Science, Faculty of
Botany, Department of
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UBCV
Cho, Anna
Doctor of Philosophy - PhD
University of British Columbia
Stramenopiles are a diverse eukaryotic supergroup with considerable genomic information available. Nevertheless, the relationships between major stramenopiles subgroups remain unresolved and incongruent between analyses, in part due to a lack of data from small nanoflagellates that make up much of the genetic diversity of the group, under-represented ochrophyte classes, and a rapid radiation leading to eroded phylogenetic signals in the tree. As a result, assessing genetic diversity and distribution, addressing character evolution, and investigating interactions of these lineages with other organisms are limited. To resolve phylogenomic relationships of stramenopiles, I generated 23 transcriptomes from the most under-sampled subgroups, such as Bigyromonadea, MAST-6, Placididea, and some classes of ochrophytes that had been scarcely represented in phylogenomic data. Of these, 11 are new species of stramenopiles, some of which have helped resolving phylogenomic relationships of Bigyromonadea and the backbone of deep-branching lineages. Some of these species were found to be abundant in sediment sampled across different geographic locations, while others can tolerate a broad range of salinities. I also described behaviours and morphological characters of these species including the ability to form pseudopods and cell-aggregates observed in some bigyromonads. This updated phylogenomic dataset now represents 14 out of 17 classes of ochrophytes and demonstrates robust support for previously contentious or under-tested lineages such as Eustigmatophyceae, Pinguiophyceae, and Olisthodiscophyceae. To address phylogenomic incongruence between multi- or single-gene trees, I explored various gene filtering criteria to identify the phylogenetically informative genes. Selecting genes with long internal phylogenetic branches or removing genes with high levels of phylogenetic noise recovered more topologies that were found in other phylogenomic analyses. Finally, I investigated the only reported case of a prokaryotic endosymbiont found among non-photosynthetic lineages of stramenopiles, in the tiny flagellate Symbiomonas scintillans. Instead of endobacteria, I detected multiple giant viruses related to prasinoviruses. This work demonstrates how little we know about symbioses, particularly in nano- or pico-flagellates. Overall, this thesis highlights complex evolutionary histories of stramenopiles inferred from the most up-to-date phylogenomic tree. This work will inform further exploration into trait evolutions related to niche occupation, morphology, and immunity.
EVOLUTION AND DIVERSITY OF STRAMENOPILES by Anna Cho M.Sc, The University of British Columbia, 2019 B.Sc (Hon.), Trent University, 2015 B.Sc., McGill University, 2010 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate and Postdoctoral Studies (Botany) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) May 2024 © Anna Cho, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Evolution and diversity of stramenopiles submitted by Anna Cho in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Botany Examining Committee: Patrick Keeling, Professor, Botany, UBC Supervisor Brian Leander, Professor, Botany and Zoology, UBC Supervisory Committee Member Sean Graham, Professor, Botany, UBC University Examiner Dolph Schluter, Professor, Zoology. UBC University Examiner Heroen Verbruggen, Professor, School of BioSciences, University of Melbourne External Examiner Additional Supervisory Committee Members: Patrick Martone, Professor, Botany, UBC Supervisory Committee Member iii Abstract Stramenopiles are a diverse eukaryotic supergroup with considerable genomic information available. Nevertheless, the relationships between major stramenopiles subgroups remain unresolved and incongruent between analyses, in part due to a lack of data from small nanoflagellates that make up much of the genetic diversity of the group, under-represented ochrophyte classes, and a rapid radiation leading to eroded phylogenetic signals in the tree. As a result, assessing genetic diversity and distribution, addressing character evolution, and investigating interactions of these lineages with other organisms are limited. To resolve phylogenomic relationships of stramenopiles, I generated 23 transcriptomes from the most under-sampled subgroups, such as Bigyromonadea, MAST-6, Placididea, and some classes of ochrophytes that had been scarcely represented in phylogenomic data. Of these, 11 are new species of stramenopiles, some of which have helped resolving phylogenomic relationships of Bigyromonadea and the backbone of deep-branching lineages. Some of these species were found to be abundant in sediment sampled across different geographic locations, while others can tolerate a broad range of salinities. I also described behaviours and morphological characters of these species including the ability to form pseudopods and cell-aggregates observed in some bigyromonads. This updated phylogenomic dataset now represents 14 out of 17 classes of ochrophytes and demonstrates robust support for previously contentious or under-tested lineages such as Eustigmatophyceae, Pinguiophyceae, and Olisthodiscophyceae. To address phylogenomic incongruence between multi- or single-gene trees, I explored various gene filtering criteria to identify the phylogenetically informative genes. Selecting genes with long internal phylogenetic branches or removing genes with high levels of phylogenetic noise recovered more topologies that were found in other phylogenomic analyses. Finally, I iv investigated the only reported case of a prokaryotic endosymbiont found among non-photosynthetic lineages of stramenopiles, in the tiny flagellate Symbiomonas scintillans. Instead of endobacteria, I detected multiple giant viruses related to prasinoviruses. This work demonstrates how little we know about symbioses, particularly in nano- or pico-flagellates. Overall, this thesis highlights complex evolutionary histories of stramenopiles inferred from the most up-to-date phylogenomic tree. This work will inform further exploration into trait evolutions related to niche occupation, morphology, and immunity. v Lay Summary Compared to other major groups of eukaryotes that are neither plants, animals, or fungi (protists), stramenopiles have been relatively well studied due to their economic and ecological significance. Notable examples are oomycetes, which infect many crops species and caused the Great Potato Famine, or the giant kelps and diatoms, which are photosynthetic stramenopiles that provide habitats for marine life and contribute >20% primary production globally. The rest of the stramenopiles are not as relevant to human interests as these groups, and indeed stramenopile studies are highly skewed towards photosynthetic or pathogenic species for this reason. In this thesis, I investigated diversity and abundance of under-studied stramenopiles by describing new species, morphologies, behaviours, symbionts, and distribution. To assess their evolutionary history, I constructed an evolutionary tree by using many genes, and inferred some relationships that were previously controversial or unknown. vi Preface A version of Chapter 2 has been published as a research article in Molecular Phylogenetics and Evolution, of which I was the first co-author. (Cho A, Tikhonenkov DV, Hehenberger E, Karnkowska A, Mylinkov AP, and Keeling PJ. 2022. Monophyly of diverse Bigyromonadea and their impact on phylogenomic relationships within stramenopiles. Molecular Phylogenetics and Evolution 171:107468). The project was done in collaboration with former members of the Keeling lab and Denis Tikhonenkov, Institute of Biology of Inland Waters in Russian Academy of Sciences (RAS), Moscow. Denis Tikhonenkov conducted field work, all cell isolations, culture establishment and maintenance, library preparation for transcriptomes. Denis Tikhonenkov also generated micrograph images of the cells with the morphology descriptions. Initial project assessment was done by Anna Karnkowska (Institute of Parasitology, Czech Academy of Science). Elisabeth Hehenberger partly processed transcriptome data for the “approach 2” mentioned in the paper. Alexander Mylinkov (RAS) was a supervisor of Denis Tikhonenkov during the time of field work. I performed all other bioinformatics analyses, determined the scientific goals of the project, and wrote the manuscript during the period when the lab was closed due to COVID restrictions. A version of Chapter 3 has also been published as a research article (Cho A, Tikhonenkov DV, Lax G, Prokina KI, and Keeling PJ. 2024. Molecular Phylogenetics and Evolution 190:107964). This work was done in collaboration with Denis Tikhonenkov (RAS), Kristina Prokina (CNRS, Université Paris-Saclay), and Gordon Lax (a postdoctoral fellow in Patrick Keeling’s UBC laboratory). Denis Tikhonenkov and Gordon Lax isolated four new species and micrograph images. Together with Denis Tikhonenkov and Gordon Lax, I wrote species description and taxonomic summaries, and conducted cDNA synthesis and library preparation to generate transcriptome data. Kristina Prokina helped with sampling. I wrote the manuscript and, vii conceptualized and conducted all other analyses including all the bioinformatics analyses and data curation. Chapter 4 is a manuscript submitted for a publication (Cho A, Lax G, and Keeling PK. Under review. Phylogenomic analyses of ochrophytes (stramenopiles) with an emphasis on neglected lineages). This project was done in collaboration with Gordon Lax, a postdoctoral fellow in the Keeling lab at UBC. Gordon Lax isolated one cell from an environmental sample and performed cDNA synthesis to generate transcriptome data. I maintained cultures, performed RNA extraction, cDNA synthesis, and generated transcriptome for the rest of the data. I conceptualized the data analysis approach, conducted all the bioinformatics analyses, and wrote the manuscript. A version of Chapter 5 has been published as a research article (Cho A, Lax G, Livingston SJ, Masukagami Y, Naumova M, Millar O, Husnik F, and Keeling PJ. PLoS Genetics 20(4):e1011218). This project was done in collaboration between Patrick Keeling’s UBC laboratory and Filip Husnik’s laboratory in Okinawa Institute of Science and Technology Graduate University, OIST, Japan. Patrick Keeling, Filip Husnik, and I identified the research question and designed the experiment. With some supervision from Gordon Lax, I generated whole genome amplification (WGA) data and Filip Husnik’s laboratory generated shotgun metagenomic data (SGM). Sam Livingston, a postdoctoral fellow in the Keeling lab generated negatively stained electron micrograph images. Yumiko Masukagami (OIST) and I performed Fluorescence in situ hybridization (FISH). Mariia Naumova and Olivia Millar, both Filip Husnik’s former students, maintained cultures in Japan and involved in laboratory work to generate the SGM data. I maintained the cultures in UBC. I wrote the manuscript and conducted viii all bioinformatics analyses of WGA and SGM, and discovered and assembled draft genomes of potential viruses associated with the culture maintained at UBC. Patrick Keeling, the principal investigator and supervisor, was involved in all aspects of conceptualization, supervision, review, and editing of all projects and manuscripts. All the co-authors were involved in reviewing and editing the manuscripts. ix Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Preface ........................................................................................................................................... vi Table of Contents ......................................................................................................................... ix List of Tables ............................................................................................................................... xiii List of Figures ............................................................................................................................. xiv List of Abbreviations ................................................................................................................... xv Acknowledgements .................................................................................................................. xviii Chapter 1: Introduction ............................................................................................................... 1 1.1 The phylogenomic relationship of non-photosynthetic stramenopiles ................................. 2 1.1.1 The unresolved relationship among the stramenopiles .................................................. 2 1.1.2 The phagoheterotroph clade in Gyrista, the Bigyromonadea, lacks taxon sampling ..... 3 1.1.3 Ecological diversity and the complex evolutionary history of the phagoheterotrophs of Bigyra ...................................................................................................................................... 6 1.2 The phylogenomic relationship and systematics of the photosynthetic stramenopiles ........ 7 1.2.1 Discrepancies between plastid and nuclear trees ......................................................... 10 1.2.2 Under representative classes of ochrophytes ............................................................... 10 1.3 Phylogenomic inference methods and computational burdens ............................................11 1.4 Endosymbionts of non-photosynthetic stramenopiles ........................................................ 13 1.5 Thesis goals and objectives ................................................................................................. 15 Chapter 2: Monophyly of diverse Bigyromonadea and their impact on phylogenomic relationships within stramenopiles ............................................................................................ 20 2.1 Introduction ......................................................................................................................... 20 2.2 Materials and Methods ........................................................................................................ 22 2.2.1 Sample collection, identification, and library preparation ........................................... 22 2.2.2 Small-subunit phylogenetic tree reconstruction........................................................... 24 2.2.3 Transcriptome processing, assembly, and decontamination ........................................ 25 2.2.4 Phylogenomic matrix construction and ortholog identification................................... 26 2.2.5 Phylogenomic tree reconstruction, fast-evolving site removal, and topology test ...... 28 2.3 Results ................................................................................................................................. 29 x 2.3.1 Multi-gene phylogenomic analysis .............................................................................. 29 2.3.2 Small-subunit ribosomal RNA gene tree reveals two different species assigned as Developayella ....................................................................................................................... 32 2.3.3 Morphology of the novel species ................................................................................. 32 2.4 Discussion ........................................................................................................................... 36 2.4.1 Monophyly and phylogenetic position of the Bigyromonadea .................................... 36 2.4.2 Morphology, evolutionary implications, and taxonomic description of the novel phagoheterotrophic Bigyromonadea ..................................................................................... 39 2.4.3 Taxonomic summary .................................................................................................... 44 Chapter 3: Phylogenomic position of genetically diverse phagotrophic stramenopile flagellates in the sediment-associated MAST-6 lineage and a potentially halotolerant placididea ..................................................................................................................................... 58 3.1 Introduction ......................................................................................................................... 58 3.2 Materials and Methods ........................................................................................................ 62 3.2.1 Sample collection and imaging .................................................................................... 62 3.2.2 cDNA synthesis, library preparation and sequencing .................................................. 63 3.2.3 Transcriptome processing, assembly, and decontamination ........................................ 65 3.2.4 Small subunit sequences and amplicon processing using QIIME 2 ............................ 66 3.2.5 Small-subunit (SSU) rRNA gene tree construction ..................................................... 68 3.2.6 Phylogenomic matrix construction using PhyloFisher ................................................ 69 3.2.7 Phylogenomic tree reconstruction, removal of fast-evolving sites, and recoding ....... 70 3.3 Results ................................................................................................................................. 71 3.3.1 Phylogenomic tree of stramenopiles ............................................................................ 71 3.3.2 New species represent phylogenetically diverse MAST-6 group in SSU rRNA analysis............................................................................................................................................... 75 3.3.3 The new Placididea may be rare in sediments ............................................................. 78 3.3.4 Morphological description and new name designation ............................................... 78 3.5 Discussion ........................................................................................................................... 84 3.5.1 Updated taxon sampling and phylogeny of MAST-6 .................................................. 84 3.5.2 The new MAST-6 species broaden the genetic diversity ............................................. 85 3.5.3 Rare and potentially halotolerant Haploplacidia sinai and its implication in trait evolution ............................................................................................................................... 86 3.5.4 Phylogenomics of stramenopiles with a twist .............................................................. 87 3.6 Conclusion .......................................................................................................................... 90 Chapter 4: Phylogenomic analyses of ochrophytes (stramenopiles) with an emphasis on neglected lineages ...................................................................................................................... 102 4.1 Introduction ....................................................................................................................... 102 xi 4.2 Materials and Methods ...................................................................................................... 105 4.2.1 Ochrophyte sample collection and processing ........................................................... 105 4.2.2 cDNA synthesis, library preparation and sequencing ................................................ 106 4.2.3 Transcriptome processing and phylogenomic matrix construction ........................... 107 4.2.4 Phylogenomic supermatrices ..................................................................................... 109 4.2.5. Phylogenomic trees: C60-PMSF, CAT-PMSF, CAT-GTR .........................................112 4.3. Results and Discussion .....................................................................................................114 4.3.1 The phylogenomic tree of stramenopiles ....................................................................114 4.3.2 No filtering criteria to select “good” or “bad” genes for phylogenomic analyses ......119 4.3.3 Phylogenomic analyses using different filtering criteria ........................................... 122 Chapter 5: Genomic analyses of Symbiomonas scintillans show no evidence for endosymbiotic bacteria but does reveal the presence of giant viruses ................................. 137 5.1 Introduction ....................................................................................................................... 137 5.2 Materials and Methods ...................................................................................................... 138 5.2.1 Culture collection and maintenance ........................................................................... 138 5.2.2 Library preparation and sequencing........................................................................... 140 5.2.3 Sequence processing: assemblies and sub-assemblies of viral reads ......................... 141 5.2.4 Draft viral genome annotation and gene content comparison ................................... 144 5.2.5 Prasinovirus hallmark gene search and phylogeny construction ............................... 145 5.2.6 Transmission Electron Microscopy (TEM) ............................................................... 146 5.2.7 Fluorescence in situ hybridization (FISH) ................................................................. 146 5.3 Results and Discussion ..................................................................................................... 148 5.3.1 No bacterial sequences from known clades of common endosymbionts .................. 148 5.3.2 Multiple prasinovirus-like vMAGs are associated with the strain RCC257 and RCC24............................................................................................................................................. 149 5.3.3 Genome characteristics of vMAGs ............................................................................ 151 5.3.4 SsV vMAGs are associated with S. scintillans .......................................................... 152 5.3.5 TEM observation of a VLP ........................................................................................ 153 5.3.6 Possible nature of associations: endobacteria, SsVs, and S. scintillans..................... 154 Chapter 6: Conclusion .............................................................................................................. 165 6.1 Major findings and significance........................................................................................ 165 6.2 Future direction and outstanding questions ...................................................................... 168 6.2.1 Character evolution and niche occupation of stramenopiles ..................................... 168 6.2.2 Minimum number of genes for a phylogenomic tree................................................. 169 6.2.3 Verifying persistent infection in Symbiomonas scintillans ........................................ 170 References .................................................................................................................................. 171 xii Appendices ................................................................................................................................. 194 Appendix A: List of published genomic level data of stramenopiles used in this study......... 194 Appendix B: Phylogenomic tree of stramenopiles with the seven new transcriptomes using the approach 2 dataset. .................................................................................................................. 196 Appendix C: Bayesian phylogenomic tree of stramenopiles .................................................. 197 Appendix D: Approximately unbiased test of constrained trees based on approach 2 dataset.................................................................................................................................................. 199 Appendix E: Phylogenomic tree of stramenopiles with the fast-evolving species removed. . 200 Appendix F: ML tree of a 18S rRNA gene including stramenopile environmental sequences.................................................................................................................................................. 201 Appendix G: Maximum-likelihood phylogenomic trees inferred from 39per- and 59per-matrix.................................................................................................................................................. 202 Appendix H: Maximum-likelihood phylogenomic trees inferred from MASTer-matrix. ...... 203 Appendix I: Pairwise-distance tree based on amino acid composition analysis. .................... 204 Appendix J: Recoded RAxML-ng multi-gene tree. ................................................................ 205 Appendix K: Consensus trees from Bayesian analysis. .......................................................... 206 Appendix L: Stacked bar plots showing relative abundance of unique MAST ASVs. .......... 207 Appendix M: A SSU rRNA phylogenetic tree of stramenopiles with SouthChina dataset. ... 208 Appendix N: A SSU rRNA phylogenetic tree of stramenopiles without ASVs. ..................... 209 Appendix O Consensus trees of stramenopiles with updated an ochrophyte data ................. 210 Appendix P: Summary of bootstraps with fast-evolving or random site and genes removed. 211 Appendix Q: Summary of 13 gene property loadings on three principal components. .......... 214 Appendix R: List of calculated properties for each gene used for building a concatenated matrix. ..................................................................................................................................... 215 Appendix S: Correlogram of 13 gene properties and PC axes. .............................................. 232 Appendix T: Heatmap of shared genes among different supermatrices. ................................ 233 Appendix U: Summary of different strains and sequencing data of S. scintillans examined . 234 Appendix V: Summary of genomic characteristics and references of prasinoviruses ............ 235 Appendix W: Summary of BlobToolKit analyses of the initial WGA assembles .................. 237 Appendix X: Fluorescence in situ hybridzation on RCC24.................................................... 239 Appendix Y: Number of shared orthologs and scaffolds among vMAGs .............................. 240 Appendix Z: Summary BlobToolKit table for green algae and prasinoviruses ...................... 241 Appendix AA: Prasinovirus phylogeny including both RCC24 and RCC257 data................ 260 Appendix AB: vMAG genome overview from RCC24 and RCC257 .................................... 261 Appendix AC: Select annotation of RCC257 BpV2-vMAG .................................................. 263 Appendix AD: Annotated HMM clusters found only in BV vMAGs from RCC257 viral-subset-scaffolds ....................................................................................................................... 267 xiii List of Tables Table 2.1 Approximately unbiased (AU) tests on tree constraints based on approach 1 dataset. . 50 Table 3.1 List of selected amplicon sequencing datasets from the European Nucleotide Archive (ENA). ........................................................................................................................................... 92 Table 4.1 List of ochrophyte cultures obtained from various culture collections. ...................... 127 Table 4.2 Summary of supermatrices generated using different filtering criteria ...................... 128 Table 4.3 Summary of bootstrap support for all the supermatrices ............................................ 133 xiv List of Figures Figure 1.1 Different versions of stramenopile phylogenomic trees recovered in different publications. .................................................................................................................................. 18 Figure 1.2 Flowchart showing steps to constructing a supermatrix.............................................. 19 Figure 2.1 Phylogenomic tree of stramenopiles with the seven new Bigyromonadea ................. 51 Figure 2.2 Summary of ultrafast bootstrap with fast-evolving sites removed .............................. 53 Figure 2.3 ML tree of stramenopiles using a 18S rRNA gene alignment ..................................... 54 Figure 2.4 Morphology of the seven new phagoheterotrophic Bigyromonadea .......................... 56 Figure 3.1 Phylogenomic tree of stramenopiles ........................................................................... 94 Figure 3.2 Removal of fast-evolving sites .................................................................................... 95 Figure 3.3 SSU-EPA tree of stramenopiles ................................................................................... 97 Figure 3.4 Relative abundance bar plots of MASTs ..................................................................... 99 Figure 3.5 Morphology of four new Bigyra................................................................................ 100 Figure 4.1 Phylogenomic of stramenopiles with 10 new ochrophyte transcriptomes ................ 129 Figure 4.2 Schematic representation of major stramenopile topologies ..................................... 131 Figure 4.3 Gene properties on a PCA plot .................................................................................. 136 Figure 5.1 FISH analyses on S. scintillans cultures .................................................................... 159 Figure 5.2 Virus-like particle and a multi-gene phylogeny of prasinoviruses ............................ 161 Figure 5.3 Circularized draft vMAGs overview ......................................................................... 164 xv List of Abbreviations aa Amino acids AU Approximately unbiased BB Bacillariophyceae-Bolidophyceae BC British Columbia BS Bootstrap supports (synonymous with STB, standard bootstrap) BLAST Basic Local Alignment Search Tool BpV Bathycoccus prasinos virus CSS Chrysophyceae-Synurophyceae-Synchromophyceae DADA2 Divisive Amplicon Denoising Algorithm 2 DAPI 4’,6-diamidino-2-phenylindole DNA Deoxyribonucleic acid dsDNA Double-stranded DNA FISH Fluorescence in situ hybridization GC Guanine-cytosine HGT Horizontal gene transfer HMM Hidden Markov Models MAFFT Multiple Alignment using fast Fourier transform MAST Marine stramenopile MCMC Markov Chain Monte Carlo ML Maximum-likelihood MOCH Marine ochrophyte MpV Micromonas pusilla virus xvi NCLDVs Nucleocytoplasmic large DNA viruses OIST Okinawa Institute of Science and Technology Graduate University OlV Ostreococcus lucimarinus virus ORFs Open reading frames PCA Principal Component Analysis PCR Polymerase chain reaction PeD Pelagophyceae-Dictyochophyceae PMSF Posterior mean site frequencies RNA Ribonucleic acid RPX Raphidophyceae-Phaeophyceae-Xanthophyceae SAR Stramenopiles, Alveolates, Rhizaria SEM Scanning electron microscopy SGM Shotgun metagenomic SSU rDNA Small subunit ribosomal DNA SSU rRNA Small subunit ribosomal RNA SsV Symbiomonas scintillans virus STB Standard bootstrap TEM Transmission electron microscopy UA Uranyl acetate UBC University of British Columbia UFB Ultrafast bootstrap vMAGs Viral metagenomically assembled genomes VLP Virus-like particle xvii VOG Virus orthologous groups WGA Whole-genome amplification xviii Acknowledgements First and foremost, I would like to thank my supervisor, Dr. Patrick Keeling, for all his supports, insightful advice, and encouragement throughout my PhD. His openness to new scientific approaches and discoveries was especially encouraging and inspiring for me to explore broad areas and, led to a serendipitous finding. I was also extremely lucky to be working with the past and present members of the Keeling Lab, despite the COVID pandemic lockdown. I was able to work with Dr. Denis Tikhonenkov on his brilliant collection of cells and learnt so much from Dr. Elisabeth Hehenberger and, my mentor, Dr. Anna Karnkowska about data processing, writing, and academic paths. In the lab, Dr. Liz Cooney, Dr. Gordon Lax, Dr. Vittorio Boscaro, Dr. Corey Halt, Dr. Filip Husnik, and Dr. Waldan Kwong were always there to help and discuss, without any hesitation. I want to thank all other present and past members who made the experience so much fun and made me truly enjoy science (Dr. Emma George, Dr. Sam Livingston, Dr. Noriko Okamoto, Dr. Juan Saldarriaga, Ina Na, Mahara Mtawali, Victoria Jacko-Reynolds, and so many others). I also want to thank Dr. Naomi Fast and her past lab members, Dr. Donald Wong, and Dr. Thomas Whelan, for their support, warm encouragements, and advice throughout the process. I would like to thank everyone for keeping the coffee room filled with sugar and carbs throughout. I was also very lucky to collaborate and work with many brilliant scientists, including Dr. Liz Cooney, Dr. Gordon Lax, Dr. Denis Tikhonenkov, Dr. Kristina Prokina, Dr. Elisabeth Hehenberger, Dr. Anna Karnkowska, and Dr. Filip Husnik. I would like to thank my committee members, Dr. Brian Leander, and Dr. Patrick Martone for all the helpful feedback and their time. I also want to thank Dr. Brett Couch who made my TA experience so much more rewarding and fun. Thank you, Alice Lou, for being so patient and kind in answering all the questions xix throughout the program. I’d like to extend my thanks to UBC Sequencing and Bioinformatics Consortium and UBC Bioimaging Facility for the fast turn-around. Many thanks to Hakai Institute for making the sampling trips and writing retreats possible. Additionally, I thank many members of Hakai Institute (Matt Whelan, Derek Van Maanen, Brenton Twist, Krystal Bachen, Gillian Sadlier-Brown), and Camosun Bog (Gerry Mignault) for providing moss samples, although the project never took off. This work was funded by the National Science and Engineering Resource Council (NSERC), the Gordon and Betty Moore Foundation, NSERC-Canada Graduate Scholarships, and the UBC Botany 4-Year Fellowship. Finally, I want to thank my partner, family, and friends who helped me get through every step of the process. Thank you to all my dear friends (especially AS) whom I shared all the adventures, late-night drinks, food, skiing, and climbing. My loving partner, Jan Finke (“Dr. Pinky”), thank you for always being there during the lows and the highs, and listening to all my breakdowns and dealing with my gremlin modes. You are my science advisor, bioinformatics saviour, and motivator. Without the love and patience of Jan, friends, and family, I would not been where I am. Lastly, I thank God for always leading me somewhere I least expect and blessing me with never-ending learning, love, and people I am with. 1 Chapter 1: Introduction Stramenopiles (= Heterokonts) are generally characterized by having two different lengths of flagella at some point in their life, with the anterior flagellum often bearing tripartite mastigonemes involved in swimming (and, rarely, gliding), and a posterior flagellum involved in beating and feeding. Such characterization does not fully reflect the morphological and genetic diversity of stramenopiles, as they cover broad ranges of sizes, occupied habitats, and nutritional modes. In fact, many stramenopiles have lost a flagellum such as Symbiomonas scintillans (Guillou et al., 1999), zoospores of Hyphochytrium catenoides (Leonard et al., 2018), or tripartite hairs such as species of Pelagomonas (Andersen et al., 1993) and Pinguiophyceae (Kawachi et al., 2002). This supergroup has been proposed (Cavalier-Smith, 1997) to be divided into two major clades: (i) Gyrista, consisting of Ochrophyta, Bigyromonadea, and Oomycota; and (ii) Bigyra, consisting of Sagenista (including Labyrinthulids and Eogyrea (Cavalier-Smith and Scoble, 2013), Bikosia, and Placidozoa (Placididea, Opalinata and Nanomonadea). Phylogenetically, a single taxon Platysulcus tardus, represents a new class Platysulcea which is sister to all other stramenopiles (Shiratori et al., 2015). Many studies have focused on the plastid-bearing autotrophs (i.e., ochrophytes) or parasitic stramenopiles (e.g.,oomycetes, labyrinthulids, and opalinids) due to their ecological and economic impacts, and as a result the diversity of the rest of the small heterotrophic stramenopiles are mostly only known through environmental sequences (Massana et al., 2004) or morphological descriptions lacking genomic level data. Characterizing these under-studied stramenopiles will thus not only provide better insights into the evolutionary history of stramenopiles as a whole, but also set a stage to further investigate character evolution that may be relevant for studying diverse niche occupation, roles in food-webs, and virulence. 2 1.1 The phylogenomic relationship of non-photosynthetic stramenopiles 1.1.1 The unresolved relationship among the stramenopiles Stramenopiles have been proposed to fall in a variety of places in the tree of eukaryotes, and these suggestions impact how we interpret the biology of the group. For example, they have been proposed to be a member of the Chromalveolata, based on the hypothesis that the last common ancestor of the stramenopiles and various other algal groups possessed a plastid of red-algal origin (Cavalier-Smith, 1999). However, this hypothesis has been challenged and alternative hypotheses have been suggested (Larkum et al., 2007; Dorrell and Smith, 2011), and overall this Chromalveolate Hypothesis remains contentious at best. Understanding phylogenetic relationships of stramenopiles affects how we interpret the evolution of non-photosynthetic stramenopile lineages, especially the way we would model events of plastid gain or loss during the diversification of stramenopiles (Burki et al., 2016; Keeling, 2009; Keeling and Burki, 2019). For instance, whether the ancestor had a plastid or not, dictates how many instances of plastid gain or loss would be required to explain the current diversity of stramenopiles, which in turn would mean non-photosynthetic stramenopiles would be interpreted as having lost a plastid versus never having had one. To assess this type of trait evolution, robust characterization of the plastid-bearing status of many lineages, as well as the construction of a well-supported phylogeny of the stramenopiles, are both necessary. However, no known plastid-associated or plastid-targeted genes, or cryptic plastids akin to the apicoplasts in the Apicomplexa, have been identified from the handful of known heterotrophic stramenopiles, including in the well-studied Oomycota and Opalinata, although the one exception being SufCB found in Blastocystis, which is hypothesized to have originated from horizontal gene transfer (Tsaousis et al., 2012). 3 Additionally, the scant amount of genomic-scale molecular data for the already small number of identified heterotrophs is associated with conflicting phylogenomic tree topologies. For example, in phylogenomic trees constructed in Thakur et al. (2019) and Derelle et al. (2016), both Gyrista sensu stricto and Bigyra sensu stricto form clades while Bigyra is paraphyletic in other publications (Burki et al., 2016; Leonard et al., 2018; Noguchi et al., 2016) (Fig. 1.1). Additional topological instability exists within Gyrista where the positioning of the Bigyromonadea alternates between ochrophytes and oomycetes depending on the publication and the number of taxa included. Increasing taxon sampling of under-studied stramenopiles will therefore contribute to resolving phylogenomic relationships and can be used to address trait evolution. 1.1.2 The phagoheterotroph clade in Gyrista, the Bigyromonadea, lacks taxon sampling The phagotrophic heterotrophs, phagoheterotrophs (Mitra et al., 2016), which lack plastid or plastid-associated pathways are found in different lineages of stramenopiles such as Bigyromonadea, Sagenista, and Opalozoa. However, prior to the work in Chapter 2, only one species, a bacterivorous Developayella elegans belonging to the subphylum Bigyromonadea, had been characterized at a transcriptome level. Other species such as Develorapax marinus, Pirsonia guinardiae, and Mediocremonas mediterraneus have small subunit ribosomal RNA (SSU rRNA) gene sequences and cellular structures described without genomic level data (Aleoshin et al., 2016; Kühn et al., 2004; Weiler et al., 2021). The rest of the Bigyromonadea are only known through environmental screening using SSU gene markers (Massana et al., 2004). The phagoheterotroph mode of nutrition, lack of known plastid-associated genes, and SSU gene phylogenies were together used to suggest that D. elegans is closely related to oomycetes 4 (Moriya et al., 2002), but subsequently a SSU gene sequence from D. marinus suggested instead it is sister to ochrophytes (Aleoshin et al., 2016). Similar to the unstable phylogenomic affinity to ochrophytes or oomycete of Bigyromonadea, the morphological traits of the flagellar apparatus also seem to vary. The presence of two roots for each of the two flagellar basal bodies in D. elegans resemble the ochrophytes while other heterotrophic stramenopiles have an additional ventral root, due to a root being split (Yubuki and Leander, 2013). When examining the flagellar transitional zone of D. elegans, a distal portion of a microtubular organizing centre (MTOC = basal body) denoting the boundary between a flagellum and a cell body, it has two connected helices (a double transitional helices) similar to those found in oomycetes and some phagoheterotrophs found in Bigyra (Tong, 1995; Aleoshin et al., 2016). The semicircular microtubule organization of the ventral flagellar root of D. marinus is similar to that of D. elegans, in addition to the presence of the double transition helices in the two basal bodies. The SSU gene sequences of the two species are also similar (94% similarity for SSU; 91% for the large subunit ribosomal RNA gene [LSU rRNA]) and together they comprise the order Developea, close to the ochrophytes (Aleoshin et al., 2016). However, unlike D. elegans, D. marinus can engulf other protists or aggregates of bacteria. This eukaryovory led to a speculation that D. marinus might resemble the ochrophyte ancestor, which in turn suggested an independent secondary endosymbiosis of ochrophyte plastid, which is also consistent with the lack of plastid or plastid-associated proteins in all non-ochrophyte stramenopiles (Aleoshin et al., 2016). Recently, a smaller bacteriovorous bigyromonad, Mediocremonas mediterraneus (Weiler et al., 2021) has been successfully cultured and its SSU rRNA gene characterized. It branched with the Developea, most closely grouping with an “abyssal” clade of deep-sea environmental 5 data from Sagami Bay sediments (Takishita et al., 2007), southeastern Atlantic plain (Scheckenbach et al., 2010), and Norwegian Sea coral reefs (Jensen et al., 2012). Collectively Developea were sister to both ochrophytes and oomycetes. Although the reliability of SSU gene tree is limited, the assignments of the characterized SSU gene sequences of Bigyromonadea to various environmental sequences point to more taxa yet to be uncovered. The order Pirsoniales (Cavalier-Smith, 1998) forms a clade in a phylogenetic trees based on SSU gene, and is consistently placed closest to ochrophytes and somewhat less stably to the order Hyphochytriales (Aleoshin et al., 2016; Kühn et al., 2004; Weiler et al., 2021). The first described species within the order was Pirsonia guinardiae, a peculiar parasite of diatoms (Schnepf et al., 1990). Unlike other osmotrophic parasites that are phylogenetically placed close to it (e.g., Oomycota and Hyphochytriomycota), Pirsonia spp. deploy their pseudopodia to squeeze through the frustule girdles of diatoms, while the main parasitic cell body (auxosome) stays outside of the host. The invading pseudopodium then forms a trophosome which phagocytoses the host cytoplasm, and sometimes chloroplasts as well (Kühn et al., 2004; Schnepf et al., 1990). Whether Pirsonia spp. have genes associated with plastid-associated pathways or cryptic plastids has not been thoroughly investigated, although there have been no reports of successful visualization of engulfed plastids that might allude to the existence of a kleptoplast (Schnepf et al., 1990). With only transcriptomic data from D. elegans available and its unstable position in the SSU phylogenetic trees, it was inconclusive whether the Bigyromonadea is monophyletic, comprising the Developea and Pirsoniales (Aleoshin et al., 2016; Weiler et al., 2021). 6 1.1.3 Ecological diversity and the complex evolutionary history of the phagoheterotrophs of Bigyra The diversity and distribution of phagoheterotrophs across stramenopile phylogeny have been inferred from environmental screenings of heterotrophic nanoflagellates using ribosomal SSU markers (Kolodziej and Stoeck, 2007; Lin et al., 2012; Massana et al., 2004). These surveys mainly focused on marine environments, and initially discovered several clades consisting of environmental sequences only. Out of these 18 MArine STramenopiles (MAST) clades, only three have been described cellularly and/or phylogenetically at the transcriptome level so far. These include Pseudophyllomitus vesiculosus as MAST-6 (Shiratori et al., 2017), Incisomonas marina as MAST-3 (Cavalier-Smith and Scoble, 2013), and MAST-4 (Roy et al., 2014). However, recent SSU environmental surveys revealed that what were previously thought to be marine-exclusive MASTs (e.g., MAST-3 and MAST-6) were also present in ecologically diverse freshwater habitats (Simon et al., 2015) and sediments (Logares et al., 2012; Massana et al., 2015; Rodríguez-Martínez et al., 2020). Additional detection of MAST-2 (Simon et al., 2015) and MAST-12 (Kolodziej and Stoeck, 2007) in an estuary demonstrated the ecological diversity of phagoheterotrophs and the biased nature of environmental survey, which are skewed towards marine pelagic environments. There is much more data from phagoheterophic Bigyra than bigyromonads, including several different strains of MAST-4, MAST-3, Bicosoecids, Placididea, Cafeteria roenbergensis, Cantina marsupialis, Wobblia lunata, and Pseudophyllomitus vesiculosus (MAST-6). More recently, genomic level data from MAST-1, -7, -8, -9, and MAST-11 were also described (Labarre et al., 2021). However, even with all these data, the phylogenetic placement of Bigrya remains contradicted, and the phylogenetic incongruence in Bigyra and Gyrista may be related. 7 Whether conflicts among published phylogenomic trees are due to different numbers or the composition of taxa or genes, quality of data, different statistical indices (e.g., Bayesian posterior probabilities vs. bootstrap percentage), phylogenetic substitution models, or phylogenetic reconstruction methods (e.g., distance vs. likelihood method), it is apparent that the stramenopiles have a complex molecular evolutionary history (e.g., genomic changes such as indels, retrotransposon integration, horizontal gene transfer (HGT), or gene fusion) and phylogenetic signal may have been masked by homoplasy and heterotachy (Delsuc et al., 2005; Lopez et al., 2002). Platysulcus tardus (Shiratori et al., 2015; Thakur et al., 2019) is the sole member of the Platysulcea, which appears to form a sister clade to the rest of the stramenopiles. At a morphological level, P. tardus is an amalgamation of different traits of various phagoheterotrophic stramenopiles. For example, its gliding movement (rare in stramenopiles) is similar to Placididea (e.g., W. lunata), but P. tardus has a longer posterior flagellum which is similar to another gliding stramenopile belonging to Bikosia (Caecitellus parvus), which itself lacks mastigonemes on its anterior flagellum (Shiratori et al., 2015). Furthermore, P. tardus lacks any helical structures at the basal bodies, which are present in a double helical form in Placidozoa, oomycetes, and the Bigyromonadea, and in a single helix form in ochrophytes. Instead, P. tardus has L-shaped microtubule organization, a similar trait to Bikosia except P. tardus has an X-fibre where the S-tubule is absent from R2 of the anterior basal body (Shiratori et al., 2015; Yubuki and Leander, 2013). 1.2 The phylogenomic relationship and systematics of the photosynthetic stramenopiles All the photosynthetic stramenopiles (excluding mixotrophic labyrinthulids harbouring green algal symbionts) are ochrophytes, but not all ochrophytes are photosynthetic. This is 8 especially the case for many chrysophytes (golden algae) that are mixotrophs or have lost the ability to photosynthesize entirely (Beisser et al., 2017; Graupner et al., 2018). Additionally, ochrophytes have diverse morphologies, ranging from multicellular giant kelps, heliozoan (“sun-like”) Ciliophrys spp., amoeboid Chrysamoeba radians (Hibberd, 1971), and silica-covered diatoms. Despite this morphological and genetic diversity, many studies involving ochrophytes are often focused on two groups, diatoms (Bacillariophyceae) and brown algae (Phaeophyceae), due to their ecological and economical importance. Genome data from most other ochrophyte groups is lacking or they are only represented by one or two species, despite being publicly available cultures. To date, only a single phylogenomic analysis includes comprehensive ochrophyte data (Terpis, 2021, data unpublished). Phylogenetic analyses including a wide variety of ochrophytes are needed, however, because conflicting phylogenetic trees have been inferred from a handful of SSU rRNA or plastid genes, and initially in combination with morphological traits (Cavalier-Smith and Chao, 2006; Cavalier-Smith and Scoble, 2013). The classification is also in flux, with new classes or families of ochrophytes being erected or existing groups correctly re-classified because of phylogenetic findings. As a result, old names have been re-used or abandoned by some authors (Riisberg et al., 2009; Ševčíková et al., 2015; Derelle et al., 2016), while others have come up with a completely different system (Yang et al., 2012), or a combination of both (Derelle et al., 2016). For example, Yang et al., (2012) proposed a new classification composed of three clades (SI, SII, and SIII), in which the SI clade consists of Raphidophyceae-Phaeophyceae-Xanthophyceae (RPX) plus Schizocladiophyceae, “Chrysomerophyceae” which was later identified as being misspelled (Graf et al., 2020), Phaeothamniophyceae, and Aurearenophyceae: the SII clade consists of the Chrysophyceae-Synchromophyceae-Synurophyceae (CSS) plus Pinguiophyceae and 9 Eustigmatophyceae, and the SIII clade consists of Bolidophyceae and Bacillariophyceae (BB), and Pelagophyceae and Dictyochophyceae (PeD). On the other hand, Derelle et al. (2016) proposed the group, Diatomista consisting of Khakista (synonymous to the originally described BB (Cavalier-Smith and Chao, 2006)) and PeD, while reusing Chrysista (Cavalier-Smith and Scoble, 2013) to describe rest of the ochrophytes. Others redefined the original classification as exemplified by Khakista and Phaeista (initially included only PeD) whose redefined groupings include BB and PeD within Khakista (essentially synonymous to Diatomista), and the rest of ochrophytes within Phaeista, essentially synonymous to Chrysista (Riisberg et al., 2009). Most of these groupings are controversial, as they are not always recovered from different phylogenetic analyses. One example of this is Limnista (Cavalier-Smith and Chao, 2006), which refers to the sister-lineage of Eustigmatophyceae and Chrysophyceae. This grouping is only recovered in phylogenetic trees inferred from plastid genes (Ševčíková et al., 2015; Di Franco et al., 2022) or a single-gene tree (Cavalier-Smith and Scoble, 2013). Pinguiophyceae is recognized to be part of Diatomista in a recent taxonomic revision by Adl et al. (2019) however many phylogenomic analyses recovered the class as being part of Chrysista (Azuma et al., 2022; Burki et al., 2016; Noguchi et al., 2016). The classification is also complicated by misnaming and misidentification [i.e., Chrysomeridophyceae was misnamed as Chrysomerophyceae in (Cavalier-Smith, 1995)], and by the lack of molecular and/or morphological data from type species (i.e., the genus Chrysomeris) (Graf et al., 2020). Even with genome-scale data, the phylogenomic position of some lineages remain unresolved, such as Eustigmatophyceae (Hibberd, 1981), Pinguiophyceae (Kawachi et al., 2002), and Actinophrys spp. (Ehrenberg, 1830) (Derelle et al., 2016; Dorrell et al., 2021; Di Franco et al., 2022; Cho et al., 2022). Other lineages are simply not represented in the dataset [except in 10 the recent study (Terpis, 2021)], or represented by a single species (Ševčíková et al., 2019; Amaral et al., 2020; Azuma et al., 2022). 1.2.1 Discrepancies between plastid and nuclear trees The most obvious controversy in ochrophyte phylogenomics is the incongruent phylogenomic trees inferred from plastid genes versus nuclear genes. This is especially true for the placements of Eustigmatophyceae and Pinguiophyceae. Why they differ and which is correct is unclear, but they appear to vary in phylogenetic signal, perhaps due to different selection pressures. In phylogenomic analyses inferred from plastid genes, Eustigmatophyceae forms a sister-lineage to CSS (Ševčíková et al., 2015; Di Franco et al., 2022). In contrast, when the phylogenomic tree is inferred from nuclear genes, Eustigmatophyceae forms a sister-lineage to RPX, with weak statistical support (Burki et al., 2016; Noguchi et al., 2016; Cho et al., 2022). Similar to Eustigmatophyceae, the placement of Pinguiophyceae can differ based on the genes used to infer a tree. It is placed sister to CSS in phylogenomic trees inferred from plastid trees but placed sister to Diatomista in some trees inferred from nuclear genes (Yang et al., 2012; Di Franco et al., 2022) or RPX in the trees inferred from SSU rRNA genes (Kawachi et al., 2002). Despite many statistical tests, such as the approximately unbiased test (Shimodaira, 2002), or comparing the level of phylogenetic signals between nuclear or plastid genes (Di Franco et al., 2022), having only one or two taxa representing each of the groups still leave the placement of these lineages contentious. 1.2.2 Under representative classes of ochrophytes There are a total of 17 currently recognized classes of ochrophytes described so far (Cavalier-Smith and Chao, 2006; Riisberg et al., 2009; Yang et al., 2012; Graf et al., 2020), with 11 detailed morphological characterization and phylogeny inferred from rRNA and/or plastid genes. In previous phylogenomic analyses, at most 10 classes of ochrophytes were represented, with much of the data collection skewed towards Phaeophyceae (brown algae), Chrysophyceae (golden algae), and Bacillariophyceae (diatoms) (Burki et al., 2016; Derelle et al., 2016; Leonard et al., 2018; Noguchi et al., 2016; Thakur et al., 2019; Cho et al., 2022, 2024). Prior to Chapter 4, four classes were under-represented in phylogenomic analyses: Schizocladiophyceae, Phaeothamniophyceae, Picophagea, and Olisthodiscophyceae. Schizocladiophyceae and Phaeothamniophyceae are positioned within the Phaeophyceae-Xanthophyceae lineage in a phylogenetic tree inferred from a combination of five plastid genes and SSU rRNA genes (Graf et al., 2020), while the position of Picophagea and Olisthodiscophyceae were only explored in single phylogenomic analysis (Terpis, 2021, unpublished data). 1.3 Phylogenomic inference methods and computational burdens Throughout the thesis, I use phylogenomic analyses to investigate and attempt to resolve phylogenomic incongruencies among reported studies. My analyses involves first finding orthologous genes (up to ~260) in genomic or transcriptomic data of species of interest. Each of these genes is aligned and then concatenated (combined) to construct a supermatrix (Fig. 1.2). The supermatrix is then used to infer a phylogenomic tree using likelihood methods (maximum likelihood and Bayesian) that incorporate models for different character (amino acids or DNA sequences) evolution. Using multiple genes allows for the “genomic” representation of the organisms when inferring a phylogeny, but often limits the number of species that can be used because it requires much more completeness of the data. My approach has been to fill in such gaps to see if newly sampled lineages can be placed, and whether including the new taxa also affects the topology of the tree more generally. Using a single gene such as SSU rRNA, to infer a 12 phylogeny was prevalent before the phylogenomic era, and although these trees lack support, they remain useful for certain purposes, especially for screening environmental data, and this approach is also taken here. Although using hundreds of genes is a more reliable way to infer a phylogeny, it comes with one of the major hurdles in the method, the computational time and resources. This is especially true for the likelihood methods used in this thesis. Likelihood inference methods utilize complex mathematical models that incorporate various parameters accounting for amino acid site heterogeneity for tree hypothesis testing (Lopez et al., 2002; Delsuc et al., 2005; Lartillot et al., 2007; Quang et al., 2008; Wang et al., 2018). Moreover, testing statistical confidence is perhaps the most computationally demanding process as multiple iterations of tree sampling are required (e.g., non-parametric bootstrap in ML inference). Bayesian inference is another model-based method that incorporates maximum likelihood and was used in conjunction with ML inference to validate the phylogenomic relationship in this thesis. As this inference method relies on a Markov chain Monte Carlo (MCMC) algorithm while measuring the statistical confidence using posterior probabilities, the computational burden can be reduced compared to the ML inference (Holder and Lewis, 2003; Delsuc et al., 2005; Lartillot et al., 2009). However, the MCMC algorithm requires tens of thousands of iterations as opposed to hundreds of bootstraps in ML, and it is difficult to estimate whether the MCMC approximation has reached local maxima or has run long enough. Additionally, Bayesian inferences are sensitive to the misspecification of an prior probabilities, and as a result, multiple chains (four chains in this thesis) are often used to conduct MCMC approximation. In the end, inferring a phylogenomic tree can be a months- to a year-long process depending on the computing resources available. To remediate the computational burden, a “divide-and-conquer” (Delsuc et 13 al., 2005) strategy has been proposed where a dataset is sub-divided by a group and then an optimal tree is generated for each (Strimmer and Von Haeseler, 1996). Then these trees are then combined to generate a supertree. Another way to reduce computational burden can be to minimize the number of genes used, by selectively using phylogenetically informative genes for the phylogenomic analyses (Salichos and Rokas, 2013; Edwards, 2016; Shen et al., 2016b; Mongiardino Koch, 2021; Di Franco et al., 2022). Although this approach has been tested on more recently diverged eukaryotes (metazoans) or prokaryotes, it is yet to be tested on a specific group of protists. 1.4 Endosymbionts of non-photosynthetic stramenopiles Many stramenopiles are known to be symbionts of other eukaryotes, either as parasites, or kleptoplasts. The best-known endosymbiotic stramenopiles include the pennate diatom, Nitzschia frustulum symbiotica residing in four families of large benthic formaniferans (Lee, 2006), while others only have the plastids sequestered by a host, such as dinoflagellates in the case of dinotoms (e.g., Durinskia capensis and D. kwazulunatalensis (Yamada et al., 2019)). Of the parasitic stramenopiles, oomycetes are known for their ability to infect a broad range of hosts, including animals, plants, and other protists (Vallet et al., 2019), whereas other parasites are scattered across the stramenopile phylogeny, including the Labyrinthulomycetes (Labyrinthula zosterae) (Muehlstein et al., 1991), Opalinata (Blastocyst hominis) (Basak et al., 2014), and Bigyromonadea (Pirsonia sp.). Stramenopiles as hosts to endosymbionts, on the other hand, have not been well investigated, and this is true for prokaryotic symbionts of heterotrophic stramenopiles. Based on a recent curation of the published ecological interactions of protists (Bjorbækmo et al., 2020), the supergroup alveolates and rhizaria are the most common hosts of non-parasitic symbionts, which 14 include dinoflagellates, diatoms, and trebouxiophyceans, and together make up 81% of the symbiont-host interaction noted in this analysis. For parasite-host interactions, alveolates again comprise two thirds of both parasite and host examples, while other common hosts were diatoms. Data from the rest of the stramenopile lineages as hosts of parasites or non-parasites is scarce, and the functional role between the pair has not been characterized (denoted as “Unresolved interaction” (Bjorbækmo et al., 2020)). The majority of these “Unresolved interactions” are dominated by protist-bacteria interactions (73%) with rest of the 23% being protist-protist interactions, possibly of under-sampled or unknown protists. The heterotrophs or mixotrophs of stramenopiles, particularly Labyrinthulomycetes and MASTs, were under-represented when compared to SSU-environmental survey (de Vargas et al., 2015), comprising ~0.5% of the interaction entries (Bjorbækmo et al., 2020). This trend can also be observed in a recent review by Husnik et al. (2020) which reports only one case of a prokaryotic endosymbiont among non-photosynthetic stramenopiles (Husnik et al., 2021). This non-photosynthetic stramenopile with reported endobacteria is Symbiomonas scintillans, a tiny phagoheterotroph (Guillou et al., 1999). What is interesting about these two reviews (Bjorbækmo et al., 2020; Husnik et al., 2021) is the number or the proportion of symbiont studies targeting stramenopiles, despite the group being relatively well characterized, if not better described than other eukaryotic supergroups. For example, only 17 cases of stramenopiles are reported to have prokaryotic symbionts (15 of which are from photosynthetic stramenopiles) while 28 cases of symbiont studies are done on rhizaria, a vastly under-studied supergroup. Although non-phototrophs play vital roles in linking carbon cycling between lower and higher trophic mode of food webs (Sherr and Sherr, 2002), it is surprising how little investigation has been done on symbionts of non-photosynthetic stramenopiles, especially for the nano- or 15 pico-flagellates. Characterizing symbionts of these under-represented stramenopiles may provide some clues into how the complex evolutionary history of stramenopiles took shape. For instance, bacterial endosymbionts may have contributed to the hosts occupying and surviving diverse ecological niches, ranging from shallow freshwater, mosses, or anoxic sediments, with or without being dependent on metabolic network of eukaryotic endosymbionts. It is still not known whether or how symbiotic interactions resulted in diverse morphologies, feeding and motility strategies, further leading to divergence of phylogenomic clades. Uncovering the evidence of the symbionts or a community thereof, may help us understand the divergence of major clades throughout the stramenopile phylogeny. 1.5 Thesis goals and objectives Compared to other protist supergroups, considerable amounts of genomic and transcriptomic data have been generated for stramenopiles (Sibbald and Archibald, 2017). Despite the amount of the data, the phylogenomic relationships among stramenopiles are largely unresolved, partly due to the complex biodiversity of ochrophytes, and under-sampling the remainder of the group, such that much of their diversity and ecological roles of which are yet to be characterized. To address these problems, the major objectives of my dissertation are: 1. To resolve phylogenomics of stramenopiles by increasing taxon sampling of small non-photosynthetic flagellates. 2. To characterize distribution and abundance of phagoheterotrophic stramenopiles that are associated with sediments and V9-region targeted (SSU) amplicon datasets. 3. To resolve phylogenomics of ochrophytes by sampling under-studied lineages and searching for phylogenetically informative genes. 16 4. To identify the function and identity of the only reported case of prokaryotic endosymbionts among a heterotrophic stramenopile, Symbiomonas scintillans. In Chapter 2, and 3, I addressed the first objective by generating transcriptomes of 13 heterotrophic stramenopiles, 11 of which are newly described species, belonging to Bigyromonadea, MAST-6, and Placididea. These groups were previously represented by one or two species in a phylogenomic analysis. After inferring phylogenomic trees, I also conducted extensive statistical tests to verify the placement of these taxa. As a result, the inferred phylogenomic tree recovered a robust monophyly of Bigyromonadea and Oomycota and a paraphyletic relationship of Bigyra. In Chapter 3, I addressed the second objective by searching for SSU rRNA sequences of the newly described sediment-associated MAST-6 and halophilic Placididea species in publicly available environmental amplicon datasets. I focused on sediment amplicon data sets and a dataset that used a V9 SSU rDNA primer pair (preferential amplification of Placididea). As a result, I found that many MAST-6 species related to one of the newly described species are abundant in sediments, while the newly described Placididea species may tolerate a broad range of salinity. In Chapter 4, I aimed to resolve the phylogenomic relationships of stramenopiles by updating under-represented classes of ochrophytes and searching for phylogenetically informative genes. As a result, I generated 10 new ochrophyte transcriptomes, mostly from public culture collections, and one obtained from a single-cell isolation for an environmental sample. I also added publicly available genomic level data and recovered robust support for some of the previously controversial relationships. To address unresolved relationships of other lineages and explore sub-sampling in a phylogenomic supermatrix, I searched for phylogenetically 17 informative genes and inferred 16 phylogenomic trees consisting of different genes with varying phylogenetic signals. I found no genes that only had phylogenetic signal without noise. However, I did find that inferring phylogenomic trees based on genes with high phylogenetic signal and quality yielded fewer variable topologies, than removing genes with high phylogenetic noise. Finally, Chapter 5 addresses the fourth objective. This chapter highlights how little we know about symbionts of non-photosynthetic stramenopiles, such as Symbiomonas scintillans, whose endosymbionts may be giant viruses and not bacteria, although further experiments are needed. The work done in Chapter 2 and Chapter 3 focused on enriching taxon sampling of these under-studied heterotrophic stramenopiles and provided both morphological descriptions and transcriptome data to provide an updated phylogenomics of stramenopiles. These chapters further provide a platform for better understanding trait evolution such as the evolution of saprotrophy in oomycetes, adaptability in specific niche occupation, horizontal gene transfers, and revisiting plastid evolution. Overall, my thesis has greatly updated phylogenomic data from stramenopiles by generating 23 transcriptomes, in addition to data curation from publicly available database. The phylogenomic analyses permit a better understanding of the evolutionary history of stramenopiles, while highlighting their diversities in terms of feeding, morphology, and relationship with other organisms. 18 Figure 1.1 Different versions of stramenopile phylogenomic trees recovered in different publications. Ochrophytes, Oomycetes, and Bigyromonadea have been proposed to form a major group called Gyrista. Sagenista and Opalozoa have been proposed to form a second major group, called Bigyra. Three out of five phylogenomic analyses (Noguchi et al., 2016; Leonard et al., 2018; Thakur et al., 2019) included Bigyromonadea in their analyses, with the clade represented by a single taxon (*). The species that branches sister to the rest of the stramenopiles, Platysulcus tardus is omitted. 19 Figure 1.2 Flowchart showing steps to constructing a supermatrix Concatenation or combination of multiple genes into a single alignment for each species can be done using several programs including PhyloFisher (Tice et al., 2021) or SCaFos (Roure et al., 2007). 20 Chapter 2: Monophyly of diverse Bigyromonadea and their impact on phylogenomic relationships within stramenopiles 2.1 Introduction Stramenopiles (= heterokonts) are one of the well-characterized members of the eukaryotic supergroup SAR (Stramenopila, Alveolate, Rhizaria) (for a review see Keeling and Burki, 2019). Stramenopiles are very diverse, comprising photoautotrophs (i.e., heterokont algae in ochrophytes), osmotrophic oomycetes and labyrinthulomycetes with a motile zoospore life-stage (e.g., Phytophthora sp., Pythium sp., and labyrinthulids) and free-living phagotrophic opalozoans (e.g., Cafeteria roenbergensis, Cantina marsupialis) that occupy a broad range of environments (Cavalier-Smith and Chao, 2006; Kolodziej and Stoeck, 2007; Stiller et al., 2009; Tsui et al., 2009; Cavalier-Smith and Scoble, 2013). Stramenopiles can be largely classified into two major groups: Gyrista consisting of Ochrophyta, Oomycota, and Bigyromonadea; and Bigyra consisting of Sagenista and Opalozoa. A single species, Platysulcus tardus, has also recently been shown to be a basal stramenopile (Thakur et al., 2019). While there is a lot of genomic data from stramenopiles, only a handful comes from phagoheterotrophs (Mitra et al., 2016), despite them representing much of the diversity as well as being key outstanding problems in resolving controversies in stramenopiles phylogeny (Burki et al., 2016; Derelle et al., 2016; Leonard et al., 2018; Shiratori et al., 2017, 2015). One such clade is the subphylum Bigyromonadea (Cavalier-Smith, 1998), which was proposed to include the class Developea (Aleoshin et al., 2016) and order Pirsoniales (Cavalier-Smith, 1998). The monophyly of the Bigyromonadea is essentially untested, since only small subunit rRNA (SSU) data are known from all but a single species (the exception being “Developayella elegans”, for which a transcriptome is available), and the two groups never 21 branch together in SSU phylogenies (Aleoshin et al., 2016; Cavalier-Smith and Chao, 2006; Kühn et al., 2004; Weiler et al., 2021). Developea are marine bacterivores, including Developayella elegans (Tong, 1995; Leipe et al., 1996) and Mediocremonas mediterraneus (Weiler et al., 2021), and the marine eukaryovore Develorapax marinus (Aleoshin et al., 2016). Pirsoniales are parasites of other microbes, including Pirsonia guinardiae (Schnepf et al., 1990) and P. punctigera (Schweikert and Schnepf, 1997). These parasites deploy a pseudopod to squeeze through the frustule girdles of their diatom host, while the main cell body (auxosome) stays outside of the host. The invading pseudopod then phagocytoses the host cytoplasm or chloroplasts forming a trophosome (food vacuole), which is then transported out to the auxosome (Kühn et al., 2004; Schnepf et al., 1990). The relationship of both groups to other stramenopiles is uncertain, and both have led to hypotheses about the evolution of other related groups. For example, the eukaryovory of D. marinus and its placement in rRNA trees has led to the hypothesis that it represents a model for a phagoheterotrophic ochrophyte ancestor (Aleoshin et al., 2016), however its position in the tree varies between grouping with ochrophytes (Leonard et al., 2018) or oomycetes (Noguchi et al., 2016; Thakur et al., 2019). Pirsoniales have also been found as the sister group of ochrophytes based on SSU rRNA trees (Aleoshin et al., 2016; Kühn et al., 2004), although once again not consistently and without strong support. To test for the monopoly of bigyromonads and more thoroughly examine their relationship to other stramenopiles, together with the collaborators, I substantially increased the diversity of genomic data from the group by adding transcriptomes from seven newly discovered species belonging to Pirsoniales (Pirsonia chemainus nom. prov., Koktebelia satura nom. prov., and Feodosia pseudopoda nom. prov.) and Developea (Develocanicus komovi n. gen. n. sp., 22 Develocanicus vyazemskyi n. sp., Develocauda condao n. gen. n. sp., and Сubaremonas variflagellatum n. gen. n. sp.). The inferred 247-gene phylogenomic tree, reconstructed with various methods, recovered for the first time the monophyly of the Bigyromonadea. Maximum likelihood (ML) recovered a robustly supported monophyly of Bigyromonadea and oomycetes, while Bayesian inference and statistical tests of alternative tree hypothesis were inconclusive. I describe several new features of the seven bigyromonads, and noted their resemblance with oomycete zoospores, and report the first observation of eukaryovory in the flagellated stages of Pirsoniales. Overall, these findings indicate bigyromonada and ooymcetes are most likely sister groups, and suggest potential ancestral state of the oomycetes resembling bigyromonada, including their ability to form auto-aggregates (=self-aggregates) (Hickman, 1970; Ko and Chase, 1973; Galiana et al., 2008) and phagoheterotrophy. 2.2 Materials and Methods 2.2.1 Sample collection, identification, and library preparation Strain Сolp-23 (Develocanicus komovi) was obtained from the black volcanic sand on the littoral zone of Maria Jimenez Beach (Playa Maria Jiménez), Puerto de la Cruz, Tenerife, Spain, October 20, 2014. Strains Colp-30 (Develocanicus vyazemskyi) and Chromo-1 (Koktebelia satura) were isolated from the near shore sediments on the littoral zone near T.I. Vyazemsky Karadag Scientific Station, Crimea, May 2015. Strain Chromo-2 (Feodosia pseudopoda) was obtained from the near shore sand on the littoral zone of the beach in the settlement Beregovoye, Feodosiya, Crimea, June 24, 2017. Strain Colp-29c (Develocauda condao) was isolated from the near shore sediments on the north-east part of Con Dao Island, South Vietnam, May 4, 2015. Strains ‘Pirsonia-like’ (Pirsonia chemainus) and Dev-1 (Сubaremonas variflagellatum) were obtained from seawater samples taken in the Strait of Georgia, British Columbia, Canada (123° 23 28'50'' W, 49°10'366'' N) at 70 m and 220 m depths, respectively using a Niskin bottle, June 13, 2017. The samples were examined on the third, sixth and ninth day of incubation in accordance with methods described previously (Tikhonenkov et al., 2008). Procryptobia sorokini strain B-69 (IBIW RAS), feeding on Pseudomonas fluorescens, was cultivated in Schmaltz-Pratt’s medium at a final salinity of 20‰, and used as a prey for clones Colp-23, Colp-29c, Colp-30, Chromo-1, Chromo-2, and ‘Pirsonia-like’ (Tikhonenkov et al., 2014). Bacterivorous strain Dev-1 was propagated on the Pseudomonas fluorescens, which was grown in Schmaltz-Pratt’s medium. Strains Colp-23, Colp-29c, and Dev-1 are currently being stored in a collection of live protozoan cultures at the Institute for Biology of Inland Waters, Russian Academy of Sciences. However, strains Colp-30, Chromo-1, Chromo-2, and ‘Pirsonia-like’ perished after several months to one year of cultivation. Studied isolates were identified using a combination of microscopic and molecular approaches. Light microscopy observations were made using a Zeiss AxioScope A.1 equipped with a DIC water immersion objective (63x) and an AVT HORN MC-1009/S analog video camera. The SSU rRNA genes (GenBank accession numbers: OL630092 to OL630098) were amplified by polymerase chain reaction (PCR) using the general eukaryotic primers EukA-EukB (for strains Colp-23, Colp-30, ‘Pirsonia-like’), PF1-FAD4 (Chromo-1), 18SFU-18SRU (Chromo-2, Dev-1), 25F-1801R (Colp-29c) (Medlin et al., 1988; Keeling, 2002; Cavalier-Smith et al., 2009; Tikhonenkov et al., 2016). PCR products were subsequently cloned (Colp-23, Colp-30, Chromo-2, ‘Pirsonia-like’) or sequenced directly (Chromo-1, Dev-1, Colp-29c) using Sanger dideoxy sequencing. 24 For cDNA preparation, cells grown in clonal laboratory cultures were harvested when the cells had reached peak abundance (strains Colp-23, Col-30, Colp29c, Chromo-1, Dev-1) and after the majority of the prey had been eaten (for eukaryovorous strains Colp-23, Col-30, Colp29c, Chromo-1). Cells were collected by centrifugation (1000 x g, room temperature) onto the 0.8 µm membrane of a Vivaclear mini column (Sartorium Stedim Biotech Gmng, Cat. No. VK01P042). Total RNA was then extracted using a RNAqueous-Micro Kit (Invitrogen, Cat. No. AM1931) and reverse transcribed into cDNA using the Smart-Seq2 protocol (Picelli et al., 2014), which uses poly-A selection to enrich mRNA. Additionally, cDNA of Colp-29c was obtained from 20 single cells using the Smart-Seq2 protocol (cells were manually picked from the culture using a glass micropipette and transferred to a 0.2 mL thin-walled PCR tube containing 2 µL of cell lysis buffer – 0.2% Triton X-100 and RNase inhibitor (Invitrogen)). The same ‘single cell’ transcriptomic approach was applied for strains Chromo-2 and ‘Pirsonia-like’, which never consumed the prey completely. Sequencing libraries were prepared using NexteraXT protocol and sequencing was performed on an Illumina MiSeq using 300 bp paired-end reads. Additionally, Chromo-1 transcriptome sequencing was performed on the Illumina HiSeq platform (UCLA Clinical Microarray Core) with read lengths of 100 bp using the KAPA stranded RNA-seq kit (Roche) to construct paired-end libraries. Raw reads are available in the NCBI Short Read Archive (SRA) (BioProject number: PRJNA782193, SRR17035338 to SRR17035344). 2.2.2 Small-subunit phylogenetic tree reconstruction SSU rRNA sequences were identified from the seven new assembled transcriptomes using Barrnap v0.9 (Seemann, 2007) and compared with the SSU sequences obtained with Sanger sequencing, and the longer sequences were used for further analysis. 25 After an initial BLASTn search of the SSU rRNA sequences against the non-redundant NCBI database to confirm stramenopile identity, the SSU sequences were aligned using MAFFT v7.222 (Katoh and Standley, 2013) with previously compiled SSU datasets (Aleoshin et al., 2016; Yubuki et al., 2015). Additionally, SSU sequences of the other stramenopile taxa that were included in the multi-gene phylogenomic dataset and other closely related taxa were included (see Results). Furthermore, to show the diversity of uncultured Gyrista and provide possible directions for future sampling efforts, environmental sequences of stramenopiles that are closely related to Pirsoniales and Developea were added. The environmental sequences were manually retrieved from NCBI database and PR2 based on previously published alignments (Massana et al., 2004; Weiler et al., 2021; Yubuki et al., 2015). After trimming using trimAl v.1.2rev59 (-gt 0.3, -st 0.001) (Capella-Gutiérrez et al., 2009), the two SSU phylogenetic trees were reconstructed based on 1650 sites and 92 taxa, and 1665 sites and 107 taxa, using IQ-TREE v1.6.12 (Nguyen et al., 2015) 1000 ultrafast bootstrap (UFB) under Bayesian information criterion (BIC): using the TIM2+R6 model selected by ModelFinder (Kalyaanamoorthy et al., 2017) implemented in IQ-TREE. 2.2.3 Transcriptome processing, assembly, and decontamination Raw sequencing reads were assessed for quality using FastQC v0.11.5 (Andrews, 2010) and remnant transposase-inserts from the library preparation were removed. The reads were assembled using Trinity-v2.4.0 with –trimmomatic option to remove NexteraXT adaptors, Smart-Seq2 IS-primer, and low quality leading and trailing ends (quality threshold cut-off:5) (Grabherr et al., 2011; Bolger et al., 2014). To identify contaminants, assembled reads were searched against the NCBI nucleotide database using megaBLAST (Basic Local Alignment Search Tool) (Altschul et al., 1990), followed by diamond BLASTX against a UniProt reference 26 proteome (The UniProt Consortium et al., 2021). To visualize the contig sizes, coverage, and remove bacterial, archaeal, and metazoan contaminants, BlobTools v1.0 (Laetsch and Blaxter, 2017) was used. PhyloFlash v3.3b2 (Gruber-Vodicka et al., 2020) was used in parallel to confirm identified contaminants and coverage based on SILVA v138 SSU database (Quast et al., 2012). To remove sequences from the prey, Procryptobia sorokini, which was used in the cultures of Pirsonia chemainus, Koktebelia satura, Feodosia pseudopoda, Develocanicus komovi, D. vyazemskyi, and Develocauda condao, the assembled reads were searched against the P. sorokini transcriptome using BLASTn in which the contigs with ≥95% sequence identity were removed from the assembled reads. To predict open reading frames (ORFs) and coding genes, TransDecoder v5.5.0 (Haas et al., 2013) was used and the longest ORFs were annotated using BLASTP search against UniProt database. To estimate the completeness of each of the assembly, BUSCO v4.0.5 (Simão et al., 2015) with eukaryotic database was used. 2.2.4 Phylogenomic matrix construction and ortholog identification To better represent each stramenopile (sub)group in the phylogenomic reconstruction, recently published and publicly available (Broad Institute and Japan Agency for Marine-Earth Science and Technology; JAMSTEC) additional 27 stramenopile genomic or transcriptome data (de Vargas et al., 2015; Hackl et al., 2020; Keeling et al., 2014; Leonard et al., 2018; Noguchi et al., 2016; Seeleuthner et al., 2018; Thakur et al., 2019; Wawrzyniak et al., 2015) were obtained and analyzed along with the seven new transcriptomes (Appendix A). The updated stramenopile dataset including all the newly added transcriptomes in this study were compiled to the existing gene-set described below. Using BLASTP, the predicted coding genes from each transcriptome were searched against 263 gene-sets (orthologs), each consisting of compiled genes from major supergroups of protists, fungi, and holozoans (Burki et al., 2016; Hehenberger et al., 2017). The 27 blast outputs contained up to four non-redundant (nr) sequences for each gene and were filtered with an e-value threshold of 1e-20 with >50% query coverage. To ensure there is no extension for each of the newly identified genes that might hinder downstream 263 gene-set analysis, the new gene-sets were used as a query for BLASTP search against the UniProt database, followed by removing poorly aligned regions. Each gene-set was aligned using MAFFT-L-INS-i v.7222 and trimmed using trimAL v1.2rev59 (-gt 0.8). To infer orthologs among nr sequences from the newly added transcriptomes aligned to the corresponding 263 gene-sets, 263 gene-trees were built using maximum-likelihood (ML) estimation with IQ-TREE v1.6.12 under the LG+I+G4 model and 1000 ultrafast bootstrap (UFB). Then, each gene tree was manually screened in FigTree v1.4.4 for paralogs and contaminants (e.g., long branching sequences or sequences nested within other distantly related taxa), which were subsequently confirmed using BLASTP search against the nr database. These paralogs and contaminants were removed from each gene-set alignment. To increase ortholog coverage from the added transcriptomes, fragmented orthologs were manually merged. To minimize the creation of artifacts, we followed several criteria for merging ortholog fragments. Up to two fragments were merged and considered fragments of the same ortholog; (1) if the fragments came from the same transcriptome; (2) if they were positioned within the same node in a given gene tree; (3) if they covered different regions of a gene with or without an overlapping region; (4) and if there was an overlapping region present among fragments aligned to a given gene, up to two mismatches were permitted. Out of the 263 gene-sets, 110 gene-sets include manually merged orthologs with up to two taxa per gene-set. The 263 gene-sets containing the selected orthologs of the newly added transcriptomes and 27 newly published stramenopile data were aligned using two approaches and compared by reconstructing two phylogenomic trees. In the first approach (approach 1), the 28 sequences were aligned by using MAFFT L-INS-i v.7.222 and trimmed via trimAL v1.2rev59 (-gt 0.8). In the second approach (approach 2), the sequences were filtered using PREQUAL (Whelan et al., 2018) to remove non-homologous regions generated due to poor transcriptome quality or assembly errors. The filtered sequences were then aligned using MAFFT G-INS-i (--allowshift and --unalignlevel 0.6 option) and processed for further filtering using Divvier (-mincol 4 and -divvygap option) (Ali et al., 2019) to identify statistically robust pairwise homology characters. The filtered gene-sets were then soft-trimmed using trimAL (-gt 0.1). The two dataset generated by two different filtering and alignment methods were separately processed using SCaFoS v1.2.5 (Roure et al., 2007), by removing gene-sets that have ≥40% missing amino acid positions in the alignment. The resulting 247 gene-set was concatenated into a phylogenomic matrix comprising 75,798 amino acid (aa) sites from 76 taxa for approach 1. For the PREQUAL/Divvier processed data (approach 2), the same 247 gene-sets were concatenated in a phylogenomic matrix comprising 101,314 aa sites from the same 76 taxa. 2.2.5 Phylogenomic tree reconstruction, fast-evolving site removal, and topology test The ML tree for the concatenated phylogenomic matrix was inferred using IQ-TREE v.1.6.12 under the empirical profile mixture model, LG+C60+F+G4 (Quang et al., 2008). The best tree under this model was used as a guide tree to estimate the “posterior mean site frequencies” (PMSF). The PMSF method allows the conduction of non-parametric bootstrap analyses under complex models on large data matrices and has been shown to mitigate long-branch attraction artifacts (Wang et al., 2018). This LG+C60+F+G-PMSF model was then used to re‐estimate the ML tree and for a non-parametric bootstrap analysis with 100 replicates. For Bayesian inference, CAT-GTR mixture model with four gamma rate categories was used with PhyloBayes-MPI v.20180420 (Lartillot and Philippe, 2004; Lartillot et al., 2009), only for the 29 dataset processed with approach 1. To estimate posterior probabilities, four independent Markov Chain Monte Carlo (MCMC) chains were run simultaneously for minimum 10,000 cycles. After discarding the first 2000 burn-in points, consensus posterior probabilities for each branch were computed by subsampling every second tree. Convergence of the four chains were tested by calculating differences in bipartition frequencies (bpcomp) with a threshold maxdiff however, no chains converged (maxdiff=1). Site-specific substitution rates were inferred using the -wsr option as implemented in IQ-TREE, under the LG+C60+F+G4 substitution model. Increments of the top 5% fastest evolving sites were removed from the phylogenomic matrix until exhaustion, defined as the point when the bootstrap support value significantly began to drop and the topology became unstable (50%; 37,899 sites). Each incremental phylogenomic matrix was analyzed using IQ-TREE for ML estimation using LG+C60+F+G4 and 1000 UFB. All fast-evolving species removal and sites tests were conducted on the dataset processed with approach 1. Approximately unbiased (AU) tests (Shimodaira, 2002; Nguyen et al., 2015) were performed on set of phylogenomic trees constructed based on the 247 gene-sets generated by the first approach (i.e., MAFFT L-INS-i and trimAL with -gt 0.8) and the second approach (i.e., PREQUAL/Divvier), separately. The set of trees includes the two ML trees generated under LG+C60+F+G4(+PMSF) with 1000UFB (100STB), four consensus trees of MCMC chains, and other hypothetical constrained trees as listed as “Chain modified” in Table 2.1. 2.3 Results 2.3.1 Multi-gene phylogenomic analysis The concatenated phylogenomic matrix was composed of 68 stramenopiles and eight alveolates (outgroup) with 247 aligned genes totaling 75,798 positions for approach 1, and 30 101,314 positions for approach 2. The average missing sites and genes were 22% and 19%, respectively (Fig. 2.1). The amount of missing data varied among the seven new transcriptomes. Chromo-1 had nearly complete data (5% missing sites and 6% missing genes) while Colp-29c had 21% missing sites and 12% missing genes. Colp-23 and Chromo-2 had the highest amount of missing data (75% missing sites and 57% genes for Chromo-2 and, 83% and 76% for Colp-23). The ML phylogenomic tree generated under LG+C60+F+G4+PMSF with STB estimation from the two approaches is shown in Figure 2.1, with the tree topology representing the dataset generated from approach 1 (i.e., MAFFT L-INS-i and trimAL with -gt 0.8). The tree topology representing the dataset generated from approach 2 (i.e., Prequal/Divvier) is shown in Appendix A. The tree topology is almost identical between the two, except the position of sub-clades in ochrophytes; for example, the positions of Chrysophyceae + Synurophyceae and Raphidophyceae + Phaeophyceae + Xanthophyceae + Eustigmatophaceae are swapped in the two trees (Fig. 2.1 and Appendix B). The newly added transcriptomes of the seven new species formed the robust monophyletic bigyromonada with either dataset (approach 1 and approach 2; Figure 2.1 and Appendix A): Develocanicus komovi, D. vyazemskyi, Develocauda condao, and Сubaremonas variflagellatum forming a Developea clade (100% STB), while Pirsoniales is composed of Pirsonia chemainus, Koktebelia satura, and Feodosia pseudopoda (100% STB). The ML tree also recovered monophyly of the bigyromonada and oomycetes with 100% STB support (Fig. 2.1). The monophyly of Gyrista was strongly supported, with Sagenista (Labyrinthulomycetes and Eogyrea) forming a sister clade to it, resulting in a paraphyletic Bigyra. Platysulcea formed a sister clade to rest of the stramenopiles with a moderate support (91%/95% STB) (Fig. 2.1; Appendix B). 31 Bayesian analyses recovered a conflicting topology for the bigyromonada, which formed a sister-clade to ochrophytes in all four consensus trees generated (Appendix C). Additionally, the topology within ochrophytes was conflicting, contributing to the lack of convergence. However, the monophyly of bigyromonada + ochrophytes was rejected by approximately unbiased (AU) tests in three of the four consensus trees. AU test failed to reject the chain 1 consensus tree at a confidence interval of 95% (p-AU ≥ 0.05) (Appendix B). Interestingly, the sub-clade topology of ochrophytes in chain 1 is the same as in the ML phylogenomic tree generated using the approach 1 (Figure 2.1; Appendix C). When the AU tests were repeated on hypothetically constrained trees where bigyromonada + oomycetes were monophyletic but the rest of the topology was unchanged for each of the MCMC chains, the tests failed to reject the monophyly of bigyromonada + oomycetes (Table 2.1). Rejection of bigyromonada + ochrophytes was also observed in constrained trees when the AU test was repeated on the dataset processed with approach 2 (Appendix D). To evaluate the effect of fast-evolving sites, bootstrap support and topology were compared among the ML trees that were reconstructed with increments of 5% fast-evolving sites removed from the dataset processed with approach 1. The topologies of the phylogenomic tree were maintained while the UFB support for Platysulcea increased up to 97% (Fig. 2.2). To account for possible artefacts due to long-branching attraction of fast-evolving species, tree reconstruction was repeated after removing Cafeteria roenbergensis, the two Blastocystis species, and Cantina marsupialis. The monophyly of bigyromonada and oomycetes was recovered with 85% UFB. However, the topology of Bigyra became unresolved with weak support for its monophyly (Appendix E). 32 2.3.2 Small-subunit ribosomal RNA gene tree reveals two different species assigned as Developayella As shown previously, the SSU rRNA phylogenetic tree recovered the bigyromonada as a paraphyletic group, with the Pirsoniales (Pirsonia chemainus, Koktebelia satura, and Feodosia pseudopoda) forming a sister clade to ochrophytes (92% UFB) while the Developea clade was recovered as sister to oomycetes (Fig. 2.3). Within the Developea clade, in addition to the SSU rRNA sequences obtained from Сubaremonas variflagellatum and the JAMSTEC Developayella elegans transcriptome, I included three publicly available SSU rRNA sequences assigned as Developayella spp.: Accession ID U37107 (Tong, 1995; Leipe et al., 1996), MT355111.1 (Unpublished) and JX272636.1 (Del Campo et al., 2013). Note: although JX272636.1 is assigned as “Cf. Developayella sp.” in GenBank, it was recently re-assigned as Mediocremonas mediterraneus (Weiler et al., 2021). The SSU rRNA sequences of the four “Developayella” fell into two separate groups, indicating two different species (and genera) were assigned as Developayella elegans, sub-clade I consisted of Developayella elegans U37107, Developayella sp. MT355111.1, Develocanicus komovi, D. vyazemskyi, and Develocauda condao, while sub-clade II consisted of M. mediterraneus (JX272636.1 and MT918788.1), JAMSTEC Developayella elegans, and Сubaremonas variflagellatum (Fig. 2.3). The SSU rRNA sequence similarity between the two sub-clade I Developayella species (U37107 and MT355111.1) is 98.987%, between the two species (JAMSTEC D. elegans and Сubaremonas variflagellatum) in sub-clade II 97.528% and between the originally described Developayella elegans U37107 and Сubaremonas variflagellatum 91.143%. 2.3.3 Morphology of the novel species Developea Karpov et Aleoshin 2016 33 Develocanicus vyazemskyi (Fig. 2.4A, B) and Develocanicus komovi (Fig. 2.4C–M) Free-swimming naked eukaryovorous heterokont flagellates. The shape of the cell is irregularly flattened ellipse, where the dorsal side is more convex, and the ventral side is flatter. Two species differ in size, Develocanicus vyazemskyi (Colp-30) is larger and rounder, 7.4 –12.5 μm long, 4.8 – 9.2 μm wide, typical dimension ranging 9.2 х 7.0 μm. Develocanicus komovi (Colp-23) is slightly smaller, with the length 5.4 – 10 μm, width 3.8 – 7.4 μm and a typical dimension of 7.1 х 5.1 μm. Cell possesses two non-acronematic heterodynamic flagella of unequal lengths (Fig. 2.4A-D, F, I, J). The posterior flagellum is two times longer than the cell, the anterior flagellum is approximately 1 – 1.5 times longer. Flagella emerge from a prominent ventral depression (Fig. 2.4A-D) which passes into a shallow wide groove (Fig. 2.4E) along the entire length of the cell. Cells predominantly exhibit active and quick swimming without rotation. During swimming, the posterior flagellum is directed backward and straight, running along the ventral depression of the cell. The anterior flagellum beats rapidly and is directed forward while slightly curved. In non-motile cells, both flagella are directed backward, beating in a slow sinusoidal wave (Fig. 2.4G, J). The medial nucleus is located closer to the dorsal side of the cell (Fig. 2.4H). A large digestive vacuole is situated at the posterior part of the cell (Fig. 2.4I, J). As it is digested, the posterior end of the cell becomes thinner. The cells can form aggregations and attach to each other (Fig. 2.4K), sometimes forming pseudopodia (Fig. 2.4L). Transverse binary fission (Fig. 2.4M). Develocauda condao (Fig. 2.4N–W) Free-swimming eukaryovorous heterokont flagellates (Colp-29c). The cells are slightly flattened, usually elongated-oval, less often narrow-oval or almost rod-shaped (Fig. 2.4Q). The anterior end is more rounded, the posterior end of the cell can be pointed, forming a 34 characteristic “tail” found in starving cells (Fig. 2.4R, S). Cell length 5.14 – 12 μm, width 2.8 – 5.42 μm typically ranging 7.14 x 4.28 μm in dimension. The caudal extension is about 4.57 x 1.42 µm in size. The cells have two heterodynamic flagella of an almost equal length with a posterior flagellum compared to the cell body. Flagella emerge from a pronounced deep ventral depression (Fig. 2.4N, O), which almost extends to the dorsal side of the cell. Depression transforms into a shallow groove (Fig. 2.4P) spanning along the entire cell, in which the posterior flagellum can fit. The cells swim very quickly without rotating along the longitudinal axis. The posterior flagellum is straight and directed backwards. The anterior flagellum is directed forward, beats actively, and is only slightly curved. Rarely, the cells lie at the bottom with both flagella directed backward while making a slow sinusoidal movement, or the posterior flagellum beating actively. The aggregated (Fig. 2.4U), partially fused cells (Fig. 2.4W) that form clusters were observed in culture. The medial nucleus is located closer to the dorsal side of the cell. Sated cells do not have a tail; at the posterior end of their cells there is a large digestive vacuole (Fig. 2.4T). Transverse binary fission (Fig. 2.4V). Сubaremonas variflagellatum (Fig. 2.4X–AE) Cells (clone Dev-1) are naked and solitary bacteriovores with a length of 3.7 – 8 μm, a width 2.6 – 5.4 μm, and a typical dimension of 5.0 х 3.7 μm. The cell shape varies from elongated oval, oviform to rounder form (Fig. 2.4X-AA). Typically, the shape is irregularly ovoid, with the convex dorsal side and the flatter ventral side. The shape and size vary depending on feeding conditions. Starving cells have a small rostrum at the anterior end (Fig. 2.4AD). Cells are larger before division. 35 The cells possess two heterodynamic flagella of unequal length, emerging from a conspicuous ventral depression (Fig. 2.4Z, AB). Ventral depression starts from the anterior tip and continues ventrally to the middle of the cell. The anterior flagellum is approximately equal to the cell length or slightly longer, while the posterior flagellum is 1.5 – 1.8 times longer than the cell. Digestive vacuole is situated at the cell posterior. An observed cell division produces two or four cells (Fig. 2.4AE). In culture condition, the cells predominantly lie at the bottom unattached with both flagella directed backward. The posterior flagellum runs along the ventral surface of the cell and beats rapidly with sinusoidal pattern to draw water through the depression. The anterior flagellum is hook-shaped and sweeps slowly down behind the posterior flagellum. Although less common, when the cells swim, the curved anterior flagellum beats actively, pulling the cell forward. It is almost invisible due to its fast beating. The posterior flagellum extends behind the cell and is likely used as a rudder. The cells swim quickly, only occasionally rotating about the axis of motion. Cells can sharply change the direction of movement. Pirsoniales Cavalier-Smith 1998 emend. 2006 Feodosia pseudopoda (Fig. 2.4AF, AG, AJ–AS), Koktebelia satura (Fig. 2.4AH), and Pirsonia chemainus (Fig. 2,4AI). Free-swimming naked, solitary and eukaryovorous heterokont flagellates. Cells are shaped as a flattened oval, with slightly pointed ends with the size 10.5 – 14 μm in length, 6 – 9.1 μm in width, and typically having the dimension of 12 x 8.2 μm. The flagellated stages of three studied Pirsoniales were almost morphologically identical except for Feodosia pseudopoda (Chromo-2) which possesses a small notch at the anterior part of the cell (Fig. 2.4AF, AG). Rarely, F. 36 pseudopoda can produce pseudopodia (Fig. 2.4AM, AN), which are up to 10 μm long and sometimes branched. Two long heterodynamic flagella originate from the pit located in the anterio-medial part of the cell (Fig. 2.4AL, AM, AO). The length of the anterior flagellum is as long as the cell, while the posterior one is 2.5 times longer. The cells swim fast in a straight line, without rotating along the longitudinal axis. The anterior flagellum is directed anteriorly, always bent towards the ventral surface. The posterior flagellum propels the cell and beats at a high speed, which can be seen as multiple posterior flagella (Fig. 2.4AI). In stationary cells, the flagella take the form of a sinusoid (Fig. 2.4AJ, AK). The nucleus is located in the middle of the cell (Fig. 2.4AJ). The cytoplasm contains many refractive granules as observed in previously described Pirsonia species (Schweikert and Schnepf, 1997). Non-flagellated cells were also observed with slightly amoeboid and round shape (Fig. 2.4AP–AR). The satiated cells have a large digestive vacuole at the posterior end (Fig. 2.4AS). The eukaryovory of the biflagellates seems to be facultative as they mostly did not actively pursue the prey but only Koktebelia satura (clone Chromo-1) consumed all the prey cells in culture. 2.4 Discussion 2.4.1 Monophyly and phylogenetic position of the Bigyromonadea Of the known subdivisions of stramenopiles, the Bigyromonadea stand out for their lack of data and contentious phylogenetic position (even the newly discovered Platysulcus tardus is represented by transcriptomic data and consistently branches at the base of the tree). From the five recent phylogenomic analyses of stramenopiles (Noguchi et al., 2016; Burki et al., 2016; Derelle et al., 2016; Leonard et al., 2018; Thakur et al., 2019), only three included a single bigyromonada representative (Developayella elegans JAMSTEC), none tested the monophyly of 37 the group, and they recovered inconsistent positions. Using transcriptomes of seven new species belonging to the Bigyromonadea representing both the Developea and Pirsoniales subgroups, I tested the monophyly of the group and its position relative to other stramenopiles. Previously, only SSU rRNA phylogenies could be used to test the monophyly of the Bigyromonadea, and such analyses consistently failed to support the monophyly, typically showing Developea with oomycetes and Pirsoniales with ochrophytes. In contrast, phylogenomic data consistently and strongly supports the monophyly of these two groups, and shows each to include multiple distinct genera. The position of Bigyromonadea within stramenopiles as a whole is also contentious, with some analyses showing the previously available transcriptome from D. elegans branching with oomycetes (Noguchi et al., 2016; Thakur et al., 2019), and based on internode consistency analyses (Kobert et al., 2016; Leonard et al., 2018, with ochrophytes). This discrepancy is not entirely eliminated by the addition of new taxa, because ML phylogenomic trees with the expanded representation recovered monophyly of the bigyromonada and oomycetes with robust support, but Bayesian analyses support a clade comprising of bigyromonada+ochrophytes, and AU tests rejected most but not all topologies with this relationship (Table 2.1; Appendix D). The discrepancy between the ML and Bayesian analyses may be due to two groups (Chrysista and Bigyromonadea) that do not fit the same model for tree reconstruction. Although it is not the aim of this study to resolve the phylogeny of ochrophytes, further examination of ochrophyte phylogeny may reveal whether the discrepancy stems from the unreconciled model used in the two groups, the different data processing approaches used, or insufficient data in one or both groups. 38 These results change how we interpret these lineages and their biological characteristics within the wider evolution of stramenopiles. For example, the phylogenetic position of Pirsoniales inferred from ribosomal genes implies they share a recent common ancestor with the ochrophytes, which naturally affected the interpretation of the ancestral state of ochrophytes and the role of phagoheterotrophy in their evolution (Aleoshin et al., 2016; Shiratori et al., 2017). However, the phylogenomic tree points instead to a phagoheterotrophic origin of the Pseudofungi. Parallels between this and recent suggestions on the origin of fungi are noteworthy, since Paraphelidium tribonemae, a phagoheterotrophic parasite belonging to phylum Aphelida, has recently been found to be sister to the osmotrophic “core” fungi by phylogenomics (Torruella et al., 2018). Close similarities in metabolism and a phagotrophy-related proteome profile of P. tribonemae and the osmotrophic “core” fungi suggested the “core” fungi have evolved from a phagoheterotrophic aphelid-like ancestor. Further information on the metabolism and feeding mechanisms of the new species should shed light on whether the origins of fungi and pseudofungi have more parallels and on the possible phagoheterotrophic ancestral state of Gyrista more widely. Of course, trait evolution is also dependent on conclusively determining the position of Bigyromonadea. Substantial advances in phylogenetic methods have been made, but challenges stemming from systematic errors, compositional bias, or long branch attraction, incomplete or contaminated data, and models that do not account for heterotachy in large datasets (Delsuc et al., 2005; Kapli et al., 2020; Zhou et al., 2007) remain. Similarly, advances in single-cell sequencing have vastly increased the taxonomic scope of phylogenomics, but the severely limited starting material and the fact that they are by definition a snapshot of gene expression in one cell remain important hurdles. Here, the removal of fast-evolving sites (Fig. 2.2), species 39 (Appendix E), extensive AU tests (Table 2.1; Appendix A-C) and two different data processing approaches collectively tip the scale in favour of the monophyly of bigyromonada and oomycetes over the alternative position of bigyromonada with ochrophytes. However, the conflicting results of Bayesian inferences show that the lack of a robust phylogenomic tree is not just due to lack of taxonomic diversity. Continued sampling efforts in phagoheterotrophic stramenopiles will expand the phylogenetic diversity of the Bigyromonadea (and environmental SSU rRNA data already show there are more new taxa to be found) (Appendix F), but other advances in data generation and analyses will also be required. 2.4.2 Morphology, evolutionary implications, and taxonomic description of the novel phagoheterotrophic Bigyromonadea 2.4.2.1 Newly observed morphological and behavioural features in bigyromonads: cell-aggregation to fusion, pseudopod-formation, and facultative phagotrophy in motile zoospores Before I compare morphological features, I need to clarify that the JAMSTEC strain of Developayella elegans has been mis-named and is a distinct species in a different genus. According to the SSU rRNA gene tree (Fig. 2.3), the originally described D. elegans U37107 (Tong, 1995) is placed in a distinct sub-clade of Developea (sub-clade I) whereas, D. elegans JAMSTEC is placed within sub-clade II with its most closely related species being C. variflagellatum. Renaming D. elegans JAMSTEC will be necessary in the future: its close relatedness to Cubaremonas is sufficient to indicate that it is mis-named, but rectifying this should take into account morphological information, which is currently unavailable. Overall, however, the novel developeans have similar morphological traits as previously described species. For example, C. variflagellatum falls in the same sub-clade as Mediocremonas mediterraneus (Del Campo et al., 2013; Weiler et al., 2021) (Fig. 2.3), and both have similar 40 morphology. C. variflagellatum is slightly larger, but measurements for M. mediterraneus (2.0 – 4.0 μm in length and 1.2 – 3.7 μm in width) were most likely based on scanning electron microscopy (SEM) images and cells tend to shrink in SEM fixatives (Weiler et al., 2021). The cell size, flagella length and swimming movement of C. variflagellatum exhibited close similarity to D. elegans U37107, which was named after its characteristic “developpé” movement of the anterior flagellum during stationary feeding (Tong, 1995). However, no thread-like substances were observed, which D. elegans uses to attach to substrate. The remaining novel Developea species, Develocanicus vyazemskyi, D. komovi, and Develocauda condao, differed from D. elegans JAMSTEC and C. variflagellatum by having a proportionately longer posterior flagellum, forward propulsion without rotating its axis, a eukaryovorous diet [like Develorapax marinus (Aleoshin et al., 2016)], and the presence of a “tail” in D. condao. Notably, the ability of the cells to form aggregates (Fig. 2.4K, U), pseudopodia (Fig. 2.4L), and to undergo partial cell fusion (Fig. 2.4W) has not been reported in this clade previously. The above-mentioned differences between D. vyazemskyi D. komovi, Develocauda condao, and C. variflagellatum are also phylogenetically reflected in the division of these species into two sub-clades (Fig. 2.1 and Fig. 2.3). The three novel Pirsoniales, Feodosia pseudopoda, Koktebelia satura, and Pirsonia chemainus, described here as nomen provisorium, most likely represent a motile zoospore stage of unknown algal parasites. The novel Pirsoniales species did not actively pursue the provided prey and only partially consumed their prey (except K. satura which consumed all the prey provided), all the cultures died after a few months to one year of cultivation. Although there has been extensive description of auxosome and trophosome formation during the parasitic stage of known Pirsoniales (Schnepf et al., 1990; Schweikert and Schnepf, 1997), the ability of motile 41 zoospores to acquire effective eukaryovory has not been described so far. The observed eukaryovory of the zoospore-like Pirsoniales is likely facultative, as the cells were cultured without potential hosts and the cells with larger food vacuoles became non-flagellated and rounded, a structure akin to an auxosome. However, further culture experimentations with their natural hosts are required to verify their ability to form parasitic auxosomes and trophosomes from motile phagotrophic zoospores. I postulate that the facultative eukaryovory at the motile zoospore stage provides a significantly increased survival rate and thus extension of the motile stage during their dispersal until a suitable host is found. This ability can be particularly advantageous before the onset of seasonal algal bloom, where the zoospores can efficiently infect multiple hosts without resource competition. Therefore, the sustained survival of the zoospores via facultative eukaryovory could be an important factor leading to the evolutionary success of Pirsoniales parasites. Feodosia pseudopoda differed from rest of the Pirsoniales studied here by an anterior notch (Fig. 2.4AF, AG) and rare occurrences of pseudopodia (Fig. 2.4AM-AO). The two characteristics have been reported in Pseudopirsonia mucosa, a cercomonad rhizarian (Kühn et al., 2004), which had been mis-assigned as Pirsonia due to the similarities in their parasitic life cycles. In starving and immobile zoospores of Pirsonia puntigerae, filopodium-like processes (Schweikert and Schnepf, 1997) have been described. However, pseudopodia in motile zoospores of Pirsoniales have not been observed previously. The presence of pseudopodia, and the ability to form aggregated cells in the newly described sub-clade I of Developea and previously reported publications of Pirsoniales may be synapomorphic traits of Bigyromonadea. It will be important for future studies to compare ultrastructure and genes putatively associated with cell-aggregation or fusion among the species 42 of bigyromonada, thus potentially addressing the evolution of an osmotrophic nutritional strategy in stramenopiles. 2.4.2.2 Similarities among Oomycetes motile zoospores, Labyrinthulomycetes, and Bigyromonadea Morphologically, the novel Developea species have similar features to motile zoospores of previously studied oomycetes, such as the general cell dimension, the ratio of anterior and posterior flagellum, and two laterally oriented flagella (with a tinsellate anterior flagellum) emerging from a ventral groove (Dick, 2000), which resembles the ventral depression observed in the novel species. Behaviourally, the swimming pattern (e.g., direction of flagella, sinusoid form) is comparable (Ho and Hickman, 1967; Hickman, 1970). Another striking similarity between the two groups is their ability to self-aggregate, which is observed in oomycete zoospores as a distinct form of self-aggregation related to aggregation towards host-plant tissues (Ko and Chase, 1973; Bassani et al., 2020). Similarly, cell aggregation observed in this study was not a result of attraction to food as this behaviour was observed rarely, and feeding of these predatory flagellates is associated with active mobile eukaryotic prey hunting. Additionally, cells attaching to each other were distinguishable from the intermediate stage of transverse binary cell division. The mechanism underlying self-aggregation in oomycetes has not been fully resolved. However, recent studies suggest that a combination of chemotaxis (Judelson and Blanco, 2005; Zheng and Mackrill, 2016; Bassani et al., 2020) and bioconvection (Savory et al., 2014), is involved in the process. The exact role of the self-aggregation in oomycete pathogenesis is still unclear. However, the fact that a similar observation was made in its sister-clade, the Bigyromonadea, indicates that self-aggregation may have been present in the ancestor of Pseudofungi, before the osmotrophic parasitism of oomycetes evolved. Cell aggregation is also 43 observed in Sorodiplophrys (Dykstra and Olive, 1975), a species belonging to another osmotrophic group of stramenopiles, the labyrinthulomycetes. Cell aggregation has convergently evolved multiple times across many other supergroups (Parfrey and Lahr, 2013), such as Opisthokonta (Brown et al., 2009), Discoba (Brown et al., 2012; He et al., 2014), Amoebozoa (Du et al., 2015), Rhizaria (Brown et al., 2012), and ciliates (Sugimoto and Endoh, 2006), and whether cell aggregation within stramenopiles arose convergently or divergently should be further investigated. As mentioned previously, some species described in this study formed pseudopodia (Fig. 2.4L,4AM,4AN) and partially fused cells (Fig. 2.4W) resembling amoeboid forms. Labyrinthulomycetes also form filose pseudopodia (Gomaa et al., 2013) akin to pseudopodia observed in this study (Fig. 2.4AM, AN). These are found in Amphitremidae, during an amoeboid stage of Diplophrys (Anderson and Cavalier-Smith, 2012), and other labyrinthulids (Raghukumar, 1992), implying this trait either evolved convergently or was present earlier than the divergence of Pseudofungi. Another notable similarity between oomycetes and the novel bigyromonada is their potential marine origin, as all known bigyromonads are exclusively marine. Molecular clock analyses indicate the Silurian period as the time of oomycete origins (Matari and Blair, 2014), while the earliest fossil evidence points to the Devonian period (Krings et al., 2011). The fossil evidence of the “deep-branching” genera have shown them to be marine parasites of seaweed or of crustaceans based on molecular studies (Beakes and Sekimoto, 2009), both suggesting a marine origin of oomycetes as a facultative parasitic osmotroph (Beakes et al., 2012, 2014; Beakes and Thines, 2017). 44 The origin and evolution of major stramenopile subgroups is coming into sharper focus with the increase in phylogenomic data from diverse species. The new taxa described here, together with future descriptions of the still-substantial diversity of bigyromonada that has not been well-characterized, can potentially shed more light on this and the origins of oomycetes in particular. I propose that the ancestor of oomycetes was a phagoheterotrophic amoeboid, as postulated in the evolution of true fungi, and that this transition might be better understood through a detailed functional examination of the novel species. Just as the highly successful analyses of choanoflagellates and unicellular opisthokonts changed our understanding of the origin of animals (Sebé-Pedrós et al., 2013; Zmitrovich, 2018; Chow et al., 2019), a similar analysis of the distribution of genes involved in Pseudofungi cell-aggregation or pseudopodia formation across the diversity of bigyromonads could be a future direction to understand the evolution of these unique phagoheterotrophs and oomycetes. 2.4.3 Taxonomic summary Taxonomy: Eukaryota; SAR Burki et al. 2008, emend. Adl et al. 2012; Stramenopiles Patterson 1989, emend. Adl et al. 2005; Gyrista Cavalier-Smith 1998; Bigyromonadea Cavalier-Smith, T. 1998; Developea Karpov et Aleoshin 2016 Сubaremonas n. gen. Tikhonenkov, Cho, and Keeling Diagnosis: naked and solitary bacteriovorous protist. Cell shape is irregularly ovoid, with the convex dorsal side and the flatter ventral side. Cells possess two heterodynamic flagella emerging from a conspicuous ventral depression, which starts from the anterior end and continues ventrally to the middle of the cell. In culture condition, the cells predominantly lie at the bottom unattached with both flagella directed backward. Etymology: from lat. cubare – to lie, to be lying down and monas (lat.) – unicellular organism. 45 Zoobank Registration. urn:lsid:zoobank.org:act: 169A2385-5669-4FB2-A728-AC5AD74B5076 Type species. Сubaremonas variflagellatum Сubaremonas variflagellatum n. sp. Tikhonenkov, Cho, and Keeling Diagnosis: cells length 3.7 - 8 μm, cell width 2.6 - 5.4 μm. Flagella of unequal length, the anterior one is approximately equal to the cell length while the posterior flagellum is 1.5 - 1.8 times longer than the cell. At lying cells, posterior flagellum runs along the ventral surface of the cell and beats rapidly with sinusoidal pattern to draw water through the depression. The anterior flagellum is hook-shaped and sweeps slowly down behind the posterior flagellum. Starving cells have a small rostrum at the anterior end. Digestive vacuole is situated at the cell posterior. An observed cell division produces two or four cells. Type Figure: Fig. 2.4X illustrates a live cell of strain Dev-1. Gene sequence: The SSU rRNA gene sequence has the GenBank Accession Number OL630098. Type locality: water column of Strait of Georgia, British Columbia, Canada Etymology: the species name means “unequal flagella”, lat. Zoobank Registration: urn:lsid:zoobank.org:act: 2152FF4A-BFC8-4064-A197-74FE6BEE2EC8 Develocanicus n. gen. Tikhonenkov, Cho, Mylnikov, and Keeling Diagnosis: Free-swimming naked eukaryovorous heterokont flagellates with two non-acronematic heterodynamic flagella of unequal lengths. The shape of the cell is irregularly flattened ellipse, where the dorsal side is more convex, and the ventral side is flatter. Flagella emerge from a prominent ventral depression which passes into a shallow wide groove along the entire length of the cell. 46 Etymology: from développé (fr.) – characteristic ballet movement and volcanicus (lat.) (found near volcanos in Kanary island and Crimea). Zoobank Registration. urn:lsid:zoobank.org:act: 74F9B793-53AD-4F4C-8A71-3F29D9F97B9E Type species. Develocanicus komovi Develocanicus komovi n. sp. Tikhonenkov, Cho, Mylnikov, and Keeling Diagnosis: cell length 5.4 - 10 μm, cell width 3.8 - 7.4 μm. The posterior flagellum is two times longer than the cell, the anterior flagellum is approximately 1 - 1.5 times longer. Cells swim without rotation. At that, posterior flagellum is directed backward and straight, running along the ventral cell of the cell. Anterior flagellum beats rapidly and is directed forward while slightly curved. Medial nucleus is located closer to the dorsal side of the cell. Large digestive vacuole is situated at the posterior part of the cell. Cells can form pseudopodia and aggregations and attach to each other. Transverse binary fission. Type Figure: Fig. 2.4C illustrates a live cell of strain Colp-23. Gene sequence: The SSU rRNA gene sequence has the GenBank Accession Number OL630096. Type locality: black volcanic sand on the littoral of Maria Jimenez Beach (Playa Maria Jiménez), Puerto de la Cruz, Tenerife, Spain Etymology: named after Prof., Dr. Viktor T. Komov, Russian ecotoxicologist, who carried out fieldwork and collect samples, where new species was discovered. Zoobank Registration: urn:lsid:zoobank.org:act: 6C543426-FAFB-4DBD-AEB3-3CA648FD53D5 Develocanicus vyazemskyi n. sp. Tikhonenkov, Cho, Mylnikov, and Keeling 47 Diagnosis: cell 7.4 - 12.5 μm long, 4.8 - 9.2 μm wide. The posterior flagellum is two times longer than the cell, the anterior flagellum is approximately 1 - 1.5 times longer. Cells swim without rotation. At that, posterior flagellum is directed backward and straight, running along the ventral cell of the cell. Anterior flagellum beats rapidly and is directed forward while slightly curved. In non-motile cells, both flagella are directed backward, beating in a slow sinusoidal wave. Medial nucleus is located closer to the dorsal side of the cell. Large digestive vacuole is situated at the posterior part of the cell. Transverse binary fission. Type Figure: Fig. 2.4A illustrates a live cell of strain Colp-30. Gene sequence: The SSU rRNA gene sequence has the GenBank Accession Number OL630097. Type locality: near shore sediments on the littoral near T.I. Vyazemsky Karadag Scientific Station, Crimea Etymology: named after Dr. T.I. Vyazemsky, founder and first director of Karadag Scientific Station, Crimea Zoobank Registration: urn:lsid:zoobank.org:act: 6A2D2D31-E16A-470F-9ED9-26546944A96C Develocauda n. gen. Tikhonenkov, Cho, and Keeling Diagnosis: Free-swimming eukaryovorous heterokont flagellates with slightly flattened elongated-oval cells and two heterodynamic flagella. The anterior end is more rounded, the posterior end of the cell can be pointed, forming a characteristic “tail” in starving cells. Flagella emerge from a pronounced deep ventral depression, which almost extends to the dorsal side of the cell. Depression transforms into a shallow groove spanning along the entire cell, in which the posterior flagellum can fit. Etymology: from développé (fr.) – characteristic ballet movement and cauda (lat.) – tail. 48 Zoobank Registration. urn:lsid:zoobank.org:act: 5BA3D9B6-0A50-45A5-83D3-7474EA31F13C Type species. Develocauda condao Develocauda condao n. sp. Tikhonenkov, Cho, and Keeling Cell length 5.14 - 12 μm, width 2.8 - 5.42 μm. The caudal extension is about 4.57 x 1.42 µm in size. Flagella of almost equal length. The cells swim very quickly without rotating along the longitudinal axis. The posterior flagellum is straight and directed backwards. The anterior flagellum is directed forward, beats actively, and is only slightly curved. Cells can be partially fused and aggregated. Medial nucleus is located closer to the dorsal side of the cell. Transverse binary fission. Type Figure: Fig. 2.4N illustrates a live cell of strain Colp-29c. Gene sequence: The SSU rRNA gene sequence has the GenBank Accession Number OL630094. Type locality: near shore sediments on the littoral of north-east part of Con Dao Island, South Vietnam Etymology: named after Con Dao Island, South Vietnam, where species was discovered. Zoobank Registration: urn:lsid:zoobank.org:act: FA73444D-79A5-498C-BB7F-139E9D82C0BA Pirsoniales Cavalier-Smith 1998, emend. 2006 Studied pirsoniales most likely represent a motile zoospore stages of unknown algal parasites. Since data on the stage of the parasitic trophonts (auxosome and a trophosome) are not available, it is premature to formulate taxonomic diagnoses. But we provide provisional names (nom. prov.) which can be used for future research. Pirsonia chemainus nom. prov. Tikhonenkov, Cho, and Keeling 49 Etymology: species epithet is after the Stz'uminus First Nation traditional territory (Strait of Georgia area) claimed by the Chemainus First Nation Type locality: water column of the Strait of Georgia, British Columbia, Canada Gene sequence: The SSU rRNA gene sequence has the GenBank Accession Number OL630095. Koktebelia satura nom. prov. Tikhonenkov, Cho, and Keeling Etymology: genus epithet reflects the place of finding, Koktebel bay, Crimea; species epithet – from satur (lat.), well-fed. Type locality: near shore sediments on the littoral near T.I. Vyazemsky Karadag Scientific Station, Crimea Gene sequence: The SSU rRNA gene sequence has the GenBank Accession Number OL630093. Feodosia pseudopoda nom. prov. Tikhonenkov, Cho, and Keeling Etymology: genus epithet reflects the place of finding, the settlement Beregovoye, Feodosiya, Crimea; species epithet reflects the ability to produce pseudopodia. Type locality: near shore sand on the littoral of the beach in the settlement Beregovoye, Feodosiya, Crimea Gene sequence: The SSU rRNA gene sequence has the GenBank Accession Number OL630092. 50 Table 2.1 Approximately unbiased (AU) tests on tree constraints based on approach 1 dataset. Approach 1 (MAFFT L-INS-i and trimAl -g 0.8) Constrained Tree p-AU logL ∆logL Unconstrained ML tree 0.78 -3935691.338 0 ML tree 0.759 -3935691.338 0.00089261 Chain 1 (C+S+Pi),(R+P+X+E) 0.0541 -3935828.083 136.75 Chain 1 Modified (Bigyromonada+oomycetes) 0.267 -3935763.845 72.508 Chain 2 (C+S+Pi+E),(R+P+X) 0.0297 -3935859.39 168.05 Chain 2 Modified (Bigyromonada+oomycetes) 0.0924 -7871604.549 108.01 Chain 3 (R+P+X+E),(C+S) 0.0119 -3935874.998 183.66 Chain 3 Modified (Bigyromonada+oomycetes) 0.0717 -3935805.205 113.87 Chain 4 (C+S+E),(R+P+X) 0.0186 -3935860.003 168.67 Chain 4 Modified (Bigyromonada+oomycetes) 0.108 -3935765.741 74.404 Except for the unconstrained ML tree, each tree was constrained under LG+C60+F+G4 using IQ-TREE with the approach 1 dataset. Chain 1 to chain 4 are generated from Bayesian analyses and contain (bigyromonada+ochrophytes). “Chain 1 Modified” to “Chain4 Modified” contain a hypothetical clade (bigyromonada+oomycetes) with the rest of topology remaining the same with their corresponding chains. Each unmodified chain is listed with different topology of Chyrisista as represented in Appendix B. The unconstrained tree is based on ML tree reconstructed under LG+C60+F+G4+PMSF as presented in Fig. 1. The p-AU values were calculated using the AU test with 10,000 RELL bootstrap replicates, implemented in IQ-TREE. The maximum log likelihoods (logL) of each constrained and their differences (∆logL) compared to the unstrained tree are listed. Constraints with P-values lower than 0.05 are rejected, indicating confidence interval below 95% (marked bold). Raphidophyceae (R), Eustigmatophyceae (E), Chrysophyceae (C), Synurophyceae (S), Phaeophyceae (P), Pinguiophyceae (Pi), and Xanthophyceae (X). 51 Figure 2.1 Phylogenomic tree of stramenopiles with the seven new Bigyromonadea Multi-gene tree of stramenopiles with the seven new transcriptomes (pink) are added to Gyrista consisting of the concatenated alignments of 247 genes of 76 taxa. The tree was reconstructed using a maximum-likelihood (ML) analysis, under the site-heterogenous model, 52 LG+C60+F+G4+PMSF, implemented in IQ-Tree. Branch support was calculated using non-parametric PMSF 100 standard bootstrap (STB). Branches with ≥99% STB for both approaches are marked with black bullets while others are labelled as “Approach 1 STB/Approach 2 STB”. The topology of the trees generated from the two approaches were the same except for the positions of Raphidophyceae, Phaeophyceae, Xanthophyceae, and Eustigmatophyceae and, Chrysophyceae and Synurophyceae, which were swapped in the tree reconstructed based on the dataset processed using approach 2 (i.e., Prequal/Divvier method); denoted by star symbols (Appendix B). The percent sites (blue) and genes (grey) present for each transcriptome is depicted on the back-to-back bar plot on the left. 53 Figure 2.2 Summary of ultrafast bootstrap with fast-evolving sites removed Summary of ultrafast bootstrap (UFB) with incremental removal of fast-evolving sites, based on the dataset processed with approach 1. Schematic representation the stramenopiles ML tree (left) with each branch marked with different shapes and colours. The line plot (right) showing the change in UFB for each branch when fast-evolving sites were incrementally removed by 5%. The monophyly of Gyrista shows full support throughout while the UFB increases incrementally for ‘Sagenista’ and ‘Platysulcea’. 54 Figure 2.3 ML tree of stramenopiles using a 18S rRNA gene alignment 55 ML tree reconstructed from a 18S rRNA gene alignment of 92 taxa (1650 sites), under BIC: TIM2+R6 with 1000 UFB. Branch support with ≥99% UFB is marked with black bullets while the values less than 50% are not shown. The seven new species described in this study are marked as pink: Pirsoniales forming a sisterhood with Ochrophytes and Developea forming a sister clade to Oomycetes. Within Developea, two previously assigned Developayella species (JAMSTEC transcriptome and the U37107 SSU rRNA sequence) are split into two sub-clades, in which the four novel Developea species are positioned. 56 Figure 2.4 Morphology of the seven new phagoheterotrophic Bigyromonadea A, B. Develocanicus vyazemskyi, general cell view with flagella (anterior flagellum [af] and posterior flagellum [pf]) and ventral depression [vd]. C–M. Develocanicus komovi, C–F – general cell view with flagella and ventral depression, shallow wide groove [g] is visible in (E), G – lying cell with posterior flagellum [pf] beating with a slow sinusoidal wave, H–J – cells with medial nucleus [n] (H) and large food vacuoles [fv] (I, J), K – cell aggregation, L – aggregated 57 cells with pseudopodia [ps], M – transverse binary fission. N–W. Develocauda condao, N–P – general cell view with two flagella and ventral depression, Q – rod-shaped cell, R,S – cells with pointed ‘tail-like’ [t] posterior end, T – cells with large food vacuole, U – cell aggregation, V – transverse binary fission, W - partially fused cells. X–AE. Сubaremonas variflagellatum, X–AA – general cell view with flagella, AB – cell with conspicuous ventral depression, AC, AD – starving cells with small rostrum [r] (AD), AE – division into 4 cells. AF, AG, AJ – AS. Feodosia pseudopoda, AF, AG – typical fast swimming cell with two flagella, AJ, AK, AO – lying cells with sinusoid shaped flagella, AL–AN – cells with pseudopodia and anterior pit [p] (AL, AM), AP–AR – metabolic cells, AS – cell with large food vacuole. AH. Koktebelia satura, typical fast swimming cell with two flagella. AI. Pirsonia chemainus, typical fast swimming cell with two flagella. Scale bar – the scale changes in different images with respect to the scale bar in the AS image: A, B, R, AK, AP, AQ, AS – 8 μm; C–H, J, N, P,Q, X–AA, AC, AD – 7 μm; I, O, T, AB – 5 μm; K, L, V, AF–AI – 15 μm; M, S, AE, AJ, AL–AO, AR – 10 μm; U – 25 μm; W – 20 μm. 58 Chapter 3: Phylogenomic position of genetically diverse phagotrophic stramenopile flagellates in the sediment-associated MAST-6 lineage and a potentially halotolerant placididea 3.1 Introduction Stramenopiles are a diverse group of eukaryotes, in terms of molecular sequences, size, trophic mode, and morphology. The best known are within one subgroup, the Ochrophyta, which includes diatoms with diverse frustule shapes, microscopic phagotrophic flagellates that have lost photosynthesis (Dorrell et al., 2019; Kayama et al., 2020), and macroscopic multicellular brown algae like kelps. The diversity is less obvious at the morphological level in some “deep-branching” groups of stramenopiles, but their molecular diversity is nonetheless significant. This is most obvious in the Bigyra Cavalier-Smith, 1998, which is a large assemblage composed of Sagenista Cavalier-Smith, 1995 and Opalozoa Cavalier-Smith, 1993, and includes the most deep-branching stramenopile, Platysulcus tardus (Shiratori et al., 2015). Other than saprotrophic Labyrinthulea (Sagenista), epiphytic Solenicola setigera (MAST-3), and symbiotic Opalinata (Opalozoa), rest of the species of Bigyra are marine phagotrophic flagellates, generally in the size range of 2–10 mm (Gómez et al., 2011; Guillou et al., 1999; Lee, 2002; Moriya et al., 2002, 2000; Schoenle et al., 2022; Shiratori et al., 2017, 2015; Yubuki et al., 2015, 2010). These small and inconspicuous flagellates had been historically mistaken for cercozoans or discobans in light microscopy surveys (Larsen and Patterson, 1990; Patterson et al., 1993; Lee, 2002). Without detailed morphological examination and molecular surveys, it is difficult to discern among flagellated species of Bigyra, or indeed even between members of the major subdivisions, leading to under-sampling and under-estimation of their diversity. The diversity and abundance of Bigyra has accordingly been determined by molecular surveys, and these have revealed a number lineages without any morphological identity simply referred to as MArine Stramenopile (MAST), a term that was originally coined to include 18 59 uncharacterized lineages, most of which are still only known from small subunit (SSU) ribosomal RNA (rRNA) sequences identified from environmental sampling efforts (Logares et al., 2012; Massana et al., 2014, 2009, 2006, 2004). These surveys also showed that differences in community composition among different environments, particularly between benthic and pelagic samples (Massana et al., 2015; Forster et al., 2016). Notably, MAST-6 (along with MAST-9, and -12) are common in sediments but rare in pelagic samples (Logares et al., 2012; Massana et al., 2015; Rodríguez-Martínez et al., 2020). Extensive ultrastructural and cellular examination of the first cultured MAST-6 lineage, Pseudophyllomitus vesiculosus (family Pseudophyllomitidae Shiratori et al., 2017) describe it as a relatively large algivore (dimensions up to 18.3 x 12.4 mm) with the characteristic flagellar apparatus ultrastructure of deep-branching stramenopiles (e.g., no x-fiber, R2 flagellar root with 13 microtubules) (Lee and Patterson, 2002; Moestrup, Ø, 1976; Shiratori et al., 2017; Yubuki et al., 2010). The genus Pseudophyllomitus (Lee, 2002) was erected to describe Phyllomitus-like taxa without two adhering flagella. This resulted in re-designation of four new species (i.e., Pseudophyllomitus apiculatus, P. granulatus, P. salinus, and P. vesiculosus). However, with limited molecular and ultrastructural data available to support the monophyly of the genus. Later, a new family Pseudophyllomitidae (Shiratori et al., 2017) was erected, apparently corresponding to a MAST-6 clade. However, the phylogenetic position of the type species, P. granulatus is unknown. As a result, we refrain from using Pseudophyllomitidae in replacement of MAST-6 in this study. In multi-gene analyses, MAST-6 are closely related to many ecologically important groups such as MAST-4 (Cho et al., 2022; Thakur et al., 2019), which is one of the most common heterotrophic flagellate groups in coastal ecosystems, substantially affecting microbial food webs (Massana et al., 2006; Rodríguez-Martínez et al., 2009; Logares et al., 2012). MAST- 60 4 and -6, in turn form a sister group to Labyrinthulea, a detrital decomposer that is also abundant in sediments (Collado-Mercado et al., 2010; Massana et al., 2015; Nakai and Naganuma, 2015; Rodríguez-Martínez et al., 2020). The diversity of MAST-6 has been demonstrated by SSU rRNA amplicon sequencing of various sediment studies from surface to deep-sea push core sediments (Massana et al., 2015; Rodríguez-Martínez et al., 2020; Schoenle et al., 2021) and the high relative abundance in sediments estimated by up to 46 Operational Tasonomic Units (OTUs) and >1000 sequencing reads in a recent study (Rodríguez-Martínez et al., 2020). Two morphotypes of MAST-6 were observed in plankton samples that differ in sizes and seasonal abundances (Piwosz and Pernthaler, 2010), with the larger morphoptype (9.9-22 mm) showing rapid increase in abundance only for a week. This study showed MAST-6 is not only phylogenetically diverse, but also that community composition can respond quickly to fluctuating environments and food availability. Despite these advances, however, only a single transcriptome is available for the MAST-6 lineage (Shiratori et al., 2017; Thakur et al., 2019), limiting our understanding of its biology and its relationship to other stramenopiles, and character evolution of Sagenista. Another major but under-sampled subgroup of Bigyra is the Placididea, a class within Opalozoa. Like Sagenista, the phylogenetic diversity of Placididea is largely represented by SSU rRNA genes, with -omic data available for only two species: Wobblia lunata (Moriya et al., 2000; Thakur et al., 2019) and Placididea sp. (the CaronLab strain formerly mis-labelled as ‘Cafeteria’) (Keeling et al., 2014). Placidideans are closely related to MAST-3, another abundant and highly diverse heterotrophic flagellate group that play an important role in marine food webs and are found in all coasts and open oceans around the world (Gómez et al., 2011; Logares et al., 2012; Massana et al., 2004). Unlike the MAST clades, SSU sequences of Placididea (Moriya et 61 al., 2002) do not amplify well with V4-targeting primers (Lee et al., 2022). Consequently, the diversity of this group was reported either using V9-targeting SSU primers (Lee et al., 2022) or by individually isolating placidideans (Park et al., 2006; Park and Simpson, 2010; Rybarski et al., 2021). Isolated placidideans are often from hypersaline environments (>40‰), however, many characterized halophilic placidideans can tolerate lower salinity (Park and Simpson, 2010; Rybarski et al., 2021), raising the possibility that they are also present in non-hypersaline environments. Here, I describe three new MAST-6 species including two new genera, and one new Placididea species, providing microscopic observation, transcriptomic data, and SSU rRNA sequence comparisons with previously generated environmental amplicon data. Strain PhM-7 (Placididea, Haloplacidia sinai) and Colp-33 (MAST-6, Vomastramonas tehuelche) were maintained in culture for a year, but subsequently lost. Two other MAST-6 species (Mastreximonas tlaamin and Pseudophyllomitus sp. BSC2) were obtained by single cell isolations. I also describe transcriptomes from two cultured species, Symbiomonas scintillans RCC257 (Guillou et al., 1999) and Caecitellus sp. RCC1078 (O’Kelly and Nerad, 1998), to further fill out the diversity of deep-branching stramenopiles for phylogenomic analyses. I report the relative abundance and diversity of the four new species of MAST-6 and Placididea in publicly available environmental sequence surveys, and re-examine stramenopile phylogeny, particularly with the aim to resolve relationships within the Bigyra using a multi-gene approach based on my new transcriptome data. 62 3.2 Materials and Methods 3.2.1 Sample collection and imaging Strain PhM-7 (Haloplacidia sinai sp. nov.) was isolated from the Red Sea (average salinity 36-41 ‰), Sharm El Sheikh, Egypt (27°50’50.5'' N, 34°18’59.4’’ E), scraped from coral at 75 m depth in April 2015. Strain Colp-33 (Vomastramonas tehuelche gen. et sp. nov.) was isolated from nearshore bottom sediments, Chile, Punta Arenas (53°37'49'' S, 70°56’58’’ W, T=9.4 °C, Salinity 24 ‰) in November 2015. These strains were propagated in a predator-prey culture with the bodonid Procryptobia sorokini as a steady food source but both perished after a year of cultivation. Light microscopy observations for PhM-7 and Colp-33 were made using a Zeiss AxioScopeA.1 equipped with phase contrast and DIC water immersion objectives (63x) and an AVT HORN MC-1009/S analog video camera. For scanning electron microscopy (SEM) imaging of PhM-7, cells from exponential growth phase were fixed at 22 °C for 10 min in a cocktail of 0.6% glutaraldehyde and 2% OsO4 (final concentration) prepared using a 0.1 M cacodylate buffer (pH 7.2), and gently drawn onto a polycarbonate filter (diameter 24 mm, pores 0.8 µm). Following filtration, the specimen was taken through a graded ethanol dehydration and acetone, and critical-point dried. The dry filters with fixed specimens were mounted on aluminum stubs, coated with gold-palladium, and observed with a JSM-6510LV scanning electron microscope (JEOL, Japan). Two uncultured single cells, PRC5 (Mastreximonas tlaamin gen. et sp. nov.) and BSC2 (Pseudophyllomitus sp. BSC2), were isolated from oxic marine intertidal sediment. Sediment for PRC5 was collected from Powell River, British Columbia, Canada (49°50'42'' N, 124°31'60'' W) in August 2020; whereas the BSC2 sample was collected from Boka Santa Cruz, Curaçao (12°18'24'' N, 69°8'44'' W) in April 2022. Both cells were manually isolated using a drawn-out 63 glass micropipette under a Leica DLIM inverted microscope and imaged with a Sony ⍺7rIII camera. The cells were rinsed twice in filtered sea water and transferred into a 0.2 mL PCR tube containing lysis buffer (Picelli et al., 2014) and stored in -80°C until cDNA synthesis. Cultures of Symbiomonas scintillans strain RCC257 and Caecitellus sp. strain RCC1078 were obtained from the Roscoff culture collection (France) in March 2022. The cultures were grown in 30 mL of 0.22 µm filtered f/2 medium (30 ‰) and autoclaved seawater (30 ‰), respectively, both with an autoclaved rice grain added. The cultures were kept in a 20°C incubator with a 12 hour:12 hour light:dark cycle and sub-cultured every two weeks. 3.2.2 cDNA synthesis, library preparation and sequencing Cells of PhM-7 (H. sinai) and Colp-33 (V. tehuelche) grown in clonal cultures were harvested when the cells had reached peak abundance and after most of the prey had been eaten. The cells were collected by centrifugation (2,000 x g for PhM-7 and 1,000 x g for Colp-33, both at room temperature) onto the 0.8 μm membrane of a Vivaclear mini column (Sartorium Stedim Biotech Gmng, Cat. No. VK01P042). Total RNA was then extracted using a RNAqueous-Micro Kit (Invitrogen, Cat. No. AM1931). In addition to the RNA extraction from the Colp-33 clonal cultures, 20 single cells were manually picked from its culture using a glass micropipette and transferred into a 0.2 mL PCR tube containing the cell lysis buffer for an additional Smart-Seq2 cDNA synthesis and library preparation. For cultures obtained from the Roscoff Culture Collection (RCC257 and RCC1078), TRIzol LS Reagent was used to extract total RNA, following the manufacturer’s instructions with a modification at the aqueous-organic layer separation step. Briefly, 100 mL of each culture was centrifuged at 3220 x g for 20 min at 4°C to pellet cells at the bottom of the centrifuge tubes. After carefully discarding the media, 1 mL of TRIzol LS was added to the pelleted cells. For 64 an easier transfer of the aqueous phase containing the RNA without an interphase contaminant, the aqueous-organic layer separation by chloroform was done in Phasemaker (Invitrogen) tubes. The quality and quantity of the RNA yield was determined using a NanoDrop 1000 Spectophotometer v3.8.1 (Thermo Fisher Scientific). Additionally, using glass micropipettes, approximately 20 cells were manually isolated from each culture and processed in the same manner as the single-cell isolation method used for Colp-33 (V. tehuelche), PRC5, and BSC2. For cDNA synthesis, the poly-A selection based Smart-Seq2 protocol was used (Picelli et al., 2014). For manually isolated single cells in the lysis buffer, 2-3 rounds of freeze-thaw steps were included prior to the cDNA synthesis (Onsbring et al., 2020). For RNA extracts, 4µL of the extract was used for cDNA synthesis. The rest of the library preparation and sequencing steps (tagmentation, quality control, and adaptor ligation) for PRC5, BSC2, RCC257 and RCC1078 were carried out by the Sequencing and Bioinformatics Consortium (University of British Columbia, BC Canada), using the Illumina NexteraTM DNA Flex Library Preparation Kit. The sequencing was performed on a NextSeq (mid-output) platform with 150 bp paired-end library constructs. For PhM-7 and Colp-33, the libraries were prepared using NexteraTM XT DNA Library Preparation Kit (Illumina, Inc., Cat. # FC-131-1024) followed by Illumina Miseq 300 bp paired-end sequencing at GenoSeq, Sequencing & Genotyping Core (University of California Los Angeles, CA USA) for PhM-7, and Sequencing and Bioinformatics Consortium (University of British Columbia, BC Canada) for Colp-33. All the raw reads of the transcriptomes are deposited in the NCBI Short Read Archive (SRA) under the BioProject number PRJNA961826 (SRR24392492 to SRR24392501). 65 3.2.3 Transcriptome processing, assembly, and decontamination Along with the six newly generated transcriptomes in this study, recently published transcriptomes of Actinophrys sol (Azuma et al., 2022) and its prey, Chlorogonium capillatum, were processed as follows. The quality of the raw sequencing reads was assessed using FastQC v0.11.9 (Andrews, 2010). To correct random sequencing errors of the short Illumina RNA-seq reads, k-mer based Rcorrector (version 3) was used on the raw reads (Song and Florea, 2015). The error-corrected reads were then trimmed using Trimmomatic v0.39 (Bolger et al., 2014) to remove remnant transposase-inserts from the library preparation, NexteraTM DNA Flex adaptors, low quality reads (-phred33), and Smart-Seq2 IS-primers with the leading and trailing cut-off at 3, SLIDINGWINDOW:4:15, and MINLEN:36. Processed forward, reverse, and unpaired transcripts were assembled using the de novo transcriptome assembler rnaSPAdes v3.15.1 (Bushmanova et al., 2019). Additionally, for species with two libraries prepared from both RNA extract and single cell isolations (i.e., Colp-33, RCC257, and RCC1078), the resulting transcripts were co-assembled. BlobTools v2.3.3 (Challis et al., 2010; Laetsch and Blaxter, 2017) was used to identify contaminants and visualize contig coverage. In short, megaBLAST was used to search assembled transcripts against the NCBI nucleotide database followed by a diamond BLASTX (Altschul et al., 1990; Buchfink et al., 2015) protein search against the UniProt reference database (Buchfink et al., 2015; The UniProt Consortium et al., 2021). Both searches were performed with an e-value cut-off 1e-25. Bacterial, Viriplantae, obazoan, and archaeal reads were removed from all transcripts. To remove prey contaminants from PhM-7, Colp-33, and A. sol, the assembled transcripts were first searched against the transcriptome of the respective prey (Procryptobia sorokini for PhM-7 and Colp-33; and C. capillatum for A. sol) using BLASTn, followed by the removal of contigs with ≥ 95% sequence identity. TransDecoder v5.5.0 (Haas et 66 al., 2013) was used to predict open reading frames (ORFs) and the longest ORFs were annotated using a BLASTP search against UniProt database with the e-value cut-off 1e-5. BUSCO v5.2.2 (Simão et al., 2015) with ‘stramenopiles_odb10’ database was used to assess the completeness of each transcriptome. 3.2.4 Small subunit sequences and amplicon processing using QIIME 2 Small subunit (SSU) rRNA sequences were extracted from PRC5 and BSC2 transcriptomes using barrnap v0.9 (Seemann, 2007). For S. scintillans and Caecitellus sp., SSU rRNA sequences were generated by polymerase chain reaction (PCR) amplification of cDNA using 18SFU and 18SRU eukaryotic primers (Tikhonenkov et al., 2016), followed by Sanger dideoxy sequencing. Although the SSU sequences for S. scintillans RCC257 and Caecitellus sp. and RCC1078 are available in GenBank, I did SSU PCR to confirm species identity and to obtain longer sequences as the published S. scintillans RCC257 (accession KT861043) SSU is 760 bp. For all the downstream analyses, I included SSU sequences from this study for the two cultured bikosia. To obtain SSU rRNA sequences of Colp-33 and PhM-7, the cells were first harvested when the cultures had reached peak abundance and after the prey had been eaten (confirmed with light microscopy), followed by centrifugation (7,000 x g, room temperature) onto an 0.8 µm membrane of a Vivaclear mini column (Sartorius Stedim Biotech Gmng, Cat. No. VK01P042). Total DNA was extracted from the filters using the MasterPure Complete DNA and RNA Purification Kit (Epicentre, Cat. No. MC85200). The SSU rRNA genes were PCR-amplified using the general eukaryotic primers EukA-EukB for strain Colp-33 (Medlin et al., 1988), and GGF-GGR for strain PhM-7 (Tikhonenkov et al., 2022). PCR products were subsequently cloned prior to sequencing (PhM-7) or sequenced directly (Colp-33), using Sanger dideoxy sequencing 67 with two additional internal sequencing primers 18SintF and 18SintR (Tikhonenkov et al., 2022). All the SSU rRNA sequences from the four newly described species and two culture strains are deposited in GenBank with the accession OQ909082-OQ909087. To compare SSU rRNA sequences of newly identified speciess to previously reported studies, five sediment datasets were obtained via European Nucleotide Archive (ENA). The datasets are designated as follows: BioMarKs (Dunthorn et al., 2014; Massana et al., 2015), SouthChina (Wu and Huang, 2019), Norway (unpublished BioProjects PRJEB24876; PRJEB24158; PRJEB24888), Deepsea (Schoenle et al., 2021), and ISME2020 (Rodríguez-Martínez et al., 2020) (Table 3.1). For the sixth dataset (designated as ESBig), I obtained ten SSU rRNA sequences (ESBig130-139) assigned to Placididea directly from the authors (Lee et al., 2022) (Table 3.1). These studies examined sediments from different bodies of water across the US, Europe, and Asia, including the South China Sea, North Atlantic Ocean, Mariana Basin, Philippine Basin, Bunnefjorden (Norway), Pacific Ocean, and a freshwater lake. The depths of the sample sites vary from 20 m to 5497 m, and cover diverse marine, brackish and freshwater environments such as push-cores or surface sediments of seafloors, fjords, abyssal plains, and continental rises. Except for ESBig, all datasets were processed using QIIME 2 (q2cli v2020.11.1) (Bolyen et al., 2018). For the 454 pyrosequencing data (BioMarKs and SouthChina), the raw reads were imported and demultiplexed with ‘--type SampleData[SequencesWithQuality]’ and ‘--input-format SingleEndFastqManifestPhred33’ options. After trimming the raw reads with respective primer-pair sequences, both 454 pyrosequencing and Illumina sequencing data were filtered with a DADA2 denoising step (Callahan et al., 2016). To remove chimeric sequences, denoised sequences were further processed with ‘uchime-denovo’ (Rognes et al., 2016). For taxonomic classification of amplicon 68 sequence variants (ASVs), a QIIME 2 compatible PR2 v4.14.0 dataset was obtained (Del Campo et al., 2018; Guillou et al., 2012) and modified by manually adding the SSU rRNA sequences of the new species described from this study and relevant sequences from the recent PR2 database and GenBank (Park and Simpson, 2010; Guillou et al., 2012; Rybarski et al., 2021). The modified PR2 dataset was used to pre-train the QIIME 2 classifier using ‘qiime feature-classifier fit-classifier-naïve-bayes’ (Pedregosa et al., 2011). The trained classifier was then used to assign taxonomy to filtered representative amplicon sequence variants (ASVs). Amplicon sequence variants assigned to MAST-6 and Placididea were extracted and added to a stramenopile SSU rDNA alignment consisting of partial to nearly full-length sequences (Cho et al., 2022; Yubuki et al., 2015). Additionally, relevant environmental sequences from the PR2 database, GenBank, and 10 placididean-associated operational taxonomic units (OTUs) from ESBig (Lee et al., 2022) were added. The extracted feature sequences were further subjected to CD-HIT to remove duplicates (Li and Godzik, 2006). To check presence and visualize relative abundance of newly acquired MAST-6 and Placididea species in the amplicon dataset (Table 3.1), feature tables from QIIME2 were exported and processed in RStudio (R v4.2.0) with ggplot2 (Wickham, 2016). 3.2.5 Small-subunit (SSU) rRNA gene tree construction The compiled SSU rRNA sequences were aligned with MAFFT v7.481 (Katoh and Standley, 2013) resulting 8,771 sites, followed by maximum likelihood inference using RAxML v8.2.12 (Stamatakis, 2014) under the GTRGAMMA model with 1000 ultrafast bootstrap replicates (UFB). To further evaluate the phylogenetic placement of short amplicon sequences from the amplicon datasets (Table 3.1), additional phylogenetic supports were estimated using the Evolutionary Placement Algorithm (EPA) (Berger et al., 2011) with EPA-ng v0.3.8 (Barbera 69 et al., 2018). This method used the reference ML tree constructed with the same conditions as above with partial to nearly full-length SSU rRNA sequences. To determine the placement probability of each amplicon sequence variant (ASVs) assigned to MAST-6 or Placididea, a likelihood weight ratio (LWR) was determined using GAPPA (Czech et al., 2020). The ASVs with an LWR value higher than 95% were inspected for chimerism using BLASTn and passing sequences were considered to be accurate with high confidence (Dunthorn et al., 2014). The SSU rRNA tree with EPA analysis is hereafter referred as SSU-EPA tree. To evaluate phylogenetic relationships of newly added MAST-6, placididean, and other species of Bigyra, another SSU rRNA phylogenetic tree was constructed without short amplicon sequences, hereafter referred as the SSU-tree. A total of 224 SSU rRNA sequences ≥ 900bp consisting of previously compiled datasets and new sequences (Aleoshin et al., 2016; Cho et al., 2022; Rybarski et al., 2021; Yubuki et al., 2015) were aligned using MAFFT v7.481 (Katoh and Standley, 2013), followed by trimming using trimAl 1.2rev59 (-gt 0.3, -st 0.001) (Capella-Gutiérrez et al., 2009). The phylogenetic tree was then constructed based on 1649 sites using IQ-TREE v2.1.0 (Minh et al., 2020) under TIM2+F+R6, the optimal model determined with ModelFinder (Kalyaanamoorthy et al., 2017) and 1000 UFB. 3.2.6 Phylogenomic matrix construction using PhyloFisher The phylogenomic matrix including the predicted proteins of the newly produced transcriptomes were generated using PhyloFisher v1.1.2 (Tice et al., 2021). Briefly, annotated ORFs from the newly generated transcriptomes were searched against the 241 gene set embedded in PhyloFisher and the resulting homologs were then added to each of the gene alignments. For each of the updated 241 gene alignments, a single-gene tree was constructed using IQ-TREE v1.6.12 (Nguyen et al., 2015) under the L+G4+X model and 1000 UFB. Each 70 single-gene tree was manually screened using ParaSorter v1.0.4 to ensure orthologs were inferred from the newly added proteins. Predicted orthologs of recently published or relevant stramenopiles (Azuma et al., 2022; Cho et al., 2022; Keeling et al., 2014; Richter et al., 2022; Thakur et al., 2019) were kept. To generate a final concatenated phylogenomic matrix, 98 taxa (including 15 taxa for an outgroup) were selected, resulting in a 240 gene set with 76,516 amino acid (aa) sites. Beside the main concatenated matrix, two additional concatenated matrices were generated to evaluate the effects of ortholog completeness in determining the phylogeny: one that included only orthologs found in ≥39% of taxa (233 orthologs with 74,531 aa sites, referred as 39per-matrix), and another that included orthologs found in ≥59% of taxa (215 orthologs with 67,630 aa sites; referred as 59per-matrix). Additionally, I generated another matrix with the most recent genomic data of other MAST lineages (MAST-1, MAST-7, MAST-8, MAST-9, and MAST-11) (Labarre et al., 2021; Richter et al., 2022) with 74,898 aa sites (234 orthologs) composed of 104 taxa (hereafter, referred as MASTer-matrix). 3.2.7 Phylogenomic tree reconstruction, removal of fast-evolving sites, and recoding The initial maximum likelihood (ML) tree of the main concatenated phylogenomic matrix was inferred using IQ-TREE v2.1.2 under the empirical profile mixture model, LG+C60+F+G4 (Quang et al., 2008) with 1000 UFB. The resulting ML tree was used as a guide to estimate posterior mean site frequencies (PMSF) (Wang et al., 2018), which was then used to re-estimate a final ML-PMSF tree with 100 non-parametric standard bootstraps under the same model. The construction of the ML-PMSF phylogenomic tree was repeated with the 39per- and 59per-matrices. To consider the effect of fast-evolving sites on tree topology, the main concatenated matrix was further subjected to a stepwise 10,000 aa site removal using PhyloFisher (fast_site_removal.py) followed by construction of ML-PMSF trees. To account for 71 potential amino acid composition bias in the dataset, web-based Composition Profiler (Vacic et al., 2007) was used with default settings to compare relative abundances of GARP vs. FYMINK amino acids with “SwissProt 51” (Bairoch, 2004) as a background, in addition to examining a distance matrix tree output generated by ‘aa_comp_calculator.py’ in PhyloFisher. To remove potential amino acid composition bias, the main concatenated matrix was recoded with the Dayhoff 18 (Dayhoff et al., 1978; Wang et al., 2018; Hernandez and Ryan, 2021) option using PhyloFisher v1.2.4 (aa_recode.py) followed by a tree reconstruction under the MULTI18_GTR+FO and 100 replicates of standard bootstrap with RAxML-NG v.1.1.0 (Kozlov et al., 2019). To infer a phylogenomic tree using Bayesian estimation, the CAT-GTR mixture model was used with the -dgm 4 option in PhyloBayes-MPI v4.0.3 (Lartillot and Philippe, 2004; Lartillot et al., 2009). Four independent Markov Chain Monte Carlo (MCMC) chains were run in parallel for at least 10,000 generations. The consensus posterior probability and topology were estimated after discarding first 20% as burn-in and subsampling every second tree. Convergence of the four chains was tested with bpcomp. 3.3 Results 3.3.1 Phylogenomic tree of stramenopiles The final phylogenomic matrix used for constructing the main phylogenomic tree is a concatenated alignment of 240 genes (76,517 sites) and 98 taxa (including 15 taxa belonging to an outgroup). The average percentage of genes present for each included transcriptome is 71.6%, with 76.4% of sites covered. These values are lower in the newly added transcriptomes: 8.5% genes and 16.3% sites for Pseudophyllomitus sp. BSC2; 21.1% genes and 25% sites for Mastreximonas tlaamin; 38.6% genes and 52.5% sites for Vomastramonas tehuelche; 38.2% 72 genes and 47.5% sites for Haloplacidia sinai; 21.42% genes and 35.42% sites for Caecitellus sp.; 42.7% genes and 58.3% sites for Symbiomonas scintillans (Fig. 3.1). The BUSCO scores showed a similar pattern where Pseudophyllomitus sp. BSC2 and M. tlaamin had the lowest values (4%:4% and 8%:2% completed:fragmented) while V. tehuelche and H. sinai had 28%:19% and 26%:16% and, S. scintillans and Caecitellus sp. 43%:10% and 11%:7%, respectively. Based on the main phylogenomic tree inferred from ML analysis under LG+C60+F+G4+PMSF, Gyrista was monophyletic and the Bigyra was paraphyletic (Fig. 3.1). Within Gyrista, Ochrophytes and Pseudofungi are monophyletic with strong support. In Ochrophyta, the Raphidophyceae, Phaeophyceae, and Xanthophyceae (RPX) clade formed a monophyly with the Chrysophyceae, Synurophyceae, and Synchromophyceae + Pinguiophyceae clade (CSS + Pi) with moderate bootstrap support (84%). Bacillariophyceae + Bolidophyceae + Pelagophyceae and Dictyochophyceae (BBDPe) formed a fully supported clade. The monophyly of RPX, CCS + Pi, and BBDPe was moderately supported (84%). However, phylogenetically unstable Eustigmatophyceae formed a weakly supported (71%) clade with Actinophrys sol, a non-photosynthetic heliozoan stramenopile. The clade comprising Eustigmatophyceae + A. sol clade branched sister to the rest of the Ochrophyta. In the ML-PMSF trees reconstructed from on MASTer, 39per- and 59per-matices, A. sol was sister to CSS + Pi while Eustigmatophyceae was sister to RPX with moderate support (81% to 94%, and 76% to 89%, respectively ) (Appendix G-H). Within Bigyra, the three new MAST-6 species, M. tlaamin, Pseudophyllomitus sp. BSC2 and V. tehuelche formed a clade with Pseudophyllomitus vesiculosus, with Pseudophyllomitus sp. BSC2 being the immediate sister lineage to P. vesiculosus. MAST-6, Eogyrea (MAST-4), and Labyrinthulea all formed a monophyletic group, Sagenista. In the tree reconstructed with 73 MASTer-matrix (Appendix H), MAST-7 and MAST-11 were robustly supported as monophyletic, which then was sister to Eogyrea. MAST-8 and MAST-9 formed close relationship to the grouping consisted of Eogyrea. MAST-7, and MAST-11. MAST-6 formed robust monophyly with this grouping composed of Eogyrea, MAST-7, -8, -9, and -11. The new Placididea species, H. sinai, is closest to Placididea sp. (Caron Lab) and, together with Wobblia lunata, comprise the Placididea clade. Placididea formed a sister lineage to the rest of the Placidozoa (MAST-3 and Blastocystis sp.). However, the support value for the Nanomonadea (MAST-3) and Opalinata (Blastocystis sp.) clade was weak (70% bootstrap). Placidozoa and Bikosia in turn comprise a robust monophyletic group, the Opalozoa, which is the sister lineage to the rest of the stramenopiles, except for Platysulcus tardus. Symbiomonas scintillans RCC257 is sister to Cafeteria burkhardae (Fenchel and Patterson, 1988; Schoenle et al., 2020) and this clade is a well-supported sister lineage to a clade composed of Caecitellus sp. RCC1078 and Halocafeteria seosinensis (Park et al., 2006) (Fig. 3.1). When fast-evolving sites were removed from the concatenated matrix to assess the effects of long-branch attraction, monophyly of each of the Ochrophyta, Gyrista, Sagenista, and Opalozoa were well supported up to 65% site removal (50,000 aa; Fig 3.2A). The monophyly of pseudofungi and the relationship between Gyrista and paraphyletic Bigyra were well supported up to 39% sites removed (30,000 aa; Fig. 3.2A). However, the groups with weak to moderate supports (70-84%) in Fig. 3.1 continue to show unstable relationships when fast-evolving sites are removed (Fig. 3.2B). Particularly, the placement of A. sol, Microchloropsis gadidata (Eustigmatophyceae) and sub-groups of opalozoans is different. An alternative placement for A. sol is as a sister lineage to rest of the ochrophytes when 20,000 aa sites (26%) and 40,000 sites (52%) are removed (Fig. 3.2B). For M. gadidata, it formed a sister lineage with Pinguiophyceae 74 or the rest of the ochrophytes except A. sol. Although the paraphyly of Bigyra was always supported with the progression of fast-evolving site removal, the relationships among the sub-groups kept changing with weak support (~70%) (Fig. 3.2B). To evaluate the effect of amino acid composition bias within sub-groups of opalozoans (namely Placidozoa), I inspected GC% of each transcriptome. All taxa belonging to Placididea are enriched in GARP amino acids compared to the background dataset, whereas all Opalinata are enriched in FYMINK, as is Nanomonadea with the exception of Incisomonas marina. Additionally, the amino acid composition of Placididea is more similar to Bikosia than the rest of the Placidozoa (Appendix I). However, when a phylogenomic tree is reconstructed using the recoded main matrix, the topology of the Placidozoa remains the same as Fig. 3.1, while the placement of A. sol and M. gadidata changes, with A. sol being recovered as the sister lineage to Pinguiophyceae and M. gadidata as the sister lineage to RPX (Appendix J). For the Bayesian analysis, the chains did not converge (maxdiff=1), with all chains conflicting with one another. When a consensus tree from each chain was compared, all the trees had the same topology of Sagenista and Bikosia that was also seen in the ML-PMSF inferred trees (Fig. 3.1; Appendix K). For Placidozoa, all the consensus trees had Nanomonadea branching sister to a clade composed of Opalinata and Placididea, a different topology from the ML-PMSF analysis, except the one constructed with the MASTer-matrix (Fig. 3.1; Appendix H and K). All chains had different Ochrophyta topologies, although the sub-clade relationship of BBPe was the same as the ML-PMSF inferred trees. Compared to the ML-PMSF tree (Fig. 3.1), the same topology was observed for the monophyletic CSS and the monophyletic RPX. The placement of M. gadidata, A. sol, and Pinguiophyceae were the most inconsistent across chains. 75 In all chains, Bigyromonadea is a sister lineage to Ochrophyta and two out of four chains recovered Bigyra as monophyletic (excluding P. tardus) (Appendix K). 3.3.2 New species represent phylogenetically diverse MAST-6 group in SSU rRNA analysis To determine the genetic diversity of MAST-6 in publicly available sediment datasets, a SSU rRNA tree was constructed with the extracted amplicon sequence variants (ASVs) trained with the modified PR2 reference database, including the SSU rRNA sequences of the newly described species in this study. All the SSU rRNA sequences obtained from the newly described MAST-6 species (>1800bp), H. sinai (>1800bp) and two bikosia (>1600bp) species are nearly full length. In total, 12 unique ASVs from BioMarKs were assigned to MAST-6 species; 9 for SouthChina; 16 for Norway; 6 for Deepsea; and 61 for the ISME2020 dataset. In general, studies that targeted the V4 region had sequence lengths between 183 to 460 bp; the V1-2 region 397 to 429 bp; and the V9 region 128-154 bp (Table 3.1). Shorter sequence lengths (~180 bp) from V4 targeted amplicon data are unpaired reads where low quality reverse reads were dropped. The SSU rRNA analysis of environmental data revealed substantial diversity of the MAST-6 group, which were largely grouped into four sub-groups. The new MAST-6 species and previously cultured species were found within the three sub-groups (Fig. 3.3); M. tlaamin in sub-group I, Pseudophyllomitus sp. BSC2 in sub-group II, and V. tehuelche in sub-group III. However, their phylogenetic relationship needs additional examination as the branch support values were weak (Appendix L). To evaluate the prevalence of these new MAST-6 species in the amplicon studies in sediment samples, relative abundance was plotted against other environmental MAST-6 ASVs (Fig. 3.4). All sediment datasets had relatively high abundance of MAST-6, particularly BioMarKs (65% of all MASTs and 2.24% of all ASVs) and ISME2020 (37% of all MASTs and 13.9% of all ASVs) studies. Amplicon sequence variants assigned to M. 76 tlaamin (PRC5) were dominant MAST-6 groups in Deepsea (67% of all MAST-6), ISME2020 (44%) and Norway (20%) datasets, while no sequences assigned to the new MAST-6 species were present in the BioMarKs study (Fig. 3.4B). It is important to note that not all ASVs assigned to M. tlaamin correspond exactly to the same species. However, they were assigned based on which most closely related MAST-6 species was available in the training dataset. This assignment may change as new MAST-6 transcriptomes representing each sub-group are added to the updated SSU reference data. I did not find shared MAST-6 ASVs across the four studies, which may be due to different sequencing technologies with different coverage, or presence of biological sequence variants by different sampling sites and time. Within sub-group I (Fig. 3.3), the ASVs from the ISME2020 and Norway datasets are placed closest to M. tlaamin, while the ASVs from the Deepsea dataset are more distantly related. This indicates that MAST-6 species closely related to M. tlaamin are not only genetically diverse and abundant, but the present in various sediment samples across different depths and geological locations. Additionally, within the MAST-6 sub-group I, M. tlaamin and the environmental sequence “SA2_3F7” are the only two with nearly full length SSU rRNA sequences, compared to sub-group II, which includes more close-to-full length SSU rRNA sequences. The addition of the M. tlaamin SSU rRNA sequence in the taxonomic assignment has markedly improved phylogenetic resolution among the MAST-6 lineages. A similar trend was observed in sub-group III where V. tehuelche is placed. Amplicon sequence variants from the BioMarKs dataset that were assigned to MAST-6, however, were mostly placed across the different sub-groups, except sub-group II. Along with many ASVs from ISME2020, six out of 12 unique MAST-6 ASVs of BioMarKs are placed within sub-group IV, which have no sequences from cell isolates with genomic data. When I visualized the abundance of different sub-groups across different datasets, Sub-group I was the 77 dominant group in all cases (Appendix L). More sub-groups were present in ISME2020 and Norway and this is likely due to sequencing techniques (i.e., pyrosequencing in BioMarKs) and limited universality of V9 primer used in Deepsea dataset (Appendix M). As QIIME2 generates ASVs, we interpreted the data without clustering. However, clustering the ASVs by ≥98% sequence similarity resulted in 10 and 37 ASVs assigned to MAST-6 in BioMarKs and ISME2020, respectively. For other MAST lineages, ASVs assigned to MAST-1, -3, -9, and -12 were present in all studies. Depending on the dataset, the relative abundance these MAST lineages were high although values fluctuated depending on the sample within the study. Two ASVs assigned to V. tehuelche were present in the SouthChina study, and no ASVs were assigned to Pseudophyllomitus sp. BSC2 (Appendix M). However, based on initial phylogenetic evaluation of assigned MAST-6 sequences from the SouthChina study, blastn searches, and the EPA analysis (low LWR values with the equal likelihood of alternative placements), the sequences were excluded from main the SSU-EPA tree (Appendix L-M). Additionally, one Deepsea ASVs assigned to MAST-6 was excluded from the downstream analysis based on the initial phylogenetic tree, and blastn search places it close to MAST-8 (Appendix L). Aside from M. tlaamin, other MAST-6 sequences from cell isolates (P. vesiculosus and NY13S_181 clone) were found in Deepsea (1.5%) and Norway (0.8%), although in low relative abundance. The rest of the MAST-6 sequences were assigned to environmental “MAST-6_X” and “SA2_3F7” from the PR2 dataset and “MAST-6”, a potentially new MAST-6 variant (Fig. 3.4B). As an additional measure to quantify the confidence of the extracted SSU rRNA sequence placements, sequences with LWR values ≥95% verified with blastn searches are highlighted in red in SSU-EPA tree (Fig. 3.3) and considered to be of highly confident (Berger et 78 al., 2011; Dunthorn et al., 2014). No Deepsea_MAST6 ASVs had LWR values ≥ 95%, with many of them having equally likely alternative placements (blue lines in nodes in Fig. 3.3). 3.3.3 The new Placididea may be rare in sediments For the Deepsea study, the only ASVs with high LWR values were the ones assigned to Placididea species. Although there was a total of 15 ASVs assigned to Placididea, none were assigned to H. sinai. When the SSU sRNA tree was constructed including the 10 Placididean OTU sequences of ESBig, H. sinai formed a sister lineage with ESBig133, which were found in water samples with salinities of 78, 124 and 380‰ (Lee et al., 2022) and, Placididea sp. (Caron Lab), cultured in 36‰ (Caron, 2000; Keeling et al., 2014) (Fig. 3.3). This clade formed a sister-lineage to “Group-D” containing Haloplacidia cosmopolita (described in Park and Simpson, 2010; Rybarski et al., 2021), which can tolerate 15–175‰ salinity. Additionally, ESBig sequences and Deepsea placididean sequences were placed across the major sub-groups of Placididea, despite being isolated from different geographical locations and a broad range of salinities (36‰ for Deepsea and 76–380‰ for ESBig) (Fig. 3.3). The confidence of the extracted SSU rRNA sequences placement within the partial-to-full length SSU sequences was inferred from LWR values ≥95%. Seven out of 15 Deepsea and four out of 10 ESBig placididean ASVs showed high confidence (red nodes in SSU-EPA tree in Fig. 3.3). 3.3.4 Morphological description and new name designation 3.3.4.1 An undescribed Pseudophyllomitus sp. BSC2 The cell is a biflagellated, naked, and free-living single-celled protist. The outline of the cell is oblong and slightly concave at the middle, measuring 22 µm in length and 7 µm in width (Fig. 3.5A-E). Both flagella emerge subapically from a gullet which continues for two-thirds (approximately 5 µm) of the cell width. The anterior flagellum is ~1x cell length and directed 79 forward. The posterior flagellum is 0.5x cell length and inserts to the left of the anterior flagellum. When the cell was stationary, the anterior flagellum beats rapidly in a sinusoidal wave, often sweeping to the right. The posterior flagellum is anchored sideways, likely attached to the surface, and occasionally trailing behind when changing direction. The two flagella are clearly visible and do not adhere to each other, the morphological trait that separates Pseudophyllomitus from Phyllomitus species (Lee, 2002). Some refractile granules are visible at the cell surface. Although no feeding was observed at the time of sampling, the cell is likely be a phagotroph. The shape of the cell is comparable to P. salinus (Lackey, 1940) in its oblong shape however, it is distinguishable by the longer anterior flagellum and the shorter posterior flagellum, and the presence of refractile granules on the cell surface (Lee, 2002). The cell is also similar to P. granulatus (Larsen and Patterson, 1990; Lee and Patterson, 2002) in terms of length and movement of both flagella and presence of the vesicles on the cell surface. However, its oblong shape is distinguished from sac-shaped P. granulatus. 3.3.4.2 New genera and species designation Mastreximonas gen. nov. Lax, Cho, and Keeling Taxonomy: Eukaryota; SAR Burki et al. 2008, emend. Sar Adl et al. 2012; Stramenopiles Patterson 1989, emend. Adl et al. 2005; Bigyra Cavalier-Smith 1998 emend. 2006; Sagenista Cavalier-Smith 1995; Eogyrea Cavalier-Smith 2013. Diagnosis: Flagellated, naked, and single-celled protist. Cell outline is elongated sac-shape with a slightly flattened anterior end. Thick anterior flagellum emerging apically, posterior flagellum may be very short and trailing under the cell, or absent. Etymology: Acronym for marine stramenopile, éxi (Greek έξι, number 6), and monas (Greek, fem.), commonly used for unicellular organisms. 80 Zoobank Registration. LSID for this publication: urn:lsid:zoobank.org:pub:583E6EDF-B1A2-4220-96D7-C4CF47DA9A6C. LSID for the new genus: urn:lsid:zoobank.org:act:960070EF-0259-4A31-936F-A372FED9B7FE Type species. Mastreximonas tlaamin Mastreximonas tlaamin sp. nov. Lax, Cho, and Keeling Diagnosis: The cell measures 15.6 µm in length and 4.8-6.4 µm in width. The prominent anterior flagellum is markedly thicker than the posterior flagellum and roughly two-thirds of the cell length (13 µm), directed forward, and emerges apically from a gullet. The posterior flagellum was not observed. Many large vesicles (approximately 1.5-2.5 µm in diameter) are present in the cytoplasm and two similarly sized golden vacuoles (2.4 µm) are present at the posterior end. The cell swims in a circular motion with the anterior flagellum beating in a sine wave. The nucleus is located just below the base of the anterior flagellum and is 3.5-4.0 µm in diameter. Although no feeding was observed at the time of sampling, the cell is likely a phagotroph. Type Figure: Fig. 3.5F. Gene sequence: The SSU rRNA gene sequence has the GenBank Accession Number OQ909084. Type material: The specimen shown in Fig. 3.5F–J is the holotype. The actual specimen (single cell) was destroyed in the process of single-cell genome sequencing by necessity (see International Code of Zoological Nomenclature, Art. 72.5.6, Declaration 45). Type locality: Oxic marine intertidal sediment of the Powell River, British Columbia, Canada (49°50'42'' N, 124°31'60'' W) 81 Etymology: The species epithet ‘tlaamin’ is derived from the Tla’amin Nation, an indigenous First Nation in Powell River, BC. It means ‘our people’ in Tla’amin language. Zoobank Registration: LSID for this publication: urn:lsid:zoobank.org:pub:583E6EDF-B1A2-4220-96D7-C4CF47DA9A6C. LSID for the new species:urn:lsid:zoobank.org:act:8B0835A7-679C-441A-AFFC-18D8596201BC Vomastramonas gen. nov. Tikhonenkov, Prokina, Cho, and Keeling Taxonomy: Eukaryota; SAR Burki et al. 2008, emend. Sar Adl et al. 2012; Stramenopiles Patterson 1989, emend. Adl et al. 2005; Bigyra Cavalier-Smith 1998 emend. 2006; Sagenista Cavalier-Smith 1995; Eogyrea Cavalier-Smith 2013. Diagnosis: Biflagellate, naked, and solitary eukaryovorous protist. Cells are slightly flattened and ovoid, with a slightly narrowed posterior end and a notch at the anterior end. Both flagella are acronematic, emerging apically from a notch at the anterior end of the cell. Etymology: Acronym for voracious, marine stramenopile, and monas (Greek, fem.) – commonly used for unicellular organisms. Zoobank Registration. LSID for this publication: urn:lsid:zoobank.org:pub:583E6EDF-B1A2-4220-96D7-C4CF47DA9A6C. LSID for the new genus:urn:lsid:zoobank.org:act:610C621F-9C18-49E5-983A-6194BE4F97CB Type species. Vomastramonas tehuelche Vomastramonas tehuelche sp. nov. Tikhonenkov, Prokina, Cho, and Keeling Diagnosis: cell body is 11.5-13 µm in length and 7.5-10 µm in width. Anterior flagellum is approximately equal to the cell length, posterior flagellum is 1.2-1.5 times longer than the cell. Anterior flagellum is markedly thicker than the posterior flagellum and clearly visible, directed 82 forward and sideways, curved in form of an arc, vibrates very rapidly with a short wavelength but doesn’t change its position during cell movement. Posterior flagellum is barely visible during cell movement, directed backwards. Cells swim close to the substrate in a circle, pushing off with the posterior flagellum, without rotation around its longitudinal axis and without changing the direction of movement. The anterior flagellum is directed towards the outer side of the circle when cell moves. When cell stops, posterior flagellum is directed sideways and curved in arc towards the anterior flagellum, so the flagella seem to stretch towards each other. Cells also can swim relatively straight, with small jerks. Numerous light-refracting granules and digestive vacuoles are present in the posterior half of the cell. No cysts. Remarks: this species differs from the other member of MAST-6 clade, Pseudophyllomitus vesiculosus Shiratori et al., 2017 because the cells are not flexible and lack the rod or bar laid against the anterior side of the nucleus (Shiratori et al., 2017). Type material: The specimen shown in Fig. 3.5K is the holotype (see International Code of Zoological Nomenclature, Art. 72.5.6, Declaration 45). Type Figure: Fig. 5K. Gene sequence: The SSU rRNA gene sequence has the GenBank Accession Number OQ909086. Type locality: Nearshore bottom sediments of the Strait of Magellan, Punta Arenas, Chile. Etymology: Tehuelche is the collective name (in Araucanian) of the indigenous peoples of Patagonia. Zoobank Registration: LSID for this publication: urn:lsid:zoobank.org:pub:583E6EDF-B1A2-4220-96D7-C4CF47DA9A6C. LSID for the new species:urn:lsid:zoobank.org:act:9C52101E-1B3E-45F2-9DB0-A6DFAB1349D1 83 Haloplacidia sinai sp. nov. Tikhonenkov, Cho, and Keeling Taxonomy: Eukaryota; SAR Burki et al. 2008, emend. Sar Adl et al. 2012; Stramenopiles Patterson 1989, emend. Adl et al. 2005; Bigyra Cavalier-Smith 1998 emend. 2006; Opalozoa Cavalier Smith 1991 emend. 2006; Placidozoa Cavalier-Smith 2013; Placididea Moriya, Nakayama & Inouye 2002; Haloplacidia Rybarski, Nitsche & Arndt 2021. Diagnosis: Cells are oval, roundish or irregularly ovoid, with the convex dorsal side and the flatter ventral side. Cell body is 5.4-8.3 µm in length and 3.4-6.6 µm in width. Anterior flagellum is approximately 1.5 times longer than the cell, posterior flagellum is approximately equal to the cell length. Posterior flagellum is acronematic and both flagella emerge from a shallow groove at the central part of the ventral side of the cell and oriented in the opposite directions. Anterior flagellum bears mastigonemes. Cells are often attached to the substrate with a posterior flagellum and produce very fast trembling movements. No cysts. Remarks: This species differs from the other member of the genus, H. cosmopolita Rybarski, Nitsche & Arndt 2021, by having a slightly different shape of the cell without pronounced kidney-like morphology, and by the absence of cysts, even under starvation conditions (Rybarski et al., 2021). Type material: The specimen shown in Fig. 3.5U is the holotype (see International Code of Zoological Nomenclature, Art. 72.5.6, Declaration 45). Type Figure: Fig. 3.5U. Gene sequence: The SSU rRNA gene sequence has the GenBank Accession Number OQ909082. Type locality: surface of corals in the Red Sea, Sharm El Sheikh, Egypt. 84 Etymology: named after the place it was found in the Mount Sinai region, where the Ten Commandments were given to Moses by God, according to the Book of Exodus in the Hebrew Bible. The English name Sinai came from Latin, ultimately from Hebrew יַניִס, pronounced /siˈnái/. Zoobank Registration: LSID for this publication: urn:lsid:zoobank.org:pub:583E6EDF-B1A2-4220-96D7-C4CF47DA9A6C. LSID for the new species:urn:lsid:zoobank.org:act:6EF3B31E-BAFB-4E5C-8010-DF3BBE3DCE43 3.5 Discussion 3.5.1 Updated taxon sampling and phylogeny of MAST-6 MAST-6 has been shown to be both abundant and diverse through various amplicon sequencing studies in sediment samples (Rodríguez-Martínez et al., 2009; Massana et al., 2015; Schoenle et al., 2021) (Table 3.1). Despite the known abundance and distribution across various sediment sites, inferring the diversity of MAST-6 species has been limited to a reference database composed of handful of SSU rRNA sequences. Moreover, only a single taxon for which genomic-level data are available (i.e., Pseudophyllomitus vesiculosus) has represented the MAST-6 clade in phylogenomic analyses. In this chapter, together with collaborators, I generated transcriptomes of three new MAST-6 taxa: Mastreximonas tlaamin, Vomastramonas tehuelche, and Pseudophyllomitus sp. BSC2, and updated the deep phylogeny of stramenopiles. These three new MAST-6 species in turn reflect broader genetic diversity by representing different sub-groups of the MAST-6 lineage. As with previously described P. vesiculosus, all new MAST-6 species described here were found in sediments, and have relatively large and numerous vesicles or granules underlying the cell surface. The new Pseudophyllomitus sp. BSC2 was the most closely related to previously 85 described P. vesiculosus and one of the longest Pseudophyllomitus species described so far (22 µm) (Lee and Patterson, 2002). The overall morphological characteristics are most similar to P. granulatus and, in a lesser extent to P. salinus. However, due to not observing feeding behaviour, I refrained from establishing a new species for this cell. Mastreximonas tlaamin had a similar oblong shape to Pseudophyllomitus sp. BSC2 and is sister to the two Pseudophyllomitus species. Vomastramonas tehuelche, on the other hand, has a more circular shape and is a sister lineage to the rest of the MAST-6 species in the phylogenomic tree. 3.5.2 The new MAST-6 species broaden the genetic diversity This chapter showed that M. tlaamin-related ASVs (sub-group I) are the most abundant MAST-6 across different sediment amplicon studies (Fig. 3.4), representing a largest MAST-6 sub-group consisting of ASVs from various sediment locations and depths (Fig. 3.3). Amplicon sequence variants assigned to M. tlaamin were absent in other studies (e.g., BioMarKs and SouthChina). This can be due to pyrosequencing, which is prone to non-homopolymer errors and has less sequencing coverage (Luo et al., 2012), or low abundance of M. tlaamin at the time of sampling. None of the new MAST-6 species from this study is found within sub-group IV despite its high relative abundance in the ISME2020 (Appendix M). Future efforts in isolating and describing cells of the subgroup IV may not only confirm phylogenetic diversity but help us better understand biology behind the sediment associated MAST-6 species. Based on the absence of Pseudophyllomitus sp. BSC2-and V. tehuelche-assigned ASVs, and low relative abundance of ASVs assigned to P. vesiculosus in sediment studies, these MAST-6 species may be rare. Additionally, sample timing may have played a role in lack of detection of some MAST-6 species, as the cell abundance has been reported to be affected by seasonality and salinity (Piwosz and Pernthaler, 2010). For example, both small and large morphotypes of MAST-6 are 86 observed to have short-lived peaks at mid-May to early-June in the Gulf of Gdansk shortly after freshwater inflow, followed by a substantial decline in relative abundance (Piwosz and Pernthaler, 2010). All datasets except BioMarKs were sampled mostly in September, while some sampled in August and July (sampling months for BioMarKs from February to October). These months were the time when the number of sub-group II associated MAST-6 were reported to be very low (Piwosz and Pernthaler, 2010). Although the work by Piwosz and Pernthaler, 2010 was done on plankton samples, the rapid and short-lived seasonal fluctuation of MAST-6 abundance revealed that this group may respond quickly to changing environment, including the ones in sediments. 3.5.3 Rare and potentially halotolerant Haploplacidia sinai and its implication in trait evolution Haloplacidia sinai is the fourth new species reported here. Haloplacidia sinai belongs to Placididea, another major clade of Bigyra that was represented by two transcriptomes before this chapter. As with some of the previously described species of Placididea (Park and Simpson, 2010), H. sinai was found in a relatively high salinity environment. Although I did not detect any ASVs assigned to H. sinai, based on its relationship (Fig. 3.3) with other isolated cells cultured in broad range of salinity, H. sinai might also be found in non-hypersaline environments. Absence of ASVs assigned to H. sinai may be due to the choices of sampling habitats in the datasets examined, as collaborators isolated the cell from coral scrapes. Three “Deepsea_Placididea” sequences formed a clade with the two Suigetsumonas spp. isolated from brackish lakes in Japan and Kenya (Okamura and Kondo, 2015; Rybarski et al., 2021) (Fig. 3.3), further demonstrating the broad range of salinity in which species of Placdidiea can be found. 87 The halophilic trait is not just limited to Placididea but can also be found in Bikosia. The extremely halophilic Halocafeteria seosinensis (Park et al., 2006; Park and Simpson, 2010) can survive between 75 to 363 ‰ (Lee and Patterson, 2002; Park et al., 2006). Furthermore, several traits including differential gene expressions involved in anti-oxidization, membrane fluidity, O-linked glycosylation, and gene-duplication were linked to high salt adaptability of H. seiosinesis (Harding et al., 2017). Exploring the evolution of halotolerancy in these deep-branching stramenopiles may lead to a better understanding of the ancestral state of the stramenopiles, determining whether the trait evolved separately in Placididea and Bikosia or arose in the last common ancestor of the two groups involving transition between different salinity barrier (Dunthorn et al., 2014; Jamy et al., 2022). 3.5.4 Phylogenomics of stramenopiles with a twist In this chapter, H. seosinensis is a sister lineage to Caecitellus sp., and this atypical mastigoneme-lacking group (O’Kelly and Nerad, 1998; Park et al., 2006) in turn formed a robust sister lineage to the clade composed of S. scintillans and Cafeteria burkhardae (Fig. 3.1; Appendix G and I). When I added the most recent genomic data of MAST-1, MAST-7, MAST-8, MAST-9, and MAST-11, the relationship remained the same (Appendix H). The bikosian phylogenomic relationship in this study (Fig. 3.1) is consistent with previous SSU phylogenetic trees (Cavalier-Smith and Chao, 2006; Cavalier-Smith and Scoble, 2013; Guillou et al., 1999; Park et al., 2006; Shiratori et al., 2017, 2015). However, an alternative SSU rRNA phylogeny showed H. seosinensis forming a sister lineage to a clade composed of Cafeteria spp. and Caecitellus spp. (Yubuki et al., 2015), similar to the SSU-tree generated in this chapter (Appendix N). This could be due to the fast-evolving nature of many bikosian SSU rRNA genes, as indicated by the long branch length of S. scintillans and C. burkhardae (Appendix N). 88 Additionally, the topology of Bikosia in the phylogenomic tree may be prone to future change as there are far more bikosia that are not represented in transcriptomic or genomic datasets, such as diverse Bicosoeca spp. (Karpov et al., 1998), Pseudobodo spp. (Griessmann, 1913), freshwater or soil bikosians, including Siluania monomastiga (Karpov et al., 1998), Nerada mexicana (Cavalier-Smith and Chao, 2006), Adriamonas peritocrescens (Verhagen et al., 1994), and Paramonas globosa (Cavalier-Smith and Chao, 2006; Saville-Kent, 1880) (Appendix N). The paraphyly of Bigyra has been repeatedly demonstrated in recent publications as more genomic data across different lineages of stramenopiles have become available (Burki et al., 2016; Noguchi et al., 2016; Azuma et al., 2022; Cho et al., 2022), including the ML-PMSF tree in my study (Fig. 3.1). However, the Bigyra are monophyletic in some other studies (Derelle et al., 2016; Thakur et al., 2019), as well as the two consensus trees obtained from MCMC chains in this study (Appendix K). As these studies all have differing numbers of taxa (as well as different taxa) and orthologs, and use different methods for data processing, it is difficult what might be causing topological incongruencies across these analyses. In contrast to previously published work (Azuma et al., 2022), the placement of A. sol is not sister to the rest of the Ochrophyta. Rather, it forms a weakly-supported clade with Microchloropsis gadidata (Eustigmatophyceae). As a single transcriptome represents each of Eustigmatophyceae and Actinophrydae, and I argue that this is the result of long branch attraction artefacts (LBA) caused by eroded phylogenetic signals (class II LBA), rather than parallel substitutions (class III) or saturation (Fig. 3.1) (Wägele and Mayer, 2007). I infer the probable existence of LBA in trees reconstructed from fast-evolving-site removal (Fig. 3.2B), Bayesian analysis (Appendix K) and, 39per-, 59per-, and recoded matrix (Appendix G and J), where the placement is chaotic rather than showing a pattern. The Ochrophyta phylogeny was 89 further complicated by other unstable relationships of Eustigmatophyceae, Pinguiophyceae, and among CCS, RPX, and BBPe. Phylogenomic discrepancies found in the Ochrophyta nuclear dataset should be addressed by more taxon sampling to break the long branches (e.g., Marine OCHrophytes (MOCH) (Massana et al., 2014) and Olisthodiscophyceae (Barcytė et al., 2021) and developing new phylogenomic models that can resolve short internal branches within early ochrophyte divergence (Philippe et al., 2011; Ševčíková et al., 2015; Di Franco et al., 2022). A similar discrepancy between maximum likelihood (ML) and Bayesian analyses was also observed in Cho et al. (2022) where all four chains yielded different topologies compared to the one from ML analysis. In my consensus trees also differed from the ML analysis in recovering Bigyromonadea as the sister lineage to Ochrophyta, as was observed in previous study (Cho et al., 2022). However, constrained AU tests (Shimodaira, 2002; Nguyen et al., 2015) failed to reject the monophyly of Bigyromonadea, together with Oomycetes (Winter 1897) and Hyphochytriomycetes (Dick 1983), forming a sister lineage to Ochrophyta in all four consensus trees (Cho et al., 2022). Within the monophyletic Placidozoa (Placididea+Nanomonadea+Blastocystis), the relationship among the sub-groups is not strongly supported, in contrast to three other studies (Azuma et al., 2022; Cho et al., 2022; Thakur et al., 2019) and my Bayesian analysis (Appendix K). Based on the amino acid composition of the placidozoan data used in this study, the topology appears to result from LBA due to enriched GARP aa in this group (Fig. 3.1; -Appendix I). However, repeating the ML-PMSF analysis without H. sinai (data not shown) recovered the same Placidozoa topology as previous studies (Azuma et al., 2022; Cho et al., 2022; Thakur et al., 2019) with weak support (76%). Despite the suspected LBA due to amino acid composition bias, the recoded tree analysis did not change the topology of Placidozoa, although the bootstrap 90 support was weak. The present placidozoan topology is likely unstable in my dataset due to a combination of long branches leading to Placididea and Opalinata, and low taxon sampling in Bigyra. As shown by the Opalinata + MAST-12 clade and diverse placidideans shown in the SSU-tree (Appendix N), future efforts in increasing taxon sampling will likely help stabilize the placidozoan topology, in addition to deploying a phylogenetic model that can resolve LBA amongst stramenopiles. 3.6 Conclusion The first impression of phagotrophic Bigyra to most observers may be a jumble of heterotrophic flagellates with few distinguishing features. It was only through SSU rRNA-amplicon sequencing that their identities and phylogenetic diversities were revealed. Even then the reference-dependent taxonomy assignment and usage of a single SSU primer-set often led to an under-detection of their diversity. Placididea on the other hand were initially discovered through cell isolates, but an assessment of their environmental distribution was limited due to its preferential amplification with a V9-targeting primer set (Lee et al., 2021; Rybarski et al., 2021). Despite the group’s diversity and ability to survive in a broad range of salinity, only very limited transcriptome or genome data had been available prior to this study. After adding another transcriptome of a placididean (H. sinai), we observed a topology change in Placidozoa that conflicts with previous studies. Based on the long unbroken branch leading to Placididea and alternative tree construction methods, the topology from the current study may be an artefactual relationship caused by long branch attraction (Felsenstein, 1978; Hendy and Penny, 1989; Delsuc et al., 2005b; Philippe et al., 2005). Combined with a lack of taxon sampling, the presence of highly divergent species, such as symbiotic Opalinata, Incisomonas marina, and their long-branching sister lineage, Bikosia, it is likely that currently available models cannot resolve the 91 true relationship of Placidozoa. Although the phylogenomics of Ochrophyta are beyond the scope of the present study, I note that it remained unresolved with conflicting ML and Bayesian analyses in this and previous studies (Azuma et al., 2022; Cho et al., 2022), which suggests more data will be required. Adding three new MAST-6 transcriptomes to my phylogenomic tree resulted in robust monophyly of MAST-6 and MAST-4, a relationship only recently revealed in phylogenetic studies (Shiratori et al., 2017; Thakur et al., 2019; Cho et al., 2022). Along with the new MAST-6 species, I also showed phylogenomic relationship among Sagenista with recently published genomic data of MAST-7, -8, -9, and -11 for the first time. Newly described MAST-6 species improved the detection of considerable phylogenetic diversity of sediment-associated MAST-6 species from various sample sites, and demonstrated a higher diversity compared to that of the most abundant MAST-4 group (Logares et al., 2012; Rodríguez-Martínez et al., 2012). One of the abundantly detected MAST-6 is closely related to the newly described M. tlaamin (PRC5), while few or no ASVs were detected for V. tehuelche and Pseudophyllomitus sp. BSC2. This indicates different MAST-6 species may be rare and have different seasonal dynamics. 92 Table 3.1 List of selected amplicon sequencing datasets from the European Nucleotide Archive (ENA). Dataset designation Sample environment Sequencing technology 18S rRNA region Length (bp) Number of ASVs Sample number BioProject Placididea MAST-6 BioMarKs* Seafloor sediment 454 GS FLX Titanium V4 380-384 0 12 24* (Run accessions: ERR861806-ERR861811, ERR861839, ERR861843, ERR861849, ERR861853, ERR861860, ERR861870, ERR861884, ERR861885, ERR861894, ERR861895, ERR861900, ERR861901, ERR861905, ERR861910, ERR861911, ERR861915- ERR861917) PRJEB9133 (Dunthorn et al., 2014; Massana et al., 2015) SouthChina Seafloor sediment 454 GS FLX Titanium V1-V2 396-429 0 9† 6 PRJNA341446 (Wu and Huang, 2019) Norway Marine and brackish sediment Illumina MiSeq paired-end V4 426-429 0 16 24 PRJEB24876, PRJEB24158, PRJEB24888 Deepsea Abyssal seafloor sediment Illumina Genome Analyzer II paired-end V9 134-138 15 6† 20 PRJNA635512 (Schoenle et al., 2021) ISME2020 Seafloor sediment Illumina MiSeq paired-end V4 182-425 0 61 49 PRJNA521526 (Rodríguez-Martínez et al., 2020) ESBig** Solar saltern Illumina MiSeq paired-end V9** 128-154 10** 0 **Accession number: MZ297173, MZ297191, MZ299824, MZ299825, MZ299969, MZ300048, MZ300314, MZ300350, MZ300439, MZ300768 **PRJNA732544 (Lee et al., 2021) 93 Accession numbers are included only if selected samples of a given BioProject were analyzed. For example, out of 139 samples for the BioMarks dataset, only sediment samples (24) were processed to access the diversity of the newly identified MAST-6 species (*). Extracted length indicates the length of the amplicon sequence variants (ASVs) assigned to MAST-6 or Placididea lineages. The ESBig dataset was not processed in this study, but the sequences assigned to placidideans were directly obtained from the authors of the BioProject (**). All SouthChina ASVs and one Deepsea_MAST6 ASV were excluded from the main figures based on LWR-values and manual blastn searches (†). 94 Figure 3.1 Phylogenomic tree of stramenopiles Maximum-likelihood (ML) multi-gene tree of stramenopiles, including six new transcriptomes; four from newly described Bigyra in this study (light red), and two from culture collections (blue). The tree was constructed from concatenated alignments of 240 genes from 98 taxa (76,516 aa sites) under the site-heterogeneous model LG+C60+F+G4+PMSF with 100 standard bootstraps. Only nodes with ≤99% support values are labelled, with unlabelled nodes indicating 100% bootstrap support. Dashed branches indicate potential long branch attraction artefacts (LBA). The % genes (dark grey) and sites occupied (light grey) for each taxon are shown on the mirrored bar plot on the left. 95 Figure 3.2 Removal of fast-evolving sites Change in bootstrap support with the incremental removal of fast-evolving sites (10,000 sites removed at each step) for the monophyly of major stramenopile groups (A) and minor unstable groups (B). A. Monophyly of major stramenopile groups show strong bootstrap support up to 30,000 sites removed. Paraphyly of Bigyra, represented by “(Gyrista+Sagenista)+Bikosia” and “Gyrista+Sagenista”. B. Monophyly of unstable groups showing fluctuation in bootstrap support. Bootstrap supports with zero values indicate alternative topology (not shown here) with weak support (22-55%). Topologies within Opalozoa (Nanomonadea, Placididea and Oplinata) were unstable and weakly supported. Topologies within the Ochrophyta were also largely unstable, especially for Eustigmatophyceae (“E”) and Actinophrys sol (“A”). “RPX” = 96 Raphidophyceae+Phaeophyceae+Xanthophyceae; “CSS+Pi” = Chyrsophyceae+Synurophyceae+Synchromophyceae; “BBDPe” = Bacillariophyceae+Bolidophyceae+Dictyophyceae+Pelagophyceae. 97 Figure 3.3 SSU-EPA tree of stramenopiles 98 A RAxML SSU rRNA phylogenetic tree (SSU-EPA tree) of stramenopiles. The tree was constructed under the GTR+GAMMA model with 1000 rapid bootstrap replicates, using an alignment of 527 stramenopile sequences and seven outgroup sequences (8,771 sites): 109 extracted ASVs assigned to MAST-6 or Placididea from the amplicon dataset, and 10 placididean OTU sequences from ESBig study. The four new Bigyra species are coloured in pink. The likelihood weight ratio (LWR) values calculated from our EPA analysis are coloured in red for high confidence (LWR ≥95%), and in blue for low confidence (LWR <95%), indicating equally likelihood of alternative placements. The label structure for the ASVs is “Dataset_MAST6/Placididea_count”. Clades other than MAST-6 and Placididea are collapsed. For bootstrap supports, see Appendix M. 99 Figure 3.4 Relative abundance bar plots of MASTs Stacked bar plots of the relative abundance of unique ASVs assigned to main MAST groups (A) and MAST-6 (B) from four sediment datasets: BioMarKs, Deepsea, ISME2020, and Norway. Deepsea is the only study with a SSU rRNA gene primer targeting the V9 region. A. Composition of different MASTs from each dataset grouped by class level. Black frames indicate the relative abundance of MAST-6. B. Composition of MAST-6 lineages from each dataset grouped by order to further show higher taxonomic assignment. “MAST-6_X” represents an unknown MAST-6 lineages classified from the PR2 database, and “MAST-6” represents a potentially new MAST-6 lineage based on the updated taxonomic training database. “Mastreximonas tlaamin” is one of the new MAST-6 species descried in the current study. “Pseudophyllomitus vesiculosus” and “NY13S_181” are previously reported cultures and “SA2_3F7” is an environmental sequence. 100 Figure 3.5 Morphology of four new Bigyra A-E. Pseudophyllomitus sp. BSC2. General view of the cell including anterior flagellum [af] and posterior flagellum [pf]. Both flagella emerge from a horizontal gullet [gu] and some refractile granules are visible on the cell surface [rg]. A diatom [d] is attached at the posterior end. F-J. Mastreximonas tlaamin, general view of the cell with an anterior flagellum. The nucleus [n] is visible just below the base of the anterior flagellum. Some vesicles [ve] and golden vacuoles [va] 101 are present from the mid to posterior end of the cell. K-O. Vomastramonas tehuelche. General view of the cell with clearly visible anterior flagellum. A notch [nt] is present at the anterior end. Refractile granules and food vacuoles are present. P-V. Haloplacidia sinai, general view of the cell with two flagella. W. H. sinai in scanning electron micrograph, showing mastigonemes [mn] on the anterior flagellum and acroneme [ac] on the posterior flagellum. Scale bars are 10 µm for A-O, 5 µm for P-V, and 1 µm for W. 102 Chapter 4: Phylogenomic analyses of ochrophytes (stramenopiles) with an emphasis on neglected lineages 4.1 Introduction Ochrophyta is a group of protists that are often used as an example of the vast molecular and morphological diversity of stramenopiles. Ochrophytes include the giant multicellular brown algae, the intricate frustule-covered diatoms, some golden algae that have lost the ability to photosynthesize and dozens of other distinct subgroups (Cavalier-Smith and Chao, 2006; Graf et al., 2020; Riisberg et al., 2009; Yang et al., 2012). Because of their ecological importance and morphological diversity, there have been many studies reconstructing ochrophyte phylogeny and trying to understand their evolutionary relationship. Yet, despite this attention, phylogenomic analyses of ochrophytes remain incongruent with one another (Burki et al., 2016; Derelle et al., 2016; Noguchi et al., 2016; Thakur et al., 2019; Di Franco et al., 2022; Cho et al., 2022; Azuma et al., 2022), especially between the trees reconstructed from nuclear and plastid genes (Ševčíková et al., 2015; Barcytė et al., 2021; Dorrell et al., 2021; Di Franco et al., 2022). Additionally, even with publicly available genomic and transcriptomic data and with many ochrophytes readily available in culture collections (Yang et al., 2012), the diversity of ochrophytes in supermatrices used in phylogenomic analyses has remained under-represented and has been somewhat static (Driskell et al., 2004; Burki et al., 2016; Derelle et al., 2016; Noguchi et al., 2016; Thakur et al., 2019; Azuma et al., 2022; Cho et al., 2022;) (for an exception, see Terpis, 2021). Current ochrophyte phylogenomic analyses all differ in dataset composition and size, processing approaches, and phylogenetic inference methods. Although there is some consensus around the backbones of the ochrophyte phylogeny (Derelle et al., 2016; Azuma et al., 2022; Cho et al., 2022), numerous recalcitrant relationships characterized by short internodes leave the 103 positioning of some important lineages contentious. These short internodes in stramenopile phylogeny are likely caused by ancient rapid radiation that can carry limited phylogenetic signals (Whitfield and Lockhart, 2007; Di Franco et al., 2022; Pardo-De La Hoz et al., 2023). To make matters worse, these short internodes are commonly found across deep, divergent lineages of stramenopiles (i.e., long-branching taxa) where data sites (i.e., nucleotide or amino acid sequences) tend to experience saturation leading to underestimation of actual sequence substitutions (Lartillot et al., 2007; Philippe et al., 2011). Consequently, these branches are prone to long branch attraction (LBA) artefacts (Felsenstein, 1978; Hendy and Penny, 1989; Wägele and Mayer, 2007). Another challenge is phylogenetic incongruence among gene trees (including organellar and nuclear gene trees), caused by non-neutral selection (Stiller et al., 2003; Edwards, 2009; Dorrell et al., 2019), incomplete lineage sorting (ILS), introgression via hybridization, and horizontal gene transfers (Maddison, 1997; Nichols, 2001; Dorrell et al., 2021; Dong et al., 2022). Several phylogenomic approaches are available to remediate the effects of these issues: incrementally removing fast-evolving sites, genes, and taxa, or increasing taxon sampling and the number of sites (Bapteste et al., 2007; Pick et al., 2010; Superson and Battistuzzi, 2022). More recently, applying the CAT-PMSF phylogenetic method (Szantho et al., 2023) was reported to be robust against LBA, while significantly decreasing computing resources. Furthermore, the importance of characterizing phylogenetically informative genes has been highlighted in resolving short internodes in ancient radiations (Salichos and Rokas, 2013; Shen et al., 2016; Smith et al., 2018). Using high variable length bootstrap values as a proxy for phylogenetic signal, ochrophyte plastid genes have been shown to have more phylogenetic signals than nuclear genes with comparable numbers of sites. However, plastid datasets are not suitable for 104 inferring evolutionary history of stramenopiles as a whole, as many stramenopiles lack plastid or its associated genes. In this chapter, I aimed to resolve relationships within ochrophytes, and by extension stramenopiles as a whole, by first updating the ochrophyte dataset to include a number of neglected, but potentially informative lineages, and by comprehensively assessing nuclear genes to identify those most phylogenetically informative and those with most noise. To update the dataset, I added ten new transcriptomes from ochrophytes some of which had not been represented in previous phylogenomic analyses, along with including all other current publicly available data. The updated dataset now represents 14 out of 17 major ochrophyte classes (Cavalier-Smith and Chao, 2006; Riisberg et al., 2009; Yang et al., 2012; Graf et al., 2020) including members of the Olisthodiscophyceae (Barcytė et al., 2021), Phaeothamniophyceae (Andersen et al., 1998), Schizocladiophyceae (Kawai et al., 2003), and Picophagea (Guillou et al., 1999). I particularly focused on “breaking” long branches leading to known lineages with conflicting placement, such as Eustigmatophyceae, Actinophrydae, and Pinguiophyceae. To identify phylogenetically informative genes and investigate a source of incongruence among various phylogenomic analyses, I explored different gene filtering criteria. I used a previously established method (Mongiardino Koch, 2021; Mongiardino Koch and Thompson, 2021), which calculates phylogenetic signal, noise, and data quality. Overall, I report robust support for previously controversial placements and some of these relationships were recovered in the majority of trees reconstructed from various subsets of genes. Phylogenetically informative genes could not be unambiguously identified however, I observed that using genes with high phylogenetic signal and quality resulted in the most stable tree topologies, as opposed to selecting genes with low phylogenetic noise or removing the ones with high noise. 105 4.2 Materials and Methods 4.2.1 Ochrophyte sample collection and processing Nine cultures of under-represented ochrophytes were obtained from various culture collections (Table 4.1). Except for Actinosphaerium sp. (which was processed immediately and the culture not maintained), I sub-cultured all cultures every two weeks in 30 mL and kept at 20°C with a 12 hour:12 hour light:dark cycle. Both Olisthodiscus luteus and O. tomasii were kept in TL30 media; Schizocladia ischiensis was maintained in L1-Si (Guillard and Ryther, 1962; Guillard, 1975); Phaeothamnion confervicola in MiEB12 (Andersen, 1991); Pseudostaurastume enorme in DYV-m (Lehman, 1967); Vacuoliviride crystalliferum in AF6 with f/2 vitamin solution (Watanae et al., 2000); Chrysamoeba radians in URO+soil (Provasoli and Pintner, 1959); and Picophagus flagellatus in 0.22 µm filtered seater water (30 ‰) with an autoclaved rice grain. I extracted RNA with TRIzolTM LS for all cultures except the two Olisthodiscus spp., P. confervicola, and Actinosphaerium sp. Forty milliliters of each culture was centrifuged at 3000 rpm for 20 min at 4°C to pellet cells at the bottom of the centrifuge tubes. After carefully removing supernatant media, 1 mL of TRIzolTM LS was added to the cells and the mixture was transferred to Lysing Matrix Y bead tubes (MP Biomedicals, USA). The mixture in the bead tubes were subjected to physical lysis using a VWRTM Mini Bead Mill at 5 m/s for 30 sec followed by 30 sec on ice. This step was repeated once more. The solution was then transferred to PhasemakerTM (Invitrogen) tubes to minimize interphase contamination during the aqueous-organic layer separation using chloroform. The precipitated and washed RNA pellets were resuspended in 30 µL PCR-grade water. 106 For both Olisthodiscus cultures, I used a cetyltrimethylammonium bromide (CTAB)-based RNA extraction protocol (Apt et al., 1995; Yao et al., 2009) to prevent co-precipitation of phenolic compounds which can hinder downstream cDNA synthesis. Briefly, 40 mL of each of the culture was centrifuged in 15 mL FalconTM tubes for 10 min at 4°C, 3000 rpm. After discarding supernatant media, 2 mL of CTAB buffer was added directly to the pelleted cells. While gently agitating the mixture, 25% v/v of 100% ethanol and 11% v/v of potassium acetate (3M, pH 4.8) were slowly added. The remainder of RNA extraction and precipitation were followed as described by Yao et al., 2009. Each of the RNA pellets were resuspended in 200 µL of PCR-grade water, followed by RNA purification using NucleoSpin® RNA XS Kit (Takara Bio USA, Inc.) with 10 µL elution volume. For P. confervicola and Actinosphaerium sp., I manually isolated each single cell (or a small filamentous colony of P. confervicola) using a glass micropipette under a Leica DLIM inverted microscope, followed by rinsing three times in PCR-grade water. Rinsed cells were then transferred into 0.2 mL PCR tube containing lysis buffer (Picelli et al., 2014) and stored at -80°C until cDNA synthesis. Similarly, my collaborator isolated three single cells of Vicicitus globosus from marine plankton near-shore tows at Hakai Institute, Quadra Island, BC Canada (50°06’54.6’’N, 125°13’10.8’’W) on August 7th and September 12th, 2021. The quality and quantity of the RNA extracts from TRIzolTM LS and CTAB-based methods were assessed using a NanoDrop 1000 Spectrophotometer v3.8.1 (Thermo Fisher Scientific) and QubitTM RNA High Sensitivity Assay Kits (Thermo Fisher Scientific). 4.2.2 cDNA synthesis, library preparation and sequencing I followed the poly-A selection based Smart-Seq2 protocol for cDNA synthesis (Picelli et al., 2014). For RNA extracts, 4 µL was used for each cDNA synthesis while single-cell isolates were 107 subject to 2-3 rounds of freeze-thaw cycles (Onsbring et al., 2020) prior to Smart-Seq2. The quantity of cDNA was measured using QubitTM dsDNA HS Assay Kits (Thermo Fisher Scientific). To confirm taxonomic identities, I performed small subunit ribosomal DNA (SSU rDNA) polymerase chain reaction (PCR) on each cDNA sample (except V. globosus), using 18SFU-18SRU primers (Tikhonenkov et al., 2016), followed by purification using QIAquick® PCR Purification Kit (Qiagen), and Sanger dideoxy sequencing (University of British Columbia, UBC BC Canada). Library preparation was done by the Sequencing and Bioinformatics Consortium (UBC, BC Canada), using the Illumina DNA Flex Library Preparation Kit, and sequenced on a NextSeq platform with 150 bp paired-end library constructs. For some cultures, RNA extraction, cDNA synthesis, library preparation and the subsequent sequencing were repeated to obtain higher completeness of the transcriptome, using the same parameters and methods. The raw transcriptome data is deposited under NCBI accession SRR27254659-SRR27254668, under BioProject PRJNA1050613. 4.2.3 Transcriptome processing and phylogenomic matrix construction Along with the ten newly generated transcriptomes, I also processed publicly available transcriptomes of Saccharina sp. (ERR2861927), Sargassum sp. (DRR042036), Uroglena sp. (ERR1368708), Glossomastix sp. (ERR3497268), Synura sp. (ERR1368706), Heterococcus sp. (SRR1099987), Vischeria sp. (SRR14572414), Monodopsis sp. (SRR14581548), Eustigmatos polyphem (SRR397983), Poteriospumella lacustris (ERR1368700) as described below. All other pre-processed (i.e., predicted open reading frames, ORFs) geomic level data were obtained from previous publications (Azuma et al., 2022; Cho et al., 2024, 2022; Labarre et al., 2021; Thakur et al., 2019), the EukProt V3 database (Richter et al., 2022), and the Marine Microbial Eukaryote 108 Transcriptome Sequencing Project, MMETSP (Keeling et al., 2014). Many of these transcriptomes represent sub-groups of ochrophytes that were otherwise represented by small numbers of taxa in previous phylogenomic analyses. First, the quality of all raw sequencing data was evaluated using FastQC v0.11.9 (Andrews, 2010), followed by random sequencing error correction using k-mer based Rcorrector v3 (Song and Florea, 2015). The corrected reads were then trimmed and filtered (-phred33 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36) using Trimmomatic v0.39 (Bolger et al., 2014) to remove transposase-inserts, Smart-Seq2 IS-primers and NexteraTM DNA Flex adaptors from library preparation. The resulting forward, reverse, and unpaired transcripts were assembled (or co-assembled if multiple transcriptomes from the same culture were generated) using de novo rnaSPAdes v3.15.1 (Bushmanova et al., 2019). The single-cell transcriptome data of V. globosus was co-assembled once the species identities were confirmed by extracting SSU rDNA sequences using barrnap v0.9 (Seemann, 2007). To evaluate assembly results (e.g., coverage and taxonomic assignments), I used BlobTools v2.3.3 (Laetsch and Blaxter, 2017; Challis et al., 2020). Taxonomic assignments were determined by searching assembled transcripts against the NCBI nt database using megaBLAST followed by a diamond BLASTX against the Uniprot reference database (Haas et al., 2009), both with e-value cut-offs 1e-25. All bacterial, Viridiplantae, Metazoa, and archaeal reads were removed. Open reading frames (ORFs) were predicted using TransDecoder v5.5.0 (Haas, 2015) and the longest ORFs were annotated with a BLASTP search against UniProt database (e-value 1e-5). To assess the completeness of each transcriptome, BUSCO v5.2.2 (Simão et al., 2015) was used with database ‘stramenopiles_odb10’. 109 4.2.4 Phylogenomic supermatrices The predicted ORFs of the newly added transcriptomes were added to an existing supermatrix using PhyloFisher v1.1.2 (Tice et al., 2016). Briefly, to identify homologs from the ORFs of each transcriptome, I searched against 241 genes compiled in PhyloFisher. The identified homolog candidates were then added to their respective gene alignments, followed by sequence processing using PREQUAL (Whelan et al., 2018), MAFFT (Katoh and Standley, 2013), Divvier (Ali et al., 2019) and trimAl (Capella-Gutiérrez et al., 2009) incorporated in PhyloFisher. Each alignment was then used to construct a single gene tree under the L+G4+X model with 1000 replicates of ultrafast bootstraps (UFB), using IQ-TREE v1.6.12 (Nguyen et al., 2015). To ensure correct orthologs were identified for each gene from each transcriptome, I manually screened 241 single-gene trees using ParaSorter v1.0.4. To generate a concatenated supermatrix, I selected 139 taxa (including 14 outgroup taxa) with 231 orthologs (≥39% taxa completeness) (‘231-supermatrix’). An additional supermatrix was generated with orthologs from MAST-1, MAST-7, MAST-8, MAST-9 and MAST-11 (Labarre et al., 2021), consisting of 146 taxa (including 14 outgroup) with 233 orthologs (≥39% taxa completeness), resulting in 73,440 sites (‘233-supermatrix’). 4.2.4.1 Filtering by gene occupancy, fast-evolving and random sites To investigate the effect of fast-evolving sites, 7,000 fast-evolving amino acid (aa) sites were incrementally removed to exhaustion from the ‘231-supermatrix’, using PhyloFisher, resulting in 10 additional supermatrices (‘fsite-supermatrix’). Similarly, 7,000 random sites were incrementally removed, resulting in yet another 10 supermatrices (‘randSite-supermatrix’). I also randomly removed genes in 20% increments to compare with trees recovered from different gene filtering criteria (‘randGene-supermatrix’). The average BS values of phylogenomic trees from 110 each of randSite- and randGene-supermatrices were calculated and used to determine minimum data size (i.e. amino acid sites) required to reduce the effect of small data size and distinguish from the effect of different gene-filtering criteria (see below). With the condition of recovering paraphyletic Bigyra and well-recognized relationship of ochrophyte lineages (e.g., Chrysista or Diatomista), I decided the cut-off BS values to be >89%. Based on the cut-off, I determined that approximately 22,000 sites are the minimum sites required. 4,2.4.2 Conceptual design for phylogenomic gene filtering To identify phylogenetically informative genes and investigate incongruence among different phylogenomic analyses, I calculated different gene properties based on previously established methods (Mongiardino Koch, 2021; Mongiardino Koch and Thompson, 2021). The calculated properties were then used to rank the genes by noise or signal (some include data quality, see below) based on correlation significance and contribution to an ordination axis (i.e. PC loadings). Phylogenomic analyses inferred from different sets of selected genes were then used to evaluate whether removing genes with high phylogenetic noise, selecting genes with low noise or high phylogenetic signal would resolve lineages that were previously conflicting, ultimately finding the most informative set of genes. Furthermore, I sought to replicate alternative placements of contentious lineages (e.g. placement of Eustigmatophyceae or Pinguiophyceae found in phylogenomic trees inferred from plastid genes), by selecting nuclear genes with high phylogenetic noise. 4.2.4.3 Filter by phylogenetic biases, signals, and other data qualities To evaluate the effects of some of the known sources of noise such as average pair-wise patristic distance (av_patristic, a proxy for LBA) (Struck, 2014; Mongiardino Koch and Thompson, 2021), variance of root-to-tip distances (root_tip_var, a proxy for inferring deviation 111 from clock-like evolution) (Smith et al., 2018; Mongiardino Koch and Thompson, 2021), saturation (Nosenko et al., 2013; Kocot et al., 2016), and relative composition frequency variability (RCFV, a proxy for amino acid compositional heterogeneity) (Zhong et al., 2011; Whelan et al., 2015; Shen et al., 2016b), and phylogenetic signal such as treeness (length of internal branches) (Lanyon, 1988), average bootstrap supports (average_BS_support), Robinson-Foulds similarity (robinson_sim, distance between a gene and species tree; proxy for incongruencies) (Robinson and Foulds, 1981; Salichos and Rokas, 2013), I applied the measurement method put together by Koch (2021) and Koch and Thompson (2021), which calculates these properties in all the genes used for constructing ‘231-supermatrix’ and visualizes them with principal component analysis (PCA). Other information that is indicative of the dataset quality such as alignment lengths, the proportion of missing data per taxon, completeness/occupancy of genes, total tree length, and tree-based evolutionary rate were also calculated (Mongiardino Koch, 2021; Mongiardino Koch and Thompson, 2021). I estimated the known possible sources of phylogenetic noise (av_patristic, root-tip-var, saturation, RCFV), signal (treeness, average_BS_support, robinson_sim), and data quality or information (rate, missing data, tree and gene length, proportion of variable sites, and occupancy) using a published R-script (https://github.com/mongiardino/genesortR) (Mongiardino Koch, 2021), with some modifications. Although the ‘233-supermatrix’ has the most up-to-date collections of stramenopile taxa, due to the timing of data analysis, I calculated phylogenetic noise, signal, and quality in all genes of the ‘231-supermatrix’. The resulting measures were plotted onto two principal component axes using the ‘factoextra’ R-package. Two genes (GDI and NSF1-I) were considered as outliers based on the estimated Mahalanobis distances and were excluded from downstream analyses. To visualize how each of the measured 112 properties are correlated to one another and, calculate correlation coefficients and significance I generated Pearson correlation graphs using R-packages ‘corrr’, ‘ggcorrplot’, ‘GGally’, ‘ggfortify’, and ‘FactoMineR’ R-packages. Based on the correlation analyses, PC loadings of each properties, I subsampled genes by eight criteria: (A) high values of treeness and occupancy; (B) high values of average_BS_support, robinson_sim, and gene length; (C) low values of av_patristic, evolutionary rate, and total tree length; (D) filtering out high values of av_patristic, evolutionary rate, and total tree length; (E) high values of PC1-associated noise (root_tip_var, av_patristic, and saturation); (F) high values of all noise; (S) high values of signal (treeness, average_BS_support, robinson_sim); and (Q) high values of data quality (occupancy and gene length). Because each criterion is a combination of multiple properties, I extracted shared genes that are found in the properties of a given criterion by searching the top 40 to180 genes of the highest or the lowest values. For example, 43 genes were present in the top 80 highest values of both treeness and occupancy (criterion A80) while 33 genes were present in the top 40 lowest values for each properties in criterion C (criterion C40). I also combined subsampled genes from criteria A to C, with the top 60-160 highest values in criteria A and B and, the lowest values in criterion C (i.e., ABC60-160). Finally, I also subsampled genes that are not well represented by any of the two PCA axes (i.e., genes with low cos2 values) (criterion N). A size of different supermatrices generated from each criterion is summarized in Table 4.2. For each of the gene sets that were filtered by different criterion or a combination thereof, I generated supermatrices as described in 4.2.4. 4.2.5. Phylogenomic trees: C60-PMSF, CAT-PMSF, CAT-GTR For all the supermatrices generated above, I inferred maximum likelihood (ML) trees using IQ-TREE v2.1.2, under the profile mixture model LG+C60+F+G4 (C60) with posterior 113 mean site frequencies (PMSF) used to generate 100 replicates of non-parametric standard bootstraps (BS) (Quang et al., 2008; Wang et al., 2018). This method involves a two-step process incorporated in IQ-TREE, first by generating initial ML trees under the LG+C60+F+G4 model with 1000 ultrafast bootstraps (UFB). The estimated guide-topologies of these initial ML trees were then used to estimate PMSF, which were then used to reconstruct the final C60-PMSF trees (Wang et al., 2018). To check whether exchangeabilities were not mis-specified with the F-class, I verified that the F-class values are < 0.11 (Baños et al., 2023), and repeated the tree reconstruction under the LG+C60+G4 model. For the ‘231-supermatrix’, I inferred a phylogenomic tree with Bayesian estimation using PhyloBayes-MPI v4.0.3, under the CAT-GTR mixture model with four independent Markov Chain Monte Carlo (MCMC) chains. These chains were run in parallel for 20,000 generations each. After discarding the first 10% of generations as burn-in, I checked for convergence using bpcomp, and estimated the consensus posterior probability and topology by subsampling every second tree. Finally, I reconstructed an additional phylogenomic tree using the CAT-PMSF pipeline (Szantho et al., 2023) to compare with our C60-PMSF analysis. Both of these two methods assess the effects of potential artefacts derived from compositional heterogeneity across amino acid sites however, CAT-PMSF estimates site-specific amino acid frequency using a non-parametric Bayesian approach while C60-PMSF uses a fixed amino acid frequency vector (Wang et al., 2018; Szantho et al., 2023). CAT-PMSF involves three steps: 1) construct an initial ML tree under a site-homogeneous model, LG+F+G4; 2) correct potential LBA artefacts using Bayesian estimation (PhyloBayes-MPI v4.0.3), under the CAT-LG model with the two Markov chains until convergence (~6,000 generations, 20% discarded as burn-in, convergence assessed with maxdiff=0) using site-specific stationary distributions obtained from step 2 to fit the tree to 114 PMSF with IQ-TREE, as described above for C60-PMSF. Each chain was used to generate the final two PMSF trees (CAT-PMSF trees) for step 3. 4.3. Results and Discussion 4.3.1 The phylogenomic tree of stramenopiles 4.3.1.1 Updating ochrophytes dataset with under-represented classes I generated ten new transcriptomes to update the taxon sampling for ochrophytes, including six taxa belonging to four classes that had not been previously represented in phylogenomic analyses (Table 4.1). The updated phylogenomic supermatrix resulted in 72,932 amino acid (aa) sites (‘231-supermatrix’), with 93 Gyrista (70 ochrophyte taxa), 32 Bigyra, and 14 outgroup taxa (Fig. 4.1). When I included MAST-1, -7, -8, -9, and MAST-11 in the supermatrix (‘233-supermatrix’), the resulting dataset consisted of 73,440 aa sites from 96 Gyrista and 36 Bigyra. The addition of MAST-1, -7, -8, -9, and MAST-11 did not change the topology of the rest of the stramenopiles, except the placement of Nanomonadea and Placididea (Fig. 4.1). The phylogenomic trees inferred from these two supermatrices are summarized in Figure 4.1. In both trees of ‘231-supermatrix’, C60-PMSF and CAT-PMSF (Figs. 4.1 and 4.2), the newly added ochrophyte transcriptomes showed similar topologies as ones reported in previous phylogenetic analyses based on SSU rDNA sequences and conserved plastid genes. With robust node support, I recovered Chrysophyceae + Synurophyceae + Synchromophyceae (CSS) + Picophagea (Pico) as monophyletic in all trees examined, as previously reported in (Guillou et al., 1999; Barcytė et al., 2021) (Fig. 4.1; Table 4.3). This relationship was also observed in the only other phylogenomic analysis with a comprehensive ochrophyte dataset (Terpis, 2021). Schizocladiophyceae is sister to Phaeophyceae, while Phaeothamniophyceae is a sister-lineage to 115 Phaeophyceae-Xanthophyceae-Schizocladiophyceae (Fig. 4.1). This placement of Schizocladiophyceae is found in previous studies (Yang et al., 2012; Graf et al., 2020; Barcytė et al., 2021). However, the placement of Phaeothamniophyceae showed more inconsistency within Raphidophyceae-Phaeophyceae-Xanthophyceae (RPX) clades. As I found here, Phaeothamniophyceae falls sister to PX-Schi zocladiophyceae in a five-gene maximum-likelihood (ML) tree in Graf et al. (2020), which had extensive taxon sampling across RPX lineages. In other studies, Phaeothamniophyceae was the sister-lineage to PX in a two-gene ML tree (Barcytė et al., 2021) or Xanthophyceae in a 10-gene ML tree (Riisberg et al., 2009; Wetherbee et al., 2019). My dataset is still missing representatives of three ochrophyte classes (Aurearenophyceae, Chrysoparadoxophyceae, and Phaeosacciophyceae). These missing classes have been shown to belong to the PX clade, which forms a monophyletic group in previous multi-gene phylogenetic analyses, along with Raphidophyceae (Yang 2012; Wetherbee et al 2019; Graf et al 2020). A recent phylogenomic study that included the latter two ochrophyte classes showed Phaeothamniophyceae as the sister group of Phaeosacciophyceae while Chrysoparadoxophyceae to Xanthophyceae, both with strong BS supports (Terpis, 2021). The absence of these classes therefore, account for the low BS values for PX in the phylogenomic analyses (53% BS in ‘231-supermatrix’ C60-PMSF; 95% in CAT-PMSF) (Fig. 4.1). The two Actinophrydae taxa are sister to CSS+Pico, although with a modest BS support of 83% (Fig. 4.1). This relationship was also recovered in Cho et al. (2024), but only when genes with a minimum 39% completeness were selected. This instability was likely due to erosion of phylogenetic signal in Actinophrydae in my dataset. The newly generated transcriptome of Vicicitus globosus was nested within the Dictyochophyceae with 100% BS support. 116 The Vicicitus globosus is known to produce fast-acting cytotoxin (Chang, 2015) and its transcriptome was included in the analyses due to its availability at the time. 4.3.1.2 Robust support for contentious lineages while breaking long branches Eustigmatophyceae (Eustig) is composed of the sub-groups Eustigmataceae, Monodopsidaceae, Neomonodaceae, and Goniochloridales (Amaral et al., 2020), but had been frequently represented only by a single taxon from Monodopsidaceae (i.e., Microchloropsis gaditana) (for an exception, see Terpis, 2021). Pinguiophyceae has been represented by one or two taxa, and is sometimes omitted entirely (Derelle et al., 2016; Thakur et al., 2019). To “break” these long branches, I added newly generated and publicly available transcriptomes belonging to different Eustigmatophyceae sub-groups and Pinguiophyceae. I recovered a robust monophyly of RPX and Eustigmatophyceae (RPX+Eustig) in a majority of the trees (Figs. 4.1 and 4.2; Table 4.3), a previously contentious topology (Di Franco et al., 2022). This relationship was also observed in the recent phylogenomic analysis that included more Eustigmatophyceae subgroups (Terpis, 2021). Eustigmatophyceae is the sister lineage to CSS in a phylogenomic tree inferred from plastid genes (Ševčíková et al., 2015; Di Franco et al., 2022), while it is sister to RPX in a nuclear phylogeny (Burki et al., 2016; Derelle et al., 2016; Noguchi et al., 2016; Thakur et al., 2019; Terpis, 2021; Azuma et al., 2022; Di Franco et al., 2022; Cho et al., 2024, 2022). However, the latter studies only included a single Eustigmatophyceae taxon, likely contributing to with weak bootstrap supports. Two chains of the Bayesian analysis did recover the Eustigmatophyceae grouping close to CSS, along with Olisthodiscophyceae and Actinophrydae (Appendix O), however with lower average posterior probabilities (PP=1 and 0.71), while the two other chains with the Eustig+RPX grouping both had PP=1. I observed close groupings of Eustigmatophyceae with CSS in only two trees 117 generated from different supermatrices. For example, clades comprising [(CSS+Pico)+Olis]+Eustig and (CSS+Pico)+(Eustig+Actino) were observed in trees inferred from C60 and F140 supermatrices, respectively (Table 4.3). Although I replicated the similar placement of Eustigmatophyceae that would be observed in trees inferred from plastid genes, I suspect that these groupings are the result of small data size (C60) and/or LBA artefact (Eustig+Actino in F140), rather than replicating evolutionary or artefactual processes of plastid genes. Instead, it is likely the incongruence observed in nuclear versus plastid trees is the result of molecular convergence arising from non-neutral selection force. Molecular convergence arising from neutral or random mutations (e.g., homoplasy) can be remediated by current phylogenomic mixture models (Lartillot and Philippe, 2004; Wang et al., 2008, 2018). However, non-neutral force on plastids such as balancing selection that selects similar sets of plastid genes across eukaryotes (Maier et al., 2013; Dorrell et al., 2019) can result strong phylogenetic signal in these genes (Stiller et al., 2003; Edwards, 2009). Further investigation on the effects on non-neutral force on plastid and nuclear genes may help understanding the incongruence between the two datasets (Stiller et al., 2003; Castoe et al., 2009). Additionally, it may be worthwhile examining gene properties of plastid genes and compare with nuclear genes. I observed a clade comprising Pinguiophyceae+Olisthodiscophyceae (Olis+Ping) in almost all trees examined, including the ones with fast-evolving sites, random sites and genes removed (Fig. 4.1-4.2; Table 4.2; Appendix P). This clade was the sister group of CSS, often with strong branch supports (Fig. 4.1; Table 4.3) and was also observed in a previous phylogenomic study (Terpis, 2021). The close relatedness between Pinguiophyceae and CSS has been demonstrated in other studies including the ones using plastid genes however these only 118 used a single taxon representing Pinguiophyceae or recovered lower bootstrap supports (Burki et al., 2016; Cho et al., 2022; Di Franco et al., 2022; Noguchi et al., 2016). As with Eustig+RPX, half of the Bayesian chains (Appendix O) had different placements of Pinguiophyceae (branching as a sister to Diatomista, consisting of Pelagophyceae, Dictyochophyceae, Bolidophyceae, and Bacillariophyceae). The newly added ochrophyte data broke many long branches leading to Eustigmatophyceae, CSS, Pinguiophyceae, and Actinophrydae. Pseudofungi (Oomycotes, Hyphochytriomycetes, and Bigyromonadea) is a clade branching sister to the rest of the Ochrophyta with 100% BS support. The same topology was observed in the tree recovered from the ‘233-supermatrix’, most with higher BS supports (Fig. 4.1). I observed a clade comprising Bigyromonadea and Ochrophyta in the CAT-PMSF tree (Fig. 4.2; Table 4.3) with up to 88% BS. 4.3.1.3 Examining phylogenomic relationships with the Bayesian analysis Overall, the Bayesian analysis was inconclusive even with 20,000 generations, as none of the chains converged (maxdiff=1). However, the topology of chain 1 and 2 were identical except for the outgroup (Appendix O), while the topology of chain 3 and 4 had the same topology in Gyrista topology (Appendix O). The topology of the ochrophytes were almost the same (except for the placement of Attheya septentrionalis; Bacillariophyceae) between the chains 1-4 and the C60-PMSF tree inferred from the ‘231-supermatrix’ (Figs. 4.1 and 4.2A). This conflicting placement of A. septentrionalis can also be found in previous studies (Theriot et al., 2010, 2015; Parks et al., 2018; Dorrell et al., 2021) where different set sizes of genes were sampled; small subunit ribosomal genes and plastid genes (Theriot et al., 2010, 2015), high occupancy orthologs (58,294 sites) found in diatoms (Parks et al., 2018) or ochrophytes (26,399 sites) (Dorrell et al., 2021). 119 For Bigyra, I found paraphyly similar to that observed by Cho et al. (2024) in addition to the unstable groupings within Placidozoa (Fig. 4.1; Appendix O). In all consensus trees from the Bayesian analysis and the ‘233-supermatrix’, Nanomonadea (MAST-3) is sister to the rest of the Placidozoa (data not shown), as was also observed in Cho et al. (2024). This is likely because of a LBA artefact due to lack of taxon sampling in Opalinata and MAST-12 (Kolodziej and Stoeck, 2007; Okamura and Kondo, 2015; Cho et al., 2024). 4.3.2 No filtering criteria to select “good” or “bad” genes for phylogenomic analyses Due to the presence of many phylogenetically contentious lineages in stramenopiles, particularly in Ochrophyta, I initially aimed to resolve phylogenomic relationships by selecting genes with high phylogenetic signal and/or low noise, while also increasing taxon sampling. A principal component analysis (PCA) of 13 gene properties that are proxies for sources of known phylogenetic noise, signal, and data quality, revealed a far more complex relationship. As a result, it was challenging to devise a suitable filtering criteria that could discern genes by the “good” or the “bad” gene properties (Fig. 4.3A; Appendix Q). In contrast to the results from the work of Mongiardino Koch (2021), who established this method by testing on more recently diverged (121.8 to 479.1 million years old) organisms (Mongiardino Koch, 2021), my stramenopile dataset did not have a clear separation between phylogenetic signal and noise affecting genes along the two PC axes. Moreover, the two PC axes only explained 51.8% of the total variance while some gene properties have high loadings in an additional PC axis (Appendix Q). This made the delineation of the “good” or the “bad” genes further challenging. All values of the 13 properties are summarised in Appendix R. I observed that the majority of noise (e.g., saturation, av_patristic, and root_tip_var - coloured in red Fig. 4.3A) had higher vector loadings with principal component 1 (PC1), 120 however the two groups of phylogenetic signal (criteria A and B) were explained with different PC axes (Fig. 4.3; Appendix Q). The rest of the noise, RCFV (coloured in red in Fig. 4.3A), a proxy for aa composition bias, was explained mostly by PC2 (i.e., higher vector loading with PC2) along with some properties that are potential indicators of the phylogenetic signal (e.g., average_BS_support, robinson_sim – coloured in blue in Fig. 4.3A), although in an opposing direction (i.e., negative correlation). The two properties, the treeness and occupancy were explained by PC1 but negatively correlated with the noise and data quality (Fig. 4.3; Appendix Q). Consequently, I included various filtering criteria (criteria A-D) by PC loadings and their correlations (Appendix Q and S) among different properties regardless of the nature (e.g., noise, signal, or data quality) of the gene properties. Additionally, not all the gene properties of the same nature showed strong positive correlations (Fig. 4.3; Appendix S). I also observed that the higher data quality does not necessarily correlate with indicators of phylogenetic signal. For example, average_BS_support and occupancy are negatively correlated while robinson_sim and rate are positively correlated (Fig 4.3; Appendix S). Presence of many recalcitrant nodes, older evolutionary history with the estimated origin of 719-414 million years ago (Ma) for ochrophytes (Brown and Sorhannus, 2010; Choi et al., 2024) and 1077-1025 Ma for the rest of the stramenopiles (Yoon et al., 2004), and early rapid radiation are likely the cause of such difference between stramenopile dataset and the dataset analysed by the initial research that established this method (Mongiardino Koch, 2021). 4.3.2.1 Evolutionary rate provides phylogenetic signals but correlates with noise Among all the gene properties calculated, ‘evolutionary rate’ had the highest vector loading (0.448) along PC1, followed closely by ‘av_patristic’ and ‘tree_length’ (0.446 and 0.415, respectively) (Appendix Q). Strictly speaking, ‘evolutionary rate’ and ‘tree length’ are a measure 121 of information. However due to strong positive correlations among the ‘evolutionary rate’ and ‘tree length’ with noise (e.g., ‘saturation’, ‘av_patristic’, and ‘root_tip_var’), and neutral or negative correlation with most of phylogenetic signal, I treated them as noise in my analyses (Fig. 4.3B). Similarly, I treated ‘gene alignment’ as an indicator of phylogenetic signal based on its strong positive correlation with ‘average_BS_support’ and ‘robinson_sim’. Along PC2, ‘alignment length’ had the highest vector loading (0.571) followed by ‘robinson_sim’ (0.513) (Appendix Q). Rapid evolutionary rate has been previously reported to cause saturation as the number of possible mutation states for each nucleotide or amino acid character is limited (Felsenstein, 1978; Philippe et al., 2005; Superson and Battistuzzi, 2022). As a result, without significantly limiting the number of sites, removal of fast-evolving sites and genes has been used to minimize noise (Philippe et al., 2005; Bapteste et al., 2007; Edwards, 2016; Superson and Battistuzzi, 2022). However, despite their correlation with other noise in this study (Fig. 4.3B), rate and tree length (both used to estimate rate) should not be solely regarded as sources of noise. In a simplified simulation of evolutionary processes, Revell et al. (2008) showed that under weak stabilizing selection, high mutation rate can provide a more informative signal, while observing no correlation with rate and phylogenetic signal under a constant genetic drift. The authors proposed that phylogenetic signal is affected by the non-neutral selection force, rather than just the rate, as it can be significantly decreased by divergent selection (leading to speciation) or increased with an initially high rate that slowed over time (i.e., rate variation), or high rate of niche occupancy This means that filtering by criteria A (selecting for genes with high values of treeness and occupancy), B (selecting for genes with high values of average_BS_support, robinson_sim, and 122 gene length), and C (selecting for genes with low values root_tip_var, av_patristic, rate, and saturation) might have resulted in significant losses of these phylogenetic signal. 4.3.3 Phylogenomic analyses using different filtering criteria Based on the 13 gene properties calculated, we generated a total of 46 supermatrices and subsequent phylogenomic trees to see the effects of gene properties on phylogenomic analysis (Table 4.3; Appendix R). To minimize the effect of the small data size (i.e., number of amino acid sites) on the phylogenomic analyses, I compared the average BS support of all trees reconstructed from random site or gene removal to the C60-PMSF tree reconstructed from the 231-supermatrix (Fig. 4.1). Based on the change of backbone topologies and their average BS supports, supermatrices with the average BS less than 89% was deemed too small to sufficiently distinguish from the effects of different gene-filtering criteria and small data size. Therefore, I only considered the topologies of supermatrices with size larger than ~22,000 sites (e.g., criteria A120-160; B120-160; C100-160; all D and ABC) (Fig. 4.2; Table 4.2 and 4.3). For criteria A (selecting high values of treeness and occupancy) and B (selecting for high values of average_BS_support, robinson_sim, and gene length), the ochrophyte topology was similar in general to the ‘231-supermatrix’ under C60-PMSF (Fig. 4.2A and D; Table 4.3). To investigate the effects of signal, noise, and data quality alone, I included additional filtering criteria (criteria S, E, F, and Q) to compare the trees with those reconstructed from supermatrices A-D and ABC (Fig. 4.2). When I compared the topologies of trees reconstructed from criteria A, B, and S, most of the topologies (including the instability of Sagenista and Opalozoa) were the same, except the placement of Actinophrydae (Fig. 4.2A and J). These criteria all selected for high signal while the criteria A and B distinguished the signal associated PC axis in addition to other highly correlated gene properties (i.e. data quality and information). 123 The trees reconstructed from high data quality (criterion Q) had the most stable topologies (Fig. 4.2K), all of which were identical to the ’231-supermatrix’ C60-PMSF, except the placement of Actinophrydae. For the trees reconstructed from supermatrices C120-160 (select genes with low noise and associated properties), there were more unstable topologies (including Pseudofungi and Actinophrydae) compared to the ones reconstructed with criteria A, B, S, and Q (Fig. 4.2D, E, J, K). Similarly, the trees reconstructed from supermatrix D120-160 (Fig. 4.2F) showed unstable topology of Pseudofungi and the placement of Actinophrydae (Fig. 4.2F). When I examined the trees reconstructed from supermatrices E and F (selecting genes with high noise), the placement of Platysulcus tardus became unstable, no longer was sister to the rest of the ochrophytes (Fig. 4.2H and I; Table 4.3). Other “deep-branching” lineages such as Opalozoa and Sagenista were also affected, although the same instability was observed in trees reconstructed from different criteria (e.g., N, A and B120-160). It is likely that these “deep-branching” lineages maybe more sensitive to a data size and phylogenetic noise, likely due to having the phylogenetic signal present is smaller set of genes compared the later diverged lineages. This was also observed when random sites and genes were removed – many lineages belonging to Gyrista remained consistent with more sites or genes removed, compared to Opalozoa and Sagenista. For some instances, Eustigmatophyceae was sister to Actinophrydae, in which the clade branched sister to Chrysista or CSS (Fig. 4.2H and I; Table 4.3). The latter topology observed in plastid multi-gene trees (Ševčíková et al., 2019; Barcytė et al., 2021; Di Franco et al., 2022). The majority of the Actinophrydae (Actino) placement was observed to be sister to CSS+Pico or Olis+Ping, each relationship with the same frequency (14 occurrences) (Fig. 4.2; Table 4.3). The latter relationship was present in supermatrices A and B120-160, S120-140, and 124 Q120-180, selecting for genes with high signal, data quality and other properties that were correlated. The clade of Actinophrydae with CSS+Pico was observed in trees reconstructed from supermatrices E and F160-180, A80-100, C140-160, D60-120, even though some criteria select for genes with high noise (criteria E and F) while others select for low noise (criterion C) or remove ones with high noise (criterion D). It is likely that as the data size increases for each criterion, there are more overlapping genes sampled (Appendix T). However, Actino+[CSS+Pico] was also recovered the ‘231-supermatrix’ C60-PMSF (Fig. 4.1). I suspect that this particular topology is influenced by a small number genes (Shen et al., 2017) and various filtering criteria that removed any of these genes may have recovered alternative placements of Actinophrydae. The placement of Actinophrydae to the rest of the ochrophytes were observed in seven out of 46 trees, mostly from supermatrices C and D with lower data size (C60-120 and D140-160) (Table 4.3) and this is the topology observed in Azuma et al. (2022). The placement of Acitnophrydae being sister to the rest of the ochrophytes is likely due to selecting for slow evolving genes thereby eroding phylogenetic signal and its effect likely more pronounced in smaller data size. To lessen the loss of rate-derived phylogenetic signal that might be present in genes affected by high rate or tree length, we combined the filtered genes of each criterion’s top-ranking values (i.e., criterion ABC60-160). Excluding the placements of Actinophrydae, rest of the topologies were the same relationship as the ones found in ‘231-supermatrix’ C60-PMSF (Fig. 4.2A and G). The placement of Bigyromonadea being sister to ochrophytes was observed in different criteria that had relatively small data size (e.g. N, A and B60-80, C80-120, D140-160, ABC60-100) and for the one that select for genes with high noise (F120-140) (Table 4.2 and 4.3). Thus, 125 the groupings of Bigyromonadea+Ochrophytes, Sagenista+Opalozoa found in other trees generated with different filtering criteria may have been the result of lack of phylogenetic signal arising from small data size or the effect of compositional bias (Fig. 4.2I). When I incrementally removed fast-evolving sites, I observed monophyly of Pseudofungi (oomycetes, hyphochytriomycetes, and Bigyromonadea) in trees with up to 67% aa sites removed (Appendix P: A). Even when I randomly removed aa sites, bigyromonads formed a monophyly with oomycetes, and Platysulcidae remained sister to rest of the stramenopiles in most cases (Appendix P). When I randomly removed genes in 20% increments, monophyly of Bigyromonadea+Oomycetes were observed most of the times, even when up to 60% of genes (139 genes) were removed (Appendix P: C). 4.3.3.1 Different types of compositional heterogeneity may recover different topologies Compositional heterogeneity in phylogenomic inferences has been known to cause LBA, mainly due to lack of models that account for this (Koshi and Goldstein, 1995; Jimenez et al., 2018; Szantho et al., 2023). We used relative composition frequency variability (RCFV) as a proxy for compositional heterogeneity among branch terminals, to evaluate disproportionate amino acid composition across different taxa. However, compositional variation also occurs across sites and through time as a result of selection pressures, constraints on protein folding sites or preferential traits due to environmental factors (Koshi and Goldstein, 1995; Boussau et al., 2008; Jimenez et al., 2018; Szantho et al., 2023). To account for across-site compositional heterogeneity, we followed the C60-PMSF (Quang et al., 2008; Wang et al., 2018) and CAT-PMSF pipelines (Szantho et al., 2023). The resulting trees largely showed the same topology, except for the placement of Bigyromonadea (Fig. 4.2B). When I compared the trees inferred from supermatrices E and F, I observed that the monophyly of Bigyromondea and Oomycetes 126 were no longer stable in trees inferred from supermatrices F (selecting for genes with all the high noise, including RCFV) (Fig. 4.2H and I). It is beyond the scope of this work to account how the two different inference methods (CAT-PMSF vs C60-PMSF) may have influenced compositional biases across sites and taxa. However, based on trees inferred from various selecting criteria (Fig. 2), we speculate that paraphylectic relationship of Bigyomonadea and Oomycetes is an artefact of across-taxa aa compositional bias (i.e., RCFV). 4.4 Conclusion To resolve the placement of several contentious lineages of stramenopiles, I updated stramenopile supermatrix and conducted phylogenomic analyses using various inference methods. I recovered robust relationships of previously phylogenomically unavailable or contentious lineages such as Eustigmatophyceae, Olisthodiscophyceae, and Pinguiophyceae. Additionally, based on 13 proxies for phylogenetic noise, signal, and quality for each gene, I constructed numerous supermatrices based on different criteria selecting for genes with high signal or low noise. I found the tree topologies were more stable when I selected for genes with high signal and data quality. Selecting the most conserved, the slowest evolving genes, resulted in the most variable and incongruent tree topologies across the trees examined. Furthermore, when considering the effect of compositional heterogeneity on phylogenomic inferences, we should be conservative in our interpretation as different types of compositional variations exist along with different methods to remediate it. Future efforts should include devising systematic evaluation criteria that select for genes with high signal and quality while removing genes highly affected by noise. Additionally, finding the minimum set of genes that encompasses all these criteria may lessen computational resources and time, a challenge inherent to phylogenomic analyses. 127 Table 4.1 List of ochrophyte cultures obtained from various culture collections. Species Class Culture collection centre (location) Culture ID Media Actinospherium sp. Actinophrydae Carolina Biological Supply (USA) item#131302 Carolina™ Springwater Chrysamoeba radians Chrysophyceae National Institute for Environmental Studies (Japan) NIES-2890 URO+soil Olisthodiscus luteus Olisthodiscophyceae Norwegian Culture Collection – Scandinavian Culture Collection (Norway) K-0444 TL30 Olisthodiscus tomasii Olisthodiscophyceae National Institute for Environmental Studies (Japan) NIES-15 TL30 Phaeothamnion confervicola Phaeothamniophyceae Roscoff Culture Collection (France) RCC7139 MiEB12 Picophagus flagellatus Picophagea Roscoff Culture Collection (France) RCC22 FSW Pseudostaurastume enorme Eustigmatophyceae Culture Collection of Algae at Göttingen University (Germany) SAG11.85 DYV-m Schizocladia ischiensis Schizocladiophyceae Roscoff Culture Collection (France) RCC7138 L1-Si Vacuoliviride crystalliferum Eustigmatophyceae National Institute for Environmental Studies (Japan) NIES-2860 AF6 128 Table 4.2 Summary of supermatrices generated using different filtering criteria The number values are size of the amino acid and the brackets indicate the number of genes. ‘Top n-value’ indicates common genes found in the top n-list for all the properties of a criterion. Each criterion is denoted by A = selecting for genes with high values of treeness and occupancy; B = selecting for genes with high values average_BS_support, robinson sim, and gene length; C = selecting for genes with low values of av_patristic, rate, and treelength; D = filter out genes with high values of av_patristic, rate, and treelength; ABC = combination of criteria A-C with corresponding ‘Top n-values’; N = genes that are not explained well by the PC axes (low cos2); C60- & CAT-PMSF, Bayesian = the same 231-supermatrix was used for constructing C60-PMSF tree, CAT-PMSF tree and Bayesian trees. Top n-value A B C D ABC E F S Q N C60- & CAT-PMSF Bayesian 60 4,816 (26) 5,116 (12) 12,932 (49) 57,819 (186) 20,817 (77) — — — — 11,353 (43) 72,932 (231) 80 9,203 (43) 9,673 (23) 17,636 (60) 52,151 (167) 32,756 (109) — — — — 100 15,794 (64) 16,118 (38) 22,884 (79) 46,180 (148) 45,376 (144) — — — — 120 22,070 (81) 22,030 (54) 30,955 (102) 40,342 (130) 53,922 (169) 21,576 (70) 14,587 (53) 18,967 (47) 23,265 (54) 140 29,095 (105) 29,914 (76) 36,544 (120) 33,211 (111) 59,940 (187) 29,228 (92) 20,234 (71) 26,804 (70) 34,067 (84) 160 36,203 (130) 40,593 (107) 44,134 (139) 25,781 (88) 66,530 (207) 37,817 (121) 28,884 (100) 35,684 (99) 40,540 (109) 180 — — — — — 46,973 (149) 38,92 (128) 45,154 (130) 49,794 (141) 129 Figure 4.1 Phylogenomic of stramenopiles with 10 new ochrophyte transcriptomes 130 Combined maximum-likelihood (ML) multi-gene trees of stramenopiles with 10 new transcriptomes from under-represented ochrophyte lineages (pink): ‘231-supermatrix’ C60-PMSF and ‘233-supermatrix’ C60-PMSF. The trees were constructed from a 231 gene-alignment of 125 stramenopiles and 14 outgroup taxa (72,932 aa sites), and a 233 gene-alignment of 132 stramenopiles and 14 outgroup (73,440 aa sites), under model LG+C60+F+G4+PMSF with 100 non-parametric bootstrap replicates each (BS). Only nodes with ≤99% support, and support values that were different between the two analyses (‘231-supermatrix’ and ‘233-supermatrix’) are labelled. All other nodes indicate BS=100. Dashed line in the BS value indicates the topology was not recovered for the corresponding supermatrix (‘231-supermatrix’/‘233-supermatrix’). The bold black branches indicate the topologies of major classes or sub-groups that were found in a majority of phylogenomic trees that were constructed using various gene filtering criteria and inference methods. The dotted lines of the tree branches indicate that the relationships were not recovered in the majority of the phylogenomic trees constructed from difference supermatrices (see Fig. 4.2 and Table 4.3). The taxa names with the gray highlights are the additional taxa used to concatenate ‘233-supermatrix’, and not included in the gene-filtering analysis. The asterisk (*) denotes Chrysista Cavalier-Smith, 1986, its description did not include Eustigmatophyceae, Actinophrydae, Pinguiophyceae, and Olisthodiscophyceae. The percent genes (light grey) and sites (dark grey) occupied for each taxon are shown on the mirrored bar plot. 131 Figure 4.2 Schematic representation of major stramenopile topologies A = unfiltered ‘231-supermatrix’ C60-PMSF, ‘233-supermatrix’ C60-PMSF; B = CAT-PMSF; C = criterion N; D = criteria A and B120-160; E = C120-160; F = D120-160; G = ABC120-160; H = E120-180; I = F140-180; J = S140-180; K = Q120-180. The sub-group topologies within the collapsed groups were ignored (e.g., placements of taxa within Opalozoa, RPX, and BB+PeD). For unstable topologies within the same criterion, the branches are marked with dotted red lines, otherwise, all other branches were consistently recovered in the phylogenomic trees generated within each criterion. Black groupings indicate outgroups. CSS=Chrysophyceae-Synurophyceae-Synchromophyceae; Pico=Picophagea; 132 Olis=Olisthodiscophyceae; Ping=Pinguiophyceae; BB=Bolidophyceae-Bacillariophyceae; PeD=Pelagophyceae-Dictyochophyceae; RPX=Raphidophyceae-Phaeophyceae-Xanthophyceae; Actino=Actinophrydae; Eustig=Eustigmatophyceae. 133 Table 4.3 Summary of bootstrap support for all the supermatrices List of stramenopile groupings and their standard bootstrap support from the highest to the lowest prevalence observed in trees constructed from supermatrices obtained with different criteria (A-F, ABC, N, S, and Q), along with ‘231-supermatrix’ C60-PMSF and CAT-PMSF. The numbers in brackets indicate the number of occurrences out of all 16 trees considered in the table. For each criterion, we selected shared genes within top 60 to 180 highest or lowest values found in all corresponding properties. Controversial groupings are bolded and underlined. Each criterion is denoted by A = selecting for genes with high values of treeness and occupancy; B = selecting for genes with high values average_BS_support, robinson_sim, and gene length; C = selecting for genes with low values of av_patristic, rate, and treelength; D= filter out gens with high values of av_patristic, rate, and treelength; ABC = combination of A-C criteria with corresponding top cut-off values; N=genes that are not explained well by the PC axes (low cos2); E = selecting genes with high values of PC1 associated biases (saturation, av_patristic, and root_tip_var); F = selecting genes with high values of all biases (RCFV, saturation, av_patristic, and root_tip_var), S = selecting genes with high signals (average_BS_support, robinson_sim, treeness); Q = selecting genes with high data quality (gene length and occupancy). CSS = Chrysophyceae-Synurophyceae-Synchromophyceae; Pico=Picophagea; Olis=Olisthodiscophyceae; Ping=Pinguiophyceae; BB=Bolidophyceae-Bacillariophyceae; PeD=Pelagophyceae-Dictyochophyceae; Bigyro=Bigyromonadea; Oomy=Oomycetes-Hyphochytriomycetes; Platy=Platysulcidae; RPX=Raphidophyceae-Phaeophyceae-Xanthophyceae; Actino=Actinophryidae; Ochro=Ochrophyta; Eustig=Eustigmatophyceae. For Diatomista+Chrysita*, the relationship only considered general grouping of (CSS+RPX)+(BB+PeD), regardless of the placements of Eustig, Actino, Olis, and Ping. Groupings Criterion A Criterion B Criterion C Criterion D N 60 80 100 120 140 160 60 80 100 120 140 160 60 80 100 120 140 160 60 80 100 120 140 160 CSS+Pico (46) 100 100 100 100 100 100 99 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 Olis+Ping (44) 89 100 99 99 100 100 62 73 83 94 99 95 — 88 99 100 100 100 100 100 100 100 100 99 79 BB+PeD (43) 95 100 100 100 100 100 — — 73 88 92 100 100 100 100 100 100 100 100 100 100 100 100 100 100 Diatomista+Chrysista (40)* — 100 100 100 100 100 — — 73 100 100 100 — 89 72 — 100 100 100 100 100 100 80 74 100 RPX+Eustig (35) 85 94 92 99 100 99 38 67 65 86 94 92 — — — 64 94 99 99 100 100 93 84 69 67 Bigyro+Oomy (32) — — 94 100 91 99 — — 67 92 96 96 74 — — — 86 93 99 100 100 83 — — — Platy+rest (26) — 100 95 100 100 100 — — — 100 100 100 — 100 100 100 100 100 100 100 100 100 100 100 — [CSS+Pico]+[Olis+Ping] (16) — — — — — — — 77 59 — — — — 77 70 73 — — — — — — 76 71 72 Bigyro+Ochro (15) 72 74 — — — — 49 63 — — — — — 79 94 98 — — — — — — 96 93 78 [CSS+Pico]+Actino (14) — 91 63 — — — — — — — — — — — — — 81 86 67 84 87 93 — — — [[CSS+Pico]+Actino]+ [Olis+Ping]]+ [RPX+Eustig] (14) — 76 82 — — — — — — — — — — — — — 77 83 100 99 93 83 — — — [Ping+Olis]+Actino (14) — — — 71 63 75 — — — 73 58 72 — — — — — — — — — — — — — [CSS+Pico]+[[Ping+Olis]+ Actino] (14) — — — 95 98 94 — — — 93 98 95 — — — — — — — — — — — — — Sagenista+Opalozoa (12) 67 96 100 100 92 — — — — 95 92 — — — — — — — — — — — — — 90 Platy+Sagenista (8) — — — — — — — 69 78 — — — — — — — — — — — — — — — — 134 Groupings Criterion A Criterion B Criterion C Criterion D N 60 80 100 120 140 160 60 80 100 120 140 160 60 80 100 120 140 160 60 80 100 120 140 160 Actino+Ochro (7) — — — — — — — — — — — — 100 100 100 100 — — — — — — 100 100 — Eustig+Actino (7) — — — — — — — — — — — — — — — — — — — — — — — — 64 [[CSS+Pico]+[Olis+Ping]]+ RPX (4) — — — — — — — — — — — — — 50 63 — — — — — — — — — — RPX+[Eustig+Actino] (3) — — — — — — — — — — — — — — — — — — — — — — — — 67 [[CSS+Pico]+[Olis+Ping]]+ Actino (3) — — — — — — — 47 71 — — — — — — — — — — — — — — — — [Gyrista+Sagenista]+ Platy (3) — — — — — — 72 — — — — — 86 — — — — — — — — — — — — [BB+PeD]+Eustig (2) — — — — — — — — — — — — — 52 82 — — — — — — — — — — BB+Ochro (2) — — — — — — 84 100 — — — — — — — — — — — — — — — — — [BB+PeD]+[CSS+Pico] (2) 70 — — — — — 52 — — — — — — — — — — — — — — — — — — [PeD+BB]+ [RPX+Eustig] (2) — — — — — — — — — — — — — — — 56 — — — — — — — — — [[[PeD+[CSS+Pico]]+ [[RPX+Eustig]+ [Ping+Olis]]]+Actino (1) — — — — — — 30 — — — — — — — — — — — — — — — — — — [[CSS+Pico]+Olis] +Eustig (1) — — — — — — — — — — — — 42 — — — — — — — — — — — — [CSS+Pico]+PeD (1) — — — — — — 52 — — — — — — — — — — — — — — — — — — [RPX+Eustig]+PeD (1) — — — — — — — 46 — — — — — — — — — — — — — — — — — [RPX+Eustig]+[Olis+Ping] (1) — — — — — — 22 — — — — — — — — — — — — — — — — — — [[BB+PeD]+[CSS+Pico]]+ Actino (1) 45 — — — — — — — — — — — — — — — — — — — — — — — — Platy+Gyrista (1) — — — — — — — — — — — — — — — — — — — — — — — — 99 [CSS+Pico]+[Eustig+Actino] (1) — — — — — — — — — — — — — — — — — — — — — — — — — Groupings Criterion E Criterion F Criterion S Criterion Q Criterion ABC 231-supermatrix ML-PMSF CAT- PMSF 120 140 160 180 120 140 160 180 120 140 160 180 120 140 160 180 60 80 100 120 140 160 CSS+Pico (46) 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 Olis+Ping (44) 75 96 100 100 68 98 100 100 70 98 99 100 91 93 98 100 96 100 100 100 100 100 100 100 BB+PeD (43) 99 100 100 100 99 98 97 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 Diatomista+Chrysista (40)* 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 — 100 100 100 100 100 100 100 135 Groupings Criterion E Criterion F Criterion S Criterion Q Criterion ABC 231- supermatrix CAT- PMSF 120 140 160 180 120 140 160 180 120 140 160 180 120 140 160 180 60 80 100 120 140 160 RPX+Eustig (35) — — 97 96 — — 96 99 67 89 — — 98 97 100 99 87 100 100 100 100 100 99 98 Bigyro+Oomy (32) 99 100 100 100 — — 98 100 89 97 93 98 92 100 93 100 — — — 93 96 98 100 — Platy+rest (26) — — — 100 — — — 100 100 100 100 100 100 100 100 100 — 100 100 100 100 100 100 100 [CSS+Pico]+[Olis+Ping] (16) 47 60 — — 49 — — — — — 94 91 — — — — 75 66 — — — — — — Bigyro+Ochro (15) — — — — 82 87 — — — — — — — — — — 96 97 99 — — — — 88/85 [CSS+Pico]+Actino (14) — — 88 91 — — 96 80 — — — — — — — — — — — — 69 66 83 78/81 [[CSS+Pico]+Actino]+ [Olis+Ping]]+ [RPX+Eustig] (14) — — 99 100 — — 99 99 — — — — — — — — — — — — 100 100 100 100 [Ping+Olis]+Actino (14) — — — — — — — — 71 69 — — 80 50 70 77 — — 100 66 — — — — [CSS+Pico]+[[Ping+Olis]+ Actino] (14) — — — — — — — — 88 84 — — 96 89 93 100 — — 98 98 — — — — Sagenista+Opalozoa (12) — — — 90 — — — 91 95 95 95 — — — — — — — — — — — — — Platy+Sagenista (8) 100 95 99 — 65 97 100 — — — — — — — — — — — — — — — — — Actino+Ochro (7) — — — — — — — — — — — — — — — — 100 — — — — — — — Eustig+Actino (7) 53 72 — — 68 66 — — — — 90 81 — — — — — — — — — — — — [[CSS+Pico]+[Olis+Ping]]+ RPX (4) 46 60 — — — — — — — — — — — — — — — — — — — — — — RPX+[Eustig+Actino] (3) — — — — — — — — — — 84 74 — — — — — — — — — — — — [[CSS+Pico]+[Olis+Ping]]+ Actino (3) — — — — — — — — — — — — — — — — — 81 — — — — — — [Gyrista+Sagenista]+ Platy (3) — — — — — — — — — — — — — — — — 89 — — — — — — — [BB+PeD]+Eustig (2) — — — — — — — — — — — — — — — — — — — — — — — — [PeD+BB]+[RPX+Eustig] (2) — — — — — — — — — — — — — — — — 49 — — — — — — — [CSS+Pico]+[Eustig+Actino] (1) — — — — — 61 — — — — — — — — — — — — — — — — — — 136 Thirteen gene properties summarized in a principal component analysis (PCA) plot and a correlation matrix. (A) PCA plot of 229 genes. Each coloured dot indicates a gene, plotted onto two principal component (PC1 and PC2) axes. High cos2 values are orange and low cos2 values are blue. Higher cos2 values indicate the genes are represented well by the two PC axes. The 13 properties are shown as variables each coloured by biases (red), signals (blue), and data quality (black). (B) Correlation matrix with hierarchical clustering of 13 gene properties. Positive correlation is indicated by red and negative by blue. Figure 4.3 Gene properties on a PCA plot 137 Chapter 5: Genomic analyses of Symbiomonas scintillans show no evidence for endosymbiotic bacteria but does reveal the presence of giant viruses 5.1 Introduction Understanding the evolutionary history of eukaryotes is inherently linked to understanding their symbiotic relationships with prokaryotes, whether it is in the form of genetically integrated organelles or the multitude of short-term endosymbioses with bacteria or archaea. Most of our understanding about the effects of endosymbiosis on eukaryotic evolution relates to the origin of mitochondria and plastids, and their involvement in eukaryotic diversification (Gray, 1999; Gray and Doolittle, 1982; Keeling, 2010, 2009). However, the impact of prokaryotic symbioses goes far beyond these rare organellogenesis events, given the diverse nature of symbioses affecting hosts in different ways (reviewed in Husnik and Keeling, 2019; Nowack and Melkonian, 2010). Thanks to genome sequencing, prokaryotic symbionts have been found to be associated with all major eukaryotic supergroups, involved in a myriad of functions such as metabolism (Fenchel and Finlay, 1991; Kneip et al., 2008; Nowack and Melkonian, 2010; Seah et al., 2019), defense (Ishida et al., 2014), parasitism (Corsaro et al., 2010, 2013), and motility (Okude et al., 2012; Ishida et al., 2014). Additionally, some bacterial lineages have evolved to be “professional symbionts” (Husnik and Keeling, 2019) such as Chlamydiae, Rickettsiales, and Holosporales, consisting entirely of obligate endosymbionts or intracellular parasites of eukaryotic hosts (Montagna et al., 2013; Boscaro et al., 2019; Husnik and Keeling, 2019; Giannotti et al., 2022). Despite these findings, most prokaryotic symbionts of eukaryotes are poorly studied, generally only observed with microscopy, and left unidentified and uncharacterized. For example, the only known case of prokaryotic endosymbiosis in a non-phototrophic stramenopiles is found in the tiny (~1.4 µm) bikosia, Symbiomonas scintillans, where two geographically 138 distinct strains were reported to harbour up to six endobacteria, and which served as the inspiration for its genus name (Guillou et al., 1999). The location of these endobacteria within the endoplasmic reticulum was of particular interest, as this is where plastids of phototrophic stramenopiles are located (Cavalier-Smith, 1989; Guillou et al., 1999). However, the identity and functional role of these apparent endobacteria has not been further investigated. To identify the endobacterium and its role in such a small protist, we conducted Fluorescent in situ hybridization (FISH) targeting various groups of bacteria and generated amplification-free shotgun metagenomics and whole-genome amplification sequencing data of two strains of S. scintillans. This showed the absence of endobacteria of known endosymbiotic lineages. Instead, we observed a viral-like particle by transmission electron microscopy (TEM) and recovered three draft viral genomes related to prasinovirus, nucleocytoplasmic large DNA viruses (NCLDVs) belonging to a member of the Phycodnaviridae family (Van Etten et al., 2002; Wilson et al., 2009). During the course of this work, one strain apparently lost the virus, while the other strain perished, so I was unable to conduct further experiments to verify the nature of the viral association. This chapter underscores how much is still unknown about endosymbioses, particularly in small heterotrophic protists. I expect that viral association is especially relevant to nano- or pico-eukaryotes, as there may simply not be enough space for endobacteria, and predict more such findings in the future. 5.2 Materials and Methods 5.2.1 Culture collection and maintenance All strains of S. scintillans used in this study are summarized in Appendix U, with the initial isolation dates and locations, sequencing methods, dates, and locations, and the culture collection centres. Two Symbiomonas scintillans culture strains RCC257 and RCC24 were 139 obtained from the Roscoff culture collection (RCC, France) on March 7th, 2022. The cultures were grown and maintained in 0.22 µm filtered and autoclaved marine f/2 media (30 PSU) with an autoclaved rice grain at the University of British Columbia (UBC), Canada. The cultures were kept in a 20°C incubator with a 12:12 h light:dark cycle and sub-cultured every two weeks in 30 mL. Using glass micropipettes, approximately 50 to 100 cells from each strain were collected and stored in 5 µL PCR-grade water after two rounds of rinsing in PCR-grade water on April 6th, 2022. The isolated cells were immediately subjected to three rounds of freeze-thaw cycles to promote lysis and stored at -80°C until whole genome amplification (WGA). Upon receiving the two strains, they were slow to grow (low culture density and no noticeable movement) and within two months of receipt, the strain RCC24 showed reduced viability and was eventually lost. This was also observed in the RCC, as their cultures perished with no identifiable cause at a similar time (M. Gachenot, assistant engineer/curator of RCC, personal communication, Oct 12th, 2022). In contrast, the strain RCC257 became denser and more active between the first round of cell collection in April 2022 and the second round of cell collection on June 28th, 2022. I suspected this boost of culture viability can be due to resistant cells or loss of viruses (see below). As a result, I also collected 50 cells from strain RCC257 on June 28th, 2022, for an additional WGA (hereafter, referred to as RCC257-late). Independently at OIST, Okinawa (Japan), the culture strains RCC257 (which I refer as RCC257-jp) and NIES-2589 (strain synonymous to RCC24) were obtained from the RCC in December 2022, and the Microbial Culture Collection at the National Institute for Environmental Studies (NIES Collection, Tsukuba, Japan) in March 2021. Strain NIES-2589 will be hereafter referred to as RCC24-jp. RCC24-jp was cryopreserved at -160°C and was thawed in f/2 medium with an added rice grain. The RCC24-jp cultures were maintained in the same condition as 140 above, except with a 10:14 h light:dark cycle, and were further processed for amplification-free shotgun metagenomics (AF-SMG; see Library preparation and sequencing). Strain RCC257-jp was grown in 20 µm filtered and autoclaved seawater with rice. All cultures were sub-cultured every four weeks. 5.2.2 Library preparation and sequencing Two strains of S. scintillans (RCC24 and RCC257) maintained at UBC were subject to WGA sequencing and one strain RCC24-jp, maintained at OIST was subject to amplification-free shotgun metagenomic (AF-SMG) sequencing. To prepare a WGA library of the isolated cells, a 4BBTM TruePrime® Single Cell WGA Kit was used following a manufacturer’s protocol with 12 h incubation at 30°C for the amplification reaction step. The amplified product was then cleaned with AMPure XP beads (Beckman Coulter, US), following a protocol described in the Nanopore Ligation Sequencing Kit protocol (SQK-LSK110, Oxford Nanopore Technologies, UK). Library preparation for WGA sequencing followed the Illumina DNA Preparation kit (Illumina, US) which uses a Bead-linked Transposome complex, resulting in ~350 bp library constructs. The WGA sequencing was performed on a NextSeq (mid-output) platform with 150 bp paired-end library constructs at the UBC Sequencing and Bioinformatics Consortium (Vancouver, Canada). Whole genome amplification sequencing was repeated twice using the same library constructs. For downstream analysis, the transcriptome of RCC257 (NCBI SRA accession number SRR24392496) was also used, which was prepared from approximately 20 isolated cells from the same sub-culture, described in Cho et al. (2024). To minimize culture-associated bacterial reads, only single-cell isolated transcriptomes were used, as opposed to cDNA prepared from whole-culture RNA extract. 141 For shotgun metagenomics, 10 mL of RCC24-jp culture was filtered through a 5 µm syringe filter for enrichment (removal of large bacteria) followed by DNA extraction using the MasterPure Complete DNA and RNA Purification kit (Lucigen, US). The DNA extractions were prepared from multiple subsequent subcultures (in March, May, June, and October 2022). PCR-free shotgun metagenomic libraries were prepared with the NEBNext Ultra II DNA Library Prep Kit for Illumina (NEB, US) and sequenced by the OIST Sequencing Centre using the Illumina MiSeq platform with 300 bp paired-end reads. The strain information and sequencing methods are summarized in Appendix U. The raw genomic data for this study is deposited in the NCBI Sequence Read Archive (SRA) with the accession numbers SRR26451788-SRR26451790, SRR26412500-SRR26412501, and SRR26943481, under the BioProject PRJNA1029166. 5.2.3 Sequence processing: assemblies and sub-assemblies of viral reads The quality of raw sequencing reads for amplification-free shotgun metagenome, WGA, and transcriptome data were all examined using FastQC v0.11.9 (Andrews, 2010). The transcriptomic data were processed as described in Cho et al. (2024). Briefly, to correct random sequencing errors of the raw data, k-mer based Rcorrector (v3) (Song and Florea, 2015) was used followed by Trimmomatic v0.39 (Bolger et al., 2014) to remove transposase-inserts, SmartSeq2 primers, adaptors, IS-primers from library preparation and, low-quality reads (-phred33 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36). Error-corrected and trimmed forward, reverse and unpaired transcriptome reads were then de novo assembled using rnaSPAdes v3.15.1 (Bushmanova et al., 2019). After removing bacterial (non-endosymbiotic lineages) and metazoan reads, open reading frames (ORFs) were predicted with TransDecoder v5.5.0 (Haas et al., 2013). The raw shotgun metagenome and WGA sequencing data were 142 trimmed as described above without Rcorrector step, with corresponding adaptors and primers removed. The trimmed WGA reads from the three rounds of sequencing runs were then co-assembled using SPAdes v3.15.1 (Prjibelski et al., 2014, 2020; Vasilinetc et al., 2015) with --sc and --phredoffset33 options. The same assembly parameters were used for the shotgun metagenome reads. For initial taxonomic and coverage screenings of the assembled transcriptomes, shotgun metagenome, and WGA assemblies, and in particular to search for reported endobacteria, BlobTools v2.3.3 (Laetsch and Blaxter, 2017; Challis et al., 2020) was used to visualize search results of assemblies against NCBI nucleotide (nt) (using megaBLAST) and UniProt reference databases (using diamond BLASTX), both with e-value cut-offs at 1e-25 (--taxrule bestsumorder). After failing to detect any obvious taxonomic signatures of endobacterial origin in both transcriptome and genomic data, a subset of WGA reads was reassembled by filtering reads with GC content below 40% (a common range for endosymbionts) and coverage above 1025 using SeqKit v2.3.0 (Shen et al., 2016), and assembling those with both SPAdes and Unicycler v0.5.0 (Wick et al., 2017; Prjibelski et al., 2020). With the initial BlobTools screening indicating the presence of prasinovirus taxonomic assignments in WGA sequencing data, trimmed WGA contigs were searched against the Reference Viral Database (RVDB) (Goodcare et al., 2018) using blastn (e-value cut-off 1e-10) followed by protein domain searches using hmmsearch (HMMER3.3) (hmmer.org) against virus orthologous groups (VOGs) (vogdb.org), Pfam, and giant VOG (GVOG) hidden Markov models (HMM) databases compiled in ViralRecall (Aylward and Moniruzzaman, 2021). The open reading frames (ORFs) were predicted using Prodigal-gv (Hyatt et al., 2010). Contigs with final ViralRecall scores above 10 were considered of viral in origin. For those with the final ViralRecall scores less than 10, if the number of VOG hits were higher than 3 and Pfam hits at 143 the same time, we also considered these contigs to be viral. Additionally, all the contigs mapping to 16 prasinovirus genomes (BIIV, BpVs, OlVs, OtVs, OmVs, and MpVs) (Appendix V) using DNAdiff v1.3 (Kurtz et al., 2004) were kept. Contigs with viral hits from NCBI nt, clustered RVDB (RVDB-c), UniProt blast searches were kept, excluding circular elements. These select contig results were cross-validated with blastx and blastn searches among VOG, RVDB, and diamond viral databases. The same searches were repeated on amplification-free shotgun metagenomic data, WGA data from RCC257-late, NIES-2589 (RCC24-jp), and ORF-predicted transcriptome data. However, neither prasinovirus nor NCLDV reads were detected. Aside from microscopic observation, to confirm the absence of green algae contamination in the culture, I searched for small subunits of ribosomal RNA (SSU rRNA) in all sequencing data using barrnap v0.9 (Seemann, 2007). We also carefully screened eukaryotic reads from the initial BlobTools results and found no evidence of green algae or other eukaryotic protist contamination. An extracted subset of WGA viral contigs (707 contigs out of 69,958) was reassembled using SPAdes v3.15.1 with –sc, --careful and –phredoffset33 options, resulting in 748 scaffolds. Scaffolds with lengths under 100 bp were removed. Additionally, blastn searches were repeated against NCBI-nt and RVDB databases to remove bacteriophage reads, resulting in 543 scaffolds with the total length of 469,314 bp with 37.31% GC content. These filtered subset assemblies are hereafter referred as “viral-subset-scaffolds”. The viral-subset-scaffolds were then further scaffolded using 16 prasinovirus genomes as a guide with homology-based RagTag v2.1.0 (Alonge et al., 2019, 2022). This reference-guided assembly method does not alter the scaffold sequences but reorients and reorders them by aligning to a reference genome, creating a single scaffold or a pseudomolecule. The pseudomolecule or the single scaffold of viral metagenomically assembled genomes will be hereafter referred to as vMAG. Out of 543 viral- 144 subset-scaffolds, 279 were recruited for the assembly of 16 vMAGs. The remainder of the 264 scaffolds were not recruited to any reference genomes despite having 194 out 264 scaffolds with ≥80% similarity to known sequence identities (ID) and e-value < 1e-25 hit to prasinovirus (the rest of the scaffolds had lower % ID or no hits to the database). This is due to the majority of the scaffolds (215/264) being shorter than 500 bp, which were filtered out due to the small alignment length threshold (1000 bp). Additionally, a pre-defined k-mer and window size (19 bp) in read mapping to the reference genomes may have affected correct scaffold placements of sequence variants in these potentially new vMAGs. The completeness of each reference-guided assembly was assessed using CheckV v0.8.1 with CheckV-db v1.5 (Nayfach et al., 2021) (Appendix V). The assembled vMAGs with the highest completeness and the corresponding reference genomes are circularized for visualization with BLAST Ring Image Generator (BRIG) v3 (Alikhan et al., 2011). 5.2.4 Draft viral genome annotation and gene content comparison The ORFs for each reference guided vMAGs (=pseudomolecules) were predicted with Prodigal-gv and further annotated with Prokka and ViralRecall (scores >=10). The annotation was repeated with the 16 prasinovirus genomes (Appendix V). To compare shared orthologs among vMAGs and published viral genomes, all the ORFs were used in all-versus-all blastx search (Burns et al., 2018). The blastx (e-value=1e-5 and query-cover=50) result was then clustered first by 95% similarity using CD-hit v4.8.1 (Fu et al., 2012) followed by MCL algorithm (inflation=2). Only clusters with hits from a minimum of three different genomes (including vMAGs) were retained (432 clusters). Amino acid sequences of each cluster were then aligned using MAFFT v7.481 (Katoh and Standley, 2013) and trimmed using trimAl v1.2rev59 (Capella-Gutiérrez et al., 2009), which was then used to build 432 HMMs. The 145 resulting HMMs were then searched against individual reference and draft genomes using hmmsearch HMMER v3.3 (e-value 1e-10 and domain e-value 1e-8) to confirm the presence of the protein clusters in the genomes. The outcome of shared protein clustering hits for each genome was summarized in an upset plot. All BpV- and BIIV-vMAGs were combined as “BV-vMAGs”, all OlV-, OmV-, and OtV-vMAGs were combined as “OV-vMAGs”. Similar grouping was done for published genomes that were used as a reference-guide to assemble vMAGs. 5.2.5 Prasinovirus hallmark gene search and phylogeny construction To construct a phylogenetic tree of prasinoviruses, we searched for 22 prasinovirus hallmark genes (Rozenberg et al., 2020; Bachy et al., 2021) in the predicted ORFs of WGA viral-subset-scaffolds using blastp (e-value 0.001) and hmmsearch (–E 1e-3 –domE 1e-3 –incE 1e-3 –incdomE 1e-3). Candidate genes from the predicted ORFs were then concatenated with the corresponding alignments and realigned with MAFFT (--auto) and trimmed with trimAl (-gt 0.3 and -st 0.001). I constructed a single gene tree for each of the prasinoviral hallmark genes using IQ-TREE v2.1.0 under the LG+G4 amino acid model with 1000 ultrafast bootstrap pseudoreplicates. Each single-gene tree and corresponding alignment were manually examined to discern viral paralogs and orthologs from cellular proteins. For prasinovirus some single-gene alignments, the candidate genes were manually merged if the gene fragments had overlapping regions and were positioned within the same clade. The 22 cleaned prasinovirus hallmark single-gene trees were then concatenated, realigned with MAFFT, trimmed with trimAl, and a multi-gene phylogenetic tree was inferred using IQ-TREE v2.1.0 under the LG+G4+F model and 1000 ultrafast bootstrap pseudoreplicates. We searched for the same prasinovirus hallmark genes in predicted ORFs from my transcriptome data in the same manner. However, no hits were found. 146 The hallmark gene-alignments, relevant intermediate files, gene-tree files, vMAG genome and protein sequences are uploaded on Dryad (10.5061/dryad.mw6m90644). 5.2.6 Transmission Electron Microscopy (TEM) To visualize a virus-like particle (VLP) in unfiltered strain RCC24, 5 µL of the culture was deposited onto glow-discharged (60 sec at 50 mA; Leica EM ACE200) formvar/carbon-coated 400 mesh copper TEM grids. Samples were stained with 2% uranyl acetate for 60 s. Excess UA was removed by gently placing a filter paper at the edge of the grids and subsequently transferred to a FEI Tecnai Spirit TEM (Thermo Fisher, USA) operating at 80 kV acceleration voltage. Images were captured with a DVC1500M camera and AMT Image Capture Engine V601 software (MA, USA). VLP diameter was measured with the AMT built-in measurement tool. All sample processing and TEM imaging were carried out in a sterile environment where no other viral experiments were done prior to the imaging. 5.2.7 Fluorescence in situ hybridization (FISH) For the RCC257 strain grown at UBC, Canada, 10 mL of culture was spun down in 15 mL centrifuge tubes at 3000 rpm, at 4°C for 10 min. The centrifuged cells were collected from the bottom of the tubes and transferred into 1.5 mL microcentrifuge tubes. Approximately 7 µL of the collected cells were placed on Poly-D-Lysine-coated glass slides (Sigma-Aldrich, US) and demarcated with a LiquidBlocker (Electron Microscopy Sciences, US). An equal amount of 4% paraformaldehyde (in water) was added to the slides. After all the liquid evaporated, 95% ethanol was added to the marked spot on the slides and incubated until complete drying. The slides were dipped in 50%, 80%, and 100% ethanol for 10 min each. The slides were then incubated overnight in a dark humidity chamber at 46°C with 10 µM of probe EUB338-Green prepared in a hybridization solution (1 M, pH 8.0 Tris HCl; 5 M NaCl, 1.3% SDS). The slides were gently 147 rinsed twice in 48°C hybridization solution for 10 min, followed by 15 min rinse in water at room temperature. After completely drying liquid, 20 µL of SlowFade Gold with DAPI (Life Technologies, US) were added and visualized with an Olympus BX53 at the UBC Bioimaging Facility, Canada. To verify the lack of endobacteria in sub-cultures grown in Japan, a separate FISH protocol was done on the RCC24-jp and RCC257-jp strains. Each of the 10 mL of culture were fixed with 3.2% formaldehyde at 20 °C for 20 min and spun down at 4000 rpm at 4 °C for 15 min. The centrifuged cells were washed with 1x PBS and seeded onto a 0.1% polyethyleneimine-coated 18 mm round coverslip (Matsunami Glass Ind., Ltd, Japan) in a 12-well plate. To allow attachment to the coverslip, the fixed cells were incubated for 3 h in 1x PBS. The attached cells were then washed three times each for 5 min in 1x PBS, 0.3% 1x PBS-Tx (0.3% Triton X-100, pH7.4), then in hybridization buffer (20 mM Tris-HCl; 30% formamide; 0.01% SDS). The fixed cells were hybridized with probes EUB338-Alexa488, EUB338-Alexa647 (Eubacteria), and CF319a-Alexa647 (Bacteroidetes) [0.1 µM] (Manz et al., 1992) (ThermoFisher Scientific, Japan) with DAPI [0.01 ug/mL] (Roche, Germany) and incubated overnight in a 42°C humidity chamber. For RCC24-jp, additional probes targeting Planctomycetes (PLA46) (Neef et al., 1998), alpha- (ALF969), and gamma-Proteobacteria (GAM42a) (Neef, 1997) were hybridized. To remove unbound probes and DAPI, the coverslip was gently rinsed three times in 0.3% 1x PBS-Tx solution for 5 min and twice in 1x PBS. After drying, the coverslip was mounted onto a glass slide with ProLong Diamond Antifade Mountant (ThermoFisher Scientific, Japan) and incubated at room temperature overnight in the dark. The hybridized sample was kept at 4 °C in the dark until visualization on Leica TCS SP8 Inverted Confocal Microscope at the OIST 148 Imaging Facility (Okinawa, Japan). The brightness and contrast of all images were adjusted using ImageJ v1.53 and sharpness with Inkscape v1.2.1. 5.3 Results and Discussion 5.3.1 No bacterial sequences from known clades of common endosymbionts To identify the symbionts of Symbiomonas scintillans, I sequenced two geographically distinct strains (RCC24 isolated from Pacific Ocean and RCC257 from the Atlantic Ocean) maintained under culture conditions. In most of the sequencing data, a large representation of the host sequence was found as expected. The exception to this is the WGA data from RCC24, where no host sequences could be identified (see below). As the original description of this taxon suggested these symbionts were bacterial, we first searched for bacterial reads assigned to well-known endosymbiotic lineages such as Rickettsiales, Holosporales, or Chlamydiae in all the analyzed genomic and transcriptomic data. No such putative symbiont reads were found, and instead the bacterial reads were largely assigned to common environmental, or culture-associated Alphaproteobacteria, Gammaproteobacteria and Balneolia such as Marinobacter spp., Epibacterium spp., Hyphomonas spp., Zhongshania spp., Balneola spp., and Labrenzia spp., (Appendix W). When sequences that had no taxonomic affiliation in WGA data were removed, a scaffold assigned to Marinobacter salinus had the third highest coverage up to x95,000 (N50=116K), after the ones assigned to Oomycota (N50=276) and Cafeteria roenbergensis, (up to x102,851 coverage with N60=63K), a species closely related to S. scintillans (Cho et al., 2024). Notably, Marinobacter spp., Labrenzia spp., and Hyphomonas spp. were all reported to be common in cultures of Ostreoccocus tauri, Symbiodiniaceae, Alexandrium spp., and discobids (Alavi et al., 2001; Seibold et al., 2001; Jasti et al., 2005; Lupette et al., 2016; Bolch et al., 2017; Aponte et al., 2021; Maire et al., 2021). Accounting for this overwhelming 149 representation of culture-associated bacteria, a subset of whole-genome amplification (WGA) data was selected and reassembled based on lower GC content, which is usually associated with endosymbionts. However, no sequences assigned to endosymbiotic bacterial lineages were detected. To account for unequal genomic amplification of WGA causing loss of AT-rich and local repeat regions, and secondary structures (Karlsson et al., 2015), I also searched bacterial reads in amplification-free shotgun metagenomic data. Many bacterial lineages with high-coverage in WGA were also found in the shotgun metagenomic data (e.g., Marinobacter spp., Hyphomonas spp., Balneola spp.) in addition to Marinovum algicola and a member of Phycisphaeraceae, but no known endosymbiotic lineages nor any draft bacterial genomes with “symbiotic features” such as small genome size, AT-rich content, or rapid sequence evolution could be identified in any of these data. The absence of endosymbiotic bacteria in all the sequencing data was further supported by the absence of a bacterial signal using fluorescence in situ hybridization (FISH) of all sub-cultures of S. scintillans grown in Canada (RCC257) and Japan (RCC257-jp) (Fig. 5.1). My collaborators observed the same trend in RCC24-jp (Appendix X) using additional probes targeting Planctomycetes, Bacteroidetes, Alphaproteobacteria, and Gammaproteobacteria. In all my assembled WGA data, no sequences were assigned to Archaea while the amplification-free shotgun metagenome data had some Archaea contigs with low coverage (x1-7). 5.3.2 Multiple prasinovirus-like vMAGs are associated with the strain RCC257 and RCC24 Instead of endobacteria, we detected contigs assigned to prasinovirus with up to x200 coverage (Appendix W). When viral-subset-scaffolds were re-assembled and further scaffolded using a reference-guide approach, we recovered three viral metagenomically assembled genomes (vMAGs) related to the prasinovirus genera, Bathycoccus prasinos virus 2 (BpV2), Ostreococcus 150 lucimarinus virus 1 (OlV1), and Micromonas pusilla virus Pl1 (MpV_Pl1). The completeness of vMAGs were the highest for the BpV2-guided assembly (BpV2-vMAG), with 100% completeness. Among OV-guided and MpV-guided vMAGs, OlV-1-guided assembly (OlV1-vMAG) and MpV_Pl1-guided assembly (MpVPl1-vMAG) had the most completeness with 54% and 18%, respectively (Appendix V). I compared the number of shared scaffolds and gene contents among BV-, OV-, and MpV-vMAGs to verify the presence of multiple different virus genomes. Only up to two recruited scaffolds were shared between vMAGs of BVs, OVs and MpVs (Appendix Y). When the shared orthologs were examined among all vMAGs using 16 reference genomes, I observed the same trend (Appendix Y). Multiple copies of single-copy-genes (e.g., DNA polB, DNA helicase, and mRNA capping enzyme) (Clerissi et al., 2014; Moniruzzaman et al., 2020) were detected in viral-subset-scaffolds, each corresponding to three groups of prasinoviruses (Moniruzzaman et al., 2020). All 22 genes were placed within a BV clade, nine genes in an OV clade, and four in a MpV clade (Fig. 5.2), and similar trends were observed in RCC24 (Appendix Y). These results support the presence of multiple giant viruses, altogether referred as S. scintillans virus (SsVs), rather than a single genome mapping to multiple reference genomes. In RCC24 we found no evidence of host reads (see above), but also found evidence for three giant viruses very similar to those found in RCC257 (Appendix Z and AA). No prasinovirus reads were detected in RCC24-jp. The presence of multiple giant viral species within a single host species is rare in protists. However, multiple viral species were detected in three different species of Ectocarpales, a group of brown algal stramenopiles (Muller and Parodi, 1993; McKeown et al., 2018). In these host species, up to two major capsid protein (MCP) genes of different Phaeoviruses 151 (Phycodnaviridae) subgroups were found. One of these phaeoviruses (EfasV), can infect different genera of Ectocarpales (Muller et al., 1996). Although prasinoviruses are reported to have a narrow host range at the strain or species level (Bellec et al., 2014; Baudoux et al., 2015; Derelle et al., 2015; Bachy et al., 2018, 2021), the close relationship to phaeoviruses might indicate wider host range is also possible for these new prasinoviral vMAGs. Additionally, the name “prasinoviruses” likely reflects a sampling bias in the first reports, as is the case for many viruses. Notably, both Monkeypox (MPXV) (Von Magnus et al., 1959) and Cucumber mosaic viruses (CMV) (Price, 1934) were named after their first isolation from Macaca fascicularis (macaque monkeys) and Cucumis sativus (cucumbers), respectively, but MPXV was subsequently reported to infect other hosts including humans and squirrels (for MPXV) (reviewed in Ullah et al., 2023), and CMV in legumes and ornamental plants (Heo et al., 2020). 5.3.3 Genome characteristics of vMAGs While many genes and ORFs were predicted on all vMAGs, only BpV-vMAGs were fully annotated (Fig. 5.3; Appendix AB and AC). For BpV-vMAG, 297 ORFs were predicted, including homologues of Hsp70 [a known protein in BpVs with a green algal host origin (Moreau et al., 2010)], DNA methyltransferase, and multiple MCPs were identified (Appendix AC). For the OlV1-vMAG, up to 149 ORFs were predicted with 146 genes while MpVPl1-vMAG had 47 ORFs predicted with 40 genes (Appendix V). OlV- and MpV-vMAGs from RCC24 had more complete assemblies (Appendix V and AB). Compared to published prasinovirus genomes with 3-5 tRNAs (three for BpVs), only two tRNAs in RCC257 BpV-vMAG were predicted (Fig. 5.3A): tRNA-Leu and tRNA-Asn. Similar to chloroviruses, four tRNAs were predicted in RCC24 BpV-vMAG, two of them being tRNA-Asn (Appendix AB) (Moreau et al., 2010). I detected five and six MCPs in RCC24 and RCC257 152 BpV-vMAGs, respectively, as was the case for BVII1-3 (Fig. 5.3; Appendix AC) (Bachy et al., 2021). A high number of MCPs (up to nine) is unique to Phycodnaviridae, however, its implications in host entry or capsid assembly are currently poorly understood (Moreau et al., 2010; Weynberg et al., 2011; Moniruzzaman et al., 2020). Along with other common prasinovirus proteins involved in carbohydrate synthesis (i.e., dTDP-4-dehydrorhamnose reductase, and five glycosyltransferases), I also detected ribulose-phosphate 3-epimerase in RCC257 BpV-vMAG (Appendix AC), which was unique to BIIV-2 and -3 among prasinoviruses (Bachy et al., 2021). To evaluate unique gene contents in BV-vMAGs, I generated protein clusters and compared them between 16 vMAGs and reference genomes. I observed that 26 protein clusters were unique to BV-vMAG (including BpVs- and BIIVs-vMAGs) (Appendix Y and AD). Although most of the annotation indicated HMM hits to hypothetical proteins of prasinoviruses, I detected a protein cluster assigned to 4-hydroxy-2-oxopentanoic acid aldolase. In prasinoviruses, this enzyme was only found in MpVs and is involved in biosynthesis of isoleucine, leucine, and valine which might be important in capsid formation (Moreau et al., 2010; Weynberg et al., 2017). Additionally, in both RCC24 and RCC257 BpV-vMAGs, we detected the IceA gene (“induced by contact with epithelium” endonuclease) gene, a putative virulence gene in Helicobacter pylori (Peek et al., 1998) which is also in the Chrysochromulina ericina virus (Mimiviridae; NCLDVs) (Gallot-Lavallée and Blanc, 2017). 5.3.4 SsV vMAGs are associated with S. scintillans As prasinoviruses are known to be host-specific and have not yet been described in other hosts, I wanted to rule out the unlikely possibility that these new viruses came from a cryptic prasinophyte in the culture. I detected no green algal SSU sequences or signals indicative of 153 green algal contaminants in any of the microscopic observation and sequencing data. In my WGA data, there were 15 scaffolds assigned mitochondrial genes of various Chlorophyta species (Appendix Z), with their counts ranging from 1 to 331. A close inspection of these scaffolds showed that these hits are likely not green algal contamination, as the taxonomic assignments were based on short read lengths. Additionally, some of the blastp hits of the same scaffolds indicated a stramenopile origin (Bikosia, ochrophytes, and oomycetes), suggesting these regions of the scaffolds are likely from the host and represent conserved homologs found in mitochondria across different eukaryotes. I observed similar patterns with scaffolds taxonomically identified as belonging to Rhodophyta. The possibility that prasinoviruses contaminated the culture media is also highly unlikely, given both the sterilizing protocol (autoclaving, filtering, and UV sterilization) and single-cell isolation. These methods could hardly result in near-complete BpV-vMAGs from contaminant viruses, which require a minimum of 105 VLP to reach the observed read depth (Illingworth et al., 2017). Due to the loss of viral signals in RCC257-late and the complete loss of the RCC24 strain, I could not conduct an infection assay or purify lysates. However, given the sequence coverage of prasinoviral reads, lack of evidence for green algae in the cultures, and sample processing method, I argue that the SsV vMAGs are indeed directly associated with S. scintillans. This is further supported by the fact that the two similar but distinct strains of S. scintillans contained two similar but distinct sets of giant virus genomes. 5.3.5 TEM observation of a VLP A virus-like particle (VLP) from RCC24 was visualized with negative stain TEM (Fig. 5.2A-B). The VLP exhibited an icosahedral shape with a diameter of 192 nm, which is unusually large compared to previously characterized prasinoviruses (Weynberg et al., 2017). However, it 154 fell within the size range (180-240 nm) of the endobacteria described by Guillou et al in 1999 (Guillou et al., 1999). Indeed, the morphology of the “endobacteria” in the original description [Figure 1D in 13] closely matches that of the VLP in Figure 5.2A-B. I did not find VLPs in the actively growing RCC257 strain, as expected as the NCLDV reads were no longer detected in RCC257-late. The S. scintillans “endobacteria” were also described to be located within the endoplasmic reticulum (ER), which continues as perinuclear space of a nuclear envelope (Guillou et al., 1999). This location was emphasized to be potentially relevant for the origin of plastids in deep-branching lineages of stramenopiles and compared to the location of plastids found in photosynthetic lineage of stramenopiles (Hibberd, 1971; Husnik et al., 2021). However, the ER is also a site for viral protein glycosylation (Agarkova et al., 2006), membrane protein folding (Doms, Robert W. et al., 1993), genome replication, and pre-capsid assembly (Inoue and Tsai, 2013; Romero-Brey and Bartenschlager, 2016). Within the Phycodnaviridae, the development of a Phaeovirus infecting Hinckisa hinckisae has been observed within the ER, in which viral capsids are derived from the ER membrane (Wolf et al., 1998; Van Etten et al., 2002). 5.3.6 Possible nature of associations: endobacteria, SsVs, and S. scintillans Two decades have passed since the original description of S. scintillans, and the present analysis, raising many questions about how to connect data from the original description with data currently at hand. There is no direct evidence to verify the exact nature of the association between SsVs and S. scintillans and similarly, there is no way to equate the SsVs to the intracellular inclusions described in 1999. Because the experimental design was to identify endobacteria and because there is no longer any living host-virus pair in culture, experiments 155 such as infection assays, virus-targeted FIHS or PCRs, or thin-section TEM to show virus particles within the cells are not possible. At the same time because there was no sequence data associated with the original genus or endobacteria descriptions, I cannot compare the current data directly with any data from the original description. There are several possible explanations that formally account for the data, and I will review several in turn here. First, it is possible that inclusions originally described are endobacteria that are still present, but were not detectable in genomic analyses, or belong to one of the normally free-living lineages we did detect. This is not readily consistent with the FISH data, however, and is also not consistent with the genomic observations from most other bacteria endosymbionts of protists (Husnik et al., 2019). Second, it is also possible the endobacteria were lost and the viruses were acquired later. The idea that the endobacteria may have been lost is not without precedent, since this has been observed in previous cultures (Boscaro et al., 2013), but how the viruses could have been gained is a much more difficult problem. The read-depth in the vMAG assemblies suggest viral DNA was highly represented in these cultures, and by extension these viruses were replicated in the cultures. Since no other eukaryotes were in the cultures, it also suggests the viurses were most likely replicating in S. scintillans (since the viruses need some host and no other eukaryote is evidence). Therefore, for the viruses to have been gained after the original description, the two cultures would have to have been exposed to two unrelated but distinct sets of viruses that could each infect and replicate in S. scintillans. Third, it is also possible the viruses have been endogenized within the host genome. This is not obviously consistent with absence of viral reads in some of the sequencing data (Appendix U) or the TEM evidence for viral particles. I also examined this possibility using ViralRecall 156 (Moniruzzaman et al., 2020; Aylward and Moniruzzaman, 2021; Bellas et al., 2023), which did not detect viral regions with potential host sequences flanking the contig. Lastly, it is possible that the initially reported endobacteria are actually giant viruses. This possibility is consistent with all the sequencing and FISH analyses, but contrary to the identification of the inclusions made in the original description based on thin section TEM. However, when this was observed the field giant viruses was relatively young, so the only logical identification of a large inclusion in the ER would be a bacterium. In retrospect, many of these TEMs actually resemble giant virus particles, and I observed an extracellular VLP that falls within a similar size range and resembles a shape of the reported endobacteria [compare Fig. 5.2A and B with Figure 1D in Guillou et al., 1999]. However, as noted above since the cultures are now gone and the data are generally non-overlapping, this possibility cannot obviously be verified either. Another complication with the last possibility is how to explain the long-term persistence of viruses in these cultures, in particular as it must have been followed after 20 years by a sudden loss of viruses (RCC257-late) or the death of the strain (RCC24). One O. mediterraneus culture with a decade-long co-existence with OmV2, was found to be a co-culture of resistant (R) and susceptible (S) strains, where the host showed two reversible phenotype phases that are thought to explain the long-term stability of the system (Yau et al., 2016, 2020). It was hypothesized that the RS-switching may be a common long-term strategy for other NCLDVs-affected hosts, and persistent infection is a known strategy for phaeoviruses, a close relative of prasinoviruses (Delaroque et al., 1999; Van Etten et al., 2002; Stevens et al., 2014). Some resistant hosts have been observed to produce infective viruses without typical lytic events (Thomas et al., 2011; Yau et al., 2016), reminiscent of the fact that no prasinovirus reads and hallmark genes were detected 157 in the RCC257 transcriptome data [also to Herpesvirales (Goodrum and McWeeney, 2018), another dsDNA virus distant related to NCLDVs]. When susceptible and different types of resistant cells (RP vs. RNP: viral-producing vs. non-producing) were cloned and co-cultivated, the viruses were eventually eliminated in the co-cultivated RP and RNP culture while, susceptible cells became dominant in the S and RNP co-cultivated culture (Thomas et al., 2011). To examine the possibility that virophages are involved in the host-virus dynamic, I searched for virophage genes or virophage-like elements (VLEs) (Yutin et al., 2013; Blanc et al., 2015) in the initial assembly without taxonomically filtering out scaffolds, due to some virophage genes being recombinant, horizontally transferred, or homologs that are shared with cellular organisms or transposable elements (i.e., polintons), and NCLDVs (Yutin et al., 2013). I detected OLV2 (an uncharacterized protein) only in RCC257 WGA, forming a sister lineage to Yellowstone Lake virophage 1 (YSLV1) (Gong et al., 2016). Although this result is insufficient to conclude the involvement of virophages or VLEs in this chapter, deeper sequencing and assembly of the S. scintillans genome could potentially verify the presence and nature of virophages or VLEs association. I suspect that the lack of prasinovirus reads in RCC24-jp is due to long-term cryopreservation. For example, in the Paramecium bursaria chlorella virus (PBCV-1), the strength of infectivity decreased upon cryopreservation and more so if the samples were frozen shortly after post-infection (Nagasaki and Yamaguchi, 1999; Coy et al., 2019). Whether this observation is based on differences in host strains or SsVs, or a combination of both, characterization of host genomes along with further searches of prasinovirus in non-Mamiellophycean hosts should provide insights into the dynamics of persistent infection. 158 The current observations echo the first discovery of the mimivirus, which was initially described as “Chlamydia-like obligate parasites” in an amoeba (Birtles et al., 1997). It took six years to correctly characterize the parasites as Mimivirus (Scola et al., 2003). Conversely, the bacterium Chromulinavorax destructans (Deeg et al., 2019) was recently been described as a bacterial parasite of Spumella elongata (a photosynthetic stramenopile), but it was initially studied as a putative giant virus due to a replicating morphology resembling a viral factory of some giant viruses. Both these cases illustrate how difficult it can be to identify the nature of an intracellular symbiont, suggesting that more studies on the diversity of symbioses in heterotrophic nano- or pico-flagellates should yield more such surprises and taxonomic re-assignments of many symbionts will also follow. 159 Figure 5.1 FISH analyses on S. scintillans cultures 160 Symbiomonas scintillans RCC257 (A-D) and RCC257-jp (E-L) showing no endobacterial signals. (A), (E) and (J) Brightfield; (B) and (F) DAPI; (C) and (G) EUB388 probe under 473 and 488 nm; (D) merged image of (A-C); (H) CF319 probe under 647 nm; (I) merged image of (E-H); (K) merged image of unstained DAPI, CF319 and (L) EUB388 images. Scale bars = 5 µm. 161 Figure 5.2 Virus-like particle and a multi-gene phylogeny of prasinoviruses (A-B) Detection of a virus-like particle (VLP) in negatively stained RCC24. (B) Close up of the VLP in (A). The diameter of the VLP is 192 nm. Scale bars = 100 nm. (C) A multi-gene prasinovirus phylogeny reconstructed from 22 core genes (5,213 sites) using IQ-TREE2 LG+F+G4 model. The right panel shows presence-absence of select core genes. Single-copy genes are DNApol (DNA polymerase B), DNAhel-SNF2 (SNF2 helicase), mRNAcap (mRNA capping enzyme), ATPase, and RNR-sm (RNR small subunit). The tree is rooted with Chlorovirus (PBCVs and ATCV) for visualization. Only nodes <100% ultrafast bootstrap supports are labelled. 162 OlV=Ostreococcus lucimarinus virus; OtV=Ostreococcus tauri virus; OmV=Ostreococcus mediterraneus virus; MpV=Micromonas pusilla virus; BpV=Bathycoccus prasino virus; BIIV=Bathycoccus sp. virus clade BII. PBCV=Paramecium bursaria chlorella virus; ATCV=Acanthocystis turfaceae chlorella virus. 163 164 Figure 5.3 Circularized draft vMAGs overview Genome overview and comparison of the most complete BpV-, OlV-, and MpV-vMAGs to corresponding reference genomes, with the size of vMAGs labelled in the centre. (A) Circularized representation of (A) RCC257 BpV-vMAG compared to BpV2 genome; (B) OlV1 genome compared to RCC257 OlV1-vMAG; (C) MpV_Pl1 genome compared to MpVPl1_vMAG, in an ordered set of coding sequences, represented by blocks shaded by similarity. (A) Mapping coverage is based on RCC257 BpV-vMAG mapped to viral-subset-scaffolds and regions with the coverage more than one standard deviation [62.1] from the mean coverage [50.8] are shown in blue spikes. The outermost ring represents predicted ORFs of the vMAG with manually annotated protein from Prodigal-gv and Viralrecall. (B) Mapping coverage is based on OlV1 genome mapped to viral-subset-scaffolds and regions with the coverage more than one standard deviation [8.4] from the mean coverage [3.1] shown in blue spikes. Only ORFs from the reference OlV1 genome is shown and the partial RCC257 OlV1-vMAG CDS are shown in the outermost ring. (C) Mapping coverage is based on MpV_Pl1 genome mapped to viral-subset-scaffolds and regions with the coverage more than one standard deviation [7.8] from the mean coverage [0.6] shown in blue spikes. Only ORFs from the reference OlV1 genome is shown and the partial RCC257 OlV1-vMAG CDS are shown in the outermost ring. See Table S2 for annotation in a tabular format. 165 Chapter 6: Conclusion 6.1 Major findings and significance In Chapter 2, I processed transcriptomes of seven new species belonging to Bigyromonadea, a poorly-understood group which was previously represented by a single taxon in phylogenomic analyses (Kühn et al., 2004; Leonard et al., 2018; Noguchi et al., 2016; Thakur et al., 2019; Susan M. Tong, 1995). Along with the new transcriptomes, I updated phylogenomic dataset for stramenopiles by curating publicly available transcriptome and genome data (Appendix D). Using this dataset, I inferred a phylogenomic tree that recovered well-supported sister-group relationships between the two Bigyromonadea subgroups, Developea and Pirsoniales. These relationships had never been observed in phylogenetic trees inferred from a handful of genes, which did not support this clade (Aleoshin et al., 2016; Kühn et al., 2004). Bigyromonadea in turn, are a sister-lineage to oomycetes. Additionally, together with collaborators, I described morphologies and behaviours of the seven new bigyromonads, in which some of the Developea species were able to form cell-aggregates that occasionally fused. Some of the new bigyromonads were also able to form pseudopods, while the zoospores of the newly described Pirsoniales were able to actively feed on smaller eukaryotic prey. Together with the phylogenomic data and morphological and the behavioural observations, I hypothesized that the last common ancestor of the oomycetes may have looked more similar to Bigyromonadea, and were likely phagotrophic amoeboids. In Chapter 3, I further updated the phylogenomic data of stramenopiles by processing and generating transcriptomes of six species belonging to Sagenista and Opalozoa, including one new species of Placididea and three new benthic species of MAST-6 (Sagenista). MAST-6 is one of the few MArine STramenopiles (MASTs) with an ultrastructure description and available 166 genomic level data (Shiratori et al., 2017). This group is associated with sediments, but has unknown phylogenetic diversity (Massana et al., 2015). By searching for SSU rRNA gene sequences of the newly characterized MAST-6 species in several sediment amplicon datasets, I found that they are not only abundant in sediments but are phylogenetically more diverse than their sister lineage, MAST-4, one of the most abundant groups in the open ocean (Rodriguez-Martinez et al., 2012; Rodríguez-Martínez et al., 2009; Thakur et al., 2019). Additionally, I observed high relative abundance of SSU sequences similar to one of the newly described MAST-6 species, Mastreximonas tlaamin in most of the sediment datasets. For phylogenetic trees including SSU sequences of the new Placididea species, Haloplacidia sinai, it is depicted as a sister-lineage to a previously described clade (“Group-D”) that consists of a number of species that can tolerate a wide range of salinities (Park and Simpson, 2010). Based on this relationship and the location of its isolation in the Red Sea, H. sinai also likely tolerates a broad range of salinities. Chapter 3 also provides an updated stramenopile phylogenomic tree by adding publicly available MAST-1, -7, -8, -9, and MAST-11 in addition to the new transcriptomes generated in this study. Together with phylogenomic data and the distribution of some of the Sagenista described in this study, I conclude that Bigyra is indeed paraphyletic and some clades showed phylogenetic parallelism with niche occupation. In Chapter 4, I focused on the ochrophytes phylogenomic dataset (supermatrix) and conducted phylogenomic analyses using various inference methods and gene selection criteria. This was done by obtaining under-represented ochrophyte classes from publicly available culture collections and generating new transcriptome data. I additionally added publicly available ochrophyte genomic or transcriptomic data to the supermatrix to “break” long branches of many clades previously represented by one or two taxa in phylogenomic analyses. The resulting 167 phylogenomic tree represented four classes that had only been included in a single phylogenomic analysis (Terpis, 2021, unpublished data). The inferred phylogenomic tree recovered robust relationships of Eustigmatophyceae + RPX and Pinguiophyceae + Olisthodiscophyceae, whose relationships were either contentious or had rarely been phylogenomically analysed prior to this study. To further investigate and resolve other lineages that were incongruent between the Bayesian and the maximum-likelihood tree, I quantified various gene properties considered to represent phylogenetic signal or noise using a previously established method (Mongiardino Koch, 2021; Mongiardino Koch and Thompson, 2021). When alternative supermatrices were concatenated with different combinations of genes using various gene properties as filtering criteria, selecting genes with high signal and data quality resulted in the similar topology as the initial phylogenomic tree, but selecting genes with low noise resulted more unstable lineages. This study not only provides the most up-to-date stramenopiles phylogenomic tree while resolving some contentious relationships, but also hints at a potential way to sub-sample a supermatrix to the least number of genes to reduce the computational resources and time – both major hurdles in phylogenomic analyses. While stramenopiles endosymbionts of eukaryotes are well documented, this group has barely been investigated for being potential endosymbiont hosts, particularly among free-living heterotrophic lineages. In Chapter 5, I investigated the only reported case of putative endobacteria among free-living heterotrophic stramenopiles, S. scintillans. However, even with extensive genomic sequencing from several strains I found no evidence of a bacterial endosymbiont. The absence of endobacteria was further confirmed with multiple eubacterial FISH probes. Instead, I recovered and assembled up to three viral genomes, and detected a VLP with similar size and appearance to the “endobacteria” described by Guillou et al. (1999). Based 168 on this finding, I proposed persistent infection of SsVs in S. scintillans, although the viruses were later lost in all my cultures, either because the strain lost the virus or the susceptible strain itself was lost. Host genome assemblies and further search on SsVs in a broader host range, particularly focusing on pico- or nano-flagellates, will not only help us understand persistent infection of NCLDVs, but also broaden our view on endosymbiosis in free-living heterotrophs. 6.2 Future direction and outstanding questions 6.2.1 Character evolution and niche occupation of stramenopiles An extensive update on the stramenopile phylogenomics in this thesis resulted in 23 new transcriptomes, which were mostly generated from newly described species and previously under-represented groups. In addition to updating our knowledge on the stramenopile phylogeny, the work from my thesis can be used to address the functional evolution of stramenopiles. For example, an amoeboid morphology and saprotrophic mode of feeding can be found across different groups of stramenopiles. Two ochrophyte species, Leukarachnion sp. (Hibberd, 1971; Jaške et al., 2022) and Chrysamoeba radians, are amoeboids that can form net-like pseudopods, both traits resembling oomycetes and labyrinthulids. Some of the newly described Bigyromonadea in this study were also able to fuse with each other and form pseudopodia. Other groups of stramenopiles seem to have evolved adaptations to specific habitats where they are found. For example, all the newly described MAST-6 species from this study were benthic and shown to be abundant in sediment samples. Many species belonging to Placididea on the other hand, have been cultured in a wide range of salinities, many isolated from hypersaline conditions. Identifying genes that are linked to the amoeboid morphology, pseudopodia formation, or their habitats in stramenopiles would enhance our current understanding of the character 169 evolution of stramenopiles. Exploring the gene expression patterns from each group with these features can bring us closer to elucidating the characteristics of the the last common ancestor of stramenopiles. Using transcriptome analyses, this type of approach has been used to infer characteristics of the last common ancestor of fungi as a phagotroph (Torruella et al., 2018), and to identify genes that were highly responsive during highly saline conditions in Halocafeteria seosinensis (Opalozoa; Bigyra). 6.2.2 Minimum number of genes for a phylogenomic tree Phylogenomic analyses have revolutionized our understanding of eukaryotic phylogeny and evolutionary history (Delsuc et al., 2005; Burki et al., 2016; Keeling and Burki, 2019). However, the problem with a heavy computational burden will only increase as I generate data from more and more of the phylogenetic diversity of protists. To reduce the computational burden, I searched for phylogenetically informative genes that can be selected to build a smaller supermatrix with decreased phylogenetic noise. Likely due to the rapid ancient diversification of stramenopiles, I was unable to identify phylogenetically informative genes that would be sufficient to replace the supermatrix consisting of hundreds of genes. However, this work provided several findings that can bring us a step closer to subsampling a supermatrix: (1) selecting for the slowest evolving genes omits significant phylogenetic information and this can especially impact deep-branching lineages; (2) selecting genes with long internal branches provide less variable topologies among multiple trees and can be informative for recently and/or rapidly diverged group; (3) removing genes with the most apparent biases yielded the same topology as the phylogenomic tree inferred from an unfiltered supermatrix. Future studies can expand on this work to investigate rate-driven phylogenetic information and establish the 170 maximum number of phylogenetically “noisy” genes that can be removed to lessen the computational burden for likelihood-based tree inference methods. 6.2.3 Verifying persistent infection in Symbiomonas scintillans Finally, I investigated the only reported case of endobacteria within a non-photosynthetic lineage of stramenopile, Symbiomonas scintillans, and found no evidence of endobacteria but instead three giant viruses. I observed this in two different strains of S. scintillans with different outcomes. Strain RCC257 appears to have lost the viruses, since the culture grew faster over time, and eventually no viral contigs could be found in the WGA data. Conversely, strain RCC24 appears to have perished, leaving only viral sequences. Due to fact that the initial experimental design was to search for endobacteria, I was not able to purify viral lysates and isolate viruses. 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Group Sample ID Type Project SRA Run GenBank Strain Ochrophytes Rhizosolenia setigera (MMETSP0789) Peptide reads PRJNA248394 SRR1296707 CCMP 1694 Synchroma pusilum (MMETSP1452) Peptide reads PRJNA248394 SRR1300531 CCMP 3072 Aureococcus anophagefferens Peptide reads PRJNA13500 GCA_000186865.1 CCMP 1984 Oomycetes Phytophthora parasitica Peptide reads PRJNA73155 GCA_000247585.2 INRA-310 Plasmopara halstedii Peptide reads PRJEB6932 GCA_900000015.1 Hyaloperonospora arabidopsidis Peptide reads PRJNA298674 GCA_001414525.1 Noks1 Nothophytophthora sp. Peptide reads PRJNA328215 GCA_001712635.2 Chile5 Pythium ultimum Peptide reads PRJNA36503 GCA_000143045.1 DAOM BR144 Pythium brassicum Peptide reads PRJNA498716 GCA_008271595.1 P1 Albugo candida Peptide reads PRJNA291031 GCA_001306755.1 Ac 7v Saprolegnia diclina Peptide reads PRJNA86859 GCA_000281045.1 VS20 Achlya hypogyna Peptide reads PRJNA169234 GCA_002081595.1 ATCC 48635 Thraustotheca clavata Peptide reads PRJNA169235 GCA_002081575.1 ATCC 34112 Aphanomyces astaci Peptide reads PRJNA187372 GCA_000520075.1 APO3 Hyphochytrium catenoides Transcripts PRJEB13950 GCA_900088475.1 195 Developea Developayella elegans Transcriptome PRJDB4370 DRR049556 CCAP:1917/1 Group Sample ID Type Project SRA Run GenBank Strain Sagenista Pseudophyllomitus vesiculosus Transcriptome PRJDB8568 DRR186658 NIES-4114 MAST4 Peptide reads PRJNA244411 SRR1263007 dcp33 MAST4A2 Transcripts PRJEB6603 GCA_900128395.1 TOSAG23-2 MAST4E Transcripts PRJEB6603 GCA_900128585.1 TOSAG23-3 Oplozoa Wobblia lunata Transcriptome PRJDB4369 DRR049555 NIES-1015 Blastocystis_ST4 Peptide reads PRJNA257240 GCA_000743755.1 WR1 MAST3 Transcripts PRJEB6603 ERR1198953 TOSAG41-2 MAST3F Transcripts PRJEB6603 GCA_900128565.1 TOSAG23-6 Cafeteria roenbergensis Peptide reads PRJNA552725 GCA_008330645.1 BVI Bicosoecid sp. (MMETSP0115) Peptide reads PRJNA231566 SRR1294380 Cantina marsupialis Transcriptome PRJDB3523 DRR030401 YPF1205 Platysulcea Platysulcus tardus Transcriptome PRJDB8466 DRR186656 NIES-3720 196 Appendix B: Phylogenomic tree of stramenopiles with the seven new transcriptomes using the approach 2 dataset. Multi-gene phylogenomic tree of stramenopiles with the seven new transcriptomes (pink) added to Gyrista, consisting of the concatenated alignments of 247 aligned gene-sets. The tree was reconstructed using a maximum-likelihood (ML) analysis, under the site-heterogenous model, LG+C60+F+G4+PMSF implemented in IQ-Tree. The tree topology is based on the tree reconstructed on the dataset process with approach 2. Branch support was calculated separately 197 Appendix C: Bayesian phylogenomic tree of stramenopiles Bayesian phylogenomic tree of stramenopiles of the seven new transcriptomes (pink) added to Gyrista. The tree was reconstructed based on the 247 gene-sets of 76 taxa processed with the approach 1 using PhyloBayes under the CAT+GTR+G4 model. No chains converged (maxdiff=1) and all chains have identical tree topologies except the sub-clades of ochrophytes as summarized as different combination of Raphidophyceae (R), Eustigmatophyceae (E), Chrysophyceae (C), Synurophyceae (S), Phaeophyceae (P), Pinguiophyceae (Pi), and Xanthophyceae (X). Rhizosolenia sp., also showed inconsistent topology across the chains. P-values were calculated using the approximately unbiased test (p-AU) with 10,000 RELL bootstrap replicates, implemented in IQ-TREE. The difference in maximum log likelihoods (ΔLogL) of each tree was calculated by comparing to the maximum log likelihood of ML tree reconstructed under the LG+C60+F+G4. Except chain1, the topologies of the trees from chain 2-4 were rejected where their p-AU were less than 0.05, indicating confidence interval below 95%. Only the Bayesian posterior probabilities (PP) lower than 1 are marked in the figure. All other nodes have PP=1. The collapsed clades in the figures indicate outgroup (alveolates). 198 199 Appendix D: Approximately unbiased test of constrained trees based on approach 2 dataset. Approach 2 (Prequal/Divvier, MAFFT G-INS-i, -gt 0.1) Constrained Tree p-AU logL ∆logL Unconstrained ML tree 0.602 -4112709.551 0 ML tree under LG+C60+F+G4+PMSF 0.569 -4112709.552 0.00035827 ML tree under LG+C60+F+G4 0.543 -4112709.552 0.00035827 ML tree Modified (Bigyromonada+ochrophytes) 0.0492 -4112783.641 74.089 Except for the unconstrained ML tree, each tree was constrained under LG+C60+F+G4 using IQ-TREE with the dataset processed with approach 2. All the ML tree generated in this study (bigyromonada + oomycetes). “ML tree Modified” is a hypothetical tree constraint containing (bigyromonada + ochrophytes) with the rest of topology remaining the same with the unconstrained ML tree. The unconstrained tree is based on ML tree reconstructed under LG+C60+F+G4+PMSF as presented in Appendix B. The p-AU values were calculated using the AU test with 10,000 RELL bootstrap replicates, implemented in IQ-TREE. The maximum log likelihoods (logL) of each constrained and their differences (∆logL) compared to the unstrained tree are listed. Constraints with P-values lower than 0.05 are rejected, indicating confidence interval below 95% (marked bold). 200 Appendix E: Phylogenomic tree of stramenopiles with the fast-evolving species removed. Multi-gene phylogenomic tree of stramenopiles with the seven new transcriptomes (pink) added to Gyrista with the fast-evolving species removed (Cafeteria roenbergensis, two species of Blastocystis sp., and Cantina marsupialis). The tree was reconstructed using the Maximum-likelihood (ML) analysis, under the site-heterogenous model (LG+C60+F+G4) implemented in IQ-Tree, comprising 75,798 aa of 247 genes from 72 taxa. Branch support was calculated using 1000 ultrafast bootstrap (UFB). Branches that have different topology from Fig. 2.1 are marked by a star symbol. 201 Appendix F: ML tree of a 18S rRNA gene including stramenopile environmental sequences. ML tree reconstructed under BIC: TIM2+R6 with 1000 UFB from a 18S rRNA gene alignment of 107 taxa (1665 sites) including environmental sequences. The seven new species described in this study are marked as pink: Pirsoniales forming a sisterhood with Ochrophytes and Developea forming a sister clade to ‘Abyssal Clade’, demonstrating potential expansion of the Bigyromonada clade with further taxon sampling. 202 Appendix G: Maximum-likelihood phylogenomic trees inferred from 39per- and 59per-matrix. Combined maximum-likelihood (ML) multi-gene trees of six new transcriptomes; four from newly described Bigyra (pink) in this study, and two from culture collections (blue). Two trees were constructed from a concatenated alignment of 39per-matrix (233 orthologs of 98 taxa with 74,531 aa), and 59per-matrix (215 orthologs of 98 taxa with 67,630 aa). The tree was estimated under the site-heterogenous model LG+C60+F+G4+PMSF with 100 standard bootstraps. The resulting trees had the same topology and bootstrap values are only shown if supports are <99% or the values between the two trees are different (39per-matrix vs 59per-matrix). 203 Appendix H: Maximum-likelihood phylogenomic trees inferred from MASTer-matrix. Maximum-likelihood (ML) multi-gene trees of six new transcriptomes; four from newly described Bigyra (pink) in this study, and two from culture collections (blue). ML tree was constructed from a concatenated alignment of MASTer-matrix (234 orthologs of 104 taxa with 74,898 aa) which includes protein data from MAST-1, -7, -8, -9, and MAST-11. The tree was estimated under the site-heterogenous model LG+C60+F+G4+PMSF with 100 standard bootstraps. Bootstrap values are only shown if supports are <100%. 204 Appendix I: Pairwise-distance tree based on amino acid composition analysis. 205 Appendix J: Recoded RAxML-ng multi-gene tree. Phylogenomic tree of six new transcriptomes; four from newly described Bigyra (pink) in this study, and two from culture collections (blue). The tree was constructed from a recoded alignment of 76,516 sites under the MULTI18_GTR model with 100 standard bootstraps. The bootstrap supports are only labelled if <99%, all other unlabelled nodes indicate 99 or 100% support. 206 Appendix K: Consensus trees from Bayesian analysis. Bayesian consensus trees of stramenopiles generated from four independent Markov Chain Monte Carlo (MCMC) chains (maxdiff=1). The tree was reconstructed based on the same matrix (240 genes and 98 taxa) used for the ML-PMSF inference, using PhyloBayes under the CAT+GTR+G4 model. There was no convergence among chains. The four MAST-6 and Placididea transcriptomes generated in this study are marked in pink, two culture collection bikosian species are marked in blue. Only posterior probabilities (PP) lower than 1 are labelled in the figure. All other nodes have PP=1. A=chain 1; B=chain 2; C=chain 3; D=chain 4 207 Appendix L: Stacked bar plots showing relative abundance of unique MAST ASVs. Bar plots showing relative abundance or count of unique ASVs assigned to different MAST groups (A), MAST-6 species (B), and MAST-6 sub-groups in five sediment datasets. SouthChina is the only dataset that used a SSU rRNA gene primer were grouped by class level. B. Composition of MAST-6 lineages from each dataset were grouped by order to further show higher taxonomic assignment. “MAST-6_X” represents unknown MAST-6 lineages classified from PR2 database and “MAST-6” represents a potentially new MAST-6 lineage based on the updated taxonomic training database. The new MAST-6 species described in this study are “M. tlaamin” and “V. tehuelche”. “P. vesiculosus” and “NY13S_181” are cell isolates and “SA2_3F7” is an environmental sequence. 208 Appendix M: A SSU rRNA phylogenetic tree of stramenopiles with SouthChina dataset. A RAxML SSU rRNA phylogenetic tree of stramenopiles. The tree was constructed under the GTR+GAMMA model with 1000 rapid bootstrap replicates, using an alignment of 548 stramenopile sequences and seven outgroup sequences, and included 119 extracted ASVs assigned to MAST-6 or Placididea from all the amplicon dataset (including SouthChina dataset) and 10 placididean OTU sequences from the ESBig study. The four new Bigyra species are coloured in pink. High confidence likelihood weight ratio values (LWR ≥95%) are denoted in red. Amplicon sequence variants assigned to MAST-6 in SouthChina and in Deepsea dataset are coloured in orange and excluded from the main figures. Blue nodes in these ASVs indicate low confidence (LWR <95%), indicating an equally likelihood of alternative placements. Clades other than MAST-6 and Placididea are collapsed. 209 Appendix N: A SSU rRNA phylogenetic tree of stramenopiles without ASVs. Maximum-likelihood SSU rRNA phylogenetic tree (SSU-tree) constructed using IQ-TREE under TIM2+F+R6 with 1000 ultrafast bootstraps, based on an alignment of 246 taxa and 1649 sites. Bootstrap supports of ≥97% are marked with black dots, while bootstrap supports of <50% are excluded. The tree includes relevant environmental or cell isolate SSU sequences but excludes extracted amplicon sequences from amplicon datasets. Sequences in pink indicate three newly added MAST-6 sequences and H. sinai sequence. Blue indicates new sequence data from S. scintillans and Caecitellus sp. Clades containing Ochrophyta, Oomycetes, and Hyphochytriomycetes are collapsed. 210 Appendix O Consensus trees of stramenopiles with updated an ochrophyte data Bayesian consensus trees of stramenopiles generated from four Markov Chain Monte Carlo (MCMC) chains, none of which converged (maxdiff=1). Using PhyloBayes under the CAT+GTR+G4 model, the analysis is based on 231-supermatrix used for the C60+PMSF tree. Only posterior probabilities (PP) lower than 1 are labelled in the figure, otherwise all other unlabelled nodes have PP=1. CSS = Chrysophyceae-Synurophyceae-Synchromophyceae; Pico=Picophagea; Olis=Olisthodiscophyceae; Ping=Pinguiophyceae; BB=Bolidophyceae-Bacillariophyceae; PeD=Pelagophyceae-Dictyochophyceae; RPX=Raphidophyceae-Phaeophyceae-Xanthophyceae; Actino=Actinophryidae; Eustig=Eustigmatophyceae. Oomycetes includes Hyphochytriomycetes. Alveolates and Rhizaria are outgroups. 211 Appendix P: Summary of bootstraps with fast-evolving or random site and genes removed. Summary of bootstrap changes of some contentious lineages with incremental removal of (A) fast-evolving sites; (B) random sites; (C) random genes. For (A), (B), increments of 7,000 amino acid sites were removed while for (C) random genes in increments of 20% were removed from ‘231-supermatrix’. For (C), two to four replicates of each increment were generated and they are denoted by “rep#”. (A) Bigyromonadea+Oomycetes (including hyphochytriomycetes) were present in trees generated from removing fast-evolving sites while (B) Chrysista+Diatomista (ignoring placement of Actino, Ping, Olis and Eustig) were the only grouping present in all trees when random sites were removed. Groupings with an asterisk (*) indicate that they are found in ‘231-supermatrix’ C60-PMSF tree. CSS=Chrysophyceae-Synurophyceae-Synchromophyceae; Pico=Picophagea; Olis=Olisthodiscophyceae; Ping=Pinguiophyceae; Platy=Platysulcidae; RPX=Raphidophyceae-Phaeophyceae-Xanthophyceae; Actino=Actinophryidae; Eustig=Eustigmatophyceae. 0204060801000 7000 14000 21000 28000 35000 42000 49000Bootstrap support (%)Fast-evolving sites removedEustig+Ochrophyta (CSS+Pico)+Actino*RPX+Eustig* Oomycetes+Bigyromonadea*Nanomonadea+Opalinata Nanomonadea+PlacidideaChrysista+Diatomista* (Olis+Ping)+ActinoEustig+ActinoA. 212 01020304050607080901000 7000 14000 21000 28000 35000 42000 49000Bootstrap support (%)Random sites removedEustig+Actino+(CSS+Pico) (CSS+Pico)+Actino*RPX+Eustig* Oomycetes+Bigyromonadea*Nanomonadea+Opalinata Nanomonadea+PlacidideaChrysista+Diatomista* (Olis+Ping)+ActinoEustig+ActinoB. 213 01020304050607080901000 20rep1 20rep2 40rep1 40rep2 40rep3 60rep1 60rep2 60rep3 60rep4Bootstrap support (%)% Random genes removedEustig+Actino+(CSS+Pico) (CSS+Pico)+Actino*RPX+Eustig* Oomycetes+Bigyromonadea*Nanomonadea+Opalinata Nanomonadea+PlacidideaChrysista+Diatomista* (Olis+Ping)+ActinoEustig+ActinoC. 214 Appendix Q: Summary of 13 gene property loadings on three principal components. Only PC1 and PC2 axes were used for a PCA plot (see Fig. 4.3A). 215 Appendix R: List of calculated properties for each gene used for building a concatenated matrix. Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 BTUB 22 0.02008698 0.54808635 0.05978261 0.03905824 4.49169805 0.43603286 0.21439934 0.06397481 432 0.92 0.61342593 73.25 0.25892857 -6.3509638 0.68434478 RPS20 188 0.00826826 0.6433892 0.18358341 0.06035343 6.21640289 0.44326397 0.34066202 0 99 0.824 0.80808081 72.6 0.39 -4.1113818 0.44308226 ATP6 17 0.02668679 0.62468552 0.02765265 0.06515336 7.23202305 0.47820723 0.336169 0.11342791 144 0.888 0.79861111 79.5277778 0.4537037 -4.499775 1.51172133 VATB 220 0.00614656 0.65638689 0.04155874 0.06665395 6.13216346 0.44989819 0.35387141 0.07434217 470 0.736 0.61489362 87.8426966 0.60674157 -4.3904991 -1.748244 H2A 72 0.01928687 0.72793959 0.03057476 0.07376735 6.56529445 0.3606335 0.40273764 0.14861435 104 0.712 0.71153846 74.9302326 0.3255814 -3.4216898 2.07107281 NSF1-L 111 0.00459541 0.65286736 0.02481524 0.0777782 6.92226013 0.42113917 0.40135104 0.08046821 374 0.712 0.68181818 88.244186 0.61627907 -3.8431137 -1.3340336 IF6 79 0.00304788 0.55673988 0.04687695 0.0780059 8.03460733 0.36513899 0.36593118 0.10617345 233 0.824 0.77682403 84.03 0.58 -3.9546322 -0.3024856 S15P 198 0.00602813 0.66228246 0.02382666 0.07888281 7.25721878 0.43523272 0.38357681 0.12574429 151 0.736 0.72847682 78.3258427 0.5505618 -3.8763 0.70279767 S15A 197 0.00664081 0.62700728 0.0250038 0.08102423 8.26447132 0.38597996 0.38492178 0.13556917 129 0.816 0.72868217 83.2277228 0.52525253 -3.8780457 0.69702386 NSF1-G 106 0.00915086 0.69148636 0.04930746 0.0820762 7.46893395 0.38729602 0.39514606 0.08290885 384 0.728 0.68229167 88.3863636 0.54545455 -3.3874696 -1.1522765 NSF1-M 112 0.00747129 0.71082468 0.04844932 0.08209447 8.12735206 0.39894314 0.39293694 0.08083367 398 0.792 0.69346734 83.90625 0.57291667 -3.362521 -1.0267824 RPS5 192 0.00285813 0.6186278 0.0287226 0.08226134 8.63744053 0.44168342 0.42702084 0.11889633 189 0.84 0.75661376 85.7647059 0.60784314 -3.9093966 0.08107101 ARPC1 14 0.00964489 0.641805 0.05529563 0.08944127 8.31803775 0.36475417 0.41412444 0.09447699 393 0.744 0.72773537 87.0326087 0.63333333 -3.0951512 -1.4691728 216 Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 RPS17 185 0.0224359 0.78203442 0.05363636 0.09095896 9.0958964 0.38991916 0.53079448 0.18077442 110 0.8 0.71818182 73.3402062 0.43298969 -2.5286197 2.03217504 NDF1 99 0.00873459 0.77739384 0.03837342 0.09102972 8.8298826 0.37988616 0.48739415 0.08041657 432 0.776 0.58333333 85.4893617 0.55319149 -3.0501017 -1.2989019 NSF1-K 110 0.01182852 0.68825193 0.05835224 0.09139624 8.86543521 0.42777242 0.42935524 0.09267779 359 0.776 0.71587744 81.4468085 0.56382979 -3.2976639 -0.5406971 NSA2 104 0.01839298 0.73802856 0.03393429 0.09205514 8.83729303 0.36335175 0.45839947 0.10859429 260 0.768 0.73461538 81.2795699 0.49462366 -2.7179496 0.24650304 RPS3 190 0.00812102 0.61563135 0.02343612 0.09238369 9.97743866 0.32620871 0.40274167 0.11372772 209 0.864 0.86124402 82.7428571 0.56190476 -2.9480894 0.25657324 RPS16 184 0.01780399 0.69048765 0.03833992 0.09418748 10.3606229 0.39524623 0.47646219 0.14937963 138 0.88 0.73913043 80.6915888 0.4953271 -3.1343948 1.21570995 RPS11 181 0.0064394 0.74110876 0.0301468 0.09448563 9.92099077 0.45823257 0.51155156 0.15017555 133 0.84 0.7593985 78.245098 0.58823529 -2.9514039 1.09718371 NSF1-J 109 0.00976128 0.73229935 0.06128126 0.09521119 9.71154142 0.44562988 0.45724655 0.08757307 363 0.816 0.70798898 88.7373737 0.56565657 -3.0528752 -0.8131616 AP4S1 9 0.0227263 0.64920428 0.04395604 0.09595532 8.73193425 0.45516573 0.46397238 0.16604647 136 0.728 0.80882353 72.9325843 0.46590909 -3.2579325 1.97802608 APBLC 10 0.00897046 0.66131836 0.0451539 0.09838504 9.54334867 0.37817313 0.4723472 0.08304066 558 0.776 0.80645161 90.2765957 0.72340426 -2.3867012 -2.3761001 IF2G 77 0.01108329 0.75740478 0.04940476 0.10023559 10.0235587 0.32258832 0.42745199 0.08851261 420 0.8 0.68333333 83.8989899 0.59793814 -2.3554488 -1.3573647 RPL44 170 0.02635473 0.79137891 0.01954079 0.10225468 9.10066646 0.3706004 0.49932949 0.15086966 92 0.712 0.67391304 78.0454545 0.25581395 -2.7900278 2.48208368 PSMB-M 139 0.0249813 0.67812516 0.02518233 0.1026702 11.3963925 0.43153743 0.56784198 0.13198825 210 0.888 0.7952381 81.9537037 0.55555556 -2.8099819 0.83145117 SUCA 210 0.00994637 0.8097273 0.04554181 0.10494347 10.0745733 0.34209072 0.49851932 0.09468555 293 0.768 0.62798635 81.2021277 0.48387097 -2.4121358 -0.2161686 217 Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 DHSB3 52 0.00552816 0.72911101 0.03654365 0.10513051 9.56687597 0.3368448 0.44308313 0.11198317 212 0.728 0.6509434 83.2159091 0.45454545 -2.9156828 0.2238067 SAP40 199 0.01021509 0.65565701 0.03820348 0.10689768 11.6518467 0.33050753 0.4591124 0.12810099 201 0.872 0.86069652 79.8018868 0.46226415 -2.5097548 1.0669771 RPS15 183 0.02976633 0.66827776 0.03386426 0.11001225 11.1112372 0.42374691 0.57975508 0.17958499 138 0.808 0.89855072 81.4795918 0.51020408 -2.1585592 1.79554701 AP3S1 7 0.04519769 0.72700448 0.04845469 0.11011109 9.13922079 0.45552818 0.56095877 0.17015986 138 0.664 0.84782609 84.725 0.5 -1.9312889 1.45114004 GTUB 71 0.01587905 0.71212805 0.08297158 0.11054284 9.61722742 0.33063404 0.47726249 0.10342945 421 0.696 0.74584323 83.1547619 0.70238095 -1.7527147 -1.8336508 RAN 146 0.0219883 0.74369907 0.05134425 0.11204555 10.8684181 0.30992923 0.44966163 0.12711072 204 0.776 0.79901961 77.106383 0.54255319 -1.7603047 0.44978767 RPS2 187 0.03244237 0.88857275 0.14845361 0.11246908 10.9095011 0.34289813 0.44260986 0 210 0.776 0.75238095 77.5744681 0.39361702 -1.3253264 -0.2062589 RPL19 158 0.01273668 0.70621984 0.0185937 0.11481444 11.8258868 0.38601839 0.54689838 0.1386279 165 0.824 0.80606061 82.2 0.55 -2.3023852 0.87633105 RPL3 163 0.02442426 0.85044907 0.14138413 0.11602288 12.1824027 0.39394509 0.61498609 0 375 0.84 0.76266667 80.8137255 0.45098039 -1.3776073 -0.8602581 BAT1 21 0.01518605 0.71471529 0.05276972 0.11608206 11.0277961 0.39486128 0.52450942 0.09626955 381 0.76 0.77952756 86.7765957 0.58695652 -2.1296143 -0.8065408 EIF4A3 61 0.02487081 0.70579811 0.0466054 0.11684894 10.0490087 0.32853085 0.4812936 0.09483652 372 0.688 0.71236559 83.1566265 0.55421687 -2.1680811 -0.8700993 DHSA1 51 0.00609136 0.76745099 0.02575536 0.11690628 11.3399091 0.33497675 0.49286025 0.07377695 578 0.776 0.62629758 87.5531915 0.59574468 -2.2887401 -2.093873 NOP5A 103 0.01268329 0.74393959 0.05544425 0.11739598 11.3874098 0.3633125 0.55092407 0.11969183 283 0.776 0.70671378 87.1595745 0.67021277 -1.9037551 -0.9758364 RPL11 151 0.00868092 0.72574593 0.03365385 0.11807674 12.2799809 0.42729952 0.61872314 0.14424543 162 0.832 0.72839506 80.3267327 0.5049505 -2.5764507 1.18660961 218 Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 RPL12 152 0.04786094 0.75813454 0.02683147 0.11845365 12.674541 0.35827248 0.66086244 0.15587349 155 0.856 0.87096774 86 0.36538462 -1.3435243 2.03824448 RPS23 189 0.01057673 0.81920961 0.15328571 0.1188498 11.8849796 0.41734504 0.5972 0 140 0.8 0.63571429 80.5876289 0.50515464 -2.2039789 -0.7017413 RF1 147 0.01006293 0.73180443 0.0386396 0.11894868 11.3001242 0.37180419 0.57625745 0.09974231 407 0.76 0.76412776 88 0.67391304 -1.7814429 -1.3976761 ATSAR2 20 0.03084502 0.73070911 0.03608722 0.1192864 12.0479264 0.36802694 0.58400447 0.13923583 183 0.808 0.80327869 0 0.43877551 -2.5180397 4.80221204 GRC5 69 0.02027505 0.75129002 0.04620986 0.11940668 12.4182951 0.42891872 0.71976159 0.13341076 206 0.832 0.74757282 85.4554455 0.52475248 -1.9869786 0.69158198 MAT 88 0.02406106 0.67352287 0.02598273 0.11974643 11.6154038 0.39960638 0.55910115 0.10379966 369 0.776 0.83197832 89.5851064 0.61702128 -2.0625649 -0.6954274 RPL43 169 0.02106997 0.72406613 0.02854767 0.12061645 9.89054875 0.39744238 0.56704082 0.20032227 88 0.656 0.75 74.6375 0.48101266 -2.1924761 2.03332301 ORF2 119 0.0118293 0.69010599 0.06690501 0.12171674 12.0499575 0.36569601 0.58952305 0.16185913 178 0.792 0.81460674 85.03125 0.57291667 -1.7732866 0.50477961 CCT-A 31 0.01342651 0.68299113 0.04732129 0.12208758 12.6971079 0.36658749 0.57089629 0.09404623 509 0.832 0.82907662 90.0792079 0.69306931 -1.6706214 -1.800047 CCT-E 34 0.00497086 0.67473198 0.03143185 0.12365715 12.6130289 0.32281633 0.53220121 0.08911866 529 0.816 0.79773157 89.1818182 0.70707071 -1.7635001 -2.1290013 GNB2L 67 0.01695068 0.69461218 0.03482774 0.12451301 11.4551966 0.33552808 0.50542416 0.10891533 289 0.736 0.82352941 84.6741573 0.58426966 -1.7738371 -0.3458439 CS 49 0.01434411 0.77625925 0.06311807 0.12708535 13.3439613 0.38117485 0.6214109 0.1090327 365 0.84 0.75342466 87.7403846 0.62745098 -1.4196516 -0.8785209 VATA 219 0.01361688 0.7912 0.06179138 0.1273974 12.4849448 0.35716871 0.63792871 0.08543214 594 0.784 0.64478114 88.6842105 0.73684211 -1.3588996 -2.7798729 RPL15 156 0.00747547 0.75183158 0.0253748 0.12906898 14.0685192 0.35279295 0.62438253 0.12993202 205 0.872 0.73170732 81.1121495 0.53773585 -1.9011091 0.60552265 219 Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 RPS18 186 0.01952431 0.73788019 0.02038014 0.12969133 13.0988241 0.45288998 0.69116144 0.16881807 137 0.808 0.83211679 79.8979592 0.52040816 -1.837319 1.81107406 RPL35 168 0.01188296 0.70976215 0.22163866 0.13022813 14.5855507 0.34641866 0.58865455 0 119 0.896 0.89915966 85.5675676 0.58715596 -0.9519041 -1.1554703 RPL24A 162 0.01842931 0.77486695 0.17272727 0.13025484 14.3280321 0.40086067 0.68026555 0 103 0.88 0.89320388 81.8224299 0.57009346 -0.9103812 -0.4309906 ODO2A 115 0.01324866 0.64913532 0.03913978 0.13065695 12.151096 0.32674308 0.61252482 0.13786427 225 0.744 0.83111111 81.4 0.46666667 -1.8923538 0.84182246 CCT-B 32 0.00672478 0.70693844 0.03510861 0.13169917 12.9065184 0.36527671 0.59653464 0.09874742 497 0.784 0.78269618 89.8247423 0.68421053 -1.5989952 -1.752416 RAD51A 145 0.03265831 0.72600439 0.05968992 0.13216018 11.8944165 0.30553026 0.71578447 0.11307555 301 0.72 0.79734219 80.1264368 0.50574713 -1.0276495 0.08952109 CPN60 46 0.00869956 0.71421696 0.03705901 0.13235055 12.9703538 0.3199411 0.59820138 0.08785079 516 0.784 0.7751938 90.0210526 0.61052632 -1.4591494 -1.6934738 COPG2 43 0.0158542 0.67451532 0.04727075 0.13462811 12.7896703 0.37742252 0.64426937 0.10895304 485 0.76 0.89896907 88.3297872 0.72826087 -1.0781198 -1.5678112 CCT-D 33 0.01344512 0.73814992 0.05848313 0.13481337 13.8857775 0.36177764 0.6592331 0.10104917 505 0.824 0.83960396 89.16 0.69 -0.903266 -1.6570949 RPL31 165 0.01668816 0.79324638 0.14732774 0.13501654 13.9067034 0.40374915 0.67838807 0 103 0.824 0.94174757 80.25 0.51 -0.7419242 0.11124158 ARP3 13 0.05854334 0.74205466 0.05827309 0.13583785 11.1387041 0.37812797 0.58055238 0.10509607 361 0.656 0.79778393 82.1265823 0.51898734 -1.205969 -0.1159641 CCT-G 35 0.00848275 0.71223167 0.04970598 0.13622026 13.4858053 0.34901474 0.62220417 0.1020185 493 0.792 0.82150101 87.7244898 0.65625 -1.2192783 -1.4833186 ODPB 117 0.03422008 0.71356656 0.0333895 0.13724357 13.4498702 0.33123679 0.58240562 0.11003571 327 0.784 0.80428135 82.8736842 0.49473684 -1.4669209 0.1893752 PSMA-B 130 0.02097946 0.72941772 0.03568137 0.13751855 14.4394475 0.4365263 0.73193784 0.14557222 217 0.84 0.83870968 85.745098 0.68627451 -1.2732826 0.21191919 220 Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 MCM-E 93 0.01514015 0.74909456 0.08754554 0.13965754 10.0553426 0.34731865 0.62412687 0.12145828 366 0.576 0.71311475 87.0289855 0.65217391 -1.0801574 -1.4896142 AMP2B 5 0.00990642 0.72171789 0.04101244 0.14006085 14.7063896 0.30658676 0.56370398 0.1058557 333 0.84 0.83783784 86.6470588 0.56862745 -1.1874213 -0.4405505 RPL32 166 0.0147603 0.77315582 0.14323507 0.14197679 14.9075625 0.35654914 0.64563014 0 126 0.84 0.9047619 78.8640777 0.49019608 -0.7939968 -0.000258 CCT-Z 38 0.01810543 0.72330299 0.05837081 0.14218409 14.7871455 0.36665643 0.69068995 0.10517554 496 0.832 0.83870968 85.8613861 0.71287129 -0.8184565 -1.5172463 RPS8 194 0.01618823 0.74864404 0.04922307 0.14273266 15.4151271 0.39011143 0.64697288 0.15688483 174 0.864 0.79885057 83.1238095 0.51428571 -1.4163504 1.168193 C3H4 23 0.05904352 0.76024165 0.04348362 0.14310856 11.7349021 0.4607048 0.94490613 0.22068298 99 0.656 0.81818182 83.6075949 0.5443038 -0.5965723 1.98786508 RPS12 182 0.03893863 0.7179818 0.01832707 0.14313716 16.0313622 0.41081637 0.73806992 0.1896964 114 0.896 0.96491228 84.0091743 0.5412844 -0.7821013 2.11425736 HSP70MT 74 0.03490921 0.80294509 0.06625963 0.14568816 15.0058805 0.29729943 0.61522933 0.08485776 582 0.824 0.75257732 86.95 0.7 -0.2886688 -2.3339627 RPS10 180 0.04127963 0.71774197 0.05499571 0.14621907 15.4992219 0.40784319 0.69020192 0.23440419 88 0.848 0.88636364 78.1456311 0.45631068 -1.00336 2.86434711 L10A 87 0.00859339 0.70996954 0.03444286 0.14657378 15.5368207 0.32255706 0.67495598 0.13638548 212 0.848 0.86792453 81.5825243 0.49514563 -1.1154957 0.98881314 PSMB-K 137 0.03518657 0.77642665 0.0362254 0.14776723 15.3677924 0.34857626 0.69252459 0.13712769 215 0.832 0.81395349 86 0.45544554 -0.8480087 1.04776885 RPL30 164 0.02156931 0.8558461 0.14146825 0.14790441 15.5299627 0.37229375 0.63260389 0 96 0.84 0.86458333 81.125 0.47058824 -0.4639341 0.13535847 PSMA-A 129 0.01150886 0.69835598 0.03785125 0.14802937 15.2470249 0.37441331 0.67748489 0.13843898 227 0.824 0.85903084 87.46 0.57 -1.2340322 0.4326673 CCT-N 36 0.01314297 0.73613215 0.04199294 0.14818636 14.5222635 0.3373196 0.68770983 0.09646693 503 0.784 0.78131213 89.7578947 0.67368421 -0.8672988 -1.7735738 221 Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 PSMA-C 131 0.03481687 0.74119413 0.0601229 0.14840245 16.0274645 0.46040029 0.81715924 0.15128032 223 0.864 0.85201794 87.8207547 0.63809524 -0.8376647 0.51534068 ODBB 114 0.00782748 0.75308217 0.07012949 0.14933712 12.9923292 0.3173133 0.61309109 0.12818301 316 0.696 0.73734177 81.6511628 0.60714286 -1.0026353 -0.6332066 DRG2 56 0.01615369 0.75341555 0.05422692 0.14996875 13.947094 0.39102555 0.75907418 0.12088485 348 0.744 0.77873563 86.8888889 0.64444444 -0.891818 -0.6943972 RPS6 193 0.02133775 0.77366559 0.04256568 0.15088947 15.9942835 0.36519956 0.7185693 0.14435432 209 0.848 0.81339713 83.7864078 0.60194175 -0.7136515 0.4844978 IF2B 76 0.02665574 0.71652497 0.02748708 0.15128448 15.582301 0.40706168 0.76938854 0.16882345 154 0.824 0.8961039 77.69 0.53 -0.9803782 1.84738228 FTSJ1 62 0.04724632 0.7994088 0.07116105 0.15147637 12.7240154 0.3571001 0.6608813 0.15603792 178 0.672 0.7752809 79.8518519 0.40740741 -0.7069203 1.46263989 GNL2 68 0.06346973 0.82428925 0.04026898 0.15198122 14.8941599 0.38791388 0.83080784 0.14535392 261 0.784 0.77777778 81.7894737 0.54736842 -0.239528 0.77802012 MCM-A 89 0.02101861 0.7554611 0.0569427 0.15244612 10.5187823 0.34519418 0.70287421 0.10047795 451 0.552 0.76053215 87.75 0.68181818 -0.5976103 -1.869209 RPL2 159 0.01581796 0.81928178 0.13449574 0.15247924 15.7053615 0.38795166 0.73373826 0 245 0.824 0.75102041 84.82 0.58 -0.7691884 -1.2354503 CLAT 40 0.02138831 0.71027981 0.05480931 0.15270319 15.4230222 0.36818147 0.76862864 0.07962181 1357 0.808 0.91967576 92.4897959 0.78571429 0.19998416 -4.971663 NDUFV2-MITO 100 0.01557541 0.80562584 0.04564487 0.15338067 15.7982087 0.34534921 0.73376718 0.137053 211 0.824 0.78199052 87.77 0.54 -0.5894677 0.40184558 VPS26B 225 0.02853684 0.72101903 0.06316964 0.15404347 12.3234778 0.33203307 0.63923132 0.16358208 168 0.64 0.86309524 81.961039 0.48051948 -0.7084554 1.10534847 DNAI2 54 0.22103738 0.80324541 0.06460548 0.15720961 10.8474633 0.52568623 0.69603654 0.1040276 450 0.552 0.86222222 88.3636364 0.78787879 0.80108583 -1.1150038 ARPC4 15 0.03383461 0.70410537 0.06336974 0.15726773 12.8959535 0.35959673 0.5979289 0.16573025 163 0.656 0.90797546 75.6375 0.51898734 -0.7471031 1.4132141 222 Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 CRFG 47 0.01906299 0.75050082 0.05675531 0.15796769 12.3214798 0.35641182 0.7517876 0.11846869 431 0.624 0.79118329 91.3866667 0.65333333 -0.4417999 -1.4536616 PSMD6 143 0.04334909 0.87343942 0.12228344 0.15807777 13.9108437 0.41974956 0.73256086 0 297 0.704 0.96296296 83.5058824 0.58823529 0.510207 -0.8497548 MRA1 95 0.02774224 0.78272405 0.05747194 0.15830998 14.4062085 0.37643643 0.81381938 0.16812181 187 0.728 0.88235294 87.0454545 0.61363636 0.0488436 0.57237239 RPL5 172 0.01754576 0.80578427 0.04646994 0.15876865 17.7820888 0.38790732 0.82370574 0.14271575 259 0.896 0.83783784 88.0909091 0.56880734 -0.2841599 0.51439444 DIMT1L 53 0.03571811 0.78359988 0.05573811 0.15900988 14.9469287 0.35281761 0.77911471 0.14491528 259 0.752 0.83397683 83.9010989 0.57142857 -0.1372542 0.37400805 RPL7A 173 0.02023134 0.74138591 0.02778596 0.15945008 16.7422589 0.33432619 0.7484368 0.15781178 194 0.84 0.88659794 85.5294118 0.54901961 -0.3999202 0.96141265 SEC23 202 0.00922164 0.71946682 0.06696259 0.15973766 14.5361267 0.32750419 0.70328906 0.09687037 609 0.728 0.87192118 88.3068182 0.72727273 -0.1644618 -2.3453338 RPF1 150 0.02949295 0.74531153 0.04220119 0.16038032 14.2738484 0.29973376 0.65974495 0.18697811 127 0.712 0.8503937 78.0465116 0.48837209 -0.4241867 1.63823636 RPS4 191 0.01795236 0.78068117 0.04762617 0.16057999 17.1820592 0.35365135 0.75401427 0.13646332 250 0.856 0.848 85.9038462 0.52884615 -0.3846127 0.62165542 NFS1-MITO 101 0.01709761 0.79980826 0.05267152 0.16116819 15.6333143 0.33245925 0.68232198 0.11156924 394 0.776 0.69543147 85.1914894 0.58510638 -0.7432056 -0.8348583 ARP2 12 0.09729413 0.82101956 0.05823632 0.16173743 15.0415806 0.42542649 0.74147558 0.10121841 363 0.744 0.86225895 88.8369565 0.64444444 0.33607365 -0.4722837 RPL9 174 0.01113704 0.69095564 0.04333751 0.16272793 18.2255278 0.38806866 0.75761279 0.15923616 171 0.896 0.9005848 87.1181818 0.52293578 -0.8973562 1.29324902 RPL4B 171 0.01259527 0.77683793 0.0462157 0.16393389 16.5573229 0.34046552 0.73222467 0.13354217 298 0.808 0.82550336 88.6565657 0.62244898 -0.2648668 -0.3194083 MCM-B 90 0.01555689 0.71218543 0.06607904 0.16562267 10.9310965 0.36104505 0.76874124 0.11174523 526 0.528 0.78136882 88.7619048 0.63492063 -0.5854966 -1.7862279 223 Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 RPL20 160 0.02363658 0.8156067 0.1665488 0.16627077 16.7933482 0.38724711 0.90022157 0 168 0.808 0.88095238 85.24 0.47959184 0.18397052 -0.2879884 SRP54 208 0.01767793 0.72867388 0.06624804 0.16637443 15.1400734 0.34978747 0.73537469 0.11733176 385 0.728 0.80519481 87.8522727 0.69318182 -0.3944674 -1.2702629 AP4M 8 0.01438146 0.77201158 0.07250765 0.1671604 16.7160397 0.31485744 0.76113738 0.12813313 327 0.8 0.9204893 83.3298969 0.6185567 0.36951381 -0.2086174 MCM-D 92 0.01545547 0.73030582 0.08314393 0.16894538 12.3330129 0.32657264 0.75084317 0.12026353 446 0.584 0.78251121 84.5714286 0.58571429 -0.3657938 -1.1290766 ATP6V0A1 18 0.06388318 0.73583724 0.0553802 0.16921701 17.2601347 0.3789658 0.81039557 0.10808017 410 0.816 0.80243902 86.1515152 0.65656566 -0.1795657 -0.767519 WD66 228 0.09393167 0.88086836 0.2980963 0.16977069 15.9584452 0.2963834 0.74880472 0 380 0.752 0.93421053 81.0989011 0.54945055 2.2110367 -1.8310927 NSF1-C 105 0.06163959 0.78960052 0.06139818 0.17010976 17.0109765 0.3518838 0.7631094 0.11034914 329 0.8 0.78419453 88.3195876 0.58762887 0.02686153 -0.3918035 RPL17 157 0.03645676 0.74070074 0.04011742 0.17088598 19.1392293 0.40480019 0.86040517 0.18254261 146 0.896 0.9109589 85.027027 0.66972477 0.05446621 1.11230128 MTLPD2 97 0.00732079 0.77425052 0.04349652 0.17163702 18.5367982 0.32768727 0.75098825 0.09981911 463 0.864 0.8012959 82.0754717 0.57142857 -0.3638403 -0.5878303 DNAL1 55 0.04402918 0.72898076 0.0630523 0.17266212 13.6403075 0.40305203 0.70143674 0.16559807 159 0.632 0.87421384 80.3815789 0.42105263 -0.6274131 1.86357418 WBSCR22 226 0.04977581 0.7968154 0.07246901 0.17294505 15.2191643 0.34836094 0.87861019 0.1701529 220 0.704 0.76363636 86.7294118 0.61176471 0.33388503 0.17727068 TRS 215 0.01467618 0.77100786 0.07610189 0.17321869 17.668306 0.382177 0.86739141 0.10560203 585 0.816 0.78974359 90.4747475 0.62626263 -0.0814025 -1.5765604 RPN1B 175 0.0078841 0.72759332 0.08623713 0.17466468 17.8157972 0.34297994 0.77722798 0.10862455 682 0.816 0.91642229 89.5252525 0.72727273 0.41981031 -2.3140251 PSMB-L 138 0.04566227 0.78795563 0.0351574 0.1748157 18.0060166 0.37902911 0.84858434 0.15246563 198 0.824 0.83838384 85.67 0.48 -0.0612092 1.39020567 224 Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 IF2P 78 0.01607012 0.74586906 0.03681082 0.17494786 17.1448907 0.36527346 0.88279183 0.10200597 563 0.784 0.8312611 88.2842105 0.72631579 0.045181 -1.7981366 PSD7 128 0.032051 0.7653706 0.04824885 0.17508431 17.6835151 0.37555288 0.79163078 0.14045724 268 0.808 0.87313433 86.622449 0.74489796 0.25091915 -0.42528 RPL13E 154 0.03813884 0.80008371 0.03552972 0.17509863 18.9106519 0.32699172 0.83492167 0.19218432 129 0.864 0.82945736 82.4285714 0.47619048 0.188183 1.94423734 MCM-C 91 0.04325589 0.81116198 0.07784154 0.17513259 12.9598116 0.33084972 0.8299071 0.11275771 438 0.592 0.76027397 88.0704225 0.66197183 0.49173006 -1.5768271 OPLAH 118 0.03716294 0.75986307 0.07390618 0.17672362 10.2499698 0.30426486 0.72074376 0.09190891 677 0.464 0.65288035 86.2909091 0.72727273 -0.1429849 -3.3071086 RPL13A 153 0.01625278 0.76278127 0.02861305 0.17839144 18.5527093 0.37366946 0.84743299 0.17396566 165 0.832 0.86060606 83.8019802 0.4950495 -0.2278427 1.66756906 CC1 26 0.03002746 0.82195773 0.02666667 0.17855102 17.8551016 0.43398798 0.98086241 0.14316118 228 0.8 0.80263158 84.979798 0.58762887 0.01241509 0.81396767 IMP4 84 0.02590962 0.74492248 0.0701488 0.18009503 16.0284577 0.35781322 0.85417212 0.15282026 259 0.712 0.84555985 87.2209302 0.62790698 0.19064573 -0.048012 RPL21 161 0.0774829 0.83148197 0.19244008 0.18207213 19.2996462 0.41349786 0.97198997 0 148 0.848 0.86486486 83.8640777 0.47572816 0.86109947 0.04195193 ATP6V0D1 19 0.03358526 0.74816568 0.06461724 0.18227543 19.1389196 0.38306662 0.86862705 0.13028687 316 0.84 0.94620253 88.372549 0.59803922 0.47992507 0.20168495 PPP2R5C 126 0.05645886 0.77348027 0.05035405 0.18304571 17.0232506 0.26355113 0.75160993 0.11426138 369 0.744 0.88346883 90.2333333 0.57777778 0.87255026 -0.6251423 ODPA2 116 0.01597502 0.78335724 0.04876441 0.18342122 19.62607 0.37994919 0.86353159 0.12113792 326 0.856 0.75766871 88.8018868 0.63461538 -0.1682217 -0.4531672 PACE2-A 120 0.04562225 0.80748677 0.05629078 0.1845387 17.1620994 0.38356181 0.87270253 0.1493866 234 0.744 0.82051282 83.4444444 0.57777778 0.37500966 0.64714677 CTP 50 0.01604509 0.76916233 0.0608854 0.18521328 16.113555 0.32858344 0.80644273 0.10605819 505 0.696 0.76237624 89.1190476 0.66666667 0.11570093 -1.7799707 225 Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 PSMA-G 134 0.02661923 0.73447281 0.05134011 0.18544634 19.8427586 0.37107084 0.88646732 0.15221751 227 0.856 0.90748899 87.9326923 0.68269231 0.41809927 0.20057211 VAPA 218 0.07195851 0.8041107 0.05396825 0.18593926 11.1563555 0.39968979 0.88274382 0.26010978 63 0.48 0.85714286 66.7457627 0.36842105 0.39084911 3.76544672 WRS 229 0.01376882 0.76342008 0.07169118 0.18615875 12.6587952 0.32490261 0.82993891 0.12921214 368 0.544 0.78804348 79.9384615 0.58461538 0.0920958 -0.5408759 PSMA-E 132 0.00841773 0.71386945 0.05276926 0.18731452 19.6680242 0.37434211 0.86152305 0.1634826 233 0.84 0.9055794 86.9019608 0.52941176 -0.0692242 1.03595924 PSMA-J 136 0.0302628 0.7849033 0.04448717 0.18899137 20.0330849 0.3985665 1.00545246 0.15948042 211 0.848 0.85308057 88.2815534 0.63106796 0.54206162 0.62866701 RPO-C 178 0.02468421 0.7568563 0.08974078 0.19181986 14.3864894 0.31141927 0.83288749 0.08462071 913 0.6 0.74479737 89.5 0.73611111 0.60852548 -3.967325 SF3B2 203 0.020971 0.79295287 0.04419809 0.1926151 17.5279742 0.35040409 0.84663799 0.14879279 227 0.728 0.86784141 84.4382022 0.56818182 0.47919101 0.6343261 IFT88 82 0.0323785 0.70674337 0.07468002 0.19291697 13.6971046 0.29421161 0.75038475 0.11886192 526 0.568 0.91254753 90.057971 0.76470588 0.88201897 -2.3089097 YKT6 231 0.0231544 0.76006705 0.03365009 0.19407696 17.8550799 0.35952139 0.93203994 0.17691981 167 0.736 0.90419162 86.0898876 0.53932584 0.51970379 1.32178937 HMT1 73 0.01856308 0.77949354 0.04117356 0.19573411 13.8971217 0.34030298 0.8368531 0.13166757 313 0.568 0.7827476 83.8571429 0.63235294 0.20874319 -0.5150636 GLCN 66 0.02399735 0.74968315 0.08389738 0.19837065 15.8696518 0.38996881 0.9178111 0.16573507 229 0.64 0.79039301 82.7142857 0.58441558 0.14759844 0.45368855 SRA 207 0.04526008 0.7847934 0.05159743 0.1994104 18.5451668 0.32366539 0.89161184 0.13681743 278 0.744 0.85971223 86.5111111 0.62222222 0.98082253 -0.0293932 RPO-A 176 0.02118258 0.76482458 0.06264547 0.19996867 13.7978383 0.34317421 0.89413212 0.09686072 741 0.552 0.75168691 88.5757576 0.72727273 0.52346993 -3.0289565 SCSB 201 0.02327438 0.77110422 0.08232888 0.2005908 16.2478546 0.32703876 0.84342376 0.12782181 338 0.648 0.80769231 80.7051282 0.46153846 0.30438238 0.34746826 226 Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 COP-BETA 41 0.01282929 0.76170397 0.06671093 0.2018437 18.1659327 0.33249203 0.88713617 0.10183824 753 0.72 0.89110226 89.183908 0.67816092 1.02764242 -2.3527855 PACE2C 121 0.01646748 0.72928846 0.08612415 0.20327145 18.2944309 0.3410786 0.9244037 0.17133777 213 0.72 0.90140845 81.5862069 0.57471264 0.71947783 0.80348957 CCT-T 37 0.02350885 0.77306477 0.04233684 0.20399094 19.3791396 0.35540233 0.91401417 0.12265593 500 0.76 0.908 89.923913 0.70652174 1.08241116 -1.2253513 SCO1-MITO 200 0.06880946 0.7915172 0.05545496 0.20447518 19.2206667 0.34469133 1.01170658 0.19700043 141 0.752 0.82978723 83.1847826 0.42857143 0.99551454 2.16218384 UBA3 216 0.01797886 0.78360854 0.07079591 0.20488952 19.6693941 0.34125034 0.91214405 0.13560744 334 0.768 0.82035928 83.7849462 0.55913978 0.63966852 0.09398159 AGB1 2 0.06884666 0.7877943 0.05873369 0.20541216 21.3628651 0.29872016 0.83646298 0.12752228 277 0.832 0.89891697 81.2079208 0.43564356 1.11423557 1.23761672 RPL33 167 0.06096707 0.85481721 0.14753597 0.20549408 20.3439141 0.38317828 0.94149343 0 99 0.792 0.88888889 81.5208333 0.52083333 1.26993906 0.14584281 IFT46 80 0.02354151 0.76940123 0.06665471 0.20703298 16.9767041 0.32008583 0.87351448 0.18033312 204 0.656 0.89215686 0 0.49367089 0.25061095 4.72590083 RPPO 179 0.02868059 0.78273818 0.0346811 0.2075227 23.8651105 0.36651416 1.00761171 0.14482584 257 0.92 0.90661479 88.6428571 0.58928571 1.05177561 0.74455458 RPO-B 177 0.03269617 0.81049127 0.07144854 0.20817767 17.6951018 0.38765148 0.97203002 0.07682093 1094 0.68 0.82449726 89.8292683 0.73170732 1.33663095 -3.8996306 PSMB-N 140 0.02069089 0.76086859 0.06750228 0.20824304 23.1149771 0.3849668 1.04759382 0.17896993 207 0.888 0.9178744 83.1759259 0.56481481 1.00397136 1.42677885 XPB 230 0.05336875 0.78905534 0.06003877 0.20852064 17.5157339 0.38041622 0.94460375 0.11609124 393 0.672 0.82697201 82.3780488 0.54320988 0.74028377 0.04718059 RRAGD 196 0.098351 0.77125231 0.09143393 0.20925743 20.297971 0.29537433 0.8859322 0.17308204 275 0.776 0.94181818 87.5520833 0.54255319 1.97555808 0.74313662 RPL14E 155 0.03299574 0.77875862 0.03325243 0.20998521 21.6284764 0.37069869 0.93455879 0.21186804 120 0.824 0.925 75.3861386 0.43 0.76641631 3.05178562 227 Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 PURA 144 0.01283027 0.75076974 0.04550305 0.21031349 13.4600632 0.32563586 0.87940638 0.11295298 410 0.512 0.76097561 87.0655738 0.59016393 0.19842432 -1.0812942 SPTC2 205 0.01377213 0.78823858 0.04973046 0.21068261 21.0682607 0.27935159 0.89652358 0.12484826 371 0.8 0.83018868 81.4747475 0.54639175 0.91134084 0.00272168 CALR 24 0.01601883 0.75735252 0.04779189 0.21088933 18.347372 0.27618612 0.8288464 0.14593119 285 0.696 0.82807018 80.1428571 0.5 0.55447967 0.57876699 NMD3 102 0.02131875 0.73681309 0.06544379 0.21235041 17.4127333 0.38372646 0.98417472 0.14829196 363 0.656 0.92561983 89.85 0.6835443 1.05012803 -0.5487078 EFG-MITO 57 0.06089716 0.89382197 0.08516031 0.21336683 19.4163818 0.31340362 0.88506509 0.10810929 607 0.728 0.80230643 87.4090909 0.67045455 2.02639185 -1.8376419 TM9SF1 212 0.02668938 0.74782881 0.05465884 0.21609639 18.8003856 0.33666908 1.00728453 0.13418681 376 0.696 0.90159574 86.5119048 0.55952381 1.0953606 0.00763905 GCST 63 0.01397668 0.7804613 0.05319981 0.21739908 22.1747057 0.33675643 1.01676384 0.14233271 326 0.816 0.80674847 84.8383838 0.52525253 0.81137592 0.45001314 PPP2R3 125 0.03642804 0.75800117 0.06043637 0.21858163 18.3608569 0.34209881 1.03496513 0.15371712 287 0.672 0.92682927 87.0617284 0.7037037 1.61960697 -0.2628765 PIK3C3 124 0.05529816 0.82539274 0.06146448 0.21959541 19.5439915 0.3804895 1.02868481 0.16070981 236 0.712 0.88983051 82.4767442 0.55813953 1.56342076 1.08323382 ATG2 16 0.0326284 0.75739122 0.05847053 0.22157262 24.151415 0.31177344 0.99580766 0.15480121 292 0.872 0.96917808 88.3679245 0.55660377 1.70742681 0.70255851 AP3M1 6 0.17097612 0.78828168 0.07433016 0.22619654 20.1314922 0.33922644 0.99962136 0.14472694 325 0.712 0.93230769 90.6511628 0.68604651 2.84657064 -0.1664244 RPTOR 195 0.06130577 0.777339 0.09090279 0.22665119 11.5592109 0.25496177 0.7732439 0.14581201 283 0.408 0.83745583 78.3125 0.5 1.31286708 0.14876309 PSMA-H 135 0.01941753 0.73466956 0.06751902 0.22800268 23.7122785 0.37688518 1.00025543 0.18702609 182 0.832 0.96153846 83.8910891 0.46534653 1.11449399 2.06303653 EIF3I 60 0.02378559 0.76055622 0.05171036 0.22843157 22.3862935 0.37910992 1.10151112 0.15579569 281 0.784 0.90747331 90.3157895 0.64210526 1.45603667 0.23914885 228 Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 CAPZ 25 0.06103567 0.81360371 0.07580645 0.22849119 18.2792948 0.3565223 0.96608112 0.17285114 217 0.64 0.88479263 80.2911392 0.45454545 1.52861875 1.6313129 PACE5 122 0.05937038 0.78407015 0.05969332 0.22987575 20.2290664 0.38664892 1.01533299 0.20574323 166 0.704 0.93373494 86.2705882 0.57647059 1.72547173 1.54505474 SYGM1 211 0.01731629 0.79480266 0.06471631 0.23236249 21.8420738 0.33079173 1.03184046 0.11992565 540 0.752 0.82222222 83.8791209 0.64835165 1.49633382 -1.1068038 PSD11 127 0.03503046 0.79345549 0.04717091 0.23571618 24.5144831 0.3344795 1.11489973 0.15631236 329 0.832 0.94832827 87.6633663 0.64356436 2.23338868 0.27912817 VATE 222 0.01512878 0.75746652 0.02304637 0.23728759 23.9660464 0.40593437 1.13979263 0.18189445 177 0.808 0.94915254 87.91 0.67346939 1.53700945 1.07094185 RPAC1 149 0.02724382 0.81932804 0.06624029 0.23733232 20.8852444 0.34296059 1.01835872 0.16251786 199 0.704 0.88442211 82.9195402 0.57647059 1.75459727 0.8953666 METTL1 94 0.02762491 0.79289879 0.06992188 0.23751237 19.0009896 0.30350598 0.87971067 0.17878659 192 0.64 0.81770833 80.1298701 0.48051948 1.17126875 1.24857615 TOPO1 214 0.03271747 0.80077678 0.06356942 0.24185158 22.9759 0.34718982 1.10596576 0.12218707 486 0.76 0.83744856 85.326087 0.61956522 1.8097085 -0.6441309 CRNL1 48 0.02319402 0.7807093 0.059015 0.24257983 20.6192857 0.31677817 1.00886859 0.13869983 553 0.68 0.89692586 86.3902439 0.65853659 1.94807819 -1.1004498 UBE12 217 0.01820689 0.79902613 0.09723517 0.24536617 22.3283214 0.29467094 0.99267311 0.11920822 719 0.728 0.88178025 90.0674157 0.70454545 2.38098695 -2.4004466 CCDC113 27 0.0661906 0.75586273 0.04951981 0.24571458 17.2000204 0.26172197 0.88167559 0.15996148 238 0.56 0.92857143 82.0588235 0.6119403 2.03466531 0.34453009 KDELR2 86 0.03572901 0.83023474 0.07632129 0.24585097 20.8973326 0.28878265 1.00804774 0.16578884 197 0.68 0.89340102 81.4146341 0.5 2.18037373 1.18983185 ADK2 1 0.02145806 0.79141845 0.03915625 0.24855457 24.8554571 0.32564948 1.09461613 0.14758816 320 0.8 0.90625 82.4848485 0.51546392 1.85417738 0.97990505 EFTUD1 58 0.1471788 0.78275882 0.08813362 0.25180723 13.345783 0.37173484 1.17595787 0.14626988 453 0.424 0.89183223 83.4 0.66 2.8343194 -0.5549882 229 Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 EIF3C 59 0.03191809 0.75257713 0.05829465 0.25414501 23.1271961 0.33584964 1.02390772 0.1477726 374 0.728 0.94652406 89.2272727 0.72727273 2.20553962 -0.6463974 CORO1C 45 0.04481252 0.79332013 0.0571003 0.25648756 27.1876809 0.32553333 1.13387644 0.12863335 380 0.848 0.94210526 87.952381 0.66990291 2.69634354 -0.251476 RHEB 148 0.03365064 0.75143835 0.0678461 0.26066376 18.5071268 0.29372699 1.0140979 0.19885513 164 0.568 0.93902439 86.2571429 0.64705882 2.28275284 0.57789597 CDK5 39 0.02593388 0.80787911 0.10661529 0.26096622 14.0921757 0.27267538 0.88946803 0.16459386 346 0.432 0.7716763 83.2156863 0.47058824 1.59509684 -0.0695554 ODBA 113 0.02244449 0.82777789 0.06007157 0.26211418 22.0175911 0.29720975 1.04392712 0.14590397 326 0.672 0.80981595 83.5121951 0.54320988 2.00079412 0.21415571 GSS 70 0.0235383 0.8290427 0.07035327 0.26232396 24.6584518 0.35064142 1.15019528 0.17824138 212 0.752 0.81132075 81.6521739 0.49450549 1.95854631 1.46624589 SPTLC1 206 0.16200843 0.82548118 0.04796033 0.2659582 23.1383636 0.31545417 1.17557421 0.16983868 255 0.696 0.88627451 84.5714286 0.5952381 3.48206015 1.02859012 HYOU1 75 0.01543057 0.7904898 0.05613649 0.26693795 24.558291 0.33463992 1.15845049 0.13800554 395 0.736 0.9443038 84.988764 0.6741573 2.5732815 -0.2950383 CCDC37 28 0.24630777 0.86905679 0.0613952 0.26978416 17.2661861 0.28120123 0.92668561 0.13200874 297 0.512 0.8989899 74.9180328 0.67213115 4.08249901 0.3393578 MTHFR 96 0.02953958 0.79722973 0.10052508 0.27317551 18.029584 0.29305598 1.09870629 0.1398869 479 0.528 0.86012526 88.7936508 0.76190476 2.80370724 -1.953775 NSF1-H 107 0.04301351 0.8088495 0.0664219 0.27435921 23.046174 0.33239923 1.17974886 0.19810749 214 0.672 0.87850467 79.2771084 0.50617284 2.51070728 1.71281936 AR21 11 0.05038364 0.84836885 0.06980392 0.27473808 23.3527372 0.4097112 1.39009719 0.21698192 150 0.68 0.93333333 79.7195122 0.57317073 3.11258231 2.16206364 PGM2 123 0.02827293 0.78663107 0.0886139 0.27935884 17.3202478 0.27571736 1.06582939 0.1516655 421 0.496 0.81710214 81.6949153 0.59322034 2.28698242 -0.6405565 VBP1 223 0.14413428 0.83399928 0.04362416 0.28013951 29.1345093 0.37301445 1.39365761 0.20821586 149 0.832 0.95302013 81.9405941 0.51485149 3.91529166 2.81265901 230 Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 COPE 42 0.05078291 0.79939772 0.06736501 0.28344141 29.1944656 0.34775455 1.30881633 0.19833086 228 0.824 0.96929825 87.3137255 0.66 3.54053239 1.06426566 VATC 221 0.05175484 0.788575 0.08050388 0.28360378 28.3603777 0.31681777 1.25485729 0.18730601 258 0.8 0.97674419 89.3505155 0.68041237 3.64605648 0.48781045 CCDC65 30 0.17916727 0.80120249 0.05506148 0.28687681 19.2207464 0.31677593 1.11561551 0.13945297 386 0.536 0.96632124 83.25 0.625 3.85786617 0.20463383 ALG11 4 0.02830347 0.82781192 0.10881365 0.28777306 21.0074336 0.28424861 1.14501903 0.19645797 265 0.584 0.81886792 81.2857143 0.47142857 2.71654827 1.01084352 WD 227 0.02055782 0.78972304 0.1856014 0.29103854 24.4472372 0.30325664 1.14553327 0 232 0.672 0.875 87.382716 0.60493827 2.75610518 -1.5092011 PSMD 141 0.02466373 0.80498588 0.23520092 0.29123442 29.9971454 0.33432832 1.32328418 0 288 0.824 0.77083333 90.62 0.71 3.14149767 -2.405255 SND1 204 0.02128991 0.77064894 0.08349045 0.29402466 19.993677 0.3140727 1.19675773 0.15227763 468 0.544 0.94230769 88.8461538 0.69230769 3.07375236 -1.0912231 AGX 3 0.03518448 0.80637923 0.09251769 0.29794636 25.6233873 0.34300465 1.15229815 0.19101946 299 0.688 0.89632107 83.7831325 0.53012048 2.93121071 1.06075966 PSMD12 142 0.05672886 0.84024518 0.1325555 0.29977142 29.6773709 0.36151355 1.32465743 0 374 0.792 0.94385027 86.7916667 0.625 3.67981761 -1.1208252 VPS18 224 0.164006 0.81036858 0.09033613 0.30032075 20.4218111 0.37289834 1.40987724 0.18421853 273 0.544 0.96703297 82.2461538 0.50769231 4.18003918 1.69729919 GDI2 65 0.21192944 0.87027673 0.08415909 0.31013753 16.4372892 0.31452071 1.212268 0 417 0.424 0.86330935 78.1 0.64 4.18678077 -1.2647313 IFT57 81 0.03645936 0.7941574 0.07374332 0.32229247 21.915888 0.32772484 1.24949887 0.18646817 275 0.544 0.95272727 82.2835821 0.64615385 3.54087445 0.54889144 NAA15 98 0.06169604 0.80531646 0.07571688 0.33114655 27.4851639 0.2995256 1.31868631 0.14596348 471 0.664 0.97664544 86.4 0.6375 4.39222615 -0.3064869 COPS6 44 0.08628763 0.8422357 0.10326024 0.34417941 26.8459937 0.34545077 1.52532789 0.20740799 221 0.624 0.95475113 82.2727273 0.66666667 5.06472965 1.10629394 231 Gene name Position in dataset Root-tip var. Saturation Missing data Evolutionary rate Tree length Treeness Av patristic dist. Comp. heterogeneity Alignment length Occupancy Prop. variable sites Av. boostrap support RF similarity PC1 PC2 STXBP1 209 0.03794078 0.86412304 0.10103496 0.36051759 29.5624424 0.31022605 1.5351851 0.17533783 337 0.656 0.95252226 86.2962963 0.65822785 5.2836845 0.19849101 IPO4 85 0.03107222 0.80310657 0.11508762 0.36260045 26.4698327 0.33917507 1.63379028 0.14520548 573 0.584 0.98429319 86.75 0.6 4.9387556 -0.5651273 CCDC40 29 0.10746314 0.84680552 0.0694224 0.37409951 22.4459706 0.35464048 1.57898888 0.13425542 756 0.48 0.96296296 90.4736842 0.77192982 5.60178857 -2.0546041 IMB1 83 0.04583816 0.82750783 0.0850364 0.37606906 34.9744227 0.31779701 1.52245402 0.12700535 644 0.744 0.98136646 88.8901099 0.71111111 5.58422886 -1.2419882 TMS 213 0.05344802 0.87290306 0.09678412 0.45693679 30.6147651 0.36930828 2.00757123 0.22079557 194 0.536 0.97938144 84.6923077 0.453125 6.65410811 2.55132115 232 Appendix S: Correlogram of 13 gene properties and PC axes. Numeric values after “Corr:” indicate the Pearson correlation coefficients followed by the significance of the correlation displayed based on p-values < 0.001 (***); < 0.01 (**); < 0.05 (*); < 0.10 (.). Two regression lines are generated using Linear Model (blue) and Loess (red). Abbreviations: RF = Robinson-Foulds; PC = principal component. 233 Appendix T: Heatmap of shared genes among different supermatrices. Genes consisting of each supermatrix are selected by different filtering criteria (i.e., A-F, N, S, Q, and ABC; see Materials and Methods in Chapter 4). The numeric values are the number of shared genes with the red colour indicating high number of shared genes and blue colour indicating low number of shared genes among different supermatrices. 234 Appendix U: Summary of different strains and sequencing data of S. scintillans examined RCC=Roscoff Culture Collection, France; NIES=Microbial Culture Collection at the National Institute of Environmental Studies, Japan; WGA=Whole genome amplification; AF-SMG=Amplification-free shotgun metagenome. Culture collection Isolation location FISH location Sequencing method Sequencing location Library Prep Date (M/Y) Reported strain in Guillou et al., 1999 RCC24 RCC Pacific Ocean Canada WGA Canada 04-2022 Yes RCC257 RCC Atlantic Ocean Canada WGA Canada 04-2022 No RCC257-late RCC Atlantic Ocean Canada WGA Canada 06-2022 No RCC257-jp RCC Atlantic Ocean Japan —— —— —— No RCC24-jp (NIES-2589) NIES Pacific Ocean Japan AF-SMG Japan 03,05,06, 10-2022 Yes RCC25 RCC Mediterranean Sea —— One of the two strains reported by Guillou et al., in 1999. Lost at RCC in 2008 235 Appendix V: Summary of genomic characteristics and references of prasinoviruses List of genomes used to guide assemblies and the subsequent vMAGs from RCC24 and RCC257. CheckV% indicates completeness for each vMAG assemblies. Virus Genomes Length (bp) GC % ORFs tRNAs Genes CheckV % Accession Publication BIIV1 174,426 35.2 220 3 NA MK522034-MK522037 Bachy et al., 2021 BIIV2 207,870 36.5 235 2 249 NA MK522038 BIIV3 211,597 36.3 230 3 241 NA MK522039 BpV1 198,519 37.2 203 3 203 NA NC014765 Moreau et al., 2010 BpV2 187,069 37 210 4 225 NA HM004430 OlV1 194,022 40.9 254 5 255 NA NC014766 Zimmerman et al., 2019 OlV2 196,300 41.2 269 5 274 NA NC028091 Derelle et al., 2015 OlV4 216,925 40.3 256 5 319 NA JF974316 OlV5 186,468 41.6 254 4 263 NA NC020852 Derelle et al., 2015 OlV6 184,949 41.7 251 5 257 NA HQ633059 OlV7 182,309 41 243 5 248 NA NC028093 Zimmerman et al., 2019 OmV1 193,301 44.6 252 5 257 NA NC028092 Derelle et al., 2015 OtV1 189,567 44.5 240 4 233 NA JN225873 MpV1 184,095 39 244 6 244 NA NC014767 Moreau et al., 2010 MpV_12T 205,622 39.8 253 7 265 NA NC020864 MpV_Pl1 197,060 43.3 259 5 270 NA HQ633072 Finke et al., 2017 RCC257_vMAG_BIIV1 104,406 36.17 153 137 54.61 This study RCC257_vMAG_BIIV2 98,732 35.35 138 130 51.51 This study RCC257_vMAG_BIIV3 83,238 36.04 116 109 43.54 This study RCC257_vMAG_BpV1 193,823 36.13 295 2 254 100 This study RCC257_vMAG_BpV2 195,514 36.27 297 2 259 100 This study 236 Virus Genomes Length (bp) GC % ORFs tRNAs Genes CheckV % Accession Publication RCC257_vMAG_MpV1 19,211 39.79 23 21 10.03 This study RCC257_vMAG_MpV12T 15,031 40.58 15 15 7.85 This study RCC257_vMAG_MpVPl1 34,707 41.52 47 40 18.11 This study RCC257_vMAG_OlV1 102,789 39.97 149 146 53.93 This study RCC257_vMAG_OlV2 87,373 39.91 119 113 45.83 This study RCC257_vMAG_OlV4 56,877 39.55 88 82 29.77 This study RCC257_vMAG_OlV5 89,948 39.99 116 114 47.18 This study RCC257_vMAG_OlV6 89,926 39.98 119 117 47.17 This study RCC257_vMAG_OlV7 102,138 40.01 152 143 53.59 This study RCC257_vMAG_OmV1 67,431 39.84 811 71 35.37 This study RCC257_vMAG_OtV1 68 39.7 85 1 73 35.89 This study RCC24_vMAG_BIIV1 125,432 37.12 183 4 165 65.61 This study RCC24_vMAG_BIIV2 97,551 37.27 113 3 108 51.03 This study RCC24_vMAG_BIIV3 90,981 37.68 112 3 105 47.6 This study RCC24_vMAG_BpV1 223,996 36.51 341 4 291 100 This study RCC24_vMAG_BpV2 221,235 36.53 338 4 286 100 This study RCC24_vMAG_MpV1 40,992 40.41 64 1 60 21.4 This study RCC24_vMAG_MpVPl1 80,383 40.7 137 1 132 41.98 This study RCC24_vMAG_OlV1 123,654 40.18 221 2 190 64.54 This study RCC24_vMAG_OlV2 149,739 39.93 244 2 220 78.52 This study RCC24_vMAG_OlV4 72,632 40.29 125 119 38.1 This study RCC24_vMAG_OlV5 121,783 40.15 196 2 188 63.87 This study RCC24_vMAG_OlV6 119,913 40.14 196 2 182 62.59 This study RCC24_vMAG_OlV7 120,063 40.46 203 2 176 62.69 This study RCC24_vMAG_OmV1 125,261 39.89 196 2 186 65.39 This study RCC24_vMAG_OtV1 92,373 39.38 137 121 48.43 This study 237 Appendix W: Summary of BlobToolKit analyses of the initial WGA assembles Visualization of sequencing results of RCC257 (top row) and RCC24 (bottom row) without filtering or sub-setting. (A) Blob plots based on mean coverage (per-base) in y-axis and mean GC contents in x-axis. Each “blob” represents a square-root scaled size (showing max size) of a scaffold with its size representing the length or span. The blobs are coloured according to the top ten taxonomic assignment at the genus level (‘bestsum’ taxrule), based on coverage. Sum lengths along each axis are plotted on histograms. All reads assigned to prasinoviruses are highlighted with purple squares around each blob. (B) Snail plots visualizing quality of the initial assembly represented by N50 and N90. The purple squares in the blob plots and ones positioned at the outermost part of the plots are scaffolds assigned to prasinoviruses. (C) Histograms showing coverage (y-axis) for top ten genus (including “no-hit”, “undefined” and “others”). 238 239 Appendix X: Fluorescence in situ hybridzation on RCC24 FISH analysis on S. scintillans RCC24-jp showing no endobacterial signals. (A), (F), (K), and (P) Brightfield; (B), (G), and (L) DAPI; (C) CF319 probe under 647 nm; (D) and (M) EUB388 probe under 488 and 647 nm; (H) γ-proteobacteria probe; (I) α-proteobacteria probe; (N) Planctomycete probe; (E), (J), (O), and (T) merged image of (A-D), (F-I), (K-N), and (P-S); (R-S) unstained controls under three different channels for DAPI, 488 and 647 nm. Scale bars = 5 µm for A-E and K-T; 20 µm for F-J. 240 Appendix Y: Number of shared orthologs and scaffolds among vMAGs (A) Upset plot showing shared number of ortholog clusters among vMAGs, reference genomes and RCC257 viral-subset-scaffolds. (B) Heatmap showing shared number of recruited scaffolds from RCC257 viral-subset-scaffolds for each genome. Red colour indicates more shared numbers of scaffolds to assemble vMAGs. OV_vMAG= combined orthologs predicted from OlVs-, OtV1-, OmV1-vMAGs; BV_vMAGs=combined orthologs predicted from BpVs-, BIIVs-vMAGs; BV-genomes=combines orthologs predicted from reference genomes of BpVs and BIIVs; OV_genomes=combined orthologs predicted from reference genomes of OlVs, OtV1 and OmV1; RCC257_subset_scaffolds=RCC257 viral-subset-scaffolds 241 Appendix Z: Summary BlobToolKit table for green algae and prasinoviruses Sequencing results of RCC257 summarized using BlobToolkit showing hits assigned to green algal lineage. Scaffold IDs are omitted. “Uni” refers to Uniprot database. GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 46 Eukaryota Viridiplantae Chlorophyta Trebouxiophyceae Trebouxiales Trebouxiaceae Lobosphaera Lobosphaera incisa 331.4455 89.9 99 9.69E-26 no hit no hit no hit no hit 31 Eukaryota Viridiplantae Chlorophyta Mamiellophyceae Mamiellales Bathycoccaceae Ostreococcus Ostreococcus tauri 185.0727 91.5 94 9.69E-26 no hit no hit no hit no hit 30 Eukaryota Viridiplantae Chlorophyta Mamiellophyceae Mamiellales Bathycoccaceae Ostreococcus Ostreococcus tauri 99.633 92.5 93 2.08E-27 no hit no hit no hit no hit 32 Eukaryota Viridiplantae Chlorophyta Mamiellophyceae Mamiellales Bathycoccaceae Ostreococcus Ostreococcus tauri 81.7545 90.625 96 9.69E-26 no hit no hit no hit no hit 54 Eukaryota Viridiplantae Chlorophyta Chlorophyceae Chlamydomonadales Chlamydomonadaceae Chlamydomonas Chlamydomonas reinhardtii 22.7182 81.2 232 1.08E-41 A0A6G0XEB7_9STRA 35 1064 3.33E-200 51 Eukaryota Viridiplantae Chlorophyta Chlorophyceae Chlamydomonadales Dunaliellaceae Dunaliella Dunaliella primolecta 6.5282 84.4 141 7.86E-26 A0A5A8CRY8_CAFRO 64 292 5.67E-125 56 Eukaryota Viridiplantae Chlorophyta Chlorophyceae Chlamydomonadales Chlamydomonadaceae Chlamydomonas Chlamydomonas reinhardtii 2.9457 83.1 207 7.67E-43 A8JAX1_CHLRE 78 76 2.39E-38 59 Eukaryota Viridiplantae Chlorophyta Chlorophyceae Sphaeropleales Selenastraceae Monoraphidium Monoraphidium neglectum 2.9033 73.2 508 1.92E-33 A0A812N4R2_9DINO 65 266 1.14E-109 60 Eukaryota Viridiplantae Chlorophyta Mamiellophyceae Mamiellales Mamiellaceae Micromonas Micromonas pusilla 2.7721 83.2 179 2.65E-34 C1MTF8_MICPC 49 162 8.52E-45 48 Eukaryota Viridiplantae Chlorophyta Trebouxiophyceae Chlorellales Chlorellaceae Auxenochlorella Auxenochlorella protothecoides 2.374 82.2 365 1.88E-78 no hit no hit no hit no hit 44 Eukaryota Viridiplantae Chlorophyta Trebouxiophyceae Trebouxiophyceae-undef Trebouxiophyceae-undef Picochlorum Picochlorum sp. 'soloecismus' 2.0939 88 175 1.71E-46 no hit no hit no hit no hit 49 Eukaryota Viridiplantae Chlorophyta Trebouxiophyceae Chlorellales Chlorellaceae Micractinium Picochlorum sp. 'soloecismus' 1.6617 95.2 269 9.45E-115 no hit no hit no hit no hit 59 Eukaryota Viridiplantae Chlorophyta Trebouxiophyceae Chlorellales Chlorellaceae Auxenochlorella Auxenochlorella protothecoides 1.6427 84.5 162 4.10E-35 A0A835ZE30_9STRA 75 1290 4.63E-53 242 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 30 Eukaryota Viridiplantae Chlorophyta Trebouxiophyceae Chlorellales Chlorellaceae Chlorella Chlorella variabilis 1.4009 87.3 212 7.77E-60 A0A4P9ZP24_9FUNG 89 70 5.55E-37 31 Eukaryota Viridiplantae Chlorophyta Trebouxiophyceae Chlorellales Chlorellaceae Helicosporidium Helicosporidium sp. ex Simulium jonesi 1.2377 78.8 226 9.51E-30 A0A4Q5PZF8_9CYAN 79 75 4.62E-31 35 3499 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 34 862 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 5819 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 32 2996 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 973 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 825 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 43 588 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 40 1430 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 4562 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 1007 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC716 virus 3 39 1190 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC716 virus 1 38 770 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 33 710 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 45 764 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 40 654 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 40 694 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 34 5830 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 373 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 399 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 268 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 243 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 32 333 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 6382 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 1079 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 33 2571 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 33 1223 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 9870 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 40 801 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 41 153 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 36 6208 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 2348 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 40 399 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 2544 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 281 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 534 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 3047 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 30 274 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 34 177 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 1641 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 977 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 819 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 853 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 472 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 43 387 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 244 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 41 445 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 33 333 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 36 1001 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 2064 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 2468 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 32 9879 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC716 virus 2 36 11754 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 1313 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 1009 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 447 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 41 1393 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 31 3472 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 42 343 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 36 3408 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 16987 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 36 1628 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 834 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 478 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 32 442 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 775 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 33 1365 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 510 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 5882 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 245 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 32 695 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 34 3953 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 32 1545 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 36 1149 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 30 378 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 454 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 33 4004 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 30 844 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 9690 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 36 1008 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 5153 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 321 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 367 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 32 4114 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 40 374 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 31 6174 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 34 414 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 32 706 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 28 307 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 33 1087 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 2409 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 34 352 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 33 996 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 246 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 37 367 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 31 622 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 343 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 41 14865 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 39 397 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Phycodnaviridae-undef Bathycoccus virus BpV178 37 474 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 32 157 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 359 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 36 213 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 43 858 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 36 2698 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 40 247 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 502 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 36 2127 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 864 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 714 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 340 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 487 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 294 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 21 263 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 34 4074 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 29 461 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 233 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 247 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 34 314 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 34 418 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 27 264 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 32 486 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 29 204 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 33 319 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 32 688 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 34 570 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 43 447 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 52 1848 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Dishui Lake phycodnavirus 3 37 474 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 1076 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 7309 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 24 306 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 36 4276 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 34 679 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 291 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 30 374 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 30 605 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 32 397 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 36 345 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 34 503 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 846 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 248 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 31 1688 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC716 virus 2 36 2292 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 5322 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus tauri virus 1 38 10264 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 35 4802 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 32 466 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 305 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 26 363 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 32 388 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 585 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 33 177 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 435 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 21 173 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 549 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 1528 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 36 1131 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 36 2017 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 1574 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus OlV5 40 8102 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus OlV5 34 409 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 34 3570 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 33 536 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 32 467 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 249 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 28 277 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 258 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 3968 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 33 158 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC716 virus 1 36 1425 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC716 virus 3 34 1091 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 388 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 33 246 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 2272 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 33 173 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 43 902 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 30 291 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 40 371 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 2189 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 31 220 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC716 virus 1 29 206 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 526 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 41 1770 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 45 257 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 35 1555 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 745 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 32 338 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 285 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 250 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 32 594 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 30 763 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 32 290 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 45 1733 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus mediterraneus virus 2 41 5875 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 33 716 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 165 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 29 615 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 31 1084 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 43 2224 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 213 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 525 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 1179 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 35 737 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC716 virus 2 38 251 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 42 270 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 40 669 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 34 280 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 43 1043 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 43 742 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus mediterraneus virus 2 33 288 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 50 844 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 34 579 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 251 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 33 254 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 53 213 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Micromonas sp. RCC1109 virus MpV1 48 650 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus tauri virus 1 42 543 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 786 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 32 443 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 48 1186 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 38 1331 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 39 1138 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 48 639 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 32 305 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 46 596 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 35 555 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 49 647 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 354 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 47 593 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 36 185 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 42 730 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 46 338 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 39 422 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 45 1864 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 32 644 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 44 499 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC716 virus 3 252 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 41 1108 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 43 1280 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 43 640 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 38 570 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 45 252 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 42 207 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 45 266 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 35 307 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 271 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 45 654 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 42 381 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 42 388 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 44 668 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Micromonas sp. RCC1109 virus MpV1 46 281 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 521 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 27 341 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 43 627 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 39 570 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 40 520 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 45 521 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Micromonas sp. RCC1109 virus MpV1 40 230 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 43 161 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 40 293 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 253 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 26 223 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 303 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 40 535 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 34 332 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 44 659 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 40 213 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 43 1281 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 43 785 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 44 364 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus tauri virus 2 28 734 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC716 virus 2 50 1062 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 51 251 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 39 254 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 40 497 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 44 320 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 44 838 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 36 390 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus mediterraneus virus 2 46 933 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 333 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 36 464 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 49 477 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 45 274 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 37 347 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 254 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 46 623 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 42 835 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 47 251 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 44 211 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 895 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 45 213 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 40 717 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 49 265 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus mediterraneus virus 2 40 288 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 40 295 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 47 443 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 40 296 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 53 369 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Micromonas sp. RCC1109 virus MpV1 42 290 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 435 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 41 266 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 33 297 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus mediterraneus virus 2 40 252 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 29 224 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 37 374 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 524 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC716 virus 1 49 750 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 2 34 225 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 255 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 32 373 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 40 458 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus mediterraneus virus 1 41 310 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 41 235 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 42 1091 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 41 469 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 42 398 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus mediterraneus virus 1 44 316 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 37 392 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 323 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus mediterraneus virus 1 42 322 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 27 244 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 404 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC716 virus 2 47 211 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus mediterraneus virus 2 46 245 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 33 245 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 30 372 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 42 328 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 43 396 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus mediterraneus virus 2 39 576 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 32 247 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 52 371 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 36 337 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 256 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 33 516 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 29 519 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC716 virus 1 41 259 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 45 346 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 26 163 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 347 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 40 263 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 49 350 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus mediterraneus virus 2 39 444 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 40 441 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 43 249 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 40 267 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus mediterraneus virus 2 36 178 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC716 virus 1 41 324 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 40 210 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 39 811 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 40 546 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 34 275 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus mediterraneus virus 1 35 370 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 37 561 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 43 281 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 47 285 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 43 286 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 257 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 38 547 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 53 298 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus tauri virus 2 44 305 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 199 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 37 309 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 409 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 31 307 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 29 208 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 41 213 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 36 316 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 43 216 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 41 218 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 46 222 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus tauri virus 1 47 223 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 45 224 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 43 226 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 47 228 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 48 341 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 46 229 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 40 230 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 39 344 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 34 225 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 42 697 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 258 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 46 232 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 40 233 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 41 217 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 39 537 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 42 247 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 41 248 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus tauri virus OtV5 38 369 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus tauri virus RT-2011 41 247 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 41 249 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 42 254 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC716 virus 3 48 254 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 50 259 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus tauri virus 2 46 518 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 32 259 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 29 268 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 45 268 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 38 266 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 34 266 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 46 265 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 43 267 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 33 272 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 32 272 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 31 270 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 259 GC% superkingdom kingdom phylum class order family genus species coverage %ID length evalue Uniprot ID Uni_%ID Uni_length Uni_evalue 53 271 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 42 246 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Micromonas sp. RCC1109 virus MpV1 37 294 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 7 36 278 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 38 305 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 43 326 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus lucimarinus virus 1 41 335 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Ostreococcus tauri virus OtV5 39 168 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 45 161 Viruses Bamfordvirae Nucleocytoviricota Megaviricetes Algavirales Phycodnaviridae Prasinovirus Bathycoccus sp. RCC1105 virus BpV 260 Appendix AA: Prasinovirus phylogeny including both RCC24 and RCC257 data A multi-gene prasinovirus phylogeny reconstructed from 22 prasinovirus core genes (5,355 sites) using IQ-TREE2 LG+F+G4 model, including genes searched from WGA data of two different S. scintillans strains, RCC24 and RCC257. The right panel shows presence-absence of select core genes. Single-copy genes are DNApol (DNA polymerase B), DNAhel-SNF2 (SNF2 helicase), mRNAcap (mRNA capping enzyme), ATPase, and RNR-sm (RNR small subunit). The tree is rooted with Chlorovirus (PBCVs and ATCV) for visualization. Only nodes <100% ultrafast bootstrap supports are labelled. OlV=Ostreococcus lucimarinus virus; OtV=Ostreococcus tauri virus; OmV=Ostreococcus mediterraneus virus; MpV=Micromonas pusilla virus; BpV=Bathycoccus prasino virus; BIIV=Bathycoccus sp. virus clade BII. PBCV=Paramecium bursaria chlorella virus; ATCV=Acanthocystis turfaceae chlorella virus. 261 Appendix AB: vMAG genome overview from RCC24 and RCC257 Genome overview and comparison of the most complete BpV-, OlV-, and MpV-vMAGs to corresponding reference genomes. (A) Circularized representation of (A) RCC24 BpV-vMAG compared to BpV2 genome and RCC257 BpV-vMAG; (B) OlV2 genome compared to RCC24 and RCC257 OlV2-vMAGs, (C) MpV-Pl1 genome compared to RCC24 and RCC257 MpVPl1-vMAGS, in an ordered set of coding sequences, represented by blocks shaded by similarity. (A) Mapping coverage is based on RCC24 BpV-vMAG mapped to RCC24 WGA viral-subset-scaffolds and regions with the coverage more than one standard deviation [59.9] from the mean coverage [44.5] are shown in blue spikes. The outermost ring represents predicted ORFs of the vMAG with manually annotated protein from Prodigal-gv and Viralrecall. (B) Mapping coverage is based on OlV2 genome mapped to RCC24 WGA viral-subset-scaffolds and regions with the coverage more than one standard deviation [5.1] from the mean coverage [1.6] shown in blue spikes. Only ORFs from the reference OlV2 genome is shown and the partial RCC24 and RCC257 OlV2-vMAG CDS are shown in the outer rings. (C) Mapping coverage is based on MpV-Pl1 mapped to RCC24 WGA viral-subset-scaffolds and regions with the coverage more than one standard deviation [2.1] from the mean coverage [0.6] are shown in blue spikes. Only ORFs from the reference MpV-Pl1 genome is shown and the partial RCC24 and RCC257 MpVPl1-vMAGs CDS are shown in the outer rings. See Table S2 for annotation in a tabular format. 262 263 Appendix AC: Select annotation of RCC257 BpV2-vMAG genome vog virbit vdesc BpV2_HM004430_RagTag GVOG13327 26.2354722 unknown | DNA polymerase III is a complex, multichain enzyme responsible for most of the replicative synthesis in bacteria. The epsilon subunit contain the editing function and is a proofreading 3'-5' exonuclease | PFAM Exonuclease, RNase T and DNA polymerase BpV2_HM004430_RagTag GVOGm0548 31.7694822 Cell shape determining protein MreB Mrl | ribosomal large subunit binding | Heat shock 70 kDa protein | unfolded protein binding | ATP binding | nuclear pore complex assembly | Heat shock 70 kDa protein | ATP binding | unknown | ATP binding | ethanolamine BpV2_HM004430_RagTag GVOG11896 16.1245155 ATP-dependent specificity component of the Clp protease. It directs the protease to specific substrates. Can perform chaperone functions in the absence of ClpP | ATPase which is responsible for recognizing, binding, unfolding and translocation of substrate BpV2_HM004430_RagTag GVOGm0173 12.9073622 termination of RNA polymerase III transcription | termination of RNA polymerase I transcription | Belongs to the archaeal rpoM eukaryotic RPA12 RPB9 RPC11 RNA polymerase family | transcription, DNA-templated BpV2_HM004430_RagTag GVOGm1326 17.6720118 RbcX protein | PFAM RbcX protein BpV2_HM004430_RagTag GVOG06656 19.6468827 Catalyzes the transfer of a two-carbon ketol group from a ketose donor to an aldose acceptor, via a covalent intermediate with the cofactor thiamine pyrophosphate | Transketolase, thiamine diphosphate binding domain | PFAM Transketolase domain protein | t BpV2_HM004430_RagTag GVOG03757 16.6072273 dehydrogenase e1 component | PFAM Transketolase central region | Transketolase | Catalyzes the acyloin condensation reaction between C atoms 2 and 3 of pyruvate and glyceraldehyde 3-phosphate to yield 1-deoxy-D-xylulose-5-phosphate (DXP) BpV2_HM004430_RagTag GVOGm0477 10.0697567 dTDP-4-dehydrorhamnose reductase | NAD-dependent epimerase dehydratase | racemase and epimerase activity, acting on carbohydrates and derivatives | Catalyzes the two-step NADP-dependent conversion of GDP- 4-dehydro-6-deoxy-D-mannose to GDP-fucose, involvi BpV2_HM004430_RagTag GVOGm0363 14.0641388 unknown | negative regulation of septation initiation signaling | unknown | negative regulation of septation initiation signaling | Glycosyl transferases group 1 BpV2_HM004430_RagTag GVOGm1048 18.6064505 COG0463 Glycosyltransferases involved in cell wall biogenesis | Glycosyl transferase family 2 BpV2_HM004430_RagTag GVOGm1447 14.8290256 no_annot BpV2_HM004430_RagTag GVOGm0873 11.2915898 unusual protein kinase | regulation of tocopherol cyclase activity | kinase activity | Is probably a protein kinase regulator of UbiI activity which is involved in aerobic coenzyme Q (ubiquinone) biosynthesis | ubiquinone biosynthetic process 264 genome vog virbit vdesc BpV2_HM004430_RagTag GVOGm0071 18.568791 Catalyzes the 6-electron oxidation of protoporphyrinogen-IX to form protoporphyrin-IX | oxidoreductase activity | Protoporphyrinogen oxidase | oxidoreductase activity | COG1233 Phytoene dehydrogenase and related proteins | tRNA (5-methylaminomethyl-2-thio BpV2_HM004430_RagTag GVOGm0013 23.2163735 PFAM helicase domain protein | Type III restriction enzyme res subunit | DEAD DEAH box helicase | Type I site-specific restriction-modification system, R (Restriction) subunit and related | DEAD DEAH box helicase domain protein | ATP-dependent DNA helicase BpV2_HM004430_RagTag GVOG03539 18.1741575 unknown BpV2_HM004430_RagTag GVOGm0003 13.6601611 Large eukaryotic DNA virus major capsid protein BpV2_HM004430_RagTag GVOGm0003 15.5627761 Large eukaryotic DNA virus major capsid protein BpV2_HM004430_RagTag GVOG10311 18.1135309 no_annot BpV2_HM004430_RagTag GVOGm0085 19.1598539 DNA primase activity BpV2_HM004430_RagTag GVOGm0760 19.7737199 Poxvirus A32 protein | unknown BpV2_HM004430_RagTag GVOGm0003 18.5418446 Large eukaryotic DNA virus major capsid protein BpV2_HM004430_RagTag GVOGm0003 16.8937858 Large eukaryotic DNA virus major capsid protein BpV2_HM004430_RagTag GVOGm0303 10.397115 ICEA Protein | ICEA Protein BpV2_HM004430_RagTag GVOG10021 17.4985714 Possesses two activities a DNA synthesis (polymerase) and an exonucleolytic activity that degrades single-stranded DNA in the 3'- to 5'-direction. Has a template-primer preference which is characteristic of a replicative DNA polymerase | unknown | DNA pac BpV2_HM004430_RagTag GVOGm0018 12.1696343 triglyceride mobilization | esterase of the alpha-beta hydrolase superfamily | Esterase of the alpha-beta hydrolase superfamily | phosphatidylethanolamine catabolic process | esterase of the alpha-beta hydrolase superfamily | Patatin-like phospholipase | 265 genome vog virbit vdesc BpV2_HM004430_RagTag GVOGm0160 11.9205705 regulation of transcription by RNA polymerase I | SWIB/MDM2 domain BpV2_HM004430_RagTag GVOGm0031 16.0592652 YqaJ-like viral recombinase domain | YqaJ-like viral recombinase domain | YqaJ-like viral recombinase domain | unknown | YqaJ-like viral recombinase domain | YqaJ viral recombinase family | YqaJ-like viral recombinase domain | YqaJ-like viral recombinase BpV2_HM004430_RagTag GVOGm0088 20.2089089 Catalyzes the reduction of ribonucleotides to deoxyribonucleotides. May function to provide a pool of deoxyribonucleotide precursors for DNA repair during oxygen limitation and or for immediate growth after restoration of oxygen BpV2_HM004430_RagTag GVOGm0088 28.4587421 Catalyzes the reduction of ribonucleotides to deoxyribonucleotides. May function to provide a pool of deoxyribonucleotide precursors for DNA repair during oxygen limitation and or for immediate growth after restoration of oxygen BpV2_HM004430_RagTag GVOG12766 24.6048776 defense response to oomycetes | Serine threonine protein kinase | histone kinase activity (H3-T3 specific) | 3-phosphoinositide-dependent protein kinase activity | protein serine/threonine kinase activity | protein serine/threonine kinase activity BpV2_HM004430_RagTag GVOGm0172 17.5840837 Stabilizes TBP binding to an archaeal box-A promoter. Also responsible for recruiting RNA polymerase II to the pre- initiation complex (DNA-TBP-TFIIB) | TBP-class protein binding | RNA polymerase III type 3 promoter DNA binding BpV2_HM004430_RagTag GVOGm0868 21.881499 mRNA guanylyltransferase activity | unknown BpV2_HM004430_RagTag GVOGm0214 13.1377319 thiol-dependent ubiquitin-specific protease activity | Opioid growth factor receptor (OGFr) conserved region | catalytic activity, acting on a protein | mitochondrion organization | metalloendopeptidase inhibitor activity | ubiquitinyl hydrolase activity BpV2_HM004430_RagTag GVOGm1594 17.0205758 polynucleotide 5'-phosphatase activity BpV2_HM004430_RagTag GVOGm1694 30.8074666 unknown | Phage plasmid primase, P4 | Phage plasmid primase P4 family BpV2_HM004430_RagTag GVOG12168 13.6821051 glycosyltransferase involved in LPS biosynthesis BpV2_HM004430_RagTag GVOGm0003 11.827933 Large eukaryotic DNA virus major capsid protein BpV2_HM004430_RagTag GVOGm0054 16.6973052 SMART DNA-directed DNA polymerase B | DNA polymerase | DNA replication proofreading | chloroplast mRNA modification | DNA replication proofreading | leading strand elongation | DNA polymerase activity 266 genome vog virbit vdesc BpV2_HM004430_RagTag GVOG03001 12.5499004 DNA replication proofreading | leading strand elongation | DNA polymerase | DNA polymerase activity BpV2_HM004430_RagTag GVOGm0003 13.0613935 Large eukaryotic DNA virus major capsid protein BpV2_HM004430_RagTag GVOG05686 12.973049 DNA topoisomerase type II (ATP-hydrolyzing) activity | A type II topoisomerase that negatively supercoils closed circular double-stranded (ds) DNA in an ATP-dependent manner to modulate DNA topology and maintain chromosomes in an underwound state. BpV2_HM004430_RagTag GVOG10851 23.0195569 PFAM Glycosyl transferases group 1 | glycogen (starch) synthase activity | COG0438 Glycosyltransferase | PFAM Glycosyl transferase, group 1 | PFAM Glycosyl transferase, group 1 | Glycosyl transferases group 1 | PFAM Glycosyl transferase, group 1 BpV2_HM004430_RagTag GVOGm0872 13.685759 unknown | unknown | unknown | atp synthase 267 Appendix AD: Annotated HMM clusters found only in BV vMAGs from RCC257 viral-subset-scaffolds HMM_cluster Uniprot or VOG Description E-value score bias I2320 YHDJ_ECOLI DNA adenine methyltransferase YhdJ OS=Escherichia coli (strain K12 1.60E-25 94.8 0.1 I2321 YP_009052178.1 putative methyltransferase [Aureococcus anophagefferens virus] 3.60E-41 145.7 4.9 I2340 YP_004061542.1 DUFF5855 similar to neurofilament protein [BpV1] 5.50E-166 557.5 67 I2341 YP_004061557 DUF5756 [BpV1] 2.70E-20 77.5 1.3 I2346 N/A N/A I2347 N/A N/A I2348 YP_001648133.2 hypothetical [OtV5] 8.50E-07 34 1 I2349 YP_004061690.1 DUF5773 [OlV1] 7.50E-09 40.8 5.9 I2350 YP_009465930.1 DUF5773 [Dishui lake phycodnavirus 1] 1.10E-12 53.1 3.3 I2351 YP_004063626.1 hypothetical [OtV2] 2.60E-40 143.6 15.5 I2369 YP_004061518.1 DUF5762 [BpV1] 3.00E-26 95.8 3.1 I2370 YP_004061552.1 similar to Ribonuclease III [BpV2] 9.62E-12 50.3 0.5 I2371 YP_004061535.1 hypothetical [BpV1] 5.50E-12 51 9.1 I2372 ARF08749.1 hypothetical [Catovirus 1] 4.30E-13 54.1 10.5 I2374 YP_004061915.1 4-hydroxy-2-oxopentanoic acid aldolase [MpV1] 1.80E-10 45.5 2.4 I2375 YP_009665084.1 Pyrimidine (PYR) binding domain of DXS, and transketolase protein [MpV_SP1] 2.50E-78 266.5 0.5 I2376 YP_009665083.1 Transketolase domain-containing protein [MpV_SP1] 1.30E-107 363.1 7.1 268 HMM_cluster Uniprot or VOG Description E-value score bias I2377 YP_009665082.1 adenosylmethionine-dependent methyltransferases (SAM or AdoMet-MTase) [MpV_SP1] 8.60E-88 298.9 8 I2380 YP_009173577.1 ICEA 1 virulence factor [Chrysochromulina ericina virus] 3.90E-04 24.9 2.5 I2381 YP_004061450.1 hypothetical [BpV1] 4.00E-18 69.5 3.6 I2405 YP_009465878.1 hypothetical [Dishui lake phycodnavirus 1] 3.40E-09 41.4 0.2 I2407 N/A N/A I2411 N/A N/A I2412 AYV75274.1 hypothetical [Terrestrivirus sp.] 1.20E-25 95.5 7.8 I2416 YP_004061591.1 nuclease family [BpV1] 6.10E-07 34.3 1.2 I2417 YP_004061616.1 hypothetical [BpV1] 1.70E-22 84 3.4
Thesis/Dissertation
eng
Botany
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Keeling, Patrick J. (Patrick John), 1969-
Evolution and diversity of stramenopiles
Text