The Molecular Life of Diatoms 303092498X, 9783030924980

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Angela Falciatore Thomas Mock Editors

The Molecular Life of Diatoms

The Molecular Life of Diatoms

Angela Falciatore • Thomas Mock Editors

The Molecular Life of Diatoms

Editors Angela Falciatore Laboratoire de Biologie du chloroplaste et perception de la lumière chez les microalgues, UMR7141 Centre national de la recherche scientifique, Sorbonne Université Institut de Biologie Physico-Chimique Paris, France

Thomas Mock School of Environmental Sciences University of East Anglia Norwich Research Park Norwich, United Kingdom

ISBN 978-3-030-92498-0 ISBN 978-3-030-92499-7 https://doi.org/10.1007/978-3-030-92499-7

(eBook)

# Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover image provided by Dr. Oliver Skibbe This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Diatoms are single-celled photosynthetic eukaryotes found throughout aquatic ecosystems wherever there is sufficient light and nutrients. A considerable number of species colonized terrestrial habitats, including soils. These organisms are mostly invisible to the naked eye and, yet, are responsible for about twenty percent of global primary production, generating about as much organic carbon each year as all the terrestrial rainforests combined. In the ocean, diatom organic carbon is rapidly consumed by other organisms and supports some of our most productive fisheries. Diatom research has a rich history dating back to the eighteenth century when they were recognized because of their beauty and ubiquity. The ability to decipher the molecular underpinning of the unique attributes of diatoms began in the 1990s with the early development of genetic tools that allowed for delivery of DNA (genetic transformation) into a few diatom species, sequence data for select genes, as well as the ability to identify proteins essential for the formation of diatom cell walls out of precipitated silica. These early molecular studies, in combination with the critical role of diatoms in the global carbon cycle, motivated a collaboration between a relatively small group of diatom researchers and researchers from the US Department of Energy’s Joint Genome Institute to generate, annotate, and interpret the first whole genome sequence of a diatom. Thalassiosira pseudonana was chosen as the inaugural diatom because of its small genome size and the cosmopolitan distribution of the genus. When the project began, nearly twenty years ago, the state-of-the-art sequencing technology was Sanger-based shotgun sequencing, which because of cost and time had been applied to few marine eukaryotes. The international consortium of diatom researchers was highly successful in analyzing the genome, and the resulting manuscript helped to put the enigmatic diatoms on everyone’s radar. Shortly thereafter, the whole genome sequencing of a second diatom, Phaeodactylum tricornutum, propelled further expansion of the diatom scientific community. As the community of researchers interested in a molecular understanding of diatoms continued to expand and diversify, a new conference was initiated, specifically dedicated to the “Molecular Life of Diatoms” or MLD as it became known. MLD1 was held in Atlanta, Georgia, USA, in 2011, seven years after the publication of the first genome. Since then, five biennial meetings have been held in different countries with great success and an increasing number of attendees, reflecting the v

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growing community of diatom researchers, including early career researchers and established scientists from neighboring disciplines such as microbiology and plant science. Over the years, additional model diatoms have been sequenced both in continued collaboration with large sequencing centers and through smaller research consortiums made possible by new sequencing technologies. These projects are complemented by additional transcriptomics and metatranscriptomics studies that provide increasing insights into diatom diversity. The current molecular and genetic tools provide new opportunities to probe more deeply into the inner workings of diatoms biology. The inspiration for this book arose during the MLD5, just before the world plunged into the COVID pandemic. Chapter authors are all leading experts in their respective fields and have generated this comprehensive synthesis despite the adversities of the times. The chapters presented here represent a synthesis and interpretation of the molecular underpinnings of the ecology and evolution (Part 1), genomics (Part 2), and cell biology (Part 3) of these fascinating organisms. Diatoms are derived from an ancient secondary endosymbiosis that created an evolutionary mosaic of genes and metabolic pathways (Chapters “Structure and Evolution of Diatom Nuclear Genes and Genomes” and “Reconstructing Dynamic Evolutionary Events in Diatom Nuclear and Organelle Genomes”), with compound exchange between the different organelles (Chapter “Cellular Hallmarks and Regulation of the Diatom Cell Cycle”). Diatoms combine features found in plants with features previously known only from heterotrophic organisms, including the presence of a complete urea cycle. Constraint-based modeling provides inroads into understanding the interconnections between the different pathways (Chapter “Constraint-based Modelling of Diatoms Metabolism and Quantitative Biology Approaches”). A defining feature of diatoms is their ability to control precipitation of silica to form beautifully intricate cell walls (Chapters “Structure and Morphogenesis of the Frustule”, “Biomolecules Involved in Frustule Biogenesis and Function”, and “Silicic Acid Uptake and Storage by Diatoms”), which not only made them early favorites of microscopists but more recently also proved useful in nanotechnological applications. The resulting species-specific frustules influence nutrient uptake and sinking rates, reduce grazing, and can act to focus light. Many diatoms are motile, due to the ability to release adhesive compounds through an opening in the frustule (Chapter “Adhesion and Motility”). Replication of the frustule influences the diatom sexual cycle (Chapters “Life-cycle Regulation” and “Cellular Hallmarks and Regulation of the Diatom Cell Cycle”), a process intimately connected to a cycle of cell size reduction during vegetative divisions and cell size restitution upon zygote formation. Highly silicified resting spores are formed by a subset of diatoms, and in response to adverse conditions, these dormant cells quickly sink to the sea floor. A recent study suggests that ancient spores up to thousands of years old can be recovered from sediment cores and germinated under controlled laboratory conditions. Sequencing of these diatom “time capsules” provides a glimpse into their recent evolutionary history (Chapter “Ancient Diatom DNA”). As a corollary to this window into the past, recent studies in experimental evolution also illustrate how diatoms respond to consistent selective pressures

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(Chapter “Structure and Evolution of Diatom Nuclear Genes and Genomes ”). And finally, discoveries that diatoms encode most of the epigenetic machinery present in higher eukaryotes have motivated genome-scale studies of the ways that methylation and histone modifications facilitate adaptation to environmental change (Chapter “Epigenetic Control of Diatom Genomes: An Overview from in silico Characterisation to Functional Studies ”). Diatoms are distributed worldwide and are dominant members of coastal and polar ecosystems, a distribution long attributed to light and nutrient availability (Chapter “Comparative and Functional Genomics of Macronutrient Utilization in Marine Diatoms”). Population genetics (Chapter “The Population Genetics and Evolutionary Potential of Diatoms”) combined with trait-based modeling (Chapter “Trait-based Ecology with Diatoms”) is shedding light into their biogeography. Diatoms can survive in low-iron environments, which characterize vast areas of the open ocean, due to several iron assimilation and storage proteins and ironindependent substitutes that together allow diatoms to optimize their iron quota (Chapter “ Molecular Mechanisms Underlying Micronutrient Utilization in Marine Diatoms”). Carbon acquisition, concentration, and fixation mechanisms, as well as photoacclimation and photoprotection mechanisms, illustrate the many attributes that allow diatoms to thrive in turbulent waters where they are continuously mixed throughout the light field (Chapters “Photosynthetic Light Reactions in Diatoms. I. The Lipids and Light-harvesting Complexes of the Thylakoid Membrane”, “Photosynthetic Light Reactions in Diatoms. II. The Dynamic Regulation of the Various Light Reactions”, “Carbohydrate Metabolism”, and “Lipid Metabolism in Diatoms”). For example, diatoms have a distinctive lipid composition of the thylakoid membrane, a unique spatial organization of the light harvesting complexes, and a pigment composition optimized to capture the available wavelengths of light. Over the past decade, diatom molecular research has taken a more holistic approach and is increasingly focusing on the role of community dynamics in shaping the attributes, evolutionary trajectories, and biogeography of diatoms (Chapter “An Integrated View of Diatom Interactions”, and Part 1). Established model systems of diatom–bacteria (Chapter “The Diatom Microbiome: New Perspectives for DiatomBacteria Symbioses”), diatom–virus (Chapter “Diatom Viruses”), and grazing interactions have helped to uncover the sophisticated mechanisms diatoms use to sense and respond to changes in their environment (Chapters “Sensing and Signalling in Diatom Responses to Abiotic Cues” and “An Ocean of Signals: Intracellular and Extracellular Signalling”). Inter-kingdom signaling and metabolite exchanges appear to allow diatoms to select for beneficial bacteria, while viral-infection dynamics can be regulated by nutrient availability. Some diatom-derived molecules such as polyunsaturated fatty acids (PUFAs) can serve both as an intriguing defense against grazers and as a signaling molecule within the diatom population. The recent discovery of the biosynthetic pathway for the neurotoxin domoic acid now makes it possible to understand when, where, and perhaps even why diatoms produce a compound that can be lethal to mammals.

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Many gaps remain in our understanding of the molecular mechanisms of diatoms. Between 30 and 60% of diatom genes still have an unknown function and the role of many other genes in diatoms remains mysterious despite sharing some sequence similarity to known functional domains. However, new tools have been developed that enable knock-outs, knock-downs, and overexpression of specific genes in several model diatoms and the creation of transgenic cell lines with random genomic integrations (Chapter “Genetic Engineering in Marine Diatoms: Current Practices and Emerging Technologies”). Different groups are using these molecular tools to painstakingly shed light on the genomic “dark matter,” one gene at a time. This book will hopefully inspire a next generation of researchers to further study the molecular life of diatoms. As highlighted at the end of each chapter, there are numerous new exciting areas ripe for study as these organisms assume increasingly important roles in bio-nanotechnology, biofuels, and nutraceuticals. As our planet warms, the aquatic ecosystems are changing. How diatoms respond to these changes will influence the structure of future marine ecosystems and inland waters including ecosystem services. Seattle, WA, USA Seattle, WA, USA Paris, France Norwich, UK

E. Virginia Armbrust Shiri Graff van Creveld Angela Falciatore Thomas Mock

Acknowledgments

We want to thank all the authors, who are leading scientists in their respective fields, for having accepted to write a chapter of this book. They started this project in 2019 just before the COVID pandemic hit. Despite the difficult circumstances to work during a pandemic, all authors completed their chapters in time, which reflects their commitment and enthusiasm for molecular diatom research. Thus, we cannot be thankful enough to all authors for their enduring efforts to contribute to this book. We are particularly grateful for the dedication and continuous support of the part coordinators: Nicole Poulsen, Wim Vyverman, Nils Kröger, Yusuke Matsuda, Peter Kroth, and Assaf Vardi, in successfully achieving the editing of this book despite the unprecedented circumstances. Nicole Poulsen also helped with the cover design of the book for which we are very grateful. We thank Springer—Life Sciences, especially Dr. Srinivasan Manavalan and Sabine Schwarz, for their support during the preparation of this book. As the inspiration for this book arose during MLD5 and therefore in part is the outcome of the MLD conference series, we want to thank Nils Kröger and Nicole Poulsen who had the idea of creating a conference focusing on the molecular biology of diatoms. Thus, their initiative led to MLD1 in Atlanta (2011) where they were the main organizers and therefore the beginning of this successful conference series. This book is dedicated to the memory of Mark Hildebrand, Kirk E. Apt, and Anna Godhe in recognition of their pioneering work and inestimable contributions to the molecular diatom community.

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Contents

Part I

Ecology and Evolution

Trait-Based Diatom Ecology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elena Litchman

3

The Population Genetics and Evolutionary Potential of Diatoms . . . . . . Tatiana A. Rynearson, Ian W. Bishop, and Sinead Collins

29

An Integrated View of Diatom Interactions . . . . . . . . . . . . . . . . . . . . . . Flora Vincent and Chris Bowler

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Ancient Diatom DNA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Matthew I. M. Pinder and Mats Töpel

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Part II

Genomics

Structure and Evolution of Diatom Nuclear Genes and Genomes . . . . . . 111 Thomas Mock, Kat Hodgkinson, Taoyang Wu, Vincent Moulton, Anthony Duncan, Cock van Oosterhout, and Monica Pichler Reconstructing Dynamic Evolutionary Events in Diatom Nuclear and Organelle Genomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Richard G. Dorrell, Fuhai Liu, and Chris Bowler Epigenetic Control of Diatom Genomes: An Overview from In Silico Characterization to Functional Studies . . . . . . . . . . . . . . . . . . . 179 Xue Zhao, Antoine Hoguin, Timothée Chaumier, and Leila Tirichine Part III

Cell Biology

Life Cycle Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 Gust Bilcke, Maria Immacolata Ferrante, Marina Montresor, Sam De Decker, Lieven De Veylder, and Wim Vyverman Cellular Hallmarks and Regulation of the Diatom Cell Cycle . . . . . . . . . 229 Petra Bulankova, Gust Bilcke, Wim Vyverman, and Lieven De Veylder

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Contents

Cell Biology of Organelles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 Uwe G. Maier, Daniel Moog, Serena Flori, Pierre-Henri Jouneau, Denis Falconet, Thomas Heimerl, Peter G. Kroth, and Giovanni Finazzi Structure and Morphogenesis of the Frustule . . . . . . . . . . . . . . . . . . . . . 287 Iaroslav Babenko, Benjamin M. Friedrich, and Nils Kröger Biomolecules Involved in Frustule Biogenesis and Function . . . . . . . . . . 313 Nils Kröger Silicic Acid Uptake and Storage by Diatoms . . . . . . . . . . . . . . . . . . . . . . 345 Felicitas Kolbe and Eike Brunner Diatom Adhesion and Motility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Nicole Poulsen, Metin Gabriel Davutoglu, and Jirina Zackova Suchanova Part IV

Primary Metabolism

Photosynthetic Light Reactions in Diatoms. I. The Lipids and Light-Harvesting Complexes of the Thylakoid Membrane . . . . . . . . . . . 397 Claudia Büchel, Reimund Goss, Benjamin Bailleul, Douglas A. Campbell, Johann Lavaud, and Bernard Lepetit Photosynthetic Light Reactions in Diatoms. II. The Dynamic Regulation of the Various Light Reactions . . . . . . . . . . . . . . . . . . . . . . . 423 Bernard Lepetit, Douglas A. Campbell, Johann Lavaud, Claudia Büchel, Reimund Goss, and Benjamin Bailleul Carbohydrate Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465 Peter G. Kroth and Yusuke Matsuda Lipid Metabolism in Diatoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493 Tsuyoshi Tanaka, Kohei Yoneda, and Yoshiaki Maeda Comparative and Functional Genomics of Macronutrient Utilization in Marine Diatoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 529 Sarah R. Smith and Andrew E. Allen Molecular Mechanisms Underlying Micronutrient Utilization in Marine Diatoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 567 Tyler H. Coale, Erin M. Bertrand, Robert H. Lampe, and Andrew E. Allen Part V

Cell Signalling and Interactions

Sensing and Signalling in Diatom Responses to Abiotic Cues . . . . . . . . . 607 Marianne Jaubert, Carole Duchêne, Peter G. Kroth, Alessandra Rogato, Jean-Pierre Bouly, and Angela Falciatore

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An Ocean of Signals: Intracellular and Extracellular Signaling in Diatoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641 Shiri Graff van Creveld, Avia Mizrachi, and Assaf Vardi The Diatom Microbiome: New Perspectives for Diatom-Bacteria Symbioses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 679 Katherine E. Helliwell, Ahmed A. Shibl, and Shady A. Amin Diatom Viruses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 713 Laure Arsenieff, Kei Kimura, Chana F. Kranzler, Anne-Claire Baudoux, and Kimberlee Thamatrakoln Part VI

Genetic and Metabolic Engineering

Genetic Engineering in Marine Diatoms: Current Practices and Emerging Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743 Mark Moosburner, Andrew E. Allen, and Fayza Daboussi Constraint-Based Modeling of Diatoms Metabolism and Quantitative Biology Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 775 Manish Kumar, Cristal Zuniga, Juan D. Tibocha-Bonilla, Sarah R. Smith, Joanna Coker, Andrew E. Allen, and Karsten Zengler

Editors and Contributors

About the Editors Angela Falciatore is Research Director of the French National Centre for Scientific Research (CNRS). She received a master’s degree in biological sciences (1995) at the University Federico II of Naples, Italy, where she studied osmotic stress responses in bacteria. Her interest for marine biology stems from the research performed at the Stazione Zoologica Anton Dohrn of Naples (SZN) in Italy, in Chris Bowler laboratory (1995–2001) where she got a PhD in 2002 on the “Molecular studies of environmental signal perception and transduction in marine diatoms.” Particularly interested in the dynamic responses of photosynthetic organisms to light, she joined Jean-David Rochaix’s laboratory at the University of Geneva, Switzerland, for her post-doc (2002–2005), devoted to the chloroplast-to-nucleus retrograde signaling in the green alga Chlamydomonas reinhardtii. Complementary activities at the Okazaki National Institute for Basic Biology, Japan (1997), and at the Carnegie Institution of Washington, Stanford University, USA (2003), contributed to enlarge her expertise in photobiology. In 2005, with a tenure-track position, she started an independent research activity at the SZN, Italy. At the end of 2009, she got a permanent position from CNRS and moved from Italy to France. She established and led the team “Diatom Functional Genomics” in the Laboratory of Computational and Quantitative Biology directed by Alessandra Carbone at Université Pierre et Marie Curie in Paris. Since 2019, she is the Head of the Laboratory of “Chloroplast Biology and Light Sensing in Microalgae,” affiliated with the CNRS and Sorbonne xv

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Université at the Institut de Biologie Physico-Chimique (IBPC) in Paris. A major focus of her research has been to establish diatoms as new model systems for marine biology and photobiology by developing genomic resources and genetic tools. Combining physiological, biophysical, biochemical, and genome-wide molecular approaches in the diatom model species Phaeodactylum tricornutum, her team has characterized diversified photoreceptors, fostering novel hypotheses on the role of these sensors in controlling growth and adaptive responses in a marine context. She also uncovered the existence of a long-foreseen diatom circadian clock, which controls essential rhythmic processes in these algae. Her team also contributed to disclose some of the diatom-specific photoacclimation properties, by identifying critical regulators of photosynthesis that also influence the natural variability of diatom photoresponses. Thomas Mock is a Professor of Marine Microbiology in the School of Environmental Sciences at the University of East Anglia (UEA), Norwich Research Park, Norwich, United Kingdom. He obtained his MSc (1998) in Biology with emphasis on Biological Oceanography at the Christian-Albrechts University in Kiel (GEOMAR) and his PhD (2003) at Bremen University (Alfred-Wegener Institute for Polar and Marine Research), Germany. Before joining UEA in 2007, most of his post-doc research was conducted with a fellowship from the German Academic Exchange Service (DAAD) in the School of Oceanography, University of Washington (E.V. Armbrust lab), in cooperation with the Biotechnology Center, University of Wisconsin (M.R. Sussman lab), USA. Before he was promoted to Professor (Personal Chair) at UEA in 2014, he was Reader (2012–2014) and had a Research Councils UK Academic Fellowship (2007–2012). The overarching aim of his research is to identify fundamental biological processes that govern the adaptation and evolution of marine microalgae (Phytoplankton) in the oceans with emphasis on diatoms. His group uses genomics (e.g., metatranscriptomics and metagenomics) and reverse genetics tools (e.g., CRISPR/Cas) for selected phytoplankton groups (e.g.,

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diatoms) and natural assemblages from the global upper ocean to understand their evolution, diversity, and adaptation. This work leads to the identification of genes that shape their phenotypes and are therefore responsible for their unique biology and evolutionary adaptation to different environments of the upper ocean from pole to pole.

List of Contributors Andrew E. Allen Department of Microbial and Environmental Genomics, J Craig Venter Institute, La Jolla, CA, USA Integrative Oceanography Division, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA Shady A. Amin New York University Abu Dhabi, Abu Dhabi, United Arab Emirates E. Virginia Armbrust School of Oceanography, University of Washington, Seattle, WA, USA Laure Arsenieff Faculty of Biology, Technion—Israel Institute of Technology, Haifa, Israel Iaroslav Babenko B CUBE, Center for Molecular and Cellular Bioengineering, TU Dresden, Dresden, Germany Cluster of Excellence Physics of Life, TU Dresden, Dresden, Germany cfaed, TU Dresden, Dresden, Germany Benjamin Bailleul Laboratoire de Biologie du chloroplaste et perception de la lumière chez les microalgues, UMR7141, Centre national de la recherche scientifique, Sorbonne Université, Institut de Biologie Physico-Chimique, Paris, France Anne-Claire Baudoux Sorbonne Université, CNRS UMR 7144, Diversity and Interactions in Oceanic Plankton—Station Biologique de Roscoff, Roscoff, France Erin M. Bertrand Department of Biology, Dalhousie University, Halifax, NS, Canada Gust Bilcke Protistology and Aquatic Ecology, Department of Biology, Ghent University, Ghent, Belgium Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium

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VIB Center for Plant Systems Biology, Ghent, Belgium Ian W. Bishop Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, USA Jean-Pierre Bouly Laboratoire de Biologie du chloroplaste et perception de la lumière chez les microalgues, UMR7141, Centre national de la recherche scientifique, Sorbonne Université, Institut de Biologie Physico-Chimique, Paris, France Chris Bowler Institut de Biologie de l’ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France Eike Brunner Bioanalytical Chemistry, Faculty of Chemistry and Food Chemistry, TU Dresden, Dresden, Germany Claudia Büchel Institute of Molecular Biosciences, Goethe University Frankfurt, Frankfurt, Germany Petra Bulankova Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium VIB Center for Plant Systems Biology, Ghent, Belgium Douglas A. Campbell Department of Biology, Mount Allison University, Sackville, NB, Canada Timothée Chaumier Nantes Université, CNRS, US2B, UMR 6286, Nantes, France Tyler H. Coale Department of Microbial and Environmental Genomics, J Craig Venter Institute, La Jolla, CA, USA Integrative Oceanography Division, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA Joanna Coker Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA Sinead Collins School of Biological Sciences, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK Shiri Graff van Creveld School of Oceanography, University of Washington, Seattle, WA, USA Fayza Daboussi Toulouse Biotechnology Institute (TBI), INSA Toulouse, Toulouse, France Toulouse White Biotechnology (TWB), INSA Toulouse, Toulouse, France Metin Gabriel Davutoglu B CUBE—Center for Molecular Bioengineering, Technical University of Dresden, Dresden, Germany

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Sam De Decker Protistology and Aquatic Ecology, Department of Biology, Ghent University, Ghent, Belgium Richard G. Dorrell Institut de Biologie de l’ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France Carole Duchêne Laboratoire de Biologie du chloroplaste et perception de la lumière chez les microalgues, UMR7141, Centre national de la recherche scientifique, Sorbonne Université, Institut de Biologie Physico-Chimique, Paris, France Anthony Duncan School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, UK Angela Falciatore Laboratoire de Biologie du chloroplaste et perception de la lumière chez les microalgues, UMR7141, Centre national de la recherche scientifique, Sorbonne Université, Institut de Biologie Physico-Chimique, Paris, France Denis Falconet Univ. Grenoble Alpes, CNRS, CEA, INRAE, IRIG-LPCV, Grenoble, France Maria Immacolata Ferrante Integrative Marine Ecology, Stazione Zoologica Anton Dohrn, Naples, Italy Giovanni Finazzi Univ. Grenoble Alpes, CNRS, CEA, INRAE, IRIG-LPCV, Grenoble, France Serena Flori The Laboratory, The Marine Biological Association, Devon, UK Benjamin M. Friedrich Cluster of Excellence Physics of Life, TU Dresden, Dresden, Germany cfaed, TU Dresden, Dresden, Germany Reimund Goss Institute of Biology, Leipzig University, Leipzig, Germany Thomas Heimerl Centre for Synthetic Microbiology (SYNMIKRO), University of Marburg, Marburg, Germany Katherine E. Helliwell Biosciences, College of Life and Environmental Sciences, University of Exeter, Exeter, UK Marine Biological Association, Plymouth, UK Kat Hodgkinson School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, UK Antoine Hoguin Nantes Université, CNRS, US2B, UMR 6286, Nantes, France Département de biologie, École normale supérieure, Institut de Biologie de l’ENS (IBENS), CNRS, INSERM, Université PSL, Paris, France

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Editors and Contributors

Marianne Jaubert Laboratoire de Biologie du chloroplaste et perception de la lumière chez les microalgues, UMR7141, Centre national de la recherche scientifique, Sorbonne Université, Institut de Biologie Physico-Chimique, Paris, France Pierre-Henri Jouneau Univ. Grenoble Alpes, CEA, IRIG-MEM, Grenoble, France Kei Kimura Faculty of Agriculture, Saga University, Saga, Japan Felicitas Kolbe Bioanalytical Chemistry, Faculty of Chemistry and Food Chemistry, TU Dresden, Dresden, Germany Chana F. Kranzler Department of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ, USA Nils Kröger B CUBE, Center for Molecular and Cellular Bioengineering, TU Dresden, Dresden, Germany Cluster of Excellence Physics of Life, TU Dresden, Dresden, Germany Faculty of Chemistry and Food Chemistry, TU Dresden, Dresden, Germany Peter G. Kroth Department of Biology, University of Konstanz, Konstanz, Germany Manish Kumar Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA Robert H. Lampe Department of Microbial and Environmental Genomics, J Craig Venter Institute, La Jolla, CA, USA Integrative Oceanography Division, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA Johann Lavaud UMI3376 Takuvik, CNRS/ULaval, Département de Biologie, Université Laval, Québec, QC, Canada UMR6539 LEMAR, CNRS/Univ Brest/Ifremer/IRD, Institut Européen de la Mer, Technopôle Brest-Iroise, rue Dumont d’Urville, Plouzané, France Bernard Lepetit Plant Ecophysiology, Department of Biology, University of Konstanz, Konstanz, Germany Elena Litchman Michigan State University, East Lansing, MI, USA Fuhai Liu Institut de Biologie de l’ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France Centre de Recherches Interdisciplinaires, Université de Paris, INSERM U1284, Paris, France

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Tsinghua-UC Berkeley Shenzhen Institute (TBSI), Tsinghua University, University of California, Berkeley, Berkeley, CA, USA Yoshiaki Maeda Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, Fuchu, Japan Uwe G. Maier Centre for Synthetic Microbiology (SYNMIKRO), University of Marburg, Marburg, Germany Cell Biology Laboratory, University of Marburg, Marburg, Germany Yusuke Matsuda Department of Bioscience, School of Science and Technology, Kwansei Gakuin University, Sanda, Japan Avia Mizrachi Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel Thomas Mock School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, UK Marina Montresor Integrative Marine Ecology, Stazione Zoologica Anton Dohrn, Naples, Italy Daniel Moog Centre for Synthetic Microbiology (SYNMIKRO), University of Marburg, Marburg, Germany Cell Biology Laboratory, University of Marburg, Marburg, Germany Mark Moosburner Department of Microbial and Environmental Genomics, J Craig Venter Institute, La Jolla, CA, USA Marine Biology Research Division, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA Vincent Moulton School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, UK Cock van Oosterhout School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, UK Monica Pichler School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, UK Matthew I. M. Pinder Department of Marine Sciences, University of Gothenburg, Gothenburg, Sweden Nicole Poulsen B CUBE -Center for Molecular Bioengineering, Technical University of Dresden, Dresden, Germany Alessandra Rogato Integrative Marine Ecology Department, Stazione Zoologica Anton Dohrn, Naples, Italy Institute of Biosciences and BioResources, IBBR/CNR, Naples, Italy

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Editors and Contributors

Tatiana A. Rynearson Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, USA Ahmed A. Shibl New York University Abu Dhabi, Abu Dhabi, United Arab Emirates Sarah R. Smith J. Craig Venter Institute, La Jolla, CA, USA Moss Landing Marine Laboratories, San José State University, Moss Landing, CA, USA Jirina Zackova Suchanova B CUBE—Center for Molecular Bioengineering, Technical University of Dresden, Dresden, Germany Tsuyoshi Tanaka Division of Biotechnology and Life Science, Institute of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan Kimberlee Thamatrakoln Department of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ, USA Juan D. Tibocha-Bonilla Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA Leila Tirichine Nantes Université, CNRS, US2B, UMR 6286, Nantes, France Mats Töpel Department of Marine Sciences, University of Gothenburg, Gothenburg, Sweden Assaf Vardi Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel Lieven De Veylder Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium VIB Center for Plant Systems Biology, Ghent, Belgium Flora Vincent Institut de Biologie de l’Ecole Normale Supérieure (IBENS), PSL Research Université, Paris, France Wim Vyverman Protistology and Aquatic Ecology, Department of Biology, Ghent University, Ghent, Belgium Taoyang Wu School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, UK Kohei Yoneda Faculty of Life and Environmental Sciences, University of Tsukuba, Ibaraki, Japan Karsten Zengler Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA

Editors and Contributors

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Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA Xue Zhao Nantes Université, CNRS, US2B, UMR 6286, Nantes, France Cristal Zuniga Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA

Part I Ecology and Evolution

Trait-Based Diatom Ecology Elena Litchman

Abstract

Diatoms play key roles in aquatic ecosystems, from being important primary producers to driving major biogeochemical cycles. Understanding what determines diatom growth and the taxonomic and functional diversity is essential for our general knowledge of aquatic ecosystems and for predicting their responses to changing environmental conditions. This chapter provides a brief overview of diatom ecology from a trait-based perspective. First, it reviews traits that determine diatom responses to the environment, including the nutrient-, light, and temperature-related traits. It then describes traits that are important for the ecological interactions of diatoms with other members of planktonic food webs, such as competitors, predators, parasites, and mutualists. The chapter also briefly discusses trait plasticity and evolution, as well as highlights traits that determine diatom responses to global environmental change. Finally, the chapter briefly outlines some challenges and future directions in the ecology of diatoms. Keywords

Traits · Ecology · Diatoms · Evolution · Plasticity

1

Introduction

Diatoms are a highly successful and abundant group of marine and freshwater phytoplankton that contributes more than 20% of global carbon fixation on Earth (Nelson et al. 1995). They are among the main primary producers in planktonic and benthic (especially in freshwaters) food webs and a major food source for primary E. Litchman (*) Michigan State University, East Lansing, MI, USA e-mail: [email protected] # Springer Nature Switzerland AG 2022 A. Falciatore, T. Mock (eds.), The Molecular Life of Diatoms, https://doi.org/10.1007/978-3-030-92499-7_1

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consumers (zooplankton in planktonic food webs) (Sommer et al. 2002). Diatoms also have a disproportionately large role in carbon sequestration because of their relatively heavy silica frustules that contribute to their sinking and carbon immobilization (Smetacek 1999; Basu and Mackey 2018; Tréguer et al. 2018). While this book is primarily about the molecular aspects of diatom life, understanding those aspects without the knowledge of ecology is impossible. Many significant advances in the field of diatom biology occurred while looking at different levels of biological organization simultaneously, from genomes to populations and communities. This chapter provides a brief overview of diatom ecology through the mechanistic lens of trait-based approaches. Focusing on traits rather than species helps understand the mechanisms of ecological responses of organisms to the environment, their biotic interactions, and how ecological communities are structured. In this chapter, I discuss key ecological dimensions of diatom life and the eco-physiological traits that determine diatom responses to the environment and their interactions with grazers, parasites, and bacterial symbionts. I also briefly discuss trait plasticity and evolution and how they may complicate our trait-based predictions by altering trait values. I finish the chapter outlining the challenges and future directions in trait-based diatom biology.

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Ecological Success of Diatoms

What makes diatoms so successful and how will this group fare in the future, with the ongoing changes in climate and other anthropogenic influences? To understand the past, current, and future ecological success of diatoms in aquatic food webs, we can use trait-based approaches that focus on functional traits rather than on species per se. Trait-based approaches are now a major research framework in terrestrial plant ecology (McGill et al. 2006; Lavorel and Garnier 2002; Lamanna et al. 2014) and is becoming more and more popular in studies of other organisms, including phytoplankton and other microbes (Litchman and Klausmeier 2008; Green et al. 2008; Martiny et al. 2015; Westoby et al. 2021). Trait-based approaches can help increase our mechanistic understanding of how ecological communities assemble and function and how they change along different environmental gradients. They can also help reduce the complexity of a system, while incorporating diversity. A consideration of different trait combinations and correlations, including trait tradeoffs, could help mechanistically define different ecological strategies, for example, a grazer-resistant strategy (through large cell size) may also be associated with poor nutrient competitive ability and vice versa, so that species highly susceptible to grazing may be good nutrient competitors (Edwards et al. 2011). Trait-based approaches can also be useful in predicting the future of ecological communities under the ongoing anthropogenic climate change by determining what traits or suites of traits could be selected for or against under different global change scenarios, such as warming, changes in physical mixing patterns and nutrient regimes, or food web alterations.

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Below I will briefly outline the main concepts relevant to trait-based ecology and discuss how they can be applied to diatoms to understand their ecology and evolution at present, in the past, and in the future. I will also outline future directions in trait-based ecology of diatoms.

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Trait-Based Framework

To develop a trait-based ecological framework for any group of organisms, we need to identify relevant traits and investigate whether and how different traits are correlated and whether there are trade-offs among traits (Litchman and Klausmeier 2008). We also need to determine how traits are related to fitness and define how fitness would be assessed (McGill et al. 2006). We should also define the most important environmental gradients and what trait combinations could be selected for under different environmental conditions (Litchman et al. 2007). We can also use trait-based mechanistic models to explain and predict the distributions of organisms with different traits in different environments (Follows et al. 2007; Klausmeier et al. 2020). Traits are often defined as “any morphological, physiological, or phenological heritable feature measurable at the level of the individual, from the cell to the whole organism, without reference to the environment or any other level of organization” (Garnier et al. 2016). Functional traits are the most commonly studied traits that can be defined as traits that affect fitness (Violle et al. 2007). Traits can also be classified as morphological, physiological, life history, behavioral, as well as genomic and metabolic traits (Litchman and Klausmeier 2008). In microbes, including diatoms, genomic and metabolic traits may be important for inferring ecological strategies. Traits can be grouped as related to different organismal functions, such as growth and reproduction, resource acquisition, and avoidance of consumers and parasites/ pathogens (Litchman and Klausmeier 2008). Another trait classification divides traits into “response” and effect” traits (Lavorel and Garnier 2002, Litchman et al. 2015). The response traits determine an organism’s responses to the environment, and the effect traits characterize the effects of an organism on the environment. Some traits can be the response and effect traits at the same time, and they may be more influential and informative than other traits. For example, nitrogen (N) fixation can be viewed both as a response trait (N-fixation increases under N limitation) and an effect trait (N-fixation can increase N availability in an ecosystem). Many traits are not independent from each other but positively or negatively correlated, and those correlations do not have to be linear. Many such trait relationships can represent trade-offs, meaning that having certain values for a trait may lead to a limited range of another trait and those values would not necessarily maximize the performance, if viewed in isolation. For example, a common trade-off in phytoplankton is a negative relationship between the ability to compete for scarce resources such as nutrients and vulnerability to grazing (Grover 1995; Leibold 1996). This trade-off is often related to cell size, so that a small-celled phytoplankter has a competitive advantage in acquiring a resource at low concentrations due to less

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diffusion limitation, but the smaller size makes it more vulnerable to grazing (Litchman and Klausmeier 2008; Edwards et al. 2013). These trade-offs likely apply to diatom algae as well. Some trade-offs are not universal but more evident in certain environments. For example, the competitive ability for phosphorus tradeoffs with the competitive ability for nitrogen in freshwater phytoplankton but appears to be positively correlated (so, no trade-off) in marine phytoplankton (Edwards et al. 2013). Trade-offs may be not only pairwise but higher-dimensional as well, where more than two traits are correlated to form a trade-off surface (Edwards et al. 2013).

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Diatom Origin and Macroevolutionary Patterns in Traits

Diatoms evolved around 150 to 200 million year ago, around the Jurassic period of the Mesozoic era (Kooistra et al. 2007; Medlin 2016). The evidence of subsequent massive diversification of diatoms is found in the Cretaceous (Medlin 2016). The three major clades are Coscinodiscophyceae (radial centrics) that appeared in the Jurassic, Mediophyceae (polar centrics + radial Thalassiosirales) that appeared in early Cretaceous, and Bacillariophyceae (pennates) that appeared in late Cretaceous (Kooistra et al. 2007, Medlin 2016). Interestingly, the major extinction of the K-T boundary was not particularly detrimental for diatoms, and they kept diversifying after that (Kooistra et al. 2007). The evolution of silica frustule in diatoms was a major innovation that contributed to diatoms’ ecological success (Smetacek 2001; Raven and Waite 2004; Kooistra et al. 2007). The evolutionary emergence of new traits often allows different groups of organisms to colonize novel ecological niches, and increase their competitive abilities and the overall fitness. In addition, with respect to trait trade-offs, where traits are not independent but correlated, leading to constraints, the evolution of novel traits may lead to breaking the trade-offs, where novel lineages become outliers, deviating from established relationships among traits. Being an outlier may help gain competitive advantage under different environmental scenarios. Diatom evolution of silica frustule may have increased diatom fitness in different ways. Cells became heavier, and their sinking rates increased (Smetacek 1985). Sinking is often viewed as contributing to phytoplankton’s ability in general and diatoms’ in particular to move toward nutrient-rich lower layers of the water column (Smetacek 1985). Both in the ocean and freshwater lakes, temperature and water density gradients and the resulting water column stratification lead to pronounced gradients in nutrients, where nutrient-rich water sits below the nutrient-depleted (due to phytoplankton uptake) water (Klausmeier and Litchman 2001). Rapid sinking of heavy cells with silica frustules to gain access to nutrients can afford a competitive advantage to diatoms over other phytoplankton groups (Raven and Waite 2004; Gemmell et al. 2016; Du Clos et al. 2021). Another likely advantage of silica frustule is the increased range of shapes and sizes that diatom cells can evolve (Ryabov et al. 2021). Compared to other phytoplankton groups, diatoms exhibit a greater diversity of shapes, including extremely

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Fig. 1 Diverse diatoms shapes, from spherical, to extremely elongated ones. Some dinoflagellates are also shown, and they exhibit much less shape variation. Photo credit: Leonilde Roselli

elongated, needle-like cells that would not be possible in the absence of the hard supporting silica crystal structure (Ryabov et al. 2021) (Fig. 1). This diversity of shapes may help achieve higher fitness under different environmental conditions and, thus, contribute to the overall ecological success of diatoms. Shape variation may help more efficient nutrient acquisition and escape from grazers by exceeding the dimensions of grazing windows by different grazers. The evolution of silica cell wall in diatoms also allowed a more efficient protection from zooplankton grazers in a more direct way, as silica frustules are exceptionally strong and can withstand significant force from grazers, both due to the frustule architecture and silica as the material (Hamm et al. 2003). The evolution of better defenses against grazers in diatoms (and other phytoplankton) has led to so called “arms race,” where both the phytoplankton (prey) and the grazers (predators) evolve various means to better defend themselves and to overcome defenses, respectively (Smetacek 2001). Over the geological time, diatom traits changed, and one such change that is relatively well documented is the change in cell size (Finkel et al. 2005). The mean frustule size decreased 2.5-fold over the Cenozoic; however, the range of sizes, as well as species diversity increased (Finkel et al. 2005). Several drivers, such as

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temperature and nutrient availability, are hypothesized to control diatom size on long time scales (Finkel et al. 2005). Diatom size has major effect on carbon sequestration and biogeochemical cycling; consequently, changes in cell size influenced these processes over the geological time.

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Traits That Determine Diatom Responses to the Environment

Diatoms, as all other phytoplankton, need nutrients (macro- and micronutrients), sunlight, and CO2 to survive and grow. These are major resources for diatoms. Nutrient- and light-dependent growth and utilization are well studied, and several mathematical relationships are commonly used to describe these processes. The parameters of those equations are often determined experimentally and viewed as traits (Litchman and Klausmeier 2008). These traits can thus be called “response” traits. Comparing those traits across groups can provide novel insights into the mechanistic basis of diatom ecological strategies and how they differ from strategies of other groups. In addition to resources (consumed by diatoms), several environmental factors, such as pH and temperature, are the important dimensions of diatom ecological niches. Below I briefly outline common ways to describe the dependence of diatom growth on those factors.

5.1

Nutrients

The ability of diatoms to take up nutrients is often described as the Michaelis– Menten relationship: V ¼ V max

R , R þ ks

ð1Þ

where V is the uptake rate, R is the resource (nutrient) concentration, Vmax is the maximum uptake rate for a given resource, and ks is the half-saturation constant for uptake. The dependence of growth on a nutrient is also assumed to follow a saturating curve (Monod curve): R μ ¼ μmax , ð2Þ R þ Ks where μ is the growth rate, μmax is the maximum growth rate, and Ks is the halfsaturation constant for growth (note that it is different from the half-saturation constant for uptake, reflected here by using the capital K ). If we want to determine the overall change of biomass or cell density, we need to include mortality m, and the simplest way is to assume constant mortality. Then, the change in biomass B with time t would be:

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dB ¼ dt

 μmax

 R m B R þ Ks

ð3Þ

Often, growth rate is assumed to depend not on the external but on the internal/ intracellular nutrient concentration or nutrient quota, according to the Droop model (Droop 1973; Grover 1991a; Litchman and Klausmeier 2008): μ ¼ μ1

ðQ  Q min Þ , Q

ð4Þ

where μ1 is the growth rate at infinite quota, Q is nutrient quota (intracellular nutrient concentration), and Qmin is the minimum nutrient quota at which growth stops. There is also a separate equation to describe nutrient uptake V (Litchman and Klausmeier 2008) (Eq. 1). The Droop model works better than the Monod model under fluctuating resource supply, as it tracks more realistically the dynamics of intracellular resource (nutrient) content (Grover 1991b). However, it has more parameters (traits) that need to be measured. A significant number of studies measured nutrient uptake in diatoms, as well as in other phytoplankton. These nutrient-related traits have been compiled for diatoms and other phytoplankton, so we can determine if diatoms tend to differ from other groups in some of those traits (Edwards et al. 2015a). Mean maximum growth rates tend to be higher in marine diatoms compared to other marine phytoplankton groups (Edwards et al. 2012). This suggests that marine diatoms should be adapted to high or fluctuating nutrient conditions (Grover 1997; Edwards et al. 2012). Interestingly, the pattern is different in freshwaters: Freshwater diatoms tend to have intermediate values of nutrient-related traits among freshwater groups. For many other nutrient-related traits, differences across taxonomic groups can be explained by cell size differences. When cell size effects are accounted for, diatoms tend to have intermediate values of nutrient-related traits (Edwards et al. 2012). Some general trends revealed by the analysis of trait compilations apply to diatoms, as well as other taxonomic groups. For example, the ability to compete for limiting nutrients declines with increasing cell size, with the scaled nutrient affinity declining with size (Edwards et al. 2012). Interestingly, marine and freshwater diatoms differ in their nutrient-related traits, such as affinities for N and P, which suggests different selective pressures (e.g., degree of limitation by different nutrients) in the two environments (Edwards et al. 2011, 2012). Diatoms may benefit from fluctuating nutrient supply because their ability to store nutrient pulses is high, as they tend to have large vacuoles (Sicko-Goad et al. 1977). This is especially true for nitrogen, as it is stored primarily in the vacuole, while phosphorus is stored in the cytoplasm, so the advantage of having a large vacuole does not matter as much for this nutrient (Syrett 1981; Litchman et al. 2009). Similarly, for iron, another frequently limiting nutrient, vacuole storage may be of a lesser importance, at least in some diatoms, as ferritins, proteins that bind to iron, may be used for storing it (Marchetti et al. 2009; Lampe et al. 2018). The scaling of the vacuole size may be greater than that of the cell volume, so that the relative

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vacuole size is greater in large-celled species, implying that large cells have enhanced storage capacity. Given that larger cells tend to have slower growth rates, there is likely a trade-off between the maximum growth rate and nutrient (at least nitrogen) storage capacity. This trade-off, as trade-offs in general, defines contrasting ecological strategies that can be favored by different environmental conditions or coexist spatially and/or temporally, especially under fluctuating conditions (Sommer 1984; Litchman et al. 2009; Edwards et al. 2013). The parameters of the equations used to describe the dependence of diatom (and other phytoplankton) growth on nutrients can be considered traits and used to predict growth under different conditions. Connecting these traits with the underlying physiological and molecular mechanisms and determining the mechanistic explanations of the observed trait differences across taxa are active areas of research that include looking at the genomes, transcriptomes, and cell molecular structure of different species. Diatoms up- or downregulate numerous genes under nutrient limitation, leading to differences in transcription and abundance of proteins, that likely lead to changes in nutrient-related traits. For example, under phosphorus limitation, the model diatom Thalassiosira pseudonana exhibited changes in the transcription and abundance of proteins associated with cellular P allocation and transport, increased utilization of dissolved organic phosphorus (DOP), change in the cell surface, and regulation of glycolysis and translation (Dyhrman et al. 2012). Under nitrogen limitation, T. pseudonana upregulated genes involved in glycolysis, the tricarboxylic acid cycle (TCA) cycle, and N metabolism and downregulated genes for the Calvin cycle, gluconeogenesis, pentose phosphate, oxidative phosphorylation, and lipid synthesis (Jian et al. 2017).

5.2

Light

For light utilization, several functions are commonly used to describe the dependence of phytoplankton, including diatom, growth on light. The growth rate can either be a saturating function of irradiance, such as an equivalent of the Monod function, or can include inhibition of growth by high light levels, called photoinhibition. Here is an example of a saturating function that is used to describe light-dependent growth (Schwaderer et al. 2011): μðI Þ ¼

μmax I I þ μmax α

ð5Þ

where μ is the growth rate, I is the irradiance, μmax is the maximum growth rate, and α is the initial slope of the growth–irradiance curve. For the growth rate that declines under high irradiance, Eilers and Peeters (1988) proposed the following function:

Trait-Based Diatom Ecology

μ ðI Þ ¼

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μmax 2 I αI 2opt

μ I  max  μmax þ 1  2 αI I þ μmax α opt

ð6Þ

where μmax is the maximum growth rate (d1) achieved at Iopt, I is the irradiance (μmol photons m2 s1), and α is the initial slope of the growth–irradiance curve (Edwards et al. 2016). Key traits that characterize diatom growth responses to light are the α, μmax, and Iopt. α characterizes the ability to grow at low irradiances, and high μmax is usually selected for at high or fluctuating nutrients (Litchman et al. 2009). High α is probably achieved through the high concentration of photosynthetic pigments, and there is a positive relationship between α and cellular chl a concentration (Edwards et al. 2015b). When diatoms are compared to other groups of phytoplankton, they tend to differ in their light-related traits and appear to have high α and high μmax (Edwards et al. 2015b). Thus, diatoms may be adapted to low light availability and high nutrients, which are the conditions associated with high mixing regimes that may favor diatoms, keeping them suspended in the water column (Lindenschmidt and Chorus 1998). Interestingly, turbulence was shown to stimulate carbon assimilation within diatom chains (Bergkvist et al. 2018), suggesting that diatoms may be adapted to turbulence in many ways. Microturbulence also changed gene expression in diatoms under nutrient-replete conditions (Amato et al. 2017), demonstrating a link between turbulent conditions and diatom physiology. There also seems to be a difference between coastal and oceanic diatoms, where coastal isolates have higher α and μmax (Edwards et al. 2015b). This could suggest adaptation to low light availability and high nutrient availability in coastal environments. There are also correlations between traits, with α and Iopt being negatively correlated, suggesting that there could be different ecological strategies with respect to light, the high light and low light-adapted species and strains (Edwards et al. 2015b).

5.3

Temperature

Similarly, for temperature, several functions have been used recently to describe the shape of dependence of growth rates or other eco-physiological processes on temperature (Thomas et al. 2012, 2017). One of them is the Norberg function (Norberg 2004; Thomas et al. 2012):    T z μ ðT Þ ¼ 1  aebT ð7Þ ω=2 where μ(T) is the temperature-dependent growth rate (day1), z is the midpoint of the growth curve, ω is the width of the unimodal response to temperature, and a and b determine the height, steepness, and skewness of the curve (Edwards et al. 2016). Key temperature-related traits are the optimum temperature for growth, Topt, minimum Tmin, and maximum Tmax temperatures that have nonnegative growth and

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bracket the thermal niche of an organism and the growth rate at Topt. These traits can be calculated from the fitted Norberg curve (Thomas et al. 2012). Most of the growth–temperature curves are left-skewed, meaning that growth rate declines more sharply when temperatures increase above the optimum temperature, at which the growth rate is the highest (Thomas et al. 2012). There are other functions that have been used, for example, a relationship where both growth and mortality processes (births and deaths) are temperature-dependent. This dependence also produces a skewed, unimodal curve (O’Donnell et al. 2018). The skewness of the growth–temperature curve or the thermal performance curve (TPC) is an important feature that means that even slight increases of temperature above the optimum lead to significantly lower growth rates (Thomas et al. 2012). This may in part explain why most species have Topt that are higher than the average temperature of their environments (Thomas et al. 2012). The compilations of temperature traits of different phytoplankton taxa allow us to determine if diatom temperature traits differ from other groups. Thomas et al. (2016) found that diatoms tend to have lower Topt, Tmax, and Tmin than other taxonomic groups, but this difference is significant only for the taxa from temperate regions, and in the tropics, different taxa have more similar Topt and Tmax. Interestingly, Tmin of marine diatoms is lower than Tmin of marine cyanobacteria even in the tropics, suggesting that tropical diatoms may have wider thermal niches compared to cyanobacteria (Thomas et al. 2016). It may thus be that diatoms are greater thermal generalists than other taxonomic groups. Diatoms tend to have higher maximum temperature-dependent growth rates, compared to dinoflagellates and cyanobacteria (Kremer et al. 2017). Interestingly, marine and freshwater diatoms differ in their temperature traits, and freshwater diatom taxa have on average higher temperature traits than the marine counterparts from similar latitudes, which in part could be explained by greater temperature variation in lakes compared to the ocean (Thomas et al. 2016). We are learning more about the underlying mechanisms of thermal tolerance in diatoms and other phytoplankton. The different scaling of photosynthesis and respiration with temperature may explain some of the thermal tolerance differences (Padfield et al. 2016; Feijão et al. 2018). Taxon-specific patterns of up- or downregulation of genes involved in lipid metabolism, heat shock protein synthesis, and photosynthetic pathways likely contribute to thermal tolerances (Kinoshita et al. 2001; Rousch et al. 2004; Schaum et al. 2018). However, we still cannot explain the taxonomic differences in temperature traits at the molecular level, and this should be one of the research directions in thermal biology.

5.4

Trait Dependence on Environmental Factors

It is important to note that traits that determine the responses of diatoms to a given environmental factor may also depend on other factors. Broadly, this dependence can be viewed as phenotypic plasticity, and it is important for both ecological and evolutionary responses of organisms to environment (Stearns 1989; Hendry 2015;

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Fig. 2 The optimum temperature for growth Topt of Thalassiosira pseudonana depends on nutrient concentrations. a) Topt vs. phosphate concentration in the medium, b) Topt vs. nitrate concentration in the medium. From Thomas et al. (2017)

Hattich et al. 2017). Determining these dependencies requires experimental measurements of traits of interest while varying other factors one at a time or in combination, which is a daunting task. Meta-analyses of existing data can also help assess the interdependencies of traits. Edwards et al. (2016) showed that light-related traits depend on temperature, and vice versa, temperature traits depend on irradiance. For example, the optimum temperature for growth Topt is a unimodal function of irradiance, with the highest Topt at about 100 μmol quanta m2 s1 and declining at low and high irradiances. In turn, the irradiance-related traits also exhibit a unimodal distribution with respect to temperature, peaking at the optimum temperature (Edwards et al. 2016). An experimental study with the model diatom Thalassiosira pseudonana showed that the optimum temperature for growth, Topt, is also a function of nutrient concentration, declining with increasing nutrient limitation (Thomas et al. 2017) (Fig. 2). This dependence may have important consequences for predicting the responses of diatoms and other phytoplankton to global environmental change. The simultaneous warming and a decreasing nutrient supply due to stronger stratification (Doney 2006) may be more detrimental than either of these factors alone (Thomas et al. 2017). Understanding how different traits depend on various environmental factors is essential for a more environment-tailored use of trait-based approaches and should be also helpful for the development of more realistic trait-based models.

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Traits Important in Ecological Interactions

Diatoms engage in a multitude of ecological interactions, such as competition for resources, predator–prey, host–parasite, and mutualistic interactions. All these interactions involve different traits that can be used to characterize those interactions. Those traits may be correlated with each other and represent trade-offs that can define different ecological strategies (Litchman et al. 2012). Here, I briefly discuss these interactions and what traits may be particularly important in defining various biotic interactions, both within and across trophic levels.

6.1

Competition for Resources

As most microalgae, diatoms require several essential nutrients such as nitrogen, phosphorus, and iron, several other micronutrients, as well as light and carbon for growth. In addition, diatoms require silica for their cell walls. Each of these resources may at times become limiting, due to either low supply or consumption by other phytoplankton or nonphytoplankton organisms (e.g., heterotrophic bacteria can consume inorganic nutrients and, thus, compete for them with phytoplankton, including diatoms). Many traits determine competitive abilities for resources, such as the maximum growth rate, maximum rate of resource uptake, and the affinity of resource that characterizes species ability to grow at low resource concentrations (Edwards et al. 2011). There are several measures of competitive ability that are widely used to predict the outcomes of competition for resources. The most widely accepted composite trait that characterizes species competitive ability is the breakeven nutrient concentration, termed R* (Tilman 1982). For the Monod equation for growth (Eq. 3 above), the R* is a function of maximum growth rate, mortality, and the half-saturation constant for growth: R ¼

mK s μmax  m

ð8Þ

For the Droop model (Eq. 4), the R* is a more complex function with more parameters (Litchman and Klausmeier 2008). The R* is thus a composite trait, so that it depends on several physiological parameters, including mortality. In different species, the same competitive ability may be achieved by modifying different parameters, such as a decrease in the half-saturation constant for growth Ks or/and mortality, or an increase in maximum growth rate μmax. For the low nutrient concentrations, the scaled nutrient uptake affinity C is also used as a measure of competitive ability (Edwards et al. 2011, 2012): C¼

V max KQmin

ð9Þ

where Vmax is the maximum rate of nutrient uptake, K is the half-saturation for nutrient uptake, and Qmin is the minimum cellular nutrient concentration allowing

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growth. High scaled affinity implies superior nutrient competitive ability. Again, nutrient competitive ability may be improved through the reduction of K or Qmin, or an increase in Vmax. When scaled nutrient affinity is compared across taxa, marine diatoms appear to be intermediate nutrient competitors, while small prymnesiophytes and haptophytes appear to be good nutrient competitors (Edwards et al. 2012). While the R* and C are measures of nutrient competitive abilities at equilibrium, under fluctuating nutrient supplies, these composite traits may not characterize the best nutrient competitors. Fluctuations may favor species that are capable of fast nutrient uptake and/or high storage capacity (Sommer 1985; Grover 1991b; Litchman et al. 2009). Some fluctuation regimes may allow coexistence of contrasting strategies, such as small, fast growing cells and large, slower growing cells with high nutrient storage capacity (Sommer 1985; Litchman et al. 2009; Edwards et al. 2013).

6.2

Consumption by Grazers

Diatoms are consumed by zooplankton and, as other phytoplankton, have evolved a number of adaptations to minimize consumption. Silica frustule can potentially be a deterrent for some grazers, and it takes significant force to break it (Smetacek 2001; Hamm et al. 2003; Pančić et al. 2019). In the presence of grazers, more thick-shelled diatoms may persist, while thin-shelled diatoms could be grazed preferentially (Assmy et al. 2013). Silica in the cell wall may also have helped diatoms to evolve diverse cell shapes and sizes that may inhibit consumption by some grazers (Ryabov et al. 2021). Diatoms exhibit an astounding diversity of shapes, greater than any other group of phytoplankton. Shapes include extremely elongated needle-like forms that may be inedible to many zooplankton grazers (Ryabov et al. 2021). In general, cell size is a good predictor of susceptibility of diatoms to grazing. The window of diatom sizes susceptible to grazing depends on the grazer, with larger grazers consuming larger diatoms. As many diatoms form chains of cells, thus modifying their overall dimensions, chain length in is an adaptive trait to decrease susceptibility to grazing. Several studies showed that some chain-forming diatoms decrease chain length under grazing pressure by copepods but not microzooplankton (O’Connors et al. 1976; Bergkvist et al. 2012; Amato et al. 2018). Interestingly, copepod clearance rates were higher when fed long chain diatoms (Bergkvist et al. 2012). Toxicity is an important trait that often mediates palatability of phytoplankton to grazers. Among diatoms, several taxa can produce toxins that may deter consumption by zooplankton (Ianora and Miralto 2010). Presence of copepod grazers was shown to induce toxin production in toxic diatom Pseudonitzschia sp. (Lundholm et al. 2018). The toxin concentration was positively correlated with a type of lipids produced by herbivorous but not carnivorous copepods (Lundholm et al. 2018). Cellular stoichiometry of macronutrients (C:N or C:P) is another trait that may affect diatom palatability to grazers and grazers’ growth (Sterner and Elser 2002; Polimene et al. 2015). Lower palatability due to changes in elemental stoichiometry may

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decrease carbon and nutrient transfer from diatoms to higher trophic levels and, thus, affect carbon sequestration (Polimene et al. 2015). Understanding how diatom–grazer interaction depends on the above-mentioned traits and, in turn, shapes them is an active area of research that should help us understand biotic interactions in aquatic environments.

6.3

Mortality from Parasites

Diatoms are known to get infected by several types of parasites, from viruses, to fungi and parasitic protists (Tomaru and Nagasaki 2011; Scholz et al. 2016). The traits that determine the interactions of diatoms with parasites are not as well-known as the traits that describe the predator–prey interactions (mostly grazing by zooplankton). Viruses of phytoplankton, including diatoms, can be more specialized, compared to grazers (Nagasaki 2008), so the traits that may determine the susceptibility to viral attacks are more difficult to identify. Viruses that infect diatoms have been documented recently, with twenty or so viruses described; they include lytic single-strand RNA or the single-strand DNA viruses with high specificity (Arsenieff et al. 2019). Diatom viruses may be among the smallest viruses (Kranzler et al. 2019), possibly because the pores of silica frustules can be as small as 40 nm, and viruses need to enter the cell through those pores (Losic et al. 2006). An interesting question is whether the structure of the frustule could in part be shaped by the selective pressure from viruses and whether there could be a coevolutionary arms race between diatoms and their viruses. Viral mortality in diatoms may be exacerbated under nutrient limitation: A recent study showed that Si limitation was associated with higher viral abundance and diatom mortality (Kranzler et al. 2019). Chapter “Diatom Viruses” of this book describes the role of viruses in diatom biology in more detail. Fungal (chytrid) infections in freshwater diatoms are well documented and may contribute to the termination of the spring diatom blooms (Reynolds 1984). These infections may increase in prevalence with warming, thus exerting more control on the bloom’s end (Frenken et al. 2016). Interestingly, chytrid infections may increase diatom genetic diversity through negative frequency-dependent selection (Gsell et al. 2013). A recent study (Chambouvet et al. 2019) found that fungal infection may be common in marine diatoms as well, where fungal symbionts are located inside diatom cells. What traits describe the likelihood and severity of such parasitic interaction is currently unknown. It may even be possible that the interaction is not completely parasitic. Understanding how diatoms interact with viruses and other pathogens is a new frontier in diatom ecology, as well as in phytoplankton ecology in general. Compared to the interactions of diatoms with zooplankton, the diatom–parasite, especially, virus, interactions appear less generalizable, as the ability of viruses to infect diatoms and other phytoplankton may be species- or even strain-specific. In contrast, zooplankton’s ability to consume diatoms mostly depends on size matching and can, thus, be predicted better (Litchman et al. 2021).

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17

Mutualistic Interactions

Diatoms engage in mutualistic interactions with many bacteria, including cyanobacteria, and these interactions include both extracellular and intracellular cross-feeding (exchange of metabolites). Recent research has revealed close mutualistic interactions between diatom cells and bacteria that live attached to diatom frustules or in the immediate vicinity of it (Seymour et al. 2017). The bacteria may provide a wide range of compounds to phytoplankton, from vitamins and growth factors, to micronutrients and lipids (Amin et al. 2015; Cooper et al. 2019; Kim et al. 2019). In turn, phytoplankton provide bacteria with organic carbon (Fu et al. 2020). The close associations of species are termed “symbiotic” and may not necessarily be mutualistic, where both (or all) partners receive benefit. Symbiotic associations may also, although probably less often, be competitive or parasitic (Baker et al. 2018; Drew et al. 2021). What kind of symbiotic relationship it is depends on many factors, including environmental drivers, such as resources (e.g., nutrients). In the associations of diatoms with attached bacteria, there may be competition for nutrients, and the overall relationship could depend on the degree of nutrient limitation. It is possible that changing environmental conditions may alter the nature of symbiotic relationships, as was found for other symbioses (Johnson et al. 1997; Grman et al. 2012). A more in-depth treatment of the diatom–bacteria interactions is presented in Chapter “Diatom Viruses” of this book. A fascinating symbiotic relationship has been observed between some oceanic diatoms and nitrogen-fixing cyanobacteria living inside diatom cells, a so called diatom diazotroph association, DDA (Villareal 1991; Caputo et al. 2019). There are some DDAs in freshwaters, but their ecological and biogeochemical importance is unknown. Time-calibrated phylogenies suggest that these associations arose ca. 100–50 MYA, in late Cretaceous, when the oceans were highly stratified, warm, and low in nutrients (Caputo et al. 2019). Diatoms that harbor cyanobacterial diazotrophs (N-fixers) occur in different oceanic regions, but mostly in low latitudes, with blooms occurring in the North Pacific Gyre and close to large river deltas, such as the Amazon (Villareal et al. 2012). Several genera of diatoms, such as Hemiaulus, Rhizosolenia, Chaetoceros, and Climacodium, are known to have cyanobacterial symbionts (heterocystous Richelia intracellularis and Calothrix rhizosoleniae) (Caputo et al. 2019). In this intracellular symbiosis, diatom and cyanobacterium exchange metabolites, with the diatom providing carbon and cyanobacterium providing fixed nitrogen (Follett et al. 2018). The association of diazotrophs with diatoms may also help diazotrophs acquire phosphorus due to efficient sinking to deeper, P-rich water (Follett et al. 2018). The efficient acquisition of P through sinking alleviates P limitation and, thus, may stimulate nitrogen fixation. The DDAs differ in how integrated the symbioses are: The oldest symbioses have partners that coevolved and adapted to relying on each other for key functions, such as uptake of nutrients and carbon fixation. Consequently, intracellular cyanobacterial symbionts have lost some genes that encode for nitrogen transporters and reductases, while more recent symbioses have diazotrophs outside diatom cells and those cyanobacteria have larger genomes (Caputo et al. 2018, 2019). Less is known

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about the changes in diatom genomes as a consequence of symbiotic association. Some of the traits found in diatoms with symbiotic diazotrophs are strong silicification and coloniality (Caputo et al. 2019). Future studies of diatom genomes should shed light on the role of symbioses in shaping genomes.

7

Traits Determining Diatom Responses to Global Environmental Change

Global environmental change has many aspects that are relevant to diatoms and other microalgae. The ongoing increase in CO2 concentration leads to increasing temperatures and ocean acidification (Pörtner 2008; Doney et al. 2012; Kroeker et al. 2013). Warming of aquatic ecosystems changes mixing and stratification patterns that affect nutrient supply and light regimes (Dutkiewicz et al. 2013; Voosen 2019; Li et al. 2020). Diatoms have many relevant traits that determine their responses to various aspects of global environmental change. Growth rate of diatoms depends on CO2 concentration. Higher CO2 concentrations were shown to enhance growth of diatoms under nutrient-replete conditions in a natural community (Bach et al. 2019). However, a long-term exposure of T. pseudonana to elevated CO2 did not show a significant change in physiological parameters or adaptation (Crawfurd et al. 2011). Interactions of global change stressors may be important for determining the growth rates of diatoms. Under supraoptimal temperatures (above Topt), high pCO2 decreased growth of T. pseudonana but not under optimal temperatures (Laws et al. 2020). Stoichiometric traits may also change in response to global change stressors, thus affecting biogeochemical cycling. Higher pCO2 increased C:N and N: P ratios in T. pseudonana, but this increase was not universal across taxa (King et al. 2015). Interestingly, high pCO2 was shown to increase diatom (T. pseudonana) resilience to other stressors, such as UV and fluctuating nitrogen limitation (Valenzuela 2018). In another study, high pCO2 under nitrogen limitation decreased growth rate, cell size, pigment content, photochemical quantum yield of PSII, and photosynthetic carbon fixation in T. pseudonana (Li et al. 2018). Temperature-related traits help us determine how different taxa, including diatoms, would respond to global warming. As discussed above, diatoms tend to have lower Topt than cyanobacteria (Thomas et al. 2016), and this trait difference may potentially lead to diatoms decreasing their abundance in the warming ocean, especially relative to cyanobacteria. Higher temperature exacerbated the negative effects of high pCO2 and nitrogen limitation in T. pseudonana (Li et al. 2018), highlighting the possibility for different stressors to interact and enhance their negative effects (Thomas et al. 2017). Because of their silica frustules, diatoms may have higher sinking rates on average compared to other taxa; consequently, stronger stratification in the future ocean may lead to decreased diatom abundances (Raven and Waite 2004; Agusti et al. 2015; Spilling et al. 2018). Ocean acidification may lead to less silicified diatom frustules, thus potentially affecting their sinking rates, decomposition, and

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carbon sequestration (Petrou et al. 2019). This may also make diatoms more susceptible to grazers, thus potentially altering the dynamics of food webs.

8

Trait Evolution

Organisms’ traits are not static but undergo evolutionary change. The time scales of evolutionary changes range from a single generation (a day or so) to geological time (millions of years) (Thompson 1998; Hendry 2020). The rates of evolutionary change were shown to depend on the time scales over which the rates are calculated. The rates are generally faster over shorter time scales than over longer (geological) time scales, in part because of the periods of stasis and reversal over the course of evolution (Hendry and Kinnison 1999; Stockwell et al. 2003). Many diatom traits experience numerous selective pressures and, as a result, evolve over different time scales. Selective forces include abiotic environmental factors, such as nutrient concentrations, temperature, as well as biotic interactions, such as competition, predation, parasitism, and mutualism. Both observational and experimental studies have demonstrated trait evolution in diatoms. The evolution of diatom traits over longer time scales and across environments has been documented (Sims et al. 2006; Connolly et al. 2008; Nakov et al. 2014). Evolutionary change may also occur relatively fast, for example, recent experimental evolution studies showed that diatoms can adapt to higher temperatures in less than a year (ca. 300 generations). The optimum temperature for growth, Topt, and, to a lesser degree, the maximum temperature allowing growth, Tmax, shifted to higher values (O’Donnell et al. 2018) (Fig. 3). High-temperature adaptation also led to changes in

Fig. 3 Thermal performance curves of T. pseudonana evolved at 16 and 31  C for ca. 350 generations. Individual curves are fits of the Norberg equation (Eq. 7) to the data from replicates evolution lines. From O'Donnell et al. (2018)

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other traits, such as fatty acid composition, cell size, and shape and elemental stoichiometry (O’Donnell et al. 2019; 2021). Interestingly, different temperature regimes can result in different evolutionary trajectories and rates of adaptation. Schaum et al. (2018) showed that adaptation to high temperature was faster under fluctuating temperature or less extreme high temperatures (26 vs. 32  C). The hightemperature-adapted diatom lines differed from the ancestral lines in elemental composition and metabolic traits. The genes involved in adaptation were related to transcriptional regulation, cellular responses to oxidative stress, and redox homeostasis (Schaum et al. 2018). How traits evolve depends on environmental conditions, so the differences not only in the drivers of selection (e.g., constant vs. fluctuating temperature) but in other factors as well may alter trait evolution. Aranguren-Gassis et al. (2019) found that adaptation to high temperature was impeded by nitrogen limitation. A possible mechanism is that high-temperature-adapted lines require more nitrogen and get competitively excluded under low N conditions, thus precluding adaptation. Documenting rapid evolution of other ecologically relevant traits, such as nutrient acquisition traits, would allow us to better predict the effects of diatoms on biogeochemical cycles and competitive interactions of diatoms with other phytoplankton.

9

Conclusions and Future Directions

Diatoms are a globally important group of phytoplankton, abundant in both marine and freshwater environments. Looking at morphological, physiological, and ecological traits, and their distribution and evolution, can help us understand what controls the abundance and diversity of diatoms in different environments and how it may change in the future. When comparing eco-physiological and morphological traits of diatoms to other phytoplankton groups, we can identify both differences and similarities. Many traits that define how diatoms interact with the environment, and other taxa, within or across trophic levels, differ from other groups. These trait differences may help explain the ecological success of diatoms over different time scales, from geological to fast ecological scales. A major challenge in trait-based research is to learn about the mechanistic underpinnings of various traits that can be used in biogeochemical models to predict phytoplankton dynamics and composition. A possible research direction to better understand the molecular basis of phenotypic traits could be a hypothesis-driven investigation of molecular differences in species with different values of relevant traits. Such comparisons could help identify the genomic, transcriptomic, and proteomic characteristics responsible for the observed trait differences. Another future direction is a better characterization of trait variation both across and within species. While for some traits there are measurements for multiple species (Edwards et al. 2015a), some traits are mostly measured in just a handful of diatoms frequently grown in the lab, especially on the model diatom T. pseudonana. Our knowledge of traits for many diatom taxa, especially open ocean oligotrophic species, is very limited. Moreover, we have even less information about the amount

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of trait variation within species for many taxa. For some traits, we are beginning to get more data on intraspecific variation, such as temperature-related traits, nutrient utilization traits, or cell size. Nevertheless, the intraspecific trait variation is still assessed just for several well-studied species. Characterizing the amount of intraspecific diversity for many relevant diatom traits across diverse taxa is needed to assess the evolutionary potential of different species, as selection would likely happen on standing genotypic/phenotypic variation. As diatom traits may change in the future in response to changing environmental conditions, understanding trait evolution in diatoms and other phytoplankton is an important frontier in trait-based diatom ecology. Experimental evolution studies should tackle measuring phenotypic trait change together with genomic, transcriptomic, and metabolomic changes to establish the link across different levels of biological organization.

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Litchman E, Edwards KF, Klausmeier CA, Thomas MK (2012) Phytoplankton niches, traits and eco-evolutionary responses to global environmental change. Marine Ecology Progres Series 470:235–248 Litchman E, Klausmeier CA (2008) Trait-based community ecology of phytoplankton. Annu Rev Ecol Evol Syst 39:615–639 Litchman E, Klausmeier CA, Schofield OM, Falkowski PG (2007) The role of functional traits and trade-offs in structuring phytoplankton communities: scaling from cellular to ecosystem level. Ecol Lett 10:1170–1181 Litchman E, Klausmeier CA, Yoshiyama K (2009) Contrasting size evolution in marine and freshwater diatoms. Proc Natl Acad Sci U S A 106:2665–2670 Litchman E, de Tezanos Pinto P, Edwards KF, Kremer CT, Klausmeier CA, Thomas MK (2015) Global biogeochemical impacts of phytoplankton: a trait-based perspective. J Ecol 103:1384– 1396 Losic D, Rosengarten G, Mitchell JG, Voelcker NH (2006) Pore architecture of diatom frustules: potential nanostructured membranes for molecular and particle separations. J Nanosci Nanotechnol 6:982–989 Lundholm N, Krock B, John U, Skov J, Cheng J, Pančić M, Wohlrab S, Rigby K, Nielsen TG, Selander E, Harðardóttir S (2018) Induction of domoic acid production in diatoms-types of grazers and diatoms are important. Harmful Algae 79:64–73 Marchetti A, Parker MS, Moccia LP, Lin EO, Arrieta AL, Ribalet F, Murphy MEP, Maldonado MT, Armbrust EV (2009) Ferritin is used for iron storage in bloom-forming marine pennate diatoms. Nature 457:467–470 Martiny JBH, Jones SE, Lennon JT, Martiny AC (2015) Microbiomes in light of traits: A phylogenetic perspective. Science 350 McGill BJ, Enquist BJ, Weiher E, Westoby M (2006) Rebuilding community ecology from functional traits. Trends Ecol Evol 21:178–185 Medlin LK (2016) Evolution of the diatoms: major steps in their evolution and a review of the supporting molecular and morphological evidence. Phycologia 55:79–103 Nagasaki K (2008) Dinoflagellates, diatoms, and their viruses. J Microbiol 46:235–243 Nakov T, Theriot EC, Alverson AJ (2014) Using phylogeny to model cell size evolution in marine and freshwater diatoms. Limnol Oceanogr 59:79–86 Nelson DM, Treguer P, Brzezinski MA, Leynaert A, Queguiner B (1995) Production and dissolution of biogeonic silica in the ocean–revised global estimates, comparison with regional data and relationship to biogenic sedimentation. Glob Biogeochem Cycles 9:359–372 Norberg J (2004) Biodiversity and ecosystem functioning: A complex adaptive systems approach. Limnol Oceanogr 49:1269–1277 O'Connors HB, Small LF, Donaghay PL (1976) Particle-size modification by two size classes of the estuarine copepod Acartia clausi. Limnol Oceanogr 21:300–308 O'Donnell DR, Du ZY, Litchman E (2019) Experimental evolution of phytoplankton fatty acid thermal reaction norms. Evol Appl 12:1201–1211 O'Donnell DR, Hamman CR, Johnson EC, Kremer CT, Klausmeier CA, Litchman E (2018) Rapid thermal adaptation in a marine diatom reveals constraints and trade-offs. Glob Chang Biol 24: 4554–4565 O’Donnell DR, Beery SM, Litchman E (2021) Temperature-dependent evolution of cell morphology and carbon and nutrient content in a marine diatom. Limnol Oceanogr 66:4334–4346 Padfield D, Yvon-Durocher G, Buckling A, Jennings S, Yvon-Durocher G (2016) Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecol Lett 19:133–142 Pančić M, Torres RR, Almeda R, Kiørboe T (2019) Silicified cell walls as a defensive trait in diatoms. Proc R Soc B Biol Sci 286:20190184 Petrou K, Baker KG, Nielsen DA, Hancock AM, Schulz KG, Davidson AT (2019) Acidification diminishes diatom silica production in the Southern Ocean. Nat Clim Chang 9:781–786

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Thomas MK, Kremer CT, Klausmeier CA, Litchman E (2012) A global pattern of thermal adaptaiton in marine phytoplankton. Science 338:1085–1088 Thomas MK, Kremer CT, Litchman E (2016) Environment and evolutionary history determine the global biogeography of phytoplankton temperature traits. Glob Ecol Biogeogr 25:75–86 Thompson JN (1998) Rapid evolution as an ecological process. Trends Ecol Evol 13:329–332 Tilman D (1982) Resource competition and community structure. Princeton University Press, Princeton, NJ Tomaru Y, Nagasaki K (2011) Diatom viruses. In: Seckbach J, Kociolek P (eds) The diatom world. Cellular origin, life in extreme habitats and astrobiology. Springer, Dordrecht Tréguer P, Bowler C, Moriceau B, Dutkiewicz S, Gehlen M, Aumont O, Bittner L, Dugdale R, Finkel Z, Iudicone D, Jahn O, Guidi L, Lasbleiz M, Leblanc K, Levy M, Pondaven P (2018) Influence of diatom diversity on the ocean biological carbon pump. Nat Geosci 11:27–37 Valenzuela JJ, de Lomana ALG, Lee A, Armbrust EV, Orellana MV, Baliga NS (2018) Ocean acidification conditions increase resilience of marine diatoms. Nat Commun 9:2328 Villareal TA (1991) Nitrogen fixation by the cyanobacterial symbiont of the diatom genus Hemiaulus. Mar Ecol Prog Ser 76:201–204 Villareal TA, Brown CG, Brzezinski MA, Krause JW, Wilson C (2012) Summer diatom blooms in the North Pacific subtropical gyre: 2008-2009. PLoS One 7:e33109 Violle C, Navas M-L, Vile D, Kazakou E, Fortunel C, Hummel I, Garnier E (2007) Let the concept of trait be functional! Oikos 116:882–892 Voosen P (2019) Warming transforms the oceans and poles. Science 365:1359–1360 Westoby M, Nielsen DA, Gillings MR, Litchman E, Madin JS, Paulsen IT, Tetu SG (2021) Cell size, genome size, and maximum growth rate are near-independent dimensions of ecological variation across bacteria and archaea. Ecol Evol 11:3956–3976

The Population Genetics and Evolutionary Potential of Diatoms Tatiana A. Rynearson, Ian W. Bishop, and Sinead Collins

Abstract

Since the Victorian era, people have remarked on the diversity and abundance of diatoms in marine waters. Diatoms are found across the sunlit ocean, are key components of marine food webs, and help to drive biogeochemical cycles. These characteristics are often used as measures of the ecological “success” of diatoms. In this chapter, we examine some of the evolutionary mechanisms that contribute to this success by reviewing the evolutionary strategies and rates that govern diatom population genetic structure and evolutionary potential. We highlight the field and lab data that have provided insights into contemporary evolution including standing genetic variation, natural selection, and rates of mutation, gene flow, recombination, and genetic drift. We also highlight the diversity of methodological approaches that have been used to examine genetic structure and contemporary evolution including single and several locus analyses, partial and whole genome sequencing efforts, experimental evolution, and modeling studies. This variety of approaches has revealed that diatom species are generally comprised of multiple genetically distinct populations. These populations are made up of genotypically diverse individuals with the ability to rapidly respond to their environment without any genetic change through acclimation. Populations also have the potential to respond to environmental change by shifting their genetic composition (evolution), due to their high genotypic diversity and ability to generate additional variation through mutation or heritable epigenetic responses. A key challenge for the future is to connect genotype with

T. A. Rynearson (*) · I. W. Bishop Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, USA e-mail: [email protected] S. Collins Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK # Springer Nature Switzerland AG 2022 A. Falciatore, T. Mock (eds.), The Molecular Life of Diatoms, https://doi.org/10.1007/978-3-030-92499-7_2

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phenotype to better interrogate and understand how contemporary evolution functions in natural populations. This effort should include the development of culture-independent methods as well as robust support for laboratory assays to evaluate phenotypic variation across a diversity of traits and species. Finally, future efforts should be focused on obtaining a deeper understanding of how life history traits in diatoms, such as sexual reproduction and resting spore formation, influence their adaptive potential. Keywords

Evolution · Genetics · Populations · Diatoms · Diversity

Abbreviations DNA GWAS HGT N Ne ROS SNP SSL Topt

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deoxyribonucleic acid genome-wide association study horizontal gene transfer nitrogen effective population size reactive oxygen species single-nucleotide polymorphism single- and several-locus optimal growth temperature

Introduction

Diatoms exert a profound influence on marine ecosystems and biogeochemical cycling, generating about 40% of marine primary production (Nelson et al. 1995; Falkowski et al. 1998). The ecological and biogeochemical impact of diatoms stems in part from their ability to form high biomass “blooms” following exponential cell division in response to favorable environmental conditions (Armbrust 2009). These blooms represent a key source of organic carbon to marine food webs and can lead to significant carbon export from surface waters (Beaulieu 2002; Jin et al. 2006; Rynearson et al. 2013). Diatoms are able to form blooms across a broad range of environments from marine to fresh waters, from tropical to polar habitats, and from eutrophic to oligotrophic nutrient regimes (Round et al. 1990). The capacity for diatoms to occupy such a diverse range of habitats stems partly from the extraordinarily high number of extant diatom species: Recent estimates of total diatom species numbers range from 5000 to 30,000 (Guiry 2012; Mann and Vanormelingen 2013; Malviya et al. 2016). The large number of species paired with the broad range of habitats in which diatoms survive suggest diatoms have a high capacity for adaptive

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evolution and radiation, large reservoirs of genetic variation that support contemporary evolution, and sufficient genetic subdivision to allow for speciation, even in planktonic environments where diatoms can experience high levels of dispersal across the surface ocean. In this chapter, we focus on marine planktonic diatoms, review the evolutionary mechanisms that influence their adaptive potential, and highlight the field and lab data that have provided insights into their population genetic structure. We examine how high-throughput sequencing approaches have influenced our understanding of adaptive potential and revealed new mechanisms for the generation of genetic variation. We cover the use of evolution experiments as windows into contemporary evolution and potential patterns of adaptation, including adaptive strategies and rates. We finish up with some thoughts on future directions that will enhance knowledge of the population genetic structure and evolutionary potential of diatoms.

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Evolutionary Mechanisms That Shape Diatom Populations and Influence Adaptive Potential

Contemporary evolution is influenced by a suite of factors that include standing genetic variation, natural selection and rates of mutation, gene flow, recombination, and genetic drift (Bell 2008). Together, these factors influence the genetic structure of populations and influence a species’ evolutionary response to environmental change (Fig. 1). In planktonic diatoms, many of these factors have rarely been determined, although they are key to interpreting diatom population genetic structure and discerning the evolutionary capacity of diatoms to respond to their environment. This paucity of evolutionary and population genetic information about diatoms is due to several challenges that include genetic marker development, single cell isolation, entraining evolutionary biologists into diatom research and diatom researchers into using evolutionary theory, obtaining sequenced genomes from field isolates, and finally, adequately surveying the sheer number of species and sampling the constantly moving planktonic environment they inhabit. Below we

Variation Generation Mutation • Point and structural • Spontaneous and stress-induced Recombination • Meiotic • Mitotic • HGT

Variation Structuring Genetic Drift • Allele fixation Selection • Environmental and ecological filtering Migration • Dispersal capacity

Evolutionary Outcomes • Local adaptation • Speciation/Extinction • Relative importance of generating and structuring forces • Life history effects

Fig. 1 Evolution is driven by processes that generate variation (mutation, recombination) and then structure that variation (genetic drift, selection, migration). Evolutionary outcomes are dependent on the interplay of forces that both generate and structure genetic variation

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describe what is known or what can be inferred about evolutionary mechanisms in diatoms. One of the key factors influencing population genetic structure and contemporary evolution is standing genetic variation, or the amount of variation present within a species on which selection can act. The first studies to examine standing genetic variation within diatom species could resolve just a handful of genotypes. These studies utilized enzyme electrophoresis (Gallagher 1980; Soudek Jr. and Robinson 1983) and examined variation in the chloroplast DNA (Stabile et al. 1990), ribosomal DNA (Zechman et al. 1994), and randomly amplified polymorphic DNA (Lewis et al. 1997). While these early approaches were similarly limited in their ability to identify genetic variation within diatom species, they revealed that diatom populations were not simply monoclonal assemblages, and they pointed to the existence of standing genetic variation within natural diatom populations. The advent of highly sensitive DNA markers called microsatellites (Freeland 2019), followed by their initial application in diatoms (Rynearson and Armbrust 2000), opened the door to examining population genetics in diatoms, including standing genetic variation. Microsatellite markers are di-, tri-, and tetra-nucleotide repeat regions generally distributed across noncoding portions of the genome. They generally have a faster mutation rate than other regions of the genome due to a process known as slipped strand mutation, which can generate many different length alleles in a population (Freeland 2019). For example, 30 to 40 alleles per locus are commonly observed in diatom microsatellites (e.g. Rynearson and Armbrust 2004; Casteleyn et al. 2009; Härnström and Ellegaard 2011; Whittaker and Rynearson 2017; Wolf et al. 2019). When multiple microsatellites containing many, variable length alleles are examined, the genetic fingerprint of a single diatom cell or chain can be obtained. Microsatellites have revealed a high level of standing genetic variation in natural diatom populations. One measure of genetic variation is genotypic or clonal diversity, which simply quantifies the number of different genotypes in a sample relative to the total number of cells analyzed. Genotypic diversity in diatoms is routinely above 90% and can reach 100%, meaning that each cell analyzed is genetically distinct (reviewed in Godhe and Rynearson 2017). Exceptions include the pennate diatom Pseudo-nitzschia multistriata, where levels of genotypic diversity as low as 6% were observed, indicating sustained periods of consistent lineage selection and subsequent clonal expansion (Ruggiero et al. 2018). Gene diversity or heterozygosity is another measure of standing genetic variation, and it quantifies the probability that any two alleles at a single locus will be different, when chosen at random from the population (Freeland 2019). In diatoms, gene diversity estimates are high (39–88%) reflecting both the large number of alleles and their relatively even frequency of occurrence in natural populations (reviewed in Godhe and Rynearson 2017). The high levels of genotypic and gene diversities in diatoms represent standing variation on which natural selection can act. Genetic variation is generated through de novo mutation and recombination, and in diatoms, rates of both mechanisms are not yet well defined. Rates of de novo

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mutation across the genome have only been directly estimated in the model pennate diatom Phaeodactylum tricornutum. Mutation rates varied between 4.8  10 10 (for base substitution mutations) and 1.6  10 11 (for insertion-deletion mutations) per site per generation (Krasovec et al. 2019). These values are similar to mutation rates determined for the green alga Ostreococcus tauri and fairly typical of genomewide mutation rates in both eukaryotes and bacteria. Not surprisingly, mutation rates at microsatellite loci were substantially faster, reaching 3  10 3 per locus per generation in the diatom P. multistriata (Tesson et al. 2013). Like mutation, the role of recombination is largely unexplored. Diatom life histories include extended periods of asexual division punctuated by sexual reproduction (Fig. 2) (Round et al. 1990). Rates of sexual recombination in diatoms are estimated to range from once per year to once every 40 years (Jewson 1992; Mann 1999; Montresor et al. 2016), leaving a large gap in our understanding of the role of sexual recombination in shaping genetic diversity of diatom populations. In at least one species, sexual recombination appears to have been lost (Koester et al. 2018). Other organisms that have lost or undergo infrequent sexual recombination, such as yeast, ascomycete fungi, and oomycetes, are able to undergo mitotic recombination between homologous chromosomes (Aguilera et al. 2000; Schoustra et al. 2007; Dale et al. 2019). This mutational mechanism occurs in vegetative cells, can be orders of magnitude more frequent than sexual recombination, leads to relatively large-scale changes in genome composition (e.g. via crossing over), and acts to increase genotypic diversity in a population in the absence of sex. Recent work showed evidence of mitotic recombination in two pennate diatoms (P. tricornutum and Seminavis robusta) occurring at a rate of 4.2 events per 100 cell divisions (Bulankova et al. 2021). Notably, this rate increased under stressful growth environments indicating that mitotic recombination may have adaptive significance in natural populations where environmental conditions can fluctuate rapidly. Population genetic structure and subsequent adaptive capacities of natural populations can be strongly influenced by genetic drift, or the fluctuation in allele frequencies in a population that occurs solely through random sampling of alleles from one generation to the next. This stochastic process can lead to the disappearance or fixation of a particular allele, and its evolutionary importance is highly influenced by effective population size (Ne) or the number of individuals contributing to the next generation of offspring (Freeland 2019). Rates of genetic drift in natural populations are unknown for diatoms, but there have been some estimates made of Ne, providing insight into the potential of genetic drift to influence the genetic structure of diatom populations. Using either the observed de novo mutation rate in P. tricornutum or an assumed rate of 10 9, Ne was estimated to be roughly 106 to 107 individuals across a range of both pennate (P. tricornutum and Fragilariopsis cylindrus) and centric (Thalassiosira rotula, Thalassiosira weissflogii, and Skeletonema spp.) diatoms (Mock et al. 2017; Krasovec and Filatov 2019; Krasovec et al. 2019). Relative to the enormous census population sizes in diatoms (~106 individuals per liter), Ne estimates for diatoms are modest. Given that diatoms are phylogenetically diverse and fluctuate between asexual and sexual modes of reproduction, these initial estimates from laboratory cultures may not be

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Fig. 2 Schematic of the diatom life cycle which includes long periods of asexual or vegetative growth and intermittent periods of sexual recombination and resting stage formation. Natural selection acts on all parts of the life cycle, including transitions between them. Population genetics (large gray circle) can measure the total amount of evolution that has elapsed, and it is measured over the entire life cycle. Laboratory evolution experiments focus mainly on selection for rapid or efficient vegetative growth (green circle), and resurrection experiments can detect evolution in resting stage formation, survival, and germination (blue circle). We currently lack studies on selection for gamete formation and recognition and sexual recombination events

reflective of diatoms in the field, and future estimates of Ne in other diatom taxa may vary widely. Combined with large census sizes, current estimates of Ne suggest that genetic drift is unlikely to play an outsized role in the genetic structure of diatom populations or strongly influence their patterns of evolution. In addition to the random fluctuations in allele frequencies imposed by genetic drift, gene flow and natural selection are the two remaining mechanisms that act to change the genetic composition of a population. Gene flow, or the transfer of genetic material from one population to another, requires both migration of individuals and

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successful subsequent interbreeding with others (Freeland 2019). Migration, or dispersal potential, is high in planktonic diatoms that drift passively with tides and currents. Passive tracers inserted into global circulation models show that the surface waters of the ocean are connected on time scales of decades (Jönsson and Watson 2016), suggesting that diatoms from any one part of the surface ocean have the potential to interact with diatoms in any other on time scales that could influence their population structure. Along their drift trajectories, environmental conditions, such as water temperature, can change dramatically (Doblin and van Sebille 2016), and the rate at which they mix with new water masses (and presumably new populations) varies with location (Kuhn et al. 2019). Finally, successful gene flow requires that members of both populations respond to the same environmental cues to initiate sexual reproduction and the ability of gametes to recognize each other (Montresor et al. 2016). Thus, despite the high dispersal potential of diatoms across the global ocean, successful gene flow requires that individuals survive the transit, mix with a new population, mate, and produce offspring. Insights into the role of gene flow in structuring diatom populations are described in Sect. 3 below. Natural selection, which alters the composition of populations as the frequency of better-adapted genotypes increases, is also important. The past action of selection in natural populations can be detected using measurements of divergence in genome sequence and, combined with expression data from laboratory studies, can also shine light on the genetic and metabolic basis of local adaptation (Mock et al. 2017). Similarly, the action of natural selection leading to local adaptation can be inferred by comparing isolates from different locations (Boyd et al. 2013). The action of natural selection can be studied using laboratory-based evolution experiments, which allow highly-replicated tests of how natural selection acts in the presence of specified biotic and abiotic challenges (Van den Bergh et al. 2018). Finally, mesocosm enclosures may offer a mid-way point between the realism of field studies and the control of laboratory ones (Scheinin et al. 2015) (Bach et al. 2019), and have shown results consistent with evolutionary processes such as genotype sorting on the timescale of weeks. The experimental evolution approach and our current understanding of how natural selection has the potential to shape diatom traits are described in Sect. 5 below.

3

Genetic Subdivision in Diatoms

The genetic subdivision of a species is characterized by population abundance, the allele frequencies of each population, and the degree of genetic isolation or divergence among populations (Chakraborty 1993). Population genetic structure plays a key role in how a species can adapt to its environment: the subdivision of a species into populations with restricted gene flow between them allows for selection to spread beneficial mutations through a population, leading to local adaptation (Bell 2008). The degree of isolation between populations dictates the integrity and longevity of a population and also influences how quickly mutations (both beneficial and deleterious) can spread through a species.

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Population structure in diatoms has been examined in a small handful of species, predominantly using microsatellite markers. Despite high potential for dispersal in planktonic diatoms, clear genetic subdivisions among populations have been identified. For example, multiple populations of the diatom Ditylum brightwellii were identified from a fjord ecosystem in the US Pacific Northwest over the course of several years (Rynearson and Armbrust 2004; Rynearson et al. 2006). Despite their predominantly asexual mode of propagation and high dispersal potential, the genetic divergence between them was high relative to distinct populations of marine metazoans such as fish (Knutsen et al. 2003). Importantly, laboratory isolates from genetically distinct populations of D. brightwellii had differential growth responses to light intensity (Rynearson and Armbrust 2004). The observed phenotypic variation suggested population genetic structure may be associated with populationspecific adaptations. Subsequent work showed that distinct field populations were associated with different nutrient and light levels (Rynearson et al. 2006), suggesting that phenotypic divergence identified in the lab can be a meaningful indicator of phenotypic differences between populations in the field. Some locations are occupied by resident populations that are present on time scales ranging from the duration of a bloom (weeks) to upward of 100 years. Intensive, daily sampling of a D. brightwellii bloom over 11 days during a sevenfold increase in cell number revealed a single population comprised of several thousand genotypes (Rynearson and Armbrust 2005). Interestingly, as the bloom progressed, some genotypes appeared to become more abundant than others suggesting that lineage or genotype selection can occur in a rapidly dividing field population, a phenomenon also observed in P. multistriata (Ruggiero et al. 2018) and inferred in Skeletonema marinoi (Scheinin et al. 2015). Individual populations may reside in or occupy a region on time scales longer than a bloom as well. For example, P. multistriata in the Gulf of Naples, Mediterranean Sea, was comprised of at least two resident populations whose dominance shifted from one year to the next (Tesson et al. 2014). On the longest time scales examined thus far, a single population of the diatom S. marinoi inhabited a Danish fjord for at least 100 years, which corresponds to over 40,000 mitotic generations (Härnström and Ellegaard 2011). Despite the movement of water and cells from a genetically distinct population identified nearby, a single fjord population was able to maintain itself over long time periods. This extreme example of a resident diatom population lends support to the hypothesis that the uniquely recirculating surface waters of a fjord provide a type of natural chemostat in which a population can become locally adapted (Rynearson and Armbrust 2004). Competition experiments between strains offer some hints as to the mechanisms that maintain genetic isolation between populations, even in the face of high dispersal potential. Strains of S. marinoi collected from the resident Danish fjord population and one found in nearby waters (Härnström and Ellegaard 2011) were grown in native and foreign water. Strains had higher fitness when grown in native water, reaching a higher biomass and outcompeting foreign strains when cocultured (Sildever et al. 2016). Although the mechanism of the growth advantage was not

Fig. 3 Conceptual diagram showing isolation by distance, or increasing genetic divergence with increasing geographic distance (blue line) and panmixia, or no change in genetic divergence with increasing geographic distance (green line)

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GeneƟc divergence between sites

The Population Genetics and Evolutionary Potential of Diatoms

Geographic distance between sites explored, the study provides experimental evidence that adaptation to local conditions can reinforce genetic subdivision in high dispersal diatoms. In contrast, this type of resident population does not appear to regularly inhabit the open ocean. In the North Atlantic, samples of the diatom Thalassiosira gravida collected from a single water mass during the progression of a spring bloom revealed that the bloom was comprised of several coexisting genetic populations (Chen and Rynearson 2016). The presence of multiple, genetically distinct populations identified within a single water mass suggests that the North Atlantic may be a site of large-scale admixture, with populations transported from disparate locations. Since diatoms are known to undergo sexual reproduction during blooms, the North Atlantic may also act as a site of gene flow between populations (Chen and Rynearson 2016). Population genetic analysis has also been used to examine geographic patterns of connectivity of planktonic diatoms. Dispersal and subsequent gene flow can influence evolutionary rescue in declining populations (Carlson et al. 2014), making it critical to understand the scale and geographic patterns of population structure. One method of examining the impact of geographic distance on the extent of gene flow is to test for isolation by distance, or the increasing genetic divergence between populations separated by increasing geographic distance (Wright 1943) (Fig. 3). Only two studies have tested for isolation by distance on global scales and their results conflict. The pennate diatom Pseudo-nitzschia pungens showed a strong isolation by distance pattern with populations becoming more genetically divergent with increasing geographic distance (Casteleyn et al. 2010). This type of pattern is significant because it indicates a geographic limit to the dispersal of cells and thus limited flow of genes from one population to the next. This pattern of geographic

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isolation can promote speciation, but it reduces the role that immigration can play in the evolutionary rescue of declining populations (Carlson et al. 2014). In contrast to P. pungens, global samples of the diatom Thalassiosira rotula revealed a set of genetically distinct populations whose divergence did not change with increasing geographic distance (Whittaker and Rynearson 2017). In several instances, the genetic divergence of T. rotula populations was taken from the same location, but at different times exceeded the genetic divergence of populations sampled from different ocean basins. Furthermore, the same genetic population was repeatedly sampled from different ocean basins, suggesting that individual populations have a realized global dispersal. Instead of geographic distance, the divergence of populations was most highly correlated with both temperature and surface water Chl a concentrations, suggesting that the realized geographic and temporal patterns of diatom populations may be driven by both environmental and ecological factors (Whittaker and Rynearson 2017). The differences in population genetic structure on large spatial scales between P. pungens and T. rotula may also be related to their divergent life histories and metabolic capacities. For example, P. pungens has no known ability to form resting stages and thus may be unable to survive periods of suboptimal growth conditions (Zhang et al. 2010). This scenario is similar to that posited in a modeling study of dispersal and selection that predicted limited phytoplankton dispersal due to the likelihood of encountering suboptimal growth conditions along drift trajectories (Ward et al. 2021). In contrast, T. rotula is known to form resting stages, enabling cells to survive periods of suboptimal environmental conditions and potentially allowing for unlimited geographic dispersal (McQuoid and Hobson 1995). With just two global-scale studies to date, it remains unknown what proportion of ecologically important diatom species follows a limited or unlimited dispersal pattern. This knowledge will be important for predicting the long-term impacts of climate change stressors on different diatom species. The importance of environmental selection on the spatial structuring of populations is evident in a number of studies in the Baltic Sea where S. marinoi populations showed a gradient of genetic divergence along a 1500 km transect that matched a strong salinity gradient (from 3 to 30 g kg 1) (Sjöqvist et al. 2015; Godhe et al. 2016). The S. marinoi populations from low salinity habitats were locally adapted and had optimal growth rates at a lower salinity than populations that were sampled from high salinity environments (Sjöqvist et al. 2015). Temporal shifts in S. marinoi population structure were additionally modified by local nutrient fields (Godhe et al. 2016), and models of gene flow and circulation indicated that genetic differentiation was also influenced by local oceanographic connectivity (Godhe et al. 2013). While rich patterns of population genetic structure have been frequently observed, there are exceptions. For example, the pennate diatom P. pungens appears to comprise one largely unstructured population in the North Sea (Evans et al. 2005). Overall, the population structure of fewer than 10 diatom species has been examined of the thousands of extant species, and thus with additional studies, exceptions may yet prove the rule.

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Genome-Wide Analyses of Diatom Population Genetics

Genetic diversity and population structure are two important elements underlying adaptive potential in all diatom species, and what we know about each is mostly supported by single- and several-locus (SSL) population genetic studies, as described above. However, recent advances in high-throughput sequencing, including whole genome sequencing and reduced representation sequencing, have led to a sea change in the ways that we both assess natural patterns of population genetic diversity and structure as well as infer the specific evolutionary mechanisms that shape and sustain them. Below, we describe some of the recent work applying these technological advances to diatom population genetics, highlighting early efforts to assess patterns of genome-wide differentiation, patterns of genomic repetition and rearrangement, and the inference of genome-wide selection. In general, early estimates of diatom genetic diversity using genome-wide markers relied on analyses of nucleotide diversity in single whole genome sequences. For example, the first diatom genome to be sequenced, T. pseudonana, had average nucleotide diversity levels similar to those found across mammal lineages (Armbrust et al. 2004). In the cold-adapted F. cylindrus, this pattern of variation was even stronger, and high levels of allelic divergence were found across the coding region of the genome, which was linked to allelic expression bias in diverse environmental conditions involving limited light and nutrient availability as well as low temperatures (Mock et al. 2017). Allelic divergence of this sort has also been explored in the model diatom P. tricornutum (Hoguin et al. 2021), which is suggestive of a broader pattern in expression bias in diatoms. These early findings using single whole genome sequences implied extensive genome-wide genetic variation, but more accurate and generalizable estimates of genetic diversity, and also substructure, require population-level genome sampling of multiple individuals, similar to SSL studies. Although rare, such studies are growing in number, mostly through the analysis of multiple strains of diatom species whose complete genomes have already been sequenced. For instance, in a recent deep whole genome resequencing (mean depth: 59x) of 10 P. tricornutum strains, Rastogi et al. (2019) comprehensively examined genetic diversity, identifying 4 distinct genetic clades with relatively high Fst values (0.2–0.4). While their Fst estimates are much higher than those found in previous studies of globally distributed diatom populations, the predicted number of populations corresponds well to previous global SSL analyses of both T. rotula and P. pungens (Casteleyn et al. 2010; Whittaker and Rynearson 2017). The P. tricornutum strains also showed a pattern of partial isolation by distance (Rastogi et al. 2019), mirroring the pattern observed in P. pungens. The P. tricornutum study illustrates how resequencing of select strains in a model species can be used to explore patterns of genetic substructure and demographic history. However, such studies have their limitations (e.g. time spent in culture varies among strains), and given massive reductions in sequencing costs, it is increasingly possible to design and

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carry out population genomic experiments that rely on contemporaneous sampling of natural populations, which in turn allow patterns of diversity and substructure to be more accurately estimated. In the only population genomics study focused on field populations published to date, Postel et al. (2020) utilized reduced representation sequencing and documented a strong pattern of genetic differentiation among Fragilariopsis kerguelensis populations along a north–south gradient in the Southern Ocean. Their results, in combination with physiological and morphometric analyses, suggest that local adaptation to warmer and colder pelagic conditions may be at the root of this divergence. These findings correspond well to earlier SSL efforts characterizing genetic diversity along salinity gradients in the Baltic Sea in S. marinoi (Sjöqvist et al. 2015, Godhe et al. 2016, see Sect. 3 above), and suggest an important role for seascape genomic analyses (Liggins et al. 2020) in identifying local adaptation in non-model diatom lineages. These initial diatom population genomic studies, using model- and non-model species, demonstrate that it is increasingly possible to interrogate specific, highly relevant aspects of marine microbial adaptive potential. One of the most intriguing topics in this regard is the role that structural variation plays as a source of genomic novelty and how its various forms both promote and constrain adaptation. To date, many types of structural variants have been found to be the genetic basis of adaptation. For example, copy-number variation can drive gene dosage effects (Dorant et al. 2020), genomic inversions can protect adaptive alleles (Fuller et al. 2019), and transposable element activity can underlie gene shuffling (Cosby et al. 2021). While none of these adaptive mechanisms have been investigated in diatoms, the growing list of sequenced diatom genomes is revealing a repeat-rich landscape that may be strongly shaped by such forces. For example, there now exist several repeat-annotated diatom genomes which demonstrate that repeat content can range from 4 to 50% (Roberts et al. 2020). Transposable elements in particular may underlie this variation, and in addition to their sheer abundance in some diatom genomes, they are highly diverse in composition (Maumus et al. 2009) and have been found to be transcriptionally active in both temperature and nutrient manipulation studies (Pargana et al. 2020). Other patterns of genome arrangement in diatoms point to additional structural variants, including inferred genomic duplication among species (Parks et al. 2018) and populations (Koester et al. 2010). Combined, these genomic architectural studies suggest that in the era of third-generation sequencing (e.g. broad use of PacBio and Oxford Nanopore long read technologies), we may come to appreciate standing structural variation, alongside standing point mutation, as a significant factor driving adaptive potential and ecological success in diatoms. Another valuable outcome of the advent of genome-wide analyses is the increased ability to infer both the degree and type of selection acting upon diatom populations. For example, it is now possible to assess in greater detail the relative importance of selection and dispersal limitation in shaping and maintaining population subdivision in diatoms (Bass-Becking 1934; de Wit and Bouvier 2006). As mentioned above, there is little consensus among SSL studies that have addressed this topic (Casteleyn et al. 2010; Whittaker and Rynearson 2017). To better infer selection’s role in structuring populations, the most useful and powerful population

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genomic methods (e.g. Fst outlier analysis, genotype–environment association analysis) require a large number of polymorphic markers, data only recently attainable to researchers of non-model natural populations. To date, only one study has addressed this topic in diatoms using genome-wide single-nucleotide polymorphism (SNP) data (Postel et al. 2020, see above). More generally, genome-wide SNP markers have also been applied to characterize patterns of selection in diatom populations, such as in the model marine benthic diatom Seminavis robusta (Osuna-Cruz et al. 2020). In this work, the authors used SNP patterns to reveal that various gene subsets (core vs. dispensable, sex-related, gene age, form of gene duplication) were subject to different selective pressures (e.g. purifying and positive selection). Such genomewide analysis of population genetic variation has greatly expanded the field’s scope of study from inferring selection in a few ecologically relevant genes among species (e.g. silica transporters; Alverson 2007) to entire exomes among individuals and populations. Overall, genome-wide analysis of marine diatom populations is facilitating the study of specific selective forces and adaptive processes that complement and reinforce SSL insights into diatom population structure and diversity. It is now within reach to attribute specific adaptive mechanisms, such as dispersal limitation or genomic rearrangement, to the unexplained patterns of genetic diversity identified in the first and second waves of molecular study of diatom populations.

5

The Power of Evolution Experiments

Given their extensive species diversity and broad range of habitats, diatoms clearly have the capacity to evolve in response to the environmental changes they have encountered thus far. Evolution experiments complement population genetic studies like those discussed above and offer tools for understanding principles of change in evolving populations by testing causal links between mechanisms that generate variation, and the action of natural selection in different types of environmental or biotic challenges. The power of microbial evolution experiments lies in being able to causally link trait evolution to a particular driver—an environmental change, demographic change, or even a change in the composition of the community present. Causal links can be established because many independently cultured replicates of a focal strain are subjected to defined environmental, demographic, or social changes for dozens or hundreds of generations, and then the evolved (or evolving) cultures are compared either to their own ancestor, or cultures that have been kept in control environments. The details of conducting an evolution experiment are discussed in detail in Elena and Lenski (2003) and Van den Bergh et al. (2018). Trait evolution is often measured as irreversible changes in trait values in a culture over time, using reciprocal transplant experiments. The component of the trait value change that is irreversible when the evolved culture is placed back in the control or ancestral environment is heritable (usually due to genetic change, but see the Chapter “Trait-Based Diatom Ecology” in this book on heritable epigenetic

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changes), and evidence of evolution. This is the same concept as is used to detect evidence of local adaptation in strains of the same genus or species isolated from different environments (Sildever et al. 2016). The amount of adaptation (fitness gain due to the action of natural selection) associated with phenotypic evolution that occurs is quantified as changes in a fitness proxy. This proxy is usually growth rate in a single-genotype culture, or change in frequency in mixed culture. Evolutionary change in traits of interest (cell size, thermal tolerance, nutrient requirements) can also be quantified. Combined with change in cell division rates or lineage frequencies, trait evolution can then be used to explore how the ecological or biogeochemical role of diatoms could shift over time in response to environmental changes. While finding the specific mutations that underlie adaptation is possible (Van den Bergh et al. 2018), it is also labor intensive and difficult, and ultimately requires being able to introduce the candidate mutation(s) into the ancestral genetic background or doing a genome-wide association study (GWAS) study. These methods are not always feasible, or a good use of resources, for many model systems and questions. More commonly, changes in trait values, combined with information about underlying metabolism or gene expression patterns, are used to understand how metabolic processes have changed, rather than directly linking causal mutations to fitness change (Collins et al. 2019). The level of control and the ability to link cause and effect unambiguously using laboratory evolution experiments come at a cost—evolution experiments are not, and are not intended to be, realistic representations of evolution in natural habitats. Evolution experiments, whether in the lab or field, necessarily use simplified environments and communities. While it is possible (and sometimes desirable) to study more complex patterns of environmental change and populations, laboratory experiments are not reconstructions of natural systems, but rather “wet simulations” that aim to simplify natural systems in order to understand how key processes within them work. Experimental evolution with marine microbes, in particular, aims to uncover the principles driving trait evolution, and it is these principles, rather than the realized phenotypic outcomes of laboratory evolution experiments, that can be applied to understanding how diatoms evolve in the ocean. For example, a recent experiment by Leung et al. (2020) evolved 32 lines of the green alga Dunaliella salina under 39 independent time series for 4 levels of predictability (a total of 156 time series), for several hundred generations. Except for the control environments, all environments had fluctuations in salinity. The fluctuation regimes were not chosen to reflect a particular environment outside the laboratory, but to study how a key characteristic of all fluctuating environments (autocorrelation) affects the evolution of trait plasticity. Because the experiment was able to statistically isolate environmental autocorrelation from the specific pattern of environmental fluctuations within a given autocorrelation regime, these results are likely to generalize to other taxonomic groups, including diatoms. One key to this level of generalization is that such experiments have high statistical power due to the level of replication of each treatment (time series predictability) as well as sufficient treatment levels (four levels) to detect a trend.

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Natural selection acts on fitness, and fitness is always context dependent. For an asexually dividing microbe, fitness can be defined as the number of descendants produced over a fixed time interval (cell divisions/day, for example). Laboratory evolution experiments can artificially constrain fitness to be highly correlated with maximum cell division rates by using semicontinuous cultures that keep cells in exponential growth, or to be highly correlated with efficient nutrient acquisition by using chemostats. In natural populations, however, the number of offspring produced will also be affected by susceptibility to grazing, resistance to viruses, formation of resting spores, and the frequency of sex and recombination (Godhe and Rynearson 2017). Since periods of asexual cell division exist in all diatom life cycles, laboratory selection experiments reasonably assume that in natural settings, cell division rates are a component of fitness. However, it is important to bear in mind when interpreting the results of experiments that they are not the only component of fitness, nor do we have estimates of how important asexual growth is relative to other life cycle stages for determining overall lineage success in natural settings. This is discussed further in Sect. 8 below.

6

Evolutionary Potential in Single-Genotype Populations

There is overwhelming evidence that diatoms and other phytoplankton, even without any standing genetic variation, readily adapt in response to environmental change in laboratory experiments, with most of the single-driver experiments focusing on responses to warming or changes in CO2. These experiments usually aim to do one or more of the following: (1) test the adaptive capacity and limits of diatoms to respond to components of projected climate change in the ocean; (2) understand how changes to particular trait values increase fitness in the focal environments; and (3) understand tradeoffs between traits. One example of how changes to particular trait values are associated with adaptation is the evolution of thermal reaction norms in diatoms, which vary dramatically between phytoplankton from different environments (Thomas et al. 2012). In experiments where diatoms from the genus Thalassiosira were evolved for at least 300 generations under different temperature conditions, cultures evolved under warmer conditions also evolved higher optimal temperatures (Topt) (Schaum et al. 2018; Zhong et al. 2021). This trait change was explained by lowered respiration, so that more carbon was allocated to growth in warm environments after populations evolved there than expected based on the initial plastic response of the populations to that same temperature. Other changes associated with adaptation to warming included transcriptional changes to genes involved in transcriptional regulation, oxidative stress, and redox homeostasis (Padfield et al. 2016; Schaum et al. 2018). In evolution experiments, responses to the environment where the cells evolved, such as an increase in Topt in populations that evolved in warmed environments, are direct responses to selection. Direct responses involve changes to traits associated with evolution in environment(s) where the population actually evolved. In addition

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to direct responses to selection, there are also indirect or correlated responses, which are changes in traits that are not directly under selection. For example, cells evolved under high temperatures, but in nutrient-replete media, have higher nutrient requirements (Thomas et al. 2017; Aranguren-Gassis et al. 2019). There are two possible reasons for this correlated response: The first is that there is simply no selection against high nutrient requirements in the high-temperature environment. Even though other adaptive solutions with lower nutrient requirements may exist, the most accessible or likely adaptive solution for increasing temperature tolerance under the experimental conditions involves using more nutrients. If this is the case, populations in warm, low-nutrient environments could still evolve high-temperature tolerances; they would simply do so in a different way from populations in warm, high-nutrient environments. A second possibility is that there is a tradeoff between adapting to high temperatures and evolving lower nutrient requirements. Since warming is often associated with decreased nutrient availability in oceans, one key tradeoff that has been examined is how nutrient levels affect evolutionary responses to warming. In this case, populations in warm, low-nutrient environments would be less able to evolve higher temperature tolerances than those in warm, high-nutrient environments. For example, Aranguren-Gassis et al. (2019) evolved Chaetoceros simplex in warm environments that were either N-replete or N-limited, and found that replicates in N-limited environments did not evolve high-temperature tolerances after 200 generations, but replicates in N-replete environments did. The authors suggested that adapting to high temperatures requires investment in nitrogenintensive processes, such as increased production of proteins associated with heatshock, photosynthesis, or other mechanisms, limiting the evolutionary response of cells in N-replete conditions. A similar tradeoff of thermal adaptation and nutrient requirement was found in a dinoflagellate system (Baker et al. 2018) where hightemperature specialists had higher nutrient requirements for carbon, nitrogen, and phosphorus, though this experiment had no biological replication. However, consistent results over several experiments and taxa suggest that the tradeoff between nutrient requirements and adaptation to warming may be general. The outcomes of evolution experiments can lend insight into the metabolic underpinnings of traits and test assumptions about the basis of fundamental tradeoffs. For example, experiments in green algae have shown that the direct responses to selection for very rapid cell division (Lindberg and Collins 2020) or size (Malerba and Marshall 2019) depend on the ability of cells to deal with reactive oxygen species (ROS). Since the production of reactive oxygen species is common to all cells, and a necessary by-product of photosynthesis, experiments like this can lend insight into principles of change that have the potential to apply across taxa, including diatoms. Malerba and Marshall (2019) selected the green alga Dunaliella for either progressively larger or smaller cells over ~290 generations, eventually obtaining a 13-fold size difference in evolved cells that originated from a common ancestor. They then tested the evolved replicates at different temperatures and found the expected result that warming increased fitness in small cells but decreased fitness in large ones. This was interesting because the usual explanation for cells being

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smaller in warmer parts of the ocean is that they are nutrient limited, but in this experiment, nutrient limitation did not co-occur with warming, so could not explain the differences in temperature sensitivity between small and large cells. Instead, these results suggest that small cells are preadapted to high temperatures by virtue of being small, perhaps because they are less vulnerable to reactive oxygen species. Along the same lines, Lindberg and Collins (2020) evolved Chlamydomonas under high-nutrient, high CO2, constant light conditions, selecting for extremely rapid population growth, and found that populations evolved under this regime also evolved higher ROS and heat-shock tolerances than populations grown under conditions favoring more moderate growth rates. While neither of these experiments initially intended to select for ROS-related traits, their outcome suggests that ROS likely plays a key role in growth rate and cell size evolution under different temperature regimes. Single driver experiments are useful in that they elucidate fundamental links between key environmental changes and adaptive trait change. However, due to tradeoffs and correlations between traits, evolutionary responses to multiple simultaneous environmental changes can differ from cases where those same drivers change individually. For example, while diatoms adapt readily to high temperatures and lowered nutrients individually, they do not adapt very much to high temperatures and low nutrients at the same time (Aranguren-Gassis et al. 2019). In addition to tradeoffs between traits limiting adaptation to multiple environmental drivers, the drop in fitness caused by drivers, which is related to the strength of selection and subsequent rate of adaptation, may differ dramatically with the number of drivers present in an environment, regardless of interactions between drivers (Brennan et al. 2017). Multiple driver experiments pose logistical challenges in laboratory and seminatural experiments, as including multiple environmental gradients, in combination, quickly leads to uninterpretable or unmanageably large experiments. In an effort to design feasible experiments, most multiple driver studies to date use an ANOVA-type design, where only two levels of each driver are used (Boyd et al. 2018), typically present and projected end of century values, though warming experiments tend to use larger steps. While this can detect driver interactions for the levels used, it assumes linear responses to individual drivers and, depending on the response curves of the drivers being considered, can lead to apparent interactions between drivers if response curves are nonlinear and differ between drivers, which we know to be the case for key drivers such as temperature and CO2 in coccolithophore responses (Seifert et al. 2020). Coming to grips with designing informative multiple-stressor experiments is a major challenge in physiological and evolutionary studies on diatoms and global change (Boyd et al. 2018).

7

Plasticity and Fluctuating Environments

In ecosystem and biogeochemical models, phytoplankton, including diatoms, are represented through their plastic responses to environmental change—temperature response curves and nutrient uptake rates are two common examples of plastic

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reaction norms. These reaction norms are determined partly by the environmental experience of the population. The question of whether or not a certain degree of plasticity is adaptive (whether populations are locally adapted to patterns of environmental fluctuation) has been thoroughly addressed theoretically (Gavrilets and Scheiner 1993; Lande 2009; Botero et al. 2015; Tufto 2015), and reviewed in the context of marine organisms (Reusch 2014; Collins et al. 2019). Theoretical studies find first that the pattern of environmental variation, in particular the predictability of environmental fluctuation, is a main driver of the adaptiveness of plasticity and its evolution (see Sect. 5). Higher plasticity is expected to evolve in predictably fluctuating environments, whereas reduced plasticity should evolve in less predictable environments, as this can lead to a mismatch between plastic responses and future selection pressures (Gavrilets and Scheiner 1993; Lande 2009; Reusch 2014; Botero et al. 2015; Tufto 2015; Collins et al. 2019). Lower plasticity is also expected to evolve in constant environments if there are costs associated with maintaining or expressing plastic responses (DeWitt 1998). These expectations are largely supported by comparative studies finding that diatoms from more variable environments tend to be more plastic than comparable isolates from less variable environments (Sackett et al. 2013; Schaum et al. 2013; Stock et al. 2019). For example, Sackett et al. (2013) and Petrou and Ralph (2011) examined the plastic responses of three Antarctic diatoms that live either in diverse Antarctic habitats (F. cylindrus and C. simplex) or are meltwater specialists (Pseudo-nitzschia subcurvata). The meltwater specialists had lower plasticity in terms of macromolecular pool production (proteins, lipids, carbohydrates, amino acids) and were less able to maintain photosynthetic efficiency under sea ice, suggesting that environmental specialization (lower variation in growth environments) selects for lower plasticity across multiple traits. Aside from the work of Leung et al. (2020) on D. salina confirming theoretical predictions that lower degrees of morphological plasticity evolved in less predictable environments, few experiments have been conducted that systematically test how differences in patterns of environmental variation affect trait plasticity. From the examples above, it is clear that the evolution of thermal tolerance can be rapid, but depends on other factors, such as nutrient availability, and whether temperature increases are stable or fluctuate. The latter was explored by Schaum et al. (2018) by evolving replicate populations of T. pseudonana in different thermal regimes: a control regime that reflected the long-term culturing conditions of the strain, moderate warming (+4  C), severe warming (+10  C) and a fluctuating regime that alternated predictably between control temperatures and severe warming every 3 to 4 generations. Here, thermal tolerance evolved in all warmed treatments. However, populations in the severe regime showed dynamics consistent with evolutionary rescue where growth rates declined and then remained low for ~100 generations, and then rapidly increased. In the other two warming regimes, growth rates increased and remained high for the duration of the experiment. This demonstrated that while adaptation to severe, constant warming was slow, adaptation to the same extreme temperature occurred rapidly if it was part of a fluctuating regime. There were also differences in the absolute growth rates of the evolved cultures after ~300 generations of evolution. While all cultures in warmed regimes

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had higher optimal temperatures than their ancestors, the populations evolved under severe warming had lower growth rates at all but the most extreme temperature tested (+18  C). The populations also showed different patterns of trait evolution and tradeoffs, depending on which thermal regime they evolved in. Plasticity can also affect subsequent evolution, and both theory and experiments suggest that plastic responses affect evolutionary ones. There are clear conceptual links between plasticity and evolution (Merilä and Hendry 2014; Ghalambor et al. 2015; Murren et al. 2015; Kelly 2019; Leung et al. 2020), yet few direct experimental tests of the links between plastic and evolutionary responses. In the case of a deleterious environmental change, adaptive plastic responses can allow organisms to “buy time” for adaptation by persisting for longer at higher abundance in poor environments, thus increasing the chances or speed of evolutionary rescue. In this case, plasticity can facilitate evolution. However, adaptive plasticity also keeps fitness high, thus shielding populations from natural selection, which can slow genetic evolution (Kronholm and Collins 2015; Walworth et al. 2020). In addition, phenotypes expressed through plasticity can become fixed in the population through genetic assimilation, leading to a new, nonplastic type, and can potentially lead to more rapid evolution than expected through random mutations alone (Denman 2017). Not all plastic responses are adaptive; initial plastic responses can be maladaptive, or become maladaptive if sustained for long periods of time, such that plastic lineages are under stronger selection, and evolve more, than less-plastic ones, provided they do not go extinct (Ghalambor et al. 2015). Finally, components of plastic responses can reverse gradually over time due to shifts in traits under selection or due to metabolic compensation, even in constant environments (Schaum et al. 2015; Barton et al. 2020; Lindberg and Collins 2020). All of these relationships are seen in evolution experiments or models with marine microbes. While diatoms do show intraspecific variation in plastic responses on which natural selection could act, they are not often used as model systems to test relationships between plastic and evolutionary responses to environmental change.

8

Dynamics in Multigenotype Populations

Diatoms exist in genetically diverse populations, where even near-monospecific blooms have high genetic diversity (reviewed in Godhe and Rynearson 2017). This means that there is ample opportunity for lineage sorting in diatom populations, where the frequency of lineages within populations changes, thus shifting bulk trait values (such as biomass production or nutrient uptake rates) associated with the population. Experimentally, this has mainly been addressed by studies that measure changes in lineage frequencies in multilineage cultures. These experiments are revealing because they test the assumptions made in ecological models where lineage frequencies depend solely on differences in resource acquisition and use (Dutkiewicz et al. 2019), or our general understanding of microbial competition (Ghoul and Mitri 2016), as well as test the assumption that the bulk properties of mixed lineage assemblages are predictable from the monoculture properties of their

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component parts. The latter is not discussed here, but we point readers toward Sjöqvist and Kremp (2016) and Wolf et al. (2019) as starting points. Broadly speaking, faster growing lineages can increase in frequency but do not do so reliably in laboratory experiments. For example, Wolf et al. (2019) grew Arctic isolates of Thalassiosira hyalina under present-day and future temperature and CO2 combinations, and found that in a 6-lineage mixture, the lineage that grew fastest in monoculture did indeed reach the highest frequency in mixed culture under future temperature/CO2 conditions, but that the frequency was higher by almost 20% than predicted based on monoculture growth. However, under present-day conditions, the fastest growing lineage failed to become the most frequent lineage in mixed culture, and overall, monoculture growth rates were poor predictors of the composition of mixed cultures. An experiment by Roger et al. (2012) grew four strains of S. marinoi under different combinations of salinity and temperature stress, and found that one of the strains rose to very high frequency (~80%) under both control and low salinity conditions, despite minimal differences in growth between the strains in monoculture. These experiments point toward the possibility that diatoms respond to the presence of nonself conspecifics, and that this response can include modulating cell division rates. This possibility was investigated explicitly in the (nondiatom) picoplankton Ostreococcus. Collins and Schaum (2021) demonstrated that Ostreococcus responded to the presence of, and cues from, nonself conspecifics at low cell densities in nutrient-replete environments by modulating their cell division rate and carbon allocation, and that the magnitude of responses to conspecifics or cues from them was strain and environment specific. While this has not been demonstrated for diatoms, it does show that in the absence of resource competition or obvious antagonistic effects such as allelopathy, phytoplankton detect and respond to nonself cues, which is consistent with the results of Wolf et al. (2019), and studies in the freshwater alga Chlamydomonas finding poor correlations between fitness estimates in pure and mixed culture (Collins et al. 2014; MeleroJiménez et al. 2021). The repercussions of lineage sorting, regardless of its predictability, can have striking effects on species dominance. For example, Listmann et al. (2020) competed multilineage assemblages of the coccolithophore Emiliania huxleyi and the diatom Chaetoceros affinis together under control and increased CO2 for >100 asexual generations. While neither species adapted strongly to high CO2 in terms of shifts in cell division rates, both experienced lineage sorting that left only a single dominant lineage. In both cases, complete lineage sorting (one lineage left) within species coincided with dominance shifting from the diatom to the coccolithophore. The link between lineage sorting and species dominance is correlational here but suggests that links between lineage sorting and species sorting bear investigating in future studies.

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Future Directions

In this last section of the chapter, we highlight some remaining challenges in the effort to determine how evolutionary processes have shaped and will continue to shape natural populations. One of the challenges of research on diatom population genetics has been the need to obtain single cell isolates from the field. Even with the advent of whole or partial genome sequencing methods, the use of individual diatom isolates has remained a key component of the work flow. New culture-independent methods are being developed that will help to alleviate the need to isolate single cells including a pooled genotyping method that requires only filtered biomass from natural communities (Wolf et al. 2021). Although this approach requires prior development of species-specific microsatellite markers, it allows for the processing and analysis of many more water samples than conventional approaches. Another promising development is the use of metagenomic sequences. New software is being developed to interrogate gene-specific polymorphisms in metagenomics datasets, to which a whole suite of classical population genetic analyses can be applied (LasoJadart et al. 2021). Due to the challenges of sequencing depth and genome complexity, metagenomics approaches have been thus far restricted to examining bacterioplankton population structure across both environmental gradients and through time (Kashtan et al. 2014; Sjöqvist et al. 2021). Improvements in accurate variant calling in diploid organisms should soon allow similar approaches to be conducted on diploid eukaryotic plankton. Overall, many newly-developed methods take advantage of low-cost, high-throughput sequencing and computational resources to address population genomics questions with high statistical power at large spatial and temporal scales. Such scales may prove invaluable for phytoplankton studies in particular given the dynamism and connectivity of the sunlit ocean. Although high-throughput sequencing has increased our ability to examine genotypic variation across whole genomes and even whole communities, we are still left with the challenge of connecting genetic diversity with trait diversity in natural populations. Because we cannot realistically measure all key variables for all key species in the lab (Boyd et al. 2013), an ultimate goal is to map functional gene variation or gene expression to trait values. This linkage will provide fundamental information to understand how populations can evolve in their natural environment (Collins et al. 2019). At present, there is no single methodology to link genotype with phenotype, and additional sequencing is unlikely to reveal phenotypes of interest. The pairing of methodologies however shows promise. For example, advancements in single-cell genomic sequencing could be combined with fluorescence-activated cell sorting and whole genome amplification to connect phenotypes measurable using flow cytometry (e.g. chl a per cell) with genotype, improving upon limited population sample sizes and allowing for increases in statistical power that will expand our ability to interrogate natural populations (Woyke et al. 2017). Finally, careful laboratory experiments that map the extent of trait variation in a species using many strains continue to be necessary and provide insights into how much standing phenotypic variation exists in natural populations

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(Rynearson and Armbrust 2000; Boyd et al. 2013; Anderson and Rynearson 2020; Ajani et al. 2021; Samuels et al. 2021). We point out that most trait measurements and evolution studies are done in cultures that are dividing asexually, often in exponential growth phases (Fig. 2). This is convenient because it allows easier intercomparison of studies, allows fitness for evolution experiments to be defined, and informs how natural selection acts during periods of rapid asexual growth, which are important for the roles of diatoms in trophic transfer and nutrient cycles, and which are necessary for fitness to be positive (cells cannot leave any descendants if they fail to divide). However, to contextualize these studies, we need a more refined view of diatom life cycles, and how natural selection acts at different points in them. We have evidence from other systems that life cycle evolution can be the main detectable evolutionary response to environmental change. For example, Hinners et al. (2017) used a resurrection study in the dinoflagellate Apocalathium malmogiense to show that “resurrected” cells from recent and 100-year-old sediments had different encystment rates along a temperature gradient, but that other key traits did not differ between recent and older cells, suggesting life cycle evolution is a key response to Baltic Sea warming in this species (Hinners et al. 2019). In terms of diatom resting stages, there appears to be variation upon which selection can act. For example, in a given species, the cues for resting stage formation are inconsistent among studies (McQuoid and Hobson 1996). It is notable that different studies used different strains, which we now know are most likely genetically distinct. In addition, Rynearson et al. (2013) showed that open ocean populations of the diatom Chaetoceros aff. diadema formed resting spores over at least a two-week period, suggesting standing variation exists in the cues for resting spore formation among natural populations. Finally, interactions with other community members such as bacteria, predators, and viruses have the potential to affect most parts of the life cycle or modulate transitions between them. For example, Cirri et al. (2019) showed that naturally co-occurring bacteria associated with the diatom S. robusta significantly decreased the success of sexual reproduction by causing reduced production of sexual pheromones. If intraspecific variation in pheromone production or detection exists, then selection has the opportunity to act on this aspect of the life cycle. Copepod predation has been shown to differentially affect genotypes of the diatom S. marinoi, including chain length and growth rates, suggesting that predation can be an important factor structuring diatom populations (Sjöqvist et al. 2014). Together, these studies suggest that contemporary evolution has the potential to play a significant role across all components of the diatom life cycle and that drivers of evolution include extrinsic factors such as bacteria and predators as well as changes to the abiotic environment, such as warming. Diatoms play key roles in aquatic systems and have high diversity that precludes making an exhaustive catalogue of the evolutionary potential of all relevant species. While no single method or framework can answer the question of how diatoms evolve, and their potential for change in the future, a combination of methods can provide a generalizable, mechanistic understanding of how both the raw material for evolution (variation) is generated and how

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natural selection sorts through the impressive genetic and trait variation present in diatoms.

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An Integrated View of Diatom Interactions Flora Vincent and Chris Bowler

Abstract

Marine microbial communities, composed of bacteria, archaea, and protists, as well as viruses, play essential roles in the functioning and regulation of Earth’s biogeochemical cycles and in providing resources at the base of marine food webs. Their roles within planktonic ecosystems have typically been studied under the prism of bottom-up research, namely, understanding how resources and abiotic factors affect their abundance, diversity, and functions. However, how species interact with each other is critical to form the ecosystems that sustain life on Earth. Top-down direct interactions (such as symbiosis, viral infection, or epibiosis) drive coevolution, influence species distribution, contribute to ecosystem stability, and affect global biogeochemical cycles. Diatoms are an extremely good case study for exploring biotic interactions. They are pivotal in marine microbial communities and are known to interact with numerous other organisms in the ocean. These interactions can provide insights about why diatoms can thrive in oligotrophic waters, how they can outcompete other organisms in eutrophic conditions, and ultimately how these interactions impact plankton communities and evolution. Keywords

Diatoms · Biogeochemistry · Microbial communities · Biotic interactions

F. Vincent (*) Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel e-mail: fl[email protected] C. Bowler Institut de Biologie de l’Ecole Normale Supérieure (IBENS), PSL Research Université, Paris, France # Springer Nature Switzerland AG 2022 A. Falciatore, T. Mock (eds.), The Molecular Life of Diatoms, https://doi.org/10.1007/978-3-030-92499-7_3

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Abbreviation 3D CO2 DDA DiOC6 DNA e-HCFM HNLC MAST NO3O2 PUA rRNA T C

three dimensions carbon dioxide diatom–diazotroph associations 3,30 -dihexyloxacarbocyanine iodide deoxyribonucleic acid environmental high content fluorescence microscopy high-nutrient, low-chlorophyll marine stramenopile nitrate dioxygen polyunsaturated aldehydes ribosomal ribonucleic acid temperature

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Introduction

1.1

Biotic Interactions in Phytoplankton

Diatoms are prolific phototrophic organisms that inhabit the open ocean, polar waters, tropical waters, all fresh water areas, soil, snow, and even glacial ice. They dominate phytoplankton communities in well-mixed coastal and upwelling regions, as long as sufficient light, inorganic nitrogen, phosphorus, silicon, and trace elements are available to sustain their growth (Morel and Price 2003; Pierella Karlusich et al. 2020). In particular, diatoms can be at the source of massive algal proliferations called “blooms” that last weeks or longer, and are often triggered by bottom-up factors such as incident irradiance, nutrient availability, and surface mixed layer shallowing (Platt et al. 2009). Diatom blooms typically occur in the early spring and last until late spring or early summer. This seasonal event is characteristic of the temperate North Atlantic Ocean, subpolar, and coastal waters. Yet, diatom blooms cannot be explained just by the fact that they have a superior environmental tolerance or more efficient nutrient uptake systems relative to other photosynthetic blooming organisms. Several additional explanations involve biotic interactions between diatoms and other members of the plankton. A decade ago, Smetacek introduced a top-down view of diatom biology, arguing that the evolution of plankton was likely ruled by protection against grazing, and not by competition for resources, and therefore that the interpretation of blooms as being the outcome of superior environmental tolerance and resource competition among photosynthetic protists was incomplete. For him, the many different morphologies and life histories of diatoms reflected responses to specific top-down pressures such as predation. Our understanding of the evolution of form and function in terrestrial

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Fig. 1 The original mandala of Margalef, R. (1978) “Life-forms of phytoplankton as survival alternatives in an unstable environment.” Oceanologica Acta, 1493–509

vegetation—driven by competition for resources and resource space (bottom-up)— could thus not be applied to phytoplankton. In particular, predators, pathogens, and parasites represent the 3 “Ps” of Smetacek: top-down drivers of phytoplankton evolution, composing the “mortality environment” (Smetacek 2012). Is this reflected in the fact that diatom species dominating blooms experience less grazing mortality than do co-occurring diatom species (Assmy et al. 2007; Strom et al. 2007)? Despite the strong biotic and abiotic selective pressures that seem to weigh on diatom biogeography and evolution, they are considered as successful r-selected species (Armbrust 2009). r-selection is an evolutionary strategy in which species can quickly produce many offspring in unstable environments, at the expense of individual “parental investment” and low probability of surviving to adulthood, such as rats. This is opposed to K-selection, in which species produce fewer descendants with increased parental investment such as elephants or whales (Pianka 1970). The r-K gradient of microalgae evolutionary strategies can be situated in Margalef’s mandala, an insightful road map providing the variations of phytoplankton composition in time and space, and the causes of these variations (Fig. 1). Margalef’s mandala maps phytoplankton species into a phase-like diagram defined by turbulence and nutrient concentrations that divide the space into four domains (Margalef 1997; Wyatt 2014). Diatoms thrive in high-nutrient and high-turbulence environments, in the top right corner of the mandala, such as upwelling regions, at the expense of the other major phytoplankton groups, for instance, dinoflagellates and haptophytes. However, Margalef’s mandala only incorporates bottom-up governing rules related to nutrient acquisition and mixing regimes, largely overlooking top-down factors. How diatom species interact is critical for life in the ocean. They support the microbial community by releasing copious amounts of photosynthesis-derived polysaccharides as well as small molecules and can extend their own ecological

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niches thanks to symbiosis with nitrogen-fixing bacteria (Foster and Zehr 2006). They are then grazed upon by eukaryotic microzooplankton, transferring carbon to higher trophic levels, which makes them the foundation of the marine food web. The discovery of viruses has challenged this view, suggesting that upon viral lysis, a large proportion of algal biomass can be redirected to heterotrophic prokaryotes and the deep sea, thereby shunting carbon away from zooplankton, and potentially remodeling our understanding of the fate of carbon in the ocean (Yamada et al. 2018). Diatoms are an extremely good case study for biotic interactions. They are pivotal in marine microbial communities and are known to interact with numerous other organisms in the plankton. These interactions can have a big impact and provide insights about why diatoms can thrive in oligotrophic waters, how they can outcompete other organisms in eutrophic conditions, and ultimately how these interactions shape planktonic communities. The goal of this chapter is to provide the reader with an integrated view of known diatom biotic interactions, across all domains of life. Excitingly, recent advances suggest that the reservoir of diatom interactions to be discovered is immense, as is their potential to transform our understanding of ecosystem functioning and eukaryotic cell evolution.

1.2

Studying Biotic Interactions

There are many ways to describe biotic interactions—by their type (antagonistic or mutualistic), their strength (weak or strong), their specialization (specialists or generalists)—though it is, in practice, difficult to make microbial interactions fit in one box as many of them are still not mechanistically understood and change depending on circumstances. The words interaction and association are used interchangeably in the chapter and can be primarily classified in two distinct groups: mutualism and antagonism (Fig. 2). Mutualism involves the exchange of goods and services among two species, which become mutualistic partners. Each partner receives a benefit from the interaction, but this generally has a cost. The benefit is not always equal, and, in any case, species do not behave altruistically. Instead, the benefit is considered as an unintended consequence of the interaction, by which species pursue their own selfish interest (Bronstein 1994). Mutualism can break apart due to changes in circumstances, or develop into mandatory ones (detailed below). Emblematic examples in the terrestrial realm are represented by flowering plants and animal pollinators, or acacia trees and the ants that live in them and protect them in return, or between plants and fungal species that form mycorrhizae. Antagonism, on the other hand, is an association in which one organism gains benefit at the expense of the other. In predation, one bigger organism often captures biomass from a smaller one and kills it. In parasitism, the smaller parasite will acquire food and shelter from a bigger host but will not kill it, contrary to parasitoids that kill their host. For instance, the Lithognathus fish is parasitized by the Cymothoa exigua crustacean, which replaces the fish’s tongue to feed on its blood and mucus,

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Fig. 2 Summary of ecological interactions between different species. The wheel display by Lidicker has been adapted to summarize all possible pairwise interactions. For each interaction partner, there are three possible outcomes: positive (+), negative (), and neutral (0). For instance, in parasitism, the parasite benefits from the relationship (+), whereas the host is harmed (); this relationship is thus represented by the symbol pair +. In some cases, one of the species has neutral feedback, while the other one benefits (commensalism) or is harmed (amensalism) by this association (figure from Faust et al. 2012)

without apparent damage to the host (Brusca et al. 1983). Hosts and parasites coevolve, shaping the evolutionary arms race, in which the short generation time of the parasite generally provides quicker adaptation relative to the host (Dunne et al. 2013). Interspecies interactions can be hard to observe in situ, especially in communities of microorganisms, and much of our understanding today comes from terrestrial environments, primarily through studies of plant–parasites, plant–pollinator, or macroorganism predation (Bascompte and Stouffer 2009). Each of these forms can be further (nonexhaustively) characterized by: • The degree of dependence: Is the interaction obligate or facultative? If obligate, species totally rely on one another for goods and services, such as obligate parasites that depend on their host to complete their life cycle. If facultative, one of the partners can be replaced by another species without affecting the benefit for the other partner(s) (Wootton and Emmerson 2005) or the interaction can completely breakdown with both partners able to survive. • The degree of specificity: Is the interaction between pairs of species (specialists), or pairs of groups (generalists)? Specific mutualism between two species is rare (e.g., fig plants and fig wasps), whereas generalist interactions are more common, e.g., whereby honey bees are known to visit the flowers of multiple plant species. Such phenomena lead to highly interconnected networks of plant–pollinator interactions (Vázquez and Aizen 2004).

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• The degree of physical associations: Are the partners physically close when they interact? They are defined as exhabitational when species such as pollinators live separately from the plants they interact with or ectoparasites that live on the skin of their host. But they are defined as being inhabitational if the partners live with one another (Dick 1999). Diatom interactions are cumbersome to study. They are difficult to observe in the natural environment and hard to establish in the laboratory. When detected in the environment, their study in situ is often limited to descriptive assays and bulk analysis of extremely complex communities. When model systems are established such as cocultures, they significantly advance molecular and mechanistic understanding, but adopt a reductionist approach, creating artificial conditions that are hardly seen in the natural context. Single-cell technologies and more advanced cell biology high-throughput techniques, that are increasingly being used both in the laboratory and in the field, have the potential to connect mechanistic and holistic approaches by providing the means to link individual microbes with population dynamics. Despite the aforementioned difficulties, the body of known diatom biotic interactions is dense, revealing the complex network of marine microbial associations, but also the current limits of our knowledge.

1.3

Diversity of Known Interactions

To date (September 2020), the most comprehensive inventory of confirmed diatom biotic interactions reports a total of 1533 associations from over 500 papers involving 83 genera of diatoms and 588 genera of other partners, illustrating a diversity of association types, such as predation, symbiosis, allelopathy, parasitism, and epibiosis, as well as a diversity of partners involved in the associations, including both prokaryotes and eukaryotes, micro- and macroorganisms (Vincent and Bowler 2020). It revealed that most validated interactions are predatory (58%), involving freshwater diatoms, and that overall our knowledge produces a highly centralized network containing a few diatoms mainly subject to grazing or epiphytic on macroorganisms, overlooking bacterial and viral interactions (Fig. 3). Yet, zooming on emblematic examples within each interaction type enables us to better appreciate the central role of biotic interactions in shaping diatom biogeography and evolution.

1.4

Antagonistic Interactions

1.4.1 Predation Diatoms are often referred to as the “pastures of the sea” (Smetacek 2001). Indeed, out of the myriad of mechanisms that can induce phytoplankton mortality or remove phytoplankton biomass, such as viral lysis or sinking, predation is considered quantitatively dominant (Calbet and Landry 2004), maintaining ratios of primary

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Fig. 3 Current knowledge of diatom biotic interactions based on literature surveys. KRONA plot based on available literature concerning diatom associations mined and manually curated from Web of Science, PubMed, and Globi and made available online (https://doi.org/10.5281/zenodo. 2619533). The outer circle represents the diatom genera (when known), the middle circle represents the interacting partner, and the inner circle represents the type of interaction (predation, parasitism, symbiosis, etc.). Adapted from (Vincent and Bowler 2020)

producers to herbivores very low, and is therefore a structuring factor in the plankton (Sherr and Sherr 2009). Unlike parasites that also feed on diatoms, it is generally assumed that predators feed on several species (not one), tend to be bigger than their prey, and tend to kill them (Lafferty and Kuris 2002). Metazoan predators such as copepods (crustaceans) presumably exercise strong pressure on diatoms by feeding on them (Lebour 1922; Campbell 2009). The classic pelagic food web involves a trophic linkage between diatom blooms, copepod production, and fish (Runge 1988). Numerous feeding experiments have investigated the coevolution between copepods and diatoms. Some evolutionary adaptations are mechanical: copepods modify their feeding tools (Itoh 1970; Michels et al. 2012), in response to which diatoms adjust their protecting frustules, leading to an arms race that fuels evolutionary processes (Hamm and Smetacek 2007). Some diatoms that dominate blooms experience less grazing mortality than do co-occurring species (Assmy et al. 2007; Strom et al. 2007). It was shown that in the presence of preconditioned media that contained herbivores, diatoms develop grazing resistant morphologies such as increased cell wall silicification (Hamm et al.

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2003; Pondaven et al. 2007). Hence, the cell wall provides not only a “constitutive mechanical protection” for the cell but also a plastic trait that responds to grazing pressure. Allelopathy has also been observed in response to copepod grazing. Allelopathy at large is a biochemically mediated interaction in which one organism can influence growth, survival, and reproduction of another organism. The effects can be either beneficial (positive allelopathy) or detrimental (negative allelopathy). These chemical signals can influence species interactions in the plankton, which is well illustrated in phytoplankton (Legrand et al. 2003), and particularly in diatoms. Copepods graze on diatoms, and there has been much debate about whether or not diatoms are a good food source for copepods, in what is known as the “Diatom-Copepod Paradox” (Harvey 1935). In the early 1990s, it was discovered that diatom-derived compounds (simple aldehydes) could decrease copepod egg hatching success from the usual 90% to 12% (Miralto et al. 1999), challenging the classical view of marine food webs wherein energy flows from diatoms to fish by means of copepods (along with the discovery of high grazing rates by dinoflagellates). Further studies discovered a myriad of polyunsaturated aldehydes named “PUAs” in the diatoms Thalassiosira rotula and Skeletonema costatum, which are released within seconds after mechanical crushing of the diatoms, up to 5 fmol of PUA per cell within 2 min (Pohnert 2000). The production of defensive chemicals and allelopathic molecules targeted toward predators is thought to contribute to diatom success, although still debated. Other adaptations are physiological: the existence of a mismatch between temperature optima for growth of diatoms relative to growth of potential predators is a strategy to escape predation pressure (Rose and Caron 2007). For instance, the maximal growth rates of herbivorous protists decline more rapidly with decreasing temperature than that of phototrophic protists, especially at the very low temperatures that are characteristic to high-latitude ecosystems where diatoms bloom. The classic food web view was challenged in the early 1990s (Kleppel et al. 1991). It was suggested that copepods rather feed preferentially on microplankton such as ciliates and dinoflagellates, supported by evidence that diatoms were nutritionally insufficient for copepod growth. Additional arguments favor low copepod grazing pressure during blooms: the inability of copepods to track diatoms over winter and the existence of grazing from heterotrophic dinoflagellates. The latter led to the concept of “loophole” (Irigoien 2005), suggesting that blooming species are those able to escape microzooplankton thanks to predation avoidance mechanisms (larger size, spines, toxic compounds) at the onset of the bloom. Followed by top-down grazing of mesozooplankton on microzooplankton, blooming conditions basically disrupt the predator–prey control, opening a “loophole” in which diatom species can thrive. Heterotrophic dinoflagellates are unicellular phagotrophic microplankton measuring between 20 and 100 microns, and are probably the highest consumers of bloom-forming diatoms, more than copepods and other mesozooplankton (Jacobson and Anderson 1986; Calbet and Landry 2004; Sherr and Sherr 2007). They can comprise more than 50% of microzooplankton biomass in diatom blooms,

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represented by thecate (armored, like Protoperidinium spp.) and athecate (Gymnodinium spp.) dinoflagellates. They exert a constant predation pressure on diatoms, by rapidly increasing their abundance through asexual reproduction when prey abundance increases—but also by their capacity to grow on diverse prey, therefore surviving in nonbloom conditions to better proliferate when diatoms bloom (Strom 2008). Attempts to compare the dinoflagellate and copepod pressures on diatom communities have been done in South Korean coastal waters. Dinoflagellates (Protoperidinium bipes) consumed 0.1% to 3.4% of diatom biomass per hour, whereas copepods (Acartia spp.) removed less than 0.2% of diatom biomass per hour, rather focusing on herbivore ingestion and relieving diatoms from grazing pressure (Jeong et al. 2004). Experimental simulation of trophic interactions among omnivorous copepods, heterotrophic dinoflagellates, and diatoms also suggests that dinoflagellates play a central role in the lower trophic levels of marine food webs by consuming diatoms and then serving as a quality food source for copepods (Chen and Liu 2011).

1.4.2 Parasitism Parasitism is described as a common consumer strategy, whereby parasites generally feed on only one prey, are smaller than their host, and do not usually kill the host, unlike parasitoids (Lafferty and Kuris 2002). Parasitic epidemics frequently follow diatom blooms in lakes worldwide, sometimes affecting over 90% of the population. Zoosporic parasites. In the marine ecosystem, the ecological role of parasites infecting diatoms is poorly understood. Knowledge about marine diatom zoosporic pathogens is summarized in Scholz et al. (2016), suggesting that marine diatom diseases may have significant impacts on the ecology of individual diatom hosts, but also at the level of the community. Zoosporic parasites are facultative or obligate and produce spores as they infect the host. Known diatom parasites involve chytrids, aphelids (Pseudaphelidium drebesii parasite of Thalassiosira punctigera), stramenopiles—including oomycetes, labyrinthuloids, and hyphochytrids— (Ectrogella perforans parasite of Licmophora hyalina), parasitic dinoflagellates (Paulsenella vonstoschii parasite of Streptotheca tamesis diatom), cercozoans (Cryothecomonas aestivalis parasite of Guinardia delicatula), and phytomyxids (Phagomyxa bellerocheae parasite of Bellerochea malleus). Scholz et al. conclude that diatom zoosporic parasites are much more abundant in the marine ecosystem than what the available literature reports. Gsell reported an interesting case of diatom–parasitic interaction in 2013 (Gsell et al. 2013). The study investigated the susceptibility to infection of seven different genotypes of the spring bloom freshwater diatom Asterionella formosa by a single genotype of the chytrid parasite Zyghorhizidium planktonicum across five environmentally relevant temperatures. The results suggested that the thermal tolerance range of the parasite genotype was narrower than that of its host, providing the diatom with a “cold” and “hot” thermal refuge in which it was not infected by the parasite. The reaction to parasitism was host-genotype specific and varied with temperature so much so that no host genotype would outcompete the others across all temperature ranges. The authors inferred that thermal variation plays a role in the

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maintenance of diatom diversity in disease-related traits. This also highlights the importance of environmental factors in the establishment—or not—of an interaction. Host parasite specificity and environmental factors such as temperature can impact diatom diversity, survival, and, consequently, their role in community structure. Other environmental parameters such as nutrient availability can trigger diatom interactions whereby organisms compete for similar resources.

1.4.3 Competition for Resource The diversity of planktonic organisms in a given environment has puzzled scientists for a long time, raising the question of how so many different plankton species could stably coexist in a given environment, especially when they are occupying the same niche and in need of the same resource, a mystery also known as the “paradox of the plankton” (Hutchinson 1959). Some—like Hardin—state that species do not cohabit but rather adhere to the “Competitive exclusion principle” according to which two species competing for the same resource cannot stably live together, as long as other ecological factors remain constant (Hardin 1960). Intra-Taxa Competition Diatoms could compete with other diatoms for nutrient resources; however, examples suggest that they avoid so by utilizing different types of resources. A metatranscriptomic study performed on the East Coast of the USA revealed that similar marine diatom species, Skeletonema spp. and Thalassiosira rotula, utilize resources differently, thereby enabling their coexistence in the same parcel of water, despite similar requirements in nitrogen and phosphorus. The former favored uptake of inorganic nitrogen sources (nitrate and nitrite), while the latter favored the utilization of nitrogen from organic sources, such as amino acids (Alexander et al. 2015a, b). Competition among diatoms can also result from the coupling of nutrient limitation, such as silica-limited environments, and physical factors such as temperature. Different diatom species grow unequally with respect to these covarying factors, suggesting a specific niche adaptation, as was also shown in freshwater diatoms (Shatwell et al. 2013). Inter-Taxa Competition Biogeochemically and ecologically, diatoms are believed to be the most important silicifiers in modern marine ecosystems, with radiolarians (polycystine and phaeodarian rhizarians), silicoflagellates (dictyochophyte and chrysophyte stramenopiles), and sponges with prominent roles as well. The diatom expansion 65 million years ago has been attributed to their superior competitive ability for silicic acid uptake relative to radiolarians, with the latter experiencing a reduction in weight of their minute skeletons, called tests (Harper and Knoll 1975). However, as the size reduction of radiolarian tests was insufficient to explain diatom expansion, strong long-term erosion of continental silicates has been proposed as a significant cofactor of diatom growth (Cermeño et al. 2015). Analysis of the distribution of silicifiers in the contemporary ocean at large spatial scale using the Tara Oceans expedition dataset can bring additional insights about the evolution of competition between different groups (Fig. 4). Functional annotation of the silicifying organisms followed by mapping of their distribution across the global

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Fig. 4 Distribution of silicifiers in the sunlit ocean based on metabarcoding abundance data from the Tara Oceans expedition. A. Silicifiers in surface waters of the 20–180 micron size fraction— divide radius by 20 for log-transformed relative abundance. B. Silicifiers in surface waters of the 0.8–5 micron size fraction—divide radius by 30 for log-transformed relative abundance. The size of the bubble corresponds to the importance of silicifiers with respect to the whole planktonic community. C. Composition of the silicifiers’ community in surface waters at each sampling station. From (Hendry et al. 2018)

ocean reveals major patterns. In larger size fractions of microplankton (20–180 microns), diversity within the silicifiers is composed essentially of Bacillariophyta and Polycystinea, so much so that both taxonomic groups represent over 99% of the microplanktonic silicifier community across the vast majority of the global ocean. Diatoms and polycystines occur in highly variable proportions, where diatoms dominate the cold high-latitude regions. Coexistence between both groups is rare,

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whereby the presence of one of the organisms appears to exclude the other, which may also reflect special adaptations to nutritional environments such as eutrophic, oligotrophic, or high-nutrient, low-chlorophyll areas (HNLC). Diatom-induced reduction of silicic acid availability coupled with grazing pressure may have further affected trends in silicoflagellate morphology in two ways: either to maintain a certain degree of silicification but to become smaller, or to lower silicon requirements and develop spines as a mean to maintain a defensive shield (Hendry et al. 2018). Inter-Taxa Competition Mediated by Allelopathy: The Case of Dinoflagellates The study of nearshore blooms of the dinoflagellate Karenia brevis proposed that allelopathic compounds were produced to inhibit growth of phytoplankton competitors, among which are diatoms (Prince 2008; Poulson 2010). However, natural offshore diatom-dominated assemblages in the Gulf of Mexico seemed resistant (Asterionellopsis glacialis, Skeletonema spp.), even displaying slight stimulation of growth, results that are more variable when brought back to the lab. The accumulation of allelopathic compounds in the water column may create an inhospitable environment for growth among competitors, although diatom responses are clearly species specific. In the lab, Karenia brevis caused suppression of growth of Thalassiosira pseudonana and Asterionellopsis glacialis, and the impact of the dinoflagellate on the competitors’ physiology was reflected in the metabolomes and proteomes of both diatoms. Cellular protection responses such as altered cell membrane components, inhibited osmoregulation, and increased oxidative stress were also triggered (Poulson-Ellestad et al. 2014).

1.4.4 Bacterial and Viral Pathogens Although predation, parasitism, and competition seem to be the prevalent types of antagonistic interactions, bacterial and viral pathogens of diatoms have also been observed. Largely overlooked, they are likely to be important players in the diversity of mortality agents affecting diatom survival. For details about bacterial and viral pathogens, please refer to dedicated Chapters “The Diatom Microbiome: New Perspectives for Diatom-Bacteria Symbioses” and “Diatom Viruses”, respectively.

1.5

Mutualistic Interactions

Thankfully, not all diatom interactions result in death. Evolutionarily speaking, diatoms are the product of successive symbiotic events revealing intricate relationships with bacteria. Intriguing ubiquitous epiphytic and photosymbiotic associations also suggest that diatom evolution is not only constrained by mortality agents. The Red and Black Queen hypotheses, denoting opposing microbial evolution driven by competition or cooperation, respectively, collide and complexify the picture (Fig. 5).

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Fig. 5 Diversity of diatom interactions. Diatoms are involved in numerous interactions, both beneficial (mutualistic) and detrimental (antagonistic). Partners of interactions can be classified according to their “domain” (Eukaryotes, Bacteria, or Viruses), and interactions can be classified by their more specific definition (Symbiosis, Predation, Competition, etc.). For clarity, this is a partial view of diatom interactions, illustrating how all those biotic top-down factors can affect diatom growth, evolution, morphology, biogeography, and sinking. Credit: Diatoms By Wipeter in the center; Predation by micro-zooplankton (Modeo et al. 2003); Parasitism by chytrids (Kagami et al. 2007); Viral infection (Kimura and Tomaru 2013); Algicidal bacteria (Sohn et al. 2004); Three-part partnership (Buck and Bentham 1998); Bacteria attachment (Gärdes et al. 2011); Hemiauluscyanobacteria (Hilton et al. 2013a, b); Symbiosis with foraminifera (Briguglio et al. 2013); Fragilariopsis doliolus and tintinnids (Vincent et al. 2018); Diatom agglutination on tintinnids (Armbrecht et al. 2017); Competition for silica (MBARI); Allelopathy with dinoflagellates (Haywood et al. 2004)

1.5.1 Symbiosis We restrict the meaning of symbiosis to close mutualistic relationships, whereby two species benefit from the association (Paracer and Ahmadjian 2000).

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Diazotrophs A highly mutually beneficial interaction involving diatoms is known to occur with diazotrophic prokaryotes, referred to as “DDAs” (Diatom–Diazotroph Associations), such as the heterocystous cyanobacteria Richelia intracellularis and Calothrix rhizosoleniae, observed in low-nutrient oligotrophic oceans. Richelia, along with Trichodesmium, is believed to be a major prokaryotic fixer of dinitrogen gas (N2) in the world’s tropical and subtropical oceans (Carpenter and Foster 2002) recently confirmed using Tara Oceans data (Pierella Karlusich et al. 2021). Richelia intracellularis converts dinitrogen gas to ammonium and then supplies the diatom with fixed bioavailable nitrogen compounds essential for metabolism (Foster et al. 2011). In these cases, the diatom serves as a protective host as the cyanobacteria lives inside the diatom. Richelia lives as an endosymbiont between the cell wall and the frustule of diatoms such as Hemiaulus, Rhizosolenia, and Bacteriastrum, while Calothrix lives externally attached to Chaetoceros spp. (Villareal 1991), and successive efforts to molecularly identify the partners, using nifH, 16S rRNA, and hetR sequences, have revealed the phylogenetic relationships between different diazotrophs (Foster and Zehr 2006). Comparative genomics studies of two obligate and facultative symbiont strains show that the location of the symbiont (intracellular or extracellular) and its dependency on the host are linked to the evolution of the symbiont genome, especially in nitrogen metabolism, assimilation genes, and genome reduction (Hilton et al. 2013a, b). The genome of the intracellular symbiont was reduced and lacked ammonium transporters and essential nitrate/nitrate reductases, illustrating metabolic streamlining. The genome of the extracellular symbiont was similar to its free-living cyanobacteria. Other less studied symbiosis involves the chain-forming pennate diatom Climacodium frauenfeldianum and a unicellular cyanobacterium similar in morphology to the free-living diazotroph Crocosphaera watsonii (Foster et al. 2011). Diatoms from the Rhopalodiacean family also contain an endosymbiont of cyanobacterial origin, named the “Spheroid body” that is obligate. Diatoms such as Rhopalodia and Epithemia can grow in nitrogen-poor habitats, suggesting that the endosymbiont fixes atmospheric nitrogen. The sequencing of the spheroid body genome found that it was considerably reduced compared to the genome of its close free living relatives, depleting the organism of key metabolic capacities such as photosynthesis, thus making it completely dependent on its host (Nakayama et al. 2014). Dinotoms Monophyletic dinoflagellates known as “dinotoms” harbor intracellular diatoms, thus establishing what is known as a stable endosymbiotic association (Tomas and Cox 1973; Kite and Dodge 2004; Yamada et al. 2019). The diatom retains its nucleus, mitochondria, and endoplasmic reticulum and is separated from the dinoflagellate’s cytosol by a single membrane; the diatom is present in all stages of the host cell cycle, and both host and endosymbiont divide simultaneously (Tippit and Pickett-Heaps 1976). Some studies suggest that the diatoms of dinotoms are an evolutionary intermediate stage of plastids, between kleptoplastids and genuine plastids, thus representing an attractive model to study steps of endosymbiosis.

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Transcriptome analysis of two dinotoms, Durinskia and Kryptoperidinium foliaceum (Hehenberger et al. 2016), shows almost no functional reduction in the diatom nuclei and that exchange of metabolites such as photosynthates seems to structure this endosymbiosis. At least 14 different diatom species, belonging to six genera, are thought to serve as endosymbionts for 19 dinoflagellate host species. Benthic Foraminifera Beyond dinoflagellates, four foraminifera families are known to host endosymbiotic diatoms (Leel et al. 2005). Foraminifera are important sediment builders in shallow-water coral-reef waters and thus contribute significantly to the carbon cycle (Scoffin and Tudhope 1985). As endosymbionts, diatoms do not form any frustules, making their identification cumbersome; thankfully, frustules appear once endosymbionts are cultured (Lee 1989). Surprisingly, in 2005, only six common diatom species were involved in over 75% of all the more than 3000 foraminifera hosts examined: Nitzschia frustulum var. symbiotica, N. laevis, N. panduriformis, Fragillaria shiloi, Amphora roettgerii, and A. erezi (Lee 2011). However, the list continues to grow, as Minutocellus has been shown to be a symbiotic species of the foraminifera Pararotalia calcariformata in the Mediterranean Sea (Schmidt et al. 2015). This photosymbiosis brings advantages to the host organisms that benefit from diatom photosynthates, enabling high population density as well as increased calcification rates (Lee et al. 2010). Three-Part Partnership A rather unusual association reported in the open ocean and eastern Arabian Sea is that established between the chain-forming centric diatom Leptocylindrus mediterraneus, the aplastidic protist Solenicola setigera (from the MAST3 stramenopile lineage), and the single-celled cyanobacterium Synechococcus sp. Even though this is an interesting case study, the fact that the diatom is devoid of cellular content questions its mutualistic nature. The benefits for each partner remain unresolved (Buck and Bentham 1998).

2

Diatom: Ciliate Interactions, from Commensalism to Epibiosis

Tintinnids (Choreotrichida) are heterotrophic planktonic ciliates enveloped in a species-specific test composed of organic material, the lorica (Agatha et al. 2013). They represent one of the morphologically most diverse groups of planktonic protists (Bachy et al. 2013), are abundant, and are ubiquitous throughout the water column. Several extracellular associations between tintinnids and diatoms have been reported, either described as “phoretic commensalism”— wherein transport is believed to be the main benefit for diatoms—or suggested as a form of obligate epibiosis enabling predation avoidance for tintinnids, and access to nutrients for diatoms such as the association involving the radial centric diatoms Chaetoceros spp. and Eutintinnus spp. (Gómez 2007). Epibiosis (from the Greek epi “on top” and bios “life”) designates “spatially close associations between two or more living organisms belonging to the same or different species” (Harder 2008).

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For example, the chain-forming pennate diatom Fragilariopsis doliolus was recorded with Eutintinnus tenuis in material collected in 10 equatorial stations between the Galapagos archipelago and the Marquesas Islands (Pavillard 1935). Small chains of F. doliolus were previously found associated with Salpingella subconica near the Prince Edward Islands in the Southern Ocean, with rates of association involving 3% to 30% of all F. doliolus and 35% to 83% of S. subconica cells encountered, as well as in the Benguela Current (Froneman et al. 1998). These authors speculate that buoyancy and protection against mesozooplankton predation are the main advantages gained by the attachment of both partners. Some of these interactions have been characterized both at large spatial scale as well as high morphogenetic resolution (Vincent et al. 2018), and behavior has been investigated using high-speed measurements (Gómez 2020). They reveal that diatoms specifically adapt their morphology to establish stable associations with tintinnids, eventually extending the ecological niche of the free-living diatom. Live flow measurements suggest that tintinnids benefit from increase in hydrodynamic drag or filtering rates, and that diatoms experience a decrease in diffusive boundary layer and enhanced antigrazing strategies. Of another nature, Laackmaniella and other tintinnids were observed in the Southern Ocean with apparently empty frustules of Fragilariopsis and other diatoms covering their lorica, for which it has been hypothesized that the ciliates retain diatom frustules following ingestion of the cellular contents, perhaps as a means of protection through camouflage (Gowing and Garrison 1992; Wasik et al. 2000; Armbrecht et al. 2017). In this case, the association seems closer to commensalism.

3

Toward an Integrated View of Biotic Interactions

The complexity of an integrated view of diatom biotic interactions does not stop with the diversity of partners involved, or the different mechanisms developed. Additionally, these interactions cannot be considered as snapshots, but rather as dynamic processes, both spatially and temporally across multiple biological scales (Fig. 8).

3.1

Temporal Scales of Diatom Interactions

Diatoms produce a class of oxylipins known as PUAs (polyunsaturated fatty acids) in the seconds following the crushing of the diatom frustule induced by predation by larger grazers. In the following hours, the copepods will continue eating in this environment garnished with teratogenic compounds. The interaction, on the long term, will have an impact on the offspring so much so that over a few years, grazers should evolve to avoid eating PUA-producing diatoms. Teeling et al. (Teeling et al. 2012) investigated the bacterioplankton response to a diatom bloom in the North Sea and managed to uncover the dynamic succession of bacterial populations at the genus level. Over a few days, bacteria known to decompose algal-derived organic matter, such as Bacteroidetes, Gammaproteobacteria, and Alphaproteobacteria,

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formed distinct, successive populations controlled by algal substrate availability. Over decades, biotic interactions leave their imprint in the seasonal succession of plankton, an annually repeated process of community assembly that is the result of community interactions such as competition, predation, and parasitism in conjunction with abiotic control mechanisms that set the start and end of the growing season. The study of these dynamics, by sampling regularly at a given location, or by following a prevailing current, also known as a time series or longitudinal study, enables scientists to examine how different organisms change in relation to one another and in relation to environmental conditions (Fuhrman et al. 2015). Over millennia, past endosymbiontic events and other gene transfers remain traceable in the genetic information within diatom and host genomes.

3.2

Spatial Scales of Diatom Interactions

The physical contact between a copiotroph bacteria and the mucus of the diatom, the bacterial diazotroph encapsulated in its host, or what happens at the cell surface in general through defense and protection against agents of mortality happens over a few micrometers. Diatom interactions enter scales of millimeters within ephemeral microlayers, centimeters when copepods feed on them, and in the vertical direction, there can be significant microbial changes over hundreds of meters. Symbiosis with cyanobacteria can form blooms measured in kilometers, as was reported in the subtropical North Atlantic (Carpenter 1999), estimating that the N supply by N2 fixation by the symbioses exceeded that of nitrate flux from below the euphotic zone, thus playing a significant role in the biogeochemistry of the surface ocean. Similarly, it was shown that DDAs drive a significant biological CO2 pump in tropical oceans off the Amazon River plume (Yeung et al. 2012), illustrating how biotic interactions can scale up to influence biogeochemical cycling of nutrients and ecosystem-wide phenomena. Patches of homogeneous diatom blooms, and thereby the interactions that happen among them, can be observed from scales of kilometers to thousands of kilometers at a given depth and over horizontal directions (Fig. 6).

3.3

New Approaches to Study Microbial Interactions

We therefore see that diatom interactions are diverse, spanning across multiple temporal and spatial scales, involving both macro- and microorganisms, prokaryotes and eukaryotes, and even viruses (see Chapters “The Diatom Microbiome: New Perspectives for Diatom-Bacteria Symbioses” and “Diatom Viruses”). Many of these studies rely on manipulative experiments, such as coculturing (two organisms in the same medium) and cross-culturing (cell-free filtrate from the culture of one organism added to the medium of the target) of potential competitors, feeding experiments to test specificity of prey and predators. Such studies have more recently incorporated omics approaches, single cell biology and secondary ion mass-

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Fig. 6 Integrated study of diatom biotic interactions across biological organization and spatiotemporal scales. Adapted from (Sunagawa et al. 2020)

spectrometry. Transcriptomic data have been used to evaluate copepod responses to harmful diatoms (Carotenuto et al. 2014), DNA barcoding has been used to analyze predator gut content (Kress et al. 2015), metabolomics has helped understand allelopathy (Scognamiglio et al. 2015), and genomics has helped interpret the evolution of host-symbiont gene transfers and evolution (Vancaester et al. 2020). But microbial communities are complex, and most studies provide a reductionist view, studying one, two, or in the best of cases three organisms in isolation. The need to develop holistic approaches emerged a few years ago in marine microbiology

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(Karsenti et al. 2011), and the possibility to study organisms in their natural habitats has opened the door to novel ways of looking at community structure in the microbial aquatic world.

3.3.1 In Silico Prediction of Microbial Interactions Co-occurrence networks using meta-omics data have increasingly been used to study microbial communities and interactions (Faust et al. 2012; Li et al. 2016), e.g., in human and soil microbiomes (Barberán et al. 2012; Faust et al. 2012) as well as in marine and lake bacterioplankton (Fuhrman and Steele 2008; Eiler et al. 2011; Milici et al. 2016). Such networks provide an opportunity to extend community analysis toward an understanding of the relational roles played by different organisms, many of which are uncultured and uncharacterized (Proulx et al. 2005; Chaffron et al. 2010). Over large spatial scales, nonrandom patterns according to which organisms frequently or never occur in the same samples are the result of several processes such as biotic interactions, habitat filtering, historical effects as well as neutral processes (Fuhrman 2009). Quantifying the relative importance of each component is still in its infancy. However, these networks can be used to reveal niche spaces, to identify potential biotic interactions, and to guide more focused studies. At large spatial scales using the Tara Oceans dataset, diatom biogeography was shown to be more constrained by biotic rather than abiotic factors (Lima-Mendez et al. 2015). Diatoms were also shown to occupy niches that were less populated with potential parasites, pathogens, and predators (Vincent and Bowler 2020), reflecting their unique ability to exclude other organisms and thrive, thus supporting Smetacek’s 3 “P’s” hypothesis (Fig. 7). However, only 6.5% edges of the largest diatom co-occurrence network have been confirmed independently in the literature. In many ways, this high proportion of unmatched interactions should be regarded as the “unknown” proportion of microbial diversity emerging from metabarcoding

Fig. 7 Major patterns of spatial co-occurrence involving diatoms. (a) Circular representation of copresences (green bands) and exclusions (red bands) within the diatom subnetworks extracted from the Tara Oceans interactome (Lima-Mendez et al. 2015). The thickness of the band corresponds to the number of interactions, and major partners are labeled around the circles if they represent more than 100 associations. Data from all size fraction networks are represented here. (b) Comparison of proportions of exclusions showing that diatoms significantly exclude potential predators, parasites, and competitors such as copepods, Syndiniales, Dinophyceae, and Radiolarians, compared to control groups. From (Vincent and Bowler 2020)

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surveys. Part of it is truly unknown, new, and very exciting, part of it is due to biases in data gathering and processing, and part of it is due to the lack of an extensive reference database. Despite the difficult and sometimes misleading interpretation of co-occurrence networks, a vast body of literature already exists in the field of ecological networks, traditionally focusing on observational, noninferred data and the modeling of food webs, host–parasite, plant–pollinator networks (Ings et al. 2009; Rohr et al. 2014). Various properties linked to the architecture of these antagonistic and mutualistic networks have been formalized, such as nestedness, modularity, or the impact of combining several types of interactions in a single framework (Fontaine et al. 2011). These works could inspire the field of biotic interactions; enhanced crossfertilization between the disciplines of ecological networks and co-occurrence networks would highly benefit both communities, ultimately helping to understand the laws governing Darwin’s “tangled bank” (Darwin 1859).

3.3.2 Seeing Is Believing After decades of omics data flooding, high-resolution environmental microscopy has caught up, to offer the same amount of information in terms of form and intracellular ultrastructure. e-HCFM—short for “environmental high content fluorescence microscopy—is a 3D-fluorescence imaging and classification tool for highthroughput analysis of microbial eukaryotes in environmental samples (Colin et al. 2017). Through high-content feature extraction, it enables accurate automated taxonomic classification and quantitative data about organism ultrastructures and interactions. Applied to environmental samples, e-HCFM has demonstrated its ability to directly detect and quantify diatom associations, involving an unknown nanoflagellate attached to the diatom Chaeoteros simplex (Fig. 8). However, this approach is based on dead cells, highlighting how live imaging is even more important but still remains a challenge. High-resolution time-lapse microscopy of diatom interactions has provided important insights into bacterial chemotaxis (Smriga et al. 2016) but remains restricted to the lab. Very few case studies investigate live imaging of diatom interactions in the natural environment, for this represents many technological barriers. However, these can provide undisputable evidence for the existence of ecologically relevant interactions, otherwise limited to genomic predictions, fixed dead samples subject to manipulation artifacts, or laboratory settings (Vincent et al. 2018). 3.3.3 Bringing the Lab to the Field Beyond previous studies, the recent expansion of our knowledge about diatom diversity and biogeography (Malviya et al. 2016) offers a huge potential to discover new types of interactions in the aquatic world. On the other hand, the broader applicability of single cell or imaging technologies and genetic manipulation is a windfall for marine microbiology, to dissect biotic interactions at the cellular scale and unravel new molecular mechanisms. If laboratory studies remain mandatory to crack down molecular mechanisms involved in microbial interactions, the field of microbial interactions is dampened by limits of cultivation and genetic transformation despite major advances (Faktorová et al. 2020). One way to circumvent these

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Fig. 8 Ultrastructure of diatom biotic interactions using high content fluorescent microscopy. From (Colin et al. 2017; Vincent et al. 2018). Pictures represent DNA (Hoechst, blue), chloroplasts (chlorophyll autofluorescence, red), membranes (DiOC6, green), and cell surface (AlexaFluor546, cyan). Arrows point to the partner of interaction with diatoms. (a,b) Fragilariopsis doliolus and the tintinnid Salpingella sp. (c) Corethron sp. with nanoflagellates. (d,e) Diatom with potential parasites. (f) Chaetoceros sp. with Vorticella sp. (g) Chaetoceros sp. with nanoflagellates. (h) Chaetoceros sp. with nanoflagellates. (i) Coscinodiscus sp. surrounded by small cells. Scale bar a, b, f, g, h ¼ 20 micron; c ¼ 10 micron; d, e, i ¼ 5 micron

current obstacles is to adapt high-end laboratory tools to real-life natural samples. This includes high-resolution live and subcellular microscopy, single-cell omics techniques, or micromanipulation of interactions in situ.

4

Conclusions

Diatoms have undoubtedly succeeded in adapting to the ocean’s fluctuating environment, shown by recurrent, predictable, and highly diverse bloom episodes (Guillard and Kilham 1977). They are considered r-selected species with high growth rates under favorable conditions that range from nutrient-rich highly turbulent environments to stratified oligotrophic waters (Margalef 1978; Alexander et al. 2015a, b; Kemp and Villareal 2018). Their success has long been attributed to this physiological trait; yet evidence suggests that abiotic factors alone are not sufficient to explain their ecological success. The present chapter shows that diatoms are involved in diverse and abundant biotic interactions, involving all domains of life across vast scales of time and space, shedding light on the top-down forces such as mortality agents that could drive diatom evolution and adaptation in the modern ocean (Fig. 9).

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Fig. 9 An artistic view of the diversity of diatom interactions in the ocean and their link with larger scale processes. Authors: Adrien Bernheim & Flora Vincent

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Excitingly, the reservoir of potential interactions involving diatoms seems to expand, as well as our capacity to study them at both mechanistic and ecosystem levels. Investigating microbial interactions involving one of the most important eukaryotic phytoplankton groups on the planet will likely shed light on novel key cellular mechanisms and provide clues about eukaryotic cell evolution.

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Ancient Diatom DNA Matthew I. M. Pinder and Mats Töpel

Abstract

An interesting feature of some diatom species is their ability to form resting stages, a dormant life stage that can allow them to survive long periods of adverse conditions. In nature, these resting stages can be revived by the return of more favourable conditions. However, they can also be revived in the laboratory and cultured for research purposes, thus allowing for population studies of natural systems over time. While the frustules and fragmented DNA of dead diatoms can indeed provide useful information, the revival and culturing of resting stages can yield both plentiful cells on which to perform phenotypic studies, and abundant DNA for genetic and genomic studies. This allows additional insight to be gained in areas such as evolution and population genomics. In this chapter, we discuss the use of ancient diatom DNA and resting stages in research, with particular attention to the chain-forming marine centric Skeletonema marinoi, on which several such studies have been performed. We also highlight some possible research directions in this field afforded by advances in DNA sequencing technologies. Keywords

Ancient DNA · Resting stages · Diatoms · Skeletonema · Resurrection ecology · Bioinformatics · Next-generation sequencing · Whole genome sequencing · Sediment cores

M. I. M. Pinder · M. Töpel (*) Department of Marine Sciences, University of Gothenburg, Gothenburg, Sweden e-mail: [email protected]; [email protected] # Springer Nature Switzerland AG 2022 A. Falciatore, T. Mock (eds.), The Molecular Life of Diatoms, https://doi.org/10.1007/978-3-030-92499-7_4

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Abbreviations CTAB DNRA LOF MAG SDC-MPN SIMS SNP SSU WGS

1

Cetyl trimethyl ammonium bromide Dissimilatory nitrate reduction to ammonium Loss of function Metagenome-assembled genome Serial dilution culture and most probable number Secondary ion mass spectrometry Single nucleotide polymorphism Small subunit (ribosomal RNA) Whole genome sequencing

Introduction

In the study of changes to our planet, the use of proxy indicators is important for gathering data from past ages. Analysis of proxies such as tree rings, ice cores, and sediment cores can give us information stretching back thousands of years. For example, in relation to climate change, one can obtain myriad details on precipitation, temperature, changes to atmospheric composition, and other similar phenomena by analysing such proxies (Sorooshian and Martinson 1995). Another informative group of proxy indicators used in, for example, studies of climate change are microalgae such as diatoms. By comparing the species composition of diatom assemblages (in the form of their silica frustules) found at different depths in the sediment, temporally-informed data can be obtained that reflect dynamic environmental conditions in the past (Mackay et al. 2003). This method has been used to study various phenomena, such as changing salinity levels in the Baltic Sea over the last 10,000 years (Emeis et al. 2003). In some cases, even in the absence of microfossils, remnants of DNA from these millennia-old diatoms have been identified in the sediments, raising the possibility of following specific lineages across similar timescales (e.g. Stoof-Leichsenring et al. 2015). Despite the scarcity of DNA after such long periods, and the presence of post-mortem DNA modifications, imposing limits on the types of studies that can be performed, improvements in laboratory techniques, sequencing technologies, and bioinformatics methods have facilitated the study of ancient DNA in recent decades. As well as the aforementioned diatom studies, ancient DNA has been sequenced from a plethora of organisms including woolly mammoths (e.g. Miller et al. 2008), domesticated crops (e.g. Mascher et al. 2016), and extinct hominids (e.g. Prüfer et al. 2017). In addition to information obtained from dead diatoms and the traces of DNA they leave behind in the sediment, living diatoms can also be used to gain information from the past. Some species are able to form dormant resting stages, which sink into the sediment and can be revived after extended periods of time, in the order of decades or centuries (e.g. Stockner and Lund 1970; McQuoid et al. 2002; Härnström

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et al. 2011), and potentially millennia (Sanyal et al. 2019). By reviving these dormant cells, information can be obtained which would otherwise be impossible to retrieve from their remains alone. This includes phenotypic responses to various environmental conditions, allowing for comparisons between revived ancient and recent strains, even in cases where the cells prove difficult to propagate in culture. Where possible, however, the germination and propagation of revived resting stages provide not only populations of cells on which to perform such phenotypic studies, but also plentiful DNA on which to perform genotypic studies, opening up further research possibilities. These advantages stand in stark contrast to the problems associated with traditional ancient DNA studies, where the material is taken from dead organisms and has therefore been subject to varying degrees of degradation. This chapter will explore the use of ancient diatom DNA for research, particularly that which is derived from revived resting stages, looking at the types of studies which have already been performed, and highlight directions for future research in light of recent advances in sequencing technology, such as the use of long-read sequencing for barcoding revived strains and assembling metagenome-assembled genomes (MAGs). Some experimental considerations for working with revived diatoms and their DNA will also be provided. Note that while ancient DNA conventionally refers to DNA derived from dead organisms, in this context, we include material from resting stages revived after timescales equivalent to many thousands of diatom generations in our definition of ancient diatom DNA. This chapter will have a particular focus on the chain-forming marine centric species Skeletonema marinoi (order Thalassiosirales, family Skeletonemataceae), given its importance as a model organism in many resting stage-based studies.

1.1

The Chain-Forming Diatom Skeletonema marinoi

Skeletonema marinoi was described in 2005 as one of four new species identified in the Skeletonema costatum complex (Sarno et al. 2005). The genome of S. marinoi strain R05AC has been sequenced and annotated (Töpel et al., in prep), with an estimated genome size of around 55 Mb and approximately 22,440 genes (Johansson et al. 2019a). The genomes of several bacteria from the S. marinoi microbiome have also been sequenced, in an effort to better understand the interactions within the diatom–bacteria holobiont (Johansson et al. 2019b, and references therein). In addition to this, several molecular techniques have been developed for the species, making it a useful model for studies of ancient DNA, population genomics, selection, and adaptation in diatoms. A method for genetic transformation, involving mutagenesis by random insertion of a construct containing the bleomycin/zeocin resistance gene into the genome by electroporation, has resulted in the establishment of a mutant collection for S. marinoi (Johansson et al. 2019a). Considering that approximately 80% of the 22,440 annotated gene models in the S. marinoi genome encode proteins of unknown function (and the fact that around half of the genes in other diatom genomes are likewise of unknown function [Armbrust et al. 2004; Bowler et al. 2008]), the creation of such a mutant collection for S. marinoi may

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prove to be a valuable resource for functional, as well as ecological, studies of diatoms.

2

Resting Stages and Ancient Diatom DNA

Various groups of phytoplankton, including diatoms, are capable of producing resting stages, with different groups producing different types, often in response to adverse conditions (reviewed in McQuoid and Hobson 1996, and Ellegaard and Ribeiro 2018). These stages then sink into seafloor or lakebed sediments, from which rapid revival is possible once more favourable conditions are presented. This mechanism can allow survival through conditions ranging from short-term nutrient depletion to, potentially, mass extinction events (Kitchell et al. 1986). Two types of resting stage are generally recognised in diatoms: resting cells, morphologically similar to vegetative cells, and the more heavily-silicified resting spores. This thickening of the frustule in resting cells is reminiscent of the thickening of the cell wall in the dormant stages of other organisms such as dinoflagellate hypnozygotes, terrestrial plant seeds, and bacterial cysts (Ellegaard and Ribeiro 2018). A third type, winter stages, has also been proposed but is distinguished by being able to undergo cell division and lacking the storage bodies associated with resting cells and resting spores (Fryxell and Prasad 1990). While diatom resting stages are discussed in more depth in Chap. 8, some brief notes will also be presented here. The ability to form resting stages presents a number of advantages to diatoms. Resting stages frequently form following diatom blooms, when nutrient levels in the surface water are low and population density is high (McQuoid and Hobson 1996). These resting stages sink out of the nutrient-depleted waters and have the potential for subsequent resuspension and germination, thereby seeding subsequent blooms (McQuoid and Godhe 2004) and anchoring distinct populations in a given area (Sundqvist et al. 2018) (Fig. 1). The formation of resting spores—more heavily silicified than resting cells or vegetative cells—also provides protection against predation, by making the diatoms less attractive prey for grazing copepods and allowing them to survive passage through predators’ digestive tracts (Kuwata and Tsuda 2005). The protective properties of the thick silica frustule also extend to the long-term preservation of the diatoms’ DNA (Grass et al. 2015; Aguirre et al. 2018), which may help to explain claims of diatom chloroplast DNA being obtained from million-year-old microfossils (Kirkpatrick et al. 2016). In addition to the benefits it affords to the diatoms themselves, resting stage formation is also a boon to scientific studies, as will be discussed in Sect. 2.2 alongside other benefits of using diatoms and their resting stages for research. While diatom resting stages have been the subject of many scientific studies (see McQuoid and Hobson 1996), the precise mechanisms behind their ability to survive in this state for decades or centuries, particularly in dark, anoxic sediments, are as yet not clearly understood. While several diatom species are capable of performing dissimilatory nitrate reduction to ammonium (DNRA) under these conditions

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Fig. 1 The process of resting stage formation and germination. In the presence of adverse conditions, such as nutrient depletion following a diatom bloom, resting stages form and sink to the bottom. Upon the return of more favourable conditions, the resting stages can germinate and become resuspended in the water column, thereby seeding subsequent blooms. Even if conditions are suitable for sediments to accumulate, perturbation (such as in the presence of burrowing macrofauna [bioturbation]) will disturb the layers and mix sediments of varying ages. Under anoxic, unperturbed conditions, the diatoms will instead be buried under stratified layers of sediment. Sediment cores taken from such an environment will display lamination, enabling reliable dating of the layers and the diatoms within them. Illustration by Paula Töpel

(Kamp et al. 2011, 2013, 2016), this expends intracellular nitrate stores within a few days. As carbohydrate stores would also be used up in a matter of months (Ellegaard and Ribeiro 2018), it remains unclear which specific metabolites and pathways allow the long-term survival of resting stages. However, secondary ion mass spectrometry (SIMS) combined with stable isotope incubations has recently been employed to assess nutrient assimilation during dormancy, showing significant assimilation of both labelled nitrate and ammonium from the environment under dark, anoxic conditions. This result could explain one facet of the mechanisms of resting stage survival (Stenow et al. 2020).

2.1

History of Ancient Diatom DNA in Research

Resting stage-related diatom research has been carried out throughout the last century, with many of these studies being directed at resting stage formation and the ways in which this benefits a diatom species. However, studies involving the DNA of resting stages have only been performed relatively recently, with few thus far taking advantage of the advances in sequencing technology.

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Several of the early DNA-based studies were performed in Scandinavian waters on S. marinoi, and analysed microsatellite markers to infer on population structure (Godhe and Härnström 2010; Härnström et al. 2011; Godhe et al. 2013). All of these studies highlighted the phenomenon of local, genetically distinct populations across relatively short geographical distances, as had previously been noted in another diatom, Ditylum brightwellii (Rynearson and Virginia Armbrust 2004). Godhe and Härnström (2010) used genetic data from both planktonic and revived surface sediment resting stage cells of S. marinoi to determine the genetic structure of a population in Gullmar Fjord, Sweden. In this study, they found that the fjord was inhabited by one dominant population, presumably well-adapted to local conditions, and differentiated from the populations in the adjacent Skagerrak and Kattegat despite the water exchange between the fjord and the open sea. Importantly, they suggested that the resting stages were likely seeding the plankton population. As discussed by Sundqvist et al. (2018), such resting stage seed banks help to anchor populations in a given area, even in the presence of migration. This is in contrast to, for example, the lack of structure observed in the North Sea population of Pseudonitzschia pungens, which unlike S. marinoi does not form resting stages (Evans et al. 2005). Another genetic study, performed by Härnström and colleagues in 2011, analysed the population structure of S. marinoi in Mariager Fjord, Denmark. Using dated sediment cores, they investigated the population structure of S. marinoi across more than a century, in response to a major shift in environmental conditions due to eutrophication. As in the case of the Godhe and Härnström (2010) study, the populations within the fjord were differentiated from those outside, with the fjord population structure having remained stable for over a hundred years and showing reduced genetic diversity compared to the open sea populations. This reduction is noted by the authors as being indicative of an extreme environment, and was particularly evident in cells revived from 1980, when the nutrient load in the fjord was especially high (e.g. 80 tons of phosphorus per year entering the fjord during the 1970s [Härnström et al. 2011]). The authors also note that additional information from coding loci would have been useful in their study, and such work is currently being undertaken on the fjord population. The S. marinoi population study by Godhe et al. (2013) looked at a broader geographic range along the Swedish west coast—Skagerrak, Kattegat, and Öresund—and sampled resting stage cells from the top 0.5 cm of the sediment, corresponding to around one year of deposits. The authors dismissed the null hypothesis of panmixia (i.e. random mating within the population), finding evidence of population structure among samples taken from each of seven sampling sites along the coast, in spite of these sites being well-connected by ocean currents. As with the aforementioned S. marinoi population studies, this highlights the fact that distinct populations can exist in the absence of apparent dispersal barriers. Outside of S. marinoi, Sanyal et al. (2019) have reported the revival of resting spores of Chaetoceros muelleri from around 1300 and 7200 years ago, although the germinated cells could not be cultured. In addition, they report successful DNA extraction from the ~1300-year-old revived spores, and PCR amplification of the

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rbcL and SSU markers. If diatoms from other taxa and environments are also found to be revivable after such lengths of time, exciting opportunities for DNA-based studies spanning most of the Holocene present themselves. Analysis of environmental DNA from sediment cores has also allowed the study of diatom populations and species diversity, in some cases across millennial timescales. Sediment cores dating back 7000 years were used in an investigation of diatom lineage relatedness across time, distance, and environmental gradients (Stoof-Leichsenring et al. 2015). DNA was extracted from sediment core slices and amplified using diatom rbcL-specific primers to infer on relatedness between Staurosira lineages. These were found to correlate more strongly with the environmental conditions over time than with geographic distances. A more recent study by Zimmerman et al. (2020) investigated whether this sedimentary ancient DNA could be used to analyse changes to diatom taxonomic composition over time using sediments up to 30,000 years old. Both of the above studies note the suitability of the rbcL gene for such metabarcoding approaches, as it is both short enough to survive the well-documented degradation of ancient DNA, and specific enough to allow classification of samples to genus or species level in many cases. Piredda et al. (2017) compared methods for the assessment of species abundance and diversity in diatom resting stages found in surface sediments from the Gulf of Naples. The group compared the conventional methods of serial dilution culture and most probable number (SDC-MPN) with a high-throughput sequencing metabarcoding approach amplifying a region of the 18S rDNA gene from DNA found in the sediment sample. The latter method was found to be promising in comparison to the more conventional method, although the authors noted that a lack of reference sequences for certain taxa was problematic for the analysis. Despite the number of studies employing diatom DNA from either revived resting stages or the environment, few have taken full advantage of the longevity of resting stages to study the genetics of diatoms from decades or centuries ago. This ‘time machine’ aspect of resting stages, which represent a snapshot from the past, offers many possibilities for study, some of which will be discussed in Sect. 4.

2.2

Advantages of Using Ancient Diatom DNA and Resting Stages in Research

Diatom resting stages are a useful resource for resurrection ecology research, the practice of studying either resurrected dormant organisms or their genetic material, in order to glean information about the past (Kerfoot et al. 1999). Although the study which coined this term looked at the resting eggs of the water flea Daphnia, the approach has since been used with many other taxa such as brine shrimps, bacteria, dinoflagellates, and diatoms (reviewed in Burge et al. 2018). This approach allows the comparison of both the phenotype and genotype of revived cells with contemporary populations, as well as permitting the observation of living snapshots of the evolutionary process through time. In the case of diatoms, resting stages of some species have been revived and cultured after a century or more (e.g. Stockner and

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Lund 1970; Härnström et al. 2011). Being able to study living organisms from so long ago allows us to assess the consequences of various events by comparing preand post-event populations, for example, the genetic effects on diatoms of the eutrophication of Mariager Fjord in Denmark (Härnström et al. 2011). In terms of the eukaryotic organisms used for resurrection ecology studies, diatoms possess relatively small genomes. Those that have been sequenced so far fall in the range of tens to hundreds of Mbp (e.g. Armbrust et al. 2004; Bowler et al. 2008; Tanaka et al. 2015; Basu et al. 2017; Mock et al. 2017; Osuna-Cruz et al. 2020), which makes whole genome sequencing (WGS) a feasible approach for DNA-based studies. This is in stark contrast to another resting stage-forming, oft-studied group of eukaryotic phytoplankton—dinoflagellates—which often possess enormous genomes, from one to several hundred Gbp (Wisecaver and Hackett 2011) (although slightly smaller dinoflagellate genomes appear possible, such as the ~100 Mb genome of the parasitic Amoebophrya ceratii [John et al. 2019]). In terms of both resting stage survival time and genome size, diatoms have comparable characteristics to some green algae, which are occasionally noted in resurrection studies (e.g. Ellegaard et al. 2016), with survival times in the order of decades (Ellegaard and Ribeiro 2018) and genome sizes in the order of tens to hundreds of Mbp (e.g. Merchant et al. 2007; Prochnik et al. 2010). As diatoms can be cultured after revival and have a short generation time, it is often possible to easily produce large quantities of cells. This provides ample material for study, facilitating the extraction of sufficient DNA for genome sequencing, which often presents a problem in ancient DNA studies of fossils, where small amounts of starting material tend to result in low genome coverage (e.g. Reich et al. 2010). In addition to the issue of quantity, fossil DNA also suffers from degradation. Due to exposure to the environment and the lack of an active repair system, DNA extracted from dead organisms is usually heavily degraded. This degradation frequently takes the form of DNA fragmentation (in part due to depurination), cytosine deamination to uracil at fragment ends, and modifications such as DNA crosslinking which block amplification and subsequent sequencing (see examples in Fig. 2; reviewed in Dabney et al. 2013). As evidenced by their ability to resume vegetative growth upon revival, diatom resting stages appear able to maintain their DNA during dormancy. Oku and Kamatani (1999) noted that light-exposed resting spores of Chaetoceros pseudocurvisetus have an active xanthophyll cycle, suggestive of photoprotection. Other protective or repair mechanisms may exist within resting stages of diatoms exposed to darkness and anoxia, although to the best of our knowledge, this has so far not been studied in depth. If the reported unculturability of resting stages revived after millennial timescales (Sanyal et al. 2019) is due to DNA damage, however, such mechanisms would appear to have their limits. With large amounts of high-quality DNA, one can confidently perform various studies— analyses of single-nucleotide polymorphisms (SNPs), genomic inversions, or loss or gain of genetic material, to name a few—on a whole genome scale, which can be paired with phenotypic studies in order to identify causal mutations for observed phenotypes. In the case of degraded ancient DNA, studies are instead limited to the

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Fig. 2 Examples of the post-mortem damage sustained by ancient DNA. (a) Depurination of A and G bases, whereby adenine or guanine residues are cleaved from the DNA strand, leading to singlestranded breaks via β elimination, where the sugar-phosphate backbone of the DNA is cleaved 30 of the abasic site (Lindahl and Andersson 1972), and subsequently fragmentation. (b) Deamination of cytosine to uracil, leading to C-to-T (and G-to-A) substitutions during DNA amplification. This deamination occurs more frequently at the 50 end of the molecule (Briggs et al. 2007). (c) Crosslinking of DNA (e.g. inter-strand cross-links, where bases on opposite strands become covalently bonded [Noll et al. 2006]), which prevent DNA polymerases from moving along the DNA strand and therefore prevent amplification. For a more in-depth review of ancient DNA damage processes, see Dabney et al. (2013)

amplification and sequencing of short markers, and issues of post-mortem DNA mutation must be considered. Finally, both ancient and modern diatom DNA can be harnessed to distinguish between cryptic species. Piredda et al. (2017) noted the greater power of highthroughput sequencing for identifying taxa compared to the use of optical methods such as light microscopy. While there are still limitations with such methods, it may yet be possible to overcome these, as discussed in Sect. 4.1.

3

Workflow

In this section, we will highlight some considerations regarding the workflow of ancient diatom DNA studies, as summarised in Fig. 3. While aspects such as culturing the organism of interest and the extraction of high-quality DNA are important for any genetic or genomic study, when working with ancient DNA, there is an especial importance in ensuring that modern sequences do not contaminate the sample and confound the results. In addition, working with resting stage

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Fig. 3 Basic workflow for an ancient diatom DNA study. The first two boxes denote the initial process of obtaining and dating the sediments from which the diatom material is taken. The boxes in the central section denote the process of obtaining the diatom DNA, either directly from the sediment or by revival, isolation, and culturing of the resting stages (note that in some instances, isolation of individual strains may be performed before revival). The final three boxes denote DNA sequencing and downstream in silico analyses. Illustration by Paula Töpel

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diatoms often requires the added step of reviving the dormant cells. These steps are each discussed below.

3.1

Isolation, Revival, and Culturing

In order to ensure the reliability of both isolation and dating of ancient diatom resting stages, there is a requirement for sedimentation in the water body in order to bury the resting stages progressively deeper (Fig. 1). The sediment must also be unperturbed by, for example, burrowing animals which cause mixing of deposits (including resting stages) and thus render dating impossible. Sediments lacking such perturbations are most often anoxic (Nordberg et al. 2001), and the stratification can be observed by X-ray imaging of the sediment cores (e.g. McQuoid et al. 2002). In such undisturbed cores, radioisotope dating can be performed to determine the ages of sediment layers and, by extension, the age of the diatom resting stages found therein. Radioisotopes typically used for this purpose include 210Pb, 226Ra, and 137 Cs (Lundholm et al. 2011), which have dated diatom resting stages to over 80 years old (Härnström et al. 2011). With the additional use of another radioisotope—14C—ancient sediments dated to several thousand years old have been found to contain diatom DNA (Stoof-Leichsenring et al. 2015). As with any study of ancient DNA, when dealing with samples of such ages, ensuring the absence of modern contaminants is vital for obtaining trustworthy results. As an alternative to the use of radioisotopes, sediment dating can be achieved by estimating sedimentation rates in the study area. This approach was used by Stockner and Lund (1970) to investigate resting stages of the diatom Aulacoseira italica (therein referred to as Melosira italica), for which they obtained an approximate age of 175 to 275 years. Given the requirement for undisturbed sediments, care must be taken when choosing appropriate sample sites for such studies so that reliable dates can be obtained. Diatom resting stages can be revived in vitro through mixing of resting stagecontaining sediment in a nutrient-rich medium (frequently a variant of f medium [Guillard and Ryther 1962] such as f/2 [Guillard 1975]), and exposure of the resultant slurry to favourable conditions of light and temperature (e.g. Godhe and Härnström 2010). This provides the cells with the conditions necessary for the resumption of vegetative growth, and is analogous with their being released from the sediment and returned to nutrient-rich waters. Once revived, individual cells or cell chains can be transferred into fresh medium and cultured as separate strains (although in some cases, this isolation of individuals takes place before the resting stages are revived). While most often done in liquid medium, culturing of diatoms on solid agar medium has also been achieved (Kourtchenko et al. 2018). Care must be exercised when selecting cells for culturing, as the presence of morphologically similar species in the study area can be problematic. For example, while S. marinoi is the dominating species of its genus in the Baltic Sea (Sjöqvist et al. 2015), the morphologically similar brackish water species S. subsalsum was first described from the Stockholm archipelago and can be found along both the

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Swedish and Finnish coasts (Hasle and Evensen 1975). Since morphological differences between species like these may be subtle, observations via light microscopy at the time of isolation of cells may not be sufficient to discriminate between them. However, with the possibility of generating species-specific primers to amplify, for example, microsatellite loci (e.g. Adams et al. 2017), such molecular methods can provide an affordable way to verify a sample’s taxonomic identity, thereby avoiding expending effort on sequencing a non-target species.

3.2

DNA Extraction and Decontamination

In contrast to approaches which involve amplification of short DNA sequences (such as microsatellite analyses or metabarcoding), where fragmented DNA molecules are often sufficient, WGS studies benefit from DNA extracts of high molecular weight (i.e. those which are obtainable from living cells, such as revived resting stages). This is of particular importance, for example, when attempting to assemble a genome de novo. For environmental samples, where DNA extraction is performed directly from the sediment, it is necessary to treat the sediment prior to cell lysis and DNA extraction, in order to remove extracellular DNA and other compounds that can inhibit DNA extraction or PCR (e.g. Stoof-Leichsenring et al. 2015; Piredda et al. 2017). Polysaccharides are one such PCR-hindering extracellular compound that inhibit a range of enzymes (Fang et al. 1992) and are produced in abundance by both diatoms and their associated bacteria (Amin et al. 2012), making it essential to separate them from DNA during purification. Methods to achieve this include the use of cetyl trimethyl ammonium bromide (CTAB), which acts by binding polysaccharides under high salt conditions (Panova et al. 2016). As in any study of ancient DNA, ensuring that samples are contaminant-free is paramount, as results can be confounded by the introduction of modern DNA. In cases where millennia-old sediment samples are being studied, this can include the use of dedicated ancient DNA laboratories, physically separated from other postPCR laboratories, where all surfaces and instruments are kept sterile through the use of UV radiation, bleach, and other techniques (e.g. Stoof-Leichsenring et al. 2015; Sanyal et al. 2019). Possible contaminants on the surface of extracted sediment cores can also be avoided by removing around 5 to 10 millimetres from the outside of the core slices being examined, which also avoids any between-layers smearing which may occur during core extraction (e.g. Lundholm et al. 2011). One particularly noteworthy contamination issue in diatom studies is that of bacteria. Diatoms are associated with a diverse microbiome (Amin et al. 2012; Johansson et al. 2019b), which makes obtaining axenic cultures of diatoms notoriously difficult, requiring various combinations of physical (ultrasonic treatment, filtration, centrifugation, etc.) and chemical methods (addition of antibiotics or detergents) (e.g. Bruckner and Kroth 2009; Shishlyannikov et al. 2011). Specific care must be taken to ensure that the antibiotics used do not also kill the diatom. For example, it has been shown that S. marinoi is exceptionally sensitive to several

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commonly used antibiotics, which has complicated initial efforts to obtain axenic cultures (Johansson et al. 2019b). As many of the associated bacteria have a positive effect on growth, removal of these symbionts may in itself be detrimental to the diatom host, thus hampering cultivation efforts. In cases where short, specific marker genes are being amplified via PCR, primer specificity should ensure that bacterial contaminant sequences aren’t amplified. However, given the agnostic nature of WGS approaches where in theory every DNA fragment is sequenced, any contaminants in the sample (such as DNA sequences from the diatom microbiome) will also appear in the sequencing data. This will necessitate further sequencing in order to generate enough diatom sequences for meaningful downstream analyses, thus increasing the sequencing costs of the project. As such, thorough removal of associated bacteria prior to sequencing is of the utmost importance for WGS approaches.

3.3

In Silico Analyses

For non-axenic diatom cultures, it is still possible to perform post-sequencing decontamination in silico. With the increasing number of reference genomes available for diatoms and their associated bacteria, it is possible to use mapping-based approaches to decontaminate the sequencing data. Using read mapping software such as Bowtie 2 (Langmead and Salzberg 2012) or BLASR (Chaisson and Tesler 2012), reads can be separated into those which map to the diatom genome and those which map to the microbiome, allowing downstream analyses to be performed only on diatom-derived reads. In this instance, a good quality reference is particularly important to ensure that as many diatom reads as possible can be identified and separated out from contaminant reads. Even if no reference genome is available, an unsupervised binning approach—not dependent on reference genomes—can also be used to separate diatom and non-diatom reads. Software such as MetaBAT 2 (Kang et al. 2019) can sort this type of metagenome data into bins based on factors such as sequence composition and abundance, followed by quality analysis using programmes such as CheckM (Parks et al. 2015). The resulting MAGs can then be taxonomically classified, and only those attributed to non-contaminant species can be included in downstream analyses. However, such a method could lead to misclassification of reads, resulting in either inadvertent inclusion or exclusion of certain sequences. As such, a reference-based method is preferable whenever good quality reference sequences are available. Bioinformatics methods can also be applied to address another issue present in studies of ancient DNA–post-mortem mutation. As noted in Sect. 2.2, an organism’s DNA can undergo extensive post-mortem degradation. Through the in silico analysis of ancient DNA sequences, statistical models for the frequency of such postmortem mutations can be generated (e.g. Briggs et al. 2007), and tools have been created which account for these modifications (e.g. Kawash et al. 2018).

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Once decontamination (and other forms of quality control common to WGS projects) has been completed, analysis of the sequence data can then be performed. As mentioned in Sect. 2.1, most previous studies of ancient diatom DNA have had a fairly narrow scope, looking at microsatellites or short marker genes. Some examples of potentially more ambitious studies, made possible by advances in sequencing technology, will be discussed in the next section.

4

Future Perspectives

On account of the ages from which diatoms can be revived, and the length of time their DNA can remain detectable in the sediment, there exist many possibilities for their use in research. The existence of resting stages allows for both comparative genomic and phenomic studies between these revived diatoms and contemporary strains. Given that the ability to form resting stages can help anchor a population to a location (Sundqvist et al. 2018) and that earlier members of that population will be adapted to a different suite of conditions, resting stages allow informative studies of that population to be performed. Considering the global success of diatoms over millions of years, and therefore their ability to adapt to a changing world, the possibility to study strains separated by both time and environmental changes (such as fluctuating nutrient levels, temperature changes, and the presence of toxic metals) can help to shed light on the specific mechanisms they have evolved to survive such changes. Some potential areas for future study will be discussed in this section.

4.1

The Potential of High-Throughput Sequencing in Ancient Diatom DNA Studies

Given the continued improvement and accessibility of DNA sequencing technologies, genetic and genomic analyses using bioinformatics methods are becoming progressively more commonplace. While early studies using DNA from revived diatom strains mainly made use of Sanger sequencing (e.g. Godhe and Härnström 2010), more and more studies are now using combinations of shortand long-read high-throughput sequencing technologies. One outcome of this paradigm shift in sequencing is the increasing number of diatom reference genomes that are becoming available (e.g. Armbrust et al. 2004; Bowler et al. 2008; Tanaka et al. 2015; Basu et al. 2017; Mock et al. 2017; OsunaCruz et al. 2020). Despite this, only two of these have thus far been assembled to chromosome level—Thalassiosira pseudonana (Armbrust et al. 2004) and Phaeodactylum tricornutum (Bowler et al. 2008). With the increases in accuracy and read length of recent sequencing technologies, however, it should be possible to generate more chromosome-level assemblies in the future, as reads of sufficient length have the capacity to span repeat regions in the genome, which would otherwise be difficult or impossible to properly assemble. Such resources will surely

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facilitate future genetic and genomic studies. An example of an initiative seeking to make advances in this area is the 100 Diatom Genomes project (https://jgi.doe.gov), which aims to sequence a diverse array of diatom species in order to better understand the group’s varied capabilities. As well as allowing for the assembly of reference genomes, and the wealth of information which these resources bring to the research community, WGS approaches can be used to answer many different research questions. Rather than focusing on a handful of genes of interest, whole genomes of individual strains from a particular time and place can be sequenced and compared to a reference, allowing research questions with a broader genomic scope to be addressed. The falling cost of DNA sequencing also makes it viable to generate sequence data from many more samples, at greater sequence depths, than previously. This allows, for example, more robust population genomics studies using SNP calling, with more reads per base to back up any given polymorphism. In their study on the diversity of diatom resting stages, Piredda et al. (2017) note that the V4 region of the 18S rDNA gene (commonly used in metabarcoding studies) does not provide sufficient resolution to distinguish between the two species, Skeletonema pseudocostatum and S. tropicum. Satisfactory taxonomic resolution can be achieved by analysing longer spans of the 18S rDNA region, but as the gene is over 1 kbp in length, this approach is not feasible with short-read sequencing technology. However, with the improving accuracy of long-read sequencing, it is becoming possible to reliably sequence the entirety of longer barcoding regions (Tedersoo et al. 2018), although to the best of our knowledge, such methods have not yet been attempted in a diatom study. Piredda et al. (2017) also point out that the existing databases of diatom reference sequences are lacking with regard to certain taxa. In parallel with efforts to improve taxonomic coverage in the reference databases, long-read sequencing of, for example, the full 18S rDNA gene will make metabarcoding approaches progressively more informative. Advances are also being made in the field of bioinformatics which will facilitate the analysis of the increasing amounts of sequence data currently being generated. One such area of advancement is in machine learning, which is being applied to a growing number of biological problems, among them the issues of modern contamination and post-mortem DNA mutations (e.g. Kawash et al. 2018). These techniques should prove highly relevant when dealing with diatom sequences obtained from millennia-old sediment samples. We also expect that further advances in machine learning will provide additional sequence analysis tools for future diatom resurrection studies.

4.2

Promising Avenues of Investigation

Through the application of modern sequencing technologies to ancient diatom DNA, many additional research possibilities present themselves. Ellegaard et al. (2018) extol the virtues of using resting stage-forming phytoplankton such as diatoms to study adaptation to environmental change, including the use of genome

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resequencing to detect variants through time. The adaptation to such changes can be tested by analysing resting stages revived from progressively deeper layers of sediment cores, functioning as a time series which enables us to observe a population’s evolution across tens of thousands of diatom generations. Changes to population structure, as well as to coding and regulatory regions, could be analysed to predict the fate of these important primary producers in response to projected changes to the world’s oceans. With indications that diatoms can be revived after over a millennium in the sediment (Sanyal et al. 2019), diatoms could potentially be used to address crucial environmental questions from throughout much of recent human history. Indeed, further investigations into the maximum possible age of still-living diatom resting stages could provide insights into diatoms’ resilience to environmental changes. Considering the length of time that diatoms can remain viable in the sediment, examining the roles of the diatom-associated microbiome throughout this period will provide answers as to whether the associated bacteria also endure the dark, anoxic sediments, or whether newly-germinated diatoms recruit a new microbiome. While the resting stage microbiomes of other organisms such as Daphnia have been investigated (Mushegian et al. 2018), to the best of our knowledge, similar studies have not yet been performed in diatoms. Using the dating information from various depths of sediment cores, one could also investigate whether the composition of the resting stages’ accompanying bacteria changes over time in a single location. While comparative metagenomics has previously been performed on sediment and permafrost cores (e.g. Garner et al. 2020; Xue et al. 2020), to the best of our knowledge, such studies have not been performed in the context of diatoms. Another possible novel area of study in diatoms lies in the adaptive potential of loss-of-function (LOF) mutations and other types of intraspecific structural variations that can affect the fitness of individual strains. Such mutations have been found in humans, one example being the APOC3 gene, whose mutation can have a beneficial effect on triglyceride levels (The TG and HDL Working Group of the Exome Sequencing Project 2014). As beneficial LOF mutations have been identified in humans, it seems reasonable to assume that such beneficial mutations could similarly be found in natural populations of diatoms and other organisms. By sequencing diatom strains from before and after certain events, such as eutrophication or an increase in water temperature caused by industries or power plants, one can potentially identify naturally-occurring LOF mutations showing signs of selection and correlation with an adaptive response to the event in question. Considering the large proportion of diatom genes encoding proteins of unknown function (Johansson et al. 2019a), such information may also aid in characterisation of these proteins. In addition to loss of function, loss (and indeed gain) of genomic regions over time can also be investigated. Given diatoms’ propensity for incorporating bacterial genes via horizontal gene transfer (Bowler et al. 2008), this could highlight more recent events of this nature. However, approaches involving mapping sequence reads to a reference genome have their limitations. For example, these approaches can only assess loss of genomic regions in relation to the reference, which in itself

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may fail to fully represent the genomic variation of the species. In order to assess regions that are not present in the reference strain, an assembly-based approach is more appropriate, followed by alignment of the ancient and contemporary genomes in order to identify the previously unknown genomic region. Many mysteries remain unsolved regarding diatoms, but by combining the use of ancient diatom DNA with ever-improving sequencing and bioinformatics techniques, the outlook for elucidating some of these is promising. Acknowledgements Many of the studies and techniques that this chapter refers to were performed, and indeed pioneered, by Professor Anna Godhe, who passed away before this chapter could be written. We, the authors, would not have become so involved in this field were it not for our close collaboration, and friendship, with Anna. She will be deeply missed by all in the wide network that she cultivated over the years.

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Part II Genomics

Structure and Evolution of Diatom Nuclear Genes and Genomes Thomas Mock, Kat Hodgkinson , Taoyang Wu , Vincent Moulton , Anthony Duncan , Cock van Oosterhout and Monica Pichler

,

Abstract

Diatoms are one of the most successful eukaryotes. There are over 100,000 diatom species contributing nearly half of total algal abundance in the oceans. Diatoms have conquered almost all aquatic environments, with high abundance especially in coastal and polar oceans and inland waters. The first diatom genomes provided important insights into their genetic, metabolic, and morphological diversity, which is unmatched by any other algal class. However, the recent application of long-read sequencing in addition to population genomics and culture-independent approaches enables a step-change in our understanding of diatom genomes. This chapter synthesizes what we have learned about the structure and evolution of diatom nuclear genes and genomes since the genome of Thalassiosira pseudonana became available in 2004. We highlight some of the key findings and discuss mechanisms and drivers of diatom genome evolution and adaptation underpinning the success of the entire class. Considering that most of their genomic diversity is still unknown, large-scale genome projects and culture-independent methods such as metagenome-assembled and single-cellamplified genomes hold great promise to reveal more of their inter- and intraspecific genomic diversity in an environmental context. Data from these studies will pave the way for novel insights into their genetic versatility, which will enable us to identify the key evolutionary innovations in diatoms, and their adaptive evolution to a wide variety of environments, including to some of the most extreme aquatic environments on Earth such as intertidal zones and polar oceans.

T. Mock (*) · K. Hodgkinson · C. van Oosterhout · M. Pichler School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, UK e-mail: [email protected] T. Wu · V. Moulton · A. Duncan School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, UK # Springer Nature Switzerland AG 2022 A. Falciatore, T. Mock (eds.), The Molecular Life of Diatoms, https://doi.org/10.1007/978-3-030-92499-7_5

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These insights are not only critical for advancing diatom-based biotechnology and synthetic biology, but will also improve our knowledge about how the various diatom lineages perform their important roles as key players for capturing CO2 and as the foundation of diverse aquatic food webs, thus providing significant ecosystem services and maintaining the continued habitability on Earth.

Abbreviations BAC DAE EGT GO HGT ISIPs JGI lincRNAs MAG MGT ncRNAs ONT ORF PacBio PUFAs SAG SMRT sRNAs

1

Bacterial Artificial Chromosome Differential Allelic Expression Endosymbiotic Gene Transfer Gene Ontology Horizontal Gene Transfer Iron Stress-Induced Proteins Joint Genome Institute long intergenic non-coding RNAs Metagenome-assembled genome Metagenomics-based transcriptome non-coding RNAs Oxford Nanopore Technology Open Reading Frame Pacific Biosciences Polyunsaturated Fatty Acids Single-Amplified Genome Single-Molecule Real-Time small non-coding RNAs

Introduction

Diatom genomics is a relatively young field, which has commenced by the publication of the genome of Thalassiosira pseudonana in 2004 (Armbrust et al. 2004). It was the first genome from the diverse group of photosynthetic stramenopiles, which represent one of the major lineages of eukaryotes (Burki et al. 2020). As it was also the first genome from a marine alga, T. pseudonana has been frequently used as a reference to study not only diatom-specific biology but also to address wider questions concerning microbial biodiversity and biotechnology, the role of endosymbiosis for the evolution of life on Earth, and for revealing intricacies of how phytoplankton orchestrate global biogeochemical cycles (Mock et al. 2008; Ashworth et al. 2013; Kustka et al. 2014; Delalat et al. 2015; Benoiston et al. 2017; Chen et al. 2018; Treguer et al. 2018). Today, approximately 17 years later, diatom research has significantly matured and not only leads the field of marine algal

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research (Falciatore et al. 2020), but is on par with research using the ‘green yeast’ Chlamydomonas reinhardtii (Chlorophyta), which is a long-standing model for plant biology and biotechnology (Sasso et al. 2018). Although other algal groups such as haptophytes, dinoflagellates, and cryptophytes are nearly equally important considering their ecology and role in the evolution of eukaryotic life, only a few genomes from these groups are currently available, and the development of reverse genetics tools for experimental cell biology is still in its infancy. Reasons for the diatom success story can be found throughout this book; they are manifold, but most of them depend on the availability of diatom genomes, easy cultivation of diatom species under laboratory conditions, and the availability of diverse methods for their exploitation to address questions from ecology to biotechnology. While multiple candidates were initially explored for genomic understanding of diatoms, research quickly focussed around two representative species, the centric diatom Thalassiosira pseudonana and the pennate diatom Phaeodactylum tricornutum. The latter was published only a few years later in 2008 (Bowler et al. 2008). Both genomes were used for comparative genomics and transcriptomics to provide first results on the evolution, ecology, and metabolic diversity of diatoms (Nisbet et al. 2004; Montsant et al. 2005, 2007; Dyhrman et al. 2012; Veluchamy et al. 2013; Levitan et al. 2015; Rastogi et al. 2018). Remarkable insights have been revealed, shedding light on some key features of diatom biology including the synthesis of their nanopatterned silica cell walls (Mock et al. 2008; Shrestha et al. 2012), the significance of the urea cycle (Allen et al. 2011), the acquisition of genes from bacteria and eukaryotic sources (Vancaester et al. 2020; Dorrell et al. 2021), and the role of transposable elements for driving metabolic versatility (Maumus et al. 2009), just to name a few. Thus, these genomes were used like a library to retrieve relevant information for addressing questions from different fields of biological research including molecular ecology and evolution, physiology, metabolism, and reverse genetics. For instance, diverse gene expression studies not only revealed how genes were regulated under relevant growth conditions, but they were also used to identify physiological markers for ecological studies with natural diatom populations such as ISIPs (iron stress-induced proteins) (Marchetti et al. 2012; Caputi et al. 2019). Genes reporting on diverse nutrient limitations (e.g. nitrate reductase) were used to assess the physiological state of natural communities across environmental gradients (Bender et al. 2014; Alipanah et al. 2015; Amato et al. 2017; Lampe et al. 2018; Cohen et al. 2019), which provided deeper insights into how natural diatom communities respond to changes of environmental conditions. These early studies also contributed to extending the known functional diversity of diatom genes as the first diatom genomes were used as important references for identifying novel gene variants from natural diatom communities through either amplicon sequencing or metatranscriptomics. Furthermore, the first diatom genomes were invaluable for mapping out the metabolism responsible for the success of diatoms in diverse ecosystems and under changing environmental conditions (Kroth et al. 2008; Rosenwasser et al. 2014; Kim et al. 2016; Levering et al. 2016). The main focus was on identifying

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metabolic pathways involved in carbon acquisition (Kroth et al. 2008), the synthesis of lipids (Sayanova et al. 2017), signalling (Helliwell et al. 2021), vitamin auxotrophy (Helliwell et al. 2011), and cell-cycle progression (Huysman et al. 2010; Kim et al. 2017). Diatom genomes were used to identify genes involved in the response to nutrient limitations with strong emphasis on silicate, nitrate, and iron metabolism (Allen et al. 2008; Mock et al. 2008; Shrestha et al. 2012; Alipanah et al. 2015). Knowledge on the gene content in combination with genome-wide expression patterns revealed the first metabolic maps and metabolic pathway models (Fabris et al. 2012; Singh et al. 2015; Gruber and Kroth 2017; Levering et al. 2017). For details on the latter, please see “Constraint-based Modeling of Diatoms Metabolism and Quantitative Biology Approaches”. Once a minimal set of cyanobacterial genomes and diverse genomes from the algal tree of life became available, it was possible to reconstruct the evolutionary mosaicism of diatom genomes as the outcome of primary and secondary endosymbiosis (Moustafa et al. 2009; Prihoda et al. 2012; Benoiston et al. 2017; Dorrell et al. 2017). In particular, the first genomes from marine green and red algae (Matsuzaki et al. 2004; Worden et al. 2009; Bhattacharya et al. 2013; van Baren et al. 2016), which were published shortly after the genome of T. pseudonana, provided a stepchange in our understanding of how endosymbionts have shaped diatom genomes through gene acquisitions. One of the most remarkable discoveries, which is still strongly debated to date, are the genetic traces of a cryptic greenalgal endosymbiont predating the acquisition of a red alga (Moustafa et al. 2009; Deschamps and Moreira 2012; Dorrell et al. 2017). The latter was known before diatom genomes became available because the plastid genome in diatom is derived from a red-algal endosymbiont. But only through the genomic lens and the availability of algal genomes from descendants of endosymbionts from the group of Archaeplastida (red and green algae), many genes were discovered in extant diatom genomes likely being of green- and red-algal origin (Moustafa et al. 2009; Dorrell et al. 2017). As there is no remnant organelle representing this green-algal endosymbiont, these genes were only discovered once the first green-algal genomes became available, i.e., Ostreococcus tauri and Micromonas species (Derelle et al. 2006; Worden et al. 2009). Despite many taxonomic and phylogenetic studies have revealed the macro- and microevolution of diatoms, diatom research has only recently begun to reveal mechanisms responsible for genetic variations between and especially within a species (Koester et al. 2018; Rastogi et al. 2020), due to the availability of more diatom genomes (Lommer et al. 2012; Tanaka et al. 2015; Traller et al. 2016; Basu et al. 2017; Mock et al. 2017; Osuna-Cruz et al. 2020) and the use of the latest sequencing and assembly technologies. The latter mechanisms are likely to significantly drive the evolution of hyperdiversity (global number of species (species richness) differs by
one order of magnitude compared to other classes of algae) in the class of diatoms. Thus, addressing the fundamental question as to how the forces of evolution have shaped the structure of diatom genomes as the consequence of natural selection is relevant for our understanding of the extraordinary diversity of diatoms, their evolvability, and their adaptability (Pinseel et al. 2020). Their

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widespread distribution especially in coastal waters, subpolar and polar oceans, and in freshwater ecosystems accompanied by their hyperdiversity suggests a significant level of evolvability unseen in other microbial eukaryotes (Nakov et al. 2018). How this evolvability has evolved (e.g. mechanisms underpinning the evolution of evolvability) and how it contributes to the genomic and phenotypic plasticity in the class of diatoms are unknown, but these questions are at the heart of revealing fundamental mechanisms responsible for the success of diatoms. This chapter not only provides basic information on the structure and evolution of diatom genomes, but it also critically reflects on how novel sequencing technologies (e.g. Oxford Nanopore, PacBio HiF, 10X Genomics) and bioinformatics tools (e.g. Hi-C-guided genome assemblies) have shaped and sometimes even revised our understanding of diatom genomes. Furthermore, it addresses the important question of evolutionary mechanisms, and it provides an outlook for diatom genomics. It is likely that the latter will be significantly shaped by culture-independent approaches and single-cell sequencing, both of which are still in their infancy, but these methods hold great promise in terms of revealing more of diatoms inter- and intraspecific genomic diversity in an environmental context (Delmont et al. 2020; Duncan et al. 2020).

2

The Basic Structure of Diatom Genes and Genomes

2.1

Genes

Coding genes in diatoms have a characteristic eukaryotic structure, although their length is relatively short due to the compactness of diatom genomes (e.g. Armbrust et al. 2004; Bowler et al. 2008; Basu et al. 2017; Mock et al. 2017). Approximately 50% of the average diatom genome space is occupied by protein-coding genes. There are a number of notable exceptions of species that possess a significant amount of repeats in the non-coding part of their genomes, such as in Cyclotella cryptica (Traller et al. 2016; Roberts et al. 2020) and Fragilariopsis cylindrus (Mock et al. 2017) (Table 1). For example, the repeat content of C. cryptica is 53%, and repeats have significantly increased the genome size (161.7 Mb) of this species. So far, no other diatom genome has been characterized by such a high repeat content, and in Table 1. Genome properties of selected diatom genomes. (1) Armbrust et al. 2004; (2) Bowler et al. 2008; (3) Lommer et al. 2012; (4) Mock et al. 2017; (5) Tanaka et al. 2015; (6) Traller et al. 2016; (7) Osuna-Cruz et al. 2020; (8) Basu et al. 2017. Species Genome Size Repeats GC Gene count Ploidy Haplotype Diversity (Polymorphisms)

Thalassiosira pseudonana1 32 Mbp ≤2% 48 % 11776 Diploid Homozygous (≤ 1 %)

Phaeodactylum tricornutum2 27 Mbp ≤6% 51 % 10402 Diploid Homozygous (≤ 1 %)

Thalassiosira oceanica3 92 Mbp N.D. 53% 10109 Diploid N.D.

Fragilariopsis cylindrus4 61 Mbp ≤ 38 % 40 % 21066 Partial Triploid Heterozygous (≤ 6 %)

Fistulifera solaris5 25 Mbp ≤ 16 % 46 % 11448 Allodiploid Heterozygous (≤ 38 %)

Cyclotella cryptica6 171 Mbp ≤ 59 % 42 % 21250 Diploid N.D.

Seminavis robusta7 126 Mbp ≤ 23 % 48 % 36254 Diploid Homozygous (≤ 1 %)

Pseudo-nitzschia multistriata8 59 Mbp ≤ 25 % 46 % 12008 Diploid Homozygous (≤ 1 %)

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general, repeats do not appear to have inflated the genome size of diatoms as much as that in dinoflagellates (Stephens et al. 2020). The relatively short coding genes in diatom genomes (mean gene length  2500 bps) have on average a single intron and two exons (Basu et al. 2017). Introns are usually being spliced out by the canonical eukaryotic splicing machinery. However, in some diatom species, the introns of genes are retained in mature mRNAs, and this alternative splicing mode is known as ‘intron retention’. Over 95% of diatom genes possess canonical splice sites (Acceptor Sites: AG/CT and Donor sites: GT/AC), but only less than 20% of genes contain more than one intron. Intron retention and exon skipping have been observed and are assumed to contribute to the diversity of the protein space, facilitating phenotypic plasticity in a dynamic aquatic environment. In Phaeodactylum tricornutum, ca. 24% of genes undergo intron retention and ca. 20% exon skipping (Rastogi et al. 2018). A small percentage of genes (2 cell divisions per day) and extensively characterized physiology. Furthermore, T. pseudonana is a model for diatom cell-wall biology and biochemistry (e.g. Sumper and Brunner 2008; Hildebrand et al. 2018), and this centric species is a representative of the Thalassiosirales, an ecologically important and diverse diatom order (Malviya et al. 2016; Branco-Vieira et al. 2020). The pennate diatom P. tricornutum was established as a model for cell biology, biochemistry and reverse genetics (Bowler et al. 2008). However, it is an unusual diatom species because it has no absolute requirement for Si, but it grows well under different laboratory settings and without bacteria in co-culture. The latter two properties likely contributed to the rise of P. tricornutum as a model species. It is also the main species of the diatom biotechnology industry focussing on alternative fuels and highvalue end products, such as essential polyunsaturated fatty acids (PUFAs) (e.g. Daboussi et al. 2014; Branco-Vieira et al. 2020; George et al. 2020). Considering the hyperdiversity of the class (Nakov et al. 2018), comprising
100,000 species, the availability of two diatom genomes was only the beginning of revealing the secrets of the molecular life of diatoms. Most of the subsequent genome projects were based on second and third generation sequencing technologies (e.g. Illumina, PacBio, Oxford Nanopore), which provided deeper insights into genome structure and diversity (Mock et al. 2017; Osuna-Cruz et al. 2020). Although resequencing of T. pseudonana and P. tricornutum has confirmed their diploid structure with a relatively low level of polymorphisms between the two haplotypes (Table 1) (Koester et al. 2018; Rastogi et al. 2020), other diatoms genomes are either comprised of more haplotypes, such as Fistulifera solaris (Tanaka et al. 2015), and/or significantly diverged haplotypes, such as Fragilariopsis cylindrus (Mock et al. 2017). Triploidy may play a role in the latter species, which only recently has been identified by combining Illumina with Oxford Nanopore sequencing and a haplotype-specific assembly strategy. Together with k-mer spectra, these data revealed that two out of three sub-genomes (haplotypes) were highly identical (up to 100% sequence identity), imposing challenges to differentiate them (Fig. 1). However, diverged alleles and extended genomic loci between the two most diverged haplotypes could still be identified (Mock et al. 2017). A small number of genomic loci even show divergence between all three haplotypes (Fig. 1). Unlike whole genome duplication, which is not uncommon in diatoms and which is considered to have significantly contributed to speciation (Parks et al. 2018), the allopolyploid genome of the coastal marine diatom Fustulifera solaris appears to be a consequence of introgressive hybridization, which has led to two sets of pseudoparental sub-genomes (Tanaka et al. 2015). Furthermore, a near chromosome-scale assembly of the F. solaris genome provided first sequence based evidence of potential aneuploidy in diatom genomes (Maeda et al. 2021). Aneuploidy can arise from errors in chromosome segregation, leading to an abnormal number of chromosomes in a cell (Compton 2011). Although allopolyploidy is prevalent in plants, it has not been reported before in algae. Transcriptome profiling with F. solaris revealed that both sub-genomes

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Distinct k-mers

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Fig. 1 (a) (i) The k-mer spectra for F. cylindrus CMP1102 shows three distinct distributions. Each distribution represents content that occurs once, twice and thrice, respectively, with the latter two occurring at harmonic frequencies to the first. The first distribution consists of unique content where the sub-genomes are diverged, while the third distribution consists of conserved content amongst all sub-genomes. (b) A de Bruijn graph of the assembly contains areas of varying coverage, as reflected in the k-mer spectra. Areas conserved between all sub-genomes (a, b, c) contain triple the coverage than the unique components. Where sub-genomes diverge, “bubbles” are formed. These may form between areas where 2 sub-genomes are very similar (a, b) opposing a unique sub-genome section (c). They may also occur in unique areas of the subgenomes (a, b, c), forming a “double-bubble”. Unique content may diverge from double-coverage content or may diverge straight from triplecoverage content

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contributed to global gene expression although the majority (61%) of homoeologous alleles were not equally expressed but showed an expression bias towards specific conditions (Nomaguchi et al. 2018). Differences in the promoter regions of homoeologous alleles might have contributed to this expression bias. A slightly more complex and not yet completely resolved genome structure was observed in the polar diatom F. cylindrus (Fig. 1) (Mock et al. 2017). The genome of F. cylindrus is characterized by significant allelic divergence between haplotypes for about 30% of the genome. Most of the polymorphisms have been identified in non-coding regions upstream of transcription start sites. Similar to F. solaris, diverged promoter regions appear to drive the differential expression of allelic pairs due to differences in binding affinities of transcription factors. Subsequent genome projects (e.g. Skeletonema marinoi (https://albiorix.bioenv.gu.se/Skeletonema_marinoi. html) confirmed that haplotype divergence is more common in diatoms than previously anticipated based on the genomes of T. pseudonana and P. tricornutum. Taken together, our understanding of the structure of diatom genes and genomes has significantly altered over the last decade because of: a) sequencing more (non-model) diatom species, and b) increasingly advanced sequencing technologies and genome assembly tools, enabling the inclusion of long-read data resulting in (near) chromosome-level assemblies (Fig. 1). The latter has also been generated for T. pseudonana and P. tricornutum, which increased the contiguity of the assembly to the chromosome level from telomere to telomere compared to the original Sangerbased assemblies. However, the biggest revelations include the recent discovery of haplotype divergence in F. cylindrus and F. solaris and its influence on allelic expression bias (Mock et al. 2017; Hoguin et al. 2021). These insights were gained by sequencing species from different habitats using new sequencing and assembly approaches. The next steps could include the ‘geography’ of chromosomes to reveal if different parts of the chromatin between telomeres contribute differently to haplotype divergence and potentially the loss of heterozygosity underpinning adaptive processes to different habitats. Furthermore, how different levels of ploidy impact the structure of diatom genomes and the expression of alleles remains enigmatic. This is a question that needs to be addressed urgently in future genome projects, as there is mounting evidence that ploidy is an important driver of diatom evolution, adaption and speciation (e.g. Nakov et al. 2018; Parks et al. 2018). Currently, the most significant large-scale genome project addressing these questions is the ‘100 Diatom Genomes Project’ funded by JGI (https://jgi.doe.gov/ csp-2021-100-diatom-genomes/). A comparative analysis of 100 genomes from carefully selected diatom species representative of their structural, metabolic and evolutionary diversity covering the diatom-tree-of-life (Fig. 2) is likely to reveal novel insights into their genetic versatility, which will enable us to differentiate between what makes a diatom a diatom and what has evolved to underpin specific metabolic demands.

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Raphid pennates Fig. 2 18S rDNA diatom phylogeny. Dots indicate strains selected for whole-genome sequencing as part of the “100 Diatom Genomes Project”; dots to the right, strains already sequenced or in preparation. Features to the left: key acquisitions (Courtesy of Wiebe Kooistra)

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3

The Evolutionary Mosaicism of Diatom Genomes Driven by Endosymbiotic and Horizontal Gene Transfer

3.1

Evolutionary Mosaicism

The era of phylogenomic studies with diatoms began once there were not only several diatom genomes and transcriptomes available but also genomes from the monophyletic supergroup of Archaeplastida, composed of primary plastid-bearing lineages, i.e., green and red algae, and the glaucophytes (e.g. Nisbet et al. 2004; Grossman 2005; Worden et al. 2009; Price et al. 2012). Genomes from these lineages provided the foundation for reconstructing major evolutionary events in eukaryotes, which possess complex plastids evolved by the engulfment of either red or green algae. These endosymbiotic gene transfer (EGT) events have left behind footprints in the genomes of extant eukaryotic lineages such as diatoms, as evidenced by gene loss and the transfer of genes from the endosymbiont to the host genome (Fig. 3) (e.g. Timmis et al. 2004; Li et al. 2006; Ponce-Toledo et al. 2019). Consequently, the genomes of diatoms can be considered a puzzle built by pieces from different sources acquired successively and over long periods of time (
1 billion years) (e.g. Benoiston et al. 2017; Brodie et al. 2017). To identify the origin of each of these pieces still remains a major challenge as the ravages of time have had their

Fig. 3 The evolution of diatoms through primary and secondary endosymbiosis. Highly debated is the process by which a heterotrophic eukaryote acquired an endosymbiont from the group of Archaeplastida (Chlorophyta, glaucophyte, rhodophyta). Phylogenomics provided evidence of a cryptic endosymbiotic event with an ancient chlorophyte predating the acquisition of a red alga. The latter turned into the plastid of extant heterokontophytes including diatoms. Adapted from Hopes and Mock 2015

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impact on the integrity of the puzzle pieces. This is especially the case for diatoms due to an ongoing controversy regarding a significant number of nuclear genes (> 1500) supposedly derived from a cryptic green algal endosymbiont (Moustafa et al. 2009). Although there is strong evidence that plastids in extant diatom species are derived from red algae, these ‘green genes’ have not only been identified in nuclear genomes of several diatoms, but also in their ancestors where they can contribute up to 25% of nucleus-encoded plastid targeted proteins (Dorrell et al. 2017). As to how these evolutionary processes have shaped the evolution of plastids in diatoms, please see “Reconstructing Dynamic Evolutionary Events in Diatom Nuclear and Organelle Genomes”. These data suggest a massive genetic mosaicism in diatom genomes likely driven by successive endosymbiotic events with the green algal endosymbiont as being acquired prior to the acquisition of the red algal endosymbiont (Fig. 3) (Hopes and Mock 2015). High-frequency horizontal gene transfer (HGT) has been discussed and potential tree reconstruction artefacts to explain the high number of these cryptic ‘green genes’ in diatom genomes (e.g. Deschamps and Moreira 2012). Thus, the origin of the mosaicism in diatom genomes is still being debated, including the relative importance HGT and EGT. A global approach encompassing all genomes of potential ‘donor’ organisms responsible for the mosaicism of diatom genomes might help addressing this fundamental question. However, as gene acquisition events took place over a period of more than a billion years, the endosymbiotic footprints likely have become eroded due to the evolutionary forces, such as mutation, genetic drift, gene flow, selection and recombination. Nonetheless, current data suggest that up to 20% of coding potential in diatom genomes has been derived from genomes of former endosymbionts, which is slightly more than in other major algal lineages (e.g. Moustafa et al. 2009). However, significant uncertainties still exist. For instance, undersampling and undersequencing of putative donors leave us with significant knowledge gaps in terms of the nature of the donors and their contribution to endosymbiosis and with respect to discriminating EGT from non-EGT derived genes. While genes were transferred from the genomes of the endosymbionts to the genome of the host, they were either lost or undergoing significant modifications. Those genes that were retained are mostly enriched in photosynthesis-related processes, such as the synthesis of photosystem subunits, pigments and other processes essential for plastid maintenance (e.g. plastid division). However, recent data suggest that plastids from endosymbionts would not be viable without reprogramming and retargeting of host genes (Dorrell et al. 2017). For instance, plastid proteomes are composed of proteins that are not derived from endosymbionts, suggesting that a significant amount of genes of non-endosymbiont origin in the host nuclear genome have undergone structural modification to target their proteins to the plastids. In diatoms, this mainly is based on modifications at their 50 -prime ends such the evolution of specific plastid-targeting motifs, and sequences for signal and transit peptides. Thus, the acquisition of an endosymbiont had an impact on the structure of at least a subset of host genes and likely also their regulation as EGT contributed to the expansion of the redox sensing capabilities of the host. Those secondarily acquired genes that were not required for the maintenance of the plastid or other

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organelles such as the mitochondria, contribute to the complexity of diatom metabolism by increasing the diversity and reticulate evolution of isoforms as described in “Reconstructing Dynamic Evolutionary Events in Diatom Nuclear and Organelle Genomes”. Many of these isoforms are part of complex genetic networks underpinning core metabolism such as amino acid and lipid metabolism in diatoms (e.g. Benoiston et al. 2017; Brodie et al. 2017; Dorrell et al. 2017).

3.2

Horizontally Acquired Genes

Although the acquisition of genes via horizontal gene transfer (HGT) has been studied intensively in prokaryotes, the significance and especially the quantitative contribution of genes acquired via HGT in eukaryotes are still controversial (e.g. Van Etten and Bhattacharya 2020). However, the question is not so much if there is HGT in microbial eukaryotes but how much. Thus, the extent of which, and the differences in HGT between species, remains a subject of ongoing investigation. Identifying HGT in microbial eukaryotes such as diatoms is significantly more challenging compared to prokaryotes, and this is assumed to be the main reason for sometimes significant differences in estimates of HGT in the evolution of microbial eukaryotes (e.g. Bowler et al. 2008; Vancaester et al. 2020). Currently, challenges to estimate the contribution of HGT in eukaryotic microbial genomes are based on: a) genome size and complexity (e.g. repeats, heterozygosity, polyploidy), b) contaminants such as bacteria and viruses, and c) co-assembly of the contaminating DNA with the target DNA. The latest research on HGT in microbial eukaryotes suggests that horizontally acquired genes do not contribute more than 1.5% of the complete gene inventory, i.e., reflecting ‘the 1% rule’ (e.g. Van Etten and Bhattacharya 2020). This estimate is based on long-read sequencing and assembly-free approaches (Rossoni et al. 2019). Thus, genes are only considered to be of horizontal origin if they are physically linked with native genes on the same single read, which can be
50 kbps long in case of Oxford Nanopore, 10X Genomics or PacBio HiFi sequencing (e.g. Jain et al. 2018). This approach minimizes issues with incorporating DNA from contaminants in assemblies derived from short-read data. In particular, genomes with a considerable number of repeats suffer from fragmentation and co-assembly of contaminating reads, especially in conjunction with reads representing repeats (e.g. Schmid et al. 2018). If contaminating reads have similar GC content and k-mer frequency, they likely will be co-assembled. To avoid co-assembly, only reads from foreign organisms (HGT) as part of long reads should be accepted in addition to applying bioinformatics pipelines to remove contaminating sequences based on sequence similarity, read coverage and GC content (e.g. Fierst and Murdock 2017; Lu and Salzberg 2018). For instance, resequencing of the Cyclotella cryptica genome using a combination of long reads (MinION, Oxford Nanopore) and high-quality sort reads (Illumina HiSeq4000) in addition to applying ‘BlobTools’ removed up to 20% of genes in version 1 of the genome, which were considered to have been acquired via HGT (Roberts et al. 2020). If axenic cultures for the resequencing project would have been

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used, it is likely that even more of the contaminating sequences would have been identified and therefore removed. However, some diatom species are known to live in a mutual relationship with bacteria (e.g. Amin et al. 2012; Monnich et al. 2020), which would require single-cell sequencing to avoid the inclusion of contaminants into the diatom genome assemblies. Although removing contaminating reads from diatom genome assemblies can be considered essential for estimating how HGT contributed to the evolutionary adaptation of diatoms, efforts to do so in the community of diatom researchers are still in their infancy. So far, the most commonly used approach to identify HGT events is to filter based on bootstrap support in phylogenetic trees (e.g. Bowler et al. 2008; Vancaester et al. 2020). However, depending on the cut-off used (e.g. 60%–80%), this may lead to misestimation and therefore false positives or false negatives (Van Etten and Bhattacharya 2020). As most diatom genomes so far have been assembled with short-read data because they were sequenced before long-read sequence technologies became available, it is likely that current estimates of how much HGT contributed to the evolution of diatoms will need to be revised (Roberts et al. 2020). Nevertheless, there is no doubt that HGT significantly contributed to the evolution of diatoms, but the suspicion is that the actual number might be much lower than currently estimated. With our current tools, we are able to dissect and study some of the more recent HGT events. Indeed, if the time of their acquisition can be traced back before the split of centric and pennate diatoms (~90 million years ago), the signature of HGT is easier to unmask. Examples include genes essential for the urea cycle (e.g. carbamate kinase and ornithine cyclodeaminase) and nitrogen storage (e.g. allantoin synthase) (e.g. Allen et al. 2011; Vancaester et al. 2020). As most of them appear to be under purifying selection, they play important roles in central metabolism shared by the majority of diatom species (Vancaester et al. 2020). More recently acquired genes (90 million years) from prokaryotes, viruses, or even other microbial eukaryotes appear to underpin specific adaptations required by diatom species that share a similar ecological niche (e.g. Raymond and Kim 2012; Nelson et al. 2021). However, we cannot exclude the possibility that our current tools only enable us to study those more recent HGT events, and that as science and technology advances, we will discover many more ancient HGT events. An example of recently acquired HGT genes is related to those of the family of ice-binding proteins (e.g. Janech et al. 2006; Sorhannus 2011). All of them appear to have been acquired from either cold-adapted bacteria or fungi as they convey freezing tolerance, a key trait required to thrive in polar ecosystems. Thus, this case of convergent evolution provides an example of how environmental conditions facilitated HGT in diatoms and therefore their ability to extend their global biogeographical distribution. Recent large-scale microalgal genome sequencing encompassing all major groups (e.g. chlorophytes, haptophytes, bacillariophytes) provided some clues as to how virus genes contributed to the evolution of diatom genomes, potentially conferring niche-specific fitness benefits (Nelson et al. 2021). To identify genes acquired from viruses, this study identified virus-specific protein families (VFAMs) in microalgal genomes. After contamination screening, one of the main results was that VFAMs were enriched in marine microalgae and specifically diatoms and other

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classes of the Ochrophyta (Photosynthetic stramenopiles). A negative correlation between the number of repeats and VFAMs in algal genomes suggests that less complex diatom genomes potentially benefit from viral genes, or that they can tolerate them better. Many of these VFAMs in marine species had elevated ratios of dN/dS indicating at least relaxed purifying selection. As a significant number of them were involved in membrane integrity, maybe they conferred halotolerance and therefore contributed to the evolutionary adaptation of diatoms to conditions of saltwater habitats (Nelson et al. 2021). An alternative ‘neutralist’ explanation is that during colonization of saltwater habitats, the early colonizers had not been exposed to the native marine viruses. These were able to invade the diatom genomes of the early colonizers, resulting in genetic hitchhiking at a genomic level. Taken together, diatom genomes are a complex mix and match of genes not only from exo- and endosymbionts (EGT) due to their intertwined vertical evolution, but also from very distantly related species via horizontal gene transfer (HGT). Whereas the former appears to have provided basic toolsets for core metabolism, genes acquired via HGT seem to have conferred habitat-specific fitness benefits that enabled diatoms to conquer specific environments. Once (axenic) culturing techniques, long-read sequencing, bioinformatic assembly and QC methods have been optimized, we will be able to address questions about the nature of the HGT and EGT genes, whether the rate of HGT and EGT differed between species and between the ecological niche they are occupying.

4

Mechanisms and Drivers of Diatom Genome Evolution and Adaptation

Historically, the evolution of diatoms has been studied in the context of systematics and taxonomy reaching back to the pioneers in the late seventeenth century (e.g. Antoni van Leeuwenhoek) who likely discovered them by just using beadlike lenses (Lane 2015). However, it was the use of molecular tools and specifically the discovery of phylogenetic marker genes such as 18S that revolutionized our understanding of diatom evolution together with their fossilized remnants (Medlin et al. 1988). Although single-gene-based phylogenies provided first insights into the complexities of diatom evolution, the availability of the first algal genomes enabled us to extend this knowledge to all genes in their genomes and therefore to reconstruct the evolutionary origins of diatom metabolism underpinning their characteristic biology. However, compared to other fields of research, such as plant and animal sciences, population genetics and especially population genomics with diatoms is still in its infancy (e.g. Godhe and Rynearson 2017; Mock et al. 2017; Rengefors et al. 2017; Whittaker and Rynearson 2017; Koester et al. 2018; Parks et al. 2018; Postel et al. 2020; Rastogi et al. 2020). However, revealing how populations evolve is essential because some of the most fundamental questions (e.g. drivers of diversification and adaptation) can only be addressed if we understand how the evolutionary forces of mutation, recombination, selection, gene flow, and genetic drift shape genetic variation within and between species. For instance, we have insights into the

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macroevolutionary diversity of diatoms but only a poor understanding of genetic variations within a diatom species. However, this knowledge is required to reveal how diatom genomes in a population change according to the evolutionary forces imposed by biotic and abiotic pressures. One of the most fundamental properties of nuclear genomes in eukaryotic organisms is their size and level of ploidy. Both are linked as endoreduplication increases the DNA content of a cell, which can lead to an increase in cell volume (Connolly et al. 2008). Polyploidization has been shown to lead to reproductive isolation and eventually speciation (e.g. Koester et al. 2010; Parks et al. 2018). Thus, it potentially is an important mechanism driving speciation and therefore might contribute to the hyper-diversity in the group of diatoms, which is the youngest of all eukaryotic phytoplankton groups (Nakov et al. 2018). Estimates based on karyotyping suggest that chromosome counts range over several orders of magnitude, with flow cytometer measurements largely confirming these results with respect to genome size (Kociolek and Stoermer 1989). However, the mechanisms of chromosome fission and fusion, and whole or partial genome duplication, are not well understood (e.g. Parks et al. 2018). Although we assume the pennate diatom F. solaris has evolved allodiploidy based on hybridization events in distant parental lineages (Tanaka et al. 2015), our understanding of the frequency of these events and their evolutionary success is still limited (Amato and Orsini 2015). Usually, significant hybrid viability is seen with autopolyploidy, which is often caused by meiotic non-reduction (Mann 1994; von Dassow et al. 2008). Recent phylogenomic approaches based on 37 diatom transcriptomes have shed light onto the importance of polyploidization in the group of diatoms. For instance, Parks et al. (2018) estimated the age distributions of duplicated genes. In combination with phylogenetically based reconciliation methods and gene counts, the authors showed that allopolyploidy as observed in F. solaris maybe as important as autopolyploidy. This study provided strong evidence for ancient allopolyploid events (>100 Myr) in the thalassiosiroid and pennate diatom clades. Although this work provides strong evidence that whole genome duplications have significantly contributed to the genome evolution in diatoms, their course sampling and macroevolutionary approach did not address intraspecific variation in genome size and ploidy as drivers of speciation. To gain insights into those processes requires a micro-evolutionary (i.e. population genomic) approach. An excellent population genomic diatom model is D. brightwellii (e.g. Rynearson et al. 2006; Koester et al. 2010). This coastal species consists of distinct populations, some of which with a global distribution, and others only found locally in coastal embayments or estuaries. High FST (Proportion of the total genetic variance contained in a subpopulation (the S subscript) relative to the total genetic variance (the T subscript) values (can range from 0 to 1) suggest that they have been reproductively isolated for considerable time, and hence, D. brightwellii may actually represent a species-complex or meta-species. Interestingly, one local population is assumed to have diverged by a whole genome duplication, as is evidenced by its marked difference in genomes size (Koester et al. 2010). Furthermore, this population has a distinct phenotype, showing a slightly larger cell diameter. This population

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co-exist with another D. brightwellii genotype at a single geographic location throughout a seasonal cycle. These observations suggest that this biodiversity can be maintained by ecological selection, and not only by geographic partitioning. Similar results have recently been found for the endemic Southern Ocean diatom Fragilariopsis kerguelensis, which can dominate phytoplankton communities in ice-free surface waters. Postel et al. (2020) identified three different genotypes across a transect in the Southern Ocean. Although two of them were separated geographically into a northern and southern genotype, the third genotype was omnipresent but reproductively isolated. Thus, diatom biodiversity can be maintained both by geographic isolation, as well as by reproductive isolation maintained across a large environmental envelope. Another recent study with T. rotula (Whittaker and Rynearson 2017) provided evidence that temporal genetic variation, as shown for D. brightwellii at a single geographical location, can be as similar as genetic variability observed over global distances (>10,000 km). These examples provide evidence that geographic structuring, local adaptations and environmental heterogeneity can all result in reproductive isolation, and that these are majors drivers for the genetic and genomic structure of diatom populations. Together, these processes can contribute to radiation and eventual speciation of diatoms, explaining the rich biodiversity of this taxon. To identify the biological processes under selection, and therefore the mechanisms of diatom genome evolution and speciation, several research groups have begun to identify structural variations in diatom genomes from different populations (e.g. Koester et al. 2018; Osuna-Cruz et al. 2020; Rastogi et al. 2020). With whole diatom genomes available, we can start to identify how environmental conditions impact the evolution of genomes and therefore the divergence of populations as a consequence of adaptive evolution. Environmental and ecological variations are significant for generating and maintaining the diversity of diatoms (e.g. Whittaker and Rynearson 2017). Considering that temperature is both a strong selecting agent for microbes including diatoms (e.g. Thomas et al. 2012) and the environmental variable that changes most quickly, diatom populations might respond with significant changes in their structure and diversity with potential knock-on effects for aquatic food webs and biogeochemical cycles they drive. The genome of the cold-adapted diatom Fragilariopsis cylindrus provided fundamental insights into how the environment drives changes in the structure of diatom genomes (Mock et al. 2017). The strong selection pressure imposed by the environmental conditions of the Southern Ocean likely contributed to a rare evolutionary mechanism of adaptation that has subsequently been confirmed in other diatom genome projects. The genome of F. cylindrus is characterized by approximately 29% of highly diverged alleles. The sequence divergence between alleles was up to 6%, a level of divergence that is typically observed only between the alleles of different species. Most remarkably, however, the most highly diverged alleles showed condition-specific differential expression (differential allelic expression, or DAE). Furthermore, the alleles appeared to have diverged by natural selection, rather than just by neutral evolution (i.e. genetic drift). A coalescence analysis showed that the majority of these alleles diverged shortly after the onset of the last glacial period,

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which began about 110,000 years ago. The alleles of F. cylindrus appear to have adapted to accommodate different environmental conditions. In turn, DAE enables this diatom to express the adapted alleles under the right conditions, allowing it to thrive under the dramatically fluctuating conditions of the polar oceans. Subsequent diatom genome projects including a re-sequencing project with P. tricornutum have confirmed that haplotype diversity and differential allelic expression appear to play a major role in the evolutionary adaptation of diatoms (Rastogi et al. 2020; Hoguin et al. 2021). Even the relatively homozygous genome of P. tricornutum (70 genera that have diverse ecological adaptations and environmental niches (Simon et al. 2017). Several studies have described symbiotic interactions between diatoms and Rhodobacteraceae species or consistent associations between the two taxa, including Silicibacter, Ruegeria, Sulfitobacter, Roseobacter, Roseovarius, and Donghicola (Amin et al. 2012; Durham et al. 2015; Grossart et al. 2005; Hünken et al. 2008; Suleiman et al. 2016). Other groups of bacteria that have been shown to benefit diatoms or consistently associate with them include Marinobacter, Alteromonadaceae, Flavobacteria, Oceanospirillales, Sphingomonadaceae, and Bacteroides (Ajani et al. 2018; Amin et al. 2009;

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Nutrients & Cofactors

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acyl-Homoserine lactones (HSLs)

Fig. 4 Chemical structures of known phycosphere metabolites. Top: Nutrients and cofactors shown to be required for diatom and/or bacterial growth in the phycosphere. L-amino acids represent a variety of diatom-derived amino acids that have been shown to support the growth of phycosphere bacteria. Vibrioferrin is a cofactor (siderophore) that binds Fe(III) and renders it more bioavailable to diatoms. Bottom: Signaling and defense molecules shown to have major effects on diatom and/or bacterial behavior and transcriptional responses. DHPS ¼ 2,3-dihydroxypropane-1sulfonate, AI-2 ¼ autoinducer-2, IAA ¼ Indole-3-acetate, DMSP¼Dimethylsulfoniopropionate. “R” in chemical structures denotes a variable chemical moiety/side chain

Johansson et al. 2019; Klindworth et al. 2014; Mönnich et al. 2020; Riemann et al. 2000; Schäfer et al. 2002; Teeling et al. 2012). While it is not clear whether diatoms have highly specific bacterial symbionts, similar to legumes (Poole et al. 2018), recently several strains of the Rhodobacteraceae bacterium Sulfitobacter pseudotnitzschiae have been isolated from the diatoms A. glacialis originating from the Persian Gulf (Fei et al. 2020), Skeletonema marinoi originating from the Baltic Sea (Töpel et al. 2019) and several cultures of the toxigenic diatom Pseudonitzschia multiseries originating from the Atlantic and Pacific Oceans (Amin et al. 2015; Hong et al. 2015). These repetitive isolations of nearly identical bacteria (>99% average nucleotide identity, ANI) (Fei et al. 2020) from different diatom species originating from starkly different environments suggest this bacterium may be a highly specific symbiont of some diatoms. Several studies have also shown that

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S. pseudonitzschiae and other Sulfitobacter spp. are attuned to diatom metabolites, enhance diatom growth, provide them with reduced nitrogen, and protect diatoms against viruses and oxidative stress (Amin et al. 2015; Fei et al. 2020; Hünken et al. 2008; Kimura and Tomaru 2014; Shibl et al. 2020), further highlighting a symbiotic role. Cumulatively, these observations suggest that diatoms possess specific microbial communities, a so-called microbiome. However, current sampling methods for laboratory and field samples hinder our ability to define a specific diatom microbiome. For example, methods typically used to isolate and culture diatoms from field samples rely on isolating a single diatom cell or chain along with bacterial communities in ~1 μL volume, while metagenomic studies that examine bacterial communities associated with diatom blooms rely on sampling liters of seawater. In contrast, phycosphere volumes of most diatoms vary between a few picoliter for small cells to hundreds of nanoliters for large cells. This large discrepancy in volume indicates that inadvertent inclusion of non-phycosphere bacteria in cultures and metagenomic samples is likely. Recent advances in fluorescence-activated cell sorting (FACS, Box 1) has aided in reducing some of the biases associated with traditional sampling methods (Baker and Kemp 2014; Crenn et al. 2018). However, FACS is mainly effective in capturing bacteria strongly attached to diatom cells or TEP and is mainly applicable to relatively small diatom cells and chains ( 410,000) mixture of different polymers, composed mainly of polysaccharides, proteins, nucleic acids, lipids, surfactants, and humic-like substances (Flemming and Wingender 2001; Toullec and Moriceau 2018). EPS can aggregate to form transparent exopolymeric particles (TEP). A specialized type of flow cytometry for sorting a heterogeneous mixture of particles, including biological cells, based on the specific light scattering and fluorescent properties of each particle.

(continued)

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Box 1 (continued) Homoserine lactones (HSLs) Infochemicals/signaling molecules

The microbial loop

Phycosphere

Particulate organic matter (POM) Quorum sensing (QS)

Quorum-quenching (QQ) Transparent exopolymeric particles (TEP) Vitamins

Acyl-homoserine lactones (also abbreviated as AHLs) are an important class of quorum sensing molecules mostly produced by the Proteobacteria. Biomolecules mediating communication and interactions between organisms. Typically, these molecules are produced in minute quantities, yet they have major influences on the organisms that perceive them. Originally coined by Azam et al. (1983), this term stems from the increased recognition of the importance of bacteria, nanoflagellates, and microzooplankton in the consumption of phytoplankton-derived dissolved organic matter, which subsequently makes its way up to higher trophic levels. The diffusive boundary layer that surrounds phytoplankton cells and creates a microenvironment where transport of metabolites is mostly governed by diffusion. Organic matter retained on a filter with pore sizes ranging between 0.2 μm and 1 μm (Verdugo et al. 2004). A type of mostly bacterial cell–cell communication that enables bacteria to coordinate their gene expression as a function of the population cell density. QS is mediated by diffusive small molecules, known as autoinducers (e.g., HSLs). Defined as the disruption of QS by any means, e.g., inactivation of AHL signals with enzymes. Defined as >0.4 μm transparent particles that consist of mostly acidic polysaccharides and are stainable with the dye Alcian blue (Alldredge et al. 1993). Organic micronutrients, necessary as cofactors for enzymes of central and secondary metabolism.

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Diatom Viruses Laure Arsenieff, Kei Kimura, Chana F. Kranzler, Anne-Claire Baudoux, and Kimberlee Thamatrakoln

Abstract

The discovery, isolation, and cultivation of the first diatom-infecting virus less than two decades ago revealed an enigmatic, ecological interaction that altered our understanding of diatom ecosystem functioning. Since that discovery, characterization of additional diatom host-virus systems has brought important insight into unique aspects of these viruses and the biogeochemical consequences of virus-mediated mortality. Emerging approaches for identifying these pathogens in natural populations are revealing widespread prevalence and geographic distribution of diatom viruses and the environmental factors that influence host-virus interactions. In this chapter, we summarize the existing literature and highlight the latest research on diatom viruses and the potential of these viruses to impact one of the most significant groups of phytoplankton on the planet. We conclude with thoughts for the future generation of diatom viral ecologists.

L. Arsenieff (*) Faculty of Biology, Technion, Israel Institute of Technology, Haifa, Israel e-mail: [email protected] K. Kimura Faculty of Agriculture, Saga University, Saga, Japan C. F. Kranzler Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel A.-C. Baudoux Sorbonne Université, CNRS UMR 7144, Diversity and Interactions in Oceanic Plankton - Station Biologique de Roscoff, Roscoff, France K. Thamatrakoln (*) Department of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ, USA e-mail: [email protected] # Springer Nature Switzerland AG 2022 A. Falciatore, T. Mock (eds.), The Molecular Life of Diatoms, https://doi.org/10.1007/978-3-030-92499-7_24

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Abbreviations dsDNA dsRNA FLDS ICTV MPN ORF RdRp ssDNA ssRNA TEM TEP

1

Double-stranded DNA Double-stranded RNA Fragmented and primer ligated dsRNA sequencing International Committee on Taxonomy of Viruses Most probable number Open-reading frame RNA-dependent RNA polymerase Single-stranded DNA Single-stranded RNA Transmission electron microscopy Transparent exopolymers

Introduction

The discovery that viruses are the most abundant biological entities in a wide range of marine and freshwater ecosystems (averaging 107 particles per milliliter of water; Bergh et al. 1989; Breitbart 2012) has considerably changed our view of the aquatic microbial food-web (Fuhrman 1999; Wilhelm and Suttle 1999). This seminal discovery has promoted research on these infectious agents and the role they play in marine environments. As obligatory pathogens, viruses depend on a living host to replicate. Virions, individual virus particles, consist of nucleic acids surrounded by a protective protein coat called the capsid. A lipid membrane can be found inside or outside of the capsid, the latter describing enveloped viruses. Viruses are traditionally classified by genome type (e.g., DNA, RNA, single or double-stranded, circular or linear, segmented or not), structural features (e.g., the symmetry and size of the virion, the capsid protein composition, the presence of an envelope), replication strategy, and host organism. Viral infection involves host recognition, adsorption, entry, and co-opting host machinery for viral genome replication and virion production. Viruses are thus specialized pathogens that act as important drivers of host population dynamics and evolution, and of ecosystem function globally (Suttle 2007; Breitbart 2012). The ecological and evolutionary consequences of viral infection depend, in part, on the virus replication strategy. Through the lytic cycle, viral progeny is released into the environment via lysis of the host cell. For unicellular organisms, lytic infection leads to host mortality, altering community structure, and stimulating the microbial loop through the release of nutrients and organic matter (Suttle 2007; Brussaard et al. 2008)—a process referred to as the “viral shunt” (Wilhelm and Suttle 1999). In contrast, temperate viruses do not cause immediate host lysis, but rather are maintained in a latent state called lysogeny (Lwoff 1953; Paul 2008), and can alter host physiology and metabolism by introducing novel functions such as virulence

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factor production (Waldor and Mekalanos 1996; Sumby and Waldor 2003; Vidgen et al. 2006) or immunity to infection by related viruses (super-infection; Lwoff 1953, Zinder 1958, Paul 2008, Blasdel and Abedon 2017). Continuous release or intermittent budding of viral progeny without host lysis can also occur, but the prevalence and environmental consequence of this mode of chronic infection is not well documented in aquatic viruses (Thomas et al. 2011; Demory et al. 2017). The first viruses discovered in the ocean were largely phages—viruses that infect bacteria—with genomes comprising double-stranded (ds) DNA (reviewed in Breitbart 2012). Among the first eukaryotic algal viruses discovered were the Phycodnaviridae—large, dsDNA-containing viruses that infect a wide range of phytoplankton including chlorophytes, prasinophytes, dinoflagellates, and haptophytes (reviewed in Brussaard 2004). Advances in high-throughput sequencing later revealed a novel community of picorna-like viruses—small, single-stranded (ss) RNA-containing viruses (Culley et al. 2003, 2006) that have since been shown to include viruses similar to those that infect diatoms and dinoflagellates (Tai et al. 2003; Nagasaki et al. 2004). Arguably one of the most globally distributed and ecologically successful protist groups in the ocean, diatoms are major players in silicon (Si) and carbon biogeochemistry, processing over 240 Tmol Si annually (Treguer and De La Rocha 2013) and contributing ~40% of marine primary production (Nelson et al. 1995) and carbon export (Falkowski et al. 1998; Smetacek 1999). The relatively recent discovery of diatom-infecting viruses revealed a unique group of marine viruses distinct in genome structure (ssRNA and ssDNA) and a virion size among the smallest on the planet (~20 to 40 nm in diameter; Nagasaki et al. 2004, Tomaru et al. 2015b). Although still in its infancy, our understanding of diatom viruses and the impact of host-virus interactions on biogeochemical cycling and ecosystem function is improving with the growing number of observations and experimental studies. In this chapter, we summarize current knowledge about diatom-infecting viruses, starting with the discovery, diversity, and phylogeny of these unique viruses. We then describe the ecology of diatom viruses, including host-virus dynamics, environmental factors that influence infection, and the role diatom viruses play in natural communities. Finally, we discuss future outlooks of this developing frontier in diatom research, implications of emerging technologies and strategies toward better integration of diatom viruses in modeling ecosystem function.

2

Discovery, Isolation, and Characterization of Diatom Host-Virus Systems

2.1

Discovery and Isolation

The first diatom virus was isolated from Ariake Sound (Japan) in 2004 by filtering surface water through a 0.2 μm pore-size filter and challenging 22 exponentially growing diatom strains with the resulting filtrate. Following inhibition of algal growth and multiple rounds of dilution to extinction, a clonal pathogen of the centric

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diatom, Rhizosolenia setigera, was isolated (Nagasaki et al. 2004). Since then, a number of diatom viruses have been isolated (Tables 1 and 2) from resuspended sediments or through a range of approaches such as dilution to extinction of filtered surface seawater, enrichment cultures, or tangential flow filtration (Wilhelm et al. 2010).

2.2

Morphological and Genomic Features

The R. setigera virus was identified as a positive-sense (+) ssRNA-containing virus and designated RsRNAV. Viral replication occurs within the host cytoplasm where small (~32 nm in diameter), naked (i.e., non-enveloped) and non-tailed hexagonal particles, suggestive of icosahedral symmetry, are formed. The linear genome (~9 kb) of RsRNAV encodes two open reading frames (ORFs; Shirai et al. 2006). ORF1 is a polyprotein gene encoding for replication proteins, including a helicase and an RNA-dependent RNA Polymerase (RdRp), a highly conserved sequence among the Picornavirales (Koonin et al. 1993). ORF2 encodes structural proteins of the viral capsid (Shirai et al. 2006). Subsequent discovery of other diatom-infecting +ssRNA viruses revealed similar features with genomes ranging between 8 and 10 kb encoding 2 ORFs, virion replication and assembly in the cytoplasm, and virion diameters ranging from 22 to 50 nm (Fig. 1, Table 1). Recently, the capsid structure of an ssRNA virus, CtenRNAV-II, infecting Chaetoceros tenuissimus was resolved using cryo-electron microscopy (cryo-EM; Munke et al. 2020). Comparison to other Picornavirales viruses revealed conserved ancestral structural traits that provide insight into the evolutionary history of this order, but the presence of structures unique to CtenRNAV-II also leave open questions about the molecular details of viral infection and host-specificity. As this is the first diatom virus structure to be determined at near atomic-resolution, resolving the structure of additional members of this family will likely provide useful insight into the propagation and transmission of these viruses. In addition to RNA viruses, a number of single-stranded DNA (ssDNA)containing diatom viruses have been isolated and characterized (Table 2). Similar to ssRNA viruses, ssDNA viruses have small (25–38 nm in diameter), icosahedral capsids. In contrast, viral replication occurs in the nucleus where rod-shaped structures have been observed (Fig. 2a). However, these rod-shaped virus-like particles have never been observed extracellularly even following host lysis and have thus been hypothesized to represent precursors of mature virions (Eissler et al. 2009). The general genomic structure of diatom ssDNA viruses is a closed, circular, single-stranded molecule of DNA approximately 5–7 kb and composed of 3–4 ORFs (Fig. 2b). Two of these ORFs, denoted VP2 and VP3, encode a structural protein of the viral capsid and replication enzyme, respectively, with the function of the other ORF(s) unknown. With the exception of CdebDNAV and CsetDNAV (Tomaru et al. 2008; Tomaru et al. 2013b), the genome also contains a ~1 kb, double-stranded DNA region with unknown function. Intriguingly, diatoms are the only protists known to be infected by ssDNA viruses (Tomaru et al. 2015a) and thus far, no

Centric

RCC3083

S3

MESCM-1 NF-DSPA-1 IT Dia-1

Guinardia delicatula

Rhizosolenia setigera

Skeletonema costatum Stephanopyxis palmeriana Thalassiosira gravida

TgraRNAV

SpalVa

ScosVa

RsRNAV

GdelRNAV

CtenRNAV type-II

CtenRNAV type-I

2–10

2–10

CsfrRNAV

Virus Csp03RNAV

L-4

Chaetoceros tenuissimus

Chaetoceros socialis f. radians Chaetoceros tenuissimus

Host Chaetoceros sp.

Host strain SS08C03

Western English Channel, France Ariake sound, Japan Jaran Bay, Korea Jaran Bay, Korea Yatsushiro Sea, Japan

Ariake sound, Japan Hiroshima Bay, Japan

Hiroshima Bay, Japan

Origin Yatsushiro Sea, Japan

Table 1 List and characteristics of ssRNA diatom host-virus systems

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25–30

45–50