Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Feldman, Erin Christie
Master of Science - MSc
University of British Columbia
As the most recently diverged mycorrhizal lineage, ericoid mycorrhizal (ErM) fungi remain facultatively symbiotic and have retained many saprotrophic enzymes. This symbiosis represents an adaptation to acidic and nutrient-poor soils that plants in the Ericaceae family typically inhabit, including tundra, boreal forests, heathlands and peatlands – environments whose carbon sequestration capabilities are particularly sensitive to changing climate. The majority of soil carbon is derived from plant or fungal biomass; one way to examine the role of soil fungi in carbon cycling is to examine their ability to break down cell wall material contained within these soils. To investigate the saprotrophic potential of the ErM fungus Oidiodendron maius, I used two complimentary techniques. First, RNA sequencing was used to examine differential expression of cell wall degrading enzymes (CWDEs) by O. maius across substrates of different carbon complexity: glucose, bovine serum albumin and peat. I found significant differential regulation of 52 CWDEs in response to substrate, 13 of which were selected as targets for quantification across a greater number of substrates. I then performed droplet digital PCR (ddPCR)-based transcript abundance profiling of these target CWDEs in O. maius grown on 11 carbon sources of varying complexity. Concurrently, I developed a method to validate reference gene selection to generate normalization factors, calculated as the geometric mean of three reference gene transcript levels . After normalization, the coefficient of variation between biological replicates was substantially reduced, though by varying degrees dependent on the target gene assayed. While differential regulation of these transcripts was not as pronounced as I had predicted, I was able to demonstrate a significant upregulation of several transcripts responsible for cellulose and hemicellulose degradation in response to peat- and cellulose-containing substrates. Overall, this thesis represents the first of its kind to employ ddPCR to quantify transcript abundance in O. maius. The results from my studies demonstrate the potential of O. maius to degrade a wide range of lignocellulosic substrates and provide fundamental understanding regarding the enzymes responsible for this decomposition. A more comprehensive analysis of ErM fungal degradation capabilities will provide a deeper understanding of carbon sequestration and release within vulnerable ecosystems.
Differential Expression of Cell Wall Degrading Enzymes in Oidiodendron maius in Response to Varying Carbon Source by Erin Christie Feldman B.Sc., University of Victoria, 2007 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) August 2021 © Erin Christie Feldman, 2021 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Differential Expression of Carbohydrate Active Enzymes in Oidiodendron maius in Response to Varying Carbon Source submitted by Erin Feldman in partial fulfillment of the requirements of the degree of Master of Science Melanie Jones, Professor, Biology, UBCO Supervisor Dan Durall, Associate Professor, Biology, UBCO Supervisor Gwen Grelet, Senior Researcher, Land Use & Ecosystems, Manaaki Whenua, NZ Supervisory Committee Member Soheil Mahmoud, Associate Professor Biochemistry and Molecular Biology, UBCO Supervisory Committee Member Mary Berbee, Professor, Botany, UBC University Examiner Click or tap here to enter text. External Examiner Click or tap here to enter text. Click or tap here to enter text. iii Abstract As the most recently diverged mycorrhizal lineage, ericoid mycorrhizal (ErM) fungi remain facultatively symbiotic and have retained many saprotrophic enzymes. This symbiosis represents an adaptation to acidic and nutrient-poor soils that plants in the Ericaceae family typically inhabit, including tundra, boreal forests, heathlands and peatlands – environments whose carbon sequestration capabilities are particularly sensitive to changing climate. The majority of soil carbon is derived from plant or fungal biomass; one way to examine the role of soil fungi in carbon cycling is to examine their ability to break down cell wall material contained within these soils. To investigate the saprotrophic potential of the ErM fungus Oidiodendron maius, I used two complimentary techniques. First, RNA sequencing was used to examine differential expression of cell wall degrading enzymes (CWDEs) by O. maius across substrates of different carbon complexity: glucose, bovine serum albumin and peat. I found significant differential regulation of 52 CWDEs in response to substrate, 13 of which were selected as targets for quantification across a greater number of substrates. I then performed droplet digital PCR (ddPCR)-based transcript abundance profiling of these target CWDEs in O. maius grown on 11 carbon sources of varying complexity. Concurrently, I developed a method to validate reference gene selection to generate normalization factors, calculated as the geometric mean of three reference gene transcript levels . After normalization, the coefficient of variation between biological replicates was substantially reduced, though by varying degrees dependent on the target gene assayed. While differential regulation of these transcripts was not as pronounced as I had predicted, I was able to demonstrate a significant upregulation of several transcripts responsible for cellulose and hemicellulose degradation in response to peat- and cellulose-containing substrates. iv Overall, this thesis represents the first of its kind to employ ddPCR to quantify transcript abundance in O. maius. The results from my studies demonstrate the potential of O. maius to degrade a wide range of lignocellulosic substrates and provide fundamental understanding regarding the enzymes responsible for this decomposition. A more comprehensive analysis of ErM fungal degradation capabilities will provide a deeper understanding of carbon sequestration and release within vulnerable ecosystems. v Lay Summary Soil is the interface at which life extracts mineral nutrients from the earth and its contribution to global carbon cycling goes largely unacknowledged. Terrestrial plants extract carbon dioxide from the atmosphere and sequester carbon in soil in exchange for nutrients in a mutually beneficial relationship with soil fungi. Some of these fungi, such as Oidiodendron maius, are also able to acquire carbon by consuming decaying plant and fungal matter. Carbon storage in soils is highest, and most prone to the effects of climate change, in ecosystems this fungus inhabits. I examined the ability of O. maius to produce carbon breakdown compounds (enzymes) in an effort to better understand how these fungi obtain their carbon. I found significant regulation of some of these enzymes in response to common plant cell wall components in an effort to contribute to our understanding of global carbon cycling in vulnerable ecosystems. vi Preface This project was developed as a result of discussions between myself, my supervisory committee and the F Martin research group at INRAe, Nancy. All chapters were written by myself and edited by MD Jones and DM Durall. All three experiments were supported by NSERC Discovery Grants awarded to MD Jones and DM Durall. Chapter 1 provides the theoretical framework for this thesis and contextualizes the work within current research. Chapter 2 is based on a large-scale transcriptome project undertaken by JGI & INRAE; protocols were adapted from Kohler et al. (2015). The experiment described here was designed, developed, conducted and interpreted by myself with intellectual guidance and support from MD Jones, DM Durall, E Martino, A Kohler and G Grelet. A Kohler was invaluable in assisting with curation and analysis of RNA sequencing data. A version of Chapter 3 was submitted to BMC Genomics on December 18, 2020: Erin Feldman, Elena Martino, Annegret Kohler, Daniel Durall, Melanie Jones. (2021). Selection and evaluation of reference genes for ddPCR-based transcript abundance studies in Oidiodendron maius across varying carbon sources. BMC Genomics. doi: 10.21203/rs.3.rs-131970/v1. It is currently being revised for submission to a methods-focused journal. This experiment was designed, developed, conducted and interpreted by myself with intellectual guidance and support from all co-authors. This study is the first publication describing the validation and use of reference genes for normalization of ddPCR data in Oidiodendron maius. Chapter 4 is a quantitative analysis of 13 CWDE genes from O. maius across 11 carbon sources; this experiment was designed, developed, conducted and interpreted by myself with intellectual guidance and support from MD Jones, DM Durall, E Martino, A Kohler and G Grelet. vii M Hart provided access to the ddPCR technology and E Vukicevich provided invaluable assistance on its use. viii Table of Contents Abstract .................................................................................................................................. iii Lay Summary .......................................................................................................................... v Preface .................................................................................................................................. vi Table of Contents ................................................................................................................. viii List of Tables ........................................................................................................................ xiii List of Figures ....................................................................................................................... xv List of Abbreviations ............................................................................................................. xvi Acknowledgements ............................................................................................................. xviii Chapter 1 Introduction .............................................................................................................. 1 1.1 General Introduction ..................................................................................................... 1 1.2 Classification of Mycorrhizal Associations .................................................................... 2 1.2.1 Oidiodendron maius ............................................................................................ 7 1.3 Tundra, Boreal Forest, Heathlands and Peatlands ....................................................... 8 1.4 Soil Organic Matter ......................................................................................................11 1.4.1 Soil organic matter composition .........................................................................12 1.4.2 Soil organic matter breakdown by cell wall degrading enzymes .........................14 1.4.2.1 Substrate specificity of cell wall degrading enzymes ..................................17 1.4.2.1.1 Cellulose and hemicellulose ....................................................................17 1.4.2.1.2 Pectin ......................................................................................................18 1.4.2.1.3 Lignin and other aromatic components ....................................................18 1.4.2.1.4 Chitin and glucans ..................................................................................20 1.4.3 Cell wall degrading enzymes in ericoid mycorrhizal fungi genomes ......................21 1.4.3.1 Transcription of cell wall degrading enzymes .................................................23 1.5 The Saprotrophy-Mutualism Continuum ......................................................................26 ix 1.6 Research Objectives & Predictions ..............................................................................28 Chapter 2 Transcriptome ........................................................................................................ 30 2.1 Synopsis ......................................................................................................................30 2.2 Materials & Methods ....................................................................................................31 2.2.1 Strain [and genome sequence] .............................................................................31 2.2.2 Culture conditions .................................................................................................32 2.2.3 RNA extraction and cDNA synthesis .....................................................................33 2.2.4 Library construction and transcriptome sequencing ..............................................34 2.2.5 In-silico analysis of RNA sequencing data ............................................................34 2.3 Results & Discussion ...................................................................................................35 2.3.1 RNA isolation, library construction and transcriptome composition .......................35 2.3.2 Differential transcript abundance across varying carbon sources..........................37 2.3.2.1 RPKM means sums by family ........................................................................37 2.3.2.1.1 Hemicellulose-active enzymes ................................................................38 2.3.2.1.2 Cellulose-active enzymes ........................................................................39 2.3.2.1.3 Pectin-active enzymes ............................................................................40 2.3.2.1.4 Enzymes active on other cell wall components .......................................40 2.3.2.2 Significantly regulated individual transcripts ...................................................41 2.3.2.2.1 Cellulose- and hemicellulose-active enzymes .........................................44 2.3.2.2.2 Pectin-active enzymes ............................................................................45 2.3.2.2.3 Lignin degradation...................................................................................46 2.3.2.2.4 Enzymes active on other cell wall components .......................................48 2.3.3 Fifteen targets selected from Oidiodendron maius CAZome .................................49 2.4 Interpretations & Implications ......................................................................................63 x Chapter 3 Selection and Evaluation of Reference Genes for ddPCR-based Transcript Abundance Studies in Oidiodendron maius Across Varying Carbon Sources .................. 65 3.1 Synopsis ......................................................................................................................65 3.2 Background .................................................................................................................66 3.3 Materials & Methods ....................................................................................................72 3.3.1 Strain ....................................................................................................................72 3.3.2 In-silico analysis of RNA sequencing data ............................................................72 3.3.3 Cultivation of Oidiodendron maius for use in ddPCR ............................................73 3.3.4 RNA extraction and cDNA synthesis .....................................................................74 3.3.5 Primer design and validation by conventional PCR detection ...............................75 3.3.6 ddPCR assay with EvaGreen® .............................................................................76 3.3.7 ddPCR stability analysis .......................................................................................77 3.4 Results & Discussion ...................................................................................................78 3.4.1 Transcriptome sequencing to identify genes with stable expression across three carbon sources and in symbiosis ...................................................................................78 3.4.2 Optimization of EvaGreen® ddPCR assays ..........................................................82 3.4.3 Stability of candidate ddPCR reference genes evaluated for Oidiodendron maius cultured on a variety of carbon sources .........................................................................83 3.5 Interpretations & Implications ......................................................................................88 Chapter 4 Quantitative Transcript Abundance Analysis of Cell Wall Degrading Enzyme Response to Varying Carbon Source .................................................................................... 89 4.1 Synopsis ......................................................................................................................89 4.2 Materials & Methods ....................................................................................................89 4.2.1 Strain ....................................................................................................................89 4.2.2 Carbon sources ....................................................................................................89 xi 4.2.3 Culture conditions for ddPCR ...............................................................................90 4.2.4 RNA extraction and cDNA synthesis .....................................................................91 4.2.5 Reference gene and target gene selection ...........................................................92 4.2.6 Primer design and validation by conventional PCR detection ...............................92 4.2.7 ddPCR assay with EvaGreen® .............................................................................93 4.2.8 ddPCR normalization based on reference gene stability analysis .........................94 4.3 Results & Discussion ...................................................................................................95 4.3.1 Variable culture growth demonstrated on different carbon sources .......................95 4.3.2 Target primers validated by conventional PCR .....................................................96 4.3.3 ddPCR assay optimized for annealing/extension temperature ..............................98 4.3.4 Differential expression of CWDEs across various carbon sources ........................99 4.3.4.1 Reduction in variation achieved by normalization ..................................... 100 4.3.5 Relative transcript abundance determined for 13 cell wall degrading enzymes ... 102 4.3.5.1 Carbohydrate esterases: CE5 acetyl xylan esterase/cutinase (hemicellulose, cellulose); CE8 pectin methylesterase (pectin) ........................................................ 102 4.3.5.2 Cellulose- and hemicellulose-active glycoside hydrolases: GH3 beta-glucosidases; GH5 cellulases; GH11 endoxylanases .............................................. 104 4.3.5.3 Chitin- and glucan-active glycoside hydrolases: GH18 chitinase and GH55 glucanase ................................................................................................................ 108 4.3.5.4 Pectin-active glycoside hydrolases: GH28 polygalacturonase and GH78 rhamnosidase .......................................................................................................... 111 4.3.5.5 CWDEs with auxiliary activity: multi-copper oxidase (MCO / AA1) (phenols/lignin); tannase and feruloyl esterase (TFE) (various incl. lignin and tannins) 113 xii 4.3.5.6 Other CWDE-associated transcripts: manganese-dependent iron superoxide dismutase (MnFeSOD) (radical protection); phosphoketolase (SugMod) (active metabolism) ............................................................................................................. 117 4.4 Interpretations & Implications .................................................................................... 120 Chapter 5 Conclusion ........................................................................................................... 123 5.1 Significance & Contribution .................................................................................. 123 5.2 Strengths & Limitations ........................................................................................ 127 5.3 Future Research Directions & Applications .......................................................... 132 Bibliography .......................................................................................................................... 134 Appendices ........................................................................................................................... 157 Appendix A ...................................................................................................................... 157 Appendix B ...................................................................................................................... 161 Appendix C.................................................................................................................... 1623 Appendix D.................................................................................................................... 1701 xiii List of Tables Table 2.1. Amendments made to Modified Melin Norkrans media to support growth of Oidiodendron maius. ........................................................................................................32 Table 2.2. Yield of RNA sequencing reads from Oidiodendron maius grown on three different substrates, with two biological replicates each, after cleaning and assembly ...................36 Table 2.3. Differentially expressed CWDEs during growth of Oidiodendron maius in response to Peat and BSA. Significant values for fold change of gene expression during cultivation of O. maius on Peat and BSA compared to MMN. ...............................................................43 Table 2.4. Differentially expressed CWDEs during growth of Oidiodendron maius in response to Peat and BSA. .................................................................................................................50 Table 2.5. Expected expression response to carbon source by Oidiodendron maius. ...............52 Table 3.1. Amendments made to Modified Melin Norkrans media to support growth of Oidiodendron maius. ........................................................................................................73 Table 3.2. Treatment groups for growth of Oidiodendron maius for use in ddPCR* ...................74 Table 3.3. Selected candidate reference genes with stable expression across various culture conditions in Oidiodendron maius* ...................................................................................81 Table 3.4. Primer pairs designed for amplification of candidate reference genes in Oidiodendron maius* ..............................................................................................................................81 Table 4.1. Treatment groups for growth of Oidiodendron maius on solid MMN media in preparation for RNA extraction and subsequent transcript abundance studies. ................91 Table 4.2. Summary of observed characteristics of sequenced amplicons resulting from conventional PCR with target primers. .............................................................................98 Table 4.3. Coefficients of Variation for raw ddPCR data (CoEVRaw). Coeffficients of variation calculated for 13 target genes in Oidiodendron maius across 11 carbon sources. ......... 100 Table 4.4. Normalized Coefficients of Variation for ddPCR data (CoEVnorm). Coeffficients of variation calculated for 13 target genes in Oidiodendron maius across 11 carbon sources. ...................................................................................................................................... 101 Table 4.5. Observed expression response to carbon source by Oidiodendron maius relative to expected response. ........................................................................................................ 121 Table A.1. CAZyme families contained in the Oidiodendron maius genome……….…………..157 Table C.1. Shortlist of 251 reference gene candidates for Oidiodendron maius ………………163 Table D.1. Primers used for amplification of cDNA from Oidiodendron maius……………..…..171 xiv Table D.2. Reduction in Coefficients of Variation for ddPCR data (CoEVReduce)…………..…...178 xv List of Figures Figure 2.1. Expression analysis of CAZyme families in mycorrhizal tissue (MYC) and free-living mycelium (FLM) of Oidiodendron maius during cultivation on Modified Melin Norkrans (MMN) media .................................................................................................................38 Figure 3.1. Gene expression analysis of 251 putative reference genes for Oidiodendron maius. Heat map illustrating RNA-Seq differential expression data and corresponding KOGG Group. ...........................................................................................................................79 Figure 3.2. Optimization of primer annealing/extension temperature for ddPCR assays using temperature gradient. ....................................................................................................82 Figure 3.3. Reference gene concentration in 36 cDNA samples of Oidiodendron maius grown on a variety of carbon sources as determined by ddPCR. ..................................................85 Figure 3.4. Identification of stable reference genes in Oidiodendron maius. ..............................87 Figure 4.1. Average colony diameter of Oidiodendron maius grown on varying carbon sources ......................................................................................................................................96 Figure 4.2. Electrophoretic gels of amplicons produced by conventional PCR using a touchdown procedure with primer pairs as indicated. ......................................................................97 Figure 4.3. Carbohydrate esterase (CE) transcript abundance across eleven carbon sources. .................................................................................................................................... 103 Figure 4.4. Cellulose- and Hemicellulose-active Glycoside Hydrolase (GH) transcript abundance across eleven carbon sources. .................................................................................... 107 Figure 4.5. Chitin- and Glucan-active Glycoside Hydrolase (GH) transcript abundance across eleven carbon sources. ............................................................................................... 110 Figure 4.6. Pectin-active Glycoside Hydrolase (GH) transcript abundance across eleven carbon sources.. ...................................................................................................................... 112 Figure 4.7. Transcript abundance of Cell Wall Degrading Enzymes with Auxiliary Activity across eleven carbon sources. ............................................................................................... 115 Figure 4.8. Abundance of other Cell Wall Degrading Enzyme-Associated Transcripts across eleven carbon sources.. .............................................................................................. 118 Figure B.1. Oidiodendron maius growth at 30 days on A) MMN + BSA B) MMN + Peat….…..162 Figure D.1. Growth of Oidiodendron maius on different carbon sources……………...……...…173 Figure D.2. Droplet digital PCR annealing / extension temperature optimization assays……..174 xvi List of Abbreviations AA Auxiliary Activity AM Arbuscular Mycorrhizae BSA bovine serum albumin CAZyme carbohydrate active enzyme CBM carbohydrate binding module CE carbohydrate esterase CWDE Cell Wall Degrading Enzyme ddPCR droplet digital polymerase chain reaction DEPC Diethylpyrocarbonate DOM dissolved organic matter EC Enzyme Commission ECM ectomycorrhizal ErM ericoid mycorrhizal EXPN expansin FAE feruloyl esterase FDR False Discovery Rate GH glycoside hydrolase GMC glucose-methanol-choline GO Gene Ontology GOO glucooligosaccharide oxidase GT glycosyl transferase HMM Hidden Markov model JGI Joint Genome Institute KEGG Kyoto Encyclopedia of Genes and Genomes KOG euKaryotic Orthologous Groups LPMO lytic polysaccharide mono‐oxygenase MCO multicopper oxidase MMN Modified Melin Norkrans MO Monooxygenase PFCWDE plant and fungal cell wall degrading enzyme PL polysaccharide lyase POD lignin peroxidases xvii REA Rhizoscyphus ericae aggregate RNA-Seq RNA sequencing / high-throughput cDNA sequencing RPKM reads per kilobase of exon model per million mapped reads SOD superoxide dismutase SOM soil organic matter SSP effector small secreted protein TFE tannase and feruloyl esterase TPM transcripts per kilobase million xviii Acknowledgements My deepest gratitude to my amazing parents, Jan and Rick, whose unending support in all my endeavours made the completion of this work possible. Thank you for teaching me the value of hard work and perseverance. Thank you for always encouraging my passions and always being there to listen and restore my confidence when the challenges seemed insurmountable. Thank you to my sweet little Shiloh, that gave me a sense of purpose on the days I had trouble finding one, and who always made the struggles melt away with huggles and dimpled smiles. Thank you to my supervisors Drs. Melanie Jones and Dan Durall for allowing me the opportunity to (finally!) study fungi in an academic capacity and for providing the exceptional direction and encouragement that made this research possible. Thank you to my committee members Drs. Soheil Mahmoud and Gwen Grelet for providing invaluable technical knowhow, advice and direction throughout this work. I would also like to extend a special thank you to Dr. Mike Deyholos whose assistance, insight, and knowledge have been indispensable to my graduate career. This research would not have been possible without the Centre INRAe Grand Est-Nancy. A special thanks to Annegret Kohler and Elena Martino for providing the O. maius culture, assistance with transcriptomic analysis and unending support on all things Oidiodendron. Thank you to Dr. Miranda Hart for providing access to the ddPCR technology, and to Eric Vukicevich for providing training on its use. Thank you Ayelign Adal for providing helpful comments on Chapter 3. Thank you to Josh Smith for always providing a hand (or an ear) when it was needed. Thank you Barb Lucente for more than words can express; your assistance and advice in matters of administration and life have been invaluable. xix I’d also like to thank my funding sources, the NSERC Discovery grant program, the UBCO university graduate fellowship and the UBC graduate travel grant. 1 Chapter 1 Introduction 1.1 General Introduction Soils contain more carbon globally than the atmosphere and vegetation combined (Tarnocai et al. 2009) and, with increasing global temperatures, it is imperative to examine the role of soil fungi in releasing this carbon in response to warming. Despite the ubiquitous nature of soil fungi, their contribution to global carbon cycling goes largely unacknowledged, in part due to our lack of understanding of their function in diverse soil communities. Combined, boreal forest, tundra and peatland biomes contain more than 50% of global soil carbon (Post et al. 1982, Rydin and Jeglum 2013). In particular, peatlands, which cover only about 3% of the world's land area, store an estimated one-third of global soil carbon as a result of extremely slow decomposition (Rydin and Jeglum 2013). As global temperatures continue to increase, with extremes predicted in high latitude and polar regions - where boreal forests, tundra and peatlands dominate - the modification of soil carbon is one of the most important consequences (Matzner and Borken 2008). The large soil carbon pools in permafrost and peatlands are especially vulnerable to accelerated decomposition and, if more belowground carbon is released into the atmosphere by this accelerated decomposition, a positive feedback to warming could occur. Therefore, understanding the mechanisms controlling the breakdown of soil organic matter in these carbon rich soils, including the role of fungi, is critical to predicting the Earth’s future climate. Fungi are the predominant decomposer organisms in boreal forests, tundra and peatland. They are loosely grouped based on trophic status – saprotrophs, mycorrhizal fungi and pathogens, although there is often overlap in their trophic capabilities. Saprotrophic fungi acquire carbon from dead or decaying tissue and are considered the organisms primarily responsible for enzymatic degradation of soil organic matter. Mycorrhizal fungi form a symbiosis with plant roots (Frank 1885), acquiring the bulk of their carbon from plant partners in exchange for limiting mineral nutrients; in temperate forests, mycorrhizal fungi are estimated to receive 57-100% of their carbon 2 from their overstory tree partners (Högberg et al. 1999). The dominant mycorrhizal morphotypes in peatlands and boreal forests are ectomycorrhizae (ECM) and ericoid mycorrhizae (ErM). The ability of ECM fungi to degrade recalcitrant organic matter has sparked much debate in recent years as to whether these organisms are saprotrophic (Koide et al. 2008, Baldrian 2009, Cullings and Courty 2009). By contrast, ErM fungi are often isolated as free-living organisms, are able to degrade a variety of complex polymers, and possess a greater number of Cell Wall Degrading Enzymes (CWDEs) in their genomes than ECM fungi (Martino et al. 2018). This thesis examines the expression of CWDE genes in the ErM fungus, Oidiodendron maius, in order to better understand the functional role of these symbiotic fungi in soil carbon cycling and to better predict their involvement in carbon sequestration and release. 1.2 Classification of Mycorrhizal Associations Mycorrhizal symbioses have evolved repeatedly across diverse, independent lineages of fungi and were critical to colonization of land by plants (Bidartondo et al. 2011). These symbioses are most often considered to be mutualistic associations, though they have also been referred to as reciprocal parasitisms (Egger & Hibbett 2004). This mutualism has often been described in terms of economics, as a balance between transfer of soil mineral nutrients to the plant from the fungus - plant nutritional ‘benefits’ - and supply of photosynthetic carbon to the fungus - carbon ‘costs’ to the plant (Jones et al. 1991, Tinker et al. 1994, Jones et al. 1998, Kummel & Salant 2006, Wyatt et al. 2014). There are several types of mycorrhizal symbioses, including ectomycorrhizae (ECM), endomycorrhizae and ectendomycorrhizae. These broad mycorrhizal types are further subdivided into seven major morphological types including arbuscular, ecto, ectendo, arbutoid, orchid, monotropoid and ericoid mycorrhizas (Smith and Read 2008). Arbuscular mycorrhizae (AM) are the most common and best studied mycorrhizal type (Smith & Read 2008). They are formed by obligately symbiotic fungi of the phylum Glomeromycota (Schüβler et al., 2001), with an exceptionally wide range of plants including 3 angiosperms, gymnosperms and pteridophytes (Smith & Read 2008). The name “arbuscular” is derived from the characteristic structures - the arbuscules - which occur within the cortical cells of many plant roots. There is, however, structural diversity within this mycorrhizal type and additional diagnostic structures can include storage vesicles located within or between the cells, or intracellular hyphal coils, which sometimes occur in the absence of any arbuscules (Smith & Read 2008). The ectomycorrhizal (ECM) symbiosis is characterized by the presence of three structural components: a fungal mantle, or sheath, which encloses the tips of fine roots; an intercellular growth of hyphae between root epidermal and cortical cells called the Hartig net; and an outwardly growing network of hyphae termed the extraradical, extramatrical or external mycelium (Smith & Read 2008). These symbioses occur between more than 20,000 species of fungal taxa in the Ascomycota and Basidiomycota and about 6,000 plant taxa, primarily woody plants, in the Gymnospermae and Angiospermae (Rinaldi et al. 2008, Tedersoo et al. 2010). Ectendomycorrhizae similarly present a thin fungal mantle and well-developed Hartig net; however, plant epidermal and cortical cells exhibit a high degree of intracellular penetration by fungal hyphae (Smith & Read 2008). This mycorrhizal form is described primarily between trees and shrubs of Pinus and Larix genera (Mikola 1998; Yu et al. 2001) and members of the ascomycetous genus Wilcoxina (Egger 1996). Arbutoid mycorrhizae maintain a similar structure to those of the ectendomycorrhizae, though the intracellular penetration does not extend into the cortical cells (Massicotte et al. 1993). These symbioses occur between plants in the ericaceous genera Arbutus and Arctostaphylos (and several genera of the ericaceous subfamily Pyrolae) and basidiomycetous fungi more typically found as ECM symbionts of trees (Zak 1974). Orchidaceae is one of the largest and most diverse families in the plant kingdom (Cribb et al., 2003) and one of the most highly and uniquely modified of all angiosperm families (Smith & Read 2008). All chlorophyllous orchids have a relatively prolonged heterotrophic stage during 4 germination and early growth; during this time, their fungal partners facilitate ‘symbiotic germination’ and produce intracellular coils in the embryos of developing seedlings and in the rhizomes or roots of adult plants. The vast majority of these fungi are of the saprotrophic anamorphic artificial form genus Rhizoctonia (Andersen & Stalpers 1994), which actually reside within the Tulasnellaceae, Ceratobasidiaceae or Sebacinaceae families (Taylor et al. 2002). Monotropoid mycorrhizas occur in several plant genera belonging to the subfamily Monotropoideae (family Ericaceae); these monotropes are all myco-heterotrophic and, therefore, depend on adjacent autotrophic plant species for their source of carbon (Leake 1994). The fungal species that form C-transferring hyphal links between the roots of monotropes and autotrophs are extremely specific to each myco-heterotrophic plant, though the majority of those known to date fall into the Bankeraceae, Russulaceae, Tricholomataceae or Rhizopogonaceae families (Bidartondo & Bruns 2001, 2005). In ericoid mycorrhizae (ErM), intracellular fungal hyphae penetrate root cortical cells to form intimate contact with the root cell plasmalemma; dense coils of fungal hyphae in root cells increase the contact area between the two symbionts. The name ericoid makes reference to the monophyletic plant family Ericaceae, characterized by tightly conserved root architecture; they have very fine (100–750 μm) multicellular hair roots and it is within the outermost epidermal layer, immediately behind the advancing root tip meristem of these hair roots, where the fungal hyphae are found in coiled masses occupying up to 80% of the hair root volume (Watkinson 2016). There is some evidence to suggest that certain bryophytes support ErM symbiosis based on the presence of fungal coils in root segments (Duckett & Read 1995, Upson et al. 2007), although functional confirmation of ErM habit is only established by resynthesis experiments involving the mycobiont and plant, including verification of nutrient exchange between partner organisms (Leake and Read 1991). In this regard, the only confirmed ErM plant species belong to the sub-families Cassiopoideae, Ericoideae, Harrimanelloideae, Styphelioideae (formally known as 5 Epacridaceae), and Vaccinioideae (Kohout 2017). These sub-families are the most recently diverging lineages of Ericaceae (Kron et al. 2002); the ~2500 species comprising these subfamilies are distributed globally, on every continent except Antarctica, primarily occurring in acidic soils where nitrogen and phosphorus are difficult for plants to access (Kohout 2017). The fungal taxa involved in the ErM symbiosis appear to be much less diverse than those of other mycorrhizal types; however, this may be due, in part, to challenges in classifying them. Whereas ECM fungi can be classified based on morphology of their sporocarps and mycorrhizal root tips in addition to well-characterized phylogenetic relationships, the ErM fungi are not as amenable to these types of categorization. Most ErM fungi are microscopic and are only known by their asexual anamorph, with little knowledge of life cycle and sexual characteristics (Grelet et al. 2016). Additionally, formation of hyphal coils in ericaceous roots (Villarreal-Ruiz 2012 and references therein) by some fungi may represent opportunistic colonization (Leopold 2016) and ErM roots are often occupied by multiple putative mycorrhizal taxa (Setaro et al. 2006; Perotto et al. 2012). The mycorrhizal status of some putative ErM fungi may remain unresolved due to limited sampling and vague functional definitions despite morphological similarities to confirmed taxa. For example, Sebacinales Clade B (Basidiomycota, Serendipitaceae) have been shown to form ericoid-like mycorrhizae with ericaceous plants through resynthesis trials; however, they remain in the ‘putative ErM’ category as nutrient exchange has not been confirmed (Selosse et al. 2007, Vohnik et al. 2016, 2012). Our current understanding of ErM functional ecology and functional diversity is restricted to a very limited number of well-studied fungal species. Molecular evidence suggests that ErM fungi developed independently multiple times in several fungal lineages; some arose from saprotrophic lineages, some apparently diversified from within clades of ECM fungi, and some may have arisen from endophytic fungi (Martino et al. 2018; Kohler et al. 2015; Smith and Read 2008). Those taxa that have been indisputably confirmed as ErM fungi, however, belong solely to the Leotiomycetes class of Ascomycota. The first ErM fungus 6 isolated was Rhizoscyphus ericae1; it was designated as a mycorrhizal fungus upon demonstration of bi-directional C/P transport with its plant partner, Calluna vulgaris (Pearson & Read 1973). The original isolate is now considered part of the Rhizoscyphus ericae1 aggregate (REA) (Vrålstad et al. 2000), a cluster of species with high phylogenetic affinity to R. ericae1 (Helotiales). While most of the REA are plant root-associated fungi, the group contains species confirmed to be ErM fungi (Rhizoscyphus ericae1, Meliniomyces2 variabilis, M2. bicolor), putative ErM fungi (M2. vraolstadiae), as well as ECM fungi (Hyaloscypha finlandica and the M2. bicolor morphotype Piceirhiza bicolorata) and numerous other related non-mycorrhizal endophytes (Hambleton and Sigler 2005). Within the REA, M2. variabilis and M2. bicolor are consistently detected in Ericaceae roots in nature (e.g., Bougoure et al. 2007; Grelet et al. 2010; Ishida and Nordin 2010, Lukesova et al. 2015), form typical hyphal coils in plant roots in resynthesis experiments (Grelet et al. 2009; Vohník et al. 2013) and have demonstrated bidirectional C/N exchange with Vaccinium in experimental microcosms (Grelet et al. 2009). The only confirmed ErM fungal taxa not contained within the REA are Cairneyella variabilis (Midgley et al. 2016) and Oidiodendron maius; both species form hyphal coils in resynthesis experiments (Couture et al., 1983; Dalpé 1986), are repeatedly detected in Ericaceae roots in natural environments (e.g., McLean et al. 1998; Bougoure and Cairney 2005; Chambers et al. 2008) and show a growth benefit to the phytobiont in symbiosis (Palmer et al. 2007; Vohnik et al. 2005; Martino et al. 2018). While ErM fungi remain understudied relative to more ‘common’ mycorrhizal types (i.e., ECM and AM), the saprotrophic capabilities of the ErM taxa make these organisms amenable to isolation 1Pezizella ericae, subsequently Hymenoscyphus ericae, are names widely used in early literature to describe Helotiales isolates from Ericaceae, presumably Rhizoscyphus ericae (and currently Pezeloma ericae (Baral & Krieglsteiner, 2006)), though there is no strong taxonomical or morphological support (Martino et al 2018)); the names used throughout this thesis are those used by the authors at time of publication. 2Meliniomyces was recently moved to Hyaloscypha (Fehrer et al. 2019); the names used throughout this thesis are those used by the authors at time of publication 7 and maintenance in pure culture as well as experimental manipulation, and continued ErM research is a critical step in improving general understanding of the ecology and evolution of mycorrhizal symbioses. 1.2.1 Oidiodendron maius Oidiodendron maius has been detected in studies that span the globe (e.g., Australia, Bougoure and Cairney, 2005; South Africa, Bizabani, 2015; China, Zhang et al., 2009; USA, Wurzburger et al., 2012), though rarely as the dominant mycobiont at any given site. From the first description of O. maius (Amorphothecaceae, Erysiphales, Leotiomycetidae, Leotiomycetes, Pezizomycotina, Ascomycota, Fungi), it was suspected that this organism had strong cellulose- and lignin-degrading capacity, due to its frequent isolation from humus, peat and decaying wood (Barron 1962; Lumley et al. 2001). Like other Oidiodendron spp., O. maius exhibits classic characteristics of saprotrophy (Hambleton and Currah 1997): it sporulates prolifically, grows relatively quickly in culture on a variety of media and produces a broad complement of enzymes. In pure culture, O. maius is capable of degrading cellulose, tannic acid, pectin, chitin, gelatin and starch, as well as causing mass losses to Sphagnum fuscum necromass and spruce wood chips (Rice and Currah 2001, Thormann et al. 2002, Piercey et al. 2002). The ability to degrade S. fuscum, specifically the complex organic residues contained in its cell walls, is a rare trait amongst fungi (Thormann 2001); O. maius simultaneously degrades all S. fuscum cell wall components, including phenolics of the amorphous cell wall and interior microfibrils, but this breakdown is limited to the areas directly adjacent to hyphae (Tsuneda et al. 2001). O. maius is also able to grow on cellulose azure and tannic acid medium and its degradation mechanisms have been likened to that of white rot fungi (Rice et al. 2006). Perotto et al. (1997) showed that O. maius could grow with pectin as the sole carbon source and secrete polygalacturonase enzymes into this medium. 8 It was not until 1989 that O. maius was confirmed to be an ErM fungus by re-inoculation in Rhododendron spp. (Douglas et al., 1989), and not until 2005 that its ability to increase plant P and N content was demonstrated (Vohník et al. 2005). O. maius is the only Oidiodendron sp. that demonstrates mycorrhizal association with Ericaceae, although its isolation from the roots of spruce (Schild et al. 1988; Qian et al. 1998) and oak (Bergero et al. 2000), suggest that this genus is not restricted to partnership with ericaceous plants (Bergero et al. 2000; Rice and Currah 2006). The vast majority of the research regarding nutrient uptake in ErM has been based on experiments with Hymenoscyphus ericae1 (Bajwa and Read 1985; Bajwa et al. 1985; Kerley & Read 1995, 1997, 1998) and few experiments have been done to determine benefits from the association of ericaceous plants with O. maius, despite its frequent detection in the roots of ericaceous plants (Couture et al. 1983, Dalpé 1986, Douglas et al. 1989, Xiao and Berch 1996, Johansson 2001). Experiments by Vohnik et al. (2005) showed improved plant growth and increased foliar P in rhododendrons inoculated with O. maius and there are no reported negative effects of O. maius on plant partners. 1.3 Tundra, Boreal Forest, Heathlands and Peatlands As dominant plant species in tundra, taiga and boreo-temperate forest zones, ericaceous plants occupy nearly 70% of the Northern Hemisphere’s terrestrial surface (Read et al. 2004) and utilize soils containing the majority of the global carbon stocks (Post et al. 1982). Due to relatively low temperatures and low nutrient availability, decomposition rates in these biomes are correspondingly low, resulting in accumulation of soil organic matter (SOM). This high organic matter content combined with the acidic, nutrient poor soil provides the optimal habitat to support ErM partnerships. The tundra biome accounts for roughly 8% of the Earth’s surface and comprises the arctic tundra, covering about 5 % of the total land surface of the earth towards the Northern pole, and the alpine tundra, which occurs in mountains worldwide (at high enough altitude at any latitude) 9 covering about 3% of the Earth’s surface (Chapin & Korner 1995). The annual precipitation (mostly snow) is only about 25 cm (Caldwell et al. 2018) and soils are extremely nutrient poor; permafrost penetrates the ground from as shallow as 25 cm down, so that the ground can only support low growing plants like mosses, heaths, and lichen. Because of the cold, dry climate, decomposition is extremely slow in the tundra, and, thus, the permafrost traps huge amounts of carbon (~1600 gigatonnes), more than double the amount of carbon in the atmosphere (Tarnocai et al. 2009). The boreal forest, or taiga, is the largest terrestrial biome in the world, stretching as a continuous 2000 km-wide circumpolar belt around the Northern Hemisphere (Odum 1971) and containing approximately 16% of the global carbon sequestered in soils (Post et al. 1982). Boreal soils are generally acidic and have a low availability of plant nutrients, particularly N (Tamm 1991). Additionally, these soils typically receive less than 100 cm of precipitation annually (Larsen 1980), resulting in low decomposition rates and, therefore, accumulation of organic matter as humus or peat (often to considerable depths). They are dominated by conifers, whose litter contributes greatly to phenolic content and soil acidity (Schlesinger 1997), but the understory shrubs form a more or less continuous ground layer comprised of a variety of Ericaceae, to the extent that this ground layer may itself be described as heathland. Northern Hemisphere heathlands are ecosystems characterized by soils with high organic matter content and by the predominance of dwarf shrubs of the family Ericaceae and its relatives, predominantly Calluna, Erica, Gaultheria, Rhododendron, and Vaccinium (Specht 1979). Heathland soils are extremely acidic (pH < 5) and the essential nutrients N and P are present almost exclusively in combination with organic residues, due to low concentrations of these nutrients in ericaceous litter; polyphenolic compounds also reach high concentrations in heathland soils (Jalal & Read 1983). 10 Peatlands can form in any biome from tropical to tundra and from sea level to high alpine conditions, though it is in the boreal region where peatlands are most abundant. Vegetation type is not a determining characteristic; instead, peatlands are characterized by high soil organic matter content (>50%) under conditions of nearly permanent water saturation (Craft 2016). These landscapes are the most efficient carbon sinks on the planet (Hugron et al. 2013) and cover over 4 million square kilometres of the terrestrial and freshwater surface (Joosten and Clarke 2002). Northern hemisphere peatlands are dominated by Sphagnum moss and ericaceous shrubs and contain up to one third of the global soil carbon (Joosten and Clarke 2002); Sphagnum mosses are particularly resistant to decay (Clymo 1965) and, consequently, peatlands dominated by these species act as stronger carbon sinks than other peatland types. As in heathlands, the rate of biomass production in peatlands exceeds the rate of decomposition (Hugron et al. 2013). This slow decomposition results primarily from acidic and anoxic conditions due to flooding and this accumulation of partially decomposed plant material is what makes peat an excellent carbon sink. Different ecosystems are characterized by variability in soil organic matter; ecosystems with high soil organic matter levels often support ErM plants, like those described here. With ericaceous soils containing the world’s largest carbon stocks, global climate is dependent on the interactions of these plants, their fungal partners and the soil organic matter in which these partnerships dominate. A recent study by Clemmenson et al. (2021) correlated ErM fungi-dominated ecosystems with slower organic matter decomposition than their ECM fungi-dominated counterparts, suggesting that, as ecosystem boundaries shift in response to changing climate, habitat loss for ErM fungi could potentially result in significant release of carbon from these currently substantial sinks. In this regard, a better understanding of ErM functioning is critical for predicting how the carbon in ericaceous soils may respond to global climate change and other anthropogenic global change factors (Clemmenson et al. 2013). 11 1.4 Soil Organic Matter Soil Organic matter (SOM), although highly variable and not well characterized, plays a critical role in many ecosystem processes. Soil organic matter can be defined as “all organic [C containing] materials found in soils irrespective of origin or state of decomposition” (Baldock & Skjemstad 1999). In addition to providing nutrients and habitat to organisms living in the soil, organic matter affects the chemical and physical properties of the soil by binding soil particles into aggregates and improving the nutrient- and water-holding capacity of soil. The composition of SOM affects soil structure and porosity, water infiltration rate and moisture holding capacity, diversity and biological activity of soil organisms, and plant nutrient availability. Soils can range from mineral to organic and contain anywhere from zero to 50 % organic matter, respectively (Trettin & Jurgensen 2003). Ten to 50 % of SOM is an “active” fraction (Osman 2013), originating from plant, microbial, and animal residues, whether fresh and undecomposed or partially decomposed; these fractions are called ‘active’ because they are readily available to soil organisms and they are degraded quickly. The active SOM is composed of easily degradable compounds such as sugars, amino acids and other small molecules; polysaccharides (i.e., cellulose, hemicellulose and chitin), lipids and proteins; as well as more resistant compounds such as waxes and lignins (Osman 2013). The remainder of the SOM is resistant, passive or stable organic matter with reduced accessibility to decomposers. This stable fraction is considered more chemically ‘recalcitrant’ (Osman 2013), though recent focus in the field has emphasized the physically-protected carbon aspect rather than the concept of chemical recalcitrance (Kleber 2010). The C:N ratio is often used to indicate the quality of the SOM, where high ratios represent resistance to decomposition, but this is a drastic oversimplification (Berg 2000). Soil organic matter is dynamic and the rate of decomposition and accumulation of SOM is determined by such soil properties as texture, pH, temperature, moisture, aeration, composition and soil biological 12 activities, as well as above-ground flora and fauna (Coleman et al. 2004); SOM, in turn, influences these factors. 1.4.1 Soil organic matter composition The majority of carbon in peat and other soils is derived from plant or fungal biomass. Plant cell walls are composed mainly of cellulose, hemicellulose, pectin and lignin, while fungal cell walls are comprised primarily of chitin and glucans. Therefore, soil microorganisms must be able to chemically degrade these major biomolecules to extract carbon and mineral nutrients from their surrounding environment. The most abundant biopolymer on earth is cellulose, a linear polysaccharide consisting of several hundred to many thousands of β-1,4 linked D-glucose units, hydrogen bonded to one another to form microfibrils and rigid, crystalline structures (Klemm et al. 2005). Plant-derived cellulose is rarely found in pure form, but, rather, is embedded in a matrix of other structural polymers such as hemicellulose, lignin, pectin and other substances such as proteins, which combine to give plant cell walls their high tensile strength (Popper et al. 2011). Hemicellulose, the second most abundant biopolymer (Somerville et al. 2004), comprises 25-50% of the biomass of most plants (Bajpai 2016). Hemicelluloses are a group of heterogenous polysaccharides composed of pentose and hexose monomers, including glucose, xylose, mannose, galactose, rhamnose, and arabinose, which provide extensive cross-linking between cellulose microfibrils (Bastawde 1992). These sugars are arranged in predominantly β-1,4 glycosidic linkages, including, but not limited to, xyloglucans, xylans, mannans, glucomannans, and β-glucans, named to reflect the predominant sugar in the backbone (Scheller and Ulvskov, 2010). Pectins are another diverse group of polysaccharides, which, like hemicellulose, cross-link cellulose microfibrils in plant cell walls. These carboxylic polysaccharides are comprised of a galacturonic acid backbone and sidechains including, but not limited to, D-galactose, L-arabinose 13 and D-xylose. These sidechains are used to characterize pectins into four major structural classes: homogalacturonan, rhamnogalacturonan I, xylogalacturonan, and rhamnogalacturonan II (Mohnen 2008). Homogalacturonan, composed of α-1,4-linked d-galacturonic acid residues, is by far the most prevalent and accounts for ~60% of the pectin in plant cell walls (Caffall & Mohnen 2009). Lignin is the most abundant aromatic biopolymer on earth and it is a large contributor to SOM (Kögel-Knabner, 2002) based on the inability of most soil organisms to degrade it. Until recently, fungi were thought to be the only organisms able to completely degrade lignin (Reid 1995); however, there are reports of bacteria that can break down lignin, represented mainly by three classes: Actinomycetes, α-Proteobacteria and γ-Proteobacteria (Bugg et al. 2011; Huang et al. 2013). Decomposition of lignin is primarily achieved by certain genera of Basidiomycota (“white‐rot fungi”), mediated by enzymes, such as laccases and peroxidases, and the free radicals produced in these reactions (Worrall et al. 1997). Lignin is the only plant cell wall polymer not composed of carbohydrate monomers; the main building blocks of this complex molecule are the hydroxycinnamyl alcohols (Lebo et al. 2001). Lignin is composed of branched polymers of coniferyl alcohol and sinapyl alcohol, with typically minor amounts of p-coumaryl alcohol. Like hemicelluloses and pectins, lignin is not a strictly defined structure and varies with source. The third most abundant biopolymer in nature (Shahidi et al. 2005), chitin, is a long-chain polymer of N-acetylglucosamine residues that form covalent β-1,4-linkages. Essentially chitin is cellulose with one hydroxyl group on each monomer replaced with an acetyl amine group, which increases hydrogen bonding, giving chitin comparatively greater strength. The other major structural polysaccharides of the fungal cell wall are glucans, polysaccharides of glucose monomers linked by glycosidic bonds (Synytsya & Novák 2013), which constitute approximately 50–60% of the cell wall by dry weight (Kapteyn et al. 1999). β-1,3-glucan 14 is the most abundant glucan (Fesel & Zuccaro 2016) and the main structural constituent to which other cell wall components attach (Bowman & Free 2006). Much of this β-1,3-glucan is covalently bound to β-1,6-glucan in the form of branching polysaccharides (Shahinian and Bussey, 2000) or to chitin through β-1,4 linkages (Latgé 2007). As with cellulose, many fungi and several bacteria produce the enzymes necessary to hydrolyze these glycosidic linkages (i.e., glycoside hydrolases), and, therefore, glucans are not a major component of SOM, but rather their hydrolysis products are (Schreiner et al. 2014). In addition to the main structural components of plant and fungal cell walls, SOM from habitats abundant in ericaceous plants are exceptionally rich in waxes (β-diketones from the ericaceous cuticle), cutins, tannins and other phenolic compounds; phenolics can reach over 30% of tissue dry weight in plants such as Calluna vulgaris (Jalal et al. 1983). Along with lignin, these lipids, waxes and other phenolics are the ‘recalcitrant’ constituents of SOM; they are considered more chemically resistant to decomposition and inaccessible to most microorganisms. One way to examine the role of ErM fungi in global carbon cycling in soils is to examine their ability to degrade the cell wall material contained in SOM. 1.4.2 Soil organic matter breakdown by cell wall degrading enzymes The majority of the enzymes involved in the breakdown of the complex molecules of SOM fall into two overlapping groups: carbohydrate active enzymes (CAZymes) and plant and fungal cell wall degrading enzymes (PFCWDEs). The CAZymes are defined as those enzymes involved in synthesis, metabolism and transport of carbohydrates (Lombard et al. 2014). Several PFCWDEs are included in this group, as well as many enzymes with auxiliary activities (AAs) such as lignin peroxidases and laccases (Lombard et al. 2014). PFCWDEs also include those enzymes responsible for non-carbohydrate components of cell walls and various other esterases and hydrolases, such as tannase and feruloyl esterases (TFEs) involved in providing accessibility for glycoside hydrolases (GHs) and polysaccharide lyases (PLs) to lignocellulose fibers by removing 15 ester bonds between plant polymers (Wong 2006). Throughout this thesis, I will use the term cell wall degrading enzymes (CWDEs) to encompass all the aforementioned enzymes. CAZyme classification is a sequence-based classification of enzymes that originated with the pivotal grouping of glycoside hydrolases by Henrissat et al. (1989) and has expanded to include different categories, further grouped into various families. These include glycoside hydrolases (GHs), glycosyltransferases (GTs), polysaccharide lyases (PLs), carbohydrate esterases (CEs) and carbohydrate-binding modules (CBMs) (Lombard et al. 2014). The AA enzymes are also included in this classification system; these enzymes do not catalyze reactions with carbohydrates, but instead cooperate with other CAZymes to provide access to polysaccharide components of cell walls (Levasseur et al. 2013). The GH enzymes (or glycosidases) catalyze the hydrolytic cleavage or rearrangement of the glycosidic bond in complex carbohydrates (Lombard et al. 2014). They currently comprise 153 families and include cellulases, chitinases, pectinases and a variety of other key metabolic enzymes that show varied frequency in different organisms. Some families are populated with many identified and characterized proteins (e.g., GH5 cellulases), other families do not target specific substrates (e.g., GH16), and still more contain only a few sequences with putative function or multiple functions dependent on the organism (Lombard et al. 2014). Glycosyl transferases (GTs) catalyze the formation of a glycosidic linkage to form a glycoside (Coutinho et al. 2003) and, as such, will not be considered in great detail here. Polysaccharide lyases (PLs) cleave polysaccharides using an elimination instead of a hydrolytic mechanism (Lombard et al. 2010), primarily modifying substrates such as pectins. The PLs are currently distributed across 28 CAZyme families, though only a few of these are found in fungi. The carbohydrate esterases (CEs) catalyze the deacylation of substituted saccharides, such as those found in xylan [a hemicellulose] (e.g., CE5) and pectin (e.g., CE8); they currently comprise 16 distinct families. 16 Redox enzymes classified as having Auxiliary Activities (AAs) are grouped together into eight families of ligninolytic enzymes – oxidases and oxidoreductases - and five families of lytic polysaccharide mono‐oxygenases (LPMOs) involved in the cleavage of chitin (AA10 and AA11), cellulose (AA9 and AA10, previously classified as GH61 and CBM33, respectively) and various other complex carbohydrates (as per CAZy database http://www.cazy.org/; Lombard et al, 2014). AA13 is the fifth family of enzyme with LPMO activity and contains only a few identified sequences (Lombard et al, 2014); the three characterized proteins are of fungal origin and are active on starch (Vu et al. 2014). Despite the huge effort towards classifying this diverse group of enzymes, many families and subfamilies are not well characterized outside of the model organism in which they were first discovered and enzymes are frequently reclassified as more information becomes available (Lombard et al. 2014). Functional characterization is available for many CAZymes, and many GH families, for example, display conserved substrate specificity; however, the substrate may be different for different organisms, or an enzyme may act on several substrates (Berlemont 2017). Only a fraction of these characterized enzymes has been assigned a PFam ID, which allows for domain identification within genomes based on Hidden Markov model (HMM) profile recognition (i.e., as of 2016, 68 of 153 GH families had been assigned a PFam ID (Finn et al. 2016)). Additionally, the vast majority of characterized CAZymes are multi-modular, which can lead to mis-annotation based on homology (Davies & Sinnott 2008); however, many fungal CWDEs contain only a single catalytic domain (Berlemont 2017), making them much more amenable to identification based on HMM profiles than multi-modular CWDEs from other organisms. CAZyme classification is continuously evolving and examination of these enzymes along with other CWDEs provides much needed fundamental knowledge on degradation of organic matter. 17 1.4.2.1 Substrate specificity of cell wall degrading enzymes Plant and fungal cell walls, while often discussed in terms of discrete compounds, are composed of complex superstructures (i.e., lignocellulose, hemicellulose, glucan/chitin) and their decomposition requires synergy between enzymes that target these chemically distinct, but physically linked substrates. For the purpose of this review, cell wall components will be considered discretely, but it is important to bear in mind that degradation of these composite biomolecules requires synergistic action by multiple enzyme groups. 1.4.2.1.1 Cellulose and hemicellulose Classical cellulose degradation is a synergistic process requiring three classes of enzymes: 1) β-1,4-endoglucanases (i.e., GH5, 12, 45), which randomly cleave the β-1,4 glycosidic linkages of cellulose fibers, generating shorter, more accessible fragments commonly referred to as cellodextrins; 2) exoglucanases/ cellobiohydrolases (i.e., GH6, 7), which hydrolyze chain ends to break the polymer into smaller sugars; and 3) a β-glucosidase (i.e.,GH1, 3) to hydrolyze the resultant disaccharides and tetrasaccharides into glucose (Béguin & Aubert 1994). These three enzyme classes are divided over several GH families (van den Brink & de Vries 2011), but cellulases may also be lytic polysaccharide mono‐oxygenases (i.e., AA9, formerly classified as GH61), which catalyze the oxidative cleavage of cellulose in the presence of an external electron donor (Beeson et al. 2012). According to the CAZy database (http://www.cazy.org/; Lombard et al, 2014), some AA10 enzymes (copper-dependent LPMOs) and all biochemically characterized AA9s (LPMOs) are active on cellulose. Recently, fungal swollenins – proteins similar to plant cell wall expansins – have also been implicated in cellulosic (and lignocellulosic) structure disruption or disaggregation (i.e., “amorphogenesis”) by way of nonhydrolytic mechanisms (Eibinger et al. 2016). The hemicellulases are either GHs or CEs depending on the hemicellulose type (i.e., xylosidase, xylanase, mannosidase, galactosidase, arabinosidase, glucanase). Similar to cellulose 18 degradation, a variety of enzymes are required for hemicellulose degradation including, but not limited to endo-β-1,4 xylanases (i.e.,GH10 and 11), α-L-arabinofuranosidases (i.e.,GH3, 10, 43, 51, 54, and 62), galactosidases (i.e.,GH27), -xylosidases (i.e.,GH31), β-xylosidases (i.e.,GH3, 39 and 43), mannosidases (i.e.,GH47), xyloglucanases (i.e.,GH12, 74), β-glucuronidases (i.e.,GH79), fucosidases (i.e.,GH95), α-glucuronidases (i.e.,115), acetylxylan esterases (i.e.,CE1–7), and ferulic acid esterases (i.e.,CE1) (Glass et al. 2013, Lombard et al. 2014). Hemicellulose degradation is also dependent on auxiliary activity (AA) enzymes such as lytic polysaccharide mono‐oxygenases (LPMOs). 1.4.2.1.2 Pectin With pectin being the most structurally diverse cell wall component, the number of CAZymes that contribute to its decomposition are copious and diverse (Martens-Uzunova & Schaap 2009). Pectins are degraded by enzymes from GH, CE, and PL families including polygalacturonases and rhamnogalacturonases (i.e., GH28), rhamnosidases (i.e., GH78), pectin and pectate lyases (i.e., PL1-3, 9), rhamnogalacturonan lyases (i.e., PL4), pectin methylesterases (i.e., CE8), acetylesterases (i.e., CE12 and 13), arabinanases (i.e., GH43 and 93), arabinosidases and α-arabinofuranosidases (i.e., GH3, 43, 51, 54, and 62), galactanases (i.e., GH5, 16, 30, 35, and 53), α- and β-galactosidases (i.e., GH1, 2, 4, 16, 27, 35, 36, 42, 43, and 53), β-glucuronidases (i.e., GH1, 2, and 79), glucuronyl hydrolases (i.e., GH88), rhamnogalacturonyl hydrolases (i.e., GH105) and feruloyl esterases (i.e., CE1), just to name a few (Glass et al. 2013). 1.4.2.1.3 Lignin and other aromatic components Lignin is metabolized by a variety of enzyme-mediated mechanisms including, but not limited to, those reactions catalyzed by laccases, ligninases, manganese peroxidases, lignin peroxidases (PODs), and glyoxal oxidase (an accessory enzyme that generates hydrogen peroxide (H2O2) as a substrate for PODs), the vast majority of which fall into various AA enzyme categories. Many fungi use Fenton-like reactions (free radical formation via H2O2 production: Fe2++ 19 H2O2 → Fe3++ OH- + OH●) to oxidize lignin (and various other polymers) via hydroxyl radical attack. The hydroxyl radical is highly reactive and is known to react rapidly with a wide variety of organic compounds in oxidative reactions; in the case of lignin, hydroxyl radicals disrupt the lignocellulose matrix (Arantes et al. 2012), allowing access to individual cell wall components (i.e., lignin, hemicellulose and cellulose). Putative ferric reductases, for example, have been implicated in lignin degradation, through examinations of Paxillus involutus (Rineau et al. 2012, Nicolás et al. 2019), whereby they modify polyphenol structure in a matter consistent with hydroxyl radical attack. The AA1 family is currently divided into three subfamilies including laccases, ferroxidases and laccase-like multicopper oxidases. The multicopper oxidases (MCO) are laccases that oxidize a variety of phenols, aromatics and diamines, including some capable of oxidizing lignin. These enzymes are found in all kingdoms of life but are best studied in fungi due to the ligninolytic function; Reiss et al. (2013) showed that fungal MCOs showed the broadest substrate specificity, compared to plant and bacterial laccases. Family AA2 contains class II lignin-modifying peroxidases (PODs); these enzymes are secreted and use H2O2 or organic peroxides as electron acceptors to catalyze a number of oxidative reactions (Levasseur et al. 2013). Examples include the manganese (Mn)-dependent peroxidases, which reduce H2O2, obtaining high redox potentials with the ability to oxidize a wide range of substrates in forest humus including phenolics (Bodeker et al. 2014). The O. maius genome does not contain any genes encoding AA2 PODs (Kohler et al. 2015). AA3 enzymes belong to the glucose-methanol-choline (GMC) oxidoreductases family, which contains four subfamilies. These enzymes are widespread and catalyze the oxidation of alcohols or carbohydrates with the simultaneous formation of H2O2 or hydroquinones (Levasseur et al. 2013), which act as the ultimate oxidizers in saprotrophic fungal decay. Most characterized 20 fungal AA3 enzymes promote lignocellulose degradation in cooperation with other AA-enzymes such as AA2 PODs (Ferreira et al. 2015) or AA9 LPMOs (Garajova et al. 2016). Family AA5 are copper radical oxidases and the family includes two subfamilies, AA5_1 containing characterized glyoxal oxidase and AA5_2 containing galactose oxidase and alcohol oxidases (http://www.cazy.org/; Lombard et al, 2014). AA5_2 enzymes from the phytopathogenic fungi Colletotrichum graminicola and C. gloeosporioides were recently implicated in Fenton-like chemistry, showing a broad substrate range, resulting in the oxidation of saturated and unsaturated primary alcohols to the corresponding aldehydes with the concomitant production of H2O2 from molecular oxygen (Yin et al. 2015) All experimentally characterized proteins in the AA6 family are 1,4-benzoquinone reductases. These are intracellular enzymes involved in the biodegradation of aromatic compounds and in the protection of fungal cells from reactive quinone compounds, like those produced during oxidative degradation. 1.4.2.1.4 Chitin and glucans Chitinases are produced by all kingdoms of life, and broadly classified as endo- and exo-chitinases; endochitinases cleave chitin chains in random locations, generating low molecular weight oligomers, such as chitotetriose, chitotriose, and diacetylochitobiose, whereas exochitinases release chitobiose from the reducing or non-reducing end of the chitin chain (Swiontek et al. 2013). The products of chitinases can be further converted to N-acetylglucosamine monomers by N-acetylglucosaminidases. Fungi produce and excrete nearly ten times more chitinases than other kingdoms (Hartl et al. 2012); these seemingly redundant enzymes presumably act synergistically to catabolize chitin in soil. Given that chitin is the main polysaccharide component of fungal cell walls, it is not surprising that there would be a high frequency and broad distribution of chitinases in fungal genomes; many are used in the maintenance, repair and degradation of endogenous cell walls. According to the CAZy database 21 (http://www.cazy.org/; Lombard et al, 2014), most characterized proteins from GH families 18, 19, 20, and 85 are chitinases, the CE4 family contains chitin deacetylases, and the AA10 LPMOs target either cellulose or chitin, whereas AA7 glucooligosaccharide oxidases (GOOs) and AA11 LPMOs are specific to chitin. In addition to chitin, fungal cell walls contain up to 60% glucan (Kapteyn et al. 1999), which is degraded by several glucanases, including GH55 (exo-β-1,3-glucanases), which are found exclusively in fungi. 1.4.3 Cell wall degrading enzymes in ericoid mycorrhizal fungi genomes The Oidiodendron maius genome was the first ErM fungal genome sequenced in the framework of the Mycorrhizal Genomics Initiative CSP#305 project headed by Francis Martin (INRA-Lorraine Centre of Nancy) in collaboration with the Joint Genome Institute (JGI, California) (v1.0 May 2011 https://genome.jgi.doe.gov/Oidma1/Oidma1.home.html, Kohler et al. 2015, Martino et al. 2018). The O. maius genome contains 835 predicted CAZyme genes, more than half of which (494) are GHs; the remainder consist of 91 carbohydrate-binding moieties (CBMs), 37 CEs, 12 PLs, 126 GTs, 68 AAs and five enzymes distantly related to plant expansins, putatively described as swollenins (Kohler et al. 2015; Appendix A). Three other ErM fungal genomes have been sequenced since then: Rhizoscyphus ericae1, Meliniomyces2 variabilis and Meliniomyces2 bicolor (Martino et al. 2018); all four ErM genomes contain a higher median number of total CAZymes than 57 other fungal genomes (six saprotrophic or pathogenic Leotiomycetes and 50 other Ascomycetes and Basidiomycetes representing different life strategies). Furthermore, all four ErM fungi possess an expansive repertoire of CWDE genes, especially those involved in the degradation of hemicelluloses, pectins, glucans and mannans, to the extent that their CWDE repertoire is more similar to that of plant pathogens and soil generalist saprotrophs than that of other mycorrhizal fungi (Martino et al. 2018). ErM genomes also contained the highest number of genes encoding GHs and GTs, while PL genes were mostly absent, except for pectin lyases (PL1) (Martino et al. 2018). In contrast to other ErM genomes, O. maius also contains genes encoding 22 PL families 4, 7 and 20 (Kohler et al. 2015, Martino et al. 2018). Two cellobiohydrolase families - GH6 and GH7 - show expansion and similar frequency in the genomes of ErM, saprotrophic and pathogenic fungi compared to ECM fungi (Kohler et al. 2015; Martino et al. 2018). Genomes of the four ErM fungi studied by Martino et al. (2018) contained nearly twice the median number of lipase genes as any other type of fungus (mostly coding for secreted CEs), a greater number of AA genes coding for laccases (AA1), cellobiose dehydrogenases (AA3) and LPMOs involved in the cleavage of chitin (AA11) and cellulose (AA9), as well as MCOs involved in lignin degradation (AA1). Monooxygenases (MOs) involved in the degradation of complex polyaromatic organic molecules such as lignin (Cerniglia & Sutherland, 2010) are also abundant in these ErM fungi (Martino et al. 2018). GH enzymes acting on cellulose (GH5s), hemicellulose and/or pectin (GH27, 28, 53, 54), -1,3-glucans (GH55, 72, 132) and mannans (GH76) were significantly enriched compared with all other fungi (Martino et al. 2018). Comparing the ErM genomes to those of ECM fungi or brown/white rot saprotrophs, genes for AA enzymes acting on cellulose (i.e., AA8, AA12) were significantly enriched (Martino et al. 2018). When examining the secreted CAZyme repertoire, O. maius was most similar to a general soil saprotroph (Thielavia terrestris) and a pathogenic Ascomycete (Cryphonectria parasitica), neither belonging to the Leotiomycetes. By contrast, members of the R. ericae1 aggregate (REA) clustered with two saprotrophic Leotiomycetes, (C. longipes and A. sarcoides), suggesting differences in substrate preference among ErM fungal species. The O. maius genome contains more genes responsible for cellulose recognition (e.g., nearly three times as many CBM1 domains), hemicellulose and pectin degradation (e.g., GH2, 27, 79), as well as a greater number of cellulose-binding domains (CBM1 and CBM6), but fewer CAZymes involved in chitin recognition and degradation (e.g., CBM18, CBM50, CE4, AA7) than those species in the REA (Martino et al. 2018). Meliniomyces2 genomes contained a larger set of genes coding for secreted lignocellulose-23 degrading enzymes (e.g., laccases – AA1, cellobiose dehydrogenases – AA3, and galactose oxidase – AA5) than O. maius and R. ericae1 (Martino et al. 2018). While the ability of ErM fungi to degrade a wide range of complex substrates is widely accepted based on the work of several key research groups (Rice & Currah 2005, 2006; Smith & Read, 2008), the recent work by Martino et al. (2018) confirms that this ability to digest a variety of organic compounds found in SOM is explained by the abundance and variety of genes coding for CWDEs. Advances in molecular techniques have allowed for a deeper examination of SOM degradation by ECM fungi and, with the continuing effort toward the 1000 fungal genomes project, the picture continues to become clearer. With the recent publication of several ErM genomes, researchers can now focus on functional gene analyses and examine in greater detail the regulation of ErM saprotrophy and symbiosis in situ, leading to a deeper understanding of carbon cycling within ecosystems. 1.4.3.1 Transcription of cell wall degrading enzymes Despite the general confidence in the SOM-degrading capabilities of ErM fungi based on genome annotation, the presence of CAZyme sequences in fungal genomes may not equate with active production of the corresponding enzymes. For example, in forest soils, only an estimated 30% of the laccase genes detected are transcribed (Luis et al. 2005). In order to be confident that these CWDEs are being actively produced in situ, expression of the genes must be demonstrated. By definition, gene expression refers to the process by which a ‘functional gene product’ is synthesized; ‘gene product’ can refer to either a functional protein or a functional RNA molecule, as in the case of non-protein coding genes (i.e., transfer RNAs, small nuclear RNAs). Several steps in the gene expression process may be modulated, including the transcription, RNA splicing, translation, and post-translational modification of a protein (Raven et al. 2013). Gene expression is typically quantified by way of messenger RNA, though this is more accurately termed transcript abundance as it does not take into account the various modifications that may occur prior to 24 protein production; however, for consistency with recent literature (Martino et al. 2018, Wu et al. 2018), the terms gene expression and transcript abundance will be used interchangeably here. The sum total of all these mRNA molecules expressed by an organism at any given time is termed its transcriptome. Once a transcriptome has been obtained, there are several ways to annotate it. One approach is to use the JGI Annotation Pipeline (Joint Genome Institute, US Department of Energy, California), which predicts genes using a variety of methods to compare gene models against highly curated databases such as SWISS-PROT (Bairoch 2000), Kyoto Encyclopedia of Genes and Genomes (KEGG; Ogata et al. 1999), and Pfam (El-Gebali et al. 2019), which are then used to map Enzyme Commission (EC) numbers (Bairoch 2000), Gene Ontology (GO) terms (Ashburner & Ball 2000) and euKaryotic Orthologous Groups (KOG) classification (Koonin et al. 2004). However, even when a high-quality genome or transcriptome is generated, it can be difficult to provide functional annotation if there is a lack of characterized homologs available within databases. In these cases, most annotation pipelines search for functional domains within the transcripts to infer biological function to infer a GO term or other classification. The vast majority of Carbohydrate Active Enzymes (CAZymes) and Cell Wall Degrading Enzymes (CWDEs), though, do not have defined GO identities, or fall into multiple categories, which renders these applications unusable. The protein sequence-based classification of CAZymes (Henrissat 1991) was initiated in 1991 as a complement to the long-standing EC number system (Webb 1992), which is based solely on enzyme activities, to unify information on enzyme structure, specificity, and mechanism. The initial classification scheme presented by Henrissat (1991), proposed 35 GH families with the caveat that it was “evident that a number of other families remain to be discovered”. As of August, 2019, the continuously updated CAZy database (www.cazy.org; Lombard et al. 2014) contained 165 GH families (four of which are subclassified), 25 107 GT families, 37 PL families, 16 CE families and 16 AA families (3 of which were discovered within the last year). The O. maius transcriptomes for both the free-living mycelium and the fungus in symbiosis with V. myrtillus (both grown on modified Melin Norkrans (MMN) media supplemented with BSA) were sequenced by Kohler et al. (2015); under these conditions O. maius transcribed a rich repertoire of CWDEs, strongly upregulated during symbiosis, suggesting a use in penetrating host cells. Additionally, several glycoside hydrolases targeting pectin, cellulose and hemicellulose, were upregulated in the symbiosis as compared to the free-living mycelium (Kohler et al. 2015 supplementary material). The recent study by Martino et al. (2018) also identified transcripts induced during ErM symbiosis in four ErM fungal genomes and found that O. maius demonstrated a higher number and percentage of symbiosis-regulated genes in most categories, including CWDEs. Of those differentially regulated CAZymes, secreted enzymes targeting pectin (PL1, CE8, GH28) and hemicellulose (GH27, GH43, GH95) were the genes most highly upregulated in symbiosis (Martino et al. 2018), whereas the highest transcript abundance in symbiosis was found for those CAZymes acting on cellulose (e.g., GH7-CBM1, GH5, GH10-CBM1, GH6-CBM1, GH62-CBM1). Despite this recent molecular work by Kohler et al. (2015) and Martino et al. (2018), studies examining gene expression in ErM fungi in response to carbon source have not been conducted. The only other transcript abundance studies on ErM fungi to date have focused on stress response and metal tolerance (i.e., Vallino et al. 2005, Abbà et al. 2009, Daghino et al. 2016). Functional complementation of yeast mutations was used for the first time in ErM fungi to demonstrate the role of Cu/Zn superoxide dismutase (SOD) enzyme in metal tolerance (Vallino et al. 2009) and heterologous expression of O. maius genes in yeast has since demonstrated transfer of cadmium resistance (Di Vietroa et al. 2014). Casarrubia et al. (2018) point out that whereas effector small secreted proteins (SSPs) have been characterized in arbuscular and ECM 26 fungi, their recent article is the first to examine the occurrence of SSPs in ErM fungi and on their potential role in symbiosis. This lack of understanding of the functional role of ErM fungi and paucity of information regarding transcript abundance of CWDEs highlights the need for research that examines the functional response of ErM to changing SOM composition. The research presented herein endeavours to gain insight into the biology and evolution of these ubiquitous fungi. 1.5 The Saprotrophy-Mutualism Continuum The early view of symbiosis suggests that all long-term intimate associations tend to evolve toward mutualism, though Hibbett et al. (2000) suggested that symbiosis can shift along a continuum of interactions ranging from mutualism to parasitism. By examining rDNA small and large subunits of 161 fungal species, these researchers were able to show that there have been at least 15 transformations between ECM and free-living lifestyle; mycorrhizal symbionts have evolved repeatedly from saprotrophic precursors and there have been multiple reversals to free-living forms (Hibbett et al. 2000). They show that the ancestor of the homobasidiomycetes was free-living and that wood decay is the plesiomorphic condition in this group (Hibbett et al. 2000). Furthermore, the lignin-degrading peroxidases expanded in the lineage leading to the ancestor of the Agaricomycetes and then contracted in parallel lineages leading to brown rot and ECM fungi and the rate of duplication of these peroxidase genes is elevated in white rot lineages (Floudas et al. 2012). These studies demonstrate the substantial plasticity of the fungal genome and emphasize the need for more information as more molecular tools become available. ErM symbiosis is the most recently evolved mycorrhizal type, with diversification of ErM-forming lineages of Ericaceae estimated at 90–75 million years ago (Carpenter et al. 2015); ErM fungi presumably evolved independently across several lineages in the same timeframe (Cairney 2000). This recent divergence is most likely the reason for the maintained presence of many ‘saprotrophic’ genes in ErM fungal genomes as compared with the significant losses of many 27 CWDEs seen in ECM fungi. Similarly, many fungal root endophytes maintained the presence of CWDE genes in the transition from saprotrophism to endophytism (Fesel & Zuccaro, 2016). Martino et al. (2018) speculate that ErM fungi retain saprotrophic genes because, unlike ECM fungi, they are still transitioning between saprotrophy and mutualism. Straker (1996) suggests that the ErM symbiosis was driven by the selective advantages conferred to the plant partner by a fungal symbiont able to live saprotrophically in environments with extreme acidity, low nutrient availability, and high accumulation of recalcitrant organic matter. Given the ‘young’ age of the ErM symbiosis, Brundrett (2002) argues that if ErM fungi can persist as soil saprotrophs, then symbiosis would not have been a strong factor driving their evolution, but this is where our knowledge of ErM fungal physiology, genetics and natural history is lacking. Koide et al. (2008) presented the first reference to a “biotrophy-saprotrophy” continuum: they proposed a re-examination of the “original arrangement of pigeonholes” that fungi have been placed in, arguing that fungi occupy a range of trophic categories. If biotrophy, or symbiosis, evolved from saprotrophy as suggested by Hibbett et al. (2000), it would be reasonable to assume that some mycorrhizal fungi would still possess saprotrophic characteristics, especially under selective pressure of occasionally losing contact with the plant partner (Koide et al. 2008). As more genomes and transcriptomes become available, the characterization of fungi along this continuum will become clearer. Aguilar-Trigueros et al. (2014) propose a trait-based approach as this data becomes available that would clarify evolutionary histories, provide understanding and predictive measures of functional role in N and carbon dynamics within ecosystems and provide a mechanistic understanding of fungal community structure and maintenance of diversity. Irrespective of how mycorrhizal fungi acquire their C, they clearly play a critical and underestimated role in global carbon cycling and carbon sequestration; therefore, the need for more research on these diverse and essential organisms is indisputable. Regardless of whether or not degradation of complex organic compounds implies saprotrophy, mycorrhizal fungi contribute 28 to soil carbon cycling. While the conventional view is that mycorrhizal fungi are vectors for plant carbon input into soils, the mounting evidence (Averill et al. 2014) suggests that even a minor involvement in the enzymatic release of carbon could have global consequences regardless of whether fungi utilize the released carbon or not. 1.6 Research Objectives & Predictions Transcriptomic studies aim to provide a functional view of an organism by identifying genes actively expressed under a set of metabolic conditions. Recent data show that O. maius transcribed a prolific variety of CWDEs in symbiosis, suggesting a use in penetrating partner plant cells (Kohler et al. 2015); several glycoside hydrolases targeting pectin, cellulose and hemicellulose, were upregulated in the symbiosis as compared to the free-living mycelium (Kohler et al. 2015 supplementary material). Therefore, I have the ideal opportunity to compare these data to transcriptome data of free-living O. maius mycelium grown on complex organic matter. This will allow assembly of a list of differentially transcribed genes in the free-living fungus in its saprotrophic state versus the fungus in symbiosis. Some of the transcripts that are upregulated in O. maius mycelium on complex organic matter would be candidates for further study of saprotrophy in this fungus. Given that O. maius has been isolated as a free-living saprotroph, genes involved in carbon acquisition are expected to be transcribed when the fungus is grown on complex organic matter. These genes would be expected to be ‘turned off’ or transcribed at basal levels when the fungus is grown on a simple carbon source, such as glucose. By targeting differentially transcribed genes in O. maius grown under different conditions (i.e., grown on soil, or the hot water extractable component of this soil), transcript abundance profiles can be compiled to gain insight into the intricacies of the fungal lifestyle. If the fungus is grown on substrates of increasing carbohydrate complexity, it would be expected that both the variety of genes transcribed and the transcript abundance would vary. 29 My first objective (presented in Chapter 2) was to determine which CWDE genes would be differentially transcribed in free-living O. maius mycelium grown on glucose-containing minimal media (Modified Melin Norkrans – MMN) compared to growth on MMN supplemented with a more complex carbon source - peat; a third growth condition included media containing supplemental bovine serum albumin (BSA) to allow comparison with previously published transcriptomes. I predicted that transcription of many CWDEs would be significantly upregulated in O. maius grown on peat as compared with the fungus grown on a defined medium with glucose as the carbon source. My second objective (presented in chapters 3 and 4) was to determine whether the transcript abundance of these CWDE genes differed when O. maius was grown on carbon sources of varying complexity. Differentially transcribed genes, as determined from whole transcriptome sequencing, were quantified by ddPCR when the fungus was grown on a greater variety of carbon sources: glucose, bovine serum albumin (BSA), cellulose, chitin, lignin, field soil, peat, and dissolved organic matter from peat and field soil. I predicted that the level of complexity of the organic matter substrate would determine the transcript abundance of these genes; the more complex the substrate, the higher the level of transcription and greater the variety of CWDE genes transcribed. Specifically, I expected the greatest diversity of CWDE genes upregulated in the field soil and peat treatments. Likewise, I expected low diversity and abundance of CWDE genes on the minimal media (Modified Melin Norkrans; MMN) treatment. Substrates containing cellulose, chitin, pectin or lignin were expected to lead to upregulation of genes corresponding to cellulases, chitinases, pectin esterases and lignin degradation enzymes, respectively. Regardless of the outcome, this research aims to make a contribution to understand the functional role of these ErM fungi in soil carbon cycling and to better predict their involvement in carbon sequestration and release. 30 Chapter 2 Transcriptome 2.1 Synopsis To investigate the influence of carbon source on transcript abundance of cell wall degrading enzyme (CWDE) genes, RNAseq analysis was conducted on Oidiodendron maius cultivated on minimal media containing glucose – an easily metabolized carbon source, with the expectation that mostly constitutive genes would be expressed – compared to growth on the more complex carbon sources bovine serum albumin (BSA) and peat. Peat was selected as a substrate of high carbon complexity, predicted to induce expression of a wide variety of carbohydrate active enzymes (CAZymes) and other CWDEs. BSA was selected as a treatment in order to allow comparison of my own results with those of Kohler et al. (2015). A total of 825 distinct transcripts containing CAZyme domains or otherwise classified as CWDEs were detected. These putative CWDEs include cellulases, hemicellulases, chitinases and pectinases, supporting the conjecture that O. maius has the ability to grow on a wide variety of complex carbon substrates. Significant upregulation of 23 transcripts on peat and 16 transcripts on BSA, in comparison to growth on glucose, was observed. Among the most highly upregulated CWDEs on peat were feruloyl esterases (active on hemicellulose, pectin, and aromatics), glycoside hydrolase families GH11 and GH27 (both active on hemicellulose), GH31 (active on cellulose and hemicellulose), GH55 (active on glucans), a multicopper oxidase belonging to Auxiliary Activity family AA1 (active on aromatics) and a GH105 (active on pectins). This comprehensive analysis of the CWDE transcriptome of O. maius provides detailed insight into its response to growth on differing carbon sources. This study facilitates further exploration of the various lignocellulolytic enzymes of this ubiquitous fungus, including the identification of candidate target genes for more detailed analysis. 31 2.2 Materials & Methods 2.2.1 Strain [and genome sequence] Oidiodendron maius Zn (CLM1381.98) was isolated from roots of Vaccinium myrtillus growing in zinc-contaminated experimental plots in the Niepolomice Forest, Poland (25 km NE of Krakow) according to Pearson and Read (1973) (Martino et al. 2000). This isolate was inoculated onto seedlings of Calluna vulgaris under axenic conditions and mycorrhization was confirmed by observation of infiltration of epidermal hair root cells with hyphal coils typical of ericoid mycorrhizae (Perotto et al. 1995). The O. maius Zn genome was sequenced (Assembly v1: May 2011) in the framework of the Mycorrhizal Genomics Initiative CSP#305 project headed by Francis Martin (Institut national de la recherche agronomique - Lorraine Centre of Nancy, France) in collaboration with the Joint Genome Institute (Department of Energy Joint Genome Institute (JGI), California) [https://genome.jgi.doe.gov/Oidma1/Oidma1.home.html]. Moreover, the O. maius transcriptomes for both the free-living mycelium and the fungus in symbiosis with Vaccinium myrtillus (both grown on MMN + BSA) were also sequenced (Kohler et al. 2015). Annotation of the O. maius Zn genome (v1 May 2011) was performed using the JGI Annotation Pipeline (Kohler et al. 2015). Both the O. maius Zn genome and the transcriptome discussed here were further subjected to the CAZy database annotation pipeline (Lombard et al. 2014), which is regarded as the gold standard method (Zhang et al. 2018) and includes some manual annotation by CAZy curators. Due, however, to the ever-expanding nature of this classification scheme, I included several additional transcripts in the analysis of CWDEs, which are not necessarily classified as CAZymes, but may have similar activity, function, substrate, specificity or mechanisms. 32 2.2.2 Culture conditions After 45 days of growth on solid Czapek-glucose medium (3 g L-1 NaNO3, 1 g L-1 K2HPO4, 0.5 g L-1 MgSO4*7H2O, 0.01 g L-1 FeSO4*7H2O, 0.5 g L-1 KCl, 20 g L-1 glucose, 10 g L-1 agar, adjusted to pH 6) in the dark at 22 °C, O. maius was transferred to media containing one of three treatments. Each plate contained solid Modified Melin-Norkrans (MMN) media (1 g L-1 glucose, 0.5 g L-1 KH2PO4, 0.066 g L-1 CaCl2*2H2O, 0.025 g L-1 NaCl, 0.15 g L-1 MgSO4*7H2O, 0.1 g L-1 thiamine HCl (filter sterilized, 0.2 m, added after autoclaving), 0.001 g L-1 FeCl3*6H2O, 0.075 g L-1 (NH4)2HPO4 (filter sterilized, 0.2 m, added after autoclaving), 10 g L-1 agar, adjusted to pH 4.7), amended as follows (Table 2.1): 1) (NH4)2HPO4 and glucose omitted, 0.1 g L-1 bovine serum albumin added [FLM/BSA], 2) no further modifications [FLM/MMN], or 3) (NH4)2HPO4 and glucose omitted, 10 g (dry weight) ion-irradiated, rehydrated peat [FLM/Peat] (Commercial peat (Sunshine brand, Sun Gro Horticulture Canada Ltd.) was sterilized via electron beam radiation at Iotron Industries Canada Inc. at 35 kGy). Each 25 mm-deep Petri dish was overlaid with a sterile cellulose membrane [Pacon® cellophane wrap clear, autoclaved twice in sterile water with 24 h in between sterilizations] and inoculated with 5-7 fungal plugs from the Czapek agar. These plates were incubated for 45 days in the dark at 22 °C, then all tissue was recovered by scraping the cellophane with a sterile scalpel, harvested with sterile forceps, flash frozen in liquid nitrogen and stored at -80 oC prior to extraction. Table 2.1. Amendments made to Modified Melin Norkrans media to support growth of Oidiodendron maius. Treatment Name Glucose (1 g L-1) (NH4)2HPO4 BSA (0.1 g L-1) Peat1 (~10 g dry weight) Notes FLM/BSA - - + - “Control” FLM/MMN + + - - FLM/Peat - - - + 1 Sunshine brand, Sun Gro Horticulture Canada Ltd.; sterilized via electron beam radiation (Iotron Industries Canada Inc.; 35 kGy) 33 2.2.3 RNA extraction and cDNA synthesis Up to 100 mg of flash-frozen fungal tissue was mechanically disrupted by grinding in liquid nitrogen in a sterile mortar and pestle and placed in RNase-free 1.5 mL Eppendorf tubes on ice. To each tube was added 700 L extraction buffer (100 mM Tris-HCl pH 8, 100 mM NaCl, 20 mM Na-EDTA, 0.1% PVP, 1% sodium-lauryl sarcosine, prepared in DEPC water) and 700 L acid phenol before gentle inversion. Tubes were centrifuged for 5 minutes at 14000 rpm (4 °C); all subsequent centrifugation was also performed at 14000 rpm (4 °C). The uppermost phase of the supernatants was transferred to new Eppendorf tubes on ice to which was added an equal volume of acid phenol-chloroform-isoamyl alcohol (25:24:1). Tubes were gently inverted several times, then centrifuged for 5 minutes. Supernatants were transferred to new tubes containing an equal volume of chloroform, gently inverted several times and then centrifuged for 5 minutes. This chloroform extraction was repeated once and tubes centrifuged for 5 minutes. Total nucleic acids were precipitated by addition of an equal volume of isopropyl alcohol to the supernatant and gentle inversion. Tubes were incubated for 30 minutes at -80 °C, then centrifuged 30 minutes. Supernatant was discarded and the pellet was resuspended in 500 L DEPC water. To each tube was added 500 L 6 M LiCl, then the tubes were gently inverted and kept overnight in ice at 4 °C. Tubes were centrifuged 30 minutes, then the supernatant was transferred to new tubes containing an equal volume of isopropyl alcohol and maintained at -80 °C for genomic DNA preparation. The remaining pellet was washed with 150 L 70% ethanol (prepared with DEPC water), centrifuged 5 minutes, supernatant discarded and dried on ice in the fume hood. Once completely dry, the pellet was resuspended in 25 L DEPC water, then concentration and quality were determined using the NanoDrop ND-1000 UV-visible light spectrophotometer (NanoDrop, Wilmington, DE, USA). Only RNA samples with 260/280 nm wavelength ratio of approximately 2 and 260/230 nm wavelength ratio of approximately 2 were retained for sequencing and further experimentation. The quality of total RNA was established by running approximately 1 μg on an RNase-free 1.5% agarose gel in 34 Tris/Borate/EDTA (TBE: 45 mM Tris-borate and 1 mM EDTA) and staining with SYBR® Green II (Invitrogen). Quality was further confirmed by RNA Integrity Number (RIN) determination on the Agilent 2100 bioanalyzer and RNA LabChip® at PlantBiosis Ltd (Lethbridge, Alberta, Canada) prior to library construction. 2.2.4 Library construction and transcriptome sequencing Library construction was performed at PlantBiosis Ltd (Lethbridge, Alberta, Canada) using the Illumina® TruSeq RNA Access Library Prep Kit (Cat. No. RS-301-2001 and RS-301-2002, now called TruSeq RNA Exome) as per manufacturer’s instructions, which enriches poly-A RNA from low input, low quality samples. Transcriptome sequencing was also performed by PlantBiosis Ltd (Lethbridge, Alberta, Canada) on the Illumina® NextSeq500 machine using the Illumina® NextSeq 500/550 High Output v2 kit (75 cycles) (Cat. No. FC-404-2005) as per manufacturer’s instructions. Two biological replicates were sequenced for each of the three treatments; one set of biological replicates was single reads, the second was paired. 2.2.5 In-silico analysis of RNA sequencing data Initial processing of RNA sequencing (RNA-Seq) data was performed by Annegret Kohler (INRAE, Nancy) using CLC Genomic workbench v6. In brief, raw reads were trimmed and aligned to the reference transcripts available at the JGI MycoCosm database (http://genome.jgi-psf.org/programs/fungi/index.jsf) and identification of CAZyme families was achieved using the CAZy database (www.cazy.org) annotation pipeline (Lombard et al. 2014). The unique and total mapped reads number for each transcript were determined, and then normalized to reads per kilobase of exon model per million mapped reads (RPKM). For complete details, refer to Kohler et al. (2015). The complete data sets were deposited in NCBI's Gene Expression Omnibus (Edgar et al., 2002) and are accessible through GEO Series accession number GSE163739. There are several well-established methods for normalizing transcriptome data, but there is no clear consensus among the scientific community as to the most effective statistical methods of 35 normalization (Soneson & Delorenzi, 2013). To obtain a comparable expression level of transcripts in my research, the average of the RPKM means of the two biological replicates was calculated for each treatment. The use of RPKM measure of read density reflects the molar concentration of a transcript in the starting sample by normalizing for RNA length and for the total read number in the measurement; this allows for clear comparison of transcript levels both within and between samples. To identify differentially regulated transcripts across the three treatment groups, Baggerly et al. (2003)’s Test was applied to the data; this test compares the proportions of counts in pairs of groups. The samples are given different weights depending on their sizes (total counts); these weights are obtained by assuming a beta distribution on the proportions in a group, and estimating these, along with the proportion of a binomial distribution, by the method of moments, resulting in a weighted t-type test statistic. In addition, Benjamini & Hochberg (1995) multiple-hypothesis testing corrections with False Discovery Rate (FDR) were used. The online tool Heatmapper (Babicki et al. 2016) was utilized to construct the heatmap from RPKM Means Sums; no clustering method was used. 2.3 Results & Discussion 2.3.1 RNA isolation, library construction and transcriptome composition RNA yields were low, with final RNA concentrations averaging about 10 ng RNA mg-1 tissue (40 ng µL-1 per 25 µL volume from 100 mg of tissue) for all three treatments. A more typical yield has been reported as five to ten times higher from other filamentous fungi using similar phenol-chloroform extractions (Leceilier & Silar 1994, Sánchez-Rodríguez et al. 2008). This low yield may be due, in part, to the arbitrary 45 days of growth; the fungi may have exhausted the available nutrients from the media, which could potentially have downstream effects on those transcripts produced. Because of the low yields, extracted RNA was pooled to obtain enough material for library construction. Cleaning and assembly of RNA-Seq-generated reads resulted in 36 over 30 million reads per treatment, with read counts and alignment summarized in Table 2.2. Yield of RNA sequencing reads from Oidiodendron maius grown on three different substrates, with two biological replicates each, after cleaning and assembly for all treatments and replicates. The alignment achieved (~70 %) is consistent with other comparable studies (Kohler et al. 2015 Supplementary Table 11). The CLC parameters used do not allow alignments with large gaps and therefore do not support mapping of spliced reads onto the reference genome, which may account for a large proportion of the unmapped reads. Similarly, reads that contain repetitive sequences or transcripts that are part of large multigene families may map to multiple locations (Treangen & Salzberg 2011), so these alignments were removed. Table 2.2. Yield of RNA sequencing reads from Oidiodendron maius grown on three different substrates, with two biological replicates each, after cleaning and assembly Substrate Replicate Longest Reads Total Reads Aligned Not Aligned % Aligned BSA 1 76 bp 28828125 20008329 8819796 69.41 BSA 2(paired)a 2956642 1984007 972635 67.10 Peat 1 76 bp 33391333 24690601 7800732 73.94 Peat 2(paired)a 3436092 2463190 972902 71.69 MMN 1 76 bp 30152819 22021177 8131642 73.03 MMN 2(paired)a 3165910 2248816 917094 71.03 a: one set of biological replicates was single reads, the second was paired A total of 825 distinct transcripts containing CAZy domains or otherwise classified as CWDEs were identified in cultures grown on at least one of the substrates; these included all 752 genes annotated as containing CAZy domains (Assembly v1: May 2011; https://genome.jgi.doe.gov/Oidma1/Oidma1.home.html). An additional 21 CAZy domain-containing transcripts were identified (17 GHs) based on putative function or gene ontology, as well as 8 superoxide dismutases (SODs), 13 tannase and feruloyl esterases (TFEs), and 31 chitinases. Those transcripts that contained only CBM or GT domains were removed from further analysis (13 and 130, respectively). The remaining 680 transcripts were classified into 70 GH, 10 CE, 8 AA and 4 PL families, with five transcripts identified as distantly related to plant expansins. The vast majority of these transcripts encode putative enzymes involved in plant biomass deconstruction, though several families are responsible for the degradation of fungal cell wall 37 components, bacterial cell wall components and various sugars including starch and trehalose. The expansin-related transcripts were included because, although the molecular mechanism by which expansin disrupts the cellulose-hemicellulose association is not yet established, it is hypothesized to disrupt the non-covalent adhesion of hemicellulose on the surface of the cellulose microfibrils (Georgelis et al. 2014). Most transcripts (>99%) were transcribed on all three carbon sources at varying levels. 2.3.2 Differential transcript abundance across varying carbon sources 2.3.2.1 RPKM means sums by family Expression levels are depicted by family in Figure 2.1. Expression analysis of CAZyme families in mycorrhizal tissue (MYC) and free-living mycelium (FLM) of Oidiodendron maius during cultivation on Modified Melin Norkrans (MMN) media. showing the RPKM means sums for each of these families with the number of representatives of each family in parenthesis across my three experimental treatments and compared to the corresponding values from Kohler et al. (2015) for free-living mycelium (FLM) and O. maius in symbiosis with V. myrtillus (MYC), both grown on MMN + BSA (see 2.2.2). The inclusion of the MMN + BSA treatment in my experiment was intended to allow direct comparison of my own results with those of Kohler et al. (2015). The absolute transcript abundance in RPKM cannot be directly compared between these analyses as the experiments were conducted independently, with gene length (K) and sequence depth (M) parameters normalized within the individual transcriptomes for each experiment. Relative expression trends in each treatment can, however, be compared to identify common expression patterns across treatments.38 Figure 2.1. Expression analysis of CAZyme families in mycorrhizal tissue (MYC) and free-living mycelium (FLM) of Oidiodendron maius during cultivation on Modified Melin Norkrans (MMN) media. Reads per kilobase of exon model per million mapped reads (RPKM) values for free-living O. maius grown on MMN (FLM/MMN), MMN without glucose and (NH4)2HPO4, poured over ~10 g (dry weight) sterile commercial peat (FLM/Peat), or MMN without glucose and (NH4)2HPO4, supplemented with 0.1 g L-1 bovine serum albumin (FLM/BSA) from the research described in this thesis are compared to corresponding values from Kohler et al. (2015) for free-living mycelium (FLM/BSA) and O. maius in symbiosis with V. myrtillus (MYC/BSA), both grown on MMN without glucose and (NH4)2HPO4, supplemented with 0.1 g L-1 BSA. The sum of RPKMs is given for each CAZyme or CWDE family and graduated in red. Colour coding of text refers to the putative substrate for each enzyme family. Number of representatives of each family is given in parentheses. 2.3.2.1.1 Hemicellulose-active enzymes Hemicelluloses are the carbohydrates present in greatest quantity in most peats, though the specific structural types can vary greatly depending on the origin (Fuchsman 1980). The greatest RPKM means sum for any family was that of glycoside hydrolase family GH16 (with 16 39 representatives). These enzymes are endo-1,3-1,4-β-glucanases, active on β-1,4 or β-1,3 glycosidic bonds in hemicelluloses (and various glucans and galactans). The highest RPKM means sums were seen in the peat treatment, followed by the BSA treatment; the Kohler et al. (2015) MYC treatment also showed upregulation compared to the FLM. Several other families active on hemicelluloses also showed higher RPKM means sums in the peat treatment than the BSA or MMN treatments, including GH17 (endo-β-1,3-glucanases); CE5 (cutinases); GH3 ( β-glucosidases and β-xylosidases); GH31 (α-glucosidases and α-xylosidases); GH11 (endo-β-1,3-1,4-xylanases); GH92 (α-mannosidases); GH27 (α-N-acetylgalactosaminidase / α-galactosidases); GH79 (β-glucuronidases); GH10 (xylanases); GH30 (β-1,6-glucanases); GH95 ( α-1,2-L-fucosidases); CE2 (acetyl xylan esterases); CE1 (Acetyl xylan/feruloyl esterases); and GH54 (α-L-arabinofuranosidases / β-xylosidase). All of these enzymes, while potentially active on hemicelluloses, have fairly broad substrate specificity and may be active on cellulose, glucans or other cell wall components; very few members of these families have been well characterized in fungi and none in non-model ascomycetes. All of these hemicellulose-active enzyme families showed similarly high RPKM means sums in the Kohler et al. (2015) MYC treatment compared to the FLM treatment. 2.3.2.1.2 Cellulose-active enzymes Nearly all cellulose-active CWDE families showed higher RPKM means sums for the peat treatment than the BSA or MMN treatments, with the exception of GH12 (endo-β-1,4-glucanases), AA11 (lytic polysaccharide monooxygenases), expansins, GH131 (exo-β-1,3/1,6-glucanases), AA7 (glucooligosaccharide oxidases / chitooligosaccharide oxidases) and AA12 (oxidoreductase) families. The MYC treatment (Kohler et al. 2015) similarly showed higher RPKM means sums than the FLM treatment (Kohler et al. 2015) for nearly all cellulose-active CWDE families, with the exception of AA3_2 (galactose oxidases / alcohol oxidases) and AA7 families, both of which have multiple putative plant cell wall substrates. It is possible that the apparent downregulation of the 40 glucanases and AA family enzymes in the peat treatment was actually upregulation in the MMN and BSA treatments, an effect of the fungus using the cellulose membrane for a carbon source due to exhaustion of easily obtainable carbon from the media (Appendix B, Figure B.1). 2.3.2.1.3 Pectin-active enzymes Enzyme families typically considered active on pectin showed relatively consistent RPKM means across my three treatments and clear upregulation in the Kohler et al. (2015) MYC treatment. The two PL families active on pectin - PL1 and 4_3 - with three and two representatives, respectively, showed RPKM means less than one for all treatments other than the mycorrhizal one, suggesting that these enzymes are important in mycorrhization. While pectin is a minor component in peat (Fuchsman 1980), it is likely that the aforementioned hemicellulose-active enzymes are partly responsible for its degradation in this experiment (Appendix B, Figure B.1). 2.3.2.1.4 Enzymes active on other cell wall components Thirteen enzymes not categorized as CAZymes but classified as tannase and feruloyl esterases – active on hemicellulose, pectin and a variety of aromatics – showed an RPKM means sum in the peat treatment more than twice the corresponding value in the BSA and MMN treatments. Correspondingly, in the Kohler et al. (2015) data, the RPKM means sum for these esterases was nearly twenty times higher in the MYC treatment than in the FLM treatment. These enzymes are known to be involved in degradation of complex plant cell wall components such as cellulose, hemicelluloses, tannins, lignins, and pectins (Dilokpimol et al. 2016). Fungal cell wall modifications are occurring continually during active growth of any fungus and this is reflected in the regulation of several enzyme families active on fungal cell wall components. Relatively high expression was seen for the putative GH72 (β-1,3-glucanosyltransglycosylases; 8 family members) in both the peat and BSA treatments of this study, as well as the MYC treatment from Kohler et al. (2015). These GH72 enzymes are involved 41 in elongation and remodeling of the fungal cell wall glucans (Ao & Free 2017). These enzymes are unlikely to be involved in degradation of plant cell walls but, rather, are important for fungal growth during nutrient abundance. Similarly, several putative chitinase-encoding families (including GH18s) also demonstrated high RPKM means sums across my three treatments; chitinases are involved in a myriad of fungal life cycle processes, including cell wall remodeling and degradation of exogenous chitin (Gruber et al. 2011). The highly regulated CE4 enzymes mentioned above may also be involved in fungal cell wall remodeling as several enzymes in this family have been categorized as chitin deacetylases (Wang et al. 2010). The GH128 family showed lower RPKM means sums for both the peat and MYC (Kohler et al. 2015) treatments than their corresponding controls. GH64 family enzymes showed a slightly lower RPKM means sum in the peat treatment and considerably lower sum in the BSA treatment; and GH55 family enzymes showed a lower RPKM means sum in the MYC treatment (Kohler et al. 2015) and increased (nearly double) RPKM means sums for the peat treatment compared to the MMN treatment. These β-1,3-glucanases are active on fungal cell wall glucans and are likely involved in softening of the cell wall during hyphal branching (Mouyna et al. 2013). 2.3.2.2 Significantly regulated individual transcripts While examining RPKM means sums across families gives a broad generalization of some of the enzyme classes being regulated between treatments, it is important to look as well at transcript abundance of individual genes. To obtain information on how transcript abundance was influenced during growth on varying carbon sources, a differential expression analysis was conducted. CWDE transcripts that were regulated more than twofold (Ratio of RPKM Means) on Peat and BSA treatments compared to MMN with significance (Baggerly’s Test (Baggerly et al. 2003): tx (FLM/Peat or FLM/BSA) vs. FLM/MMN, common dispersion - FDR p-value correction; p<0.05) were identified (Table 2.3. Differentially expressed CWDEs during growth of Oidiodendron maius in response to Peat and BSA. .3). The vast majority of these transcripts encode CAZymes 42 involved in plant cell wall deconstruction such as cellulose (e.g., GH 1, 3, 5, 7, 31, 35, 45; CE 5), hemicelluloses (e.g., GH 11, 16, 27, 31, 79, 92, 115; CE 1, 5) and pectins (e.g., GH 3, 105). Several auxiliary activity (AA) families also showed significant regulation including many active on lignocellulose and other aromatics (e.g., AA1, 3, 5). Multiple chitinases (e.g., GH18) and glucanases (e.g., GH55), active on fungal cell wall components, also demonstrated significant regulation. The remainder are enzymes active on a wide variety of substrates including lignin and other aromatics, though not classified as CAZymes (e.g., tannase and feruloyl esterases (TFE), superoxide dismutase (SOD)). The most highly upregulated transcript on the peat treatment (61.6-fold) was a tannase and feruloyl esterase (TFE); these enzymes are known to be involved in degradation of complex plant cell wall components such as cellulose, hemicelluloses, tannins, lignins, and pectins (de Vries et al. 2002). The same transcript was downregulated on the BSA treatment (2.6-fold). Interestingly, a second TFE was significantly upregulated on both treatments (2.6- and 4.8-fold), more so on BSA. The second most highly upregulated transcript on the peat treatment (19.1-fold) was a CE1 family enzyme, also significantly upregulated on the BSA treatment (4.5-fold). Acetyl-xylan, cinnamoyl, feruloyl esterase and carboxymethylesterase activities are reported for the CE1 enzymes providing the potential for broad substrate specificity with activity demonstrated primarily on xylan hemicelluloses and pectins (http://www.cazy.org/, Lombard et al. 2015). 43 Table 2.3. Differentially expressed CWDEs during growth of Oidiodendron maius in response to Peat and BSA. Significant values for fold change of gene expression during cultivation of O. maius on Peat and BSA compared to MMN. Upregulated genes (i.e., transcripts more abundant on Peat and/or BSA) are shaded green, downregulated genes (i.e., transcripts more abundant on MMN) are shaded red. Shading is stronger for higher fold changes for all cases. Transcripts were considered significantly differentially expressed when fold change was at least two (p ≤ 0.05). MMN: Modified Melin Norkrans media; Peat: MMN without glucose and (NH4)2HPO4, poured over ~10 g (dry weight) sterile commercial peat; BSA: MMN without glucose and (NH4)2HPO4, supplemented with 0.1 g L-1 bovine serum albumin. 44 2.3.2.2.1 Cellulose- and hemicellulose-active enzymes The carbon contained in peat is likely found primarily in celluloses and hemicelluloses (Fuschman 1980), so it stands to reason that several CWDEs active on these plant cell wall components would be upregulated on the treatment containing peat as a carbon source. A GH11 (xylanases active primarily on xylan hemicelluloses - previously categorized as “Cellulase Family G”) transcript was significantly upregulated on both treatments (9.2-fold on peat and 3.9-fold on BSA); though a second GH11 transcript was significantly downregulated (3.4-fold) on the BSA treatment. A GH31 (primarily glucosidases and xylosidases active on cellulose and hemicellulose) was significantly upregulated on peat (8.2-fold) and significantly downregulated on BSA (4.9-fold); this enzyme family is found in a range of organisms including archaea, bacteria, plants and animals and may have glucan lyase activity, which enables degradation of starch to fructose (Lee & Withers 2003). Two α-galactosidase (GH27 family - active on a variety of hemicelluloses) transcripts were upregulated 7.4- and 3.3-fold on the peat treatment; a third GH27 transcript was downregulated 3.1-fold on the BSA treatment. The GH27 family is widespread across the fungal kingdom and has been implicated in degradation of the main softwood hemicelluloses (galacto)glucomannans (Brumer et al. 1999). One of the most significantly upregulated enzymes on the BSA treatment was an endo-1,3-1,4-β-glucanase (GH16) active on hemicelluloses (3.9-fold); a second GH16 transcript was downregulated 2.2-fold on BSA. The O. maius genome contains 16 copies of GH16, all of which were transcribed at varying levels in this experiment; it stands to reason that each copy would be specialized for specific substrates and/or conditions and further examination of these enzymes is necessary before drawing conclusions from these seemingly conflicting results. An α-mannosidase (GH92) active on hemicelluloses was upregulated 2.9-fold on BSA, as well as a hemicellulose-active glucuronidase (GH115) (Upregulated 2.4-fold on BSA). An acetyl xylan esterase (CE3) with broad specificity, but active primarily on hemicelluloses (Poutanen et al. 45 1990) was significantly downregulated on both treatments: 27-fold and 2-fold on peat and BSA, respectively. A cutinase from family CE5 was upregulated 2.3-fold on the peat treatment and downregulated 2.6-fold on the BSA treatment; a second representative from CE5 was downregulated 3.0-fold on the BSA treatment. These CE5 enzymes are active on various hemicelluloses as well as cellulose. A β-glucuronidase from family GH79 (active on hemicelluloses) was upregulated 2.2-fold on peat and downregulated 2.2-fold on BSA. If one focuses on those enzymes needed for the deconstruction of cellulose by fungi - β-1,4-endoglucanases, cellobiohydrolases, β-glucosidases (Couturier et al. 2016) – we see significant upregulation on the peat treatment for transcripts represented by several CAZy families including GH 1 (4.2-fold), GH3 (3.9- and 2.0-fold), GH5 (2.5-fold), and GH35 (3.0-fold). Significant upregulation on the BSA treatment is also seen for transcripts represented by CAZy families GH3 (2.8- and 2.1-fold), GH5 (2.0-fold), GH7 (2.4-fold) and 45 (2.1-fold), though this upregulation may result from the fungus degrading the cellulose membrane to obtain carbon in response to depletion of available carbon from the media. Several cellulose-active enzymes showed significant downregulation on the BSA treatment including the aforementioned GH3 (downregulated 2.2-fold) and a GH35 (downregulated 2.2-fold on BSA). Interestingly, all lytic polysaccharide monooxygenases (LPMOs) from family AA9, also responsible for degradation of cellulose (Couturier et al. 2016), were expressed at relatively consistent levels across all three treatments. 2.3.2.2.2 Pectin-active enzymes When examining those enzymes needed for the deconstruction of pectin by fungi (including some also implicated in degradation of other PCW components), we see 5.1-fold upregulation of an unsaturated β-glucuronyl hydrolase (GH105) on the peat treatment and a 2.4-fold downregulation on BSA. The aforementioned TFEs, CE1 and GH3s are also active on pectin. The upregulation of these pectinases, even on low pectin substrates, suggests that pectin may be a significant barrier for enzymes targeting other plant cell wall components. Anderson (2016) 46 suggests that pectin acts like a glue in the plant cell wall, linking lignocellulose and hemicellulose fibres, which may explain the neccessity of these pectin-active enzymes. 2.3.2.2.3 Lignin degradation Fungi have developed multiple strategies for the degradation of lignin, the most common is via peroxidase (POD) enzymes; the O. maius genome, however, contains no such lignin-attacking PODs (Kohler et al. 2015). The aforementioned CE1 feruloyl esterase tannase and feruloyl esterases (TFE) that show the most significant upregulation on the peat treatment are likely involved in decomposition of the complex polymers found in this carbon source. Another method for lignin degradation is via laccases (or multipcopper oxidases (MCO)) (Bugg et al. 2011). While the number of laccases is variable between species, O. maius had the largest number (15 copies) among those ascomycetes examined by Kohler et al. (2015). Four AA1 transcripts encoding such enzymes were significantly regulated; the pattern of regulation is variable, however. One transcript was upregulated in the peat treatment (5.3-fold) and one transcript was downregulated in the peat treatment (6.4-fold). The other two AA1 enzymes were the most highly upregulated transcripts in the BSA treatment (4.1- and 18.5-fold); with these enzymes active primarily on aromatics (including lignin), they were presumably acting on the phenylalanine residues of BSA (Weijers 1977). Fungal laccases have been implicated in numerous processes, though, including pathogenesis, detoxification and degradation of lignin (Youn, Hah and Kang 1995), but also in development and morphogenesis (Leonowicz et al.2001; Morozova et al.2007). Yet another method for lignin degradation is the utilization of reactive oxygen species either by production of H2O2 by AA3 and AA5 family members (Hernandez-Ortega et al. 2012, Levasseur et al. 2013) or hydroxyl radical formation by superoxide dismutase (MnSOD; Murphy 2009). The pattern of regulation is again somewhat chaotic in the instance of these enzymes, though several are significantly regulated (Table 2.3. Differentially expressed CWDEs during 47 growth of Oidiodendron maius in response to Peat and BSA. ). An alcohol oxidase (AA3_3) enzyme was the only enzyme in this grouping to be significantly upregulated on the peat treatment (3.7-fold), while an aryl alcohol oxidase/dehydrogenase (AA3_2), a glyoxal oxidase (AA5_1) and an aryl alcohol oxidase (AA5_2) transcript were significantly upregulated on the BSA treatment (3.2-, 2.0- and 2.0-fold, respectively). A second AA3_2 transcript was significantly downregulated on the peat treatment (3.1-fold) and another AA3_2 transcript was significantly downregulated on the BSA treatment (2.6-fold). Two MnSOD transcripts were among the most highly downregulated transcripts on the peat treatment (126.0- and 12.3-fold). The mechanisms of lignin degradation described above result in radical formation, which can result in significant oxidative stress to the fungus. The superoxide dismutase (SOD) enzymes, which catalyze the conversion of superoxide to hydrogen peroxide are also indispensably involved in radical protection (Fridovich 1995). The enzymes described here are typically presumed to be active in degradation of lignin and other complex aromatics (Reading et al. 2003), but oxidative degradation has the potential to be a productive method of degrading any number of substrates. It is possible, then, that O. maius uses oxidative degradation as a constitutive method for nutrient acquisition along with concomitant production of enzymes that mediate radical protection; then, the downregulation of those protective enzymes would be seen when a suitable substrate (i.e., the lignin contained in the peat) was available to absorb those radical oxygen species produced. It would be of interest to consider this line of reasoning by examining other antioxidant proteins involved in the degradation of reactive oxygen species, such as glutathione peroxidases and thioredoxin dependent peroxiredoxins, of which O. maius has a single copy of each. The degradation of lignocellulose complexes of plant cell walls requires synergistic action from a variety of CWDEs (Busk et al. 2014). There are multiple strategies used by O. maius to access the carbon contained in these complex polymers (Martino et al. 2018) and our understanding of the function and mechanism of action of this diverse group of enzymes is limited. 48 I hypothesized that CWDEs active on cellulose, hemicellulose and lignin would be higher in the peat treatment, but it is clear that the situation is more complicated. While several enzymes active on each of these cell wall components were upregulated significantly on the complex carbon source, peat, we also saw what could be construed as conflicting evidence of downregulation of synonymous enzymes on this carbon source. Additionally, upregulation of CAZymes on the simpler carbon source (BSA) complicates the already complex picture. While I have proposed possible explanations for these apparent inconsistencies, one must keep in mind that the proposed functions for these enzymes are based on sequence similarity to enzymes from a variety of organisms and not functional analysis in O. maius; the enzymes examined may not function as putatively supposed. 2.3.2.2.4 Enzymes active on other cell wall components Detecting a pattern in the regulation of CWDEs involved in fungal cell wall synthesis, rearrangement and degradation is difficult. The most highly downregulated transcript on the BSA treatment was an α-1,4-galactosaminogalactan hydrolase of family GH135; these enzymes are active on galactosaminogalactan (a fungal heteropolysaccharide) (Bamford et al. 2015). The second most highly downregulated transcript on the peat treatment was a chitinase of unknown family affiliation (56-fold on peat) and a second chitinase of the GH18 family was significantly downregulated (4.7-fold) on peat; another chitinase of unknown family affiliation was downregulated 3.9-fold on BSA. Also significantly downregulated on the peat treatment was a glucooligosaccharide oxidase of the AA7 family, likely active on chitin (2.1-fold). Amongst those chitin-active transcripts significantly upregulated were a β-N-acetylhexosaminidase (GH20), active on chitobiose (2.3-fold on peat) and two GH18 chitinases (2.1- and 2.0-fold on peat). As mentioned previously, the β-1,3-glucanases of the GH55 family, active on fungal cell wall glucans, showed an increased RPKM means sum for the peat treatment; two such transcripts were shown to be significantly upregulated in the peat (5.5-fold and 2.6-fold). There is some evidence that 49 GH3, 5, 16 and 79 family members, which are typically considered to be active on lignocellulose components, also have activity on the glucans of fungal cell walls (Martin et al. 2008); if these families are also implicated, the picture becomes even more difficult to understand. Because fungal cell wall modifications are occurring continually during active growth of any fungus, it is impossible to distinguish those enzymes involved in normal cellular growth and reprocessing from those regulated by nutritional stimuli by looking at transcription alone. An amidohydrolase from the CE9 family was upregulated 2.2-fold on the peat treatment. These esterases catalyze the deacetylation of N-acetylglucosamine-6-phosphate to glucosamine-6-phosphate. While this has been demonstrated to be important for both amino sugar metabolism and peptidoglycan cell wall recycling in bacteria (Park 2001), its function in fungi has yet to be determined. 2.3.3 Fifteen targets selected from Oidiodendron maius CAZome Targets were selected for ddPCR assays (Chapter 4) from the 680 distinct transcripts containing CAZy domains or otherwise classified as CWDEs (CBMs and GTs omitted). Fifteen targets were selected from this dataset based on a fold change (ratio of RPKM) greater than two between at least two of the treatments within either the Feldman 2020 or Kohler et al. (2015) data sets (Table 2.4). The targets were chosen to cover a broad range of putative substrates, with a focus on those cell wall components most prevalent in soil. 50 Table 2.4. Differentially expressed CWDEs during growth of Oidiodendron maius in response to Peat and BSA. Fold Change (Ratio of RPKM) Transcript ID Protein ID Target Name Peat: MMN Peat: BSA BSA: MMN MYC: FLM* Putative Function (InterproDesc / SP Description) 50968 50893 CE5 2.26 5.87 -2.56 49 Cutinase / Acetylxylan esterase 119173 119098 CE8 5.08 1.52 3.35 1163 Pectinesterase, catalytic 172745 172670 GH3_70 3.34 9.81 -2.94 18 Glycoside hydrolase, family 3 173180 173105 GH3_05 32.69 2.97 11.01 Glycoside hydrolase, family 3 109142 109067 GH5 2.48 2.01 1.24 Endoglucanase 160759 160684 GH11 1.15 2.90 -3.33 747 Endo-1,4-beta-xylanase 147779 147704 GH18 1.44 1.83 -1.28 8 Endochitinase 130215 130140 GH28 3.90 35.61 -9.09 159 Probable exopolygalacturonase 25971 25896 GH55_8 1.54 -1.18 1.82 39 Esterase, SGNH hydrolase-type / Glucan 1,3-beta-glucosidase 165996 165921 GH55_9 5.48 4.19 1.31 Carbohydrate-binding WSC 40736 40661 GH78 5.75 4.47 1.28 Bacterial alpha-L-rhamnosidase 172675 172600 MCO 5.30 3.71 1.43 59 Multicopper oxidase, type 1 / Cupredoxin 20150 20075 MnFeSOD -100 -100 1.15 Manganese and iron superoxide dismutase 196656 196581 SugMod 7.91 1.47 5.39 D-xylulose 5-phosphate/D-fructose 6-phosphate phosphoketolase 58531 58456 TFE 61.64 157.77 -2.56 Tannase and feruloyl esterase Target selection was focused on fold change (ratio of reads per kilobase of exon model per million mapped reads (RPKM)) greater than two between at least two transcriptome treatments (highlighted in green). MMN: Modified Melin Norkrans media; Peat: MMN without glucose and (NH4)2HPO4, poured over ~10 g (dry weight) sterile commercial peat; BSA: MMN without glucose and (NH4)2HPO4, supplemented with 0.1 g L-1 bovine serum albumin; GH: Glycoside Hydrolase; MCO: Multi-copper oxidase; MnFeSOD: Manganese Iron Superoxide Dismutase; SugMod: phosphoketolase; TFE; Tannase and feruloyl esterase. *Data from Kohler et al. 2015 Transcript 50968 showed higher expression in the peat treatment compared to both the BSA and MMN treatments; data from Kohler et al. (2015) showed a pronounced symbiosis-induced increase in expression. The gene product belongs to Carbohydrate Esterase Family 5 (CE5), which contains acetyl xylan esterases and cutinases (http://www.cazy.org/, Lombard et al. 2015). Acetlyxylan esterases are hemicellulolytic enzymes responsible for the deacetylation of xylans, xylopyranosides and xylo-oligosaccharides found in hemicelluloses (McDonald et al. 2009 – EC 3.1.1.72); they catalyze the hydrolysis of acetyl groups from polymeric xylan, acetylated xylooligosaccharides, acetylated glycosides and cellulose acetate, but do not act on acetylated 51 mannan or pectin (Biely 2012). The top BLASTn alignment (https://blast.ncbi.nlm.nih.gov/Blast.cgi) for this transcript is a Colletotrichum graminicola cutinase partial mRNA (Accession: XM_008101744.1). The protein product has high scoring alignments to a putative acetylxylan esterase 2 precursor from Talaromyces marneffei (Accession: NW_002196661.1) and the axe2 gene (a putative cutinase) from Trichoderma reesei (Accession: NW_006711148.1); the T. reesei axe2 gene shows amino acid similarity with other fungal cutinases (Margolles-Clark et al. 1996). Many CE5 enzymes found in fungi are serine esterases known as cutinases, active on the structural compound cutin, a polyester composed of hydroxy and hydroxyepoxy fatty acids (Ettinger et al. 1987), found in the cuticle of plants. The upregulation on peat as compared with the BSA and MMN treatments seen here supports either gene product as it is the only plant matter-containing carbon source of the three. The upregulation seen during symbiosis would suggest acetylxylan esterase activity, which would aid the fungus in penetration of root cells. While it is unlikely that the root cells contain cutin, the CE5 enzymes could potentially have activity on the structurally similar suberin in the subepidermal cells of the hair roots. With the assumption that the product of this transcript acts on hemicellulose moieties, I expect to observe its upregulation on all carbon sources containing organic material (i.e., peat, soil, and their respective dissolved organic compounds) in further ddPCR studies (Table 2.5). However, the known substrate promiscuity of carbohydrate esterases make their classification less predictive than other CAZyme families (Davies et al. 2005). 52 Table 2.5. Expected expression response to carbon source by Oidiodendron maius. Carbon Source Transcript ID Target Name Putative Function glucose (0.5 %) peat peat DOM field soil field soil DOM BSA cellulose chitin pectin lignin 50968 CE5 Cutinase / Acetylxylan esterase + + + + 119173 CE8 Pectinesterase, catalytic + + + + + 172745 GH3_70 beta-xylosidase? + + + + 173180 GH3_05 beta-glucosidase? + + + + + 109142 GH5 Endoglucanase + + + + + 160759 GH11 Endo-1,4-beta-xylanase + + + + 147779 GH18 Endochitinase + + + + + + + ++ + + 130215 GH28 Probable exopolygalacturonase + + + + + 25971 GH55_8 β-1,3-glucanase + + + + + 165996 GH55_9 β-1,3-glucanase + + + + + 40736 GH78 Bacterial alpha-L-rhamnosidase + + + + 172675 MCO Multicopper oxidase, type 1 / Cupredoxin ++ ++ ++ ++ + + + + ++ 20150 MnFeSOD Manganese and iron superoxide dismutase - - - - - - - -- 196656 SugMod D-xylulose 5-phosphate/D-fructose 6-phosphate phosphoketolase (+) (+) (+) (+) (+) 58531 TFE Tannase and feruloyl esterase + + + + + (+) Expected response in transcript abundance of target cell wall degrading enzymes produced by O. maius in response to variable carbon source. Predicted positive (+) and negative (-) responses are relative to growth on MMN media containing 0.1 g L-1 glucose as the sole carbon source; doubled symbols predict a stronger effect, brackets predict a very slight effect. Transcript 119173 demonstrated upregulation in the peat and BSA treatments compared to the MMN treatment; data from Kohler et al. (2015) showed a symbiosis-induced increase in expression of over 1000-fold. The gene product from this transcript is a pectin methylesterase from carbohydrate esterase family 8 (CE8); it shows high sequence similarity to hypothetical proteins from Sclerotinia sclerotiorum (Accession: XP_001594743.1) and Aspergillus nidulans (Accession: NT_107013.1) as well as a pectin methylesterase from Aspergillus fumigatus (Accession: XM_742394.1). The top BLASTn alignment (https://blast.ncbi.nlm.nih.gov/Blast.cgi) for this transcript is a Hyaloscypha bicolor E carbohydrate esterase family 8 protein mRNA 53 (Accession: XM_024886186.1). Pectin methylesterases are ubiquitous enzymes that modify the degree of methylesterification of pectins by catalyzing the de-esterification of pectin into pectate and methanol (McDonald et al. 2009 – EC 3.1.1.11). These fungal pectin esterases have been strongly implicated in plant pathogenicity (Sella et al. 2016), a supposition supported by the symbiosis-induced upregulation of this transcript. The upregulation on peat as compared with the MMN treatment seen here was consistent with expectations as peat is the only plant matter-containing carbon source of the three; the upregulation seen on the BSA treatment was unexpected. With a putative pectin substrate, I expect to observe the upregulation of transcript 119173 on all carbon sources containing plant matter (i.e., peat, soil, and their respective dissolved organic compounds) and/or pectin in the ddPCR studies described in Chapter 4 (Table 2.5). Transcript 172745 showed higher expression in the peat treatment compared to both the BSA and MMN treatments; data from Kohler et al. (2015) showed a pronounced symbiosis-induced increase in expression. The top BLASTn alignment (https://blast.ncbi.nlm.nih.gov/Blast.cgi) from this transcript was to a Trichoderma gamsii glycosyl hydrolase family 3 mRNA (Accession: XM_024550354.1). The gene product from this transcript is a glycoside hydrolase family 3 (GH3) enzyme with high alignment to putative proteins from several Aspergillus spp. (Accession: XP_001210069.1, XP_002377419.1, XP_001825822.1) as well as several fungal putative beta-xylosidases (Accession: XP_002486253.1, XP_002473890.1, XP_001826724.1, XP_002385353.1). Glycoside hydrolase family 3 enzymes are ubiquitous and involved in a wide variety of activities including cellulosic biomass degradation, plant and bacterial cell wall remodeling, energy metabolism and pathogen defense; they often have broad substrate specificities with respect to both monosaccharide residues and linkage position within the substrate (Lee et al. 2003). Beta-xylosidases, specifically, are involved in the hydrolysis of (1→4)-β-D-xylans, to remove successive D-xylose residues from the non-reducing termini (McDonald et 54 al. 2009 – EC 3.2.1.37), suggesting they are important in the degradation of hemicelluloses. It is, therefore, not surprising that upregulation was demonstrated both on the peat treatment and during symbiosis. As with the previous transcripts, I expect to observe the upregulation of transcript 172745 on all carbon sources containing plant matter (i.e., peat, soil, and their respective dissolved organic compounds) and/or cellulose in further ddPCR studies (Table 2.5). Transcript 173180 showed upregulation in the peat treatment compared to both the BSA and MMN treatments as well as a less-pronounced upregulation in the BSA treatment versus the MMN treatment; no symbiosis-induced increase in expression was demonstrated by Kohler et al. (2015). The top BLASTn alignment (https://blast.ncbi.nlm.nih.gov/Blast.cgi) from this transcript was to an Amorphotheca resinae ATCC 22711 glycoside hydrolase family 3 protein mRNA (Accession: XM_024868149.1); a glycoside hydrolase family 3 protein mRNA from the ericoid mycorrhizal fungus Hyaloscypha bicolor E was also within the top 5 BLASTn hits (Accession: XM_024886983.1). As mentioned previously, the GH3 family possesses many enzymatic functions; this transcript showed high alignment to hypothetical fungal proteins from Botryotinia fuckeliana (Accession: XP_001551395.1) and Podospora ansperina (Accession: XP_001907699.1) as well as beta-glucosidases from Sclerotinia sclerotiorum (Accession: XP_001591700.1) and Neurospora crassa (Accession: XP_956104.1). Beta glucosidases possess wide substrate specificity and are primarily responsible for the hydrolysis of terminal, non-reducing β-D-glucosyl residues with release of β-D-glucose (McDonald et al. 2009 – EC 3.2.1.21); this would suggest an important role in cellulose degradation. It is surprising, therefore, that symbiosis did not induce expression of this transcript, yet BSA did; this unexpected finding suggests that the role of this enzyme requires further examination. Without knowing the precise substrate of a gene product, it is difficult to make predictions on its behaviour, though, given the expression pattern seen here, I would expect upregulation of transcript 173180 on all carbon sources containing plant 55 matter (i.e., peat, soil, and their respective dissolved organic compounds) and/or cellulose in further ddPCR studies (Table 2.5). Transcript 109142 demonstrated only slight upregulation in the peat treatment compared to both the BSA and MMN treatments with no symbiosis-induced increase in expression demonstrated by Kohler et al. (2015). This transcript belongs to the glycoside hydrolase family 5 (GH5) enzymes, one of the largest of all the GH families, previously known as "cellulase family A", currently containing 56 subfamilies (http://www.cazy.org/, Lombard et al. 2015). GH5 enzymes are found widely distributed across archaea, bacteria and eukaryotes, particularly fungi and plants, with no known human enzymes. This transcript is annotated as belonging to subfamily 24, a family that itself contains a variety of enzymes with broad substrate specificity (http://www.cazy.org/GH5_24.html), but one which is presently restricted to fungi (Aspeborg et al. 2012). The top BLASTn alignment (https://blast.ncbi.nlm.nih.gov/Blast.cgi) for this transcript is a glycoside hydrolase family 5 protein from Hyaloscypha bicolor E (Accession: XM_024882222.1); this transcript showed high alignment to hypothetical fungal proteins from N. crassa (Accession: XP_964407.2), Magnaporthe oryzae (Accession: XP_368714.1) and Podospora anserina (Accession: XP_001912480.1). Given the pattern of expression seen here, presuming a cellulosic substrate, I would expect upregulation of transcript 109142 on all carbon sources containing plant matter (i.e.,peat, soil, and their respective dissolved organic compounds) and/or cellulose in further ddPCR studies (Table 2.5). Transcript 160759 showed slight upregulation in the peat treatment versus the BSA treatment and downregulation in the BSA treatment versus the MMN treatment; data from Kohler et al. (2015) showed a substantial symbiosis-induced increase in expression. The transcript encodes a glycoside hydrolase family 11 enzyme (GH11); these enzymes are endo-β-1,4-xylanases, previously known as “Cellulase Family G”, responsible for the endohydrolysis of (1→4)-β-D-xylosidic linkages in xylans and were one of the first glycoside hydrolase families 56 classified by sequence analysis (Gilkes et al. 1991). The top ten BLASTn alignments (https://blast.ncbi.nlm.nih.gov/Blast.cgi) for this transcript are to endo-β-1,4-xylanases from various Aspergillus spp. (i.e., Accession: XM_025619954.1, EF597673.1), as are the top ten protein alignments (i.e., XP_001401361.1). Due to their application in various industrial processes, many endo-β-1,4-xylanases have been well characterized, particularly those isolated from fungal sources. With a putative hemicellulose substrate, it is not surprising to see upregulation of this transcript on those plant-matter containing carbon sources; however, the reason for the downregulation seen on the BSA treatment is unknown. Given the pattern of expression seen here, and supposing a hemicellulosic substrate, I would expect upregulation of transcript 160759 on all carbon sources containing plant matter (i.e., peat, soil, and their respective dissolved organic compounds) in further ddPCR studies (Table 2.5). Transcript 147779 demonstrated only slight upregulation in peat compared to BSA and MMN treatments, as well as a moderate symbiosis-induced increase (Kohler et al. 2015). The gene product of this transcript belongs to glycoside hydrolase family 18 (GH18), enzymes with endochitinase activity that are able to break down all forms of chitin at varying rates depending on the enzyme and the substrate (Horn et al. 2006). The top BLASTn alignments (https://blast.ncbi.nlm.nih.gov/Blast.cgi) for this transcript were a Phialocephala scopiformis glycoside hydrolase partial mRNA (Accession: XM_018219690.1) and a carbohydrate-binding module family 18 protein from H. bicolor E (Accession: XM_024878641.1). The protein sequence from this transcript demonstrated high sequence alignment to a putative chitinase from Magnaporthe oryzae (Accession: XP_361056.1) as well as class III chitinases from Trichophyton verrucosum (Accession: EFE44978.1) and Arthroderma benhamiae (Accession: EFE33907.1). Chitinases have variable physiological function, whether degrading exogenous chitin or maintaining normal cellular function; while I do not know whether this particular transcript is responsible for the former or the latter, I felt it was important to include at least one chitinase in the 57 further examination because chitin is the primary structural component of fungi and a major biomolecule in soil. The pattern of expression seen here suggests upregulation of GH18 during active growth and metabolism and, as such, I would expect to see its upregulation on all carbon sources compared with glucose in further ddPCR studies (Table 2.5). Transcript 130215 showed higher expression in the peat treatment versus the BSA and MMN treatments, as well as nearly ten-fold higher expression in the MMN treatment versus the BSA treatment; data from Kohler et al. (2015) showed a marked symbiosis-induced increase in expression. The gene product of this transcript belongs to glycoside hydrolase family 28 (GH28); the vast majority of this family are polygalacturonases that hydrolyse the α-1,4 glycosidic linkage between galacturonate residues in polygalacturonic acid (a component of many pectins). The top BLASTn alignments (https://blast.ncbi.nlm.nih.gov/Blast.cgi) for this transcript were exo-polygalacturonases from Colletotrichum spp. (Accession: XM_036720025.1, XM_037320792.1, XM_018298775.1) and a Phialocephala scopiformis pectin lyase-like protein (Accession: XM_018212378.1). The protein sequence from this transcript demonstrated high sequence alignment to hypothetical proteins from Sclerotinia sclerotiorum (Accession: XP_001584552.1) and Botryotinia fuckeliana (Accession: XP_001546213.1) and a 1,4-alpha-galacturonidase from Aspergillus fumigatus (Accession: B0XYC4). Similar to the CE8 transcript described above, the upregulation on peat as compared with the MMN treatment seen here was consistent with expectations as peat is the only plant matter-containing carbon source of the three; the downregulation seen in response to the BSA treatment was unexpected. The symbiosis-induced expression would suggest a role in degradation of the root tissue during hyphal penetration. With a putative pectin substrate, I expect to observe the upregulation of transcript 130215 on all carbon sources containing plant matter (i.e., peat, soil, and their respective dissolved organic compounds) and/or pectin in further ddPCR studies (Table 2.5). 58 Transcript 25971 demonstrated only slight upregulation in peat and BSA compared to MMN treatment and slight downregulation in peat compared with BSA; data from Kohler et al. (2015) demonstrated a moderately-high symbiosis-induced increase. Transcript 165996 demonstrated moderate upregulation in peat compared to BSA and MMN treatments, as well as slight upregulation in BSA compared to MMN treatment, with no symbiosis-induced increase in expression reported by Kohler et al. (2015). The gene product of both aforementioned transcripts belongs to glycoside hydrolase family 55 (GH55), which includes exclusively β-1,3-glucanases; all biochemical characterizations of these enzymes are so far limited to fungi and bacteria. The top BLASTn alignment (https://blast.ncbi.nlm.nih.gov/Blast.cgi) for transcript 25971 was a Trichoderma virens Gv29-8 glycoside hydrolase family 55 protein partial mRNA (Accession: XM_014101907.1); the protein sequence from transcript 25971 demonstrated high sequence alignment to exo-1,3-beta-D-glucanases from Paracoccidioides brasiliensis (Accession: EEH18278.1) and Ajellomyces dermatitidis (Accession: XP_002629505.1). The top five BLASTn alignments (https://blast.ncbi.nlm.nih.gov/Blast.cgi) for transcript 165996 were to GH55 proteins from various Trichoderma spp. (Accession: XM_014085316.1, XM_024912412.1, XM_024901273.1); the protein sequence from transcript 165996 showed high sequence alignment to beta-1,3 exoglucanases from two Trichoderma spp. (Accession: ABY19519.1, AAP33112.1) and one from Hypocrea lixii (Accession: CAA05375.1). These two GH55 transcripts were chosen for further study based on their contrasting expression profiles, despite retaining the same putative function. I am hesitant to make predictions regarding their expression patterns in further ddPCR studies, due to the opposing results demonstrated here; though, based solely on the predicted function, I expect to observe the upregulation of both transcripts on all carbon sources containing plant matter (i.e., peat, soil, and their respective dissolved organic compounds) in further ddPCR studies (Table 2.5). 59 Transcript 40736 demonstrated moderate upregulation on peat compared with BSA and MMN treatments, and slight upregulation on BSA compared with MMN treatments; data from Kohler et al. (2015) showed no symbiosis-induced expression. The gene product of this transcript is a glycoside hydrolase family 78 (GH78) enzyme; these enzymes are α-L-rhamnosidases, found exclusively in bacteria and fungi that catalyze the hydrolysis of α-L-rhamnosyl-linkages in L-rhamnosides. The top BLASTn alignments (https://blast.ncbi.nlm.nih.gov/Blast.cgi) for transcript 40736 were alpha-L-rhamnosidases from Aspergillus bombycis (Accession: XM_022531639.1) and Penicillium chrysogenum Pcrha78B (Accession: LC120364.1); the protein sequence from transcript 40736 demonstrated high sequence alignment to alpha-L-rhamnosidases from Aspergillus kawachii (Accession: BAF98236.1) and the bacteria Pyrenophora tritici-repentis Pt-1C-BFP (Accession: EDU48875.1). The higher expression exhibited on the peat treatment was expected as this was the only hemicellulose-containing substrate. While a symbiosis-induced expression would also be expected due to the presumed hemicellulose substrate, I am unaware of any studies done to determine the nature of the hemicellulose content in V. myrtillus, and it may not contain a high degree of rhamnose-based hemicelluloses. Similar to the GH11 transcript described above, I would expect upregulation of transcript 40736 on all carbon sources containing plant matter (i.e., peat, soil, and their respective dissolved organic compounds) in further ddPCR studies (Table 2.5). Transcript 172675 demonstrated moderate upregulation on peat compared to both BSA and MMN treatments as well as slight upregulation on BSA compared with MMN; data from Kohler et al. (2015) showed a moderately high symbiosis-induced increase in expression. The gene product of transcript 172675 is a multicopper oxidase that putatively belongs to auxiliary activity family 1 (AA1); these enzymes use diphenols and related substances as donors with oxygen as the acceptor, making their potential substrate specificity extremely broad and varied. The top BLASTn alignments (https://blast.ncbi.nlm.nih.gov/Blast.cgi) for transcript 172675 include 60 multicopper oxidases from Hyaloscypha bicolor E (Accession: XM_024884711.1) and Grosmannia clavigera (Accession: XM_014316170.1); the protein sequence from transcript 172675 demonstrated high sequence alignment to a multicopper oxidase family protein from Coccidioides posadasii (Accession: EER24346.1) and a putative laccase from Talaromyces stipitatus (Accession: XP_002482301.1). Multicopper oxidases and laccases have the ability to oxidize a wide variety of compounds including phenols and diamines, often with the concomitant production of a semiquinone, which may react further either enzymatically or non-enzymatically (Malmström et al. 1975). This suggests that these enzymes have the capacity to break down lignin-containing plant matter as well as the simpler compounds contained therein. This broad substrate specificity is supported by upregulation on peat and BSA treatments as well as the symbiosis-induced expression demonstrated by Kohler et al. (2015). I would, therefore, expect to see upregulation of transcript 172675 on all carbon sources of greater complexity than glucose in further ddPCR studies; further, I would expect more substantial upregulation on the complex carbon sources such as lignin, peat and soil (Table 2.5). Transcript 20150 was the most significantly downregulated transcript of the selected targets on peat compared with both MMN and BSA and was only slightly upregulated on BSA compared with MMN; no symbiosis-induced expression was reported by Kohler et al. (2015). The gene product of this transcript is a manganese and iron superoxide dismutase (MnFeSOD) which catalyzes the reaction: 2 peroxide radical + 2 H+ = O2 + H2O2; while MnFeSOD enzymes are not classified as CWDEs, they are potentially involved in radical protection during the Fenton reaction used to degrade complex carbon sources such as lignin. The top BLASTn alignments (https://blast.ncbi.nlm.nih.gov/Blast.cgi) for transcript 20150 were to hypothetical proteins from Pseudogymnoascus verrucosus (Accession: XM_018269682.1), Pseudogymnoascus destructans (Accession: XM_024466274.1) and Amorphotheca resinae (Accession: XM_024862536.1), as well as a Fe-Mn family superoxide dismutase from Exophiala dermatitidis (Accession: 61 XM_009161645.1) and a Mn superoxide dismutase from Scedosporium apiospermum (Accession: XM_016788565.1). The protein sequence from transcript 20150 demonstrated high sequence alignment to superoxide dismutases from Blumeria graminis (Accession: AF327338_1) and Neurospora crassa (Accession: Q9Y783). While I expected to see upregulation of this transcript on the most complex of the carbon sources examined, a possible explanation for this might be that the fungus does not require protection from radicals when those radicals have sufficient substrate to act upon; the complex carbohydrates in the peat treatment may provide a rich source of phenols and other reactants for the Fenton reaction, such that the radicals produced further react with those breakdown products. Given the expression pattern demonstrated here, I would expect the expression of this transcript to be inversely correlated with the complexity of the carbon source in further ddPCR studies (Table 2.5). Transcript 196656 showed moderate upregulation on peat compared with MMN and on BSA compared with MMN; no symbiosis-induced expression was reported by Kohler et al. (2015). The gene product for this transcript is a D-xylulose 5-phosphate/D-fructose 6-phosphate phosphoketolase; these enzymes are related to pyruvate-ferredoxin oxidoreductases and catalyze of the cleavage of C-C, C-O, C-N and other bonds by other means than by hydrolysis or oxidation, or, conversely, adding a group to a double bond. While they are not CWDEs, they are important in downstream carbohydrate metabolism as they modify the sugars obtained through primary carbohydrate metabolism. The top BLASTn alignments (https://blast.ncbi.nlm.nih.gov/Blast.cgi) for transcript 196656 were to an hypothetical protein from Amorphotheca resinae (Accession: XM_024861754.1) followed by D-xylulose 5-phosphate/D-fructose 6-phosphate phosphoketolases from Phialocephala scopiformis (Accession: XM_018211631.1) and Hyaloscypha bicolor E (Accession: XM_024875459.1). The protein sequence from transcript 196656 demonstrated high sequence alignment to hypothetical proteins from Sclerotinia sclerotiorum (Accession: XP_001592549.1) and Aspergillus niger (Accession: XP_001399780.1) as well as D-xylulose 5-62 phosphate/D-fructose 6-phosphate phosphoketolases from Neosartorya fischeri (Accession: XP_001263382.1) and Aspergillus clavatus (Accession: XP_001271080.1). While I am hesitant to make predictions regarding the expression of this transcript in response to a greater variety of carbon sources, it may provide some insight into the rate of metabolism in response to these sources in further ddPCR studies (Table 2.5). Transcript 58531 showed very high upregulation on peat compared with both BSA and MMN treatments and a slightly significant downregulation on BSA compared with MMN; no symbiosis-induced expression was reported by Kohler et al. (2015). The gene product for this transcript is a tannase and feruloyl esterase (TFE). The top BLASTn alignments (https://blast.ncbi.nlm.nih.gov/Blast.cgi) for transcript 58531 are a hypothetical protein from Amorphotheca resinae (Accession: XM_024866077.1) and a tannase and feruloyl esterase from Hyaloscypha bicolor E (Accession: XM_024879470.1). The protein sequence from transcript 58531 demonstrated high sequence alignment to hypothetical proteins from Aspergillus niger (Accession: XP_001401646.1), Aspergillus flavus (Accession: XP_002384763.1) and Botryotinia fuckeliana (Accession: XP_001560611.1) as well as a feruloyl esterase B precursor from Pyrenophora tritici-repentis (Accession: XP_001941383.1). Fungal TFEs hydrolyse ester linkages and have broad substrate specificity; they are known to be involved in pectin and xylan degradation (de Vries et al. 2002) and are sometimes referred to as hemicellulose accessory enzymes as they assist in the degradation of hemicellulose by xylanases and pectinases. Many feruloyl esterases found in fungi specifically catalyze the hydrolysis of ester bonds between ferulic (hydroxycinnamic) acid and plant cell wall polysaccharides (Dilokpimol et al. 2016). With a putative hemicellulose and/or pectic substrate, it is not surprising to see upregulation of this transcript on those plant matter-containing carbon sources; however, the reason for lack of induction during symbiosis is unknown. Given the pattern of expression seen here, and supposing a hemicellulosic and/or pectic substrate, I would expect upregulation of transcript 58531 on all carbon sources 63 containing plant matter (i.e., peat, soil, and their respective dissolved organic compounds) and/or pectin in further ddPCR studies (Table 2.5). 2.4 Interpretations & Implications Transcriptome sequencing of O. maius demonstrated expression and regulation of 680 CWDE genes. Significant upregulation of 23 transcripts on peat and 16 transcripts on BSA, in comparison to growth on glucose, was observed. Significant downregulation of 8 transcripts on peat and 15 transcripts on BSA, in comparison to growth on glucose was observed. Among the most highly upregulated CWDEs on peat were feruloyl esterases (active on hemicellulose, pectin, and aromatics), glycoside hydrolase families GH11 and GH27 (both active on hemicellulose), GH31 (active on cellulose and hemicellulose), GH55 (active on glucans), a multicopper oxidase belonging to Auxiliary Activity family AA1 (active on aromatics) and a GH105 (active on pectins). This comprehensive analysis of the CWDE transcriptome of O. maius provides much needed knowledge regarding its response to growth on differing carbon sources. This study provides additional insight into the carbohydrate metabolism of the ericoid mycorrhizal fungus O. maius. I have demonstrated that O. maius produces a variety of enzymes that can effectively target plant and fungal cell wall components with a wide range of chemical and structural heterogeneity. The variability of this CAZome could provide an advantage to O. maius when both free living and in symbiosis, and alleviates direct competition for resources within the soil environment. These results add to the growing body of knowledge surrounding the mechanisms that soil fungi utilize to degrade lignocellulose and other carbon containing components of the soil environment to effectively attain resources in this highly competitive environment. The inconsistent nature of regulation of enzyme categories, with relation to substrate, demonstrates that this fungus is capable of degrading plant and fungal cell wall components through a wide variety of mechanisms. It also highlights the fact that CAZyme nomenclature and 64 mechanistic understanding is still a work in progress. Many CAZyme families have only been categorized based on sequence similarity and more research is required to determine the specific mode of action and substrate preference, especially in the diverse fungal kingdom. For example, subfamilies for GH5 have been defined with specific enzyme activities and annotated structural features (Aspeborg et al. 2012), including the aforementioned GH5_24, found only in fungal genomes, but not yet for other CAZyme families that also contain multiple activities, such as GH3. Only with the accumulation of sequence data and functional experimentation from a myriad of sources can the relationship between the original families change enough to merit re-examination of family membership. Conversely, many enzymes with similar function to other CAZymes are not yet classified as such; for example, the ~30 chitinases annotated in this genome, but not identified as CAZymes through the current HMM profiles. The solution, however, is the same, in that more data accumulation through genomic and transcriptomic studies such as these is necessary to clarify function. There are many reasons that observed transcript abundance may differ from expectations, including the intrinsic stochastic nature of biochemical reactions in the cell. The aforementioned inaccuracy of enzyme function is another. The low mRNA yield attained from fungal tissue here can also contribute to ambiguity of results. Assuming that the low yield was a temporal issue, the fungi may have exhausted all available nutrients from the media, which could potentially have downstream effects on those transcripts produced, one of which is the use of the cellulose membrane as a nutrient source, causing upregulation of cellulose degrading genes under these conditions; alternatively, fungal mycelium could have been recycled as a nutrient source, thereby producing more glucan- and chitin-associated enzymes. Another possibility is concerted downregulation of CWDEs under these starvation conditions, causing the upregulation of transcripts by the peat substrate to appear inflated relative to the other two treatments. Repeated testing of these conditions with an added temporal analysis could minimize these uncertainties. 65 Chapter 3 Selection and Evaluation of Reference Genes for ddPCR-based Transcript Abundance Studies in Oidiodendron maius Across Varying Carbon Sources 3.1 Synopsis When identifying transcript abundance in response to treatment, accurate quantification is critical, especially when examining subtle differences in expression. In particular, data normalization is necessary to account for differences among samples including those associated with RNA quantity and quality. Due to the capacity of droplet digital PCR to absolutely quantify the copy number of the target gene in a given sample, normalization, such as the use of an internal control gene, has not customarily been considered obligatory. Decades of quantitative PCR research have shown, however, that the use of endogenous controls undoubtedly aid in correcting sample variability. With our limited knowledge of gene function in many fungi, typical ‘housekeeping genes’ commonly used as internal references may not be relevant in these organisms. This study aimed to identify and validate suitable reference genes for transcript abundance studies in Oidiodendron maius, a globally distributed, model ericoid mycorrhizal fungus. A shortlist of 251 non-differentially expressed genes was generated from RNA-Seq analyses of O. maius grown on three different carbon sources or in symbiosis with Vaccinium myrtillus. Subsequently, a set of criteria (stable expression, valid annotation and relatively high expression) was applied to select three candidate reference genes. These three genes were validated across a further eleven carbon sources using ddPCR and the application of geNorm and NormFinder stability analysis algorithms. Expression stability analysis of three genes - EfTu, vma, and sar - confirmed their reliability as internal references; the geometric mean of their expression values demonstrated the highest stability as a normalization factor. 66 We propose the use of the geometric mean of O. maius genes EfTu, vma and sar as a reference tool to normalize RNA expression in ddPCR assays. These newly selected and validated reference genes will increase reliability and reproducibility when studying transcriptional responses of O. maius at different developmental stages and/or under a range of physiological conditions. In addition, the list of 251 non-differentially expressed genes can serve as a valuable resource for selecting reference genes for related experiments and enhances the limited information available on O. maius. 3.2 Background The Fungi comprise a monophyletic kingdom, recently estimated at approximately 5 million species worldwide, with about 100,000 currently described (Blackwell 2011). They are essential decomposers in the nutrient cycles of many ecosystems and, as root symbionts, they are critical to the growth of nearly 90% of plant species. Despite their significance, we are still in the early stages of understanding gene function in most fungal groups. Improving knowledge of gene function often begins with investigation of the expression of genes of interest under a broad range of treatment conditions. The most commonly used and well-established analytical method to examine transcript abundance has been real-time, or quantitative, polymerase chain reaction (RT- or q-PCR), but the recent advent of droplet digital PCR (ddPCR) allows for accurate quantification of samples varying in quality and starting concentration with no requirement for standard curves or exogenous controls (Baker 2012). In the ddPCR method, samples are diluted and divided into multiple ‘partitions’ (~20,000 nanoliter-sized emulsified water-in-oil droplets in the case of the BioRad QX200™ ddPCR™ system (Bio-Rad Laboratories, Hercules, CA, USA)) and subjected to endpoint PCR. This partitioning has the effect of diluting the target DNA, such that some droplets contain zero copies of the target (negative), while others contain one or more copies (positive). The absolute quantity of DNA can then be calculated from the number of positive and negative droplets using Poisson 67 statistics (Hindson 2011; Pinheiro 2012). This partitioning into a large number of discrete tests increases sensitivity and dynamic range (McCord 2016). Droplet digital PCR technology offers several advantages over conventional qPCR including lower sensitivity to inhibitors, lack of susceptibility to variations in PCR and reverse transcriptase (RT) efficiency, absolute quantification of the target without requiring a standard curve, and increased precision in detection of small-fold changes in gene expression (<1.25, which is the limit of qPCR) (Baker 2012). Currently, the primary use of ddPCR is detecting copy number variations and rare mutations in gDNA sequences (e.g., (Zhao et al. 2013)). Increasingly, however, it is becoming an indispensable tool in transcript abundance analysis as it exhibits greater sensitivity than qPCR when examining subtle fold-differences and has benefits when mRNA is in low abundance (Whale et al. 2012). Additionally, ddPCR allows for reliable and reproducible measurements when anticipating closely related gene sequences or in the examination of highly similar mRNAs, such as those in multigene families (Vasina et al. 2017). Due to the capacity of ddPCR to absolutely quantify the number of copies of the target in a given sample, normalization, such as the use of a reference gene, is not considered obligatory. The assumed absolute quantification, however, relies on invariable sample input, which can be affected by the source of tissue as well as the efficiency of the RT step (Huggett et al. 2013). Recent research suggests that the application of a normalization method may be beneficial for ddPCR data, especially in instances of low replication or highly variable sample quality (Zmienko et al. 2015), thus providing a more accurate representation of gene expression changes. Normalization aids in correcting the variability that arises from differences in initial sample amount, variable growth conditions, recovery and quality differences during RNA extraction, cDNA synthesis efficiency, and differences in the overall transcriptional activity of the tissue in response to treatment. It is often difficult to normalize mRNA samples, so internal control genes (reference 68 genes) have frequently been used as a normalization factor in qPCR experiments, resulting in a relative quantification of the gene of interest. These internal control genes have traditionally been ‘housekeeping genes’ (constitutively expressed genes involved in normal cellular function); however, many studies use these genes as normalization factors without proper validation, disregarding the fact that qPCR results for transcript abundance studies are highly dependent on the reference genes chosen (Dheda et al. 2005). It is critical that reference genes maintain stable expression between tissues, cells or treatment groups. Reference genes typically used for quantification of transcripts include elongation factors (e.g., (Abbal et al. 2008)), actin (e.g., (Ruan & Lai 2007)), tubulin, glyceraldehyde- 3-phosphate-dehydrogenase or ribosomal RNA (18S or 28S) (Bas et al. 2004). While these genes are constitutively expressed in nearly all organisms, variation in transcript abundance due to regulation can be seen between species and even within species across experimental conditions (Solanas et al. 2001; Spanakis 1993). Suzuki et al. (2000) reviewed qPCR gene expression studies from high-impact journals and found that glyceraldehyde 3-phosphate dehydrogenase, beta-actin, 18S and 28S rRNA were used as single control genes for normalization in more than 90% of the studies reviewed. Unfortunately, the use of an inappropriate reference gene, i.e., one with variable expression, can lead to inaccurate calculations of the expression level of target genes and, therefore, incorrect interpretations regarding the function of those target genes (Dheda et al. 2005). Furthermore, because no single gene maintains constant expression in all examined tissues and cells under all experimental conditions (Vandesompele et al. 2002), multiple reference genes may be required for an experimental system. More recently, many studies examining the validity of reference genes for qPCR have been carried out in plant (Zmienko et al. 2015; Czechowsky et al. 2005), and animal models (Foss et al. 1998), including humans (Warrington et al. 2000), but very few have examined fungi. Among those studies focused on fungi, the gene 69 expression quantification method used has been exclusively qPCR and the results are variable. The general consensus is that at least two reference genes should be used for accurate normalization; however, the specific genes used for normalization are dependent on organism and experimental conditions. Cusick et al. (2014) used combined stability values from NormFinder and Best Keeper software to show that vma genes (V-type proton ATPase catalytic subunits) were the most stable transcripts for qPCR studies in the model fungus Neurospora crassa under varying environmental conditions and that actin was the least stable of 12 genes examined. In an examination of eleven potential reference genes, Song et al. (Lyu et al. 2017) found that ubiquitin-conjugating enzyme (UBC) and elongation factor 2 (EFTu) were the most stable reference genes for qPCR studies in Puccinia helianthin; they also concluded that actin and tubulin were expressed in extremely low levels in certain tissue types and were not appropriate reference genes. Lyu et al. (Song et al. 2019) had similar findings in their selection of reference genes for qPCR in Trichoderma afroharzianum using five different stability analyses; EF1 was found to be the most stably expressed reference gene, whereas alpha tubulin and actin showed the least stable expression. Llanos et al. (2015) analyzed 12 functionally unrelated candidate reference genes for Talaromyces versatilis by RT-qPCR assays over more than 30 relevant culture conditions using geNorm and found that six of these (ubcB, sac7, fis1, sarA, TFC1 and UBC6) were stable across all conditions. They suggest geometric averaging of at least three of these genes (or their homologues) and propose their use in all filamentous fungi (Llanos et al. 2015). Using the NormFinder and geNorm software, Steiger et al. (2010) evaluated six potential reference genes in 34 samples from diverse conditions for Trichoderma reesei and found that sar1, which encodes a small GTPase, was the most stable gene, whereas act (encoding actin) was not amongst the best validated ones; they suggest that sar1 and at least one other reference gene be used to normalize transcript analysis. 70 To our knowledge, studies examining appropriate reference genes for normalization in transcript abundance studies utilizing ddPCR in fungi have not been conducted. Oidiodendron maius ((G. L. Barron 1962), Leotiomycetes, Ascomycota) (Barron 1962) is a multi-trophic fungus, growing both as a saprotroph and as a symbiont (Hambleton & Currah 1997). It has been isolated from sphagnum peat bogs (Barron 1962), decaying wood (Lumley et al. 2001), and roots of spruce (Schild et al. 1988; Qian et al. 1998), salal (Xiao & Berch 1996) and oak (Bergero et al. 2000). Several Oidiodendron spp., including O. griseum have been proposed as ericoid mycorrhizal fungi due to their ability to form typical intracellular coils in the roots of the monophyletic plant family Ericaceae (Couture et al. 1982; Dalpé 1986; Stoyke & Currah 1991). However, more recent analysis of rDNA ITS sequences suggests that many Oidiodendron isolates may have been misidentified in older studies, and that only O. maius demonstrates mycorrhizal associations with Ericaceae in the field (Lacourt et al. 2001; Perotto et al. 2012). Although few experiments have been done to determine benefits from the association (Leake & Read 1991; Martino et al. 2018), there are no reported negative effects of O. maius on plant partners. Like other Oidiodendron spp., O. maius exhibits classic characteristics of saprotrophy (Hambleton & Currah 1997): it sporulates prolifically, grows relatively quickly in culture on a variety of media, produces a broad complement of degradative enzymes (Martino et al. 2018; Rice & Currah 2001), and is effective at eroding complex organic residues, such as the cell walls of Sphagnum (Tsuneda et al. 2001). Much of the work on O. maius has focused on its metal tolerance (Daghino et al. 2016), but with its published genome (Martino et al. 2018; Kohler et al. 2015) and transcriptomes (Martino et al. 2018; Casarrubia et al. 2020), it has become a model organism for the investigation of the genetic and functional basis of the ericoid mycorrhizal symbiosis. Several groups have used individual reference genes to normalize qPCR data from O. maius, including elongation factor 1 (Vallino et al. 2005; 2009) and beta-tubulin (Casarrubia et al. 2018); these genes were assumed to be stably expressed “housekeeping genes", but this was not 71 confirmed experimentally. DiVietro et al. (2014) used the Normfinder algorithm to assess the stability of beta-tubulin, elongation factor 1a and triose phosphate isomerase for use as reference genes in qPCR of O. maius; the most stable expression profile was derived from the average between b-tubulin and elongation factor 1a expression values. Attempts to validate reference genes for ddPCR of O. maius have not been made. Theoretically, genes that are stable across treatments would be potential reference genes for either classical qPCR or ddPCR, but the need to confirm stability remains. When selecting appropriate reference genes, several characteristics are important to consider. Relatively stable expression across various treatments ensures that the expression does not vary due to nutrient source or physiological condition; a relatively high expression level ensures that changes in expression will be detectable; and annotation provides some context for why the expression of these genes remains stable. Screening transcriptome datasets has been demonstrated as a quick and efficient means of selecting candidate reference genes that meet these criteria for a particular species (Deng et al. 2016; Stanton et al. 2017). In the work reported here, our objective was to select and validate appropriate reference genes for transcript abundance studies by ddPCR in O. maius. Using in-silico analysis to mine RNA-seq data from O. maius cultivated on varying carbon sources as well as during symbiosis (Kohler et al. 2015), a list of 251 annotated genes with invariant expression was generated as a resource for selecting reference genes. Three of these genes, EfTu (protein ID 102574), vma (protein ID 31957), and sar (protein ID 18772), were further investigated by ddPCR analysis and are proposed as reference genes for studying transcriptional response across different developmental stages and physiological conditions in O. maius. Additionally, we show that the geometric mean of these three genes increases their reliability as a means of normalizing variable transcript abundance data. 72 3.3 Materials & Methods 3.3.1 Strain The Oidiodendron maius (MUT1381/ATCC MYA‐4765) isolate used here had been isolated from roots of Vaccinium myrtillus growing in zinc-contaminated experimental plots in the Niepolomice Forest, Poland according to Pearson and Read (1973; Martino et al. 2000). The isolate is capable of forming typical ericoid mycorrhizae with axenic Calluna vulgaris (Perotto et al. 1995) and Vaccinium myrtillus (Martino et al. 2018) seedlings. The genome of O. maius as well as transcriptomes for both the free-living mycelium and the fungus in symbiosis with Vaccinium myrtillus, have been sequenced (Kohler et al. 2015). 3.3.2 In-silico analysis of RNA sequencing data Five RNA-seq datasets from O. maius Zn were analyzed in the current study: three unpublished data sets (E. Feldman, unpublished) and two from Kohler et al. (2015; the complete data sets have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE63947). In all cases, fungal cultures were grown for 45 days on Modified Melin Norkrans (MMN) plates, overlaid with sterile cellulose membranes (autoclaved in ddH20 twice, with 24 hours in between), with a range of amendments (Table 3.1). The MMN medium contained: 0.075 g L-1 (NH4)2HPO4 (filter sterilized, 0.2 m, added after autoclaving), 1 g L-1 glucose, 0.5 g L-1 KH2PO4, 0.066 g L-1 CaCl2.2H2O, 0.025 g L-1 NaCl, 0.15 g L-1 MgSO4.7H2O, 0.1 g L-1 thiamine HCl (filter sterilized, 0.2 m, added after autoclaving), 1 mg L-1 FeCl3.6H2O and 10 g L-1 agar; pH was adjusted to 4.7. 73 Table 3.1. Amendments made to Modified Melin Norkrans media to support growth of Oidiodendron maius. Treatment Name Study Glucose (1 g L-1) (NH4)2HPO4 BSA (0.1 g L-1) Peat1 (~10 g dry weight) Notes FLM/BSA present work - - + - “Control” FLM/NH4 present work + + - - FLM/Peat present work - - - + FLM/BSA Kohler et al. 2015 - - + - “Control” MYC/BSA Kohler et al. 2015 - - + - O. maius colonizing Vaccinium myrtillus roots 1 Sunshine brand, Sun Gro Horticulture Canada Ltd.; sterilized via electron beam radiation (Iotron Industries Canada Inc.; 35 kGy) Three criteria were applied to these five transcriptomes to generate a short list of potential candidate reference genes: (1) the fold change (based on Reads Per Kilobase Million; RPKM) was equal to approximately one (between 0.8 and 1.2) among all treatments (uniform expression despite treatment difference, as suggested by Manoli et al. (61)), (2) annotation was available (including a valid annotation from InterPro (excluding “protein of unknown function”) and a valid Gene Ontology name), and (3) homologous genes had been used previously in other fungi as internal reference genes for qPCR. The online tool Heatmapper (62) was utilized to construct the heatmap from RPKM Means; no clustering method was used. 3.3.3 Cultivation of Oidiodendron maius for use in ddPCR Because we planned to assess expression of carbohydrate-active genes in future research, we cultivated O. maius on a greater variety of carbon sources than those previously examined by RNA-Seq. O. maius cultures were maintained on Czapek-glucose agar (3 g L-1 NaNO3; 1 g L-1 K2HPO4; 0.5 g L-1 MgSO4.7H2O; 0.01 g L-1 FeSO4.7H2O; 0.5 g L-1 KCl; 20 g L-1 glucose; 10 g L-1 agar; pH adjusted to 6) for 45 days in the dark at 25 °C. Fungal plugs from these plates were transferred to glass mesh filters (Whatman Grade GF/F Glass Microfiber Filters, Binder Free, GE Healthcare, 0.6-0.8 µm particle retention) overlaid on MMN 0.1% glucose plates supplemented with one of eleven additional carbon sources (Table 3.2) for a further 45 days in the 74 dark at 25 °C (plates contained (NH4)2HPO4 as the sole nitrogen source, unless otherwise stated, to provide a nitrogen source where it may be limiting). The experimental ddPCR assays were performed on cDNA samples from these eleven experimental treatments using at least three biological replicates. Table 3.2. Treatment groups for growth of Oidiodendron maius for use in ddPCR* Carbon Source Source carbon Added (g L-1) N source (g L-1) glucose (0.1 %) (NH4)2HPO4 (0.075) glucose (0.5 % total) 0.4 (NH4)2HPO4 (0.075) peat 0.1 (NH4)2HPO4 (0.075) + peat peat dissolved organic matter (DOM) 200 mL L-1 (NH4)2HPO4 (0.075) + peat DOM field soil 0.1 (NH4)2HPO4 (0.075) + field soil field soil dissolved organic matter (DOM) 200 mL L-1 (NH4)2HPO4 (0.075) + field soil DOM Bovine Serum Albumin (BSA) 0.1 (NH4)2HPO4 (0.075) + BSA cellulose 0.113 (NH4)2HPO4 (0.075) chitin 0.113 (NH4)2HPO4 (0.075) pectin 0.119 (NH4)2HPO4 (0.075) lignin 0.08 (NH4)2HPO4 (0.075) *Cultures were grown on solid Modified Melin Norkrans media in preparation for RNA extraction and subsequent transcript abundance studies. Added source carbon is in addition to the 0.1 g L-1 glucose contained in the base media. 3.3.4 RNA extraction and cDNA synthesis Fungal tissue was removed from glass mesh filters with a sterile scalpel, placed in pre-weighed RNase-free 1.5 mL Eppendorf tubes and flash-frozen in liquid nitrogen. Up to 100 mg of flash-frozen fungal tissue was mechanically disrupted by grinding in liquid nitrogen in a sterile mortar and pestle and placed in RNase-free 1.5 mL Eppendorf tubes on ice. To each tube, 700 L extraction buffer (100 mM Tris-HCl pH 8, 100 mM NaCl, 20 mM Na-EDTA, 0.1% PVP, 1% sodium-lauryl sarcosine, prepared in diethylpyrocarbonate (DEPC) water) and 700 L acid phenol were added prior to gently inverting the tubes. Tubes were centrifuged for 5 minutes at 14000 rpm (4 °C); all subsequent centrifugation was also performed at 14000 rpm (4 °C). The uppermost phase of the supernatants was transferred to new Eppendorf tubes on ice, to which an equal volume of acid phenol-chloroform-isoamyl alcohol (25:24:1) was added. Tubes were gently inverted several times, then centrifuged for 5 minutes. Supernatants were transferred to new tubes containing an 75 equal volume of chloroform, tubes were gently inverted several times and then centrifuged for 5 minutes. This chloroform extraction and centrifugation was repeated once. Total nucleic acids were precipitated by addition of an equal volume of isopropyl alcohol to the supernatant and gentle inversion. Tubes were incubated for 30 minutes at -80 °C, then centrifuged 30 minutes. Supernatant was discarded and the pellet was resuspended in 500 L DEPC water. To each tube 500 L 6 M LiCl was added, then the tubes were gently inverted and kept overnight in ice at 4 °C. Tubes were centrifuged 30 minutes, then the supernatant was discarded and the remaining pellet was washed with 150 L 70% ethanol (prepared with DEPC water). The tubes were centrifuged for an additional 5 minutes, the supernatant discarded and the pellet was dried on ice in the fume hood. Once completely dry, the pellet was resuspended in 25 L DEPC water, then the concentration and quality were determined using a NanoDrop ND-1000 UV-visible light spectrophotometer (NanoDrop, Wilmington, DE, USA). Only RNA samples with 260/280 nm wavelength ratio of approximately 2 and 260/230 nm wavelength ratio of approximately 2 were retained. RNA solutions were treated with PerfeCTa® DNase I according to manufacturer’s instructions (Quanta Biosciences™, Beverly, MA, USA). DNase-treated RNA was converted to cDNA using the BioRad iScript RT Supermix for RT-qPCR according to manufacturer’s protocol (Bio-Rad Laboratories, Inc., Hercules, California, USA) and stored at −20°C. 3.3.5 Primer design and validation by conventional PCR detection Primers were designed for the three candidate reference genes using the IDT PrimerQuest tool which incorporates Primer3 software (version 2.2.3; Integrated DNA technologies, Skokie, Illinois) using the qPCR Intercalating Dyes parameters. Additional parameters included a product size of 75-200 bp (optimum = 125 bp), melting temperature of 50─65 °C (optimum = 59 °C), GC content of 50─60 % (optimum = 55 %), GC clamps on both ends (3’ GC clamp = 2 nt), 50 mM salt concentration, 300 nM oligonucleotide concentration and minimum overlap of 4 nt at either end. Three primer pairs were chosen for each transcript based on forward and reverse primers having 76 similar GC content and melting temperature, where the target sequence had a single hit when BLASTed against the O. maius model filtered transcript dataset (Kohler et al. 2015). cDNA from the MMN + Peat treatment was used for all primer validation by conventional PCR. PCR reaction conditions were 1x GoTaq buffer, 200 M dNTPs, 1 U GoTaq, 0.1 M F primer, 0.1 M R primer, 100 ng cDNA, 5 % DMSO. Touchdown PCR was run: 3 min. @ 94 °C + 10(1 min. @ 94 °C + 1 min. @ 65-1 °C + 1 min. @ 72 °C) + 30(1 min. @ 94 °C + 1 min. @ 60 °C + 1 min. @ 72 °C) + 9 min. @ 72 °C + ∞@ 4 °C. All PCR products were run for 60 minutes at 90 V on a 1 % agarose gel containing Invitrogen™ SYBR™ Safe. Gels were photographed under UV light and bands containing the correctly sized amplicons were excised. These excised gel fragments were cleaned using the QIAGEN QIAquick Gel Extraction kit as per manufacturer’s instructions. The resulting extractions were sequenced on an Applied Biosystems 3130xl DNA sequencer in the Fragment Analysis and DNA Sequencing Services lab at the University of British Columbia Okanagan campus. Sequenced amplicons were reBLASTed against the O. maius model filtered transcript dataset (Kohler et al. 2015) to ensure a single hit with the intended target. 3.3.6 ddPCR assay with EvaGreen® Validated primers were used in a ddPCR Assay using a QX200™ ddPCR™ system (Bio-Rad Laboratories, Hercules, CA, USA) according to the manufacturer's standard EvaGreen® protocol. Briefly, each reaction contained 2 L of cDNA, 100 nM of each forward and reverse primer, 1X ddPCR EvaGreen Supermix, 5 % DMSO and molecular-grade water to 20 l. Reactions were loaded into the sample wells of a DG8 droplet generation cartridge (Bio-Rad). Seventy l of Droplet Generation Oil for EvaGreen® (Bio-Rad) were loaded into the oil wells, and the cartridge was placed in the QX200Droplet Generator (Bio-Rad). The resulting droplets were transferred to a 96-well Bio-Rad PCR plate. The PCR plate was then heat-sealed with a foil seal and placed in the thermocycler. Reaction conditions consisted of initial enzyme activation period 77 at 95°C for 5 min; followed by 40 cycles of denaturing at 95°C for 30 s and annealing/extension for 1 min; then dye stabilization at 4°C for 5 min and 90°C for 5 min; the ramp rate was 2.5°C/sec. Optimal annealing temperature was determined by running a temperature gradient for each primer pair ranging from 54 °C to 60 °C. After the amplification, plates were loaded into the Bio-Rad QX200 DropletReader for enumeration of the number of positive and negative droplets based on fluorescence. The number of template molecules per microliter of starting material was estimated by the QuantaSoft®AP software (version 1.6.6.0320, Bio-Rad) using an internal Poisson algorithm to analyze clusters; only droplets above a minimum amplitude threshold were counted as positive. For each primer pair, the PCR reaction mixture without matrix was used as negative control (no template control, NTC). Three biological replicates were run for each carbon source, unless otherwise specified. 3.3.7 ddPCR stability analysis The stability of the putative reference genes was assessed using the geNormv3 (Vandesompele et al. 2002) and the NormFinder (Andersen et al. 2004) add-ins for Microsoft Excel. The geNorm add-in allows the calculation, for each reference gene, of the gene expression stability value M, which is the average pairwise variation of a particular gene with all other genes. The most stable genes present the lowest M values; genes with M value ≤ 1.5 are considered highly stable across analyzed samples. Normfinder uses a model-based approach that provides an estimate of both intra- and intergroup expression variation, and calculates a gene stability value; the smaller the stability value, the more appropriate the use as a reference gene. 78 3.4 Results & Discussion 3.4.1 Transcriptome sequencing to identify genes with stable expression across three carbon sources and in symbiosis Analysis of three RNA-Seq datasets from O. maius free living mycelium (FLM) grown on modified MMN supplemented with glucose, BSA and peat (E. Feldman, unpublished) and two RNA-Seq data sets from O. maius grown on modified MMN supplemented with BSA alone (FLM) or in symbiosis (MYC) with Vaccinium myrtillus (Kohler et al. 2015) revealed a list of 251 putative reference genes that met several criteria: (1) stable expression across the five treatments (based on ratio of RPKM Means); (2) valid annotation; and (3) relatively high expression level (RPKM means >15). The resultant shortlist of 251 candidate reference genes will provide an excellent resource for researchers looking to examine transcript abundance in O. maius in the future (Figure 1, Appendix C Table C.1). 79 Figure 3.1. Gene expression analysis of 251 putative reference genes for Oidiodendron maius. Heat map illustrating RNA-Seq differential expression data and corresponding KOGG Group. White to red spectrum indicates increasing fold change (Reads Per Kilobase Million ratio) for each treatment compared with free-living mycelium (FLM) expression on Modified Melin Norkrans (MMN) without glucose and (NH4)2HPO4, supplemented with 0.1 g L-1 bovine serum albumin (BSA) (FLM/BSA). Reference genes selected for validation are indicated by blue arrows; corresponding transcript IDs are in parentheses. Media used for each treatment are as follows - FLM/NH4: MMN (present work); FLM/Peat: MMN without glucose and (NH4)2HPO4, poured over ~10 g (dry weight) sterile commercial peat (present work); MYC/BSA: O. maius colonizing Vaccinium myrtillus root samples, grown on MMN without glucose and (NH4)2HPO4, supplemented with 0.1 g L-1 BSA (Kohler et al. 2015). 80 Approximately one third of the shortlisted genes were involved in cellular processes and signalling, including chaperone proteins, ubiquitins and related proteins, ADP-ribosylation factors and three tubulin genes. Approximately one quarter of the shortlisted genes were transcription factors, transcriptional regulatory proteins, elongation factors and other genes related to information storage and processing. Another twenty percent of the shortlisted genes encoded proteins involved in metabolism, primarily those involved in amino acid and coenzyme transport/metabolism as well as several enzymes involved in secondary metabolite synthesis. The remainder were poorly characterized with only a general function predicted. Other than the aforementioned tubulin genes, this shortlist did not contain any of the transcripts typically considered housekeeping genes, nor those previously suggested for use as reference genes in qPCR of O. maius (Vallino 2005; 2009; Casarrubia et al. 2018; DiVietro et al. 2014). This absence of typical housekeeping genes supports the need to validate reference genes in the context of each experimental system. Three of the shortlisted candidate reference genes were selected for further study, based on homology to reference genes previously used in quantitative expression studies in fungi (Cusick et al. 2014; Song et al. 2019; Lyu et al. 2017; Llanos et al. 2015). The genes chosen for this study (Table 3.3) were selected based on InterPro Descriptions; these homology-based annotations indicate that transcript 102649 codes for a translation elongation factor 2 (EFTu) with GTPase activity, potentially involved in translation, ribosomal structure and biogenesis; transcript 18772 codes for a small GTPase (ARF/SAR) subunit SAR1/ GTP-binding ADP-ribosylation factor Arf1, involved in GTP binding and intracellular protein transport; and transcript 32032 codes for a proton-transporting two-sector ATPase complex (ATPase_F1/V1/A1) with hydrogen ion transmembrane transporter activity. For the purpose of this work, I have named the genes according to their proposed function. The genes are categorized in three distinct gene ontology terms and their products are likely associated with three different cellular 81 components/compartments: ribosome, cytosol, and membrane. There is no evidence of functional relation to one another; therefore, it was assumed that independent confirmation of expression stability of each gene between treatments would support this reference gene selection process. Table 3.3. Selected candidate reference genes with stable expression across various culture conditions in Oidiodendron maius* RPKM Ratio Name Transcript ID Protein ID Putative function FLM/Peat vs FLM/NH4 FLM/Peat vs FLM/BSA FLM/NH4 vs FLM/BSA EfTu jgi|Oidma1|102649| 102574 Translation elongation factor EFTu/EF1A 1.119 1.055 0.943 sar jgi|Oidma1|18847| 18772 GTP-binding protein SAR1; ARF/SAR superfamily 1.058 1.059 1.001 vma jgi|Oidma1|32032| 31957 ATPase, F1/V1/A1 complex, alpha/beta subunit 0.986 0.997 1.011 *Transcript abundance data evaluated in this study was obtained through RNA-Seq. Stable expression is here defined as a Reads Per Kilobase Million (RPKM) ratio approximating one. O. maius samples were grown on the following media: NH4 = Modified Melin Norkrans (MMN); BSA = MMN without glucose and (NH4)2HPO4, supplemented with 0.1 g L-1 bovine serum albumin (BSA); Peat = MMN without glucose and (NH4)2HPO4, poured over ~10 g (dry weight) sterile commercial peat. A total of three primer pairs were designed and tested using a touchdown PCR procedure for each transcript selected as a candidate reference gene (Table 3.4). For all primer pairs except E25, a single amplicon was observed by electrophoretic separation. Primer pairs E22, S12 and V35 produced amplicons which aligned to a single, intended transcript and were subsequently used for ddPCR amplification. Table 3.4. Primer pairs designed for amplification of candidate reference genes in Oidiodendron maius* Gene Name/ Transcript ID Primer Pair Name Forward primer Sequence (5'→3') Reverse Primer Sequence (5'→3') Expected Amplicon Size EfTu / 102649 E22 ACAGGTAGCGACAAAGG GTATAGCTCGAGGATCACG 112 E23 TCTGGGTGCCATTTACG CCAATCCCTTCCTCATCC 108 E25 GATGATCTGGAGGATCTGG TCTGCTTCTCTGCATCC 130 sar / 18847 S11 TCTGATGGTTGGGTTGG GTTGAAGCCGATTGTGG 97 S12 TCGTCGACAGTAATGACC GCAGATCTTGCTTGTTGG 120 S15 CACAATCGGCTTCAACG CGACGACGAAGATAATGC 137 vma / 32032 V32 GATCCTGCTCGACTATCC GGTTGTTCAGCCTTTCC 192 V34 GAGTTTGGTTCCGTTGC CTCCATCTCTAGGTTGCC 139 V35 CTGGCAACCTAGAGATGG GTGATCCAGAATCCAGAGG 165 *The tabulated primer pairs were designed to amplify selected candidate reference genes with stable expression (as determined by in silico analysis of RNA-Seq data) and associated primer pairs for O. maius. 82 3.4.2 Optimization of EvaGreen® ddPCR assays The optimum temperature for annealing/extension during ddPCR was determined for each primer pair using a temperature gradient. This optimization determined the annealing/extension temperature that demonstrated the greatest balance between high droplet count and cluster separation of positive and negative droplets while reducing ‘rain’ (droplets with intermediate fluorescence) (Bio-Rad 2015). All gradient assays produced non-specific amplification (‘rain’) at all temperatures in the gradient, although the EfTu (E22) primer set showed the greatest cluster separation (Figure 2A). Cluster separation was strongest for all primer pairs at lower temperatures (Figure 2); consequently, 54 °C was selected as the annealing/ extension temperature for all experimental assays. Figure 3.2. Optimization of primer annealing/extension temperature for ddPCR assays using temperature gradient. Fluorescence amplitude for eight annealing/extension temperatures ranging from 60 °C to 54 °C from left to right for ddPCR amplification of candidate reference genes (A) EFTu amplification using primer pair E22 (B) sar amplification using primer pair S12 (C) vma amplification using primer pair V35. 83 3.4.3 Stability of candidate ddPCR reference genes evaluated for Oidiodendron maius cultured on a variety of carbon sources It is critical that reference genes maintain invariable expression across a wide variety of carbon sources. To evaluate whether the selected candidate reference genes could be utilized as internal controls for normalization of ddPCR expression data, their transcript levels were quantified across eleven carbon treatments. The resulting analysis showed variability among and between treatments, as expected, in response to variable amounts of starting material (Figure 3). While a few studies have examined reference gene stability for use in ddPCR, these analyses have been conducted solely on qPCR data (Zmienko et al. 2015; Zhang et al. 2019). The geNorm algorithm was designed for qPCR data and, as such, the input data used is raw expression data. While we are unaware of any other application of geNorm to ddPCR data, the algorithm is based on average pairwise variation between the gene of interest and all other genes examined (Vandesompele et al. 2002), and, therefore, should be applicable to expression values derived from ddPCR. Similarly, the NormFinder algorithm allows input of any linear scale expression quantities, hence any expression data obtained through any quantitative method can be analyzed (Andersen et al. 2004). Furthermore, it is a model-based system that compares inter- and intra-group variation, so as long as there are at least three candidate genes examined, over at least eight samples (Andersen et al. 2004), the resulting stability values should be relevant to ddPCR quantification. Another algorithm, BestKeeper (Pfaffl et al. 2004), has often been used to validate reference gene stability; however, data input is in the form of raw crossing points (CP) (Rasmussen 2001) or threshold cycles (Ct) (Livak 2001) generated by a real-time PCR platform, which are not compatible with the data obtained through ddPCR experiments. The geNorm algorithm does not allow for zero values, so all samples from this work that contained one or more “No Call” readings were omitted from the stability calculation for both algorithms (i.e.,nine assays – one for each candidate reference gene target - were excluded for the carbon sources: peat DOM, 84 field soil and lignin) resulting in 32 samples being used for the calculation (i.e.,field soil and lignin carbon sources were only present as two biological replicates in the stability analysis). 85 Figure 3.3. Reference gene concentration in 36 cDNA samples of Oidiodendron maius grown on a variety of carbon sources as determined by ddPCR. Error bars indicate the Poisson 95% confidence intervals for each concentration determination. (A) sar (B) EfTu (C) vma. Field soil sample 7 was omitted from plot A as an extreme outlier. (DOM: dissolved organic matter; BSA: bovine serum albumin; NTC: no template control). 86 NormFinder ranked the most stable reference gene as EfTu, followed by vma, then sar (Figure 3.4A). geNorm ranked vma as the most stable reference gene, followed by EfTu and sar (Figure 3.4B), though all three candidate reference genes fell below the M value stability cut-off of 1.5. The algorithms were also applied to the geometric means (Geomean) of two or all three candidate reference genes (Figure 3.4C, D). NormFinder ranked the Geomean of the three candidate genes as the most stable, followed by the Geomean of sar and vma the Geomean of EfTu and sar, and the Geomean of EfTu and vma (Figure 3.4C). Interestingly, it ranked the stability of EfTu alone as greater than the latter two Geomeans of two genes. The geNorm algorithm also ranked the Geomean of the three candidate genes as the most stable, followed by the Geomean of EfTu and sar, the Geomean of sar and vma, and the Geomean of EfTu and vma (Figure 3.4D). In the case of geNorm, the use of any Geomean was considerably more stable than the use of any single reference gene. As they are not functionally related, it is unlikely that this correlation results from co-regulation of the three genes. 87 Figure 3.4. Identification of stable reference genes in Oidiodendron maius. Analysis of the stability of three different reference genes (EfTu, sar, vma) in 33 different samples (O. maius grown on 11 different carbon sources in biological triplicate), as calculated with NormFinder (A and C) and geNorm (B and D) algorithms. In addition to the analysis of the stability of the individual genes (A and B), these analyses were also performed on the geometric means of the expression value of the three genes (GM3) and the geometric means of each pair of reference genes (GM2ES: EfTu and sar; GM2EV: EfTu and vma; GM2SV: sar and vma) (C and D). The lower the variability (NormFinder) or the M-value (geNorm) for a certain gene, the more stable it is when evaluated by the corresponding algorithm. Numerous qPCR studies have demonstrated that the use of a single reference gene is inadequate for normalizing quantitative expression and the results presented here support that. It is impractical, however, to quantify multiple reference genes when only a few target genes are being examined, or if RNA is limiting (Vandesompele et al. 2002); therefore, the geometric mean of three constitutively expressed genes is considered a suitable method to normalize quantitative expression (Vandesompele et al. 2002). Using multiple reference genes for normalization is reasonable, as it can be assumed that the variation in transcript abundance for any single gene is higher than the variation in the average transcript abundance of multiple genes. 88 3.5 Interpretations & Implications This work is the first study to show that a data set derived from a massive RNA sequencing effort for several culture conditions can be used for the identification of reference genes in O. maius. In this study, a list was generated of 251 annotated O. maius genes with invariant expression during cultivation on varying carbon sources, as well as during symbiosis. Because O. maius is considered a model species for studies of ericoid mycorrhizal fungi, this list will be a valuable resource for selecting reference genes for future gene expression studies. Furthermore, the work reported here enhances the limited information available on this organism. Included in this list, the genes EfTu (transcript 102649), vma (transcript 32032), and sar (transcript 18847) were expressed at stable levels in all samples studied and were examined as candidate reference genes for transcript abundance studies. Droplet digital PCR is a powerful tool to analyze transcript abundance profiles across multiple treatments and when fold-changes are low; however, like all transcript abundance quantification methods, it requires a stable and reproducible normalization method. Using two different algorithms to determine their stability, EfTu (transcript 102649), vma (transcript 32032), and sar (transcript 18847) were demonstrated to be stable reference genes, though their rank order differed between NormFinder and geNorm. Both algorithms demonstrate, however, that the geometric mean of all three candidate reference genes increased their stability appreciably. Therefore, we propose the geometric mean of these three genes be used as an appropriate normalization factor for future studies of transcriptional response across different developmental stages and physiological conditions in O. maius. 89 Chapter 4 Quantitative Transcript Abundance Analysis of Cell Wall Degrading Enzyme Response to Varying Carbon Source4.1 Synopsis In this chapter, I present and discuss transcript abundance profiling of several carbohydrate-active enzymes (CAZymes) and related cell wall-degrading enzymes (CWDEs) of O. maius grown on 11 different carbon sources of varying carbon complexity. The relative abundance values obtained through normalization of droplet digital PCR (ddPCR) data showed a weaker correlation between carbon source and CWDE transcript abundance than hypothesized; however, several transcripts showed significant regulation in response to substrate. 4.2 Materials & Methods 4.2.1 Strain The Oidiodendron maius (MUT1381/ATCC MYA‐4765) isolate used here is described in detail in Chapter two (Section 2.2.1). 4.2.2 Carbon sources Carbon sources of variable complexity were chosen to examine CWDE expression: glucose, peat, dissolved organic matter (DOM) from peat, field soil, field soil dissolved organic matter (DOM), bovine serum albumin (BSA), cellulose, chitin, pectin and lignin. These carbon sources were provided as either the sole carbon source or in addition to 0.1 % glucose. In an attempt to maintain similar amounts of carbon in each treatment and alter only the form of carbon present, carbon content was estimated for each source. It was added accordingly to obtain a 0.5 % (w/v) total carbon in the media (prior to 0.1 % glucose addition), with the exceptions of agar-only and glucose-only plates. Field soil from the A horizon was obtained near Sovereign Lake, British Columbia (50°22'20.8"N 119°04'25.2"W) on November 9, 2014, in an area rich in ericaceous undergrowth; 90 the carbon content of field soil was conservatively presumed to be 7.5 %. Spatial and environmental variability of soil carbon content is complex, but most estimates for boreal soil suggest 5-10 % carbon content in the upper horizons (e.g., Hossain et al. 2007) and recent examinations demonstrate that soil organic carbon content increases with increasing altitude, with organic content being highest in the upper 10 cm soil profile (Wan et al. 2019). The carbon content of the commercial peat (Sunshine brand, Sun Gro Horticulture Canada Ltd.) was estimated at 50 % (Rydin & Jeglum 2013). Dissolved organic matter (DOM) was obtained from peat and field soil based on the hot water extractable carbon method by Ghani et al. (2003), whereby 15 g soil or peat (sterilized via electron beam radiation (Iotron Industries Canada Inc.; 35 kGy)) in 30 mL water was bathed in 80 ⁰C water for 16 h with shaking, then passed through 45 mm filters. Two hundred mL of filtrate from peat and soil was added to 1 L of media. 4.2.3 Culture conditions for ddPCR Cultures of O. maius were maintained on Czapek-glucose agar (Section 2.2.2) for 45 days in the dark at 25 °C. A single fungal plug from these plates was transferred to a glass mesh filter (Whatman Grade GF/F Glass Microfiber Filters, Binder Free, GE Healthcare, 0.6-0.8 µm particle retention) overlain on MMN no glucose media (Section 2.2.2) supplemented with one of 11 carbon sources, with or without an additional 0.1 % glucose (Table 4.1), for a further 45 days in the dark at 25 °C. Plates contained 0.075 g L-1 (NH4)2HPO4 as the sole nitrogen source, unless otherwise stated, to ensure that nitrogen was not limiting. Eight replicate fungal plugs of each treatment were plated in six-well plates (VWR 29442-042) according to a number randomizer, for a total of 176 wells (22 media types X 8 wells) across 32 plates (one in four plates had two empty wells). The ddPCR assays were performed on cDNA samples from these treatments using at least three randomly-selected biological replicates. The mean colony diameter of O. maius was calculated from all eight replicates of each carbon substrate then subjected to a pairwise t test (p < 0.05 ) to determine significant differences between treatments. 91 Table 4.1. Treatment groups for growth of Oidiodendron maius on solid MMN media in preparation for RNA extraction and subsequent transcript abundance studies. Treatment Glucose added (1 g L-1) N source* % carbon in source† Amount Used to Obtain 0.5% total C 0.1 % Glucose Yes 40 n/a 0.5 % Glucose Yes 40 n/a MMN No 0 0 peat + Yes peat (~10 g dry weight) 50 0.1 g L-1 peat - No peat (~10 g dry weight) 50 0.1 g L-1 peat DOM + Yes 50 200 mL L-1 peat DOM - No 50 200 mL L-1 field soil + Yes field soil (~10 g dry weight) 7.5 0.67 g L-1 field soil - No field soil (~10 g dry weight) 7.5 0.67 g L-1 field soil DOM + Yes 7.5 200 mL L-1 field soil DOM - No 7.5 200 mL L-1 BSA + Yes BSA (0.1 g L-1) 53 0.1 g L-1 BSA - No BSA (0.1 g L-1) 53 0.1 g L-1 Cellulose + Yes 44.4 0.113 g L-1 Cellulose - No 44.4 0.113 g L-1 Chitin + Yes 44.4 0.113 g L-1 Chitin - No 44.4 0.113 g L-1 Pectin + Yes 41.9 0.119 g L-1 Pectin - No 41.9 0.119 g L-1 Lignin + Yes 63.4 0.08 g L-1 Lignin - No 63.4 0.08 g L-1 (Agar) No No *Plates contained (NH4)2HPO4 as the sole nitrogen source, unless otherwise stated †References for % carbon content of sources: BSA (Sigma A4503 - C2932 H4614 N780 O898 S39) – 53.03 % carbon (Weijers 1977); cellulose (Sigma C6288; (C6H10O5)n) – 44.4% carbon (Nordin et al. 1973); chitin (VWR CAAAJ61206-22 – (C8H13O5N)n) – 44.4 % carbon (Yen et al. 2009); pectin (VWR CAAAJ61021-22) – 41.9 % carbon (Sirotek et al. 2004); lignin (SIGMA 471003) – 63.4 % carbon (Avni et al. 1985). 4.2.4 RNA extraction and cDNA synthesis Glass mesh filters were placed atop the media to aid in the removal of fungal tissue for RNA extraction; however, the fungal hyphae penetrated into the solid media and, therefore, the entire contents of each well were flash-frozen and subjected to the extraction process. Up to 92 100 mg of flash-frozen material was mechanically disrupted by grinding in liquid nitrogen in a sterile mortar and pestle and placed in RNase-free 1.5 mL Eppendorf tubes on ice. RNA was extracted as described in Section 2.2.3. RNA solutions were treated with PerfeCTa® DNase I according to manufacturer’s instructions (Quanta Biosciences™, Beverly, MA, USA) and all extractions from the same biological replicate (i.e., the same well) were pooled. DNase-treated RNA was converted to cDNA using the BioRad iScript RT Supermix for RT-qPCR according to manufacturer’s protocol and stored at −20 °C. 4.2.5 Reference gene and target gene selection Reference genes for normalization were selected and validated, as described in Chapter 3. Fifteen target CWDE genes were selected (Section, 2.3.3, Table 2.4), where fold change (based on RPKM) was greater than two between at least two of the treatments described in Chapter 2. 4.2.6 Primer design and validation by conventional PCR detection Primers were designed using the IDT PrimerQuest tool which incorporates Primer3 software (version 2.2.3) (Integrated DNA technologies, Skokie, Illinois, http://www.idtdna.com/Primerquest/Home/Index) using the qPCR Intercalating Dyes parameters. Additional parameters included a product size of 75-200 bp (optimum = 125 bp), melting temperature of 50─65 °C (optimum = 59 °C), GC content of 50─60 % (optimum = 55 %), GC clamps on both ends (3’ GC clamp = 2 nt), 50 mM salt concentration, 300 nM oligonucleotide concentration and minimum overlap of 4 nt at either end. Two primer pairs were chosen for each target transcript (See Appendix D Table D.1) based on forward and reverse primers having similar GC content and melting temperature, where the target sequence had a single hit when BLASTed against the O. maius model filtered transcript dataset (http://genome.jgi.doe.gov/pages/blast-query.jsf?db=Oidma1). 93 cDNA from the MMN + Peat treatment (Chapter 2) was used for all primer validation by conventional PCR. PCR reaction conditions were 1x GoTaq buffer, 200 mM dNTPs, 1 U GoTaq, 0.1 mM F primer, 0.1 mM R primer, 100 ng cDNA, 5 % DMSO. Touchdown PCR was run: 3 min. @ 94 °C + 10(1 min. @ 94 °C + 1 min. @ 65D-1 °C + 1 min. @ 72 °C) + 30(1 min. @ 94 °C + 1 min. @ 60 °C + 1 min. @ 72 °C) + 9 min. @ 72 °C + ∞@ 4 °C. All PCR products were run for 60 minutes at 90 V on a 1 % agarose gel containing Invitrogen™ SYBR™ Safe. Gels were photographed under UV light and bands containing the correctly sized amplicons were excised. These excised gel fragments were cleaned using the QIAGEN QIAquick Gel Extraction kit as per manufacturer’s instructions. The resulting extractions were sequenced on the 3130xl DNA sequencer (Applied Biosystems) in the Fragment Analysis and DNA Sequencing Services lab at University of British Columbia Okanagan campus. Sequenced amplicons were reBLASTed against the O. maius model filtered transcript dataset (http://genome.jgi.doe.gov/pages/blast-query.jsf?db=Oidma1) to ensure a single hit with the intended target. Primers for reference genes were designed and validated as described in Chapter 3. 4.2.7 ddPCR assay with EvaGreen® Validated primers were used in a ddPCR Assay using a QX200™ ddPCR™ system (Bio-Rad Laboratories, Hercules, CA, USA) according to the manufacturer's standard EvaGreen® protocol. Briefly, 20 ml reaction volumes contained 2 mL of cDNA, 100 nM of each forward and reverse primer, 1X ddPCR EvaGreen® Supermix, 5 % DMSO and molecular-grade water to 20 ml. Reactions were loaded into the sample wells of a DG8 droplet generation cartridge (Bio-Rad). Seventy ml of Droplet Generation Oil for EvaGreen® (Bio-Rad) were loaded into the oil wells, and the cartridge was placed in the QX200Droplet Generator (Bio-Rad). The resulting droplets were transferred to a 96-well PCR plate (Bio-Rad). The PCR plate was then heat-sealed with a foil seal and placed in the thermocycler. Reaction conditions consisted of initial 94 enzyme activation period at 95 °C for 5 min; followed by 40 cycles of denaturing at 95 °C for 30 s and annealing/extension for 1 min; then dye stabilization at 4 °C for 5 min and 90 °C for 5 min; the ramp rate was 2.5 °C s-1. Optimal annealing temperature was determined by running a temperature gradient for each primer pair ranging from 49 °C to 61 °C. After the amplification, plates were loaded into the QX200 DropletReader (Bio-Rad) for enumeration of the number of positive and negative droplets based on fluorescence. The number of template molecules per microliter of starting material was estimated by the QuantaSoft®AP software (version 1.6.6.0320, Bio-Rad) using an internal Poisson algorithm to analyze clusters; only droplets above a minimum amplitude threshold were counted as positive. For each primer pair, the PCR reaction mixture without matrix was used as negative control (no template control, NTC). 4.2.8 ddPCR normalization based on reference gene stability analysis The stability of the putative reference genes was assessed as described in Section 3.4.3. These analyses concluded that the geometric mean of the EfTu (transcript 102649), sar (transcript 18847) and vma (transcript 32032) genes had the greatest stability. The geometric mean of the concentration of these three transcripts was used as a normalization factor for all target genes, resulting in the relative quantity (RQ) of the gene of interest. Normalized ddPCR data were statistically analyzed by the Kruskal-Wallis test by ranks (Kruskal & Wallis 1952); this non-parametric one-way analysis of variance was used to determine if there were statistically significant differences among carbon treatments. To determine which specific treatments were statistically significantly different from each other the Dunnett’s multiple comparison procedure (Dunnett 1955) and Dunn’s non-parametric Multiple Comparison Test (Dunn 1964) were used. Dunnett's test takes into consideration the special structure of comparing treatment against control (0.1 % glucose), yielding narrower confidence intervals, whereas Dunn’s test makes use of rank sums from a combined ranking of 95 independent samples in order to decide which populations differ, among all treatments. All results were considered significant at  < 0.05. The statistical analyses were generated using the Real Statistics Resource Pack software (Release 6.8, copyright 2013 – 2020; Charles Zaiontz (Zaiontz 2020)) and all p values were Bonferroni corrected. Coefficients of variation (CoEV) were calculated for ddPCR assays before (CoEVraw) and after (CoEVnorm) normalization. The CoEV values were determined by calculating the ratio of the standard deviation to the mean transcript abundance for each carbon source (across at least three biological replicates). The reduction in CoEV (CoEVreduce) was calculated as the difference between the raw and normalized CoEVs divided by raw CoEV and expressed as a percentage: CoEVreduce = (CoEVraw - CoEVnorm)/(CoEVraw). 4.3 Results & Discussion 4.3.1 Variable culture growth demonstrated on different carbon sources The O. maius cultures demonstrated variable growth on the different carbon sources (Appendix D Figure D.1). After 45 days of growth on the various carbon sources, no increase in fungal colony diameter was observable without glucose supplementation, though hyphae were present on all substrates. Consequently, it was determined that the substrates that had not been supplemented with 0.1 % glucose would not be used in further analyses. The largest colony diameters were observed when grown on MMN supplemented with 0.5% glucose and the smallest colonies were seen on sterile peat (Figure 4.1). 96 Figure 4.1. Average colony diameter of Oidiodendron maius grown on varying carbon sources. O. maius grown for 45 days in the dark at 25 °C on MMN agar, supplemented with one of eleven different carbon sources and 0.1 g L-1 glucose (n = 8). Treatments sharing a horizontal line were not significantly different according to a pairwise t test (p < 0.05). Means are plotted as horizontal lines within the boxes; outliers are plotted as individual points. 4.3.2 Target primers validated by conventional PCR Two sets of primers designed for each target transcript (Appendix D Table D.1) were used to amplify the selected target genes from cDNA prepared in the experiments described in Chapter 2. The resulting amplicons are shown in Figure 4.2 and the characteristics critical to their characterization are summarized in Table 4.2. The amplicon sequences resulting from use of the GH3 primers designed for transcript 173180 did not match the intended target sequence and this transcript was eliminated from further analysis (Table 4.2). 456789101112Diameter (mm)97 Figure 4.2. Electrophoretic gels of amplicons produced by conventional PCR using a touchdown procedure with primer pairs as indicated. Gels were run for 60 minutes at 90 V on a 1 % agarose gel containing Invitrogen™ SYBR™ Safe. Gene names are followed by a numeric indicator of the primer pair used (Appendix D Table D.1). G-Biosciences DNAmark™ 100bp Plus Ladder was run in the first lane of each gel; fragment sizes are indicated. 98 Table 4.2. Summary of observed characteristics of sequenced amplicons resulting from conventional PCR with target primers. Gene Transcript ID Primer pair4 Single band (Y/N) Intended target (Y/N) % identity with target Used in subsequent ddPCR (Y/N) CE5 50968 4 Y Y 97.8 Y 5 Y N N/A N CE8 119173 1 Y Y 92.1 Y 5 N N/A N/A N GH3_05 173180 1 Y N N/A N 2 Y N N/A N GH3_70 172745 4 Y Y 100 Y 5 Y Y 96.9 N GH5 109142 3 Y Y 100 Y 4 N N/A N/A N GH11 160759 1 Y Y 100 Y 3 Y N N/A N GH18 147779 3 Y Y 100 Y 4 Y N N/A N GH281 130215 3 Y Y 100 Y GH55_8 25971 2 N N/A N/A N 5 Y Y 100 Y GH55_9 165996 1 Y Y 97.4 Y 5 Y N N/A N GH78 40736 2 Y N/A2 N/A N 5 Y Y 100 Y MCO 172675 2 Y Y 98.3 Y 7 N N/A N/A N MnFeSOD 20150 1 Y N N/A N 4 Y Y 94.1 Y SugMod 196656 2 Y Y 100 Y 5 Y Y 100 N TFE 58531 1 Y Y3 100 Y 3 Y Y3 100 N 1 A single primer pair was selected and tested for GH28, as all other designed primer pairs had GC contents that differed by ~ 8 % 2 Primer pair two for GH78 produced a very faint band that was not excised 3 The resultant amplicons for TFE were considerably larger than the predicted model transcript (210 nt and 204 nt vs. 122 nt and 134 nt, respectively) 4 Refer to Appendix D Table D.1 for primer sequences and amplicon lengths 99 4.3.3 ddPCR assay optimized for annealing/extension temperature Droplet digital PCR assays for each functional primer pair were optimized individually; optimal annealing/extension temperature was determined by running a temperature gradient for each primer pair ranging from 49 °C to 60 °C (Appendix D Figure D.2). The results from this optimization were examined for a balance between maximum cluster separation and reduced intermediate fluorescence between clusters (Bio-Rad 2015). No separation of clusters occurred using the GH55 primers designed for transcript 25971 for any annealing/extension temperature within the 49 – 60 ⁰C range tested (Figure D.2 H), so this target was eliminated from further analyses. 4.3.4 Differential expression of CWDEs across various carbon sources The main source of variation in transcript abundance of the remaining 13 CWDE genes resulted from the inherent differences between individual RNA samples (e.g., unequal sample quantity and quality), which can affect the ddPCR assay due to its high sensitivity. The coefficients of variation (CoEV) were greater than one for nearly all field soil DOM (hot water-extractable fraction) assays and nearly half the 0.1 % glucose assays (Table 4.3). The coefficients of variation for all remaining substrates were less than one. This variability suggested that reference gene normalization could effectively minimize those variations introduced in procedural steps preceding the ddPCR assays. The development of a normalization approach based on the results of analysis using geNormv3 (Vandesompele et al. 2002) and NormFinder (Andersen et al. 2004) algorithms (Chapter 3), determined that the geometric mean of three genes - EfTu (transcript 102649), vma (transcript 32032), and sar (transcript 18847) – was sufficient to achieve significant reduction of variation between biological replicates. 100 Table 4.3. Coefficients of Variation for raw ddPCR data (CoEVRaw). Coeffficients of variation calculated for 13 target genes in Oidiodendron maius across 11 carbon sources. The CoEV values were determined by calculating the ratio of the standard deviation to the mean transcript abundance for each carbon source (across at least three biological replicates). CoEV values are shaded to reflect increasing variation; CoEV values greater than or equal to 0.9 are highlighted in darkest pink. CE5 CE8 GH3 GH5 GH11 GH18 GH28 GH55 GH78 MCO MnFe SOD Sug Mod TFE Mean 0.1 % glucose 0.77 0.82 0.76 1.07 0.79 1.02 0.81 1.08 0.81 0.92 0.67 0.38 1.03 0.84 0.5 % glucose 0.33 0.68 0.52 0.46 0.48 0.90 0.59 0.95 0.45 0.60 0.48 0.34 0.81 0.58 peat 0.17 0.36 0.19 0.51 0.16 0.59 0.18 0.34 0.02 0.19 0.52 0.27 0.29 0.29 peat DOM 0.44 0.38 0.93 0.27 0.53 0.43 0.72 0.17 0.28 0.67 0.73 0.11 0.55 0.48 field soil 0.30 0.08 0.32 0.45 0.25 0.47 0.37 0.60 0.21 0.37 0.27 0.07 0.51 0.33 field soil DOM 1.67 1.63 1.69 0.53 1.66 1.61 1.70 1.66 1.65 1.68 1.65 1.59 1.69 1.57 BSA 0.21 0.25 0.27 0.12 0.25 0.39 0.37 0.11 0.28 0.20 0.11 0.13 0.23 0.23 cellulose 0.35 0.54 0.66 0.57 0.53 0.16 0.73 0.54 0.50 0.69 0.43 0.20 0.54 0.50 chitin 0.74 * 0.39 0.52 0.35 0.35 0.17 0.37 0.11 0.10 0.40 0.05 0.28 0.32 pectin 0.24 0.07 0.29 0.45 0.31 0.11 0.29 0.37 0.10 0.30 0.27 0.13 0.32 0.25 lignin 0.52 0.61 0.45 0.55 0.40 0.49 0.40 0.69 0.34 0.55 0.45 0.20 0.27 0.46 Mean 0.52 0.54 0.59 0.50 0.52 0.59 0.58 0.63 0.43 0.57 0.54 0.32 0.59 * CE8 chitin data was omitted from CoEV calculations due to n = 1. 4.3.4.1 Reduction in variation achieved by normalization The overall effect of normalization of the transcript abundance data was a reduction in the CoEV (Table 4.4, Appendix D Table D.2); the greatest reductions were seen for those transcript/ substrate combinations which produced the highest CoEVraw values prior to normalization. On average, the abundance data for each transcript showed decreased variability with the exception of CE8, GH78 and SugMod. In general, the reduction in variability was inversely proportional to the variation seen in the raw data. 101 Table 4.4. Normalized Coefficients of Variation for ddPCR data (CoEVnorm). Coeffficients of variation calculated for 13 target genes in Oidiodendron maius across 11 carbon sources. The CoEV values were determined by calculating the ratio of the standard deviation to the mean transcript abundance for each carbon source (across at least three biological replicates). CoEVnorm values are shaded to reflect reductions in variation (green) and increases in variation (red) after normalization, where deeper shades reflect greater change; bolded values represent values for which the CoEVraw was >0.75. CE5 CE8 GH3 GH5 GH11 GH18 GH28 GH55 GH78 MCO MnFe SOD Sug Mod TFE Mean 0.1 % glucose 0.17 0.04 0.07 0.59 0.17 0.13 0.22 0.37 0.16 0.10 0.31 0.44 0.23 0.23 0.5 % glucose 0.34 0.22 0.28 0.27 0.36 0.76 0.21 0.38 0.17 0.10 0.61 0.18 0.41 0.33 peat 0.26 0.43 0.24 0.47 0.19 0.68 0.26 0.43 0.11 0.23 0.61 0.34 0.31 0.35 peat DOM 0.21 0.13 0.68 0.47 0.45 0.22 0.41 0.42 0.36 0.46 0.31 0.34 0.24 0.36 field soil 0.42 0.76 0.81 0.51 0.66 1.14 0.57 0.66 0.64 0.34 0.56 0.72 0.74 0.66 Field soil DOM 0.23 0.31 0.59 0.83 0.17 0.49 0.67 0.26 0.22 0.43 0.11 0.39 0.57 0.41 BSA 0.24 0.28 0.32 0.08 0.28 0.35 0.40 0.08 0.33 0.25 0.12 0.15 0.25 0.24 cellulose 0.13 0.38 0.28 0.28 0.19 0.32 0.44 0.27 0.18 0.35 0.84 0.36 0.22 0.33 chitin 0.69 * 0.43 0.54 0.29 0.24 0.05 0.42 0.08 0.18 0.29 0.12 0.30 0.30 pectin 0.06 0.08 0.17 0.36 0.24 0.03 0.18 0.27 0.05 0.20 0.15 0.10 0.20 0.16 lignin 0.59 0.68 0.50 0.62 0.46 0.56 0.47 0.76 0.40 0.63 0.53 0.28 0.37 0.53 Mean 0.30 0.33 0.40 0.46 0.31 0.45 0.35 0.39 0.25 0.30 0.40 0.31 0.35 * CE8 chitin data was omitted from CoEV calculations due to n = 1. The normalization was most pronounced for the control (0.1 % glucose) and the field soil DOM (hot water-extractable fraction) treatments, whereby the CoEV was reduced, on average, over 65 %; these carbon sources consistently had the highest variability in the raw data. The 0.5 % glucose and cellulose treatments were the only other two carbon sources with an average CoEVraw above 0.5 and both were substantially reduced after normalization. The BSA and pectin treatments showed the least variability prior to normalization; on average, the CoEV of the BSA treatment was increased, though never more than 25 %, while the pectin treatment on average demonstrated reduced variability after normalization. The variability of the field soil treatments was consistently increased substantially by normalization, although it remained less variable than other treatments; the variability of the peat and lignin treatments also increased slightly after normalization, though the increase was, on average, very small. The transcripts that demonstrated the greatest reduction in variability due to normalization were CE5, GH28, MCO and TFE, all producing an average CoEVreduce of approximately 20 %. Only three transcripts showed, on average, an increase in variability after normalization: CE8, GH78 and SugMod. 102 4.3.5 Relative transcript abundance determined for 13 cell wall degrading enzymes 4.3.5.1 Carbohydrate esterases: CE5 acetyl xylan esterase/cutinase (hemicellulose, cellulose); CE8 pectin methylesterase (pectin) CE5 expression (Figure 4.4A) did not differ among the carbon treatments (p = 0.55, df = 10; Kruskal-Wallis), but was upregulated in the 0.5 % glucose treatment compared with the 0.1 % glucose control treatment (p = 0.020; Dunn’s test) and downregulated in the chitin treatment compared with the 0.5 % glucose treatment (p = 0.032; Dunn’s test). Several substrates (e.g., peat- and field soil-based carbon sources), contain the acetylated xylooligosaccharides presumed to be the substrate of CE5; therefore, expression of CE5 was expected to be higher in the peat and field soil samples than on other substrates. The mean transcript abundance in response to peat, field soil DOM (hot water-extractable fraction), cellulose pectin and lignin substrates was slightly upregulated relative to the control, though these trends were not statistically significant (p = 0.129, 0.204, 0.219, 0.241, 0.234, respectively; Dunn’s test).The downregulation seen on the chitin substrate defies current explanation, but may be related to an as yet unknown function of this particular enzyme. Our knowledge of the substrate specificity of carbohydrate esterases is constantly evolving and while CE families 1-7 are all classified as acetyl xylan esterases, these families also contain cinnamoyl and feruloyl esterases (either of which would explain the upregulation seen on lignin), chitin deacetylases (thought to play a role in cell wall biosynthesis and in counteracting plant defenses) as well as acetyl and carboxyl esterases (which hydrolyze a wide range of substrate esters) (Sista Kameshwar & Qin 2018). Hypocrea jecorina (anamorph Trichoderma reesei), secretes two CE5 enzymes in response to cellulose, but only one possesses a carbohydrate binding module (Li et al. 2008); more functional examination of this CE family is needed to make informed conclusions regarding its expression. 103 Figure 4.3. Carbohydrate esterase (CE) transcript abundance across eleven carbon sources. A. CE5 (transcript 50968) (n = 4 for MMN + 0.5 % glucose and peat DOM, n = 2 for pectin, n = 3 for all other treatments) (a: p = 0.020, b: p = 0.032). B. CE8 (transcript 119173) (n = 4 for cellulose, n = 1 for chitin, n = 3 for all other treatments). Data normalized to geometric mean of three reference genes. Boxes that share a letter show significant differences in expression between treatments according to Dunn’s test (p values are listed by transcript). 104 There was no significant difference in CE8 expression among the various carbon treatments (p = 0.926, df = 10; Kruskal Wallis), nor between treatments and the control (Dunnett’s), nor between treatment pairs (Dunn’s) (Figure 4.4B). Carboxyl esterase family 8 encodes pectin methylesterases and I predicted that a higher expression level would be seen for pectin-containing substrates (e.g., pectin, peat and field soil) (Table 2.5). Unfortunately, one of the sample assays failed for the pectin treatment resulting in an n = 2 for pectin; regardless, the expression level was consistently low across all treatments, with a mean concentration of approximately one copy per microliter. 4.3.5.2 Cellulose- and hemicellulose-active glycoside hydrolases: GH3 beta-glucosidases; GH5 cellulases; GH11 endoxylanases GH3 expression among treatments did not differ significantly (p = 0.262, df = 10; Kruskal-Wallis), nor did it differ from the control (Dunnett’s) (Figure 4.5A); however, it was significantly upregulated in the peat and cellulose treatments compared with the hot water- extractable fraction of peat (peat DOM) treatment (p = 0.046 and 0.008, respectively; Dunn’s), and significantly downregulated in the pectin treatment compared with the cellulose treatment (p = 0.020; Dunn’s). The mean transcript abundance in response to the field soil DOM (hot water- extractable fraction) and cellulose was upregulated relative to the control, however, this trend was not statistically significant (p = 0.614, 0.230, respectively; Dunn’s); a slight downregulation relative to the control was also seen on peat DOM, chitin, pectin and field soil (p = 0.210, 0.670, 0.295, 0.485, respectively; Dunn’s). I hypothesized an upregulation on peat- and field soil-containing substrates (Table 2.5), so the downregulation seen on peat DOM and field soil was unexpected. There are 35 GH3 enzymes encoded in the O. maius genome, only four of which were upregulated in O. maius grown on peat compared to MMN (NCBI’s Gene Expression Omnibus GEO Series accession number GSE163739). Beta glucosidases use cellobiose (the breakdown product of cellulose) as a substrate (Shewale 1982); it is very likely that the peat, 105 field soil DOM and cellulose treatments contained high quantities of this compound. Beta glucosidases are thought to be inhibited by the end product of their reaction, glucose (Sandhu & Sidhu 1985); presumably, the chitin, peat DOM and field soil treatments would contain high levels of glucose from the degradation of complex carbon sources, though this would not explain the downregulation seen in response to pectin. The GH3 family has very broad substrate specificity (Cournoyer & Faure 2003), and, perhaps, its transcriptional control is also broad. 106 107 Figure 4.4. Cellulose- and Hemicellulose-active Glycoside Hydrolase (GH) transcript abundance across eleven carbon sources. A. GH3 (transcript 172745) (n = 4 for MMN + 0.5 % glucose, peat DOM and cellulose; n = 3 for all other treatments). Boxes that share a letter show significant differences in expression between treatments according to Dunn’s test (p values are listed by transcript). (a: p = 0.046, b: p = 0.008, c: p = 0.020). B. GH5 (transcript 109142) (n = 4 for MMN + 0.5 % glucose, peat DOM and cellulose; n = 3 for all other treatments (a: p = 0.046, b: p = 0.008, c: p = 0.020, d = 0.006, e = 0.047). C. GH11 (transcript 160759) (n = 4 for MMN + 0.5 % glucose, peat DOM and cellulose; n = 3 for all other treatments) (a: p = 0.020, b: p = 0.032). Data normalized to geometric mean of three reference genes. Expression of GH5 did not differ significantly when examined across all treatments (p = 0.165, df = 10; Kruskal-Wallis); however, significant upregulation occurred in the cellulose treatment compared with the 0.1 % glucose control, the 0.5 % glucose, peat and peat DOM treatments (p = 0.014, 0.025, 0.027 and 0.006, respectively; Dunn’s) (Figure 4.5B). GH5 was also significantly upregulated on the BSA treatment compared to the peat DOM treatment (p = 0.047; Dunn’s). There are 20 GH5 enzymes encoded in the O. maius genome, two of which belong to this subfamily (24). Although the specific activity for this subfamily has yet to be determined (Aspeborg et al. 2012), glycoside hydrolase family 5 contains a variety of cellulases; therefore, the upregulation demonstrated by the cellulose treatment was expected. Given how little is known about this subfamily, the reason behind the upregulation seen on the BSA treatment is not obvious. 108 Endo-xylanases from GH family 11 (formerly known as cellulase family G) have been widely described in the literature and their relatively small size allows them to pass through hemicellulose pores fairly easily allowing for efficient hydrolysis (Törrönen & Rouvinen 1997). I expected to see upregulation in the peat and field soil treatments, but GH11 expression did not vary among treatments (p = 0.481, df = 10; Kruskal-Wallis) nor between treatments (Dunnett’s, Dunn’s) (Figure 4.5C). It is likely that these enzymes were employed early in the time course of degradation, due to their ease of movement, but were no longer present at the time of harvest. 4.3.5.3 Chitin- and glucan-active glycoside hydrolases: GH18 chitinase and GH55 glucanase There was no significant difference in GH18 expression among treatments (p = 0.376, df = 10; Kruskal-Wallis); significant upregulation was seen on cellulose compared with the control (p = 0.035; Dunn’s), though not between any other treatment groups (Figure 4.6A). Chitinase family GH18 contains enzymes from viruses, bacteria, fungi and animals as well as several classes from plants (http://www.cazy.org/; Lombard et al, 2014). The O. maius genome contains coding regions for 27 GH18 chitinases and an additional 32 genes classified as chitinases (Kohler et al. 2015), the vast majority of which were constitutively expressed in the whole transcriptome sequencing (chapter 2). It is expected that these many chitinases would have variable physiological function, whether degrading exogenous chitin or maintaining normal cellular function. Those chitinases involved in maintenance, such as remodeling the cell wall, would be expected to be constitutively expressed during active growth. In the initial transcriptome study used to select this target (chapter 2), only slight upregulation was seen on the peat treatment. The fact that I did not detect significant upregulation of the GH18 transcript here in the chitin treatment would suggest that this particular chitinase is not involved in the breakdown of exogenous chitin. If, therefore, this transcript is involved in maintaining normal functioning of fungal cells, it would be expected to be expressed at a relatively consistent level 109 in all treatments. The upregulation seen in the whole transcriptome sequencing may have been due to greater growth and exploration and not a direct effect of the substrate, a presumption supported by the slightly higher mean expression seen on the 0.5 % glucose treatment (an easily accessible carbon source). 110 Figure 4.5. Chitin- and Glucan-active Glycoside Hydrolase (GH) transcript abundance across eleven carbon sources. Boxes that share a letter show significant differences in expression between treatments according to Dunn’s test (p values are listed by transcript). Data normalized to geometric mean of three reference genes. A. GH18 (transcript 147779) (n = 2 for MMN + 0.1 % glucose; n = 4 for MMN + 0.5 % glucose, peat DOM and cellulose; n = 3 for all other treatments) (a: p = 0.035). B. GH55 (transcript 165996) (n = 4 for peat DOM and cellulose, n = 3 for all other treatments) (a: p = 0.010, b: p = 0.001, c: p = 0.025, d: p = 0.013). 111 There was no significant difference in GH55 expression (Figure 4.6B) among treatments (p = 0.108, df = 10; Kruskal-Wallis), nor between treatments and the 0.1 % glucose control (Dunnett’s). GH55 was, however, significantly upregulated on cellulose compared with 0.5 % glucose, peat DOM (hot water-extractable fraction) and field soil (p = 0.010, 0.001 and 0.025, respectively; Dunn’s) and significantly upregulated on field soil DOM (hot water-extractable fraction) compared with peat DOM (p = 0.013; Dunn’s), though no more than two copies per L were produced under any conditions. Glucanases of GH55 family are found exclusively in fungi and bacteria and cleave the terminal glycosidic linkage at the non-reducing end of β-glucans found in fungal cell walls. The O. maius genome encodes 20 GH55 enzymes, one quarter of which were differentially regulated in response to the peat treatment examined in Chapter 2. It is not surprising that expression remained fairly low across all treatments here as there are likely no sources of exogenous glucans in the media and, if it is involved in modification of fungal cell walls, it would be expected to be expressed at a relatively consistent level in all treatments. 4.3.5.4 Pectin-active glycoside hydrolases: GH28 polygalacturonase and GH78 rhamnosidase There was no significant difference in GH28 expression among treatments (p = 0.517, df = 10; Kruskal-Wallis), nor between treatments and the 0.1 % glucose control (Dunnett’s) (Figure 4.7A); significant downregulation of GH28 was detected on the peat DOM (hot water-extractable fraction) treatment compared to both cellulose and chitin (p = 0.037 and 0.016, respectively; Dunn’s). The polygalacturonases of the GH28 family act on the galacturonate residues found primarily in pectin. There are 24 GH28 enzymes encoded in the O. maius genome and six of them showed upregulation in response to peat in the RNA-Seq dataset (Chapter 2), though this particular transcript was chosen as it was upregulated to an order of magnitude higher than any of the others. I hypothesized upregulation on the peat, field soil and pectin treatments (Table 2.5), although with numerous other genes encoding pectin-active 112 enzymes, it is possible that an alternate enzyme of similar function was utilized under these conditions. The downregulation seen on the peat DOM treatment defies reasonable explanation and may simply be a factor of lower variance in the samples from the peat DOM treatment. Figure 4.6. Pectin-active Glycoside Hydrolase (GH) transcript abundance across eleven carbon sources. Boxes that share a letter show significant differences in expression between treatments according to Dunn’s test (p values are listed by transcript). Data normalized to geometric mean of three reference genes. A. GH28 (transcript 130215) (n = 4 for MMN + 0.5 % glucose, peat DOM and cellulose; n = 3 for all other treatments) (a: p = 0.037, b: p = 0.016). B. GH78 (transcript 40736) (n = 4 for MMN + 0.5 % glucose, peat DOM and cellulose; n = 3 for all other treatments) (a: p = 0.043, b: p = 0.033, c: p = 0.039, d = 0.032, e = 0.042, f = 0.030, g = 0.044, h = 0.025). 113 No significant difference was found in GH78 expression among treatments (p = 0.098, df = 10; Kruskal-Wallis) (Figure 4.7B); significant upregulation was demonstrated on the hot water-extractable fraction of field soil (field soil DOM) and cellulose compared to the control (p = 0.010 and 0.009, respectively; Dunnett’s) as well as on 0.5 % glucose and peat compared with the 0.1 % glucose control (p = 0.043 and 0.033, respectively; Dunn’s). Significant upregulation of GH78 was also shown on 0.5 % glucose compared with field soil DOM and cellulose (p = 0.039 and 0.032, respectively; Dunn’s) as well as on peat relative to the peat DOM, field soil DOM, BSA and cellulose treatments (p = 0.042, 0.030, 0.044, 0.025, respectively; Dunn’s). The GH78 family enzymes are rhamnosidases found largely in bacteria and fungi (Lombard et al. 2014) which hydrolyse rhamnosides primarily found in pectin (but also in glycolipids) (Mutter et al. 1994; Zverlov et al. 2000). There are 15 GH78 genes encoded in the O. maius genome, about half of which were upregulated in response to the peat treatment examined in chapter 2. I expected to see upregulation on the pectin and complex carbon treatments (i.e.,peat- and field soil-containing), though it would be difficult to detect significant differences with such low levels of transcription (~1 copy uL-1). 4.3.5.5 CWDEs with auxiliary activity: multi-copper oxidase (MCO / AA1) (phenols/lignin); tannase and feruloyl esterase (TFE) (various incl. lignin and tannins) No significant difference in MCO expression was found among treatments (p = 0.152, df = 10; Kruskal-Wallis) (Figure 4.8A) or compared to the control (according to Dunnett’s); however, significant upregulation was seen on field soil DOM (hot water-extractable fraction) and cellulose compared to the 0.1 % glucose control (p = 0.023 and 0.048, respectively; Dunn’s). There was also significant upregulation of MCO on the peat, field soil DOM and cellulose treatments compared with the peat DOM (hot water-extractable fraction) treatment (p = 0.045, 0.008 and 0.017, respectively; Dunn’s). The MCO enzymes (classified as AA1 by the CAZyme nomenclature) oxidize a broad range of substrates including phenols and diamines, 114 though, even highly structurally similar MCO enzymes can have vastly different substrate specificities (Reiss et al. 2013). The O. maius genome encodes 26 genes annotated as AA1 enzymes, nearly half of which were differentially regulated in response to the peat treatment examined in Chapter 2. I expected to see upregulation on all the complex carbon sources including lignin (Table 2.5), though with numerous other genes encoding MCOs, it is possible that an alternate enzyme was utilized to oxidize phenolic compounds under these conditions. Multicopper oxidases also play a role in redox cycling, oxidizing the ferrous (Fe(II)) form of iron back to the ferric (Fe(III)) form (Frieden & Osaki 1974); tight regulation of this process prevents damage from reactive oxygen intermediates that occur with spontaneous iron oxidation. Fungal MCOs are highly conserved and some are membrane-bound and involved in iron uptake (Askwith et al. 1994). This particular MCO transcript would need to be examined further to determine its potential involvement in cell wall degradation. 115 Figure 4.7. Transcript abundance of Cell Wall Degrading Enzymes with Auxiliary Activity across eleven carbon sources. Boxes that share a letter show significant differences in expression between treatments according to Dunn’s test (p values are listed by transcript). Data normalized to geometric mean of three reference genes. A. Multicopper Oxidase (MCO – transcript 172675) (n = 2 for peat; n = 4 for MMN + 0.5 % glucose, peat DOM and cellulose; n = 3 for all other treatments) (a: p = 0.023, b: p = 0.048, c: p = 0.045, d: p = 0.008, e: p = 0.017). B. Tannase and feruloyl esterase (TFE – transcript 58531) transcript abundance across eleven carbon sources (n = 4 for MMN + 0.5 % glucose, peat DOM and cellulose; n = 3 for all other treatments) (a: p = 0.008, b: p = 0.040, c: p = 0.011, d: p = 0.007, e: p = 0.042, f: p = 0.006, g: p = 0.030). 116 While no significant differences were seen among treatments for TFE (p = 0.069, df = 10; Kruskal-Wallis) (Figure 4.8B) or compared to the control (Dunnett’s), Dunn’s test showed significant upregulation on the cellulose and lignin treatments compared with the control (p = 0.008 and 0.040, respectively). Upregulation of TFE was also shown on cellulose compared with the 0.5 % glucose treatment (p = 0.011; Dunn’s) as well as on the cellulose and lignin treatments compared with the peat DOM treatment (p = 0.007 and 0.042, respectively; Dunn’s) and the field soil treatment (p = 0.006 and 0.030, respectively; Dunn’s). The TFE enzyme is not classified as a CAZyme, though several other feruloyl esterases are included in the carbohydrate esterase classification and the O. maius genome contains 13 genes encoding TFEs. Feruloyl esterases are important for opening cell wall structure, making other cell wall components accessible to GH enzymes (Dilokpimol et al. 2016). Ferulic acid units are cross-linked with other polysaccharides, proteins and lignins in plant cell walls, and feruloyl esterases cleave these ester linkages (Hermoso et al. 2004). They display broad specificity and, while several fungal feruloyl esterases have been well characterized, those belonging to the tannase family are not as well described (Suzuki et al. 2014). While the initial transcriptomic analysis (Chapter 2) showed high upregulation of this transcript on peat-containing media, it is not surprising that few differences were demonstrated in the expression levels seen here; these enzymes are active in the early stages of plant cell wall breakdown (Dilokpimol et al. 2016), in order to access other components and it is very possible that their activity had subsided by the time measurements were taken. 117 4.3.5.6 Other CWDE-associated transcripts: manganese-dependent iron superoxide dismutase (MnFeSOD) (radical protection); phosphoketolase (SugMod) (active metabolism) There was no significant difference detected in MnFeSOD expression among treatments (p = 0.419, df = 10; Kruskal-Wallis), nor between treatments and the control (Dunnett’s) (Figure 4.9A). Further analysis showed significant upregulation of MnFeSOD on 0.5 % glucose compared with the field soil, BSA, chitin and lignin treatments (p = 0.008, 0.033, 0.050 and 0.033, respectively; Dunn’s), but this is potentially obscured in other tests due to the high variance seen in the 0.5 % glucose and cellulose treatments. The MnFeSOD enzymes are not classified as CWDE, they are potentially involved in radical protection during the Fenton reaction used to degrade complex carbon sources such as lignin (Aguirre & Culotta 2012). This transcript was one of the two most significantly downregulated genes (126-fold) in response to the peat treatment examined in Chapter 2. As such, I expected to see downregulation on the complex carbon sources including lignin (Table 2.5); however, expression seems to be constitutive. It is possible that the Fenton reaction occurs early in the degradation process, providing access to other cell wall components, or that other enzymes were used for radical protection under these conditions. The O. maius genome encodes three MnFeSOD enzymes and another five unclassified superoxide dismutase enzymes (Appendix A Table A.1), all of which are likely tightly regulated and expressed under, as yet unknown, specific conditions. 118 Figure 4.8. Abundance of other Cell Wall Degrading Enzyme-Associated Transcripts across eleven carbon sources. A. Manganese and iron superoxide dismutase (MnFeSOD – transcript 20150 (n = 2 for peat DOM, n = 4 for MMN + 0.5 % glucose and cellulose, n = 3 for all other treatments) (a: p = 0.008, b: p = 0.033, c: p = 0.050, d: p = 0.033). B. Phosphoketolase (SugMod - transcript 196656) (n = 4 for peat DOM and cellulose, n = 3 for all other treatments) (a: p = 0.026, b: p = 0.007, c: p = 0.005, d: p = 0.042, e: p = 0.028, f: p = 0.021). Data normalized to geometric mean of three reference genes. Boxes that share a letter show significant differences in expression between treatments according to Dunn’s test (p values are listed by transcript). 119 While Kruskal-Wallis tests showed no significant regulation for SugMod among treatments (p = 0.112, df = 10) (Figure 4.9B), the other analyses indicated significant upregulation on the peat, cellulose and chitin treatments compared with the 0.1 % glucose control (p = 0.026, 0.007 and 0.005, respectively; Dunn’s). Significant upregulation of SugMod was also demonstrated on the chitin treatment compared with the field soil treatment (p = 0.042; Dunn’s) as well as on both cellulose and chitin treatments compared with BSA (p = 0.028 and 0.021, respectively; Dunn’s). This transcript encodes a phosphoketolase which is not involved in cell wall degradation directly, however, it generates precursors for many biosynthetic pathways via the pentose phosphoketolase pathway (Sánchez et al. 2010). The metabolic benefits include generation of ATP and regeneration of coenzyme A (Glenn 2014) and, as such, it may be an indicator for active metabolism. The SugMod transcript was significantly upregulated in response to both peat and BSA treatments examined in chapter two and I expected upregulation on all carbon sources relative to the control and an exaggerated increase on the 0.5 % glucose treatment (Table 2.5). This lack of upregulation suggests that the fungus may have exhausted the majority of the available carbon by the time the assays were performed. 120 4.4 Interpretations & Implications My predictions of an increase in abundance and variety of CWDE transcripts in response to carbon complexity was not supported, though the trends seen in several assays support further experimentation. Both the peat and cellulose treatments produced significant upregulation of several transcripts including GH3, responsible for cellulose and hemicellulose degradation, and the phosphoketolase transcript, which may be an indicator for active metabolism. Significant upregulation on the cellulose treatment was also seen for GH5 and TFE transcripts, both of which are known to be involved in cellulose degradation. Significant upregulation was seen on the 0.5 % glucose treatment for CE5, GH78 and MnFeSOD; these enzymes, presumably, would not be required to degrade such a simple carbon source, so this may, in fact, reflect the basal level of transcription during active metabolism. While I expected to see more pronounced differences in transcript abundance across the varying carbon sources (Table 4.5), there are many possible reasons for the discrepancy. One possibility is the concentration of carbon in the plates. While the percent carbon content was equal across treatments (with the exception of the 0.5 % glucose treatment), the amount of carbon contained in each plate was considerably lower than that used to obtain the RNA-Seq data (Chapter 2), and, thereby used, to establish targets. Another related possibility is that at such low carbon concentrations, the transcript abundance was also relatively low. In many transcript datasets, no sample produced more than two copies of the transcript per microlitre, so even a slight difference between biological replicates would have a large impact on variance. This increase in variance – even after normalization – could obscure significant differences between treatments. 121 Table 4.5. Observed expression response to carbon source by Oidiodendron maius relative to expected response originally presented in Table 2.5. Carbon Source Transcript ID Target Name Putative Function glucose (0.5 %) peat peat DOM field soil field soil DOM BSA cellulose chitin pectin lignin 50968 CE5 Cutinase / Acetylxylan esterase + + + + 119173 CE8 Pectinesterase, catalytic + + + + + 172745 GH3 beta-glucosidase + + + + 109142 GH5 Endoglucanase + + + + + 160759 GH11 Endo-1,4-beta-xylanase + + + + 147779 GH18 Endochitinase + + + + + + + ++ + + 130215 GH28 Probable exopolygalacturonase + + + + + 165996 GH55 β-1,3-glucanase + + + + + 40736 GH78 Bacterial alpha-L-rhamnosidase + + + + 172675 MCO Multicopper oxidase, type 1 / Cupredoxin ++ ++ ++ ++ + + + + ++ 20150 MnFe SOD Manganese and iron superoxide dismutase - - - - - - - -- 196656 Sug Mod D-xylulose 5-phosphate/D-fructose 6-phosphate phosphoketolase (+) (+) (+) (+) (+) 58531 TFE Tannase and feruloyl esterase + + + + + (+) Observed response in transcript abundance of target cell wall degrading enzymes produced by O. maius in response to variable carbon source compared with expected response. Predicted positive (+) and negative (-) responses are relative to growth on MMN media containing 0.1 g L-1 glucose as the sole carbon source; doubled symbols predict a stronger effect, brackets predict a very slight effect. Fill colour demonstrates observed response; red tones are downregulation and green tones are upregulation, with higher intensity colours indicating significant differences. Thickened borders indicate significant regulation compared to at least one treatment other than the control. 122 The nature of cell walls is that they are composed of complex biopolymers and their decomposition by CAZymes and other CWDEs is synergistic and carefully regulated (Chettri et al. 2020; Corrêa et al. 2020). An additional possibility is that the lack of significant differences was temporal and simply a result of low substrate availability. If too much time had elapsed, the expression of the target enzymes active on the complex carbon sources would have occurred at an earlier stage of degradation and simpler carbon sources were being utilized at the time of assay, thereby diminishing the expression detected at the time of quantification. Another possibility is that O. maius used alternative enzymes of comparable function. The targets were selected based on RNA-Seq results for a single complex carbon source that stimulated upregulation of several CWDEs; these enzymes, while selected, in part, for their putative substrates, may not be active on the specific range of complex carbon sources selected in this experiment. Pectin, for example, is a term used to encompass a broad group of macromolecules, with variable sugar moieties and degrees of branching. I examined transcript abundance of three pectin active enzymes – CE8, GH28 and GH78 – which catalyze the de-esterification of pectin, the hydrolysis of galacturonic acid residues and the hydrolysis of rhamnogalacturonan residues, respectively. Kaya et al. (2014) showed that citrus pectin from different sources varied in galacturonic acid and rhamnogalacturonan content as well as varying degrees of methyl esterification. The citrus pectin used here may not be a suitable substrate for the specific pectin-active enzymes examined and may partially explain why no significant upregulation was seen for pectin-active enzymes on the pectin containing substrate. 123 Chapter 5 Conclusion 5.1 Significance & Contribution The ericoid mycorrhizal (ErM) symbiosis represents an important adaptation to acidic and nutrient poor soils that species in the monophyletic plant family Ericaceae typically inhabit, including tundra, boreal forests, heathlands and peatlands. The boreal forest is the largest terrestrial biome, and, combined with the permafrost of the tundra, comprises approximately 15% of the global land surface with nearly 30% of the carbon stock sequestered in soils found within these two biomes (Post et al. 1982). While peatlands cover only about 3% of the world's land area, they store an estimated one-third of global soil carbon due to extremely slow decomposition of resident plant litter (Rydin and Jeglum 2013). Sphagnum mosses are particularly resistant to decay (Clymo 1965) and, consequently, peatlands dominated by these species act as stronger carbon sinks than other peatland types. With the knowledge that soils contain more carbon globally than the atmosphere and vegetation combined (Tarnocai et al. 2009) and that boreal forests, tundra and peatlands combined contain more than 50% of the global soil C, the ability of ErM fungi to grow saprotrophically in these soils is especially important. With increasing global temperatures, it is imperative to examine the role of fungi in releasing carbon in response to warming. If belowground carbon is released into the atmosphere by accelerated decomposition, a positive feedback to warming would occur. Conversely, if the input of plant-derived carbon deposition exceeds this decomposition rate, the feedback would be negative. Permafrost and peatlands, especially, represent a huge carbon sink that is vulnerable to this positive feedback, though there is not enough information on the carbon flux in these systems to predict warming-induced outcomes (Bowden et al. 2014, Fekete et al. 2016, Paustian et al. 2019, Fekete et al. 2021). Therefore, understanding the mechanisms controlling the accumulation and stability of soil carbon is critical to predicting the Earth’s future climate. By inhabiting the interface between vegetation and soil, ErM fungi have the potential to 124 profoundly influence the carbon source-sink relationships that are so vulnerable in this age of imminent global climate change. The majority of carbon in soils is derived from plant or fungal biomass. One way to examine the role of ErM fungi in carbon cycling in these soils is to examine their ability to break down the cell wall material contained in this soil organic matter. Plant cell walls are composed mainly of cellulose, hemicellulose, pectin and lignin; this lignocellulose complex is both physically and chemically resistant to degradation (Rayner & Boddy 1988). The major groups of enzymes involved in the breakdown of these complex carbohydrates are carbohydrate active enzymes (CAZymes) and plant cell wall degrading enzymes (CWDEs). Despite the ubiquitous nature of ericaceous plants and their ErM fungal partners, little progress has been made in relation to the functional genomics of these essential fungi, despite the achievements in the study of arbuscular and ectomycorrhizae. The overall objectives of my thesis research were (i) to assemble a list of differentially transcribed genes in Oidiodendron maius across treatments of increasing carbohydrate complexity using a transcriptomic approach (Chapter 2) and (ii) to examine a specific set of differentially transcribed CAZymes and CWDEs over a greater variety of carbohydrate sources using digital droplet PCR (Chapter 4). I expected that transcription of CWDEs would be significantly upregulated in the fungus grown on complex organic matter as compared with the fungus grown on minimal media and that the more complex the substrate, the higher the level of transcription and greater the variety of genes transcribed. To support this effort, I developed a method to identify and validate reference genes for O. maius using ddPCR (Chapter 3); this work is the first study to show that a data set derived from a massive RNA sequencing effort for several culture conditions can be used for the identification of reference genes in O. maius. The findings of this research supply a fundamental background to understanding the saprotrophic nature of O. maius. 125 The transcriptome sequencing of O. maius presented in Chapter 2 demonstrated expression and regulation of 680 CWDE genes across three treatments of varying carbon complexity. While many of the transcripts identified as CWDEs had been annotated, their putative functions are based purely on sequence similarity to modular sections of the hypothetical resultant protein. This study highlights the fact that CAZyme nomenclature and mechanistic understanding is still a work in progress; much more research is needed to determine the specific mode of action and substrate preference of these enzymes, especially in the diverse fungal kingdom. Only with the collection of sequence data and functional experimentation from multiple sources can we clarify function and CAZy family membership. I was able to demonstrate that O. maius produces an extensive variety of enzymes capable of targeting cell wall components with a wide range of chemical and structural heterogeneity, potentially providing an advantage to this ErM fungus during both its free-living and symbiotic habits. This examination of the CWDE transcriptome of O. maius contributes fundamental knowledge regarding its growth on differing carbon sources and adds to the growing body of knowledge surrounding the mechanisms that soil fungi utilize to degrade SOM to effectively attain resources in this highly competitive environment, and, thereby, a deeper understanding of carbon cycling within the vulnerable ecosystems they inhabit. Recent studies suggest that global warming could potentially trigger significant changes to the belowground ecosystem these ErM fungi inhabit, triggering a decreased overall carbon sink capacity (Clemmensen et al. 2015, Fernandez et al. 2019, Clemmensen et al. 2021). Studies like those presented in this thesis are critical to constructing better climate and carbon models that take into consideration the involvement of soil fungi in carbon sequestration and release. Included in this CWDE transcriptome were 52 significantly regulated transcripts (more than two-fold when comparing RPKM values between treatments). Among the most highly regulated CWDEs were enzymes active on cellulose, hemicellulose, aromatics, glucans and 126 pectins, providing a robust basis for selection of targets to further examine carbohydrate metabolism in this fungus. The targets I selected to examine my second objective were chosen to cover a broad range of substrates, with a focus on those most prevalent in soil. In Chapter 3, I generated a list of 251 annotated genes with invariant expression during cultivation on varying carbon sources, as well as during symbiosis, as a resource for selecting reference genes (Appendix C Table C.1). This reference gene library can be used as a starting point for future quantitative transcript abundance studies in O. maius, while it also contributes to our understanding of O. maius physiology. To my knowledge, this is the first study to show that a data set derived from a massive RNA sequencing effort can be used for the identification of appropriate reference genes in O. maius. Three of these stable genes - EfTu (protein ID 102574), vma (protein ID 31957), and sar (protein ID 18772) - were further investigated as reference genes for studying transcriptional response across different developmental stages and physiological conditions in O. maius. I developed a method to validate their use as endogenous controls in ddPCR, based on methods previously used for qPCR. I demonstrated that using the geometric mean of these three genes increases their reliability as a means of normalizing variable transcript abundance data, thus providing a more accurate representation of gene expression changes. The methods developed in this study are the first of their kind and can be used to expand our use of ddPCR for quantitative analysis of transcript abundance in ErM fungi as well as other model organisms. In my third research chapter (Chapter 4), I examined expression of 13 highly regulated CWDEs and related genes of O. maius grown on 11 different carbon sources of varying complexity using ddPCR. The main source of variation in transcript abundance resulted from inherent differences in RNA quantity and quality, so relative transcript abundances were calculated using the normalization method developed in Chapter 3. In general, the relative abundance values obtained through normalization of droplet digital PCR (ddPCR) data showed 127 a weaker correlation between carbon source and CWDE transcript abundance than predicted; however, several transcripts showed significant regulation in response to substrate, highlighting the need for further analysis. The strongest findings from this study were the significant upregulation of transcripts seen in response to peat (e.g. GH3, GH78, AA1) and cellulose (e.g. GH3, GH5, TFE); the enzymes these transcripts correspond to are responsible for a range of functions including, but not limited to, cellulose and hemicellulose degradation as well as oxidation of aromatics. The most surprising finding from this study was the downregulation of several transcripts seen in response to the field soil treatment and the hot water-extractable fraction of peat (peat DOM), two substrates presumed to be of high carbon complexity, and, therefore, predicted to induce higher production of CWDE transcripts (though none were significant relative to the control). These downregulated transcripts included those enzymes responsible for cellulose degradation (i.e., GH3), glucan degradation (i.e., GH55) and pectin degradation (i.e., GH28, GH78) and the reason for this downregulation remains to be elucidated. The research presented in Chapters 3 and 4 also highlights the benefit of ddPCR data normalization using well-validated reference genes. Although ddPCR is an absolute quantification method, numerous biomedical examples show that combining it with the use of reference genes is a valid approach (e.g., Heredia et al. 2013, Iyer et al. 2014). In general, the positive effect of normalization was evident for ten of the 13 CWDE transcripts examined. Therefore, the use of reference genes may contribute to improving the interpretation of ddPCR data obtained from comparative analysis of individual, highly variable samples. 5.2 Strengths & Limitations The O. maius genome was sequenced in the framework of the Mycorrhizal Genomics Initiative (MGI) CSP#305 project headed by Francis Martin (INRAe-Nancy) in collaboration with the Joint Genome Institute (JGI, California), providing an ideal scaffold for transcriptome 128 studies. The availability of this assembled and annotated genome eliminates the need for de novo assembly of the acquired RNA-Seq data and affords more confidence in the transcriptome quality. This allowed for the construction of three high quality transcriptomes composed of two replicates each. This is the first study that has resulted in the production of such a large amount of publically available O. maius expression data (NCBI’s Gene Expression Omnibus GEO Series accession number GSE163739) in response to varying carbon substrate, a first step towards understanding the role these ErM fungi play in soil carbon cycling. These analysis of these transcriptomes demonstrated expression and regulation of 680 CWDE genes among the three substrates, including significant differential regulation of 52 individual transcripts. These differentially regulated transcripts included genes from four carbohydrate esterase families, 18 glycoside hydrolase families, four enzyme families with auxiliary activies as well as tannase and feruloyl esterases and superoxide dismutase enzymes. This is the first comprehensive examination of the CWDE transcriptome of O. maius and lays a foundation to understanding the physiological response of this model organism to differing carbon sources. I have demonstrated that O. maius transcribes a variety of genes targeting plant and fungal cell wall components, providing an advantage in both its saprotrophic and symbiotic habits. These results add to the growing body of knowledge surrounding the mechanisms that soil fungi utilize to degrade lignocellulose and other carbon containing components of the soil environment. As is the case in all studies involving mRNA and cDNA, it should be noted that the RNA-Seq and ddPCR data only suggest changes in the transcript abundance, and do not necessarily represent “protein levels” since translation to active protein may be post-transcriptionally and post-translationally regulated (Barrett et al., 2005; Valasek, 2005). Despite the limitations, the findings of this study provide a foundation to further examine the relationship of O. maius with soil carbon. It increases our limited understanding of carbohydrate metabolism by ErM fungi, 129 and this data, combined with similar studies examining O. maius in symbiosis, and in response to changing temperature, can be used to better inform models of warming-induced changes to global C. The method developed in Chapter 3 was successful in identifying a shortlist of 251 reference gene candidates, a successful first step toward expanding our knowledge regarding constitutively expressed genes in O. maius. This work is the first study to show that a data set derived from a massive RNA sequencing effort for several culture conditions can be used for the identification of reference genes in O. maius. The fact that this list did not contain several transcripts previously used as reference genes in O. maius highlights the need to validate the stability of endogenous controls under experimental conditions. Significant further work is required to establish which genes maintain stable expression independent of nutrient source or physiological condition. For the purposes of the research presented here, though, I was able to validate three new primer sets specific for stable reference genes (EfTu, vma, sar). I was also able to demonstrate this stability using algorithms previously used only for qPCR data and to show an increased stability by calculating the geometric mean of abundance of these three reference genes. This geometric mean was then used to effectively normalize ddPCR transcript abundance data from mRNA samples varying in starting quality and quantity, a procedure not undertaken often enough due to the assumed ‘absolute’ quantification capabilities of ddPCR. The quantitative abundance data presented in Chapter 4 was the first attempt of its kind to examine expression of a large number of target CWDEs (13) across a wide range of substrates (11) by O. maius. It was also one of only a handful of studies to use ddPCR to examine numerous transcript abundance profiles across multiple substrates for any organism. Included in this analysis was the development and validation of 13 primer sets for various CWDEs, many of which could be used for further characterization of their protein products. 130 No good research is without its challenges and one of the first problems encountered was degradation of the cellulose membranes in Chapter 2. These cellulose membranes were added to allow the fungal tissue to be easily removed from the plates, but the excessive degradation made removal of fungal tissue extremely problematic and may have influenced the transcript abundance of enzymes responsible for cellulose catabolism seen in the resultant transcriptome data. Oidiodendron maius, like many other ascomycetous fungi, produces compounds that inhibit nucleic acid extraction, so modifications had to be made to standard techniques. Acid phenol is used in phenol/chloroform extractions to denature DNA, partitioning it to the organic phase; obtaining the acid phenol used in similar studies (Roth ROTI®Aqua-Phenol, pH ≤ 4.0) was not possible in Canada, so an alternative product (Sigma 0.1 M citrate buffer saturated phenol solution, pH 4.3 ± 0.2) was used. This is most likely the reason for the low quality and yield of the resultant RNA used to construct the cDNA library. As a result, a library construction kit was used that targets “difficult” samples; the TruSeq RNA Exome library construction kit uses biotinylated capture probes that target coding RNA, and does not require RNA with poly-A tails. Another benefit of this kit is that RNA fragmentation is executed prior to cDNA synthesis which greatly improves the uniformity of sequence coverage over all the transcripts by greatly reducing 5′ bias (resulting from poly-A tails) and reducing the number of secondary structures in the RNA template (which can cause shielding of certain transcript portions while favoring others; Mortazavi, 2008). If I were to re-analyze the data, I would calculate TPM (Transcripts Per Kilobase Million) instead of RPKM due to differences in RNA-Seq depth, though the Baggerly et al. (2003) test used takes this into account. When calculating TPM, the only difference is that you normalize for gene length first, and then normalize for sequencing depth second (the reverse is true for calculation of RPKM); however, the effects of this difference are quite profound. It allows 131 comparison between samples as a proportion of reads mapped to a gene in each sample because the sum of all TPMs in each sample are the same. The CWDEs selected as targets for ddPCR quantification were selected primarily based on their response to peat added to the MMN media. Peat is a complex and indistinct composite that contains a multitude of carbon-based substrates. The primary constituent of the commercial peat product is presumably Sphagnum, however, it is harvested from Canadian “peatlands”, so it would contain partially decomposed plant and fungal matter from any and all plants and fungi which had inhabited those peatlands. The diverse composition of biological cell walls requires the synergistic and carefully regulated action of many, variable enzymes for degradation. While many of these enzymes are classified into enzyme systems, such as cellulolytic, ligninolytic, xylanolytic or pectinolytic, cell wall polymers are intricately embedded within one another to form the complex network that provides a cell with strength and rigidity, and, as such, the enzymes that act on them cannot necessarily be examined separately from one another. Enzymes induced by lignin, for example, may be produced as a means of releasing this lignin from the lignocellulose matrix and may not act directly on lignin. The pure compounds used in Chapter 4 may not have induced the same suite of enzymes produced by the more heterogenous substrates such as peat and soil, making it difficult to draw biologically relevant conclusions. The careful regulation of these enzymes may also be successive, and without examining transcript abundance at multiple timepoints, the lack of significant differences may have been simply a result of having missed the narrow window of a given enzyme’s expression. While I predicted more significant regulation of CWDEs across the varying carbon sources examined in Chapter 4, my study design may have contributed to this discrepancy. The concentration of carbon in the plates was equal across treatments (with the exception of the 0.5 % glucose treatment), but considerably lower than that used to obtain the RNA-Seq data 132 (Chapter 2), and, thereby used, to establish targets. At such low carbon concentrations, the transcript abundance was also relatively low, amplifying the impact of variance. Another limitation is our incomplete understanding of the function and substrate specificities of many of the CWDEs examined here. Target transcripts examined by ddPCR in Chapter 4 were selected, in part, for their putative substrates, but many large gene families, and in particular CAZymes, are polyspecific and may encompass functions other than cell wall degradation (Henrissat & Davies 2000). Studies such as this one contribute to the enormous amount of data necessary to draw meaningful conclusions regarding function, substrate preference and biological relevance of this diverse group of enzymes. 5.3 Future Research Directions & Applications A major goal of RNA-seq analysis is to identify differentially expressed and coregulated genes and to infer biological meaning for further studies; transcriptome datasets, therefore, provide a wealth of information for data mining. While I chose to focus on CWDEs in this study, the three transcriptomes produced during this research have been made publicly available (NCBI's Gene Expression Omnibus GEO Series accession number GSE163739) and can provide a vital resource for those wishing to examine other aspects of O. maius expression. Looking at enzymes with secretion signals, for example, could also contribute to our knowledge of carbohydrate degradation by these fungi. Studies such as this one lay the foundation for the discovery of new enzymatic strategies for cell wall deconstruction. The identification of proteins lacking functional annotation, but that are coregulated with chitinolytic genes, for example, would suggest functions associated with fungal biomass degradation that remain to be elucidated. If I had the opportunity to continue my examination of carbohydrate degradation in O. maius, I would begin by resolving the study design issues I encountered. My first experiment would be a timepoint analysis of CWDE transcript abundance; the extremely low abundance 133 values obtained in Chapter 4 suggest that degradation had slowed considerably by the time measurements were taken. If we had a better understanding of peak expression times, I speculate that more significant data could be collected. A more extensive study including a larger variety of CWDE genes and fewer well-defined substrates would likely result in an even more robust analysis and be able to distinguish between constitutive genes and genes that are reliably upregulated on specific substrates. Alternatively, the same methods could be applied to a comprehensive analysis of a single CAZy family across a range of biologically relevant substrates. This would provide a greater depth of understanding in the role that these enzymes play in carbon cycling and mycorrhizal evolution. Specifically examining a CWDE family found to be retained in O. maius, compared with other mycorrhizal fungi, such as the 31 oxidoreductase genes, could provide insight into transition of fungal species from saprotrophic to mycorrhizal. Applying the same methods to other GH families can similarly improve our capacity to understand carbohydrate degradation. Another method to better understand CAZy families is the use of gene synthesis; the declining cost of this technology allows for efforts to systematically characterize the activities and specificities of a single family in order to elucidate their contribution to carbon flux. The work here represents a small step in establishing a framework for the discovery and analysis of ErM fungal genes, and provides a base upon which a multitude of future questions can be pursued. Both in situ studies and supporting research under a broader range of conditions would greatly strengthen the results and conclusions of these studies.The continued investigation of carbohydrate degradation processes will drive forward both primary and applied research objectives, and expand our ability to understand global carbon cycling for future benefit. 134 Bibliography Abbà S, Khouja HR, Martino E, Archer DB, Perotto S (2009). SOD1-targeted gene disruption in the ericoid mycorrhizal fungus Oidiodendron maius reduces conidiation and the capacity for mycorrhization. Molecular Plant-Microbe Interactions, 22:1412–1421 Abbal P, Pradal M, Muniz L, Sauvage FX, Chatelet P, Ueda T, Tesniere C (2008). Molecular characterization and expression analysis of the Rab GTPase family in Vitis vinifera reveal the specific expression of a VvRabA protein. Journal of Experimental Botany, 59: 2403-2416 Aguilar-Trigueros CA, Powell JR, Anderson IC, Antonovics J, Rillig MC. (2014). Ecological understanding of root-infecting fungi using trait-based approaches. Trends in Plant Science, 19(7): 432-438 Aguirre JD, Culotta VC (2012). Battles with iron: manganese in oxidative stress protection. The Journal of biological chemistry, 287(17): 13541–13548 Andersen TF, Stalpers JA (1994). A checklist of Rhizoctonia epithets. Mycotaxon, 51: 437–457 Andersen, CL, Jensen, JL, Orntoft, TF (2004). Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Research, 64: 5245-5250 Anderson CT (2016). We be jammin': an update on pectin biosynthesis, trafficking and dynamics. Journal of Experimental Botany, 67: 495-502 Ao J, Free SJ. (2017). Genetic and biochemical characterization of the GH72 family of cell wall transglycosylases in Neurospora crassa. Fungal Genetics and Biology, 101:46–54 Arantes V, Jellison J, Goodell B (2012). Peculiarities of brown-rot fungi and biochemical Fenton reaction with regard to their potential as a model for bioprocessing biomass. Applied microbiology and biotechnology, 94: 323-38 Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000). Gene Ontology: tool for the unification of biology. Nature Genetics, 25:25-29 Askwith C, Eide D, Van Ho A, Bernard PS, Li L, Davis-Kaplan S, Sipe DM, Kaplan J (1994). The FET3 gene of S. cerevisiae encodes a multicopper oxidase required for ferrous iron uptake. Cell, 76(2): 403-10 Aspeborg H, Coutinho PM, Wang Y, Brumer III H, Henrissat B (2012). Evolution, substrate specificity and subfamily classification of glycoside hydrolase family 5 (GH5). BMC Evolutionary Biology, 12: 186 Averill C, Turner B, Finzi A (2014). Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature, 505: 543–545 135 Avni E, Coughlin RW, Solomon PR, King HH (1985). Mathematical modelling of lignin pyrolysis. Fuel, 64(11): 1495-1501 Babicki S, Arndt D, Marcu A, Liang Y, Grant JR, Maciejewski A, Wishart, DS (2016). Heatmapper: web-enabled heat mapping for all. Nucleic Acids Research, 44: W147-53 Baggerly K, Deng L, Morris J, Aldaz C (2003). Differential expression in SAGE: accounting for normal between-library variation. Bioinformatics, 19(12): 1477-1483 Bairoch A (2000). The ENZYME database in 2000. Nucleic Acids Research, 28:304-305 Bajpai P (2016). Chapter 2: Structure of Lignocellulosic Biomass in: Pretreatment of Lignocellulosic Biomass for Biofuel Production, SpringerBriefs in Green Chemistry for Sustainability: 7-12 Bajwa R, Read DJ (1985). The biology of mycorrhiza in the Ericaceae: IX. Peptides as nitrogen sources for mycorrhizal and non-mycorrhizal plants. New Phytologist, 101: 459–467 Bajwa R, Abuarghub S, Read DJ (1985). The biology of mycorrhiza in the Ericaceae. X. The utilization of proteins and the production of proteolytic enzymes by mycorrhizal plants. New Phytologist, 101: 469–486 Baker M (2012). Digital PCR hits its stride. Nature Methods, 9: 541–544 Baldock JA, Skjemstad JO (1999). Soil organic carbon/soil organic matter. In: Peverill KI, Sparrow LA, Reuter DJ (eds) Soil analysis: An interpretation manual. CSIRO Publishing, Collingwood Baldrian P (2009). Ectomycorrhizal fungi and their enzymes in soils: Is there enough evidence for their role as facultative soil saprotrophs? Oecologia, 161(4): 657-660 Bamford NC, Snarr BD, Gravelat FN, Little DJ, Lee MJ, Zacharias CA, Chabot JC, Geller AM, Baptista SD, Baker P, Robinson H, Howell PL, Sheppard DC (2015). Sph3 Is a Glycoside Hydrolase Required for the Biosynthesis of Galactosaminogalactan in Aspergillus fumigatus. Journal of Biological Chemistry, 290(46): 27438-50. Baral HO, Krieglsteiner L (2006). Hymenoscyphus subcarneus, a little known bryicolous discomycete found in the Białowieża National Park. Acta Mycologica, Warszawa 41(1): 11-20 Barrett T, Suzek TO, Troup DB, Wilhite SE, Ngau WC, Ledoux P, Rudenv D, Lash AE, Fugibuchi W, Edgar R (2005). NCBI GEO: Mining millions of expression profiles -database and tools. Nucleic Acids Research, 33: D562-D566 Barron GL (1962). New Species and New Records of Oidiodendron. Canadian Journal of Botany, 40(4): 589-607 Bas A, Forsberg G, Hammarstrom S, Hammarstrom ML (2004). Utility of the housekeeping genes 18S rRNA, beta-actin and glyceraldehyde- 3-phosphate-dehydrogenase for normalization in real-time quantitative reverse transcriptase-polymerase chain reaction 136 analysis of gene expression in human T lymphocytes. Scandinavian Journal of Immunology, 59: 566-573 Bastawde KB (1992). Xylan structure, microbial xylanases, and their mode of action. World Journal of Microbiology and Biotechnology, 8: 353–68 Beeson WT, Phillips CM, Cate JHD, Marletta MA (2012). Oxidative cleavage of cellulose by fungal copper-dependent polysaccharide monooxygenases. Journal of the American Chemical Society, 134: 890–92 Béguin P, Aubert JP (1994). The biological degradation of cellulose. FEMS Microbiology Reviews, 13(1): 25-58 Benjamini Y, Hochberg Y (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of Royal Statistical Society Series B, 57: 289-289 Berg B (2000). Litter decomposition and organic matter turnover in northern forest soils. Forest Ecology and Management, 133: 13-22 Bergero R, Perotto S, Girlanda M, Vidano G, Luppi MA (2000). Ericoid mycorrhizal fungi are common root associates of a Mediterranean ectomycorrhizal plant (Quercus ilex). Molecular Ecology, 9: 1639-1649 Berlemont R (2017). Distribution and diversity of enzymes for polysaccharide degradation in fungi. Scientific Reports 7: 222 Bidartondo MI, Bruns TD (2001). Extreme specificity in epiparasitic Monotropoideae Ericaceae: widespread phylogenetic and geographical structure. Molecular Ecology, 10(9): 2285-2295 Bidartondo MI, Bruns TD (2005). On the origins of extreme mycorrhizal specificity in the Monotropoideae (Ericaceae): performance trade-offs during seed germination and seedling development. Molecular Ecology, 14: 1549-1560 Bidartondo MI, Read DJ, Trappe JM, Merckx V, Ligrone R, Duckett JG (2011). The dawn of symbiosis between plants and fungi. Biology Letters, 7(4): 574-577 Bio-Rad (2015). Droplet Digital ™ PCR Applications Guide. http://www.biorad.com/webroot/web/pdf/lsr/literature/Bulletin_6407.pdf Bizabani C (2015). The Diversity of Root Fungi Associated with Erica Species Occurring in the Albany Centre of Endemism. Rhodes University, South Africa, PhD Thesis. Blackwell M (2011). The Fungi: 1, 2, 3 … 5.1 million species? Botany, 98(3): 426-438 Bodeker ITM, Clemmensen KE, de Boer W, Martin F, Olson A, Lindahl BD (2014). Ectomycorrhizal Cortinarius species participate in enzymatic oxidation of humus in northern forest ecosystems. New Phytologist, 203(1): 245-256 137 Bougoure DS, Cairney JWG (2005). Fungi associated with hair roots of Rhododendron lochiae (Ericaceae) in an Australian tropical cloud forest revealed by culturing and culture‐independent molecular methods. Environmental Microbiology, 7(11): 1743-1754 Bougoure DS, Parkin PI, Cairney JWG, Alexander IJ, Anderson IC (2007). Diversity of fungi in hair roots of Ericaceae varies along a vegetation gradient. Molecular Ecology, 16: 4624-4636 Bowden RD, Deem L, Plante AF, Peltre C, Nadelhoffer K, Lajtha K (2014), Litter Input Controls on Soil Carbon in a Temperate Deciduous Forest. Soil Science Society of America Journal, 78: S66-S75 Bowman SM, Free SJ (2006). The structure and synthesis of the fungal cell wall. BioEssays 28(8): 799–808 Brumer H 3rd, Sims PF, Sinnott ML (1999). Lignocellulose degradation by Phanerochaete chrysosporium: purification and characterization of the main alpha-galactosidase. Biochemical Journal, 339(1): 43-53 Brundrett MC (2002). Coevolution of roots and mycorrhizas of land plants. New Phytologist, 154: 275–304 Bugg TD, Ahmad M, Hardiman EM, Rahmanpour R (2011). Pathways for degradation of lignin in bacteria and fungi. Natural Product Reports, 28: 1883–1896 Busk PK, Lange M, Pilgaard B, Lange L (2014). Several Genes Encoding Enzymes with the Same Activity Are Necessary for Aerobic Fungal Degradation of Cellulose in Nature. PLoS ONE 9(12): e114138 Caffall KH, Mohnen D (2009). The structure, function, and biosynthesis of plant cell wall pectic polysaccharides. Carbohydrate Research, 344: 1879–900 Cairney JWG (2000). Evolution of mycorrhiza systems. Naturwissenchaften, 87:467–475 Caldwell R, Collins A, Collins JJ, Erdmann M, Fiorillo T, Fox H, Guralnick R, Hutchinson J, Laurin M, Lindstrom K, Monk A, Polly PD, Rieboldt S, Scotchmoor J, Simison B, Smith D, Speer B, Thanukos A, Waggoner B, Wetmore KL, and Whitney C (2018). The tundra biome. Retrieved from http://www.ucmp.berkeley.edu/exhibits/biomes/tundra.php Carpenter RJ, Macphail MK, Jordan GJ, Hill RS. (2015). Fossil evidence for open, Proteaceae-dominated heathlands and fire in the Late Cretaceous of Australia. American Journal of Botany, 12: 2092–2107 Casarrubia S, Daghino S, Kohler A, Morin E, Khouja HR, Daguerre Y, Veneault-Fourrey C, Martin FM, Perotto S, Martino E (2018). The Hydrophobin-Like OmSSP1 May Be an Effector in the Ericoid Mycorrhizal Symbiosis. Frontiers in Plant Science, 9: 546 Casarrubia S, Martino E, Daghino S, Kohler A, Morin E, Khouja H, Murat C, Barry KW, Lindquist EA, Martin FM, Perotto S (2020). Modulation of Plant and Fungal Gene Expression Upon 138 Cd Exposure and Symbiosis in Ericoid Mycorrhizal Vaccinium myrtillus. Frontiers in Microbiology, 11:341 Cerniglia CE, Sutherland JB (2010). Degradation of Polycyclic Aromatic Hydrocarbons by Fungi. In: Timmis KN (Ed) Handbook of Hydrocarbon and Lipid Microbiology, Springer, Berlin, Heidelberg: 2079-2110 Chambers SM, Curlevski NJA, Cairney JWG (2008). Ericoid mycorrhizal fungi are common root inhabitants of non-Ericaceae plants in a south-eastern Australian sclerophyll forest. FEMS Microbiology Ecology, 65(2): 263–270 Chapin III FS, Korner C (1995). Arctic and Alpine Biodiversity: Patterns, Causes and Ecosystem Consequences. Berlin: Springer-Veilag: 313-320 Chettri D, Verma AK, AKVerma (2020). Innovations in CAZyme gene diversity and its modification for biorefinery applications. Biotechnology Reports, 28: e00525 Clemmensen KE, Bahr A, Ovaskainen O, Dahlberg A, Ekblad A, Wallander H, Stenlid J, Finlay RD, Wardle DA, Lindahl BD (2013). Roots and Associated Fungi Drive Long-Term Carbon Sequestration in Boreal Forest. Science, 339(6127): 1615-1618 Clemmensen KE, Finlay RD, Dahlberg A, Stenlid J, Wardle DA, Lindahl BD (2015). Carbon sequestration is related to mycorrhizal fungal community shifts during long-term succession in boreal forests. New Phytologist, 205: 1525–1536 Clemmensen KE, Durling MB, Michelsen A, Hallin S, Finlay RD, Lindahl BD (2021). A tipping point in carbon storage when forest expands into tundra is related to mycorrhizal recycling of nitrogen. Ecology Letters, e13735 Clymo RS (1965). Experiments on breakdown of Sphagnum in two bogs. Journal of Ecology, 53(3): 747–758. Coleman DC, Crossley DA Jr, Hendrix PF (2004). Fundamentals of Soil Ecology, 2nd edn. Elsevier-Academic Press, USA Corrêa CL, Midorikawa GEO, Filho EXF, Noronha EF, Alves GSC, Togawa RC, Silva-Junior OB, Costa MMC, Grynberg P, Miller RNG (2020). Transcriptome Profiling-Based Analysis of Carbohydrate-Active Enzymes in Aspergillus terreus Involved in Plant Biomass Degradation. Frontiers in Bioengineering & Biotechnology, 8: 564527 Cournoyer B, Faure D (2003). Radiation and functional specialization of the family-3 glycoside hydrolases. Journal of Molecular Microbiology and Biotechnology, 5: 190-8 Coutinho PM, Deleury E, Davies GJ, Henrissat B (2003). An evolving hierarchical family classification for glycosyltransferases. Journal of Molecular Biology, 328(2): 307-17 Couture M, Fortin JA, Dalpe Y (1983). Oidiodendron-griseum robak - an endophyte of ericoid mycorrhiza in Vaccinium-spp. New Phytologist, 95(3): 375-380 139 Couturier M, Bennati-Granier C, Urio MB, Ramos LP, Berrin J-G (2016). Fungal enzymatic degradation of cellulose. In: Soccol C, Brar S, Faulds C, Ramos L, (eds.) Green Fuels Technol. Springer: Cham: 133–46 Craft C (2016). 7 – Peatlands. In: Creating and Restoring Wetlands; From Theory to Practice, Elsevier: 161-192 Cribb PJ, Kell SP, Dixon KW, Barrett RL (2003). Orchid conservation — a global perspective. Orchid Conservation, Natural History Publication, Kota Kinabalu, Malaysia: 1-24 Cullings K, Courty P (2009). Saprotrophic capabilities as functional traits to study functional diversity and resilience of ectomycorrhizal community. Oecologia, 161(4): 661-664 Cusick KD, Fitzgerald LA, Pirlo RK, Cockrell AL, Petersen ER, Biffinger JC (2014). Selection and Evaluation of Reference Genes for Expression Studies with Quantitative PCR in the Model Fungus Neurospora crassa under Different Environmental Conditions in Continuous Culture. PLoS ONE 9(12): e112706 Czechowsky T, Stitt M, Altmann T, Udvardi K, Scheible WR (2005). Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiology, 139: 5–17 Daghino S, Martino E, Perotto S (2016). Model system to unravel the molecular mechanisms of heavy metal tolerance in the ericoid mycorrhizal symbiosis. Mycorrhiza, 26(4): 263-274 Dalpé Y (1986). Axenic synthesis of ericoid mycorrhiza in Vaccinium angustifolium Ait. by Oidiodendron species. New Phytologist, 103: 391–396 Davies GJ, Gloster TM, Henrissat, B (2005). Recent structural insights into the expanding world of carbohydrate-active enzymes. Current Opinion in Structural Biology, 15(6): 637-645 Davies GJ, Sinnott ML (2008). Sorting the diverse: the sequence-based classifications of carbohydrate-active enzymes. The Biochemist, 30(4): 26-32 Deng LT, Wu YL, Li JC, OuYang KX, Ding MM, Zhang JJ, Li SQ, Lin MF, Chen HB, Hu XS, Chen XY (2016). Screening reliable reference genes for RT-qPCR analysis of gene expression in Moringa oleifera. PLoS ONE 11(8):e0159458 de Vries RP, vanKuyk PA, Kester HC, Visser J (2002). The Aspergillus niger faeB gene encodes a second feruloyl esterase involved in pectin and xylan degradation and is specifically induced in the presence of aromatic compounds. Biochemical Journal, 363(2): 377-386 Dheda K, Huggett JF, Chang JS, Kim LU, Bustin SA, Johnson MA, Rook GAW, Zumla A (2005). The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Analytical Biochemistry, 344: 141-143 Dilokpimol A, Mäkelä MR, Aguilar-Pontes MV, Benoit-Gelber I, Hildén KS, de Vries RP (2016). Diversity of fungal feruloyl esterases: updated phylogenetic classification, properties, and industrial applications. Biotechnology for Biofuels, 9: 231 140 Di Vietro L, Daghino S, Abbà S, Perotto S (2014). Gene expression and role in cadmium tolerance of two PLAC8-containing proteins identified in the ericoid mycorrhizal fungus Oidiodendron maius. Fungal Biology, 118(8): 695-703 Douglas GC, Heslin MC, Reid C (1989). Isolation of Oidiodendron maius from Rhododendron and ultrastructural characterization of synthesized mycorrhizas. Canadian Journal of Botany-Revue Canadienne De Botanique, 67(7): 2206-2212 Duckett JG, Read DJ (1995). Ericoid mycorrhizas and rhizoid ascomycete associations in liverworts share the same mycobiont: isolation of the partners and resynthesis of the associations in vitro. New Phytologist, 129: 439-447 Dunn OJ (1964). Multiple comparisons using rank sums. Technometrics. 6(3): 241–252 Dunnett CW (1955). A multiple comparison procedure for comparing several treatments with a control. Journal of the American Statistical Association, 50: 1096–1121 Edgar R, Domrachev M, Lash AE (2002). Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Research, 30(1): 207-210 Egger KN (1996). Molecular systematics of E-strain mycorrhizal fungi: Wilcoxina and its relationship to Tricharina Pezizales. Canadian Journal of Botany, 74: 773-779 Egger KN, Hibbett DS (2004). The evolutionary implications of exploitation in mycorrhizas. Canadian Journal of Botany, 82(8): 1110-1121 Eibinger M, Sigl K, Sattelkow J, Ganner T, Ramoni J, Seiboth B, Plank H, Nidetzky B (2016). Functional characterization of the native swollenin from Trichoderma reesei: study of its possible role as C1 factor of enzymatic lignocellulose conversion. Biotechnology for Biofuels, 9:178-197 El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, Qureshi M, Richardson LJ, Salazar GA, Smart A, Sonnhammer ELL, Hirsh L, Paladin L, Piovesan D, Tosatto SCE, Finn RD (2019). The Pfam protein families database in 2019. Nucleic Acids Research, 47(D1): D427–D432 Fehrer J, Reblova M, Bambasova V, Vohnik M (2019). The root-symbiotic Rhizoscyphus ericae aggregate and Hyaloscypha (Leotiomycetes) are congeneric: Phylogenetic and experimental evidence. Studies in Mycology, 92: 195-225 Fekete I, Varga C, Biró B, Tóth JA, Várbíró G, Lajtha K, Szabó G, Kotroczó Z (2016). The effects of litter production and litter depth on soil microclimate in a central european deciduous forest. Plant Soil, 398: 291–300 Fekete, I., Berki, I., Lajtha, K, Trumbore S, Francioso O, Gioacchini P, Montecchio D, Várbíró G, Béni Á, Makádi M, Demeter I, Madarász B, Juhos K, Kotroczó Z (2021). How will a drier climate change carbon sequestration in soils of the deciduous forests of Central Europe? Biogeochemistry, 152: 13–32 141 Fernandez CW, Heckman K, Kolka R, Kennedy PG (2019). Melanin mitigates the accelerated decay of mycorrhizal necromass with peatland warming. Ecology Letters, 22: 498–505 Ferreira P, Carro J, Serrano A, Martínez AT (2015). A survey of genes encoding H2O2-producing GMC oxidoreductases in 10 Polyporales genomes. Mycologia, 107: 1105-19 Fesel PH, Zuccaro A (2016). β-glucan: Crucial component of the fungal cell wall and elusive MAMP in plants. Fungal Genetics and Biology, 90: 53-60 Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, Potter SC, Punta M, Qureshi M, Sangrador-Vegas A, Salazar GA, Tate J, Bateman A (2016). The Pfam protein families database: towards a more sustainable future. Nucleic Acids Research, 44: D279-D285 Floudas D, Binder M, Riley R, Barry K, Blanchette RA, Henrissat B, Martínez AT, Otillar R, Spatafora JW, Yadav JS, Aerts A, Benoit I, Boyd A, Carlson A, Copeland A, Coutinho PM, de Vries RP, Ferreira P, Findley K, Foster B, Gaskell J, Glotzer D, Górecki P, Heitman J, Hesse C, Hori C, Igarashi K, Jurgens JA, Kallen N, Kersten P, Kohler A, Kües U, Kumar TK, Kuo A, LaButti K, Larrondo LF, Lindquist E, Ling A, Lombard V, Lucas S, Lundell T, Martin R, McLaughlin DJ, Morgenstern I, Morin E, Murat C, Nagy LG, Nolan M, Ohm RA, Patyshakuliyeva A, Rokas A, Ruiz-Dueñas FJ, Sabat G, Salamov A, Samejima M, Schmutz J, Slot JC, St John F, Stenlid J, Sun H, Sun S, Syed K, Tsang A, Wiebenga A, Young D, Pisabarro A, Eastwood DC, Martin F, Cullen D, Grigoriev IV, Hibbett DS (2012). The Paleozoic origin of enzymatic lignin decomposition reconstructed from 31 fungal genomes. Science, 336(6089): 1715-1719 Foss DL, Baarsch MJ, Murtaugh MP (1998). Regulation of hypoxanthine phosphoribosyltransferase, glyceraldehyde-3-phosphate dehydrogenase and beta-actin mRNA expression in porcine immune cells and tissues. Animal Biotechnology, 9: 67–78 Frank AB (1885). Ueber die auf Wurzelsymbiose beruhende Ernährung gewisser Bäume durch unterirdische Pilze. Berichte der Deutschen Botanischen Gesellschaft, 3:128–145 Fridovich I (1995). Superoxide radical and superoxide dismutases. Annual Review of Biochemistry, 64: 97–112 Freudenstein JV, Broe MB, Feldenkris ER (2016). Phylogenetic relationships at the base of Ericaceae: Implications for vegetative and mycorrhizal evolution. TAXON, 65(4): 794-804 Frieden E, Osaki S (1974). Ferroxidases and ferrireductases: their role in iron metabolism. Advances in Experimental Medicine and Biology, 48: 235-65 Fuchsman CH (1980). Peat: Industrial Chemistry and Technology. Academic Press Inc., New York. Garajova S, Mathieu Y, Beccia MR, Bennati-Granier C, Biaso F, Fanuel M, Ropartz D, Guigliarelli B, Record E, Rogniaux H, Henrissat B, and Berrin JG (2016). Single-domain flavoenzymes trigger lytic polysaccharide monooxygenases for oxidative degradation of cellulose. Scientific Reports, 6: 28276 142 Georgelis N, Nikolaidis N, Cosgrove DJ (2014) Biochemical analysis of expansin-like proteins from microbes. Carbohydrate Polymers, 100: 17–23 Ghani A, Dexter M, Perrott KW (2003). Hot-water extractable carbon in soils: a sensitive measurement for determining impacts of fertilisation, grazing and cultivation. Soil Biology & Biochemistry, 35(9): 1231–1243 Glass NL, Schmoll M, Cate JHD, Coradetti S (2013). Plant Cell Wall Deconstruction by Ascomycete Fungi. Annual Reviews Microbiology, 67: 477-498 Glenn K (2014). Allosteric Regulation of Bacterial and Fungal Xylulose 5-phosphate/ Fructose 6-phosphate Phosphoketolases (Xfps). Clemson University, South Carolina, USA. PhD Thesis. (https://tigerprints.clemson.edu/all_dissertations/1454) Grelet G, Johnson D, Paterson E, Anderson IC, Alexander IJ (2009). Reciprocal carbon and nitrogen transfer between an ericaceous dwarf shrub and fungi isolated from Piceirhiza bicolorata ectomycorrhizas. New Phytologist, 182(2): 359-366 Grelet GA, Johnson D, Vrålstad T, Alexander IJ, Anderson IC (2010). New insights into the mycorrhizal Rhizoscyphus ericae aggregate: spatial structure and co‐colonization of ectomycorrhizal and ericoid roots. New Phytologist, 188: 210–222 Grelet GA, Martino E, Dickie IA, Tajuddin R, Artz RAA (2016). Ecology of ericoid mycorrhizal fungi: what insight have we gained with molecular tools and what’s missing? In: Martin F (ed) Molecular Mycorrhizal Symbiosis, Springer: 405-419 Gruber S, Vaaje-Kolstad G, Matarese F, López-Mondéjar R, Kubicek CP, Seidl-Seiboth V (2011). Analysis of subgroup carbon of fungal chitinases containing chitin-binding and LysM modules in the mycoparasite Trichoderma atroviride. Glycobiology, 21:122–33 Hambleton S, Currah RS (1997). Fungal endophytes from the roots of alpine and boreal Ericaceae. Canadian Journal of Botany, 75: 1570-1581 Hartl L, Zach S, Seidl-Seiboth V (2012) Fungal chitinases: diversity, mechanistic properties and biotechnological potential. Applied Microbiology and Biotechnology, 93(2): 533–543 Henrissat B, Claeyssens M, Tomme P, Lemesle L, Mornon JP (1989) Cellulase families revealed by hydrophobic cluster analysis. Gene, 81: 83-95 Henrissat B (1991) A classification of glycosyl hydrolases based on amino-acid-sequence similarities. Biochemical Journal, 280: 309-316 Henrissat B, Davies GJ (2000). Glycoside hydrolases and glycosyltransferases: families, modules, and implications for genomics. Plant Physiology, 124: 1515-1519 Heredia NJ, Belgrader P, Wang S, Koehler R, Regan J, Cosman AM, Saxonov S, Hindson B, Tanner SC, Brown AS, Karlin-Neumann G (2013). Droplet Digital PCR quantitation of HER2 expression in FFPE breast cancer samples. Methods, 59(1): S20–S23 143 Hermoso J, Sanz-Aparicio J, Molina R, Juge N, Gonzalez R, Faulds CB (2004). The Crystal Structure of Feruloyl Esterase A from Aspergillus niger Suggests Evolutive Functional Convergence in Feruloyl Esterase Family. Journal of Molecular Biology, 338(3): 495-506 Hernandez-Ortega A, Ferreira P, Martinez AT (2012). Fungal Aryl-alcohol oxidase: A peroxide-producing flavoenzyme involved in lignin degradation. Applied Microbiology and Biotechnology, 93: 1395–1410 Hibbett DS, Gilbert LB, Donoghue MJ (2000). Evolutionary instability of ectomycorrhizal symbioses in basidiomycetes. Nature, 407(6803): 506-508 Hindson BJ, Ness KD, Masquelier DA, Belgrader P, Heredia NJ, Makarewicz AJ, Bright IJ, Lucero MY, Hiddessen AL, Legler TC, Kitano TK, Hodel MR, Petersen JF, Wyatt PW, Steenblock ER, Shah PH, Bousse LJ, Troup CB, Mellen JC, Wittmann DK, Erndt NG, Cauley TH, Koehler RT, So AP, Dube S, Rose KA, Montesclaros L, Wang S, Stumbo DP, Hodges SP, Romine S, Milanovich FP, White HE, Regan JF, Karlin-Neumann GA, Hindson CM, Saxonov S and Colston BW (2011). High-Throughput Droplet Digital PCR System for Absolute Quantitation of DNA Copy Number. Analytical Chemistry, 83(22): 8604-8610 Högberg P, Plamboeck A, Taylor A, Fransson P (1999). Natural C-13 abundance reveals trophic status of fungi and host-origin of carbon in mycorrhizal fungi in mixed forests. Proceedings of the National Academy of Sciences of the United States of America, 96(15): 8534-8539 Hossain MF, Zhang Y, Chen W, Wang J, Pavlic G (2007). Soil organic carbon content in northern Canada: A database of field measurements and its analysis. Canadian Journal of Soil Science, 87(3): 259-268 Huang XF , Santhanam N, Badri DV, Hunter WJ, Manter DK, Decker SR, Vivanco JM, Reardon KF (2013). Isolation and characterization of lignin-degrading bacteria from rainforest soils. Biotechnology and Bioengineering,110:1616–1626 Huggett JF, Foy CA, Benes V, Emslie K, Garson JA, Haynes R, Hellemans J, Kubista M, Mueller RD, Nolan T, Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT, Bustin SA (2013). The digital MIQE guidelines: Minimum Information for Publication of Quantitative Digital PCR Experiments. Clinical Chemistry, 59(6): 892-902 (2013) Hugron S, Bussières J, Rochefort L (2013). Tree plantations within the context of ecological restoration of peatlands: practical guide. Laval, Québec, Canada: Peatland Ecology Research Group (http://www.gret-perg.ulaval.ca/uploads/media/Tree_Plantation_guide.pdf) Ishida TA, Nordin A (2010). No evidence that nitrogen enrichment affect fungal communities of Vaccinium roots in two contrasting boreal forest types. Soil Biology and Biochemistry, 42(2): 234-243 Iyer CC, McGovern VL, Wise DO, Glass DJ, Burghes AH (2014). Deletion of atrophy enhancing genes fails to ameliorate the phenotype in a mouse model of spinal muscular atrophy. Neuromuscular Disorders, 24: 436–444 144 Jalal MAF, Read DJ (1983). The organic acid composition of Calluna heathland soil with special reference to phyto- and fungitoxicity. Plant and Soil, 70: 257-272 Johansson M (2001). Fungal associations of Danish Calluna vulgaris roots with special reference to ericoid mycorrhiza. Plant and Soil, 231: 225–232 Jones M, Durall D, Tinker P (1991). Fluxes of Carbon and Phosphorus between Symbionts in Willow Ectomycorrhizas and Their Changes with Time. New Phytologist, 119(1), 99-106 Jones M, Durall D, Tinker P (1998). A Comparison of Arbuscular and Ectomycorrhizal Eucalyptus coccifera: Growth Response, Phosphorus Uptake Efficiency and External Hyphal Production. New Phytologist, 140(1), 125-134 Joosten H, Clarke D (2002). Wise Use of Mires and Peatlands: Background and Principles including a Framework for Decision-Making. International Mire Conservation Group and International Peat Society,Totnes, Devon, UK Kapteyn JC, Van Den Ende H, Klis FM (1999). The contribution of cell wall proteins to the organization of the yeast cell wall. Biochimica et Biophysica Acta, 1426: 373 – 383 Kaya M, Sousa A, Crépeau M, Sørensen S, Ralet M (2014). Characterization of citrus pectin samples extracted under different conditions: Influence of acid type and pH of extraction. Annals of Botany, 114: 1319 – 1326 Kerley SJ, Read DJ (1995). The biology of mycorrhiza in the Ericaceae. XVIII. Chitin degradation by Hymenoscyphus ericae and transfer of chitin-nitrogen to the host plant. New Phytologist, 131: 369–375 Kerley SJ, Read DJ (1997). The biology of mycorrhiza in the Ericaceae. XIX. Fungal mycelium as a nitrogen source for the ericoid mycorrhizal fungus Hymenoscyphus ericae and its host plant. New Phytologist, 136: 691–701 Kerley SJ, Read DJ (1998). The biology of mycorrhiza in the Ericaceae. XX. Plant and mycorrhizal necromass as nitrogenous substrates for ericoid mycorrhizal fungus Hymenoscyphus ericae and its host. New Phytologist, 139 : 353–360 Kleber M (2010). What is recalcitrant soil organic matter? Environmental Chemistry, 7: 320–332 Klemm D, Heublein B, Fink H, Bohn A (2005). Cellulose: Fascinating Biopolymer and Sustainable Raw Material. Angewandte Chemie International Edition, 44(22): 3358–93 Kohler A, Kuo A, Nagy LG, Morin E, Barry KW, Buscot F, Canback B, Choi C, Cichocki N, Clum A, Colpaert J, Copeland A, Costa MD, Dore J, Floudas D, Gay G, Girlanda M, Henrissat B, Herrmann S, Hess J, Hogberg N, Johansson T, Khouja HR, LaButti K, Lahrmann U, Levasseur A, Lindquist EA, Lipzen A, Marmeisse R, Martino E, Murat C, Ngan CY, Nehls U, Plett JM, Pringle A, Ohm RA, Perotto S, Peter M, Riley R, Rineau F, Ruytinx J, Salamov A, Shah F, Sun H, Tarkka M, Tritt A, Veneault-Fourrey C, Zuccaro A, Tunlid A, Grigoriev IV, Hibbett DS, Martin F. (2015). Convergent losses of decay mechanisms and rapid turnover of symbiosis genes in mycorrhizal mutualists. Nature Genetics, 47(4), 410-415 145 Kohout P (2017) Biogeography of Ericoid Mycorrhiza. In: Tedersoo L. (ed.) Biogeography of Mycorrhizal Symbiosis. Ecological Studies (Analysis and Synthesis), vol 230: 179-193. Springer, Cham Koide RT, Sharda JN, Herr JR, Malcolm GM (2008), Ectomycorrhizal fungi and the biotrophy–saprotrophy continuum. New Phytologist, 178: 230-233 Kögel-Knabner I (2002). The macromolecular organic composition of plant and microbial residues as inputs to soil organic matter. Soil Biology and Biochemistry, 34(2): 139-162 Koonin EV, Fedorova ND, Jackson JD, Jacobs AR, Krylov DM, Makarova KS, Mazumder R, Mekhedov SL, Nikolskaya AN, Rao BS, Rogozin IB, Smirnov S, Sorokin AV, Sverdlov AV, Vasudevan S, Wolf YI, Yin JJ, Natale DA (2004). A comprehensive evolutionary classification of proteins encoded in complete eukaryotic genomes. Genome Biology, 5: R7 Kron KA, Judd WS, Stevens PF, Crayn D, Anderberg AA, Gadek P, Quinn CJ, Luteyn JL (2002). Phylogenetic classification of Ericaceae: molecular and morphological evidence. The Botanical Review, 68: 335–423 Kruskal W, Wallis WA (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260): 583–621 Kummel M, Salant SW (2006). The economics of mutualisms: Optimal utilization of mycorrhizal mutualistic partners by plants. Ecology, 87(4), 892-902 Lacourt I, Girlanda M, Perotto S, Del Pero M, Zuccon D, Luppi AM (2001). Nuclear ribosomal sequence analysis of Oidiodendron: towards a redefinition of ecologically relevant species. New Phytologist, 149: 565–576 Larsen JA (1980). The Boreal ecosystem. Academic Press, New York. Latgé J (2007). The cell wall: a carbohydrate armour for the fungal cell. Molecular Microbiology, 66: 279-290 Leake JR, Read DJ (1991). Experiments with ericoid mycorrhiza. Methods in Microbiology, 23: 435-439 Leake JR (1994). The biology of myco-heterotrophic (“saprophytic”) plants. Tansley review No. 69. New Phytologist, 127: 171–216 Lebo SE Jr., Gargulak JD, McNally TJ (2001). "Lignin". Kirk-Othmer Encyclopedia of Chemical Technology. John Wiley & Sons, Inc. Leceilier G, Silar P (1994). Rapid methods for nucleic acids extraction from Petri dish-grown mycelia. Current Genetics, 25:122-123 Lee SS, Yu S, Withers SG (2003). Detailed dissection of a new mechanism for glycoside cleavage: alpha-1,4-glucan lyase. Biochemistry, 42: 13081-90 146 Leonowicz A, Cho NS, Luterek J, Wilkolazka A, Wojtas-Wasilewska M, Matuszewska A, Hofrichter M, Wesenberg D, Rogalski J (2001). Fungal laccase: properties and activity on lignin. Journal of Basic Microbiology, 41(3-4): 185-227 Leopold D (2016). Ericoid fungal diversity: Challenges and opportunities for mycorrhizal research. Fungal Ecology, 24(B): 114-123 Levasseur A, Drula E, Lombard V, Coutinho PM, Henrissat B (2013) Expansion of the enzymatic repertoire of the CAZy database to integrate auxiliary redox enzymes. Biotechnology for Biofuels, 6: 41 Li X, Skory CD, Cotta MA, Puchart V, Biely P (2008). Novel Family of Carbohydrate Esterases, Based on Identification of the Hypocrea jecorina Acetyl Esterase Gene. Applied and Environmental Microbiology, 74 (24): 7482-7489 Livak KJ (2001) ABI Prism 7700 Sequence Detection System User Bulletin #2 Relative Quantification of Gene Expression. (http://tools.thermofisher.com/content/sfs/manuals/cms_040980.pdf) Llanos A, François JM, Parrou J-L (2015). Tracking the best reference genes for RT-qPCR data normalization in filamentous fungi. BMC Genomics, 16: 71 Lombard V, Bernard T, Rancurel C, Brumer H, Coutinho PM, Henrissat B (2010). A hierarchical classification of polysaccharide lyases for glycogenomics. Biochemical Journal, 432: 437-444 Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B (2014). The Carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Research, 42: D490–D495. Luis P, Kellner H, Martin F, Buscot F (2005). A molecular method to evaluate basidiomycete laccase gene expression in forest soils. Geoderma, 128(1-2): 18-27 Lukešová T, Kohout P, Větrovský T, Vohník M (2015). The Potential of Dark Septate Endophytes to Form Root Symbioses with Ectomycorrhizal and Ericoid Mycorrhizal Middle European Forest Plants. PLOS ONE 10(4): e0124752 Lumley TC, Gignac LD, Currah RS (2001). Microfungus communities of white spruce and trembling aspen logs at different stages of decay in disturbed and undisturbed sites in the boreal mixedwood region of Alberta. Canadian Journal of Botany, 79: 76-92 Lyu Y, Wu X, Ren H, Zhou F, Zhou H, Zhang X, Yang H (2017). Selection of reliable reference genes for gene expression studies in Trichoderma afroharzianum LTR-2 under oxalic acid stress. Journal of Microbiological Methods, 141: 28-31 McLean CB, Anthony J, Collins RA, Steinke E, Lawrie AC (1998). First synthesis of ericoid mycorrhizas in the Epacridaceae under axenic conditions. New Phytologist, 139: 589-593 147 Malmström BG, Andréasson L-E, Reinhammar B (1975). Copper-containing oxidases and superoxide dismutase. In: Boyer, P.D. (Ed.), The Enzymes, 3rd edn, vol. 12, Academic Press, New York: 507–579 Manoli A, Sturaro A, Trevisan S, Quaggiotti S, Nonis A (2012). Evaluation of candidate reference genes for qPCR in maize. Journal of Plant Physiology, 169: 807–15 Martens-Uzunova ES, Schaap PJ (2009). Assessment of the pectin degrading enzyme network of Aspergillus niger by functional genomics. Fungal Genetics and Biology, 46: S170–79 Martin F, Aerts A, Ahrén D, Brun A, Danchin EGJ, Duchaussoy F, Gibon J, Kohler A, Lindquist E, Pereda V, Salamov A, Shapiro HJ, Wuyts J, Blaudez D, Buée M, Brokstein P, Canbäck B, Cohen D, Courty PE, Coutinho PM, Delaruelle C, Detter JC, Deveau A, DiFazio S, Duplessis S, Fraissinet-Tachet L, Lucic E, Frey-Klett P, Fourrey C, Feussner I, Gay G, Grimwood J, Hoegger PJ, Jain P, Kilaru S, Labbé J, Lin YC, Legué V, Le Tacon F, Marmeisse R, Melayah D, Montanini B, Muratet M, Nehls U, Niculita-Hirzel H, Oudot-Le Secq MP, Peter M, Quesneville H, Rajashekar B, Reich M, Rouhier N, Schmutz J, Yin T, Chalot M, Henrissat B, Kües U, Lucas S, Van de Peer Y, Podila GK, Polle A, Pukkila PJ, Richardson PM, Rouzé P, Sanders IR, Stajich JE, Tunlid A, Tuskan G, Grigoriev IV (2008). The genome of Laccaria bicolor provides insights into mycorrhizal symbiosis. Nature, 452: 88–92 Martino E, Morin E, Grelet GA, Kuo A, Kohler A, Daghino S, Barry KW, Cichocki N, Clum A, Dockter RB, Hainaut M, Kuo RC, LaButti K, Lindahl BD, Lindquist EA, Lipzen A, Khouja HR, Magnuson J, Murat C, Ohm RA, Singer SW, Spatafora JW, Wang M, Veneault-Fourrey C, Henrissat B, Grigoriev IV, Martin FM, Perotto S (2018). Comparative genomics and transcriptomics depict ericoid mycorrhizal fungi as versatile saprotrophs and plant mutualists. New Phytologist, 217: 1213–1229 Martino E, Turnau K, Girlanda M, Bonfante P, Perotto S (2000). Ericoid mycorrhizal fungi from heavy metal polluted soils: their identification and growth in the presence of zinc ions. Mycological Research, 104(3): 338 – 344 Massicotte HB, Melville LH, Molina R, Peterson RL (1993). Structure and histochemistry of mycorrhizae synthesized between Arbutus menziesii (Ericaceae) and two basidiomycetes, Pisolithus tinctorius (Pisolithaceae) and Piloderma bicolor (Corticiaceae). Mycorrhiza, 3: 1–11 Matzner E, Borken W (2008). Do freeze-thaw events enhance carbon and N losses from soils of different ecosystems? A review. European Journal of Soil Science, 59: 274 – 284 McCord PH (2016). Using droplet digital PCR (ddPCR) to detect copy number variation in sugarcane, a high-level polyploid. Euphytica, 209: 439–448 Midgley DJ, Rosewarne CP, Greenfield P, Li D, Vockler CJ, Hitchcock CJ, Sawyer NA, Brett R, Edwards J, Pitt JI, Tran-Dinh N (2016). Genomic insights into the carbohydrate catabolism of Cairneyella variabilis gen. nov. sp. nov., the first reports from a genome of an ericoid mycorrhizal fungus from the southern hemisphere. Mycorrhiza, 26(4): 345-352 Mikola P (1998). Ectendomycorrhiza of conifers. Silva Fennica, 22: 19-27 148 Mohnen D (2008). Pectin structure and biosynthesis. Current Opinions in Plant Biology, 11: 266–77 Morozova OV, Shumakovich GP, Gorbacheva MA, Shleev SV, Yaropolov AI (2007). "Blue" laccases. Biochemistry (Mosc), 72(10): 1136-50. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008 Jul;5(7):621-8. doi: 10.1038/nmeth.1226. Epub 2008 May 30. PMID: 18516045 Mouyna I, Hartl L, Latgé J-P (2013). β-1,3-glucan modifying enzymes in Aspergillus fumigatus. Frontiers in Microbiology, 4: 81 Murphy MP (2009). How mitochondria produce reactive oxygen species. Biochemical Journal, 417: 1–13 Mutter M, Beldman G, Schols HA, Voragen AG (1994). Rhamnogalacturonan alpha-L-rhamnopyranohydrolase. A novel enzyme specific for the terminal nonreducing rhamnosyl unit in rhamnogalacturonan regions of pectin. Plant Physiology, 106(1): 241-50 Nicolás C, Martin-Bertelsen T, Floudas D, Bentzer J, Smits M, Johansson T, Troein C, Persson P, Tunlid A (2019). The soil organic matter decomposition mechanisms in ectomycorrhizal fungi are tuned for liberating soil organic nitrogen. ISME Journal, 13: 977–988 Nordin SB, Nyren JO, Back LL (1973). Molten cellulose produced in a laser-beam. Svensk Papperstidn, 76: 609-610 Odum EP (1971) Fundamentals of Ecology. Third Edition, W.B. Saunders Co., Philadelphia-London-Toronto: 1-574 Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M (1999). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research, 27: 29 -34 Osman KT (2013). Forest Soils: Properties and Management. Springer International Publishing. Palmer JH, Horton BM, Allaway WG, Ashford AE (2007). Growth Stimulation of Woollsia pungens by a Natural Ericoid Mycorrhizal fungal Endophyte. Australasian Mycologist, 26(1): 1-8 Park JT (2001). Identification of a dedicated recycling pathway for anhydro-N-acetylmuramic acid and N-acetylglucosamine derived from Escherichia coli cell wall murein. Journal of Bacteriology, 183: 3842-3847 Paustian K, Larson E, Kent J, Marx E, Swan A (2019). Soil C Sequestration as a Biological Negative Emission Strategy. Frontiers in Climate, 1: 8 Pearson V, Read DJ (1973). The biology of mycorrhiza in the Ericaceae. I. The isolation of the endophyte and synthesis of mycorrhizas in aseptic cultures. New Phytologist, 72(2): 371-379 149 Perotto S, Peretto R, Faccio A, Schubert A, Varma A, Bonfante P (1995). Ericoid mycorrhizal fungi: cellular and molecular bases of their interactions with the host plant. Canadian Journal of Botany, 73 suppl. I : S557-S568 Perotto S, Coisson JD, Perugini I, Cometti V, Bonfante P (1997). Production of pectin-degrading enzymes by ericoid mycorrhizal fungi. New Phytologist, 135(1): 151-162 Perotto S, Martino E, Abbà S, Vallino M (2012). 14 Genetic Diversity and Functional Aspects of Ericoid Mycorrhizal Fungi. In: Hock B. (eds) Fungal Associations. The Mycota (A Comprehensive Treatise on Fungi as Experimental Systems for Basic and Applied Research), vol 9. Springer, Berlin, Heidelberg Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP (2004). Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper – Excel-based tool using pair-wise correlations. Biotechnology Letters, 26: 509-515 Piercey M, Thormann M, Currah R (2002). Saprobic characteristics of three fungal taxa from ericalean roots and their association with the roots of Rhododendron groenlandicum and Picea mariana in culture. Mycorrhiza, 12(4): 175-180 Pinheiro LB, Coleman VA, Hindson CM, Herrmann J, Hindson BJ, Bhat S, Emslie KR (2012). Evaluation of a droplet digital polymerase chain reaction format for DNA copy number quantification. Analytical Chemistry, 84(2): 1003-1011 Popper ZA, Michel G, Herve C, Domozych DS, Willats WG, Tuohy MG, Kloareg B, Stengel DB (2011). Evolution and diversity of plant cell walls: from algae to flowering plants. Annual Reviews Plant Biology, 62: 567–90 Post WM, Emanuel WR, Zinke PJ, Stangenberger AG (1982). Soil carbon pools and world life zones. Nature, 298: 156–159 Poutanen K, Sundberg M, Korte H, Puls J (1990). Deacetylation of xylans by acetyl esterases of Trichoderma reesei. Applied Microbiology & Biotechnology, 33: 506–510 Qian XM, El-Ashker A, Kottke I, Oberwinkler F (1998). Studies of pathogenic and antagonistic microfungal populations and their potential interactions in the mycorrhizoplane of Norway spruce (Picea abies (L.) Karst.) and beech (Fagus sylvatica L.) on acidified and limed plots. Plant Soil, 199: 111-116 Rasmussen R (2001). Quantification on the LightCycler instrument. In: Meuer S, Wittwer C, Nakagawara K, eds. Rapid Cycle Real-Time PCR: Methods and Applications. Heidelberg: Springer-Verlag Press: 21–34 Raven P, Johnson GB, Mason KA, Losos JB, Singer SS (2013). Biology. 10th ed. New York: McGraw-Hill. Rayner ADM, Boddy L (1988). Wood Decomposition: its Biology and Ecology. John Wiley, Chichester, NY 150 Read DJ, Leake JR, Perez-Moreno J (2004). Mycorrhizal fungi as drivers of ecosystem processes in heathland and boreal forest biomes. Canadian Journal of Botany, 82: 1243–1263 Reading NS, Welch KD, Aust SD (2003). Free Radical Reactions of Wood-Degrading Fungi. In: ACS Symposium Series Vol. 845: Wood Deterioration and Preservation, Chapter 3: 16-31 Reid ID (1995). Biodegradation of lignin. Canadian Journal of Botany, 73(S1): S1011-S1018 Reiss R, Ihssen J, Richter M, Eichhorn E, Schilling B, Thöny-Meyer L (2013). Laccase versus Laccase-Like Multi-Copper Oxidase: A Comparative Study of Similar Enzymes with Diverse Substrate Spectra. PLOS ONE, 8(6): e65633 Rice AV, Currah RS (2001). Physiological and morphological variation in Oidiodendron maius. Mycotaxon, 79: 383-396 Rice AV, Currah RS (2005). Oidiodendron: A survey of the named species and related anamorphs of Myxotrichum. Studies in Mycology, 53: 83-120 Rice AV, Currah RS (2006). Soil biology volume 9. In Schulz, BJE, Boyle, CJC, Sieber, TN (Eds), Microbial root endophytes: 227-246 Rice AV, Tsuneda A, Currah RS (2006). In vitro decomposition of sphagnum by some microfungi resembles white rot of wood. FEMS Microbiology Ecology, 56(3): 372-382 Rinaldi AC, Comandini O, Kuyper TW (2008). Ectomycorrhizal fungal diversity: separating the wheat from the chaff. Fungal Diversity, 33: 1-45 Rineau F, Roth D, Shah F, Smits M, Johansson T, Canback B, Olsen PB, Persson P, Grell MN, Lindquist E, Grigoriev IV, Lange L, Tunlid A (2012). The ectomycorrhizal fungus Paxillus involutus converts organic matter in plant litter using a trimmed brown-rot mechanism involving Fenton chemistry. Environmental Microbiology, 14(6): 1477-1487 Ruan W, Lai M (2007). Actin, a reliable marker of internal control. Clinica Chimica Acta, 385: 1-5 Rydin H, Jeglum J K (2013). The biology of peatlands. Oxford Univ. Press, Oxford, UK. Sánchez B, Zúñiga M, González-Candelas F, de los Reyes-Gavilán CG, Margolles A (2010). Bacterial and eukaryotic phosphoketolases: phylogeny, distribution and evolution. Journal of Molecular Microbiology and Biotechnology,18(1): 37-51 Sánchez-Rodríguez A, Portal O, Rojas LE, Ocaña B, Mendoza M, Acosta M, Jiménez E, Höfte M (2008). Molecular Biotechnology, 40(3): 299-305 Sandhu DK, Sidhu MS (1985). Production, localization and glucose repression of beta-glucosidase in Trichoderma longibrachiatum. Journal of Basic Microbiology, 25(9): 591-8 Scheller HV, Ulvskov P (2010). Hemicelluloses. Annual Review of Plant Biology, 61: 263-289 151 Schild DE, Kennedy A, Staurt MR (1988). Isolation of symbiont and associated fungi from ectomycorrhizas of sitka spruce. European Journal of Forest Pathology, 18: 51-61 Schlesinger WH (1997). Biogeochemistry: An Analysis of Global Change. 2nd Edition. Academic Press. Schreiner KM, Blair NE, Egerton-Warburton L, Levinson WJ (2014). Contribution of fungal macromolecules to soil carbon sequestration. In A. Hartemink, & K. McSweeney (Eds), Soil Carbon: 155-161. Progress in Soil Science,16; Springer Press Schüßler A, Schwarzott D, Walker C (2001). A new fungal phylum, the Glomeromycota: phylogeny and evolution. Mycological Research, 105: 1413-1421 Selosse M, Setaro S, Glatard F, Richard F, Urcelay C, Weiss M (2007). Sebacinales are common mycorrhizal associates of Ericaceae. New Phytologist, 174(4): 864-878 Setaro S, Weiß M, Oberwinkler F, Kottke I (2006). Sebacinales form ectendomycorrhizas with Cavendishia nobilis, a member of the Andean clade of Ericaceae, in the mountain rain forest of southern Ecuador. New Phytologist, 169(2): 355-365 Shahidi F, Abuzaytoun R (2005). Chitin, chitosan, and co-products: chemistry, production, applications, and health effects. Advances in Food and Nutrition Research, 49: 93-135 Shahinian S, Bussey H (2000). β-1, 6-Glucan synthesis in Saccharomyces cerevisiae. Molecular Microbiology, 35(3): 477-489 Shewale JG (1982). Beta glucosidase: its role in cellulase synthesis and hydrolysis of cellulose. International Journal of Biochemistry, 14: 435–443 Sirotek K, Slováková L, Kopečný J, Marounek M (2004). Fermentation of pectin and glucose, and activity of pectin‐degrading enzymes in the rabbit caecal bacterium Bacteroides caccae. Letters in Applied Microbiology, 38(4), 327-332 Sista Kameshwar AK, Qin W (2018). Understanding the structural and functional properties of carbohydrate esterases with a special focus on hemicellulose deacetylating acetyl xylan esterases. Mycology, 9(4): 273–295 Smith SE, Read D (2008). Mycorrhizal symbiosis (3rd ed). Great Britain: Elsevier/Academic Press. Solanas M, Moral R, Esrich E (2001). Unsuitability of using ribosomal RNA as loading control for Northern blot analyses related to the imbalance between messenger and ribosomal RNA content in rat mammary tumors. Analytical Biochemistry, 288: 99-102 Somerville C, Bauer S, Brininstool G, Facette M, Hamann T, Milne J, Osborne E, Paredez A, Persson S, Raab T, Vorwerk S, Youngs H (2004). Toward a systems approach to understanding plant cell walls. Science, 306: 2206–2211 Soneson C, Delorenzi M (2013). A comparison of methods for differential expression analysis of RNA-Seq data. BMC Bioinformatics, 14: 91 152 Song Y, Wang Y, Guo D, Jing L (2019). Selection of reference genes for quantitative real-time PCR normalization in the plant pathogen Puccinia helianthi Schw. BMC Plant Biology, 19: 20 Spanakis E (1993). Problems related to the interpretation of autoradiographic data on gene expression using common constitutive transcripts as controls. Nucleic Acids Research, 21: 3809-3819 Specht RL (1979). Heathlands and related shrublands of the world. In: Ecosystems of the world. Vol. 9a: 1-18. R.L. Specht (Ed). Elsevier, Amsterdam Stanton KA, Edger PP, Puzey JR, Kinser T, Cheng P, Vernon DM, Forsthoefel NR, Cooley AM (2017). A whole-transcriptome approach to evaluating reference genes for quantitative gene expression studies: a case study in mimulus. G3: Genes, Genomes, Genetics, 7: 1085–1095 Steiger MG, Mach RL, Mach-Aigner AR (2010). An accurate normalization strategy for RT-qPCR in Hypocrea jecorina (Trichoderma reesei). Journal of Biotechnology, 145(1): 30-37 Stoyke G, Currah RS (1991). Endophytic fungi from the mycorrhizae of alpine ericoid plants. Canadian Journal of Botany, 69: 347-352 Straker CJ (1996). Ericoid mycorrhiza: ecological and host specificity. Mycorrhiza, 6: 215–225 Suzuki K, Hori A, Kawamoto K, Thangudu RR, Ishida T, Igarashi K, Samejima M, Yamada C, Arakawa T, Wakagi T, Koseki T, Fushinobu S (2014). Crystal structure of a feruloyl esterase belonging to the tannase family: a disulfide bond near a catalytic triad. Proteins, 82(10): 2857-67 Suzuki T, Higgins PJ, Crawford DR (2000). Control selection for RNA quantitation. Biotechniques, 29: 332-337 Swiontek Brzezinska M, Jankiewicz U, Burkowska A, Walczak M (2013). Chitinolytic microorganisms and their possible application in environmental protection. Current microbiology, 68(1): 71-81 Synytsya A, Novák M (2013). Structural diversity of fungal glucans. Carbohydrate Polymers, 92(1): 792-809 Tamm CO (1991). Nitrogen in terrestrial systems. Springer-Verlag, Berlin Tarnocai C, Canadell JG, Schuur EAG, Kuhry P, Mazhitova G, Zimov S (2009). Soil organic carbon pools in the northern circumpolar permafrost region. Global Biogeochemical Cycles, 23 (2): GB2023 Taylor DL, Bruns TD, Leake JR, Read DJ. (2002). Mycorrhizal specificity and function in mycoheterotrophic plants. MGA van der Heijden, IR Sanders (Eds), The Ecology of Mycorrhizas, Springer-Verlag, Berlin, Germany (2002): 375-413 153 Tedersoo L, May TW, Smith ME (2010). Ectomycorrhizal lifestyle in fungi: global diversity, distribution, and evolution of phylogenetic lineages. Mycorrhiza, 20: 217–263 Thormann MN (2001). The fungal communities of decomposing plants in southern boreal peatlands of Alberta, Canada. PhD thesis, University of Alberta, Edmonton, AB, Canada Thormann MN, Currah RS, Bayley SE (2002). The relative ability of fungi from Sphagnum fuscum to decompose selected carbon substrates. Canadian Journal of Microbiology, 48(3): 204-211 Tinker PB, Durall, DM and Jones, MD (1994). Carbon use efficiency in mycorrhizas: theory and sample calculations. New Phytologist, 128(1): 115-122 Torronen A, Rouvinen J (1997). Structural and functional properties of low molecular weight endo-1,4-β-xylanases. Journal of Biotechnology, 57: 137–149 Treangen TJ, Salzberg SL (2011). Repetitive DNA and next-generation sequencing: computational challenges and solutions. Nature reviews. Genetics, 13(1): 36–46 Tsuneda A, Thormann MN, Currah RS (2001). Modes of cell-wall degradation of Sphagnum fuscum by Acremonium cf. curvulum and Oidiodendron maius. Canadian Journal of Botany, 79(1): 93-100 Upson R, Read DJ, Newsham KK (2007). Widespread association between the ericoid mycorrhizal fungus Rhizoscyphus ericae and a leafy liverwort in the maritime and sub-Antarctic. New Phytologist, 176: 460–471 van den Brink J, de Vries RP (2011). Fungal enzyme sets for plant polysaccharide degradation. Applied Microbiology and Biotechnology, 91: 1477–1479 Valasek M, Repa J (2005). The power of real-time PCR. Advances in Physiology Education, 29: 151-159 Vallino M, Martino E, Boella F, Murat C, Chiapello M, Perotto S (2009). Cu, Zn superoxide dismutase and zinc stress in the metal-tolerant ericoid mycorrhizal fungus Oidiodendron maius Zn. FEMS Microbiology Letters, 293: 48–57 Vallino M, Drogo V, Abba S, Perotto S (2005). Gene expression of the ericoid mycorrhizal fungus Oidiodendron maius in the presence of high zinc concentrations. Mycorrhiza, 15(5): 333–344 Vandesompele J, DePreter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F (2002). Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology, 3: r0034.1 Vasina DV, Moiseenko KV, Fedorova TV, Tyazhelova TV (2017). Lignin-degrading peroxidases in white-rot fungus Trametes hirsuta 072. Absolute expression quantification of full multigene family. PLOS ONE, 12(3): e0173813 154 Villarreal-Ruiz L, Neri-Luna C, Anderson IC, Alexander IJ (2012). In vitro interactions between ectomycorrhizal fungi and ericaceous plants. Symbiosis, 56(2): 67-75 Vohník M, Albrechtová J, Vosátka M (2005). The inoculation with Oidiodendron maius and Phialocephala fortinii alters phosphorus and nitrogen uptake, foliar C:N ratio and root biomass distribution in Rhododendron cv. Azurro. Symbiosis, 40(2): 87-96 Vohník M, Sadowsky JJ, Kohout P, Lhotakova Z, Nestby R, Kolarik M (2012). Novel root-fungus symbiosis in Ericaceae: Sheathed ericoid mycorrhiza formed by a hitherto undescribed Basidiomycete with affinities to Trechisporales. Plos One, 7(6), e39524 Vohník M, Mrnka L, Lukešová T, Bruzone MC, Kohout P, Fehrer J (2013) The cultivable endophytic community of Norway spruce ectomycorrhizas from microhabitats lacking ericaceous hosts is dominated by ericoid mycorrhizal Meliniomyces variabilis. Fungal Ecology, 6(4): 281-292 Vohník M, Pánek M, Fehrer J, Selosse M-A (2016). Experimental evidence of ericoid mycorrhizal potential within Serendipitaceae (Sebacinales). Mycorrhiza, 26(8): 831-846 Vrålstad T, Fossheim T, Schumacher T (2000). Piceirhiza bicolorata - The ectomycorrhizal expression of the Hymenoscyphus ericae aggregate? New Phytologist, 145: 549 – 563 Vu VV, Beeson WT, Span EA, Farquhar ER, and Marletta MA (2014) A family of starch-active polysaccharide monooxygenases. Proceedings of the National Academy of Sciences, 111: 13822-13827 Wang Y, Song JZ, Yang Q, Liu ZH, Huang XM, ChenY (2010). Cloning of a heat-stable chitin deacetylase gene from Aspergillus nidulans and its functional expression in Escherichia coli. Applied Biochemistry and Biotechnology, 162(3): 843-854 Warrington JA, Nair A, Mahadevappa M, Tsyganskaya M (2000). Comparison of human adult and fetal expression and identification of 535 housekeeping/maintenance genes. Physiological Genomics, 2: 143–147 Watkinson SC (2016). Chapter 7 - Mutualistic Symbiosis Between Fungi and Autotrophs, In: The Fungi 3rd Edition: 205-243, Academic Press, Boston Webb EC (1992). Enzyme nomenclature 1992: Recommendations of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology on the nomenclature and classification of enzymes. 6th Edition. Academic Press Weijers RN (1977). Amino acid sequence in bovine serum albumin. Clinical Chemistry, 23(7): 1361-1362 Whale AS, Huggett JF, Cowen S, Speirs V, Shaw J, Ellison S, Foy CA, Scott DJ (2012). Comparison of microfluidic digital PCR and conventional quantitative PCR for measuring copy number variation. Nucleic Acids Research, 40(11): e82 Wong DWS (2006). Feruloyl esterase—a key enzyme in biomass degradation. Applied Biochemistry and Biotechnology, 133: 87–112 155 Worrall JJ, Anagnost SE, Zabel RD (1997). Comparison of wood decay among diverse lignicolous fungi. Mycologia, 89: 199–219 Wu B, Gaskell J, Held BW, Toapanta C, Vuong T, Ahrendt S, Lipzen A, Zhang J, Schilling JS, Master E, Grigoriev IV, Blanchette RA, Cullen D, Hibbett DS (2018). Substrate-Specific Differential Gene Expression and RNA Editing in the Brown Rot Fungus Fomitopsis pinicola. Applied and Environmental Microbiology, 84(16): e00991-18 Wurzburger N, Higgins BP, Hendrick RL (2012). Ericoid mycorrhizal root fungi and their multicopper oxidases from a temperate forest shrub. Ecology and Evolution, 2(1): 65-79 Wyatt GAK, Kiers ET, Gardner A, West SA (2014). A biological market analysis of the plant-mycorrhizal symbiosis. Evolution, 68(9): 2603-2618 Xiao G, Berch SM (1996). Diversity and abundance of ericoid mycorrhizal fungi of Gaultheria shallon on forest clearcuts. Canadian Journal of Botany, 74: 337-346 Yen M-T, Yang J-H, Mau J-L (2009). Physicochemical characterization of chitin and chitosan from crab shells. Carbohydrate Polymers, 75(1):15-21 Yin D, Urresti S, Lafond M, Johnston EM, Derikvand F, Ciano L, Berrin J-G, Henrissat B, Walton PH, Davies GJ, Brumer H (2015). Structure–function characterization reveals new catalytic diversity in the galactose oxidase and glyoxal oxidase family. Nature Communications, 6: 10197 Youn HD, Hah YC, Kang SO (1995). Role of laccase in lignin degradation by white-rot fungi. FEMS Microbiol Letters, 132: 183–188 Yu TE, Egger KN, Peterson RL (2001). Ectendomycorrhizal associations—characteristics and functions. Mycorrhiza, 11: 167-177 Zaiontz C. (2020) Real Statistics Using Excel. www.real-statistics.com Zak B (1974). Ectendomycorrhiza of Pacific madrone Arbutus menziessi. Transactions of the British Mycological Society, 62: 202-204 Zhang C, Yin L, Dai S (2009). Diversity of root-associated fungal endophytes in Rhododendron fortunei in subtropical forests of China. Mycorrhiza, 19: 417–423 Zhang L, Jing X, Chen W, Bai J, Vasseur L, He W, You M (2019). Selection of reference genes for expression analysis of plant-derived microRNAs in Plutella xylostella using qRT-PCR and ddPCR. PLoS ONE, 14(8): e0220475 Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, Busk PK, Xu Y, Yin Y (2018). dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Research, 46(W1): W95–W101 Zhao Z, Liu H, Wang C, Xu J (2013). Comparative analysis of fungal genomes reveals different plant cell wall degrading capacity in fungi. BMC Genomics, 14: 274 156 Zmienko A, Samelak-Czajka A, Goralski M, Sobieszczuk-Nowicka E, Kozlowski P, Figlerowicz M (2015). Selection of reference genes for qPCR- and ddPCR-based analyses of gene expression in senescing barley leaves. PLoS One, 10: e0118226 Zverlov VV, Hertel C, Bronnenmeier K, Hroch A, Kellermann J, Schwarz WH (2000). The thermostable α‐L‐rhamnosidase RamA of Clostridium stercorarium: biochemical characterization and primary structure of a bacterial α‐L‐rhamnoside hydrolase, a new type of inverting glycoside hydrolase. Molecular Microbiology, 35: 173-179 157 Appendices Appendix A Table A.1. CAZyme families contained in the Oidiodendron maius genome. CAZyme Family (_Subfamily) Number of Genes Substrate (If known) Putative Function GH1 13 Cellulose β-1,4-glucosidase GH2 19 Cellulose; Hemicellulose (Xyloglucan; Xylan; Galactomannan); Pectin β-1,4-galactosidase, β-glucuronidase, β-1,4-mannosidase GH3 35 Cellulose; Hemicellulose (Xyloglucan; Xylan); Pectin β-1,4-glucosidase, β-1,4-galactosidase, β-1,4-xylosidase GH5 20 Cellulose; Hemicellulose (Galactomannan); Pectin; endo-β-1,4-glucanase, β-1,4-galactosidase, endo-β-1,4-mannanase GH6 2 Cellulose; Hemicellulose (Xyloglucan; Xylan) cellobiohydrolase GH7 8 Cellulose; Glucan cellobiohydrolase; endo-β-1,4-glucanase GH10 2 Hemicellulose (Xylan) endo-β-1,4-xylanase GH11 8 Hemicellulose (Xylan) endo-β-1,4-xylanase; endo-β-1,3-xylanase GH12 7 Cellulose; Hemicellulose (Xyloglucan) endo-β-1,4-glucanase; xyloglucan endo-β-1,4-glucanase GH13 5 Starch α-amylase (glycogen branching enzyme; α-glucosidase?) GH15 4 Starch glucoamylase GH16 16 Glucan; Hemicellulose endo-1,3-1,4-β-glucanase GH17 6 Glucan endo-β-1,3-glucanase GH18 27 Chitin chitinase GH20 4 Chitin β-N-acetylhexosaminidase GH23 1 Chitin chitinase GH24 5 lysozyme GH25 2 lysozyme GH27 21 Hemicellulose (Xyloglucan; Xylan; Galactomannan) α-1,4-galactosidase ( α-N-acetylgalactosaminidase?) GH28 24 Pectin endo/exo-(rhamno)polygalacturonase GH29 6 Hemicellulose (Xyloglucan) α-L-fucosidase GH30 7 Glucan; Hemicellulose (Xylan) β-1,6-glucanase; endo-β-1,6-galactanase; glucuronoxylan hydrolase (xylanase) 158 CAZyme Family (_Subfamily) Number of Genes Substrate (If known) Putative Function GH31 13 Cellulose; Hemicellulose (Xyloglucan); Starch α-xylosidase; α-1,4-glucosidase GH32 5 Inulin inulinase (invertase) GH33 1 sialidase GH35 10 Cellulose β-galactosidase GH36 4 Hemicellulose (Xyloglucan; Xylan; Galactomannan) α-1,4-galactosidase GH37 1 Trehalose α,α-trehalase GH38 3 Hemicellulose α-mannosidase GH39 5 Hemicellulose β-xylosidase GH43 16 Hemicellulose (Xylan); Pectin β-1,4-xylosidase, endo-α-1,5-L-arabinosidase GH45 4 Cellulose endo-β-1,4-glucanase GH47 13 Hemicellulose α-mannosidase GH49 1 dextranase; isopullulanase GH51 9 Hemicellulose (Xyloglucan; Xylan); Pectin α-L-arabinofuranosidase GH53 2 Pectin endo-β-1,4-galactanase GH54 6 Hemicellulose (Xyloglucan; Xylan); Pectin α-L-arabinofuranosidase GH55 20 Glucan β-1,3-glucanase GH62 4 Hemicellulose (Xylan) arabinoxylan α-L-arabinofuranosidase GH63 2 Starch α-glucosidase GH64 6 Glucan; Hemicellulose β-1,3-glucanase GH65 3 trehalase GH67 1 Xylan α-glucuronidase GH71 6 Glucan; Hemicellulose α-1,3-glucanase GH72 8 glucanosyltransferase GH74 1 xyloglucan endo-β-1,4-glucanase GH75 3 chitosanase GH76 14 Hemicellulose α-1,6-mannanase GH78 15 Pectin α-L-rhamnosidase GH79 17 Hemicellulose β-glucuronidase GH81 2 Glucan; Hemicellulose endo-β-1,3-glucanase GH88 4 Hemicellulose; Pectin d-4,5-unsaturated β-glucuronyl hydrolase GH89 2 α-N-acetylglucosaminidase GH92 9 Hemicellulose α-mannosidase 159 CAZyme Family (_Subfamily) Number of Genes Substrate (If known) Putative Function GH93 5 Hemicellulose; Pectin exo-α-L-1,5-arabinanase GH95 8 Hemicellulose (Xyloglucan) α-L-fucosidase GH105 3 Pectin unsaturated rhamnogalacturonyl hydrolase GH114 1 endo-α-1,4-polygalactosaminidase GH115 2 Hemicellulose (Xylan) α-glucuronidase GH125 3 Metal-independent exo-α-1,6-mannosidase GH127 2 Hemicellulose β-L-arabinofuranosidase GH128 6 Glucan; Hemicellulose β-1,3-glucanase GH130 1 Hemicellulose β-1,4-mannooligosaccharide phosphorylase GH131 2 Cellulose; Glucan; Hemicellulose exo-β-1,3/1,6-glucanase GH132 2 Glucan; Hemicellulose exo-β-1,3-glucanase GH133 (formerly GH13_25) 2 Hemicellulose amylo-α-1,6-glucosidase GH135 5 α-1,4-galactosaminogalactan hydrolase GT1 16 Various Various GT2 20 UDP-N-acetyl-α-D-glucosamine chitin synthase GT 3 1 UDP-α-D-glucose + [(1→4)-α-D-glucosyl]n glycogen synthase GT4 7 glycosylphosphatidylinositol anchor biosynthesis GT8 10 lipopolysaccharide glycosyltransferase GT15 3 mannose glycolipid 2--mannosyltransferase GT17 1 N-glycans β-1,4-mannosyl-glycoprotein β-1,4-N-acetylglucosaminyltransferase GT20 4 α,α-trehalose 6-phosphate trehalose phosphatase GT21 1 UDP-α-D-glucose + a sphingosine ceramide glucosyltransferase GT22 4 dolichol-P-mannose Alg9-like mannosyltransferase family GT 24 1 [(1→4)-α-D-glucosyl]n UDP-glucose:Glycoprotein Glucosyltransferase GT25 4 lipopolysaccharide biosynthesis protein GT31 10 glycogen phosphorylase GT32 13 various various 160 CAZyme Family (_Subfamily) Number of Genes Substrate (If known) Putative Function GT33 1 mannose chitobiosyldiphosphodolichol β-mannosyltransferase GT34 2 galactosyl transferase GMA12/MNN10 family GT35 1 glycogen; starch glycogen or starch phosphorylase GT39 3 dolichyl β-D-mannosyl phosphate dolichyl-phosphate-mannose-protein mannosyltransferase GT41 1 UDP-N-acetyl-α-D-glucosamine peptide N-β-glucosyltransferase GT48 3 UDP-glucose + [(1→3)-β-D-glucosyl]n 1,3-β-glucan synthase GT50 1 dolichol-P-mannose Dol-P-Man α-1,4-mannosyltransferase GT57 2 dolichol-P-glucose α-1,3-glucosyltransferase GT58 1 dolichol-P-mannose α-1,3-mannosyltransferase GT 59 1 dolichol-P-glucose α-1,2-glucosyltransferase GT 61 1 N-glycans β-1,2-xylosyltransferase GT 62 3 mannosyltransferase GT 66 1 Glc3Man9GlcNAc2 Oligosaccharyl transferase GT 69 2 α-1,3-mannosyltransferase GT 71 4 α-mannosyltransferase GT 76 1 dolichol-P-mannose α-1,6-mannosyltransferase GT 90 3 CE1 5 Hemicellulose (Xylan); Pectin acetyl xylan/feruloyl esterase CE2 1 Hemicellulose acetyl xylan esterase CE3 5 wide substrate specificity acetyl xylan esterase CE4 3 Chitin; Hemicellulose (Xylan) polysaccharide deacetylase CE5 6 Cellulose; Hemicellulose (Xylan) cutinase CE8 5 Pectin pectin methyl esterase CE9 1 (aminosugars metabolism) amidohydrolase family CE12 3 Pectin rhamnogalacturonan acetyl esterase (pectin acetylesterase; ; acetyl xylan esterase) CE15 2 Lignin/hemicellulose covalent linkages glucuronyl esterase CE16 6 Hemicellulose acetylesterase PL1 1 Pectin pectin lyase; pectate lyase PL1_4 1 Pectin pectin Lyase PL1_7 1 Pectin Pectate lyase PL4_3 1 Pectin rhamnogalacturonan endolyase 161 CAZyme Family (_Subfamily) Number of Genes Substrate (If known) Putative Function PL4_5 1 Pectin rhamnogalacturonan endolyase PL7_4 6 [Alginate] poly(β-mannuronate) lyase; alginate lyase; α-L-guluronate lyase PL20 1 Cellouronate endo-β-1,4-glucuronan lyase AA1 8 Aromatics; Diamines; Phenols multicopper oxidase (MCO) AA1_2 3 Aromatics; Diamines; Phenols multicopper oxidase (MCO) AA1_3 15 Aromatics; Diamines; Phenols multicopper oxidase (MCO) AA3_2 20 Various, including aromatics GMC oxidoreductase AA3_3 2 Various, including aromatics GMC oxidoreductase AA5_1 3 Various, including aromatics glyoxal oxidase AA5_2 1 Various, including aromatics galactose oxidase; alcohol oxidase AA6 2 Lignin; radical protection 1,4-benzoquinone reductase (flavodoxin) AA7 1 Various, including aromatics glucooligosaccharide oxidase; chitooligosaccharide oxidase AA8 (AA3_1) 2 Cellobiose iron reductase domain; cellobiose dehydrogenase AA9 (formerly GH61) 8 Cellulose lytic polysaccharide monooxygenases (LPMOs) AA11 2 Chitin lytic polysaccharide monooxygenases (LPMOs) AA12 1 Various, including aromatics oxidoreductase EXPN 5 swollenins; distantly related to plant expansins 162 Appendix B Figure B.1. Oidiodendron maius growth at 30 days on A) MMN + BSA B) MMN + Peat. Growth of O. maius on cellulose membranes resulted in degradation, especially on the peat treatment plates. The degradation of these cellulose membranes may indicate exaggerated expression of cellulose enzymes; alternatively, the degradation could have been from oxidative enzymes. Another possibility is that the cellulose membranes could have been degraded early to better access the carbon rich peat, which would have provided the nutrition at the time of harvest; on the non-peat treatments, the cellulose membrane could have been in early stages of degradation without being visibly physically damaged. 163 Appendix C Table C.1. Shortlist of 251 reference gene candidates for Oidiodendron maius based on constant relative expression across five treatments Transcript ID Protein ID Fold Change MYC : FLM Baggerley's MYC : FLM FLM Means (Kohler et al. 2015) MYC Means (Kohler et al. 2015) Fold Change Peat : NH4 Baggerley's Peat : NH4 Fold Change Peat : BSA Baggerley's BSA : Peat Fold Change NH4 : BSA Baggerley's BSA : NH4 NH4 - Means Peat - Means BSA - Means Protein Length 16093 16018 0.84 0.71 21.94 18.36 0.97 1.00 1.10 0.95 1.14 0.83 17.78 17.18 15.66 247 16990 16915 0.90 0.61 112.27 101.59 0.95 1.00 0.91 1.00 0.95 0.97 86.39 82.39 90.96 386 17127 17052 0.85 0.11 192.90 164.30 0.87 0.52 0.98 0.99 1.13 0.25 157.78 136.85 139.41 330 17713 17638 1.02 1.00 96.38 98.76 0.98 1.00 1.05 0.71 1.07 0.57 154.84 151.07 144.54 323 18844 18769 1.18 0.38 392.33 462.38 0.86 0.20 0.90 0.87 1.04 0.53 349.18 301.63 335.17 230 18847 18772 1.04 0.98 736.59 769.26 1.06 0.76 1.06 0.75 1.00 0.82 633.68 670.41 633.17 184 19742 19667 0.81 0.70 68.38 55.47 0.98 1.00 1.06 1.00 1.08 0.96 43.61 42.71 40.21 173 20376 20301 1.09 0.69 846.96 922.50 0.87 0.78 0.94 1.00 1.08 0.50 1341.93 1172.94 1243.98 182 21080 21005 1.18 0.69 18.92 22.30 0.96 1.00 1.04 0.95 1.08 0.75 69.21 66.15 63.90 113 21375 21300 0.84 0.43 306.06 256.30 0.90 0.57 1.05 0.43 1.17 0.02 293.69 263.47 250.60 190 22303 22228 1.03 1.00 47.22 48.56 1.02 0.99 1.00 1.00 0.98 1.00 36.51 37.20 37.15 220 22925 22850 1.03 1.00 32.44 33.38 0.93 1.00 0.97 1.00 1.03 0.93 42.92 40.12 41.56 523 22961 22886 0.98 0.97 25.20 24.75 0.91 0.98 0.95 1.00 1.04 0.96 26.88 24.50 25.88 305 23491 23416 1.12 0.81 46.49 52.21 0.94 1.00 0.95 1.00 1.01 0.95 57.25 53.78 56.46 535 24373 24298 0.84 0.54 38.86 32.57 0.87 0.94 0.88 0.97 1.01 1.00 51.37 44.55 50.88 246 24451 24376 0.96 0.68 73.89 70.65 0.94 0.99 0.86 0.88 0.91 0.85 80.06 75.42 87.63 308 24839 24764 0.90 0.65 35.12 31.66 0.99 1.00 1.04 1.00 1.05 0.94 19.97 19.68 18.99 876 24933 24858 1.07 0.98 24.27 25.95 1.02 0.98 1.04 0.93 1.02 0.94 48.55 49.42 47.36 253 25581 25506 0.84 0.31 42.00 35.40 0.99 1.00 1.15 0.52 1.16 0.43 70.40 69.67 60.64 479 28436 28361 0.82 0.80 15.22 12.51 0.94 1.00 0.90 1.00 0.95 0.99 26.76 25.25 28.17 306 28770 28695 0.87 0.74 68.85 59.58 0.86 0.88 1.00 1.00 1.16 0.70 77.89 66.98 67.07 249 32002 31927 0.94 0.69 44.30 41.45 1.13 0.71 1.03 0.95 0.91 0.91 46.05 51.86 50.37 379 32032 31957 1.15 0.84 23.41 27.04 0.99 1.00 1.00 1.00 1.01 0.99 28.55 28.15 28.24 417 36628 36553 1.12 0.89 15.04 16.84 1.03 1.00 0.96 1.00 0.94 0.99 15.48 15.89 16.55 489 36994 36919 1.04 1.00 15.75 16.43 1.01 1.00 1.05 0.98 1.03 0.96 27.14 27.54 26.32 363 37961 37886 1.16 0.67 40.52 46.96 1.01 0.99 1.00 1.00 0.99 1.00 53.12 53.74 53.64 314 38337 38262 0.86 0.58 55.62 47.98 1.06 0.92 0.96 1.00 0.91 0.90 37.83 39.94 41.51 415 38392 38317 0.89 0.48 108.62 96.19 0.92 0.90 1.07 0.73 1.16 0.27 113.47 103.96 97.60 437 39411 39336 1.16 0.44 66.20 76.93 0.97 1.00 0.94 1.00 0.97 1.00 49.92 48.33 51.36 373 40095 40020 1.16 0.68 24.87 28.86 1.10 0.90 1.10 0.93 1.00 1.00 19.35 21.36 19.41 524 40314 40239 0.87 0.30 131.95 115.14 0.94 0.98 0.87 0.94 0.93 0.94 88.90 83.14 95.15 507 41030 40955 0.86 0.26 86.79 75.07 0.95 1.00 0.95 1.00 1.00 1.00 158.95 151.76 159.33 258 41734 41659 1.03 1.00 36.76 38.01 0.95 1.00 0.95 1.00 1.00 0.98 44.30 42.00 44.14 852 164 Transcript ID Protein ID Fold Change MYC : FLM Baggerley's MYC : FLM FLM Means (Kohler et al. 2015) MYC Means (Kohler et al. 2015) Fold Change Peat : NH4 Baggerley's Peat : NH4 Fold Change Peat : BSA Baggerley's BSA : Peat Fold Change NH4 : BSA Baggerley's BSA : NH4 NH4 - Means Peat - Means BSA - Means Protein Length 42161 42086 0.97 0.76 188.15 181.83 0.91 0.73 1.03 0.70 1.13 0.12 253.75 231.10 225.15 175 42250 42175 0.81 0.19 47.91 38.89 0.88 0.86 1.02 0.96 1.17 0.53 61.58 54.00 52.77 314 45501 45426 0.85 0.00 278.88 236.83 1.00 0.97 0.88 0.82 0.89 0.55 217.47 217.00 245.42 414 47645 47570 0.80 0.38 21.57 17.32 0.92 1.00 0.88 1.00 0.96 1.00 18.67 17.18 19.52 537 48409 48334 1.09 0.97 17.55 19.14 0.86 0.97 0.89 1.00 1.04 0.98 17.08 14.64 16.40 247 49613 49538 1.11 0.90 48.74 54.32 1.09 0.87 1.08 0.90 0.99 1.00 30.15 32.85 30.35 768 49614 49539 0.98 0.87 78.56 76.77 1.04 0.95 0.91 1.00 0.87 0.81 38.06 39.73 43.53 610 50182 50107 0.81 0.08 102.63 82.63 0.94 1.00 0.91 1.00 0.97 1.00 55.61 52.23 57.27 470 50357 50282 1.02 1.00 113.20 114.96 1.13 0.38 0.98 0.99 0.87 0.53 113.34 127.75 129.98 648 50726 50651 0.84 0.48 23.64 19.92 0.93 1.00 1.01 1.00 1.08 0.89 25.46 23.72 23.60 490 50865 50790 1.07 0.86 64.95 69.48 1.04 0.91 1.08 0.79 1.04 0.88 67.95 70.33 65.39 543 51771 51696 0.84 0.63 66.04 55.57 1.00 1.00 1.12 0.97 1.12 0.79 75.61 75.35 67.25 214 51868 51793 0.82 0.42 21.56 17.58 1.10 0.91 1.04 1.00 0.94 0.98 17.69 19.48 18.77 425 53619 53544 1.07 0.93 38.20 41.00 0.96 1.00 0.88 0.99 0.91 0.91 43.77 42.00 47.87 341 54052 53977 0.93 0.59 63.47 58.75 1.14 0.51 1.07 0.80 0.94 0.94 66.76 76.22 71.38 708 55017 54942 0.83 0.26 40.99 33.89 0.90 0.95 1.02 0.99 1.14 0.71 33.20 29.74 29.07 223 57769 57694 0.94 0.82 48.73 45.57 0.93 1.00 0.87 0.97 0.93 0.95 43.41 40.58 46.57 567 60118 60043 0.95 0.82 42.67 40.68 0.96 1.00 1.02 0.94 1.07 0.94 66.15 63.47 61.97 184 64195 64120 1.17 0.63 59.84 70.07 1.02 0.94 0.95 1.00 0.93 0.92 68.47 69.99 73.33 402 82333 82258 1.01 0.99 48.73 49.30 1.05 0.95 0.94 1.00 0.89 0.90 28.09 29.61 31.46 364 83504 83429 0.94 0.79 68.03 64.28 1.02 0.98 0.91 1.00 0.89 0.83 55.11 56.04 61.60 663 99951 99876 0.95 0.82 48.55 46.26 0.96 1.00 1.07 0.86 1.12 0.67 67.34 64.74 60.24 361 100148 100073 1.12 0.84 25.64 28.59 0.97 1.00 1.09 0.93 1.12 0.81 24.44 23.78 21.75 786 100575 100500 1.04 1.00 45.94 47.73 0.98 1.00 1.07 0.92 1.10 0.80 33.45 32.64 30.37 596 101034 100959 1.09 0.93 22.75 24.73 0.96 1.00 1.05 1.00 1.09 0.90 18.81 18.14 17.31 505 101240 101165 0.90 0.66 43.69 39.45 0.92 0.97 1.02 0.99 1.11 0.73 44.30 40.60 39.88 432 101506 101431 0.93 0.58 79.47 73.76 0.96 1.00 0.87 0.96 0.90 0.86 46.27 44.65 51.53 636 101861 101786 0.96 0.92 93.11 89.21 1.15 0.70 1.15 0.71 1.00 0.99 38.67 44.29 38.60 573 101886 101811 0.82 0.63 47.46 38.86 1.03 0.93 0.96 1.00 0.93 0.92 60.95 62.95 65.30 292 101933 101858 0.81 0.09 110.34 89.60 0.94 0.99 1.04 0.92 1.11 0.64 62.97 59.00 56.73 428 102334 102259 1.12 0.85 71.92 80.28 1.15 0.69 1.07 0.90 0.93 0.97 35.15 40.33 37.64 530 102649 102574 0.99 0.99 23.91 23.72 1.12 0.88 1.05 0.99 0.94 0.99 18.28 20.46 19.40 1066 102957 102882 1.18 0.84 16.09 19.05 0.93 1.00 1.07 0.98 1.15 0.80 19.30 18.00 16.75 337 103072 102997 0.98 0.86 46.19 45.35 1.03 0.97 0.96 1.00 0.93 0.95 32.15 33.21 34.59 785 103269 103194 1.16 0.70 51.29 59.60 1.03 0.97 1.00 1.00 0.97 1.00 32.52 33.51 33.58 596 103282 103207 1.12 0.80 64.81 72.72 0.97 1.00 0.89 0.99 0.92 0.91 58.78 56.77 63.85 449 165 Transcript ID Protein ID Fold Change MYC : FLM Baggerley's MYC : FLM FLM Means (Kohler et al. 2015) MYC Means (Kohler et al. 2015) Fold Change Peat : NH4 Baggerley's Peat : NH4 Fold Change Peat : BSA Baggerley's BSA : Peat Fold Change NH4 : BSA Baggerley's BSA : NH4 NH4 - Means Peat - Means BSA - Means Protein Length 103739 103664 1.02 1.00 42.60 43.56 1.04 0.93 0.95 1.00 0.92 0.92 45.16 47.00 49.24 772 103949 103874 1.19 0.26 81.75 97.28 1.06 0.85 1.13 0.62 1.07 0.81 57.79 61.39 54.13 710 104141 104066 0.91 0.62 46.21 41.86 0.87 0.89 0.96 1.00 1.10 0.79 35.76 31.23 32.41 646 104180 104105 0.97 0.81 60.78 58.82 0.90 0.94 1.00 1.00 1.11 0.74 46.12 41.69 41.56 355 104201 104126 1.00 1.00 28.90 28.93 1.01 1.00 0.96 1.00 0.95 0.98 24.80 25.03 26.18 434 105370 105295 1.02 1.00 58.36 59.74 0.91 0.94 0.85 0.90 0.94 0.95 63.78 58.01 67.95 357 105637 105562 1.02 1.00 27.54 27.97 0.93 1.00 1.07 0.95 1.15 0.74 27.59 25.72 24.03 534 106149 106074 1.05 1.00 27.79 29.21 0.89 0.94 1.03 0.99 1.17 0.68 30.23 26.82 25.94 348 106633 106558 1.10 0.60 163.71 180.18 1.00 0.94 0.91 1.00 0.91 0.86 158.64 159.38 174.64 423 106729 106654 0.80 0.03 92.71 74.50 1.10 0.70 1.11 0.72 1.00 0.98 60.50 66.63 60.21 335 107427 107352 0.91 0.87 68.52 62.05 1.14 0.59 1.05 0.88 0.93 0.92 54.62 62.26 59.03 240 107884 107809 1.19 0.78 35.39 41.99 0.99 1.00 0.96 1.00 0.97 1.00 36.95 36.58 37.94 630 108318 108243 0.96 0.75 84.66 81.62 0.93 0.98 1.08 0.81 1.15 0.47 83.80 78.25 72.60 482 109255 109180 1.19 0.63 24.20 28.91 1.03 0.99 1.07 0.99 1.04 0.97 16.86 17.42 16.28 1266 111020 110945 0.87 0.57 42.03 36.62 0.96 1.00 0.86 0.99 0.89 0.91 19.75 18.97 22.07 410 111804 111729 1.12 0.79 72.55 80.91 1.05 0.92 1.04 0.95 0.99 1.00 40.60 42.78 41.12 552 112220 112145 1.13 0.60 151.25 170.21 1.03 0.89 0.92 1.00 0.90 0.69 114.61 117.53 127.17 536 112294 112219 0.81 0.00 209.26 170.26 0.94 1.00 0.89 0.85 0.95 0.98 331.47 310.74 347.78 328 112917 112842 1.13 0.43 149.57 169.03 1.05 0.85 1.10 0.53 1.05 0.69 224.07 234.52 212.67 181 113359 113284 0.85 0.20 162.29 138.13 1.14 0.46 1.07 0.74 0.94 0.95 77.40 88.34 82.24 709 113952 113877 1.07 0.99 63.96 68.70 1.01 1.00 0.97 1.00 0.96 1.00 50.94 51.46 53.31 489 115526 115451 1.00 0.97 93.12 93.15 0.96 1.00 1.04 0.97 1.09 0.85 86.97 83.45 80.05 236 117252 117177 0.95 0.83 62.27 59.05 1.06 0.84 1.02 0.95 0.96 1.00 64.61 68.45 67.13 384 117268 117193 0.99 0.94 107.63 107.02 0.94 1.00 0.97 1.00 1.03 0.93 118.00 110.83 114.58 390 117526 117451 0.94 0.85 34.23 32.27 1.00 1.00 0.92 1.00 0.91 0.92 31.12 31.17 34.02 452 117700 117625 1.13 0.89 128.19 144.42 0.95 0.99 1.01 1.00 1.06 0.87 242.06 230.52 227.74 204 118077 118002 1.18 0.74 196.83 232.36 0.97 1.00 1.01 0.93 1.04 0.78 132.91 128.44 127.75 378 118276 118201 0.93 0.89 50.78 47.33 0.98 1.00 0.86 0.92 0.87 0.72 52.90 52.03 60.83 270 118922 118847 1.09 0.79 89.39 97.81 0.88 0.82 1.01 1.00 1.14 0.61 76.14 67.29 66.70 330 120006 119931 0.80 0.60 27.52 22.15 0.87 0.94 0.89 1.00 1.02 0.98 28.15 24.62 27.73 513 122040 121965 1.12 0.77 53.29 59.77 0.88 0.83 0.89 0.98 1.01 0.96 60.94 53.36 60.28 329 123031 122956 0.85 0.57 53.62 45.67 1.13 0.82 1.10 0.56 0.98 1.00 89.61 100.95 91.54 289 125014 124939 1.04 0.99 74.66 77.88 0.92 0.92 0.87 0.92 0.95 0.97 93.22 85.48 97.88 495 125901 125826 1.02 1.00 75.21 76.76 1.04 0.94 0.91 1.00 0.87 0.80 52.65 54.56 60.25 397 125996 125921 0.99 0.87 90.84 90.10 1.08 0.77 1.02 0.93 0.95 0.97 69.93 75.22 73.68 1421 126972 126897 0.82 0.13 107.34 88.23 0.94 0.99 1.01 0.94 1.08 0.79 102.93 96.27 95.25 283 166 Transcript ID Protein ID Fold Change MYC : FLM Baggerley's MYC : FLM FLM Means (Kohler et al. 2015) MYC Means (Kohler et al. 2015) Fold Change Peat : NH4 Baggerley's Peat : NH4 Fold Change Peat : BSA Baggerley's BSA : Peat Fold Change NH4 : BSA Baggerley's BSA : NH4 NH4 - Means Peat - Means BSA - Means Protein Length 129214 129139 0.93 0.89 26.67 24.70 0.93 1.00 0.96 1.00 1.04 0.96 21.33 19.80 20.55 234 129507 129432 1.11 0.73 65.87 73.22 1.08 0.68 1.00 0.99 0.93 0.95 98.89 106.45 106.22 249 129913 129838 1.03 1.00 24.34 25.07 0.88 0.98 0.96 1.00 1.10 0.88 24.26 21.31 22.14 396 132655 132580 1.18 0.73 21.61 25.43 1.11 0.89 1.10 0.94 0.99 1.00 19.89 22.01 20.04 721 135304 135229 1.17 0.82 31.08 36.23 1.00 1.00 1.03 0.98 1.03 0.95 35.35 35.48 34.43 510 137551 137476 0.89 0.78 32.71 29.03 0.92 0.99 1.08 0.94 1.17 0.69 27.18 25.02 23.22 634 140286 140211 0.88 0.46 98.72 87.19 0.87 0.70 0.96 1.00 1.10 0.84 117.82 102.81 107.38 201 140339 140264 1.03 1.00 61.99 63.60 1.03 0.92 1.14 0.84 1.10 0.85 74.43 76.63 67.50 537 140615 140540 0.99 0.99 40.06 39.77 0.94 1.00 0.88 1.00 0.94 0.97 51.64 48.72 55.12 588 141050 140975 1.09 0.89 20.22 21.97 0.98 1.00 0.94 1.00 0.96 1.00 20.85 20.46 21.67 948 141558 141483 0.84 0.67 46.01 38.75 0.87 0.79 1.00 0.99 1.16 0.48 63.90 55.45 55.24 358 141872 141797 0.82 0.31 60.06 49.36 1.06 0.84 1.04 0.89 0.99 1.00 66.09 69.90 66.93 309 142041 141966 0.86 0.66 75.88 65.15 0.87 0.74 0.93 1.00 1.07 0.76 84.91 73.88 79.55 404 142196 142121 1.19 0.55 78.95 93.72 0.99 1.00 0.87 0.92 0.89 0.71 83.29 82.15 93.91 381 142219 142144 0.92 0.59 124.96 114.35 0.91 0.96 0.91 1.00 1.00 0.98 77.78 70.88 77.50 363 142391 142316 1.02 1.00 49.32 50.31 0.99 1.00 0.98 1.00 0.99 1.00 41.21 40.74 41.50 582 142400 142325 1.11 0.86 32.11 35.64 1.06 0.88 0.96 1.00 0.91 0.92 62.27 66.04 68.50 635 142415 142340 0.95 0.91 33.44 31.79 0.86 0.92 0.93 1.00 1.08 0.90 21.95 18.84 20.25 634 142624 142549 1.12 0.83 59.20 66.10 0.98 1.00 1.08 0.83 1.09 0.79 62.88 61.93 57.56 644 143180 143105 0.92 0.77 32.69 30.14 1.05 0.97 1.13 0.89 1.08 0.91 21.22 22.27 19.73 713 143802 143727 1.20 0.42 118.48 142.02 1.08 0.49 1.12 0.23 1.03 0.80 160.44 173.60 155.56 588 143978 143903 0.83 0.41 51.90 43.27 0.93 1.00 0.90 1.00 0.97 1.00 51.38 47.99 53.07 356 144710 144635 1.12 0.80 52.28 58.79 0.97 1.00 1.08 0.87 1.12 0.69 45.22 43.65 40.39 417 145256 145181 0.96 0.86 35.25 33.99 0.89 0.90 1.01 1.00 1.14 0.66 50.10 44.37 44.12 324 145402 145327 0.91 0.54 88.39 80.12 1.05 0.93 0.95 1.00 0.90 0.90 45.17 47.44 50.00 710 145700 145625 0.93 0.60 57.18 53.11 1.14 0.64 1.09 0.83 0.95 0.99 44.65 50.99 46.94 763 146471 146396 0.93 0.83 21.32 19.75 0.87 0.93 0.92 1.00 1.05 0.93 24.92 21.78 23.66 632 147619 147544 0.92 0.65 55.65 51.29 0.97 1.00 0.98 1.00 1.01 0.96 58.12 56.30 57.44 375 147811 147736 0.97 0.95 24.00 23.26 1.03 0.99 1.01 1.00 0.98 1.00 15.31 15.82 15.66 621 149181 149106 1.00 0.97 57.21 57.32 0.86 0.81 0.87 0.95 1.01 0.98 47.72 41.04 47.30 682 149310 149235 1.04 1.00 31.47 32.84 0.97 1.00 1.01 1.00 1.05 0.94 23.71 22.89 22.57 565 149748 149673 0.85 0.66 33.95 28.94 0.93 1.00 0.88 1.00 0.94 0.97 25.27 23.50 26.84 597 149964 149889 1.03 1.00 82.57 85.08 1.05 0.85 0.99 1.00 0.94 0.94 82.30 86.23 87.23 322 150606 150531 0.88 0.68 39.03 34.19 1.06 0.94 0.95 1.00 0.90 0.92 23.20 24.58 25.75 408 151579 151504 0.80 0.05 156.59 125.91 1.05 0.82 1.02 0.90 0.98 1.00 99.48 104.12 101.78 455 151713 151638 1.02 1.00 36.20 36.80 1.06 0.96 1.03 1.00 0.97 1.00 20.99 22.22 21.67 1341 167 Transcript ID Protein ID Fold Change MYC : FLM Baggerley's MYC : FLM FLM Means (Kohler et al. 2015) MYC Means (Kohler et al. 2015) Fold Change Peat : NH4 Baggerley's Peat : NH4 Fold Change Peat : BSA Baggerley's BSA : Peat Fold Change NH4 : BSA Baggerley's BSA : NH4 NH4 - Means Peat - Means BSA - Means Protein Length 152069 151994 1.19 0.75 25.56 30.37 1.03 0.97 1.08 0.93 1.05 0.94 30.27 31.24 28.95 614 152111 152036 0.88 0.65 36.95 32.54 0.91 0.98 0.90 1.00 0.99 1.00 29.47 26.90 29.90 1119 152495 152420 0.93 0.75 77.24 71.59 0.99 1.00 0.90 1.00 0.91 0.85 70.43 69.82 77.42 398 152738 152663 1.07 0.91 33.70 36.12 0.92 0.98 0.88 0.99 0.95 1.00 33.83 31.01 35.43 1022 153440 153365 1.07 0.93 39.83 42.74 1.01 0.98 1.04 0.92 1.03 0.93 57.14 57.89 55.73 430 154524 154449 0.95 0.73 101.89 96.61 0.89 0.81 0.91 1.00 1.03 0.89 96.47 85.93 93.98 370 154876 154801 0.92 0.40 111.11 101.79 1.07 0.77 0.93 1.00 0.88 0.63 85.75 91.33 97.99 683 156343 156268 1.09 0.80 291.12 316.42 1.01 0.88 0.93 1.00 0.93 0.71 248.31 250.04 267.89 441 156597 156522 1.16 0.78 124.86 144.26 1.11 0.52 1.02 0.90 0.92 0.86 100.95 111.64 109.53 446 156926 156851 1.04 1.00 31.42 32.57 0.98 1.00 1.00 1.00 1.01 0.97 45.69 44.91 45.09 333 157214 157139 0.98 0.85 69.16 67.64 1.05 0.89 1.04 0.90 1.00 0.98 59.73 62.56 59.88 365 157248 157173 1.18 0.22 104.69 123.80 0.99 1.00 0.95 1.00 0.96 0.99 90.93 90.23 94.66 909 157854 157779 0.85 0.70 71.21 60.81 0.86 0.75 0.86 0.90 0.99 0.99 67.03 57.69 67.39 335 158379 158304 0.88 0.48 42.92 37.61 1.09 0.87 0.98 1.00 0.90 0.91 29.39 32.03 32.66 457 158688 158613 1.15 0.69 105.60 121.80 0.98 1.00 1.09 0.58 1.11 0.48 118.10 116.18 106.49 457 159205 159130 0.93 0.56 86.52 80.52 1.00 1.00 0.94 1.00 0.94 0.97 41.27 41.39 44.05 516 159462 159387 0.90 0.74 39.36 35.33 0.91 0.99 0.93 1.00 1.02 0.97 26.86 24.52 26.31 474 159609 159534 1.10 0.89 60.89 67.20 0.99 1.00 0.99 1.00 1.01 0.98 35.45 34.94 35.25 579 159928 159853 0.85 0.62 32.04 27.28 0.94 1.00 0.97 1.00 1.03 0.97 19.06 17.86 18.51 465 161093 161018 1.12 0.69 87.09 97.64 1.12 0.67 1.05 0.89 0.94 0.96 52.09 58.35 55.46 505 161745 161670 1.04 1.00 38.93 40.34 1.13 0.81 1.04 0.97 0.92 0.96 25.49 28.85 27.62 479 162224 162149 0.88 0.66 53.83 47.48 1.02 0.97 1.10 0.82 1.08 0.83 41.15 42.12 38.17 474 162596 162521 0.84 0.05 149.59 126.18 0.91 0.89 0.96 1.00 1.05 0.74 113.37 103.62 107.53 266 163138 163063 0.85 0.67 50.92 43.03 0.98 1.00 0.90 1.00 0.91 0.96 18.24 17.91 19.97 281 163188 163113 0.99 0.95 86.65 85.87 0.98 1.00 1.04 0.93 1.06 0.85 122.77 120.54 115.75 184 163612 163537 0.86 0.50 71.50 61.31 0.92 0.93 0.99 1.00 1.08 0.70 85.39 78.29 79.41 289 163847 163772 1.05 1.00 86.03 90.34 1.11 0.81 1.04 0.97 0.94 0.97 33.68 37.35 36.00 1098 163958 163883 0.85 0.37 39.38 33.64 1.10 0.88 1.07 0.94 0.98 1.00 24.96 27.35 25.52 919 164264 164189 1.07 0.98 47.19 50.29 1.06 0.88 0.97 1.00 0.91 0.90 46.04 48.99 50.43 409 164373 164298 0.97 0.76 67.67 65.36 0.97 1.00 0.88 0.97 0.91 0.87 63.16 61.19 69.38 720 164610 164535 0.94 0.76 86.85 82.05 1.04 0.94 1.05 0.91 1.02 0.94 68.34 70.96 67.26 417 164612 164537 0.88 0.61 49.72 43.84 0.92 0.97 1.04 0.96 1.13 0.66 48.92 44.87 43.32 353 164780 164705 1.08 0.97 21.62 23.24 0.90 0.98 0.99 1.00 1.10 0.89 18.28 16.43 16.65 626 164909 164834 1.20 0.49 108.72 130.28 1.02 0.94 1.15 0.49 1.12 0.47 94.71 96.92 84.39 400 164933 164858 1.04 1.00 47.02 48.70 0.95 1.00 0.92 1.00 0.97 1.00 33.05 31.39 34.23 533 165120 165045 0.91 0.74 96.19 87.39 0.94 0.99 0.85 0.87 0.91 0.85 69.83 65.29 76.79 329 168 Transcript ID Protein ID Fold Change MYC : FLM Baggerley's MYC : FLM FLM Means (Kohler et al. 2015) MYC Means (Kohler et al. 2015) Fold Change Peat : NH4 Baggerley's Peat : NH4 Fold Change Peat : BSA Baggerley's BSA : Peat Fold Change NH4 : BSA Baggerley's BSA : NH4 NH4 - Means Peat - Means BSA - Means Protein Length 166080 166005 0.88 0.38 133.82 117.90 1.03 0.87 1.03 0.86 1.00 0.97 106.59 110.29 107.07 448 166470 166395 0.97 0.85 96.42 93.57 0.92 0.94 1.06 0.83 1.16 0.42 76.56 70.32 66.23 390 167042 166967 0.82 0.45 42.65 34.85 1.00 1.00 0.90 1.00 0.90 0.88 32.26 32.23 35.87 353 167417 167342 1.15 0.87 207.88 238.09 0.95 0.97 0.90 0.90 0.95 0.91 326.33 309.87 342.99 298 167943 167868 1.16 0.71 27.46 31.85 0.98 1.00 1.08 0.97 1.10 0.89 18.46 18.17 16.82 694 168446 168371 0.92 0.16 223.51 206.63 1.05 0.88 0.94 1.00 0.90 0.80 111.90 117.00 124.84 623 168975 168900 0.93 0.70 41.31 38.47 0.93 0.99 0.87 0.99 0.94 0.98 34.67 32.20 36.88 859 170820 170745 0.99 0.92 87.93 87.28 0.99 1.00 1.06 0.83 1.07 0.79 76.38 75.91 71.52 874 171259 171184 0.82 0.82 15.83 12.94 1.04 1.00 0.97 1.00 0.93 0.97 26.85 27.81 28.77 378 171843 171768 1.09 0.92 30.00 32.69 1.09 0.89 1.11 0.88 1.02 0.97 25.34 27.53 24.74 560 171921 171846 1.11 0.81 36.46 40.42 1.14 0.81 1.09 0.92 0.96 1.00 22.80 26.09 23.83 1608 172187 172112 1.14 0.69 32.55 37.16 0.85 0.78 1.00 1.00 1.17 0.52 50.42 43.04 42.91 379 172289 172214 1.04 1.00 69.46 72.20 0.98 1.00 1.13 0.59 1.16 0.63 68.05 66.39 58.64 607 173725 173650 1.15 0.80 79.82 91.92 0.92 0.98 0.91 1.00 0.98 1.00 45.93 42.39 46.78 780 174504 174429 0.96 0.80 224.31 215.30 1.02 0.87 0.97 1.00 0.95 0.98 170.55 173.55 179.67 410 174709 174634 1.07 0.93 42.08 44.89 0.99 1.00 0.91 1.00 0.92 0.93 37.68 37.35 40.84 664 175009 174934 1.06 0.89 127.72 135.28 0.95 0.99 1.09 0.84 1.15 0.61 232.18 220.89 201.84 307 176049 175974 0.98 0.88 57.84 56.42 0.87 0.83 0.87 0.94 1.00 1.00 52.59 45.80 52.74 689 176268 176193 0.88 0.48 65.48 57.31 0.97 1.00 0.91 1.00 0.94 0.94 67.68 65.80 72.26 433 177675 177600 1.02 1.00 157.87 160.42 0.89 0.62 0.97 1.00 1.09 0.40 208.36 185.51 191.65 332 178393 178318 1.09 0.91 44.87 49.01 1.06 0.89 1.11 0.81 1.04 0.92 41.21 43.86 39.65 946 178634 178559 1.10 0.79 57.15 62.93 0.94 1.00 0.97 1.00 1.03 0.92 48.50 45.59 46.91 374 178990 178915 1.05 0.96 76.11 79.93 1.01 0.99 1.12 0.62 1.11 0.69 69.51 70.12 62.49 377 179104 179029 0.89 0.69 87.36 77.59 1.00 0.99 0.96 1.00 0.96 0.99 62.48 62.56 65.19 302 179529 179454 0.82 0.51 42.85 35.00 0.95 1.00 1.08 0.95 1.14 0.79 21.78 20.59 19.03 619 180228 180153 0.87 0.38 97.58 84.72 0.99 1.00 1.00 0.99 1.01 0.97 55.63 55.34 55.25 486 180348 180273 1.03 1.00 50.37 51.92 0.98 1.00 0.91 1.00 0.93 0.96 24.82 24.28 26.81 1080 182134 182059 0.99 0.84 101.01 100.39 1.05 0.88 1.10 0.73 1.05 0.86 63.04 66.05 60.16 581 182438 182363 1.02 1.00 27.08 27.65 0.94 1.00 0.89 1.00 0.95 0.99 20.33 19.06 21.37 544 182867 182792 1.00 0.99 25.75 25.70 1.07 0.94 1.12 0.86 1.04 0.96 27.47 29.51 26.37 396 183510 183435 0.96 0.87 83.08 79.66 0.92 0.97 0.87 0.93 0.94 0.95 55.29 50.83 58.72 880 184148 184073 0.86 0.53 26.60 22.98 1.00 1.00 1.04 0.98 1.05 0.93 26.77 26.70 25.60 836 184189 184114 1.07 0.92 43.60 46.81 1.08 0.91 0.99 1.00 0.91 0.93 31.39 33.84 34.32 963 186448 186373 1.12 0.87 75.89 84.98 1.00 1.00 1.03 0.98 1.04 0.91 68.76 68.48 66.41 493 186564 186489 1.00 0.95 52.29 52.11 1.06 0.87 1.09 0.79 1.03 0.92 53.32 56.51 51.65 589 186765 186690 1.06 0.91 76.83 81.46 1.06 0.88 0.94 1.00 0.89 0.80 53.65 56.63 60.47 877 169 Transcript ID Protein ID Fold Change MYC : FLM Baggerley's MYC : FLM FLM Means (Kohler et al. 2015) MYC Means (Kohler et al. 2015) Fold Change Peat : NH4 Baggerley's Peat : NH4 Fold Change Peat : BSA Baggerley's BSA : Peat Fold Change NH4 : BSA Baggerley's BSA : NH4 NH4 - Means Peat - Means BSA - Means Protein Length 187302 187227 1.00 0.95 84.68 84.85 1.08 0.80 1.07 0.82 1.00 0.99 57.58 61.99 57.84 1045 187435 187360 1.08 0.92 37.23 40.25 0.86 0.80 0.97 1.00 1.12 0.65 62.96 54.18 56.03 481 187501 187426 0.92 0.62 118.42 108.94 0.94 0.98 1.01 0.96 1.07 0.70 89.15 83.65 83.04 487 187599 187524 0.81 0.03 258.45 209.61 1.04 0.78 1.04 0.72 1.00 0.94 152.27 157.85 151.78 471 187658 187583 1.01 1.00 45.15 45.74 1.11 0.87 1.08 0.93 0.97 1.00 22.05 24.52 22.64 930 187801 187726 1.07 1.00 75.70 81.00 1.05 0.91 1.10 0.78 1.05 0.87 49.44 51.87 46.95 652 188229 188154 1.05 1.00 41.75 43.78 1.07 0.89 1.11 0.82 1.04 0.93 35.47 37.99 34.14 480 188945 188870 1.13 0.85 43.30 48.87 0.94 1.00 0.89 1.00 0.94 0.97 54.66 51.46 57.98 481 189461 189386 1.04 1.00 166.86 173.65 0.88 0.65 0.87 0.80 0.99 1.00 157.69 139.23 159.96 391 190107 190032 0.99 0.90 31.28 30.84 0.96 1.00 0.89 1.00 0.93 0.98 23.00 21.98 24.77 908 190588 190513 0.96 0.90 57.63 55.32 0.87 0.84 0.99 1.00 1.14 0.61 47.20 40.98 41.24 470 190753 190678 1.11 0.91 27.49 30.44 1.01 1.00 1.12 0.91 1.11 0.85 20.96 21.14 18.85 459 191247 191172 1.02 1.00 83.86 85.39 1.03 0.91 1.02 0.91 1.00 0.98 85.41 87.70 85.71 517 192280 192205 1.19 0.55 28.03 33.48 1.14 0.87 1.00 1.00 0.88 0.91 16.31 18.62 18.61 1076 192390 192315 0.98 0.91 81.85 80.13 0.97 1.00 1.02 0.93 1.06 0.81 69.50 67.27 65.64 548 193506 193431 1.19 0.28 86.02 102.08 0.98 1.00 1.06 0.85 1.08 0.73 71.50 70.18 66.40 970 194279 194204 0.97 0.81 240.71 233.62 0.90 0.62 0.97 0.99 1.08 0.30 263.56 236.90 243.41 293 194698 194623 1.16 0.71 44.42 51.67 0.96 1.00 1.03 0.96 1.08 0.80 48.96 46.87 45.44 498 195040 194965 1.12 0.80 55.25 61.78 1.08 0.76 1.08 0.79 1.00 0.99 66.78 72.05 66.74 490 195080 195005 0.98 0.94 86.71 84.94 1.08 0.78 1.09 0.78 1.01 0.97 77.33 83.16 76.56 322 195121 195046 1.01 1.00 67.10 67.98 1.05 0.92 0.97 1.00 0.93 0.94 40.80 42.89 44.02 460 196493 196418 0.90 0.43 129.54 117.08 0.90 0.85 1.00 0.97 1.11 0.50 104.68 94.50 94.49 461 196521 196446 0.85 0.54 160.48 136.54 1.03 0.85 0.95 1.00 0.92 0.83 200.55 206.56 217.20 392 198128 198053 0.85 0.57 38.18 32.44 0.96 1.00 1.01 1.00 1.06 0.91 27.92 26.71 26.43 855 198183 198108 0.99 0.86 71.70 70.95 0.92 0.96 0.99 1.00 1.08 0.80 51.77 47.46 47.97 542 198348 198273 0.97 0.92 41.82 40.66 0.99 1.00 1.14 0.74 1.15 0.65 40.37 40.13 35.15 391 198386 198311 0.96 0.82 36.83 35.34 0.93 1.00 1.07 0.95 1.15 0.75 25.31 23.62 22.00 1225 198667 198592 0.81 0.40 51.81 41.77 1.09 0.80 1.10 0.80 1.01 0.97 47.24 51.32 46.79 657 199680 199605 1.09 0.83 39.53 43.25 1.00 1.00 0.98 1.00 0.98 1.00 36.41 36.36 37.10 538 200535 200460 1.05 1.00 28.02 29.30 0.88 0.94 0.94 1.00 1.08 0.89 24.89 21.80 23.10 650 201382 201307 0.83 0.39 48.51 40.04 1.09 0.87 1.02 0.99 0.94 0.98 31.13 33.86 33.14 414 203192 203117 1.17 0.81 25.49 29.93 1.10 0.91 0.97 1.00 0.88 0.91 18.53 20.41 21.03 870 203268 203193 0.91 0.75 27.95 25.51 0.86 0.87 0.91 1.00 1.07 0.91 29.88 25.56 27.95 346 203521 203446 1.13 0.85 49.21 55.63 0.90 0.97 0.91 1.00 1.01 1.00 61.31 55.32 60.84 251 205297 205222 0.87 0.72 70.09 60.89 0.96 1.00 0.87 0.97 0.90 0.89 32.24 30.97 35.79 416 206782 206707 0.94 0.76 40.11 37.87 1.03 0.97 1.03 0.99 0.99 1.00 43.10 44.45 43.34 432 170 Transcript ID Protein ID Fold Change MYC : FLM Baggerley's MYC : FLM FLM Means (Kohler et al. 2015) MYC Means (Kohler et al. 2015) Fold Change Peat : NH4 Baggerley's Peat : NH4 Fold Change Peat : BSA Baggerley's BSA : Peat Fold Change NH4 : BSA Baggerley's BSA : NH4 NH4 - Means Peat - Means BSA - Means Protein Length 207135 207060 1.13 0.76 55.63 62.69 1.00 1.00 1.06 0.91 1.06 0.87 46.54 46.64 44.09 419 208492 208417 1.09 0.90 65.10 71.02 1.04 0.94 1.15 0.63 1.11 0.70 50.69 52.55 45.73 450 Media used for each treatment are as follows - FLM/NH4: MMN (present work); FLM/Peat: MMN without glucose and (NH4)2HPO4, poured over ~10 g (dry weight) sterile commercial peat (present work); MYC/BSA: O. maius colonizing Vaccinium myrtillus root samples, grown on MMN without glucose and (NH4)2HPO4, supplemented with 0.1 g L-1 BSA (Kohler et al. 2015). 171 Appendix D Table D.1. Primers used for amplification of cDNA from Oidiodendron maius* Gene Transcript ID Protein ID Primer Name Sequence Tm % GC Predicted Amplicon Length Sequenced Amplicon Length CE5 50968 50893 CE5_4F AGATCGTCCTTGTTGGC 59.38 52.94 179 164 CE5_4R ACCAAAGACGGTGATGG 59.35 52.94 CE5_5F CTCGCACTCCTGAATACC 59.39 55.56 90 n/a CE5_5R GCGCTAGAGTCATCTCC 58.68 58.82 CE8 119173 119098 CE8_1F TGGTTCTGGTTCACAGG 58.92 52.94 105 176 CE8_1R CCAGACTTGGCATAGAGG 59.16 55.56 CE8_5F GCTTGGTTTGGAGATTGC 59.40 50.00 151 n/a CE8_5R AGTAGTTTGCCTCAGTGG 59.12 50.00 GH3 172745 172670 3_70_4F TCCGCAACTAGCTTTCC 59.35 52.94 129 105 3_70_4R TGAGAATCCTGCATGTCC 59.28 50.00 3_70_5F AGAGTAGCACGCATTTCC 59.83 50.00 118 n/a 3_70_5R AGCAAGCCAACATAGTCC 59.89 50.00 GH3 173180 173105 3_05_1F GATGTCCTGTATGGGAAGG 59.34 52.63 110 260 3_05_1R CCGTTGCCATTGTTTGG 59.54 52.94 3_05_2F CGGAGGACCTAATGATCC 58.61 55.56 130 121 3_05_2R CCTGCCTAACCGTATCC 58.74 58.82 GH5 109142 109067 GH5-3F TCGTCAAGTCGATGAAGG 59.08 50.00 156 140 GH5-3R AGTCAGCGTCAACTAGC 58.94 52.94 GH5-4F ATCTGCGATACAGCTTGG 59.62 50.00 126 n/a GH5-4R GATGACTAGGGAGTTGTTGG 59.89 50.00 GH11 160759 160684 GH11-1F CTATGTACTGGGAGGATGG 58.47 52.63 132 152 GH11-1R CGTACACAGAGAGGTAGG 58.03 55.56 GH11-3F TAGCTCTGGCTCCTACC 59.52 58.82 125 125 GH11-3R AGGAGAGGGTACCAAGG 59.50 58.82 GH18 147779 147704 GH18-3F TCCAGAGCAAGGAAATGG 59.57 50.00 148 142 GH18-3R ACAGACAGGAATGTCAGC 59.46 50.00 GH18-4F CAGCTCCAATCAGAATGC 58.55 50.00 118 142 GH18-4R TGCCAGTTAGCTGATACC 58.95 50.00 GH28 130215 130140 GH28-3F ACACCTCTCCAATTCTCC 58.57 50.00 122 160 GH28-3R TGACCAGGATAGACATGC 58.66 50.00 GH55 25971 25896 55_8_2F GAGAATGTGTGGCTTTGG 58.73 50.00 126 n/a 55_8_2R AGCAGTTCCGTACATCC 58.73 52.94 55_8_5F GAGCTTCTGGATGGAAGG 59.47 55.56 125 154 55_8_5R CTGTATCGTCAGTGACTCC 59.10 52.63 GH55 165996 165921 55_9_1F ATCGACCTTGACGTTCC 58.97 52.94 134 117 55_9_1R CAGTAGATCCTGGTGTTCC 59.21 52.63 55_9_5F TGTATAGCCACGCTTCC 58.79 52.94 115 n/a 55_9_5R GAGACGGTGTCAGTATAGC 59.16 52.63 172 Gene Transcript ID Protein ID Primer Name Sequence Tm % GC Predicted Amplicon Length Sequenced Amplicon Length GH78 40736 40661 GH78-2F GATTCTTTGCAGGCATCC 58.89 50.00 120 n/a GH78-2R CTTTGCACCATCAACAGG 59.03 50.00 GH78-5F CGCGGACAATAAACTGC 58.89 52.94 135 174 GH78-5R GCCTGCAAAGAATCACC 58.63 52.94 MCO 172675 172600 MCO-2F CTCCTCTCTACAGAGTTTCC 58.69 50.00 116 149 MCO-2R TGTGCATCTGGTAGTGG 58.71 52.94 MCO-7F TTCCTGGTTGGTGATTGG 59.84 50.00 128 n/a MCO-7R CACTCAAAGTATCCGAGACC 59.83 50.00 MnFeSOD 20150 20075 MFS-1F TTGCTGCCCTCATATCG 59.22 52.94 126 n/a MFS-1R CCAGGATGTTGGTATCGG 59.51 55.56 MFS-4F TGACGCTTCACCATTCC 59.67 52.94 136 126 MFS-4R GCGTTGAAACGGATTGC 59.80 52.94 SugMod 196656 196581 SM-2F TACGCCTTCTGGTTTCC 59.00 52.94 123 209 SM-2R ATTAGGTCTGGCTTGTCC 58.95 50.00 SM-5F CTCGAACGAGATCAAGGG 59.34 55.56 108 113 SM-5R AGCATCATGTCGAAGGG 59.16 52.94 TFE 58531 58456 TFE-1F TCTCTCTACCCTCCTTCC 58.92 55.56 122 210 TFE-1R CGCCACCTCCTATATACC 58.36 55.56 TFE-3F GACGCTCCTTGGTATATAGG 59.21 50.00 134 204 TFE-3R GCAATATCGTGCTCAACC 58.83 50.00 sar 18847 18772 S11F TCTGATGGTTGGGTTGG 59.27 52.94 97 84 S11R GTTGAAGCCGATTGTGG 58.76 52.94 S12F TCGTCGACAGTAATGACC 58.95 50.00 120 110 S12R GCAGATCTTGCTTGTTGG 59.26 50.00 S13F CGGAAAGACCACAATCC 57.78 52.94 134 n/a S13R TATCCTGTCCACCAACG 58.56 52.94 S14F CCGAGATCACAGACAAGC 60.12 55.56 97 n/a S14R CCCTCATAAAGACCATCTCC 59.66 50.00 S15F CACAATCGGCTTCAACG 59.03 52.94 137 109 S15R CGACGACGAAGATAATGC 58.34 50.00 21375 21300 S22F GAAGGACTATTTCCCAGAGG 59.58 50.00 111 n/a S22R TCCTCCATAGAGAGAAGGG 59.64 52.63 S24F TCTGGGCAACAAGATCG 59.58 52.94 132 113 S24R CACATGAAGACCTCGATAGG 59.83 50.00 S25F TACGGGTAAAGGCAAGG 58.93 52.94 106 131 S25R TGACATCCACCGAATAGC 59.46 50.00 EfTu 103949 103874 E31F GTGGATGAGCGAACTATGG 59.73 52.63 128 n/a E31R CCAACTTCTACTGTCTTCCC 59.41 50.00 E33F CAGACCCAACAGATGAACC 59.92 52.63 136 n/a E33R ACTGGTCATACCCTCTCC 59.45 55.56 E35F GACTTGGTACCTCTCTTGG 58.69 52.63 108 n/a E35R CTGTATCTCCGCTTCTCC 58.35 55.56 173 Gene Transcript ID Protein ID Primer Name Sequence Tm % GC Predicted Amplicon Length Sequenced Amplicon Length 102649 102574 E22F ACAGGTAGCGACAAAGG 59.17 52.94 112 160 E22R GTATAGCTCGAGGATCACG 59.16 52.63 E23F TCTGGGTGCCATTTACG 59.61 52.94 108 n/a E23R CCAATCCCTTCCTCATCC 59.47 55.56 E25F GATGATCTGGAGGATCTGG 59.00 52.63 130 n/a E25R TCTGCTTCTCTGCATCC 58.97 52.94 vma 32032 31957 V32F GATCCTGCTCGACTATCC 58.77 55.56 192 n/a V32R GGTTGTTCAGCCTTTCC 58.39 52.94 V34F GAGTTTGGTTCCGTTGC 58.98 52.94 139 n/a V34R CTCCATCTCTAGGTTGCC 59.04 55.56 V35F CTGGCAACCTAGAGATGG 59.35 55.56 164 142 V35R GTGATCCAGAATCCAGAGG 59.49 52.63 161745 161670 V11F GCACCTTGTTTGTAGGC 58.73 52.94 167 n/a V11R CAGGAAGGGACTGTATGG 59.01 55.56 V12F AGGTTCCCGAGAGAAGG 60.29 58.82 124 n/a V12R CTCAACCACCCAGAAAGG 60.15 55.56 V15F CCAACCTACCTCTCTTCG 58.88 55.56 142 n/a V15R GTTTCCTTTGGTCCTTCC 58.25 50.00 *primer pairs highlighted in green were validated and used for subsequent ddPCR reactions Figure D.1. Growth of Oidiodendron maius on different carbon sources. O. maius grown for 45 days in the dark at 25 °C on modified Melin Norkrans agar, supplemented with one of eleven different carbon sources, with and without additional 0.1 g L-1 glucose. 174 175 176 Figure D.2. Droplet digital PCR annealing / extension temperature optimization assays. (A) CE5-4 (B) CE-8 (C) GH3_70-4 (D) GH5-3 (E) GH11-1 (F) GH18-3 (G) GH28-3 (H) GH55_8 (I) GH55_9 (J) GH78 (K) MCO (L) MnFeSOD (M) SugMod (N) TFE. Temperature used for subsequent assays is indicated by the green box Positive and negative clusters were identified for the CE5 primer set (Figure D.2 A) at all temperatures below 58 ⁰C, although non-specific amplification was also evident at these temperatures; however, cluster separation was greatest at lower temperatures and an annealing/extension temperature of 53 ⁰C was selected for all further assays. The CE8 primer set showed weak cluster separation at all temperatures (Figure D.2 B), although cluster separation was slightly stronger at lower temperatures. Thus, an annealing/extension temperature of 53.5 ⁰C was selected for all further assays. The GH3 primer set showed strong cluster separation (with non-specific amplification also present) at all temperatures tested (Figure D.2 C). Cluster separation was slightly greater at higher temperatures, hence an annealing/extension temperature of 55 ⁰C was selected for all further assays. The GH5 primer set showed weak cluster separation at all temperatures (Figure D.2 D) below 56 ⁰C, with no separation at higher temperatures. An optimal temperature of 55 ⁰C was chosen for the GH5 primer set to minimize non-specific amplification. The GH11 primer set showed moderate cluster separation at all temperatures (Figure D.2 E) below 58 ⁰C with greater separation achieved at lower temperatures; an annealing/extension temperature of 53 ⁰C was selected for 177 all further assays. The GH18 primer set showed moderate cluster separation at all temperatures (Figure D.2 F), although slightly greater separation occurred at intermediate temperatures; an annealing/extension temperature of 53.5 ⁰C was selected for all further assays. The GH28 primer set showed moderate cluster separation at all temperatures (Figure D.2 G), although there was a slight decrease in non-specific amplification at intermediate temperatures; an annealing/extension temperature of 52 ⁰C was selected for all further assays. No separation of clusters occurred using the GH55 primers designed for transcript 25971 for any annealing/extension temperature within the 49 – 60 ⁰C range tested (Figure D.2 H), so this target was eliminated from further analyses. The GH55 primer set for transcript 165996 showed weak cluster separation at all temperatures below 58 ⁰C (Figure 4.3I), with no separation at higher temperatures; slightly greater separation occurred at lower temperatures and an optimal temperature of 54 ⁰C was chosen to minimize non-specific amplification. The GH78 primer set showed strong cluster separation at all temperatures below 58 ⁰C (Figure 4.3J), with less non-specific amplification at lower temperatures; an annealing/extension temperature of 53 ⁰C was selected for all further assays. The primer set for MCO showed weak cluster separation at all temperatures examined (Figure 4.3K); an optimal temperature of 55.5 ⁰C was chosen to minimize non-specific amplification. The MnFeSOD primer set showed strong cluster separation at all temperatures (Figure 4.3L); an optimal temperature of 54.5 ⁰C was chosen to minimize non-specific amplification. The primer set for the SugMod gene showed strong cluster separation at all temperatures (Figure 4.3M) with less non-specific amplification at intermediate temperatures; an optimal temperature of 52 ⁰C was selected for all further assays. The TFE primer set demonstrated very weak cluster separation at all temperatures (Figure 4.3N), however the software was able to separate clusters at lower temperatures; an optimal temperature of 53.5 ⁰C was chosen to minimize non-specific amplification. 178 Table D.2. Reduction in Coefficients of Variation for ddPCR data (CoEVReduce). Coeffficients of variation calculated for 13 target genes in Oidiodendron maius across 11 carbon sources. The reduction in CoEV (CoEVreduce) was calculated as (CoEVraw - CoEVnorm)/(CoEVraw). CoEVreduce values are shaded to reflect positive (green) and negative (red) values, where deeper shades reflect greater change; bolded values represent values for which the CoEVraw was >0.75. CE5 CE8 GH3 GH5 GH11 GH18 GH28 GH55 GH78 MCO MFS SM TFE Mean 0.1 % glucose 78.51 95.20 90.61 44.49 78.57 87.38 73.52 66.09 80.13 89.25 54.05 -17.43 77.87 69.09 0.5 % glucose -1.35 68.11 45.91 40.27 25.59 14.85 64.96 60.53 62.56 83.07 -28.06 46.30 48.92 40.90 peat -52.53 -17.43 -23.62 8.31 -20.60 -14.76 -47.30 -25.24 -366.74 -18.47 -16.78 -26.25 -6.71 -48.32 peat DOM 53.53 64.43 27.40 -76.97 13.88 49.40 43.46 -145.88 -30.03 31.30 58.27 -195.71 57.07 -3.83 field soil -39.56 -849.08 -153.21 -15.58 -162.12 -142.61 -52.08 -10.71 -204.48 8.84 -106.30 -972.16 -47.07 -211.24 field SOM 86.14 80.81 65.27 -56.72 89.98 69.54 60.49 84.26 86.43 74.68 93.08 75.16 66.43 67.35 BSA -12.41 -12.65 -18.41 28.84 -13.23 10.20 -7.82 28.37 -14.91 -24.89 -1.99 -15.55 -9.67 -4.93 cellulose 64.72 30.22 57.26 51.75 64.37 -102.38 39.72 50.44 62.72 48.81 -95.61 -80.03 59.53 19.35 chitin 6.58 -9.20 -5.04 16.83 31.65 70.31 -14.64 29.21 -74.73 28.44 -157.02 -5.59 -6.93 pectin 73.77 -19.51 40.61 21.26 24.27 67.97 39.83 27.80 54.06 34.35 43.82 21.09 36.52 35.83 lignin -13.55 -11.83 -12.12 -13.66 -16.78 -14.37 -18.96 -10.73 -17.30 -13.37 -16.81 -38.30 -33.85 -17.82 Mean 22.17 -57.17 10.05 2.45 9.16 5.17 24.19 10.03 -23.49 21.71 1.10 -123.63 22.13 * CE8 chitin data was omitted from CoEV calculations due to n = 1.
Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Jones, Melanie D.
Durall, Daniel
Differential expression of cell wall degrading enzymes in Oidiodendron maius in response to varying carbon source
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