Forestry, Faculty of
DSpace
UBCV
Dort, Erika
Doctor of Philosophy - PhD
University of British Columbia
Filamentous plant pathogens, comprising fungi and oomycetes, are some of the most economically and ecologically damaging species in agriculture, horticulture, and forestry. These pathogens are well-suited to invade novel ecosystems, and the current era of globalization and climate change has facilitated the global spread of invasive filamentous pathogens. The introduction of invasive pathogens is particularly problematic in forestry as native host trees have no co-evolved defense mechanisms against novel invaders, which can lead some invasive pathogens to spread rapidly through forest ecosystems and cause landscape-level impacts. Significant efforts are therefore being directed towards improving the management and mitigation of forest invasive pathogens. As next generation sequencing technologies and molecular techniques have evolved and improved, management research has shifted to elucidating the genetic and genomic mechanisms that filamentous pathogens employ to infect their plant hosts, cause disease, and spread through ecosystems. The purpose of the research presented in this dissertation was to apply modern genomics methods in a forest pathology context with the goal of creating foundational techniques that can be used to further our understanding of the molecular mechanisms driving phytopathogenic lifestyles and traits in filamentous forest pathogens. In the first part of this work, large-scale comparative genomic analyses were performed on fungal genomes using a novel machine-learning method that was able to uncover distinct genomic patterns associated with phytopathogenic lifestyles and traits. These results provided a proof of concept for the use of predictive genomics in the biosurveillance of forest invasive filamentous pathogens. The second part of this work focused on developing molecular techniques for filamentous forest pathogens in the oomycete genus Phytophthora. First, a polyethylene glycol-mediated plasmid transformation method was tested in five forest Phytophthoras to determine which species was most amenable to further molecular method development. Next, CRISPR-Cas9 gene editing was tested in two forest Phytophthoras, P. cactorum and P. ramorum, using a plasmid-ribonucleoprotein co-transformation approach. The results from the plasmid transformations and CRISPR-Cas9 gene editing demonstrated both the potential and challenges for developing molecular techniques in Phytophthora species and provide a foundation for future genomic research in forest Phytophthoras.
FOREST PATHOLOGY IN THE GENOMICS ERA: COMBINING COMPARATIVE GENOMICS AND CRISPR-CAS9 GENE EDITING TO GAIN NEW INSIGHTS INTO THE GENETICS OF FILAMENTOUS PLANT PATHOGENS by Erika Dort B.Sc., The University of Victoria, 2011 M.Sc., The University of Victoria, 2015 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2024 © Erika Dort, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Forest pathology in the genomics era: combining comparative genomics and CRISPR-Cas9 gene editing to gain new insights into the genetics of filamentous plant pathogens submitted by Erika Dort in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Forestry Examining Committee: Dr. Richard Hamelin, Professor, Forest and Conservation Sciences, UBC Supervisor Dr. Nicolas Feau, Research Scientist, Natural Resources Canada Supervisory Committee Member Dr. José Úrbez-Torres, Adjunct Professor, Biology, UBCO Supervisory Committee Member Dr. Kayla King, Professor, Zoology, UBC University Examiner Dr. Guus Bakkeren, Adjunct Professor, Botany, UBC University Examiner Dr. Monica Gerth, Professor, Biological Sciences, Victoria University of Wellington External Examiner Additional Supervisory Committee Members: Dr. Jim Kronstad, Professor, Michael Smith Laboratories, UBC Supervisory Committee Member iii Abstract Filamentous plant pathogens, comprising fungi and oomycetes, are some of the most economically and ecologically damaging species in agriculture, horticulture, and forestry. These pathogens are well-suited to invade novel ecosystems, and the current era of globalization and climate change has facilitated the global spread of invasive filamentous pathogens. The introduction of invasive pathogens is particularly problematic in forestry as native host trees have no co-evolved defense mechanisms against novel invaders, which can lead some invasive pathogens to spread rapidly through forest ecosystems and cause landscape-level impacts. Significant efforts are therefore being directed towards improving the management and mitigation of forest invasive pathogens. As next generation sequencing technologies and molecular techniques have evolved and improved, management research has shifted to elucidating the genetic and genomic mechanisms that filamentous pathogens employ to infect their plant hosts, cause disease, and spread through ecosystems. The purpose of the research presented in this dissertation was to apply modern genomics methods in a forest pathology context with the goal of creating foundational techniques that can be used to further our understanding of the molecular mechanisms driving phytopathogenic lifestyles and traits in filamentous forest pathogens. In the first part of this work, large-scale comparative genomic analyses were performed on fungal genomes using a novel machine-learning method that was able to uncover distinct genomic patterns associated with phytopathogenic lifestyles and traits. These results provided a proof of concept for the use of predictive genomics in the biosurveillance of forest invasive filamentous pathogens. The second part of this work focused on developing molecular techniques for filamentous forest pathogens in the oomycete genus Phytophthora. First, a polyethylene glycol-mediated plasmid transformation method was tested iv in five forest Phytophthoras to determine which species was most amenable to further molecular method development. Next, CRISPR-Cas9 gene editing was tested in two forest Phytophthoras, P. cactorum and P. ramorum, using a plasmid-ribonucleoprotein co-transformation approach. The results from the plasmid transformations and CRISPR-Cas9 gene editing demonstrated both the potential and challenges for developing molecular techniques in Phytophthora species and provide a foundation for future genomic research in forest Phytophthoras. v Lay Summary Phytopathogenic fungi and oomycetes are microorganisms that cause diseases in plants, and many have the capacity to cause large-scale ecological and economic damage in forestry. The 21st century has brought significant technological advances, and scientists can now study the specific genetic mechanisms underlying phytopathogenic traits in fungi and oomycetes. This dissertation presents research aimed to develop novel methods for exploring forest pathogen genomes so that disease management strategies can be improved. Two broad research approaches were applied. The first approach used machine-learning to perform large-scale comparative analyses on fungal genomes, which uncovered specific patterns associated with important phytopathogenic traits. The second approach established species-scale genetic techniques in forest pathogenic oomycetes, which will allow researchers to examine the contribution of specific genes to phytopathogenicity in these species. The results from this research provide a foundation for future studies of forest pathogens and the continued improvement of disease management and mitigation strategies. vi Preface The research presented in this dissertation was conceived and designed by me with support and guidance from Dr. Richard Hamelin, Dr. Nicolas Feau, and my supervisory committee members. I performed all the research and data analyses except for the machine learning analyses presented in Chapter 2, which were performed by Elliot Layne, a PhD candidate in the School of Computer Science at McGill University (see publication notes below). A version of Chapter 2 has been published. Dort, E.N., Layne, E., Feau, N., Butyaev, A., Henrissat, B., Martin, F.M., Haridas, S., Salamov, A., Grigoriev, I.V., Blanchette, M., and Hamelin, R.C. (2023) Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits. Scientific Reports 13: 17203. I conceived and designed the study with E. Layne, N. Feau, M. Blanchette, and R.C. Hamelin. I designed and completed the lifestyle database and A. Butyaev developed the online version. The machine learning analyses were done by E. Layne, and I performed all other analyses (data scraping, PCAs, statistical analyses); during this process we received support and advice from N. Feau, M. Blanchette, and R.C. Hamelin. Data (genome annotations) and/or technical support was provided by B. Henrissat, F.M. Martin, S. Haridas, A. Salamov, and I.V. Grigoriev. The Newick version of the phylogenetic tree was created by S. Haridas. I conceived and wrote the manuscript with help from E. Layne, N. Feau, and R.C. Hamelin, and I created the figures with support and guidance from N. Feau. All the other authors reviewed the manuscript and provided edits. The chapter is presented in the form in which it was accepted for publication and is reproduced in this dissertation under the permission of a CC BY 4.0 license with minor formatting modifications made to adhere to UBC dissertation formatting requirements. vii A version of Chapter 3 has also been published. Dort, E.N., Tanguay, P., and Hamelin, R.C. (2020) CRISPR/Cas9 Gene Editing: An Unexplored Frontier for Forest Pathology. Frontiers in Plant Science 11: 1126. I conceived and wrote the manuscript, and P. Tanguay and R.C. Hamelin provided content suggestions and edits. The chapter is presented in the form in which it was accepted for publication and is reproduced in this dissertation under the permission of a CC BY 4.0 license with minor formatting modifications made to adhere to UBC dissertation formatting requirements. viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Tables ............................................................................................................................... xii List of Figures ............................................................................................................................. xiii List of Supplementary Data Files ............................................................................................. xxi List of Symbols .......................................................................................................................... xxii List of Abbreviations ............................................................................................................... xxiii Acknowledgements ....................................................................................................................xxv Dedication ................................................................................................................................ xxvii Chapter 1: Introduction ................................................................................................................1 1.1 The phytopathogenic lifestyles and ecological traits of filamentous pathogens ............. 3 1.2 Filamentous pathogens as invasive species: a threat to forest ecosystem health ............ 4 1.2.1 Biosurveillance of filamentous forest pathogens .................................................... 5 1.3 Phytophthora, a genus of highly destructive, and often invasive, plant pathogenic oomycetes ................................................................................................................................... 6 1.4 Filamentous pathogens, climate change, and forest health ............................................. 7 1.5 Forest pathology in the genomics era ............................................................................. 8 1.6 PhD research objectives ................................................................................................ 10 Chapter 2: Large-scale comparative genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits ..................13 2.1 Chapter Summary ......................................................................................................... 13 2.2 Introduction ................................................................................................................... 14 2.3 Methods......................................................................................................................... 17 2.3.1 Fungal lifestyle database ....................................................................................... 17 2.3.2 Genome analyses .................................................................................................. 19 2.3.2.1 Genome annotations .......................................................................................... 19 2.3.2.2 Phytopathogenic lifestyles and traits ................................................................ 20 2.3.2.3 Principal component analyses ........................................................................... 20 2.3.2.4 DendroNet machine learning ............................................................................ 21 2.3.2.4.1 Dataset preprocessing ................................................................................. 21 2.3.2.4.2 DendroNet architecture ............................................................................... 21 2.3.2.4.3 Model training ............................................................................................. 22 2.3.2.4.4 Model evaluation ........................................................................................ 22 2.3.2.4.5 Feature importance evaluation .................................................................... 23 2.3.2.4.6 Statistical analyses ...................................................................................... 24 2.4 Results ........................................................................................................................... 24 2.4.1 Phytopathogenic lifestyles and traits can be predicted from phylogenetic and genomic data ......................................................................................................................... 24 2.4.2 Expansions and contractions of specific CAZyme, peptidase and secondary metabolite cluster gene families drive DendroNet’s predictions of phytopathogenic lifestyles and traits ................................................................................................................ 32 ix 2.4.2.1 CAZymes .......................................................................................................... 33 2.4.2.2 Peptidases .......................................................................................................... 35 2.4.2.3 Secondary metabolite clusters ........................................................................... 36 2.5 Discussion ..................................................................................................................... 37 Chapter 3: CRISPR-Cas9 gene editing: an unexplored frontier for forest pathology ..........45 3.1 Chapter Summary ......................................................................................................... 45 3.2 Introduction ................................................................................................................... 46 3.2.1 What is CRISPR-Cas? A primer for understanding CRISPR-Cas9 gene editing . 46 3.2.2 How does CRISPR-Cas9 gene editing technology work? .................................... 49 3.2.3 CRISPR-Cas9 exploits cellular DNA repair mechanisms to edit genes ............... 50 3.2.4 CRISPR-Cas9 limitations ..................................................................................... 51 3.3 Current applications of CRISPR-Cas9 in plant pathology ........................................... 52 3.3.1 Using CRISPR-Cas9 to engineer disease resistance in plants .............................. 53 3.3.1.1 Pathogen-gene approach ................................................................................... 54 3.3.1.2 Plant-gene approach .......................................................................................... 54 3.3.2 Using CRISPR-Cas9 to target and explore genes in filamentous plant pathogens ............................................................................................................................... 58 3.3.2.1 Targeting pathogenicity genes using CRISPR-Cas9 ........................................ 59 3.4 Future perspectives for CRISPR-Cas9 in forest pathology .......................................... 61 3.4.1 Proposed CRISPR-Cas9 research in four forest pathosystems ............................. 64 3.4.1.1 Engineering disease resistance in poplars ......................................................... 64 3.4.1.2 Dutch elm disease ............................................................................................. 66 3.4.1.3 Sudden oak/larch death ..................................................................................... 67 3.4.1.4 White pine blister rust ....................................................................................... 69 3.5 Conclusions ................................................................................................................... 72 Chapter 4: Heterogeneity in establishment of polyethylene glycol-mediated plasmid transformations in five forest pathogenic Phytophthora species..............................................73 4.1 Chapter Summary ......................................................................................................... 73 4.2 Introduction ................................................................................................................... 74 4.3 Methods......................................................................................................................... 78 4.3.1 Phytophthora species, isolates, and culture conditions ........................................ 78 4.3.2 Transformation plasmids ...................................................................................... 79 4.3.2.1 Plasmid sequencing ........................................................................................... 82 4.3.3 Determining the minimum inhibitory concentrations of geneticin for Phytophthora isolates............................................................................................................ 83 4.3.4 Protoplast isolation and transformation ................................................................ 84 4.3.4.1 Preparation of Phytophthora cultures for transformation ................................. 85 4.3.4.2 Isolation of protoplasts ...................................................................................... 85 4.3.4.3 Transformation of protoplasts with plasmid DNA ........................................... 86 4.3.4.4 Antibiotic selection of transformants ................................................................ 87 4.3.5 Identification of successful transformants ............................................................ 87 4.4 Results ........................................................................................................................... 89 4.4.1 Sequencing of Phytophthora transformation plasmids indicates instability of pGFPN ............................................................................................................................... 89 4.4.2 Forest Phytophthora species differ in geneticin sensitivity .................................. 92 x 4.4.3 Plasmid DNA transformations are inconsistent and unstable in forest Phytophthora species ............................................................................................................ 92 4.5 Discussion ..................................................................................................................... 98 Chapter 5: Successes and challenges in establishing CRISPR-Cas9 gene editing for two forest pathogenic Phytophthora species via co-transformation of plasmid-ribonucleoprotein complexes ....................................................................................................................................105 5.1 Chapter Summary ....................................................................................................... 105 5.2 Introduction ................................................................................................................. 106 5.3 Methods....................................................................................................................... 111 5.3.1 Phytophthora cactorum isolates and culture conditions ..................................... 111 5.3.2 Phytophthora DNA extractions .......................................................................... 111 5.3.3 PCR primer design and equipment ..................................................................... 112 5.3.4 Identification of the PcORP1 ortholog in Phytophthora ramorum and Phytophthora cactorum ...................................................................................................... 112 5.3.4.1 Touchdown PCR of PrORP1 and PcaORP1 genes ........................................ 114 5.3.4.2 Sanger sequencing and primer-walking for PrORP1 and PcaORP1 .............. 115 5.3.4.3 Gene prediction for PrORP1 and PcaORP1 sequences ................................. 116 5.3.5 CRISPR-Cas9 guide RNA design ....................................................................... 116 5.3.6 Ribonucleoprotein-plasmid co-transformation ................................................... 117 5.3.6.1 Plasmid DNA .................................................................................................. 117 5.3.6.2 Formation of Cas9 ribonucleoprotein complexes ........................................... 118 5.3.6.3 Preparation of Phytophthora cultures for transformation ............................... 119 5.3.6.4 Isolation of protoplasts .................................................................................... 119 5.3.6.5 Formation of plasmid-RNP cationic-lipid transfection complexes ................ 119 5.3.6.6 Co-transformation of Phytophthora protoplasts with CRISPR-Cas9 ribonucleoprotein complexes and plasmid DNA ............................................................ 120 5.3.6.7 Antibiotic selection of transformants .............................................................. 121 5.3.7 Screening transformants for CRISPR-Cas9 mutants .......................................... 122 5.3.8 Generation of single zoospore-derived cultures ................................................. 123 5.3.9 Testing plasmid DNA retention in single zoospore cultures .............................. 124 5.3.10 Oxathiapiprolin growth assays ............................................................................ 124 5.3.11 Statistical analyses .............................................................................................. 125 5.4 Results ......................................................................................................................... 126 5.4.1 Structure of the PrORP1 and PcaORP1 genes ................................................... 126 5.4.2 Guide RNAs designed and tested for PrORP1 and PcaORP1 ........................... 128 5.4.3 CRISPR-Cas9 plasmid-RNP co-transformation produces mutants in Phytophthora cactorum but not Phytophthora ramorum .......................................................................... 130 5.4.3.1 Phytophthora cactorum .................................................................................. 130 5.4.3.2 Phytophthora ramorum ................................................................................... 134 5.4.4 Single zoospore cultures of Phytophthora cactorum mutants lose plasmid DNA ... ............................................................................................................................. 135 5.4.5 ΔPcaORP1 mutants exhibit decreased resistance to oxathiapiprolin ................. 136 5.5 Discussion ................................................................................................................... 138 Chapter 6: Conclusion ...............................................................................................................145 6.1 Summary of research findings .................................................................................... 145 xi 6.2 Looking forward: research implications and potential future directions .................... 147 6.2.1 Genomics-based biosurveillance of forest invasive filamentous pathogens ....... 147 6.2.2 CRISPR-Cas9 gene editing for forest pathogenic Phytophthoras ...................... 148 6.3 Concluding statements ................................................................................................ 150 References ...................................................................................................................................151 Appendices ..................................................................................................................................183 Appendix A Supplementary Methods and Results for Chapter 2 ........................................... 183 A.1 Results of statistical analyses run on DendroNet AUCs ......................................... 183 A.2 Supplementary tables .............................................................................................. 184 A.3 Supplementary figures ............................................................................................ 187 A.4 Supplementary data files ......................................................................................... 192 Appendix B Supplementary Methods and Results for Chapters 4 and 5 ................................ 194 B.1 Recipes for Growth Media and Transformation Solutions used in Chapters 4 and 5 ... ................................................................................................................................. 194 B.2 Conditions Tested for Phytophthora Transformations in Chapter 4 ...................... 200 B.3 Transformation Plasmids ........................................................................................ 200 Appendix C Supplementary Methods and Results for Chapter 5 ........................................... 209 C.1 Methods Tested for Plasmid-RNP Co-Transformations ......................................... 209 C.2 Results from Oxathiapiprolin Growth Assays ........................................................ 209 xii List of Tables Table 4.1 The five forest Phytophthora species and isolates used in this study. Phytophthora sojae was used as a control for transformations. .......................................................................... 78 Table 4.2 Geneticin (G418) concentrations tested for each Phytophthora species used in this study to determine minimum inhibitory concentrations. .............................................................. 84 Table 4.3 Primer pairs used for diagnostic PCR to detect plasmid DNA in Phytophthora transformants................................................................................................................................. 89 Table 4.4 Summary of the results of plasmid DNA transformations in the five forest Phytophthora pathogens tested in this study and the agricultural pathogen P. sojae (used as a transformation control). ................................................................................................................ 93 Table 5.1 Details of the Phytophthora cactorum and P. ramorum isolates used in this study. . 111 Table 5.2 Primer pairs designed to amplify the PcORP1 orthologs of P. ramorum (PrORP1) and P. cactorum (PcaORP1). All PCRs were performed using Phusion™️ Plus DNA Polymerase, which creates a universal annealing temperature of 60°C. ......................................................... 114 Table 5.3 Thermocycler conditions for touchdown PCR performed on P. ramorum NA2_17 and P. cactorum FF42 wildtype DNA using Pr102ORPh_ORF and Pca414ORPh_Seq primer pairs, respectively. ................................................................................................................................ 115 Table 5.4 CRISPR-Cas9 guide RNAs (gRNAs) designed to target the PrORP1 and PcaORP1 genes of Phytophthora ramorum and P. cactorum, respectively, using the Eukaryotic Pathogen CRISPR guide RNA/DNA Design Tool (EuPaGDT; Peng & Tarleton, 2015). All gRNAs are designed using a 3’ NGG protospacer adjacent motif (PAM). The number in each gRNA name represents the location of its first nucleotide in the ORP1 open reading frame sequence. The gRNAs labelled with “rc” (reverse complement) are those derived from the antisense strand of the ORP1 sequence. The gRNAs labelled with “mh” (microhomology) are those that EuPaGDT found to have microhomology pairs flanking the gRNA sequence. Also provided are the total scores from EuPaGDT for each gRNA and the IDT gRNA design checker scores. .................. 129 Table 5.5 Primer pairs designed to amplify Cas9 RNP-targeted regions of the PrORP1 and PcaORP1 genes of Phytophthora ramorum NA2_17 and P. cactorum FF42, respectively, for Sanger sequencing. The guide RNAs (gRNAs) that target a sequence within each primer-amplified region are also listed. All PCRs for Sanger sequencing were performed using Phusion™️ Plus DNA Polymerase, which uses a universal annealing temperature of 60°C. ..... 130 xiii List of Figures Figure 1.1 (A) Phylogenetic tree of life for eukaryotes; figure is from Burki et al. 2020 (Trends in Ecology & Evolution 35(1): 43-55) and is reused with permission under a CC BY 4.0 license. Oomycetes belong to the Stramenopila in the TSAR supergroup (coloured red) whereas fungi are members of the Opisthokonta in the Amorphea supergroup (coloured grey). (B) Both oomycetes (left photo) and filamentous fungi (right photo) exhibit radial growth of a mycelium, which is made up of branching filaments known as hyphae, leading to the encompassing term ‘filamentous pathogens’ being used in plant pathology. Photos: Erika Dort. ................................ 2 Figure 1.2 Phytophthora species are ubiquitous pathogens with a global distribution, and many of them have the capacity to cause large-scale damage in both agriculture and forestry. (A) Symptoms of late blight of potato caused by Phytophthora infestans, responsible for the Irish Potato Famine. Photo: Scott Bauer, USDA Agricultural Research Service, Bugwood.org, used under the permission of a CC BY 3.0 license. (B) Forest outbreak of sudden oak death, caused by Phytophthora ramorum. Photo: Joseph O’Brien, USDA Forest Service, Bugwood.org, used under the permission of a CC BY 3.0 license. C) Eucalyptus trees in Australia killed by Phytophthora cinnamomi. Photo: Florida Division of Plant Industry, Florida Department of Agriculture and Consumer Services, Bugwood.org, used under the permission of a CC BY 3.0 license. ............................................................................................................................................ 7 Figure 1.3 Graphical summary showing the simplified structure and objectives of the research presented in this dissertation. Also included are the long-term research objectives (grey box), which are not within the scope of this dissertation, but represent the bridge between Objectives 1 and 2 and the direction intended for future studies. Abbreviations: PEG (polyethylene glycol), RNP (ribonucleoprotein), gRNA (guide RNA), G418R (geneticin resistance), KO (knockout). Figure created with BioRender.com. ............................................................................................ 12 Figure 2.1 Phylogenetic groups (includes subphyla and classes) from FunLifeDB included in our genomic analyses with summaries of the categories represented for each group analysed. The tree shows the phylogenetic relationships between the groups as represented by MycoCosm (https://mycocosm.jgi.doe.gov/mycocosm/home). The circled number after each group name indicates the total number of genomes analysed for that group, and the subsequent pie charts indicate how many of those genomes belong to each pathogenic, lifestyle and trait category. Legend Abbreviations: NP: non-pathogen; PP: plant pathogen; OP: other pathogen; S: saprotroph; ENM: endomycorrhizal; ECM: ectomycorrhizal; N: necrotroph; HB: hemibiotroph; B: biotroph; OB: obligate biotroph; GP: gymnosperm pathogen; AP: angiosperm pathogen; SP: stem pathogen; FP: foliar pathogen; RP: root pathogen. .............................................................. 26 Figure 2.2 Principal component analysis (PCA) biplots of annotation data from A) carbohydrate active enzymes, B) peptidases, C) secondary metabolite clusters, D) transporters, and E) transcription factors, showing the separation of the Ascomycota and Basidiomycota phyla on the first two principal components (PCs). The two phyla are differentiated by colour, and the four subphyla are differentiated by shape. The black arrows represent the loadings of the variables included in each PCA; only the top ten variables contributing to PC1 and PC2 are shown. ....... 27 Figure 2.3 Principal component analysis (PCA) biplots for A) carbohydrate active enzymes (CAZymes), and B) peptidases, showing the clustering of obligate biotroph (OB) genomes. The four subphyla are differentiated by colour, and the obligate biotroph genomes are differentiated by shape. A statistical ellipse was drawn at a 95% confidence level around the OB genomes. The xiv black arrows represent the loadings of the variables included in each PCA; only the top ten variables contributing to PC1 and PC4 (CAZyme PCA), or PC1 and PC3 (peptidase PCA), are shown. ........................................................................................................................................... 28 Figure 2.4 Area under the receiver operating characteristic curve (AUC) scores obtained with DendroNet for predicting nine lifestyles in fungi with the three top-performing genomic feature sets (C: CAZymes; M: MEROPS (peptidases); S: secondary metabolite clusters). Results from DendroNet’s lifestyle predictions for all 31 genomic feature sets are reported in Supplementary Data S2. A) the average AUC score obtained for each lifestyle with only the phylogenetic data (parsimony model) is represented in red at the base of each graph (standard deviation of this value is represented with a dotted red line) and the average improvement of the model with the genomic feature(s) is represented with a blue column. The asterisk indicates that the AUC score from the genomic feature model was significantly (p < 0.05, N=9) greater than the AUC score from the parsimony model. B) percent gain ([[AUC parsimony – AUC genomic signal]/AUC parsimony] x 100) calculated for each lifestyle (average and standard deviation calculated from the three genomic feature sets). .................................................................................................... 30 Figure 2.5 Area under the receiver operating characteristic curve (AUC) scores obtained with DendroNet for predicting five phytopathogenic traits in fungi with the three top-performing genomic feature sets (C: CAZymes; M: MEROPS (peptidases); S: secondary metabolite clusters). Results from DendroNet’s trait predictions for all 31 genomic feature sets are reported in Supplementary Data S2. A) the average AUC score obtained for each trait with only the phylogenetic data (parsimony model) is represented in red at the base of each graph (standard deviation of this value is represented with a dotted red line) and the average improvement of the model with the genomic feature(s) is represented with a blue column. The asterisk indicates that the AUC score from the genomic feature model was significantly (p < 0.05, N=9) greater than the AUC score from the parsimony model. B) percent gain ([[AUC parsimony – AUC genomic signal]/AUC parsimony] x 100) calculated for each trait (average and standard deviation calculated from the three genomic feature sets). .......................................................................... 32 Figure 2.6 Violinplots of the gene counts from carbohydrate active enzyme (CAZyme) annotations revealed by DendroNet analyses to be expanded in phytopathogenic genomes relative to non-phytopathogenic genomes. A) Gene counts of the six CAZyme classes and the total CAZymes (all CAZyme classes and families). B) Gene counts of the three CAZyme families that were drivers for DendroNet’s predictions of fungal phytopathogenicity. The asterisks in both A and B indicate that the gene counts in phytopathogenic genomes were significantly greater than the gene counts in non-phytopathogenic genomes as per an independent t-test (N=355) (‘*’: p < 0.05, ‘**’: p < 0.01, ‘***’: p < 0.001, ‘****’: p < 0.0001). ................... 34 Figure 2.7 Violinplots of the gene counts from peptidase (MEROPS) annotations revealed by DendroNet analyses to be expanded or contracted in phytopathogenic genomes relative to non-phytopathogenic genomes. A) Gene counts of the total peptidases (all clans and families). B) Gene counts of the four peptidase families that were drivers for DendroNet’s predictions of fungal phytopathogenicity. The asterisks in both A and B indicate significance in gene counts as per an independent t-test (N=355) (‘*’: p < 0.05, ‘**’: p < 0.01, ‘***’: p < 0.001, ‘****’: p < 0.0001). For the total peptidases (‘Total_MEROPS’), S9, S10, and S33 annotations, gene counts were significantly greater in phytopathogenic genomes relative to non-phytopathogenic genomes, whereas for pepsins, gene counts were significantly lower in phytopathogenic genomes relative to non-phytopathogenic genomes. ................................................................................... 36 xv Figure 3.1 (A) A generalized CRISPR-Cas system consisting of a CRISPR array and a cas operon. The CRISPR array is composed of a series of identical repeat sequences interspaced with unique sequences called spacers, which are derived from the genetic material of invading bacteriophages and plasmids. On the 5’ end of the spacer/repeat locus is an intraspecies-conserved leader sequence likely involved in transcription of the CRISPR array. Flanking the CRISPR array is the cas operon, which contains the CRISPR-associated (cas) genes that code for proteins involved in the CRISPR-Cas defense response. (B) The three-stage CRISPR-Cas defense response. Stage 1, adaptation, occurs when the bacterial or archaeal host recognizes a fragment of DNA or RNA from an invader, named the protospacer, and integrates it into the CRISPR array as a new spacer. New spacers are integrated at the leader end of the array. Stage 2, expression, involves the transcription of the cas genes into Cas proteins, and the CRISPR array into a precursor CRISPR RNA (pre-crRNA) molecule. The pre-crRNA molecule then gets processed by Cas proteins into smaller, mature CRISPR RNA (crRNA) molecules. In Stage 3, interference, the mature crRNAs associate with one or more Cas proteins to form crRNA-effector complexes, which survey the cell for foreign nucleic acids and subsequently cleave them via recognition of the protospacer-adjacent motif (PAM) sequence. ........................................... 48 Figure 3.2 The number of CRISPR publications by year since 2012 (development of CRISPR-Cas9 gene editing technology by Jinek et al.) obtained from a Web of Science topic search with the search parameters: ‘CRISPR’ or ‘CRISPR-Cas*’, or ’CRISPR/Cas*’. The ‘Analyze Results’ function of Web of Science was used to determine publication numbers by year and research area. (A) The log-transformed number of CRISPR publications by research area shows the common trend of increasing publications for CRISPR research for all areas except forestry. (B) A breakdown of the publication distribution by topic within forestry as determined by a manual search of the Web of Science search results. ................................................................................ 62 Figure 3.3 The four forest pathosystems proposed for future CRISPR/Cas9 research: (A) uredinia of the poplar leaf rust pathogen Melampsora medusae f. sp. deltoidis Shain, a basidiomycete fungus, on eastern cottonwood (Populus deltoides W.Bartram ex Marshall) leaves in Montréal, Quebec, CA (photo: Richard Hamelin, 2015); (B) flagging symptoms typical of Dutch elm disease, caused by the ascomycete fungal pathogen Ophiostoma novo-ulmi Brasier, on an American elm (Ulmus americana L.) in Québec, Quebec, CA (photo: Philippe Tanguay, 2016); (C) sudden larch death caused by the oomycete pathogen Phytophthora ramorum Werres, De Cock, & Man in’t Veld in a larch plantation in Galloway Forest Park, Scotland, GB (photo: Richard Hamelin, 2019); (D) aecia of the basidiomycete fungal pathogen Cronartium ribicola J.C. Fisch., causal agent of white pine blister rust, on a limber pine (Pinus flexilis E.James) in Rocky Mountain National Park, Colorado, US (photo: Erika Dort, 2019). .................................. 64 Figure 3.4 Example of a generalized CRISPR/Cas9 workflow and timeline for a filamentous forest pathogen based on the protocol being developed for the Dutch elm disease pathogen Ophiostoma novo-ulmi (Onu). (A) Transformation of fungal protoplasts with a plasmid containing (see inset): a Cas9 gene fused to nuclear localization signal (NLS), a single-guide RNA (sgRNA) scaffold with a 20-nucleotide (nt) region designed to base-pair with the target genomic DNA, an antibiotic resistance gene for transformant selection, and gene promoters and terminators active in the target species. Alternatively, protoplasts can be co-transformed with a combination of Cas9-sgRNA ribonucleoprotein complexes (RNPs) and a plasmid for antibiotic transformant selection. (B) In successful transformants, Cas9 forms a complex with the sgRNA molecule, which guides the complex to the protospacer (a genomic DNA target in this example) xvi with an adjacent PAM (protospacer adjacent motif) sequence. The sgRNA-Cas9 complex is analogous to the crRNA-effector complexes of native CRISPR/Cas systems, shown in Figure 1 of this review. In the RNP approach, this sgRNA-Cas9 complex is pre-assembled and transfected directly into the protoplasts. (C) Transformants are selected by growth on antibiotic selective medium (picture shows Onu hygromycin-resistant transformants: pink colonies are CRISPR/Cas9 ade2 mutants). (D) Successful CRISPR/Cas9 mutants are confirmed through sub-culture of the putative transformants from the previous step, PCR screening, and Sanger sequencing..................................................................................................................................... 71 Figure 4.1 Map of the pYF2-PsNLS-hSpCas9-GFP (abbreviated pYF2-PsCG) transformation plasmid developed by Fang and Tyler (2016) for expression of hSpCas9 in Phytophthora sojae. Plasmid sequence determined from Fang and Tyler (2016) and Fang et al. (2017). Plasmid map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). ............................................................................................................................. 80 Figure 4.2 Map of pYF515, the 'all-in-one' plasmid developed by Fang et al. (2017) for expression of hSpCas9 and a single-guide RNA (sgRNA) for CRISPR/Cas9 gene editing. Plasmid sequence provided by Dr. Felipe Arredondo (Oregon State University). Plasmid map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). ............................................................................................................................. 81 Figure 4.3 Map of the transformation plasmid pGFPN developed by Ah-Fong and Judelson (2011). Plasmid sequence provided by Dr. Felipe Arredondo (Oregon State University). Plasmid map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). ............................................................................................................................. 82 Figure 4.4 An example of the P. cactorum isolate Larch FF-42 2Pa growth assay demonstrating how the geneticin (G418) minimum inhibitory concentration (MIC) was determined for each forest Phytophthora species in this study. Each isolate was grown on V8 agar supplemented with a gradient of G418 concentrations (labels above the image), and two replicate plates were tested for each concentration (labels to the left of image). The first G418 concentration at which mycelial growth stopped was recorded as MIC for that species/isolate. ...................................... 84 Figure 4.5 Paired alignment of the expected pYF515 sequence (top) and the Plasmidsaurus pYF515 sequence (bottom). The pairwise identity of the two sequences is 99.8% with minor differences in sequence in the nptII gene and its ham34 promoter, both the ham34 terminators, and in the origin of replication (ori). Alignment generated in Geneious Prime 2023.2.1 (https://www.geneious.com) using the Geneious assembler (Plasmidsaurus sequence mapped to expected sequence). ...................................................................................................................... 90 Figure 4.6 Paired alignment of the expected pYF2-PsCG sequence (top) and the Plasmidsaurus pYF2-PsCG sequence (bottom). The pairwise identity of the two sequences is 99.8% with minor differences in sequence at the end of the Cas9 gene, in the ham34 terminator, and in the origin of replication (ori). Alignment generated in Geneious Prime 2023.2.1 (https://www.geneious.com) using the Geneious assembler (Plasmidsaurus sequence mapped to expected sequence). ........... 90 Figure 4.7 Paired alignment of the Plasmidsaurus pGFPN (first plasmid prep) sequence (top) and the expected pGFPN sequence (bottom). The pairwise identity of the two sequences is 52.9%, with sequence inversions, repeats, and deletion of the nptII gene in the Plasmidsaurus sequence (9,842 bp). Alignment generated in Geneious Prime 2023.2.1 (https://www.geneious.com) using the Geneious assembler (expected sequence mapped to the Plasmidsaurus sequence). ............................................................................................................. 91 xvii Figure 4.8 Paired alignment of the expected pGFPN sequence (top) and the Plasmidsaurus pGFPN (second plasmid prep) sequence (bottom). The pairwise identity of the two sequences is 62.6% with sequence inversions, and a deletion of the nptII gene in the Plasmidsaurus sequence (4,927 bp). Alignment generated in Geneious Prime 2023.2.1 (https://www.geneious.com) using the Geneious assembler (Plasmidsaurus sequence mapped to expected sequence). .................... 91 Figure 4.9 Minimum inhibitory concentrations (MICs) of geneticin (G418) for the five forest Phytophthora species tested in this study and the agricultural pathogen P. sojae (used as a transformation control). Two replicates were tested for each species/isolate. *Two isolates of P. ramorum were tested, NA2 16_0386_0016 and NA2 17_0134_0030, and growth stopped for both at 5 μg/mL of G418. ............................................................................................................. 92 Figure 4.10 Transformations of Phytophthora cactorum FF42 with the pYF2-PsCG plasmid yielded stable transformants. (A) Transformants growing on V8 agar + 40 μg/mL G418 relative to (B) no growth of wildtype (untransformed) cultures on the same medium. Photos were taken five days post plating. (C) Transformants growing stably on V8 agar + 40 μg/mL G418 after multiple rounds of sub-culturing. .................................................................................................. 94 Figure 4.11 Gel electrophoresis of products from a PCR performed on DNA from Phytophthora cactorum FF42 pYF2-PsCG transformant (T) cultures using the pYF_Cas9_Diag_F3/R3 primer pair (expected product size is 824 bp). The negative PCR control was a reaction using molecular-grade water instead of DNA (well 2). The positive PCR control was purified pYF2-PsCG plasmid DNA (well 3). Note that the DNA used for the plasmid DNA control was the same preparation that was used for the P. cactorum transformations. The gel was run at 100 volts for 90 minutes on 0.85% agarose in 0.5X TBE buffer. All samples were loaded with Safe-Green™️ stain (Applied Biological Materials Inc., Richmond, BC, Canada) at a ratio of 1 μL Safe-Green: 5 μL PCR product. The pYF2-PsCG transformants highlighted in red are those that grew stably on G418 selective medium (40 μg/mL), however, there was no DNA amplification in the diagnostic PCR.............................................................................................................................. 95 Figure 4.12 Gel electrophoresis of products from a PCR performed on DNA from Phytophthora cactorum FF42 wildtype (WT) and pYF2-PsCG transformant (T) cultures using the eGFP_Diag_F1/R1 primer pair (expected product size is 274 bp). The negative PCR control was a reaction using molecular-grade water instead of DNA (well 2). The positive PCR controls were purified pYF2-PsCG plasmid DNA, both circular (well 3) and linearized (well 4). The gel was run at 100 volts for 75 minutes on 1.25% agarose in 0.5X TBE buffer. All samples were loaded with Safe-Green™️ stain (Applied Biological Materials Inc., Richmond, BC, Canada) at a ratio of 1 μL Safe-Green: 5 μL PCR product. The pYF2-PsCG T4 transformant is highlighted in red because it struggled to grow through the second round of G418 selection (40 μg/mL), with roughly only 25% mycelial growth relative to the other three transformants (T1, T5, T8), however, a strong band amplified in the diagnostic PCR. ............................................................ 96 Figure 4.13 Putative transformants (PTs) of the control species Phytophthora sojae P6497 (A) and P. ramorum NA2_17 (B) growing on V8 agar supplemented with 50 μg/mL G418 and 30 μg/mL G418, respectively, four days post transformation. While putative P. ramorum transformants did grow on three of the plates, the mycelial growth was weak and irregular (see arrows) relative to the P. sojae control. The white arrows indicate PTs that would have been considered “strong” for P. ramorum, whereas the grey arrows indicate very weak PTs. All P. ramorum PTs were taken through a second round of selection, but most did not grow at the increased concentration of G418; none of the “weak” PTs (grey arrows) grew, and only two of xviii the “strong” PTs (white arrows) grew. The same pattern emerged for every P. ramorum transformation attempted. ............................................................................................................. 97 Figure 4.14 Gel electrophoresis of products from a PCR performed on DNA from Phytophthora sojae P6497 and P. ramorum NA2_17 wildtype (WT) and pYF2-PsCG transformant (T) cultures using the AmpR_Diag_F1/R1 primer pair (expected product size is 232 bp). The negative PCR control was a reaction using molecular-grade water instead of DNA (well 2). The positive PCR controls are replicate dilutions of a single pYF2-PsCG plasmid DNA extraction (wells 3-4). Note that the DNA used for the plasmid control was the same preparation that was used for both the P. sojae and P. ramorum transformations. The gel was run at 100 volts for 80 minutes on 1.25% agarose in 0.5X TBE buffer. All samples were loaded with Safe-Green™️ stain (Applied Biological Materials Inc., Richmond, BC, Canada) at a ratio of 1 μL Safe-Green: 5 μL PCR product. ......................................................................................................................................... 98 Figure 5.1 Schematic demonstrating how radial mycelial growth measurements were taken from Phytophthora cultures at seven days post plating on V8 agar medium supplemented with a gradient of oxathiapiprolin concentrations. To account for the width of the agar plug, 5 mm was subtracted from the length of measurement 1 (M1) and measurement 2 (M2). The average of the two measurements was recorded as the radial growth of each culture at a given oxathiapiprolin concentration. .............................................................................................................................. 125 Figure 5.2 Predicted structure of the PrORP1 gene from Phytophthora ramorum NA2_17. The open reading frame was predicted using the Geneious “Find ORFs” tool (Geneious Prime 2023.2.1) and validated using the NCBI Open Reading Frame Finder (see Methods section 5.3.4.3). The exon-intron boundaries and sequences were predicted using the Softberry FGENESH eukaryotic gene finding algorithm (www.softberry.com). The orange labels indicate the locations of the four guide RNAs designed for CRISPR-Cas9 gene editing (see Table 5.4), and the purple labels show the locations of the primer pairs designed to amplify the Cas9 ribonucleoprotein-targeted regions for Sanger sequencing(see Table 5.5). Gene map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). ..... 127 Figure 5.3 Predicted structure of the PcaORP1 gene from Phytophthora cactorum FF42. The open reading frame was predicted using the Geneious “Find ORFs” tool (Geneious Prime 2023.2.1) and validated using the NCBI Open Reading Frame Finder (see Methods section 5.3.4.3). The exon-intron boundaries and sequences were predicted using the Softberry FGENESH eukaryotic gene finding algorithm (www.softberry.com). The orange labels indicate the locations of the five guide RNAs designed for CRISPR-Cas9 gene editing (see Table 5.4), and the purple labels show the locations of the primer pairs designed to amplify the Cas9 ribonucleoprotein-targeted regions for Sanger sequencing (see Table 5.5). Gene map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). ..... 127 Figure 5.4 A) Structure and size of the PrORP1 and PcaORP1 open reading frames relative to that of the P. capsici PcORP1 gene characterized by Miao et al. (2016). The numbers indicate the size of the ORFs in base pairs (bp). The exon-intron boundaries of PrORP1 and PcaORP1 were predicted using the Softberry FGENESH eukaryotic gene finding tool (http://www.softberry.com). Exon 1 is not shown for either PrORP1 or PcaORP1 as it was not predicted to be included in the ORF region of the genes. The exon-intron boundaries shown for PcORP1 are according to Miao et al. (2016). B) Structure, size, and predicted functional domains of the PrORP1 and PcaORP1 proteins relative to the P. capsici PcORP1 protein characterized by xix Miao et al. (2016). The amino acid sequences of all three proteins were analyzed using InterPro (Paysan-Lafosse et al., 2023) to predict the functional domains. ............................................... 128 Figure 5.5 Method/workflow developed for CRISPR-Cas9 gene editing in Phytophthora cactorum using a plasmid-RNP co-transformation protocol. Figure created with BioRender.com...................................................................................................................................................... 131 Figure 5.6 Details of the CRISPR-Cas9 ΔPcaORP1 indel mutant, PT10, obtained in Phytophthora cactorum FF42 using a plasmid-RNP co-transformation method with the Pca1507rcmh guide RNA (gRNA). The PT10 mutant is heterozygous, with the mutant allele comprising a single base pair insertion at the Cas9 cut site. The graphs shown were generated using TIDE software (Brinkman et al. 2014) on the forward Sanger sequencing results from PcaORPh_Cas9Edit_1F/1R PCR amplicons. The P. cactorum PcaORP1 wildtype sequencing chromatogram was used as the control sample and the ΔPcaORP1 PT10 mutant chromatograms were used as the test samples. The top graphs show the chromatogram sequence compositions with the expected Cas9 cut site indicated by the dashed blue line, the bottom graphs show the predicted percentage of sequences with wildtype vs. mutant genotypes, and the bar graphs to the right indicate the predicted nucleotide in the insertion mutants (y-axis represents probability). A) TIDE decomposition graphs for the original PT10 transformant culture recovered from G418-selective medium. The ratio of wildtype to mutant DNA indicates a mixture of edited and non-edited nuclei in the culture. B) TIDE decomposition graphs for PT10-1, one of the single zoospore-derived cultures (SZCs) of PT10. The ratio of the wildtype to mutant DNA indicates a pure heterozygous culture with one wildtype allele and one mutant allele. The TIDE results from the other two SZCs tested (PT10-2, PT10-3) were nearly identical (results not shown). The TIDE results indicate that the nucleotide inserted on the sense strand of PcaORP1 is a thymine, therefore the nucleotide inserted on the antisense strand where the gRNA was designed is an adenine. ....................................................................................................................................... 132 Figure 5.7 Details of the CRISPR-Cas9 ΔPcaORP1 indel mutant, PT9, obtained in Phytophthora cactorum FF42 using a plasmid-RNP co-transformation method with the Pca156 guide RNA (gRNA). The PT9 mutant is heterozygous, with the mutant allele comprising a single base pair deletion at the Cas9 cut site. The graphs shown were generated using TIDE software (Brinkman et al. 2014) on the forward Sanger sequencing results from PcaORPh_Cas9Edit_4F/4R PCR amplicons. The P. cactorum PcaORP1 wildtype sequencing chromatogram was used as the control sample and the ΔPcaORP1 PT9 mutant chromatograms were used as the test samples. The top graphs show the chromatogram sequence compositions with the expected Cas9 cut site indicated by the dashed blue line, and the bottom graphs show the predicted percentage of sequences with wildtype vs. mutant genotypes. A) TIDE decomposition graphs for the original PT9 transformant culture recovered from G418-selective medium. The ratio of wildtype to mutant DNA indicates a mixture of edited and non-edited nuclei in the culture. B) TIDE decomposition graphs for PT9-1, one of the single zoospore-derived cultures (SZCs) from PT9. The TIDE results indicated that PT9-1 was derived from a non-edited wildtype nucleus as it did not have any mutant alleles. C) TIDE decomposition graphs for PT9-2, another of the SZCs derived from PT9. The ratio of the wildtype to mutant DNA indicates a pure heterozygous culture with one wildtype allele and one mutant allele. The TIDE results from the other two SZCs tested (PT9-3, PT9-4) were nearly identical (results not shown). .................................... 133 Figure 5.8 Predicted protein sequences for the PcaORP1 gene of Phytophthora cactorum FF42. A) The wildtype (WT) sequence encodes an 886 amino acid polypeptide. B) The 1 base pair (bp) xx insertion mutation in one of the alleles of the ΔPcaORP1 PT10 mutant introduces a frameshift resulting in a premature stop codon at amino acid 466. C) The 1 bp deletion mutation in one of the alleles of the ΔPcaORP1 PT9 mutant also introduces a frameshift and results in a premature stop codon at amino acid 63. Protein sequences were generated using Geneious Prime 2023.2.1...................................................................................................................................................... 134 Figure 5.9 Gel electrophoresis of products from a PCR performed using eGFP_Diag_F1/R1 primers on DNA from Phytophthora cactorum FF42 ΔPcaORP1 mutant cultures (PT9, PT10) and their respective single zoospore cultures (SZCs: PT9-1 to PT9-4, PT10-1 and PT10-3). For the PT10 SZCs, DNA extractions from two replicate cultures were tested (C1, C2). The DNA from P. cactorum FF42 wildtype SZCs (WT-1 and WT-2) was used as the negative PCR control, and pYF2-PsCG plasmid DNA was used as the positive PCR control. A product amplified in sample PT10-1 C2, indicating presence of plasmid DNA; the culture from which the sample was derived was therefore not included in downstream phenotype assays. The gel was run at 100 volts for 55 minutes on 1% agarose in 0.5X TBE buffer with a 100 bp DNA ladder. All samples were loaded with Safe-Green™️ stain (Applied Biological Materials Inc., Richmond, BC, Canada) at a ratio of 1 μL Safe-Green: 5 μL PCR product. ........................................................ 136 Figure 5.10 Photos of Phytophthora cactorum FF42 wildtype (WT) and CRISPR-Cas9 ΔPcaORP1 mutant (PT9, PT10) cultures growing on oxathiapiprolin-supplemented V8 agar seven days post plating. The images shown are only a representation of the isolates and oxathiapiprolin concentrations tested, however, the same pattern of reduced growth in the ΔPcaORP1 mutant cultures was observed at every concentration tested (see Figure 5.11). ..... 137 Figure 5.11 Boxplot of radial growth measurements of Phytophthora cactorum FF42 wildtype (WT) and CRISPR-Cas9 ΔPcaORP1 mutant (PT9, PT10) cultures growing on V8 agar (V8A) supplemented with a gradient of oxathiapiprolin concentrations. Unamended V8A and V8A + 0.1% DMSO were used as control treatments. For each isolate at each treatment, the growth data from two independent single zoospore cultures (SZCs), each with four replicates, were pooled (for a total of eight replicates per isolate per treatment). The white circles indicate the mean growth value for each isolate at each treatment. Results from statistical analyses revealed that both PT9 and PT10 ΔPcaORP1 mutant cultures exhibited statistically reduced mycelial growth relative to WT cultures on every oxathiapiprolin concentration tested (‘ns’: not significant, ‘*’: p < 0.05, ‘**’: p < 0.01, ‘***’: p < 0.001, ‘****’: p < 0.0001). .................................................... 138 xxi List of Supplementary Data Files Data A.4.1 ubc_2024_may_dort_erika_dataA41.xlsx Data A.4.2 ubc_2024_may_dort_erika_dataA42.xlsx Data A.4.3 ubc_2024_may_dort_erika_dataA43.xlsx Data A.4.4 ubc_2024_may_dort_erika_dataA44.xlsx Data A.4.5 ubc_2024_may_dort_erika_dataA45.xlsx Data A.4.6 ubc_2024_may_dort_erika_dataA46.txt Data B.2.1 ubc_2024_may_dort_erika_dataB21.xlsx Data C.1.1 ubc_2024_may_dort_erika_dataC11.xlsx Data C.2.1 ubc_2024_may_dort_erika_dataC21.xlsx Data C.2.2 ubc_2024_may_dort_erika_dataC22.xlsx xxii List of Symbols ɑ alpha β beta °C degrees Celsius µ micro Δ gene mutation [ ] concentration ~ approximately xxiii List of Abbreviations BLAST/blast Basic Local Alignment Search Tool bp base pair CRISPR-Cas clustered regularly interspaced short palindromic repeats–CRISPR associated proteins crRNA CRISPR RNA dH2O distilled water DMSO dimethylsulfoxide DNA deoxyribonucleic acid EDTA ethylenediaminetetraacetic acid g gram(s) G418 geneticin IDT Integrated DNA Technologies indel insertion or deletion JGI Joint Genome Institute L litre(s) M molar (moles/litre) m- milli -m meter mol moles mRNA messenger RNA n- nano NCBI National Center for Biotechnology Information p- pico PAM protospacer-adjacent motif PCA principal component analysis PCR polymerase chain reaction PEG polyethylene glycol rcf (× g) relative centrifugal force (g-force) xxiv RNA ribonucleic acid RNP ribonucleoprotein rpm revolutions per minute sgRNA/gRNA single-guide RNA/guide RNA TBE tris-borate-EDTA buffer tracrRNA trans-activating CRISPR RNA xxv Acknowledgements I remember reading a dissertation several years ago in which it was stated that completing a PhD takes a village. That statement has increasingly resonated with me through each year of this degree, and I have many people I want to acknowledge. To start, I want to thank my supervisor, Dr. Richard Hamelin, and my committee members Drs. Nicolas Feau, Jim Kronstad, and José Úrbez-Torres, for their support and guidance over the last five years. I also want to thank the following people for their research collaboration and support: Elliot Layne and Dr. Mathieu Blanchette (McGill) for their help with machine learning, Dr. Felipe Arredondo (OSU) for the Phytophthora transformation training and for sharing plasmid DNA, and Dr. Phil Tanguay (NRCan) for his collaboration and support with my CRISPR-Cas9 research. Thank you to FPInnovations for providing me with access to a PPC-2 facility, and a huge thanks to Stacey Kus, Dr. Angela Dale, and Dr. Rod Stirling for the training and for welcoming me into their lab space. Thank you to all the Hamelin Lab members, past and current, who have provided moral support and guidance over the years, and a special thank you to Padmini Herath who was always ready to provide support when I needed it. I also want to acknowledge the students who worked with me in the lab and helped with this project: Alanna Love, Emilie Alexander, Mia Goodman, and Romy Suliteanu. And a special shout-out to Barbara Wong, Berni van der Meer, and Kelly Hrywkiw for the long-distance Zoom work/support sessions during the pandemic (and an additional thank you to Berni for help with editing). I would also like to give my heartfelt thanks to Dr. Allan Carroll, who adopted me into his lab as a “wannabe entomologist” and has provided significant mentorship and support throughout my PhD. And thank you to the FIDEL students–particularly Deb Wertman, Kate Kitchens, Stan Pokorny, and Vivek Srivastava–for the support and for always making me feel welcome. xxvi Being a part of the EvoFunPath NSERC CREATE program provided me with incredible training opportunities and moral support during these last few years. The opportunity to do a bioinformatics internship at the Broad Institute was amazing and thank you to Dr. Christina Cuomo and her team for patiently training me in genome annotation techniques. Thank you to Dr. Rebecca Shapiro and her lab, especially Viola Halder and Lauren Wensing, for their guidance on my CRISPR-Cas9 research. I also want to thank Dr. Aleeza Gerstein for always providing me with support and advice. The support I received from my family and friends over these last five years was amazing. My husband Sergio has been the linchpin holding my life together, providing constant support. Thank you to Maia Kirk and Alicia Voth, for always having my back, and for being incredible sounding boards. Thanks to my mom and dad (the Drs. Dort) for your support and for your feedback and edits on my presentations and writing. Thank you also to Deb Wertman, my teaching partner in crime, who has been there for me since my first semester, providing both scientific and moral support. Finally, I want to acknowledge the many sources of funding for both my research and stipend. The research presented in this dissertation was funded by a Genome Canada Large-Scale Applied Research Project (project #10106, bioSAFE: Biosurveillance of Alien Forest Enemies) with additional funding from Genome B.C., Genome Québec, the Canadian Food Inspection Agency, Natural Resources Canada, and FPInnovations. The stipend funding I received over the last five years was from the following sources: bioSAFE, the UBC Doctoral Affiliated Fellowship, the UBC Faculty of Forestry (SRF and Internal Grad Awards), and the Natural Sciences and Engineering Research Council of Canada (NSERC CGS-D). xxvii Dedication For my husband, Sergio. To truly support a partner through a PhD requires a level of patience and empathy possessed by few people. Thank you for all the late-night existential crisis talks, the gently forced outdoor time when I struggled to peel myself away from the computer, the delicious meals, the small milestone celebrations, and so much more. 1 Chapter 1: Introduction Plant pathogens are fundamental components of natural ecosystems, contributing to ecological succession and nutrient/carbon cycling, and driving plant evolution and diversity (Gilbert, 2002; Paseka et al., 2020). However, in the current anthropogenic era, the combination of globalization, climate change, and mismanagement have disrupted the balance between pathogens and plant hosts, leading to increased incidence of invasive pathogen species and epidemic outbreaks of plant diseases (Anderson et al., 2004; Fisher et al., 2012, 2020; Stukenbrock & Gurr, 2023). While plant diseases can be caused by a diversity of organisms including fungi, oomycetes, bacteria, viruses, protozoa, nematodes, and parasitic plants (Agrios, 2005), filamentous plant pathogens, a group that comprises fungi and oomycetes, cause some of the most economically and ecologically impactful plant diseases. Many filamentous plant pathogens have the capacity to threaten food security (Fry et al., 2015; Singh et al., 2015) or fundamentally, and sometimes irreversibly, alter natural ecosystems (Rigling & Prospero, 2018; Rizzo et al., 2002a). Filamentous fungal and oomycete plant pathogens exhibit remarkable convergences despite their phylogenetic distance (Figure 1.1A). In fact, it is the similarities of oomycetes to fungi in filamentous hyphal growth (Figure 1.1B), life cycles, heterotrophic and osmotrophic nutrition, and ecological lifestyles that led to their inclusion in the kingdom Fungi until the late 20th century. More recently, molecular evidence confirmed that oomycetes are more closely related to plants than they are to fungi (Baldauf et al., 2000; Förster et al., 1990). Specifically, oomycetes are in the kingdom Chromista (Cavalier-Smith, 1998; Cavalier-Smith & Chao, 2006) and are members of a group known as the Stramenopila along with diatoms and brown algae (Burki et 2 al., 2020; McGowan & Fitzpatrick, 2020). Despite the distance between fungi and oomycetes on the eukaryotic tree of life, convergent evolution is evident between these two groups, especially in their behaviours as plant pathogens. Furthermore, many studies have now found strong evidence for horizontal gene transfer (HGT) between fungi and oomycetes, and it is hypothesized that HGT has contributed to the phytopathogenic lifestyles of many oomycetes (Kronmiller et al., 2023; Richards et al., 2006, 2011; Savory et al., 2015). Figure 1.1 (A) Phylogenetic tree of life for eukaryotes; figure is from Burki et al. 2020 (Trends in Ecology & Evolution 35(1): 43-55) and is reused with permission under a CC BY 4.0 license. Oomycetes belong to the Stramenopila in the TSAR supergroup (coloured red) whereas fungi are members of the Opisthokonta in the Amorphea supergroup (coloured grey). (B) Both oomycetes (left photo) and filamentous fungi (right photo) exhibit radial growth of a mycelium, which is made up of branching filaments known as hyphae, leading to the encompassing term ‘filamentous pathogens’ being used in plant pathology. Photos: Erika Dort. 3 While the impacts of filamentous pathogens are widespread in agriculture, horticulture, and forestry, the focus of this dissertation is the phytopathogenicity of fungi and oomycetes in the context of forestry. Therefore, the remainder of this introduction will primarily discuss the ecological roles and impacts of filamentous pathogens as they pertain to both natural and managed forest ecosystems. 1.1 The phytopathogenic lifestyles and ecological traits of filamentous pathogens Fungi and oomycetes exhibit a diversity of trophic strategies, or lifestyles, that influence how they interact with other species and survive within ecosystems. The definitions of these lifestyles have been the source of much debate within the mycological community since the 1800s. Presently, the predominantly used and accepted system differentiates three fundamental trophic groups: saprotrophs, necrotrophs, and biotrophs; furthermore, within each of these three groups there are facultative and obligate forms (de Bary, 1887; Lewis, 1973; Thrower, 1966). Saprotrophs derive nutrients from dead and decaying plant matter and are not associated with a living host, whereas both necrotrophs and biotrophs are intimately associated with living hosts, but exhibit opposite strategies (Lewis, 1973; Thrower, 1966). Biotrophs derive nutrients from living plant cells, often using specialized structures to maintain a symbiosis (from mutualism to parasitism), whereas necrotrophs secrete toxins to kill host cells and subsequently derive nutrients from the dead tissues (Lewis, 1973; Thrower, 1966). In plant pathology, an additional lifestyle group has been recognized for fungal and oomycete pathogens: the hemibiotrophs, which exhibit an initial phase of biotrophy followed by a transition to necrotrophy (Perfect & Green, 2001). Filamentous plant pathogens are generally categorized as one of the three distinct lifestyles–biotroph, necrotroph, or hemibiotroph–which characterizes how they interact with 4 plant tissues to overcome host defenses and obtain nutrients. In addition to these three lifestyle classifications, filamentous plant pathogens can also be distinguished by several phytopathogenic traits such as their host range, the plant tissues they infect, and the symptoms they cause. The lifestyle and phytopathogenic traits a pathogen displays encompass crucial information about its infection cycle, how it will likely spread disease from host to host, and ultimately, which management strategies will be most effective. 1.2 Filamentous pathogens as invasive species: a threat to forest ecosystem health Globalization, and the corresponding increase in international trade, has led to a significant increase in biological invasions caused by invasive alien species (Vitousek et al., 1997; Westphal et al., 2008). Invasive alien species comprise a diversity of organisms, including mammals, invertebrates, plants, and microbial pathogens such as bacteria, fungi, and oomycetes. There are many biological and ecological traits of filamentous plant pathogens that make them particularly well-suited for biological invasion, including their reproductive systems (asexual and sexual), spore-based dispersal, and phenotypic plasticity (Desprez-Loustau et al., 2007; Ghelardini et al., 2017; Philibert et al., 2011). Fungal and oomycete species are often introduced via the movement of various forest products across borders for international trade, such as live plants, logs, and wood packing materials (Liebhold et al., 2012, 2017). When non-native invasive filamentous pathogens are introduced to native forests comprising host plants with no co-evolved resistance, the pathogens can spread rapidly and cause disease outbreaks across entire forest ecosystems (Allen & Humble, 2002; Loo, 2009). There are several examples of invasive filamentous pathogens that have caused irreversible damage to forest ecosystems including Ophiostoma novo-ulmi Brasier (Dutch elm disease), Cryphonectria parasitica (Murr.) Barr (chestnut blight), 5 Cronartium ribicola J.C. Fisch. (white pine blister rust), and Phytophthora ramorum Werres, De Cock & Man in’t Veld (sudden oak death). In addition to their numerous ecological impacts, invasive forest pathogens can inflict severe economic costs for governments, forestry companies, regional municipalities, and even residential land owners (Lovett et al., 2016; Pimentel et al., 2001). Given the widespread impacts of forest invasive pathogens, significant resources are invested towards their mitigation and management. 1.2.1 Biosurveillance of filamentous forest pathogens Once an invasive species is established in an ecosystem, it becomes extremely difficult and prohibitively expensive to eradicate it (Rejmánek & Pitcairn, 2002; Simberloff et al., 2013). Therefore, increasing emphasis is being placed on prevention and early detection strategies designed to monitor and remove invasive species before they become established (Epanchin-Niell & Liebhold, 2015; Kim et al., 2006; Leung et al., 2002; Lodge et al., 2006). One such prevention strategy is biosurveillance: the systematic and cyclical process of monitoring for the presence of invasive species to detect, and ideally prevent, potential outbreaks and inform subsequent management decisions. As invasive forest pests and pathogens have become increasingly prevalent, biosurveillance has become a critical component for government regulatory agencies (Bilodeau et al., 2019; Hamelin & Roe, 2020; Roe et al., 2019; Wingfield et al., 2015) and has been demonstrated to be effective in preventing the establishment and spread of forest invasive species (Fournier & Turgeon, 2017). Therefore, research efforts are being increasingly directed towards improving biosurveillance strategies for invasive filamentous forest pathogens. Furthermore, given the current era of next-generation sequencing and the unprecedented availability of genetic and genomic data for forest invasive species, researchers 6 are advocating for more genomics-based biosurveillance strategies, focused on predicting the genetic traits associated with invasive pathogens regardless of their taxonomic identity (Hamelin & Roe, 2020; McTaggart et al., 2016; Roe et al., 2019). 1.3 Phytophthora, a genus of highly destructive, and often invasive, plant pathogenic oomycetes The oomycete genus Phytophthora de Bary comprises some of the most invasive and destructive filamentous plant pathogens, especially in forestry as they are widespread nursery pathogens with the capacity to invade, and potentially destabilize, naïve forest ecosystems (Hansen, 2015; Jung et al., 2016). The genus was first described in the 1800s by Anton de Bary, one of the founders of plant pathology, who proved that the causal agent of late blight of potato (Figure 1.2A)–the disease responsible for the Irish Potato Famine–was a fungus known at the time as Peronospora infestans (de Bary, 1861). Fifteen years later, de Bary described the defining characteristics of the genus Phytophthora and renamed the potato blight pathogen as Phytophthora infestans (de Bary, 1876). Phytophthora translates to ‘plant destroyer’ from Greek, and the genus lives up to this name with over 200 currently described species that attack a wide range of important agricultural, horticultural, and forest plants (Chen et al., 2022). Phytophthora species spread primarily using their flagellated reproductive spores, called zoospores, via the soil, watercourses, rain splash, wind, and often inadvertently by humans moving soil, plants, or propagules (Ristaino & Gumpertz, 2000). There are several examples of forest invasive Phytophthora pathogens, including P. ramorum, the causal agent of sudden oak/larch death (Figure 1.2B) (Garbelotto Benzon et al., 2001; Webber et al., 2010), and P. cinnamomi Rands, the causal agent of Jarrah dieback in Australia (Figure 1.2C) (Podger et al., 1965) and a pathogen 7 of thousands of host species worldwide (Hardham & Blackman, 2018). Each Phytophthora species displays differences in host range and infection strategy, and the genus can generally be split into two groups: those that are primarily soilborne or waterborne, which tend to infect the root and collar regions of their plant hosts, and those that are primarily aerial, which tend to infect aboveground plant tissues, including leaves, fruits, shoots, branches, and stems (Chen et al., 2022; Jung et al., 2018). The most problematic forest Phytophthoras are those that can attack a broad range of plant hosts and can infect and sporulate on multiple plant tissues, allowing them to spread globally and invade novel habitats. 1.4 Filamentous pathogens, climate change, and forest health While globalization has undoubtedly aided the spread of forest invasive fungal and oomycete pathogens, climate change is also driving a shift in host-pathogen interactions, often creating more conducive conditions for both native and non-native pathogens (Anderson et al., 2004; Coakley et al., 1999; Garrett et al., 2006; Harvell et al., 2002). Increased climatic extremes, such as drought or flooding, can make host trees more stressed and therefore less able to resist Figure 1.2 Phytophthora species are ubiquitous pathogens with a global distribution, and many of them have the capacity to cause large-scale damage in both agriculture and forestry. (A) Symptoms of late blight of potato caused by Phytophthora infestans, responsible for the Irish Potato Famine. Photo: Scott Bauer, USDA Agricultural Research Service, Bugwood.org, used under the permission of a CC BY 3.0 license. (B) Forest outbreak of sudden oak death, caused by Phytophthora ramorum. Photo: Joseph O’Brien, USDA Forest Service, Bugwood.org, used under the permission of a CC BY 3.0 license. C) Eucalyptus trees in Australia killed by Phytophthora cinnamomi. Photo: Florida Division of Plant Industry, Florida Department of Agriculture and Consumer Services, Bugwood.org, used under the permission of a CC BY 3.0 license. 8 pathogen attack (Agrios, 2005; Garrett et al., 2006). Climate change has also shifted the temperature and precipitation regimes for some areas, creating environmental conditions more conducive to pathogen dispersal and survival (Garrett et al., 2006; Harvell et al., 2002). An added layer to the changing climate is historical forest management decisions, which have sometimes led to monospecies forest stands or trees being planted outside of their natural range, both of which leave forests more vulnerable to pathogen attack. A well-known example of a pathogen that has benefited from this synergy is Nothophaeocryptopus gaeumannii (T. Rohde) Videira, C. Nakash., U. Braun & Crous, the causal agent of Swiss needle cast (SNC) in Douglas-fir. Normally an innocuous endophyte, N. gaeumannii has thrived under both a warming climate and the planting of Douglas-fir outside its normal range and is now causing widespread damage in forestry (Hood, 1982; Kimberley et al., 2011; Lee et al., 2013; Stone et al., 2008). There is also increasing evidence for a link between climate change and the worsening of diseases caused by forest Phytophthoras (Contreras-Cornejo et al., 2023), including P. alni Brasier & S.A. Kirk (Aguayo et al., 2014), P. cinnamomi (Burgess et al., 2017; Serrano et al., 2022), and P. ramorum (Venette, 2009; Venette & Cohen, 2006). 1.5 Forest pathology in the genomics era In the context of globalization and climate change and their often-synergistic effects on forest disease outbreaks, it is becoming increasingly important for plant pathologists to explore the complexities of host-pathogen interactions and elucidate the specific molecular mechanisms driving phytopathogenicity in filamentous fungi and oomycetes. The 21st century brought the development of next-generation sequencing technologies, which lowered the cost of whole genome sequencing and subsequently led to the increasing availability of genomic data. 9 Additionally, the rapid innovation of highly specific and adaptable molecular techniques such as RNA interference (Fire et al., 1998), zinc finger nucleases (Bibikova et al., 2002), transcription activator-like effector nucleases (Christian et al., 2010; Miller et al., 2011), and CRISPR-Cas9 gene editing (Jinek et al., 2012) is allowing scientists to explore the genomes of organisms in novel ways. This genomics era has revolutionized most of the biological sciences, including plant and forest pathology. Whereas historically, the phytopathogenic lifestyles and traits of filamentous pathogens were determined phenotypically through morphology-based pathogen identification and observations of host-pathogen interactions, scientists are now able to employ genetic and genomic analyses to understand what drives pathogenicity on a molecular level (Derbyshire et al., 2017; Klosterman et al., 2011; Li et al., 2018; Tyler et al., 2006). There is also increased interest in developing more genomics-based approaches to forest pest and pathogen biosurveillance with the ultimate goal of uncovering the specific genomic mechanisms that forest invasive species use to invade novel ecosystems (Bilodeau et al., 2019; Hamelin & Roe, 2020; Roe et al., 2019). Despite the significant technological advancements of the genomics era, to date, most research in plant pathology exploring the use of comparative genomics and molecular techniques has focused on agricultural pathosystems, and research in forest pathosystems is lacking. The goal of the research described herein is to fill a part of this research gap in forest pathology and provide a proof-of-concept for the application of comparative genomic analyses and molecular techniques in forest pathology. 10 1.6 PhD research objectives The current genomics era facilitates molecular-based approaches for both large- and small-scale research: increased access to high quality genomes gives researchers the freedom to conduct large-scale comparative genomic studies amongst phytopathogenic species (Gan et al., 2016; Hane et al., 2020; Haridas et al., 2020; Ohm et al., 2012; Roy et al., 2020), or to use molecular techniques such as CRISPR-Cas9 gene editing to determine specific genes and molecular pathways involved in host-pathogen interactions and phytopathogenicity at the individual species level (Fang & Tyler, 2016; Gumtow et al., 2018). I believe these approaches can act in synergy, with comparative genomics uncovering specific genes or families of genes associated with phytopathogenic traits of interest, and CRISPR-Cas9 gene editing being used to functionally validate the gene candidates generated from the comparative analyses. The goal of my PhD research, presented in Chapters 2-5 of this dissertation, was to take a multidisciplinary approach, combining large-scale and small-scale methods to address two overarching objectives (Figure 1.3): Objective 1: Establish a proof of concept for the use of large-scale comparative genomics to explore the genetic mechanisms driving plant pathogenicity, uncover gene targets associated with phytopathogenic traits of interest, and discover genomic patterns that can be used in future biosurveillance of filamentous forest pathogens. (Chapter 2) Objective 2: Establish a method for CRISPR-Cas9 editing in a forest pathogenic Phytophthora species for future validation of gene candidates associated with phytopathogenic traits of interest. (Chapters 3-5) 11 Objective 1 is addressed in Chapter 2, which describes a study demonstrating the use of comparative genomic analyses in fungi to discover predictive patterns associated with phytopathogenic lifestyles and traits. The purpose of this study was to 1) establish a proof of concept for the use of predictive genomics in the biosurveillance of filamentous plant pathogens, and 2) test the use of comparative genomics to generate putative gene targets associated with the phytopathogenic traits of interest that could be functionally validated with CRISPR-Cas9 gene editing. Fungi were chosen as the test group for this research due to the readily accessible genomic data available from the online fungal genomics resource MycoCosm (Grigoriev et al., 2014). Chapter 3 provides a bridge into Objective 2 with a literature review of CRISPR-Cas9 gene editing, its current applications in plant pathology, and its potential future applications in forest pathology. Chapter 4 describes experiments conducted to establish CRISPR-Cas9 plasmid transformations in five forest Phytophthora pathogens using a polyethylene glycol (PEG)-mediated method previously established for CRISPR-Cas9 gene editing in the agricultural pathogen P. sojae Kauf. & Gerd (Fang et al., 2017a; Fang & Tyler, 2016). Finally, Chapter 5 describes the success and failures in establishing CRISPR-Cas9 gene editing in two forest pathogenic Phytophthoras, P. cactorum (Leb. And Cohn) Schröeter and P. ramorum, using a plasmid-ribonucleoprotein co-transformation method. 12 Figure 1.3 Graphical summary showing the simplified structure and objectives of the research presented in this dissertation. Also included are the long-term research objectives (grey box), which are not within the scope of this dissertation, but represent the bridge between Objectives 1 and 2 and the direction intended for future studies. Abbreviations: PEG (polyethylene glycol), RNP (ribonucleoprotein), gRNA (guide RNA), G418R (geneticin resistance), KO (knockout). Figure created with BioRender.com. 13 Chapter 2: Large-scale comparative genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits *Note that a version of this chapter has been published: Dort, E.N., Layne, E., Feau, N., Butyaev, A., Henrissat, B., Martin, F.M., Haridas, S., Salamov, A., Grigoriev, I.V., Blanchette, M., and Hamelin, R.C. (2023) Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits. Scientific Reports 13: 17203. The chapter is presented in the form in which it was accepted for publication and is reproduced in this dissertation under the permission of a CC BY 4.0 license. Modifications were made to figure and table labels, and supplementary materials to adhere to the University of British Columbia’s PhD dissertation formatting requirements. Minor modifications were also made to some figures and text to address PhD Examiner revisions. 2.1 Chapter Summary Invasive plant pathogenic fungi have a global impact, with devastating economic and environmental effects on crops and forests. Biosurveillance, a critical component of threat mitigation, requires risk prediction based on fungal lifestyles and traits. Recent studies have revealed distinct genomic patterns associated with specific groups of plant pathogenic fungi. We sought to establish whether these phytopathogenic genomic patterns hold across diverse taxonomic and ecological groups from the Ascomycota and Basidiomycota, and furthermore, if those patterns can be used in a predictive capacity for biosurveillance. Using a supervised machine learning approach that integrates phylogenetic and genomic data, we analyzed 387 fungal genomes to test a proof-of-concept for the use of genomic signatures in predicting fungal 14 phytopathogenic lifestyles and traits during biosurveillance activities. Our machine learning feature sets were derived from genome annotation data of carbohydrate-active enzymes (CAZymes), peptidases, secondary metabolite clusters (SMCs), transporters, and transcription factors. We found that machine learning could successfully predict fungal lifestyles and traits across taxonomic groups, with the best predictive performance coming from feature sets comprising CAZyme, peptidase, and SMC data. While phylogeny was an important component in most predictions, the inclusion of genomic data improved prediction performance for every lifestyle and trait tested. Plant pathogenicity was one of the best-predicted traits, showing the promise of predictive genomics for biosurveillance applications. Furthermore, our machine learning approach revealed expansions in the number of genes from specific CAZyme and peptidase families in the genomes of plant pathogens compared to non-phytopathogenic genomes (saprotrophs, endo- and ectomycorrhizal fungi). Such genomic feature profiles give insight into the evolution of fungal phytopathogenicity and could be useful to predict the risks of unknown fungi in future biosurveillance activities. 2.2 Introduction The health of many natural and managed plant ecosystems is threatened by fungal plant pathogens, which often cause emerging infectious diseases that are difficult to mitigate once established (Anderson et al., 2004; Fisher et al., 2012; Trumbore et al., 2015). Due to their perennial nature, trees are particularly vulnerable to non-native pathogens known as forest invasive alien species (FIAS), which can spread rapidly due to the lack of co-evolved resistance in native hosts, causing disease outbreaks across entire landscapes (Allen & Humble, 2002; Loo, 2009; Roy et al., 2014; Wingfield et al., 2015). Biosurveillance–the systematic and cyclical 15 process of collecting and analyzing surveillance data to detect and characterize disease outbreaks and inform subsequent management decisions–has become a crucial means for countries to reduce the threat of FIAS (Bilodeau et al., 2019; Hamelin & Roe, 2020; Roe et al., 2019; Wingfield et al., 2015). While current biosurveillance strategies enable regulatory agencies to identify known pathogens, they do not identify the specific ecological traits associated with disease outbreaks and also fail to monitor pathogens that have not yet been taxonomically identified or listed as potential threats. This missing information constitutes a blind spot in policies for pathogen regulation as most FIAS are not identified taxonomically until after they have successfully invaded ecosystems (Brasier, 2008; Roy et al., 2014). In light of these limitations, a genomics approach to biosurveillance that is focused on discovering the genomic ‘signatures’ that FIAS use to successfully invade novel ecosystems outside of their taxonomic identity has been proposed (Bilodeau et al., 2019; Hamelin & Roe, 2020; McTaggart et al., 2016; Roe et al., 2019). Indeed, a more genetics- and genomics-centered approach to plant pathogen management is an increasingly prevalent theme in recent research (Gardiner et al., 2020; Howlett et al., 2015; Keriö et al., 2020; Klosterman et al., 2016). An important question to be answered for genomics-based biosurveillance is whether there are genomic signatures associated with the lifestyles and traits of fungal plant pathogens. The success of FIAS, and of phytopathogenic fungi in general, hinges on their diverse trophic strategies, or lifestyles, which enable them to infect and colonize a variety of plant tissues. In addition to their lifestyles, fungal phytopathogens display diverse ecological traits such as host range and tissue specificity. These lifestyle and trait categories encompass generalizations about the pathogens that make up each group, including how they infect their hosts and spread disease 16 through ecosystems. However, it is often challenging to assign species to discrete lifestyle categories due to the complex behaviours of many fungi and their ability to exhibit multiple lifestyles; this becomes particularly problematic when trying to understand the mechanisms of fungal pathogenicity and plant disease resistance (De Silva et al., 2016; Oliver & Ipcho, 2004). Given the subjectivity in assigning pathogens to lifestyle and trait categories, there has been increased research to explore these categories at the genome level, especially given the increasing availability of whole genome sequences (Hane et al., 2020; Haridas et al., 2020; Pandaranayaka et al., 2019). Additionally, the continued growth of online fungal genomic resources and databases such as FungiDB (Amos et al., 2022), Ensembl Genomes (Howe et al., 2020), NCBI RefSeq (O’Leary et al., 2016), and MycoCosm (Grigoriev et al., 2014) are providing researchers with powerful resources to compare fungi with different lifestyles and traits at the genome scale. Many recently published comparative genomics studies focus on groups of important plant-interacting fungi such as wood-decay fungi (Almási et al., 2019), dark septate endophytes (Knapp et al., 2018), mycorrhizal fungi (Kohler et al., 2015; Miyauchi et al., 2020), and phytopathogenic groups including Colletotrichum (Carbú et al., 2019; Gan et al., 2016), Zymoseptoria (Krishnan et al., 2018), Fusarium (Roy et al., 2020), and Dothideomycetes (Haridas et al., 2020; Ohm et al., 2012) species. These large-scale analyses reveal genomic patterns that can help plant pathologists better understand fungal genome evolution and identify genes that may be central to phytopathogenicity. From a biosurveillance perspective, the discovery of distinct genomic signatures associated with phytopathogenic lifestyles or traits could help predict risk or impact of undescribed fungal pathogens by analyzing their gene 17 content even before they have been taxonomically described (McTaggart et al., 2016). Integrating genomic analyses into biosurveillance pipelines would allow regulatory agencies to predict the threat a pathogen poses to an ecosystem and respond accordingly. Previous studies have demonstrated that there are lifestyle-related genomic signatures present in specific groups of fungal plant pathogens (Adhikari et al., 2013; Carbú et al., 2019; Gan et al., 2016; Hane et al., 2020; Haridas et al., 2020; Krishnan et al., 2018; Ohm et al., 2012; Pandaranayaka et al., 2019; Roy et al., 2020). We sought to build on these findings in a larger and more diverse group of fungi spanning a subset of classes from the Ascomycota and Basidiomycota phyla to test a proof-of-concept for using predictive genomics in the biosurveillance of fungal plant pathogens. In addition to fungal lifestyles, we expanded our analyses to include important ecological traits associated with plant pathogens and relevant to the biosurveillance of FIAS. Our results revealed that both lifestyles and traits can be predicted from fungal genomes, and we were able to uncover genomic features influencing phytopathogenicity in fungi. Our findings have important implications for integrating predictive genomics into future fungal FIAS biosurveillance pipelines and, in the long term, the development of more effective management strategies. 2.3 Methods 2.3.1 Fungal lifestyle database We created a lifestyle database for 533 fungal species (582 genomes) with data available from the Joint Genome Institute’s (JGI) MycoCosm Fungal Portal (Grigoriev et al., 2014). The species in our database span two ascomycete subphyla (Pezizomycotina, Taphrinomycotina) and two 18 basidiomycete subphyla (Pucciniomycotina, Agaricomycotina). Each species was given taxonomic labels for phylum, class and order using the fungal nomenclature of Index Fungorum (The Royal Botanic Gardens Kew et al., 2020). To categorise each of the species in our database into its respective lifestyle(s) and identify important ecological traits, we used both the information and references from MycoCosm as well as information found in an independent literature search. We used 1014 peer-reviewed publications in assigning lifestyles and traits to fungi in the database. In total, we assembled a list of 24 different lifestyles to which species could be assigned (Supplementary Table A.2.1), including four lifestyles exhibited by important phytopathogens (biotroph, obligate biotroph, necrotroph, hemibiotroph) for both managed and natural plant systems. Given the subjectivity in the literature as well as the possibility that one species can exhibit multiple trophic strategies during its life cycle (De Silva et al., 2016; Kuo et al., 2015), species were often assigned more than one lifestyle. In cases where there was disagreement in the literature as to the lifestyle a species exhibits, we chose the lifestyle on which the majority of published studies agreed. If a species could not be definitively categorized from the literature, it was labelled as “Unknown”. For the pathogenic fungi we included ten additional ecological traits relevant to pathogenicity (Supplementary Table A.2.2). For plant pathogenic species, host type (gymnosperms, angiosperms) and targeted tissues (stem, leaves, roots) were assigned and are hereby referred to as “phytopathogenic traits”. These phytopathogenic traits were determined only from environmental studies; experimental studies conducted in controlled conditions (e.g., artificial inoculations) were not used. 19 2.3.2 Genome analyses We used both principal component analysis (PCA) and machine learning to perform genomic comparisons amongst a subset of species from the database with available data. For machine learning, we used DendroNet, a phylogeny-aware method of training machine-learning models that incorporates both phylogenetic and genomic data (Layne et al., 2020); this allowed us to separate the signals from phylogeny and gene content and determine which genomic features were associated with specific lifestyles and traits. 2.3.2.1 Genome annotations The genomic data for each MycoCosm species can only be used if there is an associated genome reference, or with explicit permission from the principal investigator (PI). Therefore, we downloaded the genome annotation data (gene counts) available from MycoCosm for a subset of 355 fungal species (387 genomes) from our lifestyle database consisting of 362 published and 25 unpublished genomes (used with PI permission). We obtained data from the five annotation groups available from MycoCosm: secondary metabolite clusters (SMCs–7 clusters), carbohydrate-active enzymes (CAZymes–220 families, subfamilies not included), peptidases (MEROPS–144 clans/families), membrane transport proteins (transporters–522 families), and transcription factors (transfactors–65 families). The gene counts from each of these five groups were then aligned to their respective species in the lifestyle database for comparative genomic analyses. While some of the genomes from MycoCosm were sequenced externally (see Data A.4.1 for original genome reference papers), all the functional annotations were generated by the JGI Annotation Pipeline (Grigoriev et al., 2006, 2014), except for CAZymes, which were 20 annotated by the Carbohydrate-Active enZYmes database (Drula et al., 2022) in collaboration with the JGI. 2.3.2.2 Phytopathogenic lifestyles and traits We performed the genomic analyses on the gene count data for all 387 genomes, but we used only a subset of lifestyles relevant to plant pathogenicity for labelling: pathogenic (includes animal, fungus, and plant pathogens), plant pathogenic (plant pathogens only), biotrophs (B), obligate biotrophs (OB), hemibiotrophs (HB), and necrotrophs (N). We also included saprotrophs (S) as a non-pathogenic lifestyle comparison, as well as ectomycorrhizal (ECM) and endomycorrhizal (ENM) species to compare plant-interacting, but non-pathogenic, fungi to phytopathogenic fungi. The ENM category comprised both ericoid mycorrhizal species and ectendomycorrhizal species. The phytopathogenic traits (gymnosperm, angiosperm, stem, foliar, and root pathogens) were also included as labels for the analyses. 2.3.2.3 Principal component analyses PCAs were performed using R software version 3.5.1 (R Core Team, 2017). Pre-processing of the data for each PCA was performed as follows: all species rows with NA values (no gene annotation) were removed, variables with zero variance and near zero variance were removed from the data using the function nearZeroVar from the caret R package (Kuhn, 2020), and the data were scaled to unit variance and centered. The function PCA was used from the R package factoextra (Kassambara & Mundt, 2020). We performed PCAs separately for each genome component (i.e., CAZymes, MEROPS, SMCs, transporters, and transfactors) to avoid excessive noise observed for joined data analyses (Hane et al., 2020). 21 2.3.2.4 DendroNet machine learning 2.3.2.4.1 Dataset preprocessing Membership in each of the lifestyles and phytopathogenic traits described above was considered as a separate binary-classification problem for the machine learning analyses, and the corresponding gene counts from each annotation group were used as features. For each of the target lifestyle/trait classes, we used a total of 31 genomic feature sets to train a predictive model: each of the five annotations (CAZymes, MEROPS, SMCs, transporters, and transfactors) as individual genomic feature sets, and every possible combination of the annotations (26 possible combinations). We performed the machine learning analyses for the phytopathogenic traits only on species belonging to the plant pathogen class (subset of 138 species). 2.3.2.4.2 DendroNet architecture DendroNet models have two components. The first component is a base model architecture, used to make predictions given a set of input data and a target output. The base model architecture used in this study was a logistic regression classifier. The second component is a neural network with the same topology as the phylogenetic tree that relates all the samples in the dataset. This neural network is used to determine the optimal weights to be used for the base classifier’s predictions at each location in the phylogenetic tree. Regularization is used to encourage the use of similar weights in species that are closely related. For this study, we retrieved the Dikarya (Ascomycota + Basidiomycota) phylogenetic tree from MycoCosm (Grigoriev et al., 2014), which was pruned to the species being analysed prior to input into DendroNet models. A pruned version of this tree with the 387 fungal genomes we analysed is included in Data A.4.6 in Newick format. To make the tree, proteins from all 387 genomes were clustered using MMseqs2 22 version 0188988235c6f1a8e90f327827c73f981db8a19a (Steinegger & Söding, 2017). Orthologous proteins were identified from the clusters allowing for paralogs and up to 100 missing genomes per cluster. When paralogs were present, only one copy from each genome was retained for alignment. Proteins from 2580 selected clusters were aligned using MAFFT version 7.123b (Katoh et al., 2002). Divergent regions and poorly aligned positions were cleaned using Gblocks (Castresana, 2000) with options ‘-t=p -e=.gb -b4=5 -b5=h’. The resulting cleaned alignments were concatenated and used for tree building with FastTree (Price et al., 2009). 2.3.2.4.3 Model training We trained a separate DendroNet model for each lifestyle/trait classification task. Each model was trained for 1,000 epochs using a learning rate of 0.001 and the Adam optimizer (Kingma & Ba, 2014). Regularization was applied to the L1 norm of the adjustments in weights made by the neural network, scaled by a factor of 1.0, via the process described previously (Layne et al., 2020). 2.3.2.4.4 Model evaluation We evaluated DendroNet model performance using the area under the receiver operating characteristic curve (AUC) and the results are reported on a 9-fold cross-validation split. The AUC value indicates the ability of the model to distinguish between members and non-members of a given class. AUC scores range in value from 0.0 (model has no ability to distinguish between classes) to 1.0 (model’s predictions perfectly separate the classes). Machine learning models producing AUC scores less than 0.6 are considered inappropriate for a classification task 23 while those above 0.7 are considered reasonable, and models producing scores above 0.8 are considered strong (Carter et al., 2016; Safari et al., 2016). 2.3.2.4.5 Feature importance evaluation We investigated the predictive power of each genomic feature set towards the lifestyle and trait classes. To separate the significance of a genomic feature set from the phylogenetic signal, we used the following process: first, the baseline predictive power of phylogeny was established for each target lifestyle and trait. This was done by training a DendroNet model that used only a bias term as a feature value, which produced predictions that were made using solely the phylogenetic placement of each species in the dataset, similar to a maximum parsimony tree. We therefore refer to this baseline phylogenetic prediction as the parsimony model. Next, for each genomic feature set, we trained a DendroNet model using both that feature set and a bias term, allowing for the use of phylogenetic placement information. We then compared the performance of this feature+parsimony model to the parsimony model alone, with an improvement in DendroNet’s performance indicating that the genomic feature set conveys information about the target lifestyle or trait beyond the phylogenetic signal. We also trained DendroNet models using each individual feature within a genomic feature set (e.g., GH5 family in CAZymes) and analysed the performance of these models relative to the model using all features of the same set (e.g., all CAZyme families) and relative to the parsimony model. If an individual feature improved DendroNet’s performance over the parsimony model for a given lifestyle or trait, we documented its corresponding AUC score increase over the parsimony model (reported as a ‘raw’ score) as well as the fraction of the total AUC improvement relative to the AUC score from the whole genomic feature set (reported as a ‘fraction’ score). Additionally, we recorded the correlation 24 direction of the features for each lifestyle and trait (i.e., whether an increase or decrease in genes from each annotation group was associated with a lifestyle or trait). 2.3.2.4.6 Statistical analyses Prior to performing statistical analyses, we determined the distributions of the relevant data with Shapiro-Wilk normality tests. To compare DendroNet’s performance across all 31 genomic feature sets, we performed a non-parametric analysis of variance with repeated measures (Friedman test) on the AUC scores for the lifestyles and traits (separate analyses) followed by a Nemenyi test for post-hoc analysis. To determine whether the feature+parsimony models resulted in a significantly increased AUC score compared to the respective parsimony models, we performed paired, one-tailed tests: either a paired t-test or a Wilcoxon signed-rank test depending on whether the paired differences followed a normal or non-normal distribution. To compare the gene counts from the top CAZyme and peptidase predictors for plant pathogenicity, we performed independent t-tests on the gene counts from phytopathogenic genomes (‘Plant Pathogen’ = YES in FunLifeDB) relative to non-phytopathogenic genomes (‘Plant Pathogen’ = NO in FunLifeDB). 2.4 Results 2.4.1 Phytopathogenic lifestyles and traits can be predicted from phylogenetic and genomic data Our fungal lifestyle database, FunLifeDB, comprising information on 533 fungal species (582 genomes; https://biosafe.cs.mcgill.ca/funlifedb/) was the source of lifestyle and trait data for the subset of 387 published genomes (from 355 species) we analysed (Data A.4.1, Figure 2.1). We 25 observed a strong phylogenetic signal in the genomic data. All PCAs using the genomic features showed a clear separation of the Ascomycota (Pezizomycotina and Taphrinomycotina) and Basidiomycota (Pucciniomycotina and Agaricomycotina), particularly on the first two principal components (Figure 2.2). Still, the obligate biotrophs of both Ascomycota and Basidiomycota clustered together on the PCAs for CAZymes and peptidases, indicating common genomic features for this lifestyle (Figure 2.3). While the phylogenetic signal for the obligate biotrophs was still evident in these PCAs, particularly for members of the order Pucciniales, there was also a clear similarity in the genomic profiles of species with this lifestyle regardless of their phylogenetic placement (Figure 2.3). For all the other lifestyles and traits, including the plant pathogens, we did not find any clear patterns in the PCAs (data not shown). 26 Figure 2.1 Phylogenetic groups (includes subphyla and classes) from FunLifeDB included in our genomic analyses with summaries of the categories represented for each group analysed. The tree shows the phylogenetic relationships between the groups as represented by MycoCosm (https://mycocosm.jgi.doe.gov/mycocosm/home). The circled number after each group name indicates the total number of genomes analysed for that group, and the subsequent pie charts indicate how many of those genomes belong to each pathogenic, lifestyle and trait category. Legend Abbreviations: NP: non-pathogen; PP: plant pathogen; OP: other pathogen; S: saprotroph; ENM: endomycorrhizal; ECM: ectomycorrhizal; N: necrotroph; HB: hemibiotroph; B: biotroph; OB: obligate biotroph; GP: gymnosperm pathogen; AP: angiosperm pathogen; SP: stem pathogen; FP: foliar pathogen; RP: root pathogen. 27 Figure 2.2 Principal component analysis (PCA) biplots of annotation data from A) carbohydrate active enzymes, B) peptidases, C) secondary metabolite clusters, D) transporters, and E) transcription factors, showing the separation of the Ascomycota and Basidiomycota phyla on the first two principal components (PCs). The two phyla are differentiated by colour, and the four subphyla are differentiated by shape. The black arrows represent the loadings of the variables included in each PCA; only the top ten variables contributing to PC1 and PC2 are shown. 28 Genomic patterns associated with fungal lifestyles and traits were revealed with the DendroNet machine learning algorithm, which showed that many lifestyles and traits could be predicted using both phylogenetic and genomic data. The inclusion of genomic features increased Figure 2.3 Principal component analysis (PCA) biplots for A) carbohydrate active enzymes (CAZymes), and B) peptidases, showing the clustering of obligate biotroph (OB) genomes. The four subphyla are differentiated by colour, and the obligate biotroph genomes are differentiated by shape. A statistical ellipse was drawn at a 95% confidence level around the OB genomes. The black arrows represent the loadings of the variables included in each PCA; only the top ten variables contributing to PC1 and PC4 (CAZyme PCA), or PC1 and PC3 (peptidase PCA), are shown. 29 predictive performance over the parsimony models for all lifestyles and phytopathogenic traits we tested. Note that all AUC scores are reported as an average score with a standard deviation across a 9-fold cross-validation split (N=9). The contribution of phylogeny to DendroNet’s lifestyle predictions varied greatly, with parsimony AUC scores ranging from 0.438 ± 0.057 for the necrotrophs to 0.899 ± 0.018 for the obligate biotrophs (Data A.4.2). The three genomic feature sets that produced the highest mean AUC scores for lifestyle predictions were the combination of CAZymes+MEROPS+SMCs (0.915 ± 0.076), the CAZymes alone (0.914 ± 0.071), and the combination of CAZymes+MEROPS (0.912 ± 0.081), respectively (Figure 2.4, Data A.4.2). All three of these top-performing feature sets resulted in statistically significant (p < 0.05, N=9) increases in AUC scores over the parsimony models for every lifestyle tested (Data A.4.3). Prediction of the endomycorrhizal lifestyle improved the most from the addition of genomic features, with an average AUC increase of 0.582 across the top three feature sets (Figure 2.4A), corresponding to a gain of 140% (Figure 2.4B). Within the plant pathogenic lifestyles, DendroNet’s predictions improved the most for necrotrophs; the AUC scores increased by up to 0.395 (CAZyme feature set; Figure 2.4A), with an average AUC gain of 87% over parsimony across the top three feature sets (Figure 2.4B). The obligate biotroph lifestyle had the highest parsimony AUC of 0.899 ± 0.018 (Figure 2.4A), resulting from a strong phylogenetic signal (there are only three orders within which obligate biotrophs are found) but increased to an AUC of 1.000 ± 0.000 (gain over the phylogeny signal of 11.2%) in all three of the top-performing genomic feature sets (Figure 2.4B). After the obligate biotrophs, prediction scores for hemibiotrophs were the highest of the phytopathogenic lifestyles, with AUC scores of up to 0.943 ± 0.007 (CAZymes+MEROPS 30 feature set), an improvement of 41.6% over the parsimony score (Figure 2.4). DendroNet also consistently predicted fungal pathogenicity (AUCs up to 0.879 ± 0.004 with CAZymes+MEROPS+SMCs) and plant pathogenicity (AUCs up to 0.947 ± 0.003 with CAZymes). However, phylogeny contributes a large proportion to this predictive capacity, with parsimony AUCs of 0.763 ± 0.023 and 0.758 ± 0.019 and gains over the phylogeny signal of 15.2% and 24.9% for the pathogen and plant pathogen lifestyles, respectively (Figure 2.4). Figure 2.4 Area under the receiver operating characteristic curve (AUC) scores obtained with DendroNet for predicting nine lifestyles in fungi with the three top-performing genomic feature sets (C: CAZymes; M: MEROPS (peptidases); S: secondary metabolite clusters). Results from DendroNet’s lifestyle predictions for all 31 genomic feature sets are reported in Supplementary Data S2. A) the average AUC score obtained for each lifestyle with only the phylogenetic data (parsimony model) is represented in red at the base of each graph (standard deviation of this value is represented with a dotted red line) and the average improvement of the model with the genomic feature(s) is represented with a blue column. The asterisk indicates that the AUC score from the genomic feature model was significantly (p < 0.05, N=9) greater than the AUC score from the parsimony model. B) percent gain ([[AUC parsimony – AUC genomic signal]/AUC parsimony] x 100) calculated for each lifestyle (average and standard deviation calculated from the three genomic feature sets). 31 The phylogenetic signals for the phytopathogenic traits tested (host type and tissues) ranged from parsimony AUCs of 0.502 ± 0.070 (root pathogens) to 0.674 ± 0.144 (foliar pathogens) (Data A.4.2). The inclusion of genomic data improved DendroNet’s predictions of all phytopathogenic traits, though the AUC gains were not as high as they were for the lifestyles (Figure 2.5, Data A.4.2). The three genomic feature sets that produced the highest average AUC scores for predicting phytopathogenic traits were the CAZymes alone (0.823 ± 0.100), the combination of CAZymes+MEROPS (0.780 ± 0.103), and the combination of CAZymes+MEROPS+SMCs (0.770 ± 0.119), respectively. All three of these top-performing feature sets resulted in statistically significant (p < 0.05, N=9) increases in AUC scores over the parsimony models for every trait tested (Data A.4.3). DendroNet’s predictions of angiosperm pathogens improved the most over the parsimony model, though the variation in performance between the top three feature sets was more than for other traits (Figure 2.5). Foliar pathogenicity was the best-predicted trait, with a mean AUC score of 0.956 ± 0.004 from the top three feature sets (Figure 2.5A). 32 2.4.2 Expansions and contractions of specific CAZyme, peptidase and secondary metabolite cluster gene families drive DendroNet’s predictions of phytopathogenic lifestyles and traits The three top-performing genomic feature sets across phytopathogenic lifestyles and traits were CAZymes alone, CAZymes+MEROPS, and CAZymes+MEROPS+SMCs (Data A.4.2). Patterns in specific gene families arose as drivers for DendroNet’s predictions (Data A.4.4). Figure 2.5 Area under the receiver operating characteristic curve (AUC) scores obtained with DendroNet for predicting five phytopathogenic traits in fungi with the three top-performing genomic feature sets (C: CAZymes; M: MEROPS (peptidases); S: secondary metabolite clusters). Results from DendroNet’s trait predictions for all 31 genomic feature sets are reported in Supplementary Data S2. A) the average AUC score obtained for each trait with only the phylogenetic data (parsimony model) is represented in red at the base of each graph (standard deviation of this value is represented with a dotted red line) and the average improvement of the model with the genomic feature(s) is represented with a blue column. The asterisk indicates that the AUC score from the genomic feature model was significantly (p < 0.05, N=9) greater than the AUC score from the parsimony model. B) percent gain ([[AUC parsimony – AUC genomic signal]/AUC parsimony] x 100) calculated for each trait (average and standard deviation calculated from the three genomic feature sets). 33 2.4.2.1 CAZymes The total number of CAZymes in the genome and the number of genes from the glycoside hydrolase (GH), carbohydrate-binding module (CBM), and auxiliary activity (AA) CAZyme classes were increased in saprotrophs and plant pathogens, compared to the other lifestyles (Supplementary Figure A.3.1). The saprotrophic lifestyle was associated with a decrease of the carbohydrate esterases (CEs), polysaccharide lyases (PLs), and glycosyltransferases (GTs), while the phytopathogenic lifestyle was associated with an increase of all these gene classes (Figure 2.6). Ectomycorrhizal genomes had decreased numbers of all CAZyme classes, including total CAZymes, whereas endomycorrhizal genomes had decreased numbers of AAs, CBMs, CEs, and PLs, but increased numbers of GHs, GTs, and total CAZymes. While none of the individual CAZyme families predicted plant pathogenicity better than using all of them together (AUC of 0.95; Figure 2.4A), the best individual CAZyme predictor of plant pathogenicity was an increase of the GT class of genes (AUC of 0.91; Data A.4.4). Within the GT class, an increase of genes in the family GT2 was associated with plant pathogens (AUC of 0.85; Figure 2.6, Data A.4.4), while a decrease of GT2 genes was the top individual predictor for saprotrophs (AUC of 0.83; Data A.4.4). The second-best predictor of plant pathogenicity was the number of CBM63 genes (AUC of 0.90; Data A.4.4), with an increased number in plant pathogens relative to non-plant pathogens (Figure 2.6). Every phytopathogenic lifestyle except the biotrophs was associated with an increase in CBM63 genes, whereas saprotrophs and both mycorrhizal lifestyles were associated with a decrease in these genes (Supplementary Figure A.3.1, Data A.4.4). Another important predictor for plant pathogens was the number of GH32 genes (Figure 2.6): almost every phytopathogenic lifestyle as well as endomycorrhizal fungi had increased GH32 genes, but biotrophs, saprotrophs, and ectomycorrhizal fungi showed decreased numbers (Supplementary 34 Figure A.3.1, Data A.4.4, Data A.4.5). An increase in GH32 genes was also associated with all phytopathogenic traits except for gymnosperm-infecting pathogens, which showed a decrease (Data A.4.4, Data A.4.5). There were three lifestyles (biotrophs, hemibiotrophs, and ectomycorrhizal fungi) and three traits (angiosperm-, root- and stem-infecting pathogens) for which there were individual CAZyme families that provided better DendroNet predictions than when all CAZyme families were used together (Data A.4.4). Figure 2.6 Violinplots of the gene counts from carbohydrate active enzyme (CAZyme) annotations revealed by DendroNet analyses to be expanded in phytopathogenic genomes relative to non-phytopathogenic genomes. A) Gene counts of the six CAZyme classes and the total CAZymes (all CAZyme classes and families). B) Gene counts of the three CAZyme families that were drivers for DendroNet’s predictions of fungal phytopathogenicity. The asterisks in both A and B indicate that the gene counts in phytopathogenic genomes were significantly greater than the gene counts in non-phytopathogenic genomes as per an independent t-test (N=355) (‘*’: p < 0.05, ‘**’: p < 0.01, ‘***’: p < 0.001, ‘****’: p < 0.0001). 35 2.4.2.2 Peptidases Several individual peptidase families produced better predictions than using all families combined for many of the lifestyles and traits (Data A.4.4). These peptidase families varied depending on the specific lifestyle or trait, but four recurring top predictor features were the prolyl oligopeptidase (S9), prolyl aminopeptidase (S33), and carboxypeptidase Y (S10) serine peptidase families as well as the pepsin A aspartic protease family (Data A.4.4). The genomes of plant pathogens exhibited increased numbers of S9, S10, and S33 genes, but decreased numbers of pepsin genes relative to non-phytopathogenic genomes (Figure 2.7, Supplementary Figure A.3.2). An important predictor for all phytopathogenic lifestyles was the number of prolyl aminopeptidase (S33) genes; plant pathogen, necrotroph, hemibiotroph, and facultative biotroph genomes had increased S33 genes, while obligate biotroph genomes had decreased S33 genes (Data A.4.4). An increase in the total number of peptidases was the best individual predictor of plant pathogenicity (AUC of 0.88; Figure 2.7, Data A.4.4) while the second-best predictor was an increase in genes from the S9 prolyl oligopeptidase family (AUC of 0.86; Figure 2.7, Data A.4.4). The total number of peptidases was also an important predictor for all phytopathogenic lifestyles, with necrotrophs and hemibiotrophs exhibiting increased peptidases, and biotrophs (facultative and obligate) exhibiting decreased peptidases (Supplementary Figure A.3.2, Data A.4.4). 36 2.4.2.3 Secondary metabolite clusters Only two lifestyles (ectomycorrhizal fungi and obligate biotrophs) and two traits (foliar and stem pathogens) had individual SMC features that improved prediction over the parsimony model (Data A.4.4). For all four of these groups, the total number of SMC genes was the strongest predictor, with ectomycorrhizal fungi, obligate biotrophs, and foliar pathogens having decreased total SMCs and stem pathogens having increased total SMCs (Supplementary Figure A.3.3, Data A.4.4). For ectomycorrhizal fungi, foliar pathogens, and stem pathogens, the fraction score of the Figure 2.7 Violinplots of the gene counts from peptidase (MEROPS) annotations revealed by DendroNet analyses to be expanded or contracted in phytopathogenic genomes relative to non-phytopathogenic genomes. A) Gene counts of the total peptidases (all clans and families). B) Gene counts of the four peptidase families that were drivers for DendroNet’s predictions of fungal phytopathogenicity. The asterisks in both A and B indicate significance in gene counts as per an independent t-test (N=355) (‘*’: p < 0.05, ‘**’: p < 0.01, ‘***’: p < 0.001, ‘****’: p < 0.0001). For the total peptidases (‘Total_MEROPS’), S9, S10, and S33 annotations, gene counts were significantly greater in phytopathogenic genomes relative to non-phytopathogenic genomes, whereas for pepsins, gene counts were significantly lower in phytopathogenic genomes relative to non-phytopathogenic genomes. 37 total SMC feature was greater than one (Data A.4.4), indicating that the total number of SMC genes was a better predictor of these lifestyles than using all SMC families combined. The total number of SMC genes was also increased in the genomes of plant pathogens, including necrotrophs and hemibiotrophs, and endomycorrhizal fungi, but was decreased in the genomes of facultative biotrophs and saprotrophs (Supplementary Figure A.3.3, Data A.4.5). 2.5 Discussion Since Anton de Bary first discussed the concept of ‘nutritive adaptation’ in the late 1800s (de Bary, 1887), plant pathologists have been exploring and refining the definitions of trophic modes, or lifestyles, for fungal species (De Silva et al., 2016; Lewis, 1973; Perfect & Green, 2001; Thrower, 1966). Identifying the lifestyles of pathogenic fungi provides important information on how pathogens cause disease, how they spread through an ecosystem, and ultimately, how best to approach disease mitigation (De Silva et al., 2016; McTaggart et al., 2016; Newton et al., 2010). Our results support the hypothesis that there are genomic patterns, or signatures, associated with the lifestyles and ecological traits of fungal plant pathogens across phylogenetic groups and that these signatures can be used in a predictive capacity. While we observed a strong association between phylogeny and many of the lifestyles and traits, the inclusion of genomic data always improved the prediction performance of DendroNet, suggesting that there are genomic signatures beyond those shaped solely by phylogenetic relationships. Our study improves our understanding of the genomics underlying fungal phytopathogenic lifestyles and traits, while also highlighting some of the challenges and limitations of integrating large-scale genomic and lifestyle data into future biosurveillance practices. 38 The lack of lifestyle-associated patterns in our PCAs contrasts with recent results obtained by Hane et al., who used a PCA-based machine learning method (CATAStrophy) for lifestyle predictions in fungi and oomycetes (Hane et al., 2020). While Hane et al. did observe a strong phylogenetic signal in their PCAs, they also observed clusters of species with similar lifestyles that were phylogenetically unrelated (Hane et al., 2020). The difference in the strength of the phylogenetic signal between the two studies could be an effect of sample size: 355 fungal species were used in our study vs. 158 in the CATAStrophy study (Hane et al., 2020). As the number of species analyzed increases, the phylogenetic signal from the 452 million years of divergence between the Ascomycota and Basidiomycota (Berbee & Taylor, 2010; J. W. Taylor & Berbee, 2006) might overwhelm any other genomic signals. The DendroNet machine learning approach allowed us to characterize the influence of phylogeny seen in our PCAs while exploring genomic signals outside phylogeny that are drivers of fungal phytopathogenic lifestyles and traits. From a biosurveillance perspective, the most promising result is DendroNet’s ability to predict plant pathogenicity with AUC scores of up to 0.95. While the parsimony scores for plant pathogens indicate a strong phylogenetic signal, the increase of 25% in AUC with the inclusion of CAZyme data suggests there are specific genomic features contributing to plant pathogenicity in fungi. This finding validates recent results obtained with a subset of the MycoCosm database (Haridas et al., 2020), which demonstrated that within the Dothideomycetes, plant pathogens could be distinguished from saprobes with greater than 95% accuracy using machine learning on genomic data. Our analyses, performed on a larger number of species with a broader range of both taxonomic and lifestyle groups, confirm that not only can plant pathogens be distinguished from saprotrophs, but they also have a unique profile relative to other pathogens. Additionally, 39 the high predictive performance of DendroNet with plant pathogen host type (angiosperms vs. gymnosperms), and particularly with foliar pathogens, indicates that there are also strong genomic signals associated with phytopathogenic traits. These findings related to plant pathogenic fungi have important implications for biosurveillance, specifically for predicting the lifestyles and traits of novel or unknown species. However, as with all machine learning algorithms, DendroNet’s performance is limited by the data that it was given for training. There is occasionally disagreement in the literature as to which lifestyle a fungal species exhibits (Kabbage et al., 2015), and as fungal-plant interactions are studied more closely in the lab, previously unobserved lifestyles can be revealed (Kuo et al., 2015) and lifestyle assignments will thus shift. We addressed this lifestyle flexibility by assigning multiple lifestyles to species when supported by evidence from the literature. Additionally, when we encountered disagreement in the literature regarding the lifestyle assignment of a species, we chose the lifestyle for which the majority of studies had categorized a fungus. This inconsistency in the categorization of fungal lifestyles is a limitation of studies such as ours that aim to assign lifestyles and traits from the literature and highlights a significant challenge in this research moving forward. Our results provide strong support for the functionality of predictive genomics, but as the lifestyles and traits of more fungi are curated and updated, DendroNet should continue to be trained and tested to determine whether its predictive power extends to a broader range of taxonomic groups and lifestyles. While we tested all possible combinations of the five genome annotations included in this study, using CAZyme data alone produced the best prediction results for most of the phytopathogenic lifestyles and traits, and CAZymes were also included in all three of the top-performing feature 40 sets. This result is not surprising given that CAZymes are crucial for most fungal plant pathogens, allowing them to colonize their hosts by overcoming the barrier of the plant cuticle, remodeling the fungal cell wall to avoid recognition, and deconstructing the host cell wall (Hématy et al., 2009; Knogge, 1996; Kubicek et al., 2014). Our finding that CAZymes are important predictors of fungal lifestyles is further supported by previous work demonstrating the predictive capacity of CAZyme content for filamentous phytopathogens (Hane et al., 2020). Differences in the patterns we observed in non-phytopathogenic and phytopathogenic lifestyles further reflect the importance of CAZymes for plant pathogenic fungi, with the genomes of plant pathogens exhibiting expansions in many CAZyme classes compared to the genomes of non-phytopathogenic fungi (saprotrophs, endo- and ectomycorrhizal fungi). An increase in CAZyme content and activity for phytopathogenic fungi relative to saprotrophs has also been documented in previous studies (B. C. King et al., 2011; Martinez et al., 2008; Zhao et al., 2013). While saprotrophs require CAZymes for the breakdown of dead plant tissues and nutrient assimilation, they do not require the extensive arsenal necessary for interacting with living plant tissues, nor are they subject to the diversifying selection that results from the co-evolutionary arms race between plants and their fungal pathogens (J. D. G. Jones & Dangl, 2006; Möller & Stukenbrock, 2017; Stahl & Bishop, 2000). Our results show that this difference in CAZyme content is also present between plant pathogens and mycorrhizal fungi, suggesting that the expansion of CAZymes in phytopathogenic species is driven by their antagonistic interactions with plants and is not simply a requirement for interacting with live plant tissues. The expansion of glycosyltransferase (GT) genes, particularly in the GT2 family (chitin synthases), that we observed as an important predictor of plant pathogenicity contrasts previous 41 findings that dothideomycete plant pathogens had a marked decrease in GTs in their genomes (Ohm et al., 2012). This discrepancy highlights the importance of increasing the availability of genome data for fungal phytopathogens from diverse taxa so that variations in genomic patterns can be observed. While GTs are well-known to be involved in fungal cell wall synthesis and have been proposed as targets for antifungal treatment of human pathogens (Klutts et al., 2006), their specific role in plant pathogens is still understudied. There is some evidence that GT2 orthologues may have been important in the evolution of fungal pathogens and likely play an important role in pathogenesis on plants (R. King et al., 2017). While our results do show a contraction in GT2 genes in saprotrophs, we found an expansion of GT2s in both plant pathogenic and mycorrhizal genomes, suggesting that this CAZyme family is not solely important for pathogenesis, but rather involved somehow in plant-fungus interactions. The second most important predictor of plant pathogenicity was an expansion of genes in the CBM63 family, which are non-catalytic cellulose-binding modules appended to proteins with similarity to plant expansins (Cosgrove, 2015). Expansins are proteins that loosen plant cell walls and have been implicated in the virulence and plant-colonizing abilities of microbial phytopathogens (Brotman et al., 2008; Jahr et al., 2000; Kerff et al., 2008; Laine et al., 2000). CBM63-containing proteins were reported to play an important role during plant infection in Botrytis cinerea Pers.:Fr. (Choquer et al., 2021) and Fusarium oxysporum f. sp. pisi (Linford) Snyder & Hansen (Achari et al., 2021). Our finding that CBM63 genes are expanded in plant pathogens, but not in saprotrophs or mycorrhizal fungi, strongly suggests that plant cell wall loosening may be an important determinant in the evolution of plant pathogenicity. 42 The expansion of GH32 genes (invertases) that we observed in plant pathogens in general, and more specifically for necrotrophs as well as foliar, root, and angiosperm pathogens contrasts with the contraction of these genes in biotrophic species. GH32 enzymes are invertases that hydrolyze sucrose to glucose and fructose, and evidence suggests that they are used by fungal pathogens to obtain carbon from their plant hosts (Chang et al., 2017; Parrent et al., 2009). GH32 was expressed during the pathogenic activities of biotrophic fungi (Chang et al., 2017; Tetlow & Farrar, 1992; Voegele et al., 2006), and an expansion of these genes has been demonstrated in plant pathogen genomes relative to saprotrophic and mycorrhizal species (Parrent et al., 2009; Van der Nest et al., 2015), suggesting that GH32 genes could be important in the evolutionary history of plant pathogenicity. In fact, the number of GH32 gene copies has been previously proposed as a predictor of the ecological strategies of fungi (Parrent et al., 2009; Van der Nest et al., 2015), lending further support to our findings that this CAZyme subfamily is a major predictor of many plant pathogenic lifestyles. We also observed that patterns in specific peptidase families, particularly serine proteases, were associated with plant pathogenicity. Serine proteases have been demonstrated by numerous studies to be important in fungal phytopathogenicity (M. Chandrasekaran et al., 2016), and an expansion in the S9 (prolyl oligopeptidase), S10 (carboxypeptidase Y), and S33 (prolyl aminopeptidase) families has been reported for plant-associated fungi (Muszewska et al., 2017). Prolyl aminopeptidases, a family that was expanded in every phytopathogenic lifestyle except obligate biotrophs, are enzymes involved in the cleavage of N-terminal proline residues from peptides (Basten et al., 2005). There has been evidence that proteins with proline-rich N-terminal domains are involved in the maintenance of biotrophy (Perfect et al., 1998; Perfect & Green, 43 2001), so it could be that obligate biotrophs require less of the enzymes that would cleave such proline-rich domains. A contraction of pepsins (MEROPS clan AA, family A1) was an important predictor of plant pathogens in general as well as necrotrophs, hemibiotrophs, and foliar pathogens. Family A1 are aspartic proteases, which are thought to play a role in the virulence of plant pathogenic fungi (Have et al., 2010; Plummer et al., 2004), so it is somewhat unexpected that we observed a contraction in this family for plant pathogenic lifestyles. Future research could expand analyses within family A1 to determine the specific subfamilies driving these contractions. Genome-based predictive approaches have important implications for the biosurveillance of invasive plant pathogens as there has been a call for more genomics-centered biosecurity strategies, especially for forest invasive alien species (FIAS) (Bilodeau et al., 2019; Hamelin & Roe, 2020; McTaggart et al., 2016; Roe et al., 2019). Here we demonstrate that predictive genomics is a promising tool that could be harnessed for biosurveillance of fungal phytopathogens. In addition to its predictive performance, our machine learning approach uncovered genomic patterns associated with specific phytopathogenic lifestyles and traits, elucidating gene families that are potentially important in the evolution of plant pathogens. These results indicate that while evolutionary differentiation is undoubtedly a major driving force for fungal lifestyles and traits, there are clearly other selective pressures influencing the genomic architecture of phytopathogenic fungi. This finding highlights the importance of moving away from solely taxonomy-focused biosurveillance towards more genomics-based strategies. Our approach used only a small group of gene families with readily available annotations; future research could be expanded to use whole genome data, including genes known to be important in 44 fungal phytopathogenicity, such as cytoplasmic effector proteins (Dodds & Rathjen, 2010; Lo Presti et al., 2015). Additional phytopathogenic traits, such as host range (e.g., broad vs narrow, monocots vs dicots), should also be tested. The use of genomic patterns for prediction of fungal lifestyles is complex. Future genomic biosurveillance may require a tailored approach that depends on the specific group of pathogens being monitored. As more genomes become publicly available, we will be able to more robustly test the capacity of tools like DendroNet to predict fungal phytopathogenic lifestyles and traits, as well as assess the integration of these predictive tools into future FIAS biosurveillance pipelines. 45 Chapter 3: CRISPR-Cas9 gene editing: an unexplored frontier for forest pathology *Note that a version of this chapter has been published: Dort, E.N., Tanguay, P., and Hamelin, R.C. (2020) CRISPR/Cas9 Gene Editing: An Unexplored Frontier for Forest Pathology. Frontiers in Plant Science 11: 1126. The chapter is presented in the form in which it was accepted for publication and is reproduced in this dissertation under the permission of a CC BY 4.0 license. Modifications were made to figure labels to adhere to the University of British Columbia’s PhD dissertation formatting requirements. 3.1 Chapter Summary CRISPR-Cas9 gene editing technology has taken the scientific community by storm since its development in 2012. First discovered in 1987, CRISPR-Cas systems act as an adaptive immune response in archaea and bacteria that defends against invading bacteriophages and plasmids. CRISPR-Cas9 gene editing technology modifies this immune response to function in eukaryotic cells as a highly specific, RNA-guided complex that can edit almost any genetic target. This technology has applications in all biological fields, including plant pathology. However, examples of its use in forest pathology are essentially nonexistent. The aim of this review is to give researchers a deeper understanding of the native CRISPR-Cas systems and how they were adapted into the CRISPR-Cas9 technology used today in plant pathology – this information is crucial for researchers aiming to use this technology in the pathosystems they study. We review the current applications of CRISPR-Cas9 in plant pathology and propose future directions for research in forest pathosystems where this technology is currently underutilized. 46 3.2 Introduction Developed in 2012, CRISPR-Cas9 gene editing is relatively new, but research using this technology has expanded rapidly in most scientific fields with 7,105 publications in 2019 alone (Clarivate Analytics, 2020). Even though human health and medicine are the most prolific fields, researchers in plant sciences are also starting to explore the applications of CRISPR-Cas9. The use of CRISPR-Cas9 in plant breeding has sparked interest in the field of plant pathology where disease resistant plant cultivars are becoming increasingly important. The applications of CRISPR-Cas9 technology in plant pathology have already been reviewed, especially in the context of agriculture (Das et al., 2019; Langner et al., 2018; S. S. Makarova et al., 2018; Muñoz et al., 2019). However, literature addressing its applications in forestry is lacking, and we believe this is because CRISPR-Cas9 is currently underutilized in this field. The purpose of this review is to fill this literature gap while giving forest pathologists a deeper understanding of CRISPR-Cas9 and its potential applications to better understand and manage tree diseases. We focus on how native CRISPR-Cas systems function as well as the mechanisms driving CRISPR-Cas9 gene editing technology as this information is crucial for implementation of this technology in forest pathosystems. 3.2.1 What is CRISPR-Cas? A primer for understanding CRISPR-Cas9 gene editing Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR associated (Cas) proteins, or CRISPR-Cas, is a bacterial and archaeal DNA-based adaptive immune system that defends against bacteriophages, plasmids, and other mobile genetic invaders (Bhaya et al., 2011; Terns & Terns, 2011). The CRISPR-Cas system was first discovered by Japanese scientists in Escherichia coli (Migula) Castellani & Chalmers (Ishino et al., 1987), but it has now 47 been found in a large array of prokaryotic species. Among these species, CRISPR-Cas DNA loci exhibit extensive genetic diversity, but they all have a common underlying architecture comprising a CRISPR array composed of direct repeats interspaced with unique sequences called spacers, which are derived from foreign nucleic acids; this array is flanked by associated cas genes organized as an operon (Bolotin et al., 2005; Jansen et al., 2002; Pourcel et al., 2005) (Figure 3.1A). These elements of the CRISPR-Cas system work in concert to direct a three-stage defense response against invading phages and plasmids (Barrangou et al., 2007; van der Oost et al., 2009) (Figure 3.1B). Stage 1 (adaptation) occurs when the bacterial or archaeal host recognizes a fragment of DNA or RNA from an invader, named the protospacer (Deveau et al., 2008), and integrates it into the CRISPR array as a new spacer sequence (van der Oost et al., 2009). Protospacer selection is dictated by the presence of highly conserved 2-3 nucleotide regions near the protospacer sequence called ‘protospacer-adjacent motifs’, or PAMs (Marraffini & Sontheimer, 2010). Stage 2 (expression) involves the transcription of the CRISPR array into large RNA transcripts called precursor CRISPR-derived RNAs (pre-crRNAs), which are processed into smaller, mature crRNAs by Cas proteins that are transcribed from the cas genes (van der Oost et al., 2009). A mature crRNA contains a unique phage-derived spacer sequence flanked by fragments of its adjacent repeat sequences from the CRISPR array (Wiedenheft et al., 2012). Finally, Stage 3 (interference) takes place when a subsequent attack occurs by the same bacteriophage or plasmid; each crRNA associates with one or more Cas proteins to form a crRNA-protein effector complex, which conducts surveillance of the cell for invading DNA or RNA (Terns & Terns, 2011). The crRNA acts as a guide for the effector complex (guide RNA), directing it to create a double-stranded break (DSB) in the complementary protospacer sequence of the invader via recognition of the PAM (Terns & Terns, 2011). The fragmented DNA or RNA 48 can no longer infect the host, thus completing successful defense by the CRISPR-Cas system. This highly adaptable nucleotide-based activity is what makes CRISPR-Cas gene editing technologies, which are designed from CRISPR-Cas systems, so effective. Figure 3.1 (A) A generalized CRISPR-Cas system consisting of a CRISPR array and a cas operon. The CRISPR array is composed of a series of identical repeat sequences interspaced with unique sequences called spacers, which are derived from the genetic material of invading bacteriophages and plasmids. On the 5’ end of the spacer/repeat locus is an intraspecies-conserved leader sequence likely involved in transcription of the CRISPR array. Flanking the CRISPR array is the cas operon, which contains the CRISPR-associated (cas) genes that code for proteins involved in the CRISPR-Cas defense response. (B) The three-stage CRISPR-Cas defense response. Stage 1, adaptation, occurs when the bacterial or archaeal host recognizes a fragment of DNA or RNA from an invader, named the protospacer, and integrates it into the CRISPR array as a new spacer. New spacers are integrated at the leader end of the array. Stage 2, expression, involves the transcription of the cas genes into Cas proteins, and the CRISPR array into a precursor CRISPR RNA (pre-crRNA) molecule. The pre-crRNA molecule then gets processed by Cas proteins into smaller, mature CRISPR RNA (crRNA) molecules. In Stage 3, interference, the mature crRNAs associate with one or more Cas proteins to form crRNA-effector complexes, which survey the cell for foreign nucleic acids and subsequently cleave them via recognition of the protospacer-adjacent motif (PAM) sequence. 49 3.2.2 How does CRISPR-Cas9 gene editing technology work? CRISPR-Cas9 gene editing technology is based on the Type II CRISPR-Cas system from the human pathogen Streptococcus pyogenes Rosenbach and was developed by an internationally collaborative research group headed by Jennifer Doudna and Emmanuelle Charpentier (Jinek et al., 2012). Type II systems are characterized by the multidomain protein Cas9 (K. S. Makarova et al., 2011, 2015), which relies on two RNA molecules to guide it to its DNA target: a trans-activating crRNA (tracrRNA), and the usual crRNA (Deltcheva et al., 2011; Gottesman, 2011; Jinek et al., 2012). The tracrRNA is complementary to the repeat sequences in the corresponding pre-crRNA, and it base-pairs to those sequences facilitating Cas9 to process the pre-crRNA into a smaller mature crRNA molecule (Deltcheva et al., 2011). The tracrRNA, mature crRNA, and Cas9 endonuclease then form an effector complex and the two RNA molecules guide Cas9 to the target protospacer sequence of an invader (Jinek et al., 2012). Cas9 then uses the complementarity between the crRNA and the protospacer as well as the adjacent PAM to create a DSB in the target DNA (Jinek et al., 2012). The Doudna/Charpentier research group developed a single chimeric RNA molecule that combined the tracrRNA and crRNA, and they demonstrated that Cas9 can be programmed to cleave any target DNA by changing only a 20-nucleotide sequence in this single-guide RNA (sgRNA) (Jinek et al., 2012). Their results were immediately significant for the scientific community, allowing the editing of DNA in a broad range of organisms and caused a surge of research in all scientific fields that continues to this day. Additionally, the original chimeric S. pyogenes Cas9 technology has served as a model system from which many different CRISPR technologies have evolved. 50 3.2.3 CRISPR-Cas9 exploits cellular DNA repair mechanisms to edit genes The success of nuclease-based technologies, including CRISPR-Cas9, as methods to edit genes relies on the highly conserved cellular DNA repair mechanisms present in all domains of life. Cas9 generates blunt-end DSBs at target DNA sites, and there are two main mechanisms that are triggered in eukaryotic cells in response to these DSBs: end-joining and homologous recombination, or homology-directed repair (HDR) (Ranjha et al., 2018). Both end-joining and HDR involve complex endogenous systems that can be divided into several sub-pathways that are triggered under different cellular conditions and generate very different repaired DNA products (Ceccaldi et al., 2016; Ranjha et al., 2018). There are two end-joining pathways, non-homologous end-joining (NHEJ) and microhomology-mediated end-joining (MMEJ), and neither require a DNA template for repair (Ranjha et al., 2018). These end-joining mechanisms are highly error prone, often resulting in insertions or deletions (indels) that create knockout mutants when they occur in the reading frame of a gene (Bortesi & Fischer, 2015; Ranjha et al., 2018). Conversely, repairs by the HDR pathways occur only in the presence of a donor DNA template containing regions homologous to the sequences surrounding the DSB induced by Cas9 (Bortesi & Fischer, 2015). The HDR pathway is more precise than the end-joining mechanisms, and thus can be used for highly specific gene modification or gene insertion. Eukaryotic organisms can use both homologous recombination and end-joining mechanisms to repair DNA damage in their cells, but the end-joining pathways are more frequent because they occur regardless of the presence of a donor DNA template and can therefore take place in any stage of the cell cycle (Ranjha et al., 2018). However, end-joining allows for less control in CRISPR-Cas9 gene editing due to the randomness of the mutations it induces (Langner et al., 2018). The less frequent HDR pathway allows for more control, but requires a homologous donor DNA 51 template that, even when present, triggers HDR at a much lower rate than end-joining (Langner et al., 2018). Consequently, researchers wishing to activate the HDR pathway using CRISPR-Cas9 have the additional task of designing a homologous donor template that can be used for recombination at the target DNA site, and they will likely have to screen larger numbers of transformants to identify successful HDR candidates. 3.2.4 CRISPR-Cas9 limitations The major limitation of using the original S. pyogenes CRISPR-Cas9 (SpCas9) is the requirement of the PAM sequence adjacent to the protospacer DNA, which is used by the Cas9 complex in conjunction with the complementary sgRNA region to recognize and cleave the target DNA sequence (Jinek et al., 2012). The SpCas9 complex recognizes the PAM sequence 5’-NGG-3’, where N represents any of the four nucleotide bases (Jinek et al., 2012). This three-base-pair sequence is a common occurrence in most genomes, but its requirement limits the genes that can be targeted, especially when attempting to study genes involved in highly specific pathways of interest (Langner et al., 2018). Additionally, research has shown that CRISPR-Cas9 can recognize alternative PAM sequences, which increases the likelihood of off-target mutations (Y. Zhang et al., 2014). In response to this limitation researchers have developed three new CRISPR-Cas9 systems from Cas9 orthologs in other bacterial species, each of which recognizes a unique PAM: SaCas9 from Staphylococcus aureus Rosenbach, StCas9 from Streptococcus thermophilus (Orla-Jensen) Schleifer, and NmCas9 from Neisseria meningitidis (Albrecht and Ghon) Murray (Kleinstiver et al., 2015). In addition, Cas9 protein variants are being engineered to recognize alternative PAMs (Agudelo et al., 2020). New CRISPR-Cas nucleases from other bacterial Type II systems have also been discovered: Cas12a, which targets DNA, and Cas13a, which targets 52 single-stranded RNA (Burstein et al., 2017; Koonin et al., 2017; Shmakov et al., 2015). While bearing some similarities to Cas9, these two systems use slightly different mechanisms for cleaving target nucleic acid sequences and processing of pre-crRNA and demonstrate advantages over Cas9 for certain applications, including within plant pathology (Langner et al., 2018). Another limitation of any CRISPR-Cas technology is the occurrence of unwanted mutations (translocations, inversions, large deletions and insertions) resulting from the complex endogenous pathways that repair the double-stranded DNA breaks induced by Cas nucleases (Després et al., 2018; Kosicki et al., 2018). Additionally, Cas9-induced DSBs can be toxic to cells, inducing cell-death pathways and resulting in low transformation and editing efficiencies (Garst et al., 2017; K. R. Roy et al., 2018). To avoid these DSB-related limitations, nuclease-deficient Cas9 proteins have been engineered and fused to other proteins such as deaminases and recombinases to achieve base editing and site-specific recombination (Nishida et al., 2016; Standage-Beier et al., 2019). However, Cas9 continues to be the most commonly used CRISPR-Cas technology, has a number of applications in plant pathology, and is a valuable untapped resource for forest pathology; it is therefore the focus of the remainder of this review. 3.3 Current applications of CRISPR-Cas9 in plant pathology Plant pathogenic viruses, bacteria, oomycetes, and fungi are natural components of healthy ecosystems, but globalization, climate change, and mismanagement have led many of these species to cause emerging infectious diseases (EIDs) that threaten natural and managed plant ecosystems (Anderson et al., 2004; Fisher et al., 2012). In the context of agriculture, plant EIDs are considered a threat to global food security (Pennisi, 2010), and in forestry they have 53 significant impacts from both economic and biodiversity conservation perspectives (Anderson et al., 2004; Fisher et al., 2012). Chemical mitigation strategies using pesticides and fungicides have proven to be inadequate and environmentally destructive (Andolfo et al., 2016), so research has turned to genetic strategies: developing disease resistance in plants and/or engineering avirulent strains of pathogens. The development of CRISPR-Cas9 as an accurate and versatile gene editing technology increased the scope of such genetic strategies and has led plant pathologists to explore its disease-mitigation applications in both hosts and pathogens. 3.3.1 Using CRISPR-Cas9 to engineer disease resistance in plants To date, most of the CRISPR-Cas9 research in plant pathology has focused on developing systems in the hosts, namely engineering disease resistance in agriculturally important plants. Not surprisingly, the first plants to be engineered using CRISPR-Cas9 were the model species Arabidopsis thaliana (L.) Heynh. (Feng et al., 2013) and Nicotiana benthamiana Domin (Nekrasov et al., 2013), but these were followed almost simultaneously by development in rice (Feng et al., 2013; W. Jiang et al., 2013; Miao et al., 2013), wheat (Y. Wang et al., 2014), sorghum (W. Jiang et al., 2013), maize (Z. Liang et al., 2014), and tomato (Brooks et al., 2014). The list of plant species engineered using CRISPR-Cas9 has expanded rapidly in the last six years, but it has remained in the realm of angiosperm species important in agriculture; use of this technology in forest species is largely absent in the literature, with the one exception of studies developing CRISPR-Cas9 systems in Populus species (Fan et al., 2015; Zhou et al., 2015). The first CRISPR-Cas9 studies in plants were proof-of-concept experiments demonstrating the use of this technology in plants, but many of these species have now been engineered for 54 resistance to viral, fungal, and bacterial diseases (Das et al., 2019). Engineering disease resistance in plants using CRISPR-Cas9 has generally been executed using one of two strategies: the pathogen-gene approach or the plant-gene approach. The former involves engineering an sgRNA into the plant chromosome that directs Cas9 to target a specific pathogen gene thereby impeding pathogenesis, whereas the latter uses an sgRNA that targets endogenous plant genes involved in pathogen interactions and modifies them to either boost the host immune response, or to interfere with the host-recognition pathway of the pathogen (S. S. Makarova et al., 2018). 3.3.1.1 Pathogen-gene approach The pathogen-gene approach is best demonstrated with plant-virus pathosystems. CRISPR-Cas9-mediated virus resistance is most commonly tackled via a transgenic approach whereby a viral DNA sequence is used to design the sgRNA and is transformed into the plant genome with the CRISPR-Cas9 system (Ali et al., 2015, 2016; T. Zhang et al., 2018). This induces a response remarkably similar to that of the native CRISPR-Cas systems in that the plant is able to use its transgenic sgRNA-Cas9 system to target invading virus DNA, RNA, or mRNA. This approach has been primarily used in the model species N. benthamiana (Ali et al., 2015, 2016; Ji et al., 2018; T. Zhang et al., 2018) and A. thaliana (Ji et al., 2018; T. Zhang et al., 2018), but it presents an intriguing paradigm for the use of CRISPR-Cas9 to fortify plant immune systems against invading pathogens. 3.3.1.2 Plant-gene approach Engineering CRISPR-Cas9-mediated disease resistance using the plant-gene approach has mostly focused on targeting plant susceptibility (S) genes, a diverse group of genes with varying 55 roles that ultimately render plants more susceptible to invading pathogens. The proteins encoded by S genes fall into two general categories: those that act as negative regulators of immunity, decreasing the plant immune response in certain contexts, and those that are part of plant development and regulatory pathways, which are targets for pathogen effector molecules (Langner et al., 2018). While traditional disease-resistance breeding has focused on disease resistance (R) genes that generally involve ‘gene-for-gene’ interactions with pathogens, it is thought that targeting S genes will yield more stable, broad-spectrum disease resistance (Pavan et al., 2009). Both R-gene- and S-gene-based resistance involve complex molecular pathways that interact with pathogens in different ways, and the details of these interactions have been reviewed elsewhere (Dangl & Jones, 2001; J. D. G. Jones & Dangl, 2006; Lapin & Van den Ackerveken, 2013; Pavan et al., 2009). Engineering resistance to viruses with CRISPR-Cas9 using the plant-gene approach involves designing the sgRNA to target a region of the plant genome that is used by the virus for replication (S. S. Makarova et al., 2018). This method has been used for disrupting RNA viruses in both Cucumis sativus L. (cucumber) and A. thaliana by targeting the eukaryotic translation initiation factor gene eIF4E in the plant (J. Chandrasekaran et al., 2016; Pyott et al., 2016). However, the plant-gene approach is best demonstrated in pathosystems involving bacteria, oomycetes, and fungi in which the proteins encoded by plant S genes are relied upon by these pathogens for host recognition and immune suppression. Generating plant resistance in these systems has predominantly been focused on designing sgRNAs to target S genes, creating host knockout mutants that the pathogen effectors have difficulty recognizing (Das et al., 2019; Langner et al., 2018). 56 A well-studied S gene system is the mildew resistance locus O (MLO), which renders both monocot and dicot plant hosts susceptible to a variety of powdery mildew pathogens. The use of CRISPR-Cas9 to disrupt the MLO gene has proven to be effective for evading pathogen effector recognition and generating disease resistance in these systems. One of the best examples of this is a study by Nekrasov et al. who used CRISPR-Cas9 to generate 48 bp deletions in an Mlo gene in tomato plants (Nekrasov et al., 2017). The CRISPR-Cas9 mutants demonstrated resistance to the powdery mildew fungus Oidium neolycopersici L. Kiss without generating any other unwanted phenotypic effects (Nekrasov et al., 2017). Second-generation progeny (F1) were then cultivated by selfing the first-generation resistant mutants (F0), which resulted in the CRISPR-Cas9 transfer DNA plasmid being segregated away (Nekrasov et al., 2017). The F1 progeny also exhibited O. neolycopersici resistance, and whole-genome sequencing showed that no off-target mutations had occurred, and that no transgenic DNA was present (Nekrasov et al., 2017). A similar study by Wang et al. generated resistance in rice plants (Oryza sativa L.) to the rice blast pathogen Magnaporthe oryzae B.C. Couch by using CRISPR-Cas9 to induce indels in an S gene that encodes proteins involved in sugar transport (SWEET proteins) (F. Wang et al., 2016). Wang et al. also used segregation to create non-transgenic, disease-resistant F1 progeny that exhibited all the desirable phenotypes from the wild-type plants (F. Wang et al., 2016). There are now a number of examples of CRISPR-Cas9 S gene mutants with enhanced disease resistance to various pathogens including: broad virus resistance in cucumber plants through disruption the eukaryotic translation initiation factor eIF4E (J. Chandrasekaran et al., 2016), resistance to bacteria and oomycete pathogens in tomato plants through deletions in a DMR6 gene (de Toledo Thomazella et al., 2016), bacterial canker-resistant Wanjincheng orange plants via mutations in 57 the CsLOB1 gene promoter (A. Peng et al., 2017), and powdery mildew resistance in wheat through Cas9-mediated mutations of the TaEDR1 susceptibility gene (Y. Zhang et al., 2017). The above studies demonstrate the advantage of using the plant-gene approach in CRISPR-Cas9 research because the disruption of these genes with indels and the subsequent segregation of the transfer DNA results in disease-resistant plants that do not contain any transgenic material. However, the backcrossing required to segregate away the CRISPR-Cas9 plasmid DNA is only feasible in annual plants with short life cycles and is not suitable for perennial crop plants or forest species (Kanchiswamy et al., 2015). Non-transgenic CRISPR-Cas9 mutants can also be generated using a plasmid-free delivery system; this involves designing a pre-assembled enzymatic ribonucleoprotein (RNP) Cas9-sgRNA complex that is transfected directly into plant protoplasts. The Cas9-sgRNA RNP complex can modify the genomic target DNA but is subsequently degraded by the cell – this results in a disease-resistant plant mutant that contains no transgenic DNA (S. S. Makarova et al., 2018). These non-transgenic approaches are especially relevant in plant-based industries where the introduction of inter-specific transgenes generates public controversy around genetically modified organisms (GMOs) and initiates prohibitively strict regulations surrounding the use of such genetically modified plants. The ability of CRISPR-Cas9 to generate highly specific disease-resistant mutants that contain no foreign DNA allows for these plants to be used outside of the GMO regulatory framework (Kanchiswamy, 2016; Kanchiswamy et al., 2015; S. S. Makarova et al., 2018). It also allows specific genetic modifications to be made in the endogenous genomic context thereby avoiding the random insertion of transgenes from unrelated species and removing the risk of any 58 unintended downstream effects from the presence of foreign DNA (Kanchiswamy, 2016; Kanchiswamy et al., 2015; S. Kim et al., 2014). 3.3.2 Using CRISPR-Cas9 to target and explore genes in filamentous plant pathogens While the use of CRISPR-Cas9 to engineer pathogen resistance in plants is a promising approach to mitigating disease outbreaks, using CRISPR-Cas9 in pathogens is of equal interest both for the generation of avirulent strains and for increasing our understanding of how these species interact with their plant hosts to induce disease. So far, CRISPR-Cas9 research in plant pathogens has been far less prolific than research on plant disease resistance, and it has focused primarily on proof-of-concept experiments in filamentous fungi and oomycetes. The first successful demonstrations of CRISPR-Cas9 technology in filamentous fungi were independently published by four research groups in 2015, who all developed CRISPR-Cas9 systems in filamentous ascomycete species including Neurospora crassa Shear & B.O. Dodge (Matsu-ura et al., 2015), Pyricularia oryzae Cavara (Arazoe et al., 2015), Trichoderma reesei E.G. Simmons (R. Liu et al., 2015), and multiple Aspergillus species (Nødvig et al., 2015). These studies used Cas9 genes that were codon-optimized for filamentous fungi, endogenous promoters for expression of the sgRNA, and common fungal selection markers. More proof of concept studies followed and CRISPR-Cas9 was developed in a number of fungal species including important plant pathogens such as Ustilago maydis (DC.) Corda (corn smut: Schuster et al., 2016), Fusarium graminearum Schwabe (Fusarium head blight of grain: Gardiner and Kazan, 2018), F. oxysporum Schltdl. (Fusarium wilt disease: Wang et al., 2018), and Sclerotinia sclerotiorum (Lib.) de Bary (white mold: Li et al., 2018). The first CRISPR-Cas9 system in oomycetes was developed in the soybean pathogen Phytophthora sojae Kaufm. & Gerd.; the study used a Cas9 gene with human- 59 optimized codons fused to the P. sojae nuclear localization signal (PsNLS) and driven by the oomycete Ham34 promoter (Fang & Tyler, 2016). CRISPR-Cas9 has now additionally been developed in P. capsici Leonian (Miao, Chi, et al., 2018) and P. palmivora (E.J. Butler) E.J. Butler (Gumtow et al., 2018). A comprehensive review of the CRISPR-Cas9 techniques being used in filamentous fungi and oomycetes has been published by Schuster and Kahmann (2019); as with the plants, the CRISPR-Cas9 studies in filamentous pathogens have been focused on agriculturally important species, with no studies on forest pathogens reported in our literature search. 3.3.2.1 Targeting pathogenicity genes using CRISPR-Cas9 The study in S. sclerotiorum by Li et al. (2018)is one of the few that has used CRISPR-Cas9 to generate pathogenicity mutants in a fungal plant pathogen. Their gene target was the oxalate biosynthesis gene Ssoah1, responsible for producing oxalic acid, which is involved in host tissue colonization by S. sclerotiorum. Li et al. generated DSBs at multiple Ssoah1 target sites and found that fragments of their Cas9 transformation plasmid had been integrated into the S. sclerotiorum genome at the DSB sites; this demonstrated that the transformation plasmid was not only providing the Cas9 protein and sgRNA molecule, but also acting as a donor DNA molecule for the NHEJ pathway to repair the Cas9-induced DSB (J. Li et al., 2018). The Cas9-generated S. sclerotiorum Ssoah1-mutant strains exhibited significantly reduced oxalic acid production and reduced pathogenicity on soybean, Abyssinian cabbage, and tomato plants (J. Li et al., 2018). Sclerotinia sclerotiorum is a highly aggressive, necrotrophic phytopathogen with a very broad host range, so these results by Li et al. are very encouraging for the use of CRISPR-Cas9 as a tool for understanding the virulence of similar plant pathogens. Improved understanding of the 60 specific modes of pathogenicity employed by different phytopathogens will subsequently improve management strategies for the diseases they cause. An important group of pathogenicity genes are those encoding the effector proteins secreted by pathogens during host interactions. Effectors are an extremely diverse group of molecules and are found in some form in all groups of plant pathogens; they have a number of functions including facilitating infection, disrupting the plant immune response, and obtaining nutrients from host tissues (Toruño et al., 2016). Their ubiquity, as well as their dominant role in plant-pathogen interactions, make them excellent candidates for CRISPR-Cas9 research. This was demonstrated by Fang and Tyler in the first study to develop a CRISPR-Cas9 system in oomycetes; they designed an sgRNA that targeted the RxLR effector gene Avr4/6 in the soybean pathogen Phytophthora sojae (Fang & Tyler, 2016). RxLR effectors are widespread in oomycetes and can enter host cells and suppress effector-triggered immune responses (R. H. Y. Jiang & Tyler, 2012). They can be detected by the receptors encoded by plant R genes (R. H. Y. Jiang & Tyler, 2012); in soybeans the R genes involved in recognition of the Avr4/6 effector protein are Rps4 and Rps6 (Fang & Tyler, 2016). Fang and Tyler examined both the pathogen and host aspects of the P. sojae pathosystem: they used CRISPR-Cas9 to create five homozygous NHEJ Avr4/6 mutants and two homozygous HDR Avr4/6 mutants, and then assessed the interactions of the P. sojae mutants with soybean plants both with and without Rps4 or Rps6 resistance loci (Fang & Tyler, 2016). Their results showed that when inoculated with P. sojae-Avr4/6 mutants, the Rps4/Rps6 soybean plants were less able to defend themselves against infection, exhibiting an impaired immune response (Fang & Tyler, 2016). 61 The results from Fang and Tyler’s 2016 study demonstrate the intricacy of plant-pathogen interactions and serve as a reminder for researchers wishing to use genetic engineering technologies to develop disease-resistant plants: using CRISPR-Cas9 to target an effector may impede the pathogen, but it might also have unintended consequences for the plant host depending on the recognition pathway associated with the effector. Systems that have coevolved for millions of years cannot be easily deconstructed, and if CRISPR-Cas9 is to be used as a tool for mitigating plant disease outbreaks the complexities of these systems must be considered. However, CRISPR-Cas9 provides a perfect opportunity to understand these complex pathosystems, and the study by Fang and Tyler is an excellent example of the use of CRISPR-Cas9 as a tool for elucidating the roles of pathogen genes via a functional genomics approach. 3.4 Future perspectives for CRISPR-Cas9 in forest pathology Given the large number of studies on CRISPR-Cas9 in plant pathology, and the promises for developing disease resistance, it is somewhat surprising that there is very little literature in the area of forest pathology (Figure 3.2). Forest pathogens are as devastating as their agricultural counterparts and can cause landscape level mortality that results in ecosystem-wide changes; well-known global examples include chestnut blight, Dutch elm disease, ash dieback, myrtle rust, white pine blister rust and sudden oak death. However, disease management options are more limited in forestry than in agriculture; for example, the use of fungicides is generally confined to forest nurseries, with chemicals rarely being applied to trees once they have been planted. Disease resistance is one of the most promising avenues to combat forest pathogens, especially given the large geographical scales that these pathogens can affect. While there is recognition of the potential applications of CRISPR-Cas9 for forest ecosystems and a call for the 62 use of this technology in these systems (Fernandez i Marti & Dodd, 2018; Fritsche et al., 2018; Tsai & Xue, 2015), no applied studies in forest pathosystems have been performed. From the perspective of engineering plant disease resistance, this lack of CRISPR-Cas9 research in forest species is understandable for three major reasons. The first is that conifer species, which dominate forest ecosystems in the Northern Hemisphere, have significantly larger and more complex genomes than most agricultural angiosperms. Thus, there are far fewer whole genome sequences available (Neale et al., 2017; Nystedt et al., 2013) – a requirement for CRISPR-Cas9 gene editing in order to design effective sgRNAs and minimize off-target effects. The second reason is the increased difficulty of transformation protocols for forest tree species; this requires not only the transformation of DNA, but also the subsequent regeneration of the whole plant, which is a more time-consuming and complex process in woody perennial plants (Fernandez i Marti & Dodd, 2018; Peña & Séguin, 2001). The final reason is the controversy surrounding GMOs, which is shared with the agricultural sector, but is perhaps greater for woody forest Figure 3.2 The number of CRISPR publications by year since 2012 (development of CRISPR-Cas9 gene editing technology by Jinek et al.) obtained from a Web of Science topic search with the search parameters: ‘CRISPR’ or ‘CRISPR-Cas*’, or ’CRISPR/Cas*’. The ‘Analyze Results’ function of Web of Science was used to determine publication numbers by year and research area. (A) The log-transformed number of CRISPR publications by research area shows the common trend of increasing publications for CRISPR research for all areas except forestry. (B) A breakdown of the publication distribution by topic within forestry as determined by a manual search of the Web of Science search results. 63 species given their perennial nature and existence within semi-natural ecosystems; furthermore, the regulatory restrictions on genetically engineered trees are far stricter than those for agricultural crops (Strauss et al., 2009, 2015, 2016). Given these limitations, very few woody perennials have been successfully engineered with CRISPR-Cas9 relative to their annual counterparts, and the list of those that have is almost exclusively made up of agricultural species including Coffea canephora Pierre ex. A. Froehner (coffee: Breitler et al., 2018), Citrus sinensis (L.) Osbeck (sweet orange: Jia and Wang, 2014), Citrus paradisi Macfad. (Duncan grapefruit: Jia et al., 2016), and Malus spp. (apple: Malnoy et al., 2016; Nishitani et al., 2016). The only forest species for which CRISPR-Cas9 systems have been developed are those in the genus Populus: P. tomentosa Carriere (Fan et al., 2015; Y. Jiang et al., 2017; Shen et al., 2018; Wan et al., 2017; L. Wang et al., 2017; Xu et al., 2017; L. Yang et al., 2017), P. tremula x alba (Aiton) Sm. (Muhr et al., 2018; Zhou et al., 2015), and P. tremula x tremuloides (Elorriaga et al., 2018). Only two of these studies used CRISPR-Cas9 to target genes involved in poplar disease resistance (Y. Jiang et al., 2017; L. Wang et al., 2017). Given the paucity of studies using CRISPR-Cas9 in forest tree species, it is not surprising that there are no published examples of the use of this technology in forest pathogens. However, there should be fewer obstacles for applying this approach to the pathogens since they are often taxonomically related to the oomycete and fungal species that cause disease on agricultural plants. The study by Fang and Tyler on Phytophthora sojae (2016) clearly demonstrated the power of CRISPR-Cas9 as a tool for gaining a deeper understanding of phytopathogens and how they interact with their plant hosts. Similar studies should be implemented in forest pathogens, and there are some forest pathosystems that are ideally suited for the use of CRISPR-Cas9 64 technology. Below, we give examples of four pathosystems that could be used to drive CRISPR-Cas9 research development in forest pathology (Figure 3.3): two using an approach of engineered resistance in the host, and two using an exploratory approach in the pathogen. 3.4.1 Proposed CRISPR-Cas9 research in four forest pathosystems 3.4.1.1 Engineering disease resistance in poplars While CRISPR-Cas9 research in poplars has already started, its focus has mainly been on modifying genes involved in growth, reproduction, and lignin development with the goal of improving Populus species for growth in plantations as well as use in the pulp and bio-refinery industries (Chanoca et al., 2019; Elorriaga et al., 2018; Fan et al., 2015; Muhr et al., 2018; Shen et al., 2018; Wan et al., 2017; Xu et al., 2017; L. Yang et al., 2017; Zhou et al., 2015). However, there are many potential applications for CRISPR-Cas9 technology for developing disease resistance, and perhaps the most promising application in forest pathology would be the genome Figure 3.3 The four forest pathosystems proposed for future CRISPR/Cas9 research: (A) uredinia of the poplar leaf rust pathogen Melampsora medusae f. sp. deltoidis Shain, a basidiomycete fungus, on eastern cottonwood (Populus deltoides W.Bartram ex Marshall) leaves in Montréal, Quebec, CA (photo: Richard Hamelin, 2015); (B) flagging symptoms typical of Dutch elm disease, caused by the ascomycete fungal pathogen Ophiostoma novo-ulmi Brasier, on an American elm (Ulmus americana L.) in Québec, Quebec, CA (photo: Philippe Tanguay, 2016); (C) sudden larch death caused by the oomycete pathogen Phytophthora ramorum Werres, De Cock, & Man in’t Veld in a larch plantation in Galloway Forest Park, Scotland, GB (photo: Richard Hamelin, 2019); (D) aecia of the basidiomycete fungal pathogen Cronartium ribicola J.C. Fisch., causal agent of white pine blister rust, on a limber pine (Pinus flexilis E.James) in Rocky Mountain National Park, Colorado, US (photo: Erika Dort, 2019). 65 editing of poplar species for resistance to their major pathogens, namely Melampsora and Sphaerulina species. In fact, the Melampsora-Populus pathosystems (Figure 3.3A) have previously been proposed as a model system to further our understanding of host-pathogen recognition mechanisms and the infection process (Feau et al., 2007), and would therefore be an excellent place to start CRISPR-Cas9 research in forest pathology. Poplars are ecologically and economically valuable trees, playing important roles in both natural and managed forests, and they have long been established as model species for molecular and genomic studies in forest trees (Bradshaw et al., 2000; G. Taylor, 2002; Wullschleger et al., 2002). Additionally, Populus trichocarpa Torr. & A.Gray ex Hook. was the first tree species to have its genome sequenced (Tuskan et al., 2006), which perfectly situates poplar pathosystems for study with CRISPR-Cas9. Melampsora leaf rusts and Sphaerulina leaf spot and stem canker pathogens are two of the most damaging groups of fungi affecting poplars in both natural stands and plantations, and they have major impacts on productivity and forest health. The barrier of entry for using CRISPR-Cas9 to engineer resistance to these pathogens is particularly low for two reasons, the first being that CRISPR-Cas9 protocols have already been established in Populus species (Fan et al., 2015; Zhou et al., 2015). The second reason is that candidate genes associated with resistance have already been identified for both Melampsora and Sphaerulina species (Duplessis et al., 2009; La Mantia et al., 2013; Muchero et al., 2018; Yin et al., 2004). As discussed previously, there have now been a number of studies using CRISPR-Cas9 in Populus species, and while two of these studies used CRISPR-Cas9 to explore the functions of genes in Melampsora resistance by creating knockout mutants (Y. Jiang et al., 2017; L. Wang et al., 2017), none of them used CRISPR-Cas9 to directly engineer disease resistance. 66 3.4.1.2 Dutch elm disease The possibility of using CRISPR-Cas9 to control the dominant Dutch elm disease (DED) pathogen Ophiostoma novo-ulmi Brasier (Figure 3.3B) is another exciting prospect. Genome sequencing and annotation of this pathogen has helped in identifying multiple candidate genes involved in the infection process (Comeau et al., 2015; Forgetta et al., 2013; Khoshraftar et al., 2013). Of particular interest are the genes involved in regulating the yeast-mycelial dimorphism exhibited by many Ophiostoma species. Ophiostoma novo-ulmi uses its budding yeast phase to travel rapidly through the tissues of its elm hosts but can also switch to a mycelial form that can penetrate xylem tissues and grow radially in the elm (Berrocal et al., 2012). This ability to switch between yeast and mycelial growth forms is thought to be involved in the pathogenicity of dimorphic fungi (Nadal et al., 2008), and has thus been explored in the DED pathogens for a number of years (Berrocal et al., 2012; Naruzawa & Bernier, 2014; W. C. Richards, 1994; Wedge et al., 2016). Transcriptomic analyses have identified candidate genes involved in this yeast-to-hypha transition (Nigg et al., 2015; Nigg & Bernier, 2016). These genes are excellent candidates for CRISPR-Cas9 gene editing: triggering the NHEJ pathway could create knockout mutants with reduced ability to switch to the yeast form thereby impeding translocation of the fungus throughout the elm tree. Another group of interest as candidate genes involved in O. novo-ulmi pathogenicity are the secondary metabolite clusters. In plant pathogenic fungi, secondary metabolites such as host-selective toxins are well known to play an important role in disease development (Macheleidt et al., 2016). Bioinformatic annotations have identified O. novo-ulmi gene clusters putatively 67 involved in biosynthesis of secondary metabolites, and interspecific comparative genomic analyses uncovered a fujikurin-like gene cluster (OpPKS8), found in the DED pathogens (O. ulmi (Buisman) Nannf. and O. novo-ulmi) but absent in related non-phytopathogenic species (Sbaraini et al., 2017). According to phylogenetic analyses the authors suggested that this toxin-related cluster may have been horizontally acquired by DED pathogens (Sbaraini et al., 2017). Genes in the OpPKS8 cluster are good candidates for exploration with CRISPR-Cas9, which could be used as an additional tool to elucidate the functions of this secondary metabolite cluster and its potential role in pathogenicity. Finally, a recent pangenomic analyses of a collection of strains from O. ulmi and O. novo-ulmi species showed that introgression has been the main driver of genomic diversity and has impacted fitness-related traits, with many of the introgressed regions containing genes involved in host–pathogen interactions and reproduction (Hessenauer et al., 2020). Hessenauer et al. (2020) further demonstrated that the virulence of O. novo-ulmi was positively or negatively affected depending on the location of the introgressed genes in the genome. As with the secondary metabolites, CRISPR-Cas9 could be used as a means of exploring the functions of some of these introgressed genes that appear to play a role in virulence. Development of a CRISPR-Cas9 system in O. novo-ulmi has already begun (Tanguay, 2019), which makes implementing such pathogenicity-related strategies an impending reality. 3.4.1.3 Sudden oak/larch death Some filamentous plant pathogens exhibit virulence via their ability to suddenly switch lifestyles. This is the case in many fungal and oomycete species that are hemibiotrophs, meaning 68 they can transition from an asymptomatic biotrophic phase to an aggressive necrotrophic phase in which they begin releasing toxins and killing host tissues (Koeck et al., 2011; S.-J. Lee & Rose, 2010). The genes enabling this dual lifestyle could be effective targets for CRISPR-Cas9 in order to better understand how hemibiotrophic pathogens cause disease. Research in the oomycete pathogen, Phytophthora infestans (Mont.) de Bary, showed that effectors are the mediators of this lifestyle transition (S.-J. Lee & Rose, 2010), which is not surprising given the dominant role these molecules play in plant-pathogen interactions. These results are very encouraging as CRISPR-Cas9 systems have already been developed in P. sojae (Fang, Cui, et al., 2017), P. capsici (Miao, Chi, et al., 2018), and P. palmivora (Gumtow et al., 2018), and it has proven successful in disrupting effector genes (Fang & Tyler, 2016; Gumtow et al., 2018). The invasive forest pathogen Phytophthora ramorum Werres, De Cock, & Man in’t Veld (Figure 3.3C), causal agent of sudden oak death in the United States (Rizzo et al., 2002a, 2002b) and sudden larch death in the United Kingdom (Webber et al., 2010), is another hemibiotrophic oomycete pathogen that causes devastating disease outbreaks in a broad range of woody hosts (Rizzo & Garbelotto, 2003). Phytophthora ramorum is classified as a highly aggressive pathogen given its ability to infect woody stems as well as foliar tissues, however, its hemibiotrophic nature allows it to remain asymptomatic anywhere from months to years before it transitions to its aggressive necrotrophic lifestyle (Rizzo & Garbelotto, 2003). It would therefore be an excellent candidate for CRISPR-Cas9 research exploring the role of effectors in mediating this lifestyle switch that influences virulence so significantly. 69 3.4.1.4 White pine blister rust White pine blister rust (WPBR), caused by the basidiomycete rust fungus Cronartium ribicola J.C. Fisch. (Figure 3.3D), has severely affected North American populations of many economically and ecologically important pine species such as eastern and western white pines, sugar pine, and whitebark pine (Sniezko et al., 2014). In many white pine species both complete and partial resistance to WPBR have been detected. Complete resistance is mediated by a dominant R gene named Cr, which causes a hypersensitive response to C. ribicola and enables the pine host to survive by restricting the infection to the needles (Kinloch et al., 2003; Sniezko et al., 2014). Partial resistance appears to be a more complex response that is likely mediated by multiple genes, but the exact mechanisms driving this response are not yet known (Sniezko et al., 2014). While the Cr genes mediating complete resistance in white pines seem like the perfect candidates for CRISPR-Cas9-generated disease resistance, these R genes are likely not stable in the long-term because only a single mutation in a corresponding C. ribicola effector gene would be required to overcome this resistance (Sniezko et al., 2014). However, plant R gene immune receptors can be mutated to provide resistance to phylogenetically divergent pathogens (Giannakopoulou et al., 2015; Segretin et al., 2014), and CRISPR-Cas9 could be used to engineer such synthetic genes in tandem in order to create stable multi-resistance plant systems (Andolfo et al., 2016). Another option to obtain more durable long-term resistance in WPBR pathosystems is to focus CRISPR-Cas9 research on partial resistance. Cas9 can be co-expressed with many sgRNAs to simultaneously target multiple genes; this multiplex gene editing could facilitate the discovery of the genes involved in partial resistance, and it could also eventually be used to target those genes simultaneously in engineered WPBR-resistant pine populations. This partial resistance strategy may be less effective than complete resistance to a single C. ribicola 70 strain, but it would be more stable in the long-term against a constantly evolving C. ribicola population exhibiting diverse mutations and could also protect pine hosts against other encroaching pathogen species. The four suggestions above demonstrate the scope of utility of CRISPR-Cas9 gene editing technology and highlight how this tool has been underutilized in forest pathology. CRISPR-Cas9 is a relatively recent development, and there are clear obstacles to its use in forest pathosystems. However, given the large number of fungal and oomycete species for which CRISPR-Cas9 systems have now been established, the barrier of entry for pathologists studying filamentous forest pathogens has been lowered, and there is a generalized research pipeline that they can follow to implement this gene editing technology in their study organisms (Figure 3.4). This pipeline can comprise various approaches for developing a CRISPR-Cas9 system, including using different Cas9 delivery methods (RNP vs. plasmid) and genetic targets (DNA vs. RNA). Figure 3.4 shows a generalized schematic of one such approach based on the CRISPR-Cas9 system being developed in the Dutch elm disease pathogen Ophiostoma novo-ulmi (Tanguay, 2019). We hope this gives forest pathologists a better understanding of the logistics involved in developing CRISPR-Cas9 systems in filamentous forest pathogens. 71 As sequencing technologies continue to improve and lower in cost, forest pathologists should aim to increase their exploration of the genetic basis of plant disease resistance via CRISPR-Cas9 gene editing. The power of this technology to aid our understanding of the intricacies of plant-pathogen interactions and generate effective strategies of disease resistance in forest pathosystems is much too great to ignore. Figure 3.4 Example of a generalized CRISPR/Cas9 workflow and timeline for a filamentous forest pathogen based on the protocol being developed for the Dutch elm disease pathogen Ophiostoma novo-ulmi (Onu). (A) Transformation of fungal protoplasts with a plasmid containing (see inset): a Cas9 gene fused to nuclear localization signal (NLS), a single-guide RNA (sgRNA) scaffold with a 20-nucleotide (nt) region designed to base-pair with the target genomic DNA, an antibiotic resistance gene for transformant selection, and gene promoters and terminators active in the target species. Alternatively, protoplasts can be co-transformed with a combination of Cas9-sgRNA ribonucleoprotein complexes (RNPs) and a plasmid for antibiotic transformant selection. (B) In successful transformants, Cas9 forms a complex with the sgRNA molecule, which guides the complex to the protospacer (a genomic DNA target in this example) with an adjacent PAM (protospacer adjacent motif) sequence. The sgRNA-Cas9 complex is analogous to the crRNA-effector complexes of native CRISPR/Cas systems, shown in Figure 1 of this review. In the RNP approach, this sgRNA-Cas9 complex is pre-assembled and transfected directly into the protoplasts. (C) Transformants are selected by growth on antibiotic selective medium (picture shows Onu hygromycin-resistant transformants: pink colonies are CRISPR/Cas9 ade2 mutants). (D) Successful CRISPR/Cas9 mutants are confirmed through sub-culture of the putative transformants from the previous step, PCR screening, and Sanger sequencing. 72 3.5 Conclusions Since the discovery of CRISPR-Cas systems in 1987 (Ishino et al., 1987), our understanding of this adaptable immune response has come a long way, and the development of CRISPR-Cas9 gene editing technology in 2012 (Jinek et al., 2012) resulted in an explosion of research with wide-reaching implications for most biological systems. Despite the development of Cas9 tools in many pathosystems, there are still limitations to the use of CRISPR-Cas technology in plant pathology, especially concerning off-target effects. However, careful design of sgRNAs and modifications to the Cas proteins prevent most of these effects, and the use of RNP delivery systems has reduced off-target mutations to zero in many systems (Das et al., 2019). Additionally, the continued development of CRISPR-Cas technology in plant pathosystems will only improve efficiency as this technology is adapted to function in a diversity of organisms. To date, most CRISPR-Cas9 research in plant pathology has been focused on agricultural pathosystems, with little to no research in forest pathology. This is understandable given the availability of genomic resources for most major crops as well as the shorter generation times of crop plants relative to their forest counterparts, which makes for a quick feedback loop on any genetic modifications made with CRISPR-Cas9. However, forest pathogens are wreaking equal havoc in the forestry sector and represent a global threat to forest ecosystems that needs to be addressed immediately. The time is now to adopt CRISPR-Cas9 in forest pathology, at the very least to improve our understanding of host-pathogen interactions, but ideally to begin integrating it into forest improvement programs to generate more effective disease resistance strategies for long-term forest sustainability. 73 Chapter 4: Heterogeneity in establishment of polyethylene glycol-mediated plasmid transformations in five forest pathogenic Phytophthora species 4.1 Chapter Summary Plasmid-mediated DNA transformation is a foundational molecular technique and the basis for most CRISPR-Cas9 gene editing systems. While plasmid transformations are well established for many agricultural Phytophthora pathogens, development of this technique in forest Phytophthoras is lacking. Given our long-term research objective to develop CRISPR-Cas9 gene editing in a forest pathogenic Phytophthora species, we sought to establish the functionality of polyethylene glycol (PEG)-mediated plasmid transformation in five species: P. cactorum, P. cinnamomi, P. cryptogea, P. ramorum, and P. syringae. We used the agricultural pathogen P. sojae, a species for which PEG-mediated transformations are well-established, as a transformation control. Using a protocol previously optimized for P. sojae, we tested transformations in the five forest Phytophthoras with three different plasmids: two developed for CRISPR-Cas9 gene editing and one developed for fluorescent protein tagging. Out of the five species tested, successful transformation, as indicated by stable growth of transformants on a high concentration of antibiotic selective growth medium and diagnostic PCR, was achieved only with P. cactorum and P. ramorum. However, while transformations in P. cactorum were consistent and stable, transformations in P. ramorum were highly variable and yielded transformants with very weak mycelial growth and abnormal morphology. Our results indicate that P. cactorum is the best candidate to move forward with CRISPR-Cas9 protocol development and provide insight for future optimization of plasmid transformations in forest Phytophthoras. 74 4.2 Introduction The oomycete genus Phytophthora, comprising almost 200 species, is well-known for containing an abundance of destructive plant pathogens (Q. Chen et al., 2022). The substantial economic and environmental impacts of Phytophthora species in horticulture, agriculture, and forestry, has led to decades of research focused on improved management and mitigation of the damage caused by these ubiquitous plant pathogens. The continued development of molecular DNA techniques and sequencing technologies in the last 20-30 years has shifted Phytophthora research increasingly towards genetics and genomics studies with the intent of elucidating the genetic mechanisms that drive phytopathogenicity and other related traits (Birch & Whisson, 2001; Feau et al., 2016; Govers & Gijzen, 2006; Huitema et al., 2004; Kamoun, 2003; M. Yang et al., 2018). The recent development of CRISPR-Cas9 gene editing (Jinek et al., 2012) and related technologies has provided new tools for the genetic exploration of phytopathogenicity, and CRISPR-Cas9 protocols have now been developed for several agricultural Phytophthora pathogens (Fang & Tyler, 2016; Gumtow et al., 2018; Miao, Chi, et al., 2018; W. Wang et al., 2019; Z. Wang et al., 2022). CRISPR-Cas gene editing technologies have proven to be invaluable in elucidating the genetic mechanisms driving Phytophthora-host interactions and phytopathogenicity (Ah-Fong et al., 2021; Fang & Tyler, 2016; Gumtow et al., 2018; X. Li et al., 2020), and exploring fungicide resistance (Miao, Chi, et al., 2018; Miao, Li, et al., 2018; Z. Wang et al., 2022). However, CRISPR-Cas9 gene editing has yet to be established in any forest pathogenic Phytophthoras, likely due to the lack of stable and efficient DNA transformation protocols in these species. Generating long-term management strategies for forest Phytophthora pathogens will require a deeper understanding of the genetics driving their phytopathogenicity, however, that will not be possible without the development of reliable molecular techniques. 75 Establishing DNA transformations in forest Phytophthoras is therefore a crucial step to bring these species into the molecular genetics era and will allow the development of exploratory techniques such as CRISPR-Cas9 gene editing. DNA transformation is a fundamental molecular technique that is used to study cellular processes and characterize specific genes involved in pathways or interactions of interest. The development of CRISPR-Cas9 protocols and similar techniques for any organism relies on a functioning and reliable DNA transformation system. The first Phytophthora species in which DNA-mediated transformations were developed were the agricultural pathogens P. capsici Leonian and P. parasitica Dastur (Bailey et al., 1991). Using a polyethylene glycol (PEG)-mediated method, Bailey et al. (1991) tested two plasmids developed for transformations in Ustilago maydis (DC.) Corda: the integrative plasmid pHL1 and the autonomously replicating plasmid pCM54. Both P. capsici and P. parasitica were successfully transformed, and the results for the plasmid DNA were as expected in that pHL1 integrated into the Phytophthora genomes, whereas pCM54 was maintained episomally (Bailey et al., 1991). However, the pHL1 and pCM54 fungal transformation plasmids were not readily adaptable for transformations in other Phytophthora species (Judelson et al., 1991). Therefore, attention turned to developing oomycete-specific transformation vectors. The first species in which these oomycete-specific plasmids were tested was P. infestans (Mont.) de Bary, the causal agent of late blight of potato (Judelson et al., 1991; Judelson & Michelmore, 1991). The plasmids constructed by Judelson and colleagues for these early transformation studies were derived from the common plasmid cloning vector pUC19 (Norrander et al., 1983), modified to include promoter and transcription terminator sequences from the ham34 and hsp70 genes of the oomycete Bremia lactucae Regel 76 (Judelson et al., 1991; Judelson & Michelmore, 1991). Neomycin phosphotransferase (nptII) and hygromycin phosphotransferase (hpt) genes were used to generate stable P. infestans transformants resistant to geneticin (G418) or hygromycin B, respectively (Judelson et al., 1991). These first Phytophthora transformations used PEG and calcium chloride (CaCl2) to transform cell-wall digested protoplasts (Judelson et al., 1991; Judelson & Michelmore, 1991), employing a protocol adapted from transformations successfully developed in fungi (Yelton et al., 1984). In the following decades, the PEG-CaCl2-mediated transformation approach was adapted and modified for other Phytophthora species, including P. sojae Kaufm. & Gerd. (Judelson et al., 1993), P. nicotianae Breda de Haan (Bottin et al., 1999), P. palmivora (E.J. Butler) E.J. Butler (van West et al., 1999), P. brassicae De Cock & Man in’t Veld (Si-Ammour et al., 2003), P. citricola Sawada (Mcleod et al., 2008), P. capsici (Dunn et al., 2013), and P. cactorum (Lebert & Cohn) J. Schröt. (X.-R. Chen et al., 2016). Alternative transformation methods were also developed for various Phytophthora species including microprojectile bombardment (Bailey et al., 1993; Cvitanich & Judelson, 2003), electroporation (Huitema et al., 2011; Latijnhouwers & Govers, 2003), and Agrobacterium-mediated transformation (Vijn & Govers, 2003; Wu et al., 2016). Each transformation method has strengths and weaknesses, however, the PEG-CaCl2 method continues to be the most common approach due to its success across Phytophthora species and its relatively adaptable methodology for research labs without highly specialized equipment (Ghimire et al., 2022; Mcleod et al., 2008). While molecular protocols have been well-developed for agricultural Phytophthoras, research is lacking for forest Phytophthoras. A literature search revealed only four studies that developed 77 DNA transformation systems in forest pathogenic Phytophthora species: two using the original PEG-CaCl2 protocol developed by Judelson et al. (1991) to transform the jarrah dieback pathogen P. cinnamomi Rands (McCarren et al., 2005) and the sudden oak death pathogen P. ramorum Werres, De Cock, & Man in’t Veld (Riedel et al., 2009); and two performing the optimized PEG-CaCl2 transformation (Mcleod et al., 2008) in P. cinnamomi (Dai et al., 2021) and the soilborne pathogen P. cactorum (X.-R. Chen et al., 2016). However, there have been no follow-up studies in either P. ramorum or P. cactorum after their initial transformation, so the long-term reliability and efficiency of DNA transformations in these species and other forest Phytophthoras remains unknown. The objective of this study was to test the optimized PEG-CaCl2 transformation (Fang, Cui, et al., 2017; Mcleod et al., 2008) protocol in five forest Phytophthora pathogens: P. cactorum, P. cinnamomi, P. cryptogea Pethybr. & Laff., P. ramorum, and P. syringae (Kleb.) Kleb. The purpose of testing five species was two-fold: first, to establish the adaptability and reliability of PEG-CaCl2 transformations across common forest pathogenic Phytophthora species; and second, to determine which Phytophthora forest pathogens can be readily transformed and would therefore be good candidates to move forward with the development of CRISPR-Cas9 gene editing protocols. 78 4.3 Methods Note: the recipes and methods used for all growth media and transformation solutions are listed in Appendix B . 4.3.1 Phytophthora species, isolates, and culture conditions The Phytophthora species and isolates used in this study are summarized in Table 4.1. All cultures were maintained on clarified 20% V8 agar (V8A: see Appendix B.1 for recipe) at room temperature (~21°C) in the dark. For long-term storage, agar plugs taken from the edge of mycelial growth, five days post plating, were stored in nuclease-free molecular water at 5-10°C in the dark. Table 4.1 The five forest Phytophthora species and isolates used in this study. Phytophthora sojae was used as a control for transformations. Species Isolate/Strain Isolate Details Phytophthora cactorum Larch FF-42 2Pa See Feau et al. (2021) for isolate details. Phytophthora cinnamomi CBS 270.55 Purchased from the CBS-KNAW culture collection (Westerdijk Fungal Biodiversity Institute). Originally isolated in 1954 by I.W. Buddenhagen from Chamaecyparis lawsoniana var. alumii in Boskoop, Netherlands. JP-09-065 (strain A-3/3/09-P-2-st) Isolated in 2009 by J. Parke from Pieris japonica in Marion, OR, USA. Culture provided by N. Grünwald. Phytophthora cryptogea RS-2006-soil-d8 Sample collected in 2017 by N. Feau from the soil surrounding Pinus monticola on Vancouver Island, BC, Canada. Culture isolated by W. Vasquez. Phytophthora ramorum NA2 17_0134_0030 Cultures of both isolates provided by G. Bilodeau from the Canadian Food Inspection Agency. NA2 16_0386_0016 Phytophthora sojae P6497 (race 2) Isolated from infected Glycine max plants in Mississippi, USA. Culture provided by F. Arredondo. Phytophthora syringae Alder 12 See Feau et al. (2022) for isolate details. 79 4.3.2 Transformation plasmids Three plasmids were tested for transformations: pYF2-PsNLS-hSpCas9-GFP (abbreviated pYF2-PsCG), pYF515, and pGFPN. Both pYF2-PsCG and pYF515 are CRISPR-Cas9 plasmids developed for P. sojae. pYF2-PsCG was originally developed to test expression of a human codon-optimized Streptococcus pyogenes Cas9 (hSpCas9) gene in P. sojae; the hSpCas9 gene is fused at its 5’ end to a nuclear localization sequence from P. sojae (PsNLS) and tagged at its 3’ end with a green fluorescent protein (GFP) (Fang, Jang, et al., 2017; Fang & Tyler, 2016). The pYF2-PsCG plasmid also contains genes for ampicillin resistance (AmpR: conferred by the β-lactamase gene from the pUC19 plasmid) and geneticin resistance (nptII) (Figure 4.1). pYF515 was developed as an ‘all-in-one’ transformation plasmid to express both hSpCas9 and a single-guide RNA (sgRNA) for CRISPR-Cas9 gene editing (Fang, Cui, et al., 2017). Like pYF2-PsCG, pYF515 has AmpR and nptII antibiotic resistance genes (Figure 4.2). pGFPN is a transformation plasmid developed for fluorescent protein tagging in Phytophthora species (Ah-Fong & Judelson, 2011) and contains a GFP gene in addition to the AmpR and nptII antibiotic resistance genes (Figure 4.3). The DNA sequences of all three transformation plasmids are provided in Appendix B.3. 80 Figure 4.1 Map of the pYF2-PsNLS-hSpCas9-GFP (abbreviated pYF2-PsCG) transformation plasmid developed by Fang and Tyler (2016) for expression of hSpCas9 in Phytophthora sojae. Plasmid sequence determined from Fang and Tyler (2016) and Fang et al. (2017). Plasmid map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). 81 Figure 4.2 Map of pYF515, the 'all-in-one' plasmid developed by Fang et al. (2017) for expression of hSpCas9 and a single-guide RNA (sgRNA) for CRISPR/Cas9 gene editing. Plasmid sequence provided by Dr. Felipe Arredondo (Oregon State University). Plasmid map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). 82 4.3.2.1 Plasmid sequencing DNA from all three plasmids was received from a collaborator (Dr. Felipe Arredondo, Oregon State University) and transformed into DH5⍺ Escherichia coli chemically competent cells (Thermo Fisher Scientific, Waltham, MA, USA) for replication. Plasmid DNA was extracted using either a QIAGEN HiSpeedⓇ Plasmid Midi Kit or Maxi Kit (QIAGEN Sciences, Figure 4.3 Map of the transformation plasmid pGFPN developed by Ah-Fong and Judelson (2011). Plasmid sequence provided by Dr. Felipe Arredondo (Oregon State University). Plasmid map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). 83 Germantown, MD, USA) as per the manufacturer’s instructions (HiSpeedⓇ Plasmid Purification Handbook). The extracted DNA was adjusted to a concentration of 30 ng/μL for sequencing. Whole plasmid sequencing was performed by Plasmidsaurus (Eugene, OR, USA) using Oxford Nanopore Technology with custom analysis and annotation. Due to issues experienced with the pGFPN plasmid in the lab, a second preparation of plasmid DNA was sent from Dr. Arredondo and therefore the two independent preparations of the pGFPN plasmid were sent for sequencing. The sequence reads returned from Plasmidsaurus for each plasmid preparation were mapped to the respective expected sequence using Geneious Prime 2023.2.1 (https://www.geneious.com) to determine the percent identity of each plasmid relative to its reference sequence. 4.3.3 Determining the minimum inhibitory concentrations of geneticin for Phytophthora isolates The antibiotic geneticin (G418) was used for selection of Phytophthora transformants. Prior to transformations, growth assays for all six Phytophthora species used in this study were performed on V8A plates supplemented with a gradient of G418 (Gibco™️ Geneticin™️ Selective Antibiotic, Thermo Fisher Scientific, Waltham, MA, USA) concentrations to determine the minimum inhibitory concentration (MIC) for each species. The G418 concentrations tested for each species and isolate are listed in Table 4.2 (note that P. cinnamomi isolate JP-09-065 was not tested). The volume of medium per plate was standardized and two replicate plates were produced for each G418 concentration. The MIC for each species was recorded as the first G418 concentration at which mycelial growth stopped (see example for P. cactorum in Figure 4.4). 84 Table 4.2 Geneticin (G418) concentrations tested for each Phytophthora species used in this study to determine minimum inhibitory concentrations. Species Isolate(s) Tested G418 Concentration Gradient (μg/mL) Phytophthora cactorum Larch FF-42 2Pa 0, 2, 5, 10, 20, 30, 50 Phytophthora cinnamomi CBS 270.55 0, 2, 5, 10, 20, 30, 40, 50 Phytophthora cryptogea RS-2006-soil-d8 0, 2, 5, 10, 20, 30, 50 Phytophthora ramorum NA2 16_0386_0016, NA2 17_0134_0030 0, 2, 5, 10, 20, 30, 40, 50 Phytophthora sojae P6497 (R2) 0, 2, 5, 10, 20, 30, 40, 50 Phytophthora syringae Alder 12 0, 2, 5, 10 4.3.4 Protoplast isolation and transformation An optimized PEG-mediated protoplast transformation protocol (Fang, Cui, et al., 2017), based on previously described methods (Dou et al., 2008; Mcleod et al., 2008), was used to introduce plasmid DNA into Phytophthora cells. The recipes for all growth media and solutions used for Figure 4.4 An example of the P. cactorum isolate Larch FF-42 2Pa growth assay demonstrating how the geneticin (G418) minimum inhibitory concentration (MIC) was determined for each forest Phytophthora species in this study. Each isolate was grown on V8 agar supplemented with a gradient of G418 concentrations (labels above the image), and two replicate plates were tested for each concentration (labels to the left of image). The first G418 concentration at which mycelial growth stopped was recorded as MIC for that species/isolate. 85 transformations can be found in Appendix B.1.Various modifications to culture media, protoplast isolation conditions, plasmid DNA concentrations, and protoplast recovery media were made to test their effects on transformation success in the five different forest Phytophthora species (see Data B.2.1 for all conditions tested). 4.3.4.1 Preparation of Phytophthora cultures for transformation Phytophthora cultures were initially grown on 1.5% agar plates (nutrient pea agar [NPA], nutrient V8 agar [NVA], or 20% V8 agar [V8A]) at room temperature (~21°C) in the dark. Once there was sufficient mycelial growth (five to seven days), cultures were transferred to a liquid medium (nutrient pea broth [NPB], nutrient V8 broth [NVB], or V8 broth [V8B]): three flasks, each containing 50 mL of broth, were inoculated with five agar plugs (6-mm diameter) taken from the growing edge of the mycelial culture (15 agar plugs in total). The liquid cultures were grown for three days at room temperature (~21°C) in the dark. 4.3.4.2 Isolation of protoplasts After three days of growth, mycelial mats were harvested and rinsed with autoclaved Milli-QⓇ water followed by 0.8 M mannitol. The mycelial mats were transferred to a 100 mm × 20 mm Petri dish, covered with 0.8 M mannitol, and shaken gently (~30 rpm) for 10 minutes. The mats were recollected, transferred to a new Petri dish containing 20 mL of an enzyme solution (lysing enzymes + cellulase; see Appendix B.1 for full recipe) and digested for 60-90 minutes at 25°C with gentle shaking (~30 rpm). Protoplast release was monitored with microscopy. After digestion, the enzyme/protoplast solution was filtered through a 70 μm cell strainer and centrifuged at 1200 rcf for 2.5 minutes. The pellet was gently washed in 35 mL of W5 solution 86 (see Appendix B.1 for recipe), recentrifuged at 1200 rcf for 3 minutes, and the resulting pellet was resuspended in 10 mL of W5 solution and placed on ice for 1-2 hours. The protoplast concentration was determined using a hemocytometer. 4.3.4.3 Transformation of protoplasts with plasmid DNA Following incubation on ice, the protoplast suspension was centrifuged at 1200 rcf for 4 minutes. The resulting pellet was resuspended in MMg solution (see Appendix B.1 for recipe) at a concentration of 2 × 10⁶ – 2 × 10⁷ protoplasts/mL and incubated at room temperature for 10 minutes. During incubation, ≤15 µL of plasmid DNA (13-55 µg, see Data B.2.1 for amounts tested), or sterile molecular-grade water for the control treatment, was added to a 50 mL centrifuge tube and placed on ice. After the 10-minute incubation, the protoplasts were gently mixed, and 1 mL of the protoplast/MMg solution was added to the DNA (or water) in each 50 mL tube, mixed, and incubated for 30 minutes on ice. A 40% (v/v) PEG solution was added to each tube in three aliquots of 580 µL for a total of 1.74 mL of PEG solution per tube, and the tubes were incubated for another 30 minutes on ice. After 30 minutes, 2 mL of pea-mannitol (PM) or V8-mannitol (VM) broth was added to each tube. The tubes were inverted to mix and incubated on ice for two minutes. Then, 8 mL of PM/VM broth was added to each tube, the tubes were inverted to mix and incubated at room temperature for two minutes. To test the viability of the protoplasts after the transformation process, ~30-50 µL of the protoplast-PM/VM solution from each centrifuge tube (including control treatment) was spread onto an agar plate (NPA or NVA) and incubated at room temperature in the dark overnight (protoplast viability test Day 1). The remainder of the protoplast-PM/VM solution (~10 mL) from each tube was added to a 100 mm × 20 mm Petri dish containing 10 mL of PM/VM broth with 100 µg/mL of ampicillin 87 (Thermo Fisher Scientific, Waltham, MA, USA) for a final concentration of 50 µg/mL ampicillin. The dishes were incubated overnight at room temperature in the dark. 4.3.4.4 Antibiotic selection of transformants The following morning, the regenerated protoplasts were transferred to a new 50 mL tube and centrifuged at 2000 rcf for 5 minutes. The pellet was resuspended in ~2 mL of the supernatant and 150 µL of the resuspended protoplasts were added to 20 mL of cooled PM/VM agar (40-45°C) without G418 and poured into a Petri dish (protoplast viability test Day 2). Cooled PM/VM agar (40-45°C) with 5-50 µg/mL of G418 (see Data B.2.1 for concentrations tested) was added to the remaining protoplasts to a total volume of 50 mL. The tubes were inverted to mix, and the protoplast/agar mixture was poured into three Petri dishes (~16-17 mL of mixture per dish). All plates were incubated at room temperature in the dark for two days, or until mycelial growth appeared (up to five days). Each mycelial colony that grew on the G418-supplemented medium was treated as a putative transformant and was transferred to a well of a 12-well culture plate containing 2 mL of V8 broth with an increased concentration of G418 (20-60 µg/mL, see Data B.2.1 for concentrations tested). 4.3.5 Identification of successful transformants Any putative transformants that grew through the second round of G418 selection were considered successful transformants based on their stable growth on selective medium. Mycelial tissue from stable transformants was harvested for DNA extraction and subsequent diagnostic PCR to confirm the presence of plasmid DNA. Wildtype (non-transformed) cultures were grown in parallel as a negative control. After 3-4 days of growth in V8 broth supplemented with G418 88 (no G418 for wildtype cultures), 50-100 mg of mycelial tissue was harvested from each culture, and flash frozen in liquid nitrogen. Total cellular DNA was extracted using a QIAGEN DNeasyⓇ Plant Mini Kit (QIAGEN Sciences, Germantown, MD, USA) as per the manufacturer’s instructions (DNeasyⓇ Plant Handbook, Mini Protocol, TissueLyser procedure). Tissue disruption was performed using a Retsch Mixer Mill MM 300 (Retsch GmbH, Haan, Germany). PCR was performed on the DNA extractions from plasmid transformants, wildtype cultures (negative control), and pure plasmid DNA (positive control) using Platinum Taq DNA Polymerase (Thermo Fisher Scientific, Waltham, MA, USA) and primers (Integrated DNA Technologies, Coralville, IA, USA) designed to amplify specific regions of each plasmid (Table 4.3). The PCR primers were designed using NCBI Primer-BLAST (Ye et al., 2012) with target specificity checked against the “RefSeq representative genomes” database for Phytophthora (taxid: 4783) to minimize off-target amplification. All PCRs were performed using a Bio-Rad T100™️ thermal cycler (Hercules, California, USA) and the following cycling conditions (as per manufacturer’s instructions for Platinum Taq): an initial hold of 94°C for 2 minutes followed by 35 cycles of 94°C for 30 seconds, the primer-specific annealing temperature (see Table 4.3) for 30 seconds, and 74°C for 1 minute. The resulting PCR products were analysed using gel electrophoresis. The presence of an amplified band at the appropriate size confirmed the transformation of plasmid DNA. 89 Table 4.3 Primer pairs used for diagnostic PCR to detect plasmid DNA in Phytophthora transformants. Primer Pair Name Primer Sequences (5’ to 3’) Annealing Temperature Plasmids Expected Product Size AmpR_Diag_F1/R1 F1: CTGGCGAACTACTTACTCTAGC 54.0°C pGFPN, pYF515, pYF2-PsCG 232 bp R1: TCATCCATAGTTGCCTGACTCC eGFP_Diag_F1/R1 F1: GGAACTGGATGGTGATGTGAAC 51.4°C pGFPN, pYF2-PsCG 274 bp R1: CTTGTAGTTCCCGTCATCCTTG pYF_Cas9_Diag_F3/R3 F3: GCTGCCTGAGAAGTACAAAGAG 62.0°C pYF515, pYF2-PsCG 824 bp R3: CCAGAATGTCCTCGTTTTCCTC 4.4 Results 4.4.1 Sequencing of Phytophthora transformation plasmids indicates instability of pGFPN The sequencing results from Plasmidsaurus showed the expected sequences for pYF515 (99.8% pairwise identity to original plasmid sequence; Figure 4.5) and pYF2-PsCG (99.8% pairwise identity to original plasmid sequence; Figure 4.6), but not for either of the two pGFPN preparations (52.9% and 62.6% pairwise identity to original plasmid sequence; Figure 4.7, Figure 4.8). The expected size of the pGFPN plasmid was 6,891 bp, however, the sequencing results indicated that the two pGFPN preparations were 9,842 bp and 4,927 bp with apparent gene repeats, deletions, and inversions relative to the original plasmid sequence (Figure 4.7, Figure 4.8). 90 Figure 4.6 Paired alignment of the expected pYF2-PsCG sequence (top) and the Plasmidsaurus pYF2-PsCG sequence (bottom). The pairwise identity of the two sequences is 99.8% with minor differences in sequence at the end of the Cas9 gene, in the ham34 terminator, and in the origin of replication (ori). Alignment generated in Geneious Prime 2023.2.1 (https://www.geneious.com) using the Geneious assembler (Plasmidsaurus sequence mapped to expected sequence). Figure 4.5 Paired alignment of the expected pYF515 sequence (top) and the Plasmidsaurus pYF515 sequence (bottom). The pairwise identity of the two sequences is 99.8% with minor differences in sequence in the nptII gene and its ham34 promoter, both the ham34 terminators, and in the origin of replication (ori). Alignment generated in Geneious Prime 2023.2.1 (https://www.geneious.com) using the Geneious assembler (Plasmidsaurus sequence mapped to expected sequence). 91 Figure 4.8 Paired alignment of the expected pGFPN sequence (top) and the Plasmidsaurus pGFPN (second plasmid prep) sequence (bottom). The pairwise identity of the two sequences is 62.6% with sequence inversions, and a deletion of the nptII gene in the Plasmidsaurus sequence (4,927 bp). Alignment generated in Geneious Prime 2023.2.1 (https://www.geneious.com) using the Geneious assembler (Plasmidsaurus sequence mapped to expected sequence). Figure 4.7 Paired alignment of the Plasmidsaurus pGFPN (first plasmid prep) sequence (top) and the expected pGFPN sequence (bottom). The pairwise identity of the two sequences is 52.9%, with sequence inversions, repeats, and deletion of the nptII gene in the Plasmidsaurus sequence (9,842 bp). Alignment generated in Geneious Prime 2023.2.1 (https://www.geneious.com) using the Geneious assembler (expected sequence mapped to the Plasmidsaurus sequence). 92 4.4.2 Forest Phytophthora species differ in geneticin sensitivity The five forest Phytophthora species tested in this study exhibited varied sensitivities to G418, with MICs ranging from 5 μg/mL to 40 μg/mL of G418 (Figure 4.9). Phytophthora syringae and P. ramorum were the most sensitive species with MICs of 5 μg/mL G418, P. cactorum and P. cryptogea were more resistant with MICs of 20 μg/mL G418, and P. cinnamomi was the most resistant with an MIC of 40 μg/mL G418. The MICs were used to customize the G418 concentration used for selection of plasmid transformants for each Phytophthora species (see Data B.2.1). 4.4.3 Plasmid DNA transformations are inconsistent and unstable in forest Phytophthora species Success of plasmid transformations, as determined by stable mycelial growth of transformants after a second round of selection on an increased concentration of G418, was inconsistent Figure 4.9 Minimum inhibitory concentrations (MICs) of geneticin (G418) for the five forest Phytophthora species tested in this study and the agricultural pathogen P. sojae (used as a transformation control). Two replicates were tested for each species/isolate. *Two isolates of P. ramorum were tested, NA2 16_0386_0016 and NA2 17_0134_0030, and growth stopped for both at 5 μg/mL of G418. 93 between the five forest Phytophthora species tested (Table 4.4, Data B.2.1). Other than the P. sojae control, the only species in which consistent and stable plasmid transformations could be reliably reproduced was P. cactorum isolate Larch FF-42 2Pa (hereafter abbreviated FF42). Transformations of P. cactorum with the pYF2-PsCG plasmid yielded stable transformants that, even after multiple rounds of sub-culturing, grew well on V8A plates supplemented with 40 μg/mL of G418 (Figure 4.10). However, the results from diagnostic PCR were variable for P. cactorum, with no DNA amplification from some stably growing transformants (Figure 4.11), and conversely, amplification in transformants that could not grow in the increased G418 concentration (Figure 4.12). Additionally, the PCR products amplified from P. cactorum transformants were sometimes larger than expected (Figure 4.11). Table 4.4 Summary of the results of plasmid DNA transformations in the five forest Phytophthora pathogens tested in this study and the agricultural pathogen P. sojae (used as a transformation control). Species Isolate/Strain Plasmid Transformation Success (stable growth after increased G418 selection) Diagnostic PCR Confirmation (plasmid amplicon generated with PCR) Phytophthora sojae P6497 (R2) pYF2-PsCG Yes Yes pGFPN No NA Phytophthora cactorum Larch FF-42 2Pa pYF2-PsCG Yes Yes pGFPN No NA pYF515 Yes Not tested Phytophthora cinnamomi CBS 270.55 pYF2-PsCG No NA JP-09-065 pGFPN No NA Phytophthora cryptogea RS-2006-soil-d8 pYF2-PsCG No NA Phytophthora ramorum NA2 17_0134_0030 pYF2-PsCG Yes Yes pGFPN No NA pYF515 Yes Yes Phytophthora syringae Alder 12 pYF2-PsCG No NA 94 Figure 4.10 Transformations of Phytophthora cactorum FF42 with the pYF2-PsCG plasmid yielded stable transformants. (A) Transformants growing on V8 agar + 40 μg/mL G418 relative to (B) no growth of wildtype (untransformed) cultures on the same medium. Photos were taken five days post plating. (C) Transformants growing stably on V8 agar + 40 μg/mL G418 after multiple rounds of sub-culturing. 95 Figure 4.11 Gel electrophoresis of products from a PCR performed on DNA from Phytophthora cactorum FF42 pYF2-PsCG transformant (T) cultures using the pYF_Cas9_Diag_F3/R3 primer pair (expected product size is 824 bp). The negative PCR control was a reaction using molecular-grade water instead of DNA (well 2). The positive PCR control was purified pYF2-PsCG plasmid DNA (well 3). Note that the DNA used for the plasmid DNA control was the same preparation that was used for the P. cactorum transformations. The gel was run at 100 volts for 90 minutes on 0.85% agarose in 0.5X TBE buffer. All samples were loaded with Safe-Green™️ stain (Applied Biological Materials Inc., Richmond, BC, Canada) at a ratio of 1 μL Safe-Green: 5 μL PCR product. The pYF2-PsCG transformants highlighted in red are those that grew stably on G418 selective medium (40 μg/mL), however, there was no DNA amplification in the diagnostic PCR. 96 Some initial success was achieved in P. ramorum isolate NA2 17_0134_0030 (hereafter abbreviated NA2_17) with both pYF2-PsCG and pYF515, however transformation efficiency was extremely low (see Data B.2.1) and the mycelial growth of putative transformants was weak and irregular relative to the P. sojae control transformants (Figure 4.13). Additionally, most of the putative transformants for P. ramorum that were transferred to the second round of increased G418 selection did not grow (data not shown). However, the Figure 4.12 Gel electrophoresis of products from a PCR performed on DNA from Phytophthora cactorum FF42 wildtype (WT) and pYF2-PsCG transformant (T) cultures using the eGFP_Diag_F1/R1 primer pair (expected product size is 274 bp). The negative PCR control was a reaction using molecular-grade water instead of DNA (well 2). The positive PCR controls were purified pYF2-PsCG plasmid DNA, both circular (well 3) and linearized (well 4). The gel was run at 100 volts for 75 minutes on 1.25% agarose in 0.5X TBE buffer. All samples were loaded with Safe-Green™️ stain (Applied Biological Materials Inc., Richmond, BC, Canada) at a ratio of 1 μL Safe-Green: 5 μL PCR product. The pYF2-PsCG T4 transformant is highlighted in red because it struggled to grow through the second round of G418 selection (40 μg/mL), with roughly only 25% mycelial growth relative to the other three transformants (T1, T5, T8), however, a strong band amplified in the diagnostic PCR. 97 transformants that did grow in the second round of G418 selection all yielded positive results for the plasmid DNA diagnostic PCR, though DNA from some transformants yielded PCR products that were larger than expected (Figure 4.14). Notably, subsequent transformations with P. ramorum under the same conditions (i.e., same protocol, plasmid DNA, recovery medium, etc.) failed, with no putative transformants growing on G418 selective medium. Control transformations in P. sojae with the pYF-PsCG plasmid always yielded stable transformants with positive diagnostic PCR results (Figure 4.14). Figure 4.13 Putative transformants (PTs) of the control species Phytophthora sojae P6497 (A) and P. ramorum NA2_17 (B) growing on V8 agar supplemented with 50 μg/mL G418 and 30 μg/mL G418, respectively, four days post transformation. While putative P. ramorum transformants did grow on three of the plates, the mycelial growth was weak and irregular (see arrows) relative to the P. sojae control. The white arrows indicate PTs that would have been considered “strong” for P. ramorum, whereas the grey arrows indicate very weak PTs. All P. ramorum PTs were taken through a second round of selection, but most did not grow at the increased concentration of G418; none of the “weak” PTs (grey arrows) grew, and only two of the “strong” PTs (white arrows) grew. The same pattern emerged for every P. ramorum transformation attempted. 98 4.5 Discussion Developing molecular techniques in Phytophthoras, and oomycetes in general, is known to be difficult and inconsistent between species (Ghimire et al., 2022; Govers & Gijzen, 2006; Judelson & Ah-Fong, 2009; Mcleod et al., 2008), and our results certainly lend evidence to this trend. Of the five forest Phytophthora species we tested with a PEG-CaCl2 transformation method, only one species, P. cactorum, could be reliably and stably transformed with plasmid DNA. Though some initial success was achieved with P. ramorum, the transformants exhibited weakened and atypical growth, and subsequent transformations suddenly failed. Regarding the Figure 4.14 Gel electrophoresis of products from a PCR performed on DNA from Phytophthora sojae P6497 and P. ramorum NA2_17 wildtype (WT) and pYF2-PsCG transformant (T) cultures using the AmpR_Diag_F1/R1 primer pair (expected product size is 232 bp). The negative PCR control was a reaction using molecular-grade water instead of DNA (well 2). The positive PCR controls are replicate dilutions of a single pYF2-PsCG plasmid DNA extraction (wells 3-4). Note that the DNA used for the plasmid control was the same preparation that was used for both the P. sojae and P. ramorum transformations. The gel was run at 100 volts for 80 minutes on 1.25% agarose in 0.5X TBE buffer. All samples were loaded with Safe-Green™️ stain (Applied Biological Materials Inc., Richmond, BC, Canada) at a ratio of 1 μL Safe-Green: 5 μL PCR product. 99 objectives of this study, these results have led us to conclude that first, PEG-CaCl2 plasmid transformations are not readily adaptable between forest Phytophthora species, and second, that of the five species tested, P. cactorum is the candidate that is most amenable to CRISPR-Cas9 protocol development. The lack of adaptability of the PEG-CaCl2 plasmid transformation method across the five forest Phytophthora pathogens tested in this study likely cannot be explained by any one single factor. The use of protoplast viability controls in our experiments allows us to rule out the physical stress of transformation itself as the reason for the failed recovery of transformants in our tested species. We will therefore explore the molecular mechanisms potentially responsible for the variation in transformation success. DNA-mediated transformation involves complex molecular pathways, and Phytophthora species are known for responding variably to PEG-mediated transformation protocols (Mcleod et al., 2008), with results sometimes even varying between isolates of the same species (Riedel et al., 2009). Notably, our results revealed no phylogenetic pattern associated with which forest Phytophthoras could be successfully transformed. For example, transformations failed in P. cinnamomi even though it is closely related to the control species P. sojae in Phytophthora Clade 7 (X. Yang et al., 2017), but the same transformation protocol and plasmid DNA worked well for P. cactorum despite its phylogenetic distance in Clade 1 (X. Yang et al., 2017). However, Phytophthora species within a clade are known to show as much variation in their biological characteristics as species between clades (C. Brasier et al., 2022). Additionally, recent comparative genomics studies have revealed that Phytophthora genomes are highly variable, even between closely related species (Gao et al., 2021; Kronmiller 100 et al., 2023). This biological and genomic variability could contribute to the lack of phylogeny-related similarities in response to taking up exogenous plasmid DNA. Our success with transformations in P. cactorum echoes that of Chen et al. (2016) who stably transformed a single isolate of P. cactorum with the pGFPN plasmid. Using the optimized PEG-mediated transformation protocol (Mcleod et al., 2008), Chen et al. obtained 25 P. cactorum transformants, all exhibiting green fluorescence from the gfp gene. Notably, Chen et al. transformed protoplasts with 13 pmol of DNA, which equates to 55 μg for the pGFPN plasmid and 100 μg for the pYF2-PsCG plasmid. We were able to consistently transform P. cactorum with 25-30 μg of plasmid DNA, suggesting that less DNA may be required for stable transformation. Taken together with previous research, our results suggest that P. cactorum is more amenable to DNA transformation. It could be that there is less intra-species genomic variation between isolates of P. cactorum, but this would need to be confirmed with comparative genomics studies. Given that P. cactorum is a broad host-range pathogen with impacts in horticulture, agriculture, and forestry (C. Brasier et al., 2022; X.-R. Chen et al., 2023), it is a good candidate species for continued development of molecular techniques such as CRISPR-Cas gene editing. The inconsistency in transformation success we observed in P. ramorum was somewhat surprising given that a previous study had demonstrated stable expression of plasmid DNA in this species (Riedel et al., 2009). Riedel et al. used a modified version of the original PEG-liposome-mediated protocol developed in P. infestans (Judelson et al., 1991) to transform four independent isolates of P. ramorum (EU1 and NA1 lineages) with the vector p34GFN (Si- 101 Ammour et al., 2003), which contains nptII and gfp genes, both driven by the B. lactucae ham34 promoter. A total of 40 transformants were obtained across all four P. ramorum isolates, all of which yielded positive results for p34GFN DNA in PCR and qPCR (Riedel et al., 2009). However, only five transformants from two of the four isolates exhibited gfp expression, indicating that the genetic background of the isolates differed enough to influence transformation results (Riedel et al., 2009). It is possible that the genotype of the P. ramorum NA2_17 isolate that we used in our experiments is less amenable to transformation than the isolates used by Riedel et al. Also of note is that Riedel et al. used Lipofectin in addition to PEG in their transformations; we did try a liposome-mediated method in one of our P. ramorum transformations, however, we found that it had no effect. Future experiments could explore optimizing liposome reagent concentrations to determine if it improves plasmid transformations in P. ramorum. Similar to P. ramorum, previous studies in P. cinnamomi were successful in obtaining stable transformants (Dai et al., 2021; McCarren et al., 2005), so our unsuccessful result in this species was unexpected. Given that Dai et al. used an almost identical protocol to ours, based on the optimized PEG-CaCl2 transformation method (Mcleod et al., 2008), there are two predominant explanations for our lack of transformants. The first possible explanation is that the genotype of the P. cinnamomi isolate we tested may be unamenable to transformation of exogenous DNA. However, the success of transformations in two previous studies with multiple isolates of P. cinnamomi (Dai et al., 2021; McCarren et al., 2005) makes this explanation somewhat less likely. The second, and more likely, possible explanation is the different plasmid DNA used for transformations. Dai et al. used a 7214 bp plasmid, pTOReGFP, which contains the nptII gene 102 for G418 resistance driven by the hsp70 promoter, while the nptII gene in the pYF2-PsCG plasmid we used for P. cinnamomi transformations is driven by the P. sojae rpl41 promoter. It is therefore possible that the hsp70 promoter is better suited to drive gene expression in P. cinnamomi than the rpl41 promoter. The possible effects that differences in promoter activity and other plasmid-related factors may have on transformation success are discussed in more detail below. An important factor to consider for transformation success is the vector DNA. Our sequencing results indicated that the pGFPN plasmid is not stable; sequences from the two independent pGFPN preparations showed recombination, inversion, and deletion of large segments of the plasmid DNA. This is particularly perplexing given that the E. coli strain used for plasmid replication was the laboratory standard DH5⍺ strain with a recA1-endA1 genotype, which should prevent unwanted recombination and cleavage in the plasmid DNA. Additionally, this instability was not observed for either pYF515 or pYF2-PsCG, which were subject to the same methods as pGFPN. The recombination events in both pGFPN preparations yielded plasmids with no nptII gene, thus the failed transformations with pGFPN in all tested Phytophthora species can confidently be attributed to the missing antibiotic selection gene. Therefore, we cannot make any meaningful conclusions regarding the efficacy of the pGFPN plasmid for transformations in forest Phytophthoras. The only plasmid that we tested across all six Phytophthora species was pYF2-PsCG. One plasmid-related factor that may have influenced transformation success is variation in the activity of the P. sojae RPL41 promoter in the five forest Phytophthora species. In pYF2-PsCG, the 103 RPL41 promoter drives expression of the nptII gene, and therefore any differences in promoter activity would have affected the recovery of transformants on G418-selective medium. Previous studies demonstrated that the P. sojae RPL41 promoter is active in other Phytophthora species, including P. capsici (Z. Wang et al., 2018), P. litchii (C.C. Chen ex W.H. Ko, H.S. Chang, H.J. Su, C.C. Chen & L.S. Leu) P.K. Chi, X.P. Pang & R. Liu (W. Wang et al., 2019), P. infestans (Ah-Fong et al., 2021), and P. colocasiae Racib. (Z. Wang et al., 2022). However, it is possible that the RPL41 promoter is more active in P. cactorum compared to the other four species we tested. Our transformations with the pGFPN plasmid could have provided more evidence for the role of promoter activity as the nptII gene in pGFPN is driven by the Bremia lactucae hsp70 promoter, however, the instability of the plasmid prevented transformation. To determine the role of promoter activity in forest Phytophthora transformation success, future studies could test plasmids with the nptII gene driven by the more standard ham34 and hsp70 oomycete promoters, which both express well in a diversity of Phytophthora (Bottin et al., 1999; X.-R. Chen et al., 2016; Judelson et al., 1991, 1993; Mcleod et al., 2008; Riedel et al., 2009; van West et al., 1999) and other oomycete species (L. Jiang et al., 2017; Vijn & Govers, 2003; J. J. Weiland, 2003). Another plasmid-related factor that could have influenced transformation success is Cas9 toxicity. Given that the pYF2-PsCG plasmid was originally constructed to test Cas9 expression (Fang, Jang, et al., 2017; Fang & Tyler, 2016), it contains the gene for SpCas9 driven by the constitutive ham34 promoter. While there is no single-guide RNA (sgRNA) construct in the plasmid, the Cas9 gene would still be transcribed and translated into a functional nuclease in transformed cells. Previous studies have demonstrated that stable expression of Cas9, even without a bound sgRNA or when in a catalytically deactivated form (dCas9), can be toxic in 104 bacteria (S. Cho et al., 2018; Rock et al., 2017), protozoa (D. Peng et al., 2014), fungi (Foster et al., 2018), and algae (W. Jiang et al., 2014). The exact mechanisms driving this toxicity are unclear, however, dCas9 has been shown to bind non-specifically to NGG PAM sites in the genome, even when the nuclease is not associated with an sgRNA (D. L. Jones et al., 2017). It has been hypothesized that this non-specific binding contributes to the toxicity of dCas9 and Cas9 when they are stably expressed in cells (Markus et al., 2019; S. Zhang & Voigt, 2018). In light of these previous studies, it is possible that the SpCas9 expressed from the pYF2-PsCG plasmid was toxic to P. cinnamomi, P. cryptogea, P. syringae, and perhaps even P. ramorum. Establishing plasmid transformations in forest Phytophthoras is clearly complex, and future success will likely require tailoring protocols to each individual species, and potentially also to individual isolates. There are many possible directions for future research, and our results lay the groundwork for the optimization of PEG-mediated DNA transformations in Phytophthora forest pathogens. The continued development of molecular techniques and species-specific protocols is necessary to improve understanding of phytopathogenicity in Phytophthoras and to ultimately develop more effective management approaches. 105 Chapter 5: Successes and challenges in establishing CRISPR-Cas9 gene editing for two forest pathogenic Phytophthora species via co-transformation of plasmid-ribonucleoprotein complexes 5.1 Chapter Summary CRISPR-Cas9 gene editing has become an important tool for the study of plant pathogenic Phytophthoras, allowing researchers to functionally characterize specific genes involved in phytopathogenicity, virulence, and fungicide resistance. However, development of this technology has exclusively focused on agricultural pathosystems, with researching lacking for forest pathosystems. We sought to develop CRISPR-Cas9 gene editing in two forest pathogenic Phytophthoras, Phytophthora cactorum and P. ramorum, using a plasmid-ribonucleoprotein (RNP) co-transformation approach. Our gene target in both species was the ortholog of PcORP1, which encodes an oxysterol binding protein that was found to be the target of the fungicide oxathiapiprolin in the agricultural pathogen P. capsici. We delivered liposome complexes, each containing plasmid DNA and CRISPR-Cas9 RNPs, to Phytophthora protoplasts using a polyethylene glycol (PEG)-mediated transformation protocol. Using this method, we obtained two ORP1 mutants in P. cactorum, however, we were unable to obtain any mutants in P. ramorum. Both P. cactorum mutants exhibited decreased resistance to oxathiapiprolin, as measured by their radial growth relative to wildtype cultures on oxathiapiprolin-supplemented medium. Our results demonstrate the potential of RNP-mediated CRISPR-Cas9 gene editing in P. cactorum and provide a foundation for the future optimization of this protocol in this species and other forest pathogenic Phytophthoras. 106 5.2 Introduction Since its development in 2012 (Jinek et al., 2012), CRISPR-Cas9 gene editing has become a valuable tool for plant pathology, facilitating the exploration of the molecular mechanisms driving host-pathogen interactions and elucidating potential targets for disease resistance and pathogen control strategies. CRISPR-Cas gene editing technologies are based on the naturally occurring adaptive immune systems of bacteria and archaea: clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR associated (Cas) proteins (Bhaya et al., 2011; Ishino et al., 1987; Terns & Terns, 2011). CRISPR-Cas9 gene editing was adapted from the type II CRISPR-Cas system from the bacterium Streptococcus pyogenes Rosenbach and targets DNA with three components: a Cas9 endonuclease (SpCas9), and a CRISPR RNA (crRNA) and trans-activating crRNA (tracrRNA) that base-pair together and bind to Cas9 (Jinek et al., 2012). A 20-nucleotide (nt) region of the crRNA guides Cas9 to the complementary DNA in the target organism’s genome, called the protospacer, where Cas9 creates a double-stranded break (DSB) near a highly conserved sequence (NGG) called the protospacer-adjacent motif, or PAM (Jinek et al., 2012). Jinek et al. created a highly adaptable CRISPR-Cas9 system with the crRNA and tracrRNA fused as a single guide RNA (sgRNA); the sgRNA can direct Cas9 to virtually any protospacer target near an NGG PAM via modifications made to the 20-nt crRNA region (Jinek et al., 2012). In eukaryotes, the DSB is then repaired by the host cell, either through an error-prone end-joining pathway in the absence of a homologous donor template, or through the highly accurate homology-directed repair (HDR) pathway in the presence of a homologous donor template (Ranjha et al., 2018). It is through the DSB repair process that mutations are made in the target gene, which allows researchers to characterize gene functions via knockout or highly specific mutations. 107 The CRISPR-Cas9 systems developed in filamentous plant pathogens enable researchers to discover how specific genes contribute to phytopathogenicity (Fang & Tyler, 2016; J. Li et al., 2018), elucidate important secondary metabolite and toxin pathways (Y. Liang et al., 2018; Q. Wang et al., 2018), and characterize genetic resistance to fungicides (Leisen et al., 2020; Miao, Chi, et al., 2018). However, most CRISPR-Cas9 research in plant pathology has focused on agricultural pathosystems and very little research has been directed towards forest pathogens. The recent development of a CRISPR-Cas9 system in the fungal pathogen Dothistroma septosporum (Dorog.) M. Morelet (McCarthy et al., 2022) is the first use of CRISPR-based gene editing in a filamentous forest pathogen. Some of the most ecologically and economically impactful filamentous plant pathogens are those in the oomycete genus Phytophthora. Well-known examples include P. infestans (Mont.) de Bary, the causal agent of late blight of potato (de Bary, 1861), and P. cinnamomi Rands, the causal agent of jarrah dieback in Australia (Podger et al., 1965) and one of the most devastating plant pathogens, with thousands of host species and a global distribution (Hardham & Blackman, 2018). Due to the global impacts of Phytophthoras in agriculture, horticulture, and forestry, characterizing the genetic mechanisms driving the phytopathogenicity of species in this genus has become a priority, and CRISPR-Cas9 gene editing is an important tool for this molecular characterization. CRISPR-Cas9 gene editing protocols have now been developed for a number of agricultural Phytophthora pathogens including P. sojae Kaufm. & Gerd. (Fang & Tyler, 2016), P. palmivora (E.J. Butler) E.J. Butler (Gumtow et al., 2018), P. capsici Leonian (Miao, Chi, et al., 2018; Z. Wang et al., 2018), P. litchii (C.C. Chen ex W.H. Ko, H.S. Chang, H.J. Su, C.C. 108 Chen & L.S. Leu) P.K. Chi, X.P. Pang & R. Liu (W. Wang et al., 2019), P. parasitica Dastur (Q. Zhang et al., 2020), and P. colocasiae Racib. (Z. Wang et al., 2022). These studies all used polyethylene glycol (PEG)-mediated transformation to deliver the CRISPR-Cas9 components to Phytophthora protoplasts. One exception is the study performed by Gumtow et al., who used Agrobacterium-mediated transformation (AMT) to deliver the Cas9 and sgRNA to P. palmivora (Gumtow et al., 2018). Despite this success of CRISPR-Cas9 systems in agricultural Phytophthoras, there have been no such methods developed for forest pathogenic Phytophthoras, which may be due to the lack of efficient and stable transformation protocols in these species (see Chapter 4). When delivering CRISPR-Cas9 components to cells via PEG-mediated transformation, there are two general methods that can be employed. The first method is plasmid delivery, where both Cas9 and its sgRNA are encoded on a plasmid (often also encoding a gene for antibiotic resistance), delivered through transformation to the nucleus, and expressed in the cell via transcription and translation. The translated Cas9 nuclease and sgRNA then form a ribonucleoprotein (RNP) complex in the cell and return to the nucleus to find the target protospacer DNA and create a DSB. The second method is direct transformation of a purified Cas9-sgRNA RNP, which also gets delivered to the nucleus through transformation, but does not require transcription and translation and thus immediately begins targeting the genomic protospacer DNA. First developed in Caenorhabditis elegans (Maupas) Dougherty (S. W. Cho et al., 2013), the RNP method for CRISPR-Cas9 gene editing has been successfully employed in mammals (S. Kim et al., 2014), plants (Woo et al., 2015), and fungi (Pohl et al., 2016). While RNP delivery of Cas9 has not been developed for any Phytophthora species, it has been used to 109 successfully edit the genome of the oomycete Aphanomyces invadans Willoughby, R.J. Roberts & Chinabut (Majeed et al., 2018). RNP delivery of CRISPR-Cas9 components has several advantages over plasmid delivery. The first is that it is possible to obtain CRISPR-Cas9 mutants with no transgenic DNA, and indeed, this has been a significant motivation for developing RNP methods in plants (Woo et al., 2015; Y. Zhang et al., 2016). Another advantage of RNP CRISPR-Cas9 delivery is that the RNP complexes can target DNA immediately after transformation into cells, as opposed to plasmid delivery which requires time for transcription and translation (S. W. Cho et al., 2013). In fact, the transgenic expression of Cas9 has toxic effects in many organisms (S. Cho et al., 2018; Foster et al., 2018; W. Jiang et al., 2014; D. Peng et al., 2014), which can usually be avoided with the transient expression of RNPs (Foster et al., 2018). Finally, the use of RNPs for CRISPR-Cas9 delivery avoids many of the common challenges of plasmid DNA transformation, including the potentially off-target effects of random transgene insertion into genomes (S. Kim et al., 2014; Latham et al., 2006; Zuris et al., 2015) and the need to optimize plasmid gene promoters and codon usage for each target organism (S. W. Cho et al., 2013; Woo et al., 2015). However, one significant limitation of the RNP approach is the lack of a selective marker for putative transformants, particularly when triggering the end-joining DNA repair pathway with no donor template. This lack of selection is particularly problematic when the target gene for CRISPR-Cas9 produces no visible phenotype when mutated. The objective of this study was to establish CRISPR-Cas9 gene editing using an RNP-mediated approach in two forest pathogenic Phytophthoras: P. ramorum and P. cactorum. Phytophthora 110 ramorum Werres, De Cock, & Man in’t Veld is the causal agent of sudden oak/larch death, a globally invasive pathogen that is causing landscape-level damage in the tanoak/oak forests of California (Garbelotto Benzon et al., 2001; Rizzo et al., 2002a) and larch plantations in the UK (Webber et al., 2010). Phytophthora cactorum (Leb. & Cohn) Schröeter is an aggressive pathogen with a broad host range spanning hundreds of species and impacts agriculture, horticulture, and forestry (X.-R. Chen et al., 2023; Erwin & Ribeiro, 1996; Nienhaus, 1960). As a forest pathogen, it is perhaps most destructive in nurseries (Jung et al., 2016; Lilja et al., 1996; Rytkönen et al., 2008), but it has also been associated with diseases on economically and ecologically important genera including Fagus (J. E. Weiland et al., 2010), Fraxinus (Orlikowski et al., 2011), Pinus (Vettraino et al., 2022), and Quercus (Jung et al., 1996). The gene we chose to target in P. ramorum and P. cactorum is the ortholog of PcORP1 in P. capsici, named due to the oxysterol binding protein (OSBP) that it encodes, a member of the OSBP-related proteins (ORPs) family (Miao et al., 2016). The ORP1 protein in Phytophthora species is the target of the fungicide oxathiapiprolin (Andreassi et al., 2013; Pasteris et al., 2016; Sweigard et al., 2014), and mutations in the ORP1 gene of P. capsici, P. colocasiae, P. nicotianae Breda de Haan, and P. sojae have yielded oxathiapiprolin-resistant mutants (Bittner et al., 2017; Miao, Chi, et al., 2018; Miao et al., 2016; Z. Wang et al., 2022). We tested a co-transformation approach for CRISPR-Cas9 editing in P. ramorum and P. cactorum, delivering liposomes containing both a Cas9-sgRNA RNP targeting the ORP1 gene and a plasmid encoding the nptII gene for geneticin (G418) resistance. We chose this hybrid approach to facilitate selection of putative mutants, hypothesizing that Phytophthora transformants growing on G418- 111 selective medium would have also received the CRISPR-Cas9 RNP complexes and therefore the mutant screening process would be more efficient than with no selection method. 5.3 Methods 5.3.1 Phytophthora cactorum isolates and culture conditions The details of the Phytophthora cactorum and P. ramorum isolates used in this study are summarized in Table 5.1. Cultures were maintained on clarified 20% V8 agar (V8A: see Appendix B.1 for recipe) at room temperature (approx. 21°C) in the dark. For long-term storage, agar plugs taken from the edge of mycelial growth, five days post plating, were stored in nuclease-free molecular water at 5-10°C in the dark. Table 5.1 Details of the Phytophthora cactorum and P. ramorum isolates used in this study. Species Isolate Isolate Details Phytophthora cactorum Larch FF-42 2Pa (abbreviated FF42) See Feau et al. (2021) for isolate details. Phytophthora ramorum NA2 17_0134_0030 (abbreviated NA2_17) Culture provided by G. Bilodeau from the Canadian Food Inspection Agency. 5.3.2 Phytophthora DNA extractions In preparation for DNA extraction, Phytophthora cultures were transferred from V8A plates to 12-well plates containing V8 broth (V8B: see Appendix B.1 for recipe). An agar plug from each culture was taken from the edge of mycelial growth and placed in a well containing 2 mL of V8B. After 3-4 days of growth in V8B, 50-100 mg of mycelial tissue was harvested from each culture, and flash frozen in liquid nitrogen. Total cellular DNA was extracted using a QIAGEN DNeasyⓇ Plant Mini Kit (QIAGEN Sciences, Germantown, MD, USA) as per the manufacturer’s instructions (DNeasyⓇ Plant Handbook, Mini Protocol, TissueLyser procedure). 112 Tissue disruption was performed using a Retsch Mixer Mill MM 300 (Retsch GmbH, Haan, Germany). 5.3.3 PCR primer design and equipment All PCR primers used in this study were designed using NCBI Primer-BLAST (Ye et al., 2012) with target specificity checked against the NCBI “RefSeq representative genomes” database for Phytophthora (taxid: 4783) to minimize off-target amplification. Primers were ordered from Integrated DNA Technologies (IDT, Coralville, IA, USA). All PCRs were performed using a Bio-Rad T100™️ thermal cycler (Hercules, California, USA). 5.3.4 Identification of the PcORP1 ortholog in Phytophthora ramorum and Phytophthora cactorum The OSBP protein target of oxathiapiprolin was first identified in P. capsici (Andreassi et al., 2013; Sweigard et al., 2014), and was labelled in the corresponding JGI PhycoCosm (Grigoriev et al., 2021) Phytophthora capsici LT1534 v11.0 genome as Protein Id: 564296 (PHYCAscaffold_14:545019–548397). The gene for the P. capsici OSBP was first referred to as PcORP1 by Miao et al. (2016); we chose to follow this naming convention in our species, referring to the orthologs in P. cactorum and P. ramorum as PcaORP1 and PrORP1, respectively. The protein, transcript, and genomic FASTA files for the PcORP1 sequence were downloaded from PhycoCosm (Grigoriev et al., 2021) using the Protein Id 564296 in the Phytophthora capsici LT1534 v11.0 genome (Lamour et al., 2012). To identify the ortholog of PcORP1 in P. 113 ramorum (PrORP1), a tblastn (Altschul et al., 1990) was performed using the PcORP1 protein sequence as the query against the P. ramorum JGI reference genome (NCBI genome assembly ASM14973v1, strain Pr102, lineage NA1). The top hit was selected, and the FASTA file for the corresponding genomic scaffold was downloaded. The genomic region corresponding to the sequence of the top hit from the tblastn with an additional 1000 nucleotides of flanking upstream and downstream DNA was copied into Geneious Prime 2023.2.1 (https://www.geneious.com) and named Pr102_NCBI_ORP1_ortholog. The Geneious “Find ORFs” tool was used to analyze the Pr102_NCBI_ORP1_ortholog sequence and identify possible open reading frames (ORFs) in the sequence corresponding to the putative PrORP1 gene. The longest ORF was selected and verified as an oxysterol-binding protein using the NCBI Open Reading Frame Finder (Wheeler et al., 2003) and SmartBLAST (NCBI Resource Coordinators, 2016). Three independent pairs of primers were designed to amplify a region including the putative PrORP1 ORF plus several hundred base pairs of flanking upstream and downstream DNA (Table 5.2). The same method was performed to find the PcORP1 ortholog in P. cactorum (PcaORP1), but with the following minor modifications: the P. cactorum NCBI reference genome (genome assembly ASM1686465v1, strain P414) was used for the tblastn of PcORP1, 1500 nucleotides of flanking upstream and downstream DNA were copied into Geneious for analysis, and only two primer pairs were designed to amplify the PcaORP1 region (Table 5.2). 114 Table 5.2 Primer pairs designed to amplify the PcORP1 orthologs of P. ramorum (PrORP1) and P. cactorum (PcaORP1). All PCRs were performed using Phusion™️ Plus DNA Polymerase, which creates a universal annealing temperature of 60°C. Species (Gene) Primer Pair Name Primer Sequences (5’ to 3’) Expected Product Size Phytophthora ramorum (PrORP1) Pr102ORPh_ORF_F1/R1 F1: CTGCGTCCAATCACAATATGCC 3,891 bp R1: TCAAGCTGGCTCGTAACTTCAT Pr102ORPh_ORF_F2/R2 F2: GTGTGACATTATTCCCAGTGGC 3,559 bp R2: AGCTTCATTTACCCCACCAAGA Pr102ORPh_ORF_F3/R3 F3: CTCATTTCGCCGGACAGTTG 3,485 bp R3: ATGCTCATCCTGCTTTGCTTG Phytophthora cactorum (PcaORP1) Pca414ORPh_Seq_F1-A/R1-A F1-A: CGATGGGGTTAGCACTACAGAG 4,502 bp R1-A: CTTTAAGGAGAAACGCACGGAG Pca414ORPh_Seq_F1-B/R1-B F1-B: CTCTATCGCTCGTGTCTCTTCG 4,731 bp R1-B: CCGGAGCTGAACCACTTCTACTTC 5.3.4.1 Touchdown PCR of PrORP1 and PcaORP1 genes Touchdown PCR was performed on DNA extractions from P. ramorum NA2_17 and P. cactorum FF42 to amplify approximately 3.5-4.8 kb regions of the PrORP1 and PcaORP1 genes, respectively. A high-fidelity polymerase, Phusion™️ Plus DNA Polymerase (Thermo Fisher Scientific, Waltham, MA, USA), was used with the primers designed for each respective gene (Table 5.2). The PCR reactions were set up according to the manufacturer’s instructions (Phusion™️ Plus DNA Polymerase User Guide), and 5-10 ng of template DNA was used. The thermocycler conditions for touchdown PCR consisted of two phases; the cycle details are shown in Table 5.3. Phusion™️ Plus DNA Polymerase uses a universal annealing temperature of 60°C, so this temperature was used for annealing in Phase 2 of the PCR. 115 Table 5.3 Thermocycler conditions for touchdown PCR performed on P. ramorum NA2_17 and P. cactorum FF42 wildtype DNA using Pr102ORPh_ORF and Pca414ORPh_Seq primer pairs, respectively. Phase Step Temperature Time 1 98°C 30 sec 1 2 98°C 10 sec 3 70°C–1°C/cycle 15 sec 4 72°C 30 sec/kb* Repeat steps 2-4 for 10 cycles 2 5 98°C 10 sec 6 60°C✢ 15 sec 7 72°C 30 sec/kb* Repeat steps 5-7 for 20 cycles 8 72°C 5 min 9 4°C Hold *Extension cycle is 30 seconds per kb of amplified product; 2 minutes was used for Pr102ORPh_ORF primers and 2.5 minutes was used for Pca414ORPh_Seq primers ✢60°C is the universal annealing temperature for Phusion™️ Plus DNA Polymerase 5.3.4.2 Sanger sequencing and primer-walking for PrORP1 and PcaORP1 Primer walking was performed along the touchdown PCR amplicons to determine the sequences of PrORP1 and PcaORP1 gene regions. For PrORP1, the PCR product from the Pr102ORPh_ORF_F1/R1 primers, named PrNA2ORPh, was sent for Sanger sequencing using the Pr102ORPh_ORF_F1 and Pr102ORPh_ORF_F2 primers for the forward direction, and the Pr102ORPh_ORF_R1 and Pr102ORPh_ORF_R3 primers for the reverse direction. The consensus sequences from the Sanger results were then used to design a new set of forward and reverse sequencing primers, which were used to sequence the next 500–700 base pairs of the original PrNA2ORPh PCR product. This process was repeated until the entire 3,891 bp PrNA2ORPh amplicon was sequenced. The same primer walking method was used for sequencing PcaORP1, using the 4,502 bp PCR amplicon from the Pca414ORPh_Seq_F1-A/R1-A primers, however, the primer walking was completed by the CHUL Sanger Sequencing Platform at Université Laval (CRCHU de Québec, Québec City, QC, Canada). 116 5.3.4.3 Gene prediction for PrORP1 and PcaORP1 sequences The PrORP1 and PcaORP1 consensus sequences from primer walking were analyzed using the Geneious “Find ORFs” tool and the NCBI Open Reading Frame Finder (Wheeler et al., 2003) to identify putative ORFs. The largest ORF was selected and its protein sequence was analyzed with the SmartBLAST tool (NCBI Resource Coordinators, 2016) on the NCBI Open Reading Frame Finder page to validate its identity as an oxysterol-binding family protein. To predict the structure of each ORP1 gene and find the exon-intron boundaries, the sequence of the putative ORF with approximately 500 nucleotides of flanking upstream and downstream DNA was analyzed using the Softberry FGENESH eukaryotic gene finding tool (http://www.softberry.com) with the Phytophthora gene-finding parameters selected. The amino acid sequences of PrORP1 and PcaORP1 were analyzed with InterPro (Paysan-Lafosse et al., 2023) to compare the predicted protein domains relative to the original P. capsici PcORP1 sequence (Miao et al., 2016). 5.3.5 CRISPR-Cas9 guide RNA design CRISPR-Cas9 guide RNAs (gRNAs) were designed within the PrORP1 and PcaORP1 predicted ORF regions. Intron regions of each ORF were avoided. The Eukaryotic Pathogen CRISPR Guide RNA/DNA Design Tool (EuPaGDT; Peng & Tarleton, 2015) was used to generate gRNAs with the SpCas9 20-nucleotide gRNA 3’ NGG PAM option selected (off-target PAM motif setting: NAG, NGA). Off-target analysis was performed in EuPaGDT using the P. ramorum Pr-102 FungiDB-26 genome for the PrORP1 gRNAs and the P. cactorum P414 NCBI reference genome for the PcaORP1 gRNAs. Note that when the first three PcaORP1 gRNAs 117 were designed, the P. cactorum P414 genome was not provided in EuPaGDT, so off-target analysis was performed manually using a blastn (Altschul et al., 1990) of each gRNA candidate against the P414 genome in NCBI. Once the P414 genome was added, the subsequent two gRNAs for PcaORP1 were designed using the off-target analysis in EuPaGDT. All gRNAs with a EuPaGDT “Total score” above 0.5 (as per Fang et al. 2017) and no off-target hits were considered potential candidates and their secondary structure was analyzed using RNAstructure (Reuter & Mathews, 2010). Only gRNAs with fewer than three consecutive paired bases were considered for further analysis (Fang, Cui, et al., 2017). Finally, candidate gRNAs were analyzed with the Integrated DNA Technologies (IDT) “CRISPR-Cas9 guide RNA design checker”, and those with the highest scores and minimal secondary structure were ordered as 2 nmol Alt-R™️ CRISPR RNAs (crRNAs) from IDT (Coralville, Iowa, USA). 5.3.6 Ribonucleoprotein-plasmid co-transformation The recipes and methods for all growth media and transformation solution are listed in Appendix B.1. The methods for preparation of Phytophthora cultures and protoplast isolation (sections 5.3.6.3 and 5.3.6.4) are based on the optimized transformation protocols previously developed for Phytophthora species (Dou et al., 2008; Fang, Cui, et al., 2017; Mcleod et al., 2008). 5.3.6.1 Plasmid DNA The pYF2-PsNLS-hSpCas9-GFP (abbreviated pYF2-PsCG) plasmid, demonstrated to generate geneticin (G418) resistant transformants in P. cactorum and P. ramorum (see Chapter 4 results), was used for all co-transformations. Plasmid DNA was obtained using a QIAGEN HiSpeedⓇ Plasmid Maxi Kit (QIAGEN Sciences, Germantown, MD, USA) as per the manufacturer’s 118 instructions (HiSpeedⓇ Plasmid Purification Handbook), and vacuum concentrated to 3-5 μg/μL prior to use in co-transformations. 5.3.6.2 Formation of Cas9 ribonucleoprotein complexes The following components for the CRISPR-Cas9 ribonucleoprotein (RNP) complexes were ordered from IDT (Coralville, Iowa, USA): Alt-R™️ S.p. HiFi Cas9 Nuclease V3 (100 μg), Alt-R™️ CRISPR-Cas9 crRNA (2 nmol), and Alt-R™️ CRISPR-Cas9 tracrRNA (20 nmol). Prior to use, the Cas9 nuclease was diluted with W5 solution to a 1 μM working solution and stored at -80°C (as per instructions from IDT). Both the crRNA and tracrRNA were resuspended to make 100 μM stocks using IDT Nuclease-Free Duplex Buffer (NFDB). Guide RNAs (gRNAs) were formed by creating 1 μM crRNA-tracrRNA duplexes as follows: equal volumes of 100 μM crRNA and 100 μM tracrRNA were mixed in a 1.5 mL microcentrifuge tube with NFDB for a final concentration of 1 μM, incubated at 95°C for 5 minutes, cooled to room temperature, and stored at -20°C. The Cas9-gRNA RNP complexes were formed as follows: equal volumes of 1 μM gRNA and 1 μM Cas9 nuclease were mixed in a 1.5 mL microcentrifuge tube with W5 solution for a final concentration of 300 nM, incubated at room temperature for 5 minutes, and stored at 4°C until used for transformation. For transformations with Lipofectamine™️ CRISPRMAX™️ (Thermo Fisher Scientific, Waltham, MA, USA), Cas9 PLUS™️ Reagent was added to the RNP mixture at a ratio of 0.6 μL of reagent to 25 μL total RNP volume (as per IDT Alt-R CRISPR-Cas9 User Guide, protocol for cationic delivery of Cas9 RNP). 119 5.3.6.3 Preparation of Phytophthora cultures for transformation Phytophthora cultures were initially grown on 1.5% nutrient V8 agar (NVA) plates at room temperature (~21°C) in the dark. Once there was sufficient mycelial growth (five to seven days), cultures were transferred to nutrient V8 broth (NVB): three flasks, each containing 50 mL of NVB, were inoculated with five agar plugs (6-mm diameter) taken from the growing edge of the mycelial culture (15 agar plugs total). The liquid cultures were grown for three to four days at room temperature (~21°C) in the dark. 5.3.6.4 Isolation of protoplasts After three days of growth, mycelial mats were harvested and rinsed with autoclaved Milli-QⓇ water followed by 0.8 M mannitol. The mycelial mats were transferred to a 100 mm × 20 mm Petri dish, covered with 0.8 M mannitol, and shaken gently (~30 rpm) for 10 minutes. The mats were recollected, transferred to a new Petri dish containing 20 mL of an enzyme solution (lysing enzymes + cellulase; see Appendix B.1 for full recipe) and digested for 60-90 minutes at 25°C with gentle shaking (~30 rpm). After digestion, the enzyme/protoplast solution was filtered through a 70 μm cell strainer and centrifuged at 1200 rcf for 2.5 minutes. The pellet was gently washed in 35 mL of W5 solution (see Appendix B.1 for recipe), recentrifuged at 1200 rcf for 3 minutes, and the resulting pellet was resuspended in 10 mL of W5 solution and placed on ice for 1-2 hours. The protoplast concentration was determined using a hemocytometer. 5.3.6.5 Formation of plasmid-RNP cationic-lipid transfection complexes While the protoplasts were incubating on ice, plasmid-RNP transfection complexes were prepared as follows: 250 μL of 300 nM RNP, 6 μL of pYF2-PsCG plasmid DNA, 12 μL of 120 Lipofectamine™️ CRISPRMAX™️ or Lipofectamine™️ RNAiMAX™️ (Thermo Fisher Scientific, Waltham, MA, USA), and 232 μL of W5 solution were mixed in a 1.5 mL microcentrifuge tube and incubated at room temperature for 20 minutes. The volumes of RNP and Lipofectamine were determined based on the picomoles of RNP per cell used in the IDT protocol for cationic lipid delivery of CRISPR RNP complexes (IDT, 2018). The final transfection complex for each transformation had a total volume of 500 μL containing 75 pmol of RNP and ~33 μg (4 pmol) of plasmid DNA. 5.3.6.6 Co-transformation of Phytophthora protoplasts with CRISPR-Cas9 ribonucleoprotein complexes and plasmid DNA Multiple protocols were tested for plasmid-ribonucleoprotein (RNP) co-transformations in P. ramorum and P. cactorum; the details of the methods tested are summarized in Data C.1.1. The protocol outlined below is the method that ultimately succeeded in P. cactorum and is a modified version of the plasmid-mediated CRISPR-Cas9 transformation developed for P. sojae (Fang, Cui, et al., 2017). Following incubation on ice (section 5.3.6.4), the protoplast suspension was centrifuged at 1200 rcf for 4 minutes. The resulting pellet was resuspended in MMg solution (see Appendix B.1 for recipe) at a concentration of ~1.0-3.0 x 106 protoplasts/mL and incubated at room temperature for 10 minutes. During incubation, 500 µL of plasmid-RNP transfection complex (TC), or 500 µL of sterile molecular-grade water/W5 solution for the negative control, was added to a 50 mL centrifuge tube and placed on ice. After the 10-minute incubation, the protoplasts were gently mixed, and 1 mL of the protoplast/MMg solution was added to the TC (or control) in each 50 mL 121 tube, mixed, and incubated for 30 minutes on ice. A 40% (v/v) PEG solution was added to each tube in three aliquots of 580 µL for a total of 1.74 mL of PEG solution per tube, and the tubes were incubated for another 30 minutes on ice. After 30 minutes, 2 mL of V8-mannitol (VM) broth was added to each tube. The tubes were inverted to mix and incubated on ice for two minutes. Then, 8 mL of VM broth was added to each tube, the tubes were inverted to mix and incubated at room temperature for an additional two minutes. To test the viability of the protoplasts after the transformation process, ~30-50 µL of the protoplast-VM solution from each centrifuge tube (including control treatment) was spread onto an agar plate (NVA) and incubated at room temperature in the dark overnight (protoplast viability test Day 1). The remainder of the protoplast-VM solution (~10 mL) from each tube was added to a 100 mm × 20 mm Petri dish containing 10 mL of VM broth with 100 µg/mL of ampicillin (Thermo Fisher Scientific, Waltham, MA, USA) for a final concentration of 50 µg/mL ampicillin. The dishes were incubated overnight at room temperature in the dark. 5.3.6.7 Antibiotic selection of transformants The following morning, the regenerated protoplasts were transferred to a new 50 mL tube and centrifuged at 2000 rcf for 5 minutes. The pellet was resuspended in ~2 mL of the supernatant and 150 µL of the resuspended protoplasts were added to 20 mL of cooled VM agar (40-45°C) without G418 and poured into a Petri dish (protoplast viability test Day 2). Cooled VM agar (40-45°C) with 30 µg/mL of G418 (Gibco™️ Geneticin™️ Selective Antibiotic, Thermo Fisher Scientific, Waltham, MA, USA) was added to the remaining protoplasts to a total volume of 50 mL. The tubes were inverted to mix, and the protoplast/agar mixture was poured into three Petri dishes (~16-17 mL of mixture in each dish). All plates were incubated at room temperature in the 122 dark for two days, or until mycelial growth appeared (up to five days). Each mycelial colony that grew on the G418-supplemented medium was treated as a putative transformant and was transferred to a well of a 12-well culture plate containing 2 mL of V8 broth with 40 µg/mL of G418. 5.3.7 Screening transformants for CRISPR-Cas9 mutants Any putative transformants that grew through the second round of G418 selection were considered successful transformants based on their stable growth on selective medium. Wildtype (WT) cultures were grown in parallel as a negative control. After 3-4 days of growth in the V8 broth + 40 µg/mL of G418 (no G418 for WT cultures), 50-100 mg of mycelial tissue was harvested from each culture and flash frozen in liquid nitrogen. Total cellular DNA was extracted using a QIAGEN DNeasyⓇ Plant Mini Kit (QIAGEN Sciences, Germantown, MD, USA) as per the manufacturer’s instructions (DNeasyⓇ Plant Handbook, Mini Protocol, TissueLyser procedure). Tissue disruption was performed using a Retsch Mixer Mill MM 300 (Retsch GmbH, Haan, Germany). PCR was performed on both transformant and wildtype DNA extractions using Phusion™️ Plus DNA Polymerase (Thermo Fisher Scientific, Waltham, MA, USA) and primers designed to amplify the regions of PrORP1 or PcaORP1 targeted by the Cas9 gRNAs. All PCRs were performed using the following thermocycler conditions (as per manufacturer’s instructions for Phusion polymerase): an initial hold of 98°C for 30 seconds; 35 cycles of 98°C for 10 seconds, 60°C for 10 seconds, 72°C for 30 seconds; a final hold of 72°C for 5 minutes. The resulting PCR products were analyzed using gel electrophoresis and sent for Sanger sequencing in the forward direction. The raw Sanger sequencing chromatograms were analyzed using the TIDE (Tracking of Indels by Decomposition) web tool to detect CRISPR- 123 Cas9-generated insertions and deletions (indels) at the Cas9 cut site for each gRNA (Brinkman et al., 2014). Any identified ΔORP1 mutants were then re-sequenced in the reverse direction and the sequence reads were re-analyzed with TIDE to confirm the mutation on both DNA strands. 5.3.8 Generation of single zoospore-derived cultures Any ΔORP1 mutants identified with Sanger sequencing and TIDE analysis were taken through a single zoospore isolation protocol to ensure that downstream phenotyping would be performed on pure cultures derived from a mononucleate gene-edited zoospore. As only ΔPcaORP1 mutants were obtained, the protocol below outlines the method used for generating single zoospore cultures (SZCs) in P. cactorum, adapted from previous studies (Eikemo et al., 2000, 2003). Wildtype P. cactorum FF42 cultures were also taken through the protocol to act as a control for downstream phenotyping. Cultures were grown on unamended V8A for two weeks. The agar on each plate was then cut into wedges with a sterile scalpel, flooded with 20 mL of autoclaved distilled water, and placed under continuous fluorescent light for 48 hours. After 48 hours, the plates were removed from the light and incubated at 4°C for 30 minutes. The liquid from the plate was then filtered through a 40 μm cell strainer into a 50 mL conical tube, and the concentration of zoospores in the resulting suspension was determined with a hemocytometer. The zoospore suspension was then diluted to the desired concentration, spread on a fresh V8A plate, and incubated in the dark at room temperature (~21°C). After 3-4 days, each SZC was transferred to its own V8A plate and maintained as described in section 5.3.1. Three to four SZCs from each ΔPcaORP1 mutant were taken through DNA extraction, PCR, Sanger sequencing, and TIDE analysis, as described in section 5.3.7, to confirm that the CRISPR-Cas9-mediated mutation was still present after single zoospore isolation. 124 5.3.9 Testing plasmid DNA retention in single zoospore cultures To test whether the pYF2-PsCG plasmid DNA was retained in mutants after single zoospore isolation, PCR was performed on DNA from both the original ΔORP1 mutant cultures and the corresponding SZCs. Phusion™️ Plus DNA Polymerase (Thermo Fisher Scientific, Waltham, MA, USA) was used with the eGFP_Diag_F1/R1 primers designed to amplify a 273 base pair product from the eGFP gene on the pYF2-PsCG plasmid (see Chapter 4 for primer details). Wildtype DNA from P. cactorum FF42 was used as the negative control, and plasmid DNA from pYF2-PsCG was used as the positive control. The PCR was performed with the same thermocycler conditions as described in section 5.3.7, and the resulting PCR products were analyzed with gel electrophoresis. 5.3.10 Oxathiapiprolin growth assays Oxathiapiprolin growth assays were performed to determine whether the CRISPR-Cas9 ΔPcaORP1 mutations would have any effect on the growth phenotype relative to wildtype P. cactorum cultures. Technical grade oxathiapiprolin (98.1%) was provided by Syngenta (Syngenta Canada Inc., Guelph, Ontario, Canada). The oxathiapiprolin was dissolved in DMSO to make a stock solution of 100 mg/mL. Phytophthora cactorum FF42 wildtype and ΔPcaORP1 mutant cultures were grown on V8A plates supplemented with a gradient of nine oxathiapiprolin concentrations: 0.0001 μg/mL, 0.0002 μg/mL, 0.0004 μg/mL, 0.0006 μg/mL, 0.0008 μg/mL, 0.001 μg/mL, 0.01 μg/mL, 0.1 μg/mL, and 1.0 μg/mL. The concentration of DMSO in the medium for each treatment was standardized to 0.1%, and the volume of V8A per plate was standardized to 15 mL. In parallel, the cultures were also grown on two control treatments: 125 unamended V8A and V8A with 0.1% DMSO. Two individual single-zoospore-derived cultures were tested from the wildtype and each of the ΔPcaORP1 mutants, and four replicates were grown for each culture on each growth medium treatment. For each treatment, a 5-mm agar plug was taken from the edge of an actively growing six-day-old culture and transferred to a treatment plate. Seven days after plating, the radial mycelial growth was measured in two perpendicular directions (Figure 5.1); 5 mm was subtracted from each measurement to account for the diameter of the agar plug. The average of these two measurements was recorded as the radial growth for each culture. 5.3.11 Statistical analyses All statistical analyses were performed using Python version 3.8 with the following packages: pandas (McKinney, 2010; The pandas development team, 2020), SciPy stats (Virtanen et al., 2020), scikit-posthocs (Terpilowski, 2019), and statsmodels (Seabold & Perktold, 2010). The resulting figures were also generated in Python 3.8 with the following packages: matplotlib Figure 5.1 Schematic demonstrating how radial mycelial growth measurements were taken from Phytophthora cultures at seven days post plating on V8 agar medium supplemented with a gradient of oxathiapiprolin concentrations. To account for the width of the agar plug, 5 mm was subtracted from the length of measurement 1 (M1) and measurement 2 (M2). The average of the two measurements was recorded as the radial growth of each culture at a given oxathiapiprolin concentration. 126 (Hunter, 2007), seaborn (Waskom, 2021), pandas (McKinney, 2010; The pandas development team, 2020), and statannotations (Charlier et al., 2022). The differences in average radial growth between P. cactorum FF42 wildtype and ΔPcaORP1 mutants on oxathiapiprolin-supplemented V8A were analyzed using either a one-way ANOVA or Kruskal-Wallis test, depending on the normality of the data. The 0.1% DMSO control treatment was tested in two separate experiments, so the results from the two experiments were pooled. The results from each of the two individual SZCs for the wildtype and ΔPcaORP1 mutants were also pooled. A Shapiro-Wilk test of normality was performed on the growth data within each treatment. For treatments with normally distributed data, the differences in radial growth between isolates were analyzed with a one-way ANOVA followed by a Tukey’s honest significance difference post-hoc test. For treatments with non-normally distributed data, the differences in radial growth between isolates were analyzed with a Kruskal-Wallis test followed by a Dunn’s multiple comparison post-hoc test. 5.4 Results 5.4.1 Structure of the PrORP1 and PcaORP1 genes The PrORP1 gene from P. ramorum NA2_17 was predicted to be a total length of 3235 bp, with two exons separated by a 76 bp intron (Figure 5.2). The predicted 2745 bp PrORP1 ORF lies within the second exon and encodes a 914 amino acid polypeptide. The PcaORP1 gene from P. cactorum FF42 was predicted to be a total length of 3166 bp, with three exons separated by two introns 82 bp and 96 bp in length (Figure 5.3). The predicted 2757 bp PcaORP1 ORF spans the second and third exons and encodes an 886 amino acid polypeptide after intron splicing. 127 Relative to the ORF of the P. capsici ORP1 gene (PcORP1), the PrORP1 and PcaORP1 ORFs were predicted to have different lengths and exon/intron structures (Figure 5.4A), but their InterPro-predicted protein domains were identical to PcORP1 (Figure 5.4B). All three ORP1 proteins have a conserved structure: an N-terminal pleckstrin homology (PH) domain, a steroidogenic acute regulatory-related lipid-transfer (START) or START-like domain, and a C-terminal oxysterol-binding protein (OBP) domain (Figure 5.4B). Figure 5.3 Predicted structure of the PrORP1 gene from Phytophthora ramorum NA2_17. The open reading frame was predicted using the Geneious “Find ORFs” tool (Geneious Prime 2023.2.1) and validated using the NCBI Open Reading Frame Finder (see Methods section 5.3.4.3). The exon-intron boundaries and sequences were predicted using the Softberry FGENESH eukaryotic gene finding algorithm (www.softberry.com). The orange labels indicate the locations of the four guide RNAs designed for CRISPR-Cas9 gene editing (see Table 5.4), and the purple labels show the locations of the primer pairs designed to amplify the Cas9 ribonucleoprotein-targeted regions for Sanger sequencing(see Table 5.5). Gene map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). Figure 5.2 Predicted structure of the PcaORP1 gene from Phytophthora cactorum FF42. The open reading frame was predicted using the Geneious “Find ORFs” tool (Geneious Prime 2023.2.1) and validated using the NCBI Open Reading Frame Finder (see Methods section 5.3.4.3). The exon-intron boundaries and sequences were predicted using the Softberry FGENESH eukaryotic gene finding algorithm (www.softberry.com). The orange labels indicate the locations of the five guide RNAs designed for CRISPR-Cas9 gene editing (see Table 5.4), and the purple labels show the locations of the primer pairs designed to amplify the Cas9 ribonucleoprotein-targeted regions for Sanger sequencing (see Table 5.5). Gene map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). 128 5.4.2 Guide RNAs designed and tested for PrORP1 and PcaORP1 We ultimately selected four guide RNAs (gRNAs) for PrORP1 and five gRNAs for PcaORP1 for testing plasmid-RNP co-transformations in P. ramorum and P. cactorum, respectively. The names, sequences, and efficiency scores for all gRNAs are shown in Table 5.4, and the primer pairs designed to amplify the targeted gene regions for the gRNAs are shown in Figure 5.4 A) Structure and size of the PrORP1 and PcaORP1 open reading frames relative to that of the P. capsici PcORP1 gene characterized by Miao et al. (2016). The numbers indicate the size of the ORFs in base pairs (bp). The exon-intron boundaries of PrORP1 and PcaORP1 were predicted using the Softberry FGENESH eukaryotic gene finding tool (http://www.softberry.com). Exon 1 is not shown for either PrORP1 or PcaORP1 as it was not predicted to be included in the ORF region of the genes. The exon-intron boundaries shown for PcORP1 are according to Miao et al. (2016). B) Structure, size, and predicted functional domains of the PrORP1 and PcaORP1 proteins relative to the P. capsici PcORP1 protein characterized by Miao et al. (2016). The amino acid sequences of all three proteins were analyzed using InterPro (Paysan-Lafosse et al., 2023) to predict the functional domains. 129 Table 5.5. Schematics of the PrORP1 and PcaORP1 genes with the locations of the gRNAs and primers are shown in Figure 5.2 and Figure 5.3, respectively. Table 5.4 CRISPR-Cas9 guide RNAs (gRNAs) designed to target the PrORP1 and PcaORP1 genes of Phytophthora ramorum and P. cactorum, respectively, using the Eukaryotic Pathogen CRISPR guide RNA/DNA Design Tool (EuPaGDT; Peng & Tarleton, 2015). All gRNAs are designed using a 3’ NGG protospacer adjacent motif (PAM). The number in each gRNA name represents the location of its first nucleotide in the ORP1 open reading frame sequence. The gRNAs labelled with “rc” (reverse complement) are those derived from the antisense strand of the ORP1 sequence. The gRNAs labelled with “mh” (microhomology) are those that EuPaGDT found to have microhomology pairs flanking the gRNA sequence. Also provided are the total scores from EuPaGDT for each gRNA and the IDT gRNA design checker scores. Species (Gene) gRNA Name (abbreviation) PAM 20-nt gRNA Sequence (5’ to 3’) EuPaGDT Total Score IDT gRNA Score Phytophthora ramorum NA2_17 (PrORP1) PrNA2ORPh_108rc (Pr108rc) AGG GTCCTTCCTCGAGATGTAAT 0.58 45 PrNA2ORPh_192 (Pr192) CGG TTACCTCACGGACCCAGCTA 0.61 51 PrNA2ORPh_368mh (Pr368mh) CGG ACACGAGGACACCGGCGCAG 0.82 54 PrNA2ORPh_751rc (Pr751rc) CGG GACGGAACACAACGGAGTTG 0.61 63 Phytophthora cactorum FF42 (PcaORP1) PcaFF42ORPh_156 (Pca156) AGG CTTGGAAGGTTGTACAGTGC 0.64 57 PcaFF42ORPh_330 (Pca330) AGG CACAAGTGAGACCAATGCAC 0.55 94 PcaFF42ORPh_1272rcmh* (Pca1272rcmh) CGG CACCACAGACTGGCCCATCT 0.59 59 PcaFF42ORPh_1507rcmh* (Pca1507rcmh) AGG CCTGGTTCACGTATTCACTG 0.57 73 PcaFF42ORPh_2248mh* (Pca2248mh) TGG CTGCCTTACTTACAGATCGG 0.56 38 *gRNAs were designed before the P. cactorum P414 NCBI reference genome was added to EuPaGDT, so off-target analysis was performed manually using a blastn analysis in NCBI. 130 Table 5.5 Primer pairs designed to amplify Cas9 RNP-targeted regions of the PrORP1 and PcaORP1 genes of Phytophthora ramorum NA2_17 and P. cactorum FF42, respectively, for Sanger sequencing. The guide RNAs (gRNAs) that target a sequence within each primer-amplified region are also listed. All PCRs for Sanger sequencing were performed using Phusion™️ Plus DNA Polymerase, which uses a universal annealing temperature of 60°C. Species (Gene) Primer Pair Name Primer Sequences (5’ to 3’) gRNA(s) Amplified Amplicon Size Phytophthora ramorum NA2_17 (PrORP1) PrNA2ORPh_F1/R1 F1: TTTTGCAGAAGAAAGGTCAGCG Pr108rc Pr192 Pr368mh 674 bp R1: CGCCTCCAAAACGTCCAGAC PrNA2ORPh_F2/R2 F2: CCAAACGTACTTCTCGCATGAC Pr751rc 740 bp R2: TTCCAGGATAAGCACCACAGAC Phytophthora cactorum FF42 (PcaORP1) PcaORPh_Cas9Edit_1F/1R 1F: CGGATCTGGTGCATTTGTCAAG Pca1272rcmh Pca1507rcmh 766 bp 1R: TCCTTGTCGATCAGCTTCAGAC PcaORPh_Cas9Edit_2F/2R 2F: GAAGCCGTTTAACCCGATCTTG Pca2248mh 571 bp 2R: CATGTAGCCGCTCTGGAATTTG PcaORPh_Cas9Edit_3F/3R 3F: ACCCTAGTGCAATCTTCCAAGC Pca156 Pca330 777 bp 3R: AAGCTCGTTTTCCAGTGCAATC PcaORPh_Cas9Edit_4F/4R 4F: TACATGAAGGAGGGCTTCTTGC Pca156 Pca330 506 bp 4R: CCATTAACCGTGGCAGTACTTG 5.4.3 CRISPR-Cas9 plasmid-RNP co-transformation produces mutants in Phytophthora cactorum but not Phytophthora ramorum 5.4.3.1 Phytophthora cactorum Using a PEG-liposome-mediated plasmid-RNP co-transformation method (Figure 5.5), we obtained two independent, heterozygous indel ΔPcaORP1 mutants in P. cactorum FF42. The first mutant was obtained with the Pca1507rcmh gRNA, and TIDE analysis revealed a single base pair (bp) insertion between nucleotides 1,491 and 1,492 in the open reading frame (ORF) of the PcaORP1 gene (Figure 5.6). The second mutant was obtained with the Pca156 gRNA, and TIDE analysis revealed a single base pair (bp) deletion of nucleotide 172 in the PcaORP1 ORF (Figure 5.7). For both mutants, the ratio of wildtype to mutant DNA in the originally obtained transformant cultures was generally around 1.5-2:1 whereas the same ratio in single zoospore cultures (SZCs) shifted to 1:1, indicating that pure heterozygous cultures from a single edited nucleus were obtained through the zoospore isolation process (Figure 5.6 and Figure 5.7). Of 131 note is that one of the SZCs obtained from the original PT9 mutant culture was no longer a ΔPcaORP1 mutant (PT9-1; Figure 5.7B), indicating that it was derived from a zoospore with a non-edited wildtype nucleus. Both the PT9 and PT10 indels lead to frameshift mutations in the PcaORP1 exons and therefore likely result in non-functional proteins translated from the mutated allele (Figure 5.8). Figure 5.5 Method/workflow developed for CRISPR-Cas9 gene editing in Phytophthora cactorum using a plasmid-RNP co-transformation protocol. Figure created with BioRender.com. 132 Figure 5.6 Details of the CRISPR-Cas9 ΔPcaORP1 indel mutant, PT10, obtained in Phytophthora cactorum FF42 using a plasmid-RNP co-transformation method with the Pca1507rcmh guide RNA (gRNA). The PT10 mutant is heterozygous, with the mutant allele comprising a single base pair insertion at the Cas9 cut site. The graphs shown were generated using TIDE software (Brinkman et al. 2014) on the forward Sanger sequencing results from PcaORPh_Cas9Edit_1F/1R PCR amplicons. The P. cactorum PcaORP1 wildtype sequencing chromatogram was used as the control sample and the ΔPcaORP1 PT10 mutant chromatograms were used as the test samples. The top graphs show the chromatogram sequence compositions with the expected Cas9 cut site indicated by the dashed blue line, the bottom graphs show the predicted percentage of sequences with wildtype vs. mutant genotypes, and the bar graphs to the right indicate the predicted nucleotide in the insertion mutants (y-axis represents probability). A) TIDE decomposition graphs for the original PT10 transformant culture recovered from G418-selective medium. The ratio of wildtype to mutant DNA indicates a mixture of edited and non-edited nuclei in the culture. B) TIDE decomposition graphs for PT10-1, one of the single zoospore-derived cultures (SZCs) of PT10. The ratio of the wildtype to mutant DNA indicates a pure heterozygous culture with one wildtype allele and one mutant allele. The TIDE results from the other two SZCs tested (PT10-2, PT10-3) were nearly identical (results not shown). The TIDE results indicate that the nucleotide inserted on the sense strand of PcaORP1 is a thymine, therefore the nucleotide inserted on the antisense strand where the gRNA was designed is an adenine. 133 Figure 5.7 Details of the CRISPR-Cas9 ΔPcaORP1 indel mutant, PT9, obtained in Phytophthora cactorum FF42 using a plasmid-RNP co-transformation method with the Pca156 guide RNA (gRNA). The PT9 mutant is heterozygous, with the mutant allele comprising a single base pair deletion at the Cas9 cut site. The graphs shown were generated using TIDE software (Brinkman et al. 2014) on the forward Sanger sequencing results from PcaORPh_Cas9Edit_4F/4R PCR amplicons. The P. cactorum PcaORP1 wildtype sequencing chromatogram was used as the control sample and the ΔPcaORP1 PT9 mutant chromatograms were used as the test samples. The top graphs show the chromatogram sequence compositions with the expected Cas9 cut site indicated by the dashed blue line, and the bottom graphs show the predicted percentage of sequences with wildtype vs. mutant genotypes. A) TIDE decomposition graphs for the original PT9 transformant culture recovered from G418-selective medium. The ratio of wildtype to mutant DNA indicates a mixture of edited and non-edited nuclei in the culture. B) TIDE decomposition graphs for PT9-1, one of the single zoospore-derived cultures (SZCs) from PT9. The TIDE results indicated that PT9-1 was derived from a non-edited wildtype nucleus as it did not have any mutant alleles. C) TIDE decomposition graphs for PT9-2, another of the SZCs derived from PT9. The ratio of the wildtype to mutant DNA indicates a pure heterozygous culture with one wildtype allele and one mutant allele. The TIDE results from the other two SZCs tested (PT9-3, PT9-4) were nearly identical (results not shown). 134 5.4.3.2 Phytophthora ramorum We were unable to obtain any CRISPR-Cas9 mutants in Phytophthora ramorum NA2_17 using two different plasmid-RNP co-transformation protocols and four unique gRNAs (Data C.1.1). The first protocol we tested in P. ramorum was based on a previous method developed to edit the Figure 5.8 Predicted protein sequences for the PcaORP1 gene of Phytophthora cactorum FF42. A) The wildtype (WT) sequence encodes an 886 amino acid polypeptide. B) The 1 base pair (bp) insertion mutation in one of the alleles of the ΔPcaORP1 PT10 mutant introduces a frameshift resulting in a premature stop codon at amino acid 466. C) The 1 bp deletion mutation in one of the alleles of the ΔPcaORP1 PT9 mutant also introduces a frameshift and results in a premature stop codon at amino acid 63. Protein sequences were generated using Geneious Prime 2023.2.1. 135 genome of the oomycete Aphanomyces invadans by delivering CRISPR-Cas9 RNPs via a PEG-mediated transformation of protoplasts (Majeed et al., 2018). However, we did not obtain any G418-resistant cultures, indicating that this transformation approach did not work for our P. ramorum protoplasts. To test the possibility that the plasmid-RNP complexes had been successfully delivered to P. ramorum protoplasts, but that the transformants were simply not expressing the nptII gene from the pYF2-PsCG plasmid, we screened a selection of cultures that we had transformed but grown on non-selective recovery medium. The Sanger sequencing results from the screened cultures (representing all four gRNAs tested) indicated that none were CRISPR-Cas9 mutants. We also tested the protoplast transformation protocol developed for CRISPR-Cas9 plasmid delivery in P. sojae (Fang, Cui, et al., 2017) to co-transform P. ramorum NA2_17 with plasmid-RNP complexes, but again, did not obtain any G418-resistant transformants. 5.4.4 Single zoospore cultures of Phytophthora cactorum mutants lose plasmid DNA The results from PCR performed on the original PT9 and PT10 mutant cultures and their respective SZCs showed loss of pYF2-PsCG plasmid DNA in the single zoospore-derived cultures (Figure 5.9). An exception was the culture PT10-1 C2, which was one of two replicate cultures from the original PT10-1 SZC (C1 and C2). The DNA from PT10-1 C2 produced a slightly larger than expected PCR amplicon. To test whether the product was due to non-specific primer amplification, the amplicon was sent for Sanger sequencing. However, the results indicated that the sequence of the PT10-1 C2 PCR product was identical to the products from both the purified pYF2-PsCG DNA and the original PT10 mutant culture DNA. It was therefore 136 assumed that the PT10-1 C2 culture contained plasmid DNA, and the culture was not used for any downstream phenotype assays. 5.4.5 ΔPcaORP1 mutants exhibit decreased resistance to oxathiapiprolin We selected two independent SZCs from each of the WT, PT9, and PT10 cultures for oxathiapiprolin growth assays: WT-1, WT-2, PT9-2, PT9-3, PT10-1 (C1), and PT10-3 (C1). To account for random variation in radial growth, the results from the two SZCs of each culture were pooled prior to statistical analysis so that changes in growth were ultimately measured between three groups: WT, PT9, and PT10. The raw growth results from the oxathiapiprolin Figure 5.9 Gel electrophoresis of products from a PCR performed using eGFP_Diag_F1/R1 primers on DNA from Phytophthora cactorum FF42 ΔPcaORP1 mutant cultures (PT9, PT10) and their respective single zoospore cultures (SZCs: PT9-1 to PT9-4, PT10-1 and PT10-3). For the PT10 SZCs, DNA extractions from two replicate cultures were tested (C1, C2). The DNA from P. cactorum FF42 wildtype SZCs (WT-1 and WT-2) was used as the negative PCR control, and pYF2-PsCG plasmid DNA was used as the positive PCR control. A product amplified in sample PT10-1 C2, indicating presence of plasmid DNA; the culture from which the sample was derived was therefore not included in downstream phenotype assays. The gel was run at 100 volts for 55 minutes on 1% agarose in 0.5X TBE buffer with a 100 bp DNA ladder. All samples were loaded with Safe-Green™️ stain (Applied Biological Materials Inc., Richmond, BC, Canada) at a ratio of 1 μL Safe-Green: 5 μL PCR product. 137 assays are reported in Data C.2.1. Radial growth of both the PT9 and PT10 ΔPcaORP1 mutant cultures was visibly reduced relative to the wildtype cultures on oxathiapiprolin-supplemented medium (Figure 5.10), and this reduction in growth was statistically significant at every oxathiapiprolin concentration tested (Figure 5.11; Data C.2.2). Note that the 0.01 μg/mL, 0.1 μg/mL, and 1.0 μg/mL oxathiapiprolin treatments were not included in the analyses because there was not enough mycelial growth on the V8A plates (Data C.2.1). Figure 5.10 Photos of Phytophthora cactorum FF42 wildtype (WT) and CRISPR-Cas9 ΔPcaORP1 mutant (PT9, PT10) cultures growing on oxathiapiprolin-supplemented V8 agar seven days post plating. The images shown are only a representation of the isolates and oxathiapiprolin concentrations tested, however, the same pattern of reduced growth in the ΔPcaORP1 mutant cultures was observed at every concentration tested (see Figure 5.11). 138 5.5 Discussion Using a plasmid-RNP co-transformation method, we successfully edited the ORP1 gene of Phytophthora cactorum, obtaining two independent heterozygous indel mutants. To our knowledge, this is the first successful use of RNPs for CRISPR-Cas9 gene editing in a Phytophthora species, and the first implementation of a CRISPR-Cas9 gene editing system in a forest Phytophthora pathogen. Delivering both the plasmid selective marker and the RNP in a Figure 5.11 Boxplot of radial growth measurements of Phytophthora cactorum FF42 wildtype (WT) and CRISPR-Cas9 ΔPcaORP1 mutant (PT9, PT10) cultures growing on V8 agar (V8A) supplemented with a gradient of oxathiapiprolin concentrations. Unamended V8A and V8A + 0.1% DMSO were used as control treatments. For each isolate at each treatment, the growth data from two independent single zoospore cultures (SZCs), each with four replicates, were pooled (for a total of eight replicates per isolate per treatment). The white circles indicate the mean growth value for each isolate at each treatment. Results from statistical analyses revealed that both PT9 and PT10 ΔPcaORP1 mutant cultures exhibited statistically reduced mycelial growth relative to WT cultures on every oxathiapiprolin concentration tested (‘ns’: not significant, ‘*’: p < 0.05, ‘**’: p < 0.01, ‘***’: p < 0.001, ‘****’: p < 0.0001). 139 liposome complex allowed us to efficiently screen for putative CRISPR-Cas9 mutants from G418-resistant transformants given the lack of visible phenotype in our ΔPcaORP1 mutants. Our results with this plasmid-RNP co-transformation approach, both the success we achieved in P. cactorum as well as our inability to obtain P. ramorum mutants, provide foundational knowledge for the use of CRISPR-Cas9 RNPs in forest Phytophthoras and give insight into potential future directions for this research. The two ΔPcaORP1 mutants we obtained in P. cactorum were the result of experiments with two gRNAs targeting different regions of the PcaORP1 gene (Pca156, Pca1507rcmh). While we were able to obtain G418-resistant transformants from the co-transformations with the other three gRNAs that we designed (Pca330, Pca1272rcmh, Pca2248mh), none of the transformants were PcaORP1 mutants. This variability in gRNA success is consistent with previous studies. For example, Majeed et al. (2018) edited the genome of the oomycete Aphanomyces invadans using RNPs with fluorescently labelled tracrRNA. By observing fluorescence in all RNP-treated protoplasts, Majeed et al. confirmed successful RNP delivery, but found that only one of the three gRNAs they tested produced successful CRISPR-Cas9 mutants. It is also well-established in many other eukaryotic organisms that gRNAs exhibit highly variable activity even when well-designed, and there are many complex factors that can contribute to the success of a gRNA in directing Cas9 to cut target DNA (Doench et al., 2014, 2016; Farboud & Meyer, 2015; G. Liu et al., 2020; Moreno-Mateos et al., 2015; Thyme et al., 2016). Previous research in human cells has shown significant variability in CRISPR-Cas9 editing performance even between gRNAs designed to target adjacent sites of the same exon (T. Wang et al., 2014). Therefore, while the Pca330, Pca1272rcmh, and Pca2248mh gRNAs had appropriate efficiency scores and minimal 140 secondary structures, it is possible they did not perform well with the Cas9 nuclease once transformed in P. cactorum cells. These results reinforce the importance of designing and testing multiple gRNAs when developing CRISPR-Cas9 protocols. The loss of pYF2-PsCG DNA in most of the single zoospore-derived cultures (SZCs) of the two ΔPcaORP1 mutants indicates that the plasmid likely remained as extrachromosomal DNA in transformed P. cactorum cells rather than integrating into the genome. The fate of plasmid DNA post transformation is variable in oomycetes. Early studies in Phytophthora and Saprolegnia species demonstrated that PEG-mediated transformations generally resulted in plasmid DNA being integrated into the genome either at one or multiple sites, and often in tandem repeats (Judelson et al., 1991, 1993; Mort-Bontemps & Fèvre, 1997; van West et al., 1999). Due to the randomness of plasmid integration sites in oomycete genomes after transformation, it is hypothesized that this process does not involve homologous recombination, but likely occurs via nonhomologous chromosomal integration (Judelson & Ah-Fong, 2009). However, plasmid integration appears to be species-specific; for example, transformation studies in P. parasitica demonstrate that plasmid DNA is often not integrated into the genome, but rather remains extrachromosomal, often leading to instability and loss of antibiotic resistance after subculturing on non-selective media (Gaulin et al., 2007). In another oomycete, Achlya ambisexualis Raper, it was demonstrated that plasmid DNA was both integrated and maintained episomally after PEG-mediated transformation (Manavathu et al., 1988), further demonstrating the diversity in cellular responses to plasmid DNA transformation in oomycetes. As the pYF2-PsCG plasmid does not contain an autonomously replicating sequence (ARS), it would not be maintained episomally in our ΔPcaORP1 mutants. Instead, the plasmid was likely partitioned away during mitosis (X. 141 Wang et al., 2016), resulting in daughter nuclei, and subsequently zoospores, with no pYF2-PsCG DNA. While our success in obtaining two ΔPcaORP1 mutants indicates that RNP-mediated CRISPR-Cas9 gene editing is feasible in P. cactorum, the rates of editing we achieved were low. We screened a total of 47 transformants and found two mutants, which translates to a rate of approximately 4%. One possible explanation for this low mutation rate could be the efficiency of the gRNAs, which was previously discussed. Future studies could test more gRNAs in different regions of the PcaORP1 gene to determine whether there is any effect on the number of mutants recovered. Another possible explanation for the low number of mutants recovered could be linked to the nuclear localization signal (NLS) fused to the Cas9 endonuclease. When developing plasmid-mediated CRISPR-Cas9 gene editing in P. sojae, Fang and Tyler found that mammalian NLS sequences were not efficient, so subsequently used a P. sojae NLS to target Cas9 to the nuclei of cells (Fang & Tyler, 2016). The IDT Alt-R™️ S.p. Cas9 nuclease we used contains a proprietary NLS of unknown origin, however, it is likely not optimized for oomycete cells given that they are a relatively niche group in CRISPR-Cas9 research. This lack of NLS customization when using a commercially available Cas9 nuclease represents an advantage of a plasmid-mediated CRISPR-Cas9 gene editing method over an RNP-mediated method. However, our results in P. cactorum indicate that the IDT Cas9 NLS is functional in P. cactorum and could therefore potentially also be used for other forest Phytophthoras. Future studies could explore optimizing the RNP concentration; it is possible that by increasing the number of RNP complexes delivered per transformation, the likelihood of delivery to nuclei, and therefore the likelihood of successful mutation would also increase. Additionally, using fluorescently labelled 142 RNP components, such as tracrRNA, would help in tracking successful delivery of the RNP to the nuclei and assessing the efficacy of the NLS. Both ΔPcaORP1 mutants we obtained in P. cactorum exhibited decreased oxathiapiprolin resistance relative to wildtype cultures, which is in direct contrast to previous studies of ORP1 gene mutations in other Phytophthora species (Bittner et al., 2017; Miao, Chi, et al., 2018; Miao et al., 2016, 2020; Z. Wang et al., 2022). However, all these previous studies induced specific non-synonymous point mutations in the ORP1 domain, demonstrating that both heterozygous and homozygous mutations confer increased resistance to oxathiapiprolin (Bittner et al., 2017; Miao, Chi, et al., 2018; Miao et al., 2016, 2020; Z. Wang et al., 2022), likely by altering the binding affinity between the fungicide and ORP1 (Z. Wang et al., 2022). The heterozygous ΔPcaORP1 indel mutations we obtained both result in frameshifts, introducing pre-mature stop codons in the PcaORP1 polypeptide encoded by the mutant allele. In theory, this would mean that only one PcaORP1 allele would be producing functional polypeptides, effectively halving the number of target molecules in the cell to which the ligand (oxathiapiprolin) can bind. Presumably, this would leave a greater concentration of ligand available to target the functional PcaORP1 proteins, leading to a lower protein saturation point. This lower saturation point would be a greater disadvantage for the ΔPcaORP1 mutants relative to wildtype individuals at lower concentrations of oxathiapiprolin, which is exactly what we observed. There are many possible explanations for our lack of success with CRISPR-Cas9 delivery in P. ramorum. However, our inability to obtain G418-resistant transformants suggests an issue encountered during the transformation process, which could manifest in one of two ways. The 143 first possible explanation is that the plasmid-RNP complex failed at the point of delivery into the P. ramorum protoplasts. As discussed in Chapter 4, Phytophthora species, and oomycetes in general, are known to respond variably to transformation protocols (Ghimire et al., 2022; Govers & Gijzen, 2006; Mcleod et al., 2008), so it could be that P. ramorum is simply not amenable to the introduction of plasmid-RNP complexes via a PEG-liposome-mediated transformation approach. The second possible explanation is that the RNP and plasmid were delivered successfully, but their presence in the cell is toxic to P. ramorum. The toxicity of Cas9, both in its catalytically active and inactive forms, has been previously demonstrated in eukaryotic organisms (Foster et al., 2018; W. Jiang et al., 2014; Morgens et al., 2017). Given that in our transformations, we delivered Cas9 in both the pYF2-PsCG plasmid and the purified RNP complex, it is possible that its presence in P. ramorum cells had toxic effects. However, the fact that we could recover apparently healthy, transformed cultures on non-G418 medium suggests that the plasmid-RNP components were not toxic, but rather were not being delivered to and/or expressed in the P. ramorum cells. These results point to the need to establish an improved transformation method for P. ramorum before further CRISPR-Cas9 experiments are attempted. Another factor to consider for future CRISPR-Cas9 protocol development in P. ramorum is the concentration of RNP delivered to protoplasts during transformation. In our experiments with P. ramorum, we delivered roughly equimolar amounts of plasmid DNA and RNP, testing co-transformations with 0.75 pmol and 1.5 pmol of both components. We selected these RNP amounts based on a combination of the previously developed RNP-mediated CRISPR-Cas9 editing protocol in Aphanomyces invadans (Majeed et al., 2018) and the picomoles of RNP per transformation used in the IDT protocol for cationic lipid delivery of CRISPR RNP complexes 144 (IDT, 2018). However, in P. cactorum, we shifted to a higher molar ratio of RNP to plasmid, ultimately using 75 pmol of RNP and 4 pmol of plasmid DNA for each transformation. Increasing the RNP concentration per transformation should therefore be tested in any future studies developing RNP-mediated CRISPR-Cas9 editing in P. ramorum once a more consistent transformation protocol has been established. Developing CRISPR-Cas9 gene editing in forest Phytophthoras is an important step for continued exploration and elucidation of the specific molecular mechanisms driving the phytopathogenic behaviours of these species. Our results provide a proof of concept for the use of RNP-mediated CRISPR-Cas9 gene editing in Phytophthora cactorum, and our findings serve as a foundation for future studies developing CRISPR-Cas9 protocols in P. ramorum and other forest Phytophthoras. 145 Chapter 6: Conclusion 6.1 Summary of research findings The research presented in this dissertation was multidisciplinary and applied state-of-the-art genomic analyses and molecular techniques in a forest pathology context. The research findings for each chapter are summarized below as they pertain to the two objectives outlined in the Introduction. Objective 1: Establish a proof of concept for the use of large-scale comparative genomics to explore the genetic mechanisms driving plant pathogenicity, uncover gene targets associated with phytopathogenic traits of interest, and discover genomic patterns that can be used in future biosurveillance of filamentous forest pathogens. In Chapter 2, I reported on the use of a novel machine learning method to perform comparative analyses on the genomes of fungi and predict phytopathogenic lifestyles and traits from distinct genomic patterns. The machine learning method was not only able to separate the phylogenetic and genomic components of each prediction but also identified specific genes and gene families associated with fungal phytopathogenic lifestyles and traits, demonstrating the potential use of this approach for generating gene candidates for downstream functional validation with CRISPR-Cas9 gene editing or similar molecular techniques. Of particular note was the strong performance of machine learning in predicting plant pathogens, which illustrates the promise of this approach for future biosurveillance pipelines. 146 Objective 2: Establish a method for CRISPR-Cas9 editing in a forest pathogenic Phytophthora species for future validation of gene candidates associated with phytopathogenic traits of interest. Objective 2 was addressed in Chapters 3-5. In Chapter 3, I reviewed CRISPR-Cas9 gene editing, summarizing its current applications in plant pathology, and proposing future applications in forest pathology, where I believe it is currently underutilized. The goal of the review was to provide forest pathologists with a framework for the use of CRISPR-Cas9 gene editing in forest pathosystems, and it has now been cited in several forest pathology and forestry-related studies (Dort et al., 2020). In Chapter 4, I outlined my experiments designed to establish plasmid transformations in five forest pathogenic Phytophthoras. Transformations are a critical component of CRISPR-Cas9 gene editing and therefore the first step in developing this technology in any organism. The results from Chapter 4 demonstrated the recalcitrance of forest Phytophthora pathogens to plasmid transformations, indicating that these species will require further research to optimize transformation and subsequent development of CRISPR-Cas9 gene editing. Transformations succeeded in two of the five species tested: P. cactorum and P. ramorum. However, the transformations in P. ramorum were inconsistent and difficult to replicate. Finally, in Chapter 5, I tested a hybrid CRISPR-Cas9 approach, co-transforming plasmid-RNP complexes in P. cactorum and P. ramorum. I successfully obtained CRISPR-Cas9 mutants in P. cactorum, demonstrating the potential of this approach for this species, and possibly other forest Phytophthoras. 147 6.2 Looking forward: research implications and potential future directions 6.2.1 Genomics-based biosurveillance of forest invasive filamentous pathogens In the current era of globalization and climate change, biological invasions will continue to threaten forest health, and therefore robust biosurveillance practices will be crucial for threat mitigation. Current biosurveillance systems enable federal regulatory agencies to monitor for the presence of known plant pathogens and respond accordingly, however they are generally not equipped to predict the risk of unknown or undescribed pathogens. Filamentous fungi and oomycetes are particularly well-suited to invade naïve ecosystems, and there are numerous examples of forest invasive pathogens, such as Phytophthora ramorum (sudden oak death), that were not taxonomically identified until after they were causing widespread ecological damage. This constitutes a blind spot for taxon-based biosurveillance, which is why researchers are now proposing a shift to genomics-centered strategies focused on predicting the genetic traits associated with invasive pathogens regardless of their taxonomic identity (Hamelin & Roe, 2020; McTaggart et al., 2016; Roe et al., 2019). The research findings I report on in Chapter 2 of this dissertation demonstrate the feasibility and potential power of integrating genomics-based strategies into future biosurveillance pipelines. The ability of machine learning to parse large and complex data sets makes it very well-suited for large-scale genomic analyses (Libbrecht & Noble, 2015). However, while access to data from hundreds of genomes is unprecedented for plant pathologists, it is considered a relatively small data set for machine learning algorithms, which are normally trained and tested with thousands of data points. Therefore, as more genomic data becomes available, it will be necessary to continue to train and test predictive machine learning approaches for biosurveillance to ensure their capacity to capture phytopathogenic signals from the genomes of filamentous plant pathogens. 148 Another important next step for the research presented in Chapter 2 is to test the same comparative genomics approach in oomycetes to determine whether machine learning can similarly parse predictive patterns associated with the genomes of phytopathogenic oomycetes. A number of recent studies have performed comparative genomic analyses in oomycetes and found associations between specific gene families and phytopathogenic traits (Adhikari et al., 2013; de Vries et al., 2020; Fletcher et al., 2018; Gao et al., 2021; Kronmiller et al., 2023). The results of these previous studies demonstrate that a large-scale analysis of oomycete genomes would likely elucidate predictive genomic signatures, similar to the results obtained with fungi in Chapter 2. However, unlike the fungi, for which there are readily available functionally annotated genomes (i.e., MycoCosm), there is no such resource for oomycetes, which makes performing a similar comparative genomics study more difficult. Publicly available oomycete genome sequences, such as those available from NCBI GenBank (Benson et al., 2013), would therefore need to be functionally annotated for comparative analysis in future studies. 6.2.2 CRISPR-Cas9 gene editing for forest pathogenic Phytophthoras CRISPR-Cas9 gene editing has become a valuable tool for the study of filamentous plant pathogens, allowing researchers to functionally characterize the role of specific genes in the phytopathogenicity of both fungi (J. Li et al., 2018; Y. Liang et al., 2018) and oomycetes (Fang & Tyler, 2016; Gumtow et al., 2018). However, while this technology is becoming well-established for studying agricultural pathosystems, it is still underutilized in forest pathology research. For example, while the first CRISPR-Cas9 gene editing studies for filamentous agricultural pathogens were published soon after the development of CRISPR-Cas9 gene editing 149 (Arazoe et al., 2015; Fang & Tyler, 2016; Schuster et al., 2016) with numerous studies since, the first, and only CRISPR-Cas9 gene editing system in a filamentous forest pathogen (Dothistroma septosporum) was only recently developed in 2022 (McCarthy et al., 2022). This lack of CRISPR-Cas9 development is likely due, at least in part, to the lack of established molecular techniques for filamentous forest pathogens, particularly forest Phytophthoras. Plasmid-mediated DNA transformation is the basis of most CRISPR-Cas9 protocols, but it is challenging to implement in pathogens with no previously established transformation protocols. Almost all the studies using CRISPR-Cas9 in filamentous pathogens to date have been in species with previously established transformation protocols in place. Oomycetes, and Phytophthoras in particular, are known to be recalcitrant to molecular protocols (Ghimire et al., 2022; Govers & Gijzen, 2006; Mcleod et al., 2008), which presents an additional barrier to the establishment of CRISPR-Cas9 gene editing, or any other CRISPR-Cas technologies (e.g., Cas12a, Cas13a), in these species. While polyethylene glycol (PEG)-mediated transformation continues to be the most common method for transformations in Phytophthoras, it does have limitations. It is recognized as a very labour-intensive protocol, and is known to have low rates of protoplast regeneration; additionally, when protoplasts do regenerate it is difficult to obtain stable transformants (Mcleod et al., 2008; Wu et al., 2016). The 2008 study by Mcleod et al. found that after transformations in three Phytophthora pathogens, protoplast regeneration rates ranged from 7% to 40% and were consistent within each species, demonstrating that certain Phytophthoras are simply less amenable to molecular manipulation. The results of my research described in Chapter 4 certainly support this trend for forest Phytophthoras also; it was evident that while P. cactorum was immediately amenable to plasmid transformation, the other four 150 species I tested were not. Continued research into optimal transformation methods for forest Phytophthoras will therefore be crucial to any future development of CRISPR-Cas technologies in these species. This future research should continue to explore the optimization of PEG-mediated transformations, especially for the development of ribonucleoprotein (RNP)-mediated CRISPR-Cas9 gene editing. Despite its limitations, PEG-mediated transformation is still the most accessible method for most labs without requiring highly specialized, and expensive, equipment. However, where feasible, alternative methods, such as Agrobacterium-mediated transformation (AMT) (Vijn & Govers, 2003), microprojectile bombardment (Bailey et al., 1993), and zoospore electroporation (Latijnhouwers & Govers, 2003) should also be explored for the transformation of forest Phytophthoras. 6.3 Concluding statements The management and mitigation of forest pathogenic Phytophthoras, and filamentous forest pathogens in general, relies on understanding how these species interact with host plants at the molecular level to cause disease and spread through ecosystems. Current management strategies such as host resistance breeding, fungicide application, and even biosurveillance require knowledge of the specific genes driving phytopathogenicity and related traits in forest pathogens. This genetic knowledge requires the continued development of both large-scale genomic analyses and species-specific molecular techniques, such as CRISPR-Cas9. My goal in this dissertation was to demonstrate a proof of concept for the application of these genomic techniques for forest pathology. I believe that my research findings, both the successes and failures, provide a useful foundation for future genomic research in filamentous forest pathogens. 151 References Achari, S. R., Edwards, J., Mann, R. C., Kaur, J. K., Sawbridge, T., & Summerell, B. A. (2021). Comparative transcriptomic analysis of races 1, 2, 5 and 6 of Fusarium oxysporum f.sp. Pisi in a susceptible pea host identifies differential pathogenicity profiles. BMC Genomics, 22(1), 734. https://doi.org/10.1186/s12864-021-08033-y Adhikari, B. N., Hamilton, J. P., Zerillo, M. M., Tisserat, N., Lévesque, C. A., & Buell, C. R. (2013). Comparative Genomics Reveals Insight into Virulence Strategies of Plant Pathogenic Oomycetes. PLoS ONE, 8(10), e75072. https://doi.org/10.1371/journal.pone.0075072 Agrios, G. N. (2005). Plant Pathology (Fifth Edition). Elsevier Academic Press. Aguayo, J., Elegbede, F., Husson, C., Saintonge, F.-X., & Marçais, B. (2014). Modeling climate impact on an emerging disease, the Phytophthora alni-induced alder decline. Global Change Biology, 20(10), 3209–3221. https://doi.org/10.1111/gcb.12601 Agudelo, D., Carter, S., Velimirovic, M., Duringer, A., Rivest, J.-F., Levesque, S., Loehr, J., Mouchiroud, M., Cyr, D., Waters, P. J., Laplante, M., Moineau, S., Goulet, A., & Doyon, Y. (2020). Versatile and robust genome editing with Streptococcus thermophilus CRISPR1-Cas9. Genome Research, 30(1), 107–117. https://doi.org/10.1101/gr.255414.119 Ah-Fong, A. M. V., Boyd, A. M., Matson, M. E. H., & Judelson, H. S. (2021). A Cas12a-based gene editing system for Phytophthora infestans reveals monoallelic expression of an elicitor. Molecular Plant Pathology, 22(6), 737–752. https://doi.org/10.1111/mpp.13051 Ah-Fong, A. M. V., & Judelson, H. S. (2011). Vectors for fluorescent protein tagging in Phytophthora: Tools for functional genomics and cell biology. Fungal Biology, 115(9), 882–890. https://doi.org/10.1016/j.funbio.2011.07.001 Ali, Z., Abulfaraj, A., Idris, A., Ali, S., Tashkandi, M., & Mahfouz, M. M. (2015). CRISPR/Cas9-mediated viral interference in plants. Genome Biology, 16(1), 238. https://doi.org/10.1186/s13059-015-0799-6 Ali, Z., Ali, S., Tashkandi, M., Zaidi, S. S.-A., & Mahfouz, M. M. (2016). CRISPR/Cas9-Mediated Immunity to Geminiviruses: Differential Interference and Evasion. Scientific Reports, 6(1), Article 1. https://doi.org/10.1038/srep26912 Allen, E. A., & Humble, L. M. (2002). Nonindigenous species introductions: A threat to Canada’s forests and forest economy. Canadian Journal of Plant Pathology, 24(2), 103–110. https://doi.org/10.1080/07060660309506983 Almási, É., Sahu, N., Krizsán, K., Bálint, B., Kovács, G. M., Kiss, B., Cseklye, J., Drula, E., Henrissat, B., Nagy, I., Chovatia, M., Adam, C., LaButti, K., Lipzen, A., Riley, R., Grigoriev, I. V., & Nagy, L. G. (2019). Comparative genomics reveals unique wood-decay strategies and fruiting body development in the Schizophyllaceae. New Phytologist, 224(2), 902–915. https://doi.org/10.1111/nph.16032 Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403–410. https://doi.org/10.1016/S0022-2836(05)80360-2 Amos, B., Aurrecoechea, C., Barba, M., Barreto, A., Basenko, E. Y., Bażant, W., Belnap, R., Blevins, A. S., Böhme, U., Brestelli, J., Brunk, B. P., Caddick, M., Callan, D., Campbell, L., Christensen, M. B., Christophides, G. K., Crouch, K., Davis, K., DeBarry, J., … Zheng, J. (2022). VEuPathDB: The eukaryotic pathogen, vector and host bioinformatics 152 resource center. Nucleic Acids Research, 50(D1), D898–D911. https://doi.org/10.1093/nar/gkab929 Anderson, P. K., Cunningham, A. A., Patel, N. G., Morales, F. J., Epstein, P. R., & Daszak, P. (2004). Emerging infectious diseases of plants: Pathogen pollution, climate change and agrotechnology drivers. Trends in Ecology & Evolution, 19(10), 535–544. https://doi.org/10.1016/j.tree.2004.07.021 Andolfo, G., Iovieno, P., Frusciante, L., & Ercolano, M. R. (2016). Genome-Editing Technologies for Enhancing Plant Disease Resistance. Frontiers in Plant Science, 7, Article 1813. https://doi.org/10.3389/fpls.2016.01813 Andreassi, J. L., Gutteridge, S., Pember, S. O., & Sweigard, J. A. (2013). Detection and Screening Method and Materials Useful in Performance Thereof (Geneva: World Intellectual Property Organization Patent International Patent No WO2013009971). https://patents.google.com/patent/WO2013009971A1/en Arazoe, T., Miyoshi, K., Yamato, T., Ogawa, T., Ohsato, S., Arie, T., & Kuwata, S. (2015). Tailor-made CRISPR/Cas system for highly efficient targeted gene replacement in the rice blast fungus. Biotechnology and Bioengineering, 112(12), 2543–2549. https://doi.org/10.1002/bit.25662 Bailey, A. M., Mena, G. L., & Herrera-Estrella, L. (1991). Genetic transformation of the plant pathogens Phytophthora capsici and Phytophthora parasitica. Nucleic Acids Research, 19(15), 4273–4278. Bailey, A. M., Mena, G. L., & Herrera-Estrella, L. (1993). Transformation of four pathogenic Phytophthora spp by microprojectile bombardment on intact mycelia. Current Genetics, 23(1), 42–46. https://doi.org/10.1007/BF00336748 Baldauf, S. L., Roger, A. J., Wenk-Siefert, I., & Doolittle, W. F. (2000). A Kingdom-Level Phylogeny of Eukaryotes Based on Combined Protein Data. Science, 290(5493), 972–977. Barrangou, R., Fremaux, C., Deveau, H., Richards, M., Boyaval, P., Moineau, S., Romero, D. A., & Horvath, P. (2007). CRISPR Provides Acquired Resistance Against Viruses in Prokaryotes. Science, 315(5819), 1709–1712. https://doi.org/10.1126/science.1138140 Basten, D. E. J. W., Moers, A. P. H. A., Ooyen, A. J. J. van., & Schaap, P. J. (2005). Characterisation of Aspergillus niger prolyl aminopeptidase. Molecular Genetics and Genomics, 272(6), 673–679. https://doi.org/10.1007/s00438-004-1094-5 Benson, D. A., Cavanaugh, M., Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J., & Sayers, E. W. (2013). GenBank. Nucleic Acids Research, 41(D1), D36–D42. https://doi.org/10.1093/nar/gks1195 Berbee, M. L., & Taylor, J. W. (2010). Dating the molecular clock in fungi – how close are we? Fungal Biology Reviews, 24(1–2), 1–16. https://doi.org/10.1016/j.fbr.2010.03.001 Berrocal, A., Navarrete, J., Oviedo, C., & Nickerson, K. W. (2012). Quorum sensing activity in Ophiostoma ulmi: Effects of fusel oils and branched chain amino acids on yeast-mycelial dimorphism. Journal of Applied Microbiology, 113(1), 126–134. https://doi.org/10.1111/j.1365-2672.2012.05317.x Bhaya, D., Davison, M., & Barrangou, R. (2011). CRISPR-Cas Systems in Bacteria and Archaea: Versatile Small RNAs for Adaptive Defense and Regulation. Annual Review of Genetics, 45(1), 273–297. https://doi.org/10.1146/annurev-genet-110410-132430 153 Bibikova, M., Golic, M., Golic, K. G., & Carroll, D. (2002). Targeted Chromosomal Cleavage and Mutagenesis in Drosophila Using Zinc-Finger Nucleases. Genetics, 161(3), 1169–1175. https://doi.org/10.1093/genetics/161.3.1169 Bilodeau, P., Roe, A. D., Bilodeau, G., Blackburn, G. S., Cui, M., Cusson, M., Doucet, D., Griess, V. C., Lafond, V. M. A., Nilausen, C., Paradis, G., Porth, I., Prunier, J., Srivastava, V., Stewart, D., Torson, A. S., Tremblay, E., Uzunovic, A., Yemshanov, D., & Hamelin, R. C. (2019). Biosurveillance of forest insects: Part II—adoption of genomic tools by end user communities and barriers to integration. Journal of Pest Science, 92(1), 71–82. https://doi.org/10.1007/s10340-018-1001-1 Birch, P. R. J., & Whisson, S. C. (2001). Phytophthora infestans enters the genomics era. Molecular Plant Pathology, 2(5), 257–263. https://doi.org/10.1046/j.1464-6722.2001.00073.x Bittner, R. J., Sweigard, J. A., & Mila, A. L. (2017). Assessing the resistance potential of Phytophthora nicotianae, the causal agent of black shank of tobacco, to oxathiopropalin with laboratory mutants. Crop Protection, 102, 63–71. https://doi.org/10.1016/j.cropro.2017.08.002 Bolotin, A., Quinquis, B., Sorokin, A., & Ehrlich, S. D. (2005). Clustered regularly interspaced short palindrome repeats (CRISPRs) have spacers of extrachromosomal origin. Microbiology, 151(8), 2551–2561. https://doi.org/10.1099/mic.0.28048-0 Bortesi, L., & Fischer, R. (2015). The CRISPR/Cas9 system for plant genome editing and beyond. Biotechnology Advances, 33(1), 41–52. https://doi.org/10.1016/j.biotechadv.2014.12.006 Bottin, A., Larche, L., Villalba, F., Gaulin, E., Esquerré-Tugayé, M.-T., & Rickauer, M. (1999). Green fluorescent protein (GFP) as gene expression reporter and vital marker for studying development and microbe-plant interaction in the tobacco pathogen Phytophthora parasitica var. Nicotianae. FEMS Microbiology Letters, 176(1), 51–56. https://doi.org/10.1111/j.1574-6968.1999.tb13641.x Bradshaw, H. D., Ceulemans, R., Davis, J., & Stettler, R. (2000). Emerging Model Systems in Plant Biology: Poplar (Populus) as A Model Forest Tree. Journal of Plant Growth Regulation, 19(3), 306–313. https://doi.org/10.1007/s003440000030 Brasier, C. M. (2008). The biosecurity threat to the UK and global environment from international trade in plants. Plant Pathology, 57(5), 792–808. https://doi.org/10.1111/j.1365-3059.2008.01886.x Brasier, C., Scanu, B., Cooke, D., & Jung, T. (2022). Phytophthora: An ancient, historic, biologically and structurally cohesive and evolutionarily successful generic concept in need of preservation. IMA Fungus, 13(1), Article 1. https://doi.org/10.1186/s43008-022-00097-z Breitler, J.-C., Dechamp, E., Campa, C., Zebral Rodrigues, L. A., Guyot, R., Marraccini, P., & Etienne, H. (2018). CRISPR/Cas9-mediated efficient targeted mutagenesis has the potential to accelerate the domestication of Coffea canephora. Plant Cell, Tissue and Organ Culture (PCTOC), 134(3), 383–394. https://doi.org/10.1007/s11240-018-1429-2 Brinkman, E. K., Chen, T., Amendola, M., & van Steensel, B. (2014). Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Research, 42(22), e168. https://doi.org/10.1093/nar/gku936 Brooks, C., Nekrasov, V., Lippman, Z. B., & Van Eck, J. (2014). Efficient Gene Editing in Tomato in the First Generation Using the Clustered Regularly Interspaced Short 154 Palindromic Repeats/CRISPR-Associated9 System. Plant Physiology, 166(3), 1292–1297. https://doi.org/10.1104/pp.114.247577 Brotman, Y., Briff, E., Viterbo, A., & Chet, I. (2008). Role of Swollenin, an Expansin-Like Protein from Trichoderma, in Plant Root Colonization. Plant Physiology, 147(2), 779–789. https://doi.org/10.1104/pp.108.116293 Burgess, T. I., Scott, J. K., Mcdougall, K. L., Stukely, M. J. C., Crane, C., Dunstan, W. A., Brigg, F., Andjic, V., White, D., Rudman, T., Arentz, F., Ota, N., & Hardy, G. E. St. J. (2017). Current and projected global distribution of Phytophthora cinnamomi, one of the world’s worst plant pathogens. Global Change Biology, 23(4), 1661–1674. https://doi.org/10.1111/gcb.13492 Burki, F., Roger, A. J., Brown, M. W., & Simpson, A. G. B. (2020). The New Tree of Eukaryotes. Trends in Ecology & Evolution, 35(1), 43–55. https://doi.org/10.1016/j.tree.2019.08.008 Burstein, D., Harrington, L. B., Strutt, S. C., Probst, A. J., Anantharaman, K., Thomas, B. C., Doudna, J. A., & Banfield, J. F. (2017). New CRISPR–Cas systems from uncultivated microbes. Nature, 542(7640), Article 7640. https://doi.org/10.1038/nature21059 Carbú, M., Moraga, J., Cantoral, J. M., Collado, I. G., & Garrido, C. (2019). Recent approaches on the genomic analysis of the phytopathogenic fungus Colletotrichum spp. Phytochemistry Reviews. https://doi.org/10.1007/s11101-019-09608-0 Carter, J. V., Pan, J., Rai, S. N., & Galandiuk, S. (2016). ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves. Surgery, 159(6), 1638–1645. https://doi.org/10.1016/j.surg.2015.12.029 Castresana, J. (2000). Selection of Conserved Blocks from Multiple Alignments for Their Use in Phylogenetic Analysis. Molecular Biology and Evolution, 17(4), 540–552. https://doi.org/10.1093/oxfordjournals.molbev.a026334 Cavalier-Smith, T. (1998). A revised six-kingdom system of life. Biological Reviews, 73(3), 203–266. https://doi.org/10.1111/j.1469-185X.1998.tb00030.x Cavalier-Smith, T., & Chao, E. E.-Y. (2006). Phylogeny and Megasystematics of Phagotrophic Heterokonts (Kingdom Chromista). Journal of Molecular Evolution, 62(4), 388–420. https://doi.org/10.1007/s00239-004-0353-8 Ceccaldi, R., Rondinelli, B., & D’Andrea, A. D. (2016). Repair Pathway Choices and Consequences at the Double-Strand Break. Trends in Cell Biology, 26(1), 52–64. https://doi.org/10.1016/j.tcb.2015.07.009 Chandrasekaran, J., Brumin, M., Wolf, D., Leibman, D., Klap, C., Pearlsman, M., Sherman, A., Arazi, T., & Gal‐On, A. (2016). Development of broad virus resistance in non-transgenic cucumber using CRISPR/Cas9 technology. Molecular Plant Pathology, 17(7), 1140–1153. https://doi.org/10.1111/mpp.12375 Chandrasekaran, M., Thangavelu, B., Chun, S. C., & Sathiyabama, M. (2016). Proteases from phytopathogenic fungi and their importance in phytopathogenicity. Journal of General Plant Pathology, 82(5), 233–239. https://doi.org/10.1007/s10327-016-0672-9 Chang, Q., Liu, J., Lin, X., Hu, S., Yang, Y., Li, D., Chen, L., Huai, B., Huang, L., Voegele, R. T., & Kang, Z. (2017). A unique invertase is important for sugar absorption of an obligate biotrophic pathogen during infection. New Phytologist, 215(4), 1548–1561. https://doi.org/10.1111/nph.14666 Chanoca, A., de Vries, L., & Boerjan, W. (2019). Lignin Engineering in Forest Trees. Frontiers in Plant Science, 10, Article 912. https://doi.org/10.3389/fpls.2019.00912 155 Charlier, F., Weber, M., Dariusz, I., Emerson, H., Marcin, M., Joseph, L., Louison, F., Chan, M., Markov, N., Proost, S., Krasoulis, A., & Repplinger, S. (2022). Statannotations (v0.6) [Python]. Zenodo. https://doi.org/10.5281/zenodo.7213391 Chen, Q., Bakhshi, M., Balci, Y., Broders, K. D., Cheewangkoon, R., Chen, S. F., Fan, X. L., Gramaje, D., Halleen, F., Horta Jung, M., Jiang, N., Jung, T., Májek, T., Marincowitz, S., Milenković, I., Mostert, L., Nakashima, C., Nurul Faziha, I., Pan, M., … Crous, P. W. (2022). Genera of phytopathogenic fungi: GOPHY 4. Studies in Mycology, 101(1), 417–564. https://doi.org/10.3114/sim.2022.101.06 Chen, X.-R., Li, Y.-P., Li, Q.-Y., Xing, Y.-P., Liu, B.-B., Tong, Y.-H., & Xu, J.-Y. (2016). SCR96, a small cysteine-rich secretory protein of Phytophthora cactorum, can trigger cell death in the Solanaceae and is important for pathogenicity and oxidative stress tolerance. Molecular Plant Pathology, 17(4), 577–587. https://doi.org/10.1111/mpp.12303 Chen, X.-R., Wen, K., Zhou, X., Zhu, M.-Y., Liu, Y., Jin, J.-H., & Nellist, C. F. (2023). The devastating oomycete phytopathogen Phytophthora cactorum: Insights into its biology and molecular features. Molecular Plant Pathology, 24(9), 1017–1032. https://doi.org/10.1111/mpp.13345 Cho, S., Choe, D., Lee, E., Kim, S. C., Palsson, B., & Cho, B.-K. (2018). High-Level dCas9 Expression Induces Abnormal Cell Morphology in Escherichia coli. ACS Synthetic Biology, 7(4), 1085–1094. https://doi.org/10.1021/acssynbio.7b00462 Cho, S. W., Lee, J., Carroll, D., Kim, J.-S., & Lee, J. (2013). Heritable Gene Knockout in Caenorhabditis elegans by Direct Injection of Cas9–sgRNA Ribonucleoproteins. Genetics, 195(3), 1177–1180. https://doi.org/10.1534/genetics.113.155853 Choquer, M., Rascle, C., Gonçalves, I. R., de Vallée, A., Ribot, C., Loisel, E., Smilevski, P., Ferria, J., Savadogo, M., Souibgui, E., Gagey, M.-J., Dupuy, J.-W., Rollins, J. A., Marcato, R., Noûs, C., Bruel, C., & Poussereau, N. (2021). The infection cushion of Botrytis cinerea: A fungal ‘weapon’ of plant-biomass destruction. Environmental Microbiology, 23(4), 2293–2314. https://doi.org/10.1111/1462-2920.15416 Christian, M., Cermak, T., Doyle, E. L., Schmidt, C., Zhang, F., Hummel, A., Bogdanove, A. J., & Voytas, D. F. (2010). Targeting DNA Double-Strand Breaks with TAL Effector Nucleases. Genetics, 186(2), 757–761. https://doi.org/10.1534/genetics.110.120717 Clarivate Analytics. (2020). Results Analysis Tool for Topic: “CRISPR.” Web of Science. https://wcs-webofknowledge-com.ezproxy.library.ubc.ca/RA/analyze.do?product=WOS&SID=5EBaGfnpdYEJCywebJU&field=PY_PublicationYear_PublicationYear_en&yearSort=true Coakley, S. M., Scherm, H., & Chakraborty, S. (1999). Climate Change and Plant Disease Management. Annual Review of Phytopathology, 37(1), 399–426. https://doi.org/10.1146/annurev.phyto.37.1.399 Comeau, A. M., Dufour, J., Bouvet, G. F., Jacobi, V., Nigg, M., Henrissat, B., Laroche, J., Levesque, R. C., & Bernier, L. (2015). Functional Annotation of the Ophiostoma novo-ulmi Genome: Insights into the Phytopathogenicity of the Fungal Agent of Dutch Elm Disease. Genome Biology and Evolution, 7(2), 410–430. https://doi.org/10.1093/gbe/evu281 Contreras-Cornejo, H. A., Larsen, J., Fernández-Pavía, S. P., & Oyama, K. (2023). Climate change, a booster of disease outbreaks by the plant pathogen Phytophthora in oak forests. Rhizosphere, 27, 100719. https://doi.org/10.1016/j.rhisph.2023.100719 156 Cosgrove, D. J. (2015). Plant expansins: Diversity and interactions with plant cell walls. Current Opinion in Plant Biology, 25, 162–172. https://doi.org/10.1016/j.pbi.2015.05.014 Cvitanich, C., & Judelson, H. S. (2003). Stable transformation of the oomycete, Phytophthora infestans, using microprojectile bombardment. Current Genetics, 42(4), 228–235. https://doi.org/10.1007/s00294-002-0354-3 Dai, T., Xu, Y., Yang, X., Jiao, B., Qiu, M., Xue, J., Arredondo, F., & Tyler, B. M. (2021). An Improved Transformation System for Phytophthora cinnamomi Using Green Fluorescent Protein. Frontiers in Microbiology, 12. https://www.frontiersin.org/articles/10.3389/fmicb.2021.682754 Dangl, J. L., & Jones, J. D. G. (2001). Plant pathogens and integrated defence responses to infection. Nature, 411(6839), Article 6839. https://doi.org/10.1038/35081161 Das, A., Sharma, N., & Prasad, M. (2019). CRISPR/Cas9: A Novel Weapon in the Arsenal to Combat Plant Diseases. Frontiers in Plant Science, 9, Article 2008. https://doi.org/10.3389/fpls.2018.02008 de Bary, A. (1861). Die gegenwärtig herrschende Kartoffelkrankenheit, ihre Ursache und ihre Verhütung (The current prevailing potato disease, its cause and treatment). Felix, Leipzig, 75. de Bary, A. (1876). Researches into the nature of the potato fungus, Phytophthora infestans. Journal of the Royal Agricultural Society of England, 12, 239–269. de Bary, A. (1887). Comparative morphology and biology of the fungi, mycetozoa and bacteria (I. B. Balfour, Ed.; H. E. F. Garnsey, Trans.). Clarendon Press. De Silva, N. I., Lumyong, S., Hyde, K. D., Bulgakov, T., Phillips, A. J. L., & Yan, J. U. (2016). Mycosphere Essays 9: Defining biotrophs and hemibiotrophs. Mycosphere, 7(5), 545–559. https://doi.org/10.5943/mycosphere/7/5/2 de Toledo Thomazella, D. P., Brail, Q., Dahlbeck, D., & Staskawicz, B. (2016). CRISPR-Cas9 mediated mutagenesis of a DMR6 ortholog in tomato confers broad-spectrum disease resistance. bioRxiv [Preprint], 064824. https://doi.org/10.1101/064824 Deltcheva, E., Chylinski, K., Sharma, C. M., Gonzales, K., Chao, Y., Pirzada, Z. A., Eckert, M. R., Vogel, J., & Charpentier, E. (2011). CRISPR RNA maturation by trans-encoded small RNA and host factor RNase III. Nature, 471(7340), Article 7340. https://doi.org/10.1038/nature09886 Derbyshire, M., Denton-Giles, M., Hegedus, D., Seifbarghy, S., Rollins, J., van Kan, J., Seidl, M. F., Faino, L., Mbengue, M., Navaud, O., Raffaele, S., Hammond-Kosack, K., Heard, S., & Oliver, R. (2017). The Complete Genome Sequence of the Phytopathogenic Fungus Sclerotinia sclerotiorum Reveals Insights into the Genome Architecture of Broad Host Range Pathogens. Genome Biology and Evolution, 9(3), 593–618. https://doi.org/10.1093/gbe/evx030 Després, P. C., Dubé, A. K., Nielly-Thibault, L., Yachie, N., & Landry, C. R. (2018). Double Selection Enhances the Efficiency of Target-AID and Cas9-Based Genome Editing in Yeast. G3: Genes, Genomes, Genetics, 8(10), 3163–3171. https://doi.org/10.1534/g3.118.200461 Desprez-Loustau, M.-L., Robin, C., Buée, M., Courtecuisse, R., Garbaye, J., Suffert, F., Sache, I., & Rizzo, D. M. (2007). The fungal dimension of biological invasions. Trends in Ecology & Evolution, 22(9), 472–480. https://doi.org/10.1016/j.tree.2007.04.005 Deveau, H., Barrangou, R., Garneau, J. E., Labonté, J., Fremaux, C., Boyaval, P., Romero, D. A., Horvath, P., & Moineau, S. (2008). Phage Response to CRISPR-Encoded Resistance in 157 Streptococcus thermophilus. Journal of Bacteriology, 190(4), 1390–1400. https://doi.org/10.1128/JB.01412-07 de Vries, S., de Vries, J., Archibald, J. M., & Slamovits, C. H. (2020). Comparative analyses of saprotrophy in Salisapilia sapeloensis and diverse plant pathogenic oomycetes reveal lifestyle-specific gene expression. FEMS Microbiology Ecology, 96(11). https://doi.org/10.1093/femsec/fiaa184 Dodds, P. N., & Rathjen, J. P. (2010). Plant immunity: Towards an integrated view of plant–pathogen interactions. Nature Reviews Genetics, 11(8), 539–548. https://doi.org/10.1038/nrg2812 Doench, J. G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E. W., Donovan, K. F., Smith, I., Tothova, Z., Wilen, C., Orchard, R., Virgin, H. W., Listgarten, J., & Root, D. E. (2016). Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nature Biotechnology, 34(2), Article 2. https://doi.org/10.1038/nbt.3437 Doench, J. G., Hartenian, E., Graham, D. B., Tothova, Z., Hegde, M., Smith, I., Sullender, M., Ebert, B. L., Xavier, R. J., & Root, D. E. (2014). Rational design of highly active sgRNAs for CRISPR-Cas9–mediated gene inactivation. Nature Biotechnology, 32(12), Article 12. https://doi.org/10.1038/nbt.3026 Dort, E. N., Tanguay, P., & Hamelin, R. C. (2020). CRISPR/Cas9 Gene Editing: An Unexplored Frontier for Forest Pathology. Frontiers in Plant Science, 11, 1126. https://doi.org/10.3389/fpls.2020.01126 Dou, D., Kale, S. D., Wang, X., Chen, Y., Wang, Q., Wang, X., Jiang, R. H. Y., Arredondo, F. D., Anderson, R. G., Thakur, P. B., McDowell, J. M., Wang, Y., & Tyler, B. M. (2008). Conserved C-Terminal Motifs Required for Avirulence and Suppression of Cell Death by Phytophthora sojae effector Avr1b. The Plant Cell, 20(4), 1118–1133. https://doi.org/10.1105/tpc.107.057067 Drula, E., Garron, M.-L., Dogan, S., Lombard, V., Henrissat, B., & Terrapon, N. (2022). The carbohydrate-active enzyme database: Functions and literature. Nucleic Acids Research, 50(D1), D571–D577. https://doi.org/10.1093/nar/gkab1045 Dunn, A. R., Fry, B. A., Lee, T. Y., Conley, K. D., Balaji, V., Fry, W. E., McLeod, A., & Smart, C. D. (2013). Transformation of Phytophthora capsici with genes for green and red fluorescent protein for use in visualizing plant-pathogen interactions. Australasian Plant Pathology, 42(5), 583–593. https://doi.org/10.1007/s13313-013-0222-2 Duplessis, S., Major, I., Martin, F., & Séguin, A. (2009). Poplar and Pathogen Interactions: Insights from Populus Genome-Wide Analyses of Resistance and Defense Gene Families and Gene Expression Profiling. Critical Reviews in Plant Sciences, 28(5), 309–334. https://doi.org/10.1080/07352680903241063 Eikemo, H., Stensvand, A., Davik, J., & Tronsmo, A. M. (2003). Resistance to crown rot (Phytophthora cactorum) in strawberry cultivars and in offspring from crosses between cultivars differing in susceptibility to the disease. Annals of Applied Biology, 142(1), 83–89. https://doi.org/10.1111/j.1744-7348.2003.tb00232.x Eikemo, H., Stensvand, A., & Tronsmo, A. M. (2000). Evaluation of methods of screening strawberry cultivars for resistance to crown rot caused by Phytophthora cactorum. Annals of Applied Biology, 137(3), 237–244. https://doi.org/10.1111/j.1744-7348.2000.tb00064.x Elorriaga, E., Klocko, A. L., Ma, C., & Strauss, S. H. (2018). Variation in Mutation Spectra Among CRISPR/Cas9 Mutagenized Poplars. Frontiers in Plant Science, 9, Article 594. https://doi.org/10.3389/fpls.2018.00594 158 Epanchin-Niell, R. S., & Liebhold, A. M. (2015). Benefits of invasion prevention: Effect of time lags, spread rates, and damage persistence. Ecological Economics, 116, 146–153. https://doi.org/10.1016/j.ecolecon.2015.04.014 Erwin, D. C., & Ribeiro, O. K. (1996). Phytophthora Diseases Worldwide. APS Press. Fan, D., Liu, T., Li, C., Jiao, B., Li, S., Hou, Y., & Luo, K. (2015). Efficient CRISPR/Cas9-mediated Targeted Mutagenesis in Populus in the First Generation. Scientific Reports, 5(1), Article 1. https://doi.org/10.1038/srep12217 Fang, Y., Cui, L., Gu, B., Arredondo, F., & Tyler, B. M. (2017). Efficient Genome Editing in the Oomycete Phytophthora sojae Using CRISPR/Cas9. Current Protocols in Microbiology, 44(1), 21A.1.1-21A.1.26. https://doi.org/10.1002/cpmc.25 Fang, Y., Jang, H. S., Watson, G. W., Wellappili, D. P., & Tyler, B. M. (2017). Distinctive Nuclear Localization Signals in the Oomycete Phytophthora sojae. Frontiers in Microbiology, 8. https://doi.org/10.3389/fmicb.2017.00010 Fang, Y., & Tyler, B. M. (2016). Efficient disruption and replacement of an effector gene in the oomycete Phytophthora sojae using CRISPR/Cas9. Molecular Plant Pathology, 17(1), 127–139. https://doi.org/10.1111/mpp.12318 Farboud, B., & Meyer, B. J. (2015). Dramatic Enhancement of Genome Editing by CRISPR/Cas9 Through Improved Guide RNA Design. Genetics, 199(4), 959–971. https://doi.org/10.1534/genetics.115.175166 Feau, N., Joly, D. L., & Hamelin, R. C. (2007). Poplar leaf rusts: Model pathogens for a model tree. Canadian Journal of Botany, 85(12), 1127–1135. https://doi.org/10.1139/B07-102 Feau, N., Mcdonald, M., Van Der Meer, B., Zang, Y., Herath, P., & Hamelin, R. C. (2022). Phytophthora species associated with red alder dieback in British Columbia, Canada. Canadian Journal of Plant Pathology, 0(ja), null. https://doi.org/10.1080/07060661.2021.2022763 Feau, N., Taylor, G., Dale, A. L., Dhillon, B., Bilodeau, G. J., Birol, I., Jones, S. J. M., & Hamelin, R. C. (2016). Genome sequences of six Phytophthora species threatening forest ecosystems. Genomics Data, 10, 85–88. https://doi.org/10.1016/j.gdata.2016.09.013 Feau, N., van der Meer, B., van der Linden, W., Herath, P., Bradley, G., Ukrainetz, N., Kope, H., & Hamelin, R. (2021). Phytophthora root rot in western white pine seed orchards in British Columbia. Canadian Journal of Plant Pathology, 43(sup1), S172–S175. https://doi.org/10.1080/07060661.2021.1932163 Feng, Z., Zhang, B., Ding, W., Liu, X., Yang, D.-L., Wei, P., Cao, F., Zhu, S., Zhang, F., Mao, Y., & Zhu, J.-K. (2013). Efficient genome editing in plants using a CRISPR/Cas system. Cell Research, 23(10), Article 10. https://doi.org/10.1038/cr.2013.114 Fernandez i Marti, A., & Dodd, R. S. (2018). Using CRISPR as a Gene Editing Tool for Validating Adaptive Gene Function in Tree Landscape Genomics. Frontiers in Ecology and Evolution, 6, Article 76. https://doi.org/10.3389/fevo.2018.00076 Fire, A., Xu, S., Montgomery, M. K., Kostas, S. A., Driver, S. E., & Mello, C. C. (1998). Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature, 391, 806–811. Fisher, M. C., Gurr, S. J., Cuomo, C. A., Blehert, D. S., Jin, H., Stukenbrock, E. H., Stajich, J. E., Kahmann, R., Boone, C., Denning, D. W., Gow, N. A. R., Klein, B. S., Kronstad, J. W., Sheppard, D. C., Taylor, J. W., Wright, G. D., Heitman, J., Casadevall, A., & Cowen, L. E. (2020). Threats Posed by the Fungal Kingdom to Humans, Wildlife, and Agriculture. mBio. https://doi.org/10.1128/mBio.00449-20 159 Fisher, M. C., Henk, D. A., Briggs, C. J., Brownstein, J. S., Madoff, L. C., McCraw, S. L., & Gurr, S. J. (2012). Emerging fungal threats to animal, plant and ecosystem health. Nature, 484(7393), Article 7393. https://doi.org/10.1038/nature10947 Fletcher, K., Klosterman, S. J., Derevnina, L., Martin, F., Bertier, L. D., Koike, S., Reyes-Chin-Wo, S., Mou, B., & Michelmore, R. (2018). Comparative genomics of downy mildews reveals potential adaptations to biotrophy. BMC Genomics, 19(1), Article 1. https://doi.org/10.1186/s12864-018-5214-8 Forgetta, V., Leveque, G., Dias, J., Grove, D., Lyons, R., Genik, S., Wright, C., Singh, S., Peterson, N., Zianni, M., Kieleczawa, J., Steen, R., Perera, A., Bintzler, D., Adams, S., Hintz, W., Jacobi, V., Bernier, L., Levesque, R., & Dewar, K. (2013). Sequencing of the Dutch Elm Disease Fungus Genome Using the Roche/454 GS-FLX Titanium System in a Comparison of Multiple Genomics Core Facilities. Journal of Biomolecular Techniques : JBT, 24(1), 39–49. https://doi.org/10.7171/jbt.12-2401-005 Förster, H., Coffey, M. D., Elwood, H., & Sogin, M. L. (1990). Sequence Analysis of the Small Subunit Ribosomal RNAs of Three Zoosporic Fungi and Implications for Fungal Evolution. Mycologia, 82(3), 306–312. Foster, A. J., Martin-Urdiroz, M., Yan, X., Wright, H. S., Soanes, D. M., & Talbot, N. J. (2018). CRISPR-Cas9 ribonucleoprotein-mediated co-editing and counterselection in the rice blast fungus. Scientific Reports, 8(1), 14355. https://doi.org/10.1038/s41598-018-32702-w Fournier, R. E., & Turgeon, J. J. (2017). Surveillance during monitoring phase of an eradication programme against Anoplophora glabripennis (Motschulsky) guided by a spatial decision support system. Biological Invasions, 19(10), 3013–3035. https://doi.org/10.1007/s10530-017-1505-2 Fritsche, S., Poovaiah, C., MacRae, E., & Thorlby, G. (2018). A New Zealand Perspective on the Application and Regulation of Gene Editing. Frontiers in Plant Science, 9, Article 1323. https://doi.org/10.3389/fpls.2018.01323 Fry, W. E., Birch, P. R. J., Judelson, H. S., Grünwald, N. J., Danies, G., Everts, K. L., Gevens, A. J., Gugino, B. K., Johnson, D. A., Johnson, S. B., McGrath, M. T., Myers, K. L., Ristaino, J. B., Roberts, P. D., Secor, G., & Smart, C. D. (2015). Five Reasons to Consider Phytophthora infestans a Reemerging Pathogen. Phytopathology®, 105(7), 966–981. https://doi.org/10.1094/PHYTO-01-15-0005-FI Gan, P., Narusaka, M., Kumakura, N., Tsushima, A., Takano, Y., Narusaka, Y., & Shirasu, K. (2016). Genus-Wide Comparative Genome Analyses of Colletotrichum Species Reveal Specific Gene Family Losses and Gains during Adaptation to Specific Infection Lifestyles. Genome Biology and Evolution, 8(5), 1467–1481. https://doi.org/10.1093/gbe/evw089 Gao, R.-F., Wang, J.-Y., Liu, K.-W., Yoshida, K., Hsiao, Y.-Y., Shi, Y.-X., Tsai, K.-C., Chen, Y.-Y., Mitsuda, N., Liang, C.-K., Wang, Z.-W., Wang, Y., Zhang, D.-Y., Huang, L., Zhao, X., Zhong, W.-Y., Cheng, Y.-H., Jiang, Z.-D., Li, M.-H., … Zhang, G.-M. (2021). Comparative analysis of Phytophthora genomes reveals oomycete pathogenesis in crops. Heliyon, 7(2), e06317. https://doi.org/10.1016/j.heliyon.2021.e06317 Garbelotto Benzon, N., Svihra, P., & Rizzo, D. (2001). New pests and diseases: Sudden oak death syndrome fells 3 oak species. California Agriculture, 55(1), 9–19. 160 Gardiner, D. M., & Kazan, K. (2018). Selection is required for efficient Cas9-mediated genome editing in Fusarium graminearum. Fungal Biology, 122(2–3), 131–137. https://doi.org/10.1016/j.funbio.2017.11.006 Gardiner, D. M., Rusu, A., Barrett, L., Hunter, G. C., & Kazan, K. (2020). Natural gene drives offer potential pathogen control strategies in plants. bioRxiv, 2020.04.05.026500. https://doi.org/10.1101/2020.04.05.026500 Garrett, K. A., Dendy, S. P., Frank, E. E., Rouse, M. N., & Travers, S. E. (2006). Climate Change Effects on Plant Disease: Genomes to Ecosystems. Annual Review of Phytopathology, 44(1), 489–509. https://doi.org/10.1146/annurev.phyto.44.070505.143420 Garst, A. D., Bassalo, M. C., Pines, G., Lynch, S. A., Halweg-Edwards, A. L., Liu, R., Liang, L., Wang, Z., Zeitoun, R., Alexander, W. G., & Gill, R. T. (2017). Genome-wide mapping of mutations at single-nucleotide resolution for protein, metabolic and genome engineering. Nature Biotechnology, 35(1), Article 1. https://doi.org/10.1038/nbt.3718 Gaulin, E., Haget, N., Khatib, M., Herbert, C., Rickauer, M., & Bottin, A. (2007). Transgenic sequences are frequently lost in Phytophthora parasitica transformants without reversion of the transgene-induced silenced state. Canadian Journal of Microbiology, 53(1), 152–157. https://doi.org/10.1139/w06-090 Ghelardini, L., Luchi, N., Pecori, F., Pepori, A. L., Danti, R., Della Rocca, G., Capretti, P., Tsopelas, P., & Santini, A. (2017). Ecology of invasive forest pathogens. Biological Invasions, 19(11), 3183–3200. https://doi.org/10.1007/s10530-017-1487-0 Ghimire, B., Saraiva, M., Andersen, C. B., Gogoi, A., Saleh, M., Zic, N., van West, P., & Brurberg, M. B. (2022). Transformation systems, gene silencing and gene editing technologies in oomycetes. Fungal Biology Reviews, 40, 37–52. https://doi.org/10.1016/j.fbr.2021.11.001 Giannakopoulou, A., Steele, J. F. C., Segretin, M. E., Bozkurt, T. O., Zhou, J., Robatzek, S., Banfield, M. J., Pais, M., & Kamoun, S. (2015). Tomato I2 Immune Receptor Can Be Engineered to Confer Partial Resistance to the Oomycete Phytophthora infestans in Addition to the Fungus Fusarium oxysporum. Molecular Plant-Microbe Interactions, 28(12), 1316–1329. https://doi.org/10.1094/MPMI-07-15-0147-R Gilbert, G. S. (2002). Evolutionary ecology of plant diseases in natural ecosystems. Annual Review of Phytopathology, 40(1), 13–43. https://doi.org/10.1146/annurev.phyto.40.021202.110417 Gottesman, S. (2011). Dicing defence in bacteria. Nature, 471(7340), Article 7340. https://doi.org/10.1038/471588a Govers, F., & Gijzen, M. (2006). Phytophthora Genomics: The Plant Destroyers’ Genome Decoded. Molecular Plant-Microbe Interactions, 19(12), 1295–1301. https://doi.org/10.1094/MPMI-19-1295 Grigoriev, I. V., Hayes, R. D., Calhoun, S., Kamel, B., Wang, A., Ahrendt, S., Dusheyko, S., Nikitin, R., Mondo, S. J., Salamov, A., Shabalov, I., & Kuo, A. (2021). PhycoCosm, a comparative algal genomics resource. Nucleic Acids Research, 49(D1), D1004–D1011. https://doi.org/10.1093/nar/gkaa898 Grigoriev, I. V., Martinez, D. A., & Salamov, A. A. (2006). Fungal Genomic Annotation. In D. K. Arora, R. M. Berka, & G. B. Singh (Eds.), Applied Mycology and Biotechnology (Vol. 6, pp. 123–142). Elsevier. https://doi.org/10.1016/S1874-5334(06)80008-0 161 Grigoriev, I. V., Nikitin, R., Haridas, S., Kuo, A., Ohm, R., Otillar, R., Riley, R., Salamov, A., Zhao, X., Korzeniewski, F., Smirnova, T., Nordberg, H., Dubchak, I., & Shabalov, I. (2014). MycoCosm portal: Gearing up for 1000 fungal genomes. Nucleic Acids Research, 42(D1), D699–D704. https://doi.org/10.1093/nar/gkt1183 Gumtow, R., Wu, D., Uchida, J., & Tian, M. (2018). A Phytophthora palmivora Extracellular Cystatin-Like Protease Inhibitor Targets Papain to Contribute to Virulence on Papaya. Molecular Plant-Microbe Interactions®, 31(3), 363–373. https://doi.org/10.1094/MPMI-06-17-0131-FI Hamelin, R. C., & Roe, A. D. (2020). Genomic biosurveillance of forest invasive alien enemies: A story written in code. Evolutionary Applications, 13(1), 95–115. https://doi.org/10.1111/eva.12853 Hane, J. K., Paxman, J., Jones, D. A. B., Oliver, R. P., & de Wit, P. (2020). “CATAStrophy,” a Genome-Informed Trophic Classification of Filamentous Plant Pathogens – How Many Different Types of Filamentous Plant Pathogens Are There? Frontiers in Microbiology, 10, 3088. https://doi.org/10.3389/fmicb.2019.03088 Hansen, E. M. (2015). Phytophthora Species Emerging as Pathogens of Forest Trees. Current Forestry Reports, 1(1), 16–24. https://doi.org/10.1007/s40725-015-0007-7 Hardham, A. R., & Blackman, L. M. (2018). Phytophthora cinnamomi. Molecular Plant Pathology, 19(2), 260–285. https://doi.org/10.1111/mpp.12568 Haridas, S., Albert, R., Binder, M., Bloem, J., LaButti, K., Salamov, A., Andreopoulos, B., Baker, S. E., Barry, K., Bills, G., Bluhm, B. H., Cannon, C., Castanera, R., Culley, D. E., Daum, C., Ezra, D., González, J. B., Henrissat, B., Kuo, A., … Grigoriev, I. V. (2020). 101 Dothideomycetes genomes: A test case for predicting lifestyles and emergence of pathogens. Studies in Mycology. https://doi.org/10.1016/j.simyco.2020.01.003 Harvell, C. D., Mitchell, C. E., Ward, J. R., Altizer, S., Dobson, A. P., Ostfeld, R. S., & Samuel, M. D. (2002). Climate Warming and Disease Risks for Terrestrial and Marine Biota. Science, 296(5576), 2158–2162. https://doi.org/10.1126/science.1063699 Have, A. ten, Espino, J. J., Dekkers, E., Van Sluyter, S. C., Brito, N., Kay, J., González, C., & van Kan, J. A. L. (2010). The Botrytis cinerea aspartic proteinase family. Fungal Genetics and Biology, 47(1), 53–65. https://doi.org/10.1016/j.fgb.2009.10.008 Hématy, K., Cherk, C., & Somerville, S. (2009). Host–pathogen warfare at the plant cell wall. Current Opinion in Plant Biology, 12(4), 406–413. https://doi.org/10.1016/j.pbi.2009.06.007 Hessenauer, P., Fijarczyk, A., Martin, H., Prunier, J., Charron, G., Chapuis, J., Bernier, L., Tanguay, P., Hamelin, R. C., & Landry, C. R. (2020). Hybridization and introgression drive genome evolution of Dutch elm disease pathogens. Nature Ecology & Evolution, 4(4), Article 4. https://doi.org/10.1038/s41559-020-1133-6 Hood, I. A. (1982). Phaeocryptopus gaeumannii on Pseudotsuga menziesii in southern British Columbia. New Zealand Journal of Forestry Science, 12, 415–424. Howe, K. L., Contreras-Moreira, B., De Silva, N., Maslen, G., Akanni, W., Allen, J., Alvarez-Jarreta, J., Barba, M., Bolser, D. M., Cambell, L., Carbajo, M., Chakiachvili, M., Christensen, M., Cummins, C., Cuzick, A., Davis, P., Fexova, S., Gall, A., George, N., … Flicek, P. (2020). Ensembl Genomes 2020—Enabling non-vertebrate genomic research. Nucleic Acids Research, 48(D1), D689–D695. https://doi.org/10.1093/nar/gkz890 162 Howlett, B. J., Lowe, R. G. T., Marcroft, S. J., & van de Wouw, A. P. (2015). Evolution of virulence in fungal plant pathogens: Exploiting fungal genomics to control plant disease. Mycologia, 107(3), 441–451. https://doi.org/10.3852/14-317 Huitema, E., Bos, J. I. B., Tian, M., Win, J., Waugh, M. E., & Kamoun, S. (2004). Linking sequence to phenotype in Phytophthora–plant interactions. Trends in Microbiology, 12(4), 193–200. https://doi.org/10.1016/j.tim.2004.02.008 Huitema, E., Smoker, M., & Kamoun, S. (2011). A Straightforward Protocol for Electro-transformation of Phytophthora capsici Zoospores. In J. M. McDowell (Ed.), Plant Immunity (Vol. 712, pp. 129–135). Humana Press. https://doi.org/10.1007/978-1-61737-998-7_11 Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55 IDT. (2018). Alt-R CRISPR-Cas9 System: Cationic lipid delivery of CRISPR ribonucleoprotein complexes into mammalian cells. Integrated DNA Technologies. Ishino, Y., Shinagawa, H., Makino, K., Amemura, M., & Nakata, A. (1987). Nucleotide sequence of the iap gene, responsible for alkaline phosphatase isozyme conversion in Escherichia coli, and identification of the gene product. Journal of Bacteriology, 169(12), 5429–5433. https://doi.org/10.1128/jb.169.12.5429-5433.1987 Jahr, H., Dreier, J., Meletzus, D., Bahro, R., & Eichenlaub, R. (2000). The Endo-β-1,4-glucanase CelA of Clavibacter michiganensis subsp. Michiganensis Is a Pathogenicity Determinant Required for Induction of Bacterial Wilt of Tomato. Molecular Plant-Microbe Interactions®, 13(7), 703–714. https://doi.org/10.1094/MPMI.2000.13.7.703 Jansen, R., Embden, J. D. A. van, Gaastra, W., & Schouls, L. M. (2002). Identification of genes that are associated with DNA repeats in prokaryotes. Molecular Microbiology, 43(6), 1565–1575. https://doi.org/10.1046/j.1365-2958.2002.02839.x Ji, X., Si, X., Zhang, Y., Zhang, H., Zhang, F., & Gao, C. (2018). Conferring DNA virus resistance with high specificity in plants using virus-inducible genome-editing system. Genome Biology, 19(1), 197. https://doi.org/10.1186/s13059-018-1580-4 Jia, H., Orbovic, V., Jones, J. B., & Wang, N. (2016). Modification of the PthA4 effector binding elements in Type I CsLOB1 promoter using Cas9/sgRNA to produce transgenic Duncan grapefruit alleviating XccΔpthA4:dCsLOB1.3 infection. Plant Biotechnology Journal, 14(5), 1291–1301. https://doi.org/10.1111/pbi.12495 Jia, H., & Wang, N. (2014). Targeted Genome Editing of Sweet Orange Using Cas9/sgRNA. PLoS ONE, 9(4), e93806. https://doi.org/10.1371/journal.pone.0093806 Jiang, L., Ye, W., Situ, J., Chen, Y., Yang, X., Kong, G., Liu, Y., Tinashe, R. J., Xi, P., Wang, Y., & Jiang, Z. (2017). A Puf RNA-binding protein encoding gene PlM90 regulates the sexual and asexual life stages of the litchi downy blight pathogen Peronophythora litchii. Fungal Genetics and Biology, 98, 39–45. https://doi.org/10.1016/j.fgb.2016.12.002 Jiang, R. H. Y., & Tyler, B. M. (2012). Mechanisms and Evolution of Virulence in Oomycetes. Annual Review of Phytopathology, 50(1), 295–318. https://doi.org/10.1146/annurev-phyto-081211-172912 Jiang, W., Brueggeman, A. J., Horken, K. M., Plucinak, T. M., & Weeks, D. P. (2014). Successful Transient Expression of Cas9 and Single Guide RNA Genes in Chlamydomonas reinhardtii. Eukaryotic Cell, 13(11), 1465–1469. https://doi.org/10.1128/ec.00213-14 163 Jiang, W., Zhou, H., Bi, H., Fromm, M., Yang, B., & Weeks, D. P. (2013). Demonstration of CRISPR/Cas9/sgRNA-mediated targeted gene modification in Arabidopsis, tobacco, sorghum and rice. Nucleic Acids Research, 41(20), e188. https://doi.org/10.1093/nar/gkt780 Jiang, Y., Guo, L., Ma, X., Zhao, X., Jiao, B., Li, C., & Luo, K. (2017). The WRKY transcription factors PtrWRKY18 and PtrWRKY35 promote Melampsora resistance in Populus. Tree Physiology, 37(5), 665–675. https://doi.org/10.1093/treephys/tpx008 Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., & Charpentier, E. (2012). A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity. Science, 337(6096), 816–821. https://doi.org/10.1126/science.1225829 Jones, D. L., Leroy, P., Unoson, C., Fange, D., Ćurić, V., Lawson, M. J., & Elf, J. (2017). Kinetics of dCas9 target search in Escherichia coli. Science, 357(6358), 1420–1424. Jones, J. D. G., & Dangl, J. L. (2006). The plant immune system. Nature, 444(7117), 323–330. https://doi.org/10.1038/nature05286 Judelson, H. S., & Ah-Fong, A. M. V. (2009). Progress and Challenges in Oomycete Transformation. In K. Lamour & S. Kamoun (Eds.), Oomycete Genetics and Genomics (pp. 435–453). John Wiley & Sons, Inc. https://doi.org/10.1002/9780470475898.ch22 Judelson, H. S., Coffey, M. D., Arredondo, F. R., & Tyler, B. M. (1993). Transformation of the oomycete pathogen Phytophthora megasperma f. Sp. Glycieea occurs by DNA integration into single or multiple chromosomes. Current Genetics, 23, 211–218. Judelson, H. S., & Michelmore, R. W. (1991). Transient expression of genes in the oomycete Phytophthora infestans using Bremia lactucae regulatory sequences. Current Genetics, 19(6), 453–459. https://doi.org/10.1007/BF00312736 Judelson, H. S., Tyler, B. M., & Michelmore, R. W. (1991). Transformation of the Oomycete Pathogen, Phytophthora infestans. Molecular Plant-Microbe Interactions, 4(6), 602–607. Jung, T., Blaschke, H., & Neumann, P. (1996). Isolation, identification and pathogenicity of Phytophthora species from declining oak stands. European Journal of Forest Pathology, 26(5), 253–272. https://doi.org/10.1111/j.1439-0329.1996.tb00846.x Jung, T., Orlikowski, L., Henricot, B., Abad-Campos, P., Aday, A. G., Aguín Casal, O., Bakonyi, J., Cacciola, S. O., Cech, T., Chavarriaga, D., Corcobado, T., Cravador, A., Decourcelle, T., Denton, G., Diamandis, S., Doğmuş-Lehtijärvi, H. T., Franceschini, A., Ginetti, B., Green, S., … Peréz-Sierra, A. (2016). Widespread Phytophthora infestations in European nurseries put forest, semi-natural and horticultural ecosystems at high risk of Phytophthora diseases. Forest Pathology, 46(2), 134–163. https://doi.org/10.1111/efp.12239 Jung, T., Pérez-Sierra, A., Durán, A., Horta Jung, M., Balci, Y., & Scanu, B. (2018). Canker and decline diseases caused by soil- and airborne Phytophthora species in forests and woodlands. Persoonia : Molecular Phylogeny and Evolution of Fungi, 40, 182–220. https://doi.org/10.3767/persoonia.2018.40.08 Kabbage, M., Yarden, O., & Dickman, M. B. (2015). Pathogenic attributes of Sclerotinia sclerotiorum: Switching from a biotrophic to necrotrophic lifestyle. Plant Science, 233, 53–60. https://doi.org/10.1016/j.plantsci.2014.12.018 Kamoun, S. (2003). Molecular Genetics of Pathogenic Oomycetes. Eukaryotic Cell, 2(2), 191–199. https://doi.org/10.1128/EC.2.2.191-199.2003 Kanchiswamy, C. N. (2016). DNA-free genome editing methods for targeted crop improvement. Plant Cell Reports, 35(7), 1469–1474. https://doi.org/10.1007/s00299-016-1982-2 164 Kanchiswamy, C. N., Malnoy, M., Velasco, R., Kim, J.-S., & Viola, R. (2015). Non-GMO genetically edited crop plants. Trends in Biotechnology, 33(9), 489–491. https://doi.org/10.1016/j.tibtech.2015.04.002 Kassambara, A., & Mundt, F. (2020). factoextra: Extract and Visualize the Results of Multivariate Data Analyses (R package version 1.0.7) [Computer software]. https://CRAN.R-project.org/package=factoextra Katoh, K., Misawa, K., Kuma, K., & Miyata, T. (2002). MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Research, 30(14), 3059–3066. https://doi.org/10.1093/nar/gkf436 Kerff, F., Amoroso, A., Herman, R., Sauvage, E., Petrella, S., Filée, P., Charlier, P., Joris, B., Tabuchi, A., Nikolaidis, N., & Cosgrove, D. J. (2008). Crystal structure and activity of Bacillus subtilis YoaJ (EXLX1), a bacterial expansin that promotes root colonization. Proceedings of the National Academy of Sciences, 105(44), 16876–16881. https://doi.org/10.1073/pnas.0809382105 Keriö, S., Daniels, H. A., Gómez-Gallego, M., Tabima, J. F., Lenz, R. R., Søndreli, K. L., Grünwald, N. J., Williams, N., Mcdougal, R., & LeBoldus, J. M. (2020). From genomes to forest management – tackling invasive Phytophthora species in the era of genomics. Canadian Journal of Plant Pathology, 42(1), 1–29. https://doi.org/10.1080/07060661.2019.1626910 Khoshraftar, S., Hung, S., Khan, S., Gong, Y., Tyagi, V., Parkinson, J., Sain, M., Moses, A. M., & Christendat, D. (2013). Sequencing and annotation of the Ophiostoma ulmi genome. BMC Genomics, 14(1), 162. https://doi.org/10.1186/1471-2164-14-162 Kim, C. S., Lubowski, R. N., Lewandrowski, J., & Eiswerth, M. E. (2006). Prevention or Control: Optimal Government Policies for Invasive Species Management. Agricultural and Resource Economics Review, 35(1), 29–40. Kim, S., Kim, D., Cho, S. W., Kim, J., & Kim, J.-S. (2014). Highly efficient RNA-guided genome editing in human cells via delivery of purified Cas9 ribonucleoproteins. Genome Research, 24(6), 1012–1019. https://doi.org/10.1101/gr.171322.113 Kimberley, M. O., Hood, I. A., & Knowles, R. L. (2011). Impact of Swiss Needle-Cast on Growth of Douglas-Fir. Phytopathology®, 101(5), 583–593. https://doi.org/10.1094/PHYTO-05-10-0129 King, B. C., Waxman, K. D., Nenni, N. V., Walker, L. P., Bergstrom, G. C., & Gibson, D. M. (2011). Arsenal of plant cell wall degrading enzymes reflects host preference among plant pathogenic fungi. Biotechnology for Biofuels, 4(1), 4. https://doi.org/10.1186/1754-6834-4-4 King, R., Urban, M., Lauder, R. P., Hawkins, N., Evans, M., Plummer, A., Halsey, K., Lovegrove, A., Hammond-Kosack, K., & Rudd, J. J. (2017). A conserved fungal glycosyltransferase facilitates pathogenesis of plants by enabling hyphal growth on solid surfaces. PLOS Pathogens, 13(10), e1006672. https://doi.org/10.1371/journal.ppat.1006672 Kingma, D. P., & Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv Preprint, arXiv:1412.6980 [cs]. Kinloch, B. B., Sniezko, R. A., & Dupper, G. E. (2003). Origin and Distribution of Cr2, a Gene for Resistance to White Pine Blister Rust in Natural Populations of Western White Pine. Phytopathology, 93(6), 691–694. https://doi.org/10.1094/PHYTO.2003.93.6.691 165 Kleinstiver, B. P., Prew, M. S., Tsai, S. Q., Topkar, V. V., Nguyen, N. T., Zheng, Z., Gonzales, A. P. W., Li, Z., Peterson, R. T., Yeh, J.-R. J., Aryee, M. J., & Joung, J. K. (2015). Engineered CRISPR-Cas9 nucleases with altered PAM specificities. Nature, 523(7561), Article 7561. https://doi.org/10.1038/nature14592 Klosterman, S. J., Rollins, J. R., Sudarshana, M. R., & Vinatzer, B. A. (2016). Disease Management in the Genomics Era—Summaries of Focus Issue Papers. Phytopathology®, 106(10), 1068–1070. https://doi.org/10.1094/PHYTO-07-16-0276-FI Klosterman, S. J., Subbarao, K. V., Kang, S., Veronese, P., Gold, S. E., Thomma, B. P. H. J., Chen, Z., Henrissat, B., Lee, Y.-H., Park, J., Garcia-Pedrajas, M. D., Barbara, D. J., Anchieta, A., de Jonge, R., Santhanam, P., Maruthachalam, K., Atallah, Z., Amyotte, S. G., Paz, Z., … Ma, L.-J. (2011). Comparative Genomics Yields Insights into Niche Adaptation of Plant Vascular Wilt Pathogens. PLoS Pathogens, 7(7), e1002137. https://doi.org/10.1371/journal.ppat.1002137 Klutts, J. S., Yoneda, A., Reilly, M. C., Bose, I., & Doering, T. L. (2006). Glycosyltransferases and their products: Cryptococcal variations on fungal themes. FEMS Yeast Research, 6(4), 499–512. https://doi.org/10.1111/j.1567-1364.2006.00054.x Knapp, D. G., Németh, J. B., Barry, K., Hainaut, M., Johnson, J., Kuo, A., Hui, J., Lim, P., Lipzen, A., Nolan, M., Ohm, R. A., Tamás, L., Grigoriev, I. V, Spatafora, J. W., & Nagy, L. G. (2018). Comparative genomics provides insights into the lifestyle and reveals functional heterogeneity of dark septate endophytic fungi. Scientific Reports, 8, 6321. https://doi.org/10.1038/s41598-018-24686-4 Knogge, W. (1996). Fungal Infection of Plants. The Plant Cell, 8(10), 1711–1722. Koeck, M., Hardham, A. R., & Dodds, P. N. (2011). The role of effectors of biotrophic and hemibiotrophic fungi in infection. Cellular Microbiology, 13(12), 1849–1857. https://doi.org/10.1111/j.1462-5822.2011.01665.x Kohler, A., Kuo, A., Nagy, L. G., Morin, E., Barry, K. W., Buscot, F., Canbäck, B., Choi, C., Cichocki, N., Clum, A., Colpaert, J., Copeland, A., Costa, M. D., Doré, J., Floudas, D., Gay, G., Girlanda, M., Henrissat, B., Herrmann, S., … Martin, F. (2015). Convergent losses of decay mechanisms and rapid turnover of symbiosis genes in mycorrhizal mutualists. Nature Genetics, 47(4), 410–415. https://doi.org/10.1038/ng.3223 Koonin, E. V., Makarova, K. S., & Zhang, F. (2017). Diversity, classification and evolution of CRISPR-Cas systems. Current Opinion in Microbiology, 37, 67–78. https://doi.org/10.1016/j.mib.2017.05.008 Kosicki, M., Tomberg, K., & Bradley, A. (2018). Repair of double-strand breaks induced by CRISPR–Cas9 leads to large deletions and complex rearrangements. Nature Biotechnology, 36(8), Article 8. https://doi.org/10.1038/nbt.4192 Krishnan, P., Ma, X., McDonald, B. A., & Brunner, P. C. (2018). Widespread signatures of selection for secreted peptidases in a fungal plant pathogen. BMC Evolutionary Biology, 18(1), 7. https://doi.org/10.1186/s12862-018-1123-3 Kronmiller, B. A., Feau, N., Shen, D., Tabima, J. F., Ali, S. S., Armitage, A. D., Arredondo, F., Bailey, B. A., Bollmann, S. R., Dale, A., Harrison, R. J., Hrywkiw, K., Kasuga, T., McDougal, R., Nellist, C. F., Panda, P., Tripathy, S., Williams, N. M., Ye, W., … Grünwald, N. J. (2023). Comparative Genomic Analysis of 31 Phytophthora Genomes Reveals Genome Plasticity and Horizontal Gene Transfer. Molecular Plant-Microbe Interactions®, 36(1), 26–46. https://doi.org/10.1094/MPMI-06-22-0133-R 166 Kubicek, C. P., Starr, T. L., & Glass, N. L. (2014). Plant Cell Wall–Degrading Enzymes and Their Secretion in Plant-Pathogenic Fungi. Annual Review of Phytopathology, 52(1), 427–451. https://doi.org/10.1146/annurev-phyto-102313-045831 Kuhn, M. (2020). caret: Classification and Regression Training (R package version 6.0-86) [Computer software]. https://CRAN.R-project.org/package=caret Kuo, H.-C., Hui, S., Choi, J., Asiegbu, F. O., Valkonen, J. P. T., & Lee, Y.-H. (2015). Secret lifestyles of Neurospora crassa. Scientific Reports, 4(1), 5135. https://doi.org/10.1038/srep05135 La Mantia, J., Klápště, J., El-Kassaby, Y. A., Azam, S., Guy, R. D., Douglas, C. J., Mansfield, S. D., & Hamelin, R. (2013). Association Analysis Identifies Melampsora ×columbiana Poplar Leaf Rust Resistance SNPs. PLoS ONE, 8(11), e78423. https://doi.org/10.1371/journal.pone.0078423 Laine, M. J., Haapalainen, M., Wahlroos, T., Kankare, K., Nissinen, R., Kassuwi, S., & Metzler, M. C. (2000). The cellulase encoded by the native plasmid of Clavibacter michiganensis ssp. Sepedonicus plays a role in virulence and contains an expansin-like domain. Physiological and Molecular Plant Pathology, 57(5), 221–233. https://doi.org/10.1006/pmpp.2000.0301 Lamour, K. H., Mudge, J., Gobena, D., Hurtado-Gonzales, O. P., Schmutz, J., Kuo, A., Miller, N. A., Rice, B. J., Raffaele, S., Cano, L. M., Bharti, A. K., Donahoo, R. S., Finley, S., Huitema, E., Hulvey, J., Platt, D., Salamov, A., Savidor, A., Sharma, R., … Kingsmore, S. F. (2012). Genome Sequencing and Mapping Reveal Loss of Heterozygosity as a Mechanism for Rapid Adaptation in the Vegetable Pathogen Phytophthora capsici. Molecular Plant-Microbe Interactions, 25(10), 1350–1360. https://doi.org/10.1094/MPMI-02-12-0028-R Langner, T., Kamoun, S., & Belhaj, K. (2018). CRISPR Crops: Plant Genome Editing Toward Disease Resistance. Annual Review of Phytopathology, 56(1), 479–512. https://doi.org/10.1146/annurev-phyto-080417-050158 Lapin, D., & Van den Ackerveken, G. (2013). Susceptibility to plant disease: More than a failure of host immunity. Trends in Plant Science, 18(10), 546–554. https://doi.org/10.1016/j.tplants.2013.05.005 Latham, J. R., Wilson, A. K., & Steinbrecher, R. A. (2006). The Mutational Consequences of Plant Transformation. Journal of Biomedicine and Biotechnology, 2006, 25376. https://doi.org/10.1155/JBB/2006/25376 Latijnhouwers, M., & Govers, F. (2003). A Phytophthora infestans G-Protein β Subunit Is Involved in Sporangium Formation. Eukaryotic Cell, 2(5), 971–977. https://doi.org/10.1128/ec.2.5.971-977.2003 Layne, E., Dort, E. N., Hamelin, R., Li, Y., & Blanchette, M. (2020). Supervised learning on phylogenetically distributed data. Bioinformatics, 36(Supplement_2), i895–i902. https://doi.org/10.1093/bioinformatics/btaa842 Lee, E. H., Beedlow, P. A., Waschmann, R. S., Burdick, C. A., & Shaw, D. C. (2013). Tree-ring analysis of the fungal disease Swiss needle cast in western Oregon coastal forests. Canadian Journal of Forest Research, 43(8), 677–690. https://doi.org/10.1139/cjfr-2013-0062 Lee, S.-J., & Rose, J. K. C. (2010). Mediation of the transition from biotrophy to necrotrophy in hemibiotrophic plant pathogens by secreted effector proteins. Plant Signaling & Behavior, 5(6), 769–772. https://doi.org/10.4161/psb.5.6.11778 167 Leisen, T., Bietz, F., Werner, J., Wegner, A., Schaffrath, U., Scheuring, D., Willmund, F., Mosbach, A., Scalliet, G., & Hahn, M. (2020). CRISPR/Cas with ribonucleoprotein complexes and transiently selected telomere vectors allows highly efficient marker-free and multiple genome editing in Botrytis cinerea. PLOS Pathogens, 16(8), e1008326. https://doi.org/10.1371/journal.ppat.1008326 Leung, B., Lodge, D. M., Finnoff, D., Shogren, J. F., Lewis, M. A., & Lamberti, G. (2002). An ounce of prevention or a pound of cure: Bioeconomic risk analysis of invasive species. Proceedings of the Royal Society of London. Series B: Biological Sciences, 269(1508), 2407–2413. https://doi.org/10.1098/rspb.2002.2179 Lewis, D. (1973). Concepts in Fungal Nutrition and the Origin of Biotrophy. Biological Reviews, 48(2), 261–277. https://doi.org/10.1111/j.1469-185x.1973.tb00982.x Li, J., Zhang, Y., Zhang, Y., Yu, P.-L., Pan, H., & Rollins, J. A. (2018). Introduction of Large Sequence Inserts by CRISPR-Cas9 To Create Pathogenicity Mutants in the Multinucleate Filamentous Pathogen Sclerotinia sclerotiorum. mBio, 9(3), e00567-18. https://doi.org/10.1128/mBio.00567-18 Li, X., Liu, Y., Tan, X., Li, D., Yang, X., Zhang, X., & Zhang, D. (2020). The high-affinity phosphodiesterase PcPdeH is involved in the polarized growth and pathogenicity of Phytophthora capsici. Fungal Biology, 124(3), 164–173. https://doi.org/10.1016/j.funbio.2020.01.006 Liang, Y., Han, Y., Wang, C., Jiang, C., & Xu, J.-R. (2018). Targeted Deletion of the USTA and UvSLT2 Genes Efficiently in Ustilaginoidea virens With the CRISPR-Cas9 System. Frontiers in Plant Science, 9, 699. https://doi.org/10.3389/fpls.2018.00699 Liang, Z., Zhang, K., Chen, K., & Gao, C. (2014). Targeted Mutagenesis in Zea mays Using TALENs and the CRISPR/Cas System. Journal of Genetics and Genomics, 41(2), 63–68. https://doi.org/10.1016/j.jgg.2013.12.001 Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), Article 6. https://doi.org/10.1038/nrg3920 Liebhold, A. M., Brockerhoff, E. G., Garrett, L. J., Parke, J. L., & Britton, K. O. (2012). Live plant imports: The major pathway for forest insect and pathogen invasions of the US. Frontiers in Ecology and the Environment, 10(3), 135–143. https://doi.org/10.1890/110198 Liebhold, A. M., Brockerhoff, E. G., Kalisz, S., Nuñez, M. A., Wardle, D. A., & Wingfield, M. J. (2017). Biological invasions in forest ecosystems. Biological Invasions, 19(11), 3437–3458. https://doi.org/10.1007/s10530-017-1458-5 Lilja, A., Rikala, R., Hietala, A., & Heinonen, R. (1996). Stem lesions on Betula pendula seedlings in Finnish forest nurseries and the pathogenicity of Phytophthora cactorum. European Journal of Forest Pathology, 26(2), 89–96. https://doi.org/10.1111/j.1439-0329.1996.tb00713.x Liu, G., Zhang, Y., & Zhang, T. (2020). Computational approaches for effective CRISPR guide RNA design and evaluation. Computational and Structural Biotechnology Journal, 18, 35–44. https://doi.org/10.1016/j.csbj.2019.11.006 Liu, R., Chen, L., Jiang, Y., Zhou, Z., & Zou, G. (2015). Efficient genome editing in filamentous fungus Trichoderma reesei using the CRISPR/Cas9 system. Cell Discovery, 1(1), Article 1. https://doi.org/10.1038/celldisc.2015.7 Lo Presti, L., Lanver, D., Schweizer, G., Tanaka, S., Liang, L., Tollot, M., Zuccaro, A., Reissmann, S., & Kahmann, R. (2015). Fungal Effectors and Plant Susceptibility. Annual 168 Review of Plant Biology, 66(1), 513–545. https://doi.org/10.1146/annurev-arplant-043014-114623 Lodge, D. M., Williams, S., MacIsaac, H. J., Hayes, K. R., Leung, B., Reichard, S., Mack, R. N., Moyle, P. B., Smith, M., Andow, D. A., Carlton, J. T., & McMichael, A. (2006). Biological Invasions: Recommendations for U.S. Policy and Management. Ecological Applications, 16(6), 2035–2054. https://doi.org/10.1890/1051-0761(2006)016[2035:BIRFUP]2.0.CO;2 Loo, J. A. (2009). Ecological impacts of non-indigenous invasive fungi as forest pathogens. Biological Invasions, 11(1), 81–96. https://doi.org/10.1007/s10530-008-9321-3 Lovett, G. M., Weiss, M., Liebhold, A. M., Holmes, T. P., Leung, B., Lambert, K. F., Orwig, D. A., Campbell, F. T., Rosenthal, J., McCullough, D. G., Wildova, R., Ayres, M. P., Canham, C. D., Foster, D. R., LaDeau, S. L., & Weldy, T. (2016). Nonnative forest insects and pathogens in the United States: Impacts and policy options. Ecological Applications, 26(5), 1437–1455. https://doi.org/10.1890/15-1176 Macheleidt, J., Mattern, D. J., Fischer, J., Netzker, T., Weber, J., Schroeckh, V., Valiante, V., & Brakhage, A. A. (2016). Regulation and Role of Fungal Secondary Metabolites. Annual Review of Genetics, 50(1), 371–392. https://doi.org/10.1146/annurev-genet-120215-035203 Majeed, M., Soliman, H., Kumar, G., El-Matbouli, M., & Saleh, M. (2018). Editing the genome of Aphanomyces invadans using CRISPR/Cas9. Parasites & Vectors, 11(1), 554. https://doi.org/10.1186/s13071-018-3134-8 Makarova, K. S., Haft, D. H., Barrangou, R., Brouns, S. J. J., Charpentier, E., Horvath, P., Moineau, S., Mojica, F. J. M., Wolf, Y. I., Yakunin, A. F., van der Oost, J., & Koonin, E. V. (2011). Evolution and classification of the CRISPR–Cas systems. Nature Reviews Microbiology, 9(6), Article 6. https://doi.org/10.1038/nrmicro2577 Makarova, K. S., Wolf, Y. I., Alkhnbashi, O. S., Costa, F., Shah, S. A., Saunders, S. J., Barrangou, R., Brouns, S. J. J., Charpentier, E., Haft, D. H., Horvath, P., Moineau, S., Mojica, F. J. M., Terns, R. M., Terns, M. P., White, M. F., Yakunin, A. F., Garrett, R. A., van der Oost, J., … Koonin, E. V. (2015). An updated evolutionary classification of CRISPR–Cas systems. Nature Reviews Microbiology, 13(11), Article 11. https://doi.org/10.1038/nrmicro3569 Makarova, S. S., Khromov, A. V., Spechenkova, N. A., Taliansky, M. E., & Kalinina, N. O. (2018). Application of the CRISPR/Cas System for Generation of Pathogen-Resistant Plants. Biochemistry (Moscow), 83(12), 1552–1562. https://doi.org/10.1134/S0006297918120131 Malnoy, M., Viola, R., Jung, M.-H., Koo, O.-J., Kim, S., Kim, J.-S., Velasco, R., & Nagamangala Kanchiswamy, C. (2016). DNA-Free Genetically Edited Grapevine and Apple Protoplast Using CRISPR/Cas9 Ribonucleoproteins. Frontiers in Plant Science, 7, Article 1904. https://doi.org/10.3389/fpls.2016.01904 Manavathu, E. K., Suryanarayana, K., Hasnain, S. E., & Leung, W.-C. (1988). DNA-mediated Transformation in the Aquatic Filamentous Fungus Achlya ambisexualis. Microbiology, 134(7), 2019–2028. https://doi.org/10.1099/00221287-134-7-2019 Markus, B. M., Bell, G. W., Lorenzi, H. A., & Lourido, S. (2019). Optimizing Systems for Cas9 Expression in Toxoplasma gondii. mSphere, 4(3), 10.1128/msphere.00386-19. https://doi.org/10.1128/msphere.00386-19 169 Marraffini, L. A., & Sontheimer, E. J. (2010). CRISPR interference: RNA-directed adaptive immunity in bacteria and archaea. Nature Reviews Genetics, 11(3), Article 3. https://doi.org/10.1038/nrg2749 Martinez, D., Berka, R. M., Henrissat, B., Saloheimo, M., Arvas, M., Baker, S. E., Chapman, J., Chertkov, O., Coutinho, P. M., Cullen, D., Danchin, E. G. J., Grigoriev, I. V., Harris, P., Jackson, M., Kubicek, C. P., Han, C. S., Ho, I., Larrondo, L. F., de Leon, A. L., … Brettin, T. S. (2008). Genome sequencing and analysis of the biomass-degrading fungus Trichoderma reesei (syn. Hypocrea jecorina). Nature Biotechnology, 26(5), Article 5. https://doi.org/10.1038/nbt1403 Matsu-ura, T., Baek, M., Kwon, J., & Hong, C. (2015). Efficient gene editing in Neurospora crassa with CRISPR technology. Fungal Biology and Biotechnology, 2(1), 4. https://doi.org/10.1186/s40694-015-0015-1 McCarren, K. L., McComb, J. A., Shearer, B., O’Brien, P., & Hardy, G. S. (2005). Transformation of Phytophthora cinnamomi with the green Fluorescent Protein (GFP) for studies on survival. 15th Australasian Plant Pathology Society Conference. McCarthy, H. M., Tarallo, M., Mesarich, C. H., McDougal, R. L., & Bradshaw, R. E. (2022). Targeted Gene Mutations in the Forest Pathogen Dothistroma septosporum Using CRISPR/Cas9. Plants, 11(8), Article 8. https://doi.org/10.3390/plants11081016 McGowan, J., & Fitzpatrick, D. A. (2020). Chapter Five—Recent advances in oomycete genomics. In D. Kumar (Ed.), Advances in Genetics (Vol. 105, pp. 175–228). Academic Press. https://doi.org/10.1016/bs.adgen.2020.03.001 McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference (SCIPY 2010), 56–61. https://doi.org/10.25080/Majora-92bf1922-00a Mcleod, A., Fry, B. A., Zuluaga, A. P., Myers, K. L., & Fry, W. E. (2008). Toward Improvements of Oomycete Transformation Protocols. Journal of Eukaryotic Microbiology, 55(2), 103–109. https://doi.org/10.1111/j.1550-7408.2008.00304.x McTaggart, A. R., van der Nest, M. A., Steenkamp, E. T., Roux, J., Slippers, B., Shuey, L. S., Wingfield, M. J., & Drenth, A. (2016). Fungal Genomics Challenges the Dogma of Name-Based Biosecurity. PLOS Pathogens, 12(5), e1005475. https://doi.org/10.1371/journal.ppat.1005475 Miao, J., Cai, M., Dong, X., Liu, L., Lin, D., Zhang, C., Pang, Z., & Liu, X. (2016). Resistance Assessment for Oxathiapiprolin in Phytophthora capsici and the Detection of a Point Mutation (G769W) in PcORP1 that Confers Resistance. Frontiers in Microbiology, 7, 615. https://doi.org/10.3389/fmicb.2016.00615 Miao, J., Chi, Y., Lin, D., Tyler, B. M., & Liu, X. (2018). Mutations in ORP1 Conferring Oxathiapiprolin Resistance Confirmed by Genome Editing using CRISPR/Cas9 in Phytophthora capsici and P. sojae. Phytopathology, 108(12), 1412–1419. https://doi.org/10.1094/PHYTO-01-18-0010-R Miao, J., Guo, D., Zhang, J., Huang, Q., Qin, G., Zhang, X., Wan, J., Gu, H., & Qu, L.-J. (2013). Targeted mutagenesis in rice using CRISPR-Cas system. Cell Research, 23(10), Article 10. https://doi.org/10.1038/cr.2013.123 Miao, J., Li, X., Lin, D., Liu, X., & Tyler, B. M. (2018). Oxysterol-binding protein-related protein 2 is not essential for Phytophthora sojae based on CRISPR/Cas9 deletions. Environmental Microbiology Reports, 10(3), 293–298. https://doi.org/10.1111/1758-2229.12638 170 Miao, J., Liu, X., Li, G., Du, X., & Liu, X. (2020). Multiple point mutations in PsORP1 gene conferring different resistance levels to oxathiapiprolin confirmed using CRISPR–Cas9 in Phytophthora sojae. Pest Management Science, 76(7), 2434–2440. https://doi.org/10.1002/ps.5784 Miller, J. C., Tan, S., Qiao, G., Barlow, K. A., Wang, J., Xia, D. F., Meng, X., Paschon, D. E., Leung, E., Hinkley, S. J., Dulay, G. P., Hua, K. L., Ankoudinova, I., Cost, G. J., Urnov, F. D., Zhang, H. S., Holmes, M. C., Zhang, L., Gregory, P. D., & Rebar, E. J. (2011). A TALE nuclease architecture for efficient genome editing. Nature Biotechnology, 29(2), Article 2. https://doi.org/10.1038/nbt.1755 Miyauchi, S., Kiss, E., Kuo, A., Drula, E., Kohler, A., Sánchez-García, M., Morin, E., Andreopoulos, B., Barry, K. W., Bonito, G., Buée, M., Carver, A., Chen, C., Cichocki, N., Clum, A., Culley, D., Crous, P. W., Fauchery, L., Girlanda, M., … Martin, F. M. (2020). Large-scale genome sequencing of mycorrhizal fungi provides insights into the early evolution of symbiotic traits. Nature Communications, 11(1), Article 1. https://doi.org/10.1038/s41467-020-18795-w Möller, M., & Stukenbrock, E. H. (2017). Evolution and genome architecture in fungal plant pathogens. Nature Reviews Microbiology, 15(12), 756–771. https://doi.org/10.1038/nrmicro.2017.76 Moreno-Mateos, M. A., Vejnar, C. E., Beaudoin, J.-D., Fernandez, J. P., Mis, E. K., Khokha, M. K., & Giraldez, A. J. (2015). CRISPRscan: Designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nature Methods, 12(10), Article 10. https://doi.org/10.1038/nmeth.3543 Morgens, D. W., Wainberg, M., Boyle, E. A., Ursu, O., Araya, C. L., Tsui, C. K., Haney, M. S., Hess, G. T., Han, K., Jeng, E. E., Li, A., Snyder, M. P., Greenleaf, W. J., Kundaje, A., & Bassik, M. C. (2017). Genome-scale measurement of off-target activity using Cas9 toxicity in high-throughput screens. Nature Communications, 8(1), Article 1. https://doi.org/10.1038/ncomms15178 Mort-Bontemps, M., & Fèvre, M. (1997). Transformation of the Oomycete Saprolegnia monoïca to hygromycin-B resistance. Current Genetics, 31(3), 272–275. https://doi.org/10.1007/s002940050205 Muchero, W., Sondreli, K. L., Chen, J.-G., Urbanowicz, B. R., Zhang, J., Singan, V., Yang, Y., Brueggeman, R. S., Franco-Coronado, J., Abraham, N., Yang, J.-Y., Moremen, K. W., Weisberg, A. J., Chang, J. H., Lindquist, E., Barry, K., Ranjan, P., Jawdy, S., Schmutz, J., … LeBoldus, J. M. (2018). Association mapping, transcriptomics, and transient expression identify candidate genes mediating plant–pathogen interactions in a tree. Proceedings of the National Academy of Sciences, 115(45), 11573–11578. https://doi.org/10.1073/pnas.1804428115 Muhr, M., Paulat, M., Awwanah, M., Brinkkötter, M., & Teichmann, T. (2018). CRISPR/Cas9-mediated knockout of Populus BRANCHED1 and BRANCHED2 orthologs reveals a major function in bud outgrowth control. Tree Physiology, 38(10), 1588–1597. https://doi.org/10.1093/treephys/tpy088 Muñoz, I. V., Sarrocco, S., Malfatti, L., Baroncelli, R., & Vannacci, G. (2019). CRISPR-Cas for Fungal Genome Editing: A New Tool for the Management of Plant Diseases. Frontiers in Plant Science, 10, Article 135. https://doi.org/10.3389/fpls.2019.00135 171 Muszewska, A., Stepniewska-Dziubinska, M. M., Steczkiewicz, K., Pawlowska, J., Dziedzic, A., & Ginalski, K. (2017). Fungal lifestyle reflected in serine protease repertoire. Scientific Reports, 7(1), 9147. https://doi.org/10.1038/s41598-017-09644-w Nadal, M., García-Pedrajas, M. D., & Gold, S. E. (2008). Dimorphism in fungal plant pathogens. FEMS Microbiology Letters, 284(2), 127–134. https://doi.org/10.1111/j.1574-6968.2008.01173.x Naruzawa, E. S., & Bernier, L. (2014). Control of yeast-mycelium dimorphism in vitro in Dutch elm disease fungi by manipulation of specific external stimuli. Fungal Biology, 118(11), 872–884. https://doi.org/10.1016/j.funbio.2014.07.006 NCBI Resource Coordinators. (2016). Database resources of the National Center for Biotechnology Information. Nucleic Acids Research, 44(D1), D7–D19. https://doi.org/10.1093/nar/gkv1290 Neale, D. B., McGuire, P. E., Wheeler, N. C., Stevens, K. A., Crepeau, M. W., Cardeno, C., Zimin, A. V., Puiu, D., Pertea, G. M., Sezen, U. U., Casola, C., Koralewski, T. E., Paul, R., Gonzalez-Ibeas, D., Zaman, S., Cronn, R., Yandell, M., Holt, C., Langley, C. H., … Wegrzyn, J. L. (2017). The Douglas-Fir Genome Sequence Reveals Specialization of the Photosynthetic Apparatus in Pinaceae. G3: Genes, Genomes, Genetics, 7(9), 3157–3167. https://doi.org/10.1534/g3.117.300078 Nekrasov, V., Staskawicz, B., Weigel, D., Jones, J. D. G., & Kamoun, S. (2013). Targeted mutagenesis in the model plant Nicotiana benthamiana using Cas9 RNA-guided endonuclease. Nature Biotechnology, 31(8), 691–693. https://doi.org/10.1038/nbt.2655 Nekrasov, V., Wang, C., Win, J., Lanz, C., Weigel, D., & Kamoun, S. (2017). Rapid generation of a transgene-free powdery mildew resistant tomato by genome deletion. Scientific Reports, 7(1), Article 1. https://doi.org/10.1038/s41598-017-00578-x Newton, A. C., Fitt, B. D. L., Atkins, S. D., Walters, D. R., & Daniell, T. J. (2010). Pathogenesis, parasitism and mutualism in the trophic space of microbe–plant interactions. Trends in Microbiology, 18(8), 365–373. https://doi.org/10.1016/j.tim.2010.06.002 Nienhaus, F. (1960). Das Wirtsspektrum von Phytophthora cactorum (Leb. Et Cohn) Schroet. Journal of Phytopathology, 38(1), 33–68. https://doi.org/10.1111/j.1439-0434.1960.tb01893.x Nigg, M., & Bernier, L. (2016). From yeast to hypha: Defining transcriptomic signatures of the morphological switch in the dimorphic fungal pathogen Ophiostoma novo-ulmi. BMC Genomics, 17(1), 920. https://doi.org/10.1186/s12864-016-3251-8 Nigg, M., Laroche, J., Landry, C. R., & Bernier, L. (2015). RNAseq Analysis Highlights Specific Transcriptome Signatures of Yeast and Mycelial Growth Phases in the Dutch Elm Disease Fungus Ophiostoma novo-ulmi. G3: Genes, Genomes, Genetics, 5(11), 2487–2495. https://doi.org/10.1534/g3.115.021022 Nishida, K., Arazoe, T., Yachie, N., Banno, S., Kakimoto, M., Tabata, M., Mochizuki, M., Miyabe, A., Araki, M., Hara, K. Y., Shimatani, Z., & Kondo, A. (2016). Targeted nucleotide editing using hybrid prokaryotic and vertebrate adaptive immune systems. Science, 353(6305), aaf8729. https://doi.org/10.1126/science.aaf8729 Nishitani, C., Hirai, N., Komori, S., Wada, M., Okada, K., Osakabe, K., Yamamoto, T., & Osakabe, Y. (2016). Efficient Genome Editing in Apple Using a CRISPR/Cas9 system. Scientific Reports, 6(1), Article 1. https://doi.org/10.1038/srep31481 172 Nødvig, C. S., Nielsen, J. B., Kogle, M. E., & Mortensen, U. H. (2015). A CRISPR-Cas9 System for Genetic Engineering of Filamentous Fungi. PLoS ONE, 10(7), e0133085. https://doi.org/10.1371/journal.pone.0133085 Norrander, J., Kempe, T., & Messing, J. (1983). Construction of improved M13 vectors using oligodeoxynucleotide-directed mutagenesis. Gene, 26(1), 101–106. https://doi.org/10.1016/0378-1119(83)90040-9 Nystedt, B., Street, N. R., Wetterbom, A., Zuccolo, A., Lin, Y.-C., Scofield, D. G., Vezzi, F., Delhomme, N., Giacomello, S., Alexeyenko, A., Vicedomini, R., Sahlin, K., Sherwood, E., Elfstrand, M., Gramzow, L., Holmberg, K., Hällman, J., Keech, O., Klasson, L., … Jansson, S. (2013). The Norway spruce genome sequence and conifer genome evolution. Nature, 497(7451), Article 7451. https://doi.org/10.1038/nature12211 Ohm, R. A., Feau, N., Henrissat, B., Schoch, C. L., Horwitz, B. A., Barry, K. W., Condon, B. J., Copeland, A. C., Dhillon, B., Glaser, F., Hesse, C. N., Kosti, I., LaButti, K., Lindquist, E. A., Lucas, S., Salamov, A. A., Bradshaw, R. E., Ciuffetti, L., Hamelin, R. C., … Grigoriev, I. V. (2012). Diverse Lifestyles and Strategies of Plant Pathogenesis Encoded in the Genomes of Eighteen Dothideomycetes Fungi. PLoS Pathogens, 8(12), e1003037. https://doi.org/10.1371/journal.ppat.1003037 O’Leary, N. A., Wright, M. W., Brister, J. R., Ciufo, S., Haddad, D., McVeigh, R., Rajput, B., Robbertse, B., Smith-White, B., Ako-Adjei, D., Astashyn, A., Badretdin, A., Bao, Y., Blinkova, O., Brover, V., Chetvernin, V., Choi, J., Cox, E., Ermolaeva, O., … Pruitt, K. D. (2016). Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. Nucleic Acids Research, 44(D1), D733-745. https://doi.org/10.1093/nar/gkv1189 Oliver, R. P., & Ipcho, S. V. S. (2004). Arabidopsis pathology breathes new life into the necrotrophs-vs. -Biotrophs classification of fungal pathogens. Molecular Plant Pathology, 5(4), 347–352. https://doi.org/10.1111/J.1364-3703.2004.00228.X Orlikowski, L. B., Ptaszek, M., Rodziewicz, A., Nechwatal, J., Thinggaard, K., & Jung, T. (2011). Phytophthora root and collar rot of mature Fraxinus excelsior in forest stands in Poland and Denmark. Forest Pathology, 41(6), 510–519. https://doi.org/10.1111/j.1439-0329.2011.00714.x Pandaranayaka, E. P., Frenkel, O., Elad, Y., Prusky, D., & Harel, A. (2019). Network analysis exposes core functions in major lifestyles of fungal and oomycete plant pathogens. BMC Genomics, 20(1), 1020. https://doi.org/10.1186/s12864-019-6409-3 Parrent, J., James, T. Y., Vasaitis, R., & Taylor, A. F. (2009). Friend or foe? Evolutionary history of glycoside hydrolase family 32 genes encoding for sucrolytic activity in fungi and its implications for plant-fungal symbioses. BMC Evolutionary Biology, 9(1), 148. https://doi.org/10.1186/1471-2148-9-148 Paseka, R. E., White, L. A., Waal, D. B. V. de, Strauss, A. T., González, A. L., Everett, R. A., Peace, A., Seabloom, E. W., Frenken, T., & Borer, E. T. (2020). Disease-mediated ecosystem services: Pathogens, plants, and people. Trends in Ecology & Evolution, 35(8), 731–743. https://doi.org/10.1016/j.tree.2020.04.003 Pasteris, R. J., Hanagan, M. A., Bisaha, J. J., Finkelstein, B. L., Hoffman, L. E., Gregory, V., Andreassi, J. L., Sweigard, J. A., Klyashchitsky, B. A., Henry, Y. T., & Berger, R. A. (2016). Discovery of oxathiapiprolin, a new oomycete fungicide that targets an oxysterol binding protein. Bioorganic & Medicinal Chemistry, 24(3), 354–361. https://doi.org/10.1016/j.bmc.2015.07.064 173 Pavan, S., Jacobsen, E., Visser, R. G. F., & Bai, Y. (2009). Loss of susceptibility as a novel breeding strategy for durable and broad-spectrum resistance. Molecular Breeding, 25(1), 1. https://doi.org/10.1007/s11032-009-9323-6 Paysan-Lafosse, T., Blum, M., Chuguransky, S., Grego, T., Pinto, B. L., Salazar, G. A., Bileschi, M. L., Bork, P., Bridge, A., Colwell, L., Gough, J., Haft, D. H., Letunić, I., Marchler-Bauer, A., Mi, H., Natale, D. A., Orengo, C. A., Pandurangan, A. P., Rivoire, C., … Bateman, A. (2023). InterPro in 2022. Nucleic Acids Research, 51(D1), D418–D427. https://doi.org/10.1093/nar/gkac993 Peña, L., & Séguin, A. (2001). Recent advances in the genetic transformation of trees. Trends in Biotechnology, 19(12), 500–506. https://doi.org/10.1016/S0167-7799(01)01815-7 Peng, A., Chen, S., Lei, T., Xu, L., He, Y., Wu, L., Yao, L., & Zou, X. (2017). Engineering canker-resistant plants through CRISPR/Cas9-targeted editing of the susceptibility gene CsLOB1 promoter in citrus. Plant Biotechnology Journal, 15(12), 1509–1519. https://doi.org/10.1111/pbi.12733 Peng, D., Kurup, S. P., Yao, P. Y., Minning, T. A., & Tarleton, R. L. (2014). CRISPR-Cas9-Mediated Single-Gene and Gene Family Disruption in Trypanosoma cruzi. mBio, 6(1), 10.1128/mbio.02097-14. https://doi.org/10.1128/mbio.02097-14 Peng, D., & Tarleton, R. (2015). EuPaGDT: A web tool tailored to design CRISPR guide RNAs for eukaryotic pathogens. Microbial Genomics, 1(4). https://doi.org/10.1099/mgen.0.000033 Pennisi, E. (2010). Armed and Dangerous. Science, 327(5967), 804–805. https://doi.org/10.1126/science.327.5967.804 Perfect, S. E., & Green, J. R. (2001). Infection structures of biotrophic and hemibiotrophic fungal plant pathogens. Molecular Plant Pathology, 2(2), 101–108. https://doi.org/10.1046/j.1364-3703.2001.00055.x Perfect, S. E., O’Connell, R. J., Green, E. F., Doering-Saad, C., & Green, J. R. (1998). Expression cloning of a fungal proline-rich glycoprotein specific to the biotrophic interface formed in the Colletotrichum–bean interaction. The Plant Journal, 15(2), 273–279. https://doi.org/10.1046/j.1365-313X.1998.00196.x Philibert, A., Desprez-Loustau, M.-L., Fabre, B., Frey, P., Halkett, F., Husson, C., Lung-Escarmant, B., Marçais, B., Robin, C., Vacher, C., & Makowski, D. (2011). Predicting invasion success of forest pathogenic fungi from species traits. Journal of Applied Ecology, 48(6), 1381–1390. https://doi.org/10.1111/j.1365-2664.2011.02039.x Pimentel, D., McNair, S., Janecka, J., Wightman, J., Simmonds, C., O’Connell, C., Wong, E., Russel, L., Zern, J., Aquino, T., & Tsomondo, T. (2001). Economic and environmental threats of alien plant, animal, and microbe invasions. Agriculture, Ecosystems & Environment, 84(1), 1–20. https://doi.org/10.1016/S0167-8809(00)00178-X Plummer, K. M., Clark, S. J., Ellis, L. M., Loganathan, A., Al-Samarrai, T. H., Rikkerink, E. H. A., Sullivan, P. A., Templeton, M. D., & Farley, P. C. (2004). Analysis of a Secreted Aspartic Peptidase Disruption Mutant of Glomerella cingulata. European Journal of Plant Pathology, 110(3), 265–274. https://doi.org/10.1023/B:EJPP.0000019796.78598.8c Podger, F. D., Doepel, R. F., & Zentmyer, G. A. (1965). Association of Phytophthora cinnamomi with a disease of Eucalyptus marginata forest in Western Australia. Plant Disease Reporter, 49(11), 943–947. 174 Pohl, C., Kiel, J. A. K. W., Driessen, A. J. M., Bovenberg, R. A. L., & Nygård, Y. (2016). CRISPR/Cas9 Based Genome Editing of Penicillium chrysogenum. ACS Synthetic Biology, 5(7), 754–764. https://doi.org/10.1021/acssynbio.6b00082 Pourcel, C., Salvignol, G., & Vergnaud, G. (2005). CRISPR elements in Yersinia pestis acquire new repeats by preferential uptake of bacteriophage DNA, and provide additional tools for evolutionary studies. Microbiology, 151(3), 653–663. https://doi.org/10.1099/mic.0.27437-0 Price, M. N., Dehal, P. S., & Arkin, A. P. (2009). FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix. Molecular Biology and Evolution, 26(7), 1641–1650. https://doi.org/10.1093/molbev/msp077 Pyott, D. E., Sheehan, E., & Molnar, A. (2016). Engineering of CRISPR/Cas9-mediated potyvirus resistance in transgene-free Arabidopsis plants. Molecular Plant Pathology, 17(8), 1276–1288. https://doi.org/10.1111/mpp.12417 R Core Team. (2017). R: A language and environment for statistical computing [R]. R Foundation for Statistical Computing. https://www.R-project.org/ Ranjha, L., Howard, S. M., & Cejka, P. (2018). Main steps in DNA double-strand break repair: An introduction to homologous recombination and related processes. Chromosoma, 127(2), 187–214. https://doi.org/10.1007/s00412-017-0658-1 Rejmánek, M., & Pitcairn, M. J. (2002). When is eradication of exotic pest plants a realistic goal? Turning the Tide: The Eradication of Invasive Species, 249–253. Reuter, J. S., & Mathews, D. H. (2010). RNAstructure: Software for RNA secondary structure prediction and analysis. BMC Bioinformatics, 11(1), Article 1. https://doi.org/10.1186/1471-2105-11-129 Richards, T. A., Dacks, J. B., Jenkinson, J. M., Thornton, C. R., & Talbot, N. J. (2006). Evolution of filamentous plant pathogens: Gene exchange across eukaryotic kingdoms. Current Biology, 16(18), 1857–1864. https://doi.org/10.1016/j.cub.2006.07.052 Richards, T. A., Soanes, D. M., Jones, M. D. M., Vasieva, O., Leonard, G., Paszkiewicz, K., Foster, P. G., Hall, N., & Talbot, N. J. (2011). Horizontal gene transfer facilitated the evolution of plant parasitic mechanisms in the oomycetes. Proceedings of the National Academy of Sciences of the United States of America, 108(37), 15258–15263. https://doi.org/10.1073/pnas.1105100108 Richards, W. C. (1994). Nonsporulation in the Dutch elm disease fungus Ophiostoma ulmi: Evidence for control by a single nuclear gene. Canadian Journal of Botany, 72(4), 461–467. https://doi.org/10.1139/b94-061 Riedel, M., Calmin, G., Belbahri, L., Lefort, F., Götz, M., Wagner, S., & Werres, S. (2009). Green Fluorescent Protein (GFP) as a Reporter Gene for the Plant Pathogenic Oomycete Phytophthora ramorum. Journal of Eukaryotic Microbiology, 56(2), 130–135. https://doi.org/10.1111/j.1550-7408.2008.00376.x Rigling, D., & Prospero, S. (2018). Cryphonectria parasitica, the causal agent of chestnut blight: Invasion history, population biology and disease control. Molecular Plant Pathology, 19(1), 7–20. https://doi.org/10.1111/mpp.12542 Ristaino, J. B., & Gumpertz, M. L. (2000). New Frontiers in the Study of Dispersal and Spatial Analysis of Epidemics Caused by Species in the Genus Phytophthora. Annual Review of Phytopathology, 38(1), 541–576. 175 Rizzo, D. M., & Garbelotto, M. (2003). Sudden oak death: Endangering California and Oregon forest ecosystems. Frontiers in Ecology and the Environment, 1(4), 197–204. https://doi.org/10.1890/1540-9295(2003)001[0197:SODECA]2.0.CO;2 Rizzo, D. M., Garbelotto, M., Davidson, J. M., Slaughter, G. W., & Koike, S. T. (2002a). Phytophthora ramorum as the Cause of Extensive Mortality of Quercus spp. And Lithocarpus densiflorus in California. Plant Disease, 86(3), 205–214. https://doi.org/10.1094/PDIS.2002.86.3.205 Rizzo, D. M., Garbelotto, M., Davidson, J. M., Slaughter, W., & Koike, S. T. (2002b). Phytophthora ramorum and Sudden Oak Death in California: I. Host Relationships. Proceedings of the Fifth Symposium on Oak Woodlands: Oaks in California’s Challenging Landscape, 733–740. https://www.fs.usda.gov/treesearch/pubs/26169 Rock, J. M., Hopkins, F. F., Chavez, A., Diallo, M., Chase, M. R., Gerrick, E. R., Pritchard, J. R., Church, G. M., Rubin, E. J., Sassetti, C. M., Schnappinger, D., & Fortune, S. M. (2017). Programmable transcriptional repression in mycobacteria using an orthogonal CRISPR interference platform. Nature Microbiology, 2(4), Article 4. https://doi.org/10.1038/nmicrobiol.2016.274 Roe, A. D., Torson, A. S., Bilodeau, G., Bilodeau, P., Blackburn, G. S., Cui, M., Cusson, M., Doucet, D., Griess, V. C., Lafond, V., Paradis, G., Porth, I., Prunier, J., Srivastava, V., Tremblay, E., Uzunovic, A., Yemshanov, D., & Hamelin, R. C. (2019). Biosurveillance of forest insects: Part I—integration and application of genomic tools to the surveillance of non-native forest insects. Journal of Pest Science, 92(1), 51–70. https://doi.org/10.1007/s10340-018-1027-4 Roy, A., Jayaprakash, A., T, R. R., Annamalai, A., & Lakshmi, P. T. V. (2020). Genome-wide annotation, comparison and functional genomics of carbohydrate-active enzymes in legumes infecting Fusarium oxysporum formae speciales. Mycology, 11(1), 56–70. https://doi.org/10.1080/21501203.2019.1706656 Roy, B. A., Alexander, H. M., Davidson, J., Campbell, F. T., Burdon, J. J., Sniezko, R., & Brasier, C. (2014). Increasing forest loss worldwide from invasive pests requires new trade regulations. Frontiers in Ecology and the Environment, 12(8), 457–465. https://doi.org/10.1890/130240 Roy, K. R., Smith, J. D., Vonesch, S. C., Lin, G., Tu, C. S., Lederer, A. R., Chu, A., Suresh, S., Nguyen, M., Horecka, J., Tripathi, A., Burnett, W. T., Morgan, M. A., Schulz, J., Orsley, K. M., Wei, W., Aiyar, R. S., Davis, R. W., Bankaitis, V. A., … Steinmetz, L. M. (2018). Multiplexed precision genome editing with trackable genomic barcodes in yeast. Nature Biotechnology, 36(6), Article 6. https://doi.org/10.1038/nbt.4137 Rytkönen, A., Lilja, A., Petäistö, R.-L., & Hantula, J. (2008). Irrigation water and Phytophthora cactorum in a forest nursery. Scandinavian Journal of Forest Research, 23(5), 404–411. https://doi.org/10.1080/02827580802419034 Safari, S., Baratloo, A., Elfil, M., & Negida, A. (2016). Evidence Based Emergency Medicine; Part 5 Receiver Operating Curve and Area under the Curve. Emergency, 4(2), 111–113. Savory, F., Leonard, G., & Richards, T. A. (2015). The Role of Horizontal Gene Transfer in the Evolution of the Oomycetes. PLOS Pathogens, 11(5), e1004805. https://doi.org/10.1371/journal.ppat.1004805 Sbaraini, N., Andreis, F. C., Thompson, C. E., Guedes, R. L. M., Junges, Â., Campos, T., Staats, C. C., Vainstein, M. H., Ribeiro de Vasconcelos, A. T., & Schrank, A. (2017). Genome-Wide Analysis of Secondary Metabolite Gene Clusters in Ophiostoma ulmi and 176 Ophiostoma novo-ulmi Reveals a Fujikurin-Like Gene Cluster with a Putative Role in Infection. Frontiers in Microbiology, 8, Article 1063. https://doi.org/10.3389/fmicb.2017.01063 Schuster, M., & Kahmann, R. (2019). CRISPR-Cas9 genome editing approaches in filamentous fungi and oomycetes. Fungal Genetics and Biology, 130, 43–53. https://doi.org/10.1016/j.fgb.2019.04.016 Schuster, M., Schweizer, G., Reissmann, S., & Kahmann, R. (2016). Genome editing in Ustilago maydis using the CRISPR–Cas system. Fungal Genetics and Biology, 89, 3–9. https://doi.org/10.1016/j.fgb.2015.09.001 Seabold, S., & Perktold, J. (2010). Statsmodels: Econometric and Statistical Modeling with Python. Proceedings of the 9th Python in Science Conference (SCIPY 2010), 92–96. https://doi.org/10.25080/Majora-92bf1922-011 Segretin, M. E., Pais, M., Franceschetti, M., Chaparro-Garcia, A., Bos, J. I. B., Banfield, M. J., & Kamoun, S. (2014). Single Amino Acid Mutations in the Potato Immune Receptor R3a Expand Response to Phytophthora Effectors. Molecular Plant-Microbe Interactions®, 27(7), 624–637. https://doi.org/10.1094/MPMI-02-14-0040-R Serrano, M. S., Romero, M. Á., Homet, P., & Gómez-Aparicio, L. (2022). Climate change impact on the population dynamics of exotic pathogens: The case of the worldwide pathogen Phytophthora cinnamomi. Agricultural and Forest Meteorology, 322, 109002. https://doi.org/10.1016/j.agrformet.2022.109002 Shen, Y., Li, Y., Xu, D., Yang, C., Li, C., & Luo, K. (2018). Molecular cloning and characterization of a brassinosteriod biosynthesis-related gene PtoDWF4 from Populus tomentosa. Tree Physiology, 38(9), 1424–1436. https://doi.org/10.1093/treephys/tpy027 Shmakov, S., Abudayyeh, O. O., Makarova, K. S., Wolf, Y. I., Gootenberg, J. S., Semenova, E., Minakhin, L., Joung, J., Konermann, S., Severinov, K., Zhang, F., & Koonin, E. V. (2015). Discovery and Functional Characterization of Diverse Class 2 CRISPR-Cas Systems. Molecular Cell, 60(3), 385–397. https://doi.org/10.1016/j.molcel.2015.10.008 Si-Ammour, A., Mauch-Mani, B., & Mauch, F. (2003). Quantification of induced resistance against Phytophthora species expressing GFP as a vital marker: β-aminobutyric acid but not BTH protects potato and Arabidopsis from infection. Molecular Plant Pathology, 4(4), 237–248. https://doi.org/10.1046/j.1364-3703.2003.00168.x Simberloff, D., Martin, J.-L., Genovesi, P., Maris, V., Wardle, D. A., Aronson, J., Courchamp, F., Galil, B., García-Berthou, E., Pascal, M., Pyšek, P., Sousa, R., Tabacchi, E., & Vilà, M. (2013). Impacts of biological invasions: What’s what and the way forward. Trends in Ecology & Evolution, 28(1), 58–66. https://doi.org/10.1016/j.tree.2012.07.013 Singh, R. P., Hodson, D. P., Jin, Y., Lagudah, E. S., Ayliffe, M. A., Bhavani, S., Rouse, M. N., Pretorius, Z. A., Szabo, L. J., Huerta-Espino, J., Basnet, B. R., Lan, C., & Hovmøller, M. S. (2015). Emergence and Spread of New Races of Wheat Stem Rust Fungus: Continued Threat to Food Security and Prospects of Genetic Control. Phytopathology®, 105(7), 872–884. https://doi.org/10.1094/PHYTO-01-15-0030-FI Sniezko, R. A., Smith, J., Liu, J.-J., & Hamelin, R. C. (2014). Genetic Resistance to Fusiform Rust in Southern Pines and White Pine Blister Rust in White Pines—A Contrasting Tale of Two Rust Pathosystems—Current Status and Future Prospects. Forests, 5(9), Article 9. https://doi.org/10.3390/f5092050 177 Stahl, E. A., & Bishop, J. G. (2000). Plant–pathogen arms races at the molecular level. Current Opinion in Plant Biology, 3(4), 299–304. https://doi.org/10.1016/S1369-5266(00)00083-2 Standage-Beier, K., Brookhouser, N., Balachandran, P., Zhang, Q., Brafman, D. A., & Wang, X. (2019). RNA-Guided Recombinase-Cas9 Fusion Targets Genomic DNA Deletion and Integration. The CRISPR Journal, 2(4), 209–222. https://doi.org/10.1089/crispr.2019.0013 Steinegger, M., & Söding, J. (2017). MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology, 35(11), Article 11. https://doi.org/10.1038/nbt.3988 Stone, J. K., Coop, L. B., & Manter, D. K. (2008). Predicting effects of climate change on Swiss needle cast disease severity in Pacific Northwest forests. Canadian Journal of Plant Pathology, 30(2), 169–176. https://doi.org/10.1080/07060661.2008.10540533 Strauss, S. H., Costanza, A., & Séguin, A. (2015). Genetically engineered trees: Paralysis from good intentions. Science, 349(6250), 794–795. https://doi.org/10.1126/science.aab0493 Strauss, S. H., Ma, C., Ault, K., & Klocko, A. L. (2016). Lessons from Two Decades of Field Trials with Genetically Modified Trees in the USA: Biology and Regulatory Compliance. In C. Vettori, F. Gallardo, H. Häggman, V. Kazana, F. Migliacci, G. Pilate, & M. Fladung (Eds.), Biosafety of Forest Transgenic Trees: Improving the Scientific Basis for Safe Tree Development and Implementation of EU Policy Directives (pp. 101–124). Springer Netherlands. https://doi.org/10.1007/978-94-017-7531-1_5 Strauss, S. H., Tan, H., Boerjan, W., & Sedjo, R. (2009). Strangled at birth? Forest biotech and the Convention on Biological Diversity. Nature Biotechnology, 27(6), Article 6. https://doi.org/10.1038/nbt0609-519 Stukenbrock, E., & Gurr, S. (2023). Address the growing urgency of fungal disease in crops. Nature, 617(7959), 31–34. https://doi.org/10.1038/d41586-023-01465-4 Sweigard, J., Andreassi, J., Pember, S., Gutteridge, S., Pasteris, R., Hanagan, M., Carroll, A., Sopa, J., & Nesnow, D. (2014). Discovery of the target site of oxathiapiprolin (DuPont Zorvec disease control). Abstracts of Papers, 13th IUPAC Congress of Pesticide Chemistry, AGRO 911, 233. Tanguay, P. (2019). CRISPR/Cas9 gene editing of the Dutch elm disease pathogen Ophiostoma novo-ulmi. Canadian Journal of Plant Pathology, 41(1), 163–163. https://doi.org/10.1080/0706066.12019.1519163 Taylor, G. (2002). Populus: Arabidopsis for Forestry. Do We Need a Model Tree? Annals of Botany, 90(6), 681–689. https://doi.org/10.1093/aob/mcf255 Taylor, J. W., & Berbee, M. L. (2006). Dating divergences in the Fungal Tree of Life: Review and new analyses. Mycologia, 98(6), 838–849. https://doi.org/10.1080/15572536.2006.11832614 Terns, M. P., & Terns, R. M. (2011). CRISPR-based adaptive immune systems. Current Opinion in Microbiology, 14(3), 321–327. https://doi.org/10.1016/j.mib.2011.03.005 Terpilowski, M. A. (2019). scikit-posthocs: Pairwise multiple comparison tests in Python. Journal of Open Source Software, 4(36), 1169. https://doi.org/10.21105/joss.01169 Tetlow, I. J., & Farrar, J. F. (1992). Sucrose-metabolizing enzymes from leaves of barley infected with brown rust (Puccinia hordei Otth.). New Phytologist, 120(4), 475–480. https://doi.org/10.1111/j.1469-8137.1992.tb01795.x 178 The pandas development team. (2020). pandas-dev/pandas: Pandas (2.1.3) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.3509134 The Royal Botanic Gardens Kew, Landcare Research-NZ, & Institute of Microbiology. (2020). Index Fungorum. http://www.indexfungorum.org/ Thrower, L. B. (1966). Terminology for Plant Parasites. Journal of Phytopathology, 56(3), 258–259. https://doi.org/10.1111/j.1439-0434.1966.tb02261.x Thyme, S. B., Akhmetova, L., Montague, T. G., Valen, E., & Schier, A. F. (2016). Internal guide RNA interactions interfere with Cas9-mediated cleavage. Nature Communications, 7(1), Article 1. https://doi.org/10.1038/ncomms11750 Toruño, T. Y., Stergiopoulos, I., & Coaker, G. (2016). Plant-Pathogen Effectors: Cellular Probes Interfering with Plant Defenses in Spatial and Temporal Manners. Annual Review of Phytopathology, 54(1), 419–441. https://doi.org/10.1146/annurev-phyto-080615-100204 Trumbore, S., Brando, P., & Hartmann, H. (2015). Forest health and global change. Science, 349(6250), 814–818. https://doi.org/10.1126/science.aac6759 Tsai, C.-J., & Xue, L.-J. (2015). CRISPRing into the woods. GM Crops & Food, 6(4), 206–215. https://doi.org/10.1080/21645698.2015.1091553 Tuskan, G. A., DiFazio, S., Jansson, S., Bohlmann, J., Grigoriev, I., Hellsten, U., Putnam, N., Ralph, S., Rombauts, S., Salamov, A., Schein, J., Sterck, L., Aerts, A., Bhalerao, R. R., Bhalerao, R. P., Blaudez, D., Boerjan, W., Brun, A., Brunner, A., … Rokhsar, D. (2006). The Genome of Black Cottonwood, Populus trichocarpa (Torr. & Gray). Science, 313(5793), 1596–1604. https://doi.org/10.1126/science.1128691 Tyler, B. M., Tripathy, S., Zhang, X., Dehal, P., Jiang, R. H. Y., Aerts, A., Arredondo, F. D., Baxter, L., Bensasson, D., Beynon, J. L., Chapman, J., Damasceno, C. M. B., Dorrance, A. E., Dou, D., Dickerman, A. W., Dubchak, I. L., Garbelotto, M., Gijzen, M., Gordon, S. G., … Boore, J. L. (2006). Phytophthora Genome Sequences Uncover Evolutionary Origins and Mechanisms of Pathogenesis. Science, 313(5791), 1261–1266. https://doi.org/10.1126/science.1128796 Van der Nest, M. A., Steenkamp, E. T., McTaggart, A. R., Trollip, C., Godlonton, T., Sauerman, E., Roodt, D., Naidoo, K., Coetzee, M. P. A., Wilken, P. M., Wingfield, M. J., & Wingfield, B. D. (2015). Saprophytic and pathogenic fungi in the Ceratocystidaceae differ in their ability to metabolize plant-derived sucrose. BMC Evolutionary Biology, 15(1), 273. https://doi.org/10.1186/s12862-015-0550-7 van der Oost, J., Jore, M. M., Westra, E. R., Lundgren, M., & Brouns, S. J. J. (2009). CRISPR-based adaptive and heritable immunity in prokaryotes. Trends in Biochemical Sciences, 34(8), 401–407. https://doi.org/10.1016/j.tibs.2009.05.002 van West, P., Reid, B., Campbell, T. A., Sandrock, R. W., Fry, W. E., Kamoun, S., & Gow, N. A. R. (1999). Green fluorescent protein (GFP) as a reporter gene for the plant pathogenic oomycete Phytophthora palmivora. FEMS Microbiology Letters, 178(1), 71–80. https://doi.org/10.1111/j.1574-6968.1999.tb13761.x Venette, R. C. (2009). Implication of global climate change on the distribution and activity of Phytophthora ramorum. 20th U.S. Department of Agriculture Interagency Research Forum on Invasive Species, 58–59. Venette, R. C., & Cohen, S. D. (2006). Potential climatic suitability for establishment of Phytophthora ramorum within the contiguous United States. Forest Ecology and Management, 231(1), 18–26. https://doi.org/10.1016/j.foreco.2006.04.036 179 Vettraino, A. M., Zikeli, F., Scarascia Mugnozza, G., Vinciguerra, V., Tabet, D., & Romagnoli, M. (2022). Lignin nanoparticles containing essential oils for controlling Phytophthora cactorum diseases. Forest Pathology, 52(2), e12739. https://doi.org/10.1111/efp.12739 Vijn, I., & Govers, F. (2003). Agrobacterium tumefaciens mediated transformation of the oomycete plant pathogen Phytophthora infestans. Molecular Plant Pathology, 4(6), 459–467. https://doi.org/10.1046/j.1364-3703.2003.00191.x Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., … van Mulbregt, P. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17(3), Article 3. https://doi.org/10.1038/s41592-019-0686-2 Vitousek, P. M., D’Antonio, C. M., Loope, L. L., Rejmánek, M., & Westbrooks, R. (1997). Introduced Species: A Significant Component of Human-Caused Global Change. New Zealand Journal of Ecology, 21(1), 1–16. Voegele, R. T., Wirsel, S., Möll, U., Lechner, M., & Mendgen, K. (2006). Cloning and Characterization of a Novel Invertase from the Obligate Biotroph Uromyces fabae and Analysis of Expression Patterns of Host and Pathogen Invertases in the Course of Infection. Molecular Plant-Microbe Interactions®, 19(6), 625–634. https://doi.org/10.1094/MPMI-19-0625 Wan, S., Li, C., Ma, X., & Luo, K. (2017). PtrMYB57 contributes to the negative regulation of anthocyanin and proanthocyanidin biosynthesis in poplar. Plant Cell Reports, 36(8), 1263–1276. https://doi.org/10.1007/s00299-017-2151-y Wang, F., Wang, C., Liu, P., Lei, C., Hao, W., Gao, Y., Liu, Y.-G., & Zhao, K. (2016). Enhanced Rice Blast Resistance by CRISPR/Cas9-Targeted Mutagenesis of the ERF Transcription Factor Gene OsERF922. PLoS ONE, 11(4), e0154027. https://doi.org/10.1371/journal.pone.0154027 Wang, L., Ran, L., Hou, Y., Tian, Q., Li, C., Liu, R., Fan, D., & Luo, K. (2017). The transcription factor MYB115 contributes to the regulation of proanthocyanidin biosynthesis and enhances fungal resistance in poplar. New Phytologist, 215(1), 351–367. https://doi.org/10.1111/nph.14569 Wang, Q., Cobine, P. A., & Coleman, J. J. (2018). Efficient genome editing in Fusarium oxysporum based on CRISPR/Cas9 ribonucleoprotein complexes. Fungal Genetics and Biology, 117, 21–29. https://doi.org/10.1016/j.fgb.2018.05.003 Wang, T., Wei, J. J., Sabatini, D. M., & Lander, E. S. (2014). Genetic Screens in Human Cells Using the CRISPR-Cas9 System. Science, 343(6166), 80–84. https://doi.org/10.1126/science.1246981 Wang, W., Xue, Z., Miao, J., Cai, M., Zhang, C., Li, T., Zhang, B., Tyler, B. M., & Liu, X. (2019). PcMuORP1, an Oxathiapiprolin-Resistance Gene, Functions as a Novel Selection Marker for Phytophthora Transformation and CRISPR/Cas9 Mediated Genome Editing. Frontiers in Microbiology, 10, 2402. https://doi.org/10.3389/fmicb.2019.02402 Wang, X., Le, N., Denoth-Lippuner, A., Barral, Y., & Kroschewski, R. (2016). Asymmetric partitioning of transfected DNA during mammalian cell division. Proceedings of the National Academy of Sciences of the United States of America, 113(26), 7177–7182. https://doi.org/10.1073/pnas.1606091113 Wang, Y., Cheng, X., Shan, Q., Zhang, Y., Liu, J., Gao, C., & Qiu, J.-L. (2014). Simultaneous editing of three homoeoalleles in hexaploid bread wheat confers heritable resistance to 180 powdery mildew. Nature Biotechnology, 32(9), Article 9. https://doi.org/10.1038/nbt.2969 Wang, Z., Ke, Q., Tao, K., Li, Q., Xia, Y., Bao, J., & Chen, Q. (2022). Activity and Point Mutation G699V in PcoORP1 Confer Resistance to Oxathiapiprolin in Phytophthora colocasiae Field Isolates. Journal of Agricultural and Food Chemistry, 70(44), 14140–14147. https://doi.org/10.1021/acs.jafc.2c06707 Wang, Z., Tyler, B. M., & Liu, X. (2018). Protocol of Phytophthora capsici Transformation Using the CRISPR-Cas9 System. In W. Ma & T. Wolpert (Eds.), Plant Pathogenic Fungi and Oomycetes: Methods and Protocols (pp. 265–274). Springer. https://doi.org/10.1007/978-1-4939-8724-5_17 Waskom, M. L. (2021). seaborn: Statistical data visualization. Journal of Open Source Software, 6(60), 3021. https://doi.org/10.21105/joss.03021 Webber, J. F., Mullett, M., & Brasier, C. M. (2010). Dieback and mortality of plantation Japanese larch (Larix kaempferi) associated with infection by Phytophthora ramorum. New Disease Reports, 22, 19. https://doi.org/10.5197/j.2044-0588.2010.022.019 Wedge, M.-È., Naruzawa, E. S., Nigg, M., & Bernier, L. (2016). Diversity in yeast-mycelium dimorphism response of the Dutch elm disease pathogens: The inoculum size effect. Canadian Journal of Microbiology, 62(6), 525–529. https://doi.org/10.1139/cjm-2015-0795 Weiland, J. E., Nelson, A. H., & Hudler, G. W. (2010). Aggressiveness of Phytophthora cactorum, P. citricola I, and P. plurivora from European Beech. Plant Disease, 94(8), 1009–1014. https://doi.org/10.1094/PDIS-94-8-1009 Weiland, J. J. (2003). Transformation of Pythium aphanidermatum to geneticin resistance. Current Genetics, 42(6), 344–352. https://doi.org/10.1007/s00294-002-0359-y Westphal, M. I., Browne, M., MacKinnon, K., & Noble, I. (2008). The link between international trade and the global distribution of invasive alien species. Biological Invasions, 10(4), 391–398. https://doi.org/10.1007/s10530-007-9138-5 Wheeler, D. L., Church, D. M., Federhen, S., Lash, A. E., Madden, T. L., Pontius, J. U., Schuler, G. D., Schriml, L. M., Sequeira, E., Tatusova, T. A., & Wagner, L. (2003). Database resources of the National Center for Biotechnology. Nucleic Acids Research, 31(1), 28–33. Wiedenheft, B., Sternberg, S. H., & Doudna, J. A. (2012). RNA-guided genetic silencing systems in bacteria and archaea. Nature, 482(7385), Article 7385. https://doi.org/10.1038/nature10886 Wingfield, M. J., Brockerhoff, E. G., Wingfield, B. D., & Slippers, B. (2015). Planted forest health: The need for a global strategy. Science, 349(6250), 832–836. https://doi.org/10.1126/science.aac6674 Woo, J. W., Kim, J., Kwon, S. I., Corvalán, C., Cho, S. W., Kim, H., Kim, S.-G., Kim, S.-T., Choe, S., & Kim, J.-S. (2015). DNA-free genome editing in plants with preassembled CRISPR-Cas9 ribonucleoproteins. Nature Biotechnology, 33(11), 1162–1164. https://doi.org/10.1038/nbt.3389 Wu, D., Navet, N., Liu, Y., Uchida, J., & Tian, M. (2016). Establishment of a simple and efficient Agrobacterium-mediated transformation system for Phytophthora palmivora. BMC Microbiology, 16(1), 204. https://doi.org/10.1186/s12866-016-0825-1 Wullschleger, S. D., Tuskan, G. A., & DiFazio, S. P. (2002). Genomics and the tree physiologist. Tree Physiology, 22(18), 1273–1276. https://doi.org/10.1093/treephys/22.18.1273 181 Xu, C., Fu, X., Liu, R., Guo, L., Ran, L., Li, C., Tian, Q., Jiao, B., Wang, B., & Luo, K. (2017). PtoMYB170 positively regulates lignin deposition during wood formation in poplar and confers drought tolerance in transgenic Arabidopsis. Tree Physiology, 37(12), 1713–1726. https://doi.org/10.1093/treephys/tpx093 Yang, L., Zhao, X., Ran, L., Li, C., Fan, D., & Luo, K. (2017). PtoMYB156 is involved in negative regulation of phenylpropanoid metabolism and secondary cell wall biosynthesis during wood formation in poplar. Scientific Reports, 7(1), Article 1. https://doi.org/10.1038/srep41209 Yang, M., Duan, S., Mei, X., Huang, H., Chen, W., Liu, Y., Guo, C., Yang, T., Wei, W., Liu, X., He, X., Dong, Y., & Zhu, S. (2018). The Phytophthora cactorum genome provides insights into the adaptation to host defense compounds and fungicides. Scientific Reports, 8, 6534. https://doi.org/10.1038/s41598-018-24939-2 Yang, X., Tyler, B. M., & Hong, C. (2017). An expanded phylogeny for the genus Phytophthora. IMA Fungus, 8(2), Article 2. https://doi.org/10.5598/imafungus.2017.08.02.09 Ye, J., Coulouris, G., Zaretskaya, I., Cutcutache, I., Rozen, S., & Madden, T. L. (2012). Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics, 13(1), Article 1. https://doi.org/10.1186/1471-2105-13-134 Yelton, M. M., Hamer, J. E., & Timberlake, W. E. (1984). Transformation of Aspergillus nidulans by using a trpC plasmid. Proceedings of the National Academy of Sciences of the United States of America, 81(5), 1470–1474. Yin, T.-M., DiFazio, S. P., Gunter, L. E., Jawdy, S. S., Boerjan, W., & Tuskan, G. A. (2004). Genetic and physical mapping of Melampsora rust resistance genes in Populus and characterization of linkage disequilibrium and flanking genomic sequence. New Phytologist, 164(1), 95–105. https://doi.org/10.1111/j.1469-8137.2004.01161.x Zhang, Q., Li, W., Yang, J., Xu, J., Meng, Y., & Shan, W. (2020). Two Phytophthora parasitica cysteine protease genes, PpCys44 and PpCys45, trigger cell death in various Nicotiana spp. And act as virulence factors. Molecular Plant Pathology, 21(4), 541–554. https://doi.org/10.1111/mpp.12915 Zhang, S., & Voigt, C. A. (2018). Engineered dCas9 with reduced toxicity in bacteria: Implications for genetic circuit design. Nucleic Acids Research, 46(20), 11115–11125. https://doi.org/10.1093/nar/gky884 Zhang, T., Zheng, Q., Yi, X., An, H., Zhao, Y., Ma, S., & Zhou, G. (2018). Establishing RNA virus resistance in plants by harnessing CRISPR immune system. Plant Biotechnology Journal, 16(8), 1415–1423. https://doi.org/10.1111/pbi.12881 Zhang, Y., Bai, Y., Wu, G., Zou, S., Chen, Y., Gao, C., & Tang, D. (2017). Simultaneous modification of three homoeologs of TaEDR 1 by genome editing enhances powdery mildew resistance in wheat. The Plant Journal, 91(4), 714–724. https://doi.org/10.1111/tpj.13599 Zhang, Y., Ge, X., Yang, F., Zhang, L., Zheng, J., Tan, X., Jin, Z.-B., Qu, J., & Gu, F. (2014). Comparison of non-canonical PAMs for CRISPR/Cas9-mediated DNA cleavage in human cells. Scientific Reports, 4(1), Article 1. https://doi.org/10.1038/srep05405 Zhang, Y., Liang, Z., Zong, Y., Wang, Y., Liu, J., Chen, K., Qiu, J.-L., & Gao, C. (2016). Efficient and transgene-free genome editing in wheat through transient expression of CRISPR/Cas9 DNA or RNA. Nature Communications, 7(1), Article 1. https://doi.org/10.1038/ncomms12617 182 Zhao, Z., Liu, H., Wang, C., & Xu, J.-R. (2013). Comparative analysis of fungal genomes reveals different plant cell wall degrading capacity in fungi. BMC Genomics, 14(1), 274. https://doi.org/10.1186/1471-2164-14-274 Zhou, X., Jacobs, T. B., Xue, L.-J., Harding, S. A., & Tsai, C.-J. (2015). Exploiting SNPs for biallelic CRISPR mutations in the outcrossing woody perennial Populus reveals 4-coumarate:CoA ligase specificity and redundancy. New Phytologist, 208(2), 298–301. https://doi.org/10.1111/nph.13470 Zuris, J. A., Thompson, D. B., Shu, Y., Guilinger, J. P., Bessen, J. L., Hu, J. H., Maeder, M. L., Joung, J. K., Chen, Z.-Y., & Liu, D. R. (2015). Cationic lipid-mediated delivery of proteins enables efficient protein-based genome editing in vitro and in vivo. Nature Biotechnology, 33(1), Article 1. https://doi.org/10.1038/nbt.3081 183 Appendices Appendix A Supplementary Methods and Results for Chapter 2 The materials provided in Appendix A directly reflect the Supplementary Materials provided with the original publication of Chapter 2: Dort et al. 2023 (Scientific Reports 13: 17203). The headings, table numbers, and figure numbers have been modified to reflect the formatting of this dissertation document. A.1 Results of statistical analyses run on DendroNet AUCs DendroNet’s ability to predict fungal lifestyles varied greatly depending on the lifestyle and genomic features included in the model, with a range in AUC scores from 0.204 ± 0.185 (endomycorrhizal fungi using SMCs+transporters+transfactors feature set) to 1.000 ± 0.000 (obligate biotrophs in 26 feature sets) (Data A.4.2). Statistical analysis with a Friedman test on the lifestyle prediction results showed a significant difference in DendroNet’s AUC scores depending on the genomic feature set used (χ² = 128.361, p = 3.932e-14). However, post-hoc analysis with a Nemenyi test revealed that these significant differences arose only between a few of the best- and worst-performing feature sets; the differences in DendroNet results between most of the 31 genomic feature sets were not statistically significant (Data A.4.3). Two consistent patterns emerged from the Nemenyi test: the first was that the SMCs alone produced significantly lower AUC scores from DendroNet when compared to the top eight feature sets, and the second was that these top eight feature sets all included the CAZyme annotation data (Data A.4.3). 184 DendroNet’s performance in predicting fungal phytopathogenic traits also varied greatly, with AUC scores ranging from 0.294 ± 0.015 (gymnosperm pathogens using SMCs+transfactors feature set) to 0.961 ± 0.004 (foliar pathogens using the CAZymes+MEROPS+SMCs feature set) (Data A.4.2). As with the lifestyles, statistical analysis with a Friedman test on the trait predictions showed a significant difference in AUC scores depending on the genomic feature set used (χ² = 59.584, p = 0.001), though the Nemenyi post-hoc test showed that these differences arose only between the poorest feature set (SMCs alone) and the top three feature sets (Data A.4.3). A.2 Supplementary tables The supplementary tables referred to in the main text of Chapter 2 are provided below. 185 Supplementary Table A.2.1 The list of 24 lifestyles to which fungal species were assigned in FunLifeDB based on a literature search, with the corresponding definitions. The lifestyles that were used for analyses in the current study are highlighted in yellow. Lifestyles (Abbreviation) Definition Acidophile (A) Fungi that can survive and grow optimally in highly acidic (pH < 3-4) environments. Alkaliphilic (AL) Fungi that can survive and grow optimally in highly alkaline (pH > 9) environments. Biotroph (B) Pathogenic fungi that grow, and often reproduce, within living host tissues. Black Yeast (BY) Fungi with highly melanized cell walls that can tolerate a wide range of extreme environments (polyextremotolerant). Brown Rot (BR) Wood-decaying fungi in the Basidiomycota that selectively degrade wood carbohydrates, but not lignin. Coprophilous (C) Saprobic fungi that grow and assimilate nutrients from animal dung. Ectomycorrhizae (ECM) Fungi that form a symbiotic association with plants by growing intercellularly in the roots. Endolichenic (EL) Fungi that grow asymptomatically within lichen thalli, akin to endophytes, but are not partners in the stable lichen symbiosis. Endomycorrhizae (ENM) Fungi that form a symbiotic association with plants by growing both inter- and intracellularly in the roots. Endophyte (E) Fungi that grow within plant tissues but are non-pathogenic. Epiphyte (EP) Fungi that grow outside or on plant tissues but are non-pathogenic. Hemibiotroph (HB) Pathogenic fungi that initially act as biotrophs within their plant host and then switch to a necrotrophic lifestyle. Lichenicolous (L) Fungi that grow obligately on lichens – can be pathogenic or saprotrophic. Lichenized (LF) Fungi that form a mutualistic symbiosis with algae as part of a lichen thallus. Mycoparasite (MP) Fungi that act as parasites of other fungi. Necrotroph (N) Pathogenic fungi that secrete toxins to kill their plant host and subsequently assimilate nutrients from the dead tissues. Obligate Biotroph (OB) Pathogenic fungi that require living host tissues to grow and reproduce – cannot survive outside their host. Opportunistic/Latent Pathogen (O) Fungi that only act as pathogens under certain environmental conditions, or towards hosts that have already been weakened by other biotic or abiotic agents. Orchid Mycorrhizal (ORM) Fungi that form mycorrhizal associations with orchid species. Parasitic (P) Fungi that grow in or on a living host; the fungus benefits from the association but there is a fitness cost for the host. Parasitic associations can range from weak to highly aggressive. Pyrophilous (PY) Fungi that produce fruiting bodies in post-fire environments. Saprotroph (S) Fungi that grow and feed on dead and decaying plant matter. Thermophile (T) Fungi that can survive and grow optimally in extremely high temperatures. White Rot (WR) Wood-decaying fungi in the Basidiomycota that degrade all wood components, including lignin. 186 Supplementary Table A.2.2 The ecological traits assigned to pathogenic fungi in FunLifeDB; host type was assigned to all pathogens whereas only plant pathogens received additional assignments for plant host information and infected/inoculated tissues. Ecological Traits Host Type Plant Fungus Vertebrate Invertebrate Plant Host Information Gymnosperms Angiosperms Infected/Diseased Tissues Main Stem Leaves Roots Supplementary Table A.2.3 The balance of genomes across pathogenic groups as well as the tested lifestyles and traits in both the original FunLifeDB and the reduced dataset of published genomes used for analyses. Number of Genomes in Original Database Number of Genomes in Reduced Database Pathogens 251 177 Non-pathogens 265 177 Pathogenicity unknown 66 33 Plant pathogens 196 138 Saprotrophs 369 241 Ectomycorrhizae 36 32 Endomycorrhizae 7 7 Necrotrophs 80 56 Hemibiotrophs 78 52 Facultative biotrophs 43 37 Obligate biotrophs 24 21 Gymnosperm pathogens 34 24 Angiosperm pathogens 181 128 Stem pathogens 131 99 Foliar pathogens 134 103 Root pathogens 64 40 Total species 533 355 Total genomes 582 387 (362 published, 8 genomes from R.H. as PI, 17 genomes from F.M. as PI) 187 A.3 Supplementary figures The supplementary figures referred to in the main text of Chapter 2 are provided below. Supplementary Figure A.3.1 Clustered heatmap of the CAZyme enzyme classes associated with DendroNet’s predictions of phytopathogenic lifestyles and traits. Also included is the ‘Total_CAZys’ feature, which is the total number of CAZyme genes annotated in each genome regardless of enzyme class. The values in the heatmap are the correlation directions for each CAZyme class input into DendroNet as a machine learning feature, which indicate whether an increase (positive correlation, red) or decrease (negative correlation, blue) in the number of genes in a CAZyme class was associated with a lifestyle or trait. Abbreviations: S, saprotrophs; ECM, ectomycorrhizal; ENM: endomycorrhizal; P, pathogens; PP, plant pathogens; N, necrotrophs; HB, hemibiotrophs; B, biotrophs; OB, obligate biotrophs; AP, angiosperm pathogens; GP, gymnosperm pathogens; FP, foliar pathogens; SP, stem pathogens; RP, root pathogens. 188 Supplementary Figure A.3.2 Clustered heatmap of the peptidase (MEROPS) clans and families associated with DendroNet’s predictions of phytopathogenic lifestyles and traits. Also included is the ‘Total_MEROPS’ feature, which is the total number of peptidase genes annotated in each genome regardless of clan or family. The values in the heatmap are the correlation directions for each MEROPS clan/family input into DendroNet as a machine learning feature, which indicate whether an increase (positive correlation, red) or decrease (negative correlation, blue) in the number of genes in a group was associated with a lifestyle or trait. Abbreviations: S, saprotrophs; ECM, ectomycorrhizal; ENM: endomycorrhizal; P, pathogens; PP, plant pathogens; N, necrotrophs; HB, hemibiotrophs; B, biotrophs; OB, obligate biotrophs; AP, angiosperm pathogens; GP, gymnosperm pathogens; FP, foliar pathogens; SP, stem pathogens; RP, root pathogens. 189 Supplementary Figure A.3.3 Clustered heatmap of the secondary metabolite cluster (SMC) classes associated with DendroNet’s predictions of phytopathogenic lifestyles and traits. Also included is the ‘Total_SMCs’ feature, which is the total number of SMC genes annotated in each genome regardless of class. The values in the heatmap are the correlation directions for each SMC class input into DendroNet as a machine learning feature, which indicate whether an increase (positive correlation, red) or decrease (negative correlation, blue) in the number of genes in a class was associated with a lifestyle or trait. Abbreviations: S, saprotrophs; ECM, ectomycorrhizal; ENM: endomycorrhizal; P, pathogens; PP, plant pathogens; N, necrotrophs; HB, hemibiotrophs; B, biotrophs; OB, obligate biotrophs; AP, angiosperm pathogens; GP, gymnosperm pathogens; FP, foliar pathogens; SP, stem pathogens; RP, root pathogens. 190 Supplementary Figure A.3.4 Clustered heatmap of the transporter superfamilies and families associated with DendroNet’s predictions of phytopathogenic lifestyles and traits. Also included is the ‘Total_Transporters’ feature, which is the total number of SMC genes annotated in each genome regardless of family. The values in the heatmap are the correlation directions for each transporter family input into DendroNet as a machine learning feature, which indicate whether an increase (positive correlation, red) or decrease (negative correlation, blue) in the number of genes in a family was associated with a lifestyle or trait. Abbreviations: S, saprotrophs; ECM, ectomycorrhizal; ENM: endomycorrhizal; P, pathogens; PP, plant pathogens; N, necrotrophs; HB, hemibiotrophs; B, biotrophs; OB, obligate biotrophs; AP, angiosperm pathogens; GP, gymnosperm pathogens; FP, foliar pathogens; SP, stem pathogens; RP, root pathogens. 191 Supplementary Figure A.3.5 Clustered heatmap of the transcription factor (TF) families associated with DendroNet’s predictions of phytopathogenic lifestyles and traits. Also included is the ‘Total_TransFactors’ feature, which is the total number of TF genes annotated in each genome regardless of family. The values in the heatmap are the correlation directions for each TF family input into DendroNet as a machine learning feature, which indicate whether an increase (positive correlation, red) or decrease (negative correlation, blue) in the number of genes in a family was associated with a lifestyle or trait. Abbreviations: S, saprotrophs; ECM, ectomycorrhizal; ENM: endomycorrhizal; P, pathogens; PP, plant pathogens; N, necrotrophs; HB, hemibiotrophs; B, biotrophs; OB, obligate biotrophs; AP, angiosperm pathogens; GP, gymnosperm pathogens; FP, foliar pathogens; SP, stem pathogens; RP, root pathogens. 192 A.4 Supplementary data files The supplementary data files referred to in the main text of Chapter 2 are provided below. Data A.4.1 Reduced version of FunLifeDB with the 387 genomes (355 species) used for analyses in this study. Also included are the raw annotation data extracted from MycoCosm for carbohydrate active enzymes, peptidases, secondary metabolite clusters, transporters, and transcription factors. These data were used as input for both the principal component analyses and the DendroNet machine learning analyses. Note: the data are provided in a separate Excel document. Data A.4.2 Area under the receiver operating characteristic curve (AUC) scores obtained with DendroNet for the parsimony models (phylogenetic placement only, no genome annotation data) and feature+parsimony models (includes phylogenetic placement and genome annotation data) from all 31 feature sets for all lifestyles and phytopathogenic traits tested. The mean and standard deviation of the AUCs from each feature set were calculated, and the results for the feature+parsimony models are listed from highest mean to lowest mean AUC. Note: the data are provided in a separate Excel document. Data A.4.3 Results from all statistical analyses on DendroNet AUCs for both the lifestyles and phytopathogenic traits. Note: the data are provided in a separate Excel document. Data A.4.4 DendroNet feature importances from the feature+parsimony models for all five genome annotation groups. The raw, fraction, and correlation values for the individual genomic features (gene families) that improved DendroNet’s predictive performance of a lifestyle or trait over the parsimony model are reported for each annotation. A fraction value greater than 1.0 for a feature indicates that a DendroNet model using only that individual feature produced better AUC scores than a model using a combination of all features from an annotation group. Note: the data are provided in a separate Excel document. 193 Data A.4.5 Correlation directions for all genomic features (gene families) included in the DendroNet feature+parsimony models, regardless of prediction performance relative to the parsimony model, for all five genome annotation groups. A positive correlation (+) indicates that a lifestyle or trait was associated with an increased number of genes relative to other lifestyles and traits, and a negative correlation (-) indicates that a lifestyle or trait was associated with a decreased number of genes. Note: the data are provided in a separate Excel document. Data A.4.6 A pruned, Newick format version of the phylogenetic tree downloaded from MycoCosm for input into DendroNet models. This reduced tree depicts the phylogenetic relationships between the 387 fungal genomes analysed in this study. Note: the tree is provided as a separate .txt file. 194 Appendix B Supplementary Methods and Results for Chapters 4 and 5 B.1 Recipes for Growth Media and Transformation Solutions used in Chapters 4 and 5 Growth Media 5X V8 Medium: Component Amount V8 juice 340 mL Calcium carbonate (CaCO3) 5 g Centrifuged at 3220 × g (~4000 rpm) for 15 minutes to clarify; supernatant retained. Not autoclaved. Stored in the fridge (~4°C) for up to three months. 1X (20%) V8 Broth/Agar (V8B/V8A): Component Amount 5X V8 medium 200 mL dH2O 800 mL Agar (for V8A) 15 g (1.5%) Autoclaved (40-minute liquid cycle) and stored in the fridge (~4°C) until used. Basic Pea Broth (PB): Component Amount Frozen sweet peas 120 g dH2O 800 mL Autoclaved (40-minute liquid cycle) and filtered through four layers of cheesecloth. Used as base for pea-mannitol and nutrient pea media (see recipes below). 195 Pea-0.5 M Mannitol (PM) & V8-0.5 M Mannitol (VM) Medium: Component Amount D-mannitol 91.1 g Calcium carbonate (CaCO3) 2 g Calcium chloride (CaCl2) 1 g Basic pea broth for PM -or- 1X V8 medium for VM 800 mL Centrifuged at 3220 × g (~4000 rpm) for 10 minutes to clarify; supernatant retained. Volume brought to 1000 mL with dH2O, and agar added (1% or 1.5%) for solid medium. Autoclaved (40-minute liquid cycle) and stored in the fridge (~4°C) until used. Nutrient Pea Broth/Agar (NPB/NPA) & Nutrient V8 Broth/Agar (NVB/NVA): Component Amount D-glucose 5.0 g D-mannitol 5.0 g D-sorbitol 5.0 g Potassium nitrate (KNO3) 3.0 g Yeast extract 2.0 g Calcium carbonate (CaCO3) 2.0 g Potassium dihydrogen phosphate (KH2PO4) 1.0 g Dipotassium hydrogen phosphate (K2HPO4) 1.0 g Magnesium sulphate (MgSO4) 0.5 g Calcium chloride (CaCl2) 0.1 g Basic pea broth for NPB -or- 1X V8 medium for NVB 800 mL Trace elements* 2.0 mL Vitamin stock* 2.0 mL Centrifuged at 3220 × g (~4000 rpm) for 10 minutes to clarify; supernatant retained. Volume brought to 1000 mL with dH2O, and agar added (1.5%) for solid medium. Autoclaved (40-minute liquid cycle), trace elements and vitamin stock added, and final NPB medium stored in the fridge (~4°C) until used. *Added after autoclaving – see recipes below 196 Trace Elements: Component Amount Iron (III) citrate trihydrate (FeC6H5O7•3H2O) -or- Iron (III) citrate (FeC6H5O7) 0.215 g 0.176 g Zinc sulphate heptahydrate (ZnSO4•7H2O) 0.150 g Copper sulphate pentahydrate (CuSO4•5H2O) 0.030 g Manganese sulphate monohydrate (MnSO4•H2O) 0.015 g Boric acid (H3BO3) 0.010 g Molybdenum trioxide (MoO3) 0.007 g Milli-QⓇ water 400 mL Stirred until dissolved. The final solution is filter sterilized, wrapped in aluminum foil (light sensitive) and stored at 4°C for up to 1 year. Vitamin Stock: Component Amount Thiamine-HCl 0.3800 g Pyridoxine-HCl 0.1800 g Nicotinic acid 0.0600 g Riboflavin 0.0150 g I-inositol (myo-inositol) 0.0120 g Biotin 0.0002 g Folic acid 0.0002 g Coconut milk (optional) 50 mL Milli-QⓇ water 300 mL Stirred until dissolved. The final solution is filter sterilized, wrapped in aluminum foil (light sensitive) and stored at 4°C for up to 1 year. 197 Transformation Solutions 0.8 M Mannitol: Component Amount D-mannitol 145.76 g Milli-QⓇ water 700 mL Stirred until dissolved. Volume brought to 1000 mL with Milli-QⓇ water, stirred, and split into 250 mL aliquots. Autoclaved (40-minute liquid cycle) and stored at room temperature until used. 0.5 M CaCl2: Component Amount Calcium chloride (CaCl2) 7.35 g Milli-QⓇ water 70 mL Stirred until dissolved. Volume brought to 100 mL with Milli-QⓇ water and stirred. Filter sterilized and stored at room temperature until used. 1 M KOH: Component Amount Potassium hydroxide (KOH) 14.03 g Milli-QⓇ water 200 mL Stirred until dissolved. Volume brought to 250 mL with Milli-QⓇ water and stirred. Stored at room temperature (used to adjust pH of MES solution). 0.5 M MES KOH: Component Amount 4-Morpholineethanesulfonic acid (MES) 4.88 g Milli-QⓇ water 40 mL Stirred until dissolved, pH adjusted to 5.7 using 1 M KOH. Volume brought to 50 mL with Milli-QⓇ water and stirred. Filter sterilized and stored at room temperature until used. 198 0.5 M KCl: Component Amount Potassium chloride (KCl) 1.86 g Milli-QⓇ water 40 mL Stirred until dissolved. Volume brought to 50 mL with Milli-QⓇ water and stirred. Filter sterilized and stored at room temperature until used. PEG (40% v/v) Solution: Component Amount Polyethylene glycol (PEG) 4000 24 g 0.8 M mannitol 15 mL 0.5 M CaCl2 12 mL Milli-QⓇ water 12 mL Stirred to dissolve and filter sterilized. Used immediately (can be stored at 4°C for a maximum of two weeks). Enzyme Solution: Component Amount Lysing enzymes from Trichoderma harzianum 0.2 g Cellulase 0.2 g 0.8 M mannitol 20 mL 0.5 M KCl 1.6 mL 0.5 M MES KOH (pH 5.7) 1.6 mL 0.5 M CaCl2 0.8 mL Volume brought to 40 mL Milli-QⓇ water. Final solution filter sterilized and stored at 4°C until use (maximum one day). 199 W5 Solution: Component Amount Glucose 7.8 g Calcium chloride (CaCl2) 4.6 g Sodium chloride (NaCl) 2.25 g Potassium chloride (KCl) 0.093 g Milli-QⓇ water 200 mL Stirred until dissolved. Volume brought to 250 mL with Milli-QⓇ water. Final solution split into 50 mL aliquots, autoclaved (40-minute liquid cycle), and stored at 4°C for a maximum of six months. MMg Solution: Component Amount D-mannitol 18.22 g Magnesium chloride hexahydrate (MgCl2•6H2O) 0.76 g 0.5 M MES KOH (pH 5.7) 2 mL Milli-QⓇ water 200 mL Stirred until dissolved. Volume brought to 250 mL with Milli-QⓇ water. Final solution split into 50 mL aliquots, autoclaved (40-minute liquid cycle), and stored at 4°C for a maximum of six months. 200 B.2 Conditions Tested for Phytophthora Transformations in Chapter 4 Beyond testing the three different plasmids (pYF2-PsNLS-Cas9-GFP, pYF515, pGFPN), a variety of protocol modifications were tested in the transformations, including changes in culture and recovery media, enzyme digestion time, plasmid DNA concentration, and G418 concentration for transformant selection. All the transformation conditions tested for each of the five forest Phytophthora species and the control species P. sojae are summarized in Data B.2.1. Data B.2.1 Summary of the conditions tested in plasmid transformations of five forest Phytophthora pathogens (P.cactorum, P. cinnamomi, P. cryptogea, P. ramorum, and P. syringae) and a control species P. sojae. The control transformations in P. sojae are listed first followed by the transformations in the five forest Phytophthora organized in alphabetical order by species name. Note: the table is provided as a separate Excel document–please open the corresponding supplementary file to view the full table. B.3 Transformation Plasmids The sequences for all three transformation plasmids tested in Chapter 4 are provided below in FASTA format in Supplementary Tables B.3.1, B.3.2, and B.3.3. 201 Supplementary Table B.3.1 Sequence of the pYF515 transformation plasmid. >pYF515 (13,032 bp) AAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCG GAAGCATAAAGTGTAAAGCCTGGGGTGCCTAATGAGTGAGCTAACTCACATTAATTGCGTTGCGCTCACTGCCCGCTTTC CAGTCGGGAAACCTGTCGTGCCAGCTGCATTAATGAATCGGCCAACGCGCGGGGAGAGGCGGTTTGCGTATTGGGCGCTC TTCCGCTTCCTCGCTCACTGACTCGCTGCGCTCGGTCGTTCGGCTGCGGCGAGCGGTATCAGCTCACTCAAAGGCGGTAA TACGGTTATCCACAGAATCAGGGGATAACGCAGGAAAGAACATGTGAGCAAAAGGCCAGCAAAAGGCCAGGAACCGTAAA AAGGCCGCGTTGCTGGCGTTTTTCCATAGGCTCCGCCCCCCTGACGAGCATCACAAAAATCGACGCTCAAGTCAGAGGTG GCGAAACCCGACAGGACTATAAAGATACCAGGCGTTTCCCCCTGGAAGCTCCCTCGTGCGCTCTCCTGTTCCGACCCTGC CGCTTACCGGATACCTGTCCGCCTTTCTCCCTTCGGGAAGCGTGGCGCTTTCTCAATGCTCACGCTGTAGGTATCTCAGT TCGGTGTAGGTCGTTCGCTCCAAGCTGGGCTGTGTGCACGAACCCCCCGTTCAGCCCGACCGCTGCGCCTTATCCGGTAA CTATCGTCTTGAGTCCAACCCGGTAAGACACGACTTATCGCCACTGGCAGCAGCCACTGGTAACAGGATTAGCAGAGCGA GGTATGTAGGCGGTGCTACAGAGTTCTTGAAGTGGTGGCCTAACTACGGCTACACTAGAAGGACAGTATTTGGTATCTGC GCTCTGCTGAAGCCAGTTACCTTCGGAAAAAGAGTTGGTAGCTCTTGATCCGGCAAACAAACCACCGCTGGTAGCGGTGG TTTTTTTGTTTGCAAGCAGCAGATTACGCGCAGAAAAAAAGGATCTCAAGAAGATCCTTTGATCTTTTCTACGGGGTCTG ACGCTCAGTGGAACGAAAACTCACGTTAAGGGATTTTGGTCATGAGATTATCAAAAAGGATCTTCACCTAGATCCTTTTA AATTAAAAATGAAGTTTTAAATCAATCTAAAGTATATATGAGTAAACTTGGTCTGACAGTTACCAATGCTTAATCAGTGA GGCACCTATCTCAGCGATCTGTCTATTTCGTTCATCCATAGTTGCCTGACTCCCCGTCGTGTAGATAACTACGATACGGG AGGGCTTACCATCTGGCCCCAGTGCTGCAATGATACCGCGGCTTCCACGCTCACCGGCTCCAGATTTATCAGCAATAAAC CAGCCAGCCGGAAGGGCCGAGCGCAGAAGTGGTCCTGCAACTTTATCCGCCTCCATCCAGTCTATTAATTGTTGCCGGGA AGCTAGAGTAAGTAGTTCGCCAGTTAATAGTTTGCGCAACGTTGTTGCCATTGCTACAGGCATCGTGGTGTCACGCTCGT CGTTTGGTATGGCTTCATTCAGCTCCGGTTCCCAACGATCAAGGCGAGTTACATGATCCCCCATGTTGTGCAAAAAAGCG GTTAGCTCCTTCGGTCCTCCGATCGTTGTCAGAAGTAAGTTGGCCGCAGTGTTATCACTCATGGTTATGGCAGCACTGCA TAATTCTCTTACTGTCATGCCATCCGTAAGATGCTTTTCTGTGACTGGTGAGTACTCAACCAAGTCATTCTGAGAATAGT GTATGCGGCGACCGAGTTGCTCTTGCCCGGCGTCAATACGGGATAATACCGCGCCACATAGCAGAACTTTAAAAGTGCTC ATCATTGGAAAACGTTCTTCGGGGCGAAAACTCTCAAGGATCTTACCGCTGTTGAGATCCAGTTCGATGTAACCCACTCG TGCACCCAACTGATCTTCAGCATCTTTTACTTTCACCAGCGTTTCTGGGTGAGCAAAAACAGGAAGGCAAAATGCCGCAA AAAAGGGAATAAGGGCGACACGGAAATGTTGAATACTCATACTCTTCCTTTTTCAATATTATTGAAGCATTTATCAGGGT TATTGTCTCATGAGCGGATACATATTTGAATGTATTTAGAAAAATAAACAAATAGGGGTTCCGCGCACATTTCCCCGAAA AGTGCCACCTGACGTCTAAGAAACCATTATTATCATGACATTAACCTATAAAAATAGGCGTATCACGAGGCCCTTTCGTC TCGCGCGTTTCGGTGATGACGGTGAAAACCTCTGACACATGCAGCTCCCGGAGACGGTCACAGCTTGTCTGTAAGCGGAT GCCGGGAGCAGACAAGCCCGTCAGGGCGCGTCAGCGGGTGTTGGCGGGTGTCGGGGCTGGCTTAACTATGCGGCATCAGA GCAGATTGTACACTGATGGACAAAGGGTCGCCTCTACGTACCCGCGACAATCTCGGCTTTTCCAATGTCGACAGCTGGCA TATCGCACGCGAGGTTAGGCGTCTTCAGCCGGCGTAACCACAAACCTCGAGTCAAGTACCCTTTTTCAGACCTTAATTTC CGTGTCCATTCCCTCGCGTTTACACGAAGGTTTCTGAGAAATACCACATGTCACAGAGAGAGAGAAGATTGGAGAACCTT CTCAACCTGACCCGTGCAGAGCGCTTTTGCGTCCTACCATCCGTTAATTATAGCACCACCACCACATGTCTCGCCGGAAC TCTCTTATCCGCTCTTTTTGATCGTCTTTTTTGCGCCCCATTAAAAGACATCTCTTGCACAGGGATCGTTTCTGATTGGC CATTTCACTTCCTCTTTTACATCCGAAAACCACCTGTTTGTGGGGTTGTGAAGCCACAACCGGACGCTCAAAAAGCCTCG CCCGACTCGCCCACGATCGGAAGGCTCATTCTCCTTTTCACTCTCACGTGCCCAAGTCCCAACCGACTCTTTTTCGACCT TCACTCTCACCGACAAACTAGCTCTTAATTAAATGATTGAACAAGATGGATTGCACGCAGGTTCTCCGGCCGCTTGGGTG GAGAGGCTATTCGGCTATGACTGGGCACAACAGACAATCGGCTGCTCTGATGCCGCCGTGTTCCGGCTGTCAGCGCAGGG GCGCCCGGTTCTTTTTGTCAAGACCGACCTGTCCGGTGCCCTGAATGAACTGCAGGACGAGCCAGCGCGGCTATCGTGGC TGGCCACGACGGGCGTTCCTTGCGCAGCTGTGCTCGACGTTGTCACTGAAGCGGGAAGGGACTGGCTGCTATTGGGCGAA GTGCCGGGGCAGGATCTCCTGTCATCTCACCTTGCTCCTGCCGAGAAAGTATCCATCATGGCTGATGCAATGCGGCGGCT GCATACGCTTGATCCGGCTACCTGCCCATTCGACCACCAAGCGAAACATCGCATCGAGCGAGCACGTACTCGGATGGAAG CCGGTCTTGTCGATCAGGATGATCTGGACGAAGAGCATCAGGGGCTCGCGCCAGCCGAACTGTTCGCCAGGCTCAAGGCG CGCATGCCCGACGGCGAGGATCTCGTCGTGACCCATGGCGATGCCTGCTTGCCGAATATCATGGTGGAAAATGGCCGCTT TTCTGGATTCATCGACTGTGGCCGGCTGGGTGTGGCGGACCGCTATCAGGACATAGCGTTGGCTACCCGTGATATTGCTG AAGAGCTTGGCGGCGAATGGGCTGACCGCTTCCTCGTGCTTTACGGTATCGCCGCTCCCGATTCGCAGCGCATCGCCTTC TATCGCCTTCTTGACGAGTTCTTCTGAGCGGCCGCAGGCGCGCCTTGACGTCTAACCGCGGGGTGCCTACTTCTTGTAAG CCGAAGGTCGAGCTGTGTGCACTGAGTGCGAAGTTGCAGATTTTGTGTCTATGCATTTAATACAATTTCCTTTTTTCCAT TTTCGTTGTTTTTTTTTGTGAAAGAACGATTGTTTTCAAAAGAAGGCCGTTTGTAGAACAATTTTTTTTTATACTAGCAT TTGGTATTCGAGTGTGACGAATCGGTGATGGCGCTTTACAAATGAAATATAACGCTGAACAAGCGCAGTGACAGCTTTAT TTTATAACAGAGAATAAAGACCAAAGTAGAGTAGACTGATGGCGGCTTGAAACAAAACTACGTGACTTCTAATGCAAAGA AAACCATACCCCACGCTACTTAGAATTGTTTCTCTTTGGTTGATGACCGCTATAGTCGTATGGAGTGATCCCTTTTTGTC AATATTTCGATTCTTGCTAAAACCGTGACTATTTTGAGGCAACTGTGTGTACTTAACATGGTAGAAATTTCTATGTTTAA GCCAGCTGACAATCATTCTTTATTTATCGTAAACCAACCAACGCGTAAGGAGAAAATACCGCATCAGGCGCCATTCGCCA TTCAGGCTGCGCAACTGTTGGGAAGGGCGATCGGTGCGGGCCTCTTCGCTATTACGCCAGCTGGCGAAAGGGGGATGTGC TGCAAGGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGGATCCCCCCAAT 202 TCCCCGGATCGTCTCATAGCTGCTCAAGACGTTTAGCAGATTGAGGATGCAGATCTTCTACTAATGAAACTACTGCAAAC TACCAACAAAGAGGCGACCACAAAATGGACAGCCTATGCCGAGATTTGTTTAAGATACTCGATTTGCCGTCCAATTTTCA TCAAGACGAAACACAGTGCATCGAACTGTGTTGTAAAAATTCATTTATTCTTGCAATTGTGACTCTCAAAGATTGATTAA ACTTGAAAGCTGGTGAAAGCTTTCCTTCTTTTGGCATGTTTGTTACTAAGGCGGCGTAAGCACAATAGGCCCAGACTCAT GCAAACCAGCCATAGTGACCTTCTCACTATGTTTCGCTTGAGATTTACTAGACTCTCGGGCATCTTCACAAAAGATGACA ACCGAATCAACAAATTAAGTCAACGCTAAATCAAATATTTAGAAAATAAATTTTATTGTCGAAAAAACTCTGCTTTGACA ATTTTCAAACTAACCAATTGCTAAGTATTCTAGTCGACACCGGTGTCCCATTCGCCATGCCGAAGCATGTTGCCCAGCCG GCGCCAGCGAGGAGGCTGGGACCATGCCGGCCAAAAGCACCGACTCGGTGCCACTTTTTCAAGTTGATAACGGACTAGCC TTATTTTAACTTGCTATTTCTAGCTCTAAAACCGAGACCCTAGCTAGCGTCACCGAACGAATTAAAACTATAGTAAGTAA AGACTGTCGATGTAGCCATGTGGGGCCAATAGCTGCGAGGCTCAGAGAGAGACAAGGGGGTCGCCACTTACCCCGAACGA TGTTCCAAGCAGAGCAGTCAGCAGAAAGTGAGGCTTGTTGCTGTCGGCGAGCGCAGCCTTGGGATCAACATGCGAGAGGA TAGTTGGCTGCGAGCTTGCCAGCGTGGAGAGACTTGGTTATTGTAGGCAACAAGTCCCGCATGAAGGGCGTTGTATGGTG CAAAAAAGAGGAGATGGAATCTTCAAACACTTTGATTGGCTGGACAGCGGGCGCGGACTACTGGCCAACCAAAATGTGAC CCCACTTGAGTGACACTTTCCCCACTATAGTGTCTTCAACTTGCACTGCAAGTGAAGTTGTTCTTGTTATTGCACTCGTT CTTACGTTATGGCTAGTACGCGCAGATGCAGAGCCTCCTCGGTGGTATTGCACGCCAACGAACCTCCATTCTCTGCGACA TAAGGCTGTGGCAGTGCGCCCAGGAACTCGTAGAGAATGGTCAACTGCGCCGTCACCACTCCCGTGTTCGAGTCTTCCGT CGTGACGAGGGCCGACTTCACCGTCGCCACATTCGACAGCGCAGTGCGGTACATCTCCATCAGCATCGTGTCCAGTTGGG CTGTCACTTCCGTCGCGTTCGCCGTGAAGTCCTCGACAAAACTGTACGCCCAGTTGCCGAGCTCGAAGCTGACGCTGGCG AATCCGCTCGCCAGGTCGAAGGTCAAGATCTGCTCTCCAGGACTGTAGTCCAGCACGATCTGCCTCCCTATTGTGGTGCT GTATGCCAGTGGACAGCTCTTCGCCCACAGACAAGCGTAGCTGGACTCGAGCAGCTCACGCACTGTACTCGACCATTCCG TGCCGTCCGATGGACTCAAGCAGTCCTGCAGCAGCCAAGTTGCATCAACGCTGCTGTCGATATCGTCCTTCTCCAGCATC GTAGCTATCAGTGTCAAGTCTGCCTCAAGCAGAACACCCATACACGCTGAGAACGCTCGGCACTCGGGCGAGCTGCTGCA ATCCTCCAATAACGTCGTGCACTCGCTGCAAGTTGGGGCTACATCATCCAGTTGTGAACCTGAATACAGTCCACCAAGTC GAGGCTGCTCCAAATCCAATGTTATGGTGATACCAGTGACTTCGTCTCCATCGTCGATCAGATCAGGAATCTCAGAGGTG ATAAATGCTGGTTCAGGCTGGCCAGTGGTGTCTACCACCGCGAGGTCGCCAAACTGCCGACGTCGCACAATGGGGATGAC AGTTGGGTCGTCAGGAACAAACTCCAGGTCTATGGTGAAGGGTGTGGTAGTGGTCATATCTTGCATCGAGTTTACAGCTT CGAACGTGAGACGGAAGCTCTGGGCATTGCCGCTTGATGACGACCCGCCTACGAAGAAGGACCACGAAGTATCCCCACCG AGACTCTGAGCACCAACCAGCACCCACGGAGCCGGATAAGTAGAAGTTGAACCAGCAACACTTGATCCGGGAGGAAGACC AATTGACGAAGATAGTGCAGCGTGAATGCTCTGCAGTTTACGAAGAAATATTGGAGCCAACTGATCGGCGCCTGTGAACC GGAGCTTGATACACAGTCTAGAAGTGGTGGAAGAGCACACGTCATCAGCATCTACCAATGCTCCAGATGATGCCGTCGAC TCAGGGTTATCGAAGCCGTAGATAAAACTTGTTTGATGTTCCAGCAATGATTCCGACGAGGTAATGAAGCCGTCGCTGAC CATGTCTATCTCTGGTACGGGATCTAACAGTCCAGGCAAATATTGCGGCCAAACGGGTGATCCATCGGCGTGGACGTCCA TCTCTCGCTCAATCAGCTCGAGGCTGCCGAGTGAATACAAAGAAATCGTGGAAGCGAGTTGCTGCGAGGAAATGACGGAA CGAATTCTTGCCATTGTTATGGTTGGTTTACGATAAATAAAGAATGATTGTCAGCTGGCTTAAACATAGAAATTTCTACC ATGTTAAGTACACACAGTTGCCTCAAAATAGTCACGGTTTTAGCAAGAATCGAAATATTGACAAAAAGGGATCACTCCAT ACGACTATAGCGGTCATCAACCAAAGAGAAACAATTCTAAGTAGCGTGGGGGATGGTTTCTTTGCATTAGAAGTCACGTA GTTTTGTTTCAAGCCGCCATCAGTCTACTCTACTTTGGTCTTTATTCTCTGTTATAAAATAAAGCTGTCACTGCGCTTGT TCAGCGTTATATTTCATTTGTAAAGCGCCATCACCGATTCGTCACACTCGAATACCAAATGCTAGTATAAAAAAAAATTG TTCTACAAACGGCCTTCTTTTGAAAACAATCGTTCTTTCACAAAAAAAAACAACGAAAATGGAAAAAAGGAAATTGTATT AAATGCATAGACACAAAATCTGCAACTTCGCACTCAGTGCACACAGCTCGACCTTCGGCTTACAAGAAGTAGGCTCCGGA ACCGGGCCCTTACTTAAGGTCGCCTCCCAGCTGAGACAGGTCGATCCGTGTCTCGTACAGGCCGGTGATGCTCTGGTGGA TCAGGGTGGCGTCCAGCACCTCTTTGGTGCTGGTGTACCTCTTCCGGTCGATGGTGGTGTCAAAGTACTTGAAGGCGGCA GGGGCTCCCAGATTGGTCAGGGTAAACAGGTGGATGATATTCTCGGCCTGCTCTCTGATGGGCTTATCCCGGTGCTTGTT GTAGGCGGACAGCACTTTGTCCAGATTAGCGTCGGCCAGGATCACTCTCTTGGAGAACTCGCTGATCTGCTCGATGATCT CGTCCAGGTAGTGCTTGTGCTGTTCCACAAACAGCTGTTTCTGCTCATTATCCTCGGGGGAGCCCTTCAGCTTCTCATAG TGGCTGGCCAGGTACAGGAAGTTCACATATTTGGAGGGCAGGGCCAGTTCGTTTCCCTTCTGCAGTTCGCCGGCAGAGGC CAGCATTCTCTTCCGGCCGTTTTCCAGCTCGAACAGGGAGTACTTAGGCAGCTTGATGATCAGGTCCTTTTTCACTTCTT TGTAGCCCTTGGCTTCCAGAAAGTCGATGGGATTCTTCTCGAAGCTGCTTCTTTCCATGATGGTGATCCCCAGCAGCTCT TTCACACTCTTCAGTTTCTTGGACTTGCCCTTTTCCACTTTGGCCACCACCAGCACAGAATAGGCCACGGTGGGGCTGTC GAAGCCGCCGTACTTCTTAGGGTCCCAGTCCTTCTTTCTGGCGATCAGCTTATCGCTGTTCCTCTTGGGCAGGATAGACT CTTTGCTGAAGCCGCCTGTCTGCACCTCGGTCTTTTTCACGATATTCACTTGGGGCATGCTCAGCACTTTCCGCACGGTG GCAAAATCCCGGCCCTTATCCCACACGATCTCCCCGGTTTCGCCGTTTGTCTCGATCAGAGGCCGCTTCCGGATCTCGCC GTTGGCCAGGGTAATCTCGGTCTTGAAAAAGTTCATGATGTTGCTGTAGAAGAAGTACTTGGCGGTAGCCTTGCCGATTT CCTGCTCGCTCTTGGCGATCATCTTCCGCACGTCGTACACCTTGTAGTCGCCGTACACGAACTCGCTTTCCAGCTTAGGG TACTTTTTGATCAGGGCGGTTCCCACGACGGCGTTCAGGTAGGCGTCGTGGGCGTGGTGGTAGTTGTTGATCTCGCGCAC TTTGTAAAACTGGAAATCCTTCCGGAAATCGGACACCAGCTTGGACTTCAGGGTGATCACTTTCACTTCCCGGATCAGCT TGTCATTCTCGTCGTACTTAGTGTTCATCCGGGAGTCCAGGATCTGTGCCACGTGCTTTGTGATCTGCCGGGTTTCCACC AGCTGTCTCTTGATGAAGCCGGCCTTATCCAGTTCGCTCAGGCCGCCTCTCTCGGCCTTGGTCAGATTGTCGAACTTTCT CTGGGTAATCAGCTTGGCGTTCAGCAGCTGCCGCCAGTAGTTCTTCATCTTCTTCACGACCTCTTCGGAGGGCACGTTGT CGCTCTTGCCCCGGTTCTTGTCGCTTCTGGTCAGCACCTTGTTGTCGATGGAGTCGTCCTTCAGAAAGCTCTGAGGCACG 203 ATATGGTCCACATCGTAGTCGGACAGCCGGTTGATGTCCAGTTCCTGGTCCACGTACATATCCCGCCCATTCTGCAGGTA GTACAGGTACAGCTTCTCGTTCTGCAGCTGGGTGTTTTCCACGGGGTGTTCTTTCAGGATCTGGCTGCCCAGCTCTTTGA TGCCCTCTTCGATCCGCTTCATTCTCTCGCGGCTGTTCTTCTGTCCCTTCTGGGTGGTCTGGTTCTCTCTGGCCATTTCG ATCACGATGTTCTCGGGCTTGTGCCGGCCCATCACTTTCACGAGCTCGTCCACCACCTTCACTGTCTGCAGGATGCCCTT CTTAATGGCGGGGCTGCCGGCCAGATTGGCAATGTGCTCGTGCAGGCTATCGCCCTGGCCGGACACCTGGGCTTTCTGGA TGTCCTCTTTAAAGGTCAGGCTGTCGTCGTGGATCAGCTGCATGAAGTTTCTGTTGGCGAAGCCGTCGGACTTCAGGAAA TCCAGGATTGTCTTGCCGGACTGCTTGTCCCGGATGCCGTTGATCAGCTTCCGGCTCAGCCTGCCCCAGCCGGTGTATCT CCGCCGCTTCAGCTGCTTCATCACTTTGTCGTCGAACAGGTGGGCATAGGTTTTCAGCCGTTCCTCGATCATCTCTCTGT CCTCAAACAGTGTCAGGGTCAGCACGATATCTTCCAGAATGTCCTCGTTTTCCTCATTGTCCAGGAAGTCCTTGTCCTTG ATAATTTTCAGCAGATCGTGGTATGTGCCCAGGGAGGCGTTGAACCGATCTTCCACGCCGGAGATTTCCACGGAGTCGAA GCACTCGATTTTCTTGAAGTAGTCCTCTTTCAGCTGCTTCACGGTCACTTTCCGGTTGGTCTTGAACAGCAGGTCCACGA TGGCCTTTTTCTGCTCGCCGCTCAGGAAGGCGGGCTTTCTCATTCCCTCGGTCACGTATTTCACTTTGGTCAGCTCGTTA TACACGGTGAAGTACTCGTACAGCAGGCTGTGCTTGGGCAGCACCTTCTCGTTGGGCAGGTTCTTATCGAAGTTGGTCAT CCGCTCGATGAAGCTCTGGGCGGAAGCGCCCTTGTCCACCACTTCCTCGAAGTTCCAGGGGGTGATGGTTTCCTCGCTCT TTCTGGTCATCCAGGCGAATCTGCTGTTTCCCCTGGCCAGAGGGCCCACGTAGTAGGGGATGCGGAAGGTCAGGATCTTC TCGATCTTTTCCCGGTTGTCCTTCAGGAATGGGTAAAAATCTTCCTGCCGCCGCAGAATGGCGTGCAGCTCTCCCAGGTG GATCTGGTGGGGGATGCTGCCGTTGTCGAAGGTCCGCTGCTTCCGCAGCAGGTCCTCTCTGTTCAGCTTCACGAGCAGTT CCTCGGTGCCGTCCATCTTTTCCAGGATGGGCTTGATGAACTTGTAGAACTCTTCCTGGCTGGCTCCGCCGTCAATGTAG CCGGCGTAGCCGTTCTTGCTCTGGTCGAAGAAAATCTCTTTGTACTTCTCAGGCAGCTGCTGCCGCACGAGAGCTTTCAG CAGGGTCAGGTCCTGGTGGTGCTCGTCGTATCTCTTGATCATAGAGGCGCTCAGGGGGGCCTTGGTGATCTCGGTGTTCA CTCTCAGGATGTCGCTCAGCAGGATGGCGTCGGACAGGTTCTTGGCGGCCAGAAACAGGTCGGCGTACTGGTCGCCGATC TGGGCCAGCAGGTTGTCCAGGTCGTCGTCGTAGGTGTCCTTGCTCAGCTGCAGTTTGGCATCCTCGGCCAGGTCGAAGTT GCTCTTGAAGTTGGGGGTCAGGCCCAGGCTCAGGGCAATCAGGTTTCCGAACAGGCCATTCTTCTTCTCGCCGGGCAGCT GGGCGATCAGATTTTCCAGCCGTCTGCTCTTGCTCAGTCTGGCAGACAGGATGGCCTTGGCGTCCACGCCGCTGGCGTTG ATGGGGTTTTCCTCGAACAGCTGGTTGTAGGTCTGCACCAGCTGGATGAACAGCTTGTCCACGTCGCTGTTGTCGGGGTT CAGGTCGCCCTCGATCAGGAAGTGGCCCCGGAACTTGATCATGTGGGCCAGGGCCAGATAGATCAGCCGCAGGTCGGCCT TGTCGGTGCTGTCCACCAGTTTCTTTCTCAGGTGGTAGATGGTGGGGTACTTCTCGTGGTAGGCCACCTCGTCCACGATG TTGCCGAAGATGGGGTGCCGCTCGTGCTTCTTATCCTCTTCCACCAGGAAGGACTCTTCCAGTCTGTGGAAGAAGCTGTC GTCCACCTTGGCCATCTCGTTGCTGAAGATCTCTTGCAGATAGCAGATCCGGTTCTTCCGTCTGGTGTATCTTCTTCTGG CGGTTCTCTTCAGCCGGGTGGCCTCGGCTGTTTCGCCGCTGTCGAACAGCAGGGCTCCGATCAGGTTCTTCTTGATGCTG TGCCGGTCGGTGTTGCCCAGCACCTTGAATTTCTTGCTGGGCACCTTGTACTCGTCGGTGATCACGGCCCAGCCCACAGA GTTGGTGCCGATGTCCAGGCCGATGCTGTACTTCTTGTCGGCTGCTGGGACTCCGTGGATACCGACCTTCCGCTTCTTCT TTGGGGCCATACTAGTGCGACGACGGACCTTGGTGTCGTCCTCGCGCTTGCGCTTGTGCATGCGACGACGGACCTTGGTG TCGTCCTCGCGCTTGCGCTTGTGCATGCGACGACGGACCTTGGTGTCGTCCTCGCGCTTGCGCTTGTGCATCCGCGGAGG CCTATCGATAAGCTTGATATCGAATTAATTCCTGCAGCCCGGGTTGTCGGTGAGAGTGAAGGTCGAAAAAGAGTCGGTTG GGACTTGGGCACGTGAGAGTGAAAAGGAGAATGAGCCTTCCGATCGTGGGCGAGTCGGGCGAGGCTTTTTGAGCGTCCGG TTGTGGCTTCACAACCCCACAAACAGGTGGTTTTCGGATGTAAAAGAGGAAGTGAAATGGCCAATCAGAAACGATCCCTG TGCAAGAGATGTCTTTAATGGGGCGCAAAAAAGACGATCAAAAAGAGCGGATAAGAGAGTTCCGGCGAGACATGTGGTGG TGGTGCTATAATTAACGGATGGTAGGACGCAAAAGCGCTCTGCACGGGTCAGGTTGAGAAGGTTCTCCAATCTTCTCTCT CTCTGTGACATGTGGTATTTCTCAGAAACCTTCGTGTAAACGCGAGGGAATGGACACGGAAATTAAGGTCTGAAAAAGGG TACTTGACTCGAGGTTTGTGGTTACGCCGGCTGAAGACGCCTAACCTCGCGTGCGATATGCCAGCTGTCGACATTGGAAA AGCCGAGATTGTCGCGGGTACGTAGAGGCGACCCTTTGTCCATCAGAGGGAGAGGAACCGACCAGTGGATAGGAGGGATA GACTTTATTTTGAGGGCGGTATAATGGTTATGGTAGCGACATTTTGATTTGACTTGAAAAAGATAAGAAAAAAAAAGGAA AAACTCGACTGCTTTGATGGGATAGCGATTTTTCCGTGGGTAGTCAGTTGGCCCTTGATTTTTACCATCAAGTACGTCCA TATGACGGGAATAGGGACGCGGGTGGATATTAGGAGCGGCAGCTGACGTGGCAGCTCATAGACAAGGGGGTGCTTGAAAG GCTGTCTATGGTGGTGACGCCGTCTTGCGGTGTGGCAATGAGTTGTATCATTTAATCGAATACGAGGTATAA 204 Supplementary Table B.3.2 Sequence of the pYF2-PsNLS-hSpCas9-GFP transformation plasmid. >pYF2-PsNLS-hSpCas9-GFP (12,509 bp) AAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCG GAAGCATAAAGTGTAAAGCCTGGGGTGCCTAATGAGTGAGCTAACTCACATTAATTGCGTTGCGCTCACTGCCCGCTTTC CAGTCGGGAAACCTGTCGTGCCAGCTGCATTAATGAATCGGCCAACGCGCGGGGAGAGGCGGTTTGCGTATTGGGCGCTC TTCCGCTTCCTCGCTCACTGACTCGCTGCGCTCGGTCGTTCGGCTGCGGCGAGCGGTATCAGCTCACTCAAAGGCGGTAA TACGGTTATCCACAGAATCAGGGGATAACGCAGGAAAGAACATGTGAGCAAAAGGCCAGCAAAAGGCCAGGAACCGTAAA AAGGCCGCGTTGCTGGCGTTTTTCCATAGGCTCCGCCCCCCTGACGAGCATCACAAAAATCGACGCTCAAGTCAGAGGTG GCGAAACCCGACAGGACTATAAAGATACCAGGCGTTTCCCCCTGGAAGCTCCCTCGTGCGCTCTCCTGTTCCGACCCTGC CGCTTACCGGATACCTGTCCGCCTTTCTCCCTTCGGGAAGCGTGGCGCTTTCTCAATGCTCACGCTGTAGGTATCTCAGT TCGGTGTAGGTCGTTCGCTCCAAGCTGGGCTGTGTGCACGAACCCCCCGTTCAGCCCGACCGCTGCGCCTTATCCGGTAA CTATCGTCTTGAGTCCAACCCGGTAAGACACGACTTATCGCCACTGGCAGCAGCCACTGGTAACAGGATTAGCAGAGCGA GGTATGTAGGCGGTGCTACAGAGTTCTTGAAGTGGTGGCCTAACTACGGCTACACTAGAAGGACAGTATTTGGTATCTGC GCTCTGCTGAAGCCAGTTACCTTCGGAAAAAGAGTTGGTAGCTCTTGATCCGGCAAACAAACCACCGCTGGTAGCGGTGG TTTTTTTGTTTGCAAGCAGCAGATTACGCGCAGAAAAAAAGGATCTCAAGAAGATCCTTTGATCTTTTCTACGGGGTCTG ACGCTCAGTGGAACGAAAACTCACGTTAAGGGATTTTGGTCATGAGATTATCAAAAAGGATCTTCACCTAGATCCTTTTA AATTAAAAATGAAGTTTTAAATCAATCTAAAGTATATATGAGTAAACTTGGTCTGACAGTTACCAATGCTTAATCAGTGA GGCACCTATCTCAGCGATCTGTCTATTTCGTTCATCCATAGTTGCCTGACTCCCCGTCGTGTAGATAACTACGATACGGG AGGGCTTACCATCTGGCCCCAGTGCTGCAATGATACCGCGAGACCCACGCTCACCGGCTCCAGATTTATCAGCAATAAAC CAGCCAGCCGGAAGGGCCGAGCGCAGAAGTGGTCCTGCAACTTTATCCGCCTCCATCCAGTCTATTAATTGTTGCCGGGA AGCTAGAGTAAGTAGTTCGCCAGTTAATAGTTTGCGCAACGTTGTTGCCATTGCTACAGGCATCGTGGTGTCACGCTCGT CGTTTGGTATGGCTTCATTCAGCTCCGGTTCCCAACGATCAAGGCGAGTTACATGATCCCCCATGTTGTGCAAAAAAGCG GTTAGCTCCTTCGGTCCTCCGATCGTTGTCAGAAGTAAGTTGGCCGCAGTGTTATCACTCATGGTTATGGCAGCACTGCA TAATTCTCTTACTGTCATGCCATCCGTAAGATGCTTTTCTGTGACTGGTGAGTACTCAACCAAGTCATTCTGAGAATAGT GTATGCGGCGACCGAGTTGCTCTTGCCCGGCGTCAATACGGGATAATACCGCGCCACATAGCAGAACTTTAAAAGTGCTC ATCATTGGAAAACGTTCTTCGGGGCGAAAACTCTCAAGGATCTTACCGCTGTTGAGATCCAGTTCGATGTAACCCACTCG TGCACCCAACTGATCTTCAGCATCTTTTACTTTCACCAGCGTTTCTGGGTGAGCAAAAACAGGAAGGCAAAATGCCGCAA AAAAGGGAATAAGGGCGACACGGAAATGTTGAATACTCATACTCTTCCTTTTTCAATATTATTGAAGCATTTATCAGGGT TATTGTCTCATGAGCGGATACATATTTGAATGTATTTAGAAAAATAAACAAATAGGGGTTCCGCGCACATTTCCCCGAAA AGTGCCACCTGACGTCTAAGAAACCATTATTATCATGACATTAACCTATAAAAATAGGCGTATCACGAGGCCCTTTCGTC TCGCGCGTTTCGGTGATGACGGTGAAAACCTCTGACACATGCAGCTCCCGGAGACGGTCACAGCTTGTCTGTAAGCGGAT GCCGGGAGCAGACAAGCCCGTCAGGGCGCGTCAGCGGGTGTTGGCGGGTGTCGGGGCTGGCTTAACTATGCGGCATCAGA GCAGATTGTACTGAGAGTGCACCATATGCGGTGTGAAATACCGCACAGATGCGTAAGGAGAAAATACCGCATCAGGCGCC ATTCGCCATTCAGGCTGCGCAACTGTTGGGAAGGGCGATCGGTGCGGGCCTCTTCGCTATTACGCCAGCTGGCGAAAGGG GGATGTGCTGCAAGGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATT CCCCCAATTCCCCGGATCGTCTCATAGCTGCTCAAGACGTTTAGCAGATTGAGGATGCAGATCTTCTACTAATGAAACTA CTGCAAACTACCAACAAAGAGGCGACCACAAAATGGACAGCCTATGCCGAGATTTGTTTAAGATACTCGATTTGCCGTCC AATTTTCATCAAGACGAAACACAGTGCATCGAACTGTGTTGTAAAAATTCATTTATTCTTGCAATTGTGACTCTCAAAGA TTGATTAAACTTGAAAGCTGGTGAAAGCTTTCCTTCTTTTGGCATGTTTGTTACTAAGGCGGCGTAAGCACAATAGGCCC AGACTCATGCAAACCAGCCATAGTGACCTTCTCACTATGTTTCGCTTGAGATTTACTAGACTCTCGGGCATCTTCACAAA AGATGACAACCGAATCAACAAATTAAGTCAACGCTAAATCAAATATTTAGAAAATAAATTTTATTGTCGAAAAAACTCTG CTTTGACAATTTTCAAACTAACCAATTGCTAAGTATTCTAGTCGACACCGGTACCTATCAGAAGAACTCGTCAAGAAGGC GATAGAAGGCGATGCGCTGCGAATCGGGAGCGGCGATACCGTAAAGCACGAGGAAGCGGTCAGCCCATTCGCCGCCAAGC TCTTCAGCAATATCACGGGTAGCCAACGCTATGTCCTGATAGCGGTCCGCCACACCCAGCCGGCCACAGTCGATGAATCC AGAAAAGCGGCCATTTTCCACCATGATATTCGGCAAGCAGGCATCGCCATGGGTCACGACGAGATCCTCGCCGTCGGGCA TGCGCGCCTTGAGCCTGGCGAACAGTTCGGCTGGCGCGAGCCCCTGATGCTCTTCGTCCAGATCATCCTGATCGACAAGA CCGGCTTCCATCCGAGTACGTGCTCGCTCGATGCGATGTTTCGCTTGGTGGTCGAATGGGCAGGTAGCCGGATCAAGCGT ATGCAGCCGCCGCATTGCATCAGCCATGATGGATACTTTCTCGGCAGGAGCAAGGTGAGATGACAGGAGATCCTGCCCCG GCACTTCGCCCAATAGCAGCCAGTCCCTTCCCGCTTCAGTGACAACGTCGAGCACAGCTGCGCAAGGAACGCCCGTCGTG GCCAGCCACGATAGCCGCGCTGCCTCGTCCTGCAGTTCATTCAGGGCACCGGACAGGTCGGTCTTGACAAAAAGAACCGG GCGCCCCTGCGCTGACAGCCGGAACACGGCGGCATCAGAGCAGCCGATTGTCTGTTGTGCCCAGTCATAGCCGAATAGCC TCTCCACCCAAGCGGCCGGAGAACCTGCGTGCAATCCATCTTGTTCAATCATTGTTGCCCGGGTGGATGCTCAGATGCTA GCGTCACCGAACGAATTAAAACTATAGTAAGTAAAGACTGTCGATGTAGCCATGTGGGGCCAATAGCTGCGAGGCTCAGA GAGAGACAAGGGGGTCGCCACTTACCCCGAACGATGTTCCAAGCAGAGCAGTCAGCAGAAAGTGAGGCTTGTTGCTGTCG GCGAGCGCAGCCTTGGGATCAACATGCGAGAGGATAGTTGGCTGCGAGCTTGCCAGCGTGGAGAGACTTGGTTATTGTAG GCAACAAGTCCCGCATGAAGGGCGTTGTATGGTGCAAAAAAGAGGAGATGGAATCTTCAAACACTTTGATTGGCTGGACA GCGGGCGCGGACTACTGGCCAACCAAAATGTGACCCCACTTGAGTGACACTTTCCCCACTATAGTGTCTTCAACTTGCAC TGCAAGTGAAGTTGTTCTTGTTATTGCACTCGTTCTTACGTTATGGCTAGTACGCGCAGATGCAGAGCCTCCTCGGTGGT ATTGCACGCCAACGAACCTCCATTCTCTGCGACATAAGGCTGTGGCAGTGCGCCCAGGAACTCGTAGAGAATGGTCAACT 205 GCGCCGTCACCACTCCCGTGTTCGAGTCTTCCGTCGTGACGAGGGCCGACTTCACCGTCGCCACATTCGACAGCGCAGTG CGGTACATCTCCATCAGCATCGTGTCCAGTTGGGCTGTCACTTCCGTCGCGTTCGCCGTGAAGTCCTCGACAAAACTGTA CGCCCAGTTGCCGAGCTCGAAGCTGACGCTGGCGAATCCGCTCGCCAGGTCGAAGGTCAAGATCTGCTCTCCAGGACTGT AGTCCAGCACGATCTGCCTCCCTATTGTGGTGCTGTATGCCAGTGGACAGCTCTTCGCCCACAGACAAGCGTAGCTGGAC TCGAGCAGCTCACGCACTGTACTCGACCATTCCGTGCCGTCCGATGGACTCAAGCAGTCCTGCAGCAGCCAAGTTGCATC AACGCTGCTGTCGATATCGTCCTTCTCCAGCATCGTAGCTATCAGTGTCAAGTCTGCCTCAAGCAGAACACCCATACACG CTGAGAACGCTCGGCACTCGGGCGAGCTGCTGCAATCCTCCAATAACGTCGTGCACTCGCTGCAAGTTGGGGCTACATCA TCCAGTTGTGAACCTGAATACAGTCCACCAAGTCGAGGCTGCTCCAAATCCAATGTTATGGTGATACCAGTGACTTCGTC TCCATCGTCGATCAGATCAGGAATCTCAGAGGTGATAAATGCTGGTTCAGGCTGGCCAGTGGTGTCTACCACCGCGAGGT CGCCAAACTGCCGACGTCGCACAATGGGGATGACAGTTGGGTCGTCAGGAACAAACTCCAGGTCTATGGTGAAGGGTGTG GTAGTGGTCATATCTTGCATCGAGTTTACAGCTTCGAACGTGAGACGGAAGCTCTGGGCATTGCCGCTTGATGACGACCC GCCTACGAAGAAGGACCACGAAGTATCCCCACCGAGACTCTGAGCACCAACCAGCACCCACGGAGCCGGATAAGTAGAAG TTGAACCAGCAACACTTGATCCGGGAGGAAGACCAATTGACGAAGATAGTGCAGCGTGAATGCTCTGCAGTTTACGAAGA AATATTGGAGCCAACTGATCGGCGCCTGTGAACCGGAGCTTGATACACAGTCTAGAAGTGGTGGAAGAGCACACGTCATC AGCATCTACCAATGCTCCAGATGATGCCGTCGACTCAGGGTTATCGAAGCCGTAGATAAAACTTGTTTGATGTTCCAGCA ATGATTCCGACGAGGTAATGAAGCCGTCGCTGACCATGTCTATCTCTGGTACGGGATCTAACAGTCCAGGCAAATATTGC GGCCAAACGGGTGATCCATCGGCGTGGACGTCCATCTCTCGCTCAATCAGCTCGAGGCTGCCGAGTGAATACAAAGAAAT CGTGGAAGCGAGTTGCTGCGAGGAAATGACGGAACGAATTCTTGCCATTGTTATGGTTGGTTTACGATAAATAAAGAATG ATTGTCAGCTGGCTTAAACATAGAAATTTCTACCATGTTAAGTACACACAGTTGCCTCAAAATAGTCACGGTTTTAGCAA GAATCGAAATATTGACAAAAAGGGATCACTCCATACGACTATAGCGGTCATCAACCAAAGAGAAACAATTCTAAGTAGCG TGGGGGATGGTTTCTTTGCATTAGAAGTCACGTAGTTTTGTTTCAAGCCGCCATCAGTCTACTCTACTTTGGTCTTTATT CTCTGTTATAAAATAAAGCTGTCACTGCGCTTGTTCAGCGTTATATTTCATTTGTAAAGCGCCATCACCGATTCGTCACA CTCGAATACCAAATGCTAGTATAAAAAAAAATTGTTCTACAAACGGCCTTCTTTTGAAAACAATCGTTCTTTCACAAAAA AAAACAACGAAAATGGAAAAAAGGAAATTGTATTAAATGCATAGACACAAAATCTGCAACTTCGCACTCAGTGCACACAG CTCGACCTTCGGCTTACAAGAAGTAGGCACCGGTACCGGGCCCTCACTTGTAGAGTTCATCCATGCCATGCGTGATCCCA GCAGCCGTCACGAACTCCAGCAGGACCATGTGGTCCCTCTTCTCGTTGGGGTCCTTGGAGAGGGCAGATTGCGTGGACAG GTAATGGTTGTCCGGCAGCAGGACAGGGCCATCGCCGATGGGCGTGTTCTGCTGGTAGTGGTCCGCCAGTTGCACGCTTC CATCTTCGATGTTGTGCCTGATCTTGAAGTTCACCTTGATGCCGTTCTTCTGCTTGTCGGCCATGATGTATACGTTGTGG GAGTTGTAGTTGTACTCCAGCTTGTGTCCGAGGATGTTTCCATCTTCCTTGAAATCGATGCCTTTCAGCTCGATGCGGTT CACCAGGGTATCACCTTCGAACTTGACTTCGGCACGTGTCTTGTAGTTCCCGTCATCCTTGAAGAAGATAGTCCTTTCTT GCACGTAGCCTTCGGGCATGGCGCTCTTGAAGAAGTCATGCTGCTTCATGTGATCTGGGTACCGGGAGAAGCACTGAACA CCGTAGGTGAAAGTGGTGACGAGGGTTGGCCACGGAACAGGGAGCTTTCCGGTAGTGCAGATGAACTTCAGGGTGAGCTT TCCGTAGGTGGCATCACCTTCACCCTCTCCGCTGACGGAGAACTTGTGCCCGTTCACATCACCATCCAGTTCCACCAGGA TTGGGACCACGCCAGTGAACAGTTCCTCGCCCTTGCCCATGGGCCCTTACTTAAGGTCGCCTCCCAGCTGAGACAGGTCG ATCCGTGTCTCGTACAGGCCGGTGATGCTCTGGTGGATCAGGGTGGCGTCCAGCACCTCTTTGGTGCTGGTGTACCTCTT CCGGTCGATGGTGGTGTCAAAGTACTTGAAGGCGGCAGGGGCTCCCAGATTGGTCAGGGTAAACAGGTGGATGATATTCT CGGCCTGCTCTCTGATGGGCTTATCCCGGTGCTTGTTGTAGGCGGACAGCACTTTGTCCAGATTAGCGTCGGCCAGGATC ACTCTCTTGGAGAACTCGCTGATCTGCTCGATGATCTCGTCCAGGTAGTGCTTGTGCTGTTCCACAAACAGCTGTTTCTG CTCATTATCCTCGGGGGAGCCCTTCAGCTTCTCATAGTGGCTGGCCAGGTACAGGAAGTTCACATATTTGGAGGGCAGGG CCAGTTCGTTTCCCTTCTGCAGTTCGCCGGCAGAGGCCAGCATTCTCTTCCGGCCGTTTTCCAGCTCGAACAGGGAGTAC TTAGGCAGCTTGATGATCAGGTCCTTTTTCACTTCTTTGTAGCCCTTGGCTTCCAGAAAGTCGATGGGATTCTTCTCGAA GCTGCTTCTTTCCATGATGGTGATCCCCAGCAGCTCTTTCACACTCTTCAGTTTCTTGGACTTGCCCTTTTCCACTTTGG CCACCACCAGCACAGAATAGGCCACGGTGGGGCTGTCGAAGCCGCCGTACTTCTTAGGGTCCCAGTCCTTCTTTCTGGCG ATCAGCTTATCGCTGTTCCTCTTGGGCAGGATAGACTCTTTGCTGAAGCCGCCTGTCTGCACCTCGGTCTTTTTCACGAT ATTCACTTGGGGCATGCTCAGCACTTTCCGCACGGTGGCAAAATCCCGGCCCTTATCCCACACGATCTCCCCGGTTTCGC CGTTTGTCTCGATCAGAGGCCGCTTCCGGATCTCGCCGTTGGCCAGGGTAATCTCGGTCTTGAAAAAGTTCATGATGTTG CTGTAGAAGAAGTACTTGGCGGTAGCCTTGCCGATTTCCTGCTCGCTCTTGGCGATCATCTTCCGCACGTCGTACACCTT GTAGTCGCCGTACACGAACTCGCTTTCCAGCTTAGGGTACTTTTTGATCAGGGCGGTTCCCACGACGGCGTTCAGGTAGG CGTCGTGGGCGTGGTGGTAGTTGTTGATCTCGCGCACTTTGTAAAACTGGAAATCCTTCCGGAAATCGGACACCAGCTTG GACTTCAGGGTGATCACTTTCACTTCCCGGATCAGCTTGTCATTCTCGTCGTACTTAGTGTTCATCCGGGAGTCCAGGAT CTGTGCCACGTGCTTTGTGATCTGCCGGGTTTCCACCAGCTGTCTCTTGATGAAGCCGGCCTTATCCAGTTCGCTCAGGC CGCCTCTCTCGGCCTTGGTCAGATTGTCGAACTTTCTCTGGGTAATCAGCTTGGCGTTCAGCAGCTGCCGCCAGTAGTTC TTCATCTTCTTCACGACCTCTTCGGAGGGCACGTTGTCGCTCTTGCCCCGGTTCTTGTCGCTTCTGGTCAGCACCTTGTT GTCGATGGAGTCGTCCTTCAGAAAGCTCTGAGGCACGATATGGTCCACATCGTAGTCGGACAGCCGGTTGATGTCCAGTT CCTGGTCCACGTACATATCCCGCCCATTCTGCAGGTAGTACAGGTACAGCTTCTCGTTCTGCAGCTGGGTGTTTTCCACG GGGTGTTCTTTCAGGATCTGGCTGCCCAGCTCTTTGATGCCCTCTTCGATCCGCTTCATTCTCTCGCGGCTGTTCTTCTG TCCCTTCTGGGTGGTCTGGTTCTCTCTGGCCATTTCGATCACGATGTTCTCGGGCTTGTGCCGGCCCATCACTTTCACGA GCTCGTCCACCACCTTCACTGTCTGCAGGATGCCCTTCTTAATGGCGGGGCTGCCGGCCAGATTGGCAATGTGCTCGTGC AGGCTATCGCCCTGGCCGGACACCTGGGCTTTCTGGATGTCCTCTTTAAAGGTCAGGCTGTCGTCGTGGATCAGCTGCAT GAAGTTTCTGTTGGCGAAGCCGTCGGACTTCAGGAAATCCAGGATTGTCTTGCCGGACTGCTTGTCCCGGATGCCGTTGA 206 TCAGCTTCCGGCTCAGCCTGCCCCAGCCGGTGTATCTCCGCCGCTTCAGCTGCTTCATCACTTTGTCGTCGAACAGGTGG GCATAGGTTTTCAGCCGTTCCTCGATCATCTCTCTGTCCTCAAACAGTGTCAGGGTCAGCACGATATCTTCCAGAATGTC CTCGTTTTCCTCATTGTCCAGGAAGTCCTTGTCCTTGATAATTTTCAGCAGATCGTGGTATGTGCCCAGGGAGGCGTTGA ACCGATCTTCCACGCCGGAGATTTCCACGGAGTCGAAGCACTCGATTTTCTTGAAGTAGTCCTCTTTCAGCTGCTTCACG GTCACTTTCCGGTTGGTCTTGAACAGCAGGTCCACGATGGCCTTTTTCTGCTCGCCGCTCAGGAAGGCGGGCTTTCTCAT TCCCTCGGTCACGTATTTCACTTTGGTCAGCTCGTTATACACGGTGAAGTACTCGTACAGCAGGCTGTGCTTGGGCAGCA CCTTCTCGTTGGGCAGGTTCTTATCGAAGTTGGTCATCCGCTCGATGAAGCTCTGGGCGGAAGCGCCCTTGTCCACCACT TCCTCGAAGTTCCAGGGGGTGATGGTTTCCTCGCTCTTTCTGGTCATCCAGGCGAATCTGCTGTTTCCCCTGGCCAGAGG GCCCACGTAGTAGGGGATGCGGAAGGTCAGGATCTTCTCGATCTTTTCCCGGTTGTCCTTCAGGAATGGGTAAAAATCTT CCTGCCGCCGCAGAATGGCGTGCAGCTCTCCCAGGTGGATCTGGTGGGGGATGCTGCCGTTGTCGAAGGTCCGCTGCTTC CGCAGCAGGTCCTCTCTGTTCAGCTTCACGAGCAGTTCCTCGGTGCCGTCCATCTTTTCCAGGATGGGCTTGATGAACTT GTAGAACTCTTCCTGGCTGGCTCCGCCGTCAATGTAGCCGGCGTAGCCGTTCTTGCTCTGGTCGAAGAAAATCTCTTTGT ACTTCTCAGGCAGCTGCTGCCGCACGAGAGCTTTCAGCAGGGTCAGGTCCTGGTGGTGCTCGTCGTATCTCTTGATCATA GAGGCGCTCAGGGGGGCCTTGGTGATCTCGGTGTTCACTCTCAGGATGTCGCTCAGCAGGATGGCGTCGGACAGGTTCTT GGCGGCCAGAAACAGGTCGGCGTACTGGTCGCCGATCTGGGCCAGCAGGTTGTCCAGGTCGTCGTCGTAGGTGTCCTTGC TCAGCTGCAGTTTGGCATCCTCGGCCAGGTCGAAGTTGCTCTTGAAGTTGGGGGTCAGGCCCAGGCTCAGGGCAATCAGG TTTCCGAACAGGCCATTCTTCTTCTCGCCGGGCAGCTGGGCGATCAGATTTTCCAGCCGTCTGCTCTTGCTCAGTCTGGC AGACAGGATGGCCTTGGCGTCCACGCCGCTGGCGTTGATGGGGTTTTCCTCGAACAGCTGGTTGTAGGTCTGCACCAGCT GGATGAACAGCTTGTCCACGTCGCTGTTGTCGGGGTTCAGGTCGCCCTCGATCAGGAAGTGGCCCCGGAACTTGATCATG TGGGCCAGGGCCAGATAGATCAGCCGCAGGTCGGCCTTGTCGGTGCTGTCCACCAGTTTCTTTCTCAGGTGGTAGATGGT GGGGTACTTCTCGTGGTAGGCCACCTCGTCCACGATGTTGCCGAAGATGGGGTGCCGCTCGTGCTTCTTATCCTCTTCCA CCAGGAAGGACTCTTCCAGTCTGTGGAAGAAGCTGTCGTCCACCTTGGCCATCTCGTTGCTGAAGATCTCTTGCAGATAG CAGATCCGGTTCTTCCGTCTGGTGTATCTTCTTCTGGCGGTTCTCTTCAGCCGGGTGGCCTCGGCTGTTTCGCCGCTGTC GAACAGCAGGGCTCCGATCAGGTTCTTCTTGATGCTGTGCCGGTCGGTGTTGCCCAGCACCTTGAATTTCTTGCTGGGCA CCTTGTACTCGTCGGTGATCACGGCCCAGCCCACAGAGTTGGTGCCGATGTCCAGGCCGATGCTGTACTTCTTGTCGGCT GCTGGGACTCCGTGGATACCGACCTTCCGCTTCTTCTTTGGGGCCATACTAGTGCGACGACGGACCTTGGTGTCGTCCTC GCGCTTGCGCTTGTGCATGCGACGACGGACCTTGGTGTCGTCCTCGCGCTTGCGCTTGTGCATGCGACGACGGACCTTGG TGTCGTCCTCGCGCTTGCGCTTGTGCATCCGCGGAGGCCTATCGATAAGCTTGATATCGAATTAATTCCTGCAGCCCGGG TTGTCGGTGAGAGTGAAGGTCGAAAAAGAGTCGGTTGGGACTTGGGCACGTGAGAGTGAAAAGGAGAATGAGCCTTCCGA TCGTGGGCGAGTCGGGCGAGGCTTTTTGAGCGTCCGGTTGTGGCTTCACAACCCCACAAACAGGTGGTTTTCGGATGTAA AAGAGGAAGTGAAATGGCCAATCAGAAACGATCCCTGTGCAAGAGATGTCTTTAATGGGGCGCAAAAAAGACGATCAAAA AGAGCGGATAAGAGAGTTCCGGCGAGACATGTGGTGGTGGTGCTATAATTAACGGATGGTAGGACGCAAAAGCGCTCTGC ACGGGTCAGGTTGAGAAGGTTCTCCAATCTTCTCTCTCTCTGTGACATGTGGTATTTCTCAGAAACCTTCGTGTAAACGC GAGGGAATGGACACGGAAATTAAGGTCTGAAAAAGGGTACTTGACTCGAGGTTTGTGGTTACGCCGGCTGAAGACGCCTA ACCTCGCGTGCGATATGCCAGCTGTCGACATTGGAAAAGCCGAGATTGTCGCGGGTACGTAGAGGCGACCCTTTGTCCAT CAGAGGGAGAGGAACCGACCAGTGGATAGGAGGGATAGACTTTATTTTGAGGGCGGTATAATGGTTATGGTAGCGACATT TTGATTTGACTTGAAAAAGATAAGAAAAAAAAAGGAAAAACTCGACTGCTTTGATGGGATAGCGATTTTTCCGTGGGTAG TCAGTTGGCCCTTGATTTTTACCATCAAGTACGTCCATATGACGGGAATAGGGACGCGGGTGGATATTAGGAGCGGCAGC TGACGTGGCAGCTCATAGACAAGGGGGTGCTTGAAAGGCTGTCTATGGTGGTGACGCCGTCTTGCGGTGTGGCAATGAGT TGTATCATTTAATCGAATACGAGGTATAA Supplementary Table B.3.3 Sequence of the pGFPN transformation plasmid. >pGFPN (6891 bp) CTAAATTGTAAGCGTTAATATTTTGTTAAAATTCGCGTTAAATTTTTGTTAAATCAGCTCATTTTTTAACCAATAGGCCG AAATCGGCAAAATCCCTTATAAATCAAAAGAATAGACCGAGATAGGGTTGAGTGTTGTTCCAGTTTGGAACAAGAGTCCA CTATTAAAGAACGTGGACTCCAACGTCAAAGGGCGAAAAACCGTCTATCAGGGCGATGGCCCACTACGTGAACCATCACC CTAATCAAGTTTTTTGGGGTCGAGGTGCCGTAAAGCACTAAATCGGAACCCTAAAGGGAGCCCCCGATTTAGAGCTTGAC GGGGAAAGCCGGCGAACGTGGCGAGAAAGGAAGGGAAGAAAGCGAAAGGAGCGGGCGCTAGGGCGCTGGCAAGTGTAGCG GTCACGCTGCGCGTAACCACCACACCCGCCGCGCTTAATGCGCCGCTACAGGGCGCGTCCCATTCGCCATTCAGGCTGCG CAACTGTTGGGAAGGGCGATCGGTGCGGGCCTCTTCGCTATTACGCCAGCTGGCGAAAGGGGGATGTGCTGCAAGGCGAT TAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAGCGCGCGTAATACGACTCACTA TAGGGCGAATTGGGTACCATTTAAATCCTAGGTGATCATGATATCTCTGATGGACAAAGGGTCGCCTCTACGTACCCGCG ACAATCTCGGCTTTTCCAATGTCGACAGCTGGCATATCGCACGCGAGGTTAGGCGTCTTCAGCCGGCGTAACCACAAACC TCGAGTCAAGTACCCTTTTTCAGACCTTAATTTCCGTGTCCATTCCCTCGCGTTTACACGAAGGTTTCTGAGAAATACCA CATGTCACAGAGAGAGAGAAGATTGGAGAACCTTCTCAACCTGACCCGTGCAGAGCGCTTTTGCGTCCTACCATCCGTTA ATTATAGCACCACCACCACATGTCTCGCCGGAACTCTCTTATCCGCTCTTTTTGATCGTCTTTTTTGCGCCCCATTAAAA GACATCTCTTGCACAGGGATCGTTTCTGATTGGCCATTTCACTTCCTCTTTTACATCCGAAAACCACCTGTTTGTGGGGT 207 TGTGAAGCCACAACCGGACGCTCAAAAAGCCTCGCCCGACTCGCCCACGATCGGAAGGCTCATTCTCCTTTTCACTCTCA CGTGCCCAAGTCCCAACCGACTCTTTTTCGACCTTCACTCTCACCGACAAACTAGCTACCGGTCTTAATTAAGGCTAGCA CCATGGGCAAGGGCGAGGAACTGTTCACTGGCGTGGTCCCAATCCTGGTGGAACTGGATGGTGATGTGAACGGGCACAAG TTCTCCGTCAGCGGAGAGGGTGAAGGTGATGCCACCTACGGAAAGCTCACCCTGAAGTTCATCTGCACTACCGGAAAGCT CCCTGTTCCGTGGCCAACCCTCGTCACCACTTTCACCTACGGTGTTCAGTGCTTCTCCCGGTACCCAGATCACATGAAGC AGCATGACTTCTTCAAGAGCGCCATGCCCGAAGGCTACGTGCAAGAAAGGACTATCTTCTTCAAGGATGACGGGAACTAC AAGACACGTGCCGAAGTCAAGTTCGAAGGTGATACCCTGGTGAACCGCATCGAGCTGAAAGGCATCGATTTCAAGGAAGA TGGAAACATCCTCGGACACAAGCTGGAGTACAACTACAACTCCCACAACGTATACATCATGGCCGACAAGCAGAAGAACG GCATCAAGGTGAACTTCAAGATCAGGCACAACATCGAAGATGGAAGCGTGCAACTGGCGGACCACTACCAGCAGAACACG CCCATCGGCGATGGCCCTGTCCTGCTGCCGGACAACCATTACCTGTCCACGCAATCTGCCCTCTCCAAGGACCCCAACGA GAAGAGGGACCACATGGTCCTGCTGGAGTTCGTGACGGCTGCTGGGATCACGCATGGCATGGATGAACTCTACAAGGCGG CCGCAGGCGCGCCTTGACGTCTAACCGCGGGGTGCCTACTTCTTGTAAGCCGAAGGTCGAGCTGTGTGCACTGAGTGCGA AGTTGCAGATTTTGTGTCTATGCATTTAATACAATTTCCTTTTTTCCATTTTCGTTGTTTTTTTTTGTGAAAGAACGATT GTTTTCAAAAGAAGGCCGTTTGTAGAACAATTTTTTTTTATACTAGCATTTGGTATTCGAGTGTGACGAATCGGTGATGG CGCTTTACAAATGAAATATAACGCTGAACAAGCGCAGTGACAGCTTTATTTTATAACAGAGAATAAAGACCAAAGTAGAG TAGACTGATGGCGGCTTGAAACAAAACTACGTGACTTCTAATGCAAAGAAAACCATACCCCACGCTACTTAGAATTGTTT CTCTTTGGTTGATGACCGCTATAGTCGTATGGAGTGATCCCTTTTTGTCAATATTTCGATTCTTGCTAAAACCGTGACTA TTTTGAGGCAACTGTGTGTACTTAACATGGTAGAAATTTCTATGTTTAAGCCAGCTGACAATCATTCTTTATTTATCGTA AACCAACCACCATATGCGTCAATGCAATTGACTGTGTCCTCGATTATTTCTATTATTAATATTATTTCCATTAAAAAAGA AATCCAGTTCGTGGCAAAATAAGACAAAGGTTTAAATAAAAATTTATCGTTATTAAACTCGTAACTTACCTTCTTCATGG CTCAACCCAATCTGTATATCGACTTATTTAATTTGGTTAACAAATCGGTTTTCGTCGCAAATAGGGGATGACAACTAAAA TACCAATTTGTGTCCTTGCGACACGTCAACAGGTGGCTGCCAATATCTCAGGTTTTGAGCACACAACGCTGTGATTGGCT ATCTGTAGATCGCTTCGAGAACTTTCGGACCGACATATCAGAACGCAGTCATGTCCAATCCAGCTAACGATTGGTCGAAA CCGCACTTCATCGCTTCATTTCAACCAATTCCCATCTACTAAAACGAGCGTCGGAATAGTCTCGAACGTAACCCCACGTA GTGGGTGGCAGCGCCCCTCGTATCTTTCAATCGCCACTCGAGTCTCAAGTTTGCTCACTTTGAAATTTTCCATCTGGACT ACACATCAAATCTAACTCAACTTTTATCAATTGAAACACAAGGGCCCCGTTTCGCATGATTGAACAAGATGGATTGCACG CAGGTTCTCCGGCCGCTTGGGTGGAGAGGCTATTCGGCTATGACTGGGCACAACAGACAATCGGCTGCTCTGATGCCGCC GTGTTCCGGCTGTCAGCGCAGGGGCGCCCGGTTCTTTTTGTCAAGACCGACCTGTCCGGTGCCCTGAATGAACTGCAGGA CGAGCCAGCGCGGCTATCGTGGCTGGCCACGACGGGCGTTCCTTGCGCAGCTGTGCTCGACGTTGTCACTGAAGCGGGAA GGGACTGGCTGCTATTGGGCGAAGTGCCGGGGCAGGATCTCCTGTCATCTCACCTTGCTCCTGCCGAGAAAGTATCCATC ATGGCTGATGCAATGCGGCGGCTGCATACGCTTGATCCGGCTACCTGCCCATTCGACCACCAAGCGAAACATCGCATCGA GCGAGCACGTACTCGGATGGAAGCCGGTCTTGTCGATCAGGATGATCTGGACGAAGAGCATCAGGGGCTCGCGCCAGCCG AACTGTTCGCCAGGCTCAAGGCGCGCATGCCCGACGGCGAGGATCTCGTCGTGACCCATGGCGATGCCTGCTTGCCGAAT ATCATGGTGGAAAATGGCCGCTTTTCTGGATTCATCGACTGTGGCCGGCTGGGTGTGGCGGACCGCTATCAGGACATAGC GTTGGCTACCCGTGATATTGCTGAAGAGCTTGGCGGCGAATGGGCTGACCGCTTCCTCGTGCTTTACGGTATCGCCGCTC CCGATTCGCAGCGCATCGCCTTCTATCGCCTTCTTGACGAGTTCTTCTGAGCGGGACTCTGGGGTTCGAAATGACCGACC AAGCGACGCCCAACCTGCCATCACGAGATTTCGATTCCACCGCCGCCTTCTATGAAAGGTTGGGCTTCGGAATCGTTTTC CGGGACGCCGGCTGGATGATCCTCCAGCGCGGGGATCTCATGCTGGAGTTCTTCGCCCACCCCCCGGATCCCCGGGGTCG ACTAGAATACTTAGCAATTGGTTAGTTTGAAAATTGTCAAAGCAGAGTTTTTTCGACAATAAAATTTATTTTCTAAATAT TTGATTTAGCGTTGACTTAATTTGTTGATTCGGTTGTCATCTTTTGTGAAGATGCCCGAGAGTCTAGTAAATCTCAAGCG AAACATAGTGAGAAGGTCACTATGGCTGGTTTGCATGAGTCTGGGCCTATTGTGCTTACGCCGCCTTAGTAACAAACATG CCAAAAGAAGGAAAGCTTTCACCAGCTTTCAAGTTTAATCAATCTTTGAGAGTCACAATTGCAAGAATAAATGAATTTTT ACAACACAGTTCGATGCACTGTGTTTCGTCTTGATGAAAATTGGACGGCAAATCGAGTATCTTAAACAAATCTCGGCATA GGCTGTCCATTTTGTGGTCGCCTCTTTGTTGGTAGTTTGCAGTAGTTTCATTAGTAGAAGATCTGCATCCTCAATCTGCT AAACGTCTTGAGCAGCTATGAGACGATCCGGGGAATTGGGGCCCGAGCTCCAGCTTTTGTTCCCTTTAGTGAGGGTTAAT TGCGCGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAG CCGGAAGCATAAAGTGTAAAGCCTGGGGTGCCTAATGAGTGAGCTAACTCACATTAATTGCGTTGCGCTCACTGCCCGCT TTCCAGTCGGGAAACCTGTCGTGCCAGCTGCATTAATGAATCGGCCAACGCGCGGGGAGAGGCGGTTTGCGTATTGGGCG CTCTTCCGCTTCCTCGCTCACTGACTCGCTGCGCTCGGTCGTTCGGCTGCGGCGAGCGGTATCAGCTCACTCAAAGGCGG TAATACGGTTATCCACAGAATCAGGGGATAACGCAGGAAAGAACATGTGAGCAAAAGGCCAGCAAAAGGCCAGGAACCGT AAAAAGGCCGCGTTGCTGGCGTTTTTCCATAGGCTCCGCCCCCCTGACGAGCATCACAAAAATCGACGCTCAAGTCAGAG GTGGCGAAACCCGACAGGACTATAAAGATACCAGGCGTTTCCCCCTGGAAGCTCCCTCGTGCGCTCTCCTGTTCCGACCC TGCCGCTTACCGGATACCTGTCCGCCTTTCTCCCTTCGGGAAGCGTGGCGCTTTCTCATAGCTCACGCTGTAGGTATCTC AGTTCGGTGTAGGTCGTTCGCTCCAAGCTGGGCTGTGTGCACGAACCCCCCGTTCAGCCCGACCGCTGCGCCTTATCCGG TAACTATCGTCTTGAGTCCAACCCGGTAAGACACGACTTATCGCCACTGGCAGCAGCCACTGGTAACAGGATTAGCAGAG CGAGGTATGTAGGCGGTGCTACAGAGTTCTTGAAGTGGTGGCCTAACTACGGCTACACTAGAAGGACAGTATTTGGTATC TGCGCTCTGCTGAAGCCAGTTACCTTCGGAAAAAGAGTTGGTAGCTCTTGATCCGGCAAACAAACCACCGCTGGTAGCGG TGGTTTTTTTGTTTGCAAGCAGCAGATTACGCGCAGAAAAAAAGGATCTCAAGAAGATCCTTTGATCTTTTCTACGGGGT CTGACGCTCAGTGGAACGAAAACTCACGTTAAGGGATTTTGGTCATGAGATTATCAAAAAGGATCTTCACCTAGATCCTT TTAAATTAAAAATGAAGTTTTAAATCAATCTAAAGTATATATGAGTAAACTTGGTCTGACAGTTACCAATGCTTAATCAG 208 TGAGGCACCTATCTCAGCGATCTGTCTATTTCGTTCATCCATAGTTGCCTGACTCCCCGTCGTGTAGATAACTACGATAC GGGAGGGCTTACCATCTGGCCCCAGTGCTGCAATGATACCGCGAGACCCACGCTCACCGGCTCCAGATTTATCAGCAATA AACCAGCCAGCCGGAAGGGCCGAGCGCAGAAGTGGTCCTGCAACTTTATCCGCCTCCATCCAGTCTATTAATTGTTGCCG GGAAGCTAGAGTAAGTAGTTCGCCAGTTAATAGTTTGCGCAACGTTGTTGCCATTGCTACAGGCATCGTGGTGTCACGCT CGTCGTTTGGTATGGCTTCATTCAGCTCCGGTTCCCAACGATCAAGGCGAGTTACATGATCCCCCATGTTGTGCAAAAAA GCGGTTAGCTCCTTCGGTCCTCCGATCGTTGTCAGAAGTAAGTTGGCCGCAGTGTTATCACTCATGGTTATGGCAGCACT GCATAATTCTCTTACTGTCATGCCATCCGTAAGATGCTTTTCTGTGACTGGTGAGTACTCAACCAAGTCATTCTGAGAAT AGTGTATGCGGCGACCGAGTTGCTCTTGCCCGGCGTCAATACGGGATAATACCGCGCCACATAGCAGAACTTTAAAAGTG CTCATCATTGGAAAACGTTCTTCGGGGCGAAAACTCTCAAGGATCTTACCGCTGTTGAGATCCAGTTCGATGTAACCCAC TCGTGCACCCAACTGATCTTCAGCATCTTTTACTTTCACCAGCGTTTCTGGGTGAGCAAAAACAGGAAGGCAAAATGCCG CAAAAAAGGGAATAAGGGCGACACGGAAATGTTGAATACTCATACTCTTCCTTTTTCAATATTATTGAAGCATTTATCAG GGTTATTGTCTCATGAGCGGATACATATTTGAATGTATTTAGAAAAATAAACAAATAGGGGTTCCGCGCACATTTCCCCG AAAAGTGCCAC 209 Appendix C Supplementary Methods and Results for Chapter 5 C.1 Methods Tested for Plasmid-RNP Co-Transformations While the baseline protocol used for co-transformations was the optimized Phytophthora transformation protocol developed in a previous study (Fang, Cui, et al., 2017), multiple methods were tested for delivering the RNP-plasmid transfection complexes to protoplasts. The methods tested for both P. ramorum and P. cactorum are summarized in Data C.1.1. Data C.1.1 Summary of the methods tested for RNP-plasmid delivery to P. ramorum and P. cactorum protoplasts. Note: the table is provided as a separate Excel document–please open the corresponding supplementary file to view the full table. C.2 Results from Oxathiapiprolin Growth Assays The raw radial growth measurements recorded for all Phytophthora cactorum FF42 isolates are recorded in Data C.2.1. Results from the statistical analyses performed on the radial growth data are reported in Data C.2.2. Data C.2.1 Radial growth measurements (in mm) recorded for Phytophthora cactorum FF42 wildtype (WT) and CRISPR-Cas9 ΔPcaORP1 mutant cultures seven days post plating on V8 agar (V8A) supplemented with a gradient of oxathiapiprolin concentrations. Unamended V8A and V8A + 0.1% DMSO were used as control treatments. For each isolate (WT, PT9, PT10), the growth of two independent single zoospore cultures (SZCs) was tested at each treatment (WT-1 RC1, WT-2 RC1, PT9-2, PT9-3, PT10-1 RC1, PT10-3 RC1). The oxathiapiprolin growth assays were carried out over two experiments (Excel Sheet 1 and Sheet 2). The results from the 0.1% DMSO control, performed in both experiments, were pooled prior to statistical analysis (Excel Sheet 3). The results from each of the SZCs were also pooled prior to analysis so that changes in growth were ultimately measured between three groups: WT, PT9, and PT10. Note: the table is provided as a separate Excel document–please open the corresponding supplementary file to view the full table. 210 Data C.2.2 Output of the Python 3.8 code for statistical analyses run on the radial growth results from Phytophthora cactorum FF42 wildtype (WT) and ΔPcaORP1 CRISPR-Cas9 mutant (PT9, PT10) cultures grown on V8 agar supplemented with a gradient of oxathiapiprolin concentrations. Note: the table is provided as a separate Excel document–please open the corresponding supplementary file to view the full table. FOREST PATHOLOGY IN THE GENOMICS ERA: COMBINING COMPARATIVE GENOMICS AND CRISPR-CAS9 GENE EDITING TO GAIN NEW INSIGHTS INTO THE GENETICS OF FILAMENTOUS PLANT PATHOGENS by Erika Dort B.Sc., The University of Victoria, 2011 M.Sc., The University of Victoria, 2015 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2024 © Erika Dort, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Forest pathology in the genomics era: combining comparative genomics and CRISPR-Cas9 gene editing to gain new insights into the genetics of filamentous plant pathogens submitted by Erika Dort in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Forestry Examining Committee: Dr. Richard Hamelin, Professor, Forest and Conservation Sciences, UBC Supervisor Dr. Nicolas Feau, Research Scientist, Natural Resources Canada Supervisory Committee Member Dr. José Úrbez-Torres, Adjunct Professor, Biology, UBCO Supervisory Committee Member Dr. Kayla King, Professor, Zoology, UBC University Examiner Dr. Guus Bakkeren, Adjunct Professor, Botany, UBC University Examiner Dr. Monica Gerth, Professor, Biological Sciences, Victoria University of Wellington External Examiner Additional Supervisory Committee Members: Dr. Jim Kronstad, Professor, Michael Smith Laboratories, UBC Supervisory Committee Member iii Abstract Filamentous plant pathogens, comprising fungi and oomycetes, are some of the most economically and ecologically damaging species in agriculture, horticulture, and forestry. These pathogens are well-suited to invade novel ecosystems, and the current era of globalization and climate change has facilitated the global spread of invasive filamentous pathogens. The introduction of invasive pathogens is particularly problematic in forestry as native host trees have no co-evolved defense mechanisms against novel invaders, which can lead some invasive pathogens to spread rapidly through forest ecosystems and cause landscape-level impacts. Significant efforts are therefore being directed towards improving the management and mitigation of forest invasive pathogens. As next generation sequencing technologies and molecular techniques have evolved and improved, management research has shifted to elucidating the genetic and genomic mechanisms that filamentous pathogens employ to infect their plant hosts, cause disease, and spread through ecosystems. The purpose of the research presented in this dissertation was to apply modern genomics methods in a forest pathology context with the goal of creating foundational techniques that can be used to further our understanding of the molecular mechanisms driving phytopathogenic lifestyles and traits in filamentous forest pathogens. In the first part of this work, large-scale comparative genomic analyses were performed on fungal genomes using a novel machine-learning method that was able to uncover distinct genomic patterns associated with phytopathogenic lifestyles and traits. These results provided a proof of concept for the use of predictive genomics in the biosurveillance of forest invasive filamentous pathogens. The second part of this work focused on developing molecular techniques for filamentous forest pathogens in the oomycete genus Phytophthora. First, a polyethylene glycol-mediated plasmid transformation method was tested iv in five forest Phytophthoras to determine which species was most amenable to further molecular method development. Next, CRISPR-Cas9 gene editing was tested in two forest Phytophthoras, P. cactorum and P. ramorum, using a plasmid-ribonucleoprotein co-transformation approach. The results from the plasmid transformations and CRISPR-Cas9 gene editing demonstrated both the potential and challenges for developing molecular techniques in Phytophthora species and provide a foundation for future genomic research in forest Phytophthoras. v Lay Summary Phytopathogenic fungi and oomycetes are microorganisms that cause diseases in plants, and many have the capacity to cause large-scale ecological and economic damage in forestry. The 21st century has brought significant technological advances, and scientists can now study the specific genetic mechanisms underlying phytopathogenic traits in fungi and oomycetes. This dissertation presents research aimed to develop novel methods for exploring forest pathogen genomes so that disease management strategies can be improved. Two broad research approaches were applied. The first approach used machine-learning to perform large-scale comparative analyses on fungal genomes, which uncovered specific patterns associated with important phytopathogenic traits. The second approach established species-scale genetic techniques in forest pathogenic oomycetes, which will allow researchers to examine the contribution of specific genes to phytopathogenicity in these species. The results from this research provide a foundation for future studies of forest pathogens and the continued improvement of disease management and mitigation strategies. vi Preface The research presented in this dissertation was conceived and designed by me with support and guidance from Dr. Richard Hamelin, Dr. Nicolas Feau, and my supervisory committee members. I performed all the research and data analyses except for the machine learning analyses presented in Chapter 2, which were performed by Elliot Layne, a PhD candidate in the School of Computer Science at McGill University (see publication notes below). A version of Chapter 2 has been published. Dort, E.N., Layne, E., Feau, N., Butyaev, A., Henrissat, B., Martin, F.M., Haridas, S., Salamov, A., Grigoriev, I.V., Blanchette, M., and Hamelin, R.C. (2023) Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits. Scientific Reports 13: 17203. I conceived and designed the study with E. Layne, N. Feau, M. Blanchette, and R.C. Hamelin. I designed and completed the lifestyle database and A. Butyaev developed the online version. The machine learning analyses were done by E. Layne, and I performed all other analyses (data scraping, PCAs, statistical analyses); during this process we received support and advice from N. Feau, M. Blanchette, and R.C. Hamelin. Data (genome annotations) and/or technical support was provided by B. Henrissat, F.M. Martin, S. Haridas, A. Salamov, and I.V. Grigoriev. The Newick version of the phylogenetic tree was created by S. Haridas. I conceived and wrote the manuscript with help from E. Layne, N. Feau, and R.C. Hamelin, and I created the figures with support and guidance from N. Feau. All the other authors reviewed the manuscript and provided edits. The chapter is presented in the form in which it was accepted for publication and is reproduced in this dissertation under the permission of a CC BY 4.0 license with minor formatting modifications made to adhere to UBC dissertation formatting requirements. vii A version of Chapter 3 has also been published. Dort, E.N., Tanguay, P., and Hamelin, R.C. (2020) CRISPR/Cas9 Gene Editing: An Unexplored Frontier for Forest Pathology. Frontiers in Plant Science 11: 1126. I conceived and wrote the manuscript, and P. Tanguay and R.C. Hamelin provided content suggestions and edits. The chapter is presented in the form in which it was accepted for publication and is reproduced in this dissertation under the permission of a CC BY 4.0 license with minor formatting modifications made to adhere to UBC dissertation formatting requirements. viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Tables ............................................................................................................................... xii List of Figures ............................................................................................................................. xiii List of Supplementary Data Files ............................................................................................. xxi List of Symbols .......................................................................................................................... xxii List of Abbreviations ............................................................................................................... xxiii Acknowledgements ....................................................................................................................xxv Dedication ................................................................................................................................ xxvii Chapter 1: Introduction ................................................................................................................1 1.1 The phytopathogenic lifestyles and ecological traits of filamentous pathogens ............. 3 1.2 Filamentous pathogens as invasive species: a threat to forest ecosystem health ............ 4 1.2.1 Biosurveillance of filamentous forest pathogens .................................................... 5 1.3 Phytophthora, a genus of highly destructive, and often invasive, plant pathogenic oomycetes ................................................................................................................................... 6 1.4 Filamentous pathogens, climate change, and forest health ............................................. 7 1.5 Forest pathology in the genomics era ............................................................................. 8 1.6 PhD research objectives ................................................................................................ 10 Chapter 2: Large-scale comparative genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits ..................13 2.1 Chapter Summary ......................................................................................................... 13 2.2 Introduction ................................................................................................................... 14 2.3 Methods......................................................................................................................... 17 2.3.1 Fungal lifestyle database ....................................................................................... 17 2.3.2 Genome analyses .................................................................................................. 19 2.3.2.1 Genome annotations .......................................................................................... 19 2.3.2.2 Phytopathogenic lifestyles and traits ................................................................ 20 2.3.2.3 Principal component analyses ........................................................................... 20 2.3.2.4 DendroNet machine learning ............................................................................ 21 2.3.2.4.1 Dataset preprocessing ................................................................................. 21 2.3.2.4.2 DendroNet architecture ............................................................................... 21 2.3.2.4.3 Model training ............................................................................................. 22 2.3.2.4.4 Model evaluation ........................................................................................ 22 2.3.2.4.5 Feature importance evaluation .................................................................... 23 2.3.2.4.6 Statistical analyses ...................................................................................... 24 2.4 Results ........................................................................................................................... 24 2.4.1 Phytopathogenic lifestyles and traits can be predicted from phylogenetic and genomic data ......................................................................................................................... 24 2.4.2 Expansions and contractions of specific CAZyme, peptidase and secondary metabolite cluster gene families drive DendroNet’s predictions of phytopathogenic lifestyles and traits ................................................................................................................ 32 ix 2.4.2.1 CAZymes .......................................................................................................... 33 2.4.2.2 Peptidases .......................................................................................................... 35 2.4.2.3 Secondary metabolite clusters ........................................................................... 36 2.5 Discussion ..................................................................................................................... 37 Chapter 3: CRISPR-Cas9 gene editing: an unexplored frontier for forest pathology ..........45 3.1 Chapter Summary ......................................................................................................... 45 3.2 Introduction ................................................................................................................... 46 3.2.1 What is CRISPR-Cas? A primer for understanding CRISPR-Cas9 gene editing . 46 3.2.2 How does CRISPR-Cas9 gene editing technology work? .................................... 49 3.2.3 CRISPR-Cas9 exploits cellular DNA repair mechanisms to edit genes ............... 50 3.2.4 CRISPR-Cas9 limitations ..................................................................................... 51 3.3 Current applications of CRISPR-Cas9 in plant pathology ........................................... 52 3.3.1 Using CRISPR-Cas9 to engineer disease resistance in plants .............................. 53 3.3.1.1 Pathogen-gene approach ................................................................................... 54 3.3.1.2 Plant-gene approach .......................................................................................... 54 3.3.2 Using CRISPR-Cas9 to target and explore genes in filamentous plant pathogens ............................................................................................................................... 58 3.3.2.1 Targeting pathogenicity genes using CRISPR-Cas9 ........................................ 59 3.4 Future perspectives for CRISPR-Cas9 in forest pathology .......................................... 61 3.4.1 Proposed CRISPR-Cas9 research in four forest pathosystems ............................. 64 3.4.1.1 Engineering disease resistance in poplars ......................................................... 64 3.4.1.2 Dutch elm disease ............................................................................................. 66 3.4.1.3 Sudden oak/larch death ..................................................................................... 67 3.4.1.4 White pine blister rust ....................................................................................... 69 3.5 Conclusions ................................................................................................................... 72 Chapter 4: Heterogeneity in establishment of polyethylene glycol-mediated plasmid transformations in five forest pathogenic Phytophthora species..............................................73 4.1 Chapter Summary ......................................................................................................... 73 4.2 Introduction ................................................................................................................... 74 4.3 Methods......................................................................................................................... 78 4.3.1 Phytophthora species, isolates, and culture conditions ........................................ 78 4.3.2 Transformation plasmids ...................................................................................... 79 4.3.2.1 Plasmid sequencing ........................................................................................... 82 4.3.3 Determining the minimum inhibitory concentrations of geneticin for Phytophthora isolates............................................................................................................ 83 4.3.4 Protoplast isolation and transformation ................................................................ 84 4.3.4.1 Preparation of Phytophthora cultures for transformation ................................. 85 4.3.4.2 Isolation of protoplasts ...................................................................................... 85 4.3.4.3 Transformation of protoplasts with plasmid DNA ........................................... 86 4.3.4.4 Antibiotic selection of transformants ................................................................ 87 4.3.5 Identification of successful transformants ............................................................ 87 4.4 Results ........................................................................................................................... 89 4.4.1 Sequencing of Phytophthora transformation plasmids indicates instability of pGFPN ............................................................................................................................... 89 4.4.2 Forest Phytophthora species differ in geneticin sensitivity .................................. 92 x 4.4.3 Plasmid DNA transformations are inconsistent and unstable in forest Phytophthora species ............................................................................................................ 92 4.5 Discussion ..................................................................................................................... 98 Chapter 5: Successes and challenges in establishing CRISPR-Cas9 gene editing for two forest pathogenic Phytophthora species via co-transformation of plasmid-ribonucleoprotein complexes ....................................................................................................................................105 5.1 Chapter Summary ....................................................................................................... 105 5.2 Introduction ................................................................................................................. 106 5.3 Methods....................................................................................................................... 111 5.3.1 Phytophthora cactorum isolates and culture conditions ..................................... 111 5.3.2 Phytophthora DNA extractions .......................................................................... 111 5.3.3 PCR primer design and equipment ..................................................................... 112 5.3.4 Identification of the PcORP1 ortholog in Phytophthora ramorum and Phytophthora cactorum ...................................................................................................... 112 5.3.4.1 Touchdown PCR of PrORP1 and PcaORP1 genes ........................................ 114 5.3.4.2 Sanger sequencing and primer-walking for PrORP1 and PcaORP1 .............. 115 5.3.4.3 Gene prediction for PrORP1 and PcaORP1 sequences ................................. 116 5.3.5 CRISPR-Cas9 guide RNA design ....................................................................... 116 5.3.6 Ribonucleoprotein-plasmid co-transformation ................................................... 117 5.3.6.1 Plasmid DNA .................................................................................................. 117 5.3.6.2 Formation of Cas9 ribonucleoprotein complexes ........................................... 118 5.3.6.3 Preparation of Phytophthora cultures for transformation ............................... 119 5.3.6.4 Isolation of protoplasts .................................................................................... 119 5.3.6.5 Formation of plasmid-RNP cationic-lipid transfection complexes ................ 119 5.3.6.6 Co-transformation of Phytophthora protoplasts with CRISPR-Cas9 ribonucleoprotein complexes and plasmid DNA ............................................................ 120 5.3.6.7 Antibiotic selection of transformants .............................................................. 121 5.3.7 Screening transformants for CRISPR-Cas9 mutants .......................................... 122 5.3.8 Generation of single zoospore-derived cultures ................................................. 123 5.3.9 Testing plasmid DNA retention in single zoospore cultures .............................. 124 5.3.10 Oxathiapiprolin growth assays ............................................................................ 124 5.3.11 Statistical analyses .............................................................................................. 125 5.4 Results ......................................................................................................................... 126 5.4.1 Structure of the PrORP1 and PcaORP1 genes ................................................... 126 5.4.2 Guide RNAs designed and tested for PrORP1 and PcaORP1 ........................... 128 5.4.3 CRISPR-Cas9 plasmid-RNP co-transformation produces mutants in Phytophthora cactorum but not Phytophthora ramorum .......................................................................... 130 5.4.3.1 Phytophthora cactorum .................................................................................. 130 5.4.3.2 Phytophthora ramorum ................................................................................... 134 5.4.4 Single zoospore cultures of Phytophthora cactorum mutants lose plasmid DNA ... ............................................................................................................................. 135 5.4.5 ΔPcaORP1 mutants exhibit decreased resistance to oxathiapiprolin ................. 136 5.5 Discussion ................................................................................................................... 138 Chapter 6: Conclusion ...............................................................................................................145 6.1 Summary of research findings .................................................................................... 145 xi 6.2 Looking forward: research implications and potential future directions .................... 147 6.2.1 Genomics-based biosurveillance of forest invasive filamentous pathogens ....... 147 6.2.2 CRISPR-Cas9 gene editing for forest pathogenic Phytophthoras ...................... 148 6.3 Concluding statements ................................................................................................ 150 References ...................................................................................................................................151 Appendices ..................................................................................................................................183 Appendix A Supplementary Methods and Results for Chapter 2 ........................................... 183 A.1 Results of statistical analyses run on DendroNet AUCs ......................................... 183 A.2 Supplementary tables .............................................................................................. 184 A.3 Supplementary figures ............................................................................................ 187 A.4 Supplementary data files ......................................................................................... 192 Appendix B Supplementary Methods and Results for Chapters 4 and 5 ................................ 194 B.1 Recipes for Growth Media and Transformation Solutions used in Chapters 4 and 5 ... ................................................................................................................................. 194 B.2 Conditions Tested for Phytophthora Transformations in Chapter 4 ...................... 200 B.3 Transformation Plasmids ........................................................................................ 200 Appendix C Supplementary Methods and Results for Chapter 5 ........................................... 209 C.1 Methods Tested for Plasmid-RNP Co-Transformations ......................................... 209 C.2 Results from Oxathiapiprolin Growth Assays ........................................................ 209 xii List of Tables Table 4.1 The five forest Phytophthora species and isolates used in this study. Phytophthora sojae was used as a control for transformations. .......................................................................... 78 Table 4.2 Geneticin (G418) concentrations tested for each Phytophthora species used in this study to determine minimum inhibitory concentrations. .............................................................. 84 Table 4.3 Primer pairs used for diagnostic PCR to detect plasmid DNA in Phytophthora transformants................................................................................................................................. 89 Table 4.4 Summary of the results of plasmid DNA transformations in the five forest Phytophthora pathogens tested in this study and the agricultural pathogen P. sojae (used as a transformation control). ................................................................................................................ 93 Table 5.1 Details of the Phytophthora cactorum and P. ramorum isolates used in this study. . 111 Table 5.2 Primer pairs designed to amplify the PcORP1 orthologs of P. ramorum (PrORP1) and P. cactorum (PcaORP1). All PCRs were performed using Phusion™️ Plus DNA Polymerase, which creates a universal annealing temperature of 60°C. ......................................................... 114 Table 5.3 Thermocycler conditions for touchdown PCR performed on P. ramorum NA2_17 and P. cactorum FF42 wildtype DNA using Pr102ORPh_ORF and Pca414ORPh_Seq primer pairs, respectively. ................................................................................................................................ 115 Table 5.4 CRISPR-Cas9 guide RNAs (gRNAs) designed to target the PrORP1 and PcaORP1 genes of Phytophthora ramorum and P. cactorum, respectively, using the Eukaryotic Pathogen CRISPR guide RNA/DNA Design Tool (EuPaGDT; Peng & Tarleton, 2015). All gRNAs are designed using a 3’ NGG protospacer adjacent motif (PAM). The number in each gRNA name represents the location of its first nucleotide in the ORP1 open reading frame sequence. The gRNAs labelled with “rc” (reverse complement) are those derived from the antisense strand of the ORP1 sequence. The gRNAs labelled with “mh” (microhomology) are those that EuPaGDT found to have microhomology pairs flanking the gRNA sequence. Also provided are the total scores from EuPaGDT for each gRNA and the IDT gRNA design checker scores. .................. 129 Table 5.5 Primer pairs designed to amplify Cas9 RNP-targeted regions of the PrORP1 and PcaORP1 genes of Phytophthora ramorum NA2_17 and P. cactorum FF42, respectively, for Sanger sequencing. The guide RNAs (gRNAs) that target a sequence within each primer-amplified region are also listed. All PCRs for Sanger sequencing were performed using Phusion™️ Plus DNA Polymerase, which uses a universal annealing temperature of 60°C. ..... 130 xiii List of Figures Figure 1.1 (A) Phylogenetic tree of life for eukaryotes; figure is from Burki et al. 2020 (Trends in Ecology & Evolution 35(1): 43-55) and is reused with permission under a CC BY 4.0 license. Oomycetes belong to the Stramenopila in the TSAR supergroup (coloured red) whereas fungi are members of the Opisthokonta in the Amorphea supergroup (coloured grey). (B) Both oomycetes (left photo) and filamentous fungi (right photo) exhibit radial growth of a mycelium, which is made up of branching filaments known as hyphae, leading to the encompassing term ‘filamentous pathogens’ being used in plant pathology. Photos: Erika Dort. ................................ 2 Figure 1.2 Phytophthora species are ubiquitous pathogens with a global distribution, and many of them have the capacity to cause large-scale damage in both agriculture and forestry. (A) Symptoms of late blight of potato caused by Phytophthora infestans, responsible for the Irish Potato Famine. Photo: Scott Bauer, USDA Agricultural Research Service, Bugwood.org, used under the permission of a CC BY 3.0 license. (B) Forest outbreak of sudden oak death, caused by Phytophthora ramorum. Photo: Joseph O’Brien, USDA Forest Service, Bugwood.org, used under the permission of a CC BY 3.0 license. C) Eucalyptus trees in Australia killed by Phytophthora cinnamomi. Photo: Florida Division of Plant Industry, Florida Department of Agriculture and Consumer Services, Bugwood.org, used under the permission of a CC BY 3.0 license. ............................................................................................................................................ 7 Figure 1.3 Graphical summary showing the simplified structure and objectives of the research presented in this dissertation. Also included are the long-term research objectives (grey box), which are not within the scope of this dissertation, but represent the bridge between Objectives 1 and 2 and the direction intended for future studies. Abbreviations: PEG (polyethylene glycol), RNP (ribonucleoprotein), gRNA (guide RNA), G418R (geneticin resistance), KO (knockout). Figure created with BioRender.com. ............................................................................................ 12 Figure 2.1 Phylogenetic groups (includes subphyla and classes) from FunLifeDB included in our genomic analyses with summaries of the categories represented for each group analysed. The tree shows the phylogenetic relationships between the groups as represented by MycoCosm (https://mycocosm.jgi.doe.gov/mycocosm/home). The circled number after each group name indicates the total number of genomes analysed for that group, and the subsequent pie charts indicate how many of those genomes belong to each pathogenic, lifestyle and trait category. Legend Abbreviations: NP: non-pathogen; PP: plant pathogen; OP: other pathogen; S: saprotroph; ENM: endomycorrhizal; ECM: ectomycorrhizal; N: necrotroph; HB: hemibiotroph; B: biotroph; OB: obligate biotroph; GP: gymnosperm pathogen; AP: angiosperm pathogen; SP: stem pathogen; FP: foliar pathogen; RP: root pathogen. .............................................................. 26 Figure 2.2 Principal component analysis (PCA) biplots of annotation data from A) carbohydrate active enzymes, B) peptidases, C) secondary metabolite clusters, D) transporters, and E) transcription factors, showing the separation of the Ascomycota and Basidiomycota phyla on the first two principal components (PCs). The two phyla are differentiated by colour, and the four subphyla are differentiated by shape. The black arrows represent the loadings of the variables included in each PCA; only the top ten variables contributing to PC1 and PC2 are shown. ....... 27 Figure 2.3 Principal component analysis (PCA) biplots for A) carbohydrate active enzymes (CAZymes), and B) peptidases, showing the clustering of obligate biotroph (OB) genomes. The four subphyla are differentiated by colour, and the obligate biotroph genomes are differentiated by shape. A statistical ellipse was drawn at a 95% confidence level around the OB genomes. The xiv black arrows represent the loadings of the variables included in each PCA; only the top ten variables contributing to PC1 and PC4 (CAZyme PCA), or PC1 and PC3 (peptidase PCA), are shown. ........................................................................................................................................... 28 Figure 2.4 Area under the receiver operating characteristic curve (AUC) scores obtained with DendroNet for predicting nine lifestyles in fungi with the three top-performing genomic feature sets (C: CAZymes; M: MEROPS (peptidases); S: secondary metabolite clusters). Results from DendroNet’s lifestyle predictions for all 31 genomic feature sets are reported in Supplementary Data S2. A) the average AUC score obtained for each lifestyle with only the phylogenetic data (parsimony model) is represented in red at the base of each graph (standard deviation of this value is represented with a dotted red line) and the average improvement of the model with the genomic feature(s) is represented with a blue column. The asterisk indicates that the AUC score from the genomic feature model was significantly (p < 0.05, N=9) greater than the AUC score from the parsimony model. B) percent gain ([[AUC parsimony – AUC genomic signal]/AUC parsimony] x 100) calculated for each lifestyle (average and standard deviation calculated from the three genomic feature sets). .................................................................................................... 30 Figure 2.5 Area under the receiver operating characteristic curve (AUC) scores obtained with DendroNet for predicting five phytopathogenic traits in fungi with the three top-performing genomic feature sets (C: CAZymes; M: MEROPS (peptidases); S: secondary metabolite clusters). Results from DendroNet’s trait predictions for all 31 genomic feature sets are reported in Supplementary Data S2. A) the average AUC score obtained for each trait with only the phylogenetic data (parsimony model) is represented in red at the base of each graph (standard deviation of this value is represented with a dotted red line) and the average improvement of the model with the genomic feature(s) is represented with a blue column. The asterisk indicates that the AUC score from the genomic feature model was significantly (p < 0.05, N=9) greater than the AUC score from the parsimony model. B) percent gain ([[AUC parsimony – AUC genomic signal]/AUC parsimony] x 100) calculated for each trait (average and standard deviation calculated from the three genomic feature sets). .......................................................................... 32 Figure 2.6 Violinplots of the gene counts from carbohydrate active enzyme (CAZyme) annotations revealed by DendroNet analyses to be expanded in phytopathogenic genomes relative to non-phytopathogenic genomes. A) Gene counts of the six CAZyme classes and the total CAZymes (all CAZyme classes and families). B) Gene counts of the three CAZyme families that were drivers for DendroNet’s predictions of fungal phytopathogenicity. The asterisks in both A and B indicate that the gene counts in phytopathogenic genomes were significantly greater than the gene counts in non-phytopathogenic genomes as per an independent t-test (N=355) (‘*’: p < 0.05, ‘**’: p < 0.01, ‘***’: p < 0.001, ‘****’: p < 0.0001). ................... 34 Figure 2.7 Violinplots of the gene counts from peptidase (MEROPS) annotations revealed by DendroNet analyses to be expanded or contracted in phytopathogenic genomes relative to non-phytopathogenic genomes. A) Gene counts of the total peptidases (all clans and families). B) Gene counts of the four peptidase families that were drivers for DendroNet’s predictions of fungal phytopathogenicity. The asterisks in both A and B indicate significance in gene counts as per an independent t-test (N=355) (‘*’: p < 0.05, ‘**’: p < 0.01, ‘***’: p < 0.001, ‘****’: p < 0.0001). For the total peptidases (‘Total_MEROPS’), S9, S10, and S33 annotations, gene counts were significantly greater in phytopathogenic genomes relative to non-phytopathogenic genomes, whereas for pepsins, gene counts were significantly lower in phytopathogenic genomes relative to non-phytopathogenic genomes. ................................................................................... 36 xv Figure 3.1 (A) A generalized CRISPR-Cas system consisting of a CRISPR array and a cas operon. The CRISPR array is composed of a series of identical repeat sequences interspaced with unique sequences called spacers, which are derived from the genetic material of invading bacteriophages and plasmids. On the 5’ end of the spacer/repeat locus is an intraspecies-conserved leader sequence likely involved in transcription of the CRISPR array. Flanking the CRISPR array is the cas operon, which contains the CRISPR-associated (cas) genes that code for proteins involved in the CRISPR-Cas defense response. (B) The three-stage CRISPR-Cas defense response. Stage 1, adaptation, occurs when the bacterial or archaeal host recognizes a fragment of DNA or RNA from an invader, named the protospacer, and integrates it into the CRISPR array as a new spacer. New spacers are integrated at the leader end of the array. Stage 2, expression, involves the transcription of the cas genes into Cas proteins, and the CRISPR array into a precursor CRISPR RNA (pre-crRNA) molecule. The pre-crRNA molecule then gets processed by Cas proteins into smaller, mature CRISPR RNA (crRNA) molecules. In Stage 3, interference, the mature crRNAs associate with one or more Cas proteins to form crRNA-effector complexes, which survey the cell for foreign nucleic acids and subsequently cleave them via recognition of the protospacer-adjacent motif (PAM) sequence. ........................................... 48 Figure 3.2 The number of CRISPR publications by year since 2012 (development of CRISPR-Cas9 gene editing technology by Jinek et al.) obtained from a Web of Science topic search with the search parameters: ‘CRISPR’ or ‘CRISPR-Cas*’, or ’CRISPR/Cas*’. The ‘Analyze Results’ function of Web of Science was used to determine publication numbers by year and research area. (A) The log-transformed number of CRISPR publications by research area shows the common trend of increasing publications for CRISPR research for all areas except forestry. (B) A breakdown of the publication distribution by topic within forestry as determined by a manual search of the Web of Science search results. ................................................................................ 62 Figure 3.3 The four forest pathosystems proposed for future CRISPR/Cas9 research: (A) uredinia of the poplar leaf rust pathogen Melampsora medusae f. sp. deltoidis Shain, a basidiomycete fungus, on eastern cottonwood (Populus deltoides W.Bartram ex Marshall) leaves in Montréal, Quebec, CA (photo: Richard Hamelin, 2015); (B) flagging symptoms typical of Dutch elm disease, caused by the ascomycete fungal pathogen Ophiostoma novo-ulmi Brasier, on an American elm (Ulmus americana L.) in Québec, Quebec, CA (photo: Philippe Tanguay, 2016); (C) sudden larch death caused by the oomycete pathogen Phytophthora ramorum Werres, De Cock, & Man in’t Veld in a larch plantation in Galloway Forest Park, Scotland, GB (photo: Richard Hamelin, 2019); (D) aecia of the basidiomycete fungal pathogen Cronartium ribicola J.C. Fisch., causal agent of white pine blister rust, on a limber pine (Pinus flexilis E.James) in Rocky Mountain National Park, Colorado, US (photo: Erika Dort, 2019). .................................. 64 Figure 3.4 Example of a generalized CRISPR/Cas9 workflow and timeline for a filamentous forest pathogen based on the protocol being developed for the Dutch elm disease pathogen Ophiostoma novo-ulmi (Onu). (A) Transformation of fungal protoplasts with a plasmid containing (see inset): a Cas9 gene fused to nuclear localization signal (NLS), a single-guide RNA (sgRNA) scaffold with a 20-nucleotide (nt) region designed to base-pair with the target genomic DNA, an antibiotic resistance gene for transformant selection, and gene promoters and terminators active in the target species. Alternatively, protoplasts can be co-transformed with a combination of Cas9-sgRNA ribonucleoprotein complexes (RNPs) and a plasmid for antibiotic transformant selection. (B) In successful transformants, Cas9 forms a complex with the sgRNA molecule, which guides the complex to the protospacer (a genomic DNA target in this example) xvi with an adjacent PAM (protospacer adjacent motif) sequence. The sgRNA-Cas9 complex is analogous to the crRNA-effector complexes of native CRISPR/Cas systems, shown in Figure 1 of this review. In the RNP approach, this sgRNA-Cas9 complex is pre-assembled and transfected directly into the protoplasts. (C) Transformants are selected by growth on antibiotic selective medium (picture shows Onu hygromycin-resistant transformants: pink colonies are CRISPR/Cas9 ade2 mutants). (D) Successful CRISPR/Cas9 mutants are confirmed through sub-culture of the putative transformants from the previous step, PCR screening, and Sanger sequencing..................................................................................................................................... 71 Figure 4.1 Map of the pYF2-PsNLS-hSpCas9-GFP (abbreviated pYF2-PsCG) transformation plasmid developed by Fang and Tyler (2016) for expression of hSpCas9 in Phytophthora sojae. Plasmid sequence determined from Fang and Tyler (2016) and Fang et al. (2017). Plasmid map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). ............................................................................................................................. 80 Figure 4.2 Map of pYF515, the 'all-in-one' plasmid developed by Fang et al. (2017) for expression of hSpCas9 and a single-guide RNA (sgRNA) for CRISPR/Cas9 gene editing. Plasmid sequence provided by Dr. Felipe Arredondo (Oregon State University). Plasmid map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). ............................................................................................................................. 81 Figure 4.3 Map of the transformation plasmid pGFPN developed by Ah-Fong and Judelson (2011). Plasmid sequence provided by Dr. Felipe Arredondo (Oregon State University). Plasmid map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). ............................................................................................................................. 82 Figure 4.4 An example of the P. cactorum isolate Larch FF-42 2Pa growth assay demonstrating how the geneticin (G418) minimum inhibitory concentration (MIC) was determined for each forest Phytophthora species in this study. Each isolate was grown on V8 agar supplemented with a gradient of G418 concentrations (labels above the image), and two replicate plates were tested for each concentration (labels to the left of image). The first G418 concentration at which mycelial growth stopped was recorded as MIC for that species/isolate. ...................................... 84 Figure 4.5 Paired alignment of the expected pYF515 sequence (top) and the Plasmidsaurus pYF515 sequence (bottom). The pairwise identity of the two sequences is 99.8% with minor differences in sequence in the nptII gene and its ham34 promoter, both the ham34 terminators, and in the origin of replication (ori). Alignment generated in Geneious Prime 2023.2.1 (https://www.geneious.com) using the Geneious assembler (Plasmidsaurus sequence mapped to expected sequence). ...................................................................................................................... 90 Figure 4.6 Paired alignment of the expected pYF2-PsCG sequence (top) and the Plasmidsaurus pYF2-PsCG sequence (bottom). The pairwise identity of the two sequences is 99.8% with minor differences in sequence at the end of the Cas9 gene, in the ham34 terminator, and in the origin of replication (ori). Alignment generated in Geneious Prime 2023.2.1 (https://www.geneious.com) using the Geneious assembler (Plasmidsaurus sequence mapped to expected sequence). ........... 90 Figure 4.7 Paired alignment of the Plasmidsaurus pGFPN (first plasmid prep) sequence (top) and the expected pGFPN sequence (bottom). The pairwise identity of the two sequences is 52.9%, with sequence inversions, repeats, and deletion of the nptII gene in the Plasmidsaurus sequence (9,842 bp). Alignment generated in Geneious Prime 2023.2.1 (https://www.geneious.com) using the Geneious assembler (expected sequence mapped to the Plasmidsaurus sequence). ............................................................................................................. 91 xvii Figure 4.8 Paired alignment of the expected pGFPN sequence (top) and the Plasmidsaurus pGFPN (second plasmid prep) sequence (bottom). The pairwise identity of the two sequences is 62.6% with sequence inversions, and a deletion of the nptII gene in the Plasmidsaurus sequence (4,927 bp). Alignment generated in Geneious Prime 2023.2.1 (https://www.geneious.com) using the Geneious assembler (Plasmidsaurus sequence mapped to expected sequence). .................... 91 Figure 4.9 Minimum inhibitory concentrations (MICs) of geneticin (G418) for the five forest Phytophthora species tested in this study and the agricultural pathogen P. sojae (used as a transformation control). Two replicates were tested for each species/isolate. *Two isolates of P. ramorum were tested, NA2 16_0386_0016 and NA2 17_0134_0030, and growth stopped for both at 5 μg/mL of G418. ............................................................................................................. 92 Figure 4.10 Transformations of Phytophthora cactorum FF42 with the pYF2-PsCG plasmid yielded stable transformants. (A) Transformants growing on V8 agar + 40 μg/mL G418 relative to (B) no growth of wildtype (untransformed) cultures on the same medium. Photos were taken five days post plating. (C) Transformants growing stably on V8 agar + 40 μg/mL G418 after multiple rounds of sub-culturing. .................................................................................................. 94 Figure 4.11 Gel electrophoresis of products from a PCR performed on DNA from Phytophthora cactorum FF42 pYF2-PsCG transformant (T) cultures using the pYF_Cas9_Diag_F3/R3 primer pair (expected product size is 824 bp). The negative PCR control was a reaction using molecular-grade water instead of DNA (well 2). The positive PCR control was purified pYF2-PsCG plasmid DNA (well 3). Note that the DNA used for the plasmid DNA control was the same preparation that was used for the P. cactorum transformations. The gel was run at 100 volts for 90 minutes on 0.85% agarose in 0.5X TBE buffer. All samples were loaded with Safe-Green™️ stain (Applied Biological Materials Inc., Richmond, BC, Canada) at a ratio of 1 μL Safe-Green: 5 μL PCR product. The pYF2-PsCG transformants highlighted in red are those that grew stably on G418 selective medium (40 μg/mL), however, there was no DNA amplification in the diagnostic PCR.............................................................................................................................. 95 Figure 4.12 Gel electrophoresis of products from a PCR performed on DNA from Phytophthora cactorum FF42 wildtype (WT) and pYF2-PsCG transformant (T) cultures using the eGFP_Diag_F1/R1 primer pair (expected product size is 274 bp). The negative PCR control was a reaction using molecular-grade water instead of DNA (well 2). The positive PCR controls were purified pYF2-PsCG plasmid DNA, both circular (well 3) and linearized (well 4). The gel was run at 100 volts for 75 minutes on 1.25% agarose in 0.5X TBE buffer. All samples were loaded with Safe-Green™️ stain (Applied Biological Materials Inc., Richmond, BC, Canada) at a ratio of 1 μL Safe-Green: 5 μL PCR product. The pYF2-PsCG T4 transformant is highlighted in red because it struggled to grow through the second round of G418 selection (40 μg/mL), with roughly only 25% mycelial growth relative to the other three transformants (T1, T5, T8), however, a strong band amplified in the diagnostic PCR. ............................................................ 96 Figure 4.13 Putative transformants (PTs) of the control species Phytophthora sojae P6497 (A) and P. ramorum NA2_17 (B) growing on V8 agar supplemented with 50 μg/mL G418 and 30 μg/mL G418, respectively, four days post transformation. While putative P. ramorum transformants did grow on three of the plates, the mycelial growth was weak and irregular (see arrows) relative to the P. sojae control. The white arrows indicate PTs that would have been considered “strong” for P. ramorum, whereas the grey arrows indicate very weak PTs. All P. ramorum PTs were taken through a second round of selection, but most did not grow at the increased concentration of G418; none of the “weak” PTs (grey arrows) grew, and only two of xviii the “strong” PTs (white arrows) grew. The same pattern emerged for every P. ramorum transformation attempted. ............................................................................................................. 97 Figure 4.14 Gel electrophoresis of products from a PCR performed on DNA from Phytophthora sojae P6497 and P. ramorum NA2_17 wildtype (WT) and pYF2-PsCG transformant (T) cultures using the AmpR_Diag_F1/R1 primer pair (expected product size is 232 bp). The negative PCR control was a reaction using molecular-grade water instead of DNA (well 2). The positive PCR controls are replicate dilutions of a single pYF2-PsCG plasmid DNA extraction (wells 3-4). Note that the DNA used for the plasmid control was the same preparation that was used for both the P. sojae and P. ramorum transformations. The gel was run at 100 volts for 80 minutes on 1.25% agarose in 0.5X TBE buffer. All samples were loaded with Safe-Green™️ stain (Applied Biological Materials Inc., Richmond, BC, Canada) at a ratio of 1 μL Safe-Green: 5 μL PCR product. ......................................................................................................................................... 98 Figure 5.1 Schematic demonstrating how radial mycelial growth measurements were taken from Phytophthora cultures at seven days post plating on V8 agar medium supplemented with a gradient of oxathiapiprolin concentrations. To account for the width of the agar plug, 5 mm was subtracted from the length of measurement 1 (M1) and measurement 2 (M2). The average of the two measurements was recorded as the radial growth of each culture at a given oxathiapiprolin concentration. .............................................................................................................................. 125 Figure 5.2 Predicted structure of the PrORP1 gene from Phytophthora ramorum NA2_17. The open reading frame was predicted using the Geneious “Find ORFs” tool (Geneious Prime 2023.2.1) and validated using the NCBI Open Reading Frame Finder (see Methods section 5.3.4.3). The exon-intron boundaries and sequences were predicted using the Softberry FGENESH eukaryotic gene finding algorithm (www.softberry.com). The orange labels indicate the locations of the four guide RNAs designed for CRISPR-Cas9 gene editing (see Table 5.4), and the purple labels show the locations of the primer pairs designed to amplify the Cas9 ribonucleoprotein-targeted regions for Sanger sequencing(see Table 5.5). Gene map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). ..... 127 Figure 5.3 Predicted structure of the PcaORP1 gene from Phytophthora cactorum FF42. The open reading frame was predicted using the Geneious “Find ORFs” tool (Geneious Prime 2023.2.1) and validated using the NCBI Open Reading Frame Finder (see Methods section 5.3.4.3). The exon-intron boundaries and sequences were predicted using the Softberry FGENESH eukaryotic gene finding algorithm (www.softberry.com). The orange labels indicate the locations of the five guide RNAs designed for CRISPR-Cas9 gene editing (see Table 5.4), and the purple labels show the locations of the primer pairs designed to amplify the Cas9 ribonucleoprotein-targeted regions for Sanger sequencing (see Table 5.5). Gene map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). ..... 127 Figure 5.4 A) Structure and size of the PrORP1 and PcaORP1 open reading frames relative to that of the P. capsici PcORP1 gene characterized by Miao et al. (2016). The numbers indicate the size of the ORFs in base pairs (bp). The exon-intron boundaries of PrORP1 and PcaORP1 were predicted using the Softberry FGENESH eukaryotic gene finding tool (http://www.softberry.com). Exon 1 is not shown for either PrORP1 or PcaORP1 as it was not predicted to be included in the ORF region of the genes. The exon-intron boundaries shown for PcORP1 are according to Miao et al. (2016). B) Structure, size, and predicted functional domains of the PrORP1 and PcaORP1 proteins relative to the P. capsici PcORP1 protein characterized by xix Miao et al. (2016). The amino acid sequences of all three proteins were analyzed using InterPro (Paysan-Lafosse et al., 2023) to predict the functional domains. ............................................... 128 Figure 5.5 Method/workflow developed for CRISPR-Cas9 gene editing in Phytophthora cactorum using a plasmid-RNP co-transformation protocol. Figure created with BioRender.com...................................................................................................................................................... 131 Figure 5.6 Details of the CRISPR-Cas9 ΔPcaORP1 indel mutant, PT10, obtained in Phytophthora cactorum FF42 using a plasmid-RNP co-transformation method with the Pca1507rcmh guide RNA (gRNA). The PT10 mutant is heterozygous, with the mutant allele comprising a single base pair insertion at the Cas9 cut site. The graphs shown were generated using TIDE software (Brinkman et al. 2014) on the forward Sanger sequencing results from PcaORPh_Cas9Edit_1F/1R PCR amplicons. The P. cactorum PcaORP1 wildtype sequencing chromatogram was used as the control sample and the ΔPcaORP1 PT10 mutant chromatograms were used as the test samples. The top graphs show the chromatogram sequence compositions with the expected Cas9 cut site indicated by the dashed blue line, the bottom graphs show the predicted percentage of sequences with wildtype vs. mutant genotypes, and the bar graphs to the right indicate the predicted nucleotide in the insertion mutants (y-axis represents probability). A) TIDE decomposition graphs for the original PT10 transformant culture recovered from G418-selective medium. The ratio of wildtype to mutant DNA indicates a mixture of edited and non-edited nuclei in the culture. B) TIDE decomposition graphs for PT10-1, one of the single zoospore-derived cultures (SZCs) of PT10. The ratio of the wildtype to mutant DNA indicates a pure heterozygous culture with one wildtype allele and one mutant allele. The TIDE results from the other two SZCs tested (PT10-2, PT10-3) were nearly identical (results not shown). The TIDE results indicate that the nucleotide inserted on the sense strand of PcaORP1 is a thymine, therefore the nucleotide inserted on the antisense strand where the gRNA was designed is an adenine. ....................................................................................................................................... 132 Figure 5.7 Details of the CRISPR-Cas9 ΔPcaORP1 indel mutant, PT9, obtained in Phytophthora cactorum FF42 using a plasmid-RNP co-transformation method with the Pca156 guide RNA (gRNA). The PT9 mutant is heterozygous, with the mutant allele comprising a single base pair deletion at the Cas9 cut site. The graphs shown were generated using TIDE software (Brinkman et al. 2014) on the forward Sanger sequencing results from PcaORPh_Cas9Edit_4F/4R PCR amplicons. The P. cactorum PcaORP1 wildtype sequencing chromatogram was used as the control sample and the ΔPcaORP1 PT9 mutant chromatograms were used as the test samples. The top graphs show the chromatogram sequence compositions with the expected Cas9 cut site indicated by the dashed blue line, and the bottom graphs show the predicted percentage of sequences with wildtype vs. mutant genotypes. A) TIDE decomposition graphs for the original PT9 transformant culture recovered from G418-selective medium. The ratio of wildtype to mutant DNA indicates a mixture of edited and non-edited nuclei in the culture. B) TIDE decomposition graphs for PT9-1, one of the single zoospore-derived cultures (SZCs) from PT9. The TIDE results indicated that PT9-1 was derived from a non-edited wildtype nucleus as it did not have any mutant alleles. C) TIDE decomposition graphs for PT9-2, another of the SZCs derived from PT9. The ratio of the wildtype to mutant DNA indicates a pure heterozygous culture with one wildtype allele and one mutant allele. The TIDE results from the other two SZCs tested (PT9-3, PT9-4) were nearly identical (results not shown). .................................... 133 Figure 5.8 Predicted protein sequences for the PcaORP1 gene of Phytophthora cactorum FF42. A) The wildtype (WT) sequence encodes an 886 amino acid polypeptide. B) The 1 base pair (bp) xx insertion mutation in one of the alleles of the ΔPcaORP1 PT10 mutant introduces a frameshift resulting in a premature stop codon at amino acid 466. C) The 1 bp deletion mutation in one of the alleles of the ΔPcaORP1 PT9 mutant also introduces a frameshift and results in a premature stop codon at amino acid 63. Protein sequences were generated using Geneious Prime 2023.2.1...................................................................................................................................................... 134 Figure 5.9 Gel electrophoresis of products from a PCR performed using eGFP_Diag_F1/R1 primers on DNA from Phytophthora cactorum FF42 ΔPcaORP1 mutant cultures (PT9, PT10) and their respective single zoospore cultures (SZCs: PT9-1 to PT9-4, PT10-1 and PT10-3). For the PT10 SZCs, DNA extractions from two replicate cultures were tested (C1, C2). The DNA from P. cactorum FF42 wildtype SZCs (WT-1 and WT-2) was used as the negative PCR control, and pYF2-PsCG plasmid DNA was used as the positive PCR control. A product amplified in sample PT10-1 C2, indicating presence of plasmid DNA; the culture from which the sample was derived was therefore not included in downstream phenotype assays. The gel was run at 100 volts for 55 minutes on 1% agarose in 0.5X TBE buffer with a 100 bp DNA ladder. All samples were loaded with Safe-Green™️ stain (Applied Biological Materials Inc., Richmond, BC, Canada) at a ratio of 1 μL Safe-Green: 5 μL PCR product. ........................................................ 136 Figure 5.10 Photos of Phytophthora cactorum FF42 wildtype (WT) and CRISPR-Cas9 ΔPcaORP1 mutant (PT9, PT10) cultures growing on oxathiapiprolin-supplemented V8 agar seven days post plating. The images shown are only a representation of the isolates and oxathiapiprolin concentrations tested, however, the same pattern of reduced growth in the ΔPcaORP1 mutant cultures was observed at every concentration tested (see Figure 5.11). ..... 137 Figure 5.11 Boxplot of radial growth measurements of Phytophthora cactorum FF42 wildtype (WT) and CRISPR-Cas9 ΔPcaORP1 mutant (PT9, PT10) cultures growing on V8 agar (V8A) supplemented with a gradient of oxathiapiprolin concentrations. Unamended V8A and V8A + 0.1% DMSO were used as control treatments. For each isolate at each treatment, the growth data from two independent single zoospore cultures (SZCs), each with four replicates, were pooled (for a total of eight replicates per isolate per treatment). The white circles indicate the mean growth value for each isolate at each treatment. Results from statistical analyses revealed that both PT9 and PT10 ΔPcaORP1 mutant cultures exhibited statistically reduced mycelial growth relative to WT cultures on every oxathiapiprolin concentration tested (‘ns’: not significant, ‘*’: p < 0.05, ‘**’: p < 0.01, ‘***’: p < 0.001, ‘****’: p < 0.0001). .................................................... 138 xxi List of Supplementary Data Files Data A.4.1 ubc_2024_may_dort_erika_dataA41.xlsx Data A.4.2 ubc_2024_may_dort_erika_dataA42.xlsx Data A.4.3 ubc_2024_may_dort_erika_dataA43.xlsx Data A.4.4 ubc_2024_may_dort_erika_dataA44.xlsx Data A.4.5 ubc_2024_may_dort_erika_dataA45.xlsx Data A.4.6 ubc_2024_may_dort_erika_dataA46.txt Data B.2.1 ubc_2024_may_dort_erika_dataB21.xlsx Data C.1.1 ubc_2024_may_dort_erika_dataC11.xlsx Data C.2.1 ubc_2024_may_dort_erika_dataC21.xlsx Data C.2.2 ubc_2024_may_dort_erika_dataC22.xlsx xxii List of Symbols ɑ alpha β beta °C degrees Celsius µ micro Δ gene mutation [ ] concentration ~ approximately xxiii List of Abbreviations BLAST/blast Basic Local Alignment Search Tool bp base pair CRISPR-Cas clustered regularly interspaced short palindromic repeats–CRISPR associated proteins crRNA CRISPR RNA dH2O distilled water DMSO dimethylsulfoxide DNA deoxyribonucleic acid EDTA ethylenediaminetetraacetic acid g gram(s) G418 geneticin IDT Integrated DNA Technologies indel insertion or deletion JGI Joint Genome Institute L litre(s) M molar (moles/litre) m- milli -m meter mol moles mRNA messenger RNA n- nano NCBI National Center for Biotechnology Information p- pico PAM protospacer-adjacent motif PCA principal component analysis PCR polymerase chain reaction PEG polyethylene glycol rcf (× g) relative centrifugal force (g-force) xxiv RNA ribonucleic acid RNP ribonucleoprotein rpm revolutions per minute sgRNA/gRNA single-guide RNA/guide RNA TBE tris-borate-EDTA buffer tracrRNA trans-activating CRISPR RNA xxv Acknowledgements I remember reading a dissertation several years ago in which it was stated that completing a PhD takes a village. That statement has increasingly resonated with me through each year of this degree, and I have many people I want to acknowledge. To start, I want to thank my supervisor, Dr. Richard Hamelin, and my committee members Drs. Nicolas Feau, Jim Kronstad, and José Úrbez-Torres, for their support and guidance over the last five years. I also want to thank the following people for their research collaboration and support: Elliot Layne and Dr. Mathieu Blanchette (McGill) for their help with machine learning, Dr. Felipe Arredondo (OSU) for the Phytophthora transformation training and for sharing plasmid DNA, and Dr. Phil Tanguay (NRCan) for his collaboration and support with my CRISPR-Cas9 research. Thank you to FPInnovations for providing me with access to a PPC-2 facility, and a huge thanks to Stacey Kus, Dr. Angela Dale, and Dr. Rod Stirling for the training and for welcoming me into their lab space. Thank you to all the Hamelin Lab members, past and current, who have provided moral support and guidance over the years, and a special thank you to Padmini Herath who was always ready to provide support when I needed it. I also want to acknowledge the students who worked with me in the lab and helped with this project: Alanna Love, Emilie Alexander, Mia Goodman, and Romy Suliteanu. And a special shout-out to Barbara Wong, Berni van der Meer, and Kelly Hrywkiw for the long-distance Zoom work/support sessions during the pandemic (and an additional thank you to Berni for help with editing). I would also like to give my heartfelt thanks to Dr. Allan Carroll, who adopted me into his lab as a “wannabe entomologist” and has provided significant mentorship and support throughout my PhD. And thank you to the FIDEL students–particularly Deb Wertman, Kate Kitchens, Stan Pokorny, and Vivek Srivastava–for the support and for always making me feel welcome. xxvi Being a part of the EvoFunPath NSERC CREATE program provided me with incredible training opportunities and moral support during these last few years. The opportunity to do a bioinformatics internship at the Broad Institute was amazing and thank you to Dr. Christina Cuomo and her team for patiently training me in genome annotation techniques. Thank you to Dr. Rebecca Shapiro and her lab, especially Viola Halder and Lauren Wensing, for their guidance on my CRISPR-Cas9 research. I also want to thank Dr. Aleeza Gerstein for always providing me with support and advice. The support I received from my family and friends over these last five years was amazing. My husband Sergio has been the linchpin holding my life together, providing constant support. Thank you to Maia Kirk and Alicia Voth, for always having my back, and for being incredible sounding boards. Thanks to my mom and dad (the Drs. Dort) for your support and for your feedback and edits on my presentations and writing. Thank you also to Deb Wertman, my teaching partner in crime, who has been there for me since my first semester, providing both scientific and moral support. Finally, I want to acknowledge the many sources of funding for both my research and stipend. The research presented in this dissertation was funded by a Genome Canada Large-Scale Applied Research Project (project #10106, bioSAFE: Biosurveillance of Alien Forest Enemies) with additional funding from Genome B.C., Genome Québec, the Canadian Food Inspection Agency, Natural Resources Canada, and FPInnovations. The stipend funding I received over the last five years was from the following sources: bioSAFE, the UBC Doctoral Affiliated Fellowship, the UBC Faculty of Forestry (SRF and Internal Grad Awards), and the Natural Sciences and Engineering Research Council of Canada (NSERC CGS-D). xxvii Dedication For my husband, Sergio. To truly support a partner through a PhD requires a level of patience and empathy possessed by few people. Thank you for all the late-night existential crisis talks, the gently forced outdoor time when I struggled to peel myself away from the computer, the delicious meals, the small milestone celebrations, and so much more. 1 Chapter 1: Introduction Plant pathogens are fundamental components of natural ecosystems, contributing to ecological succession and nutrient/carbon cycling, and driving plant evolution and diversity (Gilbert, 2002; Paseka et al., 2020). However, in the current anthropogenic era, the combination of globalization, climate change, and mismanagement have disrupted the balance between pathogens and plant hosts, leading to increased incidence of invasive pathogen species and epidemic outbreaks of plant diseases (Anderson et al., 2004; Fisher et al., 2012, 2020; Stukenbrock & Gurr, 2023). While plant diseases can be caused by a diversity of organisms including fungi, oomycetes, bacteria, viruses, protozoa, nematodes, and parasitic plants (Agrios, 2005), filamentous plant pathogens, a group that comprises fungi and oomycetes, cause some of the most economically and ecologically impactful plant diseases. Many filamentous plant pathogens have the capacity to threaten food security (Fry et al., 2015; Singh et al., 2015) or fundamentally, and sometimes irreversibly, alter natural ecosystems (Rigling & Prospero, 2018; Rizzo et al., 2002a). Filamentous fungal and oomycete plant pathogens exhibit remarkable convergences despite their phylogenetic distance (Figure 1.1A). In fact, it is the similarities of oomycetes to fungi in filamentous hyphal growth (Figure 1.1B), life cycles, heterotrophic and osmotrophic nutrition, and ecological lifestyles that led to their inclusion in the kingdom Fungi until the late 20th century. More recently, molecular evidence confirmed that oomycetes are more closely related to plants than they are to fungi (Baldauf et al., 2000; Förster et al., 1990). Specifically, oomycetes are in the kingdom Chromista (Cavalier-Smith, 1998; Cavalier-Smith & Chao, 2006) and are members of a group known as the Stramenopila along with diatoms and brown algae (Burki et 2 al., 2020; McGowan & Fitzpatrick, 2020). Despite the distance between fungi and oomycetes on the eukaryotic tree of life, convergent evolution is evident between these two groups, especially in their behaviours as plant pathogens. Furthermore, many studies have now found strong evidence for horizontal gene transfer (HGT) between fungi and oomycetes, and it is hypothesized that HGT has contributed to the phytopathogenic lifestyles of many oomycetes (Kronmiller et al., 2023; Richards et al., 2006, 2011; Savory et al., 2015). Figure 1.1 (A) Phylogenetic tree of life for eukaryotes; figure is from Burki et al. 2020 (Trends in Ecology & Evolution 35(1): 43-55) and is reused with permission under a CC BY 4.0 license. Oomycetes belong to the Stramenopila in the TSAR supergroup (coloured red) whereas fungi are members of the Opisthokonta in the Amorphea supergroup (coloured grey). (B) Both oomycetes (left photo) and filamentous fungi (right photo) exhibit radial growth of a mycelium, which is made up of branching filaments known as hyphae, leading to the encompassing term ‘filamentous pathogens’ being used in plant pathology. Photos: Erika Dort. 3 While the impacts of filamentous pathogens are widespread in agriculture, horticulture, and forestry, the focus of this dissertation is the phytopathogenicity of fungi and oomycetes in the context of forestry. Therefore, the remainder of this introduction will primarily discuss the ecological roles and impacts of filamentous pathogens as they pertain to both natural and managed forest ecosystems. 1.1 The phytopathogenic lifestyles and ecological traits of filamentous pathogens Fungi and oomycetes exhibit a diversity of trophic strategies, or lifestyles, that influence how they interact with other species and survive within ecosystems. The definitions of these lifestyles have been the source of much debate within the mycological community since the 1800s. Presently, the predominantly used and accepted system differentiates three fundamental trophic groups: saprotrophs, necrotrophs, and biotrophs; furthermore, within each of these three groups there are facultative and obligate forms (de Bary, 1887; Lewis, 1973; Thrower, 1966). Saprotrophs derive nutrients from dead and decaying plant matter and are not associated with a living host, whereas both necrotrophs and biotrophs are intimately associated with living hosts, but exhibit opposite strategies (Lewis, 1973; Thrower, 1966). Biotrophs derive nutrients from living plant cells, often using specialized structures to maintain a symbiosis (from mutualism to parasitism), whereas necrotrophs secrete toxins to kill host cells and subsequently derive nutrients from the dead tissues (Lewis, 1973; Thrower, 1966). In plant pathology, an additional lifestyle group has been recognized for fungal and oomycete pathogens: the hemibiotrophs, which exhibit an initial phase of biotrophy followed by a transition to necrotrophy (Perfect & Green, 2001). Filamentous plant pathogens are generally categorized as one of the three distinct lifestyles–biotroph, necrotroph, or hemibiotroph–which characterizes how they interact with 4 plant tissues to overcome host defenses and obtain nutrients. In addition to these three lifestyle classifications, filamentous plant pathogens can also be distinguished by several phytopathogenic traits such as their host range, the plant tissues they infect, and the symptoms they cause. The lifestyle and phytopathogenic traits a pathogen displays encompass crucial information about its infection cycle, how it will likely spread disease from host to host, and ultimately, which management strategies will be most effective. 1.2 Filamentous pathogens as invasive species: a threat to forest ecosystem health Globalization, and the corresponding increase in international trade, has led to a significant increase in biological invasions caused by invasive alien species (Vitousek et al., 1997; Westphal et al., 2008). Invasive alien species comprise a diversity of organisms, including mammals, invertebrates, plants, and microbial pathogens such as bacteria, fungi, and oomycetes. There are many biological and ecological traits of filamentous plant pathogens that make them particularly well-suited for biological invasion, including their reproductive systems (asexual and sexual), spore-based dispersal, and phenotypic plasticity (Desprez-Loustau et al., 2007; Ghelardini et al., 2017; Philibert et al., 2011). Fungal and oomycete species are often introduced via the movement of various forest products across borders for international trade, such as live plants, logs, and wood packing materials (Liebhold et al., 2012, 2017). When non-native invasive filamentous pathogens are introduced to native forests comprising host plants with no co-evolved resistance, the pathogens can spread rapidly and cause disease outbreaks across entire forest ecosystems (Allen & Humble, 2002; Loo, 2009). There are several examples of invasive filamentous pathogens that have caused irreversible damage to forest ecosystems including Ophiostoma novo-ulmi Brasier (Dutch elm disease), Cryphonectria parasitica (Murr.) Barr (chestnut blight), 5 Cronartium ribicola J.C. Fisch. (white pine blister rust), and Phytophthora ramorum Werres, De Cock & Man in’t Veld (sudden oak death). In addition to their numerous ecological impacts, invasive forest pathogens can inflict severe economic costs for governments, forestry companies, regional municipalities, and even residential land owners (Lovett et al., 2016; Pimentel et al., 2001). Given the widespread impacts of forest invasive pathogens, significant resources are invested towards their mitigation and management. 1.2.1 Biosurveillance of filamentous forest pathogens Once an invasive species is established in an ecosystem, it becomes extremely difficult and prohibitively expensive to eradicate it (Rejmánek & Pitcairn, 2002; Simberloff et al., 2013). Therefore, increasing emphasis is being placed on prevention and early detection strategies designed to monitor and remove invasive species before they become established (Epanchin-Niell & Liebhold, 2015; Kim et al., 2006; Leung et al., 2002; Lodge et al., 2006). One such prevention strategy is biosurveillance: the systematic and cyclical process of monitoring for the presence of invasive species to detect, and ideally prevent, potential outbreaks and inform subsequent management decisions. As invasive forest pests and pathogens have become increasingly prevalent, biosurveillance has become a critical component for government regulatory agencies (Bilodeau et al., 2019; Hamelin & Roe, 2020; Roe et al., 2019; Wingfield et al., 2015) and has been demonstrated to be effective in preventing the establishment and spread of forest invasive species (Fournier & Turgeon, 2017). Therefore, research efforts are being increasingly directed towards improving biosurveillance strategies for invasive filamentous forest pathogens. Furthermore, given the current era of next-generation sequencing and the unprecedented availability of genetic and genomic data for forest invasive species, researchers 6 are advocating for more genomics-based biosurveillance strategies, focused on predicting the genetic traits associated with invasive pathogens regardless of their taxonomic identity (Hamelin & Roe, 2020; McTaggart et al., 2016; Roe et al., 2019). 1.3 Phytophthora, a genus of highly destructive, and often invasive, plant pathogenic oomycetes The oomycete genus Phytophthora de Bary comprises some of the most invasive and destructive filamentous plant pathogens, especially in forestry as they are widespread nursery pathogens with the capacity to invade, and potentially destabilize, naïve forest ecosystems (Hansen, 2015; Jung et al., 2016). The genus was first described in the 1800s by Anton de Bary, one of the founders of plant pathology, who proved that the causal agent of late blight of potato (Figure 1.2A)–the disease responsible for the Irish Potato Famine–was a fungus known at the time as Peronospora infestans (de Bary, 1861). Fifteen years later, de Bary described the defining characteristics of the genus Phytophthora and renamed the potato blight pathogen as Phytophthora infestans (de Bary, 1876). Phytophthora translates to ‘plant destroyer’ from Greek, and the genus lives up to this name with over 200 currently described species that attack a wide range of important agricultural, horticultural, and forest plants (Chen et al., 2022). Phytophthora species spread primarily using their flagellated reproductive spores, called zoospores, via the soil, watercourses, rain splash, wind, and often inadvertently by humans moving soil, plants, or propagules (Ristaino & Gumpertz, 2000). There are several examples of forest invasive Phytophthora pathogens, including P. ramorum, the causal agent of sudden oak/larch death (Figure 1.2B) (Garbelotto Benzon et al., 2001; Webber et al., 2010), and P. cinnamomi Rands, the causal agent of Jarrah dieback in Australia (Figure 1.2C) (Podger et al., 1965) and a pathogen 7 of thousands of host species worldwide (Hardham & Blackman, 2018). Each Phytophthora species displays differences in host range and infection strategy, and the genus can generally be split into two groups: those that are primarily soilborne or waterborne, which tend to infect the root and collar regions of their plant hosts, and those that are primarily aerial, which tend to infect aboveground plant tissues, including leaves, fruits, shoots, branches, and stems (Chen et al., 2022; Jung et al., 2018). The most problematic forest Phytophthoras are those that can attack a broad range of plant hosts and can infect and sporulate on multiple plant tissues, allowing them to spread globally and invade novel habitats. 1.4 Filamentous pathogens, climate change, and forest health While globalization has undoubtedly aided the spread of forest invasive fungal and oomycete pathogens, climate change is also driving a shift in host-pathogen interactions, often creating more conducive conditions for both native and non-native pathogens (Anderson et al., 2004; Coakley et al., 1999; Garrett et al., 2006; Harvell et al., 2002). Increased climatic extremes, such as drought or flooding, can make host trees more stressed and therefore less able to resist Figure 1.2 Phytophthora species are ubiquitous pathogens with a global distribution, and many of them have the capacity to cause large-scale damage in both agriculture and forestry. (A) Symptoms of late blight of potato caused by Phytophthora infestans, responsible for the Irish Potato Famine. Photo: Scott Bauer, USDA Agricultural Research Service, Bugwood.org, used under the permission of a CC BY 3.0 license. (B) Forest outbreak of sudden oak death, caused by Phytophthora ramorum. Photo: Joseph O’Brien, USDA Forest Service, Bugwood.org, used under the permission of a CC BY 3.0 license. C) Eucalyptus trees in Australia killed by Phytophthora cinnamomi. Photo: Florida Division of Plant Industry, Florida Department of Agriculture and Consumer Services, Bugwood.org, used under the permission of a CC BY 3.0 license. 8 pathogen attack (Agrios, 2005; Garrett et al., 2006). Climate change has also shifted the temperature and precipitation regimes for some areas, creating environmental conditions more conducive to pathogen dispersal and survival (Garrett et al., 2006; Harvell et al., 2002). An added layer to the changing climate is historical forest management decisions, which have sometimes led to monospecies forest stands or trees being planted outside of their natural range, both of which leave forests more vulnerable to pathogen attack. A well-known example of a pathogen that has benefited from this synergy is Nothophaeocryptopus gaeumannii (T. Rohde) Videira, C. Nakash., U. Braun & Crous, the causal agent of Swiss needle cast (SNC) in Douglas-fir. Normally an innocuous endophyte, N. gaeumannii has thrived under both a warming climate and the planting of Douglas-fir outside its normal range and is now causing widespread damage in forestry (Hood, 1982; Kimberley et al., 2011; Lee et al., 2013; Stone et al., 2008). There is also increasing evidence for a link between climate change and the worsening of diseases caused by forest Phytophthoras (Contreras-Cornejo et al., 2023), including P. alni Brasier & S.A. Kirk (Aguayo et al., 2014), P. cinnamomi (Burgess et al., 2017; Serrano et al., 2022), and P. ramorum (Venette, 2009; Venette & Cohen, 2006). 1.5 Forest pathology in the genomics era In the context of globalization and climate change and their often-synergistic effects on forest disease outbreaks, it is becoming increasingly important for plant pathologists to explore the complexities of host-pathogen interactions and elucidate the specific molecular mechanisms driving phytopathogenicity in filamentous fungi and oomycetes. The 21st century brought the development of next-generation sequencing technologies, which lowered the cost of whole genome sequencing and subsequently led to the increasing availability of genomic data. 9 Additionally, the rapid innovation of highly specific and adaptable molecular techniques such as RNA interference (Fire et al., 1998), zinc finger nucleases (Bibikova et al., 2002), transcription activator-like effector nucleases (Christian et al., 2010; Miller et al., 2011), and CRISPR-Cas9 gene editing (Jinek et al., 2012) is allowing scientists to explore the genomes of organisms in novel ways. This genomics era has revolutionized most of the biological sciences, including plant and forest pathology. Whereas historically, the phytopathogenic lifestyles and traits of filamentous pathogens were determined phenotypically through morphology-based pathogen identification and observations of host-pathogen interactions, scientists are now able to employ genetic and genomic analyses to understand what drives pathogenicity on a molecular level (Derbyshire et al., 2017; Klosterman et al., 2011; Li et al., 2018; Tyler et al., 2006). There is also increased interest in developing more genomics-based approaches to forest pest and pathogen biosurveillance with the ultimate goal of uncovering the specific genomic mechanisms that forest invasive species use to invade novel ecosystems (Bilodeau et al., 2019; Hamelin & Roe, 2020; Roe et al., 2019). Despite the significant technological advancements of the genomics era, to date, most research in plant pathology exploring the use of comparative genomics and molecular techniques has focused on agricultural pathosystems, and research in forest pathosystems is lacking. The goal of the research described herein is to fill a part of this research gap in forest pathology and provide a proof-of-concept for the application of comparative genomic analyses and molecular techniques in forest pathology. 10 1.6 PhD research objectives The current genomics era facilitates molecular-based approaches for both large- and small-scale research: increased access to high quality genomes gives researchers the freedom to conduct large-scale comparative genomic studies amongst phytopathogenic species (Gan et al., 2016; Hane et al., 2020; Haridas et al., 2020; Ohm et al., 2012; Roy et al., 2020), or to use molecular techniques such as CRISPR-Cas9 gene editing to determine specific genes and molecular pathways involved in host-pathogen interactions and phytopathogenicity at the individual species level (Fang & Tyler, 2016; Gumtow et al., 2018). I believe these approaches can act in synergy, with comparative genomics uncovering specific genes or families of genes associated with phytopathogenic traits of interest, and CRISPR-Cas9 gene editing being used to functionally validate the gene candidates generated from the comparative analyses. The goal of my PhD research, presented in Chapters 2-5 of this dissertation, was to take a multidisciplinary approach, combining large-scale and small-scale methods to address two overarching objectives (Figure 1.3): Objective 1: Establish a proof of concept for the use of large-scale comparative genomics to explore the genetic mechanisms driving plant pathogenicity, uncover gene targets associated with phytopathogenic traits of interest, and discover genomic patterns that can be used in future biosurveillance of filamentous forest pathogens. (Chapter 2) Objective 2: Establish a method for CRISPR-Cas9 editing in a forest pathogenic Phytophthora species for future validation of gene candidates associated with phytopathogenic traits of interest. (Chapters 3-5) 11 Objective 1 is addressed in Chapter 2, which describes a study demonstrating the use of comparative genomic analyses in fungi to discover predictive patterns associated with phytopathogenic lifestyles and traits. The purpose of this study was to 1) establish a proof of concept for the use of predictive genomics in the biosurveillance of filamentous plant pathogens, and 2) test the use of comparative genomics to generate putative gene targets associated with the phytopathogenic traits of interest that could be functionally validated with CRISPR-Cas9 gene editing. Fungi were chosen as the test group for this research due to the readily accessible genomic data available from the online fungal genomics resource MycoCosm (Grigoriev et al., 2014). Chapter 3 provides a bridge into Objective 2 with a literature review of CRISPR-Cas9 gene editing, its current applications in plant pathology, and its potential future applications in forest pathology. Chapter 4 describes experiments conducted to establish CRISPR-Cas9 plasmid transformations in five forest Phytophthora pathogens using a polyethylene glycol (PEG)-mediated method previously established for CRISPR-Cas9 gene editing in the agricultural pathogen P. sojae Kauf. & Gerd (Fang et al., 2017a; Fang & Tyler, 2016). Finally, Chapter 5 describes the success and failures in establishing CRISPR-Cas9 gene editing in two forest pathogenic Phytophthoras, P. cactorum (Leb. And Cohn) Schröeter and P. ramorum, using a plasmid-ribonucleoprotein co-transformation method. 12 Figure 1.3 Graphical summary showing the simplified structure and objectives of the research presented in this dissertation. Also included are the long-term research objectives (grey box), which are not within the scope of this dissertation, but represent the bridge between Objectives 1 and 2 and the direction intended for future studies. Abbreviations: PEG (polyethylene glycol), RNP (ribonucleoprotein), gRNA (guide RNA), G418R (geneticin resistance), KO (knockout). Figure created with BioRender.com. 13 Chapter 2: Large-scale comparative genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits *Note that a version of this chapter has been published: Dort, E.N., Layne, E., Feau, N., Butyaev, A., Henrissat, B., Martin, F.M., Haridas, S., Salamov, A., Grigoriev, I.V., Blanchette, M., and Hamelin, R.C. (2023) Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits. Scientific Reports 13: 17203. The chapter is presented in the form in which it was accepted for publication and is reproduced in this dissertation under the permission of a CC BY 4.0 license. Modifications were made to figure and table labels, and supplementary materials to adhere to the University of British Columbia’s PhD dissertation formatting requirements. Minor modifications were also made to some figures and text to address PhD Examiner revisions. 2.1 Chapter Summary Invasive plant pathogenic fungi have a global impact, with devastating economic and environmental effects on crops and forests. Biosurveillance, a critical component of threat mitigation, requires risk prediction based on fungal lifestyles and traits. Recent studies have revealed distinct genomic patterns associated with specific groups of plant pathogenic fungi. We sought to establish whether these phytopathogenic genomic patterns hold across diverse taxonomic and ecological groups from the Ascomycota and Basidiomycota, and furthermore, if those patterns can be used in a predictive capacity for biosurveillance. Using a supervised machine learning approach that integrates phylogenetic and genomic data, we analyzed 387 fungal genomes to test a proof-of-concept for the use of genomic signatures in predicting fungal 14 phytopathogenic lifestyles and traits during biosurveillance activities. Our machine learning feature sets were derived from genome annotation data of carbohydrate-active enzymes (CAZymes), peptidases, secondary metabolite clusters (SMCs), transporters, and transcription factors. We found that machine learning could successfully predict fungal lifestyles and traits across taxonomic groups, with the best predictive performance coming from feature sets comprising CAZyme, peptidase, and SMC data. While phylogeny was an important component in most predictions, the inclusion of genomic data improved prediction performance for every lifestyle and trait tested. Plant pathogenicity was one of the best-predicted traits, showing the promise of predictive genomics for biosurveillance applications. Furthermore, our machine learning approach revealed expansions in the number of genes from specific CAZyme and peptidase families in the genomes of plant pathogens compared to non-phytopathogenic genomes (saprotrophs, endo- and ectomycorrhizal fungi). Such genomic feature profiles give insight into the evolution of fungal phytopathogenicity and could be useful to predict the risks of unknown fungi in future biosurveillance activities. 2.2 Introduction The health of many natural and managed plant ecosystems is threatened by fungal plant pathogens, which often cause emerging infectious diseases that are difficult to mitigate once established (Anderson et al., 2004; Fisher et al., 2012; Trumbore et al., 2015). Due to their perennial nature, trees are particularly vulnerable to non-native pathogens known as forest invasive alien species (FIAS), which can spread rapidly due to the lack of co-evolved resistance in native hosts, causing disease outbreaks across entire landscapes (Allen & Humble, 2002; Loo, 2009; Roy et al., 2014; Wingfield et al., 2015). Biosurveillance–the systematic and cyclical 15 process of collecting and analyzing surveillance data to detect and characterize disease outbreaks and inform subsequent management decisions–has become a crucial means for countries to reduce the threat of FIAS (Bilodeau et al., 2019; Hamelin & Roe, 2020; Roe et al., 2019; Wingfield et al., 2015). While current biosurveillance strategies enable regulatory agencies to identify known pathogens, they do not identify the specific ecological traits associated with disease outbreaks and also fail to monitor pathogens that have not yet been taxonomically identified or listed as potential threats. This missing information constitutes a blind spot in policies for pathogen regulation as most FIAS are not identified taxonomically until after they have successfully invaded ecosystems (Brasier, 2008; Roy et al., 2014). In light of these limitations, a genomics approach to biosurveillance that is focused on discovering the genomic ‘signatures’ that FIAS use to successfully invade novel ecosystems outside of their taxonomic identity has been proposed (Bilodeau et al., 2019; Hamelin & Roe, 2020; McTaggart et al., 2016; Roe et al., 2019). Indeed, a more genetics- and genomics-centered approach to plant pathogen management is an increasingly prevalent theme in recent research (Gardiner et al., 2020; Howlett et al., 2015; Keriö et al., 2020; Klosterman et al., 2016). An important question to be answered for genomics-based biosurveillance is whether there are genomic signatures associated with the lifestyles and traits of fungal plant pathogens. The success of FIAS, and of phytopathogenic fungi in general, hinges on their diverse trophic strategies, or lifestyles, which enable them to infect and colonize a variety of plant tissues. In addition to their lifestyles, fungal phytopathogens display diverse ecological traits such as host range and tissue specificity. These lifestyle and trait categories encompass generalizations about the pathogens that make up each group, including how they infect their hosts and spread disease 16 through ecosystems. However, it is often challenging to assign species to discrete lifestyle categories due to the complex behaviours of many fungi and their ability to exhibit multiple lifestyles; this becomes particularly problematic when trying to understand the mechanisms of fungal pathogenicity and plant disease resistance (De Silva et al., 2016; Oliver & Ipcho, 2004). Given the subjectivity in assigning pathogens to lifestyle and trait categories, there has been increased research to explore these categories at the genome level, especially given the increasing availability of whole genome sequences (Hane et al., 2020; Haridas et al., 2020; Pandaranayaka et al., 2019). Additionally, the continued growth of online fungal genomic resources and databases such as FungiDB (Amos et al., 2022), Ensembl Genomes (Howe et al., 2020), NCBI RefSeq (O’Leary et al., 2016), and MycoCosm (Grigoriev et al., 2014) are providing researchers with powerful resources to compare fungi with different lifestyles and traits at the genome scale. Many recently published comparative genomics studies focus on groups of important plant-interacting fungi such as wood-decay fungi (Almási et al., 2019), dark septate endophytes (Knapp et al., 2018), mycorrhizal fungi (Kohler et al., 2015; Miyauchi et al., 2020), and phytopathogenic groups including Colletotrichum (Carbú et al., 2019; Gan et al., 2016), Zymoseptoria (Krishnan et al., 2018), Fusarium (Roy et al., 2020), and Dothideomycetes (Haridas et al., 2020; Ohm et al., 2012) species. These large-scale analyses reveal genomic patterns that can help plant pathologists better understand fungal genome evolution and identify genes that may be central to phytopathogenicity. From a biosurveillance perspective, the discovery of distinct genomic signatures associated with phytopathogenic lifestyles or traits could help predict risk or impact of undescribed fungal pathogens by analyzing their gene 17 content even before they have been taxonomically described (McTaggart et al., 2016). Integrating genomic analyses into biosurveillance pipelines would allow regulatory agencies to predict the threat a pathogen poses to an ecosystem and respond accordingly. Previous studies have demonstrated that there are lifestyle-related genomic signatures present in specific groups of fungal plant pathogens (Adhikari et al., 2013; Carbú et al., 2019; Gan et al., 2016; Hane et al., 2020; Haridas et al., 2020; Krishnan et al., 2018; Ohm et al., 2012; Pandaranayaka et al., 2019; Roy et al., 2020). We sought to build on these findings in a larger and more diverse group of fungi spanning a subset of classes from the Ascomycota and Basidiomycota phyla to test a proof-of-concept for using predictive genomics in the biosurveillance of fungal plant pathogens. In addition to fungal lifestyles, we expanded our analyses to include important ecological traits associated with plant pathogens and relevant to the biosurveillance of FIAS. Our results revealed that both lifestyles and traits can be predicted from fungal genomes, and we were able to uncover genomic features influencing phytopathogenicity in fungi. Our findings have important implications for integrating predictive genomics into future fungal FIAS biosurveillance pipelines and, in the long term, the development of more effective management strategies. 2.3 Methods 2.3.1 Fungal lifestyle database We created a lifestyle database for 533 fungal species (582 genomes) with data available from the Joint Genome Institute’s (JGI) MycoCosm Fungal Portal (Grigoriev et al., 2014). The species in our database span two ascomycete subphyla (Pezizomycotina, Taphrinomycotina) and two 18 basidiomycete subphyla (Pucciniomycotina, Agaricomycotina). Each species was given taxonomic labels for phylum, class and order using the fungal nomenclature of Index Fungorum (The Royal Botanic Gardens Kew et al., 2020). To categorise each of the species in our database into its respective lifestyle(s) and identify important ecological traits, we used both the information and references from MycoCosm as well as information found in an independent literature search. We used 1014 peer-reviewed publications in assigning lifestyles and traits to fungi in the database. In total, we assembled a list of 24 different lifestyles to which species could be assigned (Supplementary Table A.2.1), including four lifestyles exhibited by important phytopathogens (biotroph, obligate biotroph, necrotroph, hemibiotroph) for both managed and natural plant systems. Given the subjectivity in the literature as well as the possibility that one species can exhibit multiple trophic strategies during its life cycle (De Silva et al., 2016; Kuo et al., 2015), species were often assigned more than one lifestyle. In cases where there was disagreement in the literature as to the lifestyle a species exhibits, we chose the lifestyle on which the majority of published studies agreed. If a species could not be definitively categorized from the literature, it was labelled as “Unknown”. For the pathogenic fungi we included ten additional ecological traits relevant to pathogenicity (Supplementary Table A.2.2). For plant pathogenic species, host type (gymnosperms, angiosperms) and targeted tissues (stem, leaves, roots) were assigned and are hereby referred to as “phytopathogenic traits”. These phytopathogenic traits were determined only from environmental studies; experimental studies conducted in controlled conditions (e.g., artificial inoculations) were not used. 19 2.3.2 Genome analyses We used both principal component analysis (PCA) and machine learning to perform genomic comparisons amongst a subset of species from the database with available data. For machine learning, we used DendroNet, a phylogeny-aware method of training machine-learning models that incorporates both phylogenetic and genomic data (Layne et al., 2020); this allowed us to separate the signals from phylogeny and gene content and determine which genomic features were associated with specific lifestyles and traits. 2.3.2.1 Genome annotations The genomic data for each MycoCosm species can only be used if there is an associated genome reference, or with explicit permission from the principal investigator (PI). Therefore, we downloaded the genome annotation data (gene counts) available from MycoCosm for a subset of 355 fungal species (387 genomes) from our lifestyle database consisting of 362 published and 25 unpublished genomes (used with PI permission). We obtained data from the five annotation groups available from MycoCosm: secondary metabolite clusters (SMCs–7 clusters), carbohydrate-active enzymes (CAZymes–220 families, subfamilies not included), peptidases (MEROPS–144 clans/families), membrane transport proteins (transporters–522 families), and transcription factors (transfactors–65 families). The gene counts from each of these five groups were then aligned to their respective species in the lifestyle database for comparative genomic analyses. While some of the genomes from MycoCosm were sequenced externally (see Data A.4.1 for original genome reference papers), all the functional annotations were generated by the JGI Annotation Pipeline (Grigoriev et al., 2006, 2014), except for CAZymes, which were 20 annotated by the Carbohydrate-Active enZYmes database (Drula et al., 2022) in collaboration with the JGI. 2.3.2.2 Phytopathogenic lifestyles and traits We performed the genomic analyses on the gene count data for all 387 genomes, but we used only a subset of lifestyles relevant to plant pathogenicity for labelling: pathogenic (includes animal, fungus, and plant pathogens), plant pathogenic (plant pathogens only), biotrophs (B), obligate biotrophs (OB), hemibiotrophs (HB), and necrotrophs (N). We also included saprotrophs (S) as a non-pathogenic lifestyle comparison, as well as ectomycorrhizal (ECM) and endomycorrhizal (ENM) species to compare plant-interacting, but non-pathogenic, fungi to phytopathogenic fungi. The ENM category comprised both ericoid mycorrhizal species and ectendomycorrhizal species. The phytopathogenic traits (gymnosperm, angiosperm, stem, foliar, and root pathogens) were also included as labels for the analyses. 2.3.2.3 Principal component analyses PCAs were performed using R software version 3.5.1 (R Core Team, 2017). Pre-processing of the data for each PCA was performed as follows: all species rows with NA values (no gene annotation) were removed, variables with zero variance and near zero variance were removed from the data using the function nearZeroVar from the caret R package (Kuhn, 2020), and the data were scaled to unit variance and centered. The function PCA was used from the R package factoextra (Kassambara & Mundt, 2020). We performed PCAs separately for each genome component (i.e., CAZymes, MEROPS, SMCs, transporters, and transfactors) to avoid excessive noise observed for joined data analyses (Hane et al., 2020). 21 2.3.2.4 DendroNet machine learning 2.3.2.4.1 Dataset preprocessing Membership in each of the lifestyles and phytopathogenic traits described above was considered as a separate binary-classification problem for the machine learning analyses, and the corresponding gene counts from each annotation group were used as features. For each of the target lifestyle/trait classes, we used a total of 31 genomic feature sets to train a predictive model: each of the five annotations (CAZymes, MEROPS, SMCs, transporters, and transfactors) as individual genomic feature sets, and every possible combination of the annotations (26 possible combinations). We performed the machine learning analyses for the phytopathogenic traits only on species belonging to the plant pathogen class (subset of 138 species). 2.3.2.4.2 DendroNet architecture DendroNet models have two components. The first component is a base model architecture, used to make predictions given a set of input data and a target output. The base model architecture used in this study was a logistic regression classifier. The second component is a neural network with the same topology as the phylogenetic tree that relates all the samples in the dataset. This neural network is used to determine the optimal weights to be used for the base classifier’s predictions at each location in the phylogenetic tree. Regularization is used to encourage the use of similar weights in species that are closely related. For this study, we retrieved the Dikarya (Ascomycota + Basidiomycota) phylogenetic tree from MycoCosm (Grigoriev et al., 2014), which was pruned to the species being analysed prior to input into DendroNet models. A pruned version of this tree with the 387 fungal genomes we analysed is included in Data A.4.6 in Newick format. To make the tree, proteins from all 387 genomes were clustered using MMseqs2 22 version 0188988235c6f1a8e90f327827c73f981db8a19a (Steinegger & Söding, 2017). Orthologous proteins were identified from the clusters allowing for paralogs and up to 100 missing genomes per cluster. When paralogs were present, only one copy from each genome was retained for alignment. Proteins from 2580 selected clusters were aligned using MAFFT version 7.123b (Katoh et al., 2002). Divergent regions and poorly aligned positions were cleaned using Gblocks (Castresana, 2000) with options ‘-t=p -e=.gb -b4=5 -b5=h’. The resulting cleaned alignments were concatenated and used for tree building with FastTree (Price et al., 2009). 2.3.2.4.3 Model training We trained a separate DendroNet model for each lifestyle/trait classification task. Each model was trained for 1,000 epochs using a learning rate of 0.001 and the Adam optimizer (Kingma & Ba, 2014). Regularization was applied to the L1 norm of the adjustments in weights made by the neural network, scaled by a factor of 1.0, via the process described previously (Layne et al., 2020). 2.3.2.4.4 Model evaluation We evaluated DendroNet model performance using the area under the receiver operating characteristic curve (AUC) and the results are reported on a 9-fold cross-validation split. The AUC value indicates the ability of the model to distinguish between members and non-members of a given class. AUC scores range in value from 0.0 (model has no ability to distinguish between classes) to 1.0 (model’s predictions perfectly separate the classes). Machine learning models producing AUC scores less than 0.6 are considered inappropriate for a classification task 23 while those above 0.7 are considered reasonable, and models producing scores above 0.8 are considered strong (Carter et al., 2016; Safari et al., 2016). 2.3.2.4.5 Feature importance evaluation We investigated the predictive power of each genomic feature set towards the lifestyle and trait classes. To separate the significance of a genomic feature set from the phylogenetic signal, we used the following process: first, the baseline predictive power of phylogeny was established for each target lifestyle and trait. This was done by training a DendroNet model that used only a bias term as a feature value, which produced predictions that were made using solely the phylogenetic placement of each species in the dataset, similar to a maximum parsimony tree. We therefore refer to this baseline phylogenetic prediction as the parsimony model. Next, for each genomic feature set, we trained a DendroNet model using both that feature set and a bias term, allowing for the use of phylogenetic placement information. We then compared the performance of this feature+parsimony model to the parsimony model alone, with an improvement in DendroNet’s performance indicating that the genomic feature set conveys information about the target lifestyle or trait beyond the phylogenetic signal. We also trained DendroNet models using each individual feature within a genomic feature set (e.g., GH5 family in CAZymes) and analysed the performance of these models relative to the model using all features of the same set (e.g., all CAZyme families) and relative to the parsimony model. If an individual feature improved DendroNet’s performance over the parsimony model for a given lifestyle or trait, we documented its corresponding AUC score increase over the parsimony model (reported as a ‘raw’ score) as well as the fraction of the total AUC improvement relative to the AUC score from the whole genomic feature set (reported as a ‘fraction’ score). Additionally, we recorded the correlation 24 direction of the features for each lifestyle and trait (i.e., whether an increase or decrease in genes from each annotation group was associated with a lifestyle or trait). 2.3.2.4.6 Statistical analyses Prior to performing statistical analyses, we determined the distributions of the relevant data with Shapiro-Wilk normality tests. To compare DendroNet’s performance across all 31 genomic feature sets, we performed a non-parametric analysis of variance with repeated measures (Friedman test) on the AUC scores for the lifestyles and traits (separate analyses) followed by a Nemenyi test for post-hoc analysis. To determine whether the feature+parsimony models resulted in a significantly increased AUC score compared to the respective parsimony models, we performed paired, one-tailed tests: either a paired t-test or a Wilcoxon signed-rank test depending on whether the paired differences followed a normal or non-normal distribution. To compare the gene counts from the top CAZyme and peptidase predictors for plant pathogenicity, we performed independent t-tests on the gene counts from phytopathogenic genomes (‘Plant Pathogen’ = YES in FunLifeDB) relative to non-phytopathogenic genomes (‘Plant Pathogen’ = NO in FunLifeDB). 2.4 Results 2.4.1 Phytopathogenic lifestyles and traits can be predicted from phylogenetic and genomic data Our fungal lifestyle database, FunLifeDB, comprising information on 533 fungal species (582 genomes; https://biosafe.cs.mcgill.ca/funlifedb/) was the source of lifestyle and trait data for the subset of 387 published genomes (from 355 species) we analysed (Data A.4.1, Figure 2.1). We 25 observed a strong phylogenetic signal in the genomic data. All PCAs using the genomic features showed a clear separation of the Ascomycota (Pezizomycotina and Taphrinomycotina) and Basidiomycota (Pucciniomycotina and Agaricomycotina), particularly on the first two principal components (Figure 2.2). Still, the obligate biotrophs of both Ascomycota and Basidiomycota clustered together on the PCAs for CAZymes and peptidases, indicating common genomic features for this lifestyle (Figure 2.3). While the phylogenetic signal for the obligate biotrophs was still evident in these PCAs, particularly for members of the order Pucciniales, there was also a clear similarity in the genomic profiles of species with this lifestyle regardless of their phylogenetic placement (Figure 2.3). For all the other lifestyles and traits, including the plant pathogens, we did not find any clear patterns in the PCAs (data not shown). 26 Figure 2.1 Phylogenetic groups (includes subphyla and classes) from FunLifeDB included in our genomic analyses with summaries of the categories represented for each group analysed. The tree shows the phylogenetic relationships between the groups as represented by MycoCosm (https://mycocosm.jgi.doe.gov/mycocosm/home). The circled number after each group name indicates the total number of genomes analysed for that group, and the subsequent pie charts indicate how many of those genomes belong to each pathogenic, lifestyle and trait category. Legend Abbreviations: NP: non-pathogen; PP: plant pathogen; OP: other pathogen; S: saprotroph; ENM: endomycorrhizal; ECM: ectomycorrhizal; N: necrotroph; HB: hemibiotroph; B: biotroph; OB: obligate biotroph; GP: gymnosperm pathogen; AP: angiosperm pathogen; SP: stem pathogen; FP: foliar pathogen; RP: root pathogen. 27 Figure 2.2 Principal component analysis (PCA) biplots of annotation data from A) carbohydrate active enzymes, B) peptidases, C) secondary metabolite clusters, D) transporters, and E) transcription factors, showing the separation of the Ascomycota and Basidiomycota phyla on the first two principal components (PCs). The two phyla are differentiated by colour, and the four subphyla are differentiated by shape. The black arrows represent the loadings of the variables included in each PCA; only the top ten variables contributing to PC1 and PC2 are shown. 28 Genomic patterns associated with fungal lifestyles and traits were revealed with the DendroNet machine learning algorithm, which showed that many lifestyles and traits could be predicted using both phylogenetic and genomic data. The inclusion of genomic features increased Figure 2.3 Principal component analysis (PCA) biplots for A) carbohydrate active enzymes (CAZymes), and B) peptidases, showing the clustering of obligate biotroph (OB) genomes. The four subphyla are differentiated by colour, and the obligate biotroph genomes are differentiated by shape. A statistical ellipse was drawn at a 95% confidence level around the OB genomes. The black arrows represent the loadings of the variables included in each PCA; only the top ten variables contributing to PC1 and PC4 (CAZyme PCA), or PC1 and PC3 (peptidase PCA), are shown. 29 predictive performance over the parsimony models for all lifestyles and phytopathogenic traits we tested. Note that all AUC scores are reported as an average score with a standard deviation across a 9-fold cross-validation split (N=9). The contribution of phylogeny to DendroNet’s lifestyle predictions varied greatly, with parsimony AUC scores ranging from 0.438 ± 0.057 for the necrotrophs to 0.899 ± 0.018 for the obligate biotrophs (Data A.4.2). The three genomic feature sets that produced the highest mean AUC scores for lifestyle predictions were the combination of CAZymes+MEROPS+SMCs (0.915 ± 0.076), the CAZymes alone (0.914 ± 0.071), and the combination of CAZymes+MEROPS (0.912 ± 0.081), respectively (Figure 2.4, Data A.4.2). All three of these top-performing feature sets resulted in statistically significant (p < 0.05, N=9) increases in AUC scores over the parsimony models for every lifestyle tested (Data A.4.3). Prediction of the endomycorrhizal lifestyle improved the most from the addition of genomic features, with an average AUC increase of 0.582 across the top three feature sets (Figure 2.4A), corresponding to a gain of 140% (Figure 2.4B). Within the plant pathogenic lifestyles, DendroNet’s predictions improved the most for necrotrophs; the AUC scores increased by up to 0.395 (CAZyme feature set; Figure 2.4A), with an average AUC gain of 87% over parsimony across the top three feature sets (Figure 2.4B). The obligate biotroph lifestyle had the highest parsimony AUC of 0.899 ± 0.018 (Figure 2.4A), resulting from a strong phylogenetic signal (there are only three orders within which obligate biotrophs are found) but increased to an AUC of 1.000 ± 0.000 (gain over the phylogeny signal of 11.2%) in all three of the top-performing genomic feature sets (Figure 2.4B). After the obligate biotrophs, prediction scores for hemibiotrophs were the highest of the phytopathogenic lifestyles, with AUC scores of up to 0.943 ± 0.007 (CAZymes+MEROPS 30 feature set), an improvement of 41.6% over the parsimony score (Figure 2.4). DendroNet also consistently predicted fungal pathogenicity (AUCs up to 0.879 ± 0.004 with CAZymes+MEROPS+SMCs) and plant pathogenicity (AUCs up to 0.947 ± 0.003 with CAZymes). However, phylogeny contributes a large proportion to this predictive capacity, with parsimony AUCs of 0.763 ± 0.023 and 0.758 ± 0.019 and gains over the phylogeny signal of 15.2% and 24.9% for the pathogen and plant pathogen lifestyles, respectively (Figure 2.4). Figure 2.4 Area under the receiver operating characteristic curve (AUC) scores obtained with DendroNet for predicting nine lifestyles in fungi with the three top-performing genomic feature sets (C: CAZymes; M: MEROPS (peptidases); S: secondary metabolite clusters). Results from DendroNet’s lifestyle predictions for all 31 genomic feature sets are reported in Supplementary Data S2. A) the average AUC score obtained for each lifestyle with only the phylogenetic data (parsimony model) is represented in red at the base of each graph (standard deviation of this value is represented with a dotted red line) and the average improvement of the model with the genomic feature(s) is represented with a blue column. The asterisk indicates that the AUC score from the genomic feature model was significantly (p < 0.05, N=9) greater than the AUC score from the parsimony model. B) percent gain ([[AUC parsimony – AUC genomic signal]/AUC parsimony] x 100) calculated for each lifestyle (average and standard deviation calculated from the three genomic feature sets). 31 The phylogenetic signals for the phytopathogenic traits tested (host type and tissues) ranged from parsimony AUCs of 0.502 ± 0.070 (root pathogens) to 0.674 ± 0.144 (foliar pathogens) (Data A.4.2). The inclusion of genomic data improved DendroNet’s predictions of all phytopathogenic traits, though the AUC gains were not as high as they were for the lifestyles (Figure 2.5, Data A.4.2). The three genomic feature sets that produced the highest average AUC scores for predicting phytopathogenic traits were the CAZymes alone (0.823 ± 0.100), the combination of CAZymes+MEROPS (0.780 ± 0.103), and the combination of CAZymes+MEROPS+SMCs (0.770 ± 0.119), respectively. All three of these top-performing feature sets resulted in statistically significant (p < 0.05, N=9) increases in AUC scores over the parsimony models for every trait tested (Data A.4.3). DendroNet’s predictions of angiosperm pathogens improved the most over the parsimony model, though the variation in performance between the top three feature sets was more than for other traits (Figure 2.5). Foliar pathogenicity was the best-predicted trait, with a mean AUC score of 0.956 ± 0.004 from the top three feature sets (Figure 2.5A). 32 2.4.2 Expansions and contractions of specific CAZyme, peptidase and secondary metabolite cluster gene families drive DendroNet’s predictions of phytopathogenic lifestyles and traits The three top-performing genomic feature sets across phytopathogenic lifestyles and traits were CAZymes alone, CAZymes+MEROPS, and CAZymes+MEROPS+SMCs (Data A.4.2). Patterns in specific gene families arose as drivers for DendroNet’s predictions (Data A.4.4). Figure 2.5 Area under the receiver operating characteristic curve (AUC) scores obtained with DendroNet for predicting five phytopathogenic traits in fungi with the three top-performing genomic feature sets (C: CAZymes; M: MEROPS (peptidases); S: secondary metabolite clusters). Results from DendroNet’s trait predictions for all 31 genomic feature sets are reported in Supplementary Data S2. A) the average AUC score obtained for each trait with only the phylogenetic data (parsimony model) is represented in red at the base of each graph (standard deviation of this value is represented with a dotted red line) and the average improvement of the model with the genomic feature(s) is represented with a blue column. The asterisk indicates that the AUC score from the genomic feature model was significantly (p < 0.05, N=9) greater than the AUC score from the parsimony model. B) percent gain ([[AUC parsimony – AUC genomic signal]/AUC parsimony] x 100) calculated for each trait (average and standard deviation calculated from the three genomic feature sets). 33 2.4.2.1 CAZymes The total number of CAZymes in the genome and the number of genes from the glycoside hydrolase (GH), carbohydrate-binding module (CBM), and auxiliary activity (AA) CAZyme classes were increased in saprotrophs and plant pathogens, compared to the other lifestyles (Supplementary Figure A.3.1). The saprotrophic lifestyle was associated with a decrease of the carbohydrate esterases (CEs), polysaccharide lyases (PLs), and glycosyltransferases (GTs), while the phytopathogenic lifestyle was associated with an increase of all these gene classes (Figure 2.6). Ectomycorrhizal genomes had decreased numbers of all CAZyme classes, including total CAZymes, whereas endomycorrhizal genomes had decreased numbers of AAs, CBMs, CEs, and PLs, but increased numbers of GHs, GTs, and total CAZymes. While none of the individual CAZyme families predicted plant pathogenicity better than using all of them together (AUC of 0.95; Figure 2.4A), the best individual CAZyme predictor of plant pathogenicity was an increase of the GT class of genes (AUC of 0.91; Data A.4.4). Within the GT class, an increase of genes in the family GT2 was associated with plant pathogens (AUC of 0.85; Figure 2.6, Data A.4.4), while a decrease of GT2 genes was the top individual predictor for saprotrophs (AUC of 0.83; Data A.4.4). The second-best predictor of plant pathogenicity was the number of CBM63 genes (AUC of 0.90; Data A.4.4), with an increased number in plant pathogens relative to non-plant pathogens (Figure 2.6). Every phytopathogenic lifestyle except the biotrophs was associated with an increase in CBM63 genes, whereas saprotrophs and both mycorrhizal lifestyles were associated with a decrease in these genes (Supplementary Figure A.3.1, Data A.4.4). Another important predictor for plant pathogens was the number of GH32 genes (Figure 2.6): almost every phytopathogenic lifestyle as well as endomycorrhizal fungi had increased GH32 genes, but biotrophs, saprotrophs, and ectomycorrhizal fungi showed decreased numbers (Supplementary 34 Figure A.3.1, Data A.4.4, Data A.4.5). An increase in GH32 genes was also associated with all phytopathogenic traits except for gymnosperm-infecting pathogens, which showed a decrease (Data A.4.4, Data A.4.5). There were three lifestyles (biotrophs, hemibiotrophs, and ectomycorrhizal fungi) and three traits (angiosperm-, root- and stem-infecting pathogens) for which there were individual CAZyme families that provided better DendroNet predictions than when all CAZyme families were used together (Data A.4.4). Figure 2.6 Violinplots of the gene counts from carbohydrate active enzyme (CAZyme) annotations revealed by DendroNet analyses to be expanded in phytopathogenic genomes relative to non-phytopathogenic genomes. A) Gene counts of the six CAZyme classes and the total CAZymes (all CAZyme classes and families). B) Gene counts of the three CAZyme families that were drivers for DendroNet’s predictions of fungal phytopathogenicity. The asterisks in both A and B indicate that the gene counts in phytopathogenic genomes were significantly greater than the gene counts in non-phytopathogenic genomes as per an independent t-test (N=355) (‘*’: p < 0.05, ‘**’: p < 0.01, ‘***’: p < 0.001, ‘****’: p < 0.0001). 35 2.4.2.2 Peptidases Several individual peptidase families produced better predictions than using all families combined for many of the lifestyles and traits (Data A.4.4). These peptidase families varied depending on the specific lifestyle or trait, but four recurring top predictor features were the prolyl oligopeptidase (S9), prolyl aminopeptidase (S33), and carboxypeptidase Y (S10) serine peptidase families as well as the pepsin A aspartic protease family (Data A.4.4). The genomes of plant pathogens exhibited increased numbers of S9, S10, and S33 genes, but decreased numbers of pepsin genes relative to non-phytopathogenic genomes (Figure 2.7, Supplementary Figure A.3.2). An important predictor for all phytopathogenic lifestyles was the number of prolyl aminopeptidase (S33) genes; plant pathogen, necrotroph, hemibiotroph, and facultative biotroph genomes had increased S33 genes, while obligate biotroph genomes had decreased S33 genes (Data A.4.4). An increase in the total number of peptidases was the best individual predictor of plant pathogenicity (AUC of 0.88; Figure 2.7, Data A.4.4) while the second-best predictor was an increase in genes from the S9 prolyl oligopeptidase family (AUC of 0.86; Figure 2.7, Data A.4.4). The total number of peptidases was also an important predictor for all phytopathogenic lifestyles, with necrotrophs and hemibiotrophs exhibiting increased peptidases, and biotrophs (facultative and obligate) exhibiting decreased peptidases (Supplementary Figure A.3.2, Data A.4.4). 36 2.4.2.3 Secondary metabolite clusters Only two lifestyles (ectomycorrhizal fungi and obligate biotrophs) and two traits (foliar and stem pathogens) had individual SMC features that improved prediction over the parsimony model (Data A.4.4). For all four of these groups, the total number of SMC genes was the strongest predictor, with ectomycorrhizal fungi, obligate biotrophs, and foliar pathogens having decreased total SMCs and stem pathogens having increased total SMCs (Supplementary Figure A.3.3, Data A.4.4). For ectomycorrhizal fungi, foliar pathogens, and stem pathogens, the fraction score of the Figure 2.7 Violinplots of the gene counts from peptidase (MEROPS) annotations revealed by DendroNet analyses to be expanded or contracted in phytopathogenic genomes relative to non-phytopathogenic genomes. A) Gene counts of the total peptidases (all clans and families). B) Gene counts of the four peptidase families that were drivers for DendroNet’s predictions of fungal phytopathogenicity. The asterisks in both A and B indicate significance in gene counts as per an independent t-test (N=355) (‘*’: p < 0.05, ‘**’: p < 0.01, ‘***’: p < 0.001, ‘****’: p < 0.0001). For the total peptidases (‘Total_MEROPS’), S9, S10, and S33 annotations, gene counts were significantly greater in phytopathogenic genomes relative to non-phytopathogenic genomes, whereas for pepsins, gene counts were significantly lower in phytopathogenic genomes relative to non-phytopathogenic genomes. 37 total SMC feature was greater than one (Data A.4.4), indicating that the total number of SMC genes was a better predictor of these lifestyles than using all SMC families combined. The total number of SMC genes was also increased in the genomes of plant pathogens, including necrotrophs and hemibiotrophs, and endomycorrhizal fungi, but was decreased in the genomes of facultative biotrophs and saprotrophs (Supplementary Figure A.3.3, Data A.4.5). 2.5 Discussion Since Anton de Bary first discussed the concept of ‘nutritive adaptation’ in the late 1800s (de Bary, 1887), plant pathologists have been exploring and refining the definitions of trophic modes, or lifestyles, for fungal species (De Silva et al., 2016; Lewis, 1973; Perfect & Green, 2001; Thrower, 1966). Identifying the lifestyles of pathogenic fungi provides important information on how pathogens cause disease, how they spread through an ecosystem, and ultimately, how best to approach disease mitigation (De Silva et al., 2016; McTaggart et al., 2016; Newton et al., 2010). Our results support the hypothesis that there are genomic patterns, or signatures, associated with the lifestyles and ecological traits of fungal plant pathogens across phylogenetic groups and that these signatures can be used in a predictive capacity. While we observed a strong association between phylogeny and many of the lifestyles and traits, the inclusion of genomic data always improved the prediction performance of DendroNet, suggesting that there are genomic signatures beyond those shaped solely by phylogenetic relationships. Our study improves our understanding of the genomics underlying fungal phytopathogenic lifestyles and traits, while also highlighting some of the challenges and limitations of integrating large-scale genomic and lifestyle data into future biosurveillance practices. 38 The lack of lifestyle-associated patterns in our PCAs contrasts with recent results obtained by Hane et al., who used a PCA-based machine learning method (CATAStrophy) for lifestyle predictions in fungi and oomycetes (Hane et al., 2020). While Hane et al. did observe a strong phylogenetic signal in their PCAs, they also observed clusters of species with similar lifestyles that were phylogenetically unrelated (Hane et al., 2020). The difference in the strength of the phylogenetic signal between the two studies could be an effect of sample size: 355 fungal species were used in our study vs. 158 in the CATAStrophy study (Hane et al., 2020). As the number of species analyzed increases, the phylogenetic signal from the 452 million years of divergence between the Ascomycota and Basidiomycota (Berbee & Taylor, 2010; J. W. Taylor & Berbee, 2006) might overwhelm any other genomic signals. The DendroNet machine learning approach allowed us to characterize the influence of phylogeny seen in our PCAs while exploring genomic signals outside phylogeny that are drivers of fungal phytopathogenic lifestyles and traits. From a biosurveillance perspective, the most promising result is DendroNet’s ability to predict plant pathogenicity with AUC scores of up to 0.95. While the parsimony scores for plant pathogens indicate a strong phylogenetic signal, the increase of 25% in AUC with the inclusion of CAZyme data suggests there are specific genomic features contributing to plant pathogenicity in fungi. This finding validates recent results obtained with a subset of the MycoCosm database (Haridas et al., 2020), which demonstrated that within the Dothideomycetes, plant pathogens could be distinguished from saprobes with greater than 95% accuracy using machine learning on genomic data. Our analyses, performed on a larger number of species with a broader range of both taxonomic and lifestyle groups, confirm that not only can plant pathogens be distinguished from saprotrophs, but they also have a unique profile relative to other pathogens. Additionally, 39 the high predictive performance of DendroNet with plant pathogen host type (angiosperms vs. gymnosperms), and particularly with foliar pathogens, indicates that there are also strong genomic signals associated with phytopathogenic traits. These findings related to plant pathogenic fungi have important implications for biosurveillance, specifically for predicting the lifestyles and traits of novel or unknown species. However, as with all machine learning algorithms, DendroNet’s performance is limited by the data that it was given for training. There is occasionally disagreement in the literature as to which lifestyle a fungal species exhibits (Kabbage et al., 2015), and as fungal-plant interactions are studied more closely in the lab, previously unobserved lifestyles can be revealed (Kuo et al., 2015) and lifestyle assignments will thus shift. We addressed this lifestyle flexibility by assigning multiple lifestyles to species when supported by evidence from the literature. Additionally, when we encountered disagreement in the literature regarding the lifestyle assignment of a species, we chose the lifestyle for which the majority of studies had categorized a fungus. This inconsistency in the categorization of fungal lifestyles is a limitation of studies such as ours that aim to assign lifestyles and traits from the literature and highlights a significant challenge in this research moving forward. Our results provide strong support for the functionality of predictive genomics, but as the lifestyles and traits of more fungi are curated and updated, DendroNet should continue to be trained and tested to determine whether its predictive power extends to a broader range of taxonomic groups and lifestyles. While we tested all possible combinations of the five genome annotations included in this study, using CAZyme data alone produced the best prediction results for most of the phytopathogenic lifestyles and traits, and CAZymes were also included in all three of the top-performing feature 40 sets. This result is not surprising given that CAZymes are crucial for most fungal plant pathogens, allowing them to colonize their hosts by overcoming the barrier of the plant cuticle, remodeling the fungal cell wall to avoid recognition, and deconstructing the host cell wall (Hématy et al., 2009; Knogge, 1996; Kubicek et al., 2014). Our finding that CAZymes are important predictors of fungal lifestyles is further supported by previous work demonstrating the predictive capacity of CAZyme content for filamentous phytopathogens (Hane et al., 2020). Differences in the patterns we observed in non-phytopathogenic and phytopathogenic lifestyles further reflect the importance of CAZymes for plant pathogenic fungi, with the genomes of plant pathogens exhibiting expansions in many CAZyme classes compared to the genomes of non-phytopathogenic fungi (saprotrophs, endo- and ectomycorrhizal fungi). An increase in CAZyme content and activity for phytopathogenic fungi relative to saprotrophs has also been documented in previous studies (B. C. King et al., 2011; Martinez et al., 2008; Zhao et al., 2013). While saprotrophs require CAZymes for the breakdown of dead plant tissues and nutrient assimilation, they do not require the extensive arsenal necessary for interacting with living plant tissues, nor are they subject to the diversifying selection that results from the co-evolutionary arms race between plants and their fungal pathogens (J. D. G. Jones & Dangl, 2006; Möller & Stukenbrock, 2017; Stahl & Bishop, 2000). Our results show that this difference in CAZyme content is also present between plant pathogens and mycorrhizal fungi, suggesting that the expansion of CAZymes in phytopathogenic species is driven by their antagonistic interactions with plants and is not simply a requirement for interacting with live plant tissues. The expansion of glycosyltransferase (GT) genes, particularly in the GT2 family (chitin synthases), that we observed as an important predictor of plant pathogenicity contrasts previous 41 findings that dothideomycete plant pathogens had a marked decrease in GTs in their genomes (Ohm et al., 2012). This discrepancy highlights the importance of increasing the availability of genome data for fungal phytopathogens from diverse taxa so that variations in genomic patterns can be observed. While GTs are well-known to be involved in fungal cell wall synthesis and have been proposed as targets for antifungal treatment of human pathogens (Klutts et al., 2006), their specific role in plant pathogens is still understudied. There is some evidence that GT2 orthologues may have been important in the evolution of fungal pathogens and likely play an important role in pathogenesis on plants (R. King et al., 2017). While our results do show a contraction in GT2 genes in saprotrophs, we found an expansion of GT2s in both plant pathogenic and mycorrhizal genomes, suggesting that this CAZyme family is not solely important for pathogenesis, but rather involved somehow in plant-fungus interactions. The second most important predictor of plant pathogenicity was an expansion of genes in the CBM63 family, which are non-catalytic cellulose-binding modules appended to proteins with similarity to plant expansins (Cosgrove, 2015). Expansins are proteins that loosen plant cell walls and have been implicated in the virulence and plant-colonizing abilities of microbial phytopathogens (Brotman et al., 2008; Jahr et al., 2000; Kerff et al., 2008; Laine et al., 2000). CBM63-containing proteins were reported to play an important role during plant infection in Botrytis cinerea Pers.:Fr. (Choquer et al., 2021) and Fusarium oxysporum f. sp. pisi (Linford) Snyder & Hansen (Achari et al., 2021). Our finding that CBM63 genes are expanded in plant pathogens, but not in saprotrophs or mycorrhizal fungi, strongly suggests that plant cell wall loosening may be an important determinant in the evolution of plant pathogenicity. 42 The expansion of GH32 genes (invertases) that we observed in plant pathogens in general, and more specifically for necrotrophs as well as foliar, root, and angiosperm pathogens contrasts with the contraction of these genes in biotrophic species. GH32 enzymes are invertases that hydrolyze sucrose to glucose and fructose, and evidence suggests that they are used by fungal pathogens to obtain carbon from their plant hosts (Chang et al., 2017; Parrent et al., 2009). GH32 was expressed during the pathogenic activities of biotrophic fungi (Chang et al., 2017; Tetlow & Farrar, 1992; Voegele et al., 2006), and an expansion of these genes has been demonstrated in plant pathogen genomes relative to saprotrophic and mycorrhizal species (Parrent et al., 2009; Van der Nest et al., 2015), suggesting that GH32 genes could be important in the evolutionary history of plant pathogenicity. In fact, the number of GH32 gene copies has been previously proposed as a predictor of the ecological strategies of fungi (Parrent et al., 2009; Van der Nest et al., 2015), lending further support to our findings that this CAZyme subfamily is a major predictor of many plant pathogenic lifestyles. We also observed that patterns in specific peptidase families, particularly serine proteases, were associated with plant pathogenicity. Serine proteases have been demonstrated by numerous studies to be important in fungal phytopathogenicity (M. Chandrasekaran et al., 2016), and an expansion in the S9 (prolyl oligopeptidase), S10 (carboxypeptidase Y), and S33 (prolyl aminopeptidase) families has been reported for plant-associated fungi (Muszewska et al., 2017). Prolyl aminopeptidases, a family that was expanded in every phytopathogenic lifestyle except obligate biotrophs, are enzymes involved in the cleavage of N-terminal proline residues from peptides (Basten et al., 2005). There has been evidence that proteins with proline-rich N-terminal domains are involved in the maintenance of biotrophy (Perfect et al., 1998; Perfect & Green, 43 2001), so it could be that obligate biotrophs require less of the enzymes that would cleave such proline-rich domains. A contraction of pepsins (MEROPS clan AA, family A1) was an important predictor of plant pathogens in general as well as necrotrophs, hemibiotrophs, and foliar pathogens. Family A1 are aspartic proteases, which are thought to play a role in the virulence of plant pathogenic fungi (Have et al., 2010; Plummer et al., 2004), so it is somewhat unexpected that we observed a contraction in this family for plant pathogenic lifestyles. Future research could expand analyses within family A1 to determine the specific subfamilies driving these contractions. Genome-based predictive approaches have important implications for the biosurveillance of invasive plant pathogens as there has been a call for more genomics-centered biosecurity strategies, especially for forest invasive alien species (FIAS) (Bilodeau et al., 2019; Hamelin & Roe, 2020; McTaggart et al., 2016; Roe et al., 2019). Here we demonstrate that predictive genomics is a promising tool that could be harnessed for biosurveillance of fungal phytopathogens. In addition to its predictive performance, our machine learning approach uncovered genomic patterns associated with specific phytopathogenic lifestyles and traits, elucidating gene families that are potentially important in the evolution of plant pathogens. These results indicate that while evolutionary differentiation is undoubtedly a major driving force for fungal lifestyles and traits, there are clearly other selective pressures influencing the genomic architecture of phytopathogenic fungi. This finding highlights the importance of moving away from solely taxonomy-focused biosurveillance towards more genomics-based strategies. Our approach used only a small group of gene families with readily available annotations; future research could be expanded to use whole genome data, including genes known to be important in 44 fungal phytopathogenicity, such as cytoplasmic effector proteins (Dodds & Rathjen, 2010; Lo Presti et al., 2015). Additional phytopathogenic traits, such as host range (e.g., broad vs narrow, monocots vs dicots), should also be tested. The use of genomic patterns for prediction of fungal lifestyles is complex. Future genomic biosurveillance may require a tailored approach that depends on the specific group of pathogens being monitored. As more genomes become publicly available, we will be able to more robustly test the capacity of tools like DendroNet to predict fungal phytopathogenic lifestyles and traits, as well as assess the integration of these predictive tools into future FIAS biosurveillance pipelines. 45 Chapter 3: CRISPR-Cas9 gene editing: an unexplored frontier for forest pathology *Note that a version of this chapter has been published: Dort, E.N., Tanguay, P., and Hamelin, R.C. (2020) CRISPR/Cas9 Gene Editing: An Unexplored Frontier for Forest Pathology. Frontiers in Plant Science 11: 1126. The chapter is presented in the form in which it was accepted for publication and is reproduced in this dissertation under the permission of a CC BY 4.0 license. Modifications were made to figure labels to adhere to the University of British Columbia’s PhD dissertation formatting requirements. 3.1 Chapter Summary CRISPR-Cas9 gene editing technology has taken the scientific community by storm since its development in 2012. First discovered in 1987, CRISPR-Cas systems act as an adaptive immune response in archaea and bacteria that defends against invading bacteriophages and plasmids. CRISPR-Cas9 gene editing technology modifies this immune response to function in eukaryotic cells as a highly specific, RNA-guided complex that can edit almost any genetic target. This technology has applications in all biological fields, including plant pathology. However, examples of its use in forest pathology are essentially nonexistent. The aim of this review is to give researchers a deeper understanding of the native CRISPR-Cas systems and how they were adapted into the CRISPR-Cas9 technology used today in plant pathology – this information is crucial for researchers aiming to use this technology in the pathosystems they study. We review the current applications of CRISPR-Cas9 in plant pathology and propose future directions for research in forest pathosystems where this technology is currently underutilized. 46 3.2 Introduction Developed in 2012, CRISPR-Cas9 gene editing is relatively new, but research using this technology has expanded rapidly in most scientific fields with 7,105 publications in 2019 alone (Clarivate Analytics, 2020). Even though human health and medicine are the most prolific fields, researchers in plant sciences are also starting to explore the applications of CRISPR-Cas9. The use of CRISPR-Cas9 in plant breeding has sparked interest in the field of plant pathology where disease resistant plant cultivars are becoming increasingly important. The applications of CRISPR-Cas9 technology in plant pathology have already been reviewed, especially in the context of agriculture (Das et al., 2019; Langner et al., 2018; S. S. Makarova et al., 2018; Muñoz et al., 2019). However, literature addressing its applications in forestry is lacking, and we believe this is because CRISPR-Cas9 is currently underutilized in this field. The purpose of this review is to fill this literature gap while giving forest pathologists a deeper understanding of CRISPR-Cas9 and its potential applications to better understand and manage tree diseases. We focus on how native CRISPR-Cas systems function as well as the mechanisms driving CRISPR-Cas9 gene editing technology as this information is crucial for implementation of this technology in forest pathosystems. 3.2.1 What is CRISPR-Cas? A primer for understanding CRISPR-Cas9 gene editing Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR associated (Cas) proteins, or CRISPR-Cas, is a bacterial and archaeal DNA-based adaptive immune system that defends against bacteriophages, plasmids, and other mobile genetic invaders (Bhaya et al., 2011; Terns & Terns, 2011). The CRISPR-Cas system was first discovered by Japanese scientists in Escherichia coli (Migula) Castellani & Chalmers (Ishino et al., 1987), but it has now 47 been found in a large array of prokaryotic species. Among these species, CRISPR-Cas DNA loci exhibit extensive genetic diversity, but they all have a common underlying architecture comprising a CRISPR array composed of direct repeats interspaced with unique sequences called spacers, which are derived from foreign nucleic acids; this array is flanked by associated cas genes organized as an operon (Bolotin et al., 2005; Jansen et al., 2002; Pourcel et al., 2005) (Figure 3.1A). These elements of the CRISPR-Cas system work in concert to direct a three-stage defense response against invading phages and plasmids (Barrangou et al., 2007; van der Oost et al., 2009) (Figure 3.1B). Stage 1 (adaptation) occurs when the bacterial or archaeal host recognizes a fragment of DNA or RNA from an invader, named the protospacer (Deveau et al., 2008), and integrates it into the CRISPR array as a new spacer sequence (van der Oost et al., 2009). Protospacer selection is dictated by the presence of highly conserved 2-3 nucleotide regions near the protospacer sequence called ‘protospacer-adjacent motifs’, or PAMs (Marraffini & Sontheimer, 2010). Stage 2 (expression) involves the transcription of the CRISPR array into large RNA transcripts called precursor CRISPR-derived RNAs (pre-crRNAs), which are processed into smaller, mature crRNAs by Cas proteins that are transcribed from the cas genes (van der Oost et al., 2009). A mature crRNA contains a unique phage-derived spacer sequence flanked by fragments of its adjacent repeat sequences from the CRISPR array (Wiedenheft et al., 2012). Finally, Stage 3 (interference) takes place when a subsequent attack occurs by the same bacteriophage or plasmid; each crRNA associates with one or more Cas proteins to form a crRNA-protein effector complex, which conducts surveillance of the cell for invading DNA or RNA (Terns & Terns, 2011). The crRNA acts as a guide for the effector complex (guide RNA), directing it to create a double-stranded break (DSB) in the complementary protospacer sequence of the invader via recognition of the PAM (Terns & Terns, 2011). The fragmented DNA or RNA 48 can no longer infect the host, thus completing successful defense by the CRISPR-Cas system. This highly adaptable nucleotide-based activity is what makes CRISPR-Cas gene editing technologies, which are designed from CRISPR-Cas systems, so effective. Figure 3.1 (A) A generalized CRISPR-Cas system consisting of a CRISPR array and a cas operon. The CRISPR array is composed of a series of identical repeat sequences interspaced with unique sequences called spacers, which are derived from the genetic material of invading bacteriophages and plasmids. On the 5’ end of the spacer/repeat locus is an intraspecies-conserved leader sequence likely involved in transcription of the CRISPR array. Flanking the CRISPR array is the cas operon, which contains the CRISPR-associated (cas) genes that code for proteins involved in the CRISPR-Cas defense response. (B) The three-stage CRISPR-Cas defense response. Stage 1, adaptation, occurs when the bacterial or archaeal host recognizes a fragment of DNA or RNA from an invader, named the protospacer, and integrates it into the CRISPR array as a new spacer. New spacers are integrated at the leader end of the array. Stage 2, expression, involves the transcription of the cas genes into Cas proteins, and the CRISPR array into a precursor CRISPR RNA (pre-crRNA) molecule. The pre-crRNA molecule then gets processed by Cas proteins into smaller, mature CRISPR RNA (crRNA) molecules. In Stage 3, interference, the mature crRNAs associate with one or more Cas proteins to form crRNA-effector complexes, which survey the cell for foreign nucleic acids and subsequently cleave them via recognition of the protospacer-adjacent motif (PAM) sequence. 49 3.2.2 How does CRISPR-Cas9 gene editing technology work? CRISPR-Cas9 gene editing technology is based on the Type II CRISPR-Cas system from the human pathogen Streptococcus pyogenes Rosenbach and was developed by an internationally collaborative research group headed by Jennifer Doudna and Emmanuelle Charpentier (Jinek et al., 2012). Type II systems are characterized by the multidomain protein Cas9 (K. S. Makarova et al., 2011, 2015), which relies on two RNA molecules to guide it to its DNA target: a trans-activating crRNA (tracrRNA), and the usual crRNA (Deltcheva et al., 2011; Gottesman, 2011; Jinek et al., 2012). The tracrRNA is complementary to the repeat sequences in the corresponding pre-crRNA, and it base-pairs to those sequences facilitating Cas9 to process the pre-crRNA into a smaller mature crRNA molecule (Deltcheva et al., 2011). The tracrRNA, mature crRNA, and Cas9 endonuclease then form an effector complex and the two RNA molecules guide Cas9 to the target protospacer sequence of an invader (Jinek et al., 2012). Cas9 then uses the complementarity between the crRNA and the protospacer as well as the adjacent PAM to create a DSB in the target DNA (Jinek et al., 2012). The Doudna/Charpentier research group developed a single chimeric RNA molecule that combined the tracrRNA and crRNA, and they demonstrated that Cas9 can be programmed to cleave any target DNA by changing only a 20-nucleotide sequence in this single-guide RNA (sgRNA) (Jinek et al., 2012). Their results were immediately significant for the scientific community, allowing the editing of DNA in a broad range of organisms and caused a surge of research in all scientific fields that continues to this day. Additionally, the original chimeric S. pyogenes Cas9 technology has served as a model system from which many different CRISPR technologies have evolved. 50 3.2.3 CRISPR-Cas9 exploits cellular DNA repair mechanisms to edit genes The success of nuclease-based technologies, including CRISPR-Cas9, as methods to edit genes relies on the highly conserved cellular DNA repair mechanisms present in all domains of life. Cas9 generates blunt-end DSBs at target DNA sites, and there are two main mechanisms that are triggered in eukaryotic cells in response to these DSBs: end-joining and homologous recombination, or homology-directed repair (HDR) (Ranjha et al., 2018). Both end-joining and HDR involve complex endogenous systems that can be divided into several sub-pathways that are triggered under different cellular conditions and generate very different repaired DNA products (Ceccaldi et al., 2016; Ranjha et al., 2018). There are two end-joining pathways, non-homologous end-joining (NHEJ) and microhomology-mediated end-joining (MMEJ), and neither require a DNA template for repair (Ranjha et al., 2018). These end-joining mechanisms are highly error prone, often resulting in insertions or deletions (indels) that create knockout mutants when they occur in the reading frame of a gene (Bortesi & Fischer, 2015; Ranjha et al., 2018). Conversely, repairs by the HDR pathways occur only in the presence of a donor DNA template containing regions homologous to the sequences surrounding the DSB induced by Cas9 (Bortesi & Fischer, 2015). The HDR pathway is more precise than the end-joining mechanisms, and thus can be used for highly specific gene modification or gene insertion. Eukaryotic organisms can use both homologous recombination and end-joining mechanisms to repair DNA damage in their cells, but the end-joining pathways are more frequent because they occur regardless of the presence of a donor DNA template and can therefore take place in any stage of the cell cycle (Ranjha et al., 2018). However, end-joining allows for less control in CRISPR-Cas9 gene editing due to the randomness of the mutations it induces (Langner et al., 2018). The less frequent HDR pathway allows for more control, but requires a homologous donor DNA 51 template that, even when present, triggers HDR at a much lower rate than end-joining (Langner et al., 2018). Consequently, researchers wishing to activate the HDR pathway using CRISPR-Cas9 have the additional task of designing a homologous donor template that can be used for recombination at the target DNA site, and they will likely have to screen larger numbers of transformants to identify successful HDR candidates. 3.2.4 CRISPR-Cas9 limitations The major limitation of using the original S. pyogenes CRISPR-Cas9 (SpCas9) is the requirement of the PAM sequence adjacent to the protospacer DNA, which is used by the Cas9 complex in conjunction with the complementary sgRNA region to recognize and cleave the target DNA sequence (Jinek et al., 2012). The SpCas9 complex recognizes the PAM sequence 5’-NGG-3’, where N represents any of the four nucleotide bases (Jinek et al., 2012). This three-base-pair sequence is a common occurrence in most genomes, but its requirement limits the genes that can be targeted, especially when attempting to study genes involved in highly specific pathways of interest (Langner et al., 2018). Additionally, research has shown that CRISPR-Cas9 can recognize alternative PAM sequences, which increases the likelihood of off-target mutations (Y. Zhang et al., 2014). In response to this limitation researchers have developed three new CRISPR-Cas9 systems from Cas9 orthologs in other bacterial species, each of which recognizes a unique PAM: SaCas9 from Staphylococcus aureus Rosenbach, StCas9 from Streptococcus thermophilus (Orla-Jensen) Schleifer, and NmCas9 from Neisseria meningitidis (Albrecht and Ghon) Murray (Kleinstiver et al., 2015). In addition, Cas9 protein variants are being engineered to recognize alternative PAMs (Agudelo et al., 2020). New CRISPR-Cas nucleases from other bacterial Type II systems have also been discovered: Cas12a, which targets DNA, and Cas13a, which targets 52 single-stranded RNA (Burstein et al., 2017; Koonin et al., 2017; Shmakov et al., 2015). While bearing some similarities to Cas9, these two systems use slightly different mechanisms for cleaving target nucleic acid sequences and processing of pre-crRNA and demonstrate advantages over Cas9 for certain applications, including within plant pathology (Langner et al., 2018). Another limitation of any CRISPR-Cas technology is the occurrence of unwanted mutations (translocations, inversions, large deletions and insertions) resulting from the complex endogenous pathways that repair the double-stranded DNA breaks induced by Cas nucleases (Després et al., 2018; Kosicki et al., 2018). Additionally, Cas9-induced DSBs can be toxic to cells, inducing cell-death pathways and resulting in low transformation and editing efficiencies (Garst et al., 2017; K. R. Roy et al., 2018). To avoid these DSB-related limitations, nuclease-deficient Cas9 proteins have been engineered and fused to other proteins such as deaminases and recombinases to achieve base editing and site-specific recombination (Nishida et al., 2016; Standage-Beier et al., 2019). However, Cas9 continues to be the most commonly used CRISPR-Cas technology, has a number of applications in plant pathology, and is a valuable untapped resource for forest pathology; it is therefore the focus of the remainder of this review. 3.3 Current applications of CRISPR-Cas9 in plant pathology Plant pathogenic viruses, bacteria, oomycetes, and fungi are natural components of healthy ecosystems, but globalization, climate change, and mismanagement have led many of these species to cause emerging infectious diseases (EIDs) that threaten natural and managed plant ecosystems (Anderson et al., 2004; Fisher et al., 2012). In the context of agriculture, plant EIDs are considered a threat to global food security (Pennisi, 2010), and in forestry they have 53 significant impacts from both economic and biodiversity conservation perspectives (Anderson et al., 2004; Fisher et al., 2012). Chemical mitigation strategies using pesticides and fungicides have proven to be inadequate and environmentally destructive (Andolfo et al., 2016), so research has turned to genetic strategies: developing disease resistance in plants and/or engineering avirulent strains of pathogens. The development of CRISPR-Cas9 as an accurate and versatile gene editing technology increased the scope of such genetic strategies and has led plant pathologists to explore its disease-mitigation applications in both hosts and pathogens. 3.3.1 Using CRISPR-Cas9 to engineer disease resistance in plants To date, most of the CRISPR-Cas9 research in plant pathology has focused on developing systems in the hosts, namely engineering disease resistance in agriculturally important plants. Not surprisingly, the first plants to be engineered using CRISPR-Cas9 were the model species Arabidopsis thaliana (L.) Heynh. (Feng et al., 2013) and Nicotiana benthamiana Domin (Nekrasov et al., 2013), but these were followed almost simultaneously by development in rice (Feng et al., 2013; W. Jiang et al., 2013; Miao et al., 2013), wheat (Y. Wang et al., 2014), sorghum (W. Jiang et al., 2013), maize (Z. Liang et al., 2014), and tomato (Brooks et al., 2014). The list of plant species engineered using CRISPR-Cas9 has expanded rapidly in the last six years, but it has remained in the realm of angiosperm species important in agriculture; use of this technology in forest species is largely absent in the literature, with the one exception of studies developing CRISPR-Cas9 systems in Populus species (Fan et al., 2015; Zhou et al., 2015). The first CRISPR-Cas9 studies in plants were proof-of-concept experiments demonstrating the use of this technology in plants, but many of these species have now been engineered for 54 resistance to viral, fungal, and bacterial diseases (Das et al., 2019). Engineering disease resistance in plants using CRISPR-Cas9 has generally been executed using one of two strategies: the pathogen-gene approach or the plant-gene approach. The former involves engineering an sgRNA into the plant chromosome that directs Cas9 to target a specific pathogen gene thereby impeding pathogenesis, whereas the latter uses an sgRNA that targets endogenous plant genes involved in pathogen interactions and modifies them to either boost the host immune response, or to interfere with the host-recognition pathway of the pathogen (S. S. Makarova et al., 2018). 3.3.1.1 Pathogen-gene approach The pathogen-gene approach is best demonstrated with plant-virus pathosystems. CRISPR-Cas9-mediated virus resistance is most commonly tackled via a transgenic approach whereby a viral DNA sequence is used to design the sgRNA and is transformed into the plant genome with the CRISPR-Cas9 system (Ali et al., 2015, 2016; T. Zhang et al., 2018). This induces a response remarkably similar to that of the native CRISPR-Cas systems in that the plant is able to use its transgenic sgRNA-Cas9 system to target invading virus DNA, RNA, or mRNA. This approach has been primarily used in the model species N. benthamiana (Ali et al., 2015, 2016; Ji et al., 2018; T. Zhang et al., 2018) and A. thaliana (Ji et al., 2018; T. Zhang et al., 2018), but it presents an intriguing paradigm for the use of CRISPR-Cas9 to fortify plant immune systems against invading pathogens. 3.3.1.2 Plant-gene approach Engineering CRISPR-Cas9-mediated disease resistance using the plant-gene approach has mostly focused on targeting plant susceptibility (S) genes, a diverse group of genes with varying 55 roles that ultimately render plants more susceptible to invading pathogens. The proteins encoded by S genes fall into two general categories: those that act as negative regulators of immunity, decreasing the plant immune response in certain contexts, and those that are part of plant development and regulatory pathways, which are targets for pathogen effector molecules (Langner et al., 2018). While traditional disease-resistance breeding has focused on disease resistance (R) genes that generally involve ‘gene-for-gene’ interactions with pathogens, it is thought that targeting S genes will yield more stable, broad-spectrum disease resistance (Pavan et al., 2009). Both R-gene- and S-gene-based resistance involve complex molecular pathways that interact with pathogens in different ways, and the details of these interactions have been reviewed elsewhere (Dangl & Jones, 2001; J. D. G. Jones & Dangl, 2006; Lapin & Van den Ackerveken, 2013; Pavan et al., 2009). Engineering resistance to viruses with CRISPR-Cas9 using the plant-gene approach involves designing the sgRNA to target a region of the plant genome that is used by the virus for replication (S. S. Makarova et al., 2018). This method has been used for disrupting RNA viruses in both Cucumis sativus L. (cucumber) and A. thaliana by targeting the eukaryotic translation initiation factor gene eIF4E in the plant (J. Chandrasekaran et al., 2016; Pyott et al., 2016). However, the plant-gene approach is best demonstrated in pathosystems involving bacteria, oomycetes, and fungi in which the proteins encoded by plant S genes are relied upon by these pathogens for host recognition and immune suppression. Generating plant resistance in these systems has predominantly been focused on designing sgRNAs to target S genes, creating host knockout mutants that the pathogen effectors have difficulty recognizing (Das et al., 2019; Langner et al., 2018). 56 A well-studied S gene system is the mildew resistance locus O (MLO), which renders both monocot and dicot plant hosts susceptible to a variety of powdery mildew pathogens. The use of CRISPR-Cas9 to disrupt the MLO gene has proven to be effective for evading pathogen effector recognition and generating disease resistance in these systems. One of the best examples of this is a study by Nekrasov et al. who used CRISPR-Cas9 to generate 48 bp deletions in an Mlo gene in tomato plants (Nekrasov et al., 2017). The CRISPR-Cas9 mutants demonstrated resistance to the powdery mildew fungus Oidium neolycopersici L. Kiss without generating any other unwanted phenotypic effects (Nekrasov et al., 2017). Second-generation progeny (F1) were then cultivated by selfing the first-generation resistant mutants (F0), which resulted in the CRISPR-Cas9 transfer DNA plasmid being segregated away (Nekrasov et al., 2017). The F1 progeny also exhibited O. neolycopersici resistance, and whole-genome sequencing showed that no off-target mutations had occurred, and that no transgenic DNA was present (Nekrasov et al., 2017). A similar study by Wang et al. generated resistance in rice plants (Oryza sativa L.) to the rice blast pathogen Magnaporthe oryzae B.C. Couch by using CRISPR-Cas9 to induce indels in an S gene that encodes proteins involved in sugar transport (SWEET proteins) (F. Wang et al., 2016). Wang et al. also used segregation to create non-transgenic, disease-resistant F1 progeny that exhibited all the desirable phenotypes from the wild-type plants (F. Wang et al., 2016). There are now a number of examples of CRISPR-Cas9 S gene mutants with enhanced disease resistance to various pathogens including: broad virus resistance in cucumber plants through disruption the eukaryotic translation initiation factor eIF4E (J. Chandrasekaran et al., 2016), resistance to bacteria and oomycete pathogens in tomato plants through deletions in a DMR6 gene (de Toledo Thomazella et al., 2016), bacterial canker-resistant Wanjincheng orange plants via mutations in 57 the CsLOB1 gene promoter (A. Peng et al., 2017), and powdery mildew resistance in wheat through Cas9-mediated mutations of the TaEDR1 susceptibility gene (Y. Zhang et al., 2017). The above studies demonstrate the advantage of using the plant-gene approach in CRISPR-Cas9 research because the disruption of these genes with indels and the subsequent segregation of the transfer DNA results in disease-resistant plants that do not contain any transgenic material. However, the backcrossing required to segregate away the CRISPR-Cas9 plasmid DNA is only feasible in annual plants with short life cycles and is not suitable for perennial crop plants or forest species (Kanchiswamy et al., 2015). Non-transgenic CRISPR-Cas9 mutants can also be generated using a plasmid-free delivery system; this involves designing a pre-assembled enzymatic ribonucleoprotein (RNP) Cas9-sgRNA complex that is transfected directly into plant protoplasts. The Cas9-sgRNA RNP complex can modify the genomic target DNA but is subsequently degraded by the cell – this results in a disease-resistant plant mutant that contains no transgenic DNA (S. S. Makarova et al., 2018). These non-transgenic approaches are especially relevant in plant-based industries where the introduction of inter-specific transgenes generates public controversy around genetically modified organisms (GMOs) and initiates prohibitively strict regulations surrounding the use of such genetically modified plants. The ability of CRISPR-Cas9 to generate highly specific disease-resistant mutants that contain no foreign DNA allows for these plants to be used outside of the GMO regulatory framework (Kanchiswamy, 2016; Kanchiswamy et al., 2015; S. S. Makarova et al., 2018). It also allows specific genetic modifications to be made in the endogenous genomic context thereby avoiding the random insertion of transgenes from unrelated species and removing the risk of any 58 unintended downstream effects from the presence of foreign DNA (Kanchiswamy, 2016; Kanchiswamy et al., 2015; S. Kim et al., 2014). 3.3.2 Using CRISPR-Cas9 to target and explore genes in filamentous plant pathogens While the use of CRISPR-Cas9 to engineer pathogen resistance in plants is a promising approach to mitigating disease outbreaks, using CRISPR-Cas9 in pathogens is of equal interest both for the generation of avirulent strains and for increasing our understanding of how these species interact with their plant hosts to induce disease. So far, CRISPR-Cas9 research in plant pathogens has been far less prolific than research on plant disease resistance, and it has focused primarily on proof-of-concept experiments in filamentous fungi and oomycetes. The first successful demonstrations of CRISPR-Cas9 technology in filamentous fungi were independently published by four research groups in 2015, who all developed CRISPR-Cas9 systems in filamentous ascomycete species including Neurospora crassa Shear & B.O. Dodge (Matsu-ura et al., 2015), Pyricularia oryzae Cavara (Arazoe et al., 2015), Trichoderma reesei E.G. Simmons (R. Liu et al., 2015), and multiple Aspergillus species (Nødvig et al., 2015). These studies used Cas9 genes that were codon-optimized for filamentous fungi, endogenous promoters for expression of the sgRNA, and common fungal selection markers. More proof of concept studies followed and CRISPR-Cas9 was developed in a number of fungal species including important plant pathogens such as Ustilago maydis (DC.) Corda (corn smut: Schuster et al., 2016), Fusarium graminearum Schwabe (Fusarium head blight of grain: Gardiner and Kazan, 2018), F. oxysporum Schltdl. (Fusarium wilt disease: Wang et al., 2018), and Sclerotinia sclerotiorum (Lib.) de Bary (white mold: Li et al., 2018). The first CRISPR-Cas9 system in oomycetes was developed in the soybean pathogen Phytophthora sojae Kaufm. & Gerd.; the study used a Cas9 gene with human- 59 optimized codons fused to the P. sojae nuclear localization signal (PsNLS) and driven by the oomycete Ham34 promoter (Fang & Tyler, 2016). CRISPR-Cas9 has now additionally been developed in P. capsici Leonian (Miao, Chi, et al., 2018) and P. palmivora (E.J. Butler) E.J. Butler (Gumtow et al., 2018). A comprehensive review of the CRISPR-Cas9 techniques being used in filamentous fungi and oomycetes has been published by Schuster and Kahmann (2019); as with the plants, the CRISPR-Cas9 studies in filamentous pathogens have been focused on agriculturally important species, with no studies on forest pathogens reported in our literature search. 3.3.2.1 Targeting pathogenicity genes using CRISPR-Cas9 The study in S. sclerotiorum by Li et al. (2018)is one of the few that has used CRISPR-Cas9 to generate pathogenicity mutants in a fungal plant pathogen. Their gene target was the oxalate biosynthesis gene Ssoah1, responsible for producing oxalic acid, which is involved in host tissue colonization by S. sclerotiorum. Li et al. generated DSBs at multiple Ssoah1 target sites and found that fragments of their Cas9 transformation plasmid had been integrated into the S. sclerotiorum genome at the DSB sites; this demonstrated that the transformation plasmid was not only providing the Cas9 protein and sgRNA molecule, but also acting as a donor DNA molecule for the NHEJ pathway to repair the Cas9-induced DSB (J. Li et al., 2018). The Cas9-generated S. sclerotiorum Ssoah1-mutant strains exhibited significantly reduced oxalic acid production and reduced pathogenicity on soybean, Abyssinian cabbage, and tomato plants (J. Li et al., 2018). Sclerotinia sclerotiorum is a highly aggressive, necrotrophic phytopathogen with a very broad host range, so these results by Li et al. are very encouraging for the use of CRISPR-Cas9 as a tool for understanding the virulence of similar plant pathogens. Improved understanding of the 60 specific modes of pathogenicity employed by different phytopathogens will subsequently improve management strategies for the diseases they cause. An important group of pathogenicity genes are those encoding the effector proteins secreted by pathogens during host interactions. Effectors are an extremely diverse group of molecules and are found in some form in all groups of plant pathogens; they have a number of functions including facilitating infection, disrupting the plant immune response, and obtaining nutrients from host tissues (Toruño et al., 2016). Their ubiquity, as well as their dominant role in plant-pathogen interactions, make them excellent candidates for CRISPR-Cas9 research. This was demonstrated by Fang and Tyler in the first study to develop a CRISPR-Cas9 system in oomycetes; they designed an sgRNA that targeted the RxLR effector gene Avr4/6 in the soybean pathogen Phytophthora sojae (Fang & Tyler, 2016). RxLR effectors are widespread in oomycetes and can enter host cells and suppress effector-triggered immune responses (R. H. Y. Jiang & Tyler, 2012). They can be detected by the receptors encoded by plant R genes (R. H. Y. Jiang & Tyler, 2012); in soybeans the R genes involved in recognition of the Avr4/6 effector protein are Rps4 and Rps6 (Fang & Tyler, 2016). Fang and Tyler examined both the pathogen and host aspects of the P. sojae pathosystem: they used CRISPR-Cas9 to create five homozygous NHEJ Avr4/6 mutants and two homozygous HDR Avr4/6 mutants, and then assessed the interactions of the P. sojae mutants with soybean plants both with and without Rps4 or Rps6 resistance loci (Fang & Tyler, 2016). Their results showed that when inoculated with P. sojae-Avr4/6 mutants, the Rps4/Rps6 soybean plants were less able to defend themselves against infection, exhibiting an impaired immune response (Fang & Tyler, 2016). 61 The results from Fang and Tyler’s 2016 study demonstrate the intricacy of plant-pathogen interactions and serve as a reminder for researchers wishing to use genetic engineering technologies to develop disease-resistant plants: using CRISPR-Cas9 to target an effector may impede the pathogen, but it might also have unintended consequences for the plant host depending on the recognition pathway associated with the effector. Systems that have coevolved for millions of years cannot be easily deconstructed, and if CRISPR-Cas9 is to be used as a tool for mitigating plant disease outbreaks the complexities of these systems must be considered. However, CRISPR-Cas9 provides a perfect opportunity to understand these complex pathosystems, and the study by Fang and Tyler is an excellent example of the use of CRISPR-Cas9 as a tool for elucidating the roles of pathogen genes via a functional genomics approach. 3.4 Future perspectives for CRISPR-Cas9 in forest pathology Given the large number of studies on CRISPR-Cas9 in plant pathology, and the promises for developing disease resistance, it is somewhat surprising that there is very little literature in the area of forest pathology (Figure 3.2). Forest pathogens are as devastating as their agricultural counterparts and can cause landscape level mortality that results in ecosystem-wide changes; well-known global examples include chestnut blight, Dutch elm disease, ash dieback, myrtle rust, white pine blister rust and sudden oak death. However, disease management options are more limited in forestry than in agriculture; for example, the use of fungicides is generally confined to forest nurseries, with chemicals rarely being applied to trees once they have been planted. Disease resistance is one of the most promising avenues to combat forest pathogens, especially given the large geographical scales that these pathogens can affect. While there is recognition of the potential applications of CRISPR-Cas9 for forest ecosystems and a call for the 62 use of this technology in these systems (Fernandez i Marti & Dodd, 2018; Fritsche et al., 2018; Tsai & Xue, 2015), no applied studies in forest pathosystems have been performed. From the perspective of engineering plant disease resistance, this lack of CRISPR-Cas9 research in forest species is understandable for three major reasons. The first is that conifer species, which dominate forest ecosystems in the Northern Hemisphere, have significantly larger and more complex genomes than most agricultural angiosperms. Thus, there are far fewer whole genome sequences available (Neale et al., 2017; Nystedt et al., 2013) – a requirement for CRISPR-Cas9 gene editing in order to design effective sgRNAs and minimize off-target effects. The second reason is the increased difficulty of transformation protocols for forest tree species; this requires not only the transformation of DNA, but also the subsequent regeneration of the whole plant, which is a more time-consuming and complex process in woody perennial plants (Fernandez i Marti & Dodd, 2018; Peña & Séguin, 2001). The final reason is the controversy surrounding GMOs, which is shared with the agricultural sector, but is perhaps greater for woody forest Figure 3.2 The number of CRISPR publications by year since 2012 (development of CRISPR-Cas9 gene editing technology by Jinek et al.) obtained from a Web of Science topic search with the search parameters: ‘CRISPR’ or ‘CRISPR-Cas*’, or ’CRISPR/Cas*’. The ‘Analyze Results’ function of Web of Science was used to determine publication numbers by year and research area. (A) The log-transformed number of CRISPR publications by research area shows the common trend of increasing publications for CRISPR research for all areas except forestry. (B) A breakdown of the publication distribution by topic within forestry as determined by a manual search of the Web of Science search results. 63 species given their perennial nature and existence within semi-natural ecosystems; furthermore, the regulatory restrictions on genetically engineered trees are far stricter than those for agricultural crops (Strauss et al., 2009, 2015, 2016). Given these limitations, very few woody perennials have been successfully engineered with CRISPR-Cas9 relative to their annual counterparts, and the list of those that have is almost exclusively made up of agricultural species including Coffea canephora Pierre ex. A. Froehner (coffee: Breitler et al., 2018), Citrus sinensis (L.) Osbeck (sweet orange: Jia and Wang, 2014), Citrus paradisi Macfad. (Duncan grapefruit: Jia et al., 2016), and Malus spp. (apple: Malnoy et al., 2016; Nishitani et al., 2016). The only forest species for which CRISPR-Cas9 systems have been developed are those in the genus Populus: P. tomentosa Carriere (Fan et al., 2015; Y. Jiang et al., 2017; Shen et al., 2018; Wan et al., 2017; L. Wang et al., 2017; Xu et al., 2017; L. Yang et al., 2017), P. tremula x alba (Aiton) Sm. (Muhr et al., 2018; Zhou et al., 2015), and P. tremula x tremuloides (Elorriaga et al., 2018). Only two of these studies used CRISPR-Cas9 to target genes involved in poplar disease resistance (Y. Jiang et al., 2017; L. Wang et al., 2017). Given the paucity of studies using CRISPR-Cas9 in forest tree species, it is not surprising that there are no published examples of the use of this technology in forest pathogens. However, there should be fewer obstacles for applying this approach to the pathogens since they are often taxonomically related to the oomycete and fungal species that cause disease on agricultural plants. The study by Fang and Tyler on Phytophthora sojae (2016) clearly demonstrated the power of CRISPR-Cas9 as a tool for gaining a deeper understanding of phytopathogens and how they interact with their plant hosts. Similar studies should be implemented in forest pathogens, and there are some forest pathosystems that are ideally suited for the use of CRISPR-Cas9 64 technology. Below, we give examples of four pathosystems that could be used to drive CRISPR-Cas9 research development in forest pathology (Figure 3.3): two using an approach of engineered resistance in the host, and two using an exploratory approach in the pathogen. 3.4.1 Proposed CRISPR-Cas9 research in four forest pathosystems 3.4.1.1 Engineering disease resistance in poplars While CRISPR-Cas9 research in poplars has already started, its focus has mainly been on modifying genes involved in growth, reproduction, and lignin development with the goal of improving Populus species for growth in plantations as well as use in the pulp and bio-refinery industries (Chanoca et al., 2019; Elorriaga et al., 2018; Fan et al., 2015; Muhr et al., 2018; Shen et al., 2018; Wan et al., 2017; Xu et al., 2017; L. Yang et al., 2017; Zhou et al., 2015). However, there are many potential applications for CRISPR-Cas9 technology for developing disease resistance, and perhaps the most promising application in forest pathology would be the genome Figure 3.3 The four forest pathosystems proposed for future CRISPR/Cas9 research: (A) uredinia of the poplar leaf rust pathogen Melampsora medusae f. sp. deltoidis Shain, a basidiomycete fungus, on eastern cottonwood (Populus deltoides W.Bartram ex Marshall) leaves in Montréal, Quebec, CA (photo: Richard Hamelin, 2015); (B) flagging symptoms typical of Dutch elm disease, caused by the ascomycete fungal pathogen Ophiostoma novo-ulmi Brasier, on an American elm (Ulmus americana L.) in Québec, Quebec, CA (photo: Philippe Tanguay, 2016); (C) sudden larch death caused by the oomycete pathogen Phytophthora ramorum Werres, De Cock, & Man in’t Veld in a larch plantation in Galloway Forest Park, Scotland, GB (photo: Richard Hamelin, 2019); (D) aecia of the basidiomycete fungal pathogen Cronartium ribicola J.C. Fisch., causal agent of white pine blister rust, on a limber pine (Pinus flexilis E.James) in Rocky Mountain National Park, Colorado, US (photo: Erika Dort, 2019). 65 editing of poplar species for resistance to their major pathogens, namely Melampsora and Sphaerulina species. In fact, the Melampsora-Populus pathosystems (Figure 3.3A) have previously been proposed as a model system to further our understanding of host-pathogen recognition mechanisms and the infection process (Feau et al., 2007), and would therefore be an excellent place to start CRISPR-Cas9 research in forest pathology. Poplars are ecologically and economically valuable trees, playing important roles in both natural and managed forests, and they have long been established as model species for molecular and genomic studies in forest trees (Bradshaw et al., 2000; G. Taylor, 2002; Wullschleger et al., 2002). Additionally, Populus trichocarpa Torr. & A.Gray ex Hook. was the first tree species to have its genome sequenced (Tuskan et al., 2006), which perfectly situates poplar pathosystems for study with CRISPR-Cas9. Melampsora leaf rusts and Sphaerulina leaf spot and stem canker pathogens are two of the most damaging groups of fungi affecting poplars in both natural stands and plantations, and they have major impacts on productivity and forest health. The barrier of entry for using CRISPR-Cas9 to engineer resistance to these pathogens is particularly low for two reasons, the first being that CRISPR-Cas9 protocols have already been established in Populus species (Fan et al., 2015; Zhou et al., 2015). The second reason is that candidate genes associated with resistance have already been identified for both Melampsora and Sphaerulina species (Duplessis et al., 2009; La Mantia et al., 2013; Muchero et al., 2018; Yin et al., 2004). As discussed previously, there have now been a number of studies using CRISPR-Cas9 in Populus species, and while two of these studies used CRISPR-Cas9 to explore the functions of genes in Melampsora resistance by creating knockout mutants (Y. Jiang et al., 2017; L. Wang et al., 2017), none of them used CRISPR-Cas9 to directly engineer disease resistance. 66 3.4.1.2 Dutch elm disease The possibility of using CRISPR-Cas9 to control the dominant Dutch elm disease (DED) pathogen Ophiostoma novo-ulmi Brasier (Figure 3.3B) is another exciting prospect. Genome sequencing and annotation of this pathogen has helped in identifying multiple candidate genes involved in the infection process (Comeau et al., 2015; Forgetta et al., 2013; Khoshraftar et al., 2013). Of particular interest are the genes involved in regulating the yeast-mycelial dimorphism exhibited by many Ophiostoma species. Ophiostoma novo-ulmi uses its budding yeast phase to travel rapidly through the tissues of its elm hosts but can also switch to a mycelial form that can penetrate xylem tissues and grow radially in the elm (Berrocal et al., 2012). This ability to switch between yeast and mycelial growth forms is thought to be involved in the pathogenicity of dimorphic fungi (Nadal et al., 2008), and has thus been explored in the DED pathogens for a number of years (Berrocal et al., 2012; Naruzawa & Bernier, 2014; W. C. Richards, 1994; Wedge et al., 2016). Transcriptomic analyses have identified candidate genes involved in this yeast-to-hypha transition (Nigg et al., 2015; Nigg & Bernier, 2016). These genes are excellent candidates for CRISPR-Cas9 gene editing: triggering the NHEJ pathway could create knockout mutants with reduced ability to switch to the yeast form thereby impeding translocation of the fungus throughout the elm tree. Another group of interest as candidate genes involved in O. novo-ulmi pathogenicity are the secondary metabolite clusters. In plant pathogenic fungi, secondary metabolites such as host-selective toxins are well known to play an important role in disease development (Macheleidt et al., 2016). Bioinformatic annotations have identified O. novo-ulmi gene clusters putatively 67 involved in biosynthesis of secondary metabolites, and interspecific comparative genomic analyses uncovered a fujikurin-like gene cluster (OpPKS8), found in the DED pathogens (O. ulmi (Buisman) Nannf. and O. novo-ulmi) but absent in related non-phytopathogenic species (Sbaraini et al., 2017). According to phylogenetic analyses the authors suggested that this toxin-related cluster may have been horizontally acquired by DED pathogens (Sbaraini et al., 2017). Genes in the OpPKS8 cluster are good candidates for exploration with CRISPR-Cas9, which could be used as an additional tool to elucidate the functions of this secondary metabolite cluster and its potential role in pathogenicity. Finally, a recent pangenomic analyses of a collection of strains from O. ulmi and O. novo-ulmi species showed that introgression has been the main driver of genomic diversity and has impacted fitness-related traits, with many of the introgressed regions containing genes involved in host–pathogen interactions and reproduction (Hessenauer et al., 2020). Hessenauer et al. (2020) further demonstrated that the virulence of O. novo-ulmi was positively or negatively affected depending on the location of the introgressed genes in the genome. As with the secondary metabolites, CRISPR-Cas9 could be used as a means of exploring the functions of some of these introgressed genes that appear to play a role in virulence. Development of a CRISPR-Cas9 system in O. novo-ulmi has already begun (Tanguay, 2019), which makes implementing such pathogenicity-related strategies an impending reality. 3.4.1.3 Sudden oak/larch death Some filamentous plant pathogens exhibit virulence via their ability to suddenly switch lifestyles. This is the case in many fungal and oomycete species that are hemibiotrophs, meaning 68 they can transition from an asymptomatic biotrophic phase to an aggressive necrotrophic phase in which they begin releasing toxins and killing host tissues (Koeck et al., 2011; S.-J. Lee & Rose, 2010). The genes enabling this dual lifestyle could be effective targets for CRISPR-Cas9 in order to better understand how hemibiotrophic pathogens cause disease. Research in the oomycete pathogen, Phytophthora infestans (Mont.) de Bary, showed that effectors are the mediators of this lifestyle transition (S.-J. Lee & Rose, 2010), which is not surprising given the dominant role these molecules play in plant-pathogen interactions. These results are very encouraging as CRISPR-Cas9 systems have already been developed in P. sojae (Fang, Cui, et al., 2017), P. capsici (Miao, Chi, et al., 2018), and P. palmivora (Gumtow et al., 2018), and it has proven successful in disrupting effector genes (Fang & Tyler, 2016; Gumtow et al., 2018). The invasive forest pathogen Phytophthora ramorum Werres, De Cock, & Man in’t Veld (Figure 3.3C), causal agent of sudden oak death in the United States (Rizzo et al., 2002a, 2002b) and sudden larch death in the United Kingdom (Webber et al., 2010), is another hemibiotrophic oomycete pathogen that causes devastating disease outbreaks in a broad range of woody hosts (Rizzo & Garbelotto, 2003). Phytophthora ramorum is classified as a highly aggressive pathogen given its ability to infect woody stems as well as foliar tissues, however, its hemibiotrophic nature allows it to remain asymptomatic anywhere from months to years before it transitions to its aggressive necrotrophic lifestyle (Rizzo & Garbelotto, 2003). It would therefore be an excellent candidate for CRISPR-Cas9 research exploring the role of effectors in mediating this lifestyle switch that influences virulence so significantly. 69 3.4.1.4 White pine blister rust White pine blister rust (WPBR), caused by the basidiomycete rust fungus Cronartium ribicola J.C. Fisch. (Figure 3.3D), has severely affected North American populations of many economically and ecologically important pine species such as eastern and western white pines, sugar pine, and whitebark pine (Sniezko et al., 2014). In many white pine species both complete and partial resistance to WPBR have been detected. Complete resistance is mediated by a dominant R gene named Cr, which causes a hypersensitive response to C. ribicola and enables the pine host to survive by restricting the infection to the needles (Kinloch et al., 2003; Sniezko et al., 2014). Partial resistance appears to be a more complex response that is likely mediated by multiple genes, but the exact mechanisms driving this response are not yet known (Sniezko et al., 2014). While the Cr genes mediating complete resistance in white pines seem like the perfect candidates for CRISPR-Cas9-generated disease resistance, these R genes are likely not stable in the long-term because only a single mutation in a corresponding C. ribicola effector gene would be required to overcome this resistance (Sniezko et al., 2014). However, plant R gene immune receptors can be mutated to provide resistance to phylogenetically divergent pathogens (Giannakopoulou et al., 2015; Segretin et al., 2014), and CRISPR-Cas9 could be used to engineer such synthetic genes in tandem in order to create stable multi-resistance plant systems (Andolfo et al., 2016). Another option to obtain more durable long-term resistance in WPBR pathosystems is to focus CRISPR-Cas9 research on partial resistance. Cas9 can be co-expressed with many sgRNAs to simultaneously target multiple genes; this multiplex gene editing could facilitate the discovery of the genes involved in partial resistance, and it could also eventually be used to target those genes simultaneously in engineered WPBR-resistant pine populations. This partial resistance strategy may be less effective than complete resistance to a single C. ribicola 70 strain, but it would be more stable in the long-term against a constantly evolving C. ribicola population exhibiting diverse mutations and could also protect pine hosts against other encroaching pathogen species. The four suggestions above demonstrate the scope of utility of CRISPR-Cas9 gene editing technology and highlight how this tool has been underutilized in forest pathology. CRISPR-Cas9 is a relatively recent development, and there are clear obstacles to its use in forest pathosystems. However, given the large number of fungal and oomycete species for which CRISPR-Cas9 systems have now been established, the barrier of entry for pathologists studying filamentous forest pathogens has been lowered, and there is a generalized research pipeline that they can follow to implement this gene editing technology in their study organisms (Figure 3.4). This pipeline can comprise various approaches for developing a CRISPR-Cas9 system, including using different Cas9 delivery methods (RNP vs. plasmid) and genetic targets (DNA vs. RNA). Figure 3.4 shows a generalized schematic of one such approach based on the CRISPR-Cas9 system being developed in the Dutch elm disease pathogen Ophiostoma novo-ulmi (Tanguay, 2019). We hope this gives forest pathologists a better understanding of the logistics involved in developing CRISPR-Cas9 systems in filamentous forest pathogens. 71 As sequencing technologies continue to improve and lower in cost, forest pathologists should aim to increase their exploration of the genetic basis of plant disease resistance via CRISPR-Cas9 gene editing. The power of this technology to aid our understanding of the intricacies of plant-pathogen interactions and generate effective strategies of disease resistance in forest pathosystems is much too great to ignore. Figure 3.4 Example of a generalized CRISPR/Cas9 workflow and timeline for a filamentous forest pathogen based on the protocol being developed for the Dutch elm disease pathogen Ophiostoma novo-ulmi (Onu). (A) Transformation of fungal protoplasts with a plasmid containing (see inset): a Cas9 gene fused to nuclear localization signal (NLS), a single-guide RNA (sgRNA) scaffold with a 20-nucleotide (nt) region designed to base-pair with the target genomic DNA, an antibiotic resistance gene for transformant selection, and gene promoters and terminators active in the target species. Alternatively, protoplasts can be co-transformed with a combination of Cas9-sgRNA ribonucleoprotein complexes (RNPs) and a plasmid for antibiotic transformant selection. (B) In successful transformants, Cas9 forms a complex with the sgRNA molecule, which guides the complex to the protospacer (a genomic DNA target in this example) with an adjacent PAM (protospacer adjacent motif) sequence. The sgRNA-Cas9 complex is analogous to the crRNA-effector complexes of native CRISPR/Cas systems, shown in Figure 1 of this review. In the RNP approach, this sgRNA-Cas9 complex is pre-assembled and transfected directly into the protoplasts. (C) Transformants are selected by growth on antibiotic selective medium (picture shows Onu hygromycin-resistant transformants: pink colonies are CRISPR/Cas9 ade2 mutants). (D) Successful CRISPR/Cas9 mutants are confirmed through sub-culture of the putative transformants from the previous step, PCR screening, and Sanger sequencing. 72 3.5 Conclusions Since the discovery of CRISPR-Cas systems in 1987 (Ishino et al., 1987), our understanding of this adaptable immune response has come a long way, and the development of CRISPR-Cas9 gene editing technology in 2012 (Jinek et al., 2012) resulted in an explosion of research with wide-reaching implications for most biological systems. Despite the development of Cas9 tools in many pathosystems, there are still limitations to the use of CRISPR-Cas technology in plant pathology, especially concerning off-target effects. However, careful design of sgRNAs and modifications to the Cas proteins prevent most of these effects, and the use of RNP delivery systems has reduced off-target mutations to zero in many systems (Das et al., 2019). Additionally, the continued development of CRISPR-Cas technology in plant pathosystems will only improve efficiency as this technology is adapted to function in a diversity of organisms. To date, most CRISPR-Cas9 research in plant pathology has been focused on agricultural pathosystems, with little to no research in forest pathology. This is understandable given the availability of genomic resources for most major crops as well as the shorter generation times of crop plants relative to their forest counterparts, which makes for a quick feedback loop on any genetic modifications made with CRISPR-Cas9. However, forest pathogens are wreaking equal havoc in the forestry sector and represent a global threat to forest ecosystems that needs to be addressed immediately. The time is now to adopt CRISPR-Cas9 in forest pathology, at the very least to improve our understanding of host-pathogen interactions, but ideally to begin integrating it into forest improvement programs to generate more effective disease resistance strategies for long-term forest sustainability. 73 Chapter 4: Heterogeneity in establishment of polyethylene glycol-mediated plasmid transformations in five forest pathogenic Phytophthora species 4.1 Chapter Summary Plasmid-mediated DNA transformation is a foundational molecular technique and the basis for most CRISPR-Cas9 gene editing systems. While plasmid transformations are well established for many agricultural Phytophthora pathogens, development of this technique in forest Phytophthoras is lacking. Given our long-term research objective to develop CRISPR-Cas9 gene editing in a forest pathogenic Phytophthora species, we sought to establish the functionality of polyethylene glycol (PEG)-mediated plasmid transformation in five species: P. cactorum, P. cinnamomi, P. cryptogea, P. ramorum, and P. syringae. We used the agricultural pathogen P. sojae, a species for which PEG-mediated transformations are well-established, as a transformation control. Using a protocol previously optimized for P. sojae, we tested transformations in the five forest Phytophthoras with three different plasmids: two developed for CRISPR-Cas9 gene editing and one developed for fluorescent protein tagging. Out of the five species tested, successful transformation, as indicated by stable growth of transformants on a high concentration of antibiotic selective growth medium and diagnostic PCR, was achieved only with P. cactorum and P. ramorum. However, while transformations in P. cactorum were consistent and stable, transformations in P. ramorum were highly variable and yielded transformants with very weak mycelial growth and abnormal morphology. Our results indicate that P. cactorum is the best candidate to move forward with CRISPR-Cas9 protocol development and provide insight for future optimization of plasmid transformations in forest Phytophthoras. 74 4.2 Introduction The oomycete genus Phytophthora, comprising almost 200 species, is well-known for containing an abundance of destructive plant pathogens (Q. Chen et al., 2022). The substantial economic and environmental impacts of Phytophthora species in horticulture, agriculture, and forestry, has led to decades of research focused on improved management and mitigation of the damage caused by these ubiquitous plant pathogens. The continued development of molecular DNA techniques and sequencing technologies in the last 20-30 years has shifted Phytophthora research increasingly towards genetics and genomics studies with the intent of elucidating the genetic mechanisms that drive phytopathogenicity and other related traits (Birch & Whisson, 2001; Feau et al., 2016; Govers & Gijzen, 2006; Huitema et al., 2004; Kamoun, 2003; M. Yang et al., 2018). The recent development of CRISPR-Cas9 gene editing (Jinek et al., 2012) and related technologies has provided new tools for the genetic exploration of phytopathogenicity, and CRISPR-Cas9 protocols have now been developed for several agricultural Phytophthora pathogens (Fang & Tyler, 2016; Gumtow et al., 2018; Miao, Chi, et al., 2018; W. Wang et al., 2019; Z. Wang et al., 2022). CRISPR-Cas gene editing technologies have proven to be invaluable in elucidating the genetic mechanisms driving Phytophthora-host interactions and phytopathogenicity (Ah-Fong et al., 2021; Fang & Tyler, 2016; Gumtow et al., 2018; X. Li et al., 2020), and exploring fungicide resistance (Miao, Chi, et al., 2018; Miao, Li, et al., 2018; Z. Wang et al., 2022). However, CRISPR-Cas9 gene editing has yet to be established in any forest pathogenic Phytophthoras, likely due to the lack of stable and efficient DNA transformation protocols in these species. Generating long-term management strategies for forest Phytophthora pathogens will require a deeper understanding of the genetics driving their phytopathogenicity, however, that will not be possible without the development of reliable molecular techniques. 75 Establishing DNA transformations in forest Phytophthoras is therefore a crucial step to bring these species into the molecular genetics era and will allow the development of exploratory techniques such as CRISPR-Cas9 gene editing. DNA transformation is a fundamental molecular technique that is used to study cellular processes and characterize specific genes involved in pathways or interactions of interest. The development of CRISPR-Cas9 protocols and similar techniques for any organism relies on a functioning and reliable DNA transformation system. The first Phytophthora species in which DNA-mediated transformations were developed were the agricultural pathogens P. capsici Leonian and P. parasitica Dastur (Bailey et al., 1991). Using a polyethylene glycol (PEG)-mediated method, Bailey et al. (1991) tested two plasmids developed for transformations in Ustilago maydis (DC.) Corda: the integrative plasmid pHL1 and the autonomously replicating plasmid pCM54. Both P. capsici and P. parasitica were successfully transformed, and the results for the plasmid DNA were as expected in that pHL1 integrated into the Phytophthora genomes, whereas pCM54 was maintained episomally (Bailey et al., 1991). However, the pHL1 and pCM54 fungal transformation plasmids were not readily adaptable for transformations in other Phytophthora species (Judelson et al., 1991). Therefore, attention turned to developing oomycete-specific transformation vectors. The first species in which these oomycete-specific plasmids were tested was P. infestans (Mont.) de Bary, the causal agent of late blight of potato (Judelson et al., 1991; Judelson & Michelmore, 1991). The plasmids constructed by Judelson and colleagues for these early transformation studies were derived from the common plasmid cloning vector pUC19 (Norrander et al., 1983), modified to include promoter and transcription terminator sequences from the ham34 and hsp70 genes of the oomycete Bremia lactucae Regel 76 (Judelson et al., 1991; Judelson & Michelmore, 1991). Neomycin phosphotransferase (nptII) and hygromycin phosphotransferase (hpt) genes were used to generate stable P. infestans transformants resistant to geneticin (G418) or hygromycin B, respectively (Judelson et al., 1991). These first Phytophthora transformations used PEG and calcium chloride (CaCl2) to transform cell-wall digested protoplasts (Judelson et al., 1991; Judelson & Michelmore, 1991), employing a protocol adapted from transformations successfully developed in fungi (Yelton et al., 1984). In the following decades, the PEG-CaCl2-mediated transformation approach was adapted and modified for other Phytophthora species, including P. sojae Kaufm. & Gerd. (Judelson et al., 1993), P. nicotianae Breda de Haan (Bottin et al., 1999), P. palmivora (E.J. Butler) E.J. Butler (van West et al., 1999), P. brassicae De Cock & Man in’t Veld (Si-Ammour et al., 2003), P. citricola Sawada (Mcleod et al., 2008), P. capsici (Dunn et al., 2013), and P. cactorum (Lebert & Cohn) J. Schröt. (X.-R. Chen et al., 2016). Alternative transformation methods were also developed for various Phytophthora species including microprojectile bombardment (Bailey et al., 1993; Cvitanich & Judelson, 2003), electroporation (Huitema et al., 2011; Latijnhouwers & Govers, 2003), and Agrobacterium-mediated transformation (Vijn & Govers, 2003; Wu et al., 2016). Each transformation method has strengths and weaknesses, however, the PEG-CaCl2 method continues to be the most common approach due to its success across Phytophthora species and its relatively adaptable methodology for research labs without highly specialized equipment (Ghimire et al., 2022; Mcleod et al., 2008). While molecular protocols have been well-developed for agricultural Phytophthoras, research is lacking for forest Phytophthoras. A literature search revealed only four studies that developed 77 DNA transformation systems in forest pathogenic Phytophthora species: two using the original PEG-CaCl2 protocol developed by Judelson et al. (1991) to transform the jarrah dieback pathogen P. cinnamomi Rands (McCarren et al., 2005) and the sudden oak death pathogen P. ramorum Werres, De Cock, & Man in’t Veld (Riedel et al., 2009); and two performing the optimized PEG-CaCl2 transformation (Mcleod et al., 2008) in P. cinnamomi (Dai et al., 2021) and the soilborne pathogen P. cactorum (X.-R. Chen et al., 2016). However, there have been no follow-up studies in either P. ramorum or P. cactorum after their initial transformation, so the long-term reliability and efficiency of DNA transformations in these species and other forest Phytophthoras remains unknown. The objective of this study was to test the optimized PEG-CaCl2 transformation (Fang, Cui, et al., 2017; Mcleod et al., 2008) protocol in five forest Phytophthora pathogens: P. cactorum, P. cinnamomi, P. cryptogea Pethybr. & Laff., P. ramorum, and P. syringae (Kleb.) Kleb. The purpose of testing five species was two-fold: first, to establish the adaptability and reliability of PEG-CaCl2 transformations across common forest pathogenic Phytophthora species; and second, to determine which Phytophthora forest pathogens can be readily transformed and would therefore be good candidates to move forward with the development of CRISPR-Cas9 gene editing protocols. 78 4.3 Methods Note: the recipes and methods used for all growth media and transformation solutions are listed in Appendix B . 4.3.1 Phytophthora species, isolates, and culture conditions The Phytophthora species and isolates used in this study are summarized in Table 4.1. All cultures were maintained on clarified 20% V8 agar (V8A: see Appendix B.1 for recipe) at room temperature (~21°C) in the dark. For long-term storage, agar plugs taken from the edge of mycelial growth, five days post plating, were stored in nuclease-free molecular water at 5-10°C in the dark. Table 4.1 The five forest Phytophthora species and isolates used in this study. Phytophthora sojae was used as a control for transformations. Species Isolate/Strain Isolate Details Phytophthora cactorum Larch FF-42 2Pa See Feau et al. (2021) for isolate details. Phytophthora cinnamomi CBS 270.55 Purchased from the CBS-KNAW culture collection (Westerdijk Fungal Biodiversity Institute). Originally isolated in 1954 by I.W. Buddenhagen from Chamaecyparis lawsoniana var. alumii in Boskoop, Netherlands. JP-09-065 (strain A-3/3/09-P-2-st) Isolated in 2009 by J. Parke from Pieris japonica in Marion, OR, USA. Culture provided by N. Grünwald. Phytophthora cryptogea RS-2006-soil-d8 Sample collected in 2017 by N. Feau from the soil surrounding Pinus monticola on Vancouver Island, BC, Canada. Culture isolated by W. Vasquez. Phytophthora ramorum NA2 17_0134_0030 Cultures of both isolates provided by G. Bilodeau from the Canadian Food Inspection Agency. NA2 16_0386_0016 Phytophthora sojae P6497 (race 2) Isolated from infected Glycine max plants in Mississippi, USA. Culture provided by F. Arredondo. Phytophthora syringae Alder 12 See Feau et al. (2022) for isolate details. 79 4.3.2 Transformation plasmids Three plasmids were tested for transformations: pYF2-PsNLS-hSpCas9-GFP (abbreviated pYF2-PsCG), pYF515, and pGFPN. Both pYF2-PsCG and pYF515 are CRISPR-Cas9 plasmids developed for P. sojae. pYF2-PsCG was originally developed to test expression of a human codon-optimized Streptococcus pyogenes Cas9 (hSpCas9) gene in P. sojae; the hSpCas9 gene is fused at its 5’ end to a nuclear localization sequence from P. sojae (PsNLS) and tagged at its 3’ end with a green fluorescent protein (GFP) (Fang, Jang, et al., 2017; Fang & Tyler, 2016). The pYF2-PsCG plasmid also contains genes for ampicillin resistance (AmpR: conferred by the β-lactamase gene from the pUC19 plasmid) and geneticin resistance (nptII) (Figure 4.1). pYF515 was developed as an ‘all-in-one’ transformation plasmid to express both hSpCas9 and a single-guide RNA (sgRNA) for CRISPR-Cas9 gene editing (Fang, Cui, et al., 2017). Like pYF2-PsCG, pYF515 has AmpR and nptII antibiotic resistance genes (Figure 4.2). pGFPN is a transformation plasmid developed for fluorescent protein tagging in Phytophthora species (Ah-Fong & Judelson, 2011) and contains a GFP gene in addition to the AmpR and nptII antibiotic resistance genes (Figure 4.3). The DNA sequences of all three transformation plasmids are provided in Appendix B.3. 80 Figure 4.1 Map of the pYF2-PsNLS-hSpCas9-GFP (abbreviated pYF2-PsCG) transformation plasmid developed by Fang and Tyler (2016) for expression of hSpCas9 in Phytophthora sojae. Plasmid sequence determined from Fang and Tyler (2016) and Fang et al. (2017). Plasmid map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). 81 Figure 4.2 Map of pYF515, the 'all-in-one' plasmid developed by Fang et al. (2017) for expression of hSpCas9 and a single-guide RNA (sgRNA) for CRISPR/Cas9 gene editing. Plasmid sequence provided by Dr. Felipe Arredondo (Oregon State University). Plasmid map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). 82 4.3.2.1 Plasmid sequencing DNA from all three plasmids was received from a collaborator (Dr. Felipe Arredondo, Oregon State University) and transformed into DH5⍺ Escherichia coli chemically competent cells (Thermo Fisher Scientific, Waltham, MA, USA) for replication. Plasmid DNA was extracted using either a QIAGEN HiSpeedⓇ Plasmid Midi Kit or Maxi Kit (QIAGEN Sciences, Figure 4.3 Map of the transformation plasmid pGFPN developed by Ah-Fong and Judelson (2011). Plasmid sequence provided by Dr. Felipe Arredondo (Oregon State University). Plasmid map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). 83 Germantown, MD, USA) as per the manufacturer’s instructions (HiSpeedⓇ Plasmid Purification Handbook). The extracted DNA was adjusted to a concentration of 30 ng/μL for sequencing. Whole plasmid sequencing was performed by Plasmidsaurus (Eugene, OR, USA) using Oxford Nanopore Technology with custom analysis and annotation. Due to issues experienced with the pGFPN plasmid in the lab, a second preparation of plasmid DNA was sent from Dr. Arredondo and therefore the two independent preparations of the pGFPN plasmid were sent for sequencing. The sequence reads returned from Plasmidsaurus for each plasmid preparation were mapped to the respective expected sequence using Geneious Prime 2023.2.1 (https://www.geneious.com) to determine the percent identity of each plasmid relative to its reference sequence. 4.3.3 Determining the minimum inhibitory concentrations of geneticin for Phytophthora isolates The antibiotic geneticin (G418) was used for selection of Phytophthora transformants. Prior to transformations, growth assays for all six Phytophthora species used in this study were performed on V8A plates supplemented with a gradient of G418 (Gibco™️ Geneticin™️ Selective Antibiotic, Thermo Fisher Scientific, Waltham, MA, USA) concentrations to determine the minimum inhibitory concentration (MIC) for each species. The G418 concentrations tested for each species and isolate are listed in Table 4.2 (note that P. cinnamomi isolate JP-09-065 was not tested). The volume of medium per plate was standardized and two replicate plates were produced for each G418 concentration. The MIC for each species was recorded as the first G418 concentration at which mycelial growth stopped (see example for P. cactorum in Figure 4.4). 84 Table 4.2 Geneticin (G418) concentrations tested for each Phytophthora species used in this study to determine minimum inhibitory concentrations. Species Isolate(s) Tested G418 Concentration Gradient (μg/mL) Phytophthora cactorum Larch FF-42 2Pa 0, 2, 5, 10, 20, 30, 50 Phytophthora cinnamomi CBS 270.55 0, 2, 5, 10, 20, 30, 40, 50 Phytophthora cryptogea RS-2006-soil-d8 0, 2, 5, 10, 20, 30, 50 Phytophthora ramorum NA2 16_0386_0016, NA2 17_0134_0030 0, 2, 5, 10, 20, 30, 40, 50 Phytophthora sojae P6497 (R2) 0, 2, 5, 10, 20, 30, 40, 50 Phytophthora syringae Alder 12 0, 2, 5, 10 4.3.4 Protoplast isolation and transformation An optimized PEG-mediated protoplast transformation protocol (Fang, Cui, et al., 2017), based on previously described methods (Dou et al., 2008; Mcleod et al., 2008), was used to introduce plasmid DNA into Phytophthora cells. The recipes for all growth media and solutions used for Figure 4.4 An example of the P. cactorum isolate Larch FF-42 2Pa growth assay demonstrating how the geneticin (G418) minimum inhibitory concentration (MIC) was determined for each forest Phytophthora species in this study. Each isolate was grown on V8 agar supplemented with a gradient of G418 concentrations (labels above the image), and two replicate plates were tested for each concentration (labels to the left of image). The first G418 concentration at which mycelial growth stopped was recorded as MIC for that species/isolate. 85 transformations can be found in Appendix B.1.Various modifications to culture media, protoplast isolation conditions, plasmid DNA concentrations, and protoplast recovery media were made to test their effects on transformation success in the five different forest Phytophthora species (see Data B.2.1 for all conditions tested). 4.3.4.1 Preparation of Phytophthora cultures for transformation Phytophthora cultures were initially grown on 1.5% agar plates (nutrient pea agar [NPA], nutrient V8 agar [NVA], or 20% V8 agar [V8A]) at room temperature (~21°C) in the dark. Once there was sufficient mycelial growth (five to seven days), cultures were transferred to a liquid medium (nutrient pea broth [NPB], nutrient V8 broth [NVB], or V8 broth [V8B]): three flasks, each containing 50 mL of broth, were inoculated with five agar plugs (6-mm diameter) taken from the growing edge of the mycelial culture (15 agar plugs in total). The liquid cultures were grown for three days at room temperature (~21°C) in the dark. 4.3.4.2 Isolation of protoplasts After three days of growth, mycelial mats were harvested and rinsed with autoclaved Milli-QⓇ water followed by 0.8 M mannitol. The mycelial mats were transferred to a 100 mm × 20 mm Petri dish, covered with 0.8 M mannitol, and shaken gently (~30 rpm) for 10 minutes. The mats were recollected, transferred to a new Petri dish containing 20 mL of an enzyme solution (lysing enzymes + cellulase; see Appendix B.1 for full recipe) and digested for 60-90 minutes at 25°C with gentle shaking (~30 rpm). Protoplast release was monitored with microscopy. After digestion, the enzyme/protoplast solution was filtered through a 70 μm cell strainer and centrifuged at 1200 rcf for 2.5 minutes. The pellet was gently washed in 35 mL of W5 solution 86 (see Appendix B.1 for recipe), recentrifuged at 1200 rcf for 3 minutes, and the resulting pellet was resuspended in 10 mL of W5 solution and placed on ice for 1-2 hours. The protoplast concentration was determined using a hemocytometer. 4.3.4.3 Transformation of protoplasts with plasmid DNA Following incubation on ice, the protoplast suspension was centrifuged at 1200 rcf for 4 minutes. The resulting pellet was resuspended in MMg solution (see Appendix B.1 for recipe) at a concentration of 2 × 10⁶ – 2 × 10⁷ protoplasts/mL and incubated at room temperature for 10 minutes. During incubation, ≤15 µL of plasmid DNA (13-55 µg, see Data B.2.1 for amounts tested), or sterile molecular-grade water for the control treatment, was added to a 50 mL centrifuge tube and placed on ice. After the 10-minute incubation, the protoplasts were gently mixed, and 1 mL of the protoplast/MMg solution was added to the DNA (or water) in each 50 mL tube, mixed, and incubated for 30 minutes on ice. A 40% (v/v) PEG solution was added to each tube in three aliquots of 580 µL for a total of 1.74 mL of PEG solution per tube, and the tubes were incubated for another 30 minutes on ice. After 30 minutes, 2 mL of pea-mannitol (PM) or V8-mannitol (VM) broth was added to each tube. The tubes were inverted to mix and incubated on ice for two minutes. Then, 8 mL of PM/VM broth was added to each tube, the tubes were inverted to mix and incubated at room temperature for two minutes. To test the viability of the protoplasts after the transformation process, ~30-50 µL of the protoplast-PM/VM solution from each centrifuge tube (including control treatment) was spread onto an agar plate (NPA or NVA) and incubated at room temperature in the dark overnight (protoplast viability test Day 1). The remainder of the protoplast-PM/VM solution (~10 mL) from each tube was added to a 100 mm × 20 mm Petri dish containing 10 mL of PM/VM broth with 100 µg/mL of ampicillin 87 (Thermo Fisher Scientific, Waltham, MA, USA) for a final concentration of 50 µg/mL ampicillin. The dishes were incubated overnight at room temperature in the dark. 4.3.4.4 Antibiotic selection of transformants The following morning, the regenerated protoplasts were transferred to a new 50 mL tube and centrifuged at 2000 rcf for 5 minutes. The pellet was resuspended in ~2 mL of the supernatant and 150 µL of the resuspended protoplasts were added to 20 mL of cooled PM/VM agar (40-45°C) without G418 and poured into a Petri dish (protoplast viability test Day 2). Cooled PM/VM agar (40-45°C) with 5-50 µg/mL of G418 (see Data B.2.1 for concentrations tested) was added to the remaining protoplasts to a total volume of 50 mL. The tubes were inverted to mix, and the protoplast/agar mixture was poured into three Petri dishes (~16-17 mL of mixture per dish). All plates were incubated at room temperature in the dark for two days, or until mycelial growth appeared (up to five days). Each mycelial colony that grew on the G418-supplemented medium was treated as a putative transformant and was transferred to a well of a 12-well culture plate containing 2 mL of V8 broth with an increased concentration of G418 (20-60 µg/mL, see Data B.2.1 for concentrations tested). 4.3.5 Identification of successful transformants Any putative transformants that grew through the second round of G418 selection were considered successful transformants based on their stable growth on selective medium. Mycelial tissue from stable transformants was harvested for DNA extraction and subsequent diagnostic PCR to confirm the presence of plasmid DNA. Wildtype (non-transformed) cultures were grown in parallel as a negative control. After 3-4 days of growth in V8 broth supplemented with G418 88 (no G418 for wildtype cultures), 50-100 mg of mycelial tissue was harvested from each culture, and flash frozen in liquid nitrogen. Total cellular DNA was extracted using a QIAGEN DNeasyⓇ Plant Mini Kit (QIAGEN Sciences, Germantown, MD, USA) as per the manufacturer’s instructions (DNeasyⓇ Plant Handbook, Mini Protocol, TissueLyser procedure). Tissue disruption was performed using a Retsch Mixer Mill MM 300 (Retsch GmbH, Haan, Germany). PCR was performed on the DNA extractions from plasmid transformants, wildtype cultures (negative control), and pure plasmid DNA (positive control) using Platinum Taq DNA Polymerase (Thermo Fisher Scientific, Waltham, MA, USA) and primers (Integrated DNA Technologies, Coralville, IA, USA) designed to amplify specific regions of each plasmid (Table 4.3). The PCR primers were designed using NCBI Primer-BLAST (Ye et al., 2012) with target specificity checked against the “RefSeq representative genomes” database for Phytophthora (taxid: 4783) to minimize off-target amplification. All PCRs were performed using a Bio-Rad T100™️ thermal cycler (Hercules, California, USA) and the following cycling conditions (as per manufacturer’s instructions for Platinum Taq): an initial hold of 94°C for 2 minutes followed by 35 cycles of 94°C for 30 seconds, the primer-specific annealing temperature (see Table 4.3) for 30 seconds, and 74°C for 1 minute. The resulting PCR products were analysed using gel electrophoresis. The presence of an amplified band at the appropriate size confirmed the transformation of plasmid DNA. 89 Table 4.3 Primer pairs used for diagnostic PCR to detect plasmid DNA in Phytophthora transformants. Primer Pair Name Primer Sequences (5’ to 3’) Annealing Temperature Plasmids Expected Product Size AmpR_Diag_F1/R1 F1: CTGGCGAACTACTTACTCTAGC 54.0°C pGFPN, pYF515, pYF2-PsCG 232 bp R1: TCATCCATAGTTGCCTGACTCC eGFP_Diag_F1/R1 F1: GGAACTGGATGGTGATGTGAAC 51.4°C pGFPN, pYF2-PsCG 274 bp R1: CTTGTAGTTCCCGTCATCCTTG pYF_Cas9_Diag_F3/R3 F3: GCTGCCTGAGAAGTACAAAGAG 62.0°C pYF515, pYF2-PsCG 824 bp R3: CCAGAATGTCCTCGTTTTCCTC 4.4 Results 4.4.1 Sequencing of Phytophthora transformation plasmids indicates instability of pGFPN The sequencing results from Plasmidsaurus showed the expected sequences for pYF515 (99.8% pairwise identity to original plasmid sequence; Figure 4.5) and pYF2-PsCG (99.8% pairwise identity to original plasmid sequence; Figure 4.6), but not for either of the two pGFPN preparations (52.9% and 62.6% pairwise identity to original plasmid sequence; Figure 4.7, Figure 4.8). The expected size of the pGFPN plasmid was 6,891 bp, however, the sequencing results indicated that the two pGFPN preparations were 9,842 bp and 4,927 bp with apparent gene repeats, deletions, and inversions relative to the original plasmid sequence (Figure 4.7, Figure 4.8). 90 Figure 4.6 Paired alignment of the expected pYF2-PsCG sequence (top) and the Plasmidsaurus pYF2-PsCG sequence (bottom). The pairwise identity of the two sequences is 99.8% with minor differences in sequence at the end of the Cas9 gene, in the ham34 terminator, and in the origin of replication (ori). Alignment generated in Geneious Prime 2023.2.1 (https://www.geneious.com) using the Geneious assembler (Plasmidsaurus sequence mapped to expected sequence). Figure 4.5 Paired alignment of the expected pYF515 sequence (top) and the Plasmidsaurus pYF515 sequence (bottom). The pairwise identity of the two sequences is 99.8% with minor differences in sequence in the nptII gene and its ham34 promoter, both the ham34 terminators, and in the origin of replication (ori). Alignment generated in Geneious Prime 2023.2.1 (https://www.geneious.com) using the Geneious assembler (Plasmidsaurus sequence mapped to expected sequence). 91 Figure 4.8 Paired alignment of the expected pGFPN sequence (top) and the Plasmidsaurus pGFPN (second plasmid prep) sequence (bottom). The pairwise identity of the two sequences is 62.6% with sequence inversions, and a deletion of the nptII gene in the Plasmidsaurus sequence (4,927 bp). Alignment generated in Geneious Prime 2023.2.1 (https://www.geneious.com) using the Geneious assembler (Plasmidsaurus sequence mapped to expected sequence). Figure 4.7 Paired alignment of the Plasmidsaurus pGFPN (first plasmid prep) sequence (top) and the expected pGFPN sequence (bottom). The pairwise identity of the two sequences is 52.9%, with sequence inversions, repeats, and deletion of the nptII gene in the Plasmidsaurus sequence (9,842 bp). Alignment generated in Geneious Prime 2023.2.1 (https://www.geneious.com) using the Geneious assembler (expected sequence mapped to the Plasmidsaurus sequence). 92 4.4.2 Forest Phytophthora species differ in geneticin sensitivity The five forest Phytophthora species tested in this study exhibited varied sensitivities to G418, with MICs ranging from 5 μg/mL to 40 μg/mL of G418 (Figure 4.9). Phytophthora syringae and P. ramorum were the most sensitive species with MICs of 5 μg/mL G418, P. cactorum and P. cryptogea were more resistant with MICs of 20 μg/mL G418, and P. cinnamomi was the most resistant with an MIC of 40 μg/mL G418. The MICs were used to customize the G418 concentration used for selection of plasmid transformants for each Phytophthora species (see Data B.2.1). 4.4.3 Plasmid DNA transformations are inconsistent and unstable in forest Phytophthora species Success of plasmid transformations, as determined by stable mycelial growth of transformants after a second round of selection on an increased concentration of G418, was inconsistent Figure 4.9 Minimum inhibitory concentrations (MICs) of geneticin (G418) for the five forest Phytophthora species tested in this study and the agricultural pathogen P. sojae (used as a transformation control). Two replicates were tested for each species/isolate. *Two isolates of P. ramorum were tested, NA2 16_0386_0016 and NA2 17_0134_0030, and growth stopped for both at 5 μg/mL of G418. 93 between the five forest Phytophthora species tested (Table 4.4, Data B.2.1). Other than the P. sojae control, the only species in which consistent and stable plasmid transformations could be reliably reproduced was P. cactorum isolate Larch FF-42 2Pa (hereafter abbreviated FF42). Transformations of P. cactorum with the pYF2-PsCG plasmid yielded stable transformants that, even after multiple rounds of sub-culturing, grew well on V8A plates supplemented with 40 μg/mL of G418 (Figure 4.10). However, the results from diagnostic PCR were variable for P. cactorum, with no DNA amplification from some stably growing transformants (Figure 4.11), and conversely, amplification in transformants that could not grow in the increased G418 concentration (Figure 4.12). Additionally, the PCR products amplified from P. cactorum transformants were sometimes larger than expected (Figure 4.11). Table 4.4 Summary of the results of plasmid DNA transformations in the five forest Phytophthora pathogens tested in this study and the agricultural pathogen P. sojae (used as a transformation control). Species Isolate/Strain Plasmid Transformation Success (stable growth after increased G418 selection) Diagnostic PCR Confirmation (plasmid amplicon generated with PCR) Phytophthora sojae P6497 (R2) pYF2-PsCG Yes Yes pGFPN No NA Phytophthora cactorum Larch FF-42 2Pa pYF2-PsCG Yes Yes pGFPN No NA pYF515 Yes Not tested Phytophthora cinnamomi CBS 270.55 pYF2-PsCG No NA JP-09-065 pGFPN No NA Phytophthora cryptogea RS-2006-soil-d8 pYF2-PsCG No NA Phytophthora ramorum NA2 17_0134_0030 pYF2-PsCG Yes Yes pGFPN No NA pYF515 Yes Yes Phytophthora syringae Alder 12 pYF2-PsCG No NA 94 Figure 4.10 Transformations of Phytophthora cactorum FF42 with the pYF2-PsCG plasmid yielded stable transformants. (A) Transformants growing on V8 agar + 40 μg/mL G418 relative to (B) no growth of wildtype (untransformed) cultures on the same medium. Photos were taken five days post plating. (C) Transformants growing stably on V8 agar + 40 μg/mL G418 after multiple rounds of sub-culturing. 95 Figure 4.11 Gel electrophoresis of products from a PCR performed on DNA from Phytophthora cactorum FF42 pYF2-PsCG transformant (T) cultures using the pYF_Cas9_Diag_F3/R3 primer pair (expected product size is 824 bp). The negative PCR control was a reaction using molecular-grade water instead of DNA (well 2). The positive PCR control was purified pYF2-PsCG plasmid DNA (well 3). Note that the DNA used for the plasmid DNA control was the same preparation that was used for the P. cactorum transformations. The gel was run at 100 volts for 90 minutes on 0.85% agarose in 0.5X TBE buffer. All samples were loaded with Safe-Green™️ stain (Applied Biological Materials Inc., Richmond, BC, Canada) at a ratio of 1 μL Safe-Green: 5 μL PCR product. The pYF2-PsCG transformants highlighted in red are those that grew stably on G418 selective medium (40 μg/mL), however, there was no DNA amplification in the diagnostic PCR. 96 Some initial success was achieved in P. ramorum isolate NA2 17_0134_0030 (hereafter abbreviated NA2_17) with both pYF2-PsCG and pYF515, however transformation efficiency was extremely low (see Data B.2.1) and the mycelial growth of putative transformants was weak and irregular relative to the P. sojae control transformants (Figure 4.13). Additionally, most of the putative transformants for P. ramorum that were transferred to the second round of increased G418 selection did not grow (data not shown). However, the Figure 4.12 Gel electrophoresis of products from a PCR performed on DNA from Phytophthora cactorum FF42 wildtype (WT) and pYF2-PsCG transformant (T) cultures using the eGFP_Diag_F1/R1 primer pair (expected product size is 274 bp). The negative PCR control was a reaction using molecular-grade water instead of DNA (well 2). The positive PCR controls were purified pYF2-PsCG plasmid DNA, both circular (well 3) and linearized (well 4). The gel was run at 100 volts for 75 minutes on 1.25% agarose in 0.5X TBE buffer. All samples were loaded with Safe-Green™️ stain (Applied Biological Materials Inc., Richmond, BC, Canada) at a ratio of 1 μL Safe-Green: 5 μL PCR product. The pYF2-PsCG T4 transformant is highlighted in red because it struggled to grow through the second round of G418 selection (40 μg/mL), with roughly only 25% mycelial growth relative to the other three transformants (T1, T5, T8), however, a strong band amplified in the diagnostic PCR. 97 transformants that did grow in the second round of G418 selection all yielded positive results for the plasmid DNA diagnostic PCR, though DNA from some transformants yielded PCR products that were larger than expected (Figure 4.14). Notably, subsequent transformations with P. ramorum under the same conditions (i.e., same protocol, plasmid DNA, recovery medium, etc.) failed, with no putative transformants growing on G418 selective medium. Control transformations in P. sojae with the pYF-PsCG plasmid always yielded stable transformants with positive diagnostic PCR results (Figure 4.14). Figure 4.13 Putative transformants (PTs) of the control species Phytophthora sojae P6497 (A) and P. ramorum NA2_17 (B) growing on V8 agar supplemented with 50 μg/mL G418 and 30 μg/mL G418, respectively, four days post transformation. While putative P. ramorum transformants did grow on three of the plates, the mycelial growth was weak and irregular (see arrows) relative to the P. sojae control. The white arrows indicate PTs that would have been considered “strong” for P. ramorum, whereas the grey arrows indicate very weak PTs. All P. ramorum PTs were taken through a second round of selection, but most did not grow at the increased concentration of G418; none of the “weak” PTs (grey arrows) grew, and only two of the “strong” PTs (white arrows) grew. The same pattern emerged for every P. ramorum transformation attempted. 98 4.5 Discussion Developing molecular techniques in Phytophthoras, and oomycetes in general, is known to be difficult and inconsistent between species (Ghimire et al., 2022; Govers & Gijzen, 2006; Judelson & Ah-Fong, 2009; Mcleod et al., 2008), and our results certainly lend evidence to this trend. Of the five forest Phytophthora species we tested with a PEG-CaCl2 transformation method, only one species, P. cactorum, could be reliably and stably transformed with plasmid DNA. Though some initial success was achieved with P. ramorum, the transformants exhibited weakened and atypical growth, and subsequent transformations suddenly failed. Regarding the Figure 4.14 Gel electrophoresis of products from a PCR performed on DNA from Phytophthora sojae P6497 and P. ramorum NA2_17 wildtype (WT) and pYF2-PsCG transformant (T) cultures using the AmpR_Diag_F1/R1 primer pair (expected product size is 232 bp). The negative PCR control was a reaction using molecular-grade water instead of DNA (well 2). The positive PCR controls are replicate dilutions of a single pYF2-PsCG plasmid DNA extraction (wells 3-4). Note that the DNA used for the plasmid control was the same preparation that was used for both the P. sojae and P. ramorum transformations. The gel was run at 100 volts for 80 minutes on 1.25% agarose in 0.5X TBE buffer. All samples were loaded with Safe-Green™️ stain (Applied Biological Materials Inc., Richmond, BC, Canada) at a ratio of 1 μL Safe-Green: 5 μL PCR product. 99 objectives of this study, these results have led us to conclude that first, PEG-CaCl2 plasmid transformations are not readily adaptable between forest Phytophthora species, and second, that of the five species tested, P. cactorum is the candidate that is most amenable to CRISPR-Cas9 protocol development. The lack of adaptability of the PEG-CaCl2 plasmid transformation method across the five forest Phytophthora pathogens tested in this study likely cannot be explained by any one single factor. The use of protoplast viability controls in our experiments allows us to rule out the physical stress of transformation itself as the reason for the failed recovery of transformants in our tested species. We will therefore explore the molecular mechanisms potentially responsible for the variation in transformation success. DNA-mediated transformation involves complex molecular pathways, and Phytophthora species are known for responding variably to PEG-mediated transformation protocols (Mcleod et al., 2008), with results sometimes even varying between isolates of the same species (Riedel et al., 2009). Notably, our results revealed no phylogenetic pattern associated with which forest Phytophthoras could be successfully transformed. For example, transformations failed in P. cinnamomi even though it is closely related to the control species P. sojae in Phytophthora Clade 7 (X. Yang et al., 2017), but the same transformation protocol and plasmid DNA worked well for P. cactorum despite its phylogenetic distance in Clade 1 (X. Yang et al., 2017). However, Phytophthora species within a clade are known to show as much variation in their biological characteristics as species between clades (C. Brasier et al., 2022). Additionally, recent comparative genomics studies have revealed that Phytophthora genomes are highly variable, even between closely related species (Gao et al., 2021; Kronmiller 100 et al., 2023). This biological and genomic variability could contribute to the lack of phylogeny-related similarities in response to taking up exogenous plasmid DNA. Our success with transformations in P. cactorum echoes that of Chen et al. (2016) who stably transformed a single isolate of P. cactorum with the pGFPN plasmid. Using the optimized PEG-mediated transformation protocol (Mcleod et al., 2008), Chen et al. obtained 25 P. cactorum transformants, all exhibiting green fluorescence from the gfp gene. Notably, Chen et al. transformed protoplasts with 13 pmol of DNA, which equates to 55 μg for the pGFPN plasmid and 100 μg for the pYF2-PsCG plasmid. We were able to consistently transform P. cactorum with 25-30 μg of plasmid DNA, suggesting that less DNA may be required for stable transformation. Taken together with previous research, our results suggest that P. cactorum is more amenable to DNA transformation. It could be that there is less intra-species genomic variation between isolates of P. cactorum, but this would need to be confirmed with comparative genomics studies. Given that P. cactorum is a broad host-range pathogen with impacts in horticulture, agriculture, and forestry (C. Brasier et al., 2022; X.-R. Chen et al., 2023), it is a good candidate species for continued development of molecular techniques such as CRISPR-Cas gene editing. The inconsistency in transformation success we observed in P. ramorum was somewhat surprising given that a previous study had demonstrated stable expression of plasmid DNA in this species (Riedel et al., 2009). Riedel et al. used a modified version of the original PEG-liposome-mediated protocol developed in P. infestans (Judelson et al., 1991) to transform four independent isolates of P. ramorum (EU1 and NA1 lineages) with the vector p34GFN (Si- 101 Ammour et al., 2003), which contains nptII and gfp genes, both driven by the B. lactucae ham34 promoter. A total of 40 transformants were obtained across all four P. ramorum isolates, all of which yielded positive results for p34GFN DNA in PCR and qPCR (Riedel et al., 2009). However, only five transformants from two of the four isolates exhibited gfp expression, indicating that the genetic background of the isolates differed enough to influence transformation results (Riedel et al., 2009). It is possible that the genotype of the P. ramorum NA2_17 isolate that we used in our experiments is less amenable to transformation than the isolates used by Riedel et al. Also of note is that Riedel et al. used Lipofectin in addition to PEG in their transformations; we did try a liposome-mediated method in one of our P. ramorum transformations, however, we found that it had no effect. Future experiments could explore optimizing liposome reagent concentrations to determine if it improves plasmid transformations in P. ramorum. Similar to P. ramorum, previous studies in P. cinnamomi were successful in obtaining stable transformants (Dai et al., 2021; McCarren et al., 2005), so our unsuccessful result in this species was unexpected. Given that Dai et al. used an almost identical protocol to ours, based on the optimized PEG-CaCl2 transformation method (Mcleod et al., 2008), there are two predominant explanations for our lack of transformants. The first possible explanation is that the genotype of the P. cinnamomi isolate we tested may be unamenable to transformation of exogenous DNA. However, the success of transformations in two previous studies with multiple isolates of P. cinnamomi (Dai et al., 2021; McCarren et al., 2005) makes this explanation somewhat less likely. The second, and more likely, possible explanation is the different plasmid DNA used for transformations. Dai et al. used a 7214 bp plasmid, pTOReGFP, which contains the nptII gene 102 for G418 resistance driven by the hsp70 promoter, while the nptII gene in the pYF2-PsCG plasmid we used for P. cinnamomi transformations is driven by the P. sojae rpl41 promoter. It is therefore possible that the hsp70 promoter is better suited to drive gene expression in P. cinnamomi than the rpl41 promoter. The possible effects that differences in promoter activity and other plasmid-related factors may have on transformation success are discussed in more detail below. An important factor to consider for transformation success is the vector DNA. Our sequencing results indicated that the pGFPN plasmid is not stable; sequences from the two independent pGFPN preparations showed recombination, inversion, and deletion of large segments of the plasmid DNA. This is particularly perplexing given that the E. coli strain used for plasmid replication was the laboratory standard DH5⍺ strain with a recA1-endA1 genotype, which should prevent unwanted recombination and cleavage in the plasmid DNA. Additionally, this instability was not observed for either pYF515 or pYF2-PsCG, which were subject to the same methods as pGFPN. The recombination events in both pGFPN preparations yielded plasmids with no nptII gene, thus the failed transformations with pGFPN in all tested Phytophthora species can confidently be attributed to the missing antibiotic selection gene. Therefore, we cannot make any meaningful conclusions regarding the efficacy of the pGFPN plasmid for transformations in forest Phytophthoras. The only plasmid that we tested across all six Phytophthora species was pYF2-PsCG. One plasmid-related factor that may have influenced transformation success is variation in the activity of the P. sojae RPL41 promoter in the five forest Phytophthora species. In pYF2-PsCG, the 103 RPL41 promoter drives expression of the nptII gene, and therefore any differences in promoter activity would have affected the recovery of transformants on G418-selective medium. Previous studies demonstrated that the P. sojae RPL41 promoter is active in other Phytophthora species, including P. capsici (Z. Wang et al., 2018), P. litchii (C.C. Chen ex W.H. Ko, H.S. Chang, H.J. Su, C.C. Chen & L.S. Leu) P.K. Chi, X.P. Pang & R. Liu (W. Wang et al., 2019), P. infestans (Ah-Fong et al., 2021), and P. colocasiae Racib. (Z. Wang et al., 2022). However, it is possible that the RPL41 promoter is more active in P. cactorum compared to the other four species we tested. Our transformations with the pGFPN plasmid could have provided more evidence for the role of promoter activity as the nptII gene in pGFPN is driven by the Bremia lactucae hsp70 promoter, however, the instability of the plasmid prevented transformation. To determine the role of promoter activity in forest Phytophthora transformation success, future studies could test plasmids with the nptII gene driven by the more standard ham34 and hsp70 oomycete promoters, which both express well in a diversity of Phytophthora (Bottin et al., 1999; X.-R. Chen et al., 2016; Judelson et al., 1991, 1993; Mcleod et al., 2008; Riedel et al., 2009; van West et al., 1999) and other oomycete species (L. Jiang et al., 2017; Vijn & Govers, 2003; J. J. Weiland, 2003). Another plasmid-related factor that could have influenced transformation success is Cas9 toxicity. Given that the pYF2-PsCG plasmid was originally constructed to test Cas9 expression (Fang, Jang, et al., 2017; Fang & Tyler, 2016), it contains the gene for SpCas9 driven by the constitutive ham34 promoter. While there is no single-guide RNA (sgRNA) construct in the plasmid, the Cas9 gene would still be transcribed and translated into a functional nuclease in transformed cells. Previous studies have demonstrated that stable expression of Cas9, even without a bound sgRNA or when in a catalytically deactivated form (dCas9), can be toxic in 104 bacteria (S. Cho et al., 2018; Rock et al., 2017), protozoa (D. Peng et al., 2014), fungi (Foster et al., 2018), and algae (W. Jiang et al., 2014). The exact mechanisms driving this toxicity are unclear, however, dCas9 has been shown to bind non-specifically to NGG PAM sites in the genome, even when the nuclease is not associated with an sgRNA (D. L. Jones et al., 2017). It has been hypothesized that this non-specific binding contributes to the toxicity of dCas9 and Cas9 when they are stably expressed in cells (Markus et al., 2019; S. Zhang & Voigt, 2018). In light of these previous studies, it is possible that the SpCas9 expressed from the pYF2-PsCG plasmid was toxic to P. cinnamomi, P. cryptogea, P. syringae, and perhaps even P. ramorum. Establishing plasmid transformations in forest Phytophthoras is clearly complex, and future success will likely require tailoring protocols to each individual species, and potentially also to individual isolates. There are many possible directions for future research, and our results lay the groundwork for the optimization of PEG-mediated DNA transformations in Phytophthora forest pathogens. The continued development of molecular techniques and species-specific protocols is necessary to improve understanding of phytopathogenicity in Phytophthoras and to ultimately develop more effective management approaches. 105 Chapter 5: Successes and challenges in establishing CRISPR-Cas9 gene editing for two forest pathogenic Phytophthora species via co-transformation of plasmid-ribonucleoprotein complexes 5.1 Chapter Summary CRISPR-Cas9 gene editing has become an important tool for the study of plant pathogenic Phytophthoras, allowing researchers to functionally characterize specific genes involved in phytopathogenicity, virulence, and fungicide resistance. However, development of this technology has exclusively focused on agricultural pathosystems, with researching lacking for forest pathosystems. We sought to develop CRISPR-Cas9 gene editing in two forest pathogenic Phytophthoras, Phytophthora cactorum and P. ramorum, using a plasmid-ribonucleoprotein (RNP) co-transformation approach. Our gene target in both species was the ortholog of PcORP1, which encodes an oxysterol binding protein that was found to be the target of the fungicide oxathiapiprolin in the agricultural pathogen P. capsici. We delivered liposome complexes, each containing plasmid DNA and CRISPR-Cas9 RNPs, to Phytophthora protoplasts using a polyethylene glycol (PEG)-mediated transformation protocol. Using this method, we obtained two ORP1 mutants in P. cactorum, however, we were unable to obtain any mutants in P. ramorum. Both P. cactorum mutants exhibited decreased resistance to oxathiapiprolin, as measured by their radial growth relative to wildtype cultures on oxathiapiprolin-supplemented medium. Our results demonstrate the potential of RNP-mediated CRISPR-Cas9 gene editing in P. cactorum and provide a foundation for the future optimization of this protocol in this species and other forest pathogenic Phytophthoras. 106 5.2 Introduction Since its development in 2012 (Jinek et al., 2012), CRISPR-Cas9 gene editing has become a valuable tool for plant pathology, facilitating the exploration of the molecular mechanisms driving host-pathogen interactions and elucidating potential targets for disease resistance and pathogen control strategies. CRISPR-Cas gene editing technologies are based on the naturally occurring adaptive immune systems of bacteria and archaea: clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR associated (Cas) proteins (Bhaya et al., 2011; Ishino et al., 1987; Terns & Terns, 2011). CRISPR-Cas9 gene editing was adapted from the type II CRISPR-Cas system from the bacterium Streptococcus pyogenes Rosenbach and targets DNA with three components: a Cas9 endonuclease (SpCas9), and a CRISPR RNA (crRNA) and trans-activating crRNA (tracrRNA) that base-pair together and bind to Cas9 (Jinek et al., 2012). A 20-nucleotide (nt) region of the crRNA guides Cas9 to the complementary DNA in the target organism’s genome, called the protospacer, where Cas9 creates a double-stranded break (DSB) near a highly conserved sequence (NGG) called the protospacer-adjacent motif, or PAM (Jinek et al., 2012). Jinek et al. created a highly adaptable CRISPR-Cas9 system with the crRNA and tracrRNA fused as a single guide RNA (sgRNA); the sgRNA can direct Cas9 to virtually any protospacer target near an NGG PAM via modifications made to the 20-nt crRNA region (Jinek et al., 2012). In eukaryotes, the DSB is then repaired by the host cell, either through an error-prone end-joining pathway in the absence of a homologous donor template, or through the highly accurate homology-directed repair (HDR) pathway in the presence of a homologous donor template (Ranjha et al., 2018). It is through the DSB repair process that mutations are made in the target gene, which allows researchers to characterize gene functions via knockout or highly specific mutations. 107 The CRISPR-Cas9 systems developed in filamentous plant pathogens enable researchers to discover how specific genes contribute to phytopathogenicity (Fang & Tyler, 2016; J. Li et al., 2018), elucidate important secondary metabolite and toxin pathways (Y. Liang et al., 2018; Q. Wang et al., 2018), and characterize genetic resistance to fungicides (Leisen et al., 2020; Miao, Chi, et al., 2018). However, most CRISPR-Cas9 research in plant pathology has focused on agricultural pathosystems and very little research has been directed towards forest pathogens. The recent development of a CRISPR-Cas9 system in the fungal pathogen Dothistroma septosporum (Dorog.) M. Morelet (McCarthy et al., 2022) is the first use of CRISPR-based gene editing in a filamentous forest pathogen. Some of the most ecologically and economically impactful filamentous plant pathogens are those in the oomycete genus Phytophthora. Well-known examples include P. infestans (Mont.) de Bary, the causal agent of late blight of potato (de Bary, 1861), and P. cinnamomi Rands, the causal agent of jarrah dieback in Australia (Podger et al., 1965) and one of the most devastating plant pathogens, with thousands of host species and a global distribution (Hardham & Blackman, 2018). Due to the global impacts of Phytophthoras in agriculture, horticulture, and forestry, characterizing the genetic mechanisms driving the phytopathogenicity of species in this genus has become a priority, and CRISPR-Cas9 gene editing is an important tool for this molecular characterization. CRISPR-Cas9 gene editing protocols have now been developed for a number of agricultural Phytophthora pathogens including P. sojae Kaufm. & Gerd. (Fang & Tyler, 2016), P. palmivora (E.J. Butler) E.J. Butler (Gumtow et al., 2018), P. capsici Leonian (Miao, Chi, et al., 2018; Z. Wang et al., 2018), P. litchii (C.C. Chen ex W.H. Ko, H.S. Chang, H.J. Su, C.C. 108 Chen & L.S. Leu) P.K. Chi, X.P. Pang & R. Liu (W. Wang et al., 2019), P. parasitica Dastur (Q. Zhang et al., 2020), and P. colocasiae Racib. (Z. Wang et al., 2022). These studies all used polyethylene glycol (PEG)-mediated transformation to deliver the CRISPR-Cas9 components to Phytophthora protoplasts. One exception is the study performed by Gumtow et al., who used Agrobacterium-mediated transformation (AMT) to deliver the Cas9 and sgRNA to P. palmivora (Gumtow et al., 2018). Despite this success of CRISPR-Cas9 systems in agricultural Phytophthoras, there have been no such methods developed for forest pathogenic Phytophthoras, which may be due to the lack of efficient and stable transformation protocols in these species (see Chapter 4). When delivering CRISPR-Cas9 components to cells via PEG-mediated transformation, there are two general methods that can be employed. The first method is plasmid delivery, where both Cas9 and its sgRNA are encoded on a plasmid (often also encoding a gene for antibiotic resistance), delivered through transformation to the nucleus, and expressed in the cell via transcription and translation. The translated Cas9 nuclease and sgRNA then form a ribonucleoprotein (RNP) complex in the cell and return to the nucleus to find the target protospacer DNA and create a DSB. The second method is direct transformation of a purified Cas9-sgRNA RNP, which also gets delivered to the nucleus through transformation, but does not require transcription and translation and thus immediately begins targeting the genomic protospacer DNA. First developed in Caenorhabditis elegans (Maupas) Dougherty (S. W. Cho et al., 2013), the RNP method for CRISPR-Cas9 gene editing has been successfully employed in mammals (S. Kim et al., 2014), plants (Woo et al., 2015), and fungi (Pohl et al., 2016). While RNP delivery of Cas9 has not been developed for any Phytophthora species, it has been used to 109 successfully edit the genome of the oomycete Aphanomyces invadans Willoughby, R.J. Roberts & Chinabut (Majeed et al., 2018). RNP delivery of CRISPR-Cas9 components has several advantages over plasmid delivery. The first is that it is possible to obtain CRISPR-Cas9 mutants with no transgenic DNA, and indeed, this has been a significant motivation for developing RNP methods in plants (Woo et al., 2015; Y. Zhang et al., 2016). Another advantage of RNP CRISPR-Cas9 delivery is that the RNP complexes can target DNA immediately after transformation into cells, as opposed to plasmid delivery which requires time for transcription and translation (S. W. Cho et al., 2013). In fact, the transgenic expression of Cas9 has toxic effects in many organisms (S. Cho et al., 2018; Foster et al., 2018; W. Jiang et al., 2014; D. Peng et al., 2014), which can usually be avoided with the transient expression of RNPs (Foster et al., 2018). Finally, the use of RNPs for CRISPR-Cas9 delivery avoids many of the common challenges of plasmid DNA transformation, including the potentially off-target effects of random transgene insertion into genomes (S. Kim et al., 2014; Latham et al., 2006; Zuris et al., 2015) and the need to optimize plasmid gene promoters and codon usage for each target organism (S. W. Cho et al., 2013; Woo et al., 2015). However, one significant limitation of the RNP approach is the lack of a selective marker for putative transformants, particularly when triggering the end-joining DNA repair pathway with no donor template. This lack of selection is particularly problematic when the target gene for CRISPR-Cas9 produces no visible phenotype when mutated. The objective of this study was to establish CRISPR-Cas9 gene editing using an RNP-mediated approach in two forest pathogenic Phytophthoras: P. ramorum and P. cactorum. Phytophthora 110 ramorum Werres, De Cock, & Man in’t Veld is the causal agent of sudden oak/larch death, a globally invasive pathogen that is causing landscape-level damage in the tanoak/oak forests of California (Garbelotto Benzon et al., 2001; Rizzo et al., 2002a) and larch plantations in the UK (Webber et al., 2010). Phytophthora cactorum (Leb. & Cohn) Schröeter is an aggressive pathogen with a broad host range spanning hundreds of species and impacts agriculture, horticulture, and forestry (X.-R. Chen et al., 2023; Erwin & Ribeiro, 1996; Nienhaus, 1960). As a forest pathogen, it is perhaps most destructive in nurseries (Jung et al., 2016; Lilja et al., 1996; Rytkönen et al., 2008), but it has also been associated with diseases on economically and ecologically important genera including Fagus (J. E. Weiland et al., 2010), Fraxinus (Orlikowski et al., 2011), Pinus (Vettraino et al., 2022), and Quercus (Jung et al., 1996). The gene we chose to target in P. ramorum and P. cactorum is the ortholog of PcORP1 in P. capsici, named due to the oxysterol binding protein (OSBP) that it encodes, a member of the OSBP-related proteins (ORPs) family (Miao et al., 2016). The ORP1 protein in Phytophthora species is the target of the fungicide oxathiapiprolin (Andreassi et al., 2013; Pasteris et al., 2016; Sweigard et al., 2014), and mutations in the ORP1 gene of P. capsici, P. colocasiae, P. nicotianae Breda de Haan, and P. sojae have yielded oxathiapiprolin-resistant mutants (Bittner et al., 2017; Miao, Chi, et al., 2018; Miao et al., 2016; Z. Wang et al., 2022). We tested a co-transformation approach for CRISPR-Cas9 editing in P. ramorum and P. cactorum, delivering liposomes containing both a Cas9-sgRNA RNP targeting the ORP1 gene and a plasmid encoding the nptII gene for geneticin (G418) resistance. We chose this hybrid approach to facilitate selection of putative mutants, hypothesizing that Phytophthora transformants growing on G418- 111 selective medium would have also received the CRISPR-Cas9 RNP complexes and therefore the mutant screening process would be more efficient than with no selection method. 5.3 Methods 5.3.1 Phytophthora cactorum isolates and culture conditions The details of the Phytophthora cactorum and P. ramorum isolates used in this study are summarized in Table 5.1. Cultures were maintained on clarified 20% V8 agar (V8A: see Appendix B.1 for recipe) at room temperature (approx. 21°C) in the dark. For long-term storage, agar plugs taken from the edge of mycelial growth, five days post plating, were stored in nuclease-free molecular water at 5-10°C in the dark. Table 5.1 Details of the Phytophthora cactorum and P. ramorum isolates used in this study. Species Isolate Isolate Details Phytophthora cactorum Larch FF-42 2Pa (abbreviated FF42) See Feau et al. (2021) for isolate details. Phytophthora ramorum NA2 17_0134_0030 (abbreviated NA2_17) Culture provided by G. Bilodeau from the Canadian Food Inspection Agency. 5.3.2 Phytophthora DNA extractions In preparation for DNA extraction, Phytophthora cultures were transferred from V8A plates to 12-well plates containing V8 broth (V8B: see Appendix B.1 for recipe). An agar plug from each culture was taken from the edge of mycelial growth and placed in a well containing 2 mL of V8B. After 3-4 days of growth in V8B, 50-100 mg of mycelial tissue was harvested from each culture, and flash frozen in liquid nitrogen. Total cellular DNA was extracted using a QIAGEN DNeasyⓇ Plant Mini Kit (QIAGEN Sciences, Germantown, MD, USA) as per the manufacturer’s instructions (DNeasyⓇ Plant Handbook, Mini Protocol, TissueLyser procedure). 112 Tissue disruption was performed using a Retsch Mixer Mill MM 300 (Retsch GmbH, Haan, Germany). 5.3.3 PCR primer design and equipment All PCR primers used in this study were designed using NCBI Primer-BLAST (Ye et al., 2012) with target specificity checked against the NCBI “RefSeq representative genomes” database for Phytophthora (taxid: 4783) to minimize off-target amplification. Primers were ordered from Integrated DNA Technologies (IDT, Coralville, IA, USA). All PCRs were performed using a Bio-Rad T100™️ thermal cycler (Hercules, California, USA). 5.3.4 Identification of the PcORP1 ortholog in Phytophthora ramorum and Phytophthora cactorum The OSBP protein target of oxathiapiprolin was first identified in P. capsici (Andreassi et al., 2013; Sweigard et al., 2014), and was labelled in the corresponding JGI PhycoCosm (Grigoriev et al., 2021) Phytophthora capsici LT1534 v11.0 genome as Protein Id: 564296 (PHYCAscaffold_14:545019–548397). The gene for the P. capsici OSBP was first referred to as PcORP1 by Miao et al. (2016); we chose to follow this naming convention in our species, referring to the orthologs in P. cactorum and P. ramorum as PcaORP1 and PrORP1, respectively. The protein, transcript, and genomic FASTA files for the PcORP1 sequence were downloaded from PhycoCosm (Grigoriev et al., 2021) using the Protein Id 564296 in the Phytophthora capsici LT1534 v11.0 genome (Lamour et al., 2012). To identify the ortholog of PcORP1 in P. 113 ramorum (PrORP1), a tblastn (Altschul et al., 1990) was performed using the PcORP1 protein sequence as the query against the P. ramorum JGI reference genome (NCBI genome assembly ASM14973v1, strain Pr102, lineage NA1). The top hit was selected, and the FASTA file for the corresponding genomic scaffold was downloaded. The genomic region corresponding to the sequence of the top hit from the tblastn with an additional 1000 nucleotides of flanking upstream and downstream DNA was copied into Geneious Prime 2023.2.1 (https://www.geneious.com) and named Pr102_NCBI_ORP1_ortholog. The Geneious “Find ORFs” tool was used to analyze the Pr102_NCBI_ORP1_ortholog sequence and identify possible open reading frames (ORFs) in the sequence corresponding to the putative PrORP1 gene. The longest ORF was selected and verified as an oxysterol-binding protein using the NCBI Open Reading Frame Finder (Wheeler et al., 2003) and SmartBLAST (NCBI Resource Coordinators, 2016). Three independent pairs of primers were designed to amplify a region including the putative PrORP1 ORF plus several hundred base pairs of flanking upstream and downstream DNA (Table 5.2). The same method was performed to find the PcORP1 ortholog in P. cactorum (PcaORP1), but with the following minor modifications: the P. cactorum NCBI reference genome (genome assembly ASM1686465v1, strain P414) was used for the tblastn of PcORP1, 1500 nucleotides of flanking upstream and downstream DNA were copied into Geneious for analysis, and only two primer pairs were designed to amplify the PcaORP1 region (Table 5.2). 114 Table 5.2 Primer pairs designed to amplify the PcORP1 orthologs of P. ramorum (PrORP1) and P. cactorum (PcaORP1). All PCRs were performed using Phusion™️ Plus DNA Polymerase, which creates a universal annealing temperature of 60°C. Species (Gene) Primer Pair Name Primer Sequences (5’ to 3’) Expected Product Size Phytophthora ramorum (PrORP1) Pr102ORPh_ORF_F1/R1 F1: CTGCGTCCAATCACAATATGCC 3,891 bp R1: TCAAGCTGGCTCGTAACTTCAT Pr102ORPh_ORF_F2/R2 F2: GTGTGACATTATTCCCAGTGGC 3,559 bp R2: AGCTTCATTTACCCCACCAAGA Pr102ORPh_ORF_F3/R3 F3: CTCATTTCGCCGGACAGTTG 3,485 bp R3: ATGCTCATCCTGCTTTGCTTG Phytophthora cactorum (PcaORP1) Pca414ORPh_Seq_F1-A/R1-A F1-A: CGATGGGGTTAGCACTACAGAG 4,502 bp R1-A: CTTTAAGGAGAAACGCACGGAG Pca414ORPh_Seq_F1-B/R1-B F1-B: CTCTATCGCTCGTGTCTCTTCG 4,731 bp R1-B: CCGGAGCTGAACCACTTCTACTTC 5.3.4.1 Touchdown PCR of PrORP1 and PcaORP1 genes Touchdown PCR was performed on DNA extractions from P. ramorum NA2_17 and P. cactorum FF42 to amplify approximately 3.5-4.8 kb regions of the PrORP1 and PcaORP1 genes, respectively. A high-fidelity polymerase, Phusion™️ Plus DNA Polymerase (Thermo Fisher Scientific, Waltham, MA, USA), was used with the primers designed for each respective gene (Table 5.2). The PCR reactions were set up according to the manufacturer’s instructions (Phusion™️ Plus DNA Polymerase User Guide), and 5-10 ng of template DNA was used. The thermocycler conditions for touchdown PCR consisted of two phases; the cycle details are shown in Table 5.3. Phusion™️ Plus DNA Polymerase uses a universal annealing temperature of 60°C, so this temperature was used for annealing in Phase 2 of the PCR. 115 Table 5.3 Thermocycler conditions for touchdown PCR performed on P. ramorum NA2_17 and P. cactorum FF42 wildtype DNA using Pr102ORPh_ORF and Pca414ORPh_Seq primer pairs, respectively. Phase Step Temperature Time 1 98°C 30 sec 1 2 98°C 10 sec 3 70°C–1°C/cycle 15 sec 4 72°C 30 sec/kb* Repeat steps 2-4 for 10 cycles 2 5 98°C 10 sec 6 60°C✢ 15 sec 7 72°C 30 sec/kb* Repeat steps 5-7 for 20 cycles 8 72°C 5 min 9 4°C Hold *Extension cycle is 30 seconds per kb of amplified product; 2 minutes was used for Pr102ORPh_ORF primers and 2.5 minutes was used for Pca414ORPh_Seq primers ✢60°C is the universal annealing temperature for Phusion™️ Plus DNA Polymerase 5.3.4.2 Sanger sequencing and primer-walking for PrORP1 and PcaORP1 Primer walking was performed along the touchdown PCR amplicons to determine the sequences of PrORP1 and PcaORP1 gene regions. For PrORP1, the PCR product from the Pr102ORPh_ORF_F1/R1 primers, named PrNA2ORPh, was sent for Sanger sequencing using the Pr102ORPh_ORF_F1 and Pr102ORPh_ORF_F2 primers for the forward direction, and the Pr102ORPh_ORF_R1 and Pr102ORPh_ORF_R3 primers for the reverse direction. The consensus sequences from the Sanger results were then used to design a new set of forward and reverse sequencing primers, which were used to sequence the next 500–700 base pairs of the original PrNA2ORPh PCR product. This process was repeated until the entire 3,891 bp PrNA2ORPh amplicon was sequenced. The same primer walking method was used for sequencing PcaORP1, using the 4,502 bp PCR amplicon from the Pca414ORPh_Seq_F1-A/R1-A primers, however, the primer walking was completed by the CHUL Sanger Sequencing Platform at Université Laval (CRCHU de Québec, Québec City, QC, Canada). 116 5.3.4.3 Gene prediction for PrORP1 and PcaORP1 sequences The PrORP1 and PcaORP1 consensus sequences from primer walking were analyzed using the Geneious “Find ORFs” tool and the NCBI Open Reading Frame Finder (Wheeler et al., 2003) to identify putative ORFs. The largest ORF was selected and its protein sequence was analyzed with the SmartBLAST tool (NCBI Resource Coordinators, 2016) on the NCBI Open Reading Frame Finder page to validate its identity as an oxysterol-binding family protein. To predict the structure of each ORP1 gene and find the exon-intron boundaries, the sequence of the putative ORF with approximately 500 nucleotides of flanking upstream and downstream DNA was analyzed using the Softberry FGENESH eukaryotic gene finding tool (http://www.softberry.com) with the Phytophthora gene-finding parameters selected. The amino acid sequences of PrORP1 and PcaORP1 were analyzed with InterPro (Paysan-Lafosse et al., 2023) to compare the predicted protein domains relative to the original P. capsici PcORP1 sequence (Miao et al., 2016). 5.3.5 CRISPR-Cas9 guide RNA design CRISPR-Cas9 guide RNAs (gRNAs) were designed within the PrORP1 and PcaORP1 predicted ORF regions. Intron regions of each ORF were avoided. The Eukaryotic Pathogen CRISPR Guide RNA/DNA Design Tool (EuPaGDT; Peng & Tarleton, 2015) was used to generate gRNAs with the SpCas9 20-nucleotide gRNA 3’ NGG PAM option selected (off-target PAM motif setting: NAG, NGA). Off-target analysis was performed in EuPaGDT using the P. ramorum Pr-102 FungiDB-26 genome for the PrORP1 gRNAs and the P. cactorum P414 NCBI reference genome for the PcaORP1 gRNAs. Note that when the first three PcaORP1 gRNAs 117 were designed, the P. cactorum P414 genome was not provided in EuPaGDT, so off-target analysis was performed manually using a blastn (Altschul et al., 1990) of each gRNA candidate against the P414 genome in NCBI. Once the P414 genome was added, the subsequent two gRNAs for PcaORP1 were designed using the off-target analysis in EuPaGDT. All gRNAs with a EuPaGDT “Total score” above 0.5 (as per Fang et al. 2017) and no off-target hits were considered potential candidates and their secondary structure was analyzed using RNAstructure (Reuter & Mathews, 2010). Only gRNAs with fewer than three consecutive paired bases were considered for further analysis (Fang, Cui, et al., 2017). Finally, candidate gRNAs were analyzed with the Integrated DNA Technologies (IDT) “CRISPR-Cas9 guide RNA design checker”, and those with the highest scores and minimal secondary structure were ordered as 2 nmol Alt-R™️ CRISPR RNAs (crRNAs) from IDT (Coralville, Iowa, USA). 5.3.6 Ribonucleoprotein-plasmid co-transformation The recipes and methods for all growth media and transformation solution are listed in Appendix B.1. The methods for preparation of Phytophthora cultures and protoplast isolation (sections 5.3.6.3 and 5.3.6.4) are based on the optimized transformation protocols previously developed for Phytophthora species (Dou et al., 2008; Fang, Cui, et al., 2017; Mcleod et al., 2008). 5.3.6.1 Plasmid DNA The pYF2-PsNLS-hSpCas9-GFP (abbreviated pYF2-PsCG) plasmid, demonstrated to generate geneticin (G418) resistant transformants in P. cactorum and P. ramorum (see Chapter 4 results), was used for all co-transformations. Plasmid DNA was obtained using a QIAGEN HiSpeedⓇ Plasmid Maxi Kit (QIAGEN Sciences, Germantown, MD, USA) as per the manufacturer’s 118 instructions (HiSpeedⓇ Plasmid Purification Handbook), and vacuum concentrated to 3-5 μg/μL prior to use in co-transformations. 5.3.6.2 Formation of Cas9 ribonucleoprotein complexes The following components for the CRISPR-Cas9 ribonucleoprotein (RNP) complexes were ordered from IDT (Coralville, Iowa, USA): Alt-R™️ S.p. HiFi Cas9 Nuclease V3 (100 μg), Alt-R™️ CRISPR-Cas9 crRNA (2 nmol), and Alt-R™️ CRISPR-Cas9 tracrRNA (20 nmol). Prior to use, the Cas9 nuclease was diluted with W5 solution to a 1 μM working solution and stored at -80°C (as per instructions from IDT). Both the crRNA and tracrRNA were resuspended to make 100 μM stocks using IDT Nuclease-Free Duplex Buffer (NFDB). Guide RNAs (gRNAs) were formed by creating 1 μM crRNA-tracrRNA duplexes as follows: equal volumes of 100 μM crRNA and 100 μM tracrRNA were mixed in a 1.5 mL microcentrifuge tube with NFDB for a final concentration of 1 μM, incubated at 95°C for 5 minutes, cooled to room temperature, and stored at -20°C. The Cas9-gRNA RNP complexes were formed as follows: equal volumes of 1 μM gRNA and 1 μM Cas9 nuclease were mixed in a 1.5 mL microcentrifuge tube with W5 solution for a final concentration of 300 nM, incubated at room temperature for 5 minutes, and stored at 4°C until used for transformation. For transformations with Lipofectamine™️ CRISPRMAX™️ (Thermo Fisher Scientific, Waltham, MA, USA), Cas9 PLUS™️ Reagent was added to the RNP mixture at a ratio of 0.6 μL of reagent to 25 μL total RNP volume (as per IDT Alt-R CRISPR-Cas9 User Guide, protocol for cationic delivery of Cas9 RNP). 119 5.3.6.3 Preparation of Phytophthora cultures for transformation Phytophthora cultures were initially grown on 1.5% nutrient V8 agar (NVA) plates at room temperature (~21°C) in the dark. Once there was sufficient mycelial growth (five to seven days), cultures were transferred to nutrient V8 broth (NVB): three flasks, each containing 50 mL of NVB, were inoculated with five agar plugs (6-mm diameter) taken from the growing edge of the mycelial culture (15 agar plugs total). The liquid cultures were grown for three to four days at room temperature (~21°C) in the dark. 5.3.6.4 Isolation of protoplasts After three days of growth, mycelial mats were harvested and rinsed with autoclaved Milli-QⓇ water followed by 0.8 M mannitol. The mycelial mats were transferred to a 100 mm × 20 mm Petri dish, covered with 0.8 M mannitol, and shaken gently (~30 rpm) for 10 minutes. The mats were recollected, transferred to a new Petri dish containing 20 mL of an enzyme solution (lysing enzymes + cellulase; see Appendix B.1 for full recipe) and digested for 60-90 minutes at 25°C with gentle shaking (~30 rpm). After digestion, the enzyme/protoplast solution was filtered through a 70 μm cell strainer and centrifuged at 1200 rcf for 2.5 minutes. The pellet was gently washed in 35 mL of W5 solution (see Appendix B.1 for recipe), recentrifuged at 1200 rcf for 3 minutes, and the resulting pellet was resuspended in 10 mL of W5 solution and placed on ice for 1-2 hours. The protoplast concentration was determined using a hemocytometer. 5.3.6.5 Formation of plasmid-RNP cationic-lipid transfection complexes While the protoplasts were incubating on ice, plasmid-RNP transfection complexes were prepared as follows: 250 μL of 300 nM RNP, 6 μL of pYF2-PsCG plasmid DNA, 12 μL of 120 Lipofectamine™️ CRISPRMAX™️ or Lipofectamine™️ RNAiMAX™️ (Thermo Fisher Scientific, Waltham, MA, USA), and 232 μL of W5 solution were mixed in a 1.5 mL microcentrifuge tube and incubated at room temperature for 20 minutes. The volumes of RNP and Lipofectamine were determined based on the picomoles of RNP per cell used in the IDT protocol for cationic lipid delivery of CRISPR RNP complexes (IDT, 2018). The final transfection complex for each transformation had a total volume of 500 μL containing 75 pmol of RNP and ~33 μg (4 pmol) of plasmid DNA. 5.3.6.6 Co-transformation of Phytophthora protoplasts with CRISPR-Cas9 ribonucleoprotein complexes and plasmid DNA Multiple protocols were tested for plasmid-ribonucleoprotein (RNP) co-transformations in P. ramorum and P. cactorum; the details of the methods tested are summarized in Data C.1.1. The protocol outlined below is the method that ultimately succeeded in P. cactorum and is a modified version of the plasmid-mediated CRISPR-Cas9 transformation developed for P. sojae (Fang, Cui, et al., 2017). Following incubation on ice (section 5.3.6.4), the protoplast suspension was centrifuged at 1200 rcf for 4 minutes. The resulting pellet was resuspended in MMg solution (see Appendix B.1 for recipe) at a concentration of ~1.0-3.0 x 106 protoplasts/mL and incubated at room temperature for 10 minutes. During incubation, 500 µL of plasmid-RNP transfection complex (TC), or 500 µL of sterile molecular-grade water/W5 solution for the negative control, was added to a 50 mL centrifuge tube and placed on ice. After the 10-minute incubation, the protoplasts were gently mixed, and 1 mL of the protoplast/MMg solution was added to the TC (or control) in each 50 mL 121 tube, mixed, and incubated for 30 minutes on ice. A 40% (v/v) PEG solution was added to each tube in three aliquots of 580 µL for a total of 1.74 mL of PEG solution per tube, and the tubes were incubated for another 30 minutes on ice. After 30 minutes, 2 mL of V8-mannitol (VM) broth was added to each tube. The tubes were inverted to mix and incubated on ice for two minutes. Then, 8 mL of VM broth was added to each tube, the tubes were inverted to mix and incubated at room temperature for an additional two minutes. To test the viability of the protoplasts after the transformation process, ~30-50 µL of the protoplast-VM solution from each centrifuge tube (including control treatment) was spread onto an agar plate (NVA) and incubated at room temperature in the dark overnight (protoplast viability test Day 1). The remainder of the protoplast-VM solution (~10 mL) from each tube was added to a 100 mm × 20 mm Petri dish containing 10 mL of VM broth with 100 µg/mL of ampicillin (Thermo Fisher Scientific, Waltham, MA, USA) for a final concentration of 50 µg/mL ampicillin. The dishes were incubated overnight at room temperature in the dark. 5.3.6.7 Antibiotic selection of transformants The following morning, the regenerated protoplasts were transferred to a new 50 mL tube and centrifuged at 2000 rcf for 5 minutes. The pellet was resuspended in ~2 mL of the supernatant and 150 µL of the resuspended protoplasts were added to 20 mL of cooled VM agar (40-45°C) without G418 and poured into a Petri dish (protoplast viability test Day 2). Cooled VM agar (40-45°C) with 30 µg/mL of G418 (Gibco™️ Geneticin™️ Selective Antibiotic, Thermo Fisher Scientific, Waltham, MA, USA) was added to the remaining protoplasts to a total volume of 50 mL. The tubes were inverted to mix, and the protoplast/agar mixture was poured into three Petri dishes (~16-17 mL of mixture in each dish). All plates were incubated at room temperature in the 122 dark for two days, or until mycelial growth appeared (up to five days). Each mycelial colony that grew on the G418-supplemented medium was treated as a putative transformant and was transferred to a well of a 12-well culture plate containing 2 mL of V8 broth with 40 µg/mL of G418. 5.3.7 Screening transformants for CRISPR-Cas9 mutants Any putative transformants that grew through the second round of G418 selection were considered successful transformants based on their stable growth on selective medium. Wildtype (WT) cultures were grown in parallel as a negative control. After 3-4 days of growth in the V8 broth + 40 µg/mL of G418 (no G418 for WT cultures), 50-100 mg of mycelial tissue was harvested from each culture and flash frozen in liquid nitrogen. Total cellular DNA was extracted using a QIAGEN DNeasyⓇ Plant Mini Kit (QIAGEN Sciences, Germantown, MD, USA) as per the manufacturer’s instructions (DNeasyⓇ Plant Handbook, Mini Protocol, TissueLyser procedure). Tissue disruption was performed using a Retsch Mixer Mill MM 300 (Retsch GmbH, Haan, Germany). PCR was performed on both transformant and wildtype DNA extractions using Phusion™️ Plus DNA Polymerase (Thermo Fisher Scientific, Waltham, MA, USA) and primers designed to amplify the regions of PrORP1 or PcaORP1 targeted by the Cas9 gRNAs. All PCRs were performed using the following thermocycler conditions (as per manufacturer’s instructions for Phusion polymerase): an initial hold of 98°C for 30 seconds; 35 cycles of 98°C for 10 seconds, 60°C for 10 seconds, 72°C for 30 seconds; a final hold of 72°C for 5 minutes. The resulting PCR products were analyzed using gel electrophoresis and sent for Sanger sequencing in the forward direction. The raw Sanger sequencing chromatograms were analyzed using the TIDE (Tracking of Indels by Decomposition) web tool to detect CRISPR- 123 Cas9-generated insertions and deletions (indels) at the Cas9 cut site for each gRNA (Brinkman et al., 2014). Any identified ΔORP1 mutants were then re-sequenced in the reverse direction and the sequence reads were re-analyzed with TIDE to confirm the mutation on both DNA strands. 5.3.8 Generation of single zoospore-derived cultures Any ΔORP1 mutants identified with Sanger sequencing and TIDE analysis were taken through a single zoospore isolation protocol to ensure that downstream phenotyping would be performed on pure cultures derived from a mononucleate gene-edited zoospore. As only ΔPcaORP1 mutants were obtained, the protocol below outlines the method used for generating single zoospore cultures (SZCs) in P. cactorum, adapted from previous studies (Eikemo et al., 2000, 2003). Wildtype P. cactorum FF42 cultures were also taken through the protocol to act as a control for downstream phenotyping. Cultures were grown on unamended V8A for two weeks. The agar on each plate was then cut into wedges with a sterile scalpel, flooded with 20 mL of autoclaved distilled water, and placed under continuous fluorescent light for 48 hours. After 48 hours, the plates were removed from the light and incubated at 4°C for 30 minutes. The liquid from the plate was then filtered through a 40 μm cell strainer into a 50 mL conical tube, and the concentration of zoospores in the resulting suspension was determined with a hemocytometer. The zoospore suspension was then diluted to the desired concentration, spread on a fresh V8A plate, and incubated in the dark at room temperature (~21°C). After 3-4 days, each SZC was transferred to its own V8A plate and maintained as described in section 5.3.1. Three to four SZCs from each ΔPcaORP1 mutant were taken through DNA extraction, PCR, Sanger sequencing, and TIDE analysis, as described in section 5.3.7, to confirm that the CRISPR-Cas9-mediated mutation was still present after single zoospore isolation. 124 5.3.9 Testing plasmid DNA retention in single zoospore cultures To test whether the pYF2-PsCG plasmid DNA was retained in mutants after single zoospore isolation, PCR was performed on DNA from both the original ΔORP1 mutant cultures and the corresponding SZCs. Phusion™️ Plus DNA Polymerase (Thermo Fisher Scientific, Waltham, MA, USA) was used with the eGFP_Diag_F1/R1 primers designed to amplify a 273 base pair product from the eGFP gene on the pYF2-PsCG plasmid (see Chapter 4 for primer details). Wildtype DNA from P. cactorum FF42 was used as the negative control, and plasmid DNA from pYF2-PsCG was used as the positive control. The PCR was performed with the same thermocycler conditions as described in section 5.3.7, and the resulting PCR products were analyzed with gel electrophoresis. 5.3.10 Oxathiapiprolin growth assays Oxathiapiprolin growth assays were performed to determine whether the CRISPR-Cas9 ΔPcaORP1 mutations would have any effect on the growth phenotype relative to wildtype P. cactorum cultures. Technical grade oxathiapiprolin (98.1%) was provided by Syngenta (Syngenta Canada Inc., Guelph, Ontario, Canada). The oxathiapiprolin was dissolved in DMSO to make a stock solution of 100 mg/mL. Phytophthora cactorum FF42 wildtype and ΔPcaORP1 mutant cultures were grown on V8A plates supplemented with a gradient of nine oxathiapiprolin concentrations: 0.0001 μg/mL, 0.0002 μg/mL, 0.0004 μg/mL, 0.0006 μg/mL, 0.0008 μg/mL, 0.001 μg/mL, 0.01 μg/mL, 0.1 μg/mL, and 1.0 μg/mL. The concentration of DMSO in the medium for each treatment was standardized to 0.1%, and the volume of V8A per plate was standardized to 15 mL. In parallel, the cultures were also grown on two control treatments: 125 unamended V8A and V8A with 0.1% DMSO. Two individual single-zoospore-derived cultures were tested from the wildtype and each of the ΔPcaORP1 mutants, and four replicates were grown for each culture on each growth medium treatment. For each treatment, a 5-mm agar plug was taken from the edge of an actively growing six-day-old culture and transferred to a treatment plate. Seven days after plating, the radial mycelial growth was measured in two perpendicular directions (Figure 5.1); 5 mm was subtracted from each measurement to account for the diameter of the agar plug. The average of these two measurements was recorded as the radial growth for each culture. 5.3.11 Statistical analyses All statistical analyses were performed using Python version 3.8 with the following packages: pandas (McKinney, 2010; The pandas development team, 2020), SciPy stats (Virtanen et al., 2020), scikit-posthocs (Terpilowski, 2019), and statsmodels (Seabold & Perktold, 2010). The resulting figures were also generated in Python 3.8 with the following packages: matplotlib Figure 5.1 Schematic demonstrating how radial mycelial growth measurements were taken from Phytophthora cultures at seven days post plating on V8 agar medium supplemented with a gradient of oxathiapiprolin concentrations. To account for the width of the agar plug, 5 mm was subtracted from the length of measurement 1 (M1) and measurement 2 (M2). The average of the two measurements was recorded as the radial growth of each culture at a given oxathiapiprolin concentration. 126 (Hunter, 2007), seaborn (Waskom, 2021), pandas (McKinney, 2010; The pandas development team, 2020), and statannotations (Charlier et al., 2022). The differences in average radial growth between P. cactorum FF42 wildtype and ΔPcaORP1 mutants on oxathiapiprolin-supplemented V8A were analyzed using either a one-way ANOVA or Kruskal-Wallis test, depending on the normality of the data. The 0.1% DMSO control treatment was tested in two separate experiments, so the results from the two experiments were pooled. The results from each of the two individual SZCs for the wildtype and ΔPcaORP1 mutants were also pooled. A Shapiro-Wilk test of normality was performed on the growth data within each treatment. For treatments with normally distributed data, the differences in radial growth between isolates were analyzed with a one-way ANOVA followed by a Tukey’s honest significance difference post-hoc test. For treatments with non-normally distributed data, the differences in radial growth between isolates were analyzed with a Kruskal-Wallis test followed by a Dunn’s multiple comparison post-hoc test. 5.4 Results 5.4.1 Structure of the PrORP1 and PcaORP1 genes The PrORP1 gene from P. ramorum NA2_17 was predicted to be a total length of 3235 bp, with two exons separated by a 76 bp intron (Figure 5.2). The predicted 2745 bp PrORP1 ORF lies within the second exon and encodes a 914 amino acid polypeptide. The PcaORP1 gene from P. cactorum FF42 was predicted to be a total length of 3166 bp, with three exons separated by two introns 82 bp and 96 bp in length (Figure 5.3). The predicted 2757 bp PcaORP1 ORF spans the second and third exons and encodes an 886 amino acid polypeptide after intron splicing. 127 Relative to the ORF of the P. capsici ORP1 gene (PcORP1), the PrORP1 and PcaORP1 ORFs were predicted to have different lengths and exon/intron structures (Figure 5.4A), but their InterPro-predicted protein domains were identical to PcORP1 (Figure 5.4B). All three ORP1 proteins have a conserved structure: an N-terminal pleckstrin homology (PH) domain, a steroidogenic acute regulatory-related lipid-transfer (START) or START-like domain, and a C-terminal oxysterol-binding protein (OBP) domain (Figure 5.4B). Figure 5.3 Predicted structure of the PrORP1 gene from Phytophthora ramorum NA2_17. The open reading frame was predicted using the Geneious “Find ORFs” tool (Geneious Prime 2023.2.1) and validated using the NCBI Open Reading Frame Finder (see Methods section 5.3.4.3). The exon-intron boundaries and sequences were predicted using the Softberry FGENESH eukaryotic gene finding algorithm (www.softberry.com). The orange labels indicate the locations of the four guide RNAs designed for CRISPR-Cas9 gene editing (see Table 5.4), and the purple labels show the locations of the primer pairs designed to amplify the Cas9 ribonucleoprotein-targeted regions for Sanger sequencing(see Table 5.5). Gene map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). Figure 5.2 Predicted structure of the PcaORP1 gene from Phytophthora cactorum FF42. The open reading frame was predicted using the Geneious “Find ORFs” tool (Geneious Prime 2023.2.1) and validated using the NCBI Open Reading Frame Finder (see Methods section 5.3.4.3). The exon-intron boundaries and sequences were predicted using the Softberry FGENESH eukaryotic gene finding algorithm (www.softberry.com). The orange labels indicate the locations of the five guide RNAs designed for CRISPR-Cas9 gene editing (see Table 5.4), and the purple labels show the locations of the primer pairs designed to amplify the Cas9 ribonucleoprotein-targeted regions for Sanger sequencing (see Table 5.5). Gene map created using SnapGeneⓇ Viewer software v. 7.0.1 (from Dotmatics; available at snapgene.com). 128 5.4.2 Guide RNAs designed and tested for PrORP1 and PcaORP1 We ultimately selected four guide RNAs (gRNAs) for PrORP1 and five gRNAs for PcaORP1 for testing plasmid-RNP co-transformations in P. ramorum and P. cactorum, respectively. The names, sequences, and efficiency scores for all gRNAs are shown in Table 5.4, and the primer pairs designed to amplify the targeted gene regions for the gRNAs are shown in Figure 5.4 A) Structure and size of the PrORP1 and PcaORP1 open reading frames relative to that of the P. capsici PcORP1 gene characterized by Miao et al. (2016). The numbers indicate the size of the ORFs in base pairs (bp). The exon-intron boundaries of PrORP1 and PcaORP1 were predicted using the Softberry FGENESH eukaryotic gene finding tool (http://www.softberry.com). Exon 1 is not shown for either PrORP1 or PcaORP1 as it was not predicted to be included in the ORF region of the genes. The exon-intron boundaries shown for PcORP1 are according to Miao et al. (2016). B) Structure, size, and predicted functional domains of the PrORP1 and PcaORP1 proteins relative to the P. capsici PcORP1 protein characterized by Miao et al. (2016). The amino acid sequences of all three proteins were analyzed using InterPro (Paysan-Lafosse et al., 2023) to predict the functional domains. 129 Table 5.5. Schematics of the PrORP1 and PcaORP1 genes with the locations of the gRNAs and primers are shown in Figure 5.2 and Figure 5.3, respectively. Table 5.4 CRISPR-Cas9 guide RNAs (gRNAs) designed to target the PrORP1 and PcaORP1 genes of Phytophthora ramorum and P. cactorum, respectively, using the Eukaryotic Pathogen CRISPR guide RNA/DNA Design Tool (EuPaGDT; Peng & Tarleton, 2015). All gRNAs are designed using a 3’ NGG protospacer adjacent motif (PAM). The number in each gRNA name represents the location of its first nucleotide in the ORP1 open reading frame sequence. The gRNAs labelled with “rc” (reverse complement) are those derived from the antisense strand of the ORP1 sequence. The gRNAs labelled with “mh” (microhomology) are those that EuPaGDT found to have microhomology pairs flanking the gRNA sequence. Also provided are the total scores from EuPaGDT for each gRNA and the IDT gRNA design checker scores. Species (Gene) gRNA Name (abbreviation) PAM 20-nt gRNA Sequence (5’ to 3’) EuPaGDT Total Score IDT gRNA Score Phytophthora ramorum NA2_17 (PrORP1) PrNA2ORPh_108rc (Pr108rc) AGG GTCCTTCCTCGAGATGTAAT 0.58 45 PrNA2ORPh_192 (Pr192) CGG TTACCTCACGGACCCAGCTA 0.61 51 PrNA2ORPh_368mh (Pr368mh) CGG ACACGAGGACACCGGCGCAG 0.82 54 PrNA2ORPh_751rc (Pr751rc) CGG GACGGAACACAACGGAGTTG 0.61 63 Phytophthora cactorum FF42 (PcaORP1) PcaFF42ORPh_156 (Pca156) AGG CTTGGAAGGTTGTACAGTGC 0.64 57 PcaFF42ORPh_330 (Pca330) AGG CACAAGTGAGACCAATGCAC 0.55 94 PcaFF42ORPh_1272rcmh* (Pca1272rcmh) CGG CACCACAGACTGGCCCATCT 0.59 59 PcaFF42ORPh_1507rcmh* (Pca1507rcmh) AGG CCTGGTTCACGTATTCACTG 0.57 73 PcaFF42ORPh_2248mh* (Pca2248mh) TGG CTGCCTTACTTACAGATCGG 0.56 38 *gRNAs were designed before the P. cactorum P414 NCBI reference genome was added to EuPaGDT, so off-target analysis was performed manually using a blastn analysis in NCBI. 130 Table 5.5 Primer pairs designed to amplify Cas9 RNP-targeted regions of the PrORP1 and PcaORP1 genes of Phytophthora ramorum NA2_17 and P. cactorum FF42, respectively, for Sanger sequencing. The guide RNAs (gRNAs) that target a sequence within each primer-amplified region are also listed. All PCRs for Sanger sequencing were performed using Phusion™️ Plus DNA Polymerase, which uses a universal annealing temperature of 60°C. Species (Gene) Primer Pair Name Primer Sequences (5’ to 3’) gRNA(s) Amplified Amplicon Size Phytophthora ramorum NA2_17 (PrORP1) PrNA2ORPh_F1/R1 F1: TTTTGCAGAAGAAAGGTCAGCG Pr108rc Pr192 Pr368mh 674 bp R1: CGCCTCCAAAACGTCCAGAC PrNA2ORPh_F2/R2 F2: CCAAACGTACTTCTCGCATGAC Pr751rc 740 bp R2: TTCCAGGATAAGCACCACAGAC Phytophthora cactorum FF42 (PcaORP1) PcaORPh_Cas9Edit_1F/1R 1F: CGGATCTGGTGCATTTGTCAAG Pca1272rcmh Pca1507rcmh 766 bp 1R: TCCTTGTCGATCAGCTTCAGAC PcaORPh_Cas9Edit_2F/2R 2F: GAAGCCGTTTAACCCGATCTTG Pca2248mh 571 bp 2R: CATGTAGCCGCTCTGGAATTTG PcaORPh_Cas9Edit_3F/3R 3F: ACCCTAGTGCAATCTTCCAAGC Pca156 Pca330 777 bp 3R: AAGCTCGTTTTCCAGTGCAATC PcaORPh_Cas9Edit_4F/4R 4F: TACATGAAGGAGGGCTTCTTGC Pca156 Pca330 506 bp 4R: CCATTAACCGTGGCAGTACTTG 5.4.3 CRISPR-Cas9 plasmid-RNP co-transformation produces mutants in Phytophthora cactorum but not Phytophthora ramorum 5.4.3.1 Phytophthora cactorum Using a PEG-liposome-mediated plasmid-RNP co-transformation method (Figure 5.5), we obtained two independent, heterozygous indel ΔPcaORP1 mutants in P. cactorum FF42. The first mutant was obtained with the Pca1507rcmh gRNA, and TIDE analysis revealed a single base pair (bp) insertion between nucleotides 1,491 and 1,492 in the open reading frame (ORF) of the PcaORP1 gene (Figure 5.6). The second mutant was obtained with the Pca156 gRNA, and TIDE analysis revealed a single base pair (bp) deletion of nucleotide 172 in the PcaORP1 ORF (Figure 5.7). For both mutants, the ratio of wildtype to mutant DNA in the originally obtained transformant cultures was generally around 1.5-2:1 whereas the same ratio in single zoospore cultures (SZCs) shifted to 1:1, indicating that pure heterozygous cultures from a single edited nucleus were obtained through the zoospore isolation process (Figure 5.6 and Figure 5.7). Of 131 note is that one of the SZCs obtained from the original PT9 mutant culture was no longer a ΔPcaORP1 mutant (PT9-1; Figure 5.7B), indicating that it was derived from a zoospore with a non-edited wildtype nucleus. Both the PT9 and PT10 indels lead to frameshift mutations in the PcaORP1 exons and therefore likely result in non-functional proteins translated from the mutated allele (Figure 5.8). Figure 5.5 Method/workflow developed for CRISPR-Cas9 gene editing in Phytophthora cactorum using a plasmid-RNP co-transformation protocol. Figure created with BioRender.com. 132 Figure 5.6 Details of the CRISPR-Cas9 ΔPcaORP1 indel mutant, PT10, obtained in Phytophthora cactorum FF42 using a plasmid-RNP co-transformation method with the Pca1507rcmh guide RNA (gRNA). The PT10 mutant is heterozygous, with the mutant allele comprising a single base pair insertion at the Cas9 cut site. The graphs shown were generated using TIDE software (Brinkman et al. 2014) on the forward Sanger sequencing results from PcaORPh_Cas9Edit_1F/1R PCR amplicons. The P. cactorum PcaORP1 wildtype sequencing chromatogram was used as the control sample and the ΔPcaORP1 PT10 mutant chromatograms were used as the test samples. The top graphs show the chromatogram sequence compositions with the expected Cas9 cut site indicated by the dashed blue line, the bottom graphs show the predicted percentage of sequences with wildtype vs. mutant genotypes, and the bar graphs to the right indicate the predicted nucleotide in the insertion mutants (y-axis represents probability). A) TIDE decomposition graphs for the original PT10 transformant culture recovered from G418-selective medium. The ratio of wildtype to mutant DNA indicates a mixture of edited and non-edited nuclei in the culture. B) TIDE decomposition graphs for PT10-1, one of the single zoospore-derived cultures (SZCs) of PT10. The ratio of the wildtype to mutant DNA indicates a pure heterozygous culture with one wildtype allele and one mutant allele. The TIDE results from the other two SZCs tested (PT10-2, PT10-3) were nearly identical (results not shown). The TIDE results indicate that the nucleotide inserted on the sense strand of PcaORP1 is a thymine, therefore the nucleotide inserted on the antisense strand where the gRNA was designed is an adenine. 133 Figure 5.7 Details of the CRISPR-Cas9 ΔPcaORP1 indel mutant, PT9, obtained in Phytophthora cactorum FF42 using a plasmid-RNP co-transformation method with the Pca156 guide RNA (gRNA). The PT9 mutant is heterozygous, with the mutant allele comprising a single base pair deletion at the Cas9 cut site. The graphs shown were generated using TIDE software (Brinkman et al. 2014) on the forward Sanger sequencing results from PcaORPh_Cas9Edit_4F/4R PCR amplicons. The P. cactorum PcaORP1 wildtype sequencing chromatogram was used as the control sample and the ΔPcaORP1 PT9 mutant chromatograms were used as the test samples. The top graphs show the chromatogram sequence compositions with the expected Cas9 cut site indicated by the dashed blue line, and the bottom graphs show the predicted percentage of sequences with wildtype vs. mutant genotypes. A) TIDE decomposition graphs for the original PT9 transformant culture recovered from G418-selective medium. The ratio of wildtype to mutant DNA indicates a mixture of edited and non-edited nuclei in the culture. B) TIDE decomposition graphs for PT9-1, one of the single zoospore-derived cultures (SZCs) from PT9. The TIDE results indicated that PT9-1 was derived from a non-edited wildtype nucleus as it did not have any mutant alleles. C) TIDE decomposition graphs for PT9-2, another of the SZCs derived from PT9. The ratio of the wildtype to mutant DNA indicates a pure heterozygous culture with one wildtype allele and one mutant allele. The TIDE results from the other two SZCs tested (PT9-3, PT9-4) were nearly identical (results not shown). 134 5.4.3.2 Phytophthora ramorum We were unable to obtain any CRISPR-Cas9 mutants in Phytophthora ramorum NA2_17 using two different plasmid-RNP co-transformation protocols and four unique gRNAs (Data C.1.1). The first protocol we tested in P. ramorum was based on a previous method developed to edit the Figure 5.8 Predicted protein sequences for the PcaORP1 gene of Phytophthora cactorum FF42. A) The wildtype (WT) sequence encodes an 886 amino acid polypeptide. B) The 1 base pair (bp) insertion mutation in one of the alleles of the ΔPcaORP1 PT10 mutant introduces a frameshift resulting in a premature stop codon at amino acid 466. C) The 1 bp deletion mutation in one of the alleles of the ΔPcaORP1 PT9 mutant also introduces a frameshift and results in a premature stop codon at amino acid 63. Protein sequences were generated using Geneious Prime 2023.2.1. 135 genome of the oomycete Aphanomyces invadans by delivering CRISPR-Cas9 RNPs via a PEG-mediated transformation of protoplasts (Majeed et al., 2018). However, we did not obtain any G418-resistant cultures, indicating that this transformation approach did not work for our P. ramorum protoplasts. To test the possibility that the plasmid-RNP complexes had been successfully delivered to P. ramorum protoplasts, but that the transformants were simply not expressing the nptII gene from the pYF2-PsCG plasmid, we screened a selection of cultures that we had transformed but grown on non-selective recovery medium. The Sanger sequencing results from the screened cultures (representing all four gRNAs tested) indicated that none were CRISPR-Cas9 mutants. We also tested the protoplast transformation protocol developed for CRISPR-Cas9 plasmid delivery in P. sojae (Fang, Cui, et al., 2017) to co-transform P. ramorum NA2_17 with plasmid-RNP complexes, but again, did not obtain any G418-resistant transformants. 5.4.4 Single zoospore cultures of Phytophthora cactorum mutants lose plasmid DNA The results from PCR performed on the original PT9 and PT10 mutant cultures and their respective SZCs showed loss of pYF2-PsCG plasmid DNA in the single zoospore-derived cultures (Figure 5.9). An exception was the culture PT10-1 C2, which was one of two replicate cultures from the original PT10-1 SZC (C1 and C2). The DNA from PT10-1 C2 produced a slightly larger than expected PCR amplicon. To test whether the product was due to non-specific primer amplification, the amplicon was sent for Sanger sequencing. However, the results indicated that the sequence of the PT10-1 C2 PCR product was identical to the products from both the purified pYF2-PsCG DNA and the original PT10 mutant culture DNA. It was therefore 136 assumed that the PT10-1 C2 culture contained plasmid DNA, and the culture was not used for any downstream phenotype assays. 5.4.5 ΔPcaORP1 mutants exhibit decreased resistance to oxathiapiprolin We selected two independent SZCs from each of the WT, PT9, and PT10 cultures for oxathiapiprolin growth assays: WT-1, WT-2, PT9-2, PT9-3, PT10-1 (C1), and PT10-3 (C1). To account for random variation in radial growth, the results from the two SZCs of each culture were pooled prior to statistical analysis so that changes in growth were ultimately measured between three groups: WT, PT9, and PT10. The raw growth results from the oxathiapiprolin Figure 5.9 Gel electrophoresis of products from a PCR performed using eGFP_Diag_F1/R1 primers on DNA from Phytophthora cactorum FF42 ΔPcaORP1 mutant cultures (PT9, PT10) and their respective single zoospore cultures (SZCs: PT9-1 to PT9-4, PT10-1 and PT10-3). For the PT10 SZCs, DNA extractions from two replicate cultures were tested (C1, C2). The DNA from P. cactorum FF42 wildtype SZCs (WT-1 and WT-2) was used as the negative PCR control, and pYF2-PsCG plasmid DNA was used as the positive PCR control. A product amplified in sample PT10-1 C2, indicating presence of plasmid DNA; the culture from which the sample was derived was therefore not included in downstream phenotype assays. The gel was run at 100 volts for 55 minutes on 1% agarose in 0.5X TBE buffer with a 100 bp DNA ladder. All samples were loaded with Safe-Green™️ stain (Applied Biological Materials Inc., Richmond, BC, Canada) at a ratio of 1 μL Safe-Green: 5 μL PCR product. 137 assays are reported in Data C.2.1. Radial growth of both the PT9 and PT10 ΔPcaORP1 mutant cultures was visibly reduced relative to the wildtype cultures on oxathiapiprolin-supplemented medium (Figure 5.10), and this reduction in growth was statistically significant at every oxathiapiprolin concentration tested (Figure 5.11; Data C.2.2). Note that the 0.01 μg/mL, 0.1 μg/mL, and 1.0 μg/mL oxathiapiprolin treatments were not included in the analyses because there was not enough mycelial growth on the V8A plates (Data C.2.1). Figure 5.10 Photos of Phytophthora cactorum FF42 wildtype (WT) and CRISPR-Cas9 ΔPcaORP1 mutant (PT9, PT10) cultures growing on oxathiapiprolin-supplemented V8 agar seven days post plating. The images shown are only a representation of the isolates and oxathiapiprolin concentrations tested, however, the same pattern of reduced growth in the ΔPcaORP1 mutant cultures was observed at every concentration tested (see Figure 5.11). 138 5.5 Discussion Using a plasmid-RNP co-transformation method, we successfully edited the ORP1 gene of Phytophthora cactorum, obtaining two independent heterozygous indel mutants. To our knowledge, this is the first successful use of RNPs for CRISPR-Cas9 gene editing in a Phytophthora species, and the first implementation of a CRISPR-Cas9 gene editing system in a forest Phytophthora pathogen. Delivering both the plasmid selective marker and the RNP in a Figure 5.11 Boxplot of radial growth measurements of Phytophthora cactorum FF42 wildtype (WT) and CRISPR-Cas9 ΔPcaORP1 mutant (PT9, PT10) cultures growing on V8 agar (V8A) supplemented with a gradient of oxathiapiprolin concentrations. Unamended V8A and V8A + 0.1% DMSO were used as control treatments. For each isolate at each treatment, the growth data from two independent single zoospore cultures (SZCs), each with four replicates, were pooled (for a total of eight replicates per isolate per treatment). The white circles indicate the mean growth value for each isolate at each treatment. Results from statistical analyses revealed that both PT9 and PT10 ΔPcaORP1 mutant cultures exhibited statistically reduced mycelial growth relative to WT cultures on every oxathiapiprolin concentration tested (‘ns’: not significant, ‘*’: p < 0.05, ‘**’: p < 0.01, ‘***’: p < 0.001, ‘****’: p < 0.0001). 139 liposome complex allowed us to efficiently screen for putative CRISPR-Cas9 mutants from G418-resistant transformants given the lack of visible phenotype in our ΔPcaORP1 mutants. Our results with this plasmid-RNP co-transformation approach, both the success we achieved in P. cactorum as well as our inability to obtain P. ramorum mutants, provide foundational knowledge for the use of CRISPR-Cas9 RNPs in forest Phytophthoras and give insight into potential future directions for this research. The two ΔPcaORP1 mutants we obtained in P. cactorum were the result of experiments with two gRNAs targeting different regions of the PcaORP1 gene (Pca156, Pca1507rcmh). While we were able to obtain G418-resistant transformants from the co-transformations with the other three gRNAs that we designed (Pca330, Pca1272rcmh, Pca2248mh), none of the transformants were PcaORP1 mutants. This variability in gRNA success is consistent with previous studies. For example, Majeed et al. (2018) edited the genome of the oomycete Aphanomyces invadans using RNPs with fluorescently labelled tracrRNA. By observing fluorescence in all RNP-treated protoplasts, Majeed et al. confirmed successful RNP delivery, but found that only one of the three gRNAs they tested produced successful CRISPR-Cas9 mutants. It is also well-established in many other eukaryotic organisms that gRNAs exhibit highly variable activity even when well-designed, and there are many complex factors that can contribute to the success of a gRNA in directing Cas9 to cut target DNA (Doench et al., 2014, 2016; Farboud & Meyer, 2015; G. Liu et al., 2020; Moreno-Mateos et al., 2015; Thyme et al., 2016). Previous research in human cells has shown significant variability in CRISPR-Cas9 editing performance even between gRNAs designed to target adjacent sites of the same exon (T. Wang et al., 2014). Therefore, while the Pca330, Pca1272rcmh, and Pca2248mh gRNAs had appropriate efficiency scores and minimal 140 secondary structures, it is possible they did not perform well with the Cas9 nuclease once transformed in P. cactorum cells. These results reinforce the importance of designing and testing multiple gRNAs when developing CRISPR-Cas9 protocols. The loss of pYF2-PsCG DNA in most of the single zoospore-derived cultures (SZCs) of the two ΔPcaORP1 mutants indicates that the plasmid likely remained as extrachromosomal DNA in transformed P. cactorum cells rather than integrating into the genome. The fate of plasmid DNA post transformation is variable in oomycetes. Early studies in Phytophthora and Saprolegnia species demonstrated that PEG-mediated transformations generally resulted in plasmid DNA being integrated into the genome either at one or multiple sites, and often in tandem repeats (Judelson et al., 1991, 1993; Mort-Bontemps & Fèvre, 1997; van West et al., 1999). Due to the randomness of plasmid integration sites in oomycete genomes after transformation, it is hypothesized that this process does not involve homologous recombination, but likely occurs via nonhomologous chromosomal integration (Judelson & Ah-Fong, 2009). However, plasmid integration appears to be species-specific; for example, transformation studies in P. parasitica demonstrate that plasmid DNA is often not integrated into the genome, but rather remains extrachromosomal, often leading to instability and loss of antibiotic resistance after subculturing on non-selective media (Gaulin et al., 2007). In another oomycete, Achlya ambisexualis Raper, it was demonstrated that plasmid DNA was both integrated and maintained episomally after PEG-mediated transformation (Manavathu et al., 1988), further demonstrating the diversity in cellular responses to plasmid DNA transformation in oomycetes. As the pYF2-PsCG plasmid does not contain an autonomously replicating sequence (ARS), it would not be maintained episomally in our ΔPcaORP1 mutants. Instead, the plasmid was likely partitioned away during mitosis (X. 141 Wang et al., 2016), resulting in daughter nuclei, and subsequently zoospores, with no pYF2-PsCG DNA. While our success in obtaining two ΔPcaORP1 mutants indicates that RNP-mediated CRISPR-Cas9 gene editing is feasible in P. cactorum, the rates of editing we achieved were low. We screened a total of 47 transformants and found two mutants, which translates to a rate of approximately 4%. One possible explanation for this low mutation rate could be the efficiency of the gRNAs, which was previously discussed. Future studies could test more gRNAs in different regions of the PcaORP1 gene to determine whether there is any effect on the number of mutants recovered. Another possible explanation for the low number of mutants recovered could be linked to the nuclear localization signal (NLS) fused to the Cas9 endonuclease. When developing plasmid-mediated CRISPR-Cas9 gene editing in P. sojae, Fang and Tyler found that mammalian NLS sequences were not efficient, so subsequently used a P. sojae NLS to target Cas9 to the nuclei of cells (Fang & Tyler, 2016). The IDT Alt-R™️ S.p. Cas9 nuclease we used contains a proprietary NLS of unknown origin, however, it is likely not optimized for oomycete cells given that they are a relatively niche group in CRISPR-Cas9 research. This lack of NLS customization when using a commercially available Cas9 nuclease represents an advantage of a plasmid-mediated CRISPR-Cas9 gene editing method over an RNP-mediated method. However, our results in P. cactorum indicate that the IDT Cas9 NLS is functional in P. cactorum and could therefore potentially also be used for other forest Phytophthoras. Future studies could explore optimizing the RNP concentration; it is possible that by increasing the number of RNP complexes delivered per transformation, the likelihood of delivery to nuclei, and therefore the likelihood of successful mutation would also increase. Additionally, using fluorescently labelled 142 RNP components, such as tracrRNA, would help in tracking successful delivery of the RNP to the nuclei and assessing the efficacy of the NLS. Both ΔPcaORP1 mutants we obtained in P. cactorum exhibited decreased oxathiapiprolin resistance relative to wildtype cultures, which is in direct contrast to previous studies of ORP1 gene mutations in other Phytophthora species (Bittner et al., 2017; Miao, Chi, et al., 2018; Miao et al., 2016, 2020; Z. Wang et al., 2022). However, all these previous studies induced specific non-synonymous point mutations in the ORP1 domain, demonstrating that both heterozygous and homozygous mutations confer increased resistance to oxathiapiprolin (Bittner et al., 2017; Miao, Chi, et al., 2018; Miao et al., 2016, 2020; Z. Wang et al., 2022), likely by altering the binding affinity between the fungicide and ORP1 (Z. Wang et al., 2022). The heterozygous ΔPcaORP1 indel mutations we obtained both result in frameshifts, introducing pre-mature stop codons in the PcaORP1 polypeptide encoded by the mutant allele. In theory, this would mean that only one PcaORP1 allele would be producing functional polypeptides, effectively halving the number of target molecules in the cell to which the ligand (oxathiapiprolin) can bind. Presumably, this would leave a greater concentration of ligand available to target the functional PcaORP1 proteins, leading to a lower protein saturation point. This lower saturation point would be a greater disadvantage for the ΔPcaORP1 mutants relative to wildtype individuals at lower concentrations of oxathiapiprolin, which is exactly what we observed. There are many possible explanations for our lack of success with CRISPR-Cas9 delivery in P. ramorum. However, our inability to obtain G418-resistant transformants suggests an issue encountered during the transformation process, which could manifest in one of two ways. The 143 first possible explanation is that the plasmid-RNP complex failed at the point of delivery into the P. ramorum protoplasts. As discussed in Chapter 4, Phytophthora species, and oomycetes in general, are known to respond variably to transformation protocols (Ghimire et al., 2022; Govers & Gijzen, 2006; Mcleod et al., 2008), so it could be that P. ramorum is simply not amenable to the introduction of plasmid-RNP complexes via a PEG-liposome-mediated transformation approach. The second possible explanation is that the RNP and plasmid were delivered successfully, but their presence in the cell is toxic to P. ramorum. The toxicity of Cas9, both in its catalytically active and inactive forms, has been previously demonstrated in eukaryotic organisms (Foster et al., 2018; W. Jiang et al., 2014; Morgens et al., 2017). Given that in our transformations, we delivered Cas9 in both the pYF2-PsCG plasmid and the purified RNP complex, it is possible that its presence in P. ramorum cells had toxic effects. However, the fact that we could recover apparently healthy, transformed cultures on non-G418 medium suggests that the plasmid-RNP components were not toxic, but rather were not being delivered to and/or expressed in the P. ramorum cells. These results point to the need to establish an improved transformation method for P. ramorum before further CRISPR-Cas9 experiments are attempted. Another factor to consider for future CRISPR-Cas9 protocol development in P. ramorum is the concentration of RNP delivered to protoplasts during transformation. In our experiments with P. ramorum, we delivered roughly equimolar amounts of plasmid DNA and RNP, testing co-transformations with 0.75 pmol and 1.5 pmol of both components. We selected these RNP amounts based on a combination of the previously developed RNP-mediated CRISPR-Cas9 editing protocol in Aphanomyces invadans (Majeed et al., 2018) and the picomoles of RNP per transformation used in the IDT protocol for cationic lipid delivery of CRISPR RNP complexes 144 (IDT, 2018). However, in P. cactorum, we shifted to a higher molar ratio of RNP to plasmid, ultimately using 75 pmol of RNP and 4 pmol of plasmid DNA for each transformation. Increasing the RNP concentration per transformation should therefore be tested in any future studies developing RNP-mediated CRISPR-Cas9 editing in P. ramorum once a more consistent transformation protocol has been established. Developing CRISPR-Cas9 gene editing in forest Phytophthoras is an important step for continued exploration and elucidation of the specific molecular mechanisms driving the phytopathogenic behaviours of these species. Our results provide a proof of concept for the use of RNP-mediated CRISPR-Cas9 gene editing in Phytophthora cactorum, and our findings serve as a foundation for future studies developing CRISPR-Cas9 protocols in P. ramorum and other forest Phytophthoras. 145 Chapter 6: Conclusion 6.1 Summary of research findings The research presented in this dissertation was multidisciplinary and applied state-of-the-art genomic analyses and molecular techniques in a forest pathology context. The research findings for each chapter are summarized below as they pertain to the two objectives outlined in the Introduction. Objective 1: Establish a proof of concept for the use of large-scale comparative genomics to explore the genetic mechanisms driving plant pathogenicity, uncover gene targets associated with phytopathogenic traits of interest, and discover genomic patterns that can be used in future biosurveillance of filamentous forest pathogens. In Chapter 2, I reported on the use of a novel machine learning method to perform comparative analyses on the genomes of fungi and predict phytopathogenic lifestyles and traits from distinct genomic patterns. The machine learning method was not only able to separate the phylogenetic and genomic components of each prediction but also identified specific genes and gene families associated with fungal phytopathogenic lifestyles and traits, demonstrating the potential use of this approach for generating gene candidates for downstream functional validation with CRISPR-Cas9 gene editing or similar molecular techniques. Of particular note was the strong performance of machine learning in predicting plant pathogens, which illustrates the promise of this approach for future biosurveillance pipelines. 146 Objective 2: Establish a method for CRISPR-Cas9 editing in a forest pathogenic Phytophthora species for future validation of gene candidates associated with phytopathogenic traits of interest. Objective 2 was addressed in Chapters 3-5. In Chapter 3, I reviewed CRISPR-Cas9 gene editing, summarizing its current applications in plant pathology, and proposing future applications in forest pathology, where I believe it is currently underutilized. The goal of the review was to provide forest pathologists with a framework for the use of CRISPR-Cas9 gene editing in forest pathosystems, and it has now been cited in several forest pathology and forestry-related studies (Dort et al., 2020). In Chapter 4, I outlined my experiments designed to establish plasmid transformations in five forest pathogenic Phytophthoras. Transformations are a critical component of CRISPR-Cas9 gene editing and therefore the first step in developing this technology in any organism. The results from Chapter 4 demonstrated the recalcitrance of forest Phytophthora pathogens to plasmid transformations, indicating that these species will require further research to optimize transformation and subsequent development of CRISPR-Cas9 gene editing. Transformations succeeded in two of the five species tested: P. cactorum and P. ramorum. However, the transformations in P. ramorum were inconsistent and difficult to replicate. Finally, in Chapter 5, I tested a hybrid CRISPR-Cas9 approach, co-transforming plasmid-RNP complexes in P. cactorum and P. ramorum. I successfully obtained CRISPR-Cas9 mutants in P. cactorum, demonstrating the potential of this approach for this species, and possibly other forest Phytophthoras. 147 6.2 Looking forward: research implications and potential future directions 6.2.1 Genomics-based biosurveillance of forest invasive filamentous pathogens In the current era of globalization and climate change, biological invasions will continue to threaten forest health, and therefore robust biosurveillance practices will be crucial for threat mitigation. Current biosurveillance systems enable federal regulatory agencies to monitor for the presence of known plant pathogens and respond accordingly, however they are generally not equipped to predict the risk of unknown or undescribed pathogens. Filamentous fungi and oomycetes are particularly well-suited to invade naïve ecosystems, and there are numerous examples of forest invasive pathogens, such as Phytophthora ramorum (sudden oak death), that were not taxonomically identified until after they were causing widespread ecological damage. This constitutes a blind spot for taxon-based biosurveillance, which is why researchers are now proposing a shift to genomics-centered strategies focused on predicting the genetic traits associated with invasive pathogens regardless of their taxonomic identity (Hamelin & Roe, 2020; McTaggart et al., 2016; Roe et al., 2019). The research findings I report on in Chapter 2 of this dissertation demonstrate the feasibility and potential power of integrating genomics-based strategies into future biosurveillance pipelines. The ability of machine learning to parse large and complex data sets makes it very well-suited for large-scale genomic analyses (Libbrecht & Noble, 2015). However, while access to data from hundreds of genomes is unprecedented for plant pathologists, it is considered a relatively small data set for machine learning algorithms, which are normally trained and tested with thousands of data points. Therefore, as more genomic data becomes available, it will be necessary to continue to train and test predictive machine learning approaches for biosurveillance to ensure their capacity to capture phytopathogenic signals from the genomes of filamentous plant pathogens. 148 Another important next step for the research presented in Chapter 2 is to test the same comparative genomics approach in oomycetes to determine whether machine learning can similarly parse predictive patterns associated with the genomes of phytopathogenic oomycetes. A number of recent studies have performed comparative genomic analyses in oomycetes and found associations between specific gene families and phytopathogenic traits (Adhikari et al., 2013; de Vries et al., 2020; Fletcher et al., 2018; Gao et al., 2021; Kronmiller et al., 2023). The results of these previous studies demonstrate that a large-scale analysis of oomycete genomes would likely elucidate predictive genomic signatures, similar to the results obtained with fungi in Chapter 2. However, unlike the fungi, for which there are readily available functionally annotated genomes (i.e., MycoCosm), there is no such resource for oomycetes, which makes performing a similar comparative genomics study more difficult. Publicly available oomycete genome sequences, such as those available from NCBI GenBank (Benson et al., 2013), would therefore need to be functionally annotated for comparative analysis in future studies. 6.2.2 CRISPR-Cas9 gene editing for forest pathogenic Phytophthoras CRISPR-Cas9 gene editing has become a valuable tool for the study of filamentous plant pathogens, allowing researchers to functionally characterize the role of specific genes in the phytopathogenicity of both fungi (J. Li et al., 2018; Y. Liang et al., 2018) and oomycetes (Fang & Tyler, 2016; Gumtow et al., 2018). However, while this technology is becoming well-established for studying agricultural pathosystems, it is still underutilized in forest pathology research. For example, while the first CRISPR-Cas9 gene editing studies for filamentous agricultural pathogens were published soon after the development of CRISPR-Cas9 gene editing 149 (Arazoe et al., 2015; Fang & Tyler, 2016; Schuster et al., 2016) with numerous studies since, the first, and only CRISPR-Cas9 gene editing system in a filamentous forest pathogen (Dothistroma septosporum) was only recently developed in 2022 (McCarthy et al., 2022). This lack of CRISPR-Cas9 development is likely due, at least in part, to the lack of established molecular techniques for filamentous forest pathogens, particularly forest Phytophthoras. Plasmid-mediated DNA transformation is the basis of most CRISPR-Cas9 protocols, but it is challenging to implement in pathogens with no previously established transformation protocols. Almost all the studies using CRISPR-Cas9 in filamentous pathogens to date have been in species with previously established transformation protocols in place. Oomycetes, and Phytophthoras in particular, are known to be recalcitrant to molecular protocols (Ghimire et al., 2022; Govers & Gijzen, 2006; Mcleod et al., 2008), which presents an additional barrier to the establishment of CRISPR-Cas9 gene editing, or any other CRISPR-Cas technologies (e.g., Cas12a, Cas13a), in these species. While polyethylene glycol (PEG)-mediated transformation continues to be the most common method for transformations in Phytophthoras, it does have limitations. It is recognized as a very labour-intensive protocol, and is known to have low rates of protoplast regeneration; additionally, when protoplasts do regenerate it is difficult to obtain stable transformants (Mcleod et al., 2008; Wu et al., 2016). The 2008 study by Mcleod et al. found that after transformations in three Phytophthora pathogens, protoplast regeneration rates ranged from 7% to 40% and were consistent within each species, demonstrating that certain Phytophthoras are simply less amenable to molecular manipulation. The results of my research described in Chapter 4 certainly support this trend for forest Phytophthoras also; it was evident that while P. cactorum was immediately amenable to plasmid transformation, the other four 150 species I tested were not. Continued research into optimal transformation methods for forest Phytophthoras will therefore be crucial to any future development of CRISPR-Cas technologies in these species. This future research should continue to explore the optimization of PEG-mediated transformations, especially for the development of ribonucleoprotein (RNP)-mediated CRISPR-Cas9 gene editing. Despite its limitations, PEG-mediated transformation is still the most accessible method for most labs without requiring highly specialized, and expensive, equipment. However, where feasible, alternative methods, such as Agrobacterium-mediated transformation (AMT) (Vijn & Govers, 2003), microprojectile bombardment (Bailey et al., 1993), and zoospore electroporation (Latijnhouwers & Govers, 2003) should also be explored for the transformation of forest Phytophthoras. 6.3 Concluding statements The management and mitigation of forest pathogenic Phytophthoras, and filamentous forest pathogens in general, relies on understanding how these species interact with host plants at the molecular level to cause disease and spread through ecosystems. Current management strategies such as host resistance breeding, fungicide application, and even biosurveillance require knowledge of the specific genes driving phytopathogenicity and related traits in forest pathogens. This genetic knowledge requires the continued development of both large-scale genomic analyses and species-specific molecular techniques, such as CRISPR-Cas9. My goal in this dissertation was to demonstrate a proof of concept for the application of these genomic techniques for forest pathology. I believe that my research findings, both the successes and failures, provide a useful foundation for future genomic research in filamentous forest pathogens. 151 References Achari, S. R., Edwards, J., Mann, R. C., Kaur, J. K., Sawbridge, T., & Summerell, B. A. (2021). Comparative transcriptomic analysis of races 1, 2, 5 and 6 of Fusarium oxysporum f.sp. Pisi in a susceptible pea host identifies differential pathogenicity profiles. BMC Genomics, 22(1), 734. https://doi.org/10.1186/s12864-021-08033-y Adhikari, B. N., Hamilton, J. P., Zerillo, M. M., Tisserat, N., Lévesque, C. A., & Buell, C. R. (2013). Comparative Genomics Reveals Insight into Virulence Strategies of Plant Pathogenic Oomycetes. PLoS ONE, 8(10), e75072. https://doi.org/10.1371/journal.pone.0075072 Agrios, G. N. (2005). Plant Pathology (Fifth Edition). Elsevier Academic Press. Aguayo, J., Elegbede, F., Husson, C., Saintonge, F.-X., & Marçais, B. (2014). Modeling climate impact on an emerging disease, the Phytophthora alni-induced alder decline. Global Change Biology, 20(10), 3209–3221. https://doi.org/10.1111/gcb.12601 Agudelo, D., Carter, S., Velimirovic, M., Duringer, A., Rivest, J.-F., Levesque, S., Loehr, J., Mouchiroud, M., Cyr, D., Waters, P. J., Laplante, M., Moineau, S., Goulet, A., & Doyon, Y. (2020). Versatile and robust genome editing with Streptococcus thermophilus CRISPR1-Cas9. Genome Research, 30(1), 107–117. https://doi.org/10.1101/gr.255414.119 Ah-Fong, A. M. V., Boyd, A. M., Matson, M. E. H., & Judelson, H. S. (2021). A Cas12a-based gene editing system for Phytophthora infestans reveals monoallelic expression of an elicitor. Molecular Plant Pathology, 22(6), 737–752. https://doi.org/10.1111/mpp.13051 Ah-Fong, A. M. V., & Judelson, H. S. (2011). Vectors for fluorescent protein tagging in Phytophthora: Tools for functional genomics and cell biology. Fungal Biology, 115(9), 882–890. https://doi.org/10.1016/j.funbio.2011.07.001 Ali, Z., Abulfaraj, A., Idris, A., Ali, S., Tashkandi, M., & Mahfouz, M. M. (2015). CRISPR/Cas9-mediated viral interference in plants. Genome Biology, 16(1), 238. https://doi.org/10.1186/s13059-015-0799-6 Ali, Z., Ali, S., Tashkandi, M., Zaidi, S. S.-A., & Mahfouz, M. M. (2016). CRISPR/Cas9-Mediated Immunity to Geminiviruses: Differential Interference and Evasion. Scientific Reports, 6(1), Article 1. https://doi.org/10.1038/srep26912 Allen, E. A., & Humble, L. M. (2002). Nonindigenous species introductions: A threat to Canada’s forests and forest economy. Canadian Journal of Plant Pathology, 24(2), 103–110. https://doi.org/10.1080/07060660309506983 Almási, É., Sahu, N., Krizsán, K., Bálint, B., Kovács, G. M., Kiss, B., Cseklye, J., Drula, E., Henrissat, B., Nagy, I., Chovatia, M., Adam, C., LaButti, K., Lipzen, A., Riley, R., Grigoriev, I. V., & Nagy, L. G. (2019). Comparative genomics reveals unique wood-decay strategies and fruiting body development in the Schizophyllaceae. New Phytologist, 224(2), 902–915. https://doi.org/10.1111/nph.16032 Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403–410. https://doi.org/10.1016/S0022-2836(05)80360-2 Amos, B., Aurrecoechea, C., Barba, M., Barreto, A., Basenko, E. Y., Bażant, W., Belnap, R., Blevins, A. S., Böhme, U., Brestelli, J., Brunk, B. P., Caddick, M., Callan, D., Campbell, L., Christensen, M. B., Christophides, G. K., Crouch, K., Davis, K., DeBarry, J., … Zheng, J. (2022). VEuPathDB: The eukaryotic pathogen, vector and host bioinformatics 152 resource center. Nucleic Acids Research, 50(D1), D898–D911. https://doi.org/10.1093/nar/gkab929 Anderson, P. K., Cunningham, A. A., Patel, N. G., Morales, F. J., Epstein, P. R., & Daszak, P. (2004). Emerging infectious diseases of plants: Pathogen pollution, climate change and agrotechnology drivers. Trends in Ecology & Evolution, 19(10), 535–544. https://doi.org/10.1016/j.tree.2004.07.021 Andolfo, G., Iovieno, P., Frusciante, L., & Ercolano, M. R. (2016). Genome-Editing Technologies for Enhancing Plant Disease Resistance. Frontiers in Plant Science, 7, Article 1813. https://doi.org/10.3389/fpls.2016.01813 Andreassi, J. L., Gutteridge, S., Pember, S. O., & Sweigard, J. A. (2013). Detection and Screening Method and Materials Useful in Performance Thereof (Geneva: World Intellectual Property Organization Patent International Patent No WO2013009971). https://patents.google.com/patent/WO2013009971A1/en Arazoe, T., Miyoshi, K., Yamato, T., Ogawa, T., Ohsato, S., Arie, T., & Kuwata, S. (2015). Tailor-made CRISPR/Cas system for highly efficient targeted gene replacement in the rice blast fungus. Biotechnology and Bioengineering, 112(12), 2543–2549. https://doi.org/10.1002/bit.25662 Bailey, A. M., Mena, G. L., & Herrera-Estrella, L. (1991). Genetic transformation of the plant pathogens Phytophthora capsici and Phytophthora parasitica. Nucleic Acids Research, 19(15), 4273–4278. Bailey, A. M., Mena, G. L., & Herrera-Estrella, L. (1993). Transformation of four pathogenic Phytophthora spp by microprojectile bombardment on intact mycelia. Current Genetics, 23(1), 42–46. https://doi.org/10.1007/BF00336748 Baldauf, S. L., Roger, A. J., Wenk-Siefert, I., & Doolittle, W. F. (2000). A Kingdom-Level Phylogeny of Eukaryotes Based on Combined Protein Data. Science, 290(5493), 972–977. Barrangou, R., Fremaux, C., Deveau, H., Richards, M., Boyaval, P., Moineau, S., Romero, D. A., & Horvath, P. (2007). CRISPR Provides Acquired Resistance Against Viruses in Prokaryotes. Science, 315(5819), 1709–1712. https://doi.org/10.1126/science.1138140 Basten, D. E. J. W., Moers, A. P. H. A., Ooyen, A. J. J. van., & Schaap, P. J. (2005). Characterisation of Aspergillus niger prolyl aminopeptidase. Molecular Genetics and Genomics, 272(6), 673–679. https://doi.org/10.1007/s00438-004-1094-5 Benson, D. A., Cavanaugh, M., Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J., & Sayers, E. W. (2013). GenBank. Nucleic Acids Research, 41(D1), D36–D42. https://doi.org/10.1093/nar/gks1195 Berbee, M. L., & Taylor, J. W. (2010). Dating the molecular clock in fungi – how close are we? Fungal Biology Reviews, 24(1–2), 1–16. https://doi.org/10.1016/j.fbr.2010.03.001 Berrocal, A., Navarrete, J., Oviedo, C., & Nickerson, K. W. (2012). Quorum sensing activity in Ophiostoma ulmi: Effects of fusel oils and branched chain amino acids on yeast-mycelial dimorphism. Journal of Applied Microbiology, 113(1), 126–134. https://doi.org/10.1111/j.1365-2672.2012.05317.x Bhaya, D., Davison, M., & Barrangou, R. (2011). CRISPR-Cas Systems in Bacteria and Archaea: Versatile Small RNAs for Adaptive Defense and Regulation. Annual Review of Genetics, 45(1), 273–297. https://doi.org/10.1146/annurev-genet-110410-132430 153 Bibikova, M., Golic, M., Golic, K. G., & Carroll, D. (2002). Targeted Chromosomal Cleavage and Mutagenesis in Drosophila Using Zinc-Finger Nucleases. Genetics, 161(3), 1169–1175. https://doi.org/10.1093/genetics/161.3.1169 Bilodeau, P., Roe, A. D., Bilodeau, G., Blackburn, G. S., Cui, M., Cusson, M., Doucet, D., Griess, V. C., Lafond, V. M. A., Nilausen, C., Paradis, G., Porth, I., Prunier, J., Srivastava, V., Stewart, D., Torson, A. S., Tremblay, E., Uzunovic, A., Yemshanov, D., & Hamelin, R. C. (2019). Biosurveillance of forest insects: Part II—adoption of genomic tools by end user communities and barriers to integration. Journal of Pest Science, 92(1), 71–82. https://doi.org/10.1007/s10340-018-1001-1 Birch, P. R. J., & Whisson, S. C. (2001). Phytophthora infestans enters the genomics era. Molecular Plant Pathology, 2(5), 257–263. https://doi.org/10.1046/j.1464-6722.2001.00073.x Bittner, R. J., Sweigard, J. A., & Mila, A. L. (2017). Assessing the resistance potential of Phytophthora nicotianae, the causal agent of black shank of tobacco, to oxathiopropalin with laboratory mutants. Crop Protection, 102, 63–71. https://doi.org/10.1016/j.cropro.2017.08.002 Bolotin, A., Quinquis, B., Sorokin, A., & Ehrlich, S. D. (2005). Clustered regularly interspaced short palindrome repeats (CRISPRs) have spacers of extrachromosomal origin. Microbiology, 151(8), 2551–2561. https://doi.org/10.1099/mic.0.28048-0 Bortesi, L., & Fischer, R. (2015). The CRISPR/Cas9 system for plant genome editing and beyond. Biotechnology Advances, 33(1), 41–52. https://doi.org/10.1016/j.biotechadv.2014.12.006 Bottin, A., Larche, L., Villalba, F., Gaulin, E., Esquerré-Tugayé, M.-T., & Rickauer, M. (1999). Green fluorescent protein (GFP) as gene expression reporter and vital marker for studying development and microbe-plant interaction in the tobacco pathogen Phytophthora parasitica var. Nicotianae. FEMS Microbiology Letters, 176(1), 51–56. https://doi.org/10.1111/j.1574-6968.1999.tb13641.x Bradshaw, H. D., Ceulemans, R., Davis, J., & Stettler, R. (2000). Emerging Model Systems in Plant Biology: Poplar (Populus) as A Model Forest Tree. Journal of Plant Growth Regulation, 19(3), 306–313. https://doi.org/10.1007/s003440000030 Brasier, C. M. (2008). The biosecurity threat to the UK and global environment from international trade in plants. Plant Pathology, 57(5), 792–808. https://doi.org/10.1111/j.1365-3059.2008.01886.x Brasier, C., Scanu, B., Cooke, D., & Jung, T. (2022). Phytophthora: An ancient, historic, biologically and structurally cohesive and evolutionarily successful generic concept in need of preservation. IMA Fungus, 13(1), Article 1. https://doi.org/10.1186/s43008-022-00097-z Breitler, J.-C., Dechamp, E., Campa, C., Zebral Rodrigues, L. A., Guyot, R., Marraccini, P., & Etienne, H. (2018). CRISPR/Cas9-mediated efficient targeted mutagenesis has the potential to accelerate the domestication of Coffea canephora. Plant Cell, Tissue and Organ Culture (PCTOC), 134(3), 383–394. https://doi.org/10.1007/s11240-018-1429-2 Brinkman, E. K., Chen, T., Amendola, M., & van Steensel, B. (2014). Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Research, 42(22), e168. https://doi.org/10.1093/nar/gku936 Brooks, C., Nekrasov, V., Lippman, Z. B., & Van Eck, J. (2014). Efficient Gene Editing in Tomato in the First Generation Using the Clustered Regularly Interspaced Short 154 Palindromic Repeats/CRISPR-Associated9 System. Plant Physiology, 166(3), 1292–1297. https://doi.org/10.1104/pp.114.247577 Brotman, Y., Briff, E., Viterbo, A., & Chet, I. (2008). Role of Swollenin, an Expansin-Like Protein from Trichoderma, in Plant Root Colonization. Plant Physiology, 147(2), 779–789. https://doi.org/10.1104/pp.108.116293 Burgess, T. I., Scott, J. K., Mcdougall, K. L., Stukely, M. J. C., Crane, C., Dunstan, W. A., Brigg, F., Andjic, V., White, D., Rudman, T., Arentz, F., Ota, N., & Hardy, G. E. St. J. (2017). Current and projected global distribution of Phytophthora cinnamomi, one of the world’s worst plant pathogens. Global Change Biology, 23(4), 1661–1674. https://doi.org/10.1111/gcb.13492 Burki, F., Roger, A. J., Brown, M. W., & Simpson, A. G. B. (2020). The New Tree of Eukaryotes. Trends in Ecology & Evolution, 35(1), 43–55. https://doi.org/10.1016/j.tree.2019.08.008 Burstein, D., Harrington, L. B., Strutt, S. C., Probst, A. J., Anantharaman, K., Thomas, B. C., Doudna, J. A., & Banfield, J. F. (2017). New CRISPR–Cas systems from uncultivated microbes. Nature, 542(7640), Article 7640. https://doi.org/10.1038/nature21059 Carbú, M., Moraga, J., Cantoral, J. M., Collado, I. G., & Garrido, C. (2019). Recent approaches on the genomic analysis of the phytopathogenic fungus Colletotrichum spp. Phytochemistry Reviews. https://doi.org/10.1007/s11101-019-09608-0 Carter, J. V., Pan, J., Rai, S. N., & Galandiuk, S. (2016). ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves. Surgery, 159(6), 1638–1645. https://doi.org/10.1016/j.surg.2015.12.029 Castresana, J. (2000). Selection of Conserved Blocks from Multiple Alignments for Their Use in Phylogenetic Analysis. Molecular Biology and Evolution, 17(4), 540–552. https://doi.org/10.1093/oxfordjournals.molbev.a026334 Cavalier-Smith, T. (1998). A revised six-kingdom system of life. Biological Reviews, 73(3), 203–266. https://doi.org/10.1111/j.1469-185X.1998.tb00030.x Cavalier-Smith, T., & Chao, E. E.-Y. (2006). Phylogeny and Megasystematics of Phagotrophic Heterokonts (Kingdom Chromista). Journal of Molecular Evolution, 62(4), 388–420. https://doi.org/10.1007/s00239-004-0353-8 Ceccaldi, R., Rondinelli, B., & D’Andrea, A. D. (2016). Repair Pathway Choices and Consequences at the Double-Strand Break. Trends in Cell Biology, 26(1), 52–64. https://doi.org/10.1016/j.tcb.2015.07.009 Chandrasekaran, J., Brumin, M., Wolf, D., Leibman, D., Klap, C., Pearlsman, M., Sherman, A., Arazi, T., & Gal‐On, A. (2016). Development of broad virus resistance in non-transgenic cucumber using CRISPR/Cas9 technology. Molecular Plant Pathology, 17(7), 1140–1153. https://doi.org/10.1111/mpp.12375 Chandrasekaran, M., Thangavelu, B., Chun, S. C., & Sathiyabama, M. (2016). Proteases from phytopathogenic fungi and their importance in phytopathogenicity. Journal of General Plant Pathology, 82(5), 233–239. https://doi.org/10.1007/s10327-016-0672-9 Chang, Q., Liu, J., Lin, X., Hu, S., Yang, Y., Li, D., Chen, L., Huai, B., Huang, L., Voegele, R. T., & Kang, Z. (2017). A unique invertase is important for sugar absorption of an obligate biotrophic pathogen during infection. New Phytologist, 215(4), 1548–1561. https://doi.org/10.1111/nph.14666 Chanoca, A., de Vries, L., & Boerjan, W. (2019). Lignin Engineering in Forest Trees. Frontiers in Plant Science, 10, Article 912. https://doi.org/10.3389/fpls.2019.00912 155 Charlier, F., Weber, M., Dariusz, I., Emerson, H., Marcin, M., Joseph, L., Louison, F., Chan, M., Markov, N., Proost, S., Krasoulis, A., & Repplinger, S. (2022). Statannotations (v0.6) [Python]. Zenodo. https://doi.org/10.5281/zenodo.7213391 Chen, Q., Bakhshi, M., Balci, Y., Broders, K. D., Cheewangkoon, R., Chen, S. F., Fan, X. L., Gramaje, D., Halleen, F., Horta Jung, M., Jiang, N., Jung, T., Májek, T., Marincowitz, S., Milenković, I., Mostert, L., Nakashima, C., Nurul Faziha, I., Pan, M., … Crous, P. W. (2022). Genera of phytopathogenic fungi: GOPHY 4. Studies in Mycology, 101(1), 417–564. https://doi.org/10.3114/sim.2022.101.06 Chen, X.-R., Li, Y.-P., Li, Q.-Y., Xing, Y.-P., Liu, B.-B., Tong, Y.-H., & Xu, J.-Y. (2016). SCR96, a small cysteine-rich secretory protein of Phytophthora cactorum, can trigger cell death in the Solanaceae and is important for pathogenicity and oxidative stress tolerance. Molecular Plant Pathology, 17(4), 577–587. https://doi.org/10.1111/mpp.12303 Chen, X.-R., Wen, K., Zhou, X., Zhu, M.-Y., Liu, Y., Jin, J.-H., & Nellist, C. F. (2023). The devastating oomycete phytopathogen Phytophthora cactorum: Insights into its biology and molecular features. Molecular Plant Pathology, 24(9), 1017–1032. https://doi.org/10.1111/mpp.13345 Cho, S., Choe, D., Lee, E., Kim, S. C., Palsson, B., & Cho, B.-K. (2018). High-Level dCas9 Expression Induces Abnormal Cell Morphology in Escherichia coli. ACS Synthetic Biology, 7(4), 1085–1094. https://doi.org/10.1021/acssynbio.7b00462 Cho, S. W., Lee, J., Carroll, D., Kim, J.-S., & Lee, J. (2013). Heritable Gene Knockout in Caenorhabditis elegans by Direct Injection of Cas9–sgRNA Ribonucleoproteins. Genetics, 195(3), 1177–1180. https://doi.org/10.1534/genetics.113.155853 Choquer, M., Rascle, C., Gonçalves, I. R., de Vallée, A., Ribot, C., Loisel, E., Smilevski, P., Ferria, J., Savadogo, M., Souibgui, E., Gagey, M.-J., Dupuy, J.-W., Rollins, J. A., Marcato, R., Noûs, C., Bruel, C., & Poussereau, N. (2021). The infection cushion of Botrytis cinerea: A fungal ‘weapon’ of plant-biomass destruction. Environmental Microbiology, 23(4), 2293–2314. https://doi.org/10.1111/1462-2920.15416 Christian, M., Cermak, T., Doyle, E. L., Schmidt, C., Zhang, F., Hummel, A., Bogdanove, A. J., & Voytas, D. F. (2010). Targeting DNA Double-Strand Breaks with TAL Effector Nucleases. Genetics, 186(2), 757–761. https://doi.org/10.1534/genetics.110.120717 Clarivate Analytics. (2020). Results Analysis Tool for Topic: “CRISPR.” Web of Science. https://wcs-webofknowledge-com.ezproxy.library.ubc.ca/RA/analyze.do?product=WOS&SID=5EBaGfnpdYEJCywebJU&field=PY_PublicationYear_PublicationYear_en&yearSort=true Coakley, S. M., Scherm, H., & Chakraborty, S. (1999). Climate Change and Plant Disease Management. Annual Review of Phytopathology, 37(1), 399–426. https://doi.org/10.1146/annurev.phyto.37.1.399 Comeau, A. M., Dufour, J., Bouvet, G. F., Jacobi, V., Nigg, M., Henrissat, B., Laroche, J., Levesque, R. C., & Bernier, L. (2015). Functional Annotation of the Ophiostoma novo-ulmi Genome: Insights into the Phytopathogenicity of the Fungal Agent of Dutch Elm Disease. Genome Biology and Evolution, 7(2), 410–430. https://doi.org/10.1093/gbe/evu281 Contreras-Cornejo, H. A., Larsen, J., Fernández-Pavía, S. P., & Oyama, K. (2023). Climate change, a booster of disease outbreaks by the plant pathogen Phytophthora in oak forests. Rhizosphere, 27, 100719. https://doi.org/10.1016/j.rhisph.2023.100719 156 Cosgrove, D. J. (2015). Plant expansins: Diversity and interactions with plant cell walls. Current Opinion in Plant Biology, 25, 162–172. https://doi.org/10.1016/j.pbi.2015.05.014 Cvitanich, C., & Judelson, H. S. (2003). Stable transformation of the oomycete, Phytophthora infestans, using microprojectile bombardment. Current Genetics, 42(4), 228–235. https://doi.org/10.1007/s00294-002-0354-3 Dai, T., Xu, Y., Yang, X., Jiao, B., Qiu, M., Xue, J., Arredondo, F., & Tyler, B. M. (2021). An Improved Transformation System for Phytophthora cinnamomi Using Green Fluorescent Protein. Frontiers in Microbiology, 12. https://www.frontiersin.org/articles/10.3389/fmicb.2021.682754 Dangl, J. L., & Jones, J. D. G. (2001). Plant pathogens and integrated defence responses to infection. Nature, 411(6839), Article 6839. https://doi.org/10.1038/35081161 Das, A., Sharma, N., & Prasad, M. (2019). CRISPR/Cas9: A Novel Weapon in the Arsenal to Combat Plant Diseases. Frontiers in Plant Science, 9, Article 2008. https://doi.org/10.3389/fpls.2018.02008 de Bary, A. (1861). Die gegenwärtig herrschende Kartoffelkrankenheit, ihre Ursache und ihre Verhütung (The current prevailing potato disease, its cause and treatment). Felix, Leipzig, 75. de Bary, A. (1876). Researches into the nature of the potato fungus, Phytophthora infestans. Journal of the Royal Agricultural Society of England, 12, 239–269. de Bary, A. (1887). Comparative morphology and biology of the fungi, mycetozoa and bacteria (I. B. Balfour, Ed.; H. E. F. Garnsey, Trans.). Clarendon Press. De Silva, N. I., Lumyong, S., Hyde, K. D., Bulgakov, T., Phillips, A. J. L., & Yan, J. U. (2016). Mycosphere Essays 9: Defining biotrophs and hemibiotrophs. Mycosphere, 7(5), 545–559. https://doi.org/10.5943/mycosphere/7/5/2 de Toledo Thomazella, D. P., Brail, Q., Dahlbeck, D., & Staskawicz, B. (2016). CRISPR-Cas9 mediated mutagenesis of a DMR6 ortholog in tomato confers broad-spectrum disease resistance. bioRxiv [Preprint], 064824. https://doi.org/10.1101/064824 Deltcheva, E., Chylinski, K., Sharma, C. M., Gonzales, K., Chao, Y., Pirzada, Z. A., Eckert, M. R., Vogel, J., & Charpentier, E. (2011). CRISPR RNA maturation by trans-encoded small RNA and host factor RNase III. Nature, 471(7340), Article 7340. https://doi.org/10.1038/nature09886 Derbyshire, M., Denton-Giles, M., Hegedus, D., Seifbarghy, S., Rollins, J., van Kan, J., Seidl, M. F., Faino, L., Mbengue, M., Navaud, O., Raffaele, S., Hammond-Kosack, K., Heard, S., & Oliver, R. (2017). The Complete Genome Sequence of the Phytopathogenic Fungus Sclerotinia sclerotiorum Reveals Insights into the Genome Architecture of Broad Host Range Pathogens. Genome Biology and Evolution, 9(3), 593–618. https://doi.org/10.1093/gbe/evx030 Després, P. C., Dubé, A. K., Nielly-Thibault, L., Yachie, N., & Landry, C. R. (2018). Double Selection Enhances the Efficiency of Target-AID and Cas9-Based Genome Editing in Yeast. G3: Genes, Genomes, Genetics, 8(10), 3163–3171. https://doi.org/10.1534/g3.118.200461 Desprez-Loustau, M.-L., Robin, C., Buée, M., Courtecuisse, R., Garbaye, J., Suffert, F., Sache, I., & Rizzo, D. M. (2007). The fungal dimension of biological invasions. Trends in Ecology & Evolution, 22(9), 472–480. https://doi.org/10.1016/j.tree.2007.04.005 Deveau, H., Barrangou, R., Garneau, J. E., Labonté, J., Fremaux, C., Boyaval, P., Romero, D. A., Horvath, P., & Moineau, S. (2008). Phage Response to CRISPR-Encoded Resistance in 157 Streptococcus thermophilus. Journal of Bacteriology, 190(4), 1390–1400. https://doi.org/10.1128/JB.01412-07 de Vries, S., de Vries, J., Archibald, J. M., & Slamovits, C. H. (2020). Comparative analyses of saprotrophy in Salisapilia sapeloensis and diverse plant pathogenic oomycetes reveal lifestyle-specific gene expression. FEMS Microbiology Ecology, 96(11). https://doi.org/10.1093/femsec/fiaa184 Dodds, P. N., & Rathjen, J. P. (2010). Plant immunity: Towards an integrated view of plant–pathogen interactions. Nature Reviews Genetics, 11(8), 539–548. https://doi.org/10.1038/nrg2812 Doench, J. G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E. W., Donovan, K. F., Smith, I., Tothova, Z., Wilen, C., Orchard, R., Virgin, H. W., Listgarten, J., & Root, D. E. (2016). Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nature Biotechnology, 34(2), Article 2. https://doi.org/10.1038/nbt.3437 Doench, J. G., Hartenian, E., Graham, D. B., Tothova, Z., Hegde, M., Smith, I., Sullender, M., Ebert, B. L., Xavier, R. J., & Root, D. E. (2014). Rational design of highly active sgRNAs for CRISPR-Cas9–mediated gene inactivation. Nature Biotechnology, 32(12), Article 12. https://doi.org/10.1038/nbt.3026 Dort, E. N., Tanguay, P., & Hamelin, R. C. (2020). CRISPR/Cas9 Gene Editing: An Unexplored Frontier for Forest Pathology. Frontiers in Plant Science, 11, 1126. https://doi.org/10.3389/fpls.2020.01126 Dou, D., Kale, S. D., Wang, X., Chen, Y., Wang, Q., Wang, X., Jiang, R. H. Y., Arredondo, F. D., Anderson, R. G., Thakur, P. B., McDowell, J. M., Wang, Y., & Tyler, B. M. (2008). Conserved C-Terminal Motifs Required for Avirulence and Suppression of Cell Death by Phytophthora sojae effector Avr1b. The Plant Cell, 20(4), 1118–1133. https://doi.org/10.1105/tpc.107.057067 Drula, E., Garron, M.-L., Dogan, S., Lombard, V., Henrissat, B., & Terrapon, N. (2022). The carbohydrate-active enzyme database: Functions and literature. Nucleic Acids Research, 50(D1), D571–D577. https://doi.org/10.1093/nar/gkab1045 Dunn, A. R., Fry, B. A., Lee, T. Y., Conley, K. D., Balaji, V., Fry, W. E., McLeod, A., & Smart, C. D. (2013). Transformation of Phytophthora capsici with genes for green and red fluorescent protein for use in visualizing plant-pathogen interactions. Australasian Plant Pathology, 42(5), 583–593. https://doi.org/10.1007/s13313-013-0222-2 Duplessis, S., Major, I., Martin, F., & Séguin, A. (2009). Poplar and Pathogen Interactions: Insights from Populus Genome-Wide Analyses of Resistance and Defense Gene Families and Gene Expression Profiling. Critical Reviews in Plant Sciences, 28(5), 309–334. https://doi.org/10.1080/07352680903241063 Eikemo, H., Stensvand, A., Davik, J., & Tronsmo, A. M. (2003). Resistance to crown rot (Phytophthora cactorum) in strawberry cultivars and in offspring from crosses between cultivars differing in susceptibility to the disease. Annals of Applied Biology, 142(1), 83–89. https://doi.org/10.1111/j.1744-7348.2003.tb00232.x Eikemo, H., Stensvand, A., & Tronsmo, A. M. (2000). Evaluation of methods of screening strawberry cultivars for resistance to crown rot caused by Phytophthora cactorum. Annals of Applied Biology, 137(3), 237–244. https://doi.org/10.1111/j.1744-7348.2000.tb00064.x Elorriaga, E., Klocko, A. L., Ma, C., & Strauss, S. H. (2018). Variation in Mutation Spectra Among CRISPR/Cas9 Mutagenized Poplars. Frontiers in Plant Science, 9, Article 594. https://doi.org/10.3389/fpls.2018.00594 158 Epanchin-Niell, R. S., & Liebhold, A. M. (2015). Benefits of invasion prevention: Effect of time lags, spread rates, and damage persistence. Ecological Economics, 116, 146–153. https://doi.org/10.1016/j.ecolecon.2015.04.014 Erwin, D. C., & Ribeiro, O. K. (1996). Phytophthora Diseases Worldwide. APS Press. Fan, D., Liu, T., Li, C., Jiao, B., Li, S., Hou, Y., & Luo, K. (2015). Efficient CRISPR/Cas9-mediated Targeted Mutagenesis in Populus in the First Generation. Scientific Reports, 5(1), Article 1. https://doi.org/10.1038/srep12217 Fang, Y., Cui, L., Gu, B., Arredondo, F., & Tyler, B. M. (2017). Efficient Genome Editing in the Oomycete Phytophthora sojae Using CRISPR/Cas9. Current Protocols in Microbiology, 44(1), 21A.1.1-21A.1.26. https://doi.org/10.1002/cpmc.25 Fang, Y., Jang, H. S., Watson, G. W., Wellappili, D. P., & Tyler, B. M. (2017). Distinctive Nuclear Localization Signals in the Oomycete Phytophthora sojae. Frontiers in Microbiology, 8. https://doi.org/10.3389/fmicb.2017.00010 Fang, Y., & Tyler, B. M. (2016). Efficient disruption and replacement of an effector gene in the oomycete Phytophthora sojae using CRISPR/Cas9. Molecular Plant Pathology, 17(1), 127–139. https://doi.org/10.1111/mpp.12318 Farboud, B., & Meyer, B. J. (2015). Dramatic Enhancement of Genome Editing by CRISPR/Cas9 Through Improved Guide RNA Design. Genetics, 199(4), 959–971. https://doi.org/10.1534/genetics.115.175166 Feau, N., Joly, D. L., & Hamelin, R. C. (2007). Poplar leaf rusts: Model pathogens for a model tree. Canadian Journal of Botany, 85(12), 1127–1135. https://doi.org/10.1139/B07-102 Feau, N., Mcdonald, M., Van Der Meer, B., Zang, Y., Herath, P., & Hamelin, R. C. (2022). Phytophthora species associated with red alder dieback in British Columbia, Canada. Canadian Journal of Plant Pathology, 0(ja), null. https://doi.org/10.1080/07060661.2021.2022763 Feau, N., Taylor, G., Dale, A. L., Dhillon, B., Bilodeau, G. J., Birol, I., Jones, S. J. M., & Hamelin, R. C. (2016). Genome sequences of six Phytophthora species threatening forest ecosystems. Genomics Data, 10, 85–88. https://doi.org/10.1016/j.gdata.2016.09.013 Feau, N., van der Meer, B., van der Linden, W., Herath, P., Bradley, G., Ukrainetz, N., Kope, H., & Hamelin, R. (2021). Phytophthora root rot in western white pine seed orchards in British Columbia. Canadian Journal of Plant Pathology, 43(sup1), S172–S175. https://doi.org/10.1080/07060661.2021.1932163 Feng, Z., Zhang, B., Ding, W., Liu, X., Yang, D.-L., Wei, P., Cao, F., Zhu, S., Zhang, F., Mao, Y., & Zhu, J.-K. (2013). Efficient genome editing in plants using a CRISPR/Cas system. Cell Research, 23(10), Article 10. https://doi.org/10.1038/cr.2013.114 Fernandez i Marti, A., & Dodd, R. S. (2018). Using CRISPR as a Gene Editing Tool for Validating Adaptive Gene Function in Tree Landscape Genomics. Frontiers in Ecology and Evolution, 6, Article 76. https://doi.org/10.3389/fevo.2018.00076 Fire, A., Xu, S., Montgomery, M. K., Kostas, S. A., Driver, S. E., & Mello, C. C. (1998). Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature, 391, 806–811. Fisher, M. C., Gurr, S. J., Cuomo, C. A., Blehert, D. S., Jin, H., Stukenbrock, E. H., Stajich, J. E., Kahmann, R., Boone, C., Denning, D. W., Gow, N. A. R., Klein, B. S., Kronstad, J. W., Sheppard, D. C., Taylor, J. W., Wright, G. D., Heitman, J., Casadevall, A., & Cowen, L. E. (2020). Threats Posed by the Fungal Kingdom to Humans, Wildlife, and Agriculture. mBio. https://doi.org/10.1128/mBio.00449-20 159 Fisher, M. C., Henk, D. A., Briggs, C. J., Brownstein, J. S., Madoff, L. C., McCraw, S. L., & Gurr, S. J. (2012). Emerging fungal threats to animal, plant and ecosystem health. Nature, 484(7393), Article 7393. https://doi.org/10.1038/nature10947 Fletcher, K., Klosterman, S. J., Derevnina, L., Martin, F., Bertier, L. D., Koike, S., Reyes-Chin-Wo, S., Mou, B., & Michelmore, R. (2018). Comparative genomics of downy mildews reveals potential adaptations to biotrophy. BMC Genomics, 19(1), Article 1. https://doi.org/10.1186/s12864-018-5214-8 Forgetta, V., Leveque, G., Dias, J., Grove, D., Lyons, R., Genik, S., Wright, C., Singh, S., Peterson, N., Zianni, M., Kieleczawa, J., Steen, R., Perera, A., Bintzler, D., Adams, S., Hintz, W., Jacobi, V., Bernier, L., Levesque, R., & Dewar, K. (2013). Sequencing of the Dutch Elm Disease Fungus Genome Using the Roche/454 GS-FLX Titanium System in a Comparison of Multiple Genomics Core Facilities. Journal of Biomolecular Techniques : JBT, 24(1), 39–49. https://doi.org/10.7171/jbt.12-2401-005 Förster, H., Coffey, M. D., Elwood, H., & Sogin, M. L. (1990). Sequence Analysis of the Small Subunit Ribosomal RNAs of Three Zoosporic Fungi and Implications for Fungal Evolution. Mycologia, 82(3), 306–312. Foster, A. J., Martin-Urdiroz, M., Yan, X., Wright, H. S., Soanes, D. M., & Talbot, N. J. (2018). CRISPR-Cas9 ribonucleoprotein-mediated co-editing and counterselection in the rice blast fungus. Scientific Reports, 8(1), 14355. https://doi.org/10.1038/s41598-018-32702-w Fournier, R. E., & Turgeon, J. J. (2017). Surveillance during monitoring phase of an eradication programme against Anoplophora glabripennis (Motschulsky) guided by a spatial decision support system. Biological Invasions, 19(10), 3013–3035. https://doi.org/10.1007/s10530-017-1505-2 Fritsche, S., Poovaiah, C., MacRae, E., & Thorlby, G. (2018). A New Zealand Perspective on the Application and Regulation of Gene Editing. Frontiers in Plant Science, 9, Article 1323. https://doi.org/10.3389/fpls.2018.01323 Fry, W. E., Birch, P. R. J., Judelson, H. S., Grünwald, N. J., Danies, G., Everts, K. L., Gevens, A. J., Gugino, B. K., Johnson, D. A., Johnson, S. B., McGrath, M. T., Myers, K. L., Ristaino, J. B., Roberts, P. D., Secor, G., & Smart, C. D. (2015). Five Reasons to Consider Phytophthora infestans a Reemerging Pathogen. Phytopathology®, 105(7), 966–981. https://doi.org/10.1094/PHYTO-01-15-0005-FI Gan, P., Narusaka, M., Kumakura, N., Tsushima, A., Takano, Y., Narusaka, Y., & Shirasu, K. (2016). Genus-Wide Comparative Genome Analyses of Colletotrichum Species Reveal Specific Gene Family Losses and Gains during Adaptation to Specific Infection Lifestyles. Genome Biology and Evolution, 8(5), 1467–1481. https://doi.org/10.1093/gbe/evw089 Gao, R.-F., Wang, J.-Y., Liu, K.-W., Yoshida, K., Hsiao, Y.-Y., Shi, Y.-X., Tsai, K.-C., Chen, Y.-Y., Mitsuda, N., Liang, C.-K., Wang, Z.-W., Wang, Y., Zhang, D.-Y., Huang, L., Zhao, X., Zhong, W.-Y., Cheng, Y.-H., Jiang, Z.-D., Li, M.-H., … Zhang, G.-M. (2021). Comparative analysis of Phytophthora genomes reveals oomycete pathogenesis in crops. Heliyon, 7(2), e06317. https://doi.org/10.1016/j.heliyon.2021.e06317 Garbelotto Benzon, N., Svihra, P., & Rizzo, D. (2001). New pests and diseases: Sudden oak death syndrome fells 3 oak species. California Agriculture, 55(1), 9–19. 160 Gardiner, D. M., & Kazan, K. (2018). Selection is required for efficient Cas9-mediated genome editing in Fusarium graminearum. Fungal Biology, 122(2–3), 131–137. https://doi.org/10.1016/j.funbio.2017.11.006 Gardiner, D. M., Rusu, A., Barrett, L., Hunter, G. C., & Kazan, K. (2020). Natural gene drives offer potential pathogen control strategies in plants. bioRxiv, 2020.04.05.026500. https://doi.org/10.1101/2020.04.05.026500 Garrett, K. A., Dendy, S. P., Frank, E. E., Rouse, M. N., & Travers, S. E. (2006). Climate Change Effects on Plant Disease: Genomes to Ecosystems. Annual Review of Phytopathology, 44(1), 489–509. https://doi.org/10.1146/annurev.phyto.44.070505.143420 Garst, A. D., Bassalo, M. C., Pines, G., Lynch, S. A., Halweg-Edwards, A. L., Liu, R., Liang, L., Wang, Z., Zeitoun, R., Alexander, W. G., & Gill, R. T. (2017). Genome-wide mapping of mutations at single-nucleotide resolution for protein, metabolic and genome engineering. Nature Biotechnology, 35(1), Article 1. https://doi.org/10.1038/nbt.3718 Gaulin, E., Haget, N., Khatib, M., Herbert, C., Rickauer, M., & Bottin, A. (2007). Transgenic sequences are frequently lost in Phytophthora parasitica transformants without reversion of the transgene-induced silenced state. Canadian Journal of Microbiology, 53(1), 152–157. https://doi.org/10.1139/w06-090 Ghelardini, L., Luchi, N., Pecori, F., Pepori, A. L., Danti, R., Della Rocca, G., Capretti, P., Tsopelas, P., & Santini, A. (2017). Ecology of invasive forest pathogens. Biological Invasions, 19(11), 3183–3200. https://doi.org/10.1007/s10530-017-1487-0 Ghimire, B., Saraiva, M., Andersen, C. B., Gogoi, A., Saleh, M., Zic, N., van West, P., & Brurberg, M. B. (2022). Transformation systems, gene silencing and gene editing technologies in oomycetes. Fungal Biology Reviews, 40, 37–52. https://doi.org/10.1016/j.fbr.2021.11.001 Giannakopoulou, A., Steele, J. F. C., Segretin, M. E., Bozkurt, T. O., Zhou, J., Robatzek, S., Banfield, M. J., Pais, M., & Kamoun, S. (2015). Tomato I2 Immune Receptor Can Be Engineered to Confer Partial Resistance to the Oomycete Phytophthora infestans in Addition to the Fungus Fusarium oxysporum. Molecular Plant-Microbe Interactions, 28(12), 1316–1329. https://doi.org/10.1094/MPMI-07-15-0147-R Gilbert, G. S. (2002). Evolutionary ecology of plant diseases in natural ecosystems. Annual Review of Phytopathology, 40(1), 13–43. https://doi.org/10.1146/annurev.phyto.40.021202.110417 Gottesman, S. (2011). Dicing defence in bacteria. Nature, 471(7340), Article 7340. https://doi.org/10.1038/471588a Govers, F., & Gijzen, M. (2006). Phytophthora Genomics: The Plant Destroyers’ Genome Decoded. Molecular Plant-Microbe Interactions, 19(12), 1295–1301. https://doi.org/10.1094/MPMI-19-1295 Grigoriev, I. V., Hayes, R. D., Calhoun, S., Kamel, B., Wang, A., Ahrendt, S., Dusheyko, S., Nikitin, R., Mondo, S. J., Salamov, A., Shabalov, I., & Kuo, A. (2021). PhycoCosm, a comparative algal genomics resource. Nucleic Acids Research, 49(D1), D1004–D1011. https://doi.org/10.1093/nar/gkaa898 Grigoriev, I. V., Martinez, D. A., & Salamov, A. A. (2006). Fungal Genomic Annotation. In D. K. Arora, R. M. Berka, & G. B. Singh (Eds.), Applied Mycology and Biotechnology (Vol. 6, pp. 123–142). Elsevier. https://doi.org/10.1016/S1874-5334(06)80008-0 161 Grigoriev, I. V., Nikitin, R., Haridas, S., Kuo, A., Ohm, R., Otillar, R., Riley, R., Salamov, A., Zhao, X., Korzeniewski, F., Smirnova, T., Nordberg, H., Dubchak, I., & Shabalov, I. (2014). MycoCosm portal: Gearing up for 1000 fungal genomes. Nucleic Acids Research, 42(D1), D699–D704. https://doi.org/10.1093/nar/gkt1183 Gumtow, R., Wu, D., Uchida, J., & Tian, M. (2018). A Phytophthora palmivora Extracellular Cystatin-Like Protease Inhibitor Targets Papain to Contribute to Virulence on Papaya. Molecular Plant-Microbe Interactions®, 31(3), 363–373. https://doi.org/10.1094/MPMI-06-17-0131-FI Hamelin, R. C., & Roe, A. D. (2020). Genomic biosurveillance of forest invasive alien enemies: A story written in code. Evolutionary Applications, 13(1), 95–115. https://doi.org/10.1111/eva.12853 Hane, J. K., Paxman, J., Jones, D. A. B., Oliver, R. P., & de Wit, P. (2020). “CATAStrophy,” a Genome-Informed Trophic Classification of Filamentous Plant Pathogens – How Many Different Types of Filamentous Plant Pathogens Are There? Frontiers in Microbiology, 10, 3088. https://doi.org/10.3389/fmicb.2019.03088 Hansen, E. M. (2015). Phytophthora Species Emerging as Pathogens of Forest Trees. Current Forestry Reports, 1(1), 16–24. https://doi.org/10.1007/s40725-015-0007-7 Hardham, A. R., & Blackman, L. M. (2018). Phytophthora cinnamomi. Molecular Plant Pathology, 19(2), 260–285. https://doi.org/10.1111/mpp.12568 Haridas, S., Albert, R., Binder, M., Bloem, J., LaButti, K., Salamov, A., Andreopoulos, B., Baker, S. E., Barry, K., Bills, G., Bluhm, B. H., Cannon, C., Castanera, R., Culley, D. E., Daum, C., Ezra, D., González, J. B., Henrissat, B., Kuo, A., … Grigoriev, I. V. (2020). 101 Dothideomycetes genomes: A test case for predicting lifestyles and emergence of pathogens. Studies in Mycology. https://doi.org/10.1016/j.simyco.2020.01.003 Harvell, C. D., Mitchell, C. E., Ward, J. R., Altizer, S., Dobson, A. P., Ostfeld, R. S., & Samuel, M. D. (2002). Climate Warming and Disease Risks for Terrestrial and Marine Biota. Science, 296(5576), 2158–2162. https://doi.org/10.1126/science.1063699 Have, A. ten, Espino, J. J., Dekkers, E., Van Sluyter, S. C., Brito, N., Kay, J., González, C., & van Kan, J. A. L. (2010). The Botrytis cinerea aspartic proteinase family. Fungal Genetics and Biology, 47(1), 53–65. https://doi.org/10.1016/j.fgb.2009.10.008 Hématy, K., Cherk, C., & Somerville, S. (2009). Host–pathogen warfare at the plant cell wall. Current Opinion in Plant Biology, 12(4), 406–413. https://doi.org/10.1016/j.pbi.2009.06.007 Hessenauer, P., Fijarczyk, A., Martin, H., Prunier, J., Charron, G., Chapuis, J., Bernier, L., Tanguay, P., Hamelin, R. C., & Landry, C. R. (2020). Hybridization and introgression drive genome evolution of Dutch elm disease pathogens. Nature Ecology & Evolution, 4(4), Article 4. https://doi.org/10.1038/s41559-020-1133-6 Hood, I. A. (1982). Phaeocryptopus gaeumannii on Pseudotsuga menziesii in southern British Columbia. New Zealand Journal of Forestry Science, 12, 415–424. Howe, K. L., Contreras-Moreira, B., De Silva, N., Maslen, G., Akanni, W., Allen, J., Alvarez-Jarreta, J., Barba, M., Bolser, D. M., Cambell, L., Carbajo, M., Chakiachvili, M., Christensen, M., Cummins, C., Cuzick, A., Davis, P., Fexova, S., Gall, A., George, N., … Flicek, P. (2020). Ensembl Genomes 2020—Enabling non-vertebrate genomic research. Nucleic Acids Research, 48(D1), D689–D695. https://doi.org/10.1093/nar/gkz890 162 Howlett, B. J., Lowe, R. G. T., Marcroft, S. J., & van de Wouw, A. P. (2015). Evolution of virulence in fungal plant pathogens: Exploiting fungal genomics to control plant disease. Mycologia, 107(3), 441–451. https://doi.org/10.3852/14-317 Huitema, E., Bos, J. I. B., Tian, M., Win, J., Waugh, M. E., & Kamoun, S. (2004). Linking sequence to phenotype in Phytophthora–plant interactions. Trends in Microbiology, 12(4), 193–200. https://doi.org/10.1016/j.tim.2004.02.008 Huitema, E., Smoker, M., & Kamoun, S. (2011). A Straightforward Protocol for Electro-transformation of Phytophthora capsici Zoospores. In J. M. McDowell (Ed.), Plant Immunity (Vol. 712, pp. 129–135). Humana Press. https://doi.org/10.1007/978-1-61737-998-7_11 Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55 IDT. (2018). Alt-R CRISPR-Cas9 System: Cationic lipid delivery of CRISPR ribonucleoprotein complexes into mammalian cells. Integrated DNA Technologies. Ishino, Y., Shinagawa, H., Makino, K., Amemura, M., & Nakata, A. (1987). Nucleotide sequence of the iap gene, responsible for alkaline phosphatase isozyme conversion in Escherichia coli, and identification of the gene product. Journal of Bacteriology, 169(12), 5429–5433. https://doi.org/10.1128/jb.169.12.5429-5433.1987 Jahr, H., Dreier, J., Meletzus, D., Bahro, R., & Eichenlaub, R. (2000). The Endo-β-1,4-glucanase CelA of Clavibacter michiganensis subsp. Michiganensis Is a Pathogenicity Determinant Required for Induction of Bacterial Wilt of Tomato. Molecular Plant-Microbe Interactions®, 13(7), 703–714. https://doi.org/10.1094/MPMI.2000.13.7.703 Jansen, R., Embden, J. D. A. van, Gaastra, W., & Schouls, L. M. (2002). Identification of genes that are associated with DNA repeats in prokaryotes. Molecular Microbiology, 43(6), 1565–1575. https://doi.org/10.1046/j.1365-2958.2002.02839.x Ji, X., Si, X., Zhang, Y., Zhang, H., Zhang, F., & Gao, C. (2018). Conferring DNA virus resistance with high specificity in plants using virus-inducible genome-editing system. Genome Biology, 19(1), 197. https://doi.org/10.1186/s13059-018-1580-4 Jia, H., Orbovic, V., Jones, J. B., & Wang, N. (2016). Modification of the PthA4 effector binding elements in Type I CsLOB1 promoter using Cas9/sgRNA to produce transgenic Duncan grapefruit alleviating XccΔpthA4:dCsLOB1.3 infection. Plant Biotechnology Journal, 14(5), 1291–1301. https://doi.org/10.1111/pbi.12495 Jia, H., & Wang, N. (2014). Targeted Genome Editing of Sweet Orange Using Cas9/sgRNA. PLoS ONE, 9(4), e93806. https://doi.org/10.1371/journal.pone.0093806 Jiang, L., Ye, W., Situ, J., Chen, Y., Yang, X., Kong, G., Liu, Y., Tinashe, R. J., Xi, P., Wang, Y., & Jiang, Z. (2017). A Puf RNA-binding protein encoding gene PlM90 regulates the sexual and asexual life stages of the litchi downy blight pathogen Peronophythora litchii. Fungal Genetics and Biology, 98, 39–45. https://doi.org/10.1016/j.fgb.2016.12.002 Jiang, R. H. Y., & Tyler, B. M. (2012). Mechanisms and Evolution of Virulence in Oomycetes. Annual Review of Phytopathology, 50(1), 295–318. https://doi.org/10.1146/annurev-phyto-081211-172912 Jiang, W., Brueggeman, A. J., Horken, K. M., Plucinak, T. M., & Weeks, D. P. (2014). Successful Transient Expression of Cas9 and Single Guide RNA Genes in Chlamydomonas reinhardtii. Eukaryotic Cell, 13(11), 1465–1469. https://doi.org/10.1128/ec.00213-14 163 Jiang, W., Zhou, H., Bi, H., Fromm, M., Yang, B., & Weeks, D. P. (2013). Demonstration of CRISPR/Cas9/sgRNA-mediated targeted gene modification in Arabidopsis, tobacco, sorghum and rice. Nucleic Acids Research, 41(20), e188. https://doi.org/10.1093/nar/gkt780 Jiang, Y., Guo, L., Ma, X., Zhao, X., Jiao, B., Li, C., & Luo, K. (2017). The WRKY transcription factors PtrWRKY18 and PtrWRKY35 promote Melampsora resistance in Populus. Tree Physiology, 37(5), 665–675. https://doi.org/10.1093/treephys/tpx008 Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., & Charpentier, E. (2012). A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity. Science, 337(6096), 816–821. https://doi.org/10.1126/science.1225829 Jones, D. L., Leroy, P., Unoson, C., Fange, D., Ćurić, V., Lawson, M. J., & Elf, J. (2017). Kinetics of dCas9 target search in Escherichia coli. Science, 357(6358), 1420–1424. Jones, J. D. G., & Dangl, J. L. (2006). The plant immune system. Nature, 444(7117), 323–330. https://doi.org/10.1038/nature05286 Judelson, H. S., & Ah-Fong, A. M. V. (2009). Progress and Challenges in Oomycete Transformation. In K. Lamour & S. Kamoun (Eds.), Oomycete Genetics and Genomics (pp. 435–453). John Wiley & Sons, Inc. https://doi.org/10.1002/9780470475898.ch22 Judelson, H. S., Coffey, M. D., Arredondo, F. R., & Tyler, B. M. (1993). Transformation of the oomycete pathogen Phytophthora megasperma f. Sp. Glycieea occurs by DNA integration into single or multiple chromosomes. Current Genetics, 23, 211–218. Judelson, H. S., & Michelmore, R. W. (1991). Transient expression of genes in the oomycete Phytophthora infestans using Bremia lactucae regulatory sequences. Current Genetics, 19(6), 453–459. https://doi.org/10.1007/BF00312736 Judelson, H. S., Tyler, B. M., & Michelmore, R. W. (1991). Transformation of the Oomycete Pathogen, Phytophthora infestans. Molecular Plant-Microbe Interactions, 4(6), 602–607. Jung, T., Blaschke, H., & Neumann, P. (1996). Isolation, identification and pathogenicity of Phytophthora species from declining oak stands. European Journal of Forest Pathology, 26(5), 253–272. https://doi.org/10.1111/j.1439-0329.1996.tb00846.x Jung, T., Orlikowski, L., Henricot, B., Abad-Campos, P., Aday, A. G., Aguín Casal, O., Bakonyi, J., Cacciola, S. O., Cech, T., Chavarriaga, D., Corcobado, T., Cravador, A., Decourcelle, T., Denton, G., Diamandis, S., Doğmuş-Lehtijärvi, H. T., Franceschini, A., Ginetti, B., Green, S., … Peréz-Sierra, A. (2016). Widespread Phytophthora infestations in European nurseries put forest, semi-natural and horticultural ecosystems at high risk of Phytophthora diseases. Forest Pathology, 46(2), 134–163. https://doi.org/10.1111/efp.12239 Jung, T., Pérez-Sierra, A., Durán, A., Horta Jung, M., Balci, Y., & Scanu, B. (2018). Canker and decline diseases caused by soil- and airborne Phytophthora species in forests and woodlands. Persoonia : Molecular Phylogeny and Evolution of Fungi, 40, 182–220. https://doi.org/10.3767/persoonia.2018.40.08 Kabbage, M., Yarden, O., & Dickman, M. B. (2015). Pathogenic attributes of Sclerotinia sclerotiorum: Switching from a biotrophic to necrotrophic lifestyle. Plant Science, 233, 53–60. https://doi.org/10.1016/j.plantsci.2014.12.018 Kamoun, S. (2003). Molecular Genetics of Pathogenic Oomycetes. Eukaryotic Cell, 2(2), 191–199. https://doi.org/10.1128/EC.2.2.191-199.2003 Kanchiswamy, C. N. (2016). DNA-free genome editing methods for targeted crop improvement. Plant Cell Reports, 35(7), 1469–1474. https://doi.org/10.1007/s00299-016-1982-2 164 Kanchiswamy, C. N., Malnoy, M., Velasco, R., Kim, J.-S., & Viola, R. (2015). Non-GMO genetically edited crop plants. Trends in Biotechnology, 33(9), 489–491. https://doi.org/10.1016/j.tibtech.2015.04.002 Kassambara, A., & Mundt, F. (2020). factoextra: Extract and Visualize the Results of Multivariate Data Analyses (R package version 1.0.7) [Computer software]. https://CRAN.R-project.org/package=factoextra Katoh, K., Misawa, K., Kuma, K., & Miyata, T. (2002). MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Research, 30(14), 3059–3066. https://doi.org/10.1093/nar/gkf436 Kerff, F., Amoroso, A., Herman, R., Sauvage, E., Petrella, S., Filée, P., Charlier, P., Joris, B., Tabuchi, A., Nikolaidis, N., & Cosgrove, D. J. (2008). Crystal structure and activity of Bacillus subtilis YoaJ (EXLX1), a bacterial expansin that promotes root colonization. Proceedings of the National Academy of Sciences, 105(44), 16876–16881. https://doi.org/10.1073/pnas.0809382105 Keriö, S., Daniels, H. A., Gómez-Gallego, M., Tabima, J. F., Lenz, R. R., Søndreli, K. L., Grünwald, N. J., Williams, N., Mcdougal, R., & LeBoldus, J. M. (2020). From genomes to forest management – tackling invasive Phytophthora species in the era of genomics. Canadian Journal of Plant Pathology, 42(1), 1–29. https://doi.org/10.1080/07060661.2019.1626910 Khoshraftar, S., Hung, S., Khan, S., Gong, Y., Tyagi, V., Parkinson, J., Sain, M., Moses, A. M., & Christendat, D. (2013). Sequencing and annotation of the Ophiostoma ulmi genome. BMC Genomics, 14(1), 162. https://doi.org/10.1186/1471-2164-14-162 Kim, C. S., Lubowski, R. N., Lewandrowski, J., & Eiswerth, M. E. (2006). Prevention or Control: Optimal Government Policies for Invasive Species Management. Agricultural and Resource Economics Review, 35(1), 29–40. Kim, S., Kim, D., Cho, S. W., Kim, J., & Kim, J.-S. (2014). Highly efficient RNA-guided genome editing in human cells via delivery of purified Cas9 ribonucleoproteins. Genome Research, 24(6), 1012–1019. https://doi.org/10.1101/gr.171322.113 Kimberley, M. O., Hood, I. A., & Knowles, R. L. (2011). Impact of Swiss Needle-Cast on Growth of Douglas-Fir. Phytopathology®, 101(5), 583–593. https://doi.org/10.1094/PHYTO-05-10-0129 King, B. C., Waxman, K. D., Nenni, N. V., Walker, L. P., Bergstrom, G. C., & Gibson, D. M. (2011). Arsenal of plant cell wall degrading enzymes reflects host preference among plant pathogenic fungi. Biotechnology for Biofuels, 4(1), 4. https://doi.org/10.1186/1754-6834-4-4 King, R., Urban, M., Lauder, R. P., Hawkins, N., Evans, M., Plummer, A., Halsey, K., Lovegrove, A., Hammond-Kosack, K., & Rudd, J. J. (2017). A conserved fungal glycosyltransferase facilitates pathogenesis of plants by enabling hyphal growth on solid surfaces. PLOS Pathogens, 13(10), e1006672. https://doi.org/10.1371/journal.ppat.1006672 Kingma, D. P., & Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv Preprint, arXiv:1412.6980 [cs]. Kinloch, B. B., Sniezko, R. A., & Dupper, G. E. (2003). Origin and Distribution of Cr2, a Gene for Resistance to White Pine Blister Rust in Natural Populations of Western White Pine. Phytopathology, 93(6), 691–694. https://doi.org/10.1094/PHYTO.2003.93.6.691 165 Kleinstiver, B. P., Prew, M. S., Tsai, S. Q., Topkar, V. V., Nguyen, N. T., Zheng, Z., Gonzales, A. P. W., Li, Z., Peterson, R. T., Yeh, J.-R. J., Aryee, M. J., & Joung, J. K. (2015). Engineered CRISPR-Cas9 nucleases with altered PAM specificities. Nature, 523(7561), Article 7561. https://doi.org/10.1038/nature14592 Klosterman, S. J., Rollins, J. R., Sudarshana, M. R., & Vinatzer, B. A. (2016). Disease Management in the Genomics Era—Summaries of Focus Issue Papers. Phytopathology®, 106(10), 1068–1070. https://doi.org/10.1094/PHYTO-07-16-0276-FI Klosterman, S. J., Subbarao, K. V., Kang, S., Veronese, P., Gold, S. E., Thomma, B. P. H. J., Chen, Z., Henrissat, B., Lee, Y.-H., Park, J., Garcia-Pedrajas, M. D., Barbara, D. J., Anchieta, A., de Jonge, R., Santhanam, P., Maruthachalam, K., Atallah, Z., Amyotte, S. G., Paz, Z., … Ma, L.-J. (2011). Comparative Genomics Yields Insights into Niche Adaptation of Plant Vascular Wilt Pathogens. PLoS Pathogens, 7(7), e1002137. https://doi.org/10.1371/journal.ppat.1002137 Klutts, J. S., Yoneda, A., Reilly, M. C., Bose, I., & Doering, T. L. (2006). Glycosyltransferases and their products: Cryptococcal variations on fungal themes. FEMS Yeast Research, 6(4), 499–512. https://doi.org/10.1111/j.1567-1364.2006.00054.x Knapp, D. G., Németh, J. B., Barry, K., Hainaut, M., Johnson, J., Kuo, A., Hui, J., Lim, P., Lipzen, A., Nolan, M., Ohm, R. A., Tamás, L., Grigoriev, I. V, Spatafora, J. W., & Nagy, L. G. (2018). Comparative genomics provides insights into the lifestyle and reveals functional heterogeneity of dark septate endophytic fungi. Scientific Reports, 8, 6321. https://doi.org/10.1038/s41598-018-24686-4 Knogge, W. (1996). Fungal Infection of Plants. The Plant Cell, 8(10), 1711–1722. Koeck, M., Hardham, A. R., & Dodds, P. N. (2011). The role of effectors of biotrophic and hemibiotrophic fungi in infection. Cellular Microbiology, 13(12), 1849–1857. https://doi.org/10.1111/j.1462-5822.2011.01665.x Kohler, A., Kuo, A., Nagy, L. G., Morin, E., Barry, K. W., Buscot, F., Canbäck, B., Choi, C., Cichocki, N., Clum, A., Colpaert, J., Copeland, A., Costa, M. D., Doré, J., Floudas, D., Gay, G., Girlanda, M., Henrissat, B., Herrmann, S., … Martin, F. (2015). Convergent losses of decay mechanisms and rapid turnover of symbiosis genes in mycorrhizal mutualists. Nature Genetics, 47(4), 410–415. https://doi.org/10.1038/ng.3223 Koonin, E. V., Makarova, K. S., & Zhang, F. (2017). Diversity, classification and evolution of CRISPR-Cas systems. Current Opinion in Microbiology, 37, 67–78. https://doi.org/10.1016/j.mib.2017.05.008 Kosicki, M., Tomberg, K., & Bradley, A. (2018). Repair of double-strand breaks induced by CRISPR–Cas9 leads to large deletions and complex rearrangements. Nature Biotechnology, 36(8), Article 8. https://doi.org/10.1038/nbt.4192 Krishnan, P., Ma, X., McDonald, B. A., & Brunner, P. C. (2018). Widespread signatures of selection for secreted peptidases in a fungal plant pathogen. BMC Evolutionary Biology, 18(1), 7. https://doi.org/10.1186/s12862-018-1123-3 Kronmiller, B. A., Feau, N., Shen, D., Tabima, J. F., Ali, S. S., Armitage, A. D., Arredondo, F., Bailey, B. A., Bollmann, S. R., Dale, A., Harrison, R. J., Hrywkiw, K., Kasuga, T., McDougal, R., Nellist, C. F., Panda, P., Tripathy, S., Williams, N. M., Ye, W., … Grünwald, N. J. (2023). Comparative Genomic Analysis of 31 Phytophthora Genomes Reveals Genome Plasticity and Horizontal Gene Transfer. Molecular Plant-Microbe Interactions®, 36(1), 26–46. https://doi.org/10.1094/MPMI-06-22-0133-R 166 Kubicek, C. P., Starr, T. L., & Glass, N. L. (2014). Plant Cell Wall–Degrading Enzymes and Their Secretion in Plant-Pathogenic Fungi. Annual Review of Phytopathology, 52(1), 427–451. https://doi.org/10.1146/annurev-phyto-102313-045831 Kuhn, M. (2020). caret: Classification and Regression Training (R package version 6.0-86) [Computer software]. https://CRAN.R-project.org/package=caret Kuo, H.-C., Hui, S., Choi, J., Asiegbu, F. O., Valkonen, J. P. T., & Lee, Y.-H. (2015). Secret lifestyles of Neurospora crassa. Scientific Reports, 4(1), 5135. https://doi.org/10.1038/srep05135 La Mantia, J., Klápště, J., El-Kassaby, Y. A., Azam, S., Guy, R. D., Douglas, C. J., Mansfield, S. D., & Hamelin, R. (2013). Association Analysis Identifies Melampsora ×columbiana Poplar Leaf Rust Resistance SNPs. PLoS ONE, 8(11), e78423. https://doi.org/10.1371/journal.pone.0078423 Laine, M. J., Haapalainen, M., Wahlroos, T., Kankare, K., Nissinen, R., Kassuwi, S., & Metzler, M. C. (2000). The cellulase encoded by the native plasmid of Clavibacter michiganensis ssp. Sepedonicus plays a role in virulence and contains an expansin-like domain. Physiological and Molecular Plant Pathology, 57(5), 221–233. https://doi.org/10.1006/pmpp.2000.0301 Lamour, K. H., Mudge, J., Gobena, D., Hurtado-Gonzales, O. P., Schmutz, J., Kuo, A., Miller, N. A., Rice, B. J., Raffaele, S., Cano, L. M., Bharti, A. K., Donahoo, R. S., Finley, S., Huitema, E., Hulvey, J., Platt, D., Salamov, A., Savidor, A., Sharma, R., … Kingsmore, S. F. (2012). Genome Sequencing and Mapping Reveal Loss of Heterozygosity as a Mechanism for Rapid Adaptation in the Vegetable Pathogen Phytophthora capsici. Molecular Plant-Microbe Interactions, 25(10), 1350–1360. https://doi.org/10.1094/MPMI-02-12-0028-R Langner, T., Kamoun, S., & Belhaj, K. (2018). CRISPR Crops: Plant Genome Editing Toward Disease Resistance. Annual Review of Phytopathology, 56(1), 479–512. https://doi.org/10.1146/annurev-phyto-080417-050158 Lapin, D., & Van den Ackerveken, G. (2013). Susceptibility to plant disease: More than a failure of host immunity. Trends in Plant Science, 18(10), 546–554. https://doi.org/10.1016/j.tplants.2013.05.005 Latham, J. R., Wilson, A. K., & Steinbrecher, R. A. (2006). The Mutational Consequences of Plant Transformation. Journal of Biomedicine and Biotechnology, 2006, 25376. https://doi.org/10.1155/JBB/2006/25376 Latijnhouwers, M., & Govers, F. (2003). A Phytophthora infestans G-Protein β Subunit Is Involved in Sporangium Formation. Eukaryotic Cell, 2(5), 971–977. https://doi.org/10.1128/ec.2.5.971-977.2003 Layne, E., Dort, E. N., Hamelin, R., Li, Y., & Blanchette, M. (2020). Supervised learning on phylogenetically distributed data. Bioinformatics, 36(Supplement_2), i895–i902. https://doi.org/10.1093/bioinformatics/btaa842 Lee, E. H., Beedlow, P. A., Waschmann, R. S., Burdick, C. A., & Shaw, D. C. (2013). Tree-ring analysis of the fungal disease Swiss needle cast in western Oregon coastal forests. Canadian Journal of Forest Research, 43(8), 677–690. https://doi.org/10.1139/cjfr-2013-0062 Lee, S.-J., & Rose, J. K. C. (2010). Mediation of the transition from biotrophy to necrotrophy in hemibiotrophic plant pathogens by secreted effector proteins. Plant Signaling & Behavior, 5(6), 769–772. https://doi.org/10.4161/psb.5.6.11778 167 Leisen, T., Bietz, F., Werner, J., Wegner, A., Schaffrath, U., Scheuring, D., Willmund, F., Mosbach, A., Scalliet, G., & Hahn, M. (2020). CRISPR/Cas with ribonucleoprotein complexes and transiently selected telomere vectors allows highly efficient marker-free and multiple genome editing in Botrytis cinerea. PLOS Pathogens, 16(8), e1008326. https://doi.org/10.1371/journal.ppat.1008326 Leung, B., Lodge, D. M., Finnoff, D., Shogren, J. F., Lewis, M. A., & Lamberti, G. (2002). An ounce of prevention or a pound of cure: Bioeconomic risk analysis of invasive species. Proceedings of the Royal Society of London. Series B: Biological Sciences, 269(1508), 2407–2413. https://doi.org/10.1098/rspb.2002.2179 Lewis, D. (1973). Concepts in Fungal Nutrition and the Origin of Biotrophy. Biological Reviews, 48(2), 261–277. https://doi.org/10.1111/j.1469-185x.1973.tb00982.x Li, J., Zhang, Y., Zhang, Y., Yu, P.-L., Pan, H., & Rollins, J. A. (2018). Introduction of Large Sequence Inserts by CRISPR-Cas9 To Create Pathogenicity Mutants in the Multinucleate Filamentous Pathogen Sclerotinia sclerotiorum. mBio, 9(3), e00567-18. https://doi.org/10.1128/mBio.00567-18 Li, X., Liu, Y., Tan, X., Li, D., Yang, X., Zhang, X., & Zhang, D. (2020). The high-affinity phosphodiesterase PcPdeH is involved in the polarized growth and pathogenicity of Phytophthora capsici. Fungal Biology, 124(3), 164–173. https://doi.org/10.1016/j.funbio.2020.01.006 Liang, Y., Han, Y., Wang, C., Jiang, C., & Xu, J.-R. (2018). Targeted Deletion of the USTA and UvSLT2 Genes Efficiently in Ustilaginoidea virens With the CRISPR-Cas9 System. Frontiers in Plant Science, 9, 699. https://doi.org/10.3389/fpls.2018.00699 Liang, Z., Zhang, K., Chen, K., & Gao, C. (2014). Targeted Mutagenesis in Zea mays Using TALENs and the CRISPR/Cas System. Journal of Genetics and Genomics, 41(2), 63–68. https://doi.org/10.1016/j.jgg.2013.12.001 Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), Article 6. https://doi.org/10.1038/nrg3920 Liebhold, A. M., Brockerhoff, E. G., Garrett, L. J., Parke, J. L., & Britton, K. O. (2012). Live plant imports: The major pathway for forest insect and pathogen invasions of the US. Frontiers in Ecology and the Environment, 10(3), 135–143. https://doi.org/10.1890/110198 Liebhold, A. M., Brockerhoff, E. G., Kalisz, S., Nuñez, M. A., Wardle, D. A., & Wingfield, M. J. (2017). Biological invasions in forest ecosystems. Biological Invasions, 19(11), 3437–3458. https://doi.org/10.1007/s10530-017-1458-5 Lilja, A., Rikala, R., Hietala, A., & Heinonen, R. (1996). Stem lesions on Betula pendula seedlings in Finnish forest nurseries and the pathogenicity of Phytophthora cactorum. European Journal of Forest Pathology, 26(2), 89–96. https://doi.org/10.1111/j.1439-0329.1996.tb00713.x Liu, G., Zhang, Y., & Zhang, T. (2020). Computational approaches for effective CRISPR guide RNA design and evaluation. Computational and Structural Biotechnology Journal, 18, 35–44. https://doi.org/10.1016/j.csbj.2019.11.006 Liu, R., Chen, L., Jiang, Y., Zhou, Z., & Zou, G. (2015). Efficient genome editing in filamentous fungus Trichoderma reesei using the CRISPR/Cas9 system. Cell Discovery, 1(1), Article 1. https://doi.org/10.1038/celldisc.2015.7 Lo Presti, L., Lanver, D., Schweizer, G., Tanaka, S., Liang, L., Tollot, M., Zuccaro, A., Reissmann, S., & Kahmann, R. (2015). Fungal Effectors and Plant Susceptibility. Annual 168 Review of Plant Biology, 66(1), 513–545. https://doi.org/10.1146/annurev-arplant-043014-114623 Lodge, D. M., Williams, S., MacIsaac, H. J., Hayes, K. R., Leung, B., Reichard, S., Mack, R. N., Moyle, P. B., Smith, M., Andow, D. A., Carlton, J. T., & McMichael, A. (2006). Biological Invasions: Recommendations for U.S. Policy and Management. Ecological Applications, 16(6), 2035–2054. https://doi.org/10.1890/1051-0761(2006)016[2035:BIRFUP]2.0.CO;2 Loo, J. A. (2009). Ecological impacts of non-indigenous invasive fungi as forest pathogens. Biological Invasions, 11(1), 81–96. https://doi.org/10.1007/s10530-008-9321-3 Lovett, G. M., Weiss, M., Liebhold, A. M., Holmes, T. P., Leung, B., Lambert, K. F., Orwig, D. A., Campbell, F. T., Rosenthal, J., McCullough, D. G., Wildova, R., Ayres, M. P., Canham, C. D., Foster, D. R., LaDeau, S. L., & Weldy, T. (2016). Nonnative forest insects and pathogens in the United States: Impacts and policy options. Ecological Applications, 26(5), 1437–1455. https://doi.org/10.1890/15-1176 Macheleidt, J., Mattern, D. J., Fischer, J., Netzker, T., Weber, J., Schroeckh, V., Valiante, V., & Brakhage, A. A. (2016). Regulation and Role of Fungal Secondary Metabolites. Annual Review of Genetics, 50(1), 371–392. https://doi.org/10.1146/annurev-genet-120215-035203 Majeed, M., Soliman, H., Kumar, G., El-Matbouli, M., & Saleh, M. (2018). Editing the genome of Aphanomyces invadans using CRISPR/Cas9. Parasites & Vectors, 11(1), 554. https://doi.org/10.1186/s13071-018-3134-8 Makarova, K. S., Haft, D. H., Barrangou, R., Brouns, S. J. J., Charpentier, E., Horvath, P., Moineau, S., Mojica, F. J. M., Wolf, Y. I., Yakunin, A. F., van der Oost, J., & Koonin, E. V. (2011). Evolution and classification of the CRISPR–Cas systems. Nature Reviews Microbiology, 9(6), Article 6. https://doi.org/10.1038/nrmicro2577 Makarova, K. S., Wolf, Y. I., Alkhnbashi, O. S., Costa, F., Shah, S. A., Saunders, S. J., Barrangou, R., Brouns, S. J. J., Charpentier, E., Haft, D. H., Horvath, P., Moineau, S., Mojica, F. J. M., Terns, R. M., Terns, M. P., White, M. F., Yakunin, A. F., Garrett, R. A., van der Oost, J., … Koonin, E. V. (2015). An updated evolutionary classification of CRISPR–Cas systems. Nature Reviews Microbiology, 13(11), Article 11. https://doi.org/10.1038/nrmicro3569 Makarova, S. S., Khromov, A. V., Spechenkova, N. A., Taliansky, M. E., & Kalinina, N. O. (2018). Application of the CRISPR/Cas System for Generation of Pathogen-Resistant Plants. Biochemistry (Moscow), 83(12), 1552–1562. https://doi.org/10.1134/S0006297918120131 Malnoy, M., Viola, R., Jung, M.-H., Koo, O.-J., Kim, S., Kim, J.-S., Velasco, R., & Nagamangala Kanchiswamy, C. (2016). DNA-Free Genetically Edited Grapevine and Apple Protoplast Using CRISPR/Cas9 Ribonucleoproteins. Frontiers in Plant Science, 7, Article 1904. https://doi.org/10.3389/fpls.2016.01904 Manavathu, E. K., Suryanarayana, K., Hasnain, S. E., & Leung, W.-C. (1988). DNA-mediated Transformation in the Aquatic Filamentous Fungus Achlya ambisexualis. Microbiology, 134(7), 2019–2028. https://doi.org/10.1099/00221287-134-7-2019 Markus, B. M., Bell, G. W., Lorenzi, H. A., & Lourido, S. (2019). Optimizing Systems for Cas9 Expression in Toxoplasma gondii. mSphere, 4(3), 10.1128/msphere.00386-19. https://doi.org/10.1128/msphere.00386-19 169 Marraffini, L. A., & Sontheimer, E. J. (2010). CRISPR interference: RNA-directed adaptive immunity in bacteria and archaea. Nature Reviews Genetics, 11(3), Article 3. https://doi.org/10.1038/nrg2749 Martinez, D., Berka, R. M., Henrissat, B., Saloheimo, M., Arvas, M., Baker, S. E., Chapman, J., Chertkov, O., Coutinho, P. M., Cullen, D., Danchin, E. G. J., Grigoriev, I. V., Harris, P., Jackson, M., Kubicek, C. P., Han, C. S., Ho, I., Larrondo, L. F., de Leon, A. L., … Brettin, T. S. (2008). Genome sequencing and analysis of the biomass-degrading fungus Trichoderma reesei (syn. Hypocrea jecorina). Nature Biotechnology, 26(5), Article 5. https://doi.org/10.1038/nbt1403 Matsu-ura, T., Baek, M., Kwon, J., & Hong, C. (2015). Efficient gene editing in Neurospora crassa with CRISPR technology. Fungal Biology and Biotechnology, 2(1), 4. https://doi.org/10.1186/s40694-015-0015-1 McCarren, K. L., McComb, J. A., Shearer, B., O’Brien, P., & Hardy, G. S. (2005). Transformation of Phytophthora cinnamomi with the green Fluorescent Protein (GFP) for studies on survival. 15th Australasian Plant Pathology Society Conference. McCarthy, H. M., Tarallo, M., Mesarich, C. H., McDougal, R. L., & Bradshaw, R. E. (2022). Targeted Gene Mutations in the Forest Pathogen Dothistroma septosporum Using CRISPR/Cas9. Plants, 11(8), Article 8. https://doi.org/10.3390/plants11081016 McGowan, J., & Fitzpatrick, D. A. (2020). Chapter Five—Recent advances in oomycete genomics. In D. Kumar (Ed.), Advances in Genetics (Vol. 105, pp. 175–228). Academic Press. https://doi.org/10.1016/bs.adgen.2020.03.001 McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference (SCIPY 2010), 56–61. https://doi.org/10.25080/Majora-92bf1922-00a Mcleod, A., Fry, B. A., Zuluaga, A. P., Myers, K. L., & Fry, W. E. (2008). Toward Improvements of Oomycete Transformation Protocols. Journal of Eukaryotic Microbiology, 55(2), 103–109. https://doi.org/10.1111/j.1550-7408.2008.00304.x McTaggart, A. R., van der Nest, M. A., Steenkamp, E. T., Roux, J., Slippers, B., Shuey, L. S., Wingfield, M. J., & Drenth, A. (2016). Fungal Genomics Challenges the Dogma of Name-Based Biosecurity. PLOS Pathogens, 12(5), e1005475. https://doi.org/10.1371/journal.ppat.1005475 Miao, J., Cai, M., Dong, X., Liu, L., Lin, D., Zhang, C., Pang, Z., & Liu, X. (2016). Resistance Assessment for Oxathiapiprolin in Phytophthora capsici and the Detection of a Point Mutation (G769W) in PcORP1 that Confers Resistance. Frontiers in Microbiology, 7, 615. https://doi.org/10.3389/fmicb.2016.00615 Miao, J., Chi, Y., Lin, D., Tyler, B. M., & Liu, X. (2018). Mutations in ORP1 Conferring Oxathiapiprolin Resistance Confirmed by Genome Editing using CRISPR/Cas9 in Phytophthora capsici and P. sojae. Phytopathology, 108(12), 1412–1419. https://doi.org/10.1094/PHYTO-01-18-0010-R Miao, J., Guo, D., Zhang, J., Huang, Q., Qin, G., Zhang, X., Wan, J., Gu, H., & Qu, L.-J. (2013). Targeted mutagenesis in rice using CRISPR-Cas system. Cell Research, 23(10), Article 10. https://doi.org/10.1038/cr.2013.123 Miao, J., Li, X., Lin, D., Liu, X., & Tyler, B. M. (2018). Oxysterol-binding protein-related protein 2 is not essential for Phytophthora sojae based on CRISPR/Cas9 deletions. Environmental Microbiology Reports, 10(3), 293–298. https://doi.org/10.1111/1758-2229.12638 170 Miao, J., Liu, X., Li, G., Du, X., & Liu, X. (2020). Multiple point mutations in PsORP1 gene conferring different resistance levels to oxathiapiprolin confirmed using CRISPR–Cas9 in Phytophthora sojae. Pest Management Science, 76(7), 2434–2440. https://doi.org/10.1002/ps.5784 Miller, J. C., Tan, S., Qiao, G., Barlow, K. A., Wang, J., Xia, D. F., Meng, X., Paschon, D. E., Leung, E., Hinkley, S. J., Dulay, G. P., Hua, K. L., Ankoudinova, I., Cost, G. J., Urnov, F. D., Zhang, H. S., Holmes, M. C., Zhang, L., Gregory, P. D., & Rebar, E. J. (2011). A TALE nuclease architecture for efficient genome editing. Nature Biotechnology, 29(2), Article 2. https://doi.org/10.1038/nbt.1755 Miyauchi, S., Kiss, E., Kuo, A., Drula, E., Kohler, A., Sánchez-García, M., Morin, E., Andreopoulos, B., Barry, K. W., Bonito, G., Buée, M., Carver, A., Chen, C., Cichocki, N., Clum, A., Culley, D., Crous, P. W., Fauchery, L., Girlanda, M., … Martin, F. M. (2020). Large-scale genome sequencing of mycorrhizal fungi provides insights into the early evolution of symbiotic traits. Nature Communications, 11(1), Article 1. https://doi.org/10.1038/s41467-020-18795-w Möller, M., & Stukenbrock, E. H. (2017). Evolution and genome architecture in fungal plant pathogens. Nature Reviews Microbiology, 15(12), 756–771. https://doi.org/10.1038/nrmicro.2017.76 Moreno-Mateos, M. A., Vejnar, C. E., Beaudoin, J.-D., Fernandez, J. P., Mis, E. K., Khokha, M. K., & Giraldez, A. J. (2015). CRISPRscan: Designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nature Methods, 12(10), Article 10. https://doi.org/10.1038/nmeth.3543 Morgens, D. W., Wainberg, M., Boyle, E. A., Ursu, O., Araya, C. L., Tsui, C. K., Haney, M. S., Hess, G. T., Han, K., Jeng, E. E., Li, A., Snyder, M. P., Greenleaf, W. J., Kundaje, A., & Bassik, M. C. (2017). Genome-scale measurement of off-target activity using Cas9 toxicity in high-throughput screens. Nature Communications, 8(1), Article 1. https://doi.org/10.1038/ncomms15178 Mort-Bontemps, M., & Fèvre, M. (1997). Transformation of the Oomycete Saprolegnia monoïca to hygromycin-B resistance. Current Genetics, 31(3), 272–275. https://doi.org/10.1007/s002940050205 Muchero, W., Sondreli, K. L., Chen, J.-G., Urbanowicz, B. R., Zhang, J., Singan, V., Yang, Y., Brueggeman, R. S., Franco-Coronado, J., Abraham, N., Yang, J.-Y., Moremen, K. W., Weisberg, A. J., Chang, J. H., Lindquist, E., Barry, K., Ranjan, P., Jawdy, S., Schmutz, J., … LeBoldus, J. M. (2018). Association mapping, transcriptomics, and transient expression identify candidate genes mediating plant–pathogen interactions in a tree. Proceedings of the National Academy of Sciences, 115(45), 11573–11578. https://doi.org/10.1073/pnas.1804428115 Muhr, M., Paulat, M., Awwanah, M., Brinkkötter, M., & Teichmann, T. (2018). CRISPR/Cas9-mediated knockout of Populus BRANCHED1 and BRANCHED2 orthologs reveals a major function in bud outgrowth control. Tree Physiology, 38(10), 1588–1597. https://doi.org/10.1093/treephys/tpy088 Muñoz, I. V., Sarrocco, S., Malfatti, L., Baroncelli, R., & Vannacci, G. (2019). CRISPR-Cas for Fungal Genome Editing: A New Tool for the Management of Plant Diseases. Frontiers in Plant Science, 10, Article 135. https://doi.org/10.3389/fpls.2019.00135 171 Muszewska, A., Stepniewska-Dziubinska, M. M., Steczkiewicz, K., Pawlowska, J., Dziedzic, A., & Ginalski, K. (2017). Fungal lifestyle reflected in serine protease repertoire. Scientific Reports, 7(1), 9147. https://doi.org/10.1038/s41598-017-09644-w Nadal, M., García-Pedrajas, M. D., & Gold, S. E. (2008). Dimorphism in fungal plant pathogens. FEMS Microbiology Letters, 284(2), 127–134. https://doi.org/10.1111/j.1574-6968.2008.01173.x Naruzawa, E. S., & Bernier, L. (2014). Control of yeast-mycelium dimorphism in vitro in Dutch elm disease fungi by manipulation of specific external stimuli. Fungal Biology, 118(11), 872–884. https://doi.org/10.1016/j.funbio.2014.07.006 NCBI Resource Coordinators. (2016). Database resources of the National Center for Biotechnology Information. Nucleic Acids Research, 44(D1), D7–D19. https://doi.org/10.1093/nar/gkv1290 Neale, D. B., McGuire, P. E., Wheeler, N. C., Stevens, K. A., Crepeau, M. W., Cardeno, C., Zimin, A. V., Puiu, D., Pertea, G. M., Sezen, U. U., Casola, C., Koralewski, T. E., Paul, R., Gonzalez-Ibeas, D., Zaman, S., Cronn, R., Yandell, M., Holt, C., Langley, C. H., … Wegrzyn, J. L. (2017). The Douglas-Fir Genome Sequence Reveals Specialization of the Photosynthetic Apparatus in Pinaceae. G3: Genes, Genomes, Genetics, 7(9), 3157–3167. https://doi.org/10.1534/g3.117.300078 Nekrasov, V., Staskawicz, B., Weigel, D., Jones, J. D. G., & Kamoun, S. (2013). Targeted mutagenesis in the model plant Nicotiana benthamiana using Cas9 RNA-guided endonuclease. Nature Biotechnology, 31(8), 691–693. https://doi.org/10.1038/nbt.2655 Nekrasov, V., Wang, C., Win, J., Lanz, C., Weigel, D., & Kamoun, S. (2017). Rapid generation of a transgene-free powdery mildew resistant tomato by genome deletion. Scientific Reports, 7(1), Article 1. https://doi.org/10.1038/s41598-017-00578-x Newton, A. C., Fitt, B. D. L., Atkins, S. D., Walters, D. R., & Daniell, T. J. (2010). Pathogenesis, parasitism and mutualism in the trophic space of microbe–plant interactions. Trends in Microbiology, 18(8), 365–373. https://doi.org/10.1016/j.tim.2010.06.002 Nienhaus, F. (1960). Das Wirtsspektrum von Phytophthora cactorum (Leb. Et Cohn) Schroet. Journal of Phytopathology, 38(1), 33–68. https://doi.org/10.1111/j.1439-0434.1960.tb01893.x Nigg, M., & Bernier, L. (2016). From yeast to hypha: Defining transcriptomic signatures of the morphological switch in the dimorphic fungal pathogen Ophiostoma novo-ulmi. BMC Genomics, 17(1), 920. https://doi.org/10.1186/s12864-016-3251-8 Nigg, M., Laroche, J., Landry, C. R., & Bernier, L. (2015). RNAseq Analysis Highlights Specific Transcriptome Signatures of Yeast and Mycelial Growth Phases in the Dutch Elm Disease Fungus Ophiostoma novo-ulmi. G3: Genes, Genomes, Genetics, 5(11), 2487–2495. https://doi.org/10.1534/g3.115.021022 Nishida, K., Arazoe, T., Yachie, N., Banno, S., Kakimoto, M., Tabata, M., Mochizuki, M., Miyabe, A., Araki, M., Hara, K. Y., Shimatani, Z., & Kondo, A. (2016). Targeted nucleotide editing using hybrid prokaryotic and vertebrate adaptive immune systems. Science, 353(6305), aaf8729. https://doi.org/10.1126/science.aaf8729 Nishitani, C., Hirai, N., Komori, S., Wada, M., Okada, K., Osakabe, K., Yamamoto, T., & Osakabe, Y. (2016). Efficient Genome Editing in Apple Using a CRISPR/Cas9 system. Scientific Reports, 6(1), Article 1. https://doi.org/10.1038/srep31481 172 Nødvig, C. S., Nielsen, J. B., Kogle, M. E., & Mortensen, U. H. (2015). A CRISPR-Cas9 System for Genetic Engineering of Filamentous Fungi. PLoS ONE, 10(7), e0133085. https://doi.org/10.1371/journal.pone.0133085 Norrander, J., Kempe, T., & Messing, J. (1983). Construction of improved M13 vectors using oligodeoxynucleotide-directed mutagenesis. Gene, 26(1), 101–106. https://doi.org/10.1016/0378-1119(83)90040-9 Nystedt, B., Street, N. R., Wetterbom, A., Zuccolo, A., Lin, Y.-C., Scofield, D. G., Vezzi, F., Delhomme, N., Giacomello, S., Alexeyenko, A., Vicedomini, R., Sahlin, K., Sherwood, E., Elfstrand, M., Gramzow, L., Holmberg, K., Hällman, J., Keech, O., Klasson, L., … Jansson, S. (2013). The Norway spruce genome sequence and conifer genome evolution. Nature, 497(7451), Article 7451. https://doi.org/10.1038/nature12211 Ohm, R. A., Feau, N., Henrissat, B., Schoch, C. L., Horwitz, B. A., Barry, K. W., Condon, B. J., Copeland, A. C., Dhillon, B., Glaser, F., Hesse, C. N., Kosti, I., LaButti, K., Lindquist, E. A., Lucas, S., Salamov, A. A., Bradshaw, R. E., Ciuffetti, L., Hamelin, R. C., … Grigoriev, I. V. (2012). Diverse Lifestyles and Strategies of Plant Pathogenesis Encoded in the Genomes of Eighteen Dothideomycetes Fungi. PLoS Pathogens, 8(12), e1003037. https://doi.org/10.1371/journal.ppat.1003037 O’Leary, N. A., Wright, M. W., Brister, J. R., Ciufo, S., Haddad, D., McVeigh, R., Rajput, B., Robbertse, B., Smith-White, B., Ako-Adjei, D., Astashyn, A., Badretdin, A., Bao, Y., Blinkova, O., Brover, V., Chetvernin, V., Choi, J., Cox, E., Ermolaeva, O., … Pruitt, K. D. (2016). Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. Nucleic Acids Research, 44(D1), D733-745. https://doi.org/10.1093/nar/gkv1189 Oliver, R. P., & Ipcho, S. V. S. (2004). Arabidopsis pathology breathes new life into the necrotrophs-vs. -Biotrophs classification of fungal pathogens. Molecular Plant Pathology, 5(4), 347–352. https://doi.org/10.1111/J.1364-3703.2004.00228.X Orlikowski, L. B., Ptaszek, M., Rodziewicz, A., Nechwatal, J., Thinggaard, K., & Jung, T. (2011). Phytophthora root and collar rot of mature Fraxinus excelsior in forest stands in Poland and Denmark. Forest Pathology, 41(6), 510–519. https://doi.org/10.1111/j.1439-0329.2011.00714.x Pandaranayaka, E. P., Frenkel, O., Elad, Y., Prusky, D., & Harel, A. (2019). Network analysis exposes core functions in major lifestyles of fungal and oomycete plant pathogens. BMC Genomics, 20(1), 1020. https://doi.org/10.1186/s12864-019-6409-3 Parrent, J., James, T. Y., Vasaitis, R., & Taylor, A. F. (2009). Friend or foe? Evolutionary history of glycoside hydrolase family 32 genes encoding for sucrolytic activity in fungi and its implications for plant-fungal symbioses. BMC Evolutionary Biology, 9(1), 148. https://doi.org/10.1186/1471-2148-9-148 Paseka, R. E., White, L. A., Waal, D. B. V. de, Strauss, A. T., González, A. L., Everett, R. A., Peace, A., Seabloom, E. W., Frenken, T., & Borer, E. T. (2020). Disease-mediated ecosystem services: Pathogens, plants, and people. Trends in Ecology & Evolution, 35(8), 731–743. https://doi.org/10.1016/j.tree.2020.04.003 Pasteris, R. J., Hanagan, M. A., Bisaha, J. J., Finkelstein, B. L., Hoffman, L. E., Gregory, V., Andreassi, J. L., Sweigard, J. A., Klyashchitsky, B. A., Henry, Y. T., & Berger, R. A. (2016). Discovery of oxathiapiprolin, a new oomycete fungicide that targets an oxysterol binding protein. Bioorganic & Medicinal Chemistry, 24(3), 354–361. https://doi.org/10.1016/j.bmc.2015.07.064 173 Pavan, S., Jacobsen, E., Visser, R. G. F., & Bai, Y. (2009). Loss of susceptibility as a novel breeding strategy for durable and broad-spectrum resistance. Molecular Breeding, 25(1), 1. https://doi.org/10.1007/s11032-009-9323-6 Paysan-Lafosse, T., Blum, M., Chuguransky, S., Grego, T., Pinto, B. L., Salazar, G. A., Bileschi, M. L., Bork, P., Bridge, A., Colwell, L., Gough, J., Haft, D. H., Letunić, I., Marchler-Bauer, A., Mi, H., Natale, D. A., Orengo, C. A., Pandurangan, A. P., Rivoire, C., … Bateman, A. (2023). InterPro in 2022. Nucleic Acids Research, 51(D1), D418–D427. https://doi.org/10.1093/nar/gkac993 Peña, L., & Séguin, A. (2001). Recent advances in the genetic transformation of trees. Trends in Biotechnology, 19(12), 500–506. https://doi.org/10.1016/S0167-7799(01)01815-7 Peng, A., Chen, S., Lei, T., Xu, L., He, Y., Wu, L., Yao, L., & Zou, X. (2017). Engineering canker-resistant plants through CRISPR/Cas9-targeted editing of the susceptibility gene CsLOB1 promoter in citrus. Plant Biotechnology Journal, 15(12), 1509–1519. https://doi.org/10.1111/pbi.12733 Peng, D., Kurup, S. P., Yao, P. Y., Minning, T. A., & Tarleton, R. L. (2014). CRISPR-Cas9-Mediated Single-Gene and Gene Family Disruption in Trypanosoma cruzi. mBio, 6(1), 10.1128/mbio.02097-14. https://doi.org/10.1128/mbio.02097-14 Peng, D., & Tarleton, R. (2015). EuPaGDT: A web tool tailored to design CRISPR guide RNAs for eukaryotic pathogens. Microbial Genomics, 1(4). https://doi.org/10.1099/mgen.0.000033 Pennisi, E. (2010). Armed and Dangerous. Science, 327(5967), 804–805. https://doi.org/10.1126/science.327.5967.804 Perfect, S. E., & Green, J. R. (2001). Infection structures of biotrophic and hemibiotrophic fungal plant pathogens. Molecular Plant Pathology, 2(2), 101–108. https://doi.org/10.1046/j.1364-3703.2001.00055.x Perfect, S. E., O’Connell, R. J., Green, E. F., Doering-Saad, C., & Green, J. R. (1998). Expression cloning of a fungal proline-rich glycoprotein specific to the biotrophic interface formed in the Colletotrichum–bean interaction. The Plant Journal, 15(2), 273–279. https://doi.org/10.1046/j.1365-313X.1998.00196.x Philibert, A., Desprez-Loustau, M.-L., Fabre, B., Frey, P., Halkett, F., Husson, C., Lung-Escarmant, B., Marçais, B., Robin, C., Vacher, C., & Makowski, D. (2011). Predicting invasion success of forest pathogenic fungi from species traits. Journal of Applied Ecology, 48(6), 1381–1390. https://doi.org/10.1111/j.1365-2664.2011.02039.x Pimentel, D., McNair, S., Janecka, J., Wightman, J., Simmonds, C., O’Connell, C., Wong, E., Russel, L., Zern, J., Aquino, T., & Tsomondo, T. (2001). Economic and environmental threats of alien plant, animal, and microbe invasions. Agriculture, Ecosystems & Environment, 84(1), 1–20. https://doi.org/10.1016/S0167-8809(00)00178-X Plummer, K. M., Clark, S. J., Ellis, L. M., Loganathan, A., Al-Samarrai, T. H., Rikkerink, E. H. A., Sullivan, P. A., Templeton, M. D., & Farley, P. C. (2004). Analysis of a Secreted Aspartic Peptidase Disruption Mutant of Glomerella cingulata. European Journal of Plant Pathology, 110(3), 265–274. https://doi.org/10.1023/B:EJPP.0000019796.78598.8c Podger, F. D., Doepel, R. F., & Zentmyer, G. A. (1965). Association of Phytophthora cinnamomi with a disease of Eucalyptus marginata forest in Western Australia. Plant Disease Reporter, 49(11), 943–947. 174 Pohl, C., Kiel, J. A. K. W., Driessen, A. J. M., Bovenberg, R. A. L., & Nygård, Y. (2016). CRISPR/Cas9 Based Genome Editing of Penicillium chrysogenum. ACS Synthetic Biology, 5(7), 754–764. https://doi.org/10.1021/acssynbio.6b00082 Pourcel, C., Salvignol, G., & Vergnaud, G. (2005). CRISPR elements in Yersinia pestis acquire new repeats by preferential uptake of bacteriophage DNA, and provide additional tools for evolutionary studies. Microbiology, 151(3), 653–663. https://doi.org/10.1099/mic.0.27437-0 Price, M. N., Dehal, P. S., & Arkin, A. P. (2009). FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix. Molecular Biology and Evolution, 26(7), 1641–1650. https://doi.org/10.1093/molbev/msp077 Pyott, D. E., Sheehan, E., & Molnar, A. (2016). Engineering of CRISPR/Cas9-mediated potyvirus resistance in transgene-free Arabidopsis plants. Molecular Plant Pathology, 17(8), 1276–1288. https://doi.org/10.1111/mpp.12417 R Core Team. (2017). R: A language and environment for statistical computing [R]. R Foundation for Statistical Computing. https://www.R-project.org/ Ranjha, L., Howard, S. M., & Cejka, P. (2018). Main steps in DNA double-strand break repair: An introduction to homologous recombination and related processes. Chromosoma, 127(2), 187–214. https://doi.org/10.1007/s00412-017-0658-1 Rejmánek, M., & Pitcairn, M. J. (2002). When is eradication of exotic pest plants a realistic goal? Turning the Tide: The Eradication of Invasive Species, 249–253. Reuter, J. S., & Mathews, D. H. (2010). RNAstructure: Software for RNA secondary structure prediction and analysis. BMC Bioinformatics, 11(1), Article 1. https://doi.org/10.1186/1471-2105-11-129 Richards, T. A., Dacks, J. B., Jenkinson, J. M., Thornton, C. R., & Talbot, N. J. (2006). Evolution of filamentous plant pathogens: Gene exchange across eukaryotic kingdoms. Current Biology, 16(18), 1857–1864. https://doi.org/10.1016/j.cub.2006.07.052 Richards, T. A., Soanes, D. M., Jones, M. D. M., Vasieva, O., Leonard, G., Paszkiewicz, K., Foster, P. G., Hall, N., & Talbot, N. J. (2011). Horizontal gene transfer facilitated the evolution of plant parasitic mechanisms in the oomycetes. Proceedings of the National Academy of Sciences of the United States of America, 108(37), 15258–15263. https://doi.org/10.1073/pnas.1105100108 Richards, W. C. (1994). Nonsporulation in the Dutch elm disease fungus Ophiostoma ulmi: Evidence for control by a single nuclear gene. Canadian Journal of Botany, 72(4), 461–467. https://doi.org/10.1139/b94-061 Riedel, M., Calmin, G., Belbahri, L., Lefort, F., Götz, M., Wagner, S., & Werres, S. (2009). Green Fluorescent Protein (GFP) as a Reporter Gene for the Plant Pathogenic Oomycete Phytophthora ramorum. Journal of Eukaryotic Microbiology, 56(2), 130–135. https://doi.org/10.1111/j.1550-7408.2008.00376.x Rigling, D., & Prospero, S. (2018). Cryphonectria parasitica, the causal agent of chestnut blight: Invasion history, population biology and disease control. Molecular Plant Pathology, 19(1), 7–20. https://doi.org/10.1111/mpp.12542 Ristaino, J. B., & Gumpertz, M. L. (2000). New Frontiers in the Study of Dispersal and Spatial Analysis of Epidemics Caused by Species in the Genus Phytophthora. Annual Review of Phytopathology, 38(1), 541–576. 175 Rizzo, D. M., & Garbelotto, M. (2003). Sudden oak death: Endangering California and Oregon forest ecosystems. Frontiers in Ecology and the Environment, 1(4), 197–204. https://doi.org/10.1890/1540-9295(2003)001[0197:SODECA]2.0.CO;2 Rizzo, D. M., Garbelotto, M., Davidson, J. M., Slaughter, G. W., & Koike, S. T. (2002a). Phytophthora ramorum as the Cause of Extensive Mortality of Quercus spp. And Lithocarpus densiflorus in California. Plant Disease, 86(3), 205–214. https://doi.org/10.1094/PDIS.2002.86.3.205 Rizzo, D. M., Garbelotto, M., Davidson, J. M., Slaughter, W., & Koike, S. T. (2002b). Phytophthora ramorum and Sudden Oak Death in California: I. Host Relationships. Proceedings of the Fifth Symposium on Oak Woodlands: Oaks in California’s Challenging Landscape, 733–740. https://www.fs.usda.gov/treesearch/pubs/26169 Rock, J. M., Hopkins, F. F., Chavez, A., Diallo, M., Chase, M. R., Gerrick, E. R., Pritchard, J. R., Church, G. M., Rubin, E. J., Sassetti, C. M., Schnappinger, D., & Fortune, S. M. (2017). Programmable transcriptional repression in mycobacteria using an orthogonal CRISPR interference platform. Nature Microbiology, 2(4), Article 4. https://doi.org/10.1038/nmicrobiol.2016.274 Roe, A. D., Torson, A. S., Bilodeau, G., Bilodeau, P., Blackburn, G. S., Cui, M., Cusson, M., Doucet, D., Griess, V. C., Lafond, V., Paradis, G., Porth, I., Prunier, J., Srivastava, V., Tremblay, E., Uzunovic, A., Yemshanov, D., & Hamelin, R. C. (2019). Biosurveillance of forest insects: Part I—integration and application of genomic tools to the surveillance of non-native forest insects. Journal of Pest Science, 92(1), 51–70. https://doi.org/10.1007/s10340-018-1027-4 Roy, A., Jayaprakash, A., T, R. R., Annamalai, A., & Lakshmi, P. T. V. (2020). Genome-wide annotation, comparison and functional genomics of carbohydrate-active enzymes in legumes infecting Fusarium oxysporum formae speciales. Mycology, 11(1), 56–70. https://doi.org/10.1080/21501203.2019.1706656 Roy, B. A., Alexander, H. M., Davidson, J., Campbell, F. T., Burdon, J. J., Sniezko, R., & Brasier, C. (2014). Increasing forest loss worldwide from invasive pests requires new trade regulations. Frontiers in Ecology and the Environment, 12(8), 457–465. https://doi.org/10.1890/130240 Roy, K. R., Smith, J. D., Vonesch, S. C., Lin, G., Tu, C. S., Lederer, A. R., Chu, A., Suresh, S., Nguyen, M., Horecka, J., Tripathi, A., Burnett, W. T., Morgan, M. A., Schulz, J., Orsley, K. M., Wei, W., Aiyar, R. S., Davis, R. W., Bankaitis, V. A., … Steinmetz, L. M. (2018). Multiplexed precision genome editing with trackable genomic barcodes in yeast. Nature Biotechnology, 36(6), Article 6. https://doi.org/10.1038/nbt.4137 Rytkönen, A., Lilja, A., Petäistö, R.-L., & Hantula, J. (2008). Irrigation water and Phytophthora cactorum in a forest nursery. Scandinavian Journal of Forest Research, 23(5), 404–411. https://doi.org/10.1080/02827580802419034 Safari, S., Baratloo, A., Elfil, M., & Negida, A. (2016). Evidence Based Emergency Medicine; Part 5 Receiver Operating Curve and Area under the Curve. Emergency, 4(2), 111–113. Savory, F., Leonard, G., & Richards, T. A. (2015). The Role of Horizontal Gene Transfer in the Evolution of the Oomycetes. PLOS Pathogens, 11(5), e1004805. https://doi.org/10.1371/journal.ppat.1004805 Sbaraini, N., Andreis, F. C., Thompson, C. E., Guedes, R. L. M., Junges, Â., Campos, T., Staats, C. C., Vainstein, M. H., Ribeiro de Vasconcelos, A. T., & Schrank, A. (2017). Genome-Wide Analysis of Secondary Metabolite Gene Clusters in Ophiostoma ulmi and 176 Ophiostoma novo-ulmi Reveals a Fujikurin-Like Gene Cluster with a Putative Role in Infection. Frontiers in Microbiology, 8, Article 1063. https://doi.org/10.3389/fmicb.2017.01063 Schuster, M., & Kahmann, R. (2019). CRISPR-Cas9 genome editing approaches in filamentous fungi and oomycetes. Fungal Genetics and Biology, 130, 43–53. https://doi.org/10.1016/j.fgb.2019.04.016 Schuster, M., Schweizer, G., Reissmann, S., & Kahmann, R. (2016). Genome editing in Ustilago maydis using the CRISPR–Cas system. Fungal Genetics and Biology, 89, 3–9. https://doi.org/10.1016/j.fgb.2015.09.001 Seabold, S., & Perktold, J. (2010). Statsmodels: Econometric and Statistical Modeling with Python. Proceedings of the 9th Python in Science Conference (SCIPY 2010), 92–96. https://doi.org/10.25080/Majora-92bf1922-011 Segretin, M. E., Pais, M., Franceschetti, M., Chaparro-Garcia, A., Bos, J. I. B., Banfield, M. J., & Kamoun, S. (2014). Single Amino Acid Mutations in the Potato Immune Receptor R3a Expand Response to Phytophthora Effectors. Molecular Plant-Microbe Interactions®, 27(7), 624–637. https://doi.org/10.1094/MPMI-02-14-0040-R Serrano, M. S., Romero, M. Á., Homet, P., & Gómez-Aparicio, L. (2022). Climate change impact on the population dynamics of exotic pathogens: The case of the worldwide pathogen Phytophthora cinnamomi. Agricultural and Forest Meteorology, 322, 109002. https://doi.org/10.1016/j.agrformet.2022.109002 Shen, Y., Li, Y., Xu, D., Yang, C., Li, C., & Luo, K. (2018). Molecular cloning and characterization of a brassinosteriod biosynthesis-related gene PtoDWF4 from Populus tomentosa. Tree Physiology, 38(9), 1424–1436. https://doi.org/10.1093/treephys/tpy027 Shmakov, S., Abudayyeh, O. O., Makarova, K. S., Wolf, Y. I., Gootenberg, J. S., Semenova, E., Minakhin, L., Joung, J., Konermann, S., Severinov, K., Zhang, F., & Koonin, E. V. (2015). Discovery and Functional Characterization of Diverse Class 2 CRISPR-Cas Systems. Molecular Cell, 60(3), 385–397. https://doi.org/10.1016/j.molcel.2015.10.008 Si-Ammour, A., Mauch-Mani, B., & Mauch, F. (2003). Quantification of induced resistance against Phytophthora species expressing GFP as a vital marker: β-aminobutyric acid but not BTH protects potato and Arabidopsis from infection. Molecular Plant Pathology, 4(4), 237–248. https://doi.org/10.1046/j.1364-3703.2003.00168.x Simberloff, D., Martin, J.-L., Genovesi, P., Maris, V., Wardle, D. A., Aronson, J., Courchamp, F., Galil, B., García-Berthou, E., Pascal, M., Pyšek, P., Sousa, R., Tabacchi, E., & Vilà, M. (2013). Impacts of biological invasions: What’s what and the way forward. Trends in Ecology & Evolution, 28(1), 58–66. https://doi.org/10.1016/j.tree.2012.07.013 Singh, R. P., Hodson, D. P., Jin, Y., Lagudah, E. S., Ayliffe, M. A., Bhavani, S., Rouse, M. N., Pretorius, Z. A., Szabo, L. J., Huerta-Espino, J., Basnet, B. R., Lan, C., & Hovmøller, M. S. (2015). Emergence and Spread of New Races of Wheat Stem Rust Fungus: Continued Threat to Food Security and Prospects of Genetic Control. Phytopathology®, 105(7), 872–884. https://doi.org/10.1094/PHYTO-01-15-0030-FI Sniezko, R. A., Smith, J., Liu, J.-J., & Hamelin, R. C. (2014). Genetic Resistance to Fusiform Rust in Southern Pines and White Pine Blister Rust in White Pines—A Contrasting Tale of Two Rust Pathosystems—Current Status and Future Prospects. Forests, 5(9), Article 9. https://doi.org/10.3390/f5092050 177 Stahl, E. A., & Bishop, J. G. (2000). Plant–pathogen arms races at the molecular level. Current Opinion in Plant Biology, 3(4), 299–304. https://doi.org/10.1016/S1369-5266(00)00083-2 Standage-Beier, K., Brookhouser, N., Balachandran, P., Zhang, Q., Brafman, D. A., & Wang, X. (2019). RNA-Guided Recombinase-Cas9 Fusion Targets Genomic DNA Deletion and Integration. The CRISPR Journal, 2(4), 209–222. https://doi.org/10.1089/crispr.2019.0013 Steinegger, M., & Söding, J. (2017). MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology, 35(11), Article 11. https://doi.org/10.1038/nbt.3988 Stone, J. K., Coop, L. B., & Manter, D. K. (2008). Predicting effects of climate change on Swiss needle cast disease severity in Pacific Northwest forests. Canadian Journal of Plant Pathology, 30(2), 169–176. https://doi.org/10.1080/07060661.2008.10540533 Strauss, S. H., Costanza, A., & Séguin, A. (2015). Genetically engineered trees: Paralysis from good intentions. Science, 349(6250), 794–795. https://doi.org/10.1126/science.aab0493 Strauss, S. H., Ma, C., Ault, K., & Klocko, A. L. (2016). Lessons from Two Decades of Field Trials with Genetically Modified Trees in the USA: Biology and Regulatory Compliance. In C. Vettori, F. Gallardo, H. Häggman, V. Kazana, F. Migliacci, G. Pilate, & M. Fladung (Eds.), Biosafety of Forest Transgenic Trees: Improving the Scientific Basis for Safe Tree Development and Implementation of EU Policy Directives (pp. 101–124). Springer Netherlands. https://doi.org/10.1007/978-94-017-7531-1_5 Strauss, S. H., Tan, H., Boerjan, W., & Sedjo, R. (2009). Strangled at birth? Forest biotech and the Convention on Biological Diversity. Nature Biotechnology, 27(6), Article 6. https://doi.org/10.1038/nbt0609-519 Stukenbrock, E., & Gurr, S. (2023). Address the growing urgency of fungal disease in crops. Nature, 617(7959), 31–34. https://doi.org/10.1038/d41586-023-01465-4 Sweigard, J., Andreassi, J., Pember, S., Gutteridge, S., Pasteris, R., Hanagan, M., Carroll, A., Sopa, J., & Nesnow, D. (2014). Discovery of the target site of oxathiapiprolin (DuPont Zorvec disease control). Abstracts of Papers, 13th IUPAC Congress of Pesticide Chemistry, AGRO 911, 233. Tanguay, P. (2019). CRISPR/Cas9 gene editing of the Dutch elm disease pathogen Ophiostoma novo-ulmi. Canadian Journal of Plant Pathology, 41(1), 163–163. https://doi.org/10.1080/0706066.12019.1519163 Taylor, G. (2002). Populus: Arabidopsis for Forestry. Do We Need a Model Tree? Annals of Botany, 90(6), 681–689. https://doi.org/10.1093/aob/mcf255 Taylor, J. W., & Berbee, M. L. (2006). Dating divergences in the Fungal Tree of Life: Review and new analyses. Mycologia, 98(6), 838–849. https://doi.org/10.1080/15572536.2006.11832614 Terns, M. P., & Terns, R. M. (2011). CRISPR-based adaptive immune systems. Current Opinion in Microbiology, 14(3), 321–327. https://doi.org/10.1016/j.mib.2011.03.005 Terpilowski, M. A. (2019). scikit-posthocs: Pairwise multiple comparison tests in Python. Journal of Open Source Software, 4(36), 1169. https://doi.org/10.21105/joss.01169 Tetlow, I. J., & Farrar, J. F. (1992). Sucrose-metabolizing enzymes from leaves of barley infected with brown rust (Puccinia hordei Otth.). New Phytologist, 120(4), 475–480. https://doi.org/10.1111/j.1469-8137.1992.tb01795.x 178 The pandas development team. (2020). pandas-dev/pandas: Pandas (2.1.3) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.3509134 The Royal Botanic Gardens Kew, Landcare Research-NZ, & Institute of Microbiology. (2020). Index Fungorum. http://www.indexfungorum.org/ Thrower, L. B. (1966). Terminology for Plant Parasites. Journal of Phytopathology, 56(3), 258–259. https://doi.org/10.1111/j.1439-0434.1966.tb02261.x Thyme, S. B., Akhmetova, L., Montague, T. G., Valen, E., & Schier, A. F. (2016). Internal guide RNA interactions interfere with Cas9-mediated cleavage. Nature Communications, 7(1), Article 1. https://doi.org/10.1038/ncomms11750 Toruño, T. Y., Stergiopoulos, I., & Coaker, G. (2016). Plant-Pathogen Effectors: Cellular Probes Interfering with Plant Defenses in Spatial and Temporal Manners. Annual Review of Phytopathology, 54(1), 419–441. https://doi.org/10.1146/annurev-phyto-080615-100204 Trumbore, S., Brando, P., & Hartmann, H. (2015). Forest health and global change. Science, 349(6250), 814–818. https://doi.org/10.1126/science.aac6759 Tsai, C.-J., & Xue, L.-J. (2015). CRISPRing into the woods. GM Crops & Food, 6(4), 206–215. https://doi.org/10.1080/21645698.2015.1091553 Tuskan, G. A., DiFazio, S., Jansson, S., Bohlmann, J., Grigoriev, I., Hellsten, U., Putnam, N., Ralph, S., Rombauts, S., Salamov, A., Schein, J., Sterck, L., Aerts, A., Bhalerao, R. R., Bhalerao, R. P., Blaudez, D., Boerjan, W., Brun, A., Brunner, A., … Rokhsar, D. (2006). The Genome of Black Cottonwood, Populus trichocarpa (Torr. & Gray). Science, 313(5793), 1596–1604. https://doi.org/10.1126/science.1128691 Tyler, B. M., Tripathy, S., Zhang, X., Dehal, P., Jiang, R. H. Y., Aerts, A., Arredondo, F. D., Baxter, L., Bensasson, D., Beynon, J. L., Chapman, J., Damasceno, C. M. B., Dorrance, A. E., Dou, D., Dickerman, A. W., Dubchak, I. L., Garbelotto, M., Gijzen, M., Gordon, S. G., … Boore, J. L. (2006). Phytophthora Genome Sequences Uncover Evolutionary Origins and Mechanisms of Pathogenesis. Science, 313(5791), 1261–1266. https://doi.org/10.1126/science.1128796 Van der Nest, M. A., Steenkamp, E. T., McTaggart, A. R., Trollip, C., Godlonton, T., Sauerman, E., Roodt, D., Naidoo, K., Coetzee, M. P. A., Wilken, P. M., Wingfield, M. J., & Wingfield, B. D. (2015). Saprophytic and pathogenic fungi in the Ceratocystidaceae differ in their ability to metabolize plant-derived sucrose. BMC Evolutionary Biology, 15(1), 273. https://doi.org/10.1186/s12862-015-0550-7 van der Oost, J., Jore, M. M., Westra, E. R., Lundgren, M., & Brouns, S. J. J. (2009). CRISPR-based adaptive and heritable immunity in prokaryotes. Trends in Biochemical Sciences, 34(8), 401–407. https://doi.org/10.1016/j.tibs.2009.05.002 van West, P., Reid, B., Campbell, T. A., Sandrock, R. W., Fry, W. E., Kamoun, S., & Gow, N. A. R. (1999). Green fluorescent protein (GFP) as a reporter gene for the plant pathogenic oomycete Phytophthora palmivora. FEMS Microbiology Letters, 178(1), 71–80. https://doi.org/10.1111/j.1574-6968.1999.tb13761.x Venette, R. C. (2009). Implication of global climate change on the distribution and activity of Phytophthora ramorum. 20th U.S. Department of Agriculture Interagency Research Forum on Invasive Species, 58–59. Venette, R. C., & Cohen, S. D. (2006). Potential climatic suitability for establishment of Phytophthora ramorum within the contiguous United States. Forest Ecology and Management, 231(1), 18–26. https://doi.org/10.1016/j.foreco.2006.04.036 179 Vettraino, A. M., Zikeli, F., Scarascia Mugnozza, G., Vinciguerra, V., Tabet, D., & Romagnoli, M. (2022). Lignin nanoparticles containing essential oils for controlling Phytophthora cactorum diseases. Forest Pathology, 52(2), e12739. https://doi.org/10.1111/efp.12739 Vijn, I., & Govers, F. (2003). Agrobacterium tumefaciens mediated transformation of the oomycete plant pathogen Phytophthora infestans. Molecular Plant Pathology, 4(6), 459–467. https://doi.org/10.1046/j.1364-3703.2003.00191.x Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., … van Mulbregt, P. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17(3), Article 3. https://doi.org/10.1038/s41592-019-0686-2 Vitousek, P. M., D’Antonio, C. M., Loope, L. L., Rejmánek, M., & Westbrooks, R. (1997). Introduced Species: A Significant Component of Human-Caused Global Change. New Zealand Journal of Ecology, 21(1), 1–16. Voegele, R. T., Wirsel, S., Möll, U., Lechner, M., & Mendgen, K. (2006). Cloning and Characterization of a Novel Invertase from the Obligate Biotroph Uromyces fabae and Analysis of Expression Patterns of Host and Pathogen Invertases in the Course of Infection. Molecular Plant-Microbe Interactions®, 19(6), 625–634. https://doi.org/10.1094/MPMI-19-0625 Wan, S., Li, C., Ma, X., & Luo, K. (2017). PtrMYB57 contributes to the negative regulation of anthocyanin and proanthocyanidin biosynthesis in poplar. Plant Cell Reports, 36(8), 1263–1276. https://doi.org/10.1007/s00299-017-2151-y Wang, F., Wang, C., Liu, P., Lei, C., Hao, W., Gao, Y., Liu, Y.-G., & Zhao, K. (2016). Enhanced Rice Blast Resistance by CRISPR/Cas9-Targeted Mutagenesis of the ERF Transcription Factor Gene OsERF922. PLoS ONE, 11(4), e0154027. https://doi.org/10.1371/journal.pone.0154027 Wang, L., Ran, L., Hou, Y., Tian, Q., Li, C., Liu, R., Fan, D., & Luo, K. (2017). The transcription factor MYB115 contributes to the regulation of proanthocyanidin biosynthesis and enhances fungal resistance in poplar. New Phytologist, 215(1), 351–367. https://doi.org/10.1111/nph.14569 Wang, Q., Cobine, P. A., & Coleman, J. J. (2018). Efficient genome editing in Fusarium oxysporum based on CRISPR/Cas9 ribonucleoprotein complexes. Fungal Genetics and Biology, 117, 21–29. https://doi.org/10.1016/j.fgb.2018.05.003 Wang, T., Wei, J. J., Sabatini, D. M., & Lander, E. S. (2014). Genetic Screens in Human Cells Using the CRISPR-Cas9 System. Science, 343(6166), 80–84. https://doi.org/10.1126/science.1246981 Wang, W., Xue, Z., Miao, J., Cai, M., Zhang, C., Li, T., Zhang, B., Tyler, B. M., & Liu, X. (2019). PcMuORP1, an Oxathiapiprolin-Resistance Gene, Functions as a Novel Selection Marker for Phytophthora Transformation and CRISPR/Cas9 Mediated Genome Editing. Frontiers in Microbiology, 10, 2402. https://doi.org/10.3389/fmicb.2019.02402 Wang, X., Le, N., Denoth-Lippuner, A., Barral, Y., & Kroschewski, R. (2016). Asymmetric partitioning of transfected DNA during mammalian cell division. Proceedings of the National Academy of Sciences of the United States of America, 113(26), 7177–7182. https://doi.org/10.1073/pnas.1606091113 Wang, Y., Cheng, X., Shan, Q., Zhang, Y., Liu, J., Gao, C., & Qiu, J.-L. (2014). Simultaneous editing of three homoeoalleles in hexaploid bread wheat confers heritable resistance to 180 powdery mildew. Nature Biotechnology, 32(9), Article 9. https://doi.org/10.1038/nbt.2969 Wang, Z., Ke, Q., Tao, K., Li, Q., Xia, Y., Bao, J., & Chen, Q. (2022). Activity and Point Mutation G699V in PcoORP1 Confer Resistance to Oxathiapiprolin in Phytophthora colocasiae Field Isolates. Journal of Agricultural and Food Chemistry, 70(44), 14140–14147. https://doi.org/10.1021/acs.jafc.2c06707 Wang, Z., Tyler, B. M., & Liu, X. (2018). Protocol of Phytophthora capsici Transformation Using the CRISPR-Cas9 System. In W. Ma & T. Wolpert (Eds.), Plant Pathogenic Fungi and Oomycetes: Methods and Protocols (pp. 265–274). Springer. https://doi.org/10.1007/978-1-4939-8724-5_17 Waskom, M. L. (2021). seaborn: Statistical data visualization. Journal of Open Source Software, 6(60), 3021. https://doi.org/10.21105/joss.03021 Webber, J. F., Mullett, M., & Brasier, C. M. (2010). Dieback and mortality of plantation Japanese larch (Larix kaempferi) associated with infection by Phytophthora ramorum. New Disease Reports, 22, 19. https://doi.org/10.5197/j.2044-0588.2010.022.019 Wedge, M.-È., Naruzawa, E. S., Nigg, M., & Bernier, L. (2016). Diversity in yeast-mycelium dimorphism response of the Dutch elm disease pathogens: The inoculum size effect. Canadian Journal of Microbiology, 62(6), 525–529. https://doi.org/10.1139/cjm-2015-0795 Weiland, J. E., Nelson, A. H., & Hudler, G. W. (2010). Aggressiveness of Phytophthora cactorum, P. citricola I, and P. plurivora from European Beech. Plant Disease, 94(8), 1009–1014. https://doi.org/10.1094/PDIS-94-8-1009 Weiland, J. J. (2003). Transformation of Pythium aphanidermatum to geneticin resistance. Current Genetics, 42(6), 344–352. https://doi.org/10.1007/s00294-002-0359-y Westphal, M. I., Browne, M., MacKinnon, K., & Noble, I. (2008). The link between international trade and the global distribution of invasive alien species. Biological Invasions, 10(4), 391–398. https://doi.org/10.1007/s10530-007-9138-5 Wheeler, D. L., Church, D. M., Federhen, S., Lash, A. E., Madden, T. L., Pontius, J. U., Schuler, G. D., Schriml, L. M., Sequeira, E., Tatusova, T. A., & Wagner, L. (2003). Database resources of the National Center for Biotechnology. Nucleic Acids Research, 31(1), 28–33. Wiedenheft, B., Sternberg, S. H., & Doudna, J. A. (2012). RNA-guided genetic silencing systems in bacteria and archaea. Nature, 482(7385), Article 7385. https://doi.org/10.1038/nature10886 Wingfield, M. J., Brockerhoff, E. G., Wingfield, B. D., & Slippers, B. (2015). Planted forest health: The need for a global strategy. Science, 349(6250), 832–836. https://doi.org/10.1126/science.aac6674 Woo, J. W., Kim, J., Kwon, S. I., Corvalán, C., Cho, S. W., Kim, H., Kim, S.-G., Kim, S.-T., Choe, S., & Kim, J.-S. (2015). DNA-free genome editing in plants with preassembled CRISPR-Cas9 ribonucleoproteins. Nature Biotechnology, 33(11), 1162–1164. https://doi.org/10.1038/nbt.3389 Wu, D., Navet, N., Liu, Y., Uchida, J., & Tian, M. (2016). Establishment of a simple and efficient Agrobacterium-mediated transformation system for Phytophthora palmivora. BMC Microbiology, 16(1), 204. https://doi.org/10.1186/s12866-016-0825-1 Wullschleger, S. D., Tuskan, G. A., & DiFazio, S. P. (2002). Genomics and the tree physiologist. Tree Physiology, 22(18), 1273–1276. https://doi.org/10.1093/treephys/22.18.1273 181 Xu, C., Fu, X., Liu, R., Guo, L., Ran, L., Li, C., Tian, Q., Jiao, B., Wang, B., & Luo, K. (2017). PtoMYB170 positively regulates lignin deposition during wood formation in poplar and confers drought tolerance in transgenic Arabidopsis. Tree Physiology, 37(12), 1713–1726. https://doi.org/10.1093/treephys/tpx093 Yang, L., Zhao, X., Ran, L., Li, C., Fan, D., & Luo, K. (2017). PtoMYB156 is involved in negative regulation of phenylpropanoid metabolism and secondary cell wall biosynthesis during wood formation in poplar. Scientific Reports, 7(1), Article 1. https://doi.org/10.1038/srep41209 Yang, M., Duan, S., Mei, X., Huang, H., Chen, W., Liu, Y., Guo, C., Yang, T., Wei, W., Liu, X., He, X., Dong, Y., & Zhu, S. (2018). The Phytophthora cactorum genome provides insights into the adaptation to host defense compounds and fungicides. Scientific Reports, 8, 6534. https://doi.org/10.1038/s41598-018-24939-2 Yang, X., Tyler, B. M., & Hong, C. (2017). An expanded phylogeny for the genus Phytophthora. IMA Fungus, 8(2), Article 2. https://doi.org/10.5598/imafungus.2017.08.02.09 Ye, J., Coulouris, G., Zaretskaya, I., Cutcutache, I., Rozen, S., & Madden, T. L. (2012). Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics, 13(1), Article 1. https://doi.org/10.1186/1471-2105-13-134 Yelton, M. M., Hamer, J. E., & Timberlake, W. E. (1984). Transformation of Aspergillus nidulans by using a trpC plasmid. Proceedings of the National Academy of Sciences of the United States of America, 81(5), 1470–1474. Yin, T.-M., DiFazio, S. P., Gunter, L. E., Jawdy, S. S., Boerjan, W., & Tuskan, G. A. (2004). Genetic and physical mapping of Melampsora rust resistance genes in Populus and characterization of linkage disequilibrium and flanking genomic sequence. New Phytologist, 164(1), 95–105. https://doi.org/10.1111/j.1469-8137.2004.01161.x Zhang, Q., Li, W., Yang, J., Xu, J., Meng, Y., & Shan, W. (2020). Two Phytophthora parasitica cysteine protease genes, PpCys44 and PpCys45, trigger cell death in various Nicotiana spp. And act as virulence factors. Molecular Plant Pathology, 21(4), 541–554. https://doi.org/10.1111/mpp.12915 Zhang, S., & Voigt, C. A. (2018). Engineered dCas9 with reduced toxicity in bacteria: Implications for genetic circuit design. Nucleic Acids Research, 46(20), 11115–11125. https://doi.org/10.1093/nar/gky884 Zhang, T., Zheng, Q., Yi, X., An, H., Zhao, Y., Ma, S., & Zhou, G. (2018). Establishing RNA virus resistance in plants by harnessing CRISPR immune system. Plant Biotechnology Journal, 16(8), 1415–1423. https://doi.org/10.1111/pbi.12881 Zhang, Y., Bai, Y., Wu, G., Zou, S., Chen, Y., Gao, C., & Tang, D. (2017). Simultaneous modification of three homoeologs of TaEDR 1 by genome editing enhances powdery mildew resistance in wheat. The Plant Journal, 91(4), 714–724. https://doi.org/10.1111/tpj.13599 Zhang, Y., Ge, X., Yang, F., Zhang, L., Zheng, J., Tan, X., Jin, Z.-B., Qu, J., & Gu, F. (2014). Comparison of non-canonical PAMs for CRISPR/Cas9-mediated DNA cleavage in human cells. Scientific Reports, 4(1), Article 1. https://doi.org/10.1038/srep05405 Zhang, Y., Liang, Z., Zong, Y., Wang, Y., Liu, J., Chen, K., Qiu, J.-L., & Gao, C. (2016). Efficient and transgene-free genome editing in wheat through transient expression of CRISPR/Cas9 DNA or RNA. Nature Communications, 7(1), Article 1. https://doi.org/10.1038/ncomms12617 182 Zhao, Z., Liu, H., Wang, C., & Xu, J.-R. (2013). Comparative analysis of fungal genomes reveals different plant cell wall degrading capacity in fungi. BMC Genomics, 14(1), 274. https://doi.org/10.1186/1471-2164-14-274 Zhou, X., Jacobs, T. B., Xue, L.-J., Harding, S. A., & Tsai, C.-J. (2015). Exploiting SNPs for biallelic CRISPR mutations in the outcrossing woody perennial Populus reveals 4-coumarate:CoA ligase specificity and redundancy. New Phytologist, 208(2), 298–301. https://doi.org/10.1111/nph.13470 Zuris, J. A., Thompson, D. B., Shu, Y., Guilinger, J. P., Bessen, J. L., Hu, J. H., Maeder, M. L., Joung, J. K., Chen, Z.-Y., & Liu, D. R. (2015). Cationic lipid-mediated delivery of proteins enables efficient protein-based genome editing in vitro and in vivo. Nature Biotechnology, 33(1), Article 1. https://doi.org/10.1038/nbt.3081 183 Appendices Appendix A Supplementary Methods and Results for Chapter 2 The materials provided in Appendix A directly reflect the Supplementary Materials provided with the original publication of Chapter 2: Dort et al. 2023 (Scientific Reports 13: 17203). The headings, table numbers, and figure numbers have been modified to reflect the formatting of this dissertation document. A.1 Results of statistical analyses run on DendroNet AUCs DendroNet’s ability to predict fungal lifestyles varied greatly depending on the lifestyle and genomic features included in the model, with a range in AUC scores from 0.204 ± 0.185 (endomycorrhizal fungi using SMCs+transporters+transfactors feature set) to 1.000 ± 0.000 (obligate biotrophs in 26 feature sets) (Data A.4.2). Statistical analysis with a Friedman test on the lifestyle prediction results showed a significant difference in DendroNet’s AUC scores depending on the genomic feature set used (χ² = 128.361, p = 3.932e-14). However, post-hoc analysis with a Nemenyi test revealed that these significant differences arose only between a few of the best- and worst-performing feature sets; the differences in DendroNet results between most of the 31 genomic feature sets were not statistically significant (Data A.4.3). Two consistent patterns emerged from the Nemenyi test: the first was that the SMCs alone produced significantly lower AUC scores from DendroNet when compared to the top eight feature sets, and the second was that these top eight feature sets all included the CAZyme annotation data (Data A.4.3). 184 DendroNet’s performance in predicting fungal phytopathogenic traits also varied greatly, with AUC scores ranging from 0.294 ± 0.015 (gymnosperm pathogens using SMCs+transfactors feature set) to 0.961 ± 0.004 (foliar pathogens using the CAZymes+MEROPS+SMCs feature set) (Data A.4.2). As with the lifestyles, statistical analysis with a Friedman test on the trait predictions showed a significant difference in AUC scores depending on the genomic feature set used (χ² = 59.584, p = 0.001), though the Nemenyi post-hoc test showed that these differences arose only between the poorest feature set (SMCs alone) and the top three feature sets (Data A.4.3). A.2 Supplementary tables The supplementary tables referred to in the main text of Chapter 2 are provided below. 185 Supplementary Table A.2.1 The list of 24 lifestyles to which fungal species were assigned in FunLifeDB based on a literature search, with the corresponding definitions. The lifestyles that were used for analyses in the current study are highlighted in yellow. Lifestyles (Abbreviation) Definition Acidophile (A) Fungi that can survive and grow optimally in highly acidic (pH < 3-4) environments. Alkaliphilic (AL) Fungi that can survive and grow optimally in highly alkaline (pH > 9) environments. Biotroph (B) Pathogenic fungi that grow, and often reproduce, within living host tissues. Black Yeast (BY) Fungi with highly melanized cell walls that can tolerate a wide range of extreme environments (polyextremotolerant). Brown Rot (BR) Wood-decaying fungi in the Basidiomycota that selectively degrade wood carbohydrates, but not lignin. Coprophilous (C) Saprobic fungi that grow and assimilate nutrients from animal dung. Ectomycorrhizae (ECM) Fungi that form a symbiotic association with plants by growing intercellularly in the roots. Endolichenic (EL) Fungi that grow asymptomatically within lichen thalli, akin to endophytes, but are not partners in the stable lichen symbiosis. Endomycorrhizae (ENM) Fungi that form a symbiotic association with plants by growing both inter- and intracellularly in the roots. Endophyte (E) Fungi that grow within plant tissues but are non-pathogenic. Epiphyte (EP) Fungi that grow outside or on plant tissues but are non-pathogenic. Hemibiotroph (HB) Pathogenic fungi that initially act as biotrophs within their plant host and then switch to a necrotrophic lifestyle. Lichenicolous (L) Fungi that grow obligately on lichens – can be pathogenic or saprotrophic. Lichenized (LF) Fungi that form a mutualistic symbiosis with algae as part of a lichen thallus. Mycoparasite (MP) Fungi that act as parasites of other fungi. Necrotroph (N) Pathogenic fungi that secrete toxins to kill their plant host and subsequently assimilate nutrients from the dead tissues. Obligate Biotroph (OB) Pathogenic fungi that require living host tissues to grow and reproduce – cannot survive outside their host. Opportunistic/Latent Pathogen (O) Fungi that only act as pathogens under certain environmental conditions, or towards hosts that have already been weakened by other biotic or abiotic agents. Orchid Mycorrhizal (ORM) Fungi that form mycorrhizal associations with orchid species. Parasitic (P) Fungi that grow in or on a living host; the fungus benefits from the association but there is a fitness cost for the host. Parasitic associations can range from weak to highly aggressive. Pyrophilous (PY) Fungi that produce fruiting bodies in post-fire environments. Saprotroph (S) Fungi that grow and feed on dead and decaying plant matter. Thermophile (T) Fungi that can survive and grow optimally in extremely high temperatures. White Rot (WR) Wood-decaying fungi in the Basidiomycota that degrade all wood components, including lignin. 186 Supplementary Table A.2.2 The ecological traits assigned to pathogenic fungi in FunLifeDB; host type was assigned to all pathogens whereas only plant pathogens received additional assignments for plant host information and infected/inoculated tissues. Ecological Traits Host Type Plant Fungus Vertebrate Invertebrate Plant Host Information Gymnosperms Angiosperms Infected/Diseased Tissues Main Stem Leaves Roots Supplementary Table A.2.3 The balance of genomes across pathogenic groups as well as the tested lifestyles and traits in both the original FunLifeDB and the reduced dataset of published genomes used for analyses. Number of Genomes in Original Database Number of Genomes in Reduced Database Pathogens 251 177 Non-pathogens 265 177 Pathogenicity unknown 66 33 Plant pathogens 196 138 Saprotrophs 369 241 Ectomycorrhizae 36 32 Endomycorrhizae 7 7 Necrotrophs 80 56 Hemibiotrophs 78 52 Facultative biotrophs 43 37 Obligate biotrophs 24 21 Gymnosperm pathogens 34 24 Angiosperm pathogens 181 128 Stem pathogens 131 99 Foliar pathogens 134 103 Root pathogens 64 40 Total species 533 355 Total genomes 582 387 (362 published, 8 genomes from R.H. as PI, 17 genomes from F.M. as PI) 187 A.3 Supplementary figures The supplementary figures referred to in the main text of Chapter 2 are provided below. Supplementary Figure A.3.1 Clustered heatmap of the CAZyme enzyme classes associated with DendroNet’s predictions of phytopathogenic lifestyles and traits. Also included is the ‘Total_CAZys’ feature, which is the total number of CAZyme genes annotated in each genome regardless of enzyme class. The values in the heatmap are the correlation directions for each CAZyme class input into DendroNet as a machine learning feature, which indicate whether an increase (positive correlation, red) or decrease (negative correlation, blue) in the number of genes in a CAZyme class was associated with a lifestyle or trait. Abbreviations: S, saprotrophs; ECM, ectomycorrhizal; ENM: endomycorrhizal; P, pathogens; PP, plant pathogens; N, necrotrophs; HB, hemibiotrophs; B, biotrophs; OB, obligate biotrophs; AP, angiosperm pathogens; GP, gymnosperm pathogens; FP, foliar pathogens; SP, stem pathogens; RP, root pathogens. 188 Supplementary Figure A.3.2 Clustered heatmap of the peptidase (MEROPS) clans and families associated with DendroNet’s predictions of phytopathogenic lifestyles and traits. Also included is the ‘Total_MEROPS’ feature, which is the total number of peptidase genes annotated in each genome regardless of clan or family. The values in the heatmap are the correlation directions for each MEROPS clan/family input into DendroNet as a machine learning feature, which indicate whether an increase (positive correlation, red) or decrease (negative correlation, blue) in the number of genes in a group was associated with a lifestyle or trait. Abbreviations: S, saprotrophs; ECM, ectomycorrhizal; ENM: endomycorrhizal; P, pathogens; PP, plant pathogens; N, necrotrophs; HB, hemibiotrophs; B, biotrophs; OB, obligate biotrophs; AP, angiosperm pathogens; GP, gymnosperm pathogens; FP, foliar pathogens; SP, stem pathogens; RP, root pathogens. 189 Supplementary Figure A.3.3 Clustered heatmap of the secondary metabolite cluster (SMC) classes associated with DendroNet’s predictions of phytopathogenic lifestyles and traits. Also included is the ‘Total_SMCs’ feature, which is the total number of SMC genes annotated in each genome regardless of class. The values in the heatmap are the correlation directions for each SMC class input into DendroNet as a machine learning feature, which indicate whether an increase (positive correlation, red) or decrease (negative correlation, blue) in the number of genes in a class was associated with a lifestyle or trait. Abbreviations: S, saprotrophs; ECM, ectomycorrhizal; ENM: endomycorrhizal; P, pathogens; PP, plant pathogens; N, necrotrophs; HB, hemibiotrophs; B, biotrophs; OB, obligate biotrophs; AP, angiosperm pathogens; GP, gymnosperm pathogens; FP, foliar pathogens; SP, stem pathogens; RP, root pathogens. 190 Supplementary Figure A.3.4 Clustered heatmap of the transporter superfamilies and families associated with DendroNet’s predictions of phytopathogenic lifestyles and traits. Also included is the ‘Total_Transporters’ feature, which is the total number of SMC genes annotated in each genome regardless of family. The values in the heatmap are the correlation directions for each transporter family input into DendroNet as a machine learning feature, which indicate whether an increase (positive correlation, red) or decrease (negative correlation, blue) in the number of genes in a family was associated with a lifestyle or trait. Abbreviations: S, saprotrophs; ECM, ectomycorrhizal; ENM: endomycorrhizal; P, pathogens; PP, plant pathogens; N, necrotrophs; HB, hemibiotrophs; B, biotrophs; OB, obligate biotrophs; AP, angiosperm pathogens; GP, gymnosperm pathogens; FP, foliar pathogens; SP, stem pathogens; RP, root pathogens. 191 Supplementary Figure A.3.5 Clustered heatmap of the transcription factor (TF) families associated with DendroNet’s predictions of phytopathogenic lifestyles and traits. Also included is the ‘Total_TransFactors’ feature, which is the total number of TF genes annotated in each genome regardless of family. The values in the heatmap are the correlation directions for each TF family input into DendroNet as a machine learning feature, which indicate whether an increase (positive correlation, red) or decrease (negative correlation, blue) in the number of genes in a family was associated with a lifestyle or trait. Abbreviations: S, saprotrophs; ECM, ectomycorrhizal; ENM: endomycorrhizal; P, pathogens; PP, plant pathogens; N, necrotrophs; HB, hemibiotrophs; B, biotrophs; OB, obligate biotrophs; AP, angiosperm pathogens; GP, gymnosperm pathogens; FP, foliar pathogens; SP, stem pathogens; RP, root pathogens. 192 A.4 Supplementary data files The supplementary data files referred to in the main text of Chapter 2 are provided below. Data A.4.1 Reduced version of FunLifeDB with the 387 genomes (355 species) used for analyses in this study. Also included are the raw annotation data extracted from MycoCosm for carbohydrate active enzymes, peptidases, secondary metabolite clusters, transporters, and transcription factors. These data were used as input for both the principal component analyses and the DendroNet machine learning analyses. Note: the data are provided in a separate Excel document. Data A.4.2 Area under the receiver operating characteristic curve (AUC) scores obtained with DendroNet for the parsimony models (phylogenetic placement only, no genome annotation data) and feature+parsimony models (includes phylogenetic placement and genome annotation data) from all 31 feature sets for all lifestyles and phytopathogenic traits tested. The mean and standard deviation of the AUCs from each feature set were calculated, and the results for the feature+parsimony models are listed from highest mean to lowest mean AUC. Note: the data are provided in a separate Excel document. Data A.4.3 Results from all statistical analyses on DendroNet AUCs for both the lifestyles and phytopathogenic traits. Note: the data are provided in a separate Excel document. Data A.4.4 DendroNet feature importances from the feature+parsimony models for all five genome annotation groups. The raw, fraction, and correlation values for the individual genomic features (gene families) that improved DendroNet’s predictive performance of a lifestyle or trait over the parsimony model are reported for each annotation. A fraction value greater than 1.0 for a feature indicates that a DendroNet model using only that individual feature produced better AUC scores than a model using a combination of all features from an annotation group. Note: the data are provided in a separate Excel document. 193 Data A.4.5 Correlation directions for all genomic features (gene families) included in the DendroNet feature+parsimony models, regardless of prediction performance relative to the parsimony model, for all five genome annotation groups. A positive correlation (+) indicates that a lifestyle or trait was associated with an increased number of genes relative to other lifestyles and traits, and a negative correlation (-) indicates that a lifestyle or trait was associated with a decreased number of genes. Note: the data are provided in a separate Excel document. Data A.4.6 A pruned, Newick format version of the phylogenetic tree downloaded from MycoCosm for input into DendroNet models. This reduced tree depicts the phylogenetic relationships between the 387 fungal genomes analysed in this study. Note: the tree is provided as a separate .txt file. 194 Appendix B Supplementary Methods and Results for Chapters 4 and 5 B.1 Recipes for Growth Media and Transformation Solutions used in Chapters 4 and 5 Growth Media 5X V8 Medium: Component Amount V8 juice 340 mL Calcium carbonate (CaCO3) 5 g Centrifuged at 3220 × g (~4000 rpm) for 15 minutes to clarify; supernatant retained. Not autoclaved. Stored in the fridge (~4°C) for up to three months. 1X (20%) V8 Broth/Agar (V8B/V8A): Component Amount 5X V8 medium 200 mL dH2O 800 mL Agar (for V8A) 15 g (1.5%) Autoclaved (40-minute liquid cycle) and stored in the fridge (~4°C) until used. Basic Pea Broth (PB): Component Amount Frozen sweet peas 120 g dH2O 800 mL Autoclaved (40-minute liquid cycle) and filtered through four layers of cheesecloth. Used as base for pea-mannitol and nutrient pea media (see recipes below). 195 Pea-0.5 M Mannitol (PM) & V8-0.5 M Mannitol (VM) Medium: Component Amount D-mannitol 91.1 g Calcium carbonate (CaCO3) 2 g Calcium chloride (CaCl2) 1 g Basic pea broth for PM -or- 1X V8 medium for VM 800 mL Centrifuged at 3220 × g (~4000 rpm) for 10 minutes to clarify; supernatant retained. Volume brought to 1000 mL with dH2O, and agar added (1% or 1.5%) for solid medium. Autoclaved (40-minute liquid cycle) and stored in the fridge (~4°C) until used. Nutrient Pea Broth/Agar (NPB/NPA) & Nutrient V8 Broth/Agar (NVB/NVA): Component Amount D-glucose 5.0 g D-mannitol 5.0 g D-sorbitol 5.0 g Potassium nitrate (KNO3) 3.0 g Yeast extract 2.0 g Calcium carbonate (CaCO3) 2.0 g Potassium dihydrogen phosphate (KH2PO4) 1.0 g Dipotassium hydrogen phosphate (K2HPO4) 1.0 g Magnesium sulphate (MgSO4) 0.5 g Calcium chloride (CaCl2) 0.1 g Basic pea broth for NPB -or- 1X V8 medium for NVB 800 mL Trace elements* 2.0 mL Vitamin stock* 2.0 mL Centrifuged at 3220 × g (~4000 rpm) for 10 minutes to clarify; supernatant retained. Volume brought to 1000 mL with dH2O, and agar added (1.5%) for solid medium. Autoclaved (40-minute liquid cycle), trace elements and vitamin stock added, and final NPB medium stored in the fridge (~4°C) until used. *Added after autoclaving – see recipes below 196 Trace Elements: Component Amount Iron (III) citrate trihydrate (FeC6H5O7•3H2O) -or- Iron (III) citrate (FeC6H5O7) 0.215 g 0.176 g Zinc sulphate heptahydrate (ZnSO4•7H2O) 0.150 g Copper sulphate pentahydrate (CuSO4•5H2O) 0.030 g Manganese sulphate monohydrate (MnSO4•H2O) 0.015 g Boric acid (H3BO3) 0.010 g Molybdenum trioxide (MoO3) 0.007 g Milli-QⓇ water 400 mL Stirred until dissolved. The final solution is filter sterilized, wrapped in aluminum foil (light sensitive) and stored at 4°C for up to 1 year. Vitamin Stock: Component Amount Thiamine-HCl 0.3800 g Pyridoxine-HCl 0.1800 g Nicotinic acid 0.0600 g Riboflavin 0.0150 g I-inositol (myo-inositol) 0.0120 g Biotin 0.0002 g Folic acid 0.0002 g Coconut milk (optional) 50 mL Milli-QⓇ water 300 mL Stirred until dissolved. The final solution is filter sterilized, wrapped in aluminum foil (light sensitive) and stored at 4°C for up to 1 year. 197 Transformation Solutions 0.8 M Mannitol: Component Amount D-mannitol 145.76 g Milli-QⓇ water 700 mL Stirred until dissolved. Volume brought to 1000 mL with Milli-QⓇ water, stirred, and split into 250 mL aliquots. Autoclaved (40-minute liquid cycle) and stored at room temperature until used. 0.5 M CaCl2: Component Amount Calcium chloride (CaCl2) 7.35 g Milli-QⓇ water 70 mL Stirred until dissolved. Volume brought to 100 mL with Milli-QⓇ water and stirred. Filter sterilized and stored at room temperature until used. 1 M KOH: Component Amount Potassium hydroxide (KOH) 14.03 g Milli-QⓇ water 200 mL Stirred until dissolved. Volume brought to 250 mL with Milli-QⓇ water and stirred. Stored at room temperature (used to adjust pH of MES solution). 0.5 M MES KOH: Component Amount 4-Morpholineethanesulfonic acid (MES) 4.88 g Milli-QⓇ water 40 mL Stirred until dissolved, pH adjusted to 5.7 using 1 M KOH. Volume brought to 50 mL with Milli-QⓇ water and stirred. Filter sterilized and stored at room temperature until used. 198 0.5 M KCl: Component Amount Potassium chloride (KCl) 1.86 g Milli-QⓇ water 40 mL Stirred until dissolved. Volume brought to 50 mL with Milli-QⓇ water and stirred. Filter sterilized and stored at room temperature until used. PEG (40% v/v) Solution: Component Amount Polyethylene glycol (PEG) 4000 24 g 0.8 M mannitol 15 mL 0.5 M CaCl2 12 mL Milli-QⓇ water 12 mL Stirred to dissolve and filter sterilized. Used immediately (can be stored at 4°C for a maximum of two weeks). Enzyme Solution: Component Amount Lysing enzymes from Trichoderma harzianum 0.2 g Cellulase 0.2 g 0.8 M mannitol 20 mL 0.5 M KCl 1.6 mL 0.5 M MES KOH (pH 5.7) 1.6 mL 0.5 M CaCl2 0.8 mL Volume brought to 40 mL Milli-QⓇ water. Final solution filter sterilized and stored at 4°C until use (maximum one day). 199 W5 Solution: Component Amount Glucose 7.8 g Calcium chloride (CaCl2) 4.6 g Sodium chloride (NaCl) 2.25 g Potassium chloride (KCl) 0.093 g Milli-QⓇ water 200 mL Stirred until dissolved. Volume brought to 250 mL with Milli-QⓇ water. Final solution split into 50 mL aliquots, autoclaved (40-minute liquid cycle), and stored at 4°C for a maximum of six months. MMg Solution: Component Amount D-mannitol 18.22 g Magnesium chloride hexahydrate (MgCl2•6H2O) 0.76 g 0.5 M MES KOH (pH 5.7) 2 mL Milli-QⓇ water 200 mL Stirred until dissolved. Volume brought to 250 mL with Milli-QⓇ water. Final solution split into 50 mL aliquots, autoclaved (40-minute liquid cycle), and stored at 4°C for a maximum of six months. 200 B.2 Conditions Tested for Phytophthora Transformations in Chapter 4 Beyond testing the three different plasmids (pYF2-PsNLS-Cas9-GFP, pYF515, pGFPN), a variety of protocol modifications were tested in the transformations, including changes in culture and recovery media, enzyme digestion time, plasmid DNA concentration, and G418 concentration for transformant selection. All the transformation conditions tested for each of the five forest Phytophthora species and the control species P. sojae are summarized in Data B.2.1. Data B.2.1 Summary of the conditions tested in plasmid transformations of five forest Phytophthora pathogens (P.cactorum, P. cinnamomi, P. cryptogea, P. ramorum, and P. syringae) and a control species P. sojae. The control transformations in P. sojae are listed first followed by the transformations in the five forest Phytophthora organized in alphabetical order by species name. Note: the table is provided as a separate Excel document–please open the corresponding supplementary file to view the full table. B.3 Transformation Plasmids The sequences for all three transformation plasmids tested in Chapter 4 are provided below in FASTA format in Supplementary Tables B.3.1, B.3.2, and B.3.3. 201 Supplementary Table B.3.1 Sequence of the pYF515 transformation plasmid. >pYF515 (13,032 bp) AAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCG GAAGCATAAAGTGTAAAGCCTGGGGTGCCTAATGAGTGAGCTAACTCACATTAATTGCGTTGCGCTCACTGCCCGCTTTC CAGTCGGGAAACCTGTCGTGCCAGCTGCATTAATGAATCGGCCAACGCGCGGGGAGAGGCGGTTTGCGTATTGGGCGCTC TTCCGCTTCCTCGCTCACTGACTCGCTGCGCTCGGTCGTTCGGCTGCGGCGAGCGGTATCAGCTCACTCAAAGGCGGTAA TACGGTTATCCACAGAATCAGGGGATAACGCAGGAAAGAACATGTGAGCAAAAGGCCAGCAAAAGGCCAGGAACCGTAAA AAGGCCGCGTTGCTGGCGTTTTTCCATAGGCTCCGCCCCCCTGACGAGCATCACAAAAATCGACGCTCAAGTCAGAGGTG GCGAAACCCGACAGGACTATAAAGATACCAGGCGTTTCCCCCTGGAAGCTCCCTCGTGCGCTCTCCTGTTCCGACCCTGC CGCTTACCGGATACCTGTCCGCCTTTCTCCCTTCGGGAAGCGTGGCGCTTTCTCAATGCTCACGCTGTAGGTATCTCAGT TCGGTGTAGGTCGTTCGCTCCAAGCTGGGCTGTGTGCACGAACCCCCCGTTCAGCCCGACCGCTGCGCCTTATCCGGTAA CTATCGTCTTGAGTCCAACCCGGTAAGACACGACTTATCGCCACTGGCAGCAGCCACTGGTAACAGGATTAGCAGAGCGA GGTATGTAGGCGGTGCTACAGAGTTCTTGAAGTGGTGGCCTAACTACGGCTACACTAGAAGGACAGTATTTGGTATCTGC GCTCTGCTGAAGCCAGTTACCTTCGGAAAAAGAGTTGGTAGCTCTTGATCCGGCAAACAAACCACCGCTGGTAGCGGTGG TTTTTTTGTTTGCAAGCAGCAGATTACGCGCAGAAAAAAAGGATCTCAAGAAGATCCTTTGATCTTTTCTACGGGGTCTG ACGCTCAGTGGAACGAAAACTCACGTTAAGGGATTTTGGTCATGAGATTATCAAAAAGGATCTTCACCTAGATCCTTTTA AATTAAAAATGAAGTTTTAAATCAATCTAAAGTATATATGAGTAAACTTGGTCTGACAGTTACCAATGCTTAATCAGTGA GGCACCTATCTCAGCGATCTGTCTATTTCGTTCATCCATAGTTGCCTGACTCCCCGTCGTGTAGATAACTACGATACGGG AGGGCTTACCATCTGGCCCCAGTGCTGCAATGATACCGCGGCTTCCACGCTCACCGGCTCCAGATTTATCAGCAATAAAC CAGCCAGCCGGAAGGGCCGAGCGCAGAAGTGGTCCTGCAACTTTATCCGCCTCCATCCAGTCTATTAATTGTTGCCGGGA AGCTAGAGTAAGTAGTTCGCCAGTTAATAGTTTGCGCAACGTTGTTGCCATTGCTACAGGCATCGTGGTGTCACGCTCGT CGTTTGGTATGGCTTCATTCAGCTCCGGTTCCCAACGATCAAGGCGAGTTACATGATCCCCCATGTTGTGCAAAAAAGCG GTTAGCTCCTTCGGTCCTCCGATCGTTGTCAGAAGTAAGTTGGCCGCAGTGTTATCACTCATGGTTATGGCAGCACTGCA TAATTCTCTTACTGTCATGCCATCCGTAAGATGCTTTTCTGTGACTGGTGAGTACTCAACCAAGTCATTCTGAGAATAGT GTATGCGGCGACCGAGTTGCTCTTGCCCGGCGTCAATACGGGATAATACCGCGCCACATAGCAGAACTTTAAAAGTGCTC ATCATTGGAAAACGTTCTTCGGGGCGAAAACTCTCAAGGATCTTACCGCTGTTGAGATCCAGTTCGATGTAACCCACTCG TGCACCCAACTGATCTTCAGCATCTTTTACTTTCACCAGCGTTTCTGGGTGAGCAAAAACAGGAAGGCAAAATGCCGCAA AAAAGGGAATAAGGGCGACACGGAAATGTTGAATACTCATACTCTTCCTTTTTCAATATTATTGAAGCATTTATCAGGGT TATTGTCTCATGAGCGGATACATATTTGAATGTATTTAGAAAAATAAACAAATAGGGGTTCCGCGCACATTTCCCCGAAA AGTGCCACCTGACGTCTAAGAAACCATTATTATCATGACATTAACCTATAAAAATAGGCGTATCACGAGGCCCTTTCGTC TCGCGCGTTTCGGTGATGACGGTGAAAACCTCTGACACATGCAGCTCCCGGAGACGGTCACAGCTTGTCTGTAAGCGGAT GCCGGGAGCAGACAAGCCCGTCAGGGCGCGTCAGCGGGTGTTGGCGGGTGTCGGGGCTGGCTTAACTATGCGGCATCAGA GCAGATTGTACACTGATGGACAAAGGGTCGCCTCTACGTACCCGCGACAATCTCGGCTTTTCCAATGTCGACAGCTGGCA TATCGCACGCGAGGTTAGGCGTCTTCAGCCGGCGTAACCACAAACCTCGAGTCAAGTACCCTTTTTCAGACCTTAATTTC CGTGTCCATTCCCTCGCGTTTACACGAAGGTTTCTGAGAAATACCACATGTCACAGAGAGAGAGAAGATTGGAGAACCTT CTCAACCTGACCCGTGCAGAGCGCTTTTGCGTCCTACCATCCGTTAATTATAGCACCACCACCACATGTCTCGCCGGAAC TCTCTTATCCGCTCTTTTTGATCGTCTTTTTTGCGCCCCATTAAAAGACATCTCTTGCACAGGGATCGTTTCTGATTGGC CATTTCACTTCCTCTTTTACATCCGAAAACCACCTGTTTGTGGGGTTGTGAAGCCACAACCGGACGCTCAAAAAGCCTCG CCCGACTCGCCCACGATCGGAAGGCTCATTCTCCTTTTCACTCTCACGTGCCCAAGTCCCAACCGACTCTTTTTCGACCT TCACTCTCACCGACAAACTAGCTCTTAATTAAATGATTGAACAAGATGGATTGCACGCAGGTTCTCCGGCCGCTTGGGTG GAGAGGCTATTCGGCTATGACTGGGCACAACAGACAATCGGCTGCTCTGATGCCGCCGTGTTCCGGCTGTCAGCGCAGGG GCGCCCGGTTCTTTTTGTCAAGACCGACCTGTCCGGTGCCCTGAATGAACTGCAGGACGAGCCAGCGCGGCTATCGTGGC TGGCCACGACGGGCGTTCCTTGCGCAGCTGTGCTCGACGTTGTCACTGAAGCGGGAAGGGACTGGCTGCTATTGGGCGAA GTGCCGGGGCAGGATCTCCTGTCATCTCACCTTGCTCCTGCCGAGAAAGTATCCATCATGGCTGATGCAATGCGGCGGCT GCATACGCTTGATCCGGCTACCTGCCCATTCGACCACCAAGCGAAACATCGCATCGAGCGAGCACGTACTCGGATGGAAG CCGGTCTTGTCGATCAGGATGATCTGGACGAAGAGCATCAGGGGCTCGCGCCAGCCGAACTGTTCGCCAGGCTCAAGGCG CGCATGCCCGACGGCGAGGATCTCGTCGTGACCCATGGCGATGCCTGCTTGCCGAATATCATGGTGGAAAATGGCCGCTT TTCTGGATTCATCGACTGTGGCCGGCTGGGTGTGGCGGACCGCTATCAGGACATAGCGTTGGCTACCCGTGATATTGCTG AAGAGCTTGGCGGCGAATGGGCTGACCGCTTCCTCGTGCTTTACGGTATCGCCGCTCCCGATTCGCAGCGCATCGCCTTC TATCGCCTTCTTGACGAGTTCTTCTGAGCGGCCGCAGGCGCGCCTTGACGTCTAACCGCGGGGTGCCTACTTCTTGTAAG CCGAAGGTCGAGCTGTGTGCACTGAGTGCGAAGTTGCAGATTTTGTGTCTATGCATTTAATACAATTTCCTTTTTTCCAT TTTCGTTGTTTTTTTTTGTGAAAGAACGATTGTTTTCAAAAGAAGGCCGTTTGTAGAACAATTTTTTTTTATACTAGCAT TTGGTATTCGAGTGTGACGAATCGGTGATGGCGCTTTACAAATGAAATATAACGCTGAACAAGCGCAGTGACAGCTTTAT TTTATAACAGAGAATAAAGACCAAAGTAGAGTAGACTGATGGCGGCTTGAAACAAAACTACGTGACTTCTAATGCAAAGA AAACCATACCCCACGCTACTTAGAATTGTTTCTCTTTGGTTGATGACCGCTATAGTCGTATGGAGTGATCCCTTTTTGTC AATATTTCGATTCTTGCTAAAACCGTGACTATTTTGAGGCAACTGTGTGTACTTAACATGGTAGAAATTTCTATGTTTAA GCCAGCTGACAATCATTCTTTATTTATCGTAAACCAACCAACGCGTAAGGAGAAAATACCGCATCAGGCGCCATTCGCCA TTCAGGCTGCGCAACTGTTGGGAAGGGCGATCGGTGCGGGCCTCTTCGCTATTACGCCAGCTGGCGAAAGGGGGATGTGC TGCAAGGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGGATCCCCCCAAT 202 TCCCCGGATCGTCTCATAGCTGCTCAAGACGTTTAGCAGATTGAGGATGCAGATCTTCTACTAATGAAACTACTGCAAAC TACCAACAAAGAGGCGACCACAAAATGGACAGCCTATGCCGAGATTTGTTTAAGATACTCGATTTGCCGTCCAATTTTCA TCAAGACGAAACACAGTGCATCGAACTGTGTTGTAAAAATTCATTTATTCTTGCAATTGTGACTCTCAAAGATTGATTAA ACTTGAAAGCTGGTGAAAGCTTTCCTTCTTTTGGCATGTTTGTTACTAAGGCGGCGTAAGCACAATAGGCCCAGACTCAT GCAAACCAGCCATAGTGACCTTCTCACTATGTTTCGCTTGAGATTTACTAGACTCTCGGGCATCTTCACAAAAGATGACA ACCGAATCAACAAATTAAGTCAACGCTAAATCAAATATTTAGAAAATAAATTTTATTGTCGAAAAAACTCTGCTTTGACA ATTTTCAAACTAACCAATTGCTAAGTATTCTAGTCGACACCGGTGTCCCATTCGCCATGCCGAAGCATGTTGCCCAGCCG GCGCCAGCGAGGAGGCTGGGACCATGCCGGCCAAAAGCACCGACTCGGTGCCACTTTTTCAAGTTGATAACGGACTAGCC TTATTTTAACTTGCTATTTCTAGCTCTAAAACCGAGACCCTAGCTAGCGTCACCGAACGAATTAAAACTATAGTAAGTAA AGACTGTCGATGTAGCCATGTGGGGCCAATAGCTGCGAGGCTCAGAGAGAGACAAGGGGGTCGCCACTTACCCCGAACGA TGTTCCAAGCAGAGCAGTCAGCAGAAAGTGAGGCTTGTTGCTGTCGGCGAGCGCAGCCTTGGGATCAACATGCGAGAGGA TAGTTGGCTGCGAGCTTGCCAGCGTGGAGAGACTTGGTTATTGTAGGCAACAAGTCCCGCATGAAGGGCGTTGTATGGTG CAAAAAAGAGGAGATGGAATCTTCAAACACTTTGATTGGCTGGACAGCGGGCGCGGACTACTGGCCAACCAAAATGTGAC CCCACTTGAGTGACACTTTCCCCACTATAGTGTCTTCAACTTGCACTGCAAGTGAAGTTGTTCTTGTTATTGCACTCGTT CTTACGTTATGGCTAGTACGCGCAGATGCAGAGCCTCCTCGGTGGTATTGCACGCCAACGAACCTCCATTCTCTGCGACA TAAGGCTGTGGCAGTGCGCCCAGGAACTCGTAGAGAATGGTCAACTGCGCCGTCACCACTCCCGTGTTCGAGTCTTCCGT CGTGACGAGGGCCGACTTCACCGTCGCCACATTCGACAGCGCAGTGCGGTACATCTCCATCAGCATCGTGTCCAGTTGGG CTGTCACTTCCGTCGCGTTCGCCGTGAAGTCCTCGACAAAACTGTACGCCCAGTTGCCGAGCTCGAAGCTGACGCTGGCG AATCCGCTCGCCAGGTCGAAGGTCAAGATCTGCTCTCCAGGACTGTAGTCCAGCACGATCTGCCTCCCTATTGTGGTGCT GTATGCCAGTGGACAGCTCTTCGCCCACAGACAAGCGTAGCTGGACTCGAGCAGCTCACGCACTGTACTCGACCATTCCG TGCCGTCCGATGGACTCAAGCAGTCCTGCAGCAGCCAAGTTGCATCAACGCTGCTGTCGATATCGTCCTTCTCCAGCATC GTAGCTATCAGTGTCAAGTCTGCCTCAAGCAGAACACCCATACACGCTGAGAACGCTCGGCACTCGGGCGAGCTGCTGCA ATCCTCCAATAACGTCGTGCACTCGCTGCAAGTTGGGGCTACATCATCCAGTTGTGAACCTGAATACAGTCCACCAAGTC GAGGCTGCTCCAAATCCAATGTTATGGTGATACCAGTGACTTCGTCTCCATCGTCGATCAGATCAGGAATCTCAGAGGTG ATAAATGCTGGTTCAGGCTGGCCAGTGGTGTCTACCACCGCGAGGTCGCCAAACTGCCGACGTCGCACAATGGGGATGAC AGTTGGGTCGTCAGGAACAAACTCCAGGTCTATGGTGAAGGGTGTGGTAGTGGTCATATCTTGCATCGAGTTTACAGCTT CGAACGTGAGACGGAAGCTCTGGGCATTGCCGCTTGATGACGACCCGCCTACGAAGAAGGACCACGAAGTATCCCCACCG AGACTCTGAGCACCAACCAGCACCCACGGAGCCGGATAAGTAGAAGTTGAACCAGCAACACTTGATCCGGGAGGAAGACC AATTGACGAAGATAGTGCAGCGTGAATGCTCTGCAGTTTACGAAGAAATATTGGAGCCAACTGATCGGCGCCTGTGAACC GGAGCTTGATACACAGTCTAGAAGTGGTGGAAGAGCACACGTCATCAGCATCTACCAATGCTCCAGATGATGCCGTCGAC TCAGGGTTATCGAAGCCGTAGATAAAACTTGTTTGATGTTCCAGCAATGATTCCGACGAGGTAATGAAGCCGTCGCTGAC CATGTCTATCTCTGGTACGGGATCTAACAGTCCAGGCAAATATTGCGGCCAAACGGGTGATCCATCGGCGTGGACGTCCA TCTCTCGCTCAATCAGCTCGAGGCTGCCGAGTGAATACAAAGAAATCGTGGAAGCGAGTTGCTGCGAGGAAATGACGGAA CGAATTCTTGCCATTGTTATGGTTGGTTTACGATAAATAAAGAATGATTGTCAGCTGGCTTAAACATAGAAATTTCTACC ATGTTAAGTACACACAGTTGCCTCAAAATAGTCACGGTTTTAGCAAGAATCGAAATATTGACAAAAAGGGATCACTCCAT ACGACTATAGCGGTCATCAACCAAAGAGAAACAATTCTAAGTAGCGTGGGGGATGGTTTCTTTGCATTAGAAGTCACGTA GTTTTGTTTCAAGCCGCCATCAGTCTACTCTACTTTGGTCTTTATTCTCTGTTATAAAATAAAGCTGTCACTGCGCTTGT TCAGCGTTATATTTCATTTGTAAAGCGCCATCACCGATTCGTCACACTCGAATACCAAATGCTAGTATAAAAAAAAATTG TTCTACAAACGGCCTTCTTTTGAAAACAATCGTTCTTTCACAAAAAAAAACAACGAAAATGGAAAAAAGGAAATTGTATT AAATGCATAGACACAAAATCTGCAACTTCGCACTCAGTGCACACAGCTCGACCTTCGGCTTACAAGAAGTAGGCTCCGGA ACCGGGCCCTTACTTAAGGTCGCCTCCCAGCTGAGACAGGTCGATCCGTGTCTCGTACAGGCCGGTGATGCTCTGGTGGA TCAGGGTGGCGTCCAGCACCTCTTTGGTGCTGGTGTACCTCTTCCGGTCGATGGTGGTGTCAAAGTACTTGAAGGCGGCA GGGGCTCCCAGATTGGTCAGGGTAAACAGGTGGATGATATTCTCGGCCTGCTCTCTGATGGGCTTATCCCGGTGCTTGTT GTAGGCGGACAGCACTTTGTCCAGATTAGCGTCGGCCAGGATCACTCTCTTGGAGAACTCGCTGATCTGCTCGATGATCT CGTCCAGGTAGTGCTTGTGCTGTTCCACAAACAGCTGTTTCTGCTCATTATCCTCGGGGGAGCCCTTCAGCTTCTCATAG TGGCTGGCCAGGTACAGGAAGTTCACATATTTGGAGGGCAGGGCCAGTTCGTTTCCCTTCTGCAGTTCGCCGGCAGAGGC CAGCATTCTCTTCCGGCCGTTTTCCAGCTCGAACAGGGAGTACTTAGGCAGCTTGATGATCAGGTCCTTTTTCACTTCTT TGTAGCCCTTGGCTTCCAGAAAGTCGATGGGATTCTTCTCGAAGCTGCTTCTTTCCATGATGGTGATCCCCAGCAGCTCT TTCACACTCTTCAGTTTCTTGGACTTGCCCTTTTCCACTTTGGCCACCACCAGCACAGAATAGGCCACGGTGGGGCTGTC GAAGCCGCCGTACTTCTTAGGGTCCCAGTCCTTCTTTCTGGCGATCAGCTTATCGCTGTTCCTCTTGGGCAGGATAGACT CTTTGCTGAAGCCGCCTGTCTGCACCTCGGTCTTTTTCACGATATTCACTTGGGGCATGCTCAGCACTTTCCGCACGGTG GCAAAATCCCGGCCCTTATCCCACACGATCTCCCCGGTTTCGCCGTTTGTCTCGATCAGAGGCCGCTTCCGGATCTCGCC GTTGGCCAGGGTAATCTCGGTCTTGAAAAAGTTCATGATGTTGCTGTAGAAGAAGTACTTGGCGGTAGCCTTGCCGATTT CCTGCTCGCTCTTGGCGATCATCTTCCGCACGTCGTACACCTTGTAGTCGCCGTACACGAACTCGCTTTCCAGCTTAGGG TACTTTTTGATCAGGGCGGTTCCCACGACGGCGTTCAGGTAGGCGTCGTGGGCGTGGTGGTAGTTGTTGATCTCGCGCAC TTTGTAAAACTGGAAATCCTTCCGGAAATCGGACACCAGCTTGGACTTCAGGGTGATCACTTTCACTTCCCGGATCAGCT TGTCATTCTCGTCGTACTTAGTGTTCATCCGGGAGTCCAGGATCTGTGCCACGTGCTTTGTGATCTGCCGGGTTTCCACC AGCTGTCTCTTGATGAAGCCGGCCTTATCCAGTTCGCTCAGGCCGCCTCTCTCGGCCTTGGTCAGATTGTCGAACTTTCT CTGGGTAATCAGCTTGGCGTTCAGCAGCTGCCGCCAGTAGTTCTTCATCTTCTTCACGACCTCTTCGGAGGGCACGTTGT CGCTCTTGCCCCGGTTCTTGTCGCTTCTGGTCAGCACCTTGTTGTCGATGGAGTCGTCCTTCAGAAAGCTCTGAGGCACG 203 ATATGGTCCACATCGTAGTCGGACAGCCGGTTGATGTCCAGTTCCTGGTCCACGTACATATCCCGCCCATTCTGCAGGTA GTACAGGTACAGCTTCTCGTTCTGCAGCTGGGTGTTTTCCACGGGGTGTTCTTTCAGGATCTGGCTGCCCAGCTCTTTGA TGCCCTCTTCGATCCGCTTCATTCTCTCGCGGCTGTTCTTCTGTCCCTTCTGGGTGGTCTGGTTCTCTCTGGCCATTTCG ATCACGATGTTCTCGGGCTTGTGCCGGCCCATCACTTTCACGAGCTCGTCCACCACCTTCACTGTCTGCAGGATGCCCTT CTTAATGGCGGGGCTGCCGGCCAGATTGGCAATGTGCTCGTGCAGGCTATCGCCCTGGCCGGACACCTGGGCTTTCTGGA TGTCCTCTTTAAAGGTCAGGCTGTCGTCGTGGATCAGCTGCATGAAGTTTCTGTTGGCGAAGCCGTCGGACTTCAGGAAA TCCAGGATTGTCTTGCCGGACTGCTTGTCCCGGATGCCGTTGATCAGCTTCCGGCTCAGCCTGCCCCAGCCGGTGTATCT CCGCCGCTTCAGCTGCTTCATCACTTTGTCGTCGAACAGGTGGGCATAGGTTTTCAGCCGTTCCTCGATCATCTCTCTGT CCTCAAACAGTGTCAGGGTCAGCACGATATCTTCCAGAATGTCCTCGTTTTCCTCATTGTCCAGGAAGTCCTTGTCCTTG ATAATTTTCAGCAGATCGTGGTATGTGCCCAGGGAGGCGTTGAACCGATCTTCCACGCCGGAGATTTCCACGGAGTCGAA GCACTCGATTTTCTTGAAGTAGTCCTCTTTCAGCTGCTTCACGGTCACTTTCCGGTTGGTCTTGAACAGCAGGTCCACGA TGGCCTTTTTCTGCTCGCCGCTCAGGAAGGCGGGCTTTCTCATTCCCTCGGTCACGTATTTCACTTTGGTCAGCTCGTTA TACACGGTGAAGTACTCGTACAGCAGGCTGTGCTTGGGCAGCACCTTCTCGTTGGGCAGGTTCTTATCGAAGTTGGTCAT CCGCTCGATGAAGCTCTGGGCGGAAGCGCCCTTGTCCACCACTTCCTCGAAGTTCCAGGGGGTGATGGTTTCCTCGCTCT TTCTGGTCATCCAGGCGAATCTGCTGTTTCCCCTGGCCAGAGGGCCCACGTAGTAGGGGATGCGGAAGGTCAGGATCTTC TCGATCTTTTCCCGGTTGTCCTTCAGGAATGGGTAAAAATCTTCCTGCCGCCGCAGAATGGCGTGCAGCTCTCCCAGGTG GATCTGGTGGGGGATGCTGCCGTTGTCGAAGGTCCGCTGCTTCCGCAGCAGGTCCTCTCTGTTCAGCTTCACGAGCAGTT CCTCGGTGCCGTCCATCTTTTCCAGGATGGGCTTGATGAACTTGTAGAACTCTTCCTGGCTGGCTCCGCCGTCAATGTAG CCGGCGTAGCCGTTCTTGCTCTGGTCGAAGAAAATCTCTTTGTACTTCTCAGGCAGCTGCTGCCGCACGAGAGCTTTCAG CAGGGTCAGGTCCTGGTGGTGCTCGTCGTATCTCTTGATCATAGAGGCGCTCAGGGGGGCCTTGGTGATCTCGGTGTTCA CTCTCAGGATGTCGCTCAGCAGGATGGCGTCGGACAGGTTCTTGGCGGCCAGAAACAGGTCGGCGTACTGGTCGCCGATC TGGGCCAGCAGGTTGTCCAGGTCGTCGTCGTAGGTGTCCTTGCTCAGCTGCAGTTTGGCATCCTCGGCCAGGTCGAAGTT GCTCTTGAAGTTGGGGGTCAGGCCCAGGCTCAGGGCAATCAGGTTTCCGAACAGGCCATTCTTCTTCTCGCCGGGCAGCT GGGCGATCAGATTTTCCAGCCGTCTGCTCTTGCTCAGTCTGGCAGACAGGATGGCCTTGGCGTCCACGCCGCTGGCGTTG ATGGGGTTTTCCTCGAACAGCTGGTTGTAGGTCTGCACCAGCTGGATGAACAGCTTGTCCACGTCGCTGTTGTCGGGGTT CAGGTCGCCCTCGATCAGGAAGTGGCCCCGGAACTTGATCATGTGGGCCAGGGCCAGATAGATCAGCCGCAGGTCGGCCT TGTCGGTGCTGTCCACCAGTTTCTTTCTCAGGTGGTAGATGGTGGGGTACTTCTCGTGGTAGGCCACCTCGTCCACGATG TTGCCGAAGATGGGGTGCCGCTCGTGCTTCTTATCCTCTTCCACCAGGAAGGACTCTTCCAGTCTGTGGAAGAAGCTGTC GTCCACCTTGGCCATCTCGTTGCTGAAGATCTCTTGCAGATAGCAGATCCGGTTCTTCCGTCTGGTGTATCTTCTTCTGG CGGTTCTCTTCAGCCGGGTGGCCTCGGCTGTTTCGCCGCTGTCGAACAGCAGGGCTCCGATCAGGTTCTTCTTGATGCTG TGCCGGTCGGTGTTGCCCAGCACCTTGAATTTCTTGCTGGGCACCTTGTACTCGTCGGTGATCACGGCCCAGCCCACAGA GTTGGTGCCGATGTCCAGGCCGATGCTGTACTTCTTGTCGGCTGCTGGGACTCCGTGGATACCGACCTTCCGCTTCTTCT TTGGGGCCATACTAGTGCGACGACGGACCTTGGTGTCGTCCTCGCGCTTGCGCTTGTGCATGCGACGACGGACCTTGGTG TCGTCCTCGCGCTTGCGCTTGTGCATGCGACGACGGACCTTGGTGTCGTCCTCGCGCTTGCGCTTGTGCATCCGCGGAGG CCTATCGATAAGCTTGATATCGAATTAATTCCTGCAGCCCGGGTTGTCGGTGAGAGTGAAGGTCGAAAAAGAGTCGGTTG GGACTTGGGCACGTGAGAGTGAAAAGGAGAATGAGCCTTCCGATCGTGGGCGAGTCGGGCGAGGCTTTTTGAGCGTCCGG TTGTGGCTTCACAACCCCACAAACAGGTGGTTTTCGGATGTAAAAGAGGAAGTGAAATGGCCAATCAGAAACGATCCCTG TGCAAGAGATGTCTTTAATGGGGCGCAAAAAAGACGATCAAAAAGAGCGGATAAGAGAGTTCCGGCGAGACATGTGGTGG TGGTGCTATAATTAACGGATGGTAGGACGCAAAAGCGCTCTGCACGGGTCAGGTTGAGAAGGTTCTCCAATCTTCTCTCT CTCTGTGACATGTGGTATTTCTCAGAAACCTTCGTGTAAACGCGAGGGAATGGACACGGAAATTAAGGTCTGAAAAAGGG TACTTGACTCGAGGTTTGTGGTTACGCCGGCTGAAGACGCCTAACCTCGCGTGCGATATGCCAGCTGTCGACATTGGAAA AGCCGAGATTGTCGCGGGTACGTAGAGGCGACCCTTTGTCCATCAGAGGGAGAGGAACCGACCAGTGGATAGGAGGGATA GACTTTATTTTGAGGGCGGTATAATGGTTATGGTAGCGACATTTTGATTTGACTTGAAAAAGATAAGAAAAAAAAAGGAA AAACTCGACTGCTTTGATGGGATAGCGATTTTTCCGTGGGTAGTCAGTTGGCCCTTGATTTTTACCATCAAGTACGTCCA TATGACGGGAATAGGGACGCGGGTGGATATTAGGAGCGGCAGCTGACGTGGCAGCTCATAGACAAGGGGGTGCTTGAAAG GCTGTCTATGGTGGTGACGCCGTCTTGCGGTGTGGCAATGAGTTGTATCATTTAATCGAATACGAGGTATAA 204 Supplementary Table B.3.2 Sequence of the pYF2-PsNLS-hSpCas9-GFP transformation plasmid. >pYF2-PsNLS-hSpCas9-GFP (12,509 bp) AAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCG GAAGCATAAAGTGTAAAGCCTGGGGTGCCTAATGAGTGAGCTAACTCACATTAATTGCGTTGCGCTCACTGCCCGCTTTC CAGTCGGGAAACCTGTCGTGCCAGCTGCATTAATGAATCGGCCAACGCGCGGGGAGAGGCGGTTTGCGTATTGGGCGCTC TTCCGCTTCCTCGCTCACTGACTCGCTGCGCTCGGTCGTTCGGCTGCGGCGAGCGGTATCAGCTCACTCAAAGGCGGTAA TACGGTTATCCACAGAATCAGGGGATAACGCAGGAAAGAACATGTGAGCAAAAGGCCAGCAAAAGGCCAGGAACCGTAAA AAGGCCGCGTTGCTGGCGTTTTTCCATAGGCTCCGCCCCCCTGACGAGCATCACAAAAATCGACGCTCAAGTCAGAGGTG GCGAAACCCGACAGGACTATAAAGATACCAGGCGTTTCCCCCTGGAAGCTCCCTCGTGCGCTCTCCTGTTCCGACCCTGC CGCTTACCGGATACCTGTCCGCCTTTCTCCCTTCGGGAAGCGTGGCGCTTTCTCAATGCTCACGCTGTAGGTATCTCAGT TCGGTGTAGGTCGTTCGCTCCAAGCTGGGCTGTGTGCACGAACCCCCCGTTCAGCCCGACCGCTGCGCCTTATCCGGTAA CTATCGTCTTGAGTCCAACCCGGTAAGACACGACTTATCGCCACTGGCAGCAGCCACTGGTAACAGGATTAGCAGAGCGA GGTATGTAGGCGGTGCTACAGAGTTCTTGAAGTGGTGGCCTAACTACGGCTACACTAGAAGGACAGTATTTGGTATCTGC GCTCTGCTGAAGCCAGTTACCTTCGGAAAAAGAGTTGGTAGCTCTTGATCCGGCAAACAAACCACCGCTGGTAGCGGTGG TTTTTTTGTTTGCAAGCAGCAGATTACGCGCAGAAAAAAAGGATCTCAAGAAGATCCTTTGATCTTTTCTACGGGGTCTG ACGCTCAGTGGAACGAAAACTCACGTTAAGGGATTTTGGTCATGAGATTATCAAAAAGGATCTTCACCTAGATCCTTTTA AATTAAAAATGAAGTTTTAAATCAATCTAAAGTATATATGAGTAAACTTGGTCTGACAGTTACCAATGCTTAATCAGTGA GGCACCTATCTCAGCGATCTGTCTATTTCGTTCATCCATAGTTGCCTGACTCCCCGTCGTGTAGATAACTACGATACGGG AGGGCTTACCATCTGGCCCCAGTGCTGCAATGATACCGCGAGACCCACGCTCACCGGCTCCAGATTTATCAGCAATAAAC CAGCCAGCCGGAAGGGCCGAGCGCAGAAGTGGTCCTGCAACTTTATCCGCCTCCATCCAGTCTATTAATTGTTGCCGGGA AGCTAGAGTAAGTAGTTCGCCAGTTAATAGTTTGCGCAACGTTGTTGCCATTGCTACAGGCATCGTGGTGTCACGCTCGT CGTTTGGTATGGCTTCATTCAGCTCCGGTTCCCAACGATCAAGGCGAGTTACATGATCCCCCATGTTGTGCAAAAAAGCG GTTAGCTCCTTCGGTCCTCCGATCGTTGTCAGAAGTAAGTTGGCCGCAGTGTTATCACTCATGGTTATGGCAGCACTGCA TAATTCTCTTACTGTCATGCCATCCGTAAGATGCTTTTCTGTGACTGGTGAGTACTCAACCAAGTCATTCTGAGAATAGT GTATGCGGCGACCGAGTTGCTCTTGCCCGGCGTCAATACGGGATAATACCGCGCCACATAGCAGAACTTTAAAAGTGCTC ATCATTGGAAAACGTTCTTCGGGGCGAAAACTCTCAAGGATCTTACCGCTGTTGAGATCCAGTTCGATGTAACCCACTCG TGCACCCAACTGATCTTCAGCATCTTTTACTTTCACCAGCGTTTCTGGGTGAGCAAAAACAGGAAGGCAAAATGCCGCAA AAAAGGGAATAAGGGCGACACGGAAATGTTGAATACTCATACTCTTCCTTTTTCAATATTATTGAAGCATTTATCAGGGT TATTGTCTCATGAGCGGATACATATTTGAATGTATTTAGAAAAATAAACAAATAGGGGTTCCGCGCACATTTCCCCGAAA AGTGCCACCTGACGTCTAAGAAACCATTATTATCATGACATTAACCTATAAAAATAGGCGTATCACGAGGCCCTTTCGTC TCGCGCGTTTCGGTGATGACGGTGAAAACCTCTGACACATGCAGCTCCCGGAGACGGTCACAGCTTGTCTGTAAGCGGAT GCCGGGAGCAGACAAGCCCGTCAGGGCGCGTCAGCGGGTGTTGGCGGGTGTCGGGGCTGGCTTAACTATGCGGCATCAGA GCAGATTGTACTGAGAGTGCACCATATGCGGTGTGAAATACCGCACAGATGCGTAAGGAGAAAATACCGCATCAGGCGCC ATTCGCCATTCAGGCTGCGCAACTGTTGGGAAGGGCGATCGGTGCGGGCCTCTTCGCTATTACGCCAGCTGGCGAAAGGG GGATGTGCTGCAAGGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATT CCCCCAATTCCCCGGATCGTCTCATAGCTGCTCAAGACGTTTAGCAGATTGAGGATGCAGATCTTCTACTAATGAAACTA CTGCAAACTACCAACAAAGAGGCGACCACAAAATGGACAGCCTATGCCGAGATTTGTTTAAGATACTCGATTTGCCGTCC AATTTTCATCAAGACGAAACACAGTGCATCGAACTGTGTTGTAAAAATTCATTTATTCTTGCAATTGTGACTCTCAAAGA TTGATTAAACTTGAAAGCTGGTGAAAGCTTTCCTTCTTTTGGCATGTTTGTTACTAAGGCGGCGTAAGCACAATAGGCCC AGACTCATGCAAACCAGCCATAGTGACCTTCTCACTATGTTTCGCTTGAGATTTACTAGACTCTCGGGCATCTTCACAAA AGATGACAACCGAATCAACAAATTAAGTCAACGCTAAATCAAATATTTAGAAAATAAATTTTATTGTCGAAAAAACTCTG CTTTGACAATTTTCAAACTAACCAATTGCTAAGTATTCTAGTCGACACCGGTACCTATCAGAAGAACTCGTCAAGAAGGC GATAGAAGGCGATGCGCTGCGAATCGGGAGCGGCGATACCGTAAAGCACGAGGAAGCGGTCAGCCCATTCGCCGCCAAGC TCTTCAGCAATATCACGGGTAGCCAACGCTATGTCCTGATAGCGGTCCGCCACACCCAGCCGGCCACAGTCGATGAATCC AGAAAAGCGGCCATTTTCCACCATGATATTCGGCAAGCAGGCATCGCCATGGGTCACGACGAGATCCTCGCCGTCGGGCA TGCGCGCCTTGAGCCTGGCGAACAGTTCGGCTGGCGCGAGCCCCTGATGCTCTTCGTCCAGATCATCCTGATCGACAAGA CCGGCTTCCATCCGAGTACGTGCTCGCTCGATGCGATGTTTCGCTTGGTGGTCGAATGGGCAGGTAGCCGGATCAAGCGT ATGCAGCCGCCGCATTGCATCAGCCATGATGGATACTTTCTCGGCAGGAGCAAGGTGAGATGACAGGAGATCCTGCCCCG GCACTTCGCCCAATAGCAGCCAGTCCCTTCCCGCTTCAGTGACAACGTCGAGCACAGCTGCGCAAGGAACGCCCGTCGTG GCCAGCCACGATAGCCGCGCTGCCTCGTCCTGCAGTTCATTCAGGGCACCGGACAGGTCGGTCTTGACAAAAAGAACCGG GCGCCCCTGCGCTGACAGCCGGAACACGGCGGCATCAGAGCAGCCGATTGTCTGTTGTGCCCAGTCATAGCCGAATAGCC TCTCCACCCAAGCGGCCGGAGAACCTGCGTGCAATCCATCTTGTTCAATCATTGTTGCCCGGGTGGATGCTCAGATGCTA GCGTCACCGAACGAATTAAAACTATAGTAAGTAAAGACTGTCGATGTAGCCATGTGGGGCCAATAGCTGCGAGGCTCAGA GAGAGACAAGGGGGTCGCCACTTACCCCGAACGATGTTCCAAGCAGAGCAGTCAGCAGAAAGTGAGGCTTGTTGCTGTCG GCGAGCGCAGCCTTGGGATCAACATGCGAGAGGATAGTTGGCTGCGAGCTTGCCAGCGTGGAGAGACTTGGTTATTGTAG GCAACAAGTCCCGCATGAAGGGCGTTGTATGGTGCAAAAAAGAGGAGATGGAATCTTCAAACACTTTGATTGGCTGGACA GCGGGCGCGGACTACTGGCCAACCAAAATGTGACCCCACTTGAGTGACACTTTCCCCACTATAGTGTCTTCAACTTGCAC TGCAAGTGAAGTTGTTCTTGTTATTGCACTCGTTCTTACGTTATGGCTAGTACGCGCAGATGCAGAGCCTCCTCGGTGGT ATTGCACGCCAACGAACCTCCATTCTCTGCGACATAAGGCTGTGGCAGTGCGCCCAGGAACTCGTAGAGAATGGTCAACT 205 GCGCCGTCACCACTCCCGTGTTCGAGTCTTCCGTCGTGACGAGGGCCGACTTCACCGTCGCCACATTCGACAGCGCAGTG CGGTACATCTCCATCAGCATCGTGTCCAGTTGGGCTGTCACTTCCGTCGCGTTCGCCGTGAAGTCCTCGACAAAACTGTA CGCCCAGTTGCCGAGCTCGAAGCTGACGCTGGCGAATCCGCTCGCCAGGTCGAAGGTCAAGATCTGCTCTCCAGGACTGT AGTCCAGCACGATCTGCCTCCCTATTGTGGTGCTGTATGCCAGTGGACAGCTCTTCGCCCACAGACAAGCGTAGCTGGAC TCGAGCAGCTCACGCACTGTACTCGACCATTCCGTGCCGTCCGATGGACTCAAGCAGTCCTGCAGCAGCCAAGTTGCATC AACGCTGCTGTCGATATCGTCCTTCTCCAGCATCGTAGCTATCAGTGTCAAGTCTGCCTCAAGCAGAACACCCATACACG CTGAGAACGCTCGGCACTCGGGCGAGCTGCTGCAATCCTCCAATAACGTCGTGCACTCGCTGCAAGTTGGGGCTACATCA TCCAGTTGTGAACCTGAATACAGTCCACCAAGTCGAGGCTGCTCCAAATCCAATGTTATGGTGATACCAGTGACTTCGTC TCCATCGTCGATCAGATCAGGAATCTCAGAGGTGATAAATGCTGGTTCAGGCTGGCCAGTGGTGTCTACCACCGCGAGGT CGCCAAACTGCCGACGTCGCACAATGGGGATGACAGTTGGGTCGTCAGGAACAAACTCCAGGTCTATGGTGAAGGGTGTG GTAGTGGTCATATCTTGCATCGAGTTTACAGCTTCGAACGTGAGACGGAAGCTCTGGGCATTGCCGCTTGATGACGACCC GCCTACGAAGAAGGACCACGAAGTATCCCCACCGAGACTCTGAGCACCAACCAGCACCCACGGAGCCGGATAAGTAGAAG TTGAACCAGCAACACTTGATCCGGGAGGAAGACCAATTGACGAAGATAGTGCAGCGTGAATGCTCTGCAGTTTACGAAGA AATATTGGAGCCAACTGATCGGCGCCTGTGAACCGGAGCTTGATACACAGTCTAGAAGTGGTGGAAGAGCACACGTCATC AGCATCTACCAATGCTCCAGATGATGCCGTCGACTCAGGGTTATCGAAGCCGTAGATAAAACTTGTTTGATGTTCCAGCA ATGATTCCGACGAGGTAATGAAGCCGTCGCTGACCATGTCTATCTCTGGTACGGGATCTAACAGTCCAGGCAAATATTGC GGCCAAACGGGTGATCCATCGGCGTGGACGTCCATCTCTCGCTCAATCAGCTCGAGGCTGCCGAGTGAATACAAAGAAAT CGTGGAAGCGAGTTGCTGCGAGGAAATGACGGAACGAATTCTTGCCATTGTTATGGTTGGTTTACGATAAATAAAGAATG ATTGTCAGCTGGCTTAAACATAGAAATTTCTACCATGTTAAGTACACACAGTTGCCTCAAAATAGTCACGGTTTTAGCAA GAATCGAAATATTGACAAAAAGGGATCACTCCATACGACTATAGCGGTCATCAACCAAAGAGAAACAATTCTAAGTAGCG TGGGGGATGGTTTCTTTGCATTAGAAGTCACGTAGTTTTGTTTCAAGCCGCCATCAGTCTACTCTACTTTGGTCTTTATT CTCTGTTATAAAATAAAGCTGTCACTGCGCTTGTTCAGCGTTATATTTCATTTGTAAAGCGCCATCACCGATTCGTCACA CTCGAATACCAAATGCTAGTATAAAAAAAAATTGTTCTACAAACGGCCTTCTTTTGAAAACAATCGTTCTTTCACAAAAA AAAACAACGAAAATGGAAAAAAGGAAATTGTATTAAATGCATAGACACAAAATCTGCAACTTCGCACTCAGTGCACACAG CTCGACCTTCGGCTTACAAGAAGTAGGCACCGGTACCGGGCCCTCACTTGTAGAGTTCATCCATGCCATGCGTGATCCCA GCAGCCGTCACGAACTCCAGCAGGACCATGTGGTCCCTCTTCTCGTTGGGGTCCTTGGAGAGGGCAGATTGCGTGGACAG GTAATGGTTGTCCGGCAGCAGGACAGGGCCATCGCCGATGGGCGTGTTCTGCTGGTAGTGGTCCGCCAGTTGCACGCTTC CATCTTCGATGTTGTGCCTGATCTTGAAGTTCACCTTGATGCCGTTCTTCTGCTTGTCGGCCATGATGTATACGTTGTGG GAGTTGTAGTTGTACTCCAGCTTGTGTCCGAGGATGTTTCCATCTTCCTTGAAATCGATGCCTTTCAGCTCGATGCGGTT CACCAGGGTATCACCTTCGAACTTGACTTCGGCACGTGTCTTGTAGTTCCCGTCATCCTTGAAGAAGATAGTCCTTTCTT GCACGTAGCCTTCGGGCATGGCGCTCTTGAAGAAGTCATGCTGCTTCATGTGATCTGGGTACCGGGAGAAGCACTGAACA CCGTAGGTGAAAGTGGTGACGAGGGTTGGCCACGGAACAGGGAGCTTTCCGGTAGTGCAGATGAACTTCAGGGTGAGCTT TCCGTAGGTGGCATCACCTTCACCCTCTCCGCTGACGGAGAACTTGTGCCCGTTCACATCACCATCCAGTTCCACCAGGA TTGGGACCACGCCAGTGAACAGTTCCTCGCCCTTGCCCATGGGCCCTTACTTAAGGTCGCCTCCCAGCTGAGACAGGTCG ATCCGTGTCTCGTACAGGCCGGTGATGCTCTGGTGGATCAGGGTGGCGTCCAGCACCTCTTTGGTGCTGGTGTACCTCTT CCGGTCGATGGTGGTGTCAAAGTACTTGAAGGCGGCAGGGGCTCCCAGATTGGTCAGGGTAAACAGGTGGATGATATTCT CGGCCTGCTCTCTGATGGGCTTATCCCGGTGCTTGTTGTAGGCGGACAGCACTTTGTCCAGATTAGCGTCGGCCAGGATC ACTCTCTTGGAGAACTCGCTGATCTGCTCGATGATCTCGTCCAGGTAGTGCTTGTGCTGTTCCACAAACAGCTGTTTCTG CTCATTATCCTCGGGGGAGCCCTTCAGCTTCTCATAGTGGCTGGCCAGGTACAGGAAGTTCACATATTTGGAGGGCAGGG CCAGTTCGTTTCCCTTCTGCAGTTCGCCGGCAGAGGCCAGCATTCTCTTCCGGCCGTTTTCCAGCTCGAACAGGGAGTAC TTAGGCAGCTTGATGATCAGGTCCTTTTTCACTTCTTTGTAGCCCTTGGCTTCCAGAAAGTCGATGGGATTCTTCTCGAA GCTGCTTCTTTCCATGATGGTGATCCCCAGCAGCTCTTTCACACTCTTCAGTTTCTTGGACTTGCCCTTTTCCACTTTGG CCACCACCAGCACAGAATAGGCCACGGTGGGGCTGTCGAAGCCGCCGTACTTCTTAGGGTCCCAGTCCTTCTTTCTGGCG ATCAGCTTATCGCTGTTCCTCTTGGGCAGGATAGACTCTTTGCTGAAGCCGCCTGTCTGCACCTCGGTCTTTTTCACGAT ATTCACTTGGGGCATGCTCAGCACTTTCCGCACGGTGGCAAAATCCCGGCCCTTATCCCACACGATCTCCCCGGTTTCGC CGTTTGTCTCGATCAGAGGCCGCTTCCGGATCTCGCCGTTGGCCAGGGTAATCTCGGTCTTGAAAAAGTTCATGATGTTG CTGTAGAAGAAGTACTTGGCGGTAGCCTTGCCGATTTCCTGCTCGCTCTTGGCGATCATCTTCCGCACGTCGTACACCTT GTAGTCGCCGTACACGAACTCGCTTTCCAGCTTAGGGTACTTTTTGATCAGGGCGGTTCCCACGACGGCGTTCAGGTAGG CGTCGTGGGCGTGGTGGTAGTTGTTGATCTCGCGCACTTTGTAAAACTGGAAATCCTTCCGGAAATCGGACACCAGCTTG GACTTCAGGGTGATCACTTTCACTTCCCGGATCAGCTTGTCATTCTCGTCGTACTTAGTGTTCATCCGGGAGTCCAGGAT CTGTGCCACGTGCTTTGTGATCTGCCGGGTTTCCACCAGCTGTCTCTTGATGAAGCCGGCCTTATCCAGTTCGCTCAGGC CGCCTCTCTCGGCCTTGGTCAGATTGTCGAACTTTCTCTGGGTAATCAGCTTGGCGTTCAGCAGCTGCCGCCAGTAGTTC TTCATCTTCTTCACGACCTCTTCGGAGGGCACGTTGTCGCTCTTGCCCCGGTTCTTGTCGCTTCTGGTCAGCACCTTGTT GTCGATGGAGTCGTCCTTCAGAAAGCTCTGAGGCACGATATGGTCCACATCGTAGTCGGACAGCCGGTTGATGTCCAGTT CCTGGTCCACGTACATATCCCGCCCATTCTGCAGGTAGTACAGGTACAGCTTCTCGTTCTGCAGCTGGGTGTTTTCCACG GGGTGTTCTTTCAGGATCTGGCTGCCCAGCTCTTTGATGCCCTCTTCGATCCGCTTCATTCTCTCGCGGCTGTTCTTCTG TCCCTTCTGGGTGGTCTGGTTCTCTCTGGCCATTTCGATCACGATGTTCTCGGGCTTGTGCCGGCCCATCACTTTCACGA GCTCGTCCACCACCTTCACTGTCTGCAGGATGCCCTTCTTAATGGCGGGGCTGCCGGCCAGATTGGCAATGTGCTCGTGC AGGCTATCGCCCTGGCCGGACACCTGGGCTTTCTGGATGTCCTCTTTAAAGGTCAGGCTGTCGTCGTGGATCAGCTGCAT GAAGTTTCTGTTGGCGAAGCCGTCGGACTTCAGGAAATCCAGGATTGTCTTGCCGGACTGCTTGTCCCGGATGCCGTTGA 206 TCAGCTTCCGGCTCAGCCTGCCCCAGCCGGTGTATCTCCGCCGCTTCAGCTGCTTCATCACTTTGTCGTCGAACAGGTGG GCATAGGTTTTCAGCCGTTCCTCGATCATCTCTCTGTCCTCAAACAGTGTCAGGGTCAGCACGATATCTTCCAGAATGTC CTCGTTTTCCTCATTGTCCAGGAAGTCCTTGTCCTTGATAATTTTCAGCAGATCGTGGTATGTGCCCAGGGAGGCGTTGA ACCGATCTTCCACGCCGGAGATTTCCACGGAGTCGAAGCACTCGATTTTCTTGAAGTAGTCCTCTTTCAGCTGCTTCACG GTCACTTTCCGGTTGGTCTTGAACAGCAGGTCCACGATGGCCTTTTTCTGCTCGCCGCTCAGGAAGGCGGGCTTTCTCAT TCCCTCGGTCACGTATTTCACTTTGGTCAGCTCGTTATACACGGTGAAGTACTCGTACAGCAGGCTGTGCTTGGGCAGCA CCTTCTCGTTGGGCAGGTTCTTATCGAAGTTGGTCATCCGCTCGATGAAGCTCTGGGCGGAAGCGCCCTTGTCCACCACT TCCTCGAAGTTCCAGGGGGTGATGGTTTCCTCGCTCTTTCTGGTCATCCAGGCGAATCTGCTGTTTCCCCTGGCCAGAGG GCCCACGTAGTAGGGGATGCGGAAGGTCAGGATCTTCTCGATCTTTTCCCGGTTGTCCTTCAGGAATGGGTAAAAATCTT CCTGCCGCCGCAGAATGGCGTGCAGCTCTCCCAGGTGGATCTGGTGGGGGATGCTGCCGTTGTCGAAGGTCCGCTGCTTC CGCAGCAGGTCCTCTCTGTTCAGCTTCACGAGCAGTTCCTCGGTGCCGTCCATCTTTTCCAGGATGGGCTTGATGAACTT GTAGAACTCTTCCTGGCTGGCTCCGCCGTCAATGTAGCCGGCGTAGCCGTTCTTGCTCTGGTCGAAGAAAATCTCTTTGT ACTTCTCAGGCAGCTGCTGCCGCACGAGAGCTTTCAGCAGGGTCAGGTCCTGGTGGTGCTCGTCGTATCTCTTGATCATA GAGGCGCTCAGGGGGGCCTTGGTGATCTCGGTGTTCACTCTCAGGATGTCGCTCAGCAGGATGGCGTCGGACAGGTTCTT GGCGGCCAGAAACAGGTCGGCGTACTGGTCGCCGATCTGGGCCAGCAGGTTGTCCAGGTCGTCGTCGTAGGTGTCCTTGC TCAGCTGCAGTTTGGCATCCTCGGCCAGGTCGAAGTTGCTCTTGAAGTTGGGGGTCAGGCCCAGGCTCAGGGCAATCAGG TTTCCGAACAGGCCATTCTTCTTCTCGCCGGGCAGCTGGGCGATCAGATTTTCCAGCCGTCTGCTCTTGCTCAGTCTGGC AGACAGGATGGCCTTGGCGTCCACGCCGCTGGCGTTGATGGGGTTTTCCTCGAACAGCTGGTTGTAGGTCTGCACCAGCT GGATGAACAGCTTGTCCACGTCGCTGTTGTCGGGGTTCAGGTCGCCCTCGATCAGGAAGTGGCCCCGGAACTTGATCATG TGGGCCAGGGCCAGATAGATCAGCCGCAGGTCGGCCTTGTCGGTGCTGTCCACCAGTTTCTTTCTCAGGTGGTAGATGGT GGGGTACTTCTCGTGGTAGGCCACCTCGTCCACGATGTTGCCGAAGATGGGGTGCCGCTCGTGCTTCTTATCCTCTTCCA CCAGGAAGGACTCTTCCAGTCTGTGGAAGAAGCTGTCGTCCACCTTGGCCATCTCGTTGCTGAAGATCTCTTGCAGATAG CAGATCCGGTTCTTCCGTCTGGTGTATCTTCTTCTGGCGGTTCTCTTCAGCCGGGTGGCCTCGGCTGTTTCGCCGCTGTC GAACAGCAGGGCTCCGATCAGGTTCTTCTTGATGCTGTGCCGGTCGGTGTTGCCCAGCACCTTGAATTTCTTGCTGGGCA CCTTGTACTCGTCGGTGATCACGGCCCAGCCCACAGAGTTGGTGCCGATGTCCAGGCCGATGCTGTACTTCTTGTCGGCT GCTGGGACTCCGTGGATACCGACCTTCCGCTTCTTCTTTGGGGCCATACTAGTGCGACGACGGACCTTGGTGTCGTCCTC GCGCTTGCGCTTGTGCATGCGACGACGGACCTTGGTGTCGTCCTCGCGCTTGCGCTTGTGCATGCGACGACGGACCTTGG TGTCGTCCTCGCGCTTGCGCTTGTGCATCCGCGGAGGCCTATCGATAAGCTTGATATCGAATTAATTCCTGCAGCCCGGG TTGTCGGTGAGAGTGAAGGTCGAAAAAGAGTCGGTTGGGACTTGGGCACGTGAGAGTGAAAAGGAGAATGAGCCTTCCGA TCGTGGGCGAGTCGGGCGAGGCTTTTTGAGCGTCCGGTTGTGGCTTCACAACCCCACAAACAGGTGGTTTTCGGATGTAA AAGAGGAAGTGAAATGGCCAATCAGAAACGATCCCTGTGCAAGAGATGTCTTTAATGGGGCGCAAAAAAGACGATCAAAA AGAGCGGATAAGAGAGTTCCGGCGAGACATGTGGTGGTGGTGCTATAATTAACGGATGGTAGGACGCAAAAGCGCTCTGC ACGGGTCAGGTTGAGAAGGTTCTCCAATCTTCTCTCTCTCTGTGACATGTGGTATTTCTCAGAAACCTTCGTGTAAACGC GAGGGAATGGACACGGAAATTAAGGTCTGAAAAAGGGTACTTGACTCGAGGTTTGTGGTTACGCCGGCTGAAGACGCCTA ACCTCGCGTGCGATATGCCAGCTGTCGACATTGGAAAAGCCGAGATTGTCGCGGGTACGTAGAGGCGACCCTTTGTCCAT CAGAGGGAGAGGAACCGACCAGTGGATAGGAGGGATAGACTTTATTTTGAGGGCGGTATAATGGTTATGGTAGCGACATT TTGATTTGACTTGAAAAAGATAAGAAAAAAAAAGGAAAAACTCGACTGCTTTGATGGGATAGCGATTTTTCCGTGGGTAG TCAGTTGGCCCTTGATTTTTACCATCAAGTACGTCCATATGACGGGAATAGGGACGCGGGTGGATATTAGGAGCGGCAGC TGACGTGGCAGCTCATAGACAAGGGGGTGCTTGAAAGGCTGTCTATGGTGGTGACGCCGTCTTGCGGTGTGGCAATGAGT TGTATCATTTAATCGAATACGAGGTATAA Supplementary Table B.3.3 Sequence of the pGFPN transformation plasmid. >pGFPN (6891 bp) CTAAATTGTAAGCGTTAATATTTTGTTAAAATTCGCGTTAAATTTTTGTTAAATCAGCTCATTTTTTAACCAATAGGCCG AAATCGGCAAAATCCCTTATAAATCAAAAGAATAGACCGAGATAGGGTTGAGTGTTGTTCCAGTTTGGAACAAGAGTCCA CTATTAAAGAACGTGGACTCCAACGTCAAAGGGCGAAAAACCGTCTATCAGGGCGATGGCCCACTACGTGAACCATCACC CTAATCAAGTTTTTTGGGGTCGAGGTGCCGTAAAGCACTAAATCGGAACCCTAAAGGGAGCCCCCGATTTAGAGCTTGAC GGGGAAAGCCGGCGAACGTGGCGAGAAAGGAAGGGAAGAAAGCGAAAGGAGCGGGCGCTAGGGCGCTGGCAAGTGTAGCG GTCACGCTGCGCGTAACCACCACACCCGCCGCGCTTAATGCGCCGCTACAGGGCGCGTCCCATTCGCCATTCAGGCTGCG CAACTGTTGGGAAGGGCGATCGGTGCGGGCCTCTTCGCTATTACGCCAGCTGGCGAAAGGGGGATGTGCTGCAAGGCGAT TAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAGCGCGCGTAATACGACTCACTA TAGGGCGAATTGGGTACCATTTAAATCCTAGGTGATCATGATATCTCTGATGGACAAAGGGTCGCCTCTACGTACCCGCG ACAATCTCGGCTTTTCCAATGTCGACAGCTGGCATATCGCACGCGAGGTTAGGCGTCTTCAGCCGGCGTAACCACAAACC TCGAGTCAAGTACCCTTTTTCAGACCTTAATTTCCGTGTCCATTCCCTCGCGTTTACACGAAGGTTTCTGAGAAATACCA CATGTCACAGAGAGAGAGAAGATTGGAGAACCTTCTCAACCTGACCCGTGCAGAGCGCTTTTGCGTCCTACCATCCGTTA ATTATAGCACCACCACCACATGTCTCGCCGGAACTCTCTTATCCGCTCTTTTTGATCGTCTTTTTTGCGCCCCATTAAAA GACATCTCTTGCACAGGGATCGTTTCTGATTGGCCATTTCACTTCCTCTTTTACATCCGAAAACCACCTGTTTGTGGGGT 207 TGTGAAGCCACAACCGGACGCTCAAAAAGCCTCGCCCGACTCGCCCACGATCGGAAGGCTCATTCTCCTTTTCACTCTCA CGTGCCCAAGTCCCAACCGACTCTTTTTCGACCTTCACTCTCACCGACAAACTAGCTACCGGTCTTAATTAAGGCTAGCA CCATGGGCAAGGGCGAGGAACTGTTCACTGGCGTGGTCCCAATCCTGGTGGAACTGGATGGTGATGTGAACGGGCACAAG TTCTCCGTCAGCGGAGAGGGTGAAGGTGATGCCACCTACGGAAAGCTCACCCTGAAGTTCATCTGCACTACCGGAAAGCT CCCTGTTCCGTGGCCAACCCTCGTCACCACTTTCACCTACGGTGTTCAGTGCTTCTCCCGGTACCCAGATCACATGAAGC AGCATGACTTCTTCAAGAGCGCCATGCCCGAAGGCTACGTGCAAGAAAGGACTATCTTCTTCAAGGATGACGGGAACTAC AAGACACGTGCCGAAGTCAAGTTCGAAGGTGATACCCTGGTGAACCGCATCGAGCTGAAAGGCATCGATTTCAAGGAAGA TGGAAACATCCTCGGACACAAGCTGGAGTACAACTACAACTCCCACAACGTATACATCATGGCCGACAAGCAGAAGAACG GCATCAAGGTGAACTTCAAGATCAGGCACAACATCGAAGATGGAAGCGTGCAACTGGCGGACCACTACCAGCAGAACACG CCCATCGGCGATGGCCCTGTCCTGCTGCCGGACAACCATTACCTGTCCACGCAATCTGCCCTCTCCAAGGACCCCAACGA GAAGAGGGACCACATGGTCCTGCTGGAGTTCGTGACGGCTGCTGGGATCACGCATGGCATGGATGAACTCTACAAGGCGG CCGCAGGCGCGCCTTGACGTCTAACCGCGGGGTGCCTACTTCTTGTAAGCCGAAGGTCGAGCTGTGTGCACTGAGTGCGA AGTTGCAGATTTTGTGTCTATGCATTTAATACAATTTCCTTTTTTCCATTTTCGTTGTTTTTTTTTGTGAAAGAACGATT GTTTTCAAAAGAAGGCCGTTTGTAGAACAATTTTTTTTTATACTAGCATTTGGTATTCGAGTGTGACGAATCGGTGATGG CGCTTTACAAATGAAATATAACGCTGAACAAGCGCAGTGACAGCTTTATTTTATAACAGAGAATAAAGACCAAAGTAGAG TAGACTGATGGCGGCTTGAAACAAAACTACGTGACTTCTAATGCAAAGAAAACCATACCCCACGCTACTTAGAATTGTTT CTCTTTGGTTGATGACCGCTATAGTCGTATGGAGTGATCCCTTTTTGTCAATATTTCGATTCTTGCTAAAACCGTGACTA TTTTGAGGCAACTGTGTGTACTTAACATGGTAGAAATTTCTATGTTTAAGCCAGCTGACAATCATTCTTTATTTATCGTA AACCAACCACCATATGCGTCAATGCAATTGACTGTGTCCTCGATTATTTCTATTATTAATATTATTTCCATTAAAAAAGA AATCCAGTTCGTGGCAAAATAAGACAAAGGTTTAAATAAAAATTTATCGTTATTAAACTCGTAACTTACCTTCTTCATGG CTCAACCCAATCTGTATATCGACTTATTTAATTTGGTTAACAAATCGGTTTTCGTCGCAAATAGGGGATGACAACTAAAA TACCAATTTGTGTCCTTGCGACACGTCAACAGGTGGCTGCCAATATCTCAGGTTTTGAGCACACAACGCTGTGATTGGCT ATCTGTAGATCGCTTCGAGAACTTTCGGACCGACATATCAGAACGCAGTCATGTCCAATCCAGCTAACGATTGGTCGAAA CCGCACTTCATCGCTTCATTTCAACCAATTCCCATCTACTAAAACGAGCGTCGGAATAGTCTCGAACGTAACCCCACGTA GTGGGTGGCAGCGCCCCTCGTATCTTTCAATCGCCACTCGAGTCTCAAGTTTGCTCACTTTGAAATTTTCCATCTGGACT ACACATCAAATCTAACTCAACTTTTATCAATTGAAACACAAGGGCCCCGTTTCGCATGATTGAACAAGATGGATTGCACG CAGGTTCTCCGGCCGCTTGGGTGGAGAGGCTATTCGGCTATGACTGGGCACAACAGACAATCGGCTGCTCTGATGCCGCC GTGTTCCGGCTGTCAGCGCAGGGGCGCCCGGTTCTTTTTGTCAAGACCGACCTGTCCGGTGCCCTGAATGAACTGCAGGA CGAGCCAGCGCGGCTATCGTGGCTGGCCACGACGGGCGTTCCTTGCGCAGCTGTGCTCGACGTTGTCACTGAAGCGGGAA GGGACTGGCTGCTATTGGGCGAAGTGCCGGGGCAGGATCTCCTGTCATCTCACCTTGCTCCTGCCGAGAAAGTATCCATC ATGGCTGATGCAATGCGGCGGCTGCATACGCTTGATCCGGCTACCTGCCCATTCGACCACCAAGCGAAACATCGCATCGA GCGAGCACGTACTCGGATGGAAGCCGGTCTTGTCGATCAGGATGATCTGGACGAAGAGCATCAGGGGCTCGCGCCAGCCG AACTGTTCGCCAGGCTCAAGGCGCGCATGCCCGACGGCGAGGATCTCGTCGTGACCCATGGCGATGCCTGCTTGCCGAAT ATCATGGTGGAAAATGGCCGCTTTTCTGGATTCATCGACTGTGGCCGGCTGGGTGTGGCGGACCGCTATCAGGACATAGC GTTGGCTACCCGTGATATTGCTGAAGAGCTTGGCGGCGAATGGGCTGACCGCTTCCTCGTGCTTTACGGTATCGCCGCTC CCGATTCGCAGCGCATCGCCTTCTATCGCCTTCTTGACGAGTTCTTCTGAGCGGGACTCTGGGGTTCGAAATGACCGACC AAGCGACGCCCAACCTGCCATCACGAGATTTCGATTCCACCGCCGCCTTCTATGAAAGGTTGGGCTTCGGAATCGTTTTC CGGGACGCCGGCTGGATGATCCTCCAGCGCGGGGATCTCATGCTGGAGTTCTTCGCCCACCCCCCGGATCCCCGGGGTCG ACTAGAATACTTAGCAATTGGTTAGTTTGAAAATTGTCAAAGCAGAGTTTTTTCGACAATAAAATTTATTTTCTAAATAT TTGATTTAGCGTTGACTTAATTTGTTGATTCGGTTGTCATCTTTTGTGAAGATGCCCGAGAGTCTAGTAAATCTCAAGCG AAACATAGTGAGAAGGTCACTATGGCTGGTTTGCATGAGTCTGGGCCTATTGTGCTTACGCCGCCTTAGTAACAAACATG CCAAAAGAAGGAAAGCTTTCACCAGCTTTCAAGTTTAATCAATCTTTGAGAGTCACAATTGCAAGAATAAATGAATTTTT ACAACACAGTTCGATGCACTGTGTTTCGTCTTGATGAAAATTGGACGGCAAATCGAGTATCTTAAACAAATCTCGGCATA GGCTGTCCATTTTGTGGTCGCCTCTTTGTTGGTAGTTTGCAGTAGTTTCATTAGTAGAAGATCTGCATCCTCAATCTGCT AAACGTCTTGAGCAGCTATGAGACGATCCGGGGAATTGGGGCCCGAGCTCCAGCTTTTGTTCCCTTTAGTGAGGGTTAAT TGCGCGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAG CCGGAAGCATAAAGTGTAAAGCCTGGGGTGCCTAATGAGTGAGCTAACTCACATTAATTGCGTTGCGCTCACTGCCCGCT TTCCAGTCGGGAAACCTGTCGTGCCAGCTGCATTAATGAATCGGCCAACGCGCGGGGAGAGGCGGTTTGCGTATTGGGCG CTCTTCCGCTTCCTCGCTCACTGACTCGCTGCGCTCGGTCGTTCGGCTGCGGCGAGCGGTATCAGCTCACTCAAAGGCGG TAATACGGTTATCCACAGAATCAGGGGATAACGCAGGAAAGAACATGTGAGCAAAAGGCCAGCAAAAGGCCAGGAACCGT AAAAAGGCCGCGTTGCTGGCGTTTTTCCATAGGCTCCGCCCCCCTGACGAGCATCACAAAAATCGACGCTCAAGTCAGAG GTGGCGAAACCCGACAGGACTATAAAGATACCAGGCGTTTCCCCCTGGAAGCTCCCTCGTGCGCTCTCCTGTTCCGACCC TGCCGCTTACCGGATACCTGTCCGCCTTTCTCCCTTCGGGAAGCGTGGCGCTTTCTCATAGCTCACGCTGTAGGTATCTC AGTTCGGTGTAGGTCGTTCGCTCCAAGCTGGGCTGTGTGCACGAACCCCCCGTTCAGCCCGACCGCTGCGCCTTATCCGG TAACTATCGTCTTGAGTCCAACCCGGTAAGACACGACTTATCGCCACTGGCAGCAGCCACTGGTAACAGGATTAGCAGAG CGAGGTATGTAGGCGGTGCTACAGAGTTCTTGAAGTGGTGGCCTAACTACGGCTACACTAGAAGGACAGTATTTGGTATC TGCGCTCTGCTGAAGCCAGTTACCTTCGGAAAAAGAGTTGGTAGCTCTTGATCCGGCAAACAAACCACCGCTGGTAGCGG TGGTTTTTTTGTTTGCAAGCAGCAGATTACGCGCAGAAAAAAAGGATCTCAAGAAGATCCTTTGATCTTTTCTACGGGGT CTGACGCTCAGTGGAACGAAAACTCACGTTAAGGGATTTTGGTCATGAGATTATCAAAAAGGATCTTCACCTAGATCCTT TTAAATTAAAAATGAAGTTTTAAATCAATCTAAAGTATATATGAGTAAACTTGGTCTGACAGTTACCAATGCTTAATCAG 208 TGAGGCACCTATCTCAGCGATCTGTCTATTTCGTTCATCCATAGTTGCCTGACTCCCCGTCGTGTAGATAACTACGATAC GGGAGGGCTTACCATCTGGCCCCAGTGCTGCAATGATACCGCGAGACCCACGCTCACCGGCTCCAGATTTATCAGCAATA AACCAGCCAGCCGGAAGGGCCGAGCGCAGAAGTGGTCCTGCAACTTTATCCGCCTCCATCCAGTCTATTAATTGTTGCCG GGAAGCTAGAGTAAGTAGTTCGCCAGTTAATAGTTTGCGCAACGTTGTTGCCATTGCTACAGGCATCGTGGTGTCACGCT CGTCGTTTGGTATGGCTTCATTCAGCTCCGGTTCCCAACGATCAAGGCGAGTTACATGATCCCCCATGTTGTGCAAAAAA GCGGTTAGCTCCTTCGGTCCTCCGATCGTTGTCAGAAGTAAGTTGGCCGCAGTGTTATCACTCATGGTTATGGCAGCACT GCATAATTCTCTTACTGTCATGCCATCCGTAAGATGCTTTTCTGTGACTGGTGAGTACTCAACCAAGTCATTCTGAGAAT AGTGTATGCGGCGACCGAGTTGCTCTTGCCCGGCGTCAATACGGGATAATACCGCGCCACATAGCAGAACTTTAAAAGTG CTCATCATTGGAAAACGTTCTTCGGGGCGAAAACTCTCAAGGATCTTACCGCTGTTGAGATCCAGTTCGATGTAACCCAC TCGTGCACCCAACTGATCTTCAGCATCTTTTACTTTCACCAGCGTTTCTGGGTGAGCAAAAACAGGAAGGCAAAATGCCG CAAAAAAGGGAATAAGGGCGACACGGAAATGTTGAATACTCATACTCTTCCTTTTTCAATATTATTGAAGCATTTATCAG GGTTATTGTCTCATGAGCGGATACATATTTGAATGTATTTAGAAAAATAAACAAATAGGGGTTCCGCGCACATTTCCCCG AAAAGTGCCAC 209 Appendix C Supplementary Methods and Results for Chapter 5 C.1 Methods Tested for Plasmid-RNP Co-Transformations While the baseline protocol used for co-transformations was the optimized Phytophthora transformation protocol developed in a previous study (Fang, Cui, et al., 2017), multiple methods were tested for delivering the RNP-plasmid transfection complexes to protoplasts. The methods tested for both P. ramorum and P. cactorum are summarized in Data C.1.1. Data C.1.1 Summary of the methods tested for RNP-plasmid delivery to P. ramorum and P. cactorum protoplasts. Note: the table is provided as a separate Excel document–please open the corresponding supplementary file to view the full table. C.2 Results from Oxathiapiprolin Growth Assays The raw radial growth measurements recorded for all Phytophthora cactorum FF42 isolates are recorded in Data C.2.1. Results from the statistical analyses performed on the radial growth data are reported in Data C.2.2. Data C.2.1 Radial growth measurements (in mm) recorded for Phytophthora cactorum FF42 wildtype (WT) and CRISPR-Cas9 ΔPcaORP1 mutant cultures seven days post plating on V8 agar (V8A) supplemented with a gradient of oxathiapiprolin concentrations. Unamended V8A and V8A + 0.1% DMSO were used as control treatments. For each isolate (WT, PT9, PT10), the growth of two independent single zoospore cultures (SZCs) was tested at each treatment (WT-1 RC1, WT-2 RC1, PT9-2, PT9-3, PT10-1 RC1, PT10-3 RC1). The oxathiapiprolin growth assays were carried out over two experiments (Excel Sheet 1 and Sheet 2). The results from the 0.1% DMSO control, performed in both experiments, were pooled prior to statistical analysis (Excel Sheet 3). The results from each of the SZCs were also pooled prior to analysis so that changes in growth were ultimately measured between three groups: WT, PT9, and PT10. Note: the table is provided as a separate Excel document–please open the corresponding supplementary file to view the full table. 210 Data C.2.2 Output of the Python 3.8 code for statistical analyses run on the radial growth results from Phytophthora cactorum FF42 wildtype (WT) and ΔPcaORP1 CRISPR-Cas9 mutant (PT9, PT10) cultures grown on V8 agar supplemented with a gradient of oxathiapiprolin concentrations. Note: the table is provided as a separate Excel document–please open the corresponding supplementary file to view the full table. ((((((((((((((((((((((((((((((((((((CocheC4_1:0.0010799999999999699,CocheC5_3:0.0017300000000000093):0.008510000000000018,Cocmi1:0.007210000000000161):0.0011599999999998278,(Cocca1:0.0024800000000000377,Cocvi1:0.0016799999999999038):0.004469999999999974):0.0014100000000001334,Cocsa1:0.008029999999999982):0.016089999999999938,Coclu2:0.022559999999999913):0.008389999999999898,Settu3:0.030529999999999946):0.01100000000000012,Stely1:0.04041000000000006):0.010399999999999965,(Altal1:0.014129999999999976,Altbr1:0.030710000000000015):0.017730000000000024):0.0042800000000000615,(Pyrtr1:0.013479999999999936,Pyrtt1:0.009579999999999922):0.03068000000000004):0.010089999999999932,Decga1:0.04292999999999991):0.009640000000000093,Clael1:0.04249000000000014):0.012969999999999926,(Lepmu1:0.03673000000000015,Photr1:0.022660000000000124):0.0319799999999999):0.007859999999999978,(Pyrsp1:0.05586000000000002,Cucbe1:0.03736000000000006):0.010499999999999954):0.010190000000000143,(((Stasp1:0.03754000000000013,Ampqui1:0.043500000000000094):0.006920000000000037,Stano2:0.04642999999999997):0.009600000000000053,(Ophdi1:0.04298000000000002,Setho1:0.040140000000000065):0.00666000000000011):0.023789999999999978):0.009909999999999863,(((Macan1:0.01855999999999991,Didex1:0.035639999999999894):0.006450000000000067,(Ascra1:0.031649999999999956,Lizem1:0.02522000000000002):0.004630000000000134):0.04793999999999987,Dotsy1:0.06323999999999996):0.00990000000000002):0.04600000000000004,(((((Maseb1:0.04679000000000011,Bysci1:0.039959999999999996):0.02082000000000006,Perma1:0.06816000000000022):0.025289999999999813,Lenfl1:0.06179000000000001):0.008590000000000098,((Parsp1:0.01607000000000003,Karrh1:0.019930000000000003):0.02732000000000001,Bimnz1:0.04339999999999988):0.050410000000000066):0.010419999999999874,Trepe1:0.05378999999999978):0.015130000000000088):0.007519999999999971,Corca1:0.07770999999999995):0.008280000000000065,Masph1:0.08644000000000007):0.009379999999999944,(Plesi1:0.0484,Melpu1:0.031460000000000043):0.04471000000000003):0.008380000000000054,(((Wesor1:0.06933999999999996,Spofi1:0.08237000000000005):0.0390299999999999,Lopma1:0.07389999999999985):0.00929000000000002,Aaoar1:0.09160999999999997):0.011230000000000073):0.00812999999999997,(((Lopnu1:0.0718399999999999,Polfu1:0.07929000000000008):0.016080000000000094,Veren1:0.0671400000000002):0.014059999999999961,Amnli1:0.09997000000000011):0.006669999999999954):0.014059999999999961,(Cloaq1:0.06187000000000009,Linin1:0.044300000000000006):0.026510000000000034):0.012250000000000094,Zoprh1:0.059699999999999864):0.017490000000000006,Delco1:0.08978999999999981):0.023519999999999985,(Hyspu1_1:0.06208999999999998,Rhyru1_1:0.08247000000000004):0.019749999999999934):0.02317999999999998,(((Cenge3:0.034009999999999874,Glost2:0.03010999999999986):0.03151000000000015,Leppa1:0.06666000000000016):0.012079999999999869,(Lopmy1:0.017169999999999908,Mytre1:0.016469999999999985):0.08461000000000007):0.012410000000000032):0.037390000000000034,Psehy1:0.18089):0.018000000000000016,(((((((Phycitr1:0.0024999999999999467,Phycpc1:0.0018700000000000383):0.0021700000000000053,Phycit1:0.004510000000000014):0.011819999999999942,(Phcitr1:0.007789999999999964,Phcit1:0.005900000000000016):0.012170000000000014):0.011560000000000015,Phycap1:0.02476999999999996):0.07296999999999998,((Botdo1_1:0.03351000000000015,Macph1:0.02783000000000002):0.012369999999999992,(Neopa1:0.03518999999999983,Dipse1:0.04589999999999983):0.008220000000000116):0.04115999999999986):0.02129000000000003,Aplpr1:0.0803799999999999):0.02079999999999993,Sacpr1:0.09489999999999998):0.04983000000000004):0.009239999999999915,(Conap1:0.11247000000000007,Patat1:0.14630999999999994):0.01924999999999999):0.010760000000000103,Linrh1:0.17596999999999996):0.010239999999999805,(((((((Venin1:0.019439999999999902,Venpi1:0.02230999999999994):0.10535000000000005,Verga1:0.14691999999999994):0.020629999999999926,Totfu1:0.11446):0.028729999999999922,Tribi1:0.15540999999999983):0.018930000000000113,Micmi1:0.22392):0.03289999999999993,Erebi1:0.2088899999999999):0.014390000000000125,(Aulhe2:0.15336000000000016,Rhili1:0.14785000000000004):0.023209999999999953):0.01634000000000002):0.023500000000000076,(((((((((Sepmu1:0.007480000000000042,Seppo1:0.01104000000000005):0.05108999999999997,Cerzm1:0.055499999999999994):0.04681000000000002,Mycfi2:0.08298000000000005):0.022340000000000027,((Clafu1:0.031159999999999966,Dotse1:0.028850000000000042):0.05900000000000005,Zasce1:0.07755000000000001):0.016639999999999988):0.012119999999999909,(((Zymps1:0.007390000000000008,Mycgr3:0.009500000000000064):0.0015399999999998748,Zymar1:0.01097999999999999):0.0022400000000000198,Zymbr1:0.007699999999999929):0.09619):0.020700000000000163,Disac1:0.14207000000000014):0.028219999999999912,(((((Aciri1_meta:5.999999999999339E-4,Aciri1_iso:7.800000000000029E-4):0.08804999999999996,Pieho1_1:0.22122999999999993):0.019360000000000044,Ternu1:0.09966999999999993):0.017099999999999893,(Horwer1:0.10114999999999996,Bauco1:0.09972999999999999):0.014339999999999908):0.02096000000000009,Horac1:0.14104000000000005):0.010680000000000023):0.016310000000000047,(Clasph1:0.12165000000000004,Polci1:0.13902000000000003):0.017589999999999995):0.06831999999999994,((((Aurpu_var_mel1:0.02124999999999999,Aurpu_var_nam1:0.022840000000000082):0.006309999999999927,(Aurpu_var_pul1:0.022760000000000002,Aurpu_var_sub1:0.026819999999999955):0.005290000000000017):0.08717999999999992,Delst1:0.09676999999999991):0.03784999999999994,Elsamp1:0.16943999999999992):0.028960000000000097):0.05503999999999998):0.051919999999999966,(((((((((((((TrireRUTC30_1:7.899999999999574E-4,Trire2:0.002630000000000021):0.006859999999999977,Trilo3:0.014009999999999967):0.0050400000000000444,Trici4:0.011440000000000006):0.02507999999999999,(((Trihar1:0.0020700000000000163,Triha1:5.500000000000504E-4):0.007879999999999998,Triatrob1:0.016619999999999968):0.012920000000000043,TriviGv29_8_2:0.018399999999999972):0.013700000000000045):0.011480000000000046,((Trias1:0.0045199999999999685,Triasp1:0.005059999999999953):0.009700000000000042,(Trigam1:0.011599999999999944,Triat2:0.009210000000000051):0.008970000000000034):0.027429999999999954):0.04959999999999998,Escweb1:0.10546):0.031200000000000006,(((((Metro1:0.017710000000000004,Metac1:0.016599999999999948):0.02466999999999997,Pocchl1:0.03881000000000001):0.023070000000000035,Ustvir1:0.09089000000000003):0.02907000000000004,Tolinf1:0.07564000000000004):0.01768000000000003,(Beaba1:0.03573000000000004,Cormi1:0.04539000000000004):0.09504999999999997):0.009549999999999947):0.014339999999999908,Acrchr1:0.11404999999999998):0.011500000000000066,((((((((((FusoxFo47:0.0017000000000000348,Fusoxrad1:0.0018099999999999783):7.700000000000484E-4,Fusoxlyc1:0.0022299999999999542):0.0010700000000000154,Fusox32931:0.003940000000000055):9.700000000000264E-4,(Fusoxmel1:0.0016300000000000203,Fusox2:0.005900000000000016):0.0020299999999999763):0.0014399999999999968,(((Fusoxrap1:0.003920000000000035,Fusoxcon1:0.005569999999999964):0.0015800000000000258,Fusoxpis1:0.0034999999999999476):0.0017300000000000093,Fusoxvas1:0.003770000000000051):0.0018200000000000438):0.0014300000000000423,Fusoxcub1:0.0023999999999999577):0.006680000000000019,(Fusve2:0.00880000000000003,Fusfu1:0.010190000000000032):0.0033400000000000096):0.023150000000000004,(Fusgr1:0.006220000000000003,Fusps1:0.0048399999999999554):0.03351000000000004):0.030689999999999995,(Necha2:0.012469999999999981,Fusso1:0.00931000000000004):0.03876000000000002):0.02514000000000005,(Neodi1:0.04288000000000003,Ilysp1:0.04688999999999999):0.032930000000000015):0.036229999999999984):0.05237999999999998,((((((((Colsi1:0.0031400000000000317,Colny1:0.0049000000000000155):0.0021299999999999653,(Gloac1:0.0040999999999999925,Colfi1:0.003190000000000026):0.0021200000000000108):0.0036800000000000166,Colsa1:0.01314000000000004):0.009079999999999977,Color1:0.015059999999999962):0.021970000000000045,(((Colto1:0.016129999999999978,Colin1:0.008360000000000034):0.005029999999999979,Colgr1:0.02844000000000002):0.005220000000000002,Colhig2:0.025619999999999976):0.009830000000000005):0.01375000000000004,Colch1:0.03622000000000003):0.008480000000000043,(Gloci1:0.039610000000000034,Colorb1:0.04910999999999999):0.011159999999999948):0.053689999999999904,((Veral1:0.03168000000000004,Verda1:0.009539999999999993):0.07865,Sodal1:0.09194999999999998):0.03933999999999993):0.034700000000000064):0.03156999999999999,(((((Gaegr1:0.02683000000000002,Magpo1:0.03369):0.05893999999999999,Magor1:0.08847000000000005):0.06694999999999995,((Ophnu1:0.022460000000000035,Ophpic1:0.02063999999999999):0.07662999999999998,Grocl1:0.09765999999999997):0.08933000000000002):0.020120000000000027,(((Cytch1:0.059660000000000046,Diaam1:0.06406999999999996):0.020059999999999967,(Crypa2:0.057089999999999974,Crypo1:0.08720000000000006):0.02646000000000004):0.06623000000000001,Phaal1:0.08931):0.026129999999999987):0.009179999999999966,((((Neucr_trp3_1:6.199999999999539E-4,Neucr2:6.599999999999939E-4):0.0029599999999999627,(Neute_mat_a1:0.0015199999999999658,Neute_matA2:5.699999999999594E-4):0.00387000000000004):0.09346999999999994,(((Spoth2:0.03842000000000001,Chagl_1:0.06647999999999998):0.01748000000000005,Thite2:0.05156000000000005):0.03127000000000002,Podan3:0.09964000000000006):0.02263000000000004):0.038510000000000044,(Conlig1:0.03474999999999995,Conioc1:0.041789999999999994):0.07900000000000007):0.02554999999999996):0.01976):0.014270000000000005,((((((HypCO275_1:0.005550000000000055,HypEC38_3:0.004210000000000047):0.029959999999999987,HypCI4A_1:0.05008999999999997):0.009000000000000008,DalEC12_1:0.04059999999999997):0.03593000000000002,Entma1:0.11053000000000002):0.016079999999999983,(Eutla1:0.09882999999999997,Micbo1:0.11719999999999997):0.020050000000000012):0.019100000000000006,Pseve2:0.10190999999999995):0.04447000000000001):0.07739999999999991,((((((((((Melva1:0.020970000000000044,Chalo1:0.01735999999999993):0.014600000000000057,Melbi2:0.02677000000000007):0.009869999999999934,Rhier1:0.029950000000000032):0.03871000000000002,(Phisc1:0.041789999999999994,Acema1:0.038899999999999935):0.03907000000000005):0.00827,(((Rhesp1:0.022889999999999966,Cadsp1:0.027429999999999954):0.02593000000000001,Marbr1:0.06682999999999995):0.027300000000000102,Greab1:0.08947000000000005):0.009429999999999938):0.00935000000000008,((((Blugr2:9.099999999999664E-4,BlugrR1_1:0.0010499999999999954):0.008770000000000056,Blugra1:0.013619999999999965):0.09933999999999998,Erynec1:0.13356):0.07575999999999994,Ascsa1:0.09331):0.012050000000000116):0.010189999999999921,Glalo1:0.10511999999999999):0.009700000000000042,(Oidma1:0.08557000000000003,Amore1:0.04704000000000008):0.03349000000000002):0.012979999999999992,(Sclsc1:0.025639999999999996,Botci1:0.03736000000000006):0.08318000000000003):0.028040000000000065,(Pseudest1:0.14294000000000007,Elyde1:0.1452500000000001):0.017630000000000035):0.029189999999999827):0.06008000000000013):0.09709000000000012,(((((((Tubma1:0.023660000000000014,Tubae1:0.029529999999999945):0.008220000000000005,Tubme1v2:0.031000000000000028):0.005149999999999988,Tubbor1:0.02956000000000003):0.010199999999999987,Chove1:0.025980000000000003):0.08687,(Morco1:0.0017899999999999583,Morimp1:0.0019000000000000128):0.10201000000000005):0.041649999999999965,(((Trihyb1:0.06966000000000006,Pyrco1:0.11473):0.02244999999999997,Sphbr2:0.08621000000000001):0.04896999999999996,Ascni1:0.17424000000000006):0.05196999999999996):0.04468000000000005,((((Tercla1:0.013519999999999976,Terbo2:0.011950000000000016):0.018900000000000028,Tirniv1:0.025700000000000056):0.06798000000000004,Kalpfe1:0.07701999999999998):0.10253999999999996,Ascim1:0.26930999999999994):0.02671000000000001):0.05255999999999994):0.1347299999999999,((((((Schoc1:0.04974999999999996,Schcy1:0.037940000000000085):0.11406000000000005,Schpo1:0.12214999999999998):0.13780000000000003,Schja1:0.23612):0.31208,Pneji1:0.39938000000000007):0.045499999999999985,((Tapde1_1:0.10250999999999999,Tappo1:0.10429999999999995):0.09697999999999996,Prola1:0.19208000000000003):0.21512999999999993):0.040610000000000035,(Saico1:0.27334,Neoirr1:0.35717):0.03339000000000003):0.032340000000000035):0.08176499999999987,(((((((((((((((((((((((Psicub1_1:0.04855000000000009,Psiser1:0.03119000000000005):0.034929999999999906,Galma1:0.05970999999999993):0.013709999999999889,Hebcy2:0.07508000000000004):0.009300000000000086,Hypsu1:0.09223000000000003):0.02018999999999993,Corgl3:0.08579000000000003):0.031609999999999916,(Lacam2:0.018769999999999953,Lacbi2:0.019530000000000047):0.0801099999999999):0.013319999999999999,Crula1:0.09024999999999994):0.012880000000000003,(((Copci1:0.0035799999999999166,Copci_AmutBmut1:0.0013199999999999878):0.07252999999999998,Copmar1:0.08474000000000004):0.040280000000000094,Copmic2:0.11739999999999995):0.06113999999999997):0.013600000000000056,((Agabi_varbur_1:0.003849999999999909,Agabi_varbisH97_2:0.004529999999999923):0.0874600000000001,Leugo1_1:0.13250000000000006):0.08139999999999992):0.013490000000000002,((Amath1:0.08614999999999995,Amamu1:0.11495999999999995):0.05754999999999999,Volvo1:0.15066000000000002):0.01629999999999998):0.0192699999999999,((((((Armosto1:0.009949999999999903,Armost1:0.006440000000000001):0.0036599999999999966,(Armga1:0.008559999999999901,Armcep1:0.01419999999999999):0.0030399999999999316):0.007139999999999924,Armme1_1:0.02658999999999989):0.08079999999999998,Cylto1:0.17964000000000002):0.04353000000000007,((((Lentinedodes1:0.007749999999999924,Lened_B_1_1:0.04512999999999989):0.06231000000000009,Gymlu1:0.06376999999999988):0.05509999999999993,Ompol1:0.09407999999999994):0.025349999999999984,Monpe1_1:0.22621000000000002):0.02554999999999996):0.021479999999999944,((Auramp1:0.05231000000000008,Schco3:0.04596):0.11395,Fishe1:0.17694999999999994):0.0484):0.013130000000000086):0.012359999999999927,(PleosPC9_1:0.007290000000000019,PleosPC15_2:0.0046600000000001085):0.1473):0.025519999999999987,((((((((Boledp1:0.007940000000000058,Boled5:0.00598999999999994):0.08708000000000005,(Paxin1:0.020510000000000028,Paxru2:0.023949999999999916):0.03794999999999993):0.016049999999999898,Hydpi2:0.051670000000000105):0.02171999999999996,((Pisti1:0.03255000000000008,Pismi1:0.03766000000000003):0.04153000000000007,Sclci1:0.07373000000000007):0.05319000000000007):0.029470000000000107,((Suilu4:0.008769999999999945,Suibr2:0.008729999999999905):0.036049999999999915,(Rhives1:0.02177999999999991,Rhivi1:0.008720000000000061):0.03488000000000002):0.059779999999999944):0.022990000000000066,(Conol1:0.0323500000000001,Conpu1:0.03143999999999991):0.12399999999999989):0.021160000000000068,((SerlaS7_9_2:0.003889999999999949,SerlaS7_3_2:0.0029399999999999427):0.02133000000000007,Serla_varsha1:0.02283999999999997):0.0777000000000001):0.039309999999999956,((Pilcr1:0.07532000000000005,Fibsp1:0.11627999999999994):0.04512000000000005,Plicr1:0.12156999999999996):0.015589999999999993):0.013360000000000039):0.021789999999999976,(((((((((Dicsqu463_1:0.0025100000000000122,Dicsqu464_1:0.0022800000000000598):0.0012700000000001044,Dicsq1:0.00383):0.001100000000000101,Dicsqu18370_1:0.003209999999999935):0.036880000000000024,Gansp1:0.05136000000000007):0.026939999999999964,Polbr1:0.05676999999999999):0.018240000000000034,((Trapub1:0.01760000000000006,Trave1:0.011130000000000084):0.04718,Pycci1:0.06044000000000005):0.01882000000000006):0.06167999999999996,(((((PosplRSB12_1:0.00822999999999996,Pospl1:0.07326999999999995):0.06705000000000005,Fibra1:0.09072999999999998):0.012029999999999985,(Wolco1:0.07999,Laesu1:0.08848000000000011):0.010760000000000103):0.009549999999999947,(Fompi3:0.04910999999999999,Daequ1:0.03952):0.05969000000000002):0.03265999999999991,(Cersu1:0.044829999999999925,Obbri1:0.041469999999999896):0.06648999999999994):0.015630000000000033):0.017679999999999918,(((((Phchr2:0.03554999999999997,Phaca1:0.0569599999999999):0.03292000000000006,Phlgi1:0.07647000000000004):0.024089999999999945,Bjead1_1:0.0847199999999999):0.028589999999999893,((Phlbr1:0.05110999999999999,Phlrad1:0.0669900000000001):0.04947999999999997,Phlcen1:0.08810000000000007):0.01593):0.031279999999999974,Abobi1:0.11477999999999988):0.021649999999999947):0.03986000000000001,(((Glotr1_1:0.049129999999999896,Neole1:0.06003000000000003):0.0646500000000001,Jaaar1:0.11038999999999999):0.03438000000000008,Punst1:0.1678900000000001):0.015980000000000105):0.01167000000000007):0.011319999999999997,((Stehi1:0.12034999999999996,Hetan2:0.09401999999999999):0.020199999999999996,(Amycha1:0.09684000000000004,Ricme1:0.17257999999999996):0.04259999999999997):0.05343000000000009):0.03589999999999993,(Schpa1:0.15365000000000006,Fomme1:0.13070000000000004):0.07225000000000004):0.03456999999999999,((Sisni1:0.002869999999999928,Sissu1:0.004010000000000069):0.20295000000000007,Sphst1:0.2114999999999999):0.026100000000000012):0.02251000000000003,(Exigl1:0.08709000000000011,Aurde3_1:0.08531):0.18113999999999997):0.021170000000000022,(Pirin1:0.10705999999999993,Sebve1:0.10011999999999999):0.21192999999999995):0.02288000000000001,(((Botbo1:0.17290000000000005,Cananz1:0.26780000000000004):0.026370000000000005,Tulca1:0.26132):0.019340000000000024,(CerAGI:0.06838999999999995,Rhiso1:0.07376999999999989):0.18318):0.02080000000000004):0.04232999999999998,((Calvi1:0.039919999999999956,Calco1:0.04251000000000005):0.02980000000000005,Dacsp1:0.07169000000000003):0.22575999999999996):0.06769000000000003,((((Treen1:0.12436000000000003,Kocim1:0.16357):0.026110000000000078,Treme1:0.16385000000000005):0.021009999999999973,(Cryne_JEC21_1:0.013319999999999999,Cryne_H99_1:0.014539999999999997):0.13971):0.04135,((Crycu1:9.999999999998899E-4,Triol1:0.0013099999999999223):0.12955000000000005,(Trias2479:0.00710999999999995,Trias8904:0.005579999999999918):0.1456900000000001):0.08077000000000001):0.23808000000000007):0.05911999999999995,((((((((Pgt_201_A1:0.00279000000000007,Pgt_Ug99_A1:0.0036400000000000876):0.0025599999999998957,Pgt_Ug99_C1:0.009009999999999962):5.900000000000905E-4,(Pgt_201_B1:0.005209999999999937,Pucgr2:0.005940000000000056):0.0012499999999999734):0.025470000000000104,Puctr1:0.03858000000000006):0.011260000000000048,((Pucst_PST78_1:0.003549999999999942,Pucstr1:0.0043800000000000505):0.0018499999999999073,Pucst1:0.016289999999999916):0.0511299999999999):0.012820000000000054,(PuccoSD80_1:0.003430000000000044,PuccoNC29_1:0.005330000000000057):0.04041000000000006):0.12159000000000009,(((Melme_tre1:0.006440000000000001,Melmed1:0.007239999999999913):0.01480999999999999,(Mellp2_3:0.01872000000000007,Melli1:0.02868000000000004):0.01106000000000007):0.07074000000000003,Croqu1:0.08421999999999996):0.07268999999999992):0.24270999999999998,(((((((Rhoto_IFO0880_2:0.004389999999999894,Rhoto_IFO1236_1:0.0035600000000000076):0.0020599999999999508,Rhoto_IFO0880_4:0.002080000000000082):0.00757999999999992,(Rhoto_IFO0559_1:0.0027399999999999647,Rhoto1:0.0016799999999999038):0.008099999999999996):0.07458000000000009,Rhosp1:0.10956999999999995):0.022380000000000067,Rhoba1_1:0.10309999999999997):0.09300999999999993,(Micld1:0.15704000000000007,Leucr1:0.1133599999999999):0.047149999999999914):0.15015,(Rhomi1:0.30228999999999995,Mixos1:0.31279999999999997):0.04917000000000005):0.02856000000000003):0.07330000000000003):0.15037500000000015);
Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hamelin, Richard C.
Forest pathology in the genomics era : combining comparative genomics and CRISPR-Cas9 gene editing to gain new insights into the genetics of filamentous plant pathogens
Text
Dataset