A Yumi

Patrick

Elina Thomas

La Sardaigne

Leila

Achal

Omer

Zhanru, Heni, Catherine, Martine, Imen, but also Camille Anne-Flore Richard

Thabo, Camille, Karl, Ulysse, Magali Julie Raymond

Xav Milan

Flora Vincent

Sébastien Colin

Sarah Romac

Eleonora Scalco

Lucie Bittner

John R Dolan

Adriana Zingone

Colomban De Vargas

Chris Bowler
email: cbowler@biologie.ens.fr

Vincent Flora

Characterisation of an abundant and widespread interaction between the pennate diatom Fragilariopsis doliolus and tintinnids

Keywords: Plancton, diatomées, biodiversité marine, interactions microbiennes, réseaux de corrélation microbiens, structure des communautés, cellule unique, biologie écosystémique Plankton, diatoms, marine biodiversity, microbial interactions, microbial correlation networks, community structure, single cell, ecosystem biology [

français) :

Les diatomées sont des micro-algues unicellulaires, qui jouent un rôle primordial dans l'ecosystème marin. En effet, elles sont responsables de 20% de l'activité photosynthétique sur Terre, et sont à la base de la chaîne alimentaire marine, toujours plus menacée par le changement climatique.

Les diatomées établissent diverses interactions microbiennes avec des organismes issus de l'ensemble de l'arbre du vivant, à travers des méchanismes complexes tels que la symbiose, le parasitisme ou la compétition. L'objectif de ma thèse a été de comprendre comment ces interactions structurent la communauté du plancton, à grande échelle spatiale. Pour ce faire, j'ai développé de nouvelles approches basées sur le jeu de données inédit de Tara Océans, une expédition mondiale qui a exploré la diversité et les fonctions des microbes marins, en récoltant plus de 40.000 échantillons à travers 210 sites autour du monde.

Grâce à l'analyse de réseaux de co-occurrence microbiens, je montre d'une part que les diatomées agissent comme des « ségrégateurs répulsifs » à l'échelle globale, en particulier envers les organismes potentiellement dangeureux tels que les prédateurs et les parasites, et d'autre part que la co-occurrence des espèces ne s'explique qu'en minorité par les facteurs environnementaux. Grâce à la richesse des données Tara Océans, j'ai par ailleurs permis la charactérisation d'une interaction biotique impliquant une diatomée et un cilié hétérotrophe à l'échelle de l'eco-système, illustrant de surcroît le succès des approches dirigées par les données. Dans l'ensemble, ma thèse contribue à notre compréhension des interactions biotiques impliquant les diatomées, de l'échelle globale à la cellule unique.

Table of figures

Marine planktonic ecosystems 1.Odyssey of plankton research

The oceans comprise the largest continuous ecosystem on Earth, and 98% of its biomass is composed of organisms that are invisible to the naked eye: marine microbes, many of which live as "plankton". The word plankton comes from the Greek planktos "errant"; it designates organisms that live in the water column and are unable to swim against the current [START_REF] Lalli | Biological oceanography: an introduction[END_REF]. Marine plankton is therefore composed of bacteria, protists (unicellular eukaryotes), fungi, viruses, archaea, but also of eggs and larval stages of larger animals.

While the larger sized plankton have been studied for more than a century, it is only with the advent of modern techniques since the late 1970's that the abundance of microbes and viruses has been appreciated [START_REF] Hobbie | Use of nuclepore filters for counting bacteria by fluorescence microscopy[END_REF]. It was shown since then that a liter of seawater contains up to 10 ^8 bacteria [START_REF] Whitman | Prokaryotes: the unseen majority[END_REF]. In the 1990's, the first cultivation-independent assessment of marine bacterial diversity -through rRNA analysisshowed that they were highly diverse, and that most groups in the ocean were previously unknown [START_REF] Stahl | Analysis of hydrothermal vent-associated symbionts by ribosomal RNA sequences[END_REF]. Simultaneously, the discovery of viruses in the ocean, reaching nearly 10 ^10 particles per liter [START_REF] Wilhelm | Freshwater and marine virioplankton: a brief overview of commonalities and differences[END_REF], added a new layer of complexity in our understanding of what generates and maintains microbial diversity [START_REF] Bergh | High abundance of viruses found in aquatic environments[END_REF].

Diversity of protists -unicellular eukaryotes -was coined in the early 2000 using rRNA, making our knowledge of the majority of planktonic diversity fairly recent (Moon-van der [START_REF] Der Staay | Oceanic 18S rDNA sequences from picoplankton reveal unsuspected eukaryotic diversity[END_REF]. Exploration of global patterns of marine microbial communities and diversity became quantitative with pyrotag sequencing in the mid 2000's [START_REF] Sogin | Microbial diversity in the deep sea and the underexplored « rare biosphere[END_REF], opening the way for large spatial scale campaigns of marine metagenomic surveys such as the Sorcerer II Global Ocean Sampling survey [START_REF] Rusch | The Sorcerer II Global Ocean Sampling Expedition: Northwest Atlantic through Eastern Tropical Pacific[END_REF] and the Malaspina deep sea expedition in 2011 (See Annexe A for details on the contribution of genomics to microbial studies). However, our knowledge today is partially restrained by the noncultivability of 90% to 99% of marine microorganisms in current laboratory settings.

Marine plankton distribution is strongly dependent on abiotic factors such as light and nutrients, turbulence, temperature, salinity, redox potential or pH, as well as biotic factors such as the presence of other planktonic organisms. If local abundance of plankton varies VINCENT Flora -Doctoral Thesis -2016 horizontally, vertically, and seasonally, planktonic organisms are virtually present everywhere in the water column across the ocean.

Importance in Earth's biogeochemical cycles

The metabolism of marine microorganisms composing plankton maintains major biogeochemical cycles on Earth [START_REF] Falkowski | The Microbial Engines That Drive Earth's Biogeochemical Cycles[END_REF], including that of carbon, oxygen, nitrogen, phosphorus and sulphur. All these chemical elements circulate through the biological and physical world, thanks to a global recycling where chemical compounds are passed from one organism to another, and from one part of the biosphere to another. A simplified version of how plankton contribute to major nutrient cycles illustrates both the complexity and the inter-connectedness of these processes.

Carbon is critical for life because it is the building block of all organic compounds. Carbon dioxide (CO 2 ) is exchanged between the surface ocean and the atmosphere, but must be transformed -or "fixed" -into a usable organic form for living organisms. In the sunlit part of the ocean, CO 2 is fixed mainly through photosynthesis performed by phytoplanktonautotrophic prokaryotes and eukaryotic algae -resulting in the production of oxygen from water [START_REF] Field | Primary Production of the Biosphere: Integrating Terrestrial and Oceanic Components[END_REF]. CO 2 fixation by phytoplankton leads to the creation of dissolved organic carbon (DOC) -any organic matter that is smaller than 0.45 micron -and particulate organic carbon (POC) -organic matter bigger than 0.45 micron -both of which can be respired back to CO 2 by other microorganisms such as zooplankton, the heterotrophic metazoans and protozoans. Part of this organic matter will sink to the ocean floor through a process called the biological pump. What is not mineralized and recycled into carbon dioxide by bacteria during the descent can be buried and stored over geological timescales, eventually leading to the formation of natural gas, petroleum, and kerogen (Figure 1.1). Carbon can be transferred from the atmosphere to the ocean depths through the carbon cycle, in which marine microbes play a key role (U.S. JGOFS).

Nitrogen is one of the most important elements because it is required for synthesis of the basic building blocks of life; many organisms depend on ammonium (NH 4 + ), nitrate (NO 3 -), or dissolved organic nitrogen (DON) to synthesize amino acids, nucleic acids or cell walls. The most abundant form of nitrogen on Earth is atmospheric dinitrogen (N 2 ), but in the ocean only a few specialized bacteria and archaea -equipped with a nitrogenase enzyme -are capable of fixing gaseous nitrogen into a bioavailable compound such as ammonium [START_REF] Galloway | A chronology of human understanding of the nitrogen cycle[END_REF]. Ammonium can be rapidly assimilated as a nutrient by microorganisms, and be incorporated into living cells. Other microbes extract energy from nitrogen by oxidizing ammonium into nitrite and nitrates through nitrification, ultimately releasing nitrates in the ocean. Some organisms can uptake nitrates, convert them back to nitrogen gas via denitrification, essentially putting nitrogen back into the atmosphere.

Incomplete denitrification may lead to the formation of nitrous oxide (N 2 O), which is a strong greenhouse gas (Figure 1.

2).

VINCENT Flora -Doctoral Thesis -2016 The nitrogen cycle is the biogeochemical cycle by which nitrogen is converted into various chemical forms as it circulates among the atmosphere and terrestrial and marine ecosystems (CMORE).

Connected micro-organisms: the example of the food web

The importance of plankton in our ecosystem is not only embodied by a vast grid of exchanged metabolic currencies. Plankton play a crucial role in the very structure of the species network in the ocean, illustrated by the role of plankton in the marine food web.

Indeed, beyond the necessary cataloguing of which species are present, how they interact complements our understanding of ecosystem structure and functioning.

Plankton are the foundation of classical marine food webs by occupying a critical bottom trophic level. Phytoplankton live in the surface water where they access enough light to perform photosynthesis and convert inorganic carbon dissolved in the surface waters to organic carbon [START_REF] Field | Primary Production of the Biosphere: Integrating Terrestrial and Oceanic Components[END_REF]. Phytoplankton are then grazed upon by zooplankton, serving as a crucial link between primary producers and the rest of the marine food web, including higher consumers such as large fish, and ultimately, top predators. However the view that fixed carbon is transferred vertically up trophic food webs has been challenged by the discovery of abundant heterotrophic prokaryotes, revealing that a large proportion of the flux of matter and energy in the marine food web passes through these smaller organisms in the form of dissolved organic matter; a concept named the microbial loop .

The microbial loop is the process by which dissolved organic material (DOM) derived from primary producers such as phytoplankton is foraged and recycled by heterotrophic bacteria that use DOM as a food source [START_REF] Hobbie | A Study Of The Distribution And Activity Of Microorganisms In Ocean Water1[END_REF][START_REF] Azam | The Ecological Role of Water-Column Microbes in the Sea[END_REF]. These bacteria are consumed by protozoans, then fed upon by zooplankton, and the link to the traditional food web is established. Marine bacteria hold a significant influence in this loop, as they allow for an energy pathway that may have otherwise been lost (Figure 1.3). Viruses, by infecting and lysing marine bacteria and phytoplankton, are also vital players in the fixation and cycling of key elements, such as carbon, nitrogen, and phosphorus. Through the viral shunt, viral lysis of microbial cells releases dissolved organic matter and particulate organic matter back into the microbial loop. By doing so, viruses drive carbon and nutrients of these cells away from grazers, and redirects it to other microorganisms through the form of DOM. It has been estimated that ocean viruses might turn over as much as 150 gigatons of carbon per year [START_REF] Suttle | Marine viruses-major players in the global ecosystem[END_REF]. Key processes of the marine carbon cycle include the conversion of inorganic carbon (ex: CO 2 ) to organic carbon by phytoplankton (step 1); the release of dissolved organic matter, dissolved organic phosphorous and particulate organic matter by phytoplankton (step 2); the consumption of phytoplankton biomass by zooplankton (step 3) and the mineralization (release of CO 2 via respiration) and recycling of organic matter by diverse heterotrophic bacteria (the microbial loop; step 4). Some heterotrophic bacteria are consumed by zooplankton, and the carbon is transferred up the food web. Heterotrophic bacteria also contribute to the remineralization of organic nutrients, to inorganic forms, which are then available for use by phytoplankton. The microbial carbon pump (step 5) refers to the transformation of organic carbon into recalcitrant dissolved organic carbon that resists further degradation and is sequestered in the ocean for thousands of years. The biological pump (step 6) refers to the export of phytoplankton-derived particulate organic matter from the surface oceans to deeper depths via sinking. Finally, the viral shunt (step 7) describes the contributions of viral-mediated cell lysis to the release of dissolved and particulate matter from both the phytoplankton and bacterial pools [START_REF] Buchan | Master recyclers: features and functions of bacteria associated with phytoplankton blooms[END_REF].

Biotic interactions such as viral infection or other types of grazing by prokaryotes have considerably complemented our vision of the marine food web, and thus of the related carbon cycle. Many other biotic interactions are likely to impact ecology and ecosystem functioning of the ocean hence the critical importance of plankton ecology in the Earth system can hardly be overstated. Yet the study of marine microbial ecology as a major component of marine science has a fairly short history [START_REF] Pomeroy | The Ocean's Food Web, A Changing Paradigm[END_REF][START_REF] Azam | The Ecological Role of Water-Column Microbes in the Sea[END_REF], in particular that of planktonic micro-eukaryotes also named protists.

The forgotten marine micro-eukaryotes: protists

The study of diversity and function of marine microbial communities has largely focused on bacteria in the past decades, and to a lesser extent on archaea and viruses. Paradoxically, much lesser attention has been given to single-celled eukaryotic microbes -the protistsdespite them being some of the first microbial taxa observed, that also play a key ecological role as primary producers, consumers, decomposers and trophic links that are extremely abundant with 10 ^6 eukaryotic cells per liter of seawater [START_REF] Brown | Microbial community structure in the North Pacific ocean[END_REF]. Reasons for this dismissal are multiple: their misleading first naming as "animalcules" (van Leewenhoek)

could have semantically related them to larger multicellular organisms thus excluding them from subsequent research, that has focused largely on prokaryotic microbes; the unfair granting of bacterial mortality to viruses in the ocean, to the expense of protistan bacterivory research; the limitations in sequencing and computation techniques to deal with protistan large genome sizes and complexity [START_REF] Caron | Protists are microbes too: a perspective[END_REF].

Nonetheless, protists are present basically in every branch of the eukaryotic tree of life and comprise the bulk of eukaryotic phylogenetic diversity (Figure 1.4). When we picture the divergence between multicellular eukaryotes (animals and fungi) just in the single supergroup of Opisthokonta, we come to appreciate the breadth of how protists populate the entire Eukarya domain. From free-living amoeboid taxa (Amoebozoa) and parasites (Excavates) to strontium sulfate or calcium carbonate skeleton builders (Rhizaria), protists display countless forms, sizes, and trophic activities. Understanding protistan ecology and evolution is likely to bring insights into the deep branches of the eukaryotic tree of life, and thus on the origins of multicellular taxa [START_REF] Baldauf | The deep roots of eukaryotes[END_REF]. Phylogenetic breadth among protists. Single-celled species (protists) occur in every supergroup within the domain Eukarya, and constitute the entirety of a number of them (Caron, 2011).

In order to increase our knowledge about marine protists, I decided to investigate the impact of biotic interactions on community structure at large spatial scales, focusing my work on a key phytoplankton group: diatoms.

Diatoms, pivotal in the plankton community 1.2.1. General biology of diatoms 1.2.1.1. From art to science

Why diatoms? Diatoms (Bacillariophyta) are unicellular photosynthetic microalgae that display a unique and distinctive feature that sparked my curiosity for it was so fascinating and atypical: they are enveloped in a silica cell wall, called the frustule, that comes in many shapes and has inspired Victorian artists in the late 19th century, a period during which arts and science were intertwined. By collecting diverse diatoms in aquatic environments, artists/scientists would meticulously shape them under the form of "diatom arrangements" leading to a hypnotizing art piece (Figure 1.5). In applied ecology, they are used to monitor change in aquatic systems particularly in rivers, as they are sensitive indicators of the ecosystem's health [START_REF] Round | Diatoms: biology and morphology of the genera[END_REF]. They are considered as serious candidates for biofuel production as they accumulate high amounts of lipids (D 'Ippolito et al., 2015), but are also known to generate a number of interesting biomolecules that have human health benefits or commercial applications, such as omega-3 fatty acids [START_REF] Petrie | Metabolic Engineering of Omega-3 Long-chain Polyunsaturated Fatty Acids in Plants Using an Acyl-CoA Δ6-desaturase with ω3-preference from the Marine Microalga Micromonas Pusilla[END_REF]. Last but not least, in the marine environment they serve as the basis of the marine food web, supporting the growth of higher organisms. They are significant players in global biogeochemical cycles, as they account for 20% of global primary production [START_REF] Nelson | Production and Dissolution of Biogenic Silica in the Ocean: Revised Global Estimates, Comparison with Regional Data and Relationship to Biogenic Sedimentation[END_REF][START_REF] Falkowski | The Ocean's Invisible Forest[END_REF] and fix as much carbon as all the tropical rainforests combined, equivalent to ~ 20 Pg of fixed carbon per year [START_REF] Field | Primary Production of the Biosphere: Integrating Terrestrial and Oceanic Components[END_REF][START_REF] Mann | The Species Concept in Diatoms[END_REF].

The silica cell wall

The word diatom comes from the Greek diatomos, meaning "cut in half", as the silica cell wall is often bilaterally symmetrical (Figure 1.7). The diatom frustule is composed of two overlapping thecae: the larger one, named epitheca, and the smaller one, the hypotheca.

Each theca consists of a valve, and a series of girdle bands (Figure 1.8). The frustule is nanopatterned with rows of pores, called interstriae, and ribs between them called striae. The pores in the interstriae serve principally for the uptake of nutrients, and exudation of metabolites. The girdle bands are generally simple; however, valves can be much more elaborated, with a flat area called the valve face, and a rim, called the mantle (Figure 1.9). The frustule elements of diatoms are formed in silica deposition vesicles (SDVs) within the cell under mildly acidic conditions. Calcium binding glycoproteins, called frustulins, coat the frustule and are then exocytosed from the protoplast [START_REF] Vrieling | Diatom Silicon Biomineralization as an Inspirational Source of New Approaches to Silica Production[END_REF].

The mechanisms by which diatoms execute their morphogenetic program to biomineralize SiO 2 and achieve such a high diversity of nano and micro patterns is nicely reviewed in [START_REF] Kröger | Diatoms-From Cell Wall Biogenesis to Nanotechnology[END_REF]. This paper also highlights how the discovery of fundamental principles in the molecular mechanism of diatom silica formation has encouraged biomimetic methods, leading to innovations in the field of nanomaterial sciences and technology. Based on the morphological characteristics of valves, including symmetry, morphology and ornamentation, as well as interstriae/striae organisation, two main categories of diatoms are distinguished: the centric and the pennate diatoms (Van den [START_REF] Hoek | Algae: an introduction to phycology[END_REF].

Centric diatoms are composed of two groups: radial centrics, and multipolar centrics. Radial centrics have circular valves and can form chains using tubes called strutted processes, through which chitin filaments are exuded and link them to the adjacent cell. Multipolar centric diatoms can form elongated, triangular, or starlike profiles (i.e., the cell form exhibits polarity of shape [START_REF] Piganeau | Genomic insights into the biology of algae[END_REF]) even though they also exhibit radial pore organization (Figure 1.10).

Pennate diatoms are elongated, and are distinguished by the midrib, from which striae and interstriae extend perpendicularly. Like the centrics, pennate diatoms can be divided in two main groups: the raphid pennates and the araphid pennates. Raphid pennates possess a slit, which enables them to move through a system of traction. Most raphid pennate diatoms are benthic, but a few have developed a truly marine planktonic lifestyle such as

Fragilariopsis and Pseudo-nitzschia [START_REF] Falkowski | The Evolution of Modern Eukaryotic Phytoplankton[END_REF]. Pseudo-nitzschia is a planktonic representative, which evolved from a benthic ancestry [START_REF] Piganeau | Genomic insights into the biology of algae[END_REF], and acquired the capacity to form chains by using its raphe and positioning cells for them to be attached by their valve apices. On the contrary, most araphid pennates are benthic, although a few genera such as Lioloma, Thalassionema, or Asterionella are planktonic (Figure 1.11). Recovery of the initial cell size is achieved by means of sexual reproduction [START_REF] Edlund | Ecological, Evolutionary, And Systematic Significance Of Diatom Life Histories1[END_REF]. Sex includes gamete formation, conjugation, and zygote formation by isometric (for radial centrics and Thalassiosirales) or anisometric (for multipolar centrics and pennates) swelling of zygotes into an auxospore, and the formation of the initial cell structure (Figure 1.13). Adapted from [START_REF] Kooistra | The Origin and Evolution of the Diatoms: Their Adaptation to a Planktonic Existence[END_REF] and [START_REF] Round | Diatoms: biology and morphology of the genera[END_REF].

Evolutionary history and divergence

Diatoms are an important component of "phytoplankton", a name first used in 1897 that defines a polyphyletic group of single celled organisms that drift with the currents, both in marine and freshwater environments. A trio of major eukaryotic phytoplankton, composed of dinoflagellates, coccolithophores, and diatoms, has dominated the ocean since the Mesozoic Era (251 to 65 million years ago). Phytoplankton represent 1% of the Earth's photosynthesis biomass, yet are responsible for 45% of the Earth's annual net primary productivity [START_REF] Behrenfeld | Photosynthetic rates derived from satellite-based chlorophyll concentration[END_REF][START_REF] Falkowski | Biogeochemical Controls and Feedbacks on Ocean Primary Production[END_REF]. Diatoms alone contribute to 20-25% of global net primary production [START_REF] Nelson | Production and Dissolution of Biogenic Silica in the Ocean: Revised Global Estimates, Comparison with Regional Data and Relationship to Biogenic Sedimentation[END_REF], which is more than all the world's rainforests [START_REF] Field | Primary Production of the Biosphere: Integrating Terrestrial and Oceanic Components[END_REF]. How did diatoms evolve to become such an important player in the global ocean?

Endosymbiotic theory explains the origins of eukaryotic organelles such as mitochondria and chloroplasts (Sagan-Margulis, 1967). Primary endosymbiosis involves the engulfment of a bacterium by another free-living organism. Early in life history, a primary endosymbiotic event occurred, during which a large, heterotrophic, eukaryotic cell engulfed a small autotrophic bacterium -probably an alphaproteobacteria -that evolved to become the mitochondrion ~1,200 Mya [START_REF] Shih | Primary endosymbiosis events date to the later Proterozoic with cross-calibrated phylogenetic dating of duplicated ATPase proteins[END_REF]. A similar process was proposed for the origin of chloroplasts: a eukaryotic host cell, already containing a mitochondrion and a nucleus, is thought to have engulfed -or been invaded by -a cyanobacterium that became the chloroplast, leading to the first oxygenic photosynthetic eukaryotes. Three major lineages arose from this event: the green algae, that use chlorophyll b as a light harvesting antenna in photosystem II, the red algae, that use chlorophyll c and derivatives as accessory photosynthetic pigments, and finally the glaucophytes [START_REF] Falkowski | The Evolution of Modern Eukaryotic Phytoplankton[END_REF]. Secondary endosymbiosis occurs when the product of primary endosymbiosis is itself engulfed by another free-living organism. A few hundred million years after the primary endosymbiosis leading to chloroplasts, a secondary endosymbiosis took place, in which a nonphotosynthetic eukaryote acquired a chloroplast by engulfing a red algae leading to Stramenopiles, the superphylum containing diatoms [START_REF] Keeling | Diversity and Evolutionary History of Plastids and Their Hosts[END_REF]. These successive events were accompanied by massive gene transfers from the swallowed cell to the host [START_REF] Armbrust | The Genome of the Diatom Thalassiosira Pseudonana: Ecology, Evolution, and Metabolism[END_REF]). However, evidence for genes of green algal origins in diatom has challenged this view [START_REF] Moustafa | Genomic Footprints of a Cryptic Plastid Endosymbiosis in Diatoms[END_REF] suggesting the potential additional engulfment of green algae that led to Chromalveolates -a eukaryotic supergroup that contains diatoms -during secondary endosymbiosis (Figure 1.14). The subsequent gains (and losses) of specific genes, largely from bacteria but also from the exosymbiont -the secondary endosymbiotic host -presumably helped diatoms to adapt to ecologically new niches. For example, diatoms display a ornithine-urea cycle, which was thought to be restricted to animals, that results from the tight coupling between exosymbiont and bacterial derived proteins, and plays an important role in the metabolic response of diatoms to episodic nitrogen availability [START_REF] Allen | Evolution and Metabolic Significance of the Urea Cycle in Photosynthetic Diatoms[END_REF]. Comparative genomics of the first two sequenced diatoms, Thalassiosira pseudonana [START_REF] Armbrust | The Genome of the Diatom Thalassiosira Pseudonana: Ecology, Evolution, and Metabolism[END_REF] and Phaeodactylum tricornutum [START_REF] Bowler | The Phaeodactylum Genome Reveals the Evolutionary History of Diatom Genomes[END_REF], representative of the centric and pennate groups, revealed over 300 bacterial genes in both diatoms, some hinting to their endosymbiotic origin but also posterior acquisition through horizontal gene transfer, constituting a major driving force of diatom evolution.

Divergence. Insights from microfossils suggest that the emergence of diatoms took place in the Triassic period, 252 to 201 Myr ago [START_REF] Sims | Evolution of the Diatoms: Insights from Fossil, Biological and Molecular Data[END_REF] At the end of the Cretaceous, about 65 Myr ago, the mass extinction induced a loss of 85% of all species of Earth including that of marine phytoplankton. However, diatoms managed to survive and began to colonize offshore areas, like the open ocean [START_REF] Armbrust | The life of diatoms in the world's oceans[END_REF]. This period saw the emergence of araphid pennates as indicated in fossil records [START_REF] Sims | Evolution of the Diatoms: Insights from Fossil, Biological and Molecular Data[END_REF]. The emergence of raphid pennates occurred 30 Myr ago, equipped with the ability to glide along surfaces hence expanding the ecological niches greatly. New estimates of diatom diversity in the Cenozoic, between 65 Myr ago and present, report major increases near the Eocene/Oligocene boundary (30 Myr ago) as well as mid Miocene (15 Myr ago), and associate warmer oceans with lower diatom diversity [START_REF] Lazarus | Cenozoic planktonic marine diatom diversity and correlation to climate change[END_REF]. [START_REF] Armbrust | The life of diatoms in the world's oceans[END_REF].

Molecular phylogeny of diatoms. The evolution of diatoms can also be investigated thanks to molecular approaches (see Annex A). The diatom phylogenic tree consensus inferred from the 18S rDNA-gene region by Kooistra (Figure 1.16) reveals the following patterns:

Ellerbeckia sol and its relatives form the basal radial centric clade, sister to the radial centrics. Basal ramifications of radial centrics are badly resolved, however they divide in a few well-supported radial centric lineages, containing chain-forming diatoms or solitary planktonic cells. The highly diverse clade with multipolar centrics and pennates emerges from the basal centric ramification. Well-supported lineages emerge in the paraphyletic multipolar centrics (group that does not include all descendants of a single common ancestor), displaying the ability to produce mucilage from the valve apical pore. All pennates form a well-supported clade, divided in two lineages. First, the araphid pennates, characterized by elongated valves and the ability to form colonies. Second, the raphid pennates that possess a raphe slit and are the only monophyletic group. Overall, it seems that the raphids evolved from araphids, and that the araphids are derived from the centrics.

This classification does come with some controversy with respect to other morphological and molecular phylogenies, which will not be discussed here for the sake of length. This tree was detailed as it is based on the 18S marker used in this thesis, contains most of the species named in this manuscript, and the nomenclature of Radial Centric, Polar Centric, Araphid Pennate and Raphid Pennate was adopted to assign the diatom molecular data. [START_REF] Cermeño | Controls on Diatom Biogeography in the Ocean[END_REF] analysed fossil diatom assemblages over 1.5 million years from the world's ocean, to show that diatoms are not only limited by dispersal.

What this paper suggests is that environmental selection -rather than dispersal -dominates diatom community structure and biogeography, contrary to macroscopic organisms for which geographic isolation is a factor of speciation. Diatom assemblages are additionally determined by the intricate synergy between bottom-up factors, such as light and nutrient supply, for which we have a fair understanding [START_REF] Falkowski | Biogeochemical Controls and Feedbacks on Ocean Primary Production[END_REF], and top-down factors such as grazing, for which our knowledge remains scarce (Smetacek, 2011).

Diatoms dominate phytoplankton communities in well-mixed coastal and upwelling regions, as well at the sea ice edge in polar regions, as long as sufficient light, inorganic nitrogen, phosphorus, silicon and trace elements are available to sustain their growth [START_REF] Morel | The Biogeochemical Cycles of Trace Metals in the Oceans[END_REF].

In particular, diatoms can be at the source of massive algal proliferations called "blooms" that last a few weeks, that are often triggered by bottom up factors such as incident irradiance, nutrient availability and surface mixed layer shallowing [START_REF] Platt | The Phenology of Phytoplankton Blooms: Ecosystem Indicators from Remote Sensing[END_REF].

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, sub-polar, and coastal waters. Yet, blooms cannot be explained just by the fact that diatoms have a superior environmental tolerance or more efficient nutrient uptake systems relative to other photosynthetic organisms. If this were the case, why would diatom species dominating blooms experience less grazing mortality than do co-occurring diatom species [START_REF] Assmy | Mechanisms Determining Species Dominance in a Phytoplankton Bloom Induced by the Iron Fertilization Experiment EisenEx in the Southern Ocean[END_REF][START_REF] Strom | Microzooplankton Grazing in the Coastal Gulf of Alaska: Variations in Top-down Control of Phytoplankton[END_REF]? Several hypotheses involve top-down effects from biotic interactions between diatoms and other members of the plankton.

The diatom social network

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 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 plankton communities. For a definition of "biotic interactions", please refer to Annex B.

A large variety of interactions

• Symbiosis

We restrict the meaning of symbiosis to close mutualistic relationships, whereby two species that interact both individually benefit from the association [START_REF] Paracer | Symbiosis: an introduction to biological associations[END_REF].

Diazotrophs.

A highly mutually beneficial interaction involving diatoms is that known to occur with diazotrophic prokaryotes, 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 (N 2 ) in the world's tropical and subtropical oceans [START_REF] Carpenter | Marine cyanobacterial symbioses. Cyanobacteria in symbiosis[END_REF]. Richelia intracellularis converts dinitrogen gas in ammonium and then supplies the diatom with fixed bioavailable nitrogen compounds essential for metabolism (Rai et al., 2002;[START_REF] Foster | Nitrogen fixation and transfer in open ocean diatom-cyanobacterial symbioses[END_REF]. In these cases the diatom serves as a protective host as the cyanobacteria lives inside the diatom. Another less studied symbiosis engages the chain-forming pennate diatom, Climacodium frauenfeldianum and a unicellular cyanobacterium similar in morphology to the free-living diazotroph, Crocosphaera watsonii [START_REF] Foster | Nitrogen fixation and transfer in open ocean diatom-cyanobacterial symbioses[END_REF]. These interactions are referred to as "DDA": Diatom-Diazotroph Associations (Figure 1.17). Richelia lives as an endosymbiont between the cell wall and the frustule of diatoms such as Hemiaulus, Rhizosolenia and Bacteriastrum, whilst Calothrix lives externally attached to Chaetoceros spp. [START_REF] Fogg | Marine plankton[END_REF][START_REF] Villareal | Widespread occurrence of the Hemiaulus-cyanobacterial symbiosis in the southwest North Atlantic Ocean[END_REF] and successive efforts to molecularly identify the partners, using nifH, 16S rRNA and hetR sequences, has revealed the phylogenetic relationships between different diazotrophs [START_REF] Janson | Host specificity in the Richeliaądiatom symbiosis revealed by hetR gene sequence analysis[END_REF][START_REF] Foster | Characterization of Diatom-cyanobacteria Symbioses on the Basis of NifH, HetR and 16S RRNA Sequences[END_REF]. 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 [START_REF] Hilton | Genomic deletions disrupt nitrogen metabolism pathways of a cyanobacterial diatom symbiont[END_REF].

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 recent sequencing of the spheroid body genome was found to be considerably reduced compared with its close free living relatives and depleted in key metabolic capacities such as photosynthesis, making it completely dependent on its host [START_REF] Nakayama | Complete genome of a nonphotosynthetic cyanobacterium in a diatom reveals recent adaptations to an intracellular lifestyle[END_REF]. Such intricate associations are not always mutualistic; instead, some are highly detrimental to the diatom host, in which case the interaction is then referred to as parasitism.

• Parasitism/Pathogenesis

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 [START_REF] Lafferty | Trophic strategies, animal diversity and body size[END_REF]. 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 [START_REF] Scholz | Zoosporic Parasites Infecting Marine Diatoms -A Black Box That Needs to Be Opened[END_REF], suggesting 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 Gsell reported an interesting case of diatom -parasitic interaction in 2013 [START_REF] Gsell | Temperature Alters Host Genotype-Specific Susceptibility to Chytrid Infection[END_REF]. 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 out-compete the others across all temperature ranges. The authors inferred that thermal variation plays a role in the 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.

• Competition

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" [START_REF] Hutchinson | Homage to Santa Rosalia or why are there so many kinds of animals?[END_REF]. 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 [START_REF] Hardin | The competitive exclusion principle[END_REF].

Intra -taxa competition. Diatoms compete for nutrient resources, for instance with other diatoms. A recent 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 favoured uptake of inorganic nitrogen sources (nitrate and nitrite), whilst the latter favoured the utilization of nitrogen from organic sources, such as amino acids [START_REF] Alexander | Metatranscriptome analyses indicate resource partitioning between diatoms in the field[END_REF]. Competition amongst diatoms can also result from the coupling of nutrient limitation, such as silicalimited environments, and physical factors such as temperature. Different diatom species grow unequally with respect to those co-varying factors, suggesting a specific niche adaptation, as was also shown in freshwater diatoms [START_REF] Shatwell | Temperature and photoperiod interactions with silicon-limited growth and competition of two diatoms[END_REF].

Inter -taxa competition. Diatoms also compete with radiolarians, another silica biomineralizing group of plankton. The diatom expansion 65 Myr ago has been attributed to their superior competitive ability for silicic acid uptake relative to radiolarians, the latter experiencing a reduction in weight of their tests [START_REF] Harper | Silica, Diatoms, and Cenozoic Radiolarian Evolution[END_REF]. 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 co-factor of diatom growth [START_REF] Cermeño | Continental Erosion and the Cenozoic Rise of Marine Diatoms[END_REF]. Diatoms also compete for resources with dinoflagellates through chemically-mediated processes detailed in the Allelopathy section below. Whereas diatoms compete for resources to grow and survive, other organisms at higher trophic levels need diatoms to grow by feeding upon them through predation. • Predation

Diatoms are often referred to as the "pastures of the sea" [START_REF] Smetacek | A Watery Arms Race[END_REF]. Indeed, out of the myriad of mechanisms that can induce phytoplankton mortality or remove phytoplankton biomass, such as viral lysis or sinking, predation is quantitatively dominant [START_REF] Calbet | Phytoplankton Growth, Microzooplankton Grazing, and Carbon Cycling in Marine Systems[END_REF]) maintaining ratios of primary producers to herbivores very low and is therefore a structuring factor in the plankton [START_REF] Sherr | Capacity of Herbivorous Protists to Control Initiation and Development of Mass Phytoplankton Blooms[END_REF]. Unlike parasites that also feed on diatoms, it is generally assumed that predators feed on several species (not one), tend to be bigger than the prey, and tend to kill it [START_REF] Lafferty | Trophic strategies, animal diversity and body size[END_REF].

Metazoan predators such as copepods (crustaceans) presumably exercise strong pressure on diatoms by feeding on them [START_REF] Lebour | The food of plankton organisms[END_REF][START_REF] Marshall | The biology of a marine copepod, Calanus finmarchicus (Gunnerus)[END_REF][START_REF] Campbell | Mesozooplankton Prey Preference and Grazing Impact in the Western Arctic Ocean[END_REF]. The classic pelagic food web involves a trophic linkage between diatom blooms, copepod production, and fish [START_REF] Runge | Should we expect a relationship between primary production and fisheries? The role of copepod dynamics as a filter of trophic variability[END_REF][START_REF] Legendre | The significance of microalgal blooms for fisheries and for the export of particulate organic carbon in oceans[END_REF]. Numerous feeding experiments have investigated the coevolution between copepods and diatoms. Some adaptations are mechanical: copepods modify their feeding tools [START_REF] Itoh | A consideration on feeding habits of planktonic copepods in relation to the structure of their oral parts[END_REF][START_REF] Michels | Tools for crushing diatoms-opal teeth in copepods feature a rubber-like bearing composed of resilin[END_REF], in response to which diatoms adjust their protecting frustules, leading to an arms race that fuels evolutionary processes [START_REF] Hamm | Evolution of primary producers in the sea[END_REF]. Some diatoms that dominate blooms experience less grazing mortality than do co-occurring species [START_REF] Assmy | Mechanisms Determining Species Dominance in a Phytoplankton Bloom Induced by the Iron Fertilization Experiment EisenEx in the Southern Ocean[END_REF][START_REF] Strom | Microzooplankton Grazing in the Coastal Gulf of Alaska: Variations in Top-down Control of Phytoplankton[END_REF]: it was shown that in the presence of preconditioned media that contained herbivores, diatoms develop grazing resistant morphologies such as increased cell wall silicification [START_REF] Hamm | Architecture and Material Properties of Diatom Shells Provide Effective Mechanical Protection[END_REF][START_REF] Pondaven | Grazing-induced Changes in Cell Wall Silicification in a Marine Diatom[END_REF]. Hence, the cell wall provides not only a "constitutive mechanical protection" for the cell but also a plastic trait that responds to grazing pressure.

Other adaptations are physiological: the existence of a mismatch between temperature optima for growth of diatoms relative to growth of potential predator is a strategy to escape predation pressure [START_REF] Rose | Does Low Temperature Constrain the Growth Rates of Heterotrophic Protists? Evidence and Implications for Algal Blooms in Cold Waters[END_REF]. Finally, the production of defensive chemicals and allelopathic molecules targeted towards predators is thought to contribute to diatom success, although largely debated, as detailed below. The classic food web view was challenged in the early 1990's [START_REF] Kleppel | Trophic interactions between copepods and microplankton: a question about the role of diatoms[END_REF]. 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 [START_REF] Jónasdóttir | Role of Diatoms in Copepod Production:good, Harmless or Toxic?[END_REF]. Additional arguments favour low copepod grazing pressure during blooms: the copepods inability to track diatoms overwinter and the existence of grazing from heterotrophic dinoflagellates.

Heterotrophic dinoflagellates are unicellular non-pigmented phagotrophic microplankton measuring between 20 and 100 microns, and are probably the highest consumers of bloomforming diatoms, more than copepods and other mesozooplankton [START_REF] Sherr | Heterotrophic Dinoflagellates: A Significant Component of Microzooplankton Biomass and Major Grazers of Diatoms in the Sea[END_REF].

They can comprise more than 50% of microzooplankton biomass in diatom blooms, represented by thecate dinoflagellates (armored, like Protoperidinium spp.) and athecate (Gymnodinium spp.). They exert a constant predation pressure on diatoms, due to asexual reproduction -therefore a rapid matching when resource abundance increases -but also by their capacity to grow on diverse prey, therefore surviving in non-bloom conditions to better proliferate when diatoms bloom [START_REF] Strom | Microbial Ecology of Ocean Biogeochemistry: A Community Perspective[END_REF]. Attempts to compare the dinoflagellate and copepod pressures on diatom communities have been done in South Korean coastal waters.

Dinoflagellates (Protoperidinium bipes) consumed 0.1-3.4%*H-1 of diatom biomass, whereas copepods (Acartia spp.) removed less than 0.2%*H-1 diatom biomass, rather focusing on herbivore ingestion and relieving diatoms from grazing pressure [START_REF] Jeong | Feeding by the Heterotrophic Dinoflagellate Protoperidinium Bipes on the Diatom Skeletonema Costatum[END_REF]. Experimental simulation of trophic interactions among omnivorous copepods, heterotrophic dinoflagellates and diatoms also suggest 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 [START_REF] Chen | Experimental Simulation of Trophic Interactions among Omnivorous Copepods, Heterotrophic Dinoflagellates and Diatoms[END_REF].

• Interactions with unclear benefits Biotic interactions cannot always be classified easily. In the following section, I focus on interactions, either beneficial or detrimental, involving micro-organisms, mediated by a form of physical attachment/proximity and for which the consequences for the organisms are unclear.

Diatom Host-Associated Microbes. With the arrival of next generation sequencing, hostassociated microbiomes have attracted much attention because microbial diversity becomes easier to characterize. By producing a thick organic rich biofilm, several diatoms are covered with bacteria, and it was estimated that 20% of bacterioplankton may actually be associated to algae [START_REF] Azam | OCEANOGRAPHY: Microbial Control of Oceanic Carbon Flux: The Plot Thickens[END_REF]. The Pseudo-nitzschia diatom genus is well studied because certain representatives produce domoic acid, a toxin causing disease in shellfish and ultimately in humans following ingestion, and produced in the presence of zooplankton (Tammilehto et al., 2015). In particular, it was shown that toxin-producing Pseudo-nitzschia had a lower associated microbial diversity than non-toxic species, suggesting that some members of the microbiota were truly specific to their native host, as they became parasitic when administrated to a foreign host [START_REF] Sison-Mangus | Host-specific adaptation governs the interaction of the marine diatom, Pseudo-nitzschia and their microbiota[END_REF]. Earlier, [START_REF] Kaczmarska | Diversity and Distribution of Epibiotic Bacteria on Pseudonitzschia Multiseries (Bacillariophyceae) in Culture, and Comparison with Those on Diatoms in Native Seawater[END_REF] used scanning electron microscopy to show that the bacterial community present on Pseudo-nitzschia multiseries in culture was very different from the one present on diatoms in native seawater, highlighting the dynamic aspect of the microbial community. Amin et al.

(2012) authored an excellent review on interactions between diatoms and bacteria, and suggested that the prominent bacterial communities associated with diatom cultures are Proteobacteria, Bacteroides (the main heterotrophic phyla associated with diatoms), and in particular Sulfitobacter, Roseobacter, Alteromonas and Flavobacterium. The respective benefit for each organism is not always straightforward though the small size of bacteria is today an obstacle that can be overcome to improve our knowledge about bacteria/phytoplankton interactions.

The first step is often to try and characterize the interactions based on external features, however understanding the mechanism of interaction is necessary to uncover the true nature of the association. In some cases diatom interactions mediated by chemical components have been better described and have enabled the understanding of many opaque associations.

• Chemical bouquets of interactions

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 [START_REF] Legrand | Allelopathy in Phytoplankton -Biochemical, Ecological and Evolutionary Aspects[END_REF], and particularly in diatoms. I will not discuss here the distinction between allelopathy and competition [START_REF] Willis | The History of Allelopathy[END_REF], but describe below a range of biotic interactions that have been identified as being chemically mediated, including competition for resources.

Allelopathy in response to copepod grazing. 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" [START_REF] Harvey | Plankton production and its control[END_REF]. In the early 1990's it was discovered that diatom-derived compounds (simple aldehydes) could decrease copepod egg hatching success from the usual 90% to 12% [START_REF] Ban | The Paradox of Diatom-copepod Interactions[END_REF][START_REF] Miralto | Embryonic Development in Invertebrates Is Arrested by Inhibitory Compounds in Diatoms[END_REF], 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 two minutes [START_REF] Pohnert | Wound-Activated Chemical Defense in Unicellular Planktonic Algae[END_REF].

PUAs are the breakdown products of the oxidative transformation of membrane fatty acids.

They have been found to inhibit cell proliferation and cell division, and to induce phagocytosis and apoptosis. While the teratogenic effect of PUAs does not appear to deter the herbivores from feeding, it does impair recruitment when female copepods are fed with PUA-rich diets, compromising the development of the offspring [START_REF] Ianora | Aldehyde Suppression of Copepod Recruitment in Blooms of a Ubiquitous Planktonic Diatom[END_REF]. Similar effects were illustrated with diatom PUAs on the polychaetes Arenicola marina, and Nereis virens. Recent transcriptome analysis using expressed sequence tags from the copepod Calanus helgolandicus showed that two days of feeding with the PUA-producing diatom Skeletonema marinoi activated a cellular stress response in the grazer, and impaired the reproductive and developmental processes in copepods such as gametogenesis, embryogenesis, egg viability and sex differentiation [START_REF] Carotenuto | Insights into the Transcriptome of the Marine Copepod Calanus Helgolandicus Feeding on the Oxylipin-producing Diatom Skeletonema Marinoi[END_REF]. Investigation of PUAs in a diatom bloom in the North Adriatic Sea [START_REF] Ribalet | Phytoplankton cell lysis associated with polyunsaturated aldehyde release in the Northern Adriatic Sea[END_REF] found that PUA concentrations correlated with diatom cell lysis, however PUA producing diatoms were not reported in cases of high PUA concentrations (2,53nM). This thickens the plot, and raises new questions: is there another PUA-producing organism, or do the enzymes responsible for PUA generation retain their activity extracellularly after diatom cell lysis? On the contrary, in 2002 Irigoien presented in situ data from which he could not observe any negative relationship between copepod egg hatching success and diatom biomass, or dominance, thus defending the classical view of food web dynamics. This was reinforced by Flynn [START_REF] Flynn | Aldehyde-induced Insidious Effects Cannot Be Considered as a Diatom Defence Mechanism against Copepods[END_REF] and Irigoien as co-author, who evaluated the effect of diatoms on copepod reproduction, and found that the aldehyde-induced negative effect could not be considered as a diatom defence mechanism against copepods, including the inhibition of egg viability.

Allelopathy by dinoflagellates for resource competition. The study of nearshore blooms of the dinoflagellate Karenia brevis proposed that allelopathic (unstable) compounds were produced to inhibit growth of phytoplankton competitors, among which diatoms [START_REF] Prince | Effects of harmful algal blooms on competitors: allelopathic mechanisms of the red tide dinoflagellate Karenia brevis[END_REF][START_REF] Poulson | Allelopathic compounds of a red tide dinoflagellate have speciesspecific and context-dependent impacts on phytoplankton[END_REF]. 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 again 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 response 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 triggered [START_REF] Poulson-Ellestad | Are Offshore Phytoplankton Susceptible to Karenia Brevis Allelopathy?[END_REF].

Allelopathy by prokaryotes. Kordia algicida belongs to the Flavobacteriaceae family, and its cell free filtrate was observed to display a high protease activity that induces an algicidal action in several marine diatoms such as Skeletonema costatum, Thalassiosira weissflogii, and Phaeodactylum tricornutum [START_REF] Paul | Interactions of the Algicidal Bacterium Kordia Algicida with Diatoms: Regulated Protease Excretion for Specific Algal Lysis[END_REF]. Lee [START_REF] Lee | Involvement of an extracellular protease in algicidal activity of the marine bacterium Pseudoalteromonas sp. strain A28[END_REF] demonstrated that

Pseudoalteromonas sp. produce a high molecular weight extracellular protease that is able to inhibit the growth of the diatom Skeletonema costatum. (+/+), (+/-), (-/-) and (+/-) signs refer to the relative positive, negative, or neutral effect both interacting species can have on each other. The first term refers to the effect of the interaction of the partner of the diatom, and the second to the effect of the interaction on the diatom. See Annex B for further details.

The relative contribution of these different interactions in shaping the evolution of diatoms is essentially unstudied. In 2001, Smetacek argued 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, and thus that we should not adapt our understanding of the evolution of form and function in terrestrial vegetation -driven by competition for resources and resource space (bottom-up) -to phytoplankton. 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 scales.

Interactions that vary through space and time

Temporal scales of diatom interactions. Diatom produce 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 will evolve to avoid eating PUA-producing diatoms. [START_REF] Teeling | Substrate-Controlled Succession of Marine Bacterioplankton Populations Induced by a Phytoplankton Bloom[END_REF] 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, 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 studies of these dynamics, by sampling regularly at a given location, or by following a prevailing current, also known as a time series study, enables scientists to examine how different organisms change in relation to one another and in relation to environmental conditions [START_REF] Fuhrman | Marine microbial community dynamics and their ecological interpretation[END_REF]. Over millennia, past endosymbiontic events and other gene transfers remain traceable in the genetic information of diatoms or their host.

Spatial scales of diatom interactions. Patches of homogeneous communities, and thereby the interactions that happen amongst them, can be observed from scales of km to thousands of km at a given depth and over horizontal directions. In the vertical direction, there can be significant changes over meters, or tens of meters or even millimeters within ephemeral microlayers [START_REF] Fuhrman | Microbial Community Structure and Its Functional Implications[END_REF]. 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 centimeters when copepods feed on them. Symbiosis with cyanobacteria can form blooms measured in kilometers, as was reported in the subtropical North Atlantic [START_REF] Carpenter | Extensive bloom of a N₂-fixing diatom/cyanobacterial association in the tropical Atlantic Ocean[END_REF], estimating that the N supply by N 2 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 [START_REF] Yeung | Impact of diatom-diazotroph associations on carbon export in the Amazon River plume[END_REF] illustrating how biotic interactions can scale up to influence ecosystem wide phenomenon.

We therefore see that diatom interactions are diverse (Figure 1.18), spanning across multiple temporal and spatial scales, involving both macro-and micro-organisms, prokaryotes and eukaryotes and even viruses (that were not mentioned here). Many of these studies rely on manipulative experiments, such as co-culturing (two organisms in same medium) and cross culturing (cell free filtrate of one organisms added to the medium of the target) of potential competitors, feeding experiments to test specificity of prey and predators, and they have more recently also embraced the omics era. Transcriptomic data has been used to evaluate copepod responses to harmful diatoms, DNA barcoding has been used to analyse predator gut content [START_REF] Kress | DNA Barcodes for Ecology, Evolution, and Conservation[END_REF], metabolomics has helped understand allelopathy, and genomics has helped interpret the evolution of host-symbiont gene transfers and evolution. But microbial communities are complex, and most studies provide a reductionist view, studying one, two, or in best of cases three organisms in isolation.

The need to develop holistic approaches emerged a few years ago in marine microbiology [START_REF] Fuhrman | Microbial Community Structure and Its Functional Implications[END_REF], 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.

With the broad empirical knowledge acquired on diatom biotic interactions, the remaining question is to understand how they structure the plankton community at large spatial scales. A dataset that would give the opportunity to go from single cell to the global ocean to characterize biotic interactions in their natural environment could be transformational for the field.

The Tara Oceans expedition

Over many centuries High throughput genome sequencing and quantitative image analysis provide evolution, metabolic, and interaction data to build community metabolome maps, taxa/gene networks, and spatial ecosystem models [START_REF] Karsenti | A holistic approach to marine eco-systems biology[END_REF].

The regular sampling program was designed to study a variety of marine ecosystems and to target well-defined meso-to large-scale features such as gyres, eddies, currents, frontal zones, upwellings, hotspots of biodiversity, low pH or low oxygen concentrations. A total of 210 stations were characterised at the mesoscale to provide richer environmental context for the morphological and genomic study of plankton. Each sample was processed and appropriately conserved to perform 18S rDNA metabarcoding (for an overview on metabarcoding techniques refer to Annex A), metagenomics, and metatranscriptomics sequencing (Figure 1.20). At each station, sampling was done at two depths: The surface water layer, sometimes labelled in the literature and databases as "surface", "SRF", "SUR", "SURF" or "S", was simply defined as a layer between 3 and 7 m below the sea surface. The deep chlorophyll maximum layer, often labelled in the literature and databases as "DCM" or "D", was determined from the chlorophyll fluorometer mounted on the Rosette Vertical Sampling

System [RVSS]. The presence of a DCM may indicate a maximum in the abundance of plankton bearing chlorophyll pigments, or it may result from the higher chlorophyll content of plankton living in a darker environment. This can be assessed a posteriori using water samples analysed for pigments by HPLC methods and from plankton counts.

At each station, sampling was done for various size fractions. Plankton sampled during the Tara Oceans Expeditions cover six orders of magnitude in size and corresponds to viruses, giant viruses (giruses), prokaryotes (bacteria and archaea), unicellular eukaryotes (protists), and multicellular eukaryotes (such as metazoans). Unicellular eukaryotes, or protists, cover a broad range of cell size (0.8-2,000 μm). Nets were used for the 5-20 μm size-fraction;

plankton from the 20-180 μm size-fraction were collected using a double plankton net with a 20 μm mesh size. Plankton from the 180-2,000 μm size-fraction were collected using a 180 μm Bongo Net.

For each station, morphological analysis was performed for different classes of organism.

This was performed through on-board and on-land FlowCams and ZooScans for quantitative recognition of organisms ranging from 20 micrometers to a few cm, light sheet and confocal microscopes for 3D imaging, on-land electron microscopes for detailed ultrastructural analyses of small protists and viruses.

The Tara Oceans project leverages powerful new technologies and analytical tools to develop the first planetary-scale data collection effort that links biogeography with ecology, genetics, and morphology bringing together an international community of researchers from a wide range of disciplines including marine ecologists, oceanographers, statisticians, molecular biologist, biochemists and engineers. Five foundational papers have been published in the journal Science in May 2015, and I contributed to three of them.

Thesis outline

"When we try to pick out anything by itself, we find it hitched to everything else in the universe" John Muir 1911

The goal of my thesis was to understand how diatom biotic interactions structure the planktonic community at large spatial scales, by developing new approaches based on the heterogeneous and unprecedented Tara Oceans dataset. This thesis is divided in four result chapters beyond this general introduction (Chapter 1), and before the conclusions (Chapter 6).

• • Chapter 4: Characterisation of an abundant and widespread interaction Chapter 4 is the long version of a draft manuscript that will be submitted to ISME Journal.

The wealth of the Tara Oceans data offers the unique opportunity to investigate microbial associations, in situ, at large spatial scale combining a morphological, genetic, and ecological perspective. The initial observation of a prevalent diatom biotic interaction in the South African Agulhas Current initiated the development of an integrated analysis that enabled (1) the illustration of successful data-driven experimental approaches, and ( 2) the characterization of a ubiquitous, specific, and fragile association between a diatom and a heterotrophic ciliate. The South African Agulhas Current and its impact on plankton dispersal is studied in Villar et al., 2015 on which I am 33 rd author (Annex E).

• Chapter 5: Single cell genomics to explore diatom biotic interactions Chapter 5 explores the use of single cell genomics to propose a holistic approach to the study of diatom biotic interactions in situ, extending the case study to diatom interactions with parasites and nanoflagellates.

• Annexes

Annex A : definition of biotic interactions.

Annex B: detailed explanation of the contribution of genomics to the study of microbial diversity.

Annex C : the pipeline used to process metabarcoding data used in this thesis is detailed in de Vargas et al., 2015, a paper on which I was 27 th author out of 55. When the text cites "Table S " (bold, underlined), this refers to online material provided with the manuscript.

Chapter 2: Fishing the unknown

Chapter 2 is a reprint of PNAS paper on which I am fourth author out of 12.

I was in charge of the paragraph dedicated to unassigned sequences, by performing the manual assignment of the top 100 unassigned barcodes with Lucie Bittner, thereby improving the assignment of those most abundant unassigned barcodes.

I was in charge of developing the appropriate bioinformatics pipeline and performing the experiments in order to assign cosmopolitan ribotypes that could not be assigned to a known diatom even after manual curation. The methodology is available under the "Reassignment of unknown diatom ribotypes" section of Supporting Information and will be extended to ribotypes in the whole data set that cannot be assigned, in collaboration with Sarah Romac. Diatoms (Bacillariophyta) constitute one of the most diverse and ecologically important groups of phytoplankton. They are considered to be particularly important in nutrient-rich coastal ecosystems and at high latitudes, but considerably less so in the oligotrophic open ocean. The Tara Oceans circumnavigation collected samples from a wide range of oceanic regions using a standardized sampling procedure. Here, a total of ∼12 million diatom V9-18S ribosomal DNA (rDNA) ribotypes, derived from 293 sizefractionated plankton communities collected at 46 sampling sites across the global ocean euphotic zone, have been analyzed to explore diatom global diversity and community composition. We provide a new estimate of diversity of marine planktonic diatoms at 4,748 operational taxonomic units (OTUs). Based on the total assigned ribotypes, Chaetoceros was the most abundant and diverse genus, followed by Fragilariopsis, Thalassiosira,a n d Corethron. We found only a few cosmopolitan ribotypes displaying an even distribution across stations and high abundance, many of which could not be assigned with confidence to any known genus. Three distinct communities from South Pacific, Mediterranean, and Southern Ocean waters were identified that share a substantial percentage of ribotypes within them. Sudden drops in diversity were observed at Cape Agulhas, which separates the Indian and Atlantic Oceans, and across the Drake Passage between the Atlantic and Southern Oceans, indicating the importance of these ocean circulation choke points in constraining diatom distribution and diversity. We also observed high diatom diversity in the open ocean, suggesting that diatoms may be more relevant in these oceanic systems than generally considered.

I was in charge of analyzing and producing

biodiversity | diatoms | metabarcoding | Tara Oceans | choke points D iatoms are single-celled photosynthetic eukaryotes deemed to be of global significance in biogeochemical cycles and the functioning of aquatic food webs (1)(2)(3). They constitute a large component of aquatic biomass, particularly during conspicuous seasonal phytoplankton blooms, and have been estimated to contribute as much as 20% of the total primary production on Earth (4)[START_REF] Fuhrman | Community Structure of Marine Bacterioplankton: Patterns, Networks, and Relationships to Function[END_REF](6). They are widely distributed in almost all aquatic habitats, except the warmest and most hypersaline environments, and can also occur as endosymbionts in dinoflagellates and foraminifers [START_REF] Amin | Interactions between Diatoms and Bacteria[END_REF].

Because of their complex evolutionary history (8), diatoms have a "mix-and-match genome" (3) that provides them with a range of potentially useful attributes, such asar i g i ds i l i c i f i e dc e l lw a l l ,t h e presence of vacuoles for nutrient storage, fast responses to changes in ambient light, resting stage formation, proton pump-like rhodopsins, ice-binding proteins, and a urea cycle (9). In general, planktonic diatoms seem well-adapted to regimes of intermittent light and nutrient exposure; however, they are particularly common in nutrient-rich regions encompassing polar as well as upwelling and coastal areas (10), highlighting their success in occupying a wide range of ecological niches and biomes. The quantification of diatom diversity and its variations across space (and time) is thus important for understanding fundamental questions of diatom speciation and their tight coupling with the global silica and carbon cycles (8,11), as well as for understanding marine ecosystem resilience to human perturbations.

Estimations of the numbers of diatom species vary widely, from a low of 1,800 planktonic species (12) to a high of 200,000 (13). Most recent estimates range from 12,000 to 30,000 species (14, 15). But such global estimates are confounded by the fact that most studies are focused toward understanding the patterns of diversity in a particular diatom genus at a local or regional scale (e.g., refs. 16-18). Furthermore, as evidenced from the Ocean Biogeographic Information System (OBIS) database, although diatom distributions have been explored extensively in numerous studies, they have predominantly focused on the Northern Hemisphere (19,20).

Characterization of diatom diversity requires accurate and consistent taxon identification. Morphological analyses alone fail to provide a complete description of diatom diversity so complementary investigations are often performed to provide a uniform means of standardization (e.g., ref. 21). During the past decade, the introduction of DNA sequence analysis to systematics has facilitated the discovery of numerous previously undescribed taxa, revealing distinct species identified by subtle or no morphological variations (e.g., ref. 22). Allozyme electrophoresis (23), DNA fingerprinting Significance Diatoms, considered one of the most diverse and ecologically important phytoplanktonic groups, contribute around 20% of global primary productivity. They are particularly abundant in nutrient-rich coastal ecosystems and at high latitudes. Here, we have explored the dataset generated by Tara Oceans from a wide range of oceanic regions to characterize diatom diversity patterns on a global scale. We confirm the dominance of diatoms as a major photosynthetic group and identify the most widespread and diverse genera. We also provide a new estimate of marine planktonic diatom diversity and a global view of their distribution in the world's ocean.

Investigating microbial interactions at large spatial scale : introduction

Our insight about global diatom and marine microbes biodiversity has considerably moved forward with advances in sequencing as the ocean contains an immense diversity of marine microbes. Different functional groups of bacteria, archaea and protists arise from this diversity to dominate various habitats and drive globally important biogeochemical cycles [START_REF] Menden-Deuer | Small Bugs with a Big Impact: Linking Plankton Ecology with Ecosystem Processes[END_REF]. The study of their distribution and associated activities often focused on resource availability and abiotic conditions, but those factors are insufficient to explain major patterns of functional group dominance in the sea [START_REF] Green | Microbial Biogeography: From Taxonomy to Traits[END_REF]. The continual reshaping of communities by mortality, allelopathy, symbiosis and other processes show that such community interactions exert strong selective pressure on marine microbes [START_REF] Strom | Microbial Ecology of Ocean Biogeochemistry: A Community Perspective[END_REF]. This reflects the "Eltonian shortfall", introduced by Hortal et al., 2015 in his review on current major flaws in biodiversity research and refers to our lack of knowledge about "biotic interactions" (see Annex B) among species, and among groups of species especially in the marine microbial world. However, because interactions can affect population dynamics, it is expected that the signatures of microbial interactions are imprinted in microbial survey datasets.

The study of microbes associations in other systems has demonstrated that they are essential for community stability -such as a healthy microbiota -and that their disturbance such as the overgrowth of a competitive pathogenic species can induce dysbioses (microbial imbalance) and diseases [START_REF] Silverman | Success of Self-Administered Home Fecal Transplantation for Chronic Clostridium Difficile Infection[END_REF][START_REF] Frank | Investigating the Biological and Clinical Significance of Human Dysbioses[END_REF]. Understanding how marine microbial interactions, and that of diatoms in particular, structure the planktonic community is of key importance in a changing ocean. Microbial interactions have been increasingly investigated using "co-occurrence networks", an approach presented below.

Biotic interaction and species co-occurrence

The impact of biotic associations on the distribution and diversity of organisms is a topic that has been debated intensively. The degree to which non-climatic factors could shape the VINCENT Flora -Doctoral Thesis -2016 distribution of species has been discussed for near a century [START_REF] Wallace | Tropical nature, and other essays[END_REF][START_REF] Baselga | Global patterns in the shape of species geographical ranges reveal range determinants[END_REF]. Specifically, there is interest in understanding the extent to which occurrences of species are constrained by the distributions of other species, at broad scales of resolution and extent [START_REF] Gravel | Trophic theory of island biogeography[END_REF]. Empirical studies have historically focused on competition [START_REF] Gause | Experimental analysis of Vito Volterra's mathematical theory of the struggle for existence[END_REF][START_REF] Hardin | The competitive exclusion principle[END_REF] showing that in its extreme form, competition leads to coexclusion of the interacting species [START_REF] Macarthur | Geographical ecology: patterns in the distribution of species[END_REF].

The earliest significant work on the relationship between biotic interactions and community assembly was developed by Diamond [START_REF] Diamond | Assembly of species communities[END_REF], that he formalized as the "Assembly

Rules". These rules suggested that competitive exclusion, and not dispersal, was responsible for the avian assemblages in Guinea that seemed like a "checkerboard". [START_REF] Hubbell | The unified neutral theory of biodiversity and biogeography[END_REF].

As reviewed in Faust and Raes, 2012, the application of niche and neutral theory based analysis to microbial distributions supported both models as determining factors of species composition. A recent study provided the first comprehensive simulation of the expected co-occurrence between two species arising from all possible combinations of direct biotic interaction types [START_REF] Araújo | The geographic scaling of biotic interactions[END_REF]. The study shows that similar co-occurrences can be achieved by different interactions, leading to the conclusion that co-occurrences alone are However, there is today experimental evidence that biotic interactions affect species range [START_REF] Araújo | The geographic scaling of biotic interactions[END_REF][START_REF] Bateman | Biotic Interactions Influence the Projected Distribution of a Specialist Mammal under Climate Change[END_REF], inducing non-random co-distribution of species at large spatial scales of hundreds of kilometers for macro-organisms [START_REF] Gotelli | Macroecological signals of species interactions in the Danish avifauna[END_REF], both at regional and continental scale. Due to sequencing technologies, our knowledge about the composition of microbial communities from diverse environments has greatly expanded through alpha and beta analysis of diversity. It is time to investigate community structure through the characterization of inter-taxa interactions, by exploring multi speciesspecific microbial associations in a holistic manner. Co-occurrence networks are becoming a useful tool based on the rationale that community structure is driven, amongst other things, by ecological interactions between species and therefore that the non-random patterns of species distribution can be used to infer these interactions.

VINCENT Flora -Doctoral Thesis -2016

Co-occurrence inference with microbial survey data

The ultimate goal of microbial associations inference in large spatial metabarcoding datasets is to find which pairs of species co-occur more than what would be expected at random, or on the contrary if two species tend not to co-occur in the same samples. The main starting point underlying co-occurrence patterns prediction is the existence of a community matrix, where sites are in columns and species are in rows. The matrix is filled by abundance or presence/absence data. The similarity between the distribution of any two species is quantified using different types of measures (Figure 3.2). The data is then compared to a set of randomized matrixes, in order to detect non-random co-occurrence patterns and assess the significance of the measures [START_REF] Faust | Microbial interactions: from networks to models[END_REF].

Here, I will focus only on methodologies and challenges faced by inference methods related to predicting pairwise relationships between two species (correlative techniques) as implemented in the CoNet software used in this thesis, and leave out those that can predict more complex ones such as regression-based interactions (Figure 3.2).

Choosing the correlation measure. When performing pairwise comparison between species abundances to assess their distribution similarity, the choice of the measure is crucial, in that each measure captures different trends and this is often neglected. Pearson correlation captures linear dependencies; Spearman correlations will detect monotonic relationships through rank ordering. More exotic measures such as KullBack-Leibler divergence can be used that calculates information gap, or the Bray-Curtis score that measures dissimilarity rather than similarity. However, methods such as association rule mining or regression analysis can also help reveal complex associations, though being more computationally intensive. In order to maximize chances of detecting pairwise interactions, it is generally advised to combine measures and keep the statistically significant interactions that meet a consensus. Data sparseness. When a species is displayed as "0" in the abundance matrix, it can either be attributed to the physical absence of a species (called structural 0) or insufficient sequencing depth to capture the species (sampling 0). This is currently a bottleneck in microbial co-occurrence inference, as "absence of presence" is hard to prove. This is particularly problematic for any analysis based on log transformation and presence/absence, as scientists need to add a pseudo-count and thus assume that all species are present. Moreover, data sparseness can cause what is known as the "double 0 problem": many species are rare in large-scale metagenomic samplings, and will display VINCENT Flora -Doctoral Thesis -2016 many "0" across the data that will induce a spurious positive correlation between the two organisms. The Bray-Curtis dissimilarity, which is robust to spurious correlations from presence-absence data, can be used. Keeping organisms present in at least 25% of the samples can lower down the impact of 0's (Figure 3.3).

Compositional effects. Due to unequal sampling effort or unequal sequencing depth, working with absolute read counts in high throughput sequencing is rare, following which scientists generally normalize their data and obtain relative abundance data. As the total abundance across all the samples is often constrained by a constant sum (1, if the abundance is divided by the total abundance in each sample), an increase in the relative abundance of one species will induce the decrease of the other, and result in a negative correlation and falsely predict negative interactions (Table 3.1). This negative bias is known as the compositional bias [START_REF] Aitchison | A New Approach to Null Correlations of Proportions[END_REF] and is common in spatial metagenomic surveys, because abundance of species is usually very uneven. Dealing with compositionality using a permutation procedure is one solution that is detailed below, in order to discard any interactions that are due to skewed distributions.

Assessing significance of a score. Once the initial correlation score matrix is computed, several procedures can be carried out to ensure statistical robustness. One can compute the confidence interval around initial score with bootstrapping. By sub sampling the initial abundance matrix (with replacement) 1,000 times, and recomputing all pairwise correlation measures, this step aims to create a confidence interval around the initial scores. All co-Diat

Diatom interactions in the open ocean

occurrences not within the limits of the 95% confidence interval are discarded. Once a correlation score is obtained, it is necessary to assess its significance with respect to a null distribution by calculating the p-value -the probability of finding the observed or more extreme result under the null hypothesis. A random score distribution is generated by repeating the scoring step a large number of times (often 1,000 times or more), through shuffling and renormalisation of the matrix. This null distribution represents the distribution of scores if the two organisms were distributed at random. In CoNet, a final specific p-value, per method and per pairwise correlation is computed as the probability of the null value (the mean of the null distribution) under the bootstrap distribution; the corresponding interaction is considered significant if the p-value < 0.05. Finally, when performing a large number of tests (i.e., a large number of comparisons), as is the case when we perform pairwise comparisons between one organism and all the others in an abundance matrix, the probability of finding a significant test by chance alone increases (also known as false positives, or Type I error). Multiple testing corrections control the number of false positive interactions, and produces an adjusted p-value threshold for each interaction whose significance under the null hypothesis can then be assessed. The p-value is usually adjusted for multiple testing with procedures such as Bonferroni or Benjamini-Hochberg.

Confounding factors and indirect dependencies.

Co-occurrence analysis reflects the sum of all possible factors affecting species distribution, both biotic (other species), and abiotic.

Sometimes, two species can be positively correlated because both of them are negatively correlated to a third environmental parameter. Another confounding factor is the potential effect of other species on a particular pairwise association (Morueta-Holme et al., 2015).

However, tools such as interaction information [START_REF] Meyer | minet: AR/Bioconductor package for inferring large transcriptional networks using mutual information[END_REF], and network deconvolution [START_REF] Feizi | Network deconvolution as a general method to distinguish direct dependencies in networks[END_REF] can help detecting indirect dependencies (Figure 3.

3).

VINCENT Flora -Doctoral Thesis -2016 The abundances of species 1 and species 2 are not correlated. However, if there are sites where neither of the species is present, the two species can have an observed positive correlation [START_REF] Li | Predicting Microbial Interactions through Computational Approaches[END_REF].

The final output of microbial co-occurrence inference is a table, or a matrix that should contain the following information: identification of significant pairwise correlations, the corresponding scores, and associated p-value. However, when co-occurrence analysis is performed on large amounts of data, extracting the information requires appropriate tools to visualize, and analyze the resulting graph.

The contribution of graph theory

"Good information design reveals the greatest number of ideas in the shortest time, with the least ink, in the smallest amount of space" Edward Tufte, 1983, The Visual Display of Quantitative Information.

Networks in biology emerged from the need to investigate a system not only as an individual component, but as a whole. From protein-protein interactions to metabolic pathways or transcriptional regulations, networks have flooded biology. The best way to represent all the elements of a system, and their interactions, is to draw the connections between them, forming a graph. Graph theory can measure modularity, connectance, degree distributions, and help connect elements of graph structure with notions of functions in the system. Each graph G, is formed from nodes, the objects, connected by edges, the relationships. We often say that the graph G = (N, E) is composed of N nodes, and E edges [START_REF] Newman | The structure and function of complex networks[END_REF]. Many descriptors and network topologies can be mathematically derived from the structure and shape of the graph, a discipline known as graph theory. As soon at the graph represents a system with defined objects, it becomes a network.

Graphs come in many different shapes, connected or not, weighted or not, directed or undirected (Figure 3.4). A connected graph is when all nodes can be reached by following a path, meaning a succession of connected nodes. A graph is not connected if a node, or a cluster of nodes, can't be reached by following a path; we say that the graph has more than one connected component. The number of connected components in a network is an indicator of the global connectivity of the network so that a low number of connected components relates to strong network connectivity, because many nodes are connected. In a weighted graph, the relationships between nodes can be quantitatively characterized by their weight, for example, the number of collaborations between two research institutes.

Finally, directed or undirected graphs represent existence or not of a direction between two nodes, for example a trophic cascade between a predator (node 1), that eats a prey (node 2)

and the relation is always in the same direction. Graphs are composed of nodes and edges, here represented as circles and links between circles, respectively. A) Graphs can be simple, directed, weighted, completely connected or composed of connected components. B) Two graph topologies that differ in their degree distribution are shown: a power law degree distribution and the characteristic bino-mial degree distribution of randomly generated graphs. C) The topology of nodes is characterized by graph theory metrics including degree, clustering coefficient and centrality measures such as betweenness centrality. The number next to each graph corresponds to the metric value of the coloured-in node [START_REF] Perez | Exploring microbial community structure and resilience through visualization and analysis of microbial co-occurrence networks[END_REF].

Several properties and parameters can be derived from a network. Parameters such as the number of nodes, and the number of edges, will give estimates of the graph's size. Two graphs, with different numbers of nodes or edges can furthermore be compared using other Diat

Diatom interactions in the open ocean

classes of measures such as connectance, which measures the proportion of realized edges out of the total possible combinations.

(

1.1) Connectance = |!| |!|(|!| ! !) / !
With |E| the number of edges in the graph and |N| the number of nodes.

In general, graph descriptors can be divided in two categories, the ones that measure properties of individual nodes and edges, and the ones that evaluate global properties of the graph. Node Degree is the number of edges connected to a node and can be used to classify nodes by their connectivity d :

(1.2) d = ! !!! ! !!! ! !" |!| = |!| |!|
where |N | is the number of nodes, |E | is the number of edges, and the edge e !" = 1 if the i !" and j !" nodes are connected, otherwise e !" = 0 [START_REF] Newman | The structure and function of complex networks[END_REF]. Once the node degree of each node is computed, a common analysis is the node degree distribution, that sometimes follows the characteristic power law distribution, meaning that the graph has a very limited number of highly connected nodes (called hubs) and a majority of nodes that are lowly connected.

Clustering coefficients. On a node basis, it is also possible to measure local connective behavior of each node, also known as the clustering coefficient of the node [START_REF] Newman | The structure and function of complex networks[END_REF].

(1.3) c ! = ! ! ! ! ! !" * ! !" * !"# ! ! (! ! !!)/! VINCENT Flora -Doctoral Thesis -2016
Where the numerator of the fraction is the number of triangles (a set of three nodes connected in triangle) through node i, with e !" * e !" * ejk = 1 if i, j , and k are all connected. d ! is the degree of the node. The clustering coefficient (always between 0 and 1) of a node expresses the connectivity between neighbours: if all its neighbours are connected then a node has a clustering coefficient of 1. The global clustering coefficient of a graph is the average of the nodes' clustering coefficients. The average clustering coefficient distribution gives the average of the clustering coefficients for all nodes n with d = 2, 3, ... degrees. In particular, the average clustering coefficient distribution was used to identify the modular organization of metabolic networks [START_REF] Ravasz | Hierarchical organization of modularity in metabolic networks[END_REF].

Centrality measures. Centrality measures are used to evaluate the position of a node within a graph, to determine its centrality with respect to other nodes in the graph. For instance, betweenness centrality will evaluate the position of a node globally, by evaluating the importance of that node relative to all paths in the graph, and reflects its importance in the overall structure of the network. (1.4) bc

! = ! !,!,!!! ! !" (!) ! !"
Where p !" (i) is the number of paths between node j and k that go through i while p !" is the total number of paths going through j and k [START_REF] Newman | The structure and function of complex networks[END_REF]. The betweenness centrality of each node is a number between 0 and 1 and reflects the amount of control that this node exerts over the interactions of other nodes in the network. The centrality measure probably reflects the importance of a node in maintaining the overall structure of a network [START_REF] Newman | The structure and function of complex networks[END_REF]. Though the most appropriate centrality measure to use is not always straightforward [START_REF] Albert | Error and attack tolerance of complex networks[END_REF][START_REF] Iyer | Attack robustness and centrality of complex networks[END_REF].

All the above measures characterize global topological properties of a graph and its nodes.

However, modules can be used to reveal sub global topology by finding structurally meaningful subgraphs, which can be interpreted as a form of topological clustering on the graph [START_REF] Newman | Modularity and community structure in networks[END_REF]. A module is defined as a subgraph whose connectivity pattern Diat

Diatom interactions in the open ocean

between its members is greater than the connectivity patterns with nodes outside that subgraph. The right algorithm to partition a graph in modules depends on the context and the system studied. They have been useful in protein protein networks, to detect groups of proteins that perform specific biological functions [START_REF] Li | A fast agglomerate algorithm for mining functional modules in protein interaction networks[END_REF] or in food webs to find functional modules such as trophic networks [START_REF] Brilli | The Structural Network Properties of Biological Systems[END_REF][START_REF] Pascual | Ecological Networks: Linking Structure to Dynamics in Food Webs[END_REF].

Ecological quantitative methods have been used since a long time to understand ecosystems, and their relations to their abiotic environment; seemingly, graph theory is now being applied to microbial co-occurrence to evaluate a community's inter-connected structure, and its relation to its environment. For example, the Human Microbiome Project has collected 1200 samples across different body sites, providing relative abundance for more than 40,000 taxonomic groups in total through a spatial metabarcoding sampling [START_REF] Qin | A human gut microbial gene catalogue established by metagenomic sequencing[END_REF]. This data has been used to infer interactions between species based on cooccurrence networks, revealing strong niche speciation, with most microbial associations occurring within body sites, and a number of accompanying inter body site relationships [START_REF] Faust | Microbial interactions: from networks to models[END_REF]. Later, analysis of gut microbial data revealed that healthy subjects have a more robust network than diseased subjects [START_REF] Naqvi | Network-based modeling of the human gut microbiome[END_REF]. In another ecosystem, the soil, robustness simulations of networks were conducted by removing OTUs with decreasing centrality, to identify key microbial genera from natural forest and agricultural plantations [START_REF] Steele | Marine bacterial, archaeal and protistan association networks reveal ecological linkages[END_REF][START_REF] Lupatini | Network Topology Reveals High Connectance Levels and Few Key Microbial Genera within Soils[END_REF]. The combination of graph theory and microbial association networks has increased in the past few years and can reveal meaningful correlations between taxa and environmental conditions [START_REF] Barberán | Using network analysis to explore co-occurrence patterns in soil microbial communities[END_REF]De Menezes et al., 2015).

Networks are not the only means of visualisation of inferred co-occurrences. Adjacency matrices are the linear, algebraic formulation but also a visual representation of graphs, where nodes are represented in rows and columns, while edges are encoded as entries in the matrix. While this has been an appropriate and efficient way to analyse graphs until now, it scales poorly to large networks and is not suitable to look at individual node topologies such as the clustering coefficient that detects potential keystone species.

VINCENT Flora -Doctoral Thesis -2016

Interpretation of microbial co-occurrence networks

The structural properties of microbial co-occurrence networks have been characterized to infer biological attributes of the community, such as resilience and disturbance, but despite the promises and power of graph theory, finding the relevant methods to discern patterns with such complex assemblages is challenging. From structure of co-occurrence networks to systems properties, the following section investigates some current research directions in the field of microbial co-occurrence inference.

• Interpreting co-occurrence networks

Comparing microbial co-occurrence networks with social, ecological, and protein protein networks. Steele has applied network analysis based on natural environmental cooccurrence patterns to examine the more complex interactions amongst microbes [START_REF] Steele | Marine bacterial, archaeal and protistan association networks reveal ecological linkages[END_REF] by including marine bacteria, archaea, protists and environmental parameters.

The obtained network was compared with a random network (equal number of edges and nodes) generated based on the Erdös-Réyni model using the Random Network plugin in Cytoscape [START_REF] Shannon | Cytoscape: a software environment for integrated models of biomolecular interaction networks[END_REF]. Subnetworks are created with taxonomic assignment rather than module detection and high positive correlation values are interpreted as potential strong direct dependencies such as symbiosis or parasitism. By calculating the clustering coefficient, the characteristic path length, and the node degree distribution, and comparing these descriptors with a random network but also to non-biological networks, they show that the microbial association network has small world properties (i.e., that nodes are more connected than a random network of similar size), and is more highly correlated than at random. The fitting of a truncated power law function, to their degree connectivity distribution, yielding similar parameter values to other ecological networks (and smaller than that of social or protein interaction networks), is an argument to claim the observation of "meaningful, non-random relationships over time". going through a given taxon to another, is proposed as a proxy for keystone species.

Linking

One option to identify potential keystone OTUs is network robustness analysis that consists in iteratively removing nodes and evaluating the structure of the network resulting from the removal, especially secondary extinctions [START_REF] Pascual | Ecological Networks: Linking Structure to Dynamics in Food Webs[END_REF]. In the gut microbiome, the data has revealed that healthy subjects have more robust networks than diseased subjects (i.e., the removal of particular nodes had less impact on the overall structure) [START_REF] Naqvi | Network-based modeling of the human gut microbiome[END_REF].

Linking specialists and generalist organisms with network structure. Soil microbial communities were investigated over large spatial scales by Barberan et al., 2012.

Pyrosequencing of the 16S rRNA gene followed by filtering and Spearman's rank correlation were used to infer networks. Regarding network topology descriptions, average node connectivity, average path length (value of the average distance between all pairs of node:

5.53 edges), diameter (value of the longest distance: 18 edges), cumulative degree distribution, clustering coefficient (value of how nodes are embedded in their neighborhood and thus, the degree to which they tend to cluster together: 0.33), and modularity (value: 0.77; above 0.4, it is considered that networks form modules) were calculated. The authors applied the terms generalist and specialist to OTUs that are more or less present in the samples. This can be confusing in the ecological interpretation of co-occurrence network, where specialists and generalists rather refer to whom OTUs are connected to, i.e., connected to a restricted or broad number of different species. Nonetheless, networks of VINCENT Flora -Doctoral Thesis -2016 generalist species were less connected and more compartmentalized than specialist networks, attributed to the higher habitat variability covered by generalist network.

Size fractionated sampling reveal differential connectivity patterns. [START_REF] Milici | Co-occurrence analysis of microbial taxa in the Atlantic Ocean reveals high connectivity in the free-living bacterioplankton[END_REF] explored spatial co-occurrences of bacterioplankton taxa in the Atlantic Ocean using stateof-the-art co-occurrence inferences (SparCC). Three different size fractioned networks were computed, corresponding to Free Living, Small Particle Associated, and Large Particle Associated communities across 27 stations and five different depths, and are further compared with respect to the absolute number of correlations in the different networks.

Results suggest high connectivity of Free Living bacterioplankton, primarily based on the comparison of positive correlation numbers and node degree.

• Validating the network and framework foundations

It is obvious that correlation does not imply true biotic interactions. Positive associations can reflect parasitism, cross-feeding, symbiosis, shared preference for a similar niche.

Similarly, negative associations can reflect competition, allelopathy, predator -prey interactions or an opposite preference for a specific niche. Several proposals have been made in order to validate co-occurrence networks like the use of simulation, amongst which the possibility to model microbial populations using simple rules about their growth (Lotka-Volterra for example) in order to simulate dynamics of complex multispecies assemblages.

This will define the range of parameters, equations, and situations in which a robust inference about biotic interactions can be made. Cazelles et al., 2015, performed multispecies simulations and discussed whether the inference of the structure of interaction networks is feasible from co-occurrence data. This is illustrated through modeling the considerable variety of mechanisms, causing pairwise associations and reveals how hard is can be to infer species interactions from co-occurrence analysis.

Testing co-occurrence inference using Lotka-Volterra dynamics. Berry et al., in VINCENT Flora -Doctoral Thesis -2016

Mining the scientific literature for available interactions was advanced as an option to test true positives [START_REF] Li | Predicting Microbial Interactions through Computational Approaches[END_REF] as was used for the study of Protein Protein Interactions [START_REF] Cohen | A Survey of Current Work in Biomedical Text Mining[END_REF]. Each cited paper above should however be interpreted in light of software biases exposed by [START_REF] Weiss | Correlation detection strategies in microbial data sets vary widely in sensitivity and precision[END_REF], where the authors compare popular microbial association tools (LSA, CoNet, SparCC and simple Pearson and Spearman) on mock communities.

The arrival of graph theory and graphical visualization was key for the field of microbial ecology, because pairwise correlations in tabular format were impossible to interpret. Due to the lack of appropriate framework and technical issues, very few community scale network studies have been performed in marine microbial ecology, however this is not true for macroecology, in which the field of Ecological Networks could be of extreme use for us.

Inspiration from ecological networks and insight from the macro world

Investigating the role of species associations through forms of networks, and their impact on the community structure started early in ecology. Before the microbial and HTS era, biotic interactions were restricted to the macro realm. Data was collected from small-scale field observations and/or manipulative experimentation, generally using macro-organisms such as plant-pollinators, food webs, or host parasites associations.

Back in 1880, Lorenzo Camerano was the first one to depict food webs in a diagrammatic manner (Figure 3.5), in the manuscript "On the equilibrium of living beings by means of reciprocal destruction." At the time, the scientific community struggled to know which species were beneficial or harmful for agriculture. In response to this binary way of thinking, Camerano responded: "To have an exact and clear idea, I repeat, of the relations between, for example, insectivorous birds and phytophagous insects, and between these and plants, these groups cannot be studied separately. Rather it is necessary to study each in relation to all other animals to see the general laws governing the equilibrium of animal and plant species." In practice these studies are restricted to studying a few species on a local scale, for collecting data is time consuming, and impossible to use in order to understand small scale associations across large regions. But, along with Darwin's first observation of the "tangled bank", this sparked the concept of the "Web of Life" that represents the global interdependence among species without neglecting species interactions. Network approaches in ecological research emphasize the patterns of interactions among species and have proved their usefulness in order to establish frameworks and to ask questions about ecology and evolution of observed patterns [START_REF] Bascompte | Disentangling the Web of Life[END_REF].

The vast majority of all ecological networks are centered on a focused, single type of interaction creating bipartite networks: predator-prey [START_REF] Pimm | Food Webs[END_REF], host-parasite [START_REF] Hawkins | Parasitoid-Host Food Webs and Donor Control[END_REF], or plant-pollinator. This partitioning is subjective, assuming that ecological and evolutionary dynamics among biotic interactions are independent from each other; this is currently evolving and has opened the way for theoretical frameworks to be developed and a more robust interpretation of network science. The study of ecological networks is traditionally based on the coupling between observed interaction networks, and subsequent

VINCENT Flora -Doctoral Thesis -2016 modeling and analysis of the structure. Visualizing networks as adjacency matrices is more common that force directed layouts, in which modules are easily visible.

Thanks to traditional ecological networks, several misleading conceptions about macroorganismal coevolution were squeezed out: (1) that coevolution leads to highly specific, directed and one to one interactions and that ( 2) coevolution within species-rich communities generates complicated assemblages that are intractable to generalization [START_REF] Bascompte | Structure and Dynamics of Ecological Networks[END_REF]. Network analysis has shown that community interactions maximize robustness and functionality [START_REF] Montoya | Ecological Networks and Their Fragility[END_REF]Thébault et Fontaine, 2010) (1) they are heterogeneous, meaning a majority of species interact with a few species, and a few species have a much higher number of interactions than what would be expected at random, (2) they are nested, in which specialist species interact with a subset of the group of species that generalists also interact with, and (3) they are asymmetric, so that a plant species will highly depend on an animal species for seed dispersal, but the animal weakly depends on the plant. It was proposed that asymmetries in these mutualistic coevolutionary networks would enhance long term coexistence and facilitate biodiversity maintenance [START_REF] Bascompte | Asymmetric Coevolutionary Networks Facilitate Biodiversity Maintenance[END_REF]. The effect of the interaction intimacy, whether high or low was studied, showing high intimacy mutualistic networks were nested whereas low intimacy mutualistic networks were modular (Guimaraes et al., 2007). Diat

Diatom interactions in the open ocean

Antagonistic networks. Antagonistic networks often treat predator-prey networks, and host-parasitoid interactions. Food webs from different habitats and of different species richness have a scale-free network structure -where the degree distribution follows a power law with a parameter comprised between 2 and 3 -which makes them robust to disturbance such as extinction of species (Dunne et al., 2002;[START_REF] Kitano | Computational systems biology[END_REF][START_REF] Pascual | Ecological Networks: Linking Structure to Dynamics in Food Webs[END_REF]. Intimacy in antagonistic networks also shapes the structure, between predators (low), parasites (intermediate) or parasitoid (high): from low to high, the network architecture changes from highly connected and weakly modular, to weakly connected and highly modular [START_REF] Ings | Review: Ecological Networks -beyond Food Webs[END_REF] for a review).

Merging different networks. Thébault and Fontaine, in 2011, opened the way to comparing different ecological networks based on models of population dynamics. Mutualistic plantpollinator networks tend to be organized in nested patterns highly connected with increased stability. Antagonistic plant-herbivore networks tend to be stable when they are organized in compartments, and weakly connected. Antagonistic networks (studied through systems such as predator-prey or host-parasitoids) should be more specific than mutualistic ones, because the arms race between host and antagonist often leads to adaptation at the expense of the ability to attack alternative hosts, whereas in mutualistic interactions, organisms often specialize in traits shared by several species within a community, resulting in enhanced generalism. Later, Dunne [START_REF] Dunne | Parasites Affect Food Web Structure Primarily through Increased Diversity and Complexity[END_REF] investigated the reasons behind the altered structure of a food web after introduction of known parasites, asking if the perturbation was due to the true nature of parasites, or variation in diversity and complexity of the network. While most of the changes in the structure were related to scale-dependent phenomena (meaning that adding parasites or adding free living species is alike) changes in frequency of motifs of interaction among three taxa was parasite dependent.

Inferring biotic interactions from proxies. [START_REF] Morales-Castilla | Inferring Biotic Interactions from Proxies[END_REF] proposed to construct biotic interaction networks based on proxies of macro-organisms, such as geographical data (species co-occurrence). In a multistep procedure, interacting groups of species are defined a priori based, for example, on trophic hierarchy (primary producers, grazers). This first step defined forbidden links, removing a large proportion of potential VINCENT Flora -Doctoral Thesis -2016 links that could occur. This is clearly a hypothesis-driven approach, assuming our knowledge is sufficient to define those impossible interactions and that we know enough about trophic strategies regarding species. This is followed by calculation of the probabilities of interactions for the remaining links, through tricky estimation of strength and asymmetry of interactions.

To what extent can the analogy between ecological networks and microbial co-occurrence networks make sense? Can we really apply the macro-ecological network theory to the microbial world? The theoretical frameworks developed with terrestrial ecological network theory, and the questions they have asked, certainly resonate in the marine microbial world

Introduction

Marine microbes, composed of bacteria, archaea and protists, play an essential role in the functioning and regulation of the Earth's biogeochemical cycles [START_REF] Falkowski | The Microbial Engines That Drive Earth's Biogeochemical Cycles[END_REF].

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. On the other hand the effect of mortality, allelopathy, symbiosis and other biotic processes are also likely to shape their communities and to exert strong selective pressures on microbes [START_REF] Strom | Microbial Ecology of Ocean Biogeochemistry: A Community Perspective[END_REF]. Indeed, with concentrations reaching 10 ^7/L protists and 10 ^9/L prokaryotes in a liter of seawater, biotic interactions are likely to impact community structure from the microscale to the ecosystem level.

Among marine protists, diatoms (Bacillariophyta) are of key ecological importance. They are a ubiquitous and predominant component of phytoplankton, characterized by their ornate silica cell walls, and responsible for approximately 40% of marine net primary productivity Diat

Diatom interactions in the open ocean

(NPP; [START_REF] Nelson | Production and Dissolution of Biogenic Silica in the Ocean: Revised Global Estimates, Comparison with Regional Data and Relationship to Biogenic Sedimentation[END_REF]. The array of biotic interactions in which marine diatoms have been described is vast. They are fed upon by heterotrophic microzooplankton such as ciliates and phagotrophic dinoflagellates [START_REF] Sherr | Heterotrophic Dinoflagellates: A Significant Component of Microzooplankton Biomass and Major Grazers of Diatoms in the Sea[END_REF], as well as metazoan grazers such as copepods [START_REF] Runge | Should we expect a relationship between primary production and fisheries? The role of copepod dynamics as a filter of trophic variability[END_REF][START_REF] Falkowski | The Ocean's Invisible Forest[END_REF][START_REF] Smetacek | Biological oceanography: diatoms and the silicate factor[END_REF]. Other well known interactions include symbiosis with nitrogen-fixing cyanobacteria [START_REF] Foster | Characterization of Diatom-cyanobacteria Symbioses on the Basis of NifH, HetR and 16S RRNA Sequences[END_REF], parasitism by chytrids and diplonemids [START_REF] Gsell | Temperature Alters Host Genotype-Specific Susceptibility to Chytrid Infection[END_REF], diatom-targeted allelopathy by algicidal prokaryotes and dinoflagellates [START_REF] Paul | Interactions of the Algicidal Bacterium Kordia Algicida with Diatoms: Regulated Protease Excretion for Specific Algal Lysis[END_REF]Poulsen-Ellestad et al., 2014) and diatom-derived compounds detrimental to copepod growth (extensively reviewed in [START_REF] Pohnert | Diatom/Copepod Interactions in Plankton: The Indirect Chemical Defense of Unicellular Algae[END_REF]. Nutrients also play a key role in the distribution and abundance of planktonic organisms. In the ocean, nutrients are often provided by upwelling processes that bring deep, cold and nutrient rich waters towards the surface. Diatoms typically thrive in high nutrient and high turbulent environments at the expense of the other major phytoplankton groups, dinoflagellates and haptophytes [START_REF] Margalef | The Organization of Space[END_REF]. Competition for silica between diatoms and radiolarians, another silica utilizing member of the plankton has also been evoked [START_REF] Harper | Silica, Diatoms, and Cenozoic Radiolarian Evolution[END_REF].

Despite the strong biotic and abiotic selective pressures that likely influence diatom biogeography and evolution, they are considered as successful r-selected species [START_REF] Armbrust | The life of diatoms in the world's oceans[END_REF]. 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 [START_REF] Pianka | On r-and K-selection[END_REF]. Diatoms are one of the most diverse planktonic groups in terms of species, found to be widely distributed across the world's sunlit ocean [START_REF] Malviya | Insights into global diatom distribution and diversity in the world's ocean[END_REF] and capable of performing massive "blooms" in which diatom biomass can increase up to three orders of magnitude in just a few days [START_REF] Platt | The Phenology of Phytoplankton Blooms: Ecosystem Indicators from Remote Sensing[END_REF]. Their success has been attributed, in part, to a broad range of predation avoidance mechanisms [START_REF] Irigoien | Phytoplankton Blooms: A 'loophole' in Microzooplankton Grazing Impact[END_REF] such as their solid mineral skeleton [START_REF] Hamm | Evolution of primary producers in the sea[END_REF], chain and spine formation in some species, and toxic aldehyde production. However, a global view of their capacity to interact with other organisms and an assessment of mechanisms shaping contemporary community structures are still lacking.

VINCENT Flora -Doctoral Thesis -2016

Co-occurrence networks using meta-omics data are increasingly being used to study microbial communities and interactions [START_REF] Faust | Microbial interactions: from networks to models[END_REF][START_REF] Li | Predicting Microbial Interactions through Computational Approaches[END_REF], e.g., in human and soil microbiomes [START_REF] Faust | Microbial interactions: from networks to models[END_REF]Barberan et al., 2012) as well as in marine and lake bacterioplankton [START_REF] Fuhrman | Community Structure of Marine Bacterioplankton: Patterns, Networks, and Relationships to Function[END_REF][START_REF] Eiler | Coherent Dynamics and Association Networks among Lake Bacterioplankton Taxa[END_REF][START_REF] Milici | Co-occurrence analysis of microbial taxa in the Atlantic Ocean reveals high connectivity in the free-living bacterioplankton[END_REF]. Such networks provide an opportunity to extend community analysis beyond alpha and beta diversity towards an understanding of the relational roles played by different organisms, many of whom are uncultured and uncharacterized [START_REF] Proulx | Network Thinking in Ecology and Evolution[END_REF][START_REF] Chaffron | A Global Network of Coexisting Microbes from Environmental and Whole-genome Sequence Data[END_REF]. Over large spatial scales, non random 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 [START_REF] Fuhrman | Microbial Community Structure and Its Functional Implications[END_REF]. 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. Much like in protein-protein networks, interpreting microbial association networks also relies on literature-curated gold standard databases [START_REF] Li | Predicting Microbial Interactions through Computational Approaches[END_REF], although such references do not yet exist for most planktonic groups.

As part of the recent Tara Oceans expedition [START_REF] Karsenti | A holistic approach to marine eco-systems biology[END_REF][START_REF] Bork | Tara Oceans studies plankton at planetary scale[END_REF], determinants of community structure in global ocean plankton communities were assessed using co-occurrence networks [START_REF] Lima-Mendez | Determinants of Community Structure in the Global Plankton Interactome[END_REF]. Pairwise links between species were computed based on how frequently they were found to co-occur in similar samples (positive correlations), or on the contrary if the presence of one organism negatively correlated with the presence of another (negative correlations). In order to prevent spurious correlations due to the presence of a third confounding factor such as abiotic factors, interaction information was furthermore calculated to assess whether or not edges were driven by an environmental parameter. The Tara Oceans Interactome represents an ideal case to investigate global scale processes involving diatoms, as it maximizes spatio-temporal variance across a global sampling campaign and captures system-level properties. Diat

Diatom interactions in the open ocean

Here, we reveal diatoms to be the organismal group with proportionally the most negative interactions within the Tara Oceans Interactome. We then hypothesize that as good competitors, diatoms are likely to act as repulsive segregators -they display more negative than positive associations (Morueta-Holme et al., 2016) -in particular against potentially harmful organisms in the global ocean, and ask what are the underlying distribution patterns that drive these interactions. By extracting knowledge from a recent biogeography study of diatoms [START_REF] Malviya | Insights into global diatom distribution and diversity in the world's ocean[END_REF], we show that abundant diatoms are not necessarily key players in the community structure. We then performed an extensive literature survey of current knowledge regarding diatom biotic interactions, revealing that it is highly skewed towards predation interactions and macroorganisms. Finally, by combining empirical knowledge and data-driven studies of microbial associations, we reveal important yet understudied players of the diatom environmental interactome. The global ocean interactome reports over 90,000 statistically significant correlations, with ~68,000 of them being positive, ~ 26,000 of them being negative, and ~ 9,000 due to the simultaneous higher correlation of two organisms (OTUs) with a third environmental parameter. Diatoms are involved in 4,369 interactions, making them the 7th most VINCENT Flora -Doctoral Thesis -2016 connected taxonomic groups after syndiniales (MALVs), arthropods, dinophyceae, polycystines, MASTs and prymnesiophyceae. All groups except diatoms and polycystines display a higher number of positive edges than negative ones (Table S1). Overall, diatoms represent around 3% of all the positive associations (2,120/68,856) and 9.5% of all negative associations (2,249/23,777) showing that their contribution to negative associations is much higher than their contribution to positive co-occurrences, unlike major taxonomic groups involved in the interactome. The positive to negative ratio provides a measure of the species role in the network, and suggests that diatoms act as repulsive segregators, meaning they have more negative than positive associations [START_REF] Morueta-Holme | A Network Approach for Inferring Species Associations from Co-occurrence Data[END_REF]. Amongst all the pairwise associations involving diatoms and other organisms in the plankton (N=4,369), only 13% were due to a third environmental parameter illustrating a shared preference for a particular abiotic condition (N=566), leaving 87% of the associations solely explained by the abundance of the two organisms (Figure S3.

Results

Diatoms

3).

A finer analysis revealed the major taxonomic groups with which diatoms correlate or anticorrelate. Positive correlations involve mainly arthropoda (9.2% of diatom negative correlations), dinophyceae (8.7%), and syndiniales (11.7%). Negative correlations include the three previous groups -arthropoda (11.5%), dinophyceae (11.3%), syndiniales (11.1%)as well as the polycystina (6%), a major group of radiolarians that produce mineral skeletons made from silica. We investigated whether or not these patterns were consistent across the ocean, asking if the other taxonomic groups of plankton displayed similar patterns with these organisms. The number of negative correlations involving diatoms with copepods, syndiniales, dinoflagellates and radiolarians was much higher than what would be expected at random based on binomial testing (Table S2, Figure S3.4-S3.6). For example, out of the 64 groups that display at least 10 associations with copepods, only 6 anti-correlate more than they co-occur with metazoan predators (Figure 3.7).

Sub-networks were then extracted for both positive and negative associations involving diatoms with syndiniales (MALVs), MASTs, dinophyceae, and copepods (Figure 3.8). The clustering coefficient measures the tendency of a graph to form clusters compared to a random network, and it was higher in positive association networks than in negative association networks, meaning that the positive co-occurrence networks tend here to form more modules than the negative ones. In network biology, two nodes are connected if there is a path of edges between them, so all nodes that are pairwise connected form a connected component. Consequently, low connected components relate to strong connectivity because many nodes are connected. Connected components were higher in MALV and MAST sub-networks, and lower in dinophyceae, meaning that the former sub-networks had lower connectivity. Associations with MASTs were more centralized, meaning that the subnetwork has a star-like topology in which nodes of the network, on average, do not have the same connectivity and are not uniformly connected (Figure S3.7). In addition, many MAST nodes belonged to the MAST-3 clade, known to harbor the diatom parasite Solenicola setigara [START_REF] Gómez | Solenicola setigera is the first characterized member of the abundant and cosmopolitan uncultured marine stramenopile group MAST-3[END_REF]. In order to investigate the strength of repulsive interactions, average Spearman correlations of the values were computed (Table S3). Contrary to expectations, they were higher for copresences than for exclusions. However, we compared diatom Spearman scores with those of polycystines as control group and found that diatoms have stronger negative scores, reflecting a higher potential as repulsive segregators with respect to potential harmful organisms. Average scores were higher for MASTs (-0.66+-0.09) and MALVs (-0.59+-0.09).

Global-scale genus abundance does not determine importance in community structuring

While abundant diatoms are likely to be important players in biogeochemical cycles such as NPP (Net Primary Production) and carbon export, are they also relevant in structuring plankton communities? To address this question, the ten most abundant diatom genera (based on 18S rDNA V9 read abundance) were analyzed with respect to their positions in the interactome (Table S4). This analysis revealed that no significant correlation was found between the total abundance of the genus, and the number of edges the genus is involved in (Spearman p.value = 0.96), nor the number of nodes involved (Spearman p.value = 0.45).

Some barely play a role in the interactome, for example Attheya is the tenth most abundant genus (96,926 reads), yet is only represented in 10 edges across the interactome. On the other hand, the diatom genus Synedra, that is not notably abundant at the global level (ranked as the 22 nd most abundant diatom with 28,700 reads), was involved in over 100

VINCENT Flora -Doctoral Thesis -2016 significant associations. Leptocylindrus (710,295 reads), is involved in 667 interactions

(Figure S3.8).
Statistics of network level properties provide further insights into the overall structure of species-specific assemblages, and was investigated at the genus level for Leptocylindrus, Proboscia, and Pseudo-Nitzschia, each of which displayed high clustering coefficients meaning neighbors of nodes are connected between each other. Similarly, these genera displayed a higher average number of neighbors, meaning that the average connectivity of the nodes in the network was higher, which could be interpreted as species being generalist in their interactions. On the other hand, the Chaetoceros, Eucampia, and Thalassiosira subnetworks displayed larger diameters, thus inferring the largest distance between two nodes. This is illustrated by the fact that a few diatom nodes are connected both positively and negatively to a large number of nodes that are not connected to any diatom partners, therefore a more specific type of behavior with respect to interactions. (Figure S3.9).

Species level repulsive segregation determined by blooming strategies

Due to the small number of individual barcodes involved at the species level, we decided to conduct a finer analysis and ask whether or not different barcodes of the same (abundant) genera displayed specificity in the type of interactions and partners they interact with. We illustrate this barcode specificity with three different genera: Chaetoceros, Pseudo-nitzschia and Thalassiosira. Chaetoceros interactions reveal that different species display very different co-occurrence patterns (Figure 3.9). The barcode "29f84ed97c31eabfc3e787fd686442a4," assigned to Chaetoceros rostratus is essentially only involved in positive co-occurrences, while barcode "8fd6d889852840ef8c8863cebdc14d10," assigned to Chaetoceros debilis, is the major driver of negative associations involving dinophyceae, MASTs, syndiniales and arthropods. This could reflect the different species tolerance to other organisms, since several Chaetoceros species are known to be harmful to aquaculture industries [START_REF] Albright | Sub-lethal Concentrations of the Harmful Diatoms, Chaetoceros Concavicornis and C. Convolutus, Increase Mortality Rates of Penned Pacific Salmon[END_REF];

Chaetoceros debilis in particular can cause physical damage in fish gills [START_REF] Kraberg | Coastal Phytoplankton: Photo Guide for Northern European Seas[END_REF].

Pseudo-nitzschia barcodes are primarily involved in positive correlations. However, they display exclusions with organisms such as arthropoda and dinophyceae, and are known to Diat

Diatom interactions in the open ocean

produce toxic domoic acid in the presence of copepods (Tammilehto et al., 2015). No exclusions regarding syndiniales appear, although barcode-level specificity is observed with "1d16c182297bf5aa52f3d99c522ade94," which is involved in a much higher number of interactions than "b56c311cb937f906f5fd96ee5983fcac." Unfortunately, diatoms were not assigned at the species level. Finally, the Thalassiosira sub-network displays major negative associations with syndiniales, arthropoda, and polycystines, with one of the three representative barcodes being highly involved in structuring the community (53bb764df052b8ba43d74f8b4e3b7e92

Segregation towards heterotrophic prokaryotes is not significant

We hypothesized that repulsive segregation by diatoms could also apply to heterotrophic bacteria, leading to higher than at random negative associations towards heterotrophic than autotrophic bacteria. Diatom -prokaryote associations represent 19% of the whole diatom co-occurrence network which is considered as average when compared to Bacteria associations in copepod interactions (28%), dinophyceae (18.5%), radiolarian (20.5%) and syndiniales (16.3%). Bacteria were classified according to their primary nutritional group based on the literature. No significant exclusion toward heterotrophic bacteria was found based on the current data (Figure S3.10).

A skewed knowledge about diatom biotic associations

To review current knowledge about diatom interactions we generated a database that assembled the minable knowledge in the literature about diatom associations from both VINCENT Flora -Doctoral Thesis -2016 marine and freshwater habitats. A total of 1,533 associations from over 500 analyzed papers involving 83 unique genera of diatoms and 588 unique genera of other partners are reported here, illustrating the diversity of association types such as predation, symbiosis, allelopathy, parasitism, and epibiosis, as well as the diversity of partners involved in the associations, including both prokaryotes and eukaryotes, micro-and macro-organisms

(Figure 3.6).
We noted that 58% (883 out of 1,533) of the interactions are labelled "eatenBy" and involve mainly insects (267 interactions; 30% of diatom predators) and crustaceans (15% of diatom predators). Cases of epibiosis, representing approximately 10% of the literature database, were largely dominated by epiphytic diatoms living on plants (40% of epibionts) and epizoic diatoms living on copepods (9% of epibionts). Parasitic and photosymbiotic interactions, although known to have significant ecological implications at the individual host level as well as at the community composition scale [START_REF] Veen | Food Web Structure of Three Guilds of Natural Enemies: Predators, Parasitoids and Pathogens of Aphids[END_REF], represented only 15% of the literature database for a total of 219 interactions, involving principally diatom associations with radiolarians and cyanobacteria. Interactions involving bacteria represent 72 associations (4.8 % of the literature database).

The distribution of habitats amongst the studied diatoms reveals a singular pattern: the majority of diatom interactions in the literature are represented by a handful of freshwater diatoms, whereas many marine species are reported in just a small number of interactions

(

Discussion

We provide a detailed study of the diatom interactome based on co-occurrence networks, derived from the largest spatial metagenomic survey conducted to our knowledge, combined with an up-to-date review of current empirical knowledge regarding diatom biotic interactions. Our literature survey reveals a skewed knowledge, focusing on freshwater diatoms and predation by macro-organisms, with very few parasitic, photosymbiotic or bacterial associations. The relative poverty of marine microbial studies can be explained by VINCENT Flora -Doctoral Thesis -2016 the difficulty of accessing these interactions in the field, which obviously limits our understanding of how such interactions structure the community at global scale.

Out of the complete diatom association network extracted from the Tara Oceans Interactome, co-localization and co-exclusion of diatoms with other organisms are due to shared preferences for an environmental niche in 13% of the cases, emphasizing the importance of biotic factors in 87% of the associations. By excluding major functional groups such as predators, parasites and competitors, diatoms act, at a global scale, as a repulsive segregator preventing their co-occurrence in similar samples [START_REF] Smetacek | Making Sense of Ocean Biota: How Evolution and Biodiversity of Land Organisms Differ from That of the Plankton[END_REF]. Diatoms are known to have developed an effective arsenal composed of silicified cell walls, spines, toxic oxylipins, and chain formation to increase size, so we propose that the observed exclusion pattern reflects the worldwide impact of the diatoms arms race against harmful organisms.

Additionally, building upon the phylogenetic affiliation of individual sequences, barcodes can be assigned to a plankton functional type that refers to traits such as the trophic strategy and role in biogeochemical cycles [START_REF] Quéré | Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models[END_REF]. As demonstrated in the Tara

Oceans interactome [START_REF] Lima-Mendez | Determinants of Community Structure in the Global Plankton Interactome[END_REF], diatoms compose the "phytoplankton silicifiers" metanode, and display a variety of mutual exclusions that distinguish them from other phytoplankton groups.

Sub-network topologies reveal that MASTs -diatom and MALV -diatom networks all display lower connectivity, suggesting more specialist interactions than with copepods or dinophyceae. MAST networks were also more centralized, showing non-uniform connections among components and higher specificity in the diatom -marine stramenopile links. Correlation values are often neglected in co-occurrence analysis but here they reveal stronger exclusion patterns of diatoms against MASTs and MALVs. Exclusion between diatoms and MASTs is therefore more specific, and stronger, than compared to copepods or dinoflagellates. Analysis at the genus level shows that abundant diatoms such as Attheya do not play a central role in structuring the community, contrary to Synedra that, at a global scale, had small significance in terms of abundance but is highly connected to the plankton community. We show the existence of a species level segregation effect, that can be attributed to harmful traits [START_REF] Kraberg | Coastal Phytoplankton: Photo Guide for Northern European Seas[END_REF], reflected by blooming distribution patterns for the top repulsive segregating diatoms. If diatom blooms are known to be triggered by light and nutrient perturbation, the negative associations were not driven by a third environmental parameter emphasizing the biotic component of the segregative effect.

Comparing empirical knowledge and data-driven association networks reveal understudied genera such as Leptocylindrus, or Actinocyclus, and those that are not even present in the literature such as Proboscia and Haslea. However, we stress that Proboscia is a homotypic synonym of Rhizosolenia that is found in the interactome, which gives rise to the long lasting debate about non-universal taxonomic denomination and its incidence on diversity analysis.

18.5% of the literature database was recovered in the interactome, however this explained only 6.5% of the 4,369 edges composing the diatom network. The gap between the 20% diatom-bacteria interactions in the Tara Oceans Interactome and 4.8% diatom-bacteria associations described in the literature highlights how little we know about host-associated microbiomes at this stage. In many ways, this high proportion of unmatched interactions should be regarded as the "Unknown" proportion of microbial diversity emerging from metabarcoding surveys. Part of it is truly unknown and new, 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.

Many challenges remain regarding the computation, analysis, and interpretation of cooccurrence networks despite their potential to uncover major processes shaping diatomrelated microbial communities. Recent studies are exploring the methodological bias of each co-occurrence method by attempting to detect specific associations within mock communities [START_REF] Weiss | Correlation detection strategies in microbial data sets vary widely in sensitivity and precision[END_REF] whilst others admit that applying network statistics to microbial relationships is submitted to high variability depending on taxonomic level of the study and criteria used to compute networks [START_REF] Williams | Demonstrating Microbial Co-occurrence Pattern Analyses within and between Ecosystems[END_REF]. Assigning biological interactions such as predation, parasitism or symbiosis to correlations is still cumbersome and will require both proper references of biotic interactions [START_REF] Li | Predicting Microbial Interactions through Computational Approaches[END_REF], and further studies that investigate dynamics of interactions through space and time, and their sensitivity to co-occurrence detection. Attempts to develop a theoretical framework for species interaction detection based on co-occurrence networks is underway [START_REF] Morales-Castilla | Inferring Biotic Interactions from Proxies[END_REF][START_REF] Cazelles | A Theory for Species Co-occurrence in Interaction Networks[END_REF]Morueta-Holme et al., 2015), but must be confronted with actual in situ data. Furthermore, a vast body of literature already exists in the field of ecological networks, traditionally focusing on observational non-inferred data and the modeling of foodwebs, host-parasite and plant-pollinator networks [START_REF] Ings | Review: Ecological Networks -beyond Food Webs[END_REF][START_REF] Bascompte | Structure and Dynamics of Ecological Networks[END_REF]. 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 (Thébault et al., 2010;[START_REF] Fontaine | The Ecological and Evolutionary Implications of Merging Different Types of Networks[END_REF]. As the fields of ecological networks and cooccurrence networks both focus on biotic interactions and how they structure the community, more cross fertilization between the two disciplines would highly benefit both communities, ultimately helping to understand the laws governing the "tangled bank" (Darwin, 1859). S5. Four independent matrices were created from the interactome regarding the major partners interacting with diatoms (copepods, dinophyceae, syndiniales and radiolaria) containing only pairwise interactions that involved the major partner and binomial testing was done using the dbinom and pbinom function as implemented in the {stats} package of R version 3.3.0. Subnetwork topologies were analyzed using the NetworkAnalyzer plugin in Cytoscape [START_REF] Shannon | Cytoscape: a software environment for integrated models of biomolecular interaction networks[END_REF] as described in [START_REF] Doncheva | Topological Analysis and Interactive Visualization of Biological Networks and Protein Structures[END_REF]. Network topologies for major groups are available in Table S6.

Materials and methods

Construction of Diatom Interaction

Major diatom interactions. The 10 most abundant diatom genera in the surface ocean were selected based on the work published by [START_REF] Malviya | Insights into global diatom distribution and diversity in the world's ocean[END_REF]. Network topologies are available in Table S7. Their co-occurrence network was then extracted from the global interactome and analyzed at the ribotype level. Distribution of individual barcodes was assessed across the Tara Oceans sampling stations.

Comparison of literature interactions and diatom interactome. All partner genera interacting

with diatoms based on the literature were searched for in the Tara Oceans dataset based on the lineage of the barcode. For each barcode that had a match, identifiers ("md5sum") were extracted creating a list of 954110 barcodes to be searched for in the global interactome. All interactions involving copepods were extracted from the Tara Oceans interactome, and only taxonomic groups with over 10 edges were kept. For each group, the relative proportion of positive (green) and negative (red) edges are displayed, and absolute number of edges involved is indicated in the abscissa. Diatoms (Bacillariophyta) are highlighted in blue. S8). For each available diatom genera in the literature interaction database, habitat was assigned based on available knowledge. et al., 2015) and the relative proportion of positive (green) and negative (red) interactions was computed for each major taxonomic group and plotted by decreasing negative edges. The absolute number of interactions between the taxonomic group and radiolaria is given in the legend. Diatoms are the 12 th group from the left.

Figures and supplementary material

Introduction

Phytoplankton are photosynthetic marine microbes that are responsible for half of Earth's net primary production [START_REF] Field | Primary Production of the Biosphere: Integrating Terrestrial and Oceanic Components[END_REF]. Diatoms, a ubiquitous and predominant component of phytoplankton enveloped in a characteristic silica cell wall, are believed to be responsible for approximately 40% of marine net primary productivity [START_REF] Nelson | Production and Dissolution of Biogenic Silica in the Ocean: Revised Global Estimates, Comparison with Regional Data and Relationship to Biogenic Sedimentation[END_REF]. They serve as the basis of the marine food web and are significant players in global biogeochemical cycles, representing a key unit of the marine ecosystem [START_REF] Falkowski | The Ocean's Invisible Forest[END_REF][START_REF] Smetacek | Biological oceanography: diatoms and the silicate factor[END_REF].

Diatoms are frequently reported to dominate phytoplankton communities in well-mixed coastal as well as upwelling regions, where light, iron, silica, phosphorus, and nitrogen are available [START_REF] Morel | The Biogeochemical Cycles of Trace Metals in the Oceans[END_REF] however they can be constrained by nutrient availability in the open ocean [START_REF] Cullen | Hypotheses to Explain High-nutrient Conditions in the Open Sea[END_REF]. They are also frequent and diverse in open ocean oligotrophic systems [START_REF] Malviya | Insights into global diatom distribution and diversity in the world's ocean[END_REF] where their survival in such low-nutrient regions is sometimes dependent on the presence of heterocystous N2-fixing cyanobacteria known as diazotrophs, which live in obligate or facultative symbioses with them [START_REF] Foster | Characterization of Diatom-cyanobacteria Symbioses on the Basis of NifH, HetR and 16S RRNA Sequences[END_REF]. These associations are cases of mutualistic symbiosis, wherein diazotrophic bacteria like Richelia intracellularis and Calothrix rhizololeniae provide nitrogen in usable forms to diatoms including Hemiaulus spp and Rhizosolenia sp., which in return serve as protective hosts. This strategy to survive or adapt to a planktonic lifestyle is one example of the high diversity of biological interactions involved in the structure and functioning of the marine ecosystem, being likely the result of millions of years of co-existence and co-evolution of these planktonic organisms [START_REF] Kiørboe | A Mechanistic Approach to Plankton Ecology[END_REF]. Planktonic diatoms have been described in numerous other biological interactions, involving a range of organisms across all domains of life, as well as viruses. Beyond predation and competition, examples include host-parasite associations (e.g., between the chytrid parasites Zygorhizidium planktonicum and Rhyzidium planktonicum with the spring bloom diatom Asterionella formosa [START_REF] Gsell | Temperature Alters Host Genotype-Specific Susceptibility to Chytrid Infection[END_REF]), various synergistic and parasitic interactions with bacteria [START_REF] Amin | Interactions between Diatoms and Bacteria[END_REF][START_REF] Sison-Mangus | Host-specific adaptation governs the interaction of the marine diatom, Pseudo-nitzschia and their microbiota[END_REF], endosymbiotic diatoms in nummulitid foraminifera (e.g., Thalassionema sp. related sequences in Heterostegina depressa foraminifera, [START_REF] Holzmann | Molecular identification of diatom endosymbionts in nummulitid Foraminifera[END_REF]) and dinoflagellates (e.g., Galeidinium rugatum and Durinskia baltica [START_REF] Schnepf | Dinophyte chloroplasts and phylogeny-a review[END_REF]Takano et al., 2008)) or even less understood forms of physical attachement with bacteria [START_REF] Kaczmarska | Diversity and Distribution of Epibiotic Bacteria on Pseudonitzschia Multiseries (Bacillariophyceae) in Culture, and Comparison with Those on Diatoms in Native Seawater[END_REF], copepods [START_REF] Gárate-Lizárraga | Primer registro de la diatomea epibionte Pseudohimantidium pacificum y de otras asociaciones simbióticas en el Golfo de California[END_REF][START_REF] Fernandes | Morfologia e distribuição de duas diatomáceas (Bacillariophyta) epizóicas no Brasil[END_REF], diatoms (e.g., Pseudo-nitzschia linea and Chaetoceros sp. [START_REF] Ruggiero | Diversity and Temporal Pattern of Pseudo-nitzschia Species (Bacillariophyceae) through the Molecular Lens[END_REF]), Phaeocystis colonies [START_REF] Sazhin | The Colonization of Two Phaeocystis Species (Prymnesiophyceae) by Pennate Diatoms and Other Protists: A Significant Contribution to Colony Biomass. Phaeocystis, Major Link in the Biogeochemical Cycling of Climate-relevant Elements[END_REF], flagellated stramenopiles [START_REF] Gómez | Solenicola setigera is the first characterized member of the abundant and cosmopolitan uncultured marine stramenopile group MAST-3[END_REF], vorticellids [START_REF] Nagasawa | Redescription of Vorticella Oceanica Zacharias, 1906 (Ciliophora: Peritrichia) with Notes on Its Host, the Marine Planktonic Diatom Chaetoceros Coarctatum Lauder[END_REF] and tintinnids (Gómez et al., 2007;[START_REF] Froneman | Observations on the association between the diatom, Fragilariopsis doliolus Wallich, and the tintinnid, Salpingella subconica Kafoid[END_REF]. The latter examples, are representative of an interspecific association known as epibiosis (from the greek epi "on top" and bios "life"), which result in "spatially close associations between two or more living organisms belonging to the same or different species" [START_REF] Harder | Marine Epibiosis: Concepts, Ecological Consequences and Host Defence[END_REF]. Tintinnids (Choreotrichida) are heterotrophic planktonic ciliates enveloped in a speciesspecific test composed of organic material, the lorica [START_REF] Agatha | The biology and ecology of tintinnid ciliates: models for marine plankton[END_REF]. They represent one of the most morphologically diverse groups of planktonic protists (Bachy, 2013). They are abundant and ubiquitous, commonly found in marine surface waters in concentrations ranging from 10 ^1 to 10 ^4 cells per liter (McManus & Santoferrara, 2013). Various associations between tintinnids and diatoms have been reported in the literature even though the true nature of the association remains unknown [START_REF] Decelle | Photosymbiosis in marine planktonic protists[END_REF] such as the one involving Chaetoceros sp. and Eutintinnus sp. known for over a century [START_REF] Fol | Nouvelle contribution à la connaissance de la famille des Tintinnodea[END_REF][START_REF] Pavillard | Observations sur les Diatomées (2e série)[END_REF]. With regards to Fragilariopsis sp. there have been occasional reports of occurrences with tintinnids. It was found with Eutintinnus sp. in material from near the Galapagos [START_REF] Pavillard | Péridiens et diatomées pélagiques recueillis par Alain Gerbault entre les iles Marquises et les ils Galapagos[END_REF] and the Central South Pacific [START_REF] Balech | Tintinnoinea y dinoflagellata del Pacífico: según material de las expediciones Norpac y Downwind del Instituto Scripps de Oceanografía[END_REF]. More recently, Fragilariopsis doliolus was found associated with Salpingella subconica in material from the Southern Ocean near Prince Edward Islands with rates of occurrence close to 30% of all Fragilariopsis sp. encountered [START_REF] Froneman | Observations on the association between the diatom, Fragilariopsis doliolus Wallich, and the tintinnid, Salpingella subconica Kafoid[END_REF]. Of a different nature would be the association of Laackmanniella and other tintinnids with empty frustules of Fragilariopsis spp. and several other diatoms covering their lorica in the Antarctic, for which it was hypothesized that the ciliates retain diatom frustules following ingestion of the cellular contents [START_REF] Gowing | Abundance and Feeding Ecology of Larger Protozooplankton in the Ice Edge Zone of the Weddell and Scotia Seas during the Austral Winter. Deep Sea Research Part A[END_REF][START_REF] Wasik | Agglutinated Loricae of Some Baltic and Antarctic Tintinnina Species (Ciliophora)[END_REF]). However, a detailed molecular and morphological characterization of the partners is lacking so far, while an appraisal of the large-scale distribution and ecology of these consortia has never been attempted.

The recent Tara Oceans worldwide expedition has generated an immense amount of multidisciplinary information focusing on open ocean plankton communities in the surface ocean [START_REF] Karsenti | A holistic approach to marine eco-systems biology[END_REF][START_REF] Bork | Tara Oceans studies plankton at planetary scale[END_REF], enabling data-driven studies of biotic associations to emerge, using high-throughput genomics coupled with bioinformatics, as well as with high-content microscopic analysis of conserved fixed samples [START_REF] Lima-Mendez | Determinants of Community Structure in the Global Plankton Interactome[END_REF][START_REF] Mordret | The Symbiotic Life of Symbiodinium in the Open Ocean within a New Species of Calcifying Ciliate (Tiarina Sp.)[END_REF]. One of the analytical platforms of the Tara Oceans project involves the high-throughput analysis by confocal laser scanning microscopy of formolglutaraldehyde-fixed fractions enriched in nano-and micro-plankton (CLSM; [START_REF] Karsenti | A holistic approach to marine eco-systems biology[END_REF]see Methods). Analysis of samples from station 66 in the Benguela current (off South Africa) led to the initial observation of high abundances of the Fragilariopsis doliolus -Salpingella sp. consortia (Figure S4.1 & Figure S4.2). We identified the V9 subregion of the 18S rDNA for both partners in the consortia and traced their worldwide abundance across the Tara Oceans metabarcoding data set, revealing eleven stations in which both organisms were highly abundant (see Methods). By examining those new samples through single cell microscopy, we investigated the morphological specificity of the consortia and whether mechanical adaptations permitted the physical attachement of the two partners. Through single cell sequencing, we addressed the phylogenetic diversity of the organisms including if the morphological features and geographic location of the partners involved had a signature at the genetic level. Finally, we examined in which environmental conditions the association occurred, hypothesing that the gain of motility for diatoms and protection for the ciliates was likely to happen in predator abundant nutrient rich samples.

Results

Morphological diversity of diatom-tintinnid consortia

Diatom-associated ciliates displayed diverse morphologies, of at least four different species of tintinnid ciliates.

Phylogenetic identification of the interacting partners

A total of 25 Fragilariopsis doliolus -Salpingella spp. individual consortia were isolated by advanced micromanipulation from eleven stations identified through a data driven approach (see Methods). Following DNA extraction, group specific PCR amplification was performed to sequence the 18S, ITS, 5.8S, and partial 28S molecular markers for both the diatom and the ciliate. Sequences were obtained for at least one of the partners, yielding sequences for 21 diatoms and 17 tintinnids for which at least one of the markers was obtained (Table 4.1). We genetically identified both partners for 17 different consortia originating from Stations 66, 70, 102, 106, 122 and 124.

Phylogenetic analysis of the tintinnid 18S rDNA confirmed the presence of Salpingella specimens, as all sequences grouped in a monophyletic clade with strong support with all other sequences of the genus (BV 97,6% PP 1). The tintinnid sequences obtained here all grouped together (with low support (BV 61,5% PP 0,56), however the topology and the branch length suggest they might correspond to the same phylogenetic species. We were not able to identify Salpingella faurei, Salpingella acuminata and Salpingella curta at the genetic level in our sequences, despite their reported description in the consortia, or if the sequences correspond to a new species due to the limited resolution of the available sequence information ( S1). Focusing on the region without missing data, three variable positions out of 130 base pairs were identified with respect to other V9 sequences of Fragilariopsis species available on NCBI, in positions 58, 70 and 86.

Fragilariopsis species therefore display a 98% similiarity for this molecular marker. All V9 sequences could be retrieved for the 17 sequenced tintinnids and were complete. Only one significant variable site was reported in position 55 and the sequences displayed a three base pair difference with the V9 sequence available on NCBI of a Salpingella acuminata isolate FG304. Interestingly, all tintinnids belonging to Haplotype 1 (except TI_887) displayed a T in position 55, and all tintinnids belonging to Haplotype 2 displayed a C in position 55. Initial cloning of 18S rDNA amplified from a single clonal diatom chain also revealed individual genetic micro diversity, with at least three different copies of 18S containing 28 nucleotide differences on a 1,246 bp alignment The three 18S copies also happened to be different at the level of the V9 subregion as shown in Table S4, with a maximum distance of four nucleotides difference between two V9 subregions. A single 18S rDNA tintinnid sequence showed at least four different copies of 18S displaying four polymorphic sites. However, three of those copies had an identical V9 sequence.

Geographic distribution and ecological context of the interaction

Amongst the 15 complete diatom V9 sequences obtained, eleven matched to a 100% identity with the Tara Oceans metabarcode f2f8b6bc0f4f3b6be690e0bd0f740a20 (f2f8) ; one diatom V9 matched 100% identity with the barcode 53cf4eb045f0a758fc188f13fcd54504 (53cf), both of which were assigned to Raphid_pennate_X+sp. (i.e. the environmental sequence JQ782062.1.1788_U); three complete V9 from isolated diatoms did not match any barcode in Tara Oceans (Table S1).

F2f8 represented the fourth most abundant unassigned diatom barcode, with 301,093 reads over the 293 samples analysed in [START_REF] Malviya | Insights into global diatom distribution and diversity in the world's ocean[END_REF], representing 24,5% of the abundance of all the Raphid-pennates_X+sp in the top 100 unassigned barcodes. This single barcode, is nearly as abundant as all barcodes assigned to the Pseudo-nitzschia genera (down to 80% of sequence identity) grouped together that represents the 7th most In total, the tintinnid V9 sequences matched at 100% identity to three unique barcodes in the Tara Oceans dataset: b61a7b36be9517d26828b362d8e147e1

(deb2a), all three of which were assigned to Choreotrichia_XX+sp (i.e. an environmental lineage GB GU819299.1.1181_U). A7cbc was particularily abundant and widely distributed, compared to b61a and deb2a, with a total abundance of 106,292 reads over the complete data set ( All the tintinnids belonging to haplotype 1 (except TI_887) displayed a V9 corresponding to b61a, and all the tintinnids belonging to haplotype 2 displayed a V9 corresponding to a7cbc ;

no particular pairing the the V9 sequences of the two partners was observed (Table S2).

Individual barcodes extracted from fractions 20-180 and 180-2000 micron of the surface layer were analysed with respect to the most ecologically relevant oceanographic variables, such as temperature, salinity, phosphate and silica through partial least square analysis Tintinnid barcodes displayed different explanatory variables, but abundances of a7cbc and b61a were best explained by temperature with a negative (-0,4) and positive (0,22) regression coefficient respectively, followed by density (0,27) and chlorophyll (0,25) for a7cbc, chlorophyll (-0,18) and silica (-0,15) for b61a (Table S7c). F2f8 (diatom) and a7cbc

(tintinnid) display an antagonitic response to their major abiotic explanatory variable (temperature), yet the organisms are found paired together in similar samples.

A common feature amongst the majority of samples in which the consortia was observed is their proximity to nutrient rich regions illustrated by, in general, higher nitrite concentrations as shown in Figure S4.4 but also oceanic province. For example, station 66 is located in the Benguela upwelling current, station 70 is situated at the limit of the nitrate plume originating from the Benguala current flowing northwest, stations 102, 106 and 109 are in the Peru current, stations 122 and 124 benefit from the island mass effect of the Marquesas and stations 137 and 138 were sampled in the Californian current.

Beyond abiotic variables, co-occurrence of the two partners and other organisms of the plankton was explored, as tintinnid competitors such as oligotrichs and potential predators such as copepods are known to impact tintinnid distribution [START_REF] Dolan | Microzooplankton Diversity: Relationships of Tintinnid Ciliates with Resources, Competitors and Predators from the Atlantic Coast of Morocco to the Eastern Mediterranean[END_REF]. Their total barcode abundance was assessed in the 150 stations sampled during the Tara Oceans expedition and compared with the occurrence of the interaction (Figure S4.5). Barcode abundance of potential competitors did not appear to be particularly relevant as it did not display an overlapping pattern with the diatom-tintinnid association; however, copepod abundance in fractions 20-180 and 180-2000 microns was found to be high in samples in which the association was found. We investigated the occurrence of the molecular sequences from our isolated interaction within the global ocean interactome [START_REF] Lima-Mendez | Determinants of Community Structure in the Global Plankton Interactome[END_REF]. One tintinnid barcode appeared and was a7cbc (Figure S4.6), which cooccurred significantly with Eutintinnus (found in association with Fragilariopsis in our samples) and Amphorides ciliates. Both diatom barcodes positively correlated with Salpingella were assigned to Bacillariophyta_X and Ditylum, and were highly divergent from

Fragilariopsis doliolus based on the V9 sequence. Our conclusion is that a co-occurrence network at a large scale was inefficient in recovering this biotic interaction.

Finally, the ecological importance of the diatom-tintinnid association was quantified through actual cell counts in four different samples based on the same data driven process (Figure between lugol-and formol-fixed samples showed high discrepancy, the association typically being much represented in acidic lugol, which is not effective in preserving colonies either.

Fixative bias is well documented for tintinnid cells [START_REF] Modigh | Effects of Fixatives on Ciliates as Related to Cell Size[END_REF], but also appears to affect the detection of biotic interactions.

Discussion

Using a data-driven approach enabled us to efficiently target individual samples for further study and characterize at a global scale the epibiotic association between Fragilariopsis doliolus and tintinnid ciliates. Microscopic analysis revealed high morphological diversity of tintinnids involved in the consortia, enveloped in a barrel shaped diatom chain. Different stages of the association were found, showing a tintinnid fully or partially encapsulated by

Fragilariopsis doliolus, either due to an early stage of the association, or an unbalanced diatom/tintinnid length ratio. At each stage, the diatom and tintinnid displayed intact cell content that could signal a potential benefit of the association for both partners. No mechanical adaptation to attachment was observed, suggesting that the association is maintained by the secretion of adhesive extracellular polymeric substances (EPS) by the diatom, as already described in other raphid pennate diatoms and reviewed in [START_REF] Wetherbee | Minireview-The First Kiss: Establishment And Control Of Initial Adhesion By Raphid Diatoms[END_REF], an idea reinforced by the quantitative discrepancy between formol and lugol fixed samples.

Single pair sequencing and 18S rDNA phylogeny obtained for 17 specimens confirmed the genus level assignation of the tintinnids, that grouped with and within Salpingella spp.

reference sequences. The ITS+5.8S+28S tintinnid revealed two major haplotypes were concomitant with a unique polymorphic site in the V9 subregion of the 18S rDNA marker,

showing Haplotype 2 as more abundant than Haplotype 1. However, these markers were not sufficient to identify morphologically characterized tintinnids. Diatom sequences formed a monophyletic clade strongly supported based on ITS+5.8S+28S alignement, confirming the good potential of those markers to study Fragilariopsis species (notably in comparison to the 18S rDNA sequences). However, the V9 subregion showed three polymorphic sites when compared to other Fragilariopsis species available on NCBI. A classic 97% identity clustering of OTUs from a metabarcoding survey would have certainly grouped together V9 sequences from Fragilariopsis doliolus and Fragilariopsis curta or Fragilariopsis cylindrus, yet none of the two last species could possibly interact the way Fragilariopsis doliolus does.

The biogeography of both partners was recovered by identifying the V9 in the Tara Oceans global metabarcoding data set. Fragilariopsis doliolus is the fourth most abundant Raphid-Pennate in Tara Oceans, occurring in both North and South hemispheres, coastal and open ocean regions as well as eutrophic, and oligotrophic waters. A similar distribution was observed for barcodes assigned to Salpingella with an additional occurrence in the Mediterranean Sea, suggesting that when both partners co-occur, they associate to form the described consortia. The most abundant barcodes of diatoms and tintinnids differed in their principal abiotic explanatory variables and opposite preferences to temperature. The fact the organisms co-occur, despite antagonistic responses to abiotic drivers, illustrates the importance of studying biotic interactions to understand how they structure the community, as was already highlighted in [START_REF] Lima-Mendez | Determinants of Community Structure in the Global Plankton Interactome[END_REF]. Analysing the presence of other members of the community such as competitors and predators can provide insight, in our case showing the association was concomitant with the abundance of copepods that feed both on diatoms and tintinnids. This leads to the remaining missing link with respect to the study of the diatom epibiotic assemblage involving the ecological and evolutionary implications of this association.

Pennate diatoms and tintinnid assemblages have been reported periodically for decades, in a wide range of ecological contexts from sub-antarctic regions to equatorial waters, involving different species of tintinnids and diatoms. The respective benefits for each partner to undergo such an association are difficult to assess but a few advantages both for the diatom and the tintinnid can be envisaged. Diatoms use nutrients from the surface layer and assimilate them through photosynthesis, which can lead to the production of exudates of particulate organic matter that tintinnids can potentially benefit from [START_REF] Gügi | Diatom-Specific Oligosaccharide and Polysaccharide Structures Help to Unravel Biosynthetic Capabilities in Diatoms[END_REF].

It should also be noted that tintinnids can feed on diatoms [START_REF] Gowing | Abundance and Feeding Ecology of Larger Protozooplankton in the Ice Edge Zone of the Weddell and Scotia Seas during the Austral Winter. Deep Sea Research Part A[END_REF][START_REF] Verity | The Relative Food Value of Diatoms, Dinoflagellates, Flagellates, and Cyanobacteria for Tintinnid Ciliates[END_REF]. In parallel, the association with diatoms could lower the tintinnid's susceptibility to predation by enveloping the ciliate in an "armour", and increasing the size, as the association occurs in regions where the pressure of potential predation is high. A further hypothesis is that attached ciliates see changes in fluid dynamics of the feeding current, that leads to steeper velocity gradients and higher flow rates close to the lorica [START_REF] Jonsson | Attachment to Suspended Particles May Improve Foraging and Reduce Predation Risk for Tintinnid Ciliates[END_REF].

In return, diatoms may benefit from increased motility by attaching to tintinnids, with potential higher access to nutrients or maintenance in the euphotic layer. However, this last hypothesis is weakened by the occurrence of diatom-tintinnid associations in resource plenty regions. Finally, it is interesting to note that few truly planktonic raphid pennates exist, Pseudo-nitzschia and Fragilariopsis amongst the most cited ones, and that the epiphytic lifestyle of some araphid pennate diatoms is considered as an ancestral stage .

Most of them form chains and colonies in the plankton, a trait that has emerged over and over again in the life history of pennate diatoms [START_REF] Kooistra | Adaptations of Araphid Pennate Diatoms to a Planktonic Existence[END_REF]. Reasons that have been advanced to underlie the low incidence of planktonic pennate diatoms is their sexual reproduction mode involving amoeboid gametes, for which encounter rates would be expected to be decreased in planktonic environments [START_REF] Falkowski | Evolution of Primary Producers in the Sea[END_REF]. Planktonic lineages have overcome this constraint by sinking to the bottom, forming blooms, or aggregating at the surface. Here, Fragilariopsis doliolus demonstrates its capacity to adopt an ancestral lifestyle tailored to the organisms co-occurring with it, potentially increasing their encounter rate. Such a hypothesis could implicate epibiosis as a supplementary strategy to warrant their ecological success.

Further studies are needed to elucidate the nature of the diatom-tintinnid association, although experimentation will be hampered by the absence of cultures of either partner.

Bacterial communities from single and associated partners could be analysed, and modeling of fluxes may enable investigation of biophysical aspects of the association. Transcriptome analysis may allow exploration of bioadhesion-related genes involved in cell adhesion or EPS synthesis. Our study not only characterizes a new diatom species of global ecological importance, but also highlights an abundant and ubiquitous marine microbial interaction by integrating data from the single cell to the global ocean.

Materials and methods

Morphological investigation of the consortium

The sampling strategy used in the Tara Oceans expedition is described in [START_REF] Pesant | Open Science Resources for the Discovery and Analysis of Tara Oceans Data[END_REF], and samples used in the present study are listed in Table S3. The Tara Oceans nucleotide sequences are available at the European Nucleotide Archive (ENA) under the project PRJEB402 and PRJEB6610. (Ex488/Em500-520), respectively. Red autofluorescence of the chlorophyll (Ex638/Em680-700) was also visualized. Image processing and three-dimensional reconstructions were conducted with Fiji [START_REF] Schindelin | Fiji: An Open-source Platform for Biological-image Analysis[END_REF] and IMARIS (Bitplane) software. Amplifications were conducted in the PCR with the following PCR program: initial denaturation step at 98°C for 30s, followed by 28 cycles of 10s at 98°C, 45s at 55°C, 15s at 72°C and final elongation step at 72°C. Amplicons were purified using the Nucleospin Gel and PCR Clean-up (Macherey-Nagel) with two NT3 rinsing due to the small quantities of DNA. In order to recover the partial 18S rDNA of both partners in the interaction, a cloning approach was adopted: purification was followed by a poly-A tailing reaction using GoTaq DNA Polymerase (Promega) and ligated in pGEM-T Easy Vector Systems (Promega). The product was cloned using chemically competent Escherichia coli cells, colonies were selected with blue white IPTG/Xgal and length of the insert was checked by PCR before picking. Plasmids were purified with the Plasmid DNA purification kit (Macherey-Nagel) and double-end sequenced by GATC Sanger sequencing.

CLSM

V9 identification in the Tara Oceans data. Sequencing results were assembled and trimmed to retain the V4 to V9 18S fragment. The V9 fragments obtained from Station 66 were blasted against the Tara Oceans ribotype database and matched as the best result two different diatom barcodes (version of the V9), and three different tintinnid barcodes (Table S4). For each partner, one of the barcodes was particularly abundant and found present in 70, 102, 106, 109, 111, 122, 124, 128, 137, and 139, in fraction 20-180 micron in surface samples. We obtained morphological confirmation for the presence of the diatom Fragilariopsis doliolus associated with three different species of the tintinnid genus Salpingella and one species of Eutintinnus in all those stations. Abundance of the five different barcodes are available in Table S5. S1.

PCR amplification of small subunit rRNA genes, internal transcribed spacers ITS1 and ITS2, and 5.8S rRNA genes. To obtain different phylogenetic ribosomal markers for both partners, initial group specific amplifications were conducted with the Phusion High-Fidelity DNA Polymerase (Finnzymes). Group specific primers were designed and inspired from (Bachy et al., 2013) and[START_REF] Mcdonald | Identifying Pseudo-nitzschia Species in Natural Samples Using Genus-specific PCR Primers and Clone Libraries[END_REF] and are available in Table S6. The PCR mixture (25 μl final volume) contained 2 μl of DNA, 0.35 μM (final concentration) of each primer, 3% of dimethyl sulfoxide and 2× of GC buffer Phusion MasterMix (Finnzymes). Amplifications were conducted in a PCR thermocycler (Applied Biosystems) with the following PCR program: initial denaturation step at 98 °C for 30 s, followed by 37 cycles of 10 s at 98 °C, 30 s at the annealing temperature of 50°C 30 s at 72 °C and final elongation step at 72 °C for 10 min.

Amplicons were purified using Nucleospin Gel and PCR Clean-up (Macherey-Nagel), reamplified using internal primers to cover the 18S, ITS1&2, 5.8S and partial 28S then Sanger sequenced using the ABI-PRISM Big Dye Terminator Sequencing kit (Applied Biosystems). Amplicon sequences of both the diatom and the tintinnid were cleaned, trimmed, and assembled using Sequencher (version 5.4).

Phylogenetic analysis.

For the tintinnid, contigs of the amplicons were obtained, and two matrixes of 18S rDNA and ITS+5.8S+28S rDNA were built, including reference sequences from (Bachy et al., 2012). For diatoms, similar matrixes were built including reference sequences from blast top hits in GenBank and reference sequences from [START_REF] Theriot | A Preliminary Multigene Phylogeny of the Diatoms (Bacillariophyta): Challenges for Future Research[END_REF].

The four matrices were completed with appropriate outgroup sequences, and aligned using MAFFT version 7 [START_REF] Katoh | MAFFT: Iterative Refinement and Additional Methods[END_REF]. Sequences were trimmed using Gblocks [START_REF] Talavera | Improvement of Phylogenies after Removing Divergent and Ambiguously Aligned Blocks from Protein Sequence Alignments[END_REF] as implemented in SeaView (Gouy et al., 2010) allowing for smaller final blocks, gap positions within the final block, and less strict flanking positions.with various degrees of stringent set selection. JmodelTest [START_REF] Darriba | JModelTest 2: More Models, New Heuristics and Parallel Computing[END_REF] was used to determine the best model of nucleotide substitution for each matrix. The general time-reversible model with gamma distribution of rate variation (GTR+G) was selected for the diatom ITS+5.8S+28S, and the tintinnid 18S and ITS+28S tree. Phylogenetic inference was done using PhyML 3.0 [START_REF] Guindon | New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0[END_REF] and robustness of topologies assessed by performing 1000 bootstraps. Bayesian inference analyses were carried out on the tintinnid 18S rDNA tree, with the program MrBayes [START_REF] Huelsenbeck | Bayesian Inference of Phylogeny and Its Impact on Evolutionary Biology[END_REF], with two independent runs and 1,000,000 generations per run. After checking convergence (maximum difference between all bipartitions <0.01) and eliminating the first 3,500 trees (burn-in), a consensus tree was constructed sampling every 100 trees. Trees were visualized and edited using FigTree v.1.4.2. For ITS+5.8S+28S rDNA, a statistical parsimony networks was constructed with TCS software (95% connection limit, 10 or less connection steps, gaps considered as 5th state) [START_REF] Clement | TCS: a computer program to estimate gene genealogies[END_REF], and visualised using TCS. S7c.

Co-occurrence network. The five different V9 barcodes corresponding to isolated organisms were looked up for in the Tara Oceans interactome (Lima-Mendez et al., 2016), in order to characterize the organisms with which the barcodes were detected to be significantly correlated to, both positively and negatively. The network of co-occurrence is shown in S8.

Cell quantification. To assess the ecological importance of the association in terms of abundance, cell/mL counts were performed in four Formol fixed samples as shown in Figure 4.7. Aliquots of formol fixed plankton net material (0.5 -2.0 ml), equivalent to approximately 10 L whole water filtered, were examined in plankton settling chambers using an Inverted Olympus microscope (model IX71) equipped with DIC optics at 200x total magnification. Tintinnid identifications were made based on lorica characteristics and dimensions following standard taxonomic monographs (Kofoid, 1939;Hada, 1938). They were compared to cell/mL counts of total tintinnids encountered in other Tara Oceans samples as shown in Figure S4.7 and raw counts are available in Table S9. Each isolated interaction has a unique identifier starting with "TI_". Visual evidence was obtained either under light microscopy, confocal microscopy or scanning electron microscopy. For each single cell barcoding, at least one of the molecular markers (18S, ITS or partial 28S) was obtained. Regression coefficients are available in Table S7c.

Figures and Supplementary Material

Introduction

Microbial community genomics have opened the way for integrated studies, that connect multiple scales of biological information, from metabolic pathways to ecosystem modeling [START_REF] Delong | Microbial community genomics in the ocean[END_REF] and have rapidly evolved in the past decades thanks to high throughput sequencing and computational advances [START_REF] Raes | Molecular eco-systems biology: towards an understanding of community function[END_REF]. Many of these holistic approaches in plankton biology rely upon population level frameworks. However, understanding biotic interactions at the single-cell level is still in its infancy, which limits the successful integration of information spanning the cell to the ecosystem (Brehm- [START_REF] Brehm-Stecher | Single-cell microbiology: tools, technologies, and applications[END_REF]. Biotic interactions in the plankton can be considered as small systems, so much so that comprehensive approaches ought to be developed for a better understanding of their impact on organismal evolution and function. The study of single biotic interactions in natural samples faces several challenges, amongst which the identification of partners of interactions, the metabolic processes involved, and the importance of host associated bacterial communities [START_REF] Hilton | Genomic deletions disrupt nitrogen metabolism pathways of a cyanobacterial diatom symbiont[END_REF][START_REF] Carotenuto | Insights into the Transcriptome of the Marine Copepod Calanus Helgolandicus Feeding on the Oxylipin-producing Diatom Skeletonema Marinoi[END_REF].

Accurate identification of species is critical for plankton biogeography studies, in particular regarding partners of biotic interactions. For more than a century, identification was ensured by recognition of morphological traits, performed by an expert -but raretaxonomist in best of cases, who's knowledge is both crucial and troublesome to transfer.

On the other hand, the arrival of DNA-based barcoding methods has transformed identification of plankton in a rapid, inexpensive highly resolved task, though it is not without problems either (see Annex A). The discovery of cryptic species -genetic diversity hidden behind apparent morphological homogeneity in one or a group of species -and planktonic species difficult to identify by eye, has put forward the necessity of combining simultaneous morphological and molecular methods for plankton identification [START_REF] Mcmanus | Molecular and morphological methods for identifying plankton: what makes a successful marriage?[END_REF]. This transition from morphological to molecular techniques is accompanied by major challenges in terms of DNA extraction, particularly from formalin-diluted preserved samples, a common fixator used in plankton studies also known to degrade DNA (Bucklin et al., 2004). This brings forward the need to optimize new approaches in order to rapidly and reliably identify partners of in situ isolated interactions, by keeping track of both the morphology and the molecular identification of the partners.

Moreover, if we consider the biotic interaction system as a whole, interest regarding hostassociated bacterial communities arises. Plant microbiomes are critical to host adaptations, and influence plant health as well as productivity (Compant, 2010;Berendsen, 2012). In phytoplankton, host-associated bacteria vary between strains that display different functional traits [START_REF] Kaczmarska | Diversity and Distribution of Epibiotic Bacteria on Pseudonitzschia Multiseries (Bacillariophyceae) in Culture, and Comparison with Those on Diatoms in Native Seawater[END_REF]. It is likely that biotic interactions between two major partners benefit as much from the functions carried out by the partner organisms than from the bacteria associated with them. If, and how, the interaction-associated bacteria vary through space is also hardly explored, as it relies upon large-scale sampling initiatives.

Beyond phylogenetic identification of major partners and bacteria, understanding the metabolic processes engaged in biotic interactions is of key interest, in order to bridge the gap between functional traits and community ecology. Functional genomics have enabled the era of "transcriptomics", that is the sequencing of a targeted or complete set of expressed genes that ultimately track the transcriptional activity of an organism [START_REF] Wang | RNA-Seq: a revolutionary tool for transcriptomics[END_REF]. Current studies of diatom biotic interactions monitor gene expression variation based on co-cultures exposed to a range of changing parameters [START_REF] Carotenuto | Insights into the Transcriptome of the Marine Copepod Calanus Helgolandicus Feeding on the Oxylipin-producing Diatom Skeletonema Marinoi[END_REF].

But if we ultimately seek to understand how biotic interactions regulate the fate and activity of marine microbes, it is timely to investigate gene expression of microbial associations in the natural environment.

By adopting high throughput sequencing of individualized interactions isolated from environmental samples fixed in formol-glutaraldehyde and ethanol, I propose a novel approach to identify both eukaryotic and prokaryotic entities forming a biotic association, based on three different types of diatom associations observed in Tara Oceans samples. I further analyze the full transcriptome of 147 pooled diatom interactions presented in Chapter 4 and provide preliminary results on the functional content of such a consortia.

Materials and methods

Isolation of single diatom interactions and DNA extraction

Based on micropipette manipulation, three different diatom interactions were isolated from Tara Oceans samples fixed in both formol-glutaraldehyde and ethanol. A total of 14 interactions were isolated from formol-glutaraldehyde samples and 40 from ethanol samples (Table S1). DNA was extracted using the MasterPure™ Complete DNA and RNA Purification Kit protocol (Epicenter) adapted to formol and ethanol fixation, respectively.

HTS of unidentified eukaryotic partners and host-associated bacteria

A first batch of interactions isolated from formol-glutaraldehyde preserved samples was PCR amplified with 18S-V9 primers (1389F -1510R, fragment length 170) used in the Tara Oceans expedition (de [START_REF] Vargas | Eukaryotic plankton diversity in the sunlit ocean[END_REF] and sent for high throughput sequencing with duplicate PCR (Table S1), using genomic DNA of the Phaeodactylum tricornutum Pt1 8.6

(CCMP2561) culture as a positive control. Pt1 8.6 is a clonal, axenic culture of a single species of diatom so we expected no traces of contaminants in the results; however, multiple 18S copies are expected. Collaborators in Station Biologique de Roscoff performed sequencing and annotation of this batch.

The second batch of interactions isolated from ethanol preserved samples was amplified with 18S-V9 primers (1389F -1510R length 170) as well as 18S-V4 primers (TAReukF1-TAReukR length 380 [START_REF] Massana | Protistan Diversity in Environmental Molecular Surveys[END_REF]) and triplicates of PCR products were used for sequencing (Table S1). The V9 region was amplified in order to identify the interacting partners in the Tara Oceans metabarcoding dataset, whereas the V4 region was sequenced to improve phylogenetic assignment. Technical replicates were performed to enable rigorous interpretation of amplicon variants [START_REF] Decelle | Intracellular Diversity of the V4 and V9 Regions of the 18S rRNA in Marine Protists (Radiolarians) Assessed by High-Throughput Sequencing[END_REF]. DNA extracts from the same ethanol fixed interactions were used to identify the prokaryotic community by amplifying the 16S-rDNA using the Fuhrman Primers (515F-926R length 411 [START_REF] Parada | Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples[END_REF]). Sequencing was performed using dual indexed primers on miSEQ Illumina platforms (Protocol, J.Raes Lab).

RNA-Seq of diatoms associated to heterotrophic ciliates

Sequencing and assembly. A total of 147 individual diatom-tintinnid associations were isolated in the microscope using micropipettes from ethanol-preserved samples. DNA and RNA were simultaneously extracted from the pooled collection of cells using a combined kit (J. Poulain, Génoscope). After checking that RNA was not contaminated with DNA, cDNA synthesis was performed using the Kit Ovation® RNA-Seq System V2 as concentration of total RNA was approximately 400 picograms, following which an Illumina library was built.

Sequencing was performed in two different runs (1) using 2% of a HiSeq 2500 to validate the Illumina library, and then (2) using 25% of a HiSeq 2500 rapid run in 2*100bp for a higher coverage. Assembly of both sequencing reactions (2% and 25% of rapid run) was performed using Velvet 1.2.07 [START_REF] Zerbino | Velvet: Algorithms for de novo short read assembly using de Bruijn graphs[END_REF] -kmer of length 63, insert size 200 -and Oases 0.2.08 [START_REF] Schulz | Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels[END_REF] to treat transcript isoforms. A second assembly was done using the Trinity Software [START_REF] Haas | De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis[END_REF] as the algorithm used for de novo assembly can highly impact the remapping rate of reads on assembled contigs Taxonomic and functional annotation. Reads assembled with Velvet/Oases were cleaned from ribosomal sequences and taxonomically annotated using the metatranscriptome pipeline developed by Genoscope to assign Tara Oceans metatranscriptomic data (E.

Pelletier). Functional annotation was performed using the InterProScan software [START_REF] Jones | InterProScan 5: genome-scale protein function classification Bioinformatics[END_REF] after transcripts were translated using Transdecoder [START_REF] Haas | De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis[END_REF] that detects different Open Reading Frames (ORFs). InterProScan uses InterPro [START_REF] Mitchell | The InterPro protein families database: the classification resource after 15 year[END_REF], the most up to date database that integrates many sources of functional annotation (Pfam, Gene3D, Panther, ProSite) giving access to protein families, as well as domains that a protein contains. Query sequences were compared with all the members of the database:

the higher the consensus of functional annotation amongst different sources for a given protein, the more reliable it could be considered.

Preliminary results

Abundant and unidentified interactions in the

HTS enables phylogenetic identification of partners in direct biotic interactions

Amplicons obtained from high throughput sequencing of formol-glutaraldehyde fixed samples are shown in the Table S2. Amplicons were analyzed by BLAST against the Tara Oceans metabarcoding database (lineaget) and against NCBI (lineageb). The analysis was done on all the reads, but the table only gives barcodes that have above 100 reads across the interaction replicates. then restricted the analysis to barcodes that co-occurred in the same replicates; a potential candidate was assigned to the Ascomycota phylum of the Fungi kingdom (ff30009a2ecee7eaf0e2e6c2d48c4192), most of which are parasitic, and some of which have adapted to arctic environments [START_REF] Zhang | Diversity and distribution of fungal communities in the marine sediments of Kongsfjorden, Svalbard[END_REF]. The total abundance across 10 replicates was of 247 reads. The same filtering strategy was adopted for the Diatom -Epiphyte interaction (refer to Figure 5.1 D1-E3) with no distinction of both morphotypes.

The absence of a clear conserved phylogenetic signature across a majority of replicates complicates interpretation of the results. Two amplicons were assigned as Chaetoceros rostratus, for a total cumulative abundance of 11 reads across 8 replicates. However, I could not determine the true nature of the epiphytic partners. Sequencing results of ethanol fixed samples for the 18S-V9, 18S-V4 and 16S are not available yet but will certainly confirm or refute the above preliminary candidates and filtering method with less ambiguity. range of functions such as cell adhesion, pathogen recognition and known to be involved in symbiotic interactions in Metazoa [START_REF] Meyer | Study of cnidarian-algal symbiosis in the "omics" age[END_REF] and were recently found in transcriptomic studies of Rhizaria symbiotic taxa [START_REF] Balzano | Transcriptome analyses to investigate symbiotic relationships between marine protists[END_REF]. Many molecular functions related to iron ion binding (GO:0005506) and zinc ion binding (GO:0008270) are found, as well as inorganic cation transmembrane transporter (GO:002290). However, a more refined analysis of diatom specific and tintinnid transcripts is necessary to make reliable conclusions.

Discussion

Species identification despite variants and contaminants

High throughput sequencing (HTS) of diatom biotic interactions amplified with universal primers based on formol-glutaraldehyde samples revealed many contaminant sequences with abundances orders of magnitude higher than expected. Due to the omnipresence of fungal sequences in many other PCR amplified samples fixed in formol-glutaraldehyde (personal communication) it is likely that the initial fixator was the source of contamination, rather than the downstream isolation and DNA extraction for which several rinsing and control of PCR reactants were performed. However, this does not exclude the fact that these steps add additional contamination. Indeed, the positive control using Phaeodactylum tricornutum diatom should show no traces of other DNA except the diatom, yet small amounts of fungal sequences were also present. This positive control was a culture extract, from several cells of a "single" population. Many factors can cause the appearance of the 391 V9 variants: cultures can display high intra-individual variability and multiple 18S copies per individual (Phaedactylum has 3-4 18S copies, personal com.); PCR and sequencing errors can occur. Attributing the different variants to each of these processes has been attempted [START_REF] Kennedy | Evaluating Bias of Illumina-Based Bacterial 16S RRNA Gene Profiles[END_REF] but I would expect all these variants except the most abundant ones to be biases rather than to represent true diversity.

The HTS procedure was successful in recovering barcodes of the partners in the case of the Diatom -Tintinnid consortia. However, they would have been hard to discover without prior knowledge of the sequences, as each replicate contains fungal sequences representing 100 times more reads than the barcodes of interest. It should be noted that these interactions were isolated with manual micropipette, and not the micromanipulator on the inverted microscope (described in Chapter 4), which also adds uncertainties in the cleanliness of the isolation. Future results of isolated interactions from ethanol samples, with the micromanipulator, should be less prone to contamination.

In summary, high throughput sequencing from formol-glutaraldehyde samples can provide taxonomic assignment of individualized interactions, when data is treated with a set of filtering steps based on several decision criteria such as: the plausibility of observing certain reads, prior idea of which taxonomic group the partners belong to, abundance of specific reads, co-occurrence with another identified partner. Ultimately, optimizing this procedure will be necessary for the diatom-parasite interactions, consortia for which fluorescence observation is mandatory (and thus formol-glutaraldehyde fixation) to identify potentially infected cells. I was able to isolate them without fluorescence, yet bright-field pictures are insufficient to distinguish the parasites properly. For other types of eukaryotic interactions, such as diatom-tintinnids, high throughput sequencing from ethanol-fixed samples is likely to be much more effective than cloning, as contamination will certainly be lower and plausible reads easier to detect (unlike parasites or nanoflagellates). Continuous progress in the field of sequencing creates the opportunity to move forward and consider whole genome sequencing of both partners, asking whether this peculiar association leaves imprints in the genome of one of the partners, much like recent exciting discoveries in diatom-diazotroph symbiosis [START_REF] Hilton | Genomic deletions disrupt nitrogen metabolism pathways of a cyanobacterial diatom symbiont[END_REF].

RNA Seq of diatom-tintinnid interactions

The initial idea behind the pooling of different diatom-tintinnid associations was to increase the RNA material. Of course, many bacteria are also likely to be attached to the diatom biofilm, and many nanoflagellates and dinoflagellates can be consumed by the tintinnid. It is therefore expected to find moderate to high eukaryotic and prokaryotic (no oligo-dT selection) diversity in the phylogenetic annotation of the transcripts, as indeed reported.

The initial concentration of RNA present in the sample was barely detectable by Bioanalyzer, around 400 pg total RNA; therefore assessing the quality of RNA was not possible. Yet, we can consider that the identified transcripts represent the functional pool of the association.

To refine the biological interpretation of the transcripts, I plan to map the sequences against both Fragilariopsis and Paramecium transcriptomic sequences (MMETSP; [START_REF] Keeling | The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing[END_REF], and to assign contigs at a finer taxonomic and functional level, subsequently decreasing the proportion of unknown contigs. In particular, I will investigate over-or under-expressed genes with respect to culture conditions of Fragilariopsis cultures. This should reveal additional features with respect to metabolic and functional genes in the consortia.

Investigating transcripts encoding glycoproteins of the extracellular matrix and involved in cell adhesion such as fibronectin, fascin, fasciclin will be of interest; molecular functions such as nutrient and metabolite transport should also be explored. Ultimately, even though this is still extremely challenging, single interaction transcriptomics from ethanol preserved samples could be an extremely exciting future step to apply on the Tara Oceans data [START_REF] Kanter | Single cell transcriptomics: methods and applications[END_REF], and has already been applied on Emiliana huxleyi cultures infected by viruses (Assaf Vardi, personal com). By single cell sequencing of the diatom, the tintinnid, and the individualized consortia, we are likely to gain insight on what the association really brings in terms of cost and benefit. Metabolomics on this association could have brought insight about exchanged metabolites, likewise NanoSIMS analysis from in situ live samples would help understand the microbial activity of both partners, however our case study is likely to be constrained by the non-culturability of both species and the difficulty of obtaining live specimens that will hold back study of the temporal dynamics of such an association. I then plan to explore the spatial distribution of Fragilariopsis-and tintinnidrelated genes in the Tara Oceans metatranscriptomic data, in relationship to the different environmental conditions in which the consortia was observed.

Overall these preliminary results are encouraging, in order to combine the morphological, molecular, functional and ecological characterization of diatom biotic interactions.

Successful high throughput sequencing from formol-glutaraldehyde samples enables simultaneous fluorescence microscopy and molecular identification, even though contamination is present. This is promising for further bacterial and eukaryotic molecular identification from ethanol fixed samples. Moreover, even though a large proportion of transcripts remain unassigned both functionally and taxonomically, many steps remain to be tested that are likely to help us gain insight into the metabolic processes involved in diatom biotic interactions. Ultimately, the combination of microfluidics applied to individualized interactions and single cell genomics make it reasonable to believe that the gap between metabolic pathways, cells, and eco-systems is gradually shrinking.

Chapter 6: Conclusions and perspectives

The goal of my thesis was to explore global scale patterns of diatom biotic interactions in the open ocean, in order to understand how they structure planktonic assemblages. By combining both theoretical and experimental procedures, whilst building upon previous empirical knowledge regarding diatom associations, I provide ecosystem level description of the impact of both direct and indirect interactions on assemblages, in relation with a changing environment. The unprecedented extent of the Tara Oceans data enables questioning in ways and to a scale that have hardly been at reach for marine microbiologists. Developing new methods, approaches, and interpretation of the data is of utmost importance in order to extract the most relevant and significant meaning of it.

A methodology to crack the case of the "unknown" diatom barcodes is proposed in Chapter 2. It is based on a full study of diatom biogeography that has recently been published by Dr.

Malviya -former PhD student and mentor in my lab -revealing global diatom distribution and diversity in the sunlit ocean. Very early whilst learning how to deal with the Tara Oceans data, I have been intrigued by the proportion of "unknown" sequences in the metabarcoding survey, particularly those that had a low percentage identity with any sequence in NCBI; they represented nearly 50% of the data in terms of richness (unique OTU), and sometimes up to 15% percent of a sample's abundance. Moreover, preliminary co-occurrence networks inferred from Tara Oceans data, displayed highly connected nodes barely assigned to "Bacillariophyta", for which further interpretation would reveal itself shaky.

Amongst the top 100 most abundant "unassigned" diatoms, 18 have been assigned just by a careful look at BLAST results in NCBI, revealing that sequences often matched "uncultured stramenopiles" along with a known diatom genera, following which the automated Tara Oceans pipeline would keep the last common ancestor as a precaution. In other cases, the V9 portion of the 18S rDNA was simply insufficient to correctly assign the barcodes, and involved the amplification of larger portions of the corresponding rDNA gene, by following a specifically developed primer design procedure. The assignation of four abundant and ubiquitous diatom barcodes revealed three new genera completely absent from the Tara Oceans assignation: Shionodiscus, Thalassiothrix, Asteromphalus. The last barcode was reassigned to Pseudo-nitzschia delicatissima and curiously found in great abundance in the metabarcoding data set.

The Tara Oceans data is so immense that this primer pipeline would deserve a dedicated online service, which could, depending on the barcode, automatically detect the Tara sample in which the barcode is the most abundant, and based on all the sequences present in the sample, edit a list of optimal primers to test. The scientists would then order such samples (for which 18S pre-amplification has been done), and proceed to a finer assignation of abundant, poorly annotated sequences. I am currently working with Sarah Romac, close collaborator from the Station Biologique de Roscoff, to design primers for eukaryotic sequences that are "unassigned" and are not even related to a supergroup. Moreover, facing the problem of designing species-specific probes that would simultaneously exclude specific classes of organisms, I was somehow surprised that specialists still rely on manual curation of aligned sequences to design their primers. I have therefore developed another pipeline to target a maximum of sequences from alignment A, whilst excluding a maximum of sequences from alignment B. This was tested in silico, but not experimentally and could reveal itself useful in Tara Oceans samples, if one wants to design truly universal diatom primers that would exclude all other eukaryotes in environmental samples. Future research directions involve using the Tara Oceans samples that have been preserved in fixatives for microscopy in order to quantify biotic interactions and confront this with cooccurrence data. It has also recently been demonstrated that a large bias was inherent to the co-occurrence method used to infer the community structure [START_REF] Weiss | Correlation detection strategies in microbial data sets vary widely in sensitivity and precision[END_REF]. I have therefore decided to launch a collaboration to apply different co-occurrence methods to the global Tara Oceans data set and hence provide more robust statements about diatom pairwise associations. Connecting with the research community working on ecological networks is likely to help us interpret co-occurrence networks. Finally, a research topic of interest to my questions would be the impact of metabolic dependencies on species cooccurrence [START_REF] Zelezniak | Metabolic Dependencies Drive Species Co-occurrence in Diverse Microbial Communities[END_REF] that could be predicted using the Tara Oceans metatranscriptomic data.

The Tara Oceans data also provides a unique opportunity to integrate genetic, morphological and functional information at ecosystem scale. By focusing on a single type of diatom direct association involving Fragilariopsis doliolus and heterotrophic ciliates (Chapter 4), I have shown that this consortium occurs worldwide, both in eutrophic and oligotrophic regions, illustrating the success of data-driven decisions, where co-occurrence is a marker of physical association that enables new discovery. However, the interpretation of the true biological consequences of the association remains unknown, as both organisms are not available to culture. Many research directions can help solve this question. Borrowing from physics, modeling fluid dynamics around the consortia can be of use, as it was suggested that ciliates influence fluxes at the microscale to improve feeding rates. Buoyancy could also be tested, as well as protection from grazing. Chapter 5 presents preliminary data that in the future could go a step further to help our understanding of a given consortium (Figure 6.1). Bacterial communities are sequenced, in order to understand if the association also serves as a microbial recruitment vector; for example, the tintinnids that feed on bacteria could benefit from those associated the Fragilariopsis' biofilm. Transcriptomic data has been extracted from the isolated consortia, with the aim of gaining insight into the metabolic functions carried out by the association, such as exchanges of metabolites, and how they vary through environmental conditions.

Our understanding is currently held back by the vast amount of taxonomically and functionally unassigned transcripts, for which better resolved databases and algorithms will help to unlock the situation. Future research directions involve quantification through high throughput microscopy imaging, which could be directly confronted with V9 molecular data.

This systemic approach could also be adopted with other novel biotic interactions observed Species interactions at the individual level have imprints much larger than the microscopic scales on which they occur. With species-specific mechanistic studies at microscale on the one hand, and ecosystem multispecies and biogeochemical cycle modeling on the other, the two ends of the spectrum have been reasonably explored, though much remains to be discovered between the two approaches. However, we currently lack the analytical and theoretical framework to integrate this into one single picture that can bridge this gap. This will certainly require more interdisciplinarity, increased collaboration and communication between scientists, to face the challenges of understanding microbial life in a changing ocean. (a) Typical organisation of tandemly repeated rDNA clusters in eukaryotes. 18S, 5.8S, and 28S ribosomal RNA-encoding genes; ITS1 and ITS2 internal transcribed regions; IGS intergenic regions [START_REF] Zagoskin | Phylogenetic information content of Copepoda ribosomal DNA repeat units: ITS1 and ITS2 impact[END_REF].

Metabarcoding. The impact of barcodes as a molecular tool goes beyond a better-resolved phylogeny of known species, and serves for conservation, species discovery, ecological forensic and community ecology [START_REF] Kress | DNA Barcodes for Ecology, Evolution, and Conservation[END_REF]. Thanks to the arrival of next generation sequencing, the 18S rDNA has become a versatile tool for eukaryotic community ecology through what is now known as metabarcoding, "meta" referring to the fact that barcoding is done on environmental samples. More formally, "DNA metabarcoding refers to the automated identification of multiple species from a single bulk sample containing entire organisms or from a single environmental sample containing degraded DNA (soil, water, faeces, etc.)" [START_REF] Taberlet | Towards Next-generation Biodiversity Assessment Using DNA Metabarcoding[END_REF]. A suitable barcode for metabarcoding studies should, amongst other things (i) correspond to a gene region nearly identical among individuals of the same species, but different between species (ii) be suitable for all chosen taxonomic groups, (iii) allow taxonomic assignment, and (iv) should permit the definition of taxonomic levels [START_REF] Valentini | DNA Barcoding for Ecologists[END_REF]. DNA sequences from complex mixtures of organisms representing different species are obtained through DNA sequencing that localize and simultaneously recover the taxonomic marker gene from the majority of individuals in the sample. Based on sequence similarity, amplicons are then clustered to form Operational Taxonomic Units -OTUs - [START_REF] Blaxter | Defining operational taxonomic units using DNA barcode data[END_REF], a pragmatic proxy to define microbial species. The taxonomic assignment of DNA barcodes from a given environmental sample is done by comparing the amplified sequence with reference databases, typically obtained from organisms grown in culture (Figure A2).

A periodic concern with metabarcoding studies is the incomplete and non-representative reference databases used to assign amplified barcodes. It was estimated that Earth is home to as many as one trillion microbial species (10 ^12 ) reducing our knowledge of microbial Biological identifications through DNA barcodes [START_REF] Hebert | Biological Identifications through DNA Barcodes[END_REF].

B. Defining 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 practical, difficult to make microbial interactions fit in one box because many of them are still not mechanistically understood. The words interaction and association are used interchangeably in the dissertation and can be primarily classified in two distinct groups : mutualism and antagonism.

Mutualism involves the exchange of goods and services amongst 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 [START_REF] Bronstein | Our current understanding of mutualism[END_REF].

Emblematic examples in the terrestrial realm are represented by flowering plants and animal pollinators [START_REF] Ollerton | Biological barter": patterns of specialization compared across different mutualisms[END_REF], 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 benefit food and shelter from a bigger host but will not kill it, contrary to parasitoids that kill the host. For instance, the Lithognathus fish is parasitized by the Cymothoa exigua crustacean, that replaces the fish's tongue to feed on its blood and mucus, without apparent damage to the host [START_REF] Brusca | Tongue replacement in a marine fish (Lutjanus guttatus) by a parasitic isopod (Crustacea: Isopoda)[END_REF]. Hosts and parasites coevolve, shaping the evolutionary arms race, in which short generation time of the parasite generally confers an advantage with quicker adaptation [START_REF] Dunne | Parasites Affect Food Web Structure Primarily through Increased Diversity and Complexity[END_REF].

Annexes 209

VINCENT Flora -Doctoral Thesis -2016

A fine-tuned classification of biotic interactions can be based on the combination of the relative positive, negative, or neutral effects that the organisms have on each other (Figure B1). 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 +- [START_REF] Faust | Microbial interactions: from networks to models[END_REF].

Each of these forms can be further (non-exhaustively) 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) [START_REF] Wootton | Measurement of interaction strength in nature[END_REF].

• The degree of specificity: is the interaction between pairs of species (specialists), or pairs of groups (generalists)? Specific mutualism between two species is rare (fig plants, and fig wasps), whereas non-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 (Vazquez, 2004).

Figure 1 . 1 .

 11 Figure 1.1. Marine microbes and the carbon cycle in the ocean. ............................................ Figure 1.2. Marine microbes and the nitrogen cycle in the ocean. .......................................... Figure 1.3. Carbon cycle processes mediated by micro-organisms. ....................................... Figure 1.4. The eukaryotic tree of life populated with protists. ............................................. Figure 1.5. Diatom arrangements. .......................................................................................... Figure 1.6. First illustration of the diatom Tabellaria (Anonymous, 1703). ........................... Figure 1.7. Diatom illustrations. ............................................................................................. Figure 1.8. Diatom thecae. ..................................................................................................... Figure 1.9. Diatom valve, mantle and girdle. .......................................................................... Figure 1.10. Centric diatom structure under electron microscopy. ....................................... Figure 1.11. Pennate diatom structure. ................................................................................. Figure 1.12. Diatom size reduction : the MacDonald-Pfitzer hypothesis. .............................. Figure 1.13. Diatom life cycle. ................................................................................................ Figure 1.14. Secondary endosymbionsis. ............................................................................... Figure 1.15. Estimated timing of divergence of the four major diatom lineages and coincident events in Earth's history. ...................................................................................... Figure 1.16. Diatom Neighbor joining phylogeny. .................................................................. Figure 1.17. Diatoms are involved in a large variety of interactions. ..................................... Figure 1.18. Diatoms, pivotal in marine microbial interactions. ............................................ Figure 1.19. Tara Oceans sampling and analysis pipeline. ..................................................... Figure 1.20. The Tara Oceans route from 2009 to 2013. ....................................................... Figure 3.1. Interaction strength and probability of co occurrence. ....................................... Figure 3.2. Principle of similarity-and regression-based network inference. ........................ Figure 3.3. Challenges in using correlations from metagenomic survey data to infer microbial interactions. ........................................................................................................................... Figure 3.4. An overview of graph types and graph theory metrics. ....................................... Figure 3.5. Camerano's food web (plate 2). ........................................................................... Figure 3.6. Current knowledge of diatom biotic interactions. ............................................. Figure 3.7. Relative proportion of exclusions and co-occurrences of copepods. ................. Figure 3.8. Subnetwork topology of diatoms and major partners. ...................................... Figure 3.9. Barcode level associations of the diatom genus Chaetoceros. .......................... Figure 3.10. Literature confirmed associations from the Tara Oceans interactome. .......... Figure S3.1. Habitats of diatoms involved in known interactions. ....................................... Figure S3.2. Main partners involved in diatom interactions based on the literature. ......... Figure S3.3. Major environmental drivers of diatom edges. ................................................ Figure S3.4. Relative proportion of positive and negative interactions for syndiniales. ...... Figure S3.5. Relative proportion of positive and negative interactions for dinophyceae. ... Figure S3.6. Relative proportion of positive and negative interactions for radiolaria. ........ Figure S3.8. Major diatom groups involved in the Tara Oceans interactome. .................... Figure S3.9. Subnetwork topologies of the top 10 most connected diatoms. .................... Figure S3.10. Distribution of diatom -bacteria interactions in the open ocean. ................. Figure S3.11. Comparison of diatom occurrence in the literature and in Tara Oceans interactome . ........................................................................................................................

Figure

  Figure S3.12. Understudied important interactors. ............................................................. Figure S3.13. Predation pressure on diatoms in the open ocean. ........................................ Figure S3.14. Diatom copresence (positive correlations) in the open ocean. ...................... Figure S3.15. Ocean province significantly drives the observed interactions. .....................Figure 4.1. Diatom-tintinnid couples display high morphological diversity. ........................Figure 4.2. Close up view of the interface between the two organisms. .............................Figure 4.3. Phylogeny of the two partners. ..........................................................................Figure 4.4. Statistical parsimony network with ITS+5.8S+28S rDNA. ...................................Figure 4.5. Spatial distribution of the isolated diatom and tintinnid metabarcodes across the 150 Tara Oceans stations. ....................................................................................................Figure 4.6. Circle of correlation for partial least square regression 2. .................................Figure 4.7. Number of diatom-tintinnid consortia across the ocean based on quantification of cell per mL. ....................................................................................................................... Figure S4.1. Microscopy images of a unique epibiotic assemblage between the diatom Fragilariopsis doliolus and the ciliate Salpingella sp. as initially observed in surface samples of Station 66 in Cape Agulhas. .............................................................................................. Figure S4.2. Tara Oceans 150 stations. ................................................................................ Figure S4.3. Spatial distribution of the V9 sequences obtained by single sequencing of the diatom -tintinnid consortia. ................................................................................................ Figure S4.4. Heatmap of Tara Oceans 150 stations metadata in surface samples of fraction 20-180 micron. ..................................................................................................................... Figure S4.5. Spatial distribution of tintinnid predators and competitors in the Tara Oceans data. ..................................................................................................................................... Figure S4.6. A7cbc co-occurrence network. ......................................................................... Figure S4.7. Tintinnid cell counts in Tara Oceans stations. .................................................. Figure 5.1. Interactions isolated from formol-glutaraldehyde samples. .............................. Figure 5.2. Rank abundance of Phaeodactylum barcodes obtained by high throughput sequencing. .......................................................................................................................... Figure 5.3. Network of Phaeodactylum variants using statistical parsimony. ...................... Figure 5.4. Taxonomic affiliation of non rRNA eukaryotic transcripts of 147 diatom-tintinnid pooled transcriptomes. ........................................................................................................ Figure 5.5. High level GO terms for three major ontology type (level 2 GO). ...................... Figure 5.6. Details of GO from cellular components subtype (level 3 GO). .......................... Figure 5.7. Details of GO from biological processes subtype (level 3 GO). .......................... Figure 5.8. Details of GO from molecular functions subtype (level 3 GO). ..........................Figure 6.1. Towards a comprehensive characterization of microbial interactions based on the Tara Oceans data. ................................................................................................................. Figure A1. Eukaryotic structure of the 18sRNA. ................................................................... Figure A2. Barcoding versus metabarcoding. ....................................................................... Figure B1. Summary of ecological interactions between different species. ........................

Figure 4 . 1 .

 41 Figure S3.12. Understudied important interactors. ............................................................. Figure S3.13. Predation pressure on diatoms in the open ocean. ........................................ Figure S3.14. Diatom copresence (positive correlations) in the open ocean. ...................... Figure S3.15. Ocean province significantly drives the observed interactions. .....................Figure 4.1. Diatom-tintinnid couples display high morphological diversity. ........................Figure 4.2. Close up view of the interface between the two organisms. .............................Figure 4.3. Phylogeny of the two partners. ..........................................................................Figure 4.4. Statistical parsimony network with ITS+5.8S+28S rDNA. ...................................Figure 4.5. Spatial distribution of the isolated diatom and tintinnid metabarcodes across the 150 Tara Oceans stations. ....................................................................................................Figure 4.6. Circle of correlation for partial least square regression 2. .................................Figure 4.7. Number of diatom-tintinnid consortia across the ocean based on quantification of cell per mL. ....................................................................................................................... Figure S4.1. Microscopy images of a unique epibiotic assemblage between the diatom Fragilariopsis doliolus and the ciliate Salpingella sp. as initially observed in surface samples of Station 66 in Cape Agulhas. .............................................................................................. Figure S4.2. Tara Oceans 150 stations. ................................................................................ Figure S4.3. Spatial distribution of the V9 sequences obtained by single sequencing of the diatom -tintinnid consortia. ................................................................................................ Figure S4.4. Heatmap of Tara Oceans 150 stations metadata in surface samples of fraction 20-180 micron. ..................................................................................................................... Figure S4.5. Spatial distribution of tintinnid predators and competitors in the Tara Oceans data. ..................................................................................................................................... Figure S4.6. A7cbc co-occurrence network. ......................................................................... Figure S4.7. Tintinnid cell counts in Tara Oceans stations. .................................................. Figure 5.1. Interactions isolated from formol-glutaraldehyde samples. .............................. Figure 5.2. Rank abundance of Phaeodactylum barcodes obtained by high throughput sequencing. .......................................................................................................................... Figure 5.3. Network of Phaeodactylum variants using statistical parsimony. ...................... Figure 5.4. Taxonomic affiliation of non rRNA eukaryotic transcripts of 147 diatom-tintinnid pooled transcriptomes. ........................................................................................................ Figure 5.5. High level GO terms for three major ontology type (level 2 GO). ...................... Figure 5.6. Details of GO from cellular components subtype (level 3 GO). .......................... Figure 5.7. Details of GO from biological processes subtype (level 3 GO). .......................... Figure 5.8. Details of GO from molecular functions subtype (level 3 GO). ..........................Figure 6.1. Towards a comprehensive characterization of microbial interactions based on the Tara Oceans data. ................................................................................................................. Figure A1. Eukaryotic structure of the 18sRNA. ................................................................... Figure A2. Barcoding versus metabarcoding. ....................................................................... Figure B1. Summary of ecological interactions between different species. ........................

Figure 4 . 2 .

 42 Figure S3.12. Understudied important interactors. ............................................................. Figure S3.13. Predation pressure on diatoms in the open ocean. ........................................ Figure S3.14. Diatom copresence (positive correlations) in the open ocean. ...................... Figure S3.15. Ocean province significantly drives the observed interactions. .....................Figure 4.1. Diatom-tintinnid couples display high morphological diversity. ........................Figure 4.2. Close up view of the interface between the two organisms. .............................Figure 4.3. Phylogeny of the two partners. ..........................................................................Figure 4.4. Statistical parsimony network with ITS+5.8S+28S rDNA. ...................................Figure 4.5. Spatial distribution of the isolated diatom and tintinnid metabarcodes across the 150 Tara Oceans stations. ....................................................................................................Figure 4.6. Circle of correlation for partial least square regression 2. .................................Figure 4.7. Number of diatom-tintinnid consortia across the ocean based on quantification of cell per mL. ....................................................................................................................... Figure S4.1. Microscopy images of a unique epibiotic assemblage between the diatom Fragilariopsis doliolus and the ciliate Salpingella sp. as initially observed in surface samples of Station 66 in Cape Agulhas. .............................................................................................. Figure S4.2. Tara Oceans 150 stations. ................................................................................ Figure S4.3. Spatial distribution of the V9 sequences obtained by single sequencing of the diatom -tintinnid consortia. ................................................................................................ Figure S4.4. Heatmap of Tara Oceans 150 stations metadata in surface samples of fraction 20-180 micron. ..................................................................................................................... Figure S4.5. Spatial distribution of tintinnid predators and competitors in the Tara Oceans data. ..................................................................................................................................... Figure S4.6. A7cbc co-occurrence network. ......................................................................... Figure S4.7. Tintinnid cell counts in Tara Oceans stations. .................................................. Figure 5.1. Interactions isolated from formol-glutaraldehyde samples. .............................. Figure 5.2. Rank abundance of Phaeodactylum barcodes obtained by high throughput sequencing. .......................................................................................................................... Figure 5.3. Network of Phaeodactylum variants using statistical parsimony. ...................... Figure 5.4. Taxonomic affiliation of non rRNA eukaryotic transcripts of 147 diatom-tintinnid pooled transcriptomes. ........................................................................................................ Figure 5.5. High level GO terms for three major ontology type (level 2 GO). ...................... Figure 5.6. Details of GO from cellular components subtype (level 3 GO). .......................... Figure 5.7. Details of GO from biological processes subtype (level 3 GO). .......................... Figure 5.8. Details of GO from molecular functions subtype (level 3 GO). ..........................Figure 6.1. Towards a comprehensive characterization of microbial interactions based on the Tara Oceans data. ................................................................................................................. Figure A1. Eukaryotic structure of the 18sRNA. ................................................................... Figure A2. Barcoding versus metabarcoding. ....................................................................... Figure B1. Summary of ecological interactions between different species. ........................

Figure 4 . 3 .

 43 Figure S3.12. Understudied important interactors. ............................................................. Figure S3.13. Predation pressure on diatoms in the open ocean. ........................................ Figure S3.14. Diatom copresence (positive correlations) in the open ocean. ...................... Figure S3.15. Ocean province significantly drives the observed interactions. .....................Figure 4.1. Diatom-tintinnid couples display high morphological diversity. ........................Figure 4.2. Close up view of the interface between the two organisms. .............................Figure 4.3. Phylogeny of the two partners. ..........................................................................Figure 4.4. Statistical parsimony network with ITS+5.8S+28S rDNA. ...................................Figure 4.5. Spatial distribution of the isolated diatom and tintinnid metabarcodes across the 150 Tara Oceans stations. ....................................................................................................Figure 4.6. Circle of correlation for partial least square regression 2. .................................Figure 4.7. Number of diatom-tintinnid consortia across the ocean based on quantification of cell per mL. ....................................................................................................................... Figure S4.1. Microscopy images of a unique epibiotic assemblage between the diatom Fragilariopsis doliolus and the ciliate Salpingella sp. as initially observed in surface samples of Station 66 in Cape Agulhas. .............................................................................................. Figure S4.2. Tara Oceans 150 stations. ................................................................................ Figure S4.3. Spatial distribution of the V9 sequences obtained by single sequencing of the diatom -tintinnid consortia. ................................................................................................ Figure S4.4. Heatmap of Tara Oceans 150 stations metadata in surface samples of fraction 20-180 micron. ..................................................................................................................... Figure S4.5. Spatial distribution of tintinnid predators and competitors in the Tara Oceans data. ..................................................................................................................................... Figure S4.6. A7cbc co-occurrence network. ......................................................................... Figure S4.7. Tintinnid cell counts in Tara Oceans stations. .................................................. Figure 5.1. Interactions isolated from formol-glutaraldehyde samples. .............................. Figure 5.2. Rank abundance of Phaeodactylum barcodes obtained by high throughput sequencing. .......................................................................................................................... Figure 5.3. Network of Phaeodactylum variants using statistical parsimony. ...................... Figure 5.4. Taxonomic affiliation of non rRNA eukaryotic transcripts of 147 diatom-tintinnid pooled transcriptomes. ........................................................................................................ Figure 5.5. High level GO terms for three major ontology type (level 2 GO). ...................... Figure 5.6. Details of GO from cellular components subtype (level 3 GO). .......................... Figure 5.7. Details of GO from biological processes subtype (level 3 GO). .......................... Figure 5.8. Details of GO from molecular functions subtype (level 3 GO). ..........................Figure 6.1. Towards a comprehensive characterization of microbial interactions based on the Tara Oceans data. ................................................................................................................. Figure A1. Eukaryotic structure of the 18sRNA. ................................................................... Figure A2. Barcoding versus metabarcoding. ....................................................................... Figure B1. Summary of ecological interactions between different species. ........................

Figure 4 . 4 .

 44 Figure S3.12. Understudied important interactors. ............................................................. Figure S3.13. Predation pressure on diatoms in the open ocean. ........................................ Figure S3.14. Diatom copresence (positive correlations) in the open ocean. ...................... Figure S3.15. Ocean province significantly drives the observed interactions. .....................Figure 4.1. Diatom-tintinnid couples display high morphological diversity. ........................Figure 4.2. Close up view of the interface between the two organisms. .............................Figure 4.3. Phylogeny of the two partners. ..........................................................................Figure 4.4. Statistical parsimony network with ITS+5.8S+28S rDNA. ...................................Figure 4.5. Spatial distribution of the isolated diatom and tintinnid metabarcodes across the 150 Tara Oceans stations. ....................................................................................................Figure 4.6. Circle of correlation for partial least square regression 2. .................................Figure 4.7. Number of diatom-tintinnid consortia across the ocean based on quantification of cell per mL. ....................................................................................................................... Figure S4.1. Microscopy images of a unique epibiotic assemblage between the diatom Fragilariopsis doliolus and the ciliate Salpingella sp. as initially observed in surface samples of Station 66 in Cape Agulhas. .............................................................................................. Figure S4.2. Tara Oceans 150 stations. ................................................................................ Figure S4.3. Spatial distribution of the V9 sequences obtained by single sequencing of the diatom -tintinnid consortia. ................................................................................................ Figure S4.4. Heatmap of Tara Oceans 150 stations metadata in surface samples of fraction 20-180 micron. ..................................................................................................................... Figure S4.5. Spatial distribution of tintinnid predators and competitors in the Tara Oceans data. ..................................................................................................................................... Figure S4.6. A7cbc co-occurrence network. ......................................................................... Figure S4.7. Tintinnid cell counts in Tara Oceans stations. .................................................. Figure 5.1. Interactions isolated from formol-glutaraldehyde samples. .............................. Figure 5.2. Rank abundance of Phaeodactylum barcodes obtained by high throughput sequencing. .......................................................................................................................... Figure 5.3. Network of Phaeodactylum variants using statistical parsimony. ...................... Figure 5.4. Taxonomic affiliation of non rRNA eukaryotic transcripts of 147 diatom-tintinnid pooled transcriptomes. ........................................................................................................ Figure 5.5. High level GO terms for three major ontology type (level 2 GO). ...................... Figure 5.6. Details of GO from cellular components subtype (level 3 GO). .......................... Figure 5.7. Details of GO from biological processes subtype (level 3 GO). .......................... Figure 5.8. Details of GO from molecular functions subtype (level 3 GO). ..........................Figure 6.1. Towards a comprehensive characterization of microbial interactions based on the Tara Oceans data. ................................................................................................................. Figure A1. Eukaryotic structure of the 18sRNA. ................................................................... Figure A2. Barcoding versus metabarcoding. ....................................................................... Figure B1. Summary of ecological interactions between different species. ........................

Figure 4 . 5 .

 45 Figure S3.12. Understudied important interactors. ............................................................. Figure S3.13. Predation pressure on diatoms in the open ocean. ........................................ Figure S3.14. Diatom copresence (positive correlations) in the open ocean. ...................... Figure S3.15. Ocean province significantly drives the observed interactions. .....................Figure 4.1. Diatom-tintinnid couples display high morphological diversity. ........................Figure 4.2. Close up view of the interface between the two organisms. .............................Figure 4.3. Phylogeny of the two partners. ..........................................................................Figure 4.4. Statistical parsimony network with ITS+5.8S+28S rDNA. ...................................Figure 4.5. Spatial distribution of the isolated diatom and tintinnid metabarcodes across the 150 Tara Oceans stations. ....................................................................................................Figure 4.6. Circle of correlation for partial least square regression 2. .................................Figure 4.7. Number of diatom-tintinnid consortia across the ocean based on quantification of cell per mL. ....................................................................................................................... Figure S4.1. Microscopy images of a unique epibiotic assemblage between the diatom Fragilariopsis doliolus and the ciliate Salpingella sp. as initially observed in surface samples of Station 66 in Cape Agulhas. .............................................................................................. Figure S4.2. Tara Oceans 150 stations. ................................................................................ Figure S4.3. Spatial distribution of the V9 sequences obtained by single sequencing of the diatom -tintinnid consortia. ................................................................................................ Figure S4.4. Heatmap of Tara Oceans 150 stations metadata in surface samples of fraction 20-180 micron. ..................................................................................................................... Figure S4.5. Spatial distribution of tintinnid predators and competitors in the Tara Oceans data. ..................................................................................................................................... Figure S4.6. A7cbc co-occurrence network. ......................................................................... Figure S4.7. Tintinnid cell counts in Tara Oceans stations. .................................................. Figure 5.1. Interactions isolated from formol-glutaraldehyde samples. .............................. Figure 5.2. Rank abundance of Phaeodactylum barcodes obtained by high throughput sequencing. .......................................................................................................................... Figure 5.3. Network of Phaeodactylum variants using statistical parsimony. ...................... Figure 5.4. Taxonomic affiliation of non rRNA eukaryotic transcripts of 147 diatom-tintinnid pooled transcriptomes. ........................................................................................................ Figure 5.5. High level GO terms for three major ontology type (level 2 GO). ...................... Figure 5.6. Details of GO from cellular components subtype (level 3 GO). .......................... Figure 5.7. Details of GO from biological processes subtype (level 3 GO). .......................... Figure 5.8. Details of GO from molecular functions subtype (level 3 GO). ..........................Figure 6.1. Towards a comprehensive characterization of microbial interactions based on the Tara Oceans data. ................................................................................................................. Figure A1. Eukaryotic structure of the 18sRNA. ................................................................... Figure A2. Barcoding versus metabarcoding. ....................................................................... Figure B1. Summary of ecological interactions between different species. ........................

Figure 4 . 6 .

 46 Figure S3.12. Understudied important interactors. ............................................................. Figure S3.13. Predation pressure on diatoms in the open ocean. ........................................ Figure S3.14. Diatom copresence (positive correlations) in the open ocean. ...................... Figure S3.15. Ocean province significantly drives the observed interactions. .....................Figure 4.1. Diatom-tintinnid couples display high morphological diversity. ........................Figure 4.2. Close up view of the interface between the two organisms. .............................Figure 4.3. Phylogeny of the two partners. ..........................................................................Figure 4.4. Statistical parsimony network with ITS+5.8S+28S rDNA. ...................................Figure 4.5. Spatial distribution of the isolated diatom and tintinnid metabarcodes across the 150 Tara Oceans stations. ....................................................................................................Figure 4.6. Circle of correlation for partial least square regression 2. .................................Figure 4.7. Number of diatom-tintinnid consortia across the ocean based on quantification of cell per mL. ....................................................................................................................... Figure S4.1. Microscopy images of a unique epibiotic assemblage between the diatom Fragilariopsis doliolus and the ciliate Salpingella sp. as initially observed in surface samples of Station 66 in Cape Agulhas. .............................................................................................. Figure S4.2. Tara Oceans 150 stations. ................................................................................ Figure S4.3. Spatial distribution of the V9 sequences obtained by single sequencing of the diatom -tintinnid consortia. ................................................................................................ Figure S4.4. Heatmap of Tara Oceans 150 stations metadata in surface samples of fraction 20-180 micron. ..................................................................................................................... Figure S4.5. Spatial distribution of tintinnid predators and competitors in the Tara Oceans data. ..................................................................................................................................... Figure S4.6. A7cbc co-occurrence network. ......................................................................... Figure S4.7. Tintinnid cell counts in Tara Oceans stations. .................................................. Figure 5.1. Interactions isolated from formol-glutaraldehyde samples. .............................. Figure 5.2. Rank abundance of Phaeodactylum barcodes obtained by high throughput sequencing. .......................................................................................................................... Figure 5.3. Network of Phaeodactylum variants using statistical parsimony. ...................... Figure 5.4. Taxonomic affiliation of non rRNA eukaryotic transcripts of 147 diatom-tintinnid pooled transcriptomes. ........................................................................................................ Figure 5.5. High level GO terms for three major ontology type (level 2 GO). ...................... Figure 5.6. Details of GO from cellular components subtype (level 3 GO). .......................... Figure 5.7. Details of GO from biological processes subtype (level 3 GO). .......................... Figure 5.8. Details of GO from molecular functions subtype (level 3 GO). ..........................Figure 6.1. Towards a comprehensive characterization of microbial interactions based on the Tara Oceans data. ................................................................................................................. Figure A1. Eukaryotic structure of the 18sRNA. ................................................................... Figure A2. Barcoding versus metabarcoding. ....................................................................... Figure B1. Summary of ecological interactions between different species. ........................

Figure 4 . 7 .

 47 Figure S3.12. Understudied important interactors. ............................................................. Figure S3.13. Predation pressure on diatoms in the open ocean. ........................................ Figure S3.14. Diatom copresence (positive correlations) in the open ocean. ...................... Figure S3.15. Ocean province significantly drives the observed interactions. .....................Figure 4.1. Diatom-tintinnid couples display high morphological diversity. ........................Figure 4.2. Close up view of the interface between the two organisms. .............................Figure 4.3. Phylogeny of the two partners. ..........................................................................Figure 4.4. Statistical parsimony network with ITS+5.8S+28S rDNA. ...................................Figure 4.5. Spatial distribution of the isolated diatom and tintinnid metabarcodes across the 150 Tara Oceans stations. ....................................................................................................Figure 4.6. Circle of correlation for partial least square regression 2. .................................Figure 4.7. Number of diatom-tintinnid consortia across the ocean based on quantification of cell per mL. ....................................................................................................................... Figure S4.1. Microscopy images of a unique epibiotic assemblage between the diatom Fragilariopsis doliolus and the ciliate Salpingella sp. as initially observed in surface samples of Station 66 in Cape Agulhas. .............................................................................................. Figure S4.2. Tara Oceans 150 stations. ................................................................................ Figure S4.3. Spatial distribution of the V9 sequences obtained by single sequencing of the diatom -tintinnid consortia. ................................................................................................ Figure S4.4. Heatmap of Tara Oceans 150 stations metadata in surface samples of fraction 20-180 micron. ..................................................................................................................... Figure S4.5. Spatial distribution of tintinnid predators and competitors in the Tara Oceans data. ..................................................................................................................................... Figure S4.6. A7cbc co-occurrence network. ......................................................................... Figure S4.7. Tintinnid cell counts in Tara Oceans stations. .................................................. Figure 5.1. Interactions isolated from formol-glutaraldehyde samples. .............................. Figure 5.2. Rank abundance of Phaeodactylum barcodes obtained by high throughput sequencing. .......................................................................................................................... Figure 5.3. Network of Phaeodactylum variants using statistical parsimony. ...................... Figure 5.4. Taxonomic affiliation of non rRNA eukaryotic transcripts of 147 diatom-tintinnid pooled transcriptomes. ........................................................................................................ Figure 5.5. High level GO terms for three major ontology type (level 2 GO). ...................... Figure 5.6. Details of GO from cellular components subtype (level 3 GO). .......................... Figure 5.7. Details of GO from biological processes subtype (level 3 GO). .......................... Figure 5.8. Details of GO from molecular functions subtype (level 3 GO). ..........................Figure 6.1. Towards a comprehensive characterization of microbial interactions based on the Tara Oceans data. ................................................................................................................. Figure A1. Eukaryotic structure of the 18sRNA. ................................................................... Figure A2. Barcoding versus metabarcoding. ....................................................................... Figure B1. Summary of ecological interactions between different species. ........................

Figure 6 . 1 .

 61 Figure S3.12. Understudied important interactors. ............................................................. Figure S3.13. Predation pressure on diatoms in the open ocean. ........................................ Figure S3.14. Diatom copresence (positive correlations) in the open ocean. ...................... Figure S3.15. Ocean province significantly drives the observed interactions. .....................Figure 4.1. Diatom-tintinnid couples display high morphological diversity. ........................Figure 4.2. Close up view of the interface between the two organisms. .............................Figure 4.3. Phylogeny of the two partners. ..........................................................................Figure 4.4. Statistical parsimony network with ITS+5.8S+28S rDNA. ...................................Figure 4.5. Spatial distribution of the isolated diatom and tintinnid metabarcodes across the 150 Tara Oceans stations. ....................................................................................................Figure 4.6. Circle of correlation for partial least square regression 2. .................................Figure 4.7. Number of diatom-tintinnid consortia across the ocean based on quantification of cell per mL. ....................................................................................................................... Figure S4.1. Microscopy images of a unique epibiotic assemblage between the diatom Fragilariopsis doliolus and the ciliate Salpingella sp. as initially observed in surface samples of Station 66 in Cape Agulhas. .............................................................................................. Figure S4.2. Tara Oceans 150 stations. ................................................................................ Figure S4.3. Spatial distribution of the V9 sequences obtained by single sequencing of the diatom -tintinnid consortia. ................................................................................................ Figure S4.4. Heatmap of Tara Oceans 150 stations metadata in surface samples of fraction 20-180 micron. ..................................................................................................................... Figure S4.5. Spatial distribution of tintinnid predators and competitors in the Tara Oceans data. ..................................................................................................................................... Figure S4.6. A7cbc co-occurrence network. ......................................................................... Figure S4.7. Tintinnid cell counts in Tara Oceans stations. .................................................. Figure 5.1. Interactions isolated from formol-glutaraldehyde samples. .............................. Figure 5.2. Rank abundance of Phaeodactylum barcodes obtained by high throughput sequencing. .......................................................................................................................... Figure 5.3. Network of Phaeodactylum variants using statistical parsimony. ...................... Figure 5.4. Taxonomic affiliation of non rRNA eukaryotic transcripts of 147 diatom-tintinnid pooled transcriptomes. ........................................................................................................ Figure 5.5. High level GO terms for three major ontology type (level 2 GO). ...................... Figure 5.6. Details of GO from cellular components subtype (level 3 GO). .......................... Figure 5.7. Details of GO from biological processes subtype (level 3 GO). .......................... Figure 5.8. Details of GO from molecular functions subtype (level 3 GO). ..........................Figure 6.1. Towards a comprehensive characterization of microbial interactions based on the Tara Oceans data. ................................................................................................................. Figure A1. Eukaryotic structure of the 18sRNA. ................................................................... Figure A2. Barcoding versus metabarcoding. ....................................................................... Figure B1. Summary of ecological interactions between different species. ........................

Figure 1 . 1 .

 11 Figure 1.1. Marine microbes and the carbon cycle in the ocean.Carbon can be transferred from the atmosphere to the ocean depths through the carbon cycle, in which marine microbes play a key role (U.S. JGOFS).

Figure 1 . 2 .

 12 Figure 1.2. Marine microbes and the nitrogen cycle in the ocean. The nitrogen cycle is the biogeochemical cycle by which nitrogen is converted into various chemical forms as it circulates among the atmosphere and terrestrial and marine ecosystems (CMORE).

Figure 1 . 3 .

 13 Figure 1.3. Carbon cycle processes mediated by micro-organisms.Key processes of the marine carbon cycle include the conversion of inorganic carbon (ex: CO 2 ) to organic carbon by phytoplankton (step 1); the release of dissolved organic matter, dissolved organic phosphorous and particulate organic matter by phytoplankton (step 2); the consumption of phytoplankton biomass by zooplankton (step 3) and the mineralization (release of CO 2 via respiration) and recycling of organic matter by diverse heterotrophic bacteria (the microbial loop; step 4). Some heterotrophic bacteria are consumed by zooplankton, and the carbon is transferred up the food web. Heterotrophic bacteria also contribute to the remineralization of organic nutrients, to inorganic forms, which are then available for use by phytoplankton. The microbial carbon pump (step 5) refers to the transformation of organic carbon into recalcitrant dissolved organic carbon that resists further degradation and is sequestered in the ocean for thousands of years. The biological pump (step 6) refers to the export of phytoplankton-derived particulate organic matter from the surface oceans to deeper depths via sinking. Finally, the viral shunt (step 7) describes the contributions of viral-mediated cell lysis to the release of dissolved and particulate matter from both the phytoplankton and bacterial pools[START_REF] Buchan | Master recyclers: features and functions of bacteria associated with phytoplankton blooms[END_REF].

Figure 1 . 4 .

 14 Figure 1.4. The eukaryotic tree of life populated with protists.Phylogenetic breadth among protists. Single-celled species (protists) occur in every supergroup within the domain Eukarya, and constitute the entirety of a number of them(Caron, 2011).

Figure 1 . 5 .

 15 Figure 1.5. Diatom arrangements. (Source: Google Image).

Figure 1 . 6 .

 16 Figure 1.6. First illustration of the diatom Tabellaria (Anonymous, 1703).

Figure 1 . 7 .Figure 1 . 8 .Figure 1 . 9 .

 171819 Figure 1.7. Diatom illustrations.(Haeckel, 1899)

Figure 1 .Figure 1 . 1 . 2 . 1 . 3 .

 111213 Figure 1.10. Centric diatom structure under electron microscopy. (a) Aulacoseira lirata, Ehrenberg -radial (Photo: Hartley, 1986) (b) Stephanopyxis antiqua -radial (Photo: Ekaterina Nikitina) (c) Triceratium synicum -multipolar (Photo: Ekaterina Nikitina) (d) Odontella sp. -multipolar (Photo: PhytoPedia)

Figure 1 .

 1 Figure 1.12. Diatom size reduction : the MacDonald-Pfitzer hypothesis. At each division the new valve is always formed within the parental theca, causing the average size of the frustules in a population to slowly decrease as shown down the left arrow (MacDonald 1869; Pfitzer 1869).

Figure 1 .

 1 Figure 1.13. Diatom life cycle.Adapted from[START_REF] Kooistra | The Origin and Evolution of the Diatoms: Their Adaptation to a Planktonic Existence[END_REF] and[START_REF] Round | Diatoms: biology and morphology of the genera[END_REF].

Figure 1 .

 1 Figure 1.14. Secondary endosymbionsis.Representation of the origin of diatom plastids through sequential primary (a) and secondary (b) endosymbioses, and their potential effects on genome evolution. a| During primary endosymbiosis, a large proportion of the engulfed cyanobacterial genome is transferred to the host nucleus (N1), with few of the original genes retained within the plastid genome. The progenitor plant cell subsequently diverged into red and green algae and land plants, readily distinguished by their plastid genomes. b| During secondary endosymbiosis, a different heterotroph engulfs a eukaryotic red alga. Potential engulfment of a green algal cell as well is indicated with a dashed arrow. The algal mitochondrion and nucleus are lost, and crucial algal nuclear and plastid genes (indicated in blue, purple and pink) are transferred to the heterotrophic host nucleus[START_REF] Armbrust | The life of diatoms in the world's oceans[END_REF].

  although the earliest wellpreserved diatom fossils come from the Early Jurassic, around 190 Myr ago. During the Cretaceous period, between 145 and 65 Myr ago, diatoms are believed to have played an important role in the carbon cycle along with other photosynthetic organisms such as dinoflagellates and coccolithophorids. This was accompanied by an increase of oxygen in the surface waters, and decrease in iron availability that is favorable to algal growth. Multipolar centrics, a major lineage of Bacillariophyta, diverged in the early Cretaceous (Figure 1.15).

Figure 1 .

 1 Figure 1.15. Estimated timing of divergence of the four major diatom lineages and coincident events in Earth's history. Shown above two of the branches are images of the four species for which the whole genome sequence is available: the multipolar centric Thalassiosira pseudonana, and the raphid pennates (from left to right) Pseudo-nitzschia multiseries, Fragilariopsis cylindrus and Phaeodactylum tricornutum. Maps are palaeographic reconstructions of continent locations during the emergence of the diatom lineages. Shallower depths in the ocean are indicated by lighter blues[START_REF] Armbrust | The life of diatoms in the world's oceans[END_REF].

Figure 1 .

 1 Figure 1.16. Diatom Neighbor joining phylogeny.Inferred from maximum likelihood pair-wise distances among nuclear SSU rDNA sequences of various diatom species Sequences of Bolidomonas spp. were included as outgroup. Maximum likelihood calculations were constrained with substitution rate parameters: ADC = 0.9, ADG = 2.4,ADT = 1.2, CDG = 1.1, CDT = 3.8 versus GDT set to 1.0. Assumed proportion of invariable sites along the alignment = 0.4 and a gamma distribution of rates at variable sites with shape parameter (alpha) = 0.5. Assumed nucleotide frequencies: A = 0.25, C = 0.18, G = 0.25, T = 0.32. Bootstrap values (1000 replicates) have been generated using the same settings; values associated to minor clades to the right have been omitted[START_REF] Kooistra | The Origin and Evolution of the Diatoms: Their Adaptation to a Planktonic Existence[END_REF].

Figure 1 .

 1 Figure 1.17. Diatoms are involved in a large variety of interactions. (a)Parasitism by the Chytrid (Zygorhizidium planktonicum) on Asterionella formosa (NIOO) (b) Symbiosis between the diatom Hemiaulus sp. (red: chloroplast) and the N2 fixing cyanobacteria, Richelia (Yellow) (Foster et al., 2011). (c) Chaetoceros costatum and epiphytic ciliate Vorticella oceanica (Sachiko et al., 1996) (d) Copepods feeding on Skeletonema diatom (Hicks et Coull, 1983).

Figure 1 . 18 .

 118 Figure 1.18. Diatoms, pivotal in marine microbial interactions.(+/+), (+/-), (-/-) and (+/-) signs refer to the relative positive, negative, or neutral effect both interacting species can have on each other. The first term refers to the effect of the interaction of the partner of the diatom, and the second to the effect of the interaction on the diatom. See Annex B for further details.

  , global expeditions have led to major scientific breakthroughs, notably with the early voyages of the H.M.S. Beagle (1831-1836) and the H.M.S. Challenger (1872-1876). Ocean exploration now provides promising first steps towards understanding the role of the ocean in global biogeochemical cycles and the impact of global climate change on ocean processes and marine biodiversity. Recently, the Sorcerer II expeditions (2003-2010) (Rusch et al., 2007) and the Malaspina expedition (2010-2011) carried out global surveys of prokaryotic metagenomes from the ocean's surface and bathypelagic layer (>1,000 m), respectively. The Tara Oceans expedition (2009-2013) complemented these surveys by collecting a wide variety of planktonic organisms (from viruses to fish larvae) from the ocean's surface (0-200 m) and mesopelagic zone (200-1,000 m) at a global scale. Overall, Tara Oceans surveyed 210 ecosystems in 20 biogeographic provinces, collecting over 40,000 samples of seawater and plankton. Many questions about marine microbial organisms can be addressed in light of current technologies: what is the real nature of microbial diversity in the ocean? What are the organisms carrying out the most important functions in the ocean? What is the influence of environmental parameters? How do microbial interactions influence the marine ecology and ecosystem? In order to address these questions, the Tara Oceans expedition's goal was to create a publicly available data set able to: visualize, quantify, and genetically characterize ocean biodiversity within entire plankton ecosystems, as well as to find patterns across unprecedentedly comprehensive data types. These objectives involved sampling across multiple depths, and multiple size fractions, as well as state-of-the-art monitoring of environmental parameters (Figure 1.19). As a research infrastructure, the Tara Oceans project mobilised over 100 scientists to sample the world oceans on board a 36 m long schooner (SV Tara) refitted to operate state-of-the-art oceanographic equipment.

Figure 1 .

 1 Figure 1.19. Tara Oceans sampling and analysis pipeline. (A) Methods for sampling organisms by size classes and abundance. The blue background indicates the filtered volume required to obtain sufficient organism numbers for analysis. (B) Methods for analyzing samples. Data on the right are from Tara Oceans sampling stations. (C)High throughput genome sequencing and quantitative image analysis provide evolution, metabolic, and interaction data to build community metabolome maps, taxa/gene networks, and spatial ecosystem models[START_REF] Karsenti | A holistic approach to marine eco-systems biology[END_REF].

Figure 1 .

 1 Figure 1.20. The Tara Oceans route from 2009 to 2013.

Chapter 2 :

 2 Fishing the Unknown Chapter 2 is a reprint of the material as it appears in: Malviya et al., "Insights into global diatom distribution and diversity in the world's ocean." Proceedings of the National Academy of Sciences (2016): 201509523. On this paper I am 4 th of 12 authors.The analysis of diatom metabarcoding data (see Annex C for in-depth description of metabarcoding dataset reported inde Vargas et al., 2015, a paper on which I am 27 th author) emanating from the Tara Oceans expedition, by Dr.Shruti Malviya, revealed that nearly 50% of the OTUs could not be assigned at the genus level. Some of them are abundant, and ubiquitous, therefore representing a strong ecological interest for community ecology. By developing a bioinformatics pipeline and experimentally validating the method taking full advantage of the global scale nature of the data set, I have successfully assigned previously "unknown ribotypes". This was complemented by manual curation of other supposed unassigned diatoms. Overall, this work allowed (1) a better taxonomic identification of diatom OTUs that represented ~ 12% of the total diatom abundance data (1,400,510 reads), (2) the extension of the ecological niche of known diatoms, and (3) a proof of concept to extend this methodology to other unassigned groups.• Chapter 3: Global scale patterns of diatom interactions in the open ocean Chapter 3 is composed of a detailed introduction to the methods related to co-occurrence network inference, followed by a draft manuscript that will be submitted to Frontiers in Microbiology (Section 3.2).Based on the large-scale co-occurrence network published in Lima-Mendez et al., 2015 (a paper on which I was 8 th author; Annex D), my goal was to investigate co-occurrence patterns of diatoms and relate them to diatom biology and biogeography, with the help of graph theory. This chapter results in (1) a literature database of our current knowledge about diatom biotic interactions to detect true positives, and (2) a global scale understanding of how diatoms impact the plankton community structure by acting as repulsive segregators.

  Annex D : the pipeline used to compute the plankton co-occurrence network interpreted in Chapter 3 is available in Lima Mendez et al., 2015, a paper on which I was 8 th author out of 51. Annex E : the oceanic regime of the Agulhas Current mentioned in Chapter 4 is avaiable in Villar et al., 2015, a paper on which I was 33 rd author out of 54.

Figure 5 4 a

 54 on the comparison between V9 barcodes and cell abundance based on Light Microscopy counts data provided by Eleonora Scalco. Insights into global diatom distribution and diversity in the world's ocean Shruti Malviya a,1 , Eleonora Scalco b , Stéphane Audic c , Flora Vincent a , Alaguraj Veluchamy a,2 , Julie Poulain d , Patrick Wincker d,e,f , Daniele Iudicone b , Colomban de Vargas c , Lucie Bittner a,3 , Adriana Zingone b , and Chris Bowler a,Institut de Biologie de l'École Normale Supérieure, École Normale Supérieure, Paris Sciences et Lettres Research University, CNRS UMR 8197, INSERM U1024, F-75005 Paris, France; b Stazione Zoologica Anton Dohrn, 80121 Naples, Italy; c CNRS, UMR 7144, Station Biologique de Roscoff, 29680 Roscoff, France; d Institut de Génomique, GENOSCOPE, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, 91057 Évry, France; e UMR 8030, CNRS, CP5706, 91057 Évry, France; and f UMR 8030, Université d'Evry, CP5706, 91057 Évry, France Edited by Paul G. Falkowski, Rutgers, The State University of New Jersey, New Brunswick, NJ, and approved January 26, 2016 (received for review May 14, 2015)

Chapter 3 :

 3 Global scale patterns of diatom interactions in the open ocean Summary 3.1. INVESTIGATING MICROBIAL INTERACTIONS AT LARGE SPATIAL SCALE : INTRODUCTION ............................. 3.1.1. BIOTIC INTERACTION AND SPECIES CO-OCCURRENCE .......................................................................... 3.1.2. CO-OCCURRENCE INFERENCE WITH MICROBIAL SURVEY DATA .............................................................. 3.1.3. THE CONTRIBUTION OF GRAPH THEORY ........................................................................................... 3.1.4. INTERPRETATION OF MICROBIAL CO-OCCURRENCE NETWORKS ............................................................ 3.1.5. INSPIRATION FROM ECOLOGICAL NETWORKS AND INSIGHT FROM THE MACRO WORLD ............................. 3.2. DIATOMS ACT AS REPULSIVE SEGREGATORS IN THE OCEAN ................................................................ ABSTRACT ............................................................................................................................................... 3.2.1. INTRODUCTION ........................................................................................................................... 3.2.2. RESULTS .................................................................................................................................... 3.2.3. DISCUSSION ............................................................................................................................... 3.2.4. MATERIALS AND METHODS ......................................................................................................... 3.2.5. FIGURES AND SUPPLEMENTARY MATERIAL ..................................................................................... Diat Diatom interactions in the open ocean

  not sufficient to provide insight into the biotic interactions generating them. Similarly, it has been argued that pairwise interactions between species cannot provide general principles about the dynamics and organization of complex communities at global scale (McGill et al., 2006) (Figure 3.1).

Figure 3 . 1 .

 31 Figure 3.1. Interaction strength and probability of co occurrence. Co-occurrence probability between two different species across biotic interaction space. Biotic interaction space is a representation of all possible types of interactions across two axes, each indicating the direction and the strength of the interaction for each species. Examples are for (A) predation of Oryctolagus cuniculus by Lynx pardinus, (B) hervibory by Odocoileus virginianus, commensalism by (C) Remora brachyptera and Carcharhinus melanopterus, (D) epiphytic bromeliad (fam. Bromeliaceae), and (E) Amphiprion percula and Entacmea quadricolor; examples of mutualism for (F) shelter-defense interaction between Pseudomyrmex ferruginea and Cecropia peltata, of (G) pollination of Helicornia caribaea by Eulampis jugularis and of (H) pollination of Stenocereus thurberi by Leptonycteris curasoae; competition between (I) Panthera leo and Crocuta crocuta and between (J) Swietenia mahagoni individuals; amensalism produced by (K) Penicillium expansum, and parasitism of (L) virus of genus Ebolavirus and (M) Anopheles gambiae mosquito, which is itself host for Plasmodium falciparum. (Morales-Castilla et al., 2015)

Figure 3 . 2 .

 32 Figure 3.2. Principle of similarity-and regression-based network inference.The goal of network inference is to identify combinations of microorganisms that show significant co-presence or mutual exclusion patterns across samples and to combine them into a network. a | Network inference starts from an incidence or an abundance matrix, both of which store observations across different samples, locations or time points. b | Pairwise scores between taxa are then computed using a suitable similarity or distance measure). In contrast to similarity-based approaches, multiple regression can detect relationships that involve more than two taxa. c | In the next step, a random score distribution is generated by repeating the scoring step a large number of times (often 1,000 times or more). The random score distribution computes the P value (that is, the probability of obtaining a score by chance that is equal to or better than the observed score) to measure the significance of the predicted relationship. The P value is usually adjusted for multiple testing with procedures such as Bonferroni or Benjamini-Hochberg.d | Taxon pairs with P values below the threshold are visualized as a network, where nodes represent taxa and edges represent the significant relationships between them. The edge thickness can reflect the strength of the relationship[START_REF] Faust | Microbial interactions: from networks to models[END_REF].

Figure 3 . 3 .

 33 Figure 3.3. Challenges in using correlations from metagenomic survey data to infer microbial interactions.(A) Compositional Effect: In a community with five species, where species 1 and species 2 have uncorrelated absolute abundances, their abundances appear correlated after being normalized into relative abundances. (B) Indirect Correlations: The abundances of species 2 and species 3 are positively correlated not because the two species interact with each other, but because they both interact with species 1 and are negatively correlated with it. (C) The abundances of species 1 and species 2 are not correlated. However, if there are sites where neither of the species is present, the two species can have an observed positive correlation[START_REF] Li | Predicting Microbial Interactions through Computational Approaches[END_REF].

Figure 3 . 4 .

 34 Figure 3.4. An overview of graph types and graph theory metrics.Graphs are composed of nodes and edges, here represented as circles and links between circles, respectively. A) Graphs can be simple, directed, weighted, completely connected or composed of connected components. B) Two graph topologies that differ in their degree distribution are shown: a power law degree distribution and the characteristic bino-mial degree distribution of randomly generated graphs. C) The topology of nodes is characterized by graph theory metrics including degree, clustering coefficient and centrality measures such as betweenness centrality. The number next to each graph corresponds to the metric value of the coloured-in node[START_REF] Perez | Exploring microbial community structure and resilience through visualization and analysis of microbial co-occurrence networks[END_REF].

  keystone species with betweenness centrality and network robustness. In 2014, Lupatini et al., investigated the soil microbiome in Brazilian biomes using correlation network analysis and high throughput sequencing (HTS). Co-occurrence is inferred based on Diat Diatom interactions in the open ocean OTUs grouped by genus (i.e., all OTUs assigned to the same genus are clustered into one, and the abundance is the sum of all those OTUs), arguing that this prevents potential taxonomic misclassification due to sequencing bias, which is debatable. The only score used is the Pearson correlation, no p-value correction for multiple testing was performed, no importance of indirect association was assessed. The resulting network was compared to 1000 random networks using the Erdös-Renyi model to compare the structures, based on average clustering coefficient, average path length, and modularity and assign p-value to topological measures obtained from the original dataset. Significant p-values justified that the original network was non-random, however no clear biological interpretation of the network descriptors is provided. Betweenness centrality, the fraction of shortest paths

  2014 made a good step forward by simulating multispecies microbial communities with known interaction patterns using the generalized Lotka-Volterra dynamics. Co-occurrence networks Diat Diatom interactions in the open ocean were built using different association metrics and evaluated to see how well the networks revealed the underlying interactions, and how experimental and ecological parameters could affect network inference and interpretation. The result show that co-occurrence works under certain conditions, but that they lose interpretability (i.e., decreased specificity) when the effects of habitat filtering become significant. Topological features are associated with keystone species such as betweenness centrality. Through simulations, Berry was able to propose conditions increasing reliability of inference: extensive sampling breadth, collecting intrinsic ecological parameters such as diversity, appropriate association metric used. He showed that networks computed with a low number of sites are susceptible to false positives and that samples should originate from similar environments. He further articulated best practices that are coherent in light of the current methodological challenges: filter out infrequent species and try to have a least 20% similarity between samples and high species richness, have a high coverage sequencing, include as many samples as possible (at least 25), include samples from similar environments to avoid habitat filtering, use absolute abundance if possible, or correct for compositionality, and check indirect correlations. Developing a framework with alternative null models. In 2016, Morueta-Holme et al.,proposed a few interpretations based on simulated interactions at local scale. For example, the degree of each species characterizes the number of its association partners that are either positive or negative associations. The ratio between both can measure the species role, as an attractive aggregator, or a repulsive segregator. The modularity represents the overall structure of the network, indicating the amount of division of the network into clusters that are more densely connected to each other. The modules correspond to subsets of species more likely to be mutually associated. Their probability of co-occurring across local communities is higher as compared with the probability of co-occurring with other species of the whole community. These modules could represent guilds, trophic motifs, coevolved organisms and species. An interesting input from their work is the use of alternative explicit regional null models, taking into account processes such as dispersal and broad scale climate patterns.

Figure 3 . 5 .

 35 Figure 3.5. Camerano's food web (plate 2).Web of interactions between Coleoptera, enemies of Coleoptera, and ecological enemies of those enemies.

  such that interactions are fundamental units for understanding community dynamics, and productivity.Araújo et al., in 2011, suggested that species more exposed to climate change were poorly connected to other species of the network, while species more connected were less exposed. Ecological networks have helped defining keystone species, showing their disappearance could induce co-extinction and entire network collapses very rapidly, especially when confronted with climate change. Mutualistic networks. They typically analyse plant-pollinator interactions, or plant-seed dispersers, by investigating how coevolutionary interactions shape species rich communities. Bascompte et al., 2007, summarised three features regarding mutualistic networks:

3 . 2 .

 32 and our diatoms. Do phylogenetically related diatoms display similar interactions? How do diatom interactions impact diatom distribution? At what scales of space and time should diatom biotic interactions leave an imprint? How does the diatom network structure inform us on functions and stability of the ecosystem? Are the diatom species generalists or specialists in the predators or bacteria they co-occur with? Diat Diatom interactions in the open ocean Diatoms act as repulsive segregators in the ocean Target Journals for the following draft manuscript: Frontiers in Microbiology, Frontiers in Environmental Science, Environmental Microbiology.Diatoms structure the plankton community based on repulsive segregation in the world's oceanFlora VINCENT 1 , Gipsi LIMA-MENDEZ 2 , Karoline FAUST 2 , Jeroen RAES 2 , Chris BOWLER 1 1 Ecole Normale Supérieure, PSL Research University, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), CNRS UMR 8197, INSERM U1024, 46 rue d'Ulm, F-75005 Paris, France VINCENT Flora -Doctoral Thesis -2016AbstractDiatoms are a major component of phytoplankton, responsible for 25% of the annual primary production on Earth. As abundant and ubiquitous organisms, they are known to establish biotic interactions with many other members of the plankton realm, likely contributing to the global community structure and assemblages. Through the analysis of co-occurrence networks derived from the Tara Oceans expedition metagenomic data, and accounting for the importance of biotic and abiotic factors in shaping species' spatial distribution, we show that 13% of diatom pairwise associations are driven by environmental conditions. Diatoms act as repulsive segregators in the ocean, particularly towards harmful organisms such as potential predators and parasites. Genus level analysis supports the fact that abundant diatoms are not necessarily important in structuring the community, that exclusion patterns are species specific, and driven by characteristic distribution patterns. An extensive literature survey of diatom biotic interactions was compiled, of which 18.5% was recovered in the computed network. This is an exciting result that reveals the amount of what remains to be discovered in the field of planktonic biotic interactions.

  are repulsive segregators in the open ocean.Co-occurrence amongst different micro-organisms in the plankton has recently been investigated at a large scale byLima-Mendez et al., 2015 (Annex D). The resulting interactome represents species (nodes), connected by links (edges) that represent either positive or negative associations. Positive associations should be understood as two organisms that are often abundant in the same sample and negative associations as two organisms that are rarely abundant in the same sample. Due to the potential impact of environmental drivers on the co-occurrence of two organisms, the Tara Oceans Interactome also provides insight into the effects of abiotic factors on pairwise correlations. This assessment provides the opportunity to disentangle biotic from abiotic factors at the pairwise level.

Figure S3. 1 ).

 1 In terms of partners involved (FigureS3.2), one third are represented by insects feeding upon diatoms in streams and crustaceans feeding upon diatoms in both marine and freshwater environments. Other principal partners are plants, upon which diatoms attach as "epiphytes," such as Posidonia (seagrass), Potamogeton (pondweed), Ruppia (ditchgrass) and Thalassia (seagrass). Consequently, our knowledge based on the literature produces a highly centralized network containing a few diatoms mainly subject to grazing. Major diatom genera for which interactions are reported in the literature are Chaetoceros spp (215 interactions, marine and freshwater), Epithemia sorex (135 interactions, freshwater), and Cymbella aspera (115 interactions, freshwater). DiatDiatom interactions in the open oceanOverlapping empirical evidence on data driven results reveals gaps in knowledge and extends it to the global oceanIn order to go towards edge annotation of the co-occurrence network, the literature database presented here was used. The occurrence of a specific genera in the literature was compared to its occurrence in the Tara Oceans interactome. On average, the co-occurrence network revealed much more potential links between species than what was reported in the literature. Disparity was especially high for Pseudo-Nitzschia, mentioned in 17 interactions in the literature compared to 307 associations in the interactome. On the other hand, many diatoms involved in several associations in the interactome are absent from the literature, such as Proboscia and Haslea (FigureS3.11& S3.12).Out of 1,533 literature-based interactions, 178 could potentially be found in the Tara Oceans Interactome, as both partners had a representative barcode in the Tara Oceans database. A total of 33 literature-based interactions (18.5% of the literature associations) were recovered in the network at the genus level, representing a total of 289 interactions from the interactome and 209 different barcodes. These 289 interactions represent 6.5% of all the associations involving Bacillariophyta in the Tara Oceans co-occurrence network. By mapping available literature on the co-occurrence network, we can see that the major interactions recovered are those involving competition, predation and symbiosis with arthropods, dinoflagellates and bacteria (Figure3.10). However predation by polychaetes and parasitism by cercozoa and chytrids are missing from the interactome.

3 . 6 .

 36 Literature Data Base. Literature was screened to look for all ecological interactions involving diatoms to establish the current state of knowledge regarding the diatom interactome, both in marine and freshwater environments. Diatom ecological interactions as defined in this paper are a very large group of associations, characterized by (i) the nature of the association defined by the ecological interaction or the mechanism(predation, symbiosis, mutualism, competition, epibiosis), (ii) the diatom involved, and (iii) the partners of the interaction.The protocol to build the list of literature-based interactions was the following (i) collect publications involving diatom associations using (a) the Web of Science query TITLE : (diatom*) AND TOPIC : (symbio* OR competition OR parasit* OR predat* OR epiphyte OR allelopathy OR epibiont OR mutualism) ; (b) Eutils tools to mine Pubmed and extract ID of all publications with the search url http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=diatom+symbi osis&usehistory=y and the same keywords; (c) the get_interactions_by_taxa(sourcetaxon = "Bacillariophyta") function from the RGlobi package (Polen et al., 2014), the most recent and extensive automated database of biotic interactions; and (d) personal mining from other publication browsers and input from experts (ii) extract when relevant the partners of the interactions based on the title and on the abstract for Web of Science, Pubmed and personal references and normalize the label of the interaction based on Globi nomenclature (iii) display KRONA plot with Type of Interaction / Partner Class / Diatom genus / Partner genus_species as shown in Figure Cases of episammic (sand) and epipelon (mud) interactions were not considered as they involved association with non-living surfaces. Relative proportion of co-occurrences and exclusions with respect to major partners and network analysis. All analyses were performed on the published co-occurrence network in Lima-Mendez et al., 2015. Environmental drivers of diatom related edges are available in Table

Figure 3 . 7 .

 37 Figure 3.7. Relative proportion of exclusions and co-occurrences of copepods.All interactions involving copepods were extracted from the Tara Oceans interactome, and only taxonomic groups with over 10 edges were kept. For each group, the relative proportion of positive (green) and negative (red) edges are displayed, and absolute number of edges involved is indicated in the abscissa. Diatoms (Bacillariophyta) are highlighted in blue.

Figure 3 . 6 .

 36 Figure 3.6. Current knowledge of diatom biotic interactions. KRONA plot based on available literature concerning diatom associations, mined and manually curated from Web Of Science, PubMed and Globi (TableS8).

Figure 3 . 9 .

 39 Figure 3.9. Barcode level associations of the diatom genus Chaetoceros. Interactions are colored by the corresponding barcodes that are named according to the identifier in the Tara Oceans metabarcoding data. Partners of interactions are coloured by domain of life (Green: Eukaryotes, Red: Prokaryotes, Black: Archaea). The higher pannel represents positive associations, the lower panel represents exclusions.

Figure 3 .

 3 Figure 3.10. Literature confirmed associations from the Tara Oceans interactome. The biotic interactions known from the literature were searched from in the Tara Oceans interactions. Dotted lines represent negative correlations in the interactome, full lines represent positive correlation in the interactome. Edges are colored by their respective taxonomic labelling in the literature (Purple: predation; Blue: Symbiosis/Parasitism; Red: Competition). Diatoms are represented in red triangles (TableS9for the whole list of recovered edges).

Figure S3. 1 .

 1 Figure S3.1. Habitats of diatoms involved in known interactions.For each available diatom genera in the literature interaction database, habitat was assigned based on available knowledge.

Figure S3. 2 .

 2 Figure S3.2. Main partners involved in diatom interactions based on the literature.

Figure S3. 3 .

 3 Figure S3.3. Major environmental drivers of diatom edges. The methodology used to disentangle the relative influence of abiotic factors on diatom interactions is available in Annex D under "Methods/Indirect taxon edge detection" and was implemented by K.Faust. Environmental Parameters: Phosphate (PO4); Mixed Layer Depth (MLD, layer in which active turbulence homogenizes water, estimated by density -sigma-and temperature); Nitrite (NO2); Light scattering by suspended particles (Beam attenuation 660nm); Backscattering coefficient of particle (bbp470); HPLC Chlorophyll pigment measurement (HPLC-adjusted); Ocean perturbation (Lyapunov exponent), Silicate (SI) and categorical variable for season. A full description of the environmental parameters is available on the PANGEAE website (https://doi.pangaea.de/10.1594/PANGAEA.840718).

Figure S3. 4 .

 4 Figure S3.4. Relative proportion of positive and negative interactions for syndiniales.All the interactions involving syndiniales were extracted from the Tara Oceans Interactome (Lima-Mendez, 2015) and the relative proportion of positive (green) and negative (red) interactions was computed for each major taxonomic group and plotted by decreasing negative edges. The absolute number of interactions between the taxonomic group and syndiniales is given in the legend. Diatoms are the 6 th group from the left. Refer to TableS2

Figure S3. 5 .

 5 Figure S3.5. Relative proportion of positive and negative interactions for dinophyceae. All the interactions involving dinophyceaes were extracted from the Tara Oceans Interactome (Lima-Mendez et al., 2015) and the relative proportion of positive (green) and negative (red) interactions was computed for each major taxonomic group and plotted by decreasing negative edges. The absolute number of interactions between the taxonomic group and Dinophyceaes is given in the legend. Diatom are the 6 th group from the left.

Figure S3. 6 .

 6 Figure S3.6. Relative proportion of positive and negative interactions for radiolaria. All the interactions involving radiolaria were extracted from the Tara Oceans Interactome (Lima-Mendezet al., 2015) and the relative proportion of positive (green) and negative (red) interactions was computed for each major taxonomic group and plotted by decreasing negative edges. The absolute number of interactions between the taxonomic group and radiolaria is given in the legend. Diatoms are the 12 th group from the left.

Figure S3. 7 .

 7 Figure S3.7. Subnetwork descriptors for major groups interacting with diatoms. The diatom network was divided in sub networks based on the taxonomic affiliation of the nodes. For each subnetworks, relevant metrics of network topology were calculated under Cytoscape, for both copresence and exclusion subnetworks. The colours correspond to the measures, the taxonomic groups of the subnetwork is on the top part of the figure, and the type of interaction (positive or negative) in the bottom part of the figure.

Figure S3. 8 .

 8 Figure S3.8. Major diatom groups involved in the Tara Oceans interactome. KRONA plot of the most important diatoms in the Tara Oceans Interactome based on the taxonomic affiliation of nodes. For example, 2% of the diatom interactions involved mutual exclusion between Leptocylindrus and another organism. A total of 81 unique diatom nodes (md5sum) were involved in the interactome.

Figure S3. 9 .

 9 Figure S3.9. Subnetwork topologies of the top 10 most connected diatoms.

Figure S3. 10 .

 10 Figure S3.10. Distribution of diatom -bacteria interactions in the open ocean. Diatom-bacteria interactions were derived from the global interactome. Bacteria involved are listed in abscissa and colored by trophic mode (Pink: unknown; Green:heterotroph; Red: chemoheterotroph; Blue: photoautotroph; Cyan: photoheterotroph; Black: chemoautotroph ). The number of interactions in which they are involved in is represented by the coloured bars.

Figure S3. 11 .

 11 Figure S3.11. Comparison of diatom occurrence in the literature and in Tara Oceans interactome . The interactome contains 32 unique genera of diatoms; 17 are completely absent from the literature (including poorly assigned ribotypes).

Figure S3. 12 .

 12 Figure S3.12. Understudied important interactors. Many diatoms that play an important role in the network are absent from the literature, such as Proboscia (sub arctic diatom). The number indicates the number of edges in the Interactome.

Figure S3. 13 .

 13 Figure S3.13. Predation pressure on diatoms in the open ocean. Selection of Tara Oceans interactions confirmed by the literature, labeled as "eatenBy". Diatoms are red triangles at the bottom part of the graph; potential predators are on the upper part of the graph, colored by their taxonomic group. Green filled lines correspond to positive edges, and red dotted lines correspond to negative edges.

Figure

  Figure S3.14. Diatom copresence (positive correlations) in the open ocean.CIRCOS plot of diatom positive correlations in the Tara Oceans Interactome. Each node was grouped by its labeling (external ring with taxonomic affiliation), and is assigned to a color shown in the inner circle. The inner circle also provides the number of interactions for each taxonomy (Bacillariophyta, in orange, are involved in 350 interactions). The three external circles with stacked bars correspond to incoming (inner), outgoing (middle), and total (external) proportion of edges relative to partners of interaction (In total, Bacillariophyta have 17% of their total interactions with Syndiniales in purple).

Figure

  Figure S3.15. Ocean province significantly drives the observed interactions. For each edge in the Tara Oceans Interactome, the importance of sample from a specific ocean province was assessed (Lima-Mendez et al., 2015). Here, we see which ocean province are significant for diatom interaction in sub networks of major interacting partners. Indian Ocean [INDO], Mediterranean Sea [MEDI], no driving ocean province [none], Red Sea [REDS], South Atlantic Ocean [SATL], South Pacific Ocean [SPAC]

Figure 4 . 1 (

 41 a-b) shows the typical smooth lorica and trumpet shaped oral opening of Salpingella acuminata pictured in station 137, whereas Figure 4.1 (e-f) displays the less-differentiated oral end and ridged lorica joined in the aboral end, typical of Salpingella faurei extracted from station 66. In the consortia, the diatom was also identified along with Salpingella curta in Station 102 (not shown) and Eutintinnus sp. found in Station 124 on Figure 4.1 (c-d). Morphological features allowed unambiguous and systematic identification of the diatom Fragilariopsis doliolus. In girdle view, rectangular shaped cells are united into curved ribbons by the valve surface (Figure 4.1 (g)). Valves are semilanceolate with bluntly rounded ends, with one straight margin and one broadly curved margin. (Wallich, 1860). Chloroplasts are present on either side of the median trans-apical plane and located close to the convex side of the valve (Figure S4.1 (c)). Scanning electron microscopy (SEM) of the consortia from Station 102 (Figure 4.2 (a)) in the Peruvian upwelling revealed a diatom displaying transverse striae with two alternating rows of poroids, confirming the species assignation to Fragilariopsis doliolus(Medlin and Sims, 1993).Several features of the consortia are noteworthy. Contrary to other cases of diatomtintinnid associations in which lorica are coated with diatom empty frustules, the diatoms had intact cell contents (FigureS4.1 (b)). The tintinnid also showed the ciliate cell within the test, with the exception of Eutintinnus, in which it was most likely the fixative, lugol, formol or formol-glutaraldehyde, that induced cell loss of the ciliate, as it was repeatdly observed containing its cell under light microscope on ethanol fixed samples. Presumptive colony formation of Fragilariopsis doliolus was found, as we observed apparently different and progressive stages, from less than half of the tintinnid being surrounded (Figure 4.1 (f)) to a nearly complete barrel (Figure 4.1 (g)) totally surrounding the ciliate. The diatom -tintinnid size ratio differs strongly depending on the ciliate cell involved, differentiating cases in which a complete ring would either fully hide the ciliate whose lorica opening is generally located just at the end of the diatom cell (Figure 4.1 (e-f-g-h-k-j)), or only partially in which case the diatom barrel is located closer to the oral end than aboral end (Figure 4.1 (a-b-c-di). SEM observations also revealed details of the contact between the two organisms, which apparently consisted of membranelles along the surface of the ciliate test adhering to the intact diatom frustule (Figure 4.2 (a-b)). Conversely, three dimensional scanning reconstructions showed no evidence of further physical attachment structures nor adaptation (Figure 4.2 (c-d-e)).

Figure 4 . 3 (

 43 a)). None of the Fragilariopsis -Eutintinnus isolated consortia were successfully sequenced. The ITS and 28S markers were chosen to perform the diatom phylogeny due to the fact that 18S rDNA is a relatively poor informative marker to distinguish Fragilariopsis species. The diatom sequences of the isolated specimens all branched with and within the Fragilariopsis genus (BV 91%) and formed a monophyletic group (BV 96%) (Figure 4.3 (b)). At this date (22/07/2016) these are the first publicly available and assigned sequences of the diatom Fragilariopsis doliolus. To estimate population divergence among sequences for both taxonomic groups, a finer analysis to estimate divergence among sequences was performed using the ITS1&2, 5.8S and partial 28S sequences (D1 region). The tintinnid sequences reveal a total of eight different haplotypes based on 17 sequences of 721 base pairs length with two major haplotypes composed of sequences originating from stations belonging to distant provinces (Figure 4.4 (a)). No morphological differentiation corresponding to haplotypes was observed, although haplotype 1 generally corresponded to tintinnids with a short-looking lorica under light microscopy and haplotype 2 generally corresponded to a longer shaped morphotype. Overall, two tintinnid sequences (TI_823 and TI_884) displayed more than eight nucleotide differences with these two major haplotypes ; besides these the other tintinnid sequences diverged by less than five nucleotides over a 721 base pair alignment. The diatom ITS+5.8S+28S sequences showed 15 different haplotypes (Figure 4.4 (b)) from 20 sequences, which diverged up to nine nucleotides over a 727 base pair alignment. Diatoms originating from the South Atlantic region of Cape Agulhas (station 66) grouped all together compared to the rest of the sequences extracted from station 70 and from South Pacific Ocean samples. The matching of the pairs revealed no specificity in combination of tintinnid haplotypes along with diatom haplotypes. The sequence of the V9 18S rDNA sub-region (Amaral-Zettler et al., 2009) was trimmed from the amplified contigs for both partners of the consortia. It was recovered for 21 diatoms, although six of them were partial (Table

  abundant diatom genera with 305,115 reads, based on Malviya et al. In light of the extended Tara Oceans dataset (in prep), encompassing 150 stations, f2f8 is the second most prevalent Fragilariopsis sequence found in the open ocean with a total abundance of 414,113 reads. It is distributed across the whole Tara Oceans sampling expedition, with higher abundances reported below the Equator and occurrences both in coastal and open ocean stations (Figure 4.5 (a)). Comparatively, 53cf had a total abundance of 853 reads across the whole Tara Oceans data (Figure S4.3 (c)).

Figure 4 . 5 (

 45 b)). B61a had a total abundance of 24,839 reads and deb2a of 3,660 reads, the second being restricted to the Benguela's upwelling current (Figure S4.3 (a-b)).

(Figure 4 . 6 ).

 46 F2f8 and 53cf barcode abundances in surface samples for fractions 20-180 microns and 180-2000 microns were best explained by temperature (regression coefficient 0.38 and 0.019 respectively), nitrate (0.35 and 0.014) and density (-0.28 and -0.014).

4. 7 ).

 7 The results reveal that diatom-associated Salpingella can represent up to 93.5% of all Salpingella present in a sample and constitute over 50% of the total tintinnid community (Figure S4.7) with 600 diatom-tintinnid associations interactions per ml of net sample from fraction 20-180 µm equivalent, to ~60 interactions/L, such as in station 102 located in the upwelling Peruvian west coastal current. The consortia was also observed off in the North Pacific Equatorial Countercurrent (Station 139), with approximately 200 interactions per ml of net sample from fraction 20-180 µm equivalent to ~20 interactions/L. Interaction counts

.

  Several consortia were analysed from formaldehyde-preserved samples and imaged with confocal laser scanning microscope (Leica TCS SP8), equipped with an HC PL APO 40 × /1.10 W motCORR CS2 objective. Multiple fluorescent dyes were used sequentially to observe the cellular components of the ciliate (host) and the microalgae, such as the nuclei (blue) and the cellular membranes (green) with Hoechst (Ex405/Em420-470) and DiOC6

  the V9 18S rDNA subregion. Single consortia composed of the association between the diatom and the tintinnid were initially isolated from formolglutaraldehyde-fixed surface samples collected by a plankton net (20 -180 μm mesh-size) in Station 66 located in the Agulhas current, in which the association was initially observed as highly prevalent (Figure S4.1). Using a glass micropipette, interactions were rinsed two to three times in a minimum volume of sterile artificial sea water, before being immersed in 300 μL of Tissue and Cell lysis buffer from MasterPure DNA and RNA purification kit (Epicenter), and stored at -20°C. DNA extraction was done following the protocol of the MasterPure DNA and RNA purification kit (Epicenter). Polymerase chain reactions (PCR) using universal-eukaryotic primers V4F 5'-CCAGCASCTGCGGTAATTC -3' and 15101R 5'-CCTTCYGCAGGTTCACCTAC-3' were performed on total DNA extracts. PCR amplifications were conducted with the Phusion High-Fidelity DNA Polymerase (ThermoFisher Scientific). The PCR mixture (25 μL final volume) contained 2 μL of DNA, 0.5 μM (final concentration) of each primer, 3% of dimethyl sulfoxide, 200 μM dNTPs and 5X Phusion HF Buffer.

  is assigned toRaphid-pennate_X+sp. and a7cbcf338bce632eeb1d94a010707449 (a7cbc) is assigned to Choreotrichia_X+sp. The search for these sequences in the global metabarcoding dataset revealed that both were widely distributed and displayed broadly overlapping patterns (Figure4.5). Ten stations were chosen for additional analysis, based on the presence of at least 100 copies of each barcode (f2f8 and a7cbc) in the sample : Stations

  cell isolation. Diatom-tintinnid consortia were isolated from ethanol-preserved samples collected by a plankton net (20 μm mesh-size) in the eleven Tara Oceans stations of the Indian, Atlantic and Pacific Oceans. By using an OLYMPUS IX51 inverted microscope equipped with an Eppendorf's manual microinjection CellTram® Air, single consortia were carefully rinsed exactly three times in 100% ethanol Labtech wells before being taken in picture, and then treated with a protocol for DNA extraction according to steps in the MasterPure DNA and RNA purification kit (Epicenter). Samples used in this study for molecular identification are available in Table

  We investigated the environmental parameters explaining the abundance of the two partners. As variables were shown to be multicollinear, Partial Least Square Regression analysis was conducted on range transformed oceanographic data as predictors and Hellinger transformed abundance data of corresponding barcodes in the Tara Oceans data as responses using the « plsdepot » package in R version 3.3.0. The resulting Circle of Correlation is displayed in Figure 4.6 and regression coefficients are available in Table

Figure S4. 6

 6 Figure S4.6 and correlation values are given in TableS8.

Figure 4 . 1 .

 41 Figure 4.1. Diatom-tintinnid couples display high morphological diversity. (a-b) Salpingella acuminata in Station 137. (c-d) Eutintinnus fraknoi with one or two diatom chains in Station 124. (e-f) Salpingella faurei in which CSLM reveals the presence of potential prey chloroplast (arrow) in the tintinnid lorica in Station 66. (g-h) Advanced status of diatom chain formation taken in Station 102. (i-j-k) Relative position of the lorica opening with respect to the diatom barrel.

Figure 4 . 2 .

 42 Figure 4.2. Close up view of the interface between the two organisms. (a-b) Scanning electron microscopy in Station 102 and (c-d-e) CSLM.

Figure 4 . 3 .Figure 4 . 4 .Figure 4 . 5 .

 434445 Figure 4.3. Phylogeny of the two partners. (a) Maximum likelihood rooted phylogenetic tree of choreotrich SSU-rDNA, based on 1,455 aligned positions. Reference sequences were extracted from(Bachy et al., 2012). Numbers at nodes are bootstrap values in percentage rounded up for 1000 bootstraps (value below 70% are omitted). Bayesian posterior probabilities higher than 0.90 are indicated by filled circles. Accession numbers are provided between brackets. Tintinnid isolates are coloured according to their respective haplotypes based on ITS analysis. (b) Maximum likelihood rooted phylogenetic tree of the diatom ITS+28S-rDNA based on 854 aligned positions. Numbers at nodes are bootstrap values in percentage rounded up for 1000 bootstraps. The branch rooting the tree has been shortened for the more clarity and isolates are coloured according to their respective haplotypes.

Figure 4 . 6 .

 46 Figure 4.6. Circle of correlation for partial least square regression 2.The five diatom and tintinnid barcodes were used as response matrix; tintinnid barcodes are labelled in orange and diatom barcodes labelled in green. The environmental parameters of surface samples in fraction 20-180 and 180-2000 microns were implemented as predictor variables and labelled in blue. Projection of explanatory variables on response variables reflect the sign and amplitude of the regression coefficients of responses on predictors. A7cbc is more abundant in high salinity samples, when the main abiotic driver for b61a7 is high temperature. Regression coefficients are available in TableS7c.

Figure 4 . 7 .

 47 Figure 4.7. Number of diatom-tintinnid consortia across the ocean based on quantification of cell per mL. Cells were counted from formol fixed net sample from 20-180 micron sized fraction, equivalent to 10L of filtered sea water. Four different species of tintinnids were counted when attached to Fragilariopsis doliolus across four different stations in South and North Pacific as well as Benguela current.

Figure S4. 1 . 2 .Figure S4. 3 .Figure S4. 4 .

 1234 Figure S4.1. Microscopy images of a unique epibiotic assemblage between the diatom Fragilariopsis doliolus and the ciliate Salpingella sp. as initially observed in surface samples of Station 66 in Cape Agulhas. The three dimensional (3D) reconstructions of the epibiosis were imaged with confocal laser scanning microscopy (CLSM). Figures (a-d) are taken under the same angle.(a) The diatom exhibits the Fragilariopsis doliolus barrel shape and the tintinnid Salpingella looking lorica. (b) DNA content from living cells revealed by Hoescht fluorescence signal, potential prey nuclei located in the tintinnid are indicated with a white arrox. (c) Chloroplasts of the diatom chain are highlighted by the red chlorophyll autofluorescence and displayed one on either side of the median transapical plane. (d) Membrane marker. (e-f) show both oral and aboral ends of the lorica. (g) Relative position of chloroplasts located on the convex side of the chain and (h) shows the interface between two organisms.

Figure S4. 5 .

 5 Figure S4.5. Spatial distribution of tintinnid predators and competitors in the Tara Oceans data. (a) Absolute abundance of all Copepode barcodes merged together. (b) Abundance of all Oligotrichs barcodes grouped together. Absolute abundance in surface samples of both fractions 20-180 (lower panel) and 180-2000 microns (upper panel). The colours correspond to ocean provinces, as in Figure S4.3(d)..

Figure S4. 6 .

 6 Figure S4.6. A7cbc co-occurrence network.Co-occurrence network extracted from the Tara Oceans interactome[START_REF] Lima-Mendez | Determinants of Community Structure in the Global Plankton Interactome[END_REF]. Each node represents a unique barcodes and is coloured and named by its taxonomic group. Red edges represent mutual exclusions, and green edges represent highly positively correlated barcodes.

Figure S4. 7 .

 7 Figure S4.7. Tintinnid cell counts in Tara Oceans stations. The counts are based on quantification per ml of formol fixed samples in 20-180 micron size fraction, equivalent to 10L of filtered sea water. Total tintinnid counts in blue, diatom associated Salpingella species in red and single Salpingella species in green.

  analytical platforms of the Tara Oceans project involves the high-throughput analysis by confocal laser scanning microscopy of formol-glutaraldehyde-fixed fractions enriched in nano-and micro-plankton. The screening of the samples led to the observations of several diatom interactions of ecological interest. One of them was observed in Tara Oceans Polar Circle samples in the North East of the Kara Sea (Station 188, Lat 78.304 / Long 91.725) and involved a chain-forming diatom (probably a pennate diatom) that showed the presence of potential parasites in high abundance (Figure 5.1 A1-C2) under epi-fluorescence microscopy. In this case, we cannot completely rule out the possibility that such cells may be a consequence of sexual reproduction, although auxospore formation is believed to be restricted to centric diatoms. A second interaction was observed in the oligotrophic Station 72 in the Southern Atlantic Ocean and involved the diatom genus Chaetoceros in association with small epiphytes, from which we could distinguish two major types: one in which the epiphytes were attached to the setae (Figure 5.1 D1-D3), and another in which they were directly attached to the diatom frustule (Figure 5.1 E1-E3).

Figure 5 . 1 .

 51 Figure 5.1. Interactions isolated from formol-glutaraldehyde samples. A1-C2: Interaction involving diatoms and potential parasites in the Arctic. D1-D3: interaction between Chaetoceros sp. and epiphytes attached directly to the setae. E1-E3: interaction between Chaetoceros sp. and epiphytes attached to the frustule. A3-D3-E3: Confocal Laser Scanning Microscope images with nuclei (blue), cellular membranes (green), chlorophyll (red), courtesy S.Colin. A1-B1-C1: Epifluorescence Microscope images, nuclei (blue).

  The positive controls (F15-F16) contained genomic DNA extracted from Phaeodactylum tricornutum Pt1 8.6 (CCMP2561) lowered to a concentration of 10 nanograms /L before 18S-V9 amplification (preserved in water). Out of the four replicates, 391 different barcodes were assigned to CCMP2561 with one highly abundant representative barcode followed by a very skewed rank abundance curve (Figure5.2).

Figure 5 . 2 .

 52 Figure 5.2. Rank abundance of Phaeodactylum barcodes obtained by high throughput sequencing.

Figure 5 . 4 .

 54 Figure 5.4. Taxonomic affiliation of non rRNA eukaryotic transcripts of 147 diatom-tintinnid pooled transcriptomes.

Figure 5 . 5 .

 55 Figure 5.5. High level GO terms for three major ontology type (level 2 GO). Transcripts corresponding to unique proteins were kept to avoid redundancy from InterPro output. The transcripts were assembled with Velvet/Oases.

Figure 5 . 6 .

 56 Figure 5.6. Details of GO from cellular components subtype (level 3 GO).

Figure 5 . 7 .

 57 Figure 5.7. Details of GO from biological processes subtype (level 3 GO).

Figure 5 . 8 .

 58 Figure 5.8. Details of GO from molecular functions subtype (level 3 GO).

Chapter 2

 2 therefore emphasizes the extent to which diatoms are ubiquitous in the open ocean. My previous work on coral symbiosis and parasitic biological control guided my interest towards diatom interactions for my PhD, in order to understand how biotic associations might impact the planktonic community structure at large spatial scale. Several questions directed my research, as to whether the competitive reputation of diatoms would have a signature at large global scale, if abundant diatoms were key players in the community structure, or if specific co-occurrences were driven by a shared preference for a specific niche. Many answers are provided in Chapter 3, demonstrating the fact that diatoms are, along with polycystines, the only supergroup that acts as repulsive segregators and that species co-occurrence is driven by abiotic factors in a minority of cases. It will be important to confront these results with the extended dataset of 126 stations. Ultimately, I would have wished to obtain a form of decision tree to go towards biological edge annotation. Given an edge between two nodes, we could assign a biotic interaction based on the abundance profile, the size fraction, support from the literature and microscopy, correlation value or betweenness centrality of the node. Despite the recent literature applying graph theory to microbial association networks, many challenges remain such as 1) which procedure to use, 2) obstacles to assess statistical significance of computed interactions, 3) confounding factors, 4) the obvious lack of procedure to analyse constructed networks and, 5) most of all, the difficulty to ecologically interpret the global network structural properties.

Figure 6 . 1 .

 61 Figure 6.1. Towards a comprehensive characterization of microbial interactions based on the Tara Oceans data.

  expression in Tara Oceans data in the samples, as is done on diatom -parasitic and diatom -epiphytic interactions, using high throughput approaches.The arrival of high-throughput studies in environmental microbial research has revealed the extent to which our knowledge of marine microbial ecology is scarce. Most environmental studies are based on metabarcoding, the current gold standard for genetic identification, and they regularly report significant proportions of unidentified organisms despite the massive efforts to build up reference databases and control for biases. When a single sample of soil or seawater reveals so many unknown organisms, it makes the empirical knowledge accumulated over the years seem quite limited, most likely because of inclinations towards studying easily cultivable organisms. It nonetheless still gives an incredible estimate of the genetic diversity that is present, even though we often lack any morphological characterization or cultured representatives. In many aspects, the study of interactions in the protistan world seems destined to the same fate. We still lack proper reference databases dedicated to marine interactions, and knowledge is often restricted to robust co-cultures. Co-occurrence networks have flourished in the literature, but have also revealed a lack of interpretability of correlation edges, and even though microbial correlation networks appear as a seductive tool to predict biotic interactions, we still have a long way to go before these methods can reliably and quantitatively propose new associations. However, coupled with empirical knowledge, microbial correlation networks can serve as an excellent way to describe community structure and bring new insights for future research directions of potential key biotic interactions.

Figure A1 .

 A1 Figure A1. Eukaryotic structure of the 18sRNA.(a) Typical organisation of tandemly repeated rDNA clusters in eukaryotes. 18S, 5.8S, and 28S ribosomal RNA-encoding genes; ITS1 and ITS2 internal transcribed regions; IGS intergenic regions[START_REF] Zagoskin | Phylogenetic information content of Copepoda ribosomal DNA repeat units: ITS1 and ITS2 impact[END_REF].

Figure A2 .

 A2 Figure A2. Barcoding versus metabarcoding.Biological identifications through DNA barcodes[START_REF] Hebert | Biological Identifications through DNA Barcodes[END_REF].

Figure B1 .

 B1 Figure B1. 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 +-[START_REF] Faust | Microbial interactions: from networks to models[END_REF].

1.2.1.5. Diatom biogeography and assemblages Biogeography.

  Diatoms are prolific phototrophic organisms, and inhabit the open ocean, polar waters, tropical waters, all fresh water areas, soil, snow, and even glacial ice, for which they have developed different adaptations to survive. These adaptations include strong silica frustules for grazing protection, energy storage under the form of chrysolaminarin and oil, as well as physiological adaptations to maintain positive buoyancy, such as vacuole content less dense than seawater, achieved by containing light ions. But why do diatoms live where they do? Diatoms are drifting organisms, and we poorly understand to what extent

their spatial distribution is controlled by local environmental selection, or if it is limited by dispersal. Cermeño

Table 3 .1.

 3

		Site 1	Site 2	Site 3
	Species 1	30 (0,3%)	30 (0.15%)	30 (0.1%)
	Species 2	70 (0.7%)	170 (0.85%)	270 (0.9%)

Table 3 .1. Compositional bias

 3

Cell value corresponds to absolute number of species counts; relative abundance is indicated in parenthesis. The abundance of species 1 is constant, the abundance of species 2 increases. Even though absolute number of species 1&2 are not correlated, when normalized they are negatively correlated. This is a spurious correlation.

Table 4 .1. Summary table of available information for each investigated station.

 4

	Station	Ocean Province	Picture	N Single	Identifier
				cell	"TI_"
				barcoding
	66	Eastern Africa Coastal Prov.	Yes	6	882, 884, 886,
					887, 888, 890
	70	South Atlantic Gyral Prov.	Yes	1	881
	102	Pacific Equatorial	Yes	5	820,821, 823,
		Divergence Prov.			825
	106	Pacific Equatorial	Yes	4	851, 852, 853,
		Divergence Prov.			854
	109	Pacific Equatorial	Yes	0
		Divergence Prov.
	111	South Pacific Subtropical	Yes	0
		Gyre
	122	South Pacific Subtropical	Yes	2	863, 864
		Gyre
	124	South Pacific Subtropical	Yes	7	805, 807, 808,
		Gyre			811, 813, 814,
					815
	128	South Pacific Subtropical	Yes	0
		Gyre
	137	North Pacific Equatorial	Yes	0
		Countercurrent
	139	North Pacific Equatorial	Yes	0
		Countercurrent

Present address: Biological Oceanography Division, National Institute of Oceanography, Dona Paula, Goa 403 004, India.

Present address: Biological and Environmental Sciences and Engineering Division, Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.

Present address: Sorbonne Universités, Université Pierre et Marie Curie (UPMC), CNRS, Institut de Biologie Paris-Seine (IBPS), Evolution Paris Seine, F-75005 Paris, France.

To whom correspondence should be addressed. Email: cbowler@biologie.ens.fr. This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. 1073/pnas.1509523113/-/DCSupplemental. E1516-E1525 | PNAS | Published online February 29, 2016 www.pnas.org/cgi/doi/10.1073/pnas.1509523113

VINCENT Flora -Doctoral Thesis -2016

DiatDiatom interactions in the open ocean

Department of Microbiology and Immunology, Rega Institute KU Leuven, Herestraat 49,

Acknowledgments

This work was supported by the project OCEANOMICS that has received funding from the French government, managed by the Agence Nationale de la Recherche, under the grant agreement 'Investissements dʹAvenir' ANR-11-BTBR-0008. We thank the European Molecular Biology Laboratory (EMBL, Heidelberg) and more specifically Rainer Pepperkok for providing access to the advanced light microscopy facility (ALMF). We also thank the coordinators and members of the Tara Oceans expedition. We thank Charles Bachy for his precious help on tintinnid phylogeny, Thibaut Pollina for technical development of advanced micromanipulation and Yann Thomas for initial on help on molecular identification of the partners.

Author contributions: S.M., A.Z., and C.B. designed research; S.M., E.S., F.V., and J.P. performed research; S.A., J.P., P.W., and C.d.V. contributed new reagents/analytic tools; S.M., E.S., F.V., A.V., D.I., L.B., and A.Z. analyzed data; S.M. and C.B. wrote the paper; S.A. and C.d.V. provided the eukaryotic v9-18s rDNA metabarcoding dataset; and J.P. and P.W. provided sequencing of the v9-18s rDNA metabarcoding dataset.

The authors declare no conflict of interest. This article is a PNAS Direct Submission.

Freely available online through the PNAS open access option.

Diatom -Copepoda

Diatom -Dinophyceae Diatom -MALV Diatom -MAST

Summary

Abstract

Microbial community genomics have opened the way for integrated studies that connect multiple scales of biological information, from metabolic pathways to ecosystem modeling. However, understanding biotic interactions at the single-cell level is still in its infancy, thus limiting the successful integration of information from the cell to the ecosystem. The study of single biotic interactions in natural samples faces several challenges, amongst which the identification of partners of interactions, the metabolic processes involved and the importance of host associated bacterial communities. By adopting high throughput sequencing of individualized interactions isolated from environmental samples fixed in formol-glutaraldehyde and ethanol, I propose a novel approach to identify both eukaryotic and prokaryotic entities forming biotic associations, based on three different types of diatom associations observed in Tara Oceans samples. I analyze the full transcriptome of 147-pooled diatom interactions presented in Chapter 4 and provide preliminary results on the functional content of such a consortia. each sequence had two to three base pair differences with all the other variants, resulting in this highly clustered network. The number of reads for the most representative Phaeodactylum sp barcode (bd6a2410e9b77fc2dba49500ecafe8a0) was highly variable between two replicates, as illustrated by F15 replicates F15N1 and F15N2, containing 15,243 and 3,693 reads, respectively, or between F16N1 and F16N2, containing 0 and 10,365 reads, respectively (Table S2, Control). Sequences of Mus musculus and Uncultured fungus, considered as contaminants, are present in low abundances with orders of magnitude in read counts 5 to 200 times lower than the representative diatom barcode.

For the Diatom -Tintinnid interaction (Table S2, Diatom-Tinti), reads assigned to Fungi, Uncultured Fungus, Metazoan, Bacteria, were filtered out. Sequences of Uncultured fungus

RNA-Seq of diatoms associated with heterotrophic ciliates

The transcriptome of 147 diatom-tintinnid associations was sequenced, leading to a total of 135 million reads (Table 5.1). As no mRNA isolation nor rRNA depletion was performed prior to library creation, rRNA that compose a large majority of RNA molecules present in a cell are highly abundant (111 million reads). Taxonomic affiliation of rRNA-free transcripts using the Tara Oceans pipeline revealed that over 60% of the transcripts are unassigned, i.e., they do not have a single match in the reference database that enables their taxonomic affiliation. 9% of the transcripts match bacteria. The remaining 31% of transcripts match eukaryotic sequences, representing 5,159 contigs (Figure 5.4). Amongst these, 31% of eukaryotic matches, 30% are unassigned eukaryotes, 20% are fungi, metazoans and dinoflagellates, 23% are assigned to diatoms and 24% to ciliates. After in silico transcription and translation, the 16,498 contigs were translated in 2,674 unique proteins for which the available GO terms are summarized below.

Annexes

A. The contribution of genomics to chart microbial diversity

Massively parallel DNA sequencing made it possible to explore microbial genetic diversity of environmental samples both qualitatively and quantitatively.

Barcoding. Morphological studies have underpinned organisms classification for many decades, based on successive diagnostic features. In recent years, molecular markers (barcodes) have been used for phylogenetic analyses to elucidate the evolutionary history of living organisms, based on the -oversimplified -idea that the further apart two organisms are evolutionarily, the bigger the differences between their genomic sequences. In the strict sense, a DNA barcode is a short sequence taken from a standardized portion of the genome that can be used to identify species, very similar to the barcodes used in supermarkets to equate products with prices. The specific sequence is chosen based on a set of important criteria summarized in [START_REF] Valentini | DNA Barcoding for Ecologists[END_REF] that includes high intra-species similarity balanced with sufficient inter-species dissimilarity, standardized amongst different taxonomic groups, containing enough phylogenetic information.

One such widely used example of a DNA region for reconstructing phylogenies are the genes encoding the ribosomal RNA subunits, rDNAs. rDNAs encode the RNA components of the ribosome (rRNAs) and form two subunits, the large subunit (LSU) and the small subunit (SSU). In most eukaryotes, the 18S rRNA is the small ribosomal subunit, and the large subunit contains three rRNA species (5S, 5.8S, 28S in mammals and 25S rRNA in plants). The rRNA encoding genes are typically organized in clusters and are separated by internal transcribed spacers (ITS1 and ITS2), and an intergenic spacer (Figure A1).

diversity to 0.001% [START_REF] Locey | Scaling laws predict global microbial diversity[END_REF] and it is unrealistic to think that cultivating this diversity is the solution to the problem. For relatively well-studied groups such as diatoms, it was shown that the short V9 portion of the 18S rDNA sequence, a 130 base pair long sequence used in metabarcoding studies, does not always contain sufficient phylogenetic information to discriminate between species (Zimmerman et al., 2014). Obtaining larger DNA fragments including other molecular markers could help solve this issue.

An additional technology-related problem concerns the primer specificity, particularly well illustrated by the "Fuhrman primers", which revealed that a whole class of prokaryotes remained under sequenced based on the falsely universal primers used for the Earth Microbiome Project [START_REF] Parada | Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples[END_REF]. Amplification and sequencing error, as well as clustering (or absence of clustering) of amplicons could furthermore bias the diversity estimates, often overestimating the total amount of individual sequences present in the sample. Beyond diversity estimates, it appears that evaluating abundance of organisms based on high throughput sequencing is prone to error. Indeed, a single diatom cell can display multiple copies of the 18S rDNA, ranging from 61 to 36,896 copies [START_REF] Godhe | Quantification of diatom and dinoflagellate biomasses in coastal marine seawater samples by real-time PCR[END_REF]. Recent comparative studies which confront molecular diversity and abundance estimates with morphological data in diatoms (Zimmerman et al., 2014), ciliates (Bachy et al., 2012;[START_REF] Gong | Extremely high copy numbers and polymorphisms of the rDNA operon estimated from single cell analysis of oligotrich and peritrich ciliates[END_REF] and collodarians [START_REF] Biard | Towards an Integrative Morpho-molecular Classification of the Collodaria (Polycystinea, Radiolaria)[END_REF] help solve these concerns.

• The degree of physical associations: are the partners physically close when they interact? They are 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 [START_REF] Diamond | Agroecology: Ecological processes in sustainable agriculture[END_REF].

Interspecies interactions can be hard to observe in situ, especially in communities of microorganisms, and most of our understanding today comes from terrestrial environments, primarily through studies of plant-parasites, plant-pollinator or macroorganism predation [START_REF] Bascompte | Disentangling the Web of Life[END_REF]. I was in charge of providing all the interpretation regarding diatom interactions in the network, including the in silico search for symbiotic interactions or parasitism, and their particular role in the PFT graph (Figure 3). I was also in charge of providing a table of comparison for diatom interactions between litterature, co-occurrence inference, and light microscopy evidence provided by Sébastien Colin as a means of method assessment, as well as contributing to the literature database used in the Interactome paper. Finally, even though unsuccessful, the search for diatom-related interactions in the fixed samples was done, based on selected inferred interactions.

C. Co-authored

Annexes 233

VINCENT Flora -Doctoral Thesis -2016 To normalize for differences in sequencing effort, V9 rDNA barcode libraries were resampled 50 times for the number of reads corresponding to the smallest library in each size fraction (provided by E.Villar): 0.8 to 5 microns, 776,358 reads; 20 to 180 microns, 1,170,592 reads; and 180 to 2000 microns, 767,940 reads. V9 rDNA barcode counts were then converted to the average number of times seen in the 50 resampling events, and barcodes with less than 10 reads were removed as potential sequencing artifacts. We used downsampled barcode richness (number of distinct V9 rDNA barcodes) as a diversity descriptor because using V9 rDNA barcode abundances to compare plankton assemblages would likely be biased.

The figure was finally replaced by Table S4 of the manuscript.

Annexes 245

VINCENT Flora -Doctoral Thesis -2016

Résumé (français) :

Les diatomées sont des micro-algues unicellulaires, qui jouent un rôle primordial dans l'ecosystème marin. En effet, elles sont responsables de 20% de l'activité photosynthétique sur Terre, et sont à la base de la chaîne alimentaire marine, toujours plus menacée par le changement climatique.

Les diatomées établissent diverses interactions microbiennes avec des organismes issus de l'ensemble de l'arbre du vivant, à travers des méchanismes complexes tels que la symbiose, le parasitisme ou la compétition. L'objectif de ma thèse a été de comprendre comment ces interactions structurent la communauté du plancton, à grande échelle spatiale. Pour ce faire, j'ai développé de nouvelles approches basées sur le jeu de données inédit de Tara

Océans, une expédition mondiale qui a exploré la diversité et les fonctions des microbes marins, en récoltant plus de 40.000 échantillons à travers 210 sites autour du monde.

Grâce à l'analyse de réseaux de co-occurrence microbiens, je montre d'une part que les diatomées agissent comme des « ségrégateurs répulsifs » à l'échelle globale, en particulier envers les organismes potentiellement dangeureux tels que les prédateurs et les parasites, et d'autre part que la co-occurrence des espèces ne s'explique qu'en minorité par les facteurs environnementaux. Grâce à la richesse des données Tara Océans, j'ai par ailleurs permis la charactérisation d'une interaction biotique impliquant une diatomée et un cilié hétérotrophe à l'échelle de l'eco-système, illustrant de surcroît le succès des approches dirigées par les données. Dans l'ensemble, ma thèse contribue à notre compréhension des interactions biotiques impliquant les diatomées, de l'échelle globale à la cellule unique.

Title:

Diatom interactions in the open ocean: from the global patterns to the single cell.

Abstract :

Diatoms are unicellular photosynthetic microeukaryotes that play a critical role in the functioning of marine ecosystems. They are responsible for 20% of global photosynthesis on

Earth and lie at the base of marine food webs, ever more threatened by climate change.

Diatoms establish microbial interactions with numerous organisms across the whole tree of life, through complex mechanisms including symbiosis, parasitism and competition. The goal of my thesis was to understand how those biotic interactions structure the planktonic community at large spatial scales, by using new approaches based on the unprecedented Tara Oceans dataset, a unique and worldwide circumnavigation that collected over 40.000 samples across 210 sites to explore the diversity and functions of marine microbes.

Through the analysis of microbial association networks, I show that diatoms act as repulsive segregators in the ocean, in particular towards potentially harmful organisms such as predators as well as parasites, and that species co-occurrence is driven by environmental factors in a minority of cases. By leveraging the singularity of the Tara Oceans data, I provide a comprehensive characterization of a prevalent biotic interaction between a diatom and heterotrophic ciliates at large spatial scale, illustrating the success of data-driven research.

Overall, my thesis contributes to our understanding of diatom biotic interactions, from the global patterns to the single cell.