are the central topics of this thesis. Here, I focus on the heterotrophic bacterial communities associated with Emiliania huxleyi, a cosmopolitan coccolithophore species. In the introduction of this manuscript the reader is guided through the main concepts and the state of the art in phytoplankton microbiome research, which are necessary for understanding the following chapters. Three chapters then present my experimental research regarding the microbiome of E. huxleyi and other phytoplankton species. Finally, a general discussion synthesizes all the contributions of this thesis work into the current state of the art and proposes new perspectives for a better understanding of phytoplankton microbiomes.

. Another important group is Chloropicophyceae, which has been found as the main green algae in oceanic waters (Lopes dos Santos, Gourvil, et al., 2017; Lopes dos Santos, Pollina, et al., 2017).

. Eukaryotic tree of life with the main phytoplanktonic groups highlighted (purple stars). This schematic tree represents a synthesis of information on morphologic, phylogenetic (based on a few genes from a large diversity of organisms) and phylogenomic analyses (based on many genes from representatives of major lineages). The phytoplankton diversity is distributed in many branches of this tree, mainly Alveolates, Stramenopiles and Archaeplastids.

.

outdoor mesocosms, each microalga harbored taxonomic distinct free-living and algaassociated communities, indicating a niche-based selection (Kimbrel et al., 2019). By inoculating the free-living community from the outdoor mesocosms (one from monoalgal P. tricornutum and one from polyalgal P. tricornutum/M. salina) into axenic P. tricornutum cultures, similar communities emerged, demonstrating that the host has also an important role in its microbiome assembly, which also agreed with the niche-based theory (Kimbrel et al., 2019). In another experiment, these authors inoculated the attached bacterial community in axenic P. tricornutum and submitted this community to several washings and growth cycles in order to keep only the community attached to the microalga. By sequencing the final established community, they detected random patterns in community composition (Kimbrel et al., 2019).

These last results agree with the competitive lottery by showing that the attached community (which was equally able to colonize the surface of P. tricornutum) was randomly selected in different P. tricornutum

INTRODUCTION Preamble

Preamble

The term microbiome nowadays incorporates all the microbial communities (microbiota) associated with an environment and their 'theatre of activity' (structural elements, metabolites/signal molecules, and the surrounding environmental biotic and abiotic conditions) [START_REF] Whipps | Mycoparasitism and plant disease control[END_REF]Berg et al., 2020). This term has evolved together with the microbiome research in order to fit all the elements involved in the interactions between hosts and associated microbial communities (Berg et al., 2020). Although the first microbes' observations are dated from the 17th century by Antonie van Leeuwenhoek of what he called 'animalcules', the onset of microbiome research started approximately 20 years ago with the first 'next-generation' sequencing technologies (Berg et al., 2020). Notably, the microbiome research became popular at both the scientific community and general public levels with the Human Microbiome Project which started in 2007. It is common to hear that the human microbiome is an additional organ which impacts our health and emotions (Baquero and Nombela, 2012). Nowadays, microbiome studies are revolutionizing the way that we understand interactions, functions, and traits between visible and invisible organisms.

Along the decades of microbiome studies much has been discovered about the microbiomes of different taxa, including marine microorganisms such as phytoplankton. These The term "phytoplankton" (from the Greek terms "phyton" or plant and "planktos" or wanderer) was first used in 1897 to describe unicellular photosynthetic organisms that drift on the sunlit layer of the oceans and fresh waters [START_REF] Falkowski | Aquatic Photosynthesis[END_REF][START_REF] Falkowski | The evolution of modern eukaryotic phytoplankton[END_REF]. The phytoplankton comprise organisms from 0.2 to > 200 µm that capture the energy from sunlight and transform carbon dioxide (CO2) into organic matter (i.e., primary production) (Figure 1, step 1) (Field et al., 1998). This organic matter can be dissolved (DOM; < 0.45 µm) or particulate (POM; > 0.45 µm) and is defined into carbon, nitrogen and phosphorus partitions (Figure 1, step 2). In addition to organic matter, the primary production results in the release of oxygen produced from water. Together, the marine phytoplankton contribute to about 50% of the global net primary production (Field et al., 1998). Buchan et al., (2014). Key processes of the marine carbon cycle include the conversion of inorganic carbon (such as CO2) to organic carbon by photosynthetic phytoplankton species (step 1); the release of both dissolved organic matter and particulate organic matter which contain various proportions of carbon, nitrogen and phosphorus (step 2); the consumption of phytoplankton biomass by zooplankton grazers (step 3) and the mineralization (that is the release of inorganic compounds and CO2 via respiration during the catabolism of organic matter) and recycling of organic matter by diverse heterotrophic bacteria (known as the microbial loop) (step 4). A fraction of the heterotrophic bacteria is consumed by zooplankton, and the carbon is further transferred up the food web. Heterotrophic bacteria also contribute to the remineralization of organic nutrients, which are then available for use by phytoplankton. The microbial carbon pump (step 5) refers to the transformation of organic carbon into recalcitrant DOM that resists further degradation and is sequestered in the ocean for thousands of years. The biological pump (step 6) refers to the export of phytoplanktonderived POM 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.

The organic matter released by phytoplankton sustains the aquatic food webs. Part of it can be respired, by grazers (Figure 1 -step 3) and bacteria (Figure 1-step 4). In addition, a portion of the organic matter can sink to the deep oceans as sinking biogenic particles or dissolved organic matter, a process named the biological carbon pump, which is responsible for export and accumulation of organic carbon from photosynthetic CO2 fixation in deep layers of the ocean down to the sediments (Figure 1 -step 6) (Ducklow et al., 2001). Besides being key actors in the carbon cycle, the phytoplankton functional groups are also actively involved in the other major biogeochemical cycles such as nitrogen, phosphorus and silica (Litchman et al., 2015).

The phytoplankton is formed by a tremendous taxonomic diversity of prokaryotes and eukaryotes displaying a wide range of sizes, shapes, and structures. The prokaryotic phytoplankton belongs to the phylum Cyanobacteria, a group previously known as blue-green algae [START_REF] Garcia-Pichel | What's in a name? The case of cyanobacteria[END_REF] capable of performing oxygenic photosynthesis.

Cyanobacteria have played a central role in the primary oxygenation of the Earth's atmosphere about 2.4 billion years ago (Bekker et al., 2004;[START_REF] Whitton | Ecology of cyanobacteria II: Their diversity in space and time[END_REF]. The main marine cyanobacterial groups are the dominant Prochlorococcus and Synechococcus in addition of the nitrogen fixers Trichodesmium, Crocosphaera and Richelia which form symbioses with diatoms and other protists [START_REF] Foster | Nitrogen fixation and transfer in open ocean diatomcyanobacterial symbioses[END_REF][START_REF] Karlusich | Exploration of marine phytoplankton: from their historical appreciation to the omics era[END_REF]. Prochlorococcus and Synechococcus are considered the most abundant photosynthetic organisms on Earth (Partensky et al., 1999) and important primary producers [START_REF] Garcia-Pichel | Estimates of global cyanobacterial biomass and its distribution[END_REF].

Among the eukaryotic phytoplankton, the most diverse and ecologically relevant groups are the prasinophytes, diatoms, dinoflagellates and haptophytes (Not et al., 2012) (Figure 2). Evolution of photosynthetic eukaryotes started at the Proterozoic oceans about 1.5 billion years ago [START_REF] Falkowski | The evolution of modern eukaryotic phytoplankton[END_REF][START_REF] Karlusich | Exploration of marine phytoplankton: from their historical appreciation to the omics era[END_REF]. Thanks to at least one endosymbiotic event when a cyanobacterium-like organism was engulfed by a heterotrophic eukaryote two main lineages of plastids evolved (primary endosymbiosis) (Hackett et al., 2007;[START_REF] Leliaert | Into the deep: New discoveries at the base of the green plant phylogeny[END_REF]. The first one, characterized by the presence of chlorophyll b, is dominated by the green algae prasinophytes (Chlorophyta) while the second lineage, the "red lineage", is characterized by the presence of chlorophyll a + c, and eventually gave rise, through secondary endosymbiosis, to the diatoms, haptophytes and dinoflagellates [START_REF] Falkowski | The evolution of modern eukaryotic phytoplankton[END_REF].

The prasinophytes appear to be the prevalent eukaryotic phytoplankton in the Paleozoic era before the rising of the red lineages when they were largely displaced (Falkowski et al., Another important phytoplankton group emerged at a time when phytoplankton diversity consisted mainly of cyanobacteria and green algae, the diatoms [START_REF] Falkowski | The evolution of modern eukaryotic phytoplankton[END_REF]. Diatoms are ubiquitous photosynthetic unicellular eukaryotes surrounded by porous silica shells called frustules. The name of this group is derived from the Greek diatomos which means "cut in half" in reference to their cell wall divided in two parts [START_REF] Armbrust | The life of diatoms in the world's oceans[END_REF]. Thanks to the frustules, diatoms are very well conserved in the fossil record, which allows to estimate their origin and diversity over the geological time [START_REF] Armbrust | The life of diatoms in the world's oceans[END_REF]. Their first appearance in the fossil record is dated at about 190 Myr ago in the Jurassic [START_REF] Sims | Evolution of the diatoms: Insights from fossil, biological and molecular data[END_REF]. Diatoms cells can be solitary or colonial and vary in size from a few micrometers to a few millimeters. Due to their high sinking rates, caused by the frustules, diatoms are a major driver of the biological pump, carrying photosynthetically fixed carbon to the deep ocean. It has been estimated that they are responsible for about 20% of the primary production on Earth (Nelson et al., 1995;Field et al., 1998).

Together with diatoms, the dinoflagellates diversified in the Mesozoic era, with a major radiation in the early Jurassic [START_REF] Falkowski | The evolution of modern eukaryotic phytoplankton[END_REF]. This major group of protists has diversified into a tremendous morphological and trophic complexity comprising phototrophic organisms, predators, mixotrophs, symbionts and parasites [START_REF] Gomez | A quantitative review of carbon cycling in a mosaic of phytoplankton blooms[END_REF]. Roughly, about 50% of the dinoflagellates are considered photosynthetic, harboring different types of plastids although mixotrophy is very common (Taylor et al., 2007). Members of this group are well known as harmful algal blooms forming species of global importance. About 70-80% of the toxic eukaryotic phytoplankton species are dinoflagellates (Janouskovec et al., 2017).

Dinoflagellates can cover their cell with cellulose-like polysaccharide plates forming together an organic theca (Not et al., 2012). Remarkably, their nucleus contains permanently condensed chromosomes (dinokaryon), and their cells are moved by two flagella, a ribbon-like flagellum with multiple waves situated in a transverse groove (cingulum) and another one emerging from the ventral furrow (sulcus) (Not et al., 2012). They are typically dominant biota in pelagic marine and freshwater ecosystems where they can form large blooms at optimal conditions (Taylor et al., 2007;[START_REF] Gomez | A quantitative review of carbon cycling in a mosaic of phytoplankton blooms[END_REF].

Last but not least, the haptophytes form a monophyletic widespread group of marine phytoplankton characterized by the presence of an organelle called haptonema which harbors similarities with a flagellum but differs in the arrangement of its microtubules (Parke et al., 1955). The haptonema can be used to capture preys, to attach to substrates and as sensory structures (de [START_REF] De Vargas | Origin and Evolution of Coccolithophores. From Coastal Hunters to Oceanic Farmers[END_REF]. However, in some groups the haptonema is only reduced to a vestigial structure (de [START_REF] De Vargas | Origin and Evolution of Coccolithophores. From Coastal Hunters to Oceanic Farmers[END_REF]. The haptophytes are found everywhere in aquatic environments, mainly in marine, and can be found as solitary free-living cells but also as colonial and as symbionts of foraminifera and radiolarians (Jordan and Chamberlain, 1997).

Coccolithophores are one of the most abundant and widespread groups of haptophytes. These organisms form an important part of the oceanic phytoplankton since the Jurassic (de [START_REF] De Vargas | Origin and Evolution of Coccolithophores. From Coastal Hunters to Oceanic Farmers[END_REF]. The name of the group is attributed to the calcium carbonate scales covering their cells, called coccoliths. Coccolithophores play important roles in the carbon cycle as primary producers and calcifiers and are involved in the control of the alkalinity and carbonate chemistry of the photic zone of the oceans (de [START_REF] De Vargas | Origin and Evolution of Coccolithophores. From Coastal Hunters to Oceanic Farmers[END_REF]. Besides being involved in the classical organic carbon pump, which fixes CO2, they also contribute to the carbonate counter pump, which releases CO2 to the atmosphere (Rost and Riebesell, 2004). Coccolithophores are responsible for the largest production of calcite on earth [START_REF] Brownlee | Calcification in coccolithophores: a cellular perspective[END_REF]. Another important role of coccolithophores in the carbon cycle involves the formation of aggregates of coccoliths and organic matter, which represents an enormous source of carbon that sink to the deep oceans (de [START_REF] De Vargas | Origin and Evolution of Coccolithophores. From Coastal Hunters to Oceanic Farmers[END_REF]. Additionally, they are important actors in the global sulfur cycle by the production of dimethylsulfoniopropionate (DMSP) and emission of dimethylsulfide (DMS) to the atmosphere [START_REF] Alcolombri | Identification of the algal dimethyl sulfide-releasing enzyme: A missing link in the marine sulfur cycle[END_REF].

1.2) Marine heterotrophic bacteria

Marine heterotrophic bacteria, together with Archaea, can be seen as the engines of Earth's biogeochemical cycles (Falkowski et al., 2008). They are responsible for the remineralization of organic nutrients into inorganic nutrients (mostly nitrogen and phosphorus).

They are also involved in reintroduction of phytoplankton released carbon in the food web through the production of biomass, which is consumed by protists, a process named 'microbial loop' (Figure 1-step 4) [START_REF] Azam | Symbiont Phaeobacter inhibens Shapes the Life History of Its Algal Host Emiliania huxleyi[END_REF]Pomeroy et al., 2007). Moreover, they produce a pool of recalcitrant organic matter that represents a relevant carbon storage pathway, a process named the "microbial carbon pump" (Figure 1 -step 5) [START_REF] Jackrel | Microbial production of recalcitrant dissolved organic matter: Long-term carbon storage in the global ocean[END_REF]. It has been estimated that about 50% of the carbon fixed by marine phytoplankton is processed by bacteria [START_REF] Azam | Symbiont Phaeobacter inhibens Shapes the Life History of Its Algal Host Emiliania huxleyi[END_REF].

In order to survive in the oceans, bacteria compete for nutrients and organic matter to support their growth (Smriga et al., 2016). Over the evolutionary time, bacteria have developed different strategies to acquire organic matter that are reflected in their genomic content [START_REF] Lauro | The genomic basis of trophic strategy in marine bacteria[END_REF]. Among these strategies, the oligotrophy and the copiotrophy are two extremes.

Oligotrophic bacteria are the ones that thrive in poor nutrients concentrations [START_REF] Lauro | The genomic basis of trophic strategy in marine bacteria[END_REF]. Considering that the oceans are in majority oligotroph, they are among the most abundant organisms in the world (Giovannoni, Tripp, et al., 2005;Giovannoni, 2017). Some strategies developed by this group to thrive in nutrient-limited environments are their ability to grow slowly and their relatively small cell size (Cavicchioli et al., 2003). The benefits of having small cell sizes can be to avoid predation, to increase the surface/volume ratio which allows efficient nutrient acquisition, and to decrease the replication costs (Cavicchioli et al., 2003). In addition, oligotrophic bacteria present streamlined genomes [START_REF] Giovannoni | Implications of streamlining theory for microbial ecology[END_REF], retaining only core metabolic genes, while reducing pseudogenes and intergenic spacers (Giovannoni, Tripp, et al., 2005). The streamlining favors cell architecture that minimizes resources required for replication (Giovannoni, Tripp, et al., 2005;[START_REF] Giovannoni | Implications of streamlining theory for microbial ecology[END_REF]. An example of marine oligotrophic lineages is the ubiquitous SAR11 clade from the Alphaproteobacteria class, the most abundant organisms in the oceans. This clade accounts to about 30% of bacterial abundance in surface oligotrophic waters (Morris et al., 2002;Giovannoni, 2017) and can be found everywhere in the global ocean [START_REF] Wietz | Latitudinal patterns in the abundance of major marine bacterioplankton groups[END_REF]. Other oligotrophic lineages belonging to Gammaproteobacteria are SAR92, OM60/NOR5, OM182, BD1-7, and KI89A, which are all part of the oligotrophic marine gammaproteobacteria (OMG) clade (Giovannoni et al., 2005). Members of this clade are ultrasmall (volume < 0.1 µm 3 ), ubiquitous, phylogenetically diverse and seem unable to grow at high nutrient concentrations (> 351 mg of carbon per liter) (see Giovannoni, Tripp, et al., 2005 and references therein).

On the other end of the trophic spectrum, copiotrophs are bacteria that favor nutrientrich conditions and complex organic matter. In general, copiotrophic bacteria display relatively large genomes sizes (> 4Mb), including a higher genetic potential to rapidly respond to changing environmental conditions [START_REF] Lauro | The genomic basis of trophic strategy in marine bacteria[END_REF]. They present higher capacity to sense, transduce and integrate extracellular stimuli, and they are also able to activate alternative catabolic pathways when the high energy compounds have been exhausted [START_REF] Lauro | The genomic basis of trophic strategy in marine bacteria[END_REF].

In addition, these bacteria have a developed chemical sensing and locomotion system (Smriga et al., 2016). This allows them to sense and swim towards hotspots of organic carbon [START_REF] Stocker | Ecology and Physics of Bacterial Chemotaxis in the Ocean[END_REF]. It has been shown that copiotrophs have more than 10% of their genes under transcriptional control, which allow them to respond to environmental stimuli (Cottrell and Kirchman, 2016). Moreover, a high fraction of marine bacteria (from 40% to 70% during summer) are motile and strongly respond to the chemical stimuli sent by phytoplankton cells and detrital aggregates that serve as organic matter sources (Grossart et al., 2001). Bacterial families that contain mainly copiotrophic members known to thrive in phytoplankton blooms are the Alteromonadaceae [START_REF] López-Pérez | The family Alteromonadaceae[END_REF], Marinobacteraceae (Handley and Lloyd, 2013), Rhodobacteraceae and Flavobacteriaceae (Buchan et al., 2014;Teeling et al., 2016). (Figure 3 -legend can be found in the next page).

Figure 3. Maximum-likelihood phylogenetic tree of the bacterial domain, highlighting heterotrophic taxa most commonly found associated with diatoms. Figure and legend (adapted) from Amin et al., (2012). Also shown are the autotrophic nitrogen-fixing cyanobacteria known to be associated with diatoms. Bacterial phyla are color coded and labeled in the correspondingcolored ring. Taxa reported to be associated with diatoms in culture or field samples are labeled in the outer ring. Boldface genera were reported in two or more independent studies. The tree is based on a concatenated alignment of 31 conserved predicted proteins from 350 bacterial species with whole genome sequences. Asterisks indicate taxonomic positions that are estimated from nearest 16S rRNA neighbor on the tree because they were not included in the original alignment. See Amin et al., (2012) for more details on the figure and phylogeny construction.

These extreme opposite strategies (copiotrophs vs oligotrophs) indicate the ability of bacteria to adapt to their surrounding environment. However, this is a rather simplistic view, and a complex spectrum of eco-evolutionary strategies does exist between these two extremes [START_REF] Lauro | The genomic basis of trophic strategy in marine bacteria[END_REF]. In the context of this thesis, although simplistic, these concepts help to understand why certain bacterial groups are often found surrounding the phytoplankton cells.

1.3) The phycosphere: Where phytoplankton and bacteria interact A) The concept of phycosphere

The phycosphere defines the microenvironment surrounding a phytoplankton cell which is enriched in organic molecules (Seymour et al., 2017). This term was first used by Bell and Mitchell in the 70's as an analogue of the rhizosphere in plants [START_REF] Bell | Chemotactic and growth responses of marine bacteria to algal extracellular products[END_REF]. The rhizosphere is the vicinity region around the plant roots which is influenced by the nutrients and oxygen released by the plant [START_REF] Philippot | Going back to the roots: the microbial ecology of the rhizosphere[END_REF] (Figure 4). This region is characterized by a gradient of chemical, biological and physical properties that change along the roots (Trivedi et al., 2020). The colonization at the rhizosphere is in majority done by chemotactic bacteria, which are attracted by the plant exudates and able to thrive in these particular conditions [START_REF] Philippot | Going back to the roots: the microbial ecology of the rhizosphere[END_REF]. In addition, the plants have evolved strategies to avoid the colonization by pathogens and non-desired organisms such as the production of antibiotics and antifungal compounds, and the stimulation of the growth of specific bacterial groups (Trivedi et al., 2020). Seymour et al., (2017).

Similarly, the phycosphere is also characterized by a gradient of oxygen, pH and organic molecules exuded by the microalgal cell which can attract heterotrophic bacteria [START_REF] Bell | Chemotactic and growth responses of marine bacteria to algal extracellular products[END_REF] (Figure 4). As it will be exposed in the next sections, chemotactic bacteria are amongst the most important colonizers of the phycosphere (Smriga et al., 2016;Seymour et al., 2017). In addition, some chemical currencies are commonly found in both regions, such as sugars, amino acids and sulfur compounds (Durham et al., 2015). Finally, these two regions also share some bacterial groups. This is the case of Rhizobium members that are often associated with plant roots and represent important members of the phycosphere of green algae [START_REF] Ramanan | Phycosphere bacterial diversity in green algae reveals an apparent similarity across habitats[END_REF]Seymour et al., 2017;Haberkorn et al., 2020). On the other hand, a remarkable particularity of the phycosphere is that phytoplankton is inserted in a turbulent environment (Seymour et al., 2017). This feature impacts the size and shape of the phycosphere and ultimately the rates to which bacteria encounters the phytoplankton cell (Smriga et al., 2016;Seymour et al., 2017).

In their pioneer work in 1972 Bell and Mitchell stated, "It would appear that the phycosphere is a region of interactions that have only begun to be evaluated" [START_REF] Bell | Chemotactic and growth responses of marine bacteria to algal extracellular products[END_REF]. Since then, much has been discovered regarding the interactions occurring in the phycosphere and this will be detailed in the next sections.

B) The microbiome assembly processes in the phycosphere

The microalgal microbiome assembly is a result of complex interactions involving the bacterial community, the microalgal host and the environment (Seymour et al., 2017). The most investigated process of assembly in the phycosphere is the traditional niche-based theory, which postulates that specific variables (biotic and abiotic interactions, life history traits) determine how communities are organized [START_REF] Hutchinson | Concluding remarks[END_REF].

Considering that physical and chemical conditions present in the phycosphere are different from that of the bulk seawater (Seymour et al., 2017), it is expected that the bacteria found in the phycosphere environment are selected by its conditions. Moreover, it is suspected that microalgae can select which bacteria are able to grow in the phycosphere by different mechanisms such as the release of specific organic molecules that will favor specific groups and/or inhibit others (Fu et al., 2020). Many studies investigating the phycosphere of different phytoplankton have found deterministic processes as the main drivers of the bacterial diversity in natural blooms (Landa et al., 2016;Teeling et al., 2016;Hattenrath-Lehmann and Gobler, 2017) and in cultures (Ajani et al., 2018;Behringer et al., 2018;Lawson et al., 2018;Shin et al., 2018;Kimbrel et al., 2019;Sörenson et al., 2019;Sara L Jackrel et al., 2020;Mönnich et al., 2020). The deterministic factors pointed out by these studies were the place/time of isolation (Ajani et al., 2018), the host genotype (Hattenrath-Lehmann and Gobler, 2017;Behringer et al., 2018;Lawson et al., 2018;Shin et al., 2018;Kimbrel et al., 2019;Sörenson et al., 2019;Sara L Jackrel et al., 2020;Mönnich et al., 2020) and the composition of the organic matter released by different phytoplankton species (Teeling et al., 2012;Landa et al., 2016). During a diatom bloom, the bacterial community composition differs drastically from before, during, and at late stages and appear to be strongly controlled by the substrate succession (Teeling et al., 2012). Changes in community composition are mainly associated with the decrease of oligotrophic bacterial clades (such as SAR11 and Actinobacteria) and the increase of copiotrophs (such as Rhodobacteraceae, Alteromonadaceae and Flavobacteriaceae members) (Teeling et al., 2012). Moreover, these patterns are similar among different phytoplankton groups, such as diatoms, dinoflagellates and coccolithophores (Teeling et al., 2012). The bacterial groups of bacteria often found associated to microalgal blooms have been called 'archetypal phytoplankton-associated taxa' and are mainly members of Alphaproteobacteria, Flavobacteriia and Gammaproteobacteria (Buchan et al., 2014) (see Figure 3 for details). Not surprisingly, they are also the most dominant groups found in cultures of diatoms (Ajani et al., 2018;Behringer et al., 2018;[START_REF] Crenn | Bacterial epibiotic communities of ubiquitous and abundant marine diatoms are distinct in short-and long-term associations[END_REF]Mönnich et al., 2020), dinoflagellates [START_REF] Bolch | Bacterial associates modify growth dynamics of the dinoflagellate Gymnodinium catenatum[END_REF]Lawson et al., 2018;Shin et al., 2018;Sörenson et al., 2019), coccolithophores (Green et al., 2015), green algae [START_REF] Abby | Bacteria in Ostreococcus tauri cultures -friends, foes or hitchhikers?[END_REF]Geng et al., 2016;Lupette et al., 2016;Jackrel et al., 2019) and cyanobacteria (Dziallas and Grossart, 2011).

Contrary to earlier studies in other marine models, such as macroalgae (Burke, Steinberg, et al., 2011;Burke, Thomas, et al., 2011), microbiomes assembly mechanisms other than the niche-based theory started only recently to be considered in the phycosphere (Kimbrel et al., 2019;Zhou et al., 2019;Mönnich et al., 2020). The absence of consistent microbiomes across individuals of the macroalga Ulva australis led the authors to hypothesize that the microbiome assembly is a process rather governed by random selection (Burke, Thomas, et al., 2011). The main theory considering the influence of stochasticity on community structure is the neutral theory of biodiversity (Hubbell, 2001). It postulates that community assembly is largely independent of specific traits of individual species, and mostly influenced by stochastic processes such as births, deaths, immigration, extinction, and speciation (Hubbell, 2001). The broad assumption from the neutral theory that all species are equivalent was not supported by results showing that the bacterial community composition of U. australis was significantly different from that of bulk seawater which favored a niche-based selection (Burke, Thomas, et al., 2011). In this context, the competitive lottery which considers both the niche-based theory and random processes seems to better explain the microbiome assembly on U. australis (Sale, 1979). In the competitive lottery, a guild of organisms with similar capacity to colonize an empty niche, e. g. bacteria capable of colonizing the U. australis surface, will be selected by stochasticity and priority effects (Sale, 1979). Since all organisms of this guild are equally able to colonize the given environment, the one arriving first wins the lottery (Sale, 1979). The competitive lottery differs from neutral theory by considering that the organisms able to colonize that environment belong to a guild with similar functional traits which are not equivalent to all members of the community (Burke, Steinberg, et al., 2011). This model is gaining attention in microalgal microbiome studies because it can justify why bacteria enriched at different phytoplankton blooms and in cultures are often assigned the same taxonomic groups (Rhodobacteraceae, Gammaproteobacteria and Flavobacteriaceae) which are often found in lower abundance in bulk seawater (Buchan et al., 2014). At the same time, this model can also justify why microbiomes of the same phytoplankton species (e. g. the Leptocylindrus sp. microbiomes) can differ between strains (stochasticity) (Ajani et al., 2018).

Two recent studies investigating phytoplankton microbiomes discussed the possible participation of the competitive lottery on the assembly of microbiomes in culture (Kimbrel et al., 2019;Mönnich et al., 2020). Kimbrel et al., (2019) studied the assembly of attached and free-living communities associated to the diatom Phaeodactylum tricornutum and to the eustigmatophyte Microchloropsis salina in outdoor mesocosms and in culture enrichments. In natural and controlled conditions are necessary to further understand how the microbiomes are assembled in the phycosphere.

C) Diversity and remarkable traits of phycosphere colonizers

Although our understanding of the patterns of community assembly in the phycosphere remains limited, valuable information regarding the traits and diversity of the organisms around phytoplankton cells has been obtained over the last decades. Considering that phytoplankton exudates have different diffusion rates and thus will spread more or less around the cells, the chemotaxis and cell motility have important roles in the first steps of phycosphere colonization (Slightom and Buchan, 2009;Seymour et al., 2010;[START_REF] Stocker | Ecology and Physics of Bacterial Chemotaxis in the Ocean[END_REF]Smriga et al., 2016). Other important traits for phycosphere colonizers can be signaling (such as quorum sensing and quorum quenching) and attachment capacities (Slightom and Buchan, 2009;[START_REF] Rolland | Quorum Sensing and Quorum Quenching in the Phycosphere of Phytoplankton: a Case of Chemical Interactions in Ecology[END_REF]. Chemotaxis has been shown to be an important mechanism for phycosphere colonization and is widespread among copiotrophic bacteria (Slightom and Buchan, 2009;Smriga et al., 2016). Prokaryotic chemotaxis includes both stimuli recognition and motility towards the stimuli source or away from it and involves sensory pathways such as the histidine-aspartate phosphorelay system [START_REF] Wadhams | Making sense of it all: Bacterial chemotaxis[END_REF]. These systems are involved in measuring chemical concentrations around the cells and processing the information, which is then used to tune the motility accordingly [START_REF] Wadhams | Making sense of it all: Bacterial chemotaxis[END_REF][START_REF] Stocker | Ecology and Physics of Bacterial Chemotaxis in the Ocean[END_REF]. Marine bacteria have developed efficient ways to swim faster and precisely which guarantee their fast response to the chemical stimuli [START_REF] Stocker | Ecology and Physics of Bacterial Chemotaxis in the Ocean[END_REF] and increase the probability that they will reach its source (Johansen et al., 2002).

Bacteria with a strong chemotactic ability, such as Proteobacteria (Vibrio, Pseudoalteromonas, roseobacters, Caulobacter and Marinobacter members) and Bacteroidetes (Sphingomonas, Flavobacteria), are often enriched in the phycosphere of different microalgal groups in natural environments [START_REF] Gonzalez | Bacterial community structure associated with a dimethylsulfoniopropionate-producing North Atlantic algal bloom[END_REF]Zubkov et al., 2001;Teeling et al., 2012;Zhou et al., 2018Zhou et al., , 2019) ) and cultures [START_REF] Ramanan | Phycosphere bacterial diversity in green algae reveals an apparent similarity across habitats[END_REF]Lupette et al., 2016;[START_REF] Bolch | Bacterial associates modify growth dynamics of the dinoflagellate Gymnodinium catenatum[END_REF]Krohn-Molt et al., 2017;Ajani et al., 2018;Behringer et al., 2018;[START_REF] Zheng | Data Descriptor : Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition[END_REF]Lawson et al., 2018;Shin et al., 2018;Sörenson et al., 2019;Kimbrel et al., 2019;Mönnich et al., 2020) (see Figure 3 for more details). The response of Ruegeria pomeroyi (Roseobacter clade) to Alexandrium tamarense depicted through transcriptomic data showed an increase in the expression of genes related to flagellar motility [START_REF] Landa | Bacterial transcriptome remodeling during sequential co-culture with a marine dinoflagellate and diatom[END_REF]. Motile bacteria are estimated to consume up to 5 times more DOM in the phycosphere than non-motile bacteria (Smriga et al., 2016). Interestingly, the partitioning of DOM in coastal conditions (at usual bacterial and phytoplankton cell concentrations) seems unaffected by motility. However, in nutrient-rich conditions (such as in phytoplankton blooms), chemotaxis play an important role in the fate of DOM, which highlights the important role of chemotaxis on phytoplanktonbacteria interactions (Smriga et al., 2016).

Besides adjusting their swimming speed and trajectory, bacteria need to be able to modulate their cell concentrations and behavior. Quorum sensing is the term used to describe the cell-cell communication system used by bacteria to control their population density and gene expression according to the ambient bacterial density [START_REF] Fuqua | Quorum sensing in bacteria: The LuxR-LuxI family of cell density-responsive transcriptional regulators[END_REF][START_REF] Rolland | Quorum Sensing and Quorum Quenching in the Phycosphere of Phytoplankton: a Case of Chemical Interactions in Ecology[END_REF]. This mechanism is controlled by autoinducers released by bacteria which above a certain threshold triggers the change of bacterial phenotype and behavior from individual to collective [START_REF] Fuqua | Quorum sensing in bacteria: The LuxR-LuxI family of cell density-responsive transcriptional regulators[END_REF]. DMSP released by phytoplankton is another signal hypothesized to trigger quorum sensing in bacteria (Johnson et al., 2016). In the phycosphere, quorum sensing is important for the formation of biofilms (Fei et al., 2020), acquisition of nutrients [START_REF] Van Mooy | Quorum sensing control of phosphorus acquisition in Trichodesmium consortia[END_REF], regulation of microbial population dynamics [START_REF] Rolland | Quorum Sensing and Quorum Quenching in the Phycosphere of Phytoplankton: a Case of Chemical Interactions in Ecology[END_REF] and modulating virulence of algicidal bacteria (Harvey et al., 2016;[START_REF] Wang | Investigation of the genetics and biochemistry of roseobacticide production in the Roseobacter clade bacterium Phaeobacter inhibens[END_REF]. Quorum-sensing will influence the persistency of the bacteria around the phytoplankton cell and phytoplankton survival. Members of the Roseobacter clade, such as several Phaeobacter species, Ruegeria pomeroyi (formerly Silicibacter pomeroyi), and Dinoroseobacter shibae have been shown to produce quorum sensing molecules (Slightom and Buchan, 2009;Seyedsayamdost, Case, et al., 2011;Patzelt et al., 2013;Johnson et al., 2016;[START_REF] Wang | Investigation of the genetics and biochemistry of roseobacticide production in the Roseobacter clade bacterium Phaeobacter inhibens[END_REF]Fei et al., 2020). However, this cell-to-cell signaling mechanism does not seem to be phylogenetically conserved by all Roseobacter clade members (Slightom and Buchan, 2009).

Attachment of bacteria onto the microalgal cell represents an additional important mechanism in microbiome assembly. In the turbulent conditions of the oceans, attachment gives to the bacteria the advantage to stay close to the host, to profit of its exudates, and potentially to influence phytoplankton aggregation (Grossart, Czub, et al., 2006;Fei et al., 2020). Different phytoplankton groups have been shown to harbor a variety of epiphytic bacteria [START_REF] Gärdes | Effects of Marinobacter adhaerens HP15 on polymer exudation by Thalassiosira weissflogii at different N: P ratios[END_REF]Sonnenschein et al., 2012;Crenn et al., 2016;Segev, Wyche, et al., 2016;[START_REF] Crenn | Bacterial epibiotic communities of ubiquitous and abundant marine diatoms are distinct in short-and long-term associations[END_REF][START_REF] Zheng | Data Descriptor : Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition[END_REF]Zhou et al., 2019;Haberkorn et al., 2020). Alphaproteobacteria (Algimonas, Erythrobacter, Paracoccus and Silicimonas algicola), Gammaproteobacteria (Pseudoalteromonas and Marinobacter) and Flavobacteriia (Tenacibaculum) are common diatom-attached bacteria from cultures and/or from the field [START_REF] Gärdes | Diatom-associated bacteria are required for aggregation of Thalassiosira weissflogii[END_REF]Sonnenschein et al., 2012;Crenn et al., 2016[START_REF] Crenn | Bacterial epibiotic communities of ubiquitous and abundant marine diatoms are distinct in short-and long-term associations[END_REF]. The attachment of bacteria is not restricted to diatoms.

For example, [START_REF] Zheng | Data Descriptor : Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition[END_REF] found Flavobacteriales attached to Synechococcus cells at all growth phases. In addition, bacterial groups such as Rhodobacterales, Vibrionales, and Flavobacteriales dominated in the particle-attached fraction (>3 µm) during a bloom of dinoflagellates (Zhou et al., 2019). During the decline phase of this bloom, other groups such as Gammaproteobacteria (Vibrionales, Oceanospirillales, Alteromonadales, Thiotrichales, and Legionellales) and Epsilonproteobacteria (Pseudomonadales and Enterobacteriales) emerged and their abundance increased in the post-bloom phase (Zhou et al., 2019).

Besides facilitating the access of bacteria to phytoplankton exudates, attachment is often used by pathogenic bacteria for infection. Alphaproteobacteria (Tistrella sp.) attach to Chlorella vulgaris and ultimately kill the microalga (Haberkorn et al., 2020). Another example is Phaeobacter inhibens which attach to senescent E. huxleyi cells and promote cell lysis by the production of specific compounds (Segev et al., 2016).

The traits presented here are some of the characteristics common among bacteria inhabiting the phycosphere which seems shared between different phytoplankton species. These characteristics are important because they will determine how bacteria encounter the phycosphere, their behavior and their residence time which will ultimately impact phytoplankton-bacteria interactions.

D) Metabolic exchanges at the phycosphere: How these organisms interact?

Phytoplankton-bacteria dynamics have been studied in a large-scale perspective (from meters to kilometers) and across long temporal scales (seasonal to annual) (see Seymour et al., 2017 and references therein). Although large scale investigations are able to show a tight coupling between distribution and seasonal patterns of both partners (Bird and Kalff, 1984), they are not precise enough to explore their interactions. The term 'interaction' is related to the influence of one species on the growth rate or metabolism of another; interactions range from negative, neutral to positive (Figure 5) [START_REF] Faust | Microbial interactions: From networks to models[END_REF]Tipton et al., 2019). Some authors defend the use of the term 'symbiosis' to define all the continuum of interactions between host and microbiome (Tipton et al., 2019). This continuum includes the mutualistic interactions which are beneficial to both partners (Tipton et al., 2019). Other type of interactions included are parasitism, which is positive for one and negative to the other, and competition, which is negative for both (Figure 5) (Tipton et al., 2019). effect for a microbe and a negative (red) effect for the host, this is known as parasitism (top right corner). Some symbioses may have more positive or negative effects for a symbiont or host than others, and these may shift depending upon their environmental context as shown in the figure by the gradation of red and blue values between the two axes. In addition, an interaction that is neutral for both partners is considered simply co-occurrence. To decipher metabolic and molecular mechanisms of phytoplankton-bacteria interactions at larger scales represents a challenge considering that the metabolites exchanged are found in very low concentrations in seawater and many of these molecules are yet not described [START_REF] Moran | Deciphering ocean carbon in a changing world[END_REF]. Yet this challenge can be overcome by the association and complementarity of different techniques. In this context, co-culture experiments (where host and bacterial partners can be controlled) associated with the 'omics' tools such as genomics, transcriptomics and metabolomics represent a powerful approach, allowing to identify the genes differentially expressed and the metabolites potentially exchanged. These studies provide key information on specific interaction metabolites that can then be targeted in environmental investigations. For example, co-culture experiments allowed to tease apart the interaction between a Sulfitobacter species with the globally distributed diatom Pseudo-nitzschia multiseries (Amin et al., 2015). This interaction involves the production and release of the phytohormone indole-3-acetic acid (IAA) by the bacterium using algal released and endogenous tryptophan, which promotes diatom cell division (Amin et al., 2015). Moreover, by targeting the IAA gene expression by metatranscriptomic analysis, the authors found that IAA production by Sulfitobacter-related bacteria is widespread in the oceans, particularly in coastal environments, revealing the large-scale relevance of these interactions (Amin et al., 2015).

In the past decades, many studies have expanded the list of molecules involved in phytoplankton-bacteria interactions (Croft et al., 2005;Amin et al., 2009;Seyedsayamdost, Case, et al., 2011;[START_REF] Paul | Interactions of the algicidal bacterium Kordia algicida with diatoms: Regulated protease excretion for specific algal lysis[END_REF][START_REF] Paul | A co-culturing/metabolomics approach to investigate chemically mediated interactions of planktonic organisms reveals influence of bacteria on diatom metabolism[END_REF]Mausz and Pohnert, 2015;[START_REF] Wang | Investigation of the genetics and biochemistry of roseobacticide production in the Roseobacter clade bacterium Phaeobacter inhibens[END_REF]Durham et al., 2017;Barak-Gavish et al., 2018;Bramucci et al., 2018;Mühlenbruch et al., 2018;[START_REF] Brown | Chemical ecology of the marine plankton[END_REF]Shibl et al., 2020;Fei et al., 2020). Different molecules are exchanged depending on the nature of the interaction.

Mutualistic phytoplankton-bacteria interactions can involve the exchange of compounds released by bacteria such as vitamins (for example B12) (Croft et al., 2005), iron (Amin et al., 2009), ammonium and growth promoters (Amin et al., 2015). In exchange, bacteria can benefit from organic nitrogen compounds (Amin et al., 2015) and organic sulfur molecules such as DMSP that are released by microalgae. Overall, recent literature reports that these interactions are sophisticated and involve different mechanisms of recognition and control by both microalgae and bacteria (Durham et al., 2017;Shibl et al., 2020). They can involve a cascade of recognition that allows the microalgae to identify the presence of the bacterium partner as shown for Thalassiosira pseudonana in the presence of R. pomeroyi (Durham et al., 2017). The cascade involved the differential expression of genes responsible in recognition of biotic stimuli (calcium-binding proteins and calcium-dependent protein kinase), transmission of signal, and protein-protein interactions (leucine-rich repeat receptors) analogous to that observed in plants (Durham et al., 2017). Besides recognizing the presence of bacteria, microalgae seem able to control which bacteria will thrive in the phycosphere by the production and release of secondary metabolites (Shibl et al., 2020). One of them, rosmarinic acid, suppresses motility and promotes attachment of beneficial bacteria, while the other, azelaic acid, inhibits the growth of opportunistic bacteria but stimulates the growth of beneficial ones (Shibl et al., 2020). This sophisticated strategy allows the microalgae to benefit from their microbiome by controlling the most favorable partners (Shibl et al., 2020). Similar results were found in synthetic phycospheres containing common metabolites released by diatoms (xylose, glutamate, glycolate, ectoine, and dihydroxypropanesulfonate) and dinoflagellates (ribose, spermidine, trimethylamine, isethionate, and DMSP) (Fu et al., 2020). By inoculating natural bacterial communities in treatments with single metabolites and with different combinations of them, the authors showed that the final communities were different between the treatments, depending on the metabolites used (Fu et al., 2020). Furthermore, by using modeling approach, the authors were able to predict the community composition of treatments with multiple metabolites based on the community composition of single metabolites treatments (Fu et al., 2020).

Negative phytoplankton-bacteria interactions (parasitism and competition) have been extensively investigated (Mayali and Azam, 2004;[START_REF] Paul | Interactions of the algicidal bacterium Kordia algicida with diatoms: Regulated protease excretion for specific algal lysis[END_REF][START_REF] Seyedsayamdost | Roseobacticides: Small molecule modulators of an algal-bacterial symbiosis[END_REF]Seyedsayamdost, Case, et al., 2011;Meyer et al., 2017;Bigalke and Pohnert, 2019). Here, I will focus on some examples of the mechanisms used by parasitic bacteria to interact with phytoplankton (see Mayali andAzam, 2004 andMeyer et al., 2017 for a detailed review on algicidal bacteria). Certain bacteria are capable of releasing compounds that display algicidal activity against different phytoplankton species. Among them, Kordia algicida killed the diatoms Skeletonema costatum, Thalassiosira weissflogii and Phaeodactylum tricornutum while Chaetoceros didymus was unaffected [START_REF] Paul | Interactions of the algicidal bacterium Kordia algicida with diatoms: Regulated protease excretion for specific algal lysis[END_REF]. The mechanisms of the algicidal activity of K. algicida do not require direct contact with the microalgae and involve protease activity [START_REF] Paul | Interactions of the algicidal bacterium Kordia algicida with diatoms: Regulated protease excretion for specific algal lysis[END_REF]. This protease activity is controlled by the bacterial cell concentration, suggesting a quorum sensing control [START_REF] Paul | Interactions of the algicidal bacterium Kordia algicida with diatoms: Regulated protease excretion for specific algal lysis[END_REF]. Further studies revealed that the resistance of C. didymus against K. algicida was affected when a microalgal competitor (S. costatum) was added in the co-cultures (Bigalke and Pohnert, 2019).

These interactions are however complex because growth of C. didymus was stimulated in the presence of low concentrations of S. costatum/K. algicida but inhibited when these partners were provided in high concentrations (Bigalke and Pohnert, 2019). Interestingly, the algicidal activity of K. algicida was also effective on natural Chaetoceros socialis populations collected in the North Sea (Bigalke et al., 2019). Algicidal compounds released by bacteria have been shown to kill different harmful phytoplankton species (Lovejoy et al., 1998;Li et al., 2018).

The dominance of Pseudoalteromonadaceae members in a Prorocentrum donghaiense coastal bloom was associated with high concentrations of putative algicidal molecules (beta-glucosidase) which were hypothesized to have important roles in the bloom termination (Li et al., 2018). Algicidal bacteria play an important role in shaping species composition in pelagic environments. Thanks to the decades of intense investigation, their ecological role is not far from being fully understood (Mayali & Azam, 2004;Meyer et al., 2017).

1.4) Large scale impacts of phytoplankton-bacteria interactions

Given the diversity, abundance and global distribution of phytoplankton and bacteria, their interactions are critical for marine ecosystems functions. Although these interactions are restricted to µm-mm spatial scales, their impact can scale up to major ecological processes in the oceans (Seymour et al., 2017). Phytoplankton-bacteria interactions can strongly impact carbon and nutrient cycling and the productivity and stability of aquatic food webs (Field et al., 1998). For example, bacteria can sustain phytoplankton growth in areas where the availability of limiting resources, such as nutrients, are scarce [START_REF] Roth-Rosenberg | Prochlorococcus cells rely on microbial interactions rather than on chlorotic resting stages to survive long-term nutrient starvation[END_REF]. It has been shown that the cosmopolitan cyanobacteria Prochlorococcus relied on associated bacteria to survive starvation when their nutrient-saving mechanisms are not sufficient [START_REF] Roth-Rosenberg | Prochlorococcus cells rely on microbial interactions rather than on chlorotic resting stages to survive long-term nutrient starvation[END_REF]. This can be related to the recycling of inorganic nutrients by bacteria, as well as the production of organic compounds which contain nutrients such as N and P (Roth-Rosenberg et al., 2020). This might affect the distribution and activity of Prochlorococcus on a global scale and may have profound impacts on the overall oceanic productivity and carbon cycling [START_REF] Roth-Rosenberg | Prochlorococcus cells rely on microbial interactions rather than on chlorotic resting stages to survive long-term nutrient starvation[END_REF]. Moreover, microscale phytoplankton-bacteria interactions play an important role in the carbon cycle by facilitating the formation of aggregates [START_REF] Gärdes | Diatom-associated bacteria are required for aggregation of Thalassiosira weissflogii[END_REF]. Several studies have shown that bacteria can stimulate the formation of aggregates by phytoplankton, a phenomenon that varies among microalgal species (Grossart, Czub, et al., 2006;Grossart, Kiørboe, et al., 2006;[START_REF] Gärdes | Diatom-associated bacteria are required for aggregation of Thalassiosira weissflogii[END_REF]Tran et al., 2020). Such interactions may potentially have important implications for mediating vertical carbon flux in the oceans (Tran et al., 2020) and enhancing the efficiency of the biological carbon pump [START_REF] Gärdes | Diatom-associated bacteria are required for aggregation of Thalassiosira weissflogii[END_REF].

Another large-scale impact of phytoplankton-bacteria interactions is the termination of harmful algal blooms. Harmful algal blooms have considerable economic and ecological impacts (Assmy and Smetacek, 2009). They are responsible for the loss of marine biodiversity and their damage can extend to public health problems. As previously mentioned, algicidal compounds released by bacteria have been shown to kill different harmful phytoplankton species and are considered as potential controllers of harmful algal blooms (Bates et al., 1995;Lovejoy et al., 1998;Li et al., 2018). Certain bacteria can however stimulate the growth of harmful phytoplankton species [START_REF] Bolch | Bacterial associates modify growth dynamics of the dinoflagellate Gymnodinium catenatum[END_REF].

Pathogenic bacteria may also contribute to the regulation of major oceanic cycles. In an experimental approach, a Sulfitobacter strain, isolated from an E. huxleyi bloom, showed increased virulence against E. huxleyi at higher concentrations of DMSP (Barak-Gavish et al., 2018). Although the mechanisms used by the bacterium to kill the microalgae are yet not known, the authors suggest that the bacterial consumption of large amounts of DMSP can reduce the production of DMS by the algal degradation (through DMSP-lyase -Alma1 enzyme) (Barak-Gravish et al., 2018). Since DMS is a volatile gas which accounts for 90% of the sulfur emissions by biological processes [START_REF] Sievert | The sulfur cycle[END_REF], these interactions, taking place in the demise phase of E. huxleyi blooms, could contribute to the regulation of oceanic sulfur cycling and feedback to the atmosphere (Barak-Gravish et al., 2018).

These few examples do not resume all the large-scale impacts that phytoplanktonbacteria interactions can have in the oceans. However, they exemplify the importance of investigating the phycosphere microbiomes in order to achieve a better understanding of global ocean ecology.

2) Emiliania huxleyi: a model organism to study phytoplankton-bacteria interactions

Emiliania huxleyi, the main phytoplankton taxa used in this work, is a relevant model organism to explore the phytoplankton-bacteria interactions. The next sections will explain its relevance and also present Gephyrocapsa oceanica, sister group of E. huxleyi, that was used for comparative purposes. The characteristics of G. oceanica will be described when they are relevant for the study.

2.1) Evolution, ecology and distribution

Emiliania huxleyi (Lohmann, 1902;Hay et al., 1967) is the most abundant coccolithophore in modern oceans [START_REF] Paasche | A review of the coccolithophorid Emiliania huxleyi (Prymnesiophyceae), with particular reference to growth, coccolith formation, and calcification-photosynthesis interactions[END_REF]Hagino et al., 2011). It belongs to the order Isochrysidales of the Haptophyta phylum. Together with the genus Gephyrocaspa and Reticulofenestra, they form the family Noelaerhabdaceae (Young et al., 2003). E. huxleyi is a relatively recent taxa, with a first appearance in the fossil record well documented at 291,000 years ago [START_REF] Raffi | A review of calcareous nannofossil astrobiochronology encompassing the past 25 million years[END_REF]. Fossil evidence suggests that E. huxleyi evolved directly from a Gephyrocaspa species [START_REF] Samtleben | Die Evolution der Coccolithophoriden-Gattung Gephyrocapsa nach Befunden im Atlantik[END_REF] during glacial periods with low atmospheric CO2 partial pressure and presumably high oceanic productivity [START_REF] Paasche | A review of the coccolithophorid Emiliania huxleyi (Prymnesiophyceae), with particular reference to growth, coccolith formation, and calcification-photosynthesis interactions[END_REF].

Contrary to their close relatives, E. huxleyi is globally distributed from tropical to subpolar oceans, from oligotrophic to eutrophic waters and they can form massive annual blooms in high latitudes such as the North Sea, the Western English Channel, the Bay of Biscay and in the North Atlantic [START_REF] Gonzalez | Bacterial community structure associated with a dimethylsulfoniopropionate-producing North Atlantic algal bloom[END_REF]Zubkov et al., 2001;Tyrrell and Merico, 2004).

Emiliania blooms can cover areas up to 250,000 km 2 and reach cell concentrations of 10 5 cells/mL (Tyrrell and Merico, 2004). G. oceanica (Kamptner, 1943) presents a more restricted distribution, being found mainly in lower latitudes, in tropical and temperate regions, and in eutrophic waters. It is more widespread in the Pacific than Atlantic Oceans (Bollmann, 1997), and can also form large blooms in coastal waters [START_REF] Blackburn | A coccolithophorid bloom in Jervis Bay, Australia[END_REF]Rhodes et al., 1995;Kai et al., 1999).

Both Emiliania and Gephyrocaspa blooms are characterized by milky-turquoise waters that can be observed by satellite sensors (Figure 6) although E. huxleyi blooms are more frequent (Tyrrell and Merico, 2004). The highest reflectance observed are mainly generated by detached coccoliths that float in surface water at the bloom declining phase [START_REF] Neukermans | Optical modeling of spectral backscattering and remote sensing reflectance from Emiliania huxleyi Blooms[END_REF] . Both taxa are easy to cultivate, comprising most of the coccolithophores diversity in culture collections (Probert and Houdan, 2004) which make them attractive models. E. huxleyi is indeed one the best-studied phytoplankton in many aspects: physiology (Beaufort et al., 2011;Van Oostende et al., 2013;[START_REF] Plummer | Dynamic regulation of extracellular superoxide production by the coccolithophore Emiliania huxleyi (CCMP 374)[END_REF]), ecology (Cook et al., 2011;Seyedsayamdost, Case, et al., 2011;[START_REF] Patil | Biogeographic distribution of extant Coccolithophores in the Indian sector of the Southern Ocean[END_REF][START_REF] Poulton | Coccolithophore ecology in the tropical and subtropical Atlantic Ocean: New perspectives from the Atlantic meridional transect (AMT) programme[END_REF], phylogeny and genomics [START_REF] Read | Pan genome of the phytoplankton Emiliania underpins its global distribution[END_REF]Bendif et al., 2014Bendif et al., , 2015Bendif et al., , 2016Bendif et al., , 2019)), life cycle (Von Dassow et al., 2009;[START_REF] Frada | In situ survey of life cycle phases of the coccolithophore Emiliania huxleyi (Haptophyta)[END_REF]Mausz and Pohnert, 2015), blooms characteristics (Tyrrell and Merico, 2004;[START_REF] Neukermans | Optical modeling of spectral backscattering and remote sensing reflectance from Emiliania huxleyi Blooms[END_REF] and interactions with viruses [START_REF] Frada | The "Cheshire Cat" escape strategy of the coccolithophore Emiliania huxleyi in response to viral infection[END_REF][START_REF] Frada | Morphological switch to a resistant subpopulation in response to viral infection in the bloom-forming coccolithophore Emiliania huxleyi[END_REF]Vardi et al., 2012;Lehahn et al., 2014).

2.2) Morphology and phylogenomics

E. huxleyi and G. oceanica presents similar extracellular architecture (Figure 7). The main morphological feature used to differentiate these coccolithophores rely on the coccoliths.

Both E. huxleyi and G. oceanica produce heterococcoliths in their diploid stage. Formed of a radial array of complex crystal-units (Young et al., 2003), the heterococcoliths are produced in Golgi-derived calcifying vesicles and extruded to the cell surface when fully calcified (Billard and Inouye, 2004;[START_REF] Brownlee | Calcification in coccolithophores: a cellular perspective[END_REF]. While Emiliania can have one or more layers of coccoliths, Gephyrocapsa presents only a single layer (Young et al., 2003). The main distinguishing feature among both groups is the absence of the bridge in the central area of E. huxleyi cells (Young et al., 2003) (Figure 7). Beyond morphologic characterization, several studies have used genomic information to unveil the evolutionary history of the Emiliania/Gephyrocapsa species complex. The first attempts highlighted the difficulty of using genetic markers to solve such a recent diversification (Bendif et al., 2014). Usual genetic markers used to detect phytoplankton species, such as the ribosomal 18S and 28S, are evolving too slowly to evidence differences between species that diversified relatively recently (Bendif et al., 2014). A recent study applied phylogenomics based on full genome comparisons of different morpho-species of Gephyrocapsa (G. oceanica, G. muellerae, G. parvula, G. ericsonii) and Emiliania including first appearance dates from the fossil record (Bendif et al., 2019). This approach allowed to solve their phylogenetic relationship and showed that E. huxleyi is nested within the Gephyrocapsa genus and was thus proposed to be renamed Gephyrocapsa huxleyi (Bendif et al., 2019). In addition, the consensus phylogeny reflected well the morphological diversity within the genus, suggesting that they belong indeed to different species (Bendif et al., 2019).

Fossil record suggests that these species evolved by repeated species radiation causing pulses a b

of coccolith size changes (Bendif et al., 2019). Of note, these species appear to have formed in sympatry or parapatry, with occasional gene flow between them (Bendif et al., 2019).

E. huxleyi presents different cellular morphotypes. Based on morphological

observations it is possible to subdivide E. huxleyi morphotypes into two main morphogroups, A and B, (Young and Westbroek, 1991;Hagino et al., 2011) (Table 1). These groups are divided by the coccoliths' profile shapes, the structure of central areas, and the degree of calcification (Young et al., 2003) (Table 1).

Table 1. Emiliania huxleyi morphotypes characteristics, including coccoliths size, shield elements, central area elements, as well as geographic distribution (Young and Westbroek, 1991;Young et al., 2003). Southern Ocean and Subpolar waters [START_REF] Patil | Biogeographic distribution of extant Coccolithophores in the Indian sector of the Southern Ocean[END_REF] The group A is formed by the typical A morphotype (Young and Westbroek, 1991), besides the morphotype A-overcalcified (Young et al., 2003), the morphotype R (Young et al., 2003), and the corona type (Okada and McIntyre, 1977) (Figure 7). The group B is formed by the morphotype B (Young and Westbroek, 1991), B-C (Young et al., 2003), C (Young and Westbroek, 1991) and the type O (Hagino et al., 2011) (Figure 8). The objective here is not to provide a detailed characterization of Emiliania morphotypes but rather point out to the morphological diversity inside the taxa. These different morphotypes display biogeographical patterns across the global ocean and thus reflect different niche adaptations. Furthermore, recent investigation evidenced that Emiliania morphotypes isolated from different oceanic regions reflect intraspecific differentiation (Figure 9) (Bendif et al., in prep). These differences were evidenced by phylogenomic analysis based on fragments (10,000 bp) of 100 random genes extracted from full Emiliania isolates genomes. Results

Group

showed that morphological groups A and B represent two different species (Table 1 and Figure 9a). Within group A, two species were identified (A1 and A2). Clades included in A1 group reflect an environmental gradient from low latitudes (A1a -found mainly in Mediterranean Sea and English Channel) to high latitudes (A1d -Arctic strains) (Figure 9a andb). The sub-clades also represent the morphotypes differentiation. Clade A1a is the classic A morphotype, A1b

represents the morphotypes overcalcified A and R and A1c is a hybrid between A1b and A1d (Figure 9a).

Species B incorporates all the morphotypes mentioned for group B (Table 1 and Figure 9a). The traditional morphotype B is common in the North East Atlantic, more specifically in the seas surrounding the United Kingdom (Hagino et al., 2011) (Figure 9b). Morphotypes B/C and C can be found dominating samples from the Polar frontal zone and Indian sector of the Southern Ocean [START_REF] Patil | Biogeographic distribution of extant Coccolithophores in the Indian sector of the Southern Ocean[END_REF]. Their dominance seems to be associated with high nutrient concentrations and low temperature regions [START_REF] Patil | Biogeographic distribution of extant Coccolithophores in the Indian sector of the Southern Ocean[END_REF]. More specifically, morphotype C has also been reported from temperate surface waters and lower photic zones of stratified tropical waters and appear to change its depth habitat depending on the levels of stratification, temperature and nutrients (Hagino et al., 2011). Morphotype O is extensively distributed in the Southern Ocean and is a dominant morphotype in the Northern North Pacific (Hagino et al., 2011). In addition, morphotype O seems rare or absent in the Pacific tropical surface waters (Hagino et al., 2011). black continuous lines represent isotherms (Annual SST; Ocean Global Database).

2.3) Life cycle

Emiliania huxleyi presents a haplodiplontic and heteromorphic life cycle dominated by the coccolith-bearing non-motile diploid phase (Figure 10) (Green et al., 1996). The coccolith bearing phase alternates with a diploid non-calcifying non-motile phase (naked cells) and a motile haploid flagellated phase where the cell is covered by organic scales (Green et al., 1996).

These three forms are capable of independent mitotic division producing populations that likely interconnect through sex and meiosis. However, sexual reproduction has never been observed in E. huxleyi [START_REF] Frada | Morphological switch to a resistant subpopulation in response to viral infection in the bloom-forming coccolithophore Emiliania huxleyi[END_REF]. In addition, the factors triggering meiosis division are still intriguing [START_REF] Frada | In situ survey of life cycle phases of the coccolithophore Emiliania huxleyi (Haptophyta)[END_REF]. Experimental and bloom surveys revealed that meiosis may be induced at the end of exponential and stationary phases as a resistance response to viral infection, a mechanism coined the "Cheshire Cat" [START_REF] Frada | The "Cheshire Cat" escape strategy of the coccolithophore Emiliania huxleyi in response to viral infection[END_REF][START_REF] Frada | In situ survey of life cycle phases of the coccolithophore Emiliania huxleyi (Haptophyta)[END_REF]. In the Cheshire Cat, the haploid cells are not recognized by the viruses and thus are resistant to infection [START_REF] Frada | The "Cheshire Cat" escape strategy of the coccolithophore Emiliania huxleyi in response to viral infection[END_REF]. This mechanism releases the host from short-term pathogen pressure and might select for a biphasic life-cycle strategy over evolutionary time [START_REF] Frada | The "Cheshire Cat" escape strategy of the coccolithophore Emiliania huxleyi in response to viral infection[END_REF]. Together with haploid flagellated cells, the naked diploid cells also increase in abundance at the end of blooms [START_REF] Frada | In situ survey of life cycle phases of the coccolithophore Emiliania huxleyi (Haptophyta)[END_REF]. It has been hypothesized that this increase could be a result of cellcell signaling among E. huxleyi cells mediated by IAA (Labeeuw et al., 2016). Culture experiments showed that calcifying cells are able to produce IAA while the naked cells cannot produce but are sensible to variable concentrations of it, showing changes in growth rates depending on the concentration (Labeeuw et al., 2016). While susceptible to viral infection, these naked cells appear to be resistant against the pathogenic bacteria Ruegeria R11 [START_REF] Mayers | A bacterial pathogen displaying temperature-enhanced virulence of the microalga Emiliania huxleyi[END_REF]. Together, the complex morphogenetic strategy of E. huxleyi could guarantee its resistance to different viral and bacterial pathogens and maintain its bloom dynamics over time. In a new study, the authors show that in response to infection by EhV, E. huxleyi can produce diploid biflagellate and scale-bearing cells that are resistant to infection as indicated by the black arrow. This mechanism seems to be decoupled from the regular sexual cycle and to enable E. huxleyi cells to rapidly respond to and escape EhV infection. Figure andlegend (adapted) from [START_REF] Frada | Morphological switch to a resistant subpopulation in response to viral infection in the bloom-forming coccolithophore Emiliania huxleyi[END_REF].

Recently, two new types of cells have been observed in response to viral infection, adding more complexity to E. huxleyi life cycle [START_REF] Frada | Morphological switch to a resistant subpopulation in response to viral infection in the bloom-forming coccolithophore Emiliania huxleyi[END_REF]. The first, produced by coccolith bearing diploid cells, is diploid, biflagellate and covered by organic scales (Figure 9) [START_REF] Frada | Morphological switch to a resistant subpopulation in response to viral infection in the bloom-forming coccolithophore Emiliania huxleyi[END_REF]. This cellular type was called 'decoupled', because it characterizes a morphological switch from diploid coccolith bearing cell to a diploid scale-bearing cell (similar to haploid cells) as a response to escape to viral infection [START_REF] Frada | Morphological switch to a resistant subpopulation in response to viral infection in the bloom-forming coccolithophore Emiliania huxleyi[END_REF]. Contrary to the haploid cells, the decoupled has not experienced meiosis [START_REF] Frada | Morphological switch to a resistant subpopulation in response to viral infection in the bloom-forming coccolithophore Emiliania huxleyi[END_REF]. The other type of cells is produced by naked E. huxleyi strain and is also diploid and covered by organic scales, however it does not present flagella [START_REF] Frada | Morphological switch to a resistant subpopulation in response to viral infection in the bloom-forming coccolithophore Emiliania huxleyi[END_REF]. Both new types of cells have lower growth rates compared to their parent strains however they are both resistant to virus and increase in abundance during viral infection [START_REF] Frada | Morphological switch to a resistant subpopulation in response to viral infection in the bloom-forming coccolithophore Emiliania huxleyi[END_REF]. Although the fate of these cells is yet not known it is possible that they revert back to the calcified state or undergo meiosis to produce the haploid cells [START_REF] Frada | Morphological switch to a resistant subpopulation in response to viral infection in the bloom-forming coccolithophore Emiliania huxleyi[END_REF].

The differences among diploid and haploid cells are not restricted to the cellular architecture but also at the transcript level. Transcriptomes of diploid and haploid cells demonstrated that diploid cells present a richness of transcripts 20% higher than the haploid ones, and just 50% of transcripts were estimated to be shared between both stages (Von Dassow et al., 2009). This suggests that haploid flagellated cells have more streamlined functions in order to adapt to specific niches, while diploid non-motile cells are more versatile being able to explore a wide variety of productive environments (Von Dassow et al., 2009). The major functional categories distinguishing haploid cells were signal transduction and motility genes (Von Dassow et al., 2009). In addition, both life stages differ also in the metabolite production (Mausz and Pohnert, 2015). The main differences are related to the primary metabolites (such as isoleucine, xylose, citric acid, fructose and others) which were enriched in haploid cells (Mausz and Pohnert, 2015).

2.4) Interactions with bacteria

Many characteristics presented above make E. huxleyi an interesting model to study phytoplankton-bacteria interactions. Their close phylogenetic relationship with G. oceanica, together with their morpho-genomic variants and their different life-cycle stages, allow investigation of both the inter-and intra-specific specificity of the associated bacterial community. In addition, their ecological relevance as primary producers, calcifiers, and main actors in the sulfur cycle, call attention to the possible large-scale impacts that these interactions might have on ocean biogeochemistry. However, the state of the art regarding the bacterial diversity associated with coccolithophore species and the patterns and mechanisms of interactions between coccolithophores and bacteria is much less complete than for other phytoplankton groups such as diatoms (Amin et al., 2012).

The first gap concerns the bacterial diversity associated with these microalgae in cultures and in environmental samples. Two studies have investigated the associations between E. huxleyi and bacteria in natural environments [START_REF] Gonzalez | Bacterial community structure associated with a dimethylsulfoniopropionate-producing North Atlantic algal bloom[END_REF]Zubkov et al., 2001). An investigation of an algal bloom with dominance of E. huxleyi cells in the North Atlantic using different approaches (16S rRNA clone libraries, group-specific oligonucleotide probe hybridizations, and terminal restriction fragment length poly-morphism fingerprinting) found significant contribution of Roseobacter clade members to the bacterial community composition which accounted for 13% of the clones [START_REF] Gonzalez | Bacterial community structure associated with a dimethylsulfoniopropionate-producing North Atlantic algal bloom[END_REF]. Other abundant groups were SAR86 (24%) and SAR11 (11%) phylotypes [START_REF] Gonzalez | Bacterial community structure associated with a dimethylsulfoniopropionate-producing North Atlantic algal bloom[END_REF]. The abundance of Roseobacter genus members associated with E. huxleyi bloom was also revealed in the North Sea, using a Lagrangian sampling approach (Zubkov et al., 2001). In this study, Roseobacter, accounted for 24% of bacterial abundance and more than 50% of the total prokaryotic biomass (Zubkov et al., 2001). When investigated in cultures, the association between coccolithophores (two cultures of Coccolithus pelagicus f. braarudii and three of E. huxleyi) and bacteria revealed the presence of five phyla: Proteobacteria, Actinobacteria, Acidobacteria, Bacteroidetes and Planctomycetes (Green et al., 2015). Alphaproteobacteria (average 53% of the richness of isolates, mainly from Roseobacter clade), Gammaproteobacteria (average 21%) and Bacteroidetes (average 17%) were present in all cultures (Figure 3). Two genera, Marinobacter and Marivita, were observed in all coccolithophores cultures which may represent a specific interdependence of bacteria and microalgae (Green et al., 2015). Other groups such as Acidobacteria, Schlegelella, and Thermomonas were reported for the first time associated with microalgal cultures (Green et al., 2015). The acidobacterial strain was closely related to a marine sponge isolate, while Schlegelella and Thermomonas were observed in marine springs, suggesting that their associations with coccolithophores may be related to their calcification capacity (Green et al., 2015).

Furthermore, the genetic and metabolic features underlying positive E. huxleyi-bacteria interactions are poorly known. Co-culture experiments revealed E. huxleyi-bacteria interactions that are ultimately pathogenic (Seyedsayamdost, Case, et al., 2011;Harvey et al., 2016;Labeeuw et al., 2016;[START_REF] Mayers | A bacterial pathogen displaying temperature-enhanced virulence of the microalga Emiliania huxleyi[END_REF]Segev, Wyche, et al., 2016;Barak-Gavish et al., 2018;Bramucci et al., 2018). Although some interactions start as mutualistic, their nature can change depending on the signals released by E. huxleyi cells (Seyedsayamdost et al., 2011).

Two important released bacterial molecules have been identified with potential to kill E. huxleyi cells: roseobacticides and IAA. Roseobacticides are potent algicidal compounds produced by Roseobacter clade members that are toxic to the algae in nM to μM concentrations (Seyedsayamdost et al., 2011). The role of roseobacticides on E. huxleyi-bacteria interactions was described for two roseobacters: Phaeobacter gallaeciensis and Phaeobacter inhibens (Seyedsayamdost, Case, et al., 2011;Seyedsayamdost et al., 2014). These interactions are described in two phases. In a first phase young E. huxleyi cells are favored by bacterial tropodithietic acid (TDA), which protects against pathogenic bacteria. In a second phase, roseobacticides production is induced by senescence signals (p-coumaric acid -pCa) released by E. huxleyi cells combined with a quorum sensing molecule N-acyl homoserine lactone signal [START_REF] Seyedsayamdost | Roseobacticides: Small molecule modulators of an algal-bacterial symbiosis[END_REF]Seyedsayamdost, Case, et al., 2011;Seyedsayamdost et al., 2014;[START_REF] Wang | Investigation of the genetics and biochemistry of roseobacticide production in the Roseobacter clade bacterium Phaeobacter inhibens[END_REF]. Interestingly, a common set of bacterial genes is necessary for the production of antibiotics that protect E. huxleyi during the mutualistic phase, and for the production of roseobacticides during the pathogenic phase. This allows a fast change from mutualism to parasitism, since the biosynthetic pathway is already active [START_REF] Wang | Investigation of the genetics and biochemistry of roseobacticide production in the Roseobacter clade bacterium Phaeobacter inhibens[END_REF].

Interactions between E. huxleyi and P. inhibens can also be controlled by IAA produced by the bacterium. This phytohormone promotes algal growth at low concentrations but is harmful when it accumulates in the culture medium, inducing E. huxleyi population decline (Segev, Wyche, et al., 2016). This strategy seems to ensure nutrient supply for the bacterium by promoting the algal growth (Segev et al., 2016). When the host is killed, bacteria utilize the least amount of nutrients released from dead cells before swimming away to attach to younger host cells (Segev et al., 2016). Other chemical compounds involved in E. huxleyi-bacteria negative interactions are 2-heptyl-4-quinolone (a quorum sensing molecule produced by bacteria) that reduce E. huxleyi growth (Harvey et al., 2016) and DMSP that modulates Sulfitobacter D7 virulence against E. huxleyi ( Barak-Gravish et al., 2018).

The virulence of bacteria against E. huxleyi can also be controlled by temperature and strain-specific, as observed for the Ruegeria sp. R11 [START_REF] Mayers | A bacterial pathogen displaying temperature-enhanced virulence of the microalga Emiliania huxleyi[END_REF]. E. huxleyi cultures were shown to bleach at 25°C while unaffected at 18°C [START_REF] Mayers | A bacterial pathogen displaying temperature-enhanced virulence of the microalga Emiliania huxleyi[END_REF]. In addition, while the diploid coccolith bearing cells and haploid flagellate cells were susceptible to R11 infection, the non-calcifying strain showed resistance [START_REF] Mayers | A bacterial pathogen displaying temperature-enhanced virulence of the microalga Emiliania huxleyi[END_REF]. This change in susceptibility among strains could be related to the cell composition among these strains (i.e, lack of coccolith and organic scales on naked strain, which may change the sensitivity to pathogens) [START_REF] Mayers | A bacterial pathogen displaying temperature-enhanced virulence of the microalga Emiliania huxleyi[END_REF]. Another possible reason pointed out by the authors is a geographic specificity of R11 [START_REF] Mayers | A bacterial pathogen displaying temperature-enhanced virulence of the microalga Emiliania huxleyi[END_REF]. While the susceptible strains were isolated from the same geographic region as R11 (Tasman Sea), the resistant strain was isolated from Equator [START_REF] Mayers | A bacterial pathogen displaying temperature-enhanced virulence of the microalga Emiliania huxleyi[END_REF]. The specific virulence of R11 brings the question whether beneficial bacteria also present strain or life stages specificity when associated to E. huxleyi and/or whether they are related to the geographic origin of the host strains.

Main PhD objectives

Altogether, the relevance of phytoplankton-bacteria interactions, the global abundance and key ecological role of cosmopolitan coccolithophores and the recognized gap regarding their associated bacterial diversity motivated the construction of this thesis.

The main goal of my thesis was to investigate the microbiome diversity associated with coccolithophores (mainly E. huxleyi) in cultures and to explore the mechanisms of microbiome selection in the phycosphere.

More specifically, in the first chapter, I aimed to identify how the inter-and intraspecific specificities in the Emiliania/Gephyrocapsa species complex would be reflected in the diversity of the microbiomes. Moreover, I also aimed to identify the main drivers of microbiome diversity in cultures. The results of this investigation are incorporated in the Chapter 1 entitled "The microbiome of the cosmopolitan coccolithophore Emiliania huxleyi".

The second specific goal was to investigate the drivers of microbiome diversity in different phytoplankton cultures that were isolated from the same seawater samples. This work is entitled "Microbiome assembly in phytoplankton cultures is partially driven by deterministic processes" and composes the Chapter 2 of this thesis.

The final objective was to investigate how bacteria from different inocula are selected by an axenic E. huxleyi strain and the compositional changes at short and long-time scales. This work is detailed in the Chapter 3 with the title "Microbiome assembly in axenic Emiliania huxleyi cultures is influenced by the source community composition and is resilient to disturbance".

The specific objectives and motivation underlying each study will be detailed in the outline before each chapter.

CHAPTER 1 Chapter 1

In this first chapter I report a high-resolution investigation of the microbiomes associated with the coccolithophore Emiliania huxleyi and its sister species Gephyrocapsa oceanica, for which a large diversity of isolates is available in culture collections. I have tried to answer fundamental questions regarding the diversity, drivers, and short-term stability of the E. huxleyi/G. oceanica microbiomes in cultures. In total 58 E. huxleyi and 15 G. oceanica cultures were selected with the main objective to test whether their microbiome diversity was driven by inter-species specificities, ocean origin of isolation, and/or age of the cultures. The E. huxleyi isolates selected which contained different morphotypes, that displayed different levels of calcification and niche adaptations, also allowed us to investigate microbiome patterns from inter-to intra-specific levels. The stability of the community composition was tested by sampling the microbiomes over three consecutive serial dilution transfers. We expected to find microbiomes with stable composition over time and differences related to biogeographical patterns and/or host specificities. We also hypothesized that the different levels of calcification in E. huxleyi morphospecies could have an impact on the diversity of the microbiomes by changing the pH of the medium.

To decipher the specificities of coccolithophore microbiomes I further compared our data to that from published datasets for cyanobacteria, diatoms and dinoflagellates microbiomes using a home-made standard bioinformatic pipeline. Finally, to expand our knowledge on these associations, I investigated the abundance and distribution of the most abundant coccolithophore-associated bacterial taxa in the datasets from the circum-global Tara Oceans and Malaspina expeditions. This chapter resulted in an unprecedented catalogue of bacterial diversity associated with E. huxleyi and G. oceanica in cultures providing valuable knowledge for further studies.

Additionally, this work involved careful selection of strains and morphological identification of E. huxleyi by scanning electron microscopy, in collaboration with the Drs. Ian Probert and El Mahdi Bendif. Part of the identification work will be included in a publication entitled "Coccolith morphometrics partially delineate morphotypes in worldwide isolates of Gephyrocapsa huxleyi" (Annex 1).

Introduction

Phytoplankton, including microalgae and cyanobacteria, are responsible for nearly half of photosynthetic carbon fixation on Earth, making them major drivers of global carbon fluxes [START_REF] Longhurst | An estimate of global primary production in the ocean from satellite radiometer data[END_REF]Field et al., 1998). Marine heterotrophic bacteria consume a significant fraction of phytoplankton-derived organic matter and thereby heavily influence major biogeochemical cycles (Pomeroy et al., 2007;Falkowski et al., 2008). Microscale interactions between phytoplankton and bacteria are therefore one of the most important interspecies relationships in the oceans and exert an ecosystem-scale influence on fundamental biological and biogeochemical processes [START_REF] Cole | Interactions between bacteria and algae in aquatic ecosystems[END_REF][START_REF] Azam | Microbial structuring of marine ecosystems[END_REF]Seymour et al., 2017).

Obligate relationships between phytoplankton and bacteria are known to be widespread in the marine environment (Foster et al., 2006). By far the most abundant of these symbiotic associations occur in the phycosphere, i.e. the region immediately surrounding and influenced by phytoplankton cells [START_REF] Bell | Chemotactic and growth responses of marine bacteria to algal extracellular products[END_REF]. Phytoplankton-bacteria associations in the phycosphere are numerous, varied and often complex (Amin et al., 2012). The exchange of metabolites and info-chemicals in the phycosphere governs phytoplankton-bacteria associations, which span the spectrum of ecological relationships from cooperative to antagonistic (Amin et al., 2012).

Several recent studies suggest that diverse phytoplankton taxa harbor unique prokaryotic communities in cultures which are consistently associated with the same host and across temporal scales, suggesting that associated bacteria carry out important and specific functions for the host [START_REF] Sison-Mangus | Host-specific adaptation governs the interaction of the marine diatom, Pseudo-nitzschia and their microbiota[END_REF]Krohn-Molt et al., 2017;Behringer et al., 2018;Lawson et al., 2018;Sörenson et al., 2019). However, although a core microbiome (i.e., stable and consistent members of the microbiome) can be identified for a host species, other factors like the sampling time and location can determine variations in the composition of phytoplankton microbiomes in cultures (Ajani et al., 2018).

Most of our current knowledge of the microbiomes associated with phytoplankton cultures is derived from examining diatoms (Ajani et al., 2018;Behringer et al., 2018), dinoflagellates (Lawson et al., 2018;Shin et al., 2018;Sörenson et al., 2019), cyanobacteria [START_REF] Zheng | Data Descriptor : Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition[END_REF]Kearney et al., 2021), and green algae [START_REF] Abby | Bacteria in Ostreococcus tauri cultures -friends, foes or hitchhikers?[END_REF]Lupette et al., 2016). Remarkably, the bacterial diversity associated with one of the most important groups of marine phytoplankton, the coccolithophores, has been poorly investigated. These widespread and abundant marine microalgae characterized by their ability to cover their cells with delicate calcite platelets, the coccoliths [START_REF] Paasche | A review of the coccolithophorid Emiliania huxleyi (Prymnesiophyceae), with particular reference to growth, coccolith formation, and calcification-photosynthesis interactions[END_REF], are estimated to account for more than 50% of the particulate inorganic carbon produced in the pelagic ocean each year (Taylor et al., 2017).

Emiliania huxleyi and Gephyrocapsa oceanica represent the most abundant extant coccolithophore morphospecies and display different geographic distribution. The ubiquitous E. huxleyi frequently forms extensive "milky water" blooms in high latitude ecosystems (Probert and Houdan, 2004;Tyrrell and Merico, 2004). G. oceanica is more restricted to tropical and subtropical waters and occasionally forms massive blooms in transitional coastal waters in the Pacific Ocean [START_REF] Blackburn | A coccolithophorid bloom in Jervis Bay, Australia[END_REF]. Gephyrocapsa and Emiliania have distinguished coccolith morphology, E. huxleyi lacking the conjunct bridge over the central area found in Gephyrocapsa (Young et al., 2003). Variability in the degree of calcification of E. huxleyi coccolith elements, their profile shapes and central area characteristics has led to the definition of two main groups A and B (Young and Westbroek, 1991) comprising intermediate (A) and heavily-calcified (A-overcalcified and R) to lightlycalcified (B, B/C, C, and O) morphotypes, respectively. Importantly, some of these morphotypes display specific physiological features and biogeographical patterns [START_REF] Paasche | A review of the coccolithophorid Emiliania huxleyi (Prymnesiophyceae), with particular reference to growth, coccolith formation, and calcification-photosynthesis interactions[END_REF]Hagino et al., 2011;[START_REF] Patil | Biogeographic distribution of extant Coccolithophores in the Indian sector of the Southern Ocean[END_REF]. In addition, many strains of E. huxleyi that have been maintained in laboratory culture for several years are only partially calcified or have lost the ability to calcify entirely [START_REF] Paasche | A review of the coccolithophorid Emiliania huxleyi (Prymnesiophyceae), with particular reference to growth, coccolith formation, and calcification-photosynthesis interactions[END_REF].

In spite of the biogeochemical and ecological importance of coccolithophores and the large amount of data from laboratory, mesocosm and field observations, the prokaryotic diversity associated with coccolithophores is poorly known. This is surprising given the considerable potential for addressing this question by further exploiting existing cultures of coccolithophore species currently maintained in collections around the world. The only study that investigated the microbiome of coccolithophore cultures analyzed a few strains (Green et al., 2015). In this study, bacterial membership of three E. huxleyi and two Coccolithus pelagicus f. braarudii cultures was assessed using bacterial cultivation and cultivationindependent methods. Alphaproteobacteria and Gammaproteobacteria dominated and specific taxa, including Marinobacter and Marivita, occurred in all cultures (Green et al., 2015).

In order to perform a high-resolution investigation of the microbiome diversity associated with coccolithophores in cultures, we focused on the sister taxa Emiliania huxleyi and Gephyrocapsa oceanica and selected a large set of strains isolated from the world ocean and maintained for several years in the Roscoff Culture Collection (RCC), including different E. huxleyi morphotypes. Our aim was to evaluate whether the microbiome diversity was stable over time and could be related to the inter and/or intra-species specificities of their hosts, different geographic origins, and time since isolation. To further understand the specificity and ecology of Emiliania/Gephyrocapsa microbiomes, we then compared their bacterial composition to that of other phytoplankton cultures and examined how the most abundant associated bacteria were represented in DNA metabarcoding datasets from the global oceans.

Materials and Methods

Origin and growth conditions of algal cultures

The 73 non axenic coccolithophore cultures (58 E. huxleyi and 15 G. oceanica clonal strains) used in this study were obtained from the RCC (http://roscoff-culture-collection.org/) and from the National Institute of Environmental studies (NIES) Microbial culture collection (https://mcc.nies.go.jp/) (Supplementary Table 1 and Supplementary figure 1). Most of the E. huxleyi strains (33 strains) belonged to the morphogroup A (comprising morphotypes A, A overcalcified, or R), while 8 were part of the morphogroup B (morphotypes B and O), 16 were non-calcifying strains, and one was a haploid strain (RCC1217; haploid version of RCC1216).

Upon receipt, the cultures were maintained in K/2 medium prepared with natural aged seawater collected at the SOMLIT-ASTAN monitoring site (48°46'18''N-3°58' 6''W) (Brittany, France) (Keller et al., 1987;Probert, 2019). Seawater was first filtered-sterilized (0.2 µm pore size), heated at 100°C for 20 min, cooled, and enhanced to K/2 by adding the nutrients and vitamins. Then, the pH was adjusted to 8.2 and the medium was filtered-sterilized (0.1 µm) (Probert, 2019). Cultures were grown in 50 mL vented tissue culture flasks at their maintenance temperature (Supplementary Table 1) on a 12:12 light:dark cycle under a white light irradiance (Philips Master TL_D 18W/865) of about 60 μmol photons m -2 s -1 . Cultures were acclimated to light, temperature and medium conditions for at least two growth cycles prior to experiments. Biological replication was achieved through time. Briefly, 200 µL of culture from each strain were transferred into a new medium (20 mL) every 2 weeks after sampling to maintain healthy cultures. Replication over time was conducted at two or three consecutive transfers yielding two or three replicates for each of the 73 strains (total n=204).

Culture monitoring

Fluorimetry. Photosystem II fluorescence quantum yield (FV/FM) was measured using a pulse amplitude-modulated fluorometer (Phyto-PAM II, Walz, Germany). Two mL of each culture was dark-acclimated for 20 min at room temperature prior to analysis. A 5-wavelength (440, 480, 540, 590, 625 nm) low irradiance, modulated light (1 Hz) was applied to measure the mean basal chlorophyll fluorescence (F0). Then, a saturating light pulse (400 ms, 8000 µmol photons m -2 s -1 ) was applied, and the mean maximum fluorescence was measured (FM). The PSII quantum yield was calculated as !V/FM = (!M -!0)/!M. Low values of PSII quantum yield result from photoinhibition or down-regulation of PSII, indicative of culture stress. Five days after transfer, the FV/FM ratio was measured from actively growing cultures of each strain.

When FV/FM ratios were lower than 0.5, cultures were grown for 24 extra hours.

Cytometry. At each time point, 1 mL of culture was fixed using glutaraldehyde 25% (0.125% final concentration) incubated for 20 min in the dark, flash frozen in liquid nitrogen and stored at -80°C. A FACSCanto flow cytometer (Becton Dickinson, San Jose, CA, USA) equipped with 488 and 633 nm lasers and standard filter setup was used to enumerate coccolithophores and bacterial cells (Marie et al., 1999). To avoid attachment of coccolithophore cells to the tube wall, Polaxamer 188 solution 10% (Sigma-Aldrich) (0.1% final concentration) was first added to the thawed fixed-frozen samples [START_REF] Marie | An improved protocol for flow cytometry analysis of phytoplankton cultures and natural samples[END_REF].

For coccolithophores, data acquisition was triggered on the red fluorescence signal and samples diluted 5-to 10-fold were run for 1 min at medium rate (∼50 µL/min). To quantify prokaryotes, samples were diluted 10-to 100-fold in TE (10 mM Tris, 1 mM EDTA [pH 7.5]), stained with SYBR Green-I (1/10,000, final concentration) and incubated for 15 min in the dark. The discriminator was set on green fluorescence, and the samples were analyzed as before.

DNA extraction, 16S rRNA PCR amplification and sequencing

To examine variability in the coccolithophores microbiome across strains, 2 mL of culture from each strain was centrifuged at 2,000 g for 30 sec to reduce the microalgal load.

The supernatants were transferred into new tubes containing 2 μL of Poloxamer 188 solution 10% (Sigma-Aldrich) and centrifuged at 5,600 g for 5 min. Bacterial cell pellets were stored at -20°C until DNA extraction. DNA extraction was carried out using NucleoSpin Plant II kit (Macherey Nagel) following the manufacturer protocol. To prevent protein contamination and improve bacterial cell lysis, proteinase K (25 µl of a 20 mg/mL solution) and lysozyme (100 µl of a 20 mg/mL solution) were added to the cell lysis step, respectively. The V4-V5 variable region of the prokaryotic 16S rRNA gene was amplified by PCR, using primers (5′-CTTTCCCTACACGACGCTCTTCCGATC-515F-Y) and (5′-GGAGTTCAGACGTGTGCTCTTCCGATCT-926R).

Primers 515F-Y (5'-GTGYCAGCMGCCGCGGTAA-3') and 926R (5'-CCGYCAATTYMTTTRAGTTT-3') were previously described by (Parada et al., 2016).

Triplicate PCR reactions (30 µL) contained 10 ng of DNA, 0.625 units of GoTaq G2 Flexi polymerase (Promega), 1X of enzyme buffer, 1.5 mM of MgCl2, 0.4 mM of each dNTPs, and 0.32 µM of each primer. Cycling conditions consisted in an initial denaturation step of 10 min at 95°C followed by 32 cycles of 95 °C for 30 s, 50 °C for 1 min, 72 °C for 1 min, and a final extension of 72 °C for 10 min. The pooled PCR products were sent for sequencing at the GeT-PlaGe platform of Genotoul (Toulouse, France). Detailed sequencing procedures are described in the Supplementary Methods.

Sequence processing and bacterial community analyses

Sequencing reads were processed as outlined in the Supplementary Figure 2 and https://github.com/mcamarareis/Coccolithophores_microbiomes. Briefly, primers were removed from the raw demultiplexed reads with cutadapt (version 2.8.1) using anchored primer removal allowing errors rate of 0.1 (Martin, 2011). Read pairs without primers or shorter than 75 nt were discarded. The resultant reads were processed using DADA2 (version 1.14.0) (Callahan et al., 2016) in R (version 3.6.1) (R Core Team, 2017). Reads were filtered using the function filterAndTrim default filtering parameters and truncation according to the quality profile of the sequences. Then, the error models were produced using the function lernErrors.

Amplicon sequence variants (ASVs) were inferred from forward and reverse reads independently using dada function in pooled mode. After merging, chimeras were removed using the function isBimeraDenovo method pooled. The taxonomy was assigned using the Silva database v138 (Quast et al., 2013) by both, IDtaxa (using confidence threshold of 50) (Murali et al., 2018) and vsearch global alignment (using id threshold of 0.9) (Rognes et al., 2016). The following analyzes were performed using R software version 4.0.2 in Rstudio

(1.1.442) (RStudio Team, 2016;R Core Team, 2017). ASVs shorter than 366 bp and longer than 376 bp, resulting mostly from non-specific priming, were removed. In addition, ASVs assigned to chloroplasts or identified as algal mitochondrial sequences were also removed as well as ASVs that accounted for less than 0.001% of the total number of reads (corresponding to 52 reads). Abundance filters removed 74% of the ASVs while keeping 99% of the reads.

A Bray-Curtis dissimilarity matrix was produced using relative abundance of ASVs (Bray and Curtis, 1957;Borcard et al., 2011). This matrix was used to test the stability of the microbiomes over time by hierarchical cluster analysis (complete linkage method) (Borcard et al., 2011;Oksanen et al., 2015). The function meandist was used to calculate the mean Bray-Curtis dissimilarity among the microbiome samples of the same strain and between strains (Oksanen et al., 2015). Then the independent microbiome replicates samples of each strain were merged for the next analysis by keeping the mean number of reads to avoid large discrepancies. In this case, only ASVs that were present in all the replicates of the same strain were kept.

A Principal Component Analysis using the Hellinger transformed data was performed to identify possible beta-diversity patterns (van den Wollenberg, 1977;Legendre and Gallagher, 2001). A heatmap was constructed using the pheatmap function to visualize the dominant ASVs (more than 5% of the reads in each sample) [START_REF] Kolde | Package 'pheatmap[END_REF]. The clustering of the samples was performed using Euclidean distance and complete linkage method (Borcard et al., 2011). Clusters formed using ASVs with more than 5% of the reads were consistent with PCA grouping. The same clusters were used to plot taxonomic diversity in the cultures. The three most abundant families were displayed at the plot while the others were merged together.

In order to investigate the possible drivers of the groups found in the PCA, a redundancy analysis (RDA) was performed using the function rda on the Hellinger transformed data (van den Wollenberg, 1977;Legendre and Gallagher, 2001). We first studied the effect of culture age (years since isolation) and of the place of isolation (latitude and longitude coordinates) and inter-specific differences on the composition of E. huxleyi and G. oceanica microbiomes. Then, the influence of cellular morphogroups (A, B, and non-calcifying), and culture age, place of isolation on the E. huxleyi microbiome composition was further tested. Statistical significance of RDA models was tested using an anova-like permutation test with anova (1,000 permutations). To test for homogeneity of multivariate dispersions among the hosts and morphogroups, the function betadisper was used followed by an anova-like permutation test as before (Oksanen et al., 2015).

Comparative analysis of coccolithophore and other phytoplankton culture datasets

We compared 16S rRNA sequences obtained in this study to sequences found in other phytoplankton cultures (Behringer et al., 2018;Sörenson et al., 2019;Kearney et al., 2021). (Callahan et al., 2016). The details of the informatic steps used for each study are available in the Github repository. Taxonomy was first assigned to ASVs using vsearch --usearch_global (90% identity threshold) and Silva database v138 (Quast et al., 2013;Rognes et al., 2016). Then, ASV table of each study was pre-filtered to remove all sequences not classified as prokaryotes. The ASVs with their abundance information were then pooled together and clustered as operational taxonomic units (OTUs) at 97% similarity threshold using vsearch --cluster_size (Rognes et al., 2016). The OTU table was produced using vsearch --usearch_global at 97% similarity and the flag --otutabout. The centroids were used to assign taxonomy as before mentioned (Quast et al., 2013). We performed a second filtering to remove remaining sequences not classified or classified as chloroplasts and mitochondria. In addition, OTUs with less than 0.001% of the total of reads were filtered out.

The OTU table was rarefied to the minimum number of reads (2,015) and used to produce the Venn diagram with the function venn from the package gplots [START_REF] Warnes | Package 'gplots.' Var R Program tools plotting data[END_REF].

Abundance and distribution of dominant coccolithophore associated ASVs in global ocean DNA metabarcoding datasets

The 10 most abundant ASVs found in this study were compared to ASVs and denoised zOTUs (zero-radius OTUs, i.e. OTUs defined at 100% sequence similarity) (Edgar, 2010) obtained from high-throughput sequencing of the 16S rRNA sequences retrieved from the Tara Oceans (Tara Oceans 2009-2012 and Tara Oceans Polar Circle 2013) and Malaspina

Expeditions datasets, respectively. Sample collection which covered surface, DCM and mesopelagic layers (Tara Oceans), and surface to bathypelagic layers (Malaspina), DNA extraction, sequence processing and data treatment are detailed in Supplementary Methods. For the Tara Oceans dataset, raw reads were processed using DADA2 (Callahan et al., 2016). Once the final ASV table was obtained, relative abundance of each ASV was calculated by station, depth and plankton size fraction. For the Malaspina dataset, reads were processed to obtain a zOTUs (100% identity OTUs) table using USEARCH v10.0.240 (Edgar, 2010;[START_REF] Sanz-Sáez | Diversity and distribution of marine heterotrophic bacteria from a large culture collection[END_REF] . Like for Tara Oceans data, we calculated the relative abundance of each zOTU by station and depth. To find zOTUs and Tara Oceans ASVs corresponding to the ASVs from cultures, we used vsearch --usearch_global method and selected zOTU and ASVs with > 97.3 and 100% similarity to the culture ASVs, respectively (Rognes et al., 2016). Finally, the maps and barplots were produced for both datasets using ggplot2 (Wickham, 2016).

Phylogenetic placement

For phylogenetic placement Marinobacter reference sequences and outgroups of about 1,382 bp were aligned in MAFFT version 7.453 (Katoh and Standley, 2013). Maximumlikelihood phylogenetic trees were calculated using RAxML 8.2.12 [START_REF] Stamatakis | RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies[END_REF] with GTRCAT evolution model and 1,000 bootstraps. ASVs sequences were aligned to the reference tree using PaPara software v2 [START_REF] Berger | PaPaRa 2.0: a vectorized algorithm for probabilistic phylogeny-aware alignment extension[END_REF]. Phylogenetic placement was produced using EPA-ng [START_REF] Barbera | EPA-ng: massively parallel evolutionary placement of genetic sequences[END_REF]. Accumulation analysis was performed with GAPPA using a 95% threshold [START_REF] Czech | Genesis and Gappa: processing, analyzing and visualizing phylogenetic (placement) data[END_REF]. The Interactive Tree of Life (iTOL) tool was used for the display and visualization of the trees [START_REF] Letunic | Interactive Tree Of Life (iTOL) v4: recent updates and new Chapter 1 80 developments[END_REF].

Results

Coccolithophore collection and microbiome stability across time

The 58 E. huxleyi and 15 G. oceanica clonal strains used in this study (Supplementary Table 1) were derived from water samples collected as part of research cruises in many oceanic and coastal regions of the Pacific, Atlantic, Indian and Arctic Oceans, and the Mediterranean Sea (Supplementary figure 1). The coccolithophores were isolated between 1959 and 2015 and co-cultivated with their associated heterotrophic prokaryotic communities through roughly monthly serial transfer since then. Because of the long-term co-cultivation, it can be assumed that presumably well-adapted microbial communities have been established under these conditions.

Chapter 1 62

To test microbiome stability over culturing time, we first monitored bacterial and microalgal cell populations along 2 to 3 consecutive culture transfers. In the cultures, bacterial cells were on average 111 times more abundant than coccolithophores (2.07 x 10 7 ± 1.23 x 10 7 cells.ml -1 versus 2.87 x 10 5 ± 2.54 x 10 5 cells.ml -1 (Supplementary Figures 3 and4), which is consistent with bacterial communities commonly reported from other microalgal cultures (Amin et al., 2015). Mid to late exponential phase E. huxleyi cultures had average fluorescencebased maximum quantum yields for PSII (FV/FM) of 0.57 ± 0.03 (Supplementary Figure 5)

indicating that microbiomes were collected from healthy cultures [START_REF] Loebl | Physiological basis for high resistance to photoinhibition under nitrogen depletion in Emiliania huxleyi[END_REF].

A total of 204 samples (3 replicates for 58 strains, and 2 of 3 replicates for 15 strains yielded enough sequences) were analyzed for their prokaryotic community composition. After filtering, 430 ASVs corresponding to 5,158,560 reads were obtained across all the replicate samples and were used to compare them. Although a few exceptions were noticed, clustering analysis (Supplementary Figure 6) and community dissimilarities measured between biological replicates (average Bray-Curtis of 0.12; Supplementary Figure 7) and between coccolithophore strains (0.86) indicated that microbiomes of culture replicates were highly similar, suggesting that the composition of prokaryotic communities associated with E. huxleyi and G. oceanica cultures is stable across time while differing between coccolithophore strains. A substantial drift was however observed between the microbiomes of the same strain (RCC1844) maintained alive by subculturing and regrown after cryopreservation.

Composition and structure of the bacterial communities associated to coccolithophore cultures

A total of 418 ASVs (1,822,779 reads) were kept after merging replicates of each strain.

Prokaryotic reads from E. huxleyi and G. oceanica cultures were dominated by Proteobacteria (95.7%) with Alpha-and Gammaproteobacteria (21.2% and 74.5%, respectively) as the most abundant classes within the dataset (Figure 1). Among the other phyla, Rhodothermaeota

(1.8%) [START_REF] Munoz | Revised phylogeny of Bacteroidetes and proposal of sixteen new taxa and two new combinations including Rhodothermaeota phyl[END_REF] were frequently found and exceptionally dominated (RC3962)

while Bdellovibrionata and Actinobacteriota represented each less than 0.4%. Noticeably, sequences belonging to the two later phyla formed however considerable proportions (> 20%)

in certain strains (i.e. RCC3711 and RCC3985). Only a few reads (0.02% of total sequences) could not be classified at the phylum level. Marinobacteraceae (61% of total sequences) predominated in 71% of the cultures whereas Rhodobacteraceae (19% of total sequences), Alteromonadaceae (8% of total sequences), and Balneolaceae (2% of total sequences) were also found as prevalent families. Less than 1% of total sequences could not be classified at the family level.

The widespread dominance of Gammaproteobacteria was due to the prevalence of the genus Marinobacter (present in all coccolithophore cultures) that also represented the top four most abundant ASVs (ASV2 to 5). In total, we identified 28 ASVs (61% of total sequences) assigned to Marinobacter, most of them displaying a prevalence higher than 20%. The most abundant Marinobacter-related ASV, ASV2, was found in all cultures and accounted for 28%

of the total sequences. ASV3 (19% of total sequences) occurred in all cultures but one, while less prevalent ASV4 and ASV5 (9.92% of the total sequences) were present in 47% and 79%

of the cultures, respectively (Supplementary figure 8). Phylogenetic placement of the Marinobacter ASVs and 16S rRNA similarities revealed that they represent different species (Supplementary Figure 9). The affiliation of ASV3 and 5 other ASVs within M. salarius was highly supported (95% likelihood weight ratio). The short 16S rRNA region we sequenced was not resolutive enough (Supplementary Figure 9) to confidently place the ASVs. However, since the relatedness between Marinobacter ASVs was in the range of the 16S rRNA similarities that separate validated species of this genus, we assume that they may represent different species.

Other abundant genera (>1% of the sequences) included several members of the family Rhodobacteraceae such as an uncultured bacterium (ASV6), Marivita (ASV8 and ASV9), Shimia (ASV10), Pseudophaeobacter (ASV13) and members of the Alteromononadaceae (Alteromonas-ASV7) and the Balneolaceae (Balneola-ASV11) (Supplementary figure 8).

Other prevalent ASVs (> 20% of the samples) belonged to the genera Hoflea, Hyphomonas, Pseudomonas, Roseovarius, Janthinobacterium, and unclassified Rhodobacteraceae. Among them, those affiliated with Pseudomonas and with Janthinobacterium exhibited a very high prevalence (99 and 94% of the samples, respectively). Colors on the right side of the clusters correspond to the groups formed at the PCA.

Comparison of coccolithophore microbiome composition with that of other phytoplankton

To assess the similarity of other phytoplankton-associated microbiomes to that of coccolithophores, we combined our dataset with the data from published diatom (Behringer et al., 2018), Alexandrium (Sörenson et al., 2019) and Synechococcus and Prochlorococcusassociated prokaryotic communities (Kearney et al., 2021) (Figure 5). 16S rDNA ASVs were clustered at 97% identity (hereafter, OTUs) due to differences in the generation of the amplicon data in the published studies (see Material and Methods).

Heterotrophic communities in coccolithophore cultures shared about half of their OTUs with those found in cyanobacterial (51%) and Alexandrium spp. cultures (40%) (Figure 3), but only 10% (18/172) with those obtained from diatoms. The four groups of phytoplankton shared seven OTUs. The most prevalent (43%) among the total phytoplankton samples was and that of diatoms (Behringer et al., 2018), Alexandrium sp. (Sörenson et al., 2019), and cyanobacteria (Kearney et al., 2021).

Chapter 1 67

Relative abundance and distribution of dominant coccolithophore-associated bacteria in the world ocean

To determine the representation of dominant ASVs from coccolithophore cultures in the global oceans, we used the Tara Oceans and Malaspina 16S rRNA metabarcoding datasets, which together span 888 samples (723 Tara Oceans and 165 Malaspina) at multiple depths in locations throughout the globe (see Materials and Methods for details). We found perfect matches of our ASVs in the Tara Oceans dataset, while in the Malaspina dataset we considered only zOTUs with > 97.3% identity with our ASVs. ASV2 and ASV3, our two most abundant ASVs in cultures, were widely distributed across stations of both global expeditions but not abundant (Figure 4). Interestingly, they were enriched in the larger size fractions (> 3 µm).

Although their relative abundance differed in the upper ocean layers (Figure 4C Other Marinobacter ASVs highly abundant in coccolithophores (ASV4 and ASV5)

showed similar distribution patterns in the wild, and preference for surface, DCM and bathypelagic layers (Supplementary Figure 10). ASV7 (Alteromonas spp.) was the only ASV dominating in coccolithophore cultures that was abundant in the global oceans. Poorly found in the 3-20 µm fractions collected during the Tara Oceans Polar Circle expedition, ASV7

favored large size fractions of Mediterranean and subtropical waters (Supplementary Figure 11). ASV6, assigned to Rhodobacteraceae unclassified and Marivita (ASV8 and ASV9) had more restricted distributions (Supplementary Figures 11 and12). Contrasting with the above depth profiles, they were mostly found in surface waters, but not enriched in the largest plankton fractions although they populated fractions below 20 µm. Shimia (ASV10) did not follow the above patterns; although it was more abundant in size fractions > 5 µm, it was equally present in surface and bathypelagic layers (Supplementary figure 13).

Discussion

E. huxleyi and G. oceanica are the most intensively studied members of the coccolithophorid microalgae because they are common bloom-forming species and are easily cultured (Rhodes et al., 1995;Taylor et al., 2017). Surprisingly, information on the bacterial assemblages associated with coccolithophore blooms and laboratory cultures is currently limited [START_REF] Gonzalez | Bacterial community structure associated with a dimethylsulfoniopropionate-producing North Atlantic algal bloom[END_REF]Zubkov et al., 2001;Green et al., 2015;Orata et al., 2016;Rosana et al., 2016). Today, the largest body of research on coccolithophores-bacteria interactions is devoted to antagonistic relationships that lead to E. huxleyi cell death (Seymour et al., 2010;Seyedsayamdost et al., 2011;Harvey et al., 2016;[START_REF] Mayers | A bacterial pathogen displaying temperature-enhanced virulence of the microalga Emiliania huxleyi[END_REF]Segev et al., 2016), suggesting that bacteria could participate in bloom demises (Barak-Gavish et al., 2018). We address the paucity of knowledge on the total diversity and variation of bacteria associated with coccolithophores by performing the first high resolution investigation of the bacterial communities associated with a large set of E. huxleyi and G. oceanica cultures.

Coccolithophore microbiomes were dominated by Gammaproteobacteria followed by Alphaproteobacteria. Both classes are often reported as abundant in microalgal microbiomes however with varying magnitude. Similarly, to our study, Gammaproteobacteria dominated the microbiomes of the green algae Ostreococcus tauri and the dinoflagellate Cochlodinium polykrikoides (Lupette et al., 2016;Shin et al., 2018). However, a dominance of Alphaproteobacteria is more common in microalgal cultures and it was reported for several species of diatoms (Ajani et al., 2018;Behringer et al., 2018;Chernikova et al., 2020), dinoflagellates (Shin et al., 2018;Sörenson et al., 2019) and the haptophyte Pavlova lutheri (Chernikova et al., 2020). Alphaproteobacteria were also found to dominate 16S rRNA gene clone libraries from E. huxleyi and Coccolithus pelagicus f. braarudii cultures (Green et al., 2015), but only a limited number of cultures and clones were analyzed. The reason of differential dominance of Gammaproteobacteria or Alphaproteobacteria in phytoplankton microbiomes is yet unknown because both classes contain r-strategists (most copiotrophs that prefer nutrient-rich niches and have a metabolism of low efficiency but high plasticity) and Kstrategists (most oligotrophs that have long phases of continuous growth characterized by a constant low growth rate). However, the boundary between these two major trophic strategies is sometimes unclear, and representatives belonging to the oligotrophic guild of both classes show an improved growth under nutrient-rich conditions [START_REF] Luo | Evolution of divergent life history strategies in marine alphaproteobacteria[END_REF]Spring and Riedel, 2013). Indeed, the higher proportion of Gammaproteobacteria in coccolithophore cultures could correspond to species with higher versatility and capacity to degrade complex carbohydrates derived from these hosts.

The core microbiome is often defined as the bacteria that are found in all the samples of a given host with a relative abundance higher than 0.0001% (Lawson et al., 2018;Sörenson et al., 2019). The presence of a core microbiome suggests stable and potentially symbiotic interactions [START_REF] Hernandez-Agreda | Defining the Core Microbiome in Corals' Microbial Soup[END_REF]. Here we identified a Marinobacter sp. as the unique core ASV. Marinobacter is a very diversified genus (Gauthier et al., 1992;[START_REF] De La Haba | Taxonomy of halophiles[END_REF] and its phenotypic versatility contributes to its ubiquity and ability to occupy an exceptionally wide range of marine habitats. While Marinobacter spp. account numerically for only a small proportion of the total bacteria present in the field (Green et al., 2015), they are often the dominant and/or core genus associated with cultures of cyanobacteria, coccolithophores, dinoflagellates, and a chlorophyte (Green et al., 2015;Lupette et al., 2016;Lawson et al., 2018;Sörenson et al., 2019;Kearney et al., 2021). In terms of functions, the Marinobacter symbionts can promote growth of the dinoflagellate G. catenatum (Bolch et al., 2011[START_REF] Bolch | Bacterial associates modify growth dynamics of the dinoflagellate Gymnodinium catenatum[END_REF] and of the cyanobacterium Prochlorococcus [START_REF] Sher | Response of Prochlorococcus ecotypes to co-culture with diverse marine bacteria[END_REF]. In contrast, Marinobacter spp. are much less abundant/prevalent in diatom cultures (Amin et al., 2009;Green et al., 2015). Diatom-bacteria co-culture experiments suggest however their functional importance, either by promoting algal assimilation of iron (Amin et al., 2009), inducing cell aggregation [START_REF] Gärdes | Diatom-associated bacteria are required for aggregation of Thalassiosira weissflogii[END_REF][START_REF] Gärdes | Effects of Marinobacter adhaerens HP15 on polymer exudation by Thalassiosira weissflogii at different N: P ratios[END_REF], or reducing mating success without affecting growth [START_REF] Cirri | Biofilm interactions-bacteria modulate sexual reproduction success of the diatom Seminavis robusta[END_REF]. Negative growth-affecting properties of Marinobacter strains have also been demonstrated in co-culturing experiments with diatoms (Wang et al., 2014;[START_REF] Johansson | Friends With Benefits: Exploring the Phycosphere of the Marine Diatom Skeletonema marinoi[END_REF][START_REF] Stock | Host specificity in diatom-bacteria interactions alleviates antagonistic effects[END_REF].

In order to unveil possible drivers of the bacterial composition in coccolithophore cultures, we compared 73 strains from two closely related taxa including all the E. huxleyi morphotypes, covering the global ocean and isolated between 61 and 6 years before our experiment. None of the parameters tested could explain the bacterial composition patterns.

Recent studies have shown that the microbiome diversity of phytoplankton strains becomes rapidly stable and consistent across time once cultures have been established (Behringer et al., 2018;Sörenson et al., 2019;Kearney et al., 2021). Taken together, our results also show that the microbiomes of coccolithophores are consistent and stable among strains although we identified shifts in relative abundance for some members. This probably explains why no significant differences were found between cultures maintained for up to 61 years and others obtained from more recent cruises. We did not find significant differences either between the microbiomes of E. huxleyi and G. oceanica, or between the E. huxleyi morphotypes whose varying carbonate contents could be putatively influence associated bacterial communities (Green et al., 2015). Similarly, no significant differences were found between the microbiome structure of Leptocylindrus species (Ajani et al., 2018). However, place/time from which these diatoms were isolated had a stronger effect on the microbiome selection than host specificity.

A similar situation was observed in Thalassiosira rotula whose microbiomes differed across seasons (Mönnich et al., 2020).

We found that variability in the bacterial community between cultures was ascribed to differences in relative abundance of the most abundant ASVs. This finding supports previous results showing the influence of certain bacteria on the microbiome assembly in axenic cultures of Thalassiosira rotula (Majzoub et al., 2019), and of a Marinobacter strain on the microbiome of Cylindrotheca closterium [START_REF] Stock | Host specificity in diatom-bacteria interactions alleviates antagonistic effects[END_REF]. Interestingly, by comparing coccolithophoreassociated bacteria with that of diatoms (Behringer et al., 2018), cyanobacteria (Kearney et al., 2021) and Alexandrium spp. (Sörenson et al., 2019), we demonstrated that all the microbiomes dominated by Marinobacter shared the highest number of OTUs. A possible explanation is that these hosts may release common specific carbon and nutrient compounds selecting for the associated copiotrophs like Marinobacteraceae and Alteromonadaceae that outcompete other heterotrophic bacteria. Marinobacter and Alteromonas can metabolize hydrocarbons and C1 compounds derived from lipid catabolism, suggesting that hydrocarbons might play an important role in their success in phytoplankton cultures (Green et al., 2015). Our results suggest that mechanisms such as bacterial signaling or competition for common resources may be important for determining the overall bacterial community composition and structure in coccolithophore cultures.

The OTUs shared between coccolithophores, cyanobacteria, diatoms and Alexandrium spp. belong to archetypal phytoplankton-associated families (Buchan et al., 2014) such as the Rhodobacteraceae, Alteromonadaceae, Flavobacteriaceae and Pseudomonadaceae. The presence of Alteromonas sp. among the most shared OTUs is not surprising since these metabolically versatile copiotrophs respond rapidly to increases in dissolved organic matter [START_REF] Shi | Transcriptional responses of surface water marine microbial assemblages to deep-sea water amendment[END_REF][START_REF] Hogle | Copiotrophic marine bacteria are associated with strong iron-binding ligand production during phytoplankton blooms[END_REF] including algal exudates [START_REF] Romera-Castillo | Net production and consumption of fluorescent colored dissolved organic matter by natural bacterial assemblages growing on marine phytoplankton exudates[END_REF] and often dominate in phytoplankton cultures and blooms (Buchan et al., 2014). Alteromonas bacteria have previously been shown to interact with individual eukaryotic and prokaryotic phytoplankton species. These interactions range from impairing algal growth (Mayali and Azam, 2004) to effects that are either neutral or beneficial to algal growth in co-culture [START_REF] Sher | Response of Prochlorococcus ecotypes to co-culture with diverse marine bacteria[END_REF][START_REF] Le Chevanton | Screening and selection of growth-promoting bacteria for Dunaliella cultures[END_REF]. Not surprisingly, the E. huxleyi ASV7 clustered into Alteromonas OTU was the most abundant found in 16S rDNA metabarcoding datasets from the world ocean. Our cross-phytoplankton analysis also allowed identification of OTUs specific to coccolithophores, suggesting some level of selection from or adaptation to the host.

The most prevalent coccolithophore-specific OTUs, assigned to Pseudomonas sp. and Janthinobacterium sp., were not abundant. Like Marinobacter sp., members of the genus Pseudomonas are r-strategists, generalist bacteria that are typically enriched in the attached communities of microalgal cultures (Kimbrel et al., 2019). They can also exert negative effects on phytoplankton growth performance [START_REF] Le Chevanton | Screening and selection of growth-promoting bacteria for Dunaliella cultures[END_REF]. The presence of the Janthinobacterium sp. in marine settings is not well documented. These betaproteobacteria are usually rare members of the bacterioplankton, yet Alonso-Sáez et al., (2014) reported a bloom of a Janthinobacterium sp. population in the Arctic Ocean. The genus reached up to 22% of the total of bacteria in the epipelagic zone and remained abundant in the mesopelagic layer. Janthinobacterium sp. also displayed high abundance in the particle-attached fractions of mesopelagic and bathypelagic water masses of the bathypelagic Southern Ocean, suggesting an obligate particle-associated lifestyle [START_REF] Milici | Diversity and community composition of particle-associated and free-living bacteria in mesopelagic and bathypelagic Southern Ocean water masses: Evidence of dispersal limitation in the Bransfield Strait[END_REF]. The high prevalence of Janthinobacterium sp. in coccolithophores cultures may be due to the exchange of common goods. Interestingly, an isolate from snowfield, that displayed the capacity to produce siderophores and auxins, significatively enhanced the growth of Chlorella vulgaris compared to axenic controls (Krug et al., 2020). Although Bacteroidota are found as substantial components in phytoplankton-associated assemblages (Ajani et al., 2018;Sörenson et al., 2019), they represented low proportions in our cultures. However, about 30% of the coccolithophore-specific OTUs belonged to this phylum. In the environment, low abundance microbes have been proposed to have important ecological roles such as stabilizing ecosystem processes after disturbance or maintaining critical biogeochemical functions [START_REF] Lynch | Ecology and exploration of the rare biosphere[END_REF]. Their role in phytoplankton cultures needs further investigation.

Interestingly, heterotrophic bacteria most prominently enriched in coccolithophore cultures are not very abundant in oceanic waters (Figure 4). Marinoacteraceae and other copiotrophs typically occur at a rather low abundance in oligotrophic marine habitats [START_REF] Eilers | Succession of pelagic marine bacteria during enrichment: A close look at cultivation-induced shifts[END_REF] but can grow rapidly in changing environments and dominate. Local variations in nutrient content can occur in the ocean because of physical processes, such as upwelling of nutrient rich deep waters or biological processes such as phytoplankton blooms or aggregation of particulate organic matter [START_REF] Tada | Differing growth responses of major phylogenetic groups of marine bacteria to natural phytoplankton blooms in the Western North Pacific Ocean[END_REF]Teeling et al., 2012Teeling et al., , 2016)). In cultures, associated bacteria experience nutrient-rich conditions provided by coccolithophore released exudates and from the cultivation medium. Such exudates, mainly polysaccharides (Mühlenbruch et al., 2018), are also likely abundant in nutrient-rich particles, or ephemeral patches of high organic carbon distributed in open ocean waters [START_REF] Stocker | Marine microbes see a sea of gradients[END_REF]Seymour et al., 2017). The distribution patterns in the open ocean of coccolithophore-associated copiotrophs are not well documented. Our analyses demonstrated that they are ubiquitous, most of them being preferably associated with relatively large plankton size fractions, and enriched in bathypelagic waters. These large plankton size fractions contain phytoplankton, heterotrophic protists, and zooplankton, as well as detritus, fecal pellets, and diverse types of marine aggregates. Marinobacter sp. and Alteromonas sp., the dominant members in our cultures, are good candidates for attachment to macroaggregates (marine snow; 500 μm to centimeters in diameter) and microaggregates (1 to 500μm) (Simon et al., 2002), and further sinking to the deep ocean layers and sediments. Indeed, previous studies reported the presence of Marinobacter in the particle-attached fraction [START_REF] Li | Variation in abundance and community structure of particle-attached and free-living bacteria in the South China Sea[END_REF], their isolation from bathypelagic waters [START_REF] Kai | Diversity of culturable bacteria in deep-sea water from the South Atlantic Ocean[END_REF][START_REF] Sanz-Sáez | Diversity and distribution of marine heterotrophic bacteria from a large culture collection[END_REF], as well as their potential for organic matter degradation (Fontanez et al., 2015) and colonization of the deep ocean [START_REF] Sebastián | High growth potential of long-term starved deep ocean opportunistic heterotrophic bacteria[END_REF]. A strain of M. adherens, a very close relative of ASV5, was isolated from marine aggregates and it was found to specifically attach to the surface of the diatom Thalassiosira weissflogii grown in culture, inducing exopolymer and aggregate formation and thus generating marine snow particles [START_REF] Gärdes | Diatom-associated bacteria are required for aggregation of Thalassiosira weissflogii[END_REF][START_REF] Gärdes | Effects of Marinobacter adhaerens HP15 on polymer exudation by Thalassiosira weissflogii at different N: P ratios[END_REF]. Finally, Alteromonadaceae were recently found being enriched in diatom-produced and natural transparent exopolymer particles (TEP) [START_REF] Taylor | Coastal bacterioplankton community response to diatomderived polysaccharide microgels[END_REF][START_REF] Zäncker | Bacterial communities associated with individual transparent exopolymer particles (TEP)[END_REF], that are ubiquitous microgels formed by the aggregation of biogenic precursors [START_REF] Passow | Transparent exopolymer particles (TEP) in aquatic environments[END_REF]. Our findings support the hypothesis that bacteria from the surface thrive at depth and that a strong vertical connectivity via particle sinking exists through the entire water column in the ocean [START_REF] Mestre | Sinking particles promote vertical connectivity in the ocean microbiome[END_REF].

They also suggest that phytoplankton-associated copiotrophs likely contribute to these communities. The demise of E. huxleyi's oceanic blooms is often attributed to coccolithoviruses and the transport of cellular debris and associated particulate organic carbon to depth is facilitated by TEP [START_REF] Bratbak | Viral mortality of the marine alga Emiliania huxleyi (Haptophyceae) and termination of algal blooms[END_REF]Vardi et al., 2012;Laber et al., 2018).

However, some coccolithophore-associated ASVs (Marivita ASV8 and ASV9) did not follow this prevalent pattern. Indeed, our results also showed that bacteria preferentially associated with smaller particles (0.8-5 µm) were less abundant in the bathypelagic layers. Although we don't know yet how biological processes control the size and amount of particles produced in the surface ocean, they will differently impact the bathypelagic realm [START_REF] Ruiz-González | Major imprint of surface plankton on deep ocean prokaryotic structure and activity[END_REF]. This suggests that the vertical connectivity from surface to depth is highly dependent on bacteria-phytoplankton interactions in sunlit water masses. To confirm our hypothesis in natural populations, future field studies in natural E. huxleyi blooms should involve detailed investigations of the relative involvement of bacteria in the production and/or utilization of particles throughout the water column.

Conclusions

Microbiomes of coccolithophore cultures represent stable communities and their taxonomic composition bore notable similarities to that of other marine phytoplankton. Our study confirms the propensity of Marinobacter to colonize the phycosphere in phytoplankton cultures. Although a large number of cultivated representatives and several sequenced genomes exist, the functional breath of Marinobacter species remains largely unexplored. The ability to metabolize hydrocarbons has been tested in relatively few species and we know little about the nature and magnitude of their actual function and interactions in phytoplankton cultures and in the field. Stable and specific microbiomes over time imply that abundant associated taxa may have functional importance to their host. To understand the stability of interspecies and interkingdom interactions, we need to test whether the relative taxonomic variability between phytoplankton-associated bacteria is related to functional variations and to which extent specific organisms can affect the cellular physiology of the interacting partners and ultimately the stability of the community. Our current knowledge of microalgae-bacteria interactions is based upon pairwise species interactions. The challenge is now to design approaches involving multispecies interactions, including viruses, that could help to better understand the stability of species interactions and ultimately how the dynamical networks building microbiomes shape community dynamics. Fontanez, K.M., Eppley, J.M., Samo, T.J., Karl, D.M., and DeLong, E.F. ( 2015

SUPPLEMENTARY MATERIAL The microbiome of the cosmopolitan coccolithophore Emiliania huxleyi

Supplementary Material and Methods

Sequencing

At the Genotoul platform, single multiplexing was performed using homemade 6 bp index, which were added to reverse primer during a second PCR with 12 cycles using forward primer (AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGAC) and reverse primer (CAAGCAGAAGACGGCATACGAGAT-index-GTGACTGGAGTTCAGACGTGT). The resulting PCR products were purified and loaded onto the Illumina MiSeq V3 cartridge according to the manufacturer instructions. The quality of the run was checked internally using PhiX, and then each pair-end sequence was assigned to its sample with the help of the previously integrated index.

Global ocean sequence datasets

Datasets

ASVs identified in coccolithophore cultures were compared to two datasets of Illumina 16S rRNA gene sequences retrieved across the world's oceans. The first dataset comprised a total of 723 samples collected during the Tara Oceans 2009 and Tara Oceans Polar Circle 2013 expeditions which covered the major oceanic provinces and latitudes (65º S -80º N). Sampling strategy and methodology are described in [START_REF] Pesant | Open science resources for the discovery and analysis of Tara Oceans data[END_REF]. In this dataset, six size fractions were collected in the surface, DCM and mesopelagic layers [0.2-3 µm (50 samples), 0.8-5 µm oceans. Surface seawater (3m) samples were collected and filtered as described previously in [START_REF] Ruiz-González | Higher contribution of globally rare bacterial taxa reflects environmental transitions across the surface ocean[END_REF]. For these surface samples we focused on the 0.2-3 μm fraction, which represents mostly free-living bacteria. On the other hand, bathypelagic samples (from 2150 to 4018 m depth) were collected and filtered as described in [START_REF] Salazar | Global diversity and biogeography of deep-sea pelagic prokaryotes[END_REF]. In these cases, two different size fractions were analyzed representing the free-living (0.2-0.8 μm)

and the particle-attached (0.8-20 μm) bacterial communities. In both datasets, once seawater was processed, filters were flash-frozen in liquid nitrogen and stored at -80 ºC until DNA extraction.

DNA extraction, amplification and sequencing

The DNA from the samples of the different datasets described was extracted with a phenol-chloroform protocol, as described elsewhere [START_REF] Massana | Vertical distribution and phylogenetic characterization of marine planktonic Archaea in the Santa Barbara Channel[END_REF][START_REF] Salazar | Global diversity and biogeography of deep-sea pelagic prokaryotes[END_REF][START_REF] Alberti | Data Descriptor : Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition[END_REF]. Prokaryotic barcodes for each of the datasets was generated by amplifying the V4 and V5 hypervariable regions of the 16S rRNA gene using primers 515F-Y (5'-GTG YCA GCM GCC GCG GTA A-3') and 926R (5′-CCG YCA ATT YMT TTR AGT TT-3′) described in (Parada et al., 2016). Sequencing was performed in an Illumina MiSeq platform (iTAGs) using 2x250 bp paired-end approach at the Research and Testing Laboratory facility (Lubbock, TX, USA) for the Malaspina datasets and at Genoscope (Evry Cedex, France) for the Tara Oceans and Tara Polar Oceans dataset.

16S rRNA Illumina sequences processing

The ASV table from the Tara Oceans dataset was obtained following the methods as described for the E. huxleyi microbiome dataset with few exceptions. First, since raw reads were in mixed orientation and R1 and R2 files contained reads with forward and reverse primers, the primer removal was run twice generating forward and reverse reads for each file.

These two runs were performed in a non-anchored mode with the addition of the flag --pairadapters and a length threshold of 75 (Martin, 2011). Additional steps of primer removal were added in order to remove remaining primers using the flag --times 5. Since R1 and R2 cycles can have different error rates, the forward and reverse reads obtained from each cycle were analyzed independently during all the DADA2 processing (Callahan et al., 2016). Another difference was that default parameters were used at the learnerrors step. Then, after the ASV inference in pooled mode, the forward and reverse reads from the R1 file were merged using the function mergePairs. The reads coming from the R2 file were also merged but in an inverse position (reverse + forward). The sequences tables were built using the function makeSequenceTable and the sequence table obtained for R2 file was reverse complemented. Sequence tables from R1 and R2 files were merged and the chimera removal was done using method consensus. Finally, the taxonomic assignation was performed using IDtaxa (confidence threshold of 40) (Murali et al., 2018) and vsearch (using usearch_global and 90% similarity) (Rognes et al., 2016).

Computing analyses of the Malaspina dataset were run at the MARBITS bioinformatics platform at the Institut de Ciències del Mar and at the Euler scientific compute cluster of the ETH Zürich University. The obtained amplicons were processed through the bioinformatic pipeline described in the github repository https://github.com/SushiLab/Amplicon_Recipes.

Briefly, pair-end reads were merged at a minimum 90% of identity alignment, and those with ≤ 1 expected error were selected (quality filtering). Primer matching was performed with CUTADAPT v.1.9.1 (Martin, 2011). Dereplication and zOTU (zero-radius OTUs) denoising at 100% similarity (UNOISE algorithm) were performed with USEARCH v.10.0.240 (Edgar, 2010). zOTUs were taxonomically annotated against the SILVA database v132 (2017) with the LCA (lowest common ancestor) approach. Finally, zOTUs were quantified to obtain zOTUabundance tables. Non-prokaryotic ASVs and zOTUs (eukaryotes, chloroplast and mitochondria) were removed from the datasets. Supplementary figure 6. Hierarchical cluster analysis using complete linkage method of Bray-Curtis dissimilarity of the microbiomes sampled at two/three consecutive culture transfers for maintenance. We hypothesized that if the environmental filters drive the microbiome selection this would be identified by correlating community composition and environmental parameters.

Supplementary

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Conversely, if the host drives the assembly, we would find consistency in the microbiomes of each microalgal group whatever their sample of origin. Similar conclusions were expected if the medium contributed to the selection of phytoplankton microbiomes. Consequently, microalgae isolated using the same medium, independent of host or sample of origin would have the most similar microbiomes. Finally, if the assembly process was guided by random selection, we expected to find no consistency in the microbiomes at any level tested.

This chapter expanded the knowledge regarding the factors involved in microbiome selection by showing that environmental parameters at the time of isolation were significantly responsible for the microbiome composition in cultures while the influence of the hosts was low. In addition, we also identified the influence of the culture medium and a shared effect of medium and environment that could not be disentangled. To our knowledge, this is the first study investigating the influence of environmental parameters on the diversity of microbiomes maintained for many years (up to 22 years) in laboratory conditions. This work will be submitted to Environmental Microbiology Reports.

Introduction

Marine phytoplankton and bacteria display tightly connected interactions that can strongly influence global biogeochemical processes, such as the carbon and nutrient cycles in the oceans (Amin et al., 2015;Seymour et al., 2017). The surrounding region of the phytoplankton cell, the phycosphere, is characterized by a gradient of oxygen and organic compounds which connects phytoplankton to the surrounding bacterial communities through the exchanges of molecules (Seymour et al., 2017;Kimbrel et al., 2019). In the past decades, numerous studies documented the diversity of phycosphere-associated bacteria in marine natural phytoplankton populations (Buchan et al., 2014;Zhou et al., 2018Zhou et al., , 2019) ) and in cultures [START_REF] Grossart | Interactions between marine bacteria and axenic diatoms (Cylindrotheca fusiformis, Nitzschia laevis, and Thalassiosira weissflogii) incubated under various conditions in the lab[END_REF]Green et al., 2015;Krohn-Molt et al., 2017;Ajani et al., 2018;Behringer et al., 2018;Lawson et al., 2018;Sörenson et al., 2019;Mönnich et al., 2020). Roseobacters, Flavobacteriia and members of the Gammaproteobacteria are typically the most dominant phytoplankton-associated bacteria (Buchan et al., 2014;Amin et al., 2015). However, little is known about the drivers determining the composition and structure of these complex assemblages.

Assembly process in the phycosphere is often discussed in the framework of the traditional niche-based theory, which postulates that specific variables (biotic and abiotic interactions, life history traits) determine how communities are organized [START_REF] Hutchinson | Concluding remarks[END_REF].

Deterministic processes, such as the host genotypes [START_REF] Eigemann | Bacterial community composition associated with freshwater algae: Species specificity vs. dependency on environmental conditions and source community[END_REF]Behringer et al., 2018;Lawson et al., 2018;Sörenson et al., 2019) and the place of origin (Ajani et al., 2018), were shown to have an important role on selecting the microbiome diversity of marine phytoplankton cultures. Phytoplankton species can release specific organic compounds which select for their phycosphere-associated bacteria (Smriga et al., 2016;Fu et al., 2020).

Phytoplankton cultures can be affected by culture conditions (light intensity and medium composition), which are suspected to have also a critical role in the establishment and diversity of associated bacterial communities (Behringer et al., 2018;Jackrel et al., 2020). In the ocean, local physico-chemical conditions are key drivers of the abundance and distribution of phytoplankton and bacterial populations, which in turn, can impact their community composition [START_REF] Vargas | Eukaryotic plankton diversity in the sunlit ocean[END_REF]Sunagawa et al., 2015;Zhou et al., 2018;[START_REF] Logares | Disentangling the mechanisms shaping the surface ocean microbiota[END_REF]. Finally, biological interactions are also forces that shape phytoplankton-associated bacterial communities in culture [START_REF] Eigemann | Bacterial community composition associated with freshwater algae: Species specificity vs. dependency on environmental conditions and source community[END_REF]Majzoub et al., 2019) and in the field (Zhou et al., 2018).

Recently, assembly models such as the 'competitive lottery', that take into account the influence of stochasticity in determining the community structure, have been used in phycosphere studies (Kimbrel et al., 2019;Zhou et al., 2019;Mönnich et al., 2020). The competitive lottery model proposes that a pool of species sharing the same niche are equally able to colonize it and the one arriving first wins the lottery (Sale, 1979). The model considers the redundancy of species that are equally able to colonize a niche and the influence of stochastic and priority effects determining which species will succeed. Increasing attention is currently brought to this model in microalgal microbiome studies because it can explain why bacterial groups present in low abundance in the natural environment (as free living) are typically enriched in phytoplankton blooms and cultures, suggesting that they represent a functional guild able to colonize the phycosphere (Buchan et al., 2014). Meanwhile, this model can also explain how stochasticity is involved in the recruitment of different microbiomes within strains of the same phytoplankton species (Ajani et al., 2018).

In this study, our objective was to disentangle deterministic and stochastic factors driving the diversity of phytoplankton-associated bacteria. We used cultures of different phytoplankton groups isolated from the same seawater samples in various oceanic regions. We identified their associated bacterial taxa and examined the effect of oceanic environmental parameters, hosts type, and culture media composition on the microbiome assembly. Our results demonstrate the detectable influence of environmental filtering on the bacterial communities associated with phytoplankton cultures even after years of laboratory maintenance and question the role of the host in the microbiome assembly.

Material and Methods

Origin and growth conditions of phytoplankton cultures

The 43 strains used in this study were isolated from six oceanographic cruises and were obtained from the Roscoff Culture Collection (RCC, http://roscoff-culture-collection.org/) (Supplementary table 1). These sampling cruises covered a wide variety of oceanic regions that displayed different trophic regimes in the Mediterranean Sea, the North to South Atlantic, and the South-East and South-West Pacific oceans (reference information on these cruises is detailed in the Supplementary Methods). Monoclonal phytoplankton batch cultures were established by isolating single cells either by micropipetting, filtration, serial dilution or flow cytometry cell sorting, transferred into culture medium and maintained under specific conditions by the RCC (Supplementary Table 1). Upon receipt, cultures were grown using their routine maintenance conditions (Supplementary Table 1) in different culture media prepared using natural aged seawater collected at the SOMLIT-ASTAN monitoring site (48°46'18''N-

Sequencing data processing and bacterial community analysis

The first steps of the bioinformatic treatment and quality control were performed by Fasteris SA. First, in order to separate the libraries, the base calling was done based on a 6 nt unique index, using the softwares MiSeq Control Software 2.6.2.1, RTA 1.18.54 and bcl2fastq2.17 v2.17.1.14 and allowing 1 mismatch. Then, Illumina standard adapters removal and quality trimming were done using the Trimmomatic package (version 0.32) (Bolger et al., 2014). Briefly paired-end reads were globally aligned to ensure an end-to-end match. The adapters sequences were identified allowing a maximum of 2 mismatches and removed if the quality score was higher than 30. Then, bases were filtered by quality using a 4-base sliding window scan and trimming was done when the average quality per base dropped below 5. Reads without insert and with ambiguities were removed.

All the scripts used for the further bioinformatic analyses can be downloaded from:

https://github.com/mcamarareis/. Briefly, raw reads from each sequencing run were demultiplexed based on the 8 nucleotide tag sequences with cutadapt (version 2.8.1) using anchored mode and allowing no mismatches, insertions or deletions (Martin, 2011). Since R1 and R2 files (corresponding to each cycle of paired-end sequencing) contained reads with forward and reverse primers (further called forward and reverse reads), the demultiplexing was run two times. The first and second demultiplexing searched for adapters in the R1 file and in the R2 file, respectively, which separated forward and reverse reads for each sample corresponding to each cycle. The primer sequences were removed using the same software allowing mismatches at a rate of 0.1 (insertions and deletions were not allowed) (Martin, 2011).

Since R1 and R2 cycles can have different error rates, they were analyzed independently. We used the DADA2 processing to obtain a table of amplicon sequence variants (ASVs) (Callahan et al., 2016). First, the sequences were filtered using the function filterAndTrim with default values and trimmed according to the sequence quality. Then, a random subset of the reads (nbases = 2e+08) was used to learn the error models from forward and reverse reads using the function learnerrors. The ASVs were inferred in pooled mode.

Following ASV inference, the forward and reverse reads of R1 were merged. The reads coming from the R2 file were also merged but in an inverse position (reverse + forward). The sequences tables were built using the function makeSequenceTable and the sequence table obtained for R2 file was reverse complemented. Before chimera removal, the sequence tables obtained independently for R1 and R2 were merged. Six samples (3 biological replicates of 2 cultures) that were sequenced in a previous run (see Chapter 1) and processed independently were included in the sequence table for chimera removal, also performed in pooled mode. The sequences were filtered according to the range of length of the primers (from 366 to 376 bp) and the resulting fasta file was used to assign taxonomy using Silva database v138 by IDtaxa (using a confidence threshold of 50) (Quast et al., 2013;Murali et al., 2018). Chloroplasts and mitochondrial sequences were removed. Sequences not classified at the domain level by IDtaxa were classified using vsearch global alignment (90% identity) using Silva v138 (Rognes et al., 2016) (Rognes et al., 2016). These sequences were removed if they could not be classified and/or classified as chloroplasts or mitochondrial sequences by vsearch (Rognes et al., 2016).

In addition, since we noticed that IDtaxa failed to assign taxonomy to many Rhodobacteraceae sequences at the genus level, we used vsearch taxonomy (at 94.5% threshold (Yarza et al., 2014)) for the taxonomic diversity plots. The resultant ASV table was filtered to remove ASVs accounting for less than 0.001% (24 reads) of the total number of reads. Consistency of technical replicates was evaluated by hierarchical cluster analysis (HCA) using Bray-Curtis dissimilarity of relative abundance data. After consistency was confirmed, independent replicates (technical and biological) of each culture were merged. Abundance and prevalence filters removed 58% of the total number of ASVs while keeping 99% of the reads.

Statistical analyses

Statistical analyses were performed in R (v 4.0.2) using Rstudio (1.1.442) (RStudio Team, 2016; R Core Team, 2017) and graphs were produced using ggplot2 (v3.3.3) (Wickham, 2016), unless otherwise stated. Analyses of presence-absence data (alpha-diversity indexes, Jaccard dissimilarity and IndVal analysis) were performed using the rarefied ASV table.

Analyses of relative abundance (diversity plots, relative abundance of groups and calculation of the other metrics) were performed using the non-rarefied ASV table to keep as much information as possible. The ASV table was rarefied 100 times at the minimum number of reads (8,522) using the function rtk (seed=1,000) from rtk package (v0.2.6.1) (Saary et al., 2017).

The mean alpha diversity indexes were obtained using the function get.mean.diversity. The diversity plot was produced using relative abundance of ASVs at the family level.

To investigate beta diversity patterns and their consistency we compared 5 betadiversity metrics: Jaccard dissimilarity (using presence-absence) [START_REF] Jaccard | Étude comparative de la distribution florale dans une portion des Alpes et des Jura[END_REF], Bray-Curtis dissimilarity calculated using the relative abundance data (Bray and Curtis, 1957), Euclidean distance of Hellinger-transformed data (Hellinger distance) (Legendre and Gallagher, 2001),

Aitchison distance, i.e. Euclidean distance Centered-Log Ratio (CLR)-transformed data [START_REF] Gloor | Microbiome datasets are compositional: And this is not optional[END_REF] and Euclidean distance of Phylogenetic Isometric-Log Ratio (PhILR)-transformed data [START_REF] Silverman | A phylogenetic transform enhances analysis of compositional microbiota data[END_REF]. To account for the compositional nature of microbiome data from high-throughput sequencing we tested two compositional transformation methods [START_REF] Gloor | Microbiome datasets are compositional: And this is not optional[END_REF]. CLR and PhILR transformations take into account the compositional nature of microbiomes data by applying log-transformation [START_REF] Gloor | Microbiome datasets are compositional: And this is not optional[END_REF][START_REF] Silverman | A phylogenetic transform enhances analysis of compositional microbiota data[END_REF].

While CLR considers only the abundance of ASVs, PhILR additionally incorporates phylogenetic models to log-transform the abundance data [START_REF] Silverman | A phylogenetic transform enhances analysis of compositional microbiota data[END_REF]. [START_REF] Palarea-Albaladejo | ZCompositions -R package for multivariate imputation of left-censored data under a compositional approach[END_REF]. CLR transformation was performed by using the function clr from easyCoda package (v0.34.3) [START_REF] Greenacre | Compositional Data Analysis in Practice[END_REF]. For PhILR transformation, a constant of 1 was added to each abundance value on the community data to avoid zeros [START_REF] Silverman | A phylogenetic transform enhances analysis of compositional microbiota data[END_REF]. This transformation uses phylogenetic information of the ASVs. For this, sequences were aligned using mafft 7.110 (Katoh and Standley, 2013) and the phylogenetic tree was built using GTRCAT model from FastTree Version 2.1.11 [START_REF] Price | FastTree 2 -Approximately maximumlikelihood trees for large alignments[END_REF]. In the absence of archaeal ASVs, the phylogenetic tree was rooted at 2 Planctomycetota ASVs using the function root of the seqinr package (v.2.5) [START_REF] Charif | Seqin{R} 1.0-2: a contributed package to the {R} project for statistical computing devoted to biological sequences retrieval and analysis[END_REF][START_REF] Silverman | A phylogenetic transform enhances analysis of compositional microbiota data[END_REF]. Finally, PhILR transformation was carried using the phylogenetic tree by applying the function philr (package philr v1.14.0) [START_REF] Silverman | A phylogenetic transform enhances analysis of compositional microbiota data[END_REF].

To explore beta diversity patterns, we performed HCA for the different distance metrics using method Ward (ward.D, function hclust) (Borcard et al., 2011). The influence of environmental parameters (temperature, salinity, phosphate and chlorophyll a (Chl a)

concentrations transformed into z-scores) on the microbiome's diversity was tested by constrained distance-based redundancy analysis (db-RDA) for Jaccard and Bray-Curtis dissimilarities [START_REF] Legendre | Distance-based redundancy analysis: Testing multispecies responses in multifactorial ecological experiments[END_REF] and by RDA for CLR, Hellinger and PhILR transformed data (van den Wollenberg, 1977). The significance of the model was tested by anova-like permutation test (1,000 permutations) and plots of the influence of the environmental data on the community were produced using the function plot.

To quantify and disentangle the influence of the environment and microalgal hosts on the microbiome assembly, we performed variation partitioning (function varpart) including hosts [grouped as Emiliania huxleyi (14 strains), Pelagophyceae (10 strains) and other ( 19strains)] and transformed environmental parameters [START_REF] Borcard | Partialling out the spatial component of ecological variation[END_REF]. Before running the variation partitioning, homogeneity of multivariate beta-dispersion in the microalgal groups was checked using the function betadisper (using bias.adjust to account for different sample size among groups) and significance of the model and variables was tested using anova-like permutation test (Anderson and Walsh, 2013). The categorical variable corresponding to hosts was converted in numerical data by using the function model.matrix. Significance of the partitions was tested by partial db-RDA and RDA and anova-like permutation test (1,000 permutations). Variation partitioning was also used to quantify the influence of culture media and environmental parameters driving the microbiome composition. For this, we selected groups of culture media that displayed homogeneous multivariate beta-dispersion (tested as before). Microbiomes from phytoplankton strains grown in K+Si (4 strains), L1 (11 strains), F/2 (4 strains) and K/2 (6 strains) media were included in the analysis (Supplementary Table 2). F/2 and L1 media have a similar nutrient composition but different trace metal solutions [START_REF] Guillard | Studies of marine planktonic diatoms: I. Cyclotella nana Hustedt, and Detonula confervacea (Cleve) Gran[END_REF][START_REF] Guillard | Stichochrysis immobilis is a diatom, not a chrysophyte[END_REF]. K+Si and K/2 media are two variations of the medium designed by Keller et al., (1987), with addition of silica to allow diatom growth and diluted two times, respectively. Then, variation partitioning was performed as before. Significance of individual variables in both models was tested by Type III anovalike permutation test, which is more robust to unbalanced sampling design [START_REF] Legendre | Distance-based redundancy analysis: Testing multispecies responses in multifactorial ecological experiments[END_REF].

In order to identify the ASVs responsible for the differences in Jaccard db-RDA, we plotted the 20 ASVs that accounted for most of the variance (presence-absence) among the samples. Then, indicative species were identified by Indicator Value tests (IndVal) [START_REF] Dufrene | Species assemblages and indicator species: the need for a flexible asymmetrical approach[END_REF]De Cáceres and Legendre, 2009). The IndVal was based on the comparison of occurrences of taxa across predefined groups of microbiomes. The analysis provides an index ranging between 0 and 1, the maximum value indicating an ASV exclusively present in all samples of one group. IndVal is calculated as the product of A (specificity, i.e., the probability that a site belongs to the group given the fact that a given species is found in that site) and B (fidelity, i.e., the probability of finding a given taxon at a site when the site belongs to that group) (Dufrêne & [START_REF] Dufrene | Species assemblages and indicator species: the need for a flexible asymmetrical approach[END_REF]. To test for the presence of indicative ASVs in the Jaccard HCA groups, hosts and medium, Indval analysis using the rarefied table was run using the function multipatt (package indicspecies v1.7.9) with 10,000 permutations (De Cáceres and Legendre 2009). P-values were adjusted for multiple comparison using false discovery rate correction (p.adjust function).

Results and Discussion

The phytoplankton cultures used in this study were isolated from 13 different seawater samples collected during 6 oceanographic cruises. The sampling stations covered different oceanic regions (South West and East Pacific, North to South Atlantic, and Mediterranean Sea) (Supplementary Figure 1A) and encompassed large ranges of salinities, temperatures, distinct nutrient status, depths, and oceanic regimes (Supplementary Figure 1B, Supplementary Table 1). The 43 diverse phytoplankton cultures originating from these samples spanned 7 phyla (Hapophyta, Ochrophyta, Chlorophyta, Bacillariophyta, Myzozoa, Cercozoa and Chyptophyta)

and more than 15 genera (Supplementary Figure 1A and Supplementary Table 2).

Bacterial diversity associated to phytoplankton cultures

Comparative sequence analysis of the 16S rRNA amplicons revealed a total of 275 ASVs and 2,346,348 reads remaining after abundance and prevalence filters. Alpha diversity indexes of bacterial communities associated with phytoplankton cultures from the same water samples were variable and rather low (Figure 1A). Overall, richness values ranged from 9 to 48 ASVs with a mean and standard deviation of 24 ± 10 (n=43) and Shannon index ranged from 0.22 to 2.85 (mean of 1.42 ± 0.76). The highest richness values were observed in microbiomes of phytoplankton cultures from the BIOSOPE cruise (average of 30 ± 9 ASVs; n=15), while TAN1810 displayed the lowest ones (13 ± 4 ASVs; n=6).

The taxonomic diversity was dominated by the phylum Proteobacteria (91.0% of the total of reads), followed by Bacteroidota (8.6% of the reads). Other phyla (Planctomycetota, Desulfobacterota, Firmicutes, Actinobacteriota, Myxococcota) accounted for 0.4% of the reads.

Gammaproteobacteria (62%) and Alphaproteobacteria (29%) were the most abundant classes followed by Bacteroidia (6.2%). These bacterial classes are typically the most abundant in natural blooms (Buchan et al., 2014;Teeling et al., 2016;Zhou et al., 2018Zhou et al., , 2019) ) and in phytoplankton cultures (Krohn-Molt et al., 2017;Ajani et al., 2018;Behringer et al., 2018;Lawson et al., 2018;Kimbrel et al., 2019;Sörenson et al., 2019;Mönnich et al., 2020;Kearney et al., 2021).

Chapter 2 120 Differences in the taxonomic composition at the family level could be observed between the microbiomes of phytoplankton cultures isolated from the same seawater sample (Figure 1B). The most abundant family Marinobacteraceae (56% of the total number of reads) dominated most TAN1702 and TAN1810 phytoplankton-associated communities, as well as some microbiomes from all other cruises. Rhodobacteraceae (17% of the total reads) were also found to dominate in cultures from diverse geographical origins. Although they were absent or low in most cultures, Thalassospiraceae (4%) and Alteromonadaceae (3%) were relatively abundant in one PROSOPE culture, and in two cultures from TAN1810 and AMT 24, respectively. Flavobacteriaceae (4%) were commonly found but never dominated (Figure 1B).

At the genus level, Marinobacter dominated (56.2% of the total of reads) and was present in all our phytoplankton cultures (non rarefied ASV table ). Marinobacter spp. are wellknown to be associated with cultures of cyanobacteria, coccolithophores, dinoflagellates, and a chlorophyte (Ostreococcus) as the dominant genus (Green et al., 2015;Lupette et al., 2016;Kearney et al., 2021) or a core member (Lawson et al., 2018;Sörenson et al., 2019). A comparison of bacterial communities associated to diverse phytoplankton cultures (Sörenson et al., 2019;Kearney et al., 2021) revealed that Marinobacter was the most prevalent operational taxonomic unit (OTU) shared between coccolithophores, cyanobacteria, and Alexandrium sp.

(Chapter 1). Among the other most abundant genera, Marinovum (4%), Marivita (3%), Roseovarius (3%), Alteromonas (2%), and Balneola (2%) are frequently associated with phytoplankton [START_REF] Pradella | Genome organisation of the marine Roseobacter clade member Marinovum algicola[END_REF][START_REF] Taniguchi | Seasonal variations in the community structure of actively growing bacteria in neritic waters of Hiroshima bay, western Japan[END_REF][START_REF] Zhang | The fate of marine bacterial exopolysaccharide in natural marine microbial communities[END_REF]Rosana et al., 2016;Ajani et al., 2018;[START_REF] Hou | Benefit from decline: the primary transcriptome of Alteromonas macleodii str. Te101 during Trichodesmium demise[END_REF][START_REF] Zheng | Metagenomic and metaproteomic insights into photoautotrophic and heterotrophic interactions in a Synechococcus culture[END_REF]. On the other hand, potential relationships with hydrocarbon-degraders of the genus Thalassospira (4%), commonly found on plastic debris, are yet not established for phytoplankton [START_REF] Kodama | Thalassospira tepidiphila sp. nov., a polycyclic aromatic hydrocarbon-degrading bacterium isolated from seawater[END_REF][START_REF] Wright | Food or just a free ride? A metaanalysis reveals the global diversity of the Plastisphere[END_REF]. Cultivated representatives of the Roseobacter clade, Marinobacter, Pseudoalteromonas and Alteromonas are frequently isolated from blooms and phytoplankton cultures [START_REF] Schäfer | Genetic diversity of 'satellite'bacteria present in cultures of marine diatoms[END_REF][START_REF] Berg | High diversity of cultivable heterotrophic bacteria in association with cyanobacterial water blooms[END_REF]Green et al., 2015;Hahnke et al., 2015;[START_REF] Crenn | Bacterial epibiotic communities of ubiquitous and abundant marine diatoms are distinct in short-and long-term associations[END_REF]. The presence in our cultures of these genera and other less abundant but prevalent ones (Hyphomonas, Muricauda, and Alcanivorax) may be due to their capacity to degrade hydrocarbon molecules and complex organic compounds produced by phytoplankton (Chernikova et al., 2020). Co-culture experiments have shown intimate interactions between these copiotrophic bacteria and their phytoplankton hosts that range from mutualistic to parasitic (Amin et al., 2012;Seymour et al., 2017), suggesting that strong chemotaxis towards phytoplankton organic matter has an important role in shaping phycosphere-associated communities (Slightom and Buchan, 2009;Sonnenschein et al., 2012;Smriga et al., 2016).

Importantly, the constant association of different phytoplankton groups that diversified hundreds of millions of years ago [START_REF] Karlusich | Exploration of marine phytoplankton: from their historical appreciation to the omics era[END_REF] with the same bacterial taxa raises important questions into the functional aspects of microbiomes. Copiotrophic bacteria are involved in broad functions such as the degradation of organic compounds (Cottrell and Kirchman, 2016;Smriga et al., 2016). This can assure the release of inorganic nutrients to the microalga (Amin et al., 2012) in addition of the degradation of toxic compounds, such as hydrocarbons [START_REF] Mishamandani | Response of the bacterial community associated with a cosmopolitan marine diatom to crude oil shows a preference for the biodegradation of aromatic hydrocarbons[END_REF] and reactive oxygen species (Bolch et al., 2011).

Copiotrophic bacteria can also display specific functions such as the production of vitamins (Cruz-López and Maske, 2016) and of specific siderophores which increase microalgal uptake of iron (Amin et al., 2009). However, the extent to which the specific functions are spread among interactions with different phytoplankton taxa is still poorly understood. Experimental approaches involving a bacterium isolate and multiple phytoplankton species will provide further insights into the mechanism of interaction of copiotrophs and phytoplankton and their impact on ecosystem function in the oceans.

Figure 1. A) Box plots of alpha diversity indexes (richness and Shannon) of the microbiomes for each sample. The boxes mark the interquartile range. The thin horizontal lines represent the 25th and 75th percentiles while the thick horizontal line represents the median. The vertical lines indicate the minimum and maximum values (using 1.5 coefficients above and below the percentiles). The dots represent the values obtained for each culture. Dots further than the vertical lines represent potential outliers. Indexes were calculated from 100 rarefactions of the ASV table at the minimum of reads (8,522). B) Taxonomic diversity of the phytoplanktonassociated microbiomes at the family level. Microbiomes are sorted according to the water samples from where the phytoplankton species were isolated. The relative abundance at family level was calculated on the raw ASV table. Families accounting to less than 5% of the total of reads at each microbiome were merged as "others" to ease visualization. Figure 2. Distance-based redundancy analysis bi-plot using Jaccard dissimilarity matrix and environmental parameters (temperature, salinity, phosphate (PO4) and total Chl a concentration). The two main axes explain the highest proportion of the variation of the data. Vectors represent the environmental variables; their direction represents the correlation with each axis and the length of the vector is proportional to its importance in the ordination. Asterisks indicate outliers identified in the HCA (Supplementary Figure 2).

To disentangle the influence of environmental parameters and phytoplankton hosts on the observed beta diversity patterns, we performed variation partitioning using several betadiversity metrics that consider different features of the data (presence-absence, relative abundance, phylogenetic-weighted abundance, compositional transformation). Multivariate beta dispersion of the phytoplankton groups was only significant for PhILR matrix (p=0.03) (data not shown). Using the full dataset, environmental parameters alone explained consistently and significantly most of the variance whatever the metric used, whereas microalgal hosts, categorized as E. huxleyi, Pelagophyceae and other microalgae, were low contributors (Table 1). Temperature (F-value ranging from 2.6 to 10.6) was consistently identified as the main factor responsible for the differences observed between the microbiomes (Supplementary Table 3), the significance of other parameters (salinity, phosphate and Chl a concentrations) being metric-dependent. 1. Disentangling the influence of microalgal host and environmental parameters on the beta diversity patterns of microbiomes. Adjusted R2 of variation partitioning analysis using different beta diversity metrics. Aitchison distance was produced as the Euclidean distance of the centered log-ratio transformed data. Bray-Curtis dissimilarity was calculated on the relative abundance data. Hellinger was calculated as the Euclidean distance of the Hellinger transformed data. Jaccard dissimilarity was calculated with presence/absence of ASVs of the rarefied table. PhILR was produced as the Euclidean distance of the phylogenetic isometric logratio transformed data. For the all the metrics (except Jaccard) the raw table was used. Significance of the partitions was tested by partial db-RDA (Jaccard and Bray-Curtis) A variation partitioning analysis carried out on a subset of the data was performed to evaluate the influence of culture media in the selection of the phytoplankton-associated microbiomes. To identify medium effect on the significance (and not dispersion effect), we selected a subset of the data which contained homogeneous multivariate beta dispersion among the groups of culture media (Anderson and Walsh 2013). Similar explained variations were generally obtained for media and environmental parameters among the different beta diversity metrics (Table 2). However, the significance of the partial tests varied. Pure contribution of medium was found significant with Jaccard, Hellinger and Bray-Curtis metrics, while pure environmental parameters were only significant with Jaccard and Hellinger metrics (Table 2).

Importantly, both variables shared an important proportion (from 6 to 17%) of the explained variation. Significance of individual parameters of the model are detailed in Supplementary Table 4. [START_REF] Vargas | Eukaryotic plankton diversity in the sunlit ocean[END_REF]Sunagawa et al., 2015;[START_REF] Logares | Disentangling the mechanisms shaping the surface ocean microbiota[END_REF]. Among these parameters, temperature is considered as the main driver of prokaryotic communities in the global ocean (Sunagawa et al., 2015;[START_REF] Logares | Disentangling the mechanisms shaping the surface ocean microbiota[END_REF]. Phytoplankton composition, abundance, as well as the quantity and quality of organic matter they excrete, also affect the prokaryotic community (Landa et al., 2016[START_REF] Landa | Major changes in the composition of a Southern Ocean bacterial community in response to diatom-derived dissolved organic matter[END_REF][START_REF] Sarmento | Bacterioplankton niche partitioning in the use of phytoplankton-derived dissolved organic carbon: Quantity is more important than quality[END_REF]. Temperature and other environmental parameters (salinity and nutrients) were found to account for a substantial part of the variation in the bacterioplankton community structure associated with a bloom of the dinoflagellate Scripsiella trochoidea (Zhou et al., 2018). To our knowledge, no studies have evaluated the influence of environmental parameters from the isolation place on the diversity of cultivated microbiomes. Ajani et al., (2018) showed that the place/time of isolation had also a stronger influence on the microbiome diversity in cultures than host genotypes on diatom-associated bacteria in culture. Other cultivation experiments demonstrated the significant influence of the host selection on the microbiome assembly (Jackrel et al., 2020;Mönnich et al., 2020). Here, we showed that the environmental conditions at the time of sampling, and mainly temperature, had detectable effects on heterotrophic bacterial communities associated with phytoplankton cultures that have been maintained in the laboratory for years, while the host influence, although it was detectable, was low. A possible explanation for the weak influence of hosts in our study could be the limited number of strains of each phytoplankton species in our dataset and the grouping of different species together (i.e. Pelagophyceae group assembled different species).

However, since E. huxleyi-associated microbiomes did not cluster together (Supplementary Figure 2), it indicates that other variables, e.g., temperature, had stronger influence than host specificity.

A concern regarding the use of phytoplankton cultures to study the microbiome composition is the possible effect of the culture medium on the bacterial community (Kearney et al., 2021). Up to now, most studies that characterized phytoplankton-associated microbiomes did not investigate the influence of the culture medium on the microbiome composition (Ajani et al., 2018;Behringer et al., 2018;Lawson et al., 2018;Kimbrel et al., 2019;Sörenson et al., 2019;Mönnich et al., 2020). Lupette et al., (2016) showed however that different culture media and photoperiods affected the growth of a strain of Ostreococcus tauri but they had no effects on the associated microbiomes diversity, dominated by Marinobacter in all culture conditions and phases of growth. Here, using a subset of the data, we showed that the culture medium had an influence similar to that of the environmental parameters on the microbiome composition.

Since it has been shown that different media and culture conditions can affect growth and cellular metabolism of microalgae (Wang et al., 2014;[START_REF] Sánchez-Bayo | Cultivation of microalgae and cyanobacteria: effect of operating conditions on growth and biomass composition[END_REF], a possibility was that the medium composition (e.g. inorganic nutrient and vitamins concentrations) may have selected for particular phytoplankton species during the initial isolation/enrichment phase of our phytoplankton cultures and that the dissolved organic matter they excreted may have favored specific bacterial communities [START_REF] Sarmento | Use of phytoplankton-derived dissolved organic carbon by different types of bacterioplankton[END_REF]. However, our variation partitioning results consistently demonstrated that phytoplankton hosts had a weak influence on the variation of the microbiome structure. We cannot also fully exclude that the media formulation had a direct effect on the bacterial composition. However, since environmental parameters and medium have a shared effect (adjusted R2 = 0.07-0.17) on the microbiome composition that we could not disentangle, we rather hypothesize that the initial composition of heterotrophic bacteria in the seawater collected during the cruises and initial culture conditions were probably decisive. A balanced design of experiments comparing different media would help to tackle this question.

Identification of indicator species

To identify species that could be indicative of the two main clusters defined by the hierarchical clustering and constrained analyses using Jaccard index (Supplementary Figure 2 and Figure 2), we performed IndVal analysis (De Cáceres and Legendre, 2009). Among the 20

ASVs that accounted for most of the variance among the microbiomes, IndVal analysis identified 8 bacterial species indicative of group A (Figure 3), mostly associated with phytoplankton from the BIOSOPE and PROSOPE cruises, (Supplementary Figure 2). These indicator species, that displayed high specificity (0.7-1), were mostly present in the microbiomes of group A but not in all cultures (Figure 3 and Supplementary Table 5).

Figure 3.

Presence-absence of the 20 ASVs that accounted for most of the variance (presenceabsence) among the microbiomes. The ASVs in red represent significant indicator ASVs of group A. The groups were extracted from hierarchical cluster analysis and were consistent with the groups obtained at the db-RDA using Jaccard dissimilarity (Supplementary Figure 2).

High specificity and fidelity values (Supplementary Table 5) of ASV8 (Marinovum), ASV16 (Hyphomonas), ASV25 (Sulfitobacter) and ASV57 (Labrenzia), identified them as the best indicators for group A. These taxa, commonly associated with phytoplankton blooms (Buchan et al., 2014;[START_REF] Landa | Major changes in the composition of a Southern Ocean bacterial community in response to diatom-derived dissolved organic matter[END_REF]Li et al., 2018) and cultures (Amin et al., 2015;Lupette et al., 2016;Ajani et al., 2018;Kimbrel et al., 2019;Mönnich et al., 2020;Kearney et al., 2021), are known to produce and exchange infochemicals within the phycosphere. Sulfitobacter provides indole-3-acetic acid, one of the most important plant auxins, and ammonium to diatoms in exchange of tryptophan (Amin et al., 2015). Other Rhodobacteraceae, such as

Roseobacter symbionts display sophisticated quorum-sensing guided behavior to colonize the diatom phycosphere (Fei et al., 2020). Labrenzia, the most abundant core member of produce dimethylsulfoniopropionate [START_REF] Curson | Dimethylsulfoniopropionate biosynthesis in marine bacteria and identification of the key gene in this process[END_REF] which has central role in mutualistic interactions [START_REF] Miller | Dimethylsulfoniopropionate metabolism by Pfiesteriaassociated Roseobacter spp[END_REF][START_REF] Pohnert | Chemical cues , defence metabolites and the shaping of pelagic interspecific interactions[END_REF]Seymour et al., 2010).

Symbiodinium

Phytoplankton-Hyphomonas interactions are not well documented but several Hyphomonas strains significantly influenced the development of normal morphology in axenic protoplasts of the red alga Pyropia yezoensis [START_REF] Fukui | Isolation of Hyphomonas strains that induce normal morphogenesis in protoplasts of the marine red alga Pyropia yezoensis[END_REF], whose normal development is entirely dependent on the incubation with certain bacteria.

The presence of indicator ASVs in the group formed mainly by bacterial communities associated with phytoplankton isolated from relatively warm (>15°C) samples can have ecological implications regarding the influence of temperature on phytoplankton-bacteria interactions. Temperature can influence bacterial community composition directly as well as through bacterial interactions with phytoplankton. The concentration and composition of DOC excreted by phytoplankton depend on temperature conditions [START_REF] Zhou | Microbial Community Structure and Associations During a Marine Dinoflagellate Bloom[END_REF][START_REF] Parker | Synergistic effects of light, temperature, and nitrogen source on transcription of genes for carbon and nitrogen metabolism in the centric diatom Thalassiosira pseudonana (Bacillariophyceae) 1[END_REF]. Experimental manipulation of natural communities showed that an increase of temperature induces a mutualistic relationship between microalgae and heterotrophic bacteria through exchange of metabolites (C transfer to bacteria) or nutrients (N uptake by microalgae) mediated by attachment [START_REF] Arandia-Gorostidi | Elevated temperature increases carbon and nitrogen fluxes between phytoplankton and heterotrophic bacteria through physical attachment[END_REF]. The effect of temperature was also shown to significantly increase the carbon assimilation of copiotrophic Rhodobacteraceae and Flavobacteria commonly associated with phytoplankton (Arandia- et al., 2020). It is worth mentioning that we tested also for indicator species related to the microalgal hosts and culture media but did not find any.

Gorostidi

Conclusions

We found that deterministic processes (environmental filtering and medium) significantly contribute to shape the microbiome composition in phytoplankton cultures while hosts had a low influence. In light of these results, we propose that the initial environmental conditions act as a primary filter selecting the bacterial community in the inoculum (Ajani et al., 2018). Once the microalgae are isolated, the culture medium will represent another force (possibly stronger than the environment) determining the diversity of the microbiomes.

However, we observed that most of the variation was unexplained (ranging from to 0.71 to 0.84) in addition to high differences among the phytoplankton-associated microbiomes originating from the same samples. This high unexplained variation could be related to the decrease of the predictive power of environmental factors over the cultivation time as recently shown by Jackrel et al., (2020). Indeed, these authors demonstrated that the magnitude of the host and environmental signatures decreased over the 30-day duration of their experiment (Jackrel et al., 2020). The microbiomes analyzed in our study were kept in cultivation from 3 to 22 years. During this time, many biotic and abiotic processes could have influenced their composition. For example, bacterial communities may have experienced ecological drift and bottlenecks imposed through weekly culture transfers and changing conditions (light, natural seawater used in the medium formulation) leading to possible selection by their algal hosts (Jackrel et al., 2020). A recent study showed that the enrichment of phytoplankton-attached bacterial community induced stochasticity in the composition of selected microbiomes (Kimbrel et al., 2019), and favored the competitive lottery hypothesis (Sale, 1979). The principle behind, is that the bacterial community present in the cultures represent a guild with similar capacity of colonizing a niche (Sale, 1979;Kimbrel et al., 2019). Priority effects and stochasticity could therefore induce changes in the bacterial community composition that may explain the high dissimilarity among microbiomes originating from the same samples. Our results bring light to the complex process of microbiome assembly in phytoplankton cultures.

Future experimental studies incorporating phytoplankton and heterotrophic model organisms could clarify the role of environment, host, and stochastic processes in shaping host microbiome composition.

Supplementary

CHAPTER 3

Chapter 3

This chapter brings answers to questions opened in previous parts of this work. In

Chapter 1, we could not associate beta diversity patterns of the microbiomes with any of the variables tested (place of isolation, host genotype, etc). This questions whether the microbiomes in cultures result from environmental selection at the place of isolation and/or through host specificities. In Chapter 2, we partially answered this question by showing that environmental filters and culture media were the main drivers of the microbiome composition of different phytoplankton cultures that were isolated from the same seawater samples.

However, we were not able to identify the influence of host selection. Importantly, in previous chapters we used cultures isolated many years ago and for which the initial microbiome composition was unknown. Although we showed in Chapter 1 that the cultures are stable over culture transfers for maintenance across a short period of time (three consecutive culture transfers), we did not test for putative microbiome changes over longer periods of time. To answer these questions, this chapter brings results of an experiment of microbiome selection by an axenic E. huxleyi strain.

We took the opportunity of the Tara BreizhBloom cruise organized by the Ecology of Marine Plankton team (ECOMAP, Roscoff) and the Tara Ocean Foundation in Spring 2019 to tackle these questions. This cruise was set up to follow an E. huxleyi bloom in the Celtic Sea for five days using a Lagrangian strategy. This was achieved by the deployment of an Argo float at the bloom patch. Every day, the positions of the sampling sites were determined next to the position of the drifting float. Besides analyzing the prokaryotic and eukaryotic microbial community dynamics during this natural bloom, we used environmental samples collected inside and outside the bloom to inoculate an axenic E. huxleyi culture and followed the microbiome changes over one year. The main objectives of this study were (i) to investigate how the microbiome composition of different sources influence the microbiomes grown from axenic E. huxleyi cultures; (ii) to identify the short-to-long term changes in the microbiome composition; and (iii) to identify whether the exposition of the natural bacterial community to high E. huxleyi cell concentrations in the bloom would result in different microbiomes in cultures.

Introduction

The oceans cover more than 70% of the world's surface harboring key processes, such as photosynthesis and organic matter degradation, that sustain and control the nutrient and carbon cycles and global productivity [START_REF] Falkowski | Evolution of the nitrogen cycle and its influence on the biological sequestration of CO 2 in the ocean[END_REF][START_REF] Behrenfeld | Climate-driven trends in contemporary ocean productivity[END_REF].

Phytoplankton and heterotrophic bacteria, as major primary producers and decomposers, respectively, are two fundamental actors in these processes [START_REF] Falkowski | The role of phytoplankton photosynthesis in global biogeochemical cycles[END_REF]Field et al., 1998;Pomeroy et al., 2007;Falkowski, Fenchel and Delong, 2008;[START_REF] Jackrel | Microbial production of recalcitrant dissolved organic matter: Long-term carbon storage in the global ocean[END_REF]. To investigate interactions between these major contributors is of central relevance to better understand microbial processes in the oceans.

The surrounding region of a microalgal cell is enriched in organic molecules which attract chemotactic bacteria (Smriga et al., 2016;Seymour et al., 2017). This area, known as the phycosphere, is an analogous of the rhizosphere in plants (Seymour et al., 2017). It is at the phycosphere level that the interactions, including exchanges of molecules and metabolites [START_REF] Cirri | Algae-bacteria interactions that balance the planktonic microbiome[END_REF] between both partners, take place. Heterotrophic bacteria take advantage of the complex organic matter released by microalgae to sustain their growth while in exchange they can stimulate microalgal growth by releasing promoters (Amin et al., 2015), by providing vitamins (such as vitamin B12) (Cruz-López and Maske, 2016), by facilitating iron uptake (Amin et al., 2009), or by protecting them against pathogenic bacteria (Seyedsayamdost et al., 2014). Microalgae can release specific metabolites which will select the bacteria growing in the phycosphere (Buchan et al., 2014;Shibl et al., 2020). On the other hand, the bacterial composition of the phycosphere will determine how the organic matter will be degraded and which molecules will be made available to the microalga (Fu et al., 2020).

However, to study the bacterial composition and selection in the phycosphere still represents a challenge considering the microscale where this process takes place. As a consequence, the selection processes of many phytoplankton groups remain poorly explored (however see Chapter 2; Kimbrel et al., 2019;Mönnich et al., 2020).

To overcome this challenge, one strategy is to study the processes of selection in natural algal blooms (Zhou et al., 2019) or by experimental manipulation (e.g. meso/microcosms and cultures) (Ajani et al., 2018;Kimbrel et al., 2019;Sörenson et al., 2019;Fu et al., 2020;Mönnich et al., 2020), when phytoplankton cells are at high concentrations. Natural blooms are short-lived phenomena that happen when specific physical, chemical and biological conditions allow microalgae to reach enormous cell concentrations and spread over kilometers in the sunlit layer of the aquatic systems (Assmy and Smetacek, 2009). Although phytoplankton blooms are common, less than 5% of the described phytoplankton species have been reported so far as to contribute to these phenomena in lakes and oceans (Assmy and Smetacek, 2009).

Emiliania huxleyi is the most abundant and cosmopolitan coccolithophore species and is able to form massive annual blooms in temperate and subpolar oceans mostly in Spring (Tyrrell and Merico, 2004). E. huxleyi blooms are characterized by blue turquoise waters that can be observed from satellite images (Tyrrell and Merico, 2004). These blooms have a critical relevance for carbon and sulfur cycles due to the ecological and biogeochemical roles of coccolithophores as primary producers, calcifiers, and main contributors to the emission of dimethylsulfoniopropionate (DMSP) to the atmosphere [START_REF] Malin | Dimethyl sulfide production: what is the contribution of the coccolithophores?[END_REF]Rost et al., 2004). Although the role of viruses in bloom termination has been thoroughly investigated (e.g. [START_REF] Bratbak | Viral mortality of the marine alga Emiliania huxleyi (Haptophyceae) and termination of algal blooms[END_REF]Vardi et al., 2012;Lehahn et al., 2014), only few studies have targeted the microbiome diversity associated with E. huxleyi in natural environments [START_REF] Gonzalez | Bacterial community structure associated with a dimethylsulfoniopropionate-producing North Atlantic algal bloom[END_REF]Zubkov et al., 2001) and cultures (Green et al., 2015;Orata et al., 2016;Rosana et al., 2016; Chapter 1). Roseobacters and SAR86 have been pointed out as the main bacterial groups in natural E. huxleyi blooms [START_REF] Gonzalez | Bacterial community structure associated with a dimethylsulfoniopropionate-producing North Atlantic algal bloom[END_REF]Zubkov et al., 2001). Meanwhile, the microbiome of E. huxleyi in cultures is highly dominated by Marinobacter members (Chapter 1) and by Rhodobacteraceae members (including Roseobacter clade members) (Green et al., 2015).

In this study, we allied investigation in a natural E. huxleyi bloom and selection experiment in a manipulated phycosphere to explore the microbiome selection by an axenic E.

huxleyi culture. By following the prokaryotic community composition in the natural bloom patch over five days, we were able to detect changes in alpha and beta diversity patterns and the taxonomic diversity of the dominant groups. In parallel, to identify the influence of the initial inoculum on the microbiome selected by an axenic E. huxleyi culture, we collected prokaryotic communities from two sites (located 34 km apart, inside and outside the bloom event), and from two depths (surface and deep chlorophyll maximum -DCM) to inoculate in 12 independent cultures. The composition and dynamics of the established prokaryotic communities were followed for more than a year. More specifically, we aimed to identify if the exposition of the bacterial community to high concentrations of E. huxleyi in the bloom would be reflected in the final microbiomes. Finally, by following the microbiome cell concentrations and diversity from the establishment of the cultures (9 days after inoculation)

until day 392, we also aimed at identifying short-to-long term microbiome diversity changes that are poorly documented (Behringer et al., 2018). We hypothesized that established microbiomes would be enriched in Marinobacter and Rhodobacteraceae members often found in E. huxleyi cultures and that selected microbiomes would differ according to the initial inoculum. All our cultures experienced a severe stress during the first weeks of cultivation. Our results showed that compositional changes were induced by this event and that microbiomes tend to partially recover their initial composition once normal growth of the algal host was restored.

Material and Methods

Study site and sample collection

Samples used in this study were collected aboard the schooner Tara [START_REF] Sunagawa | Tara Oceans: towards global ocean ecosystems biology[END_REF] in the Celtic Sea (from 48°19-48°24 N/6°28-7°02 W; Figure 1A), during the 'Tara BreizhBloom' cruise from May 27 to June 2, 2019. To follow an E. huxleyi bloom event that was occurring in this area (Figure 1B), an Argo float was deployed in the center of the bloom patch and its position was used twice a day (early morning and end of afternoon) to determine the geographical locations of the sampling stations. On the last sampling day, a site about 34 km apart from the bloom area was sampled to serve as a reference (Figure 1A). For each sampling event, surface to 50 m depth profiles of temperature, salinity, turbidity, pressure, photosynthetic active radiation (PAR), chlorophyll a (chla) fluorescence, oxygen concentrations and pH were conducted by deploying the SBE19+ profiler. Water samples from surface and bloom depth were collected twice a day using an 8L

Niskin bottle for nutrient analyses. After collection, nutrient samples (125 mL) were stored at -20°C for further analysis. Concentrations of nitrate, nitrite, phosphate and silicate were measured using a AA3 auto-analyzer (Seal Analytical) following the methods described by [START_REF] Tréguer | Manuel d'analyse des sels nutritifs dans l'eau de mer (utilisation de l'autoanalyseur Technicon R)[END_REF]. Samples for flow cytometry (FCM), scanning electron microscopy (SEM), and metabarcoding analysis, were collected at the bloom depth and Chapter 3 159 prefiltered through a 20 µm mesh to eliminate large microzooplankton. For FCM analysis of photosynthetic eukaryotes and prokaryotic communities, two replicates (1.5 mL) were fixed twice a day using glutaraldehyde (0.25% final concentration) and Poloxamer 10% (0.1% final concentration) and incubated for 15 min at 4°C before flash freezing in liquid nitrogen. At the last bloom site (Day 5) and at the reference site, samples for nutrients and FCM were collected from surface and bloom depth (DCM for reference site). For SEM analysis, samples of morning sites (two replicates of 250 mL) were gently filtered onto PC membranes (47 mm in diameter;

1.2 µm pore-size) (Millipore). Filters were placed onto Petri slides, dried at least 2h at 50°C, and finally stored at room temperature. For metagenomic analysis, cell biomass was collected from ~ 14 L of seawater by successive filtration onto large (142 mm in diameter) 3 µm poresize and then 0.2 µm pore-size polycarbonate membranes (Millipore). Filters were flash-frozen and stored in liquid nitrogen. Back in the lab, filters were transferred to -80°C until DNA extraction and purification steps.

Scanning electron microscopy analysis

Representative filter portions were fixed in aluminum stubs and sputter coated with gold-palladium (20 nm) [START_REF] Keuter | Life cycle association of the coccolithophore Syracosphaera gaarderae comb. nov. (ex Alveosphaera bimurata): Taxonomy, ecology and evolutionary implications[END_REF]. Quantitative assessment of E. huxleyi cells was performed using a Phenom Pro scanning electron microscope. Cells were counted in twenty random screens (area analyzed = 0.16 mm) and cell concentrations were calculated based on the filtered sample volume corresponding to the area analyzed (0.042 mL).

Community assembly experiments

(i) Axenization. E. huxleyi strain RCC1212, isolated from the Atlantic Ocean near to South Africa on September 1st, 2000, was obtained from the Roscoff Culture Collection. This strain was axenized following a sequence of washing and centrifugation steps, and variable incubation periods with increasing concentrations of an antibiotic solution mixture (ASM). The original detailed protocol developed at the Scottish Association for Marine Science (SAMS, Oban, UK) is available at: assemblemarine.org/assets/Uploads/Documents/toolbox/Antibiotic-treatmentSAMS.pdf.

Briefly, 15 mL of an exponential phase culture (~ 10 6 cells/mL) were centrifuged at 1600 g for 4 min. The pellet was resuspended in sterile K/2 medium (Keller et al., 1987;Probert, 2019) prepared with reconstituted red sea water (https://www.redseafish.com/red-sea-salts/red-sea-salt/; 37.7 grams of salts in 1L of ultrapure water, boiled 100°C for 20 min) and sterilized using 0.1 µm filtration unit. After centrifugation at 1600 g for 2 min, the supernatant was discarded, and the same process was repeated for 6 times. The final pellet resuspended in sterile K/2 medium was further used as inoculum. The axenization medium (final volume per tube, 2 mL) contained variable volumes of K/2 medium (according to the ASM concentration used), increasing concentrations (from 0 to 35%) of the ASM, and 25 µL of Marine Broth (1/10 strength) added to promote bacterial growth. The 10X ASM contained the following antibiotics: cefotaxime (5 g/L), carbenicillin (5 g/L), kanamycin (2 g/L) and augmentin (2 g/L).

The ASM was filtered sterilized (0.1 µm) and kept at -20°C for long-term storage. Culture tubes (one tube per ASM condition) inoculated with 100 µL of prewashed RCC1212 culture were incubated at 17°C with an irradiance of 70 ± 20 µmol photons s -1 m -2 . Culture aliquots (50 µL) of each ASM condition were sampled daily for 5 days and transferred into new culture tubes containing sterile K/2 medium that were incubated as before.

Once these new cultures were dense (about 15 days later), the presence of bacteria was checked by FCM. For FCM, 196 µL of each positive culture was fixed with glutaraldehyde 25% (0.25% final concentration) for 15 min in the dark. Then fixed cultures were stained with SYBR green (1/10,000 final concentration). Samples were analyzed in an FACSCanto flow cytometer (Becton Dickinson, San Jose, CA, USA) equipped with 488 and 633 nm lasers and standard filter setup. Bacterial data acquisition was triggered on the green fluorescence signal and non-diluted samples were run for 1 min at medium rate (∼50 µL/min). Bacteria-free cultures were transferred into fresh K/2 medium and reinspected by FCM after the next culture cycle. For maintenance, axenic cultures were grown in K/2 medium prepared as previously mentioned. They were routinely checked for bacterial contamination by FCM as mentioned above. When used for specific experiments, culture aliquots (3-5 mL) stained with SYBR green

(1/10,000 final concentration) and filtered onto 0.2 µm polycarbonate black membrane (Millipore Isopore) were examined by fluorescence microscopy to ensure cultures remained axenic.

(ii) Sample preparation and inoculation. Four seawater samples were used in the bacterial community assembly experiment. They consisted of a surface and a DCM sample collected in the bloom area on day 5 (station D5_IN_PM) and a surface and a DCM sample collected the same day apart the bloom area (station D5_OUT_PM). Seawater was collected using the Niskin and immediately processed. In order to remove the autotrophic picoeukaryotes and Chapter 3 161 cyanobacteria from the inoculum, samples were gently filtered through a 0.45 µm pore size membrane (Millipore). To estimate the number of prokaryotic cells lost during the filtration step, aliquots of total and filtered seawater samples were fixed for FCM analysis using the methods previously mentioned. After filtration, 150 µL of each prokaryotic community were transferred in triplicates into 50 mL culture flasks filled with 15 mL of K/2 medium prepared as described before. Finally, 150 µL of stationary phase (17 days old) RCC1212 axenic culture was added to each flask. Six axenic RCC1212 cultures were used as controls. Overall, the 18 cultures (12 treatments and 6 controls) were incubated at 15°C and a 12:12 photoperiod regime.

For the first 27 days the light intensity was set up to 20 µmol photons s -1 m -2 and a blue filter was used to mimic in situ conditions of the natural bloom. After 27 days, in order to recover the E. huxleyi cultures that were crashing (see results section), the light was increased to 70 ± 20 µmol photons s -1 m -2 intensity and the blue filter was removed.

(iii) Molecular survey of the microbiomes in culture. Once the inoculated E. huxleyi cultures reached the exponential growth phase (9 days after inoculation), the axenic controls were checked by FCM to confirm that no contamination happened during the experiment's preparation. Once the axeny was confirmed, 13 flasks (12 treatments + one axenic control)

were transferred by inoculating 100 µL of the culture in 10 mL of fresh K/2 medium. Then each treatment was sampled for prokaryotic community composition and FCM analyses. For community composition analysis, 2 mL of culture was centrifuged at 2,000 g for 30 sec to reduce the microalgal load. The supernatants were transferred into new tubes containing 2 μL of Poloxamer 188 solution 10% (Sigma-Aldrich) and centrifuged at 5,600 g for 5 min. The supernatants were discarded, and the pellets were stored at -80°C until DNA extraction. For FCM analysis, duplicate samples were fixed as previously described for environmental samples and stored at -80°C. Cultures were transferred and sampled using the same procedures every 11-14 days for the first 70 days of maintenance. Two additional samplings were performed at days 175 and 392, while still ensuring culture transfers every 3 weeks. The axenic control was regularly checked to ensure the clean handling of the cultures. In addition, culture flasks were daily randomized in the incubator in order to achieve homogeneous light conditions.

FCM analysis

(i) Environmental samples. Back to the laboratory, samples were thawed at room temperature.

Fluorescent microspheres (0.95 µm PolySciences) were added to each sample as internal reference at a final concentration between 6000 to 8000 per mL. Phytoplanktonic cells were first analyzed based on their autofluorescence using a FaCSARIA flow cytometer (Becton Dickinson, CA, USA) equipped with 488 and 633 nm lasers and the standard filter setup at a flow rate of 64 µL/min. A second analysis was run after staining samples with SYBR Green-I

(1/10,000, final concentration) to enumerate heterotrophic microorganisms.

(ii) Laboratory experimental samples. A FACSCanto flow cytometer (Becton Dickinson, San Jose, CA, USA) equipped with 488 and 633 nm lasers and standard filter setup was used to enumerate E. huxleyi and bacterial cells (Marie et al., 1999). For E. huxleyi, data acquisition was triggered on the red fluorescence signal and samples were run for 1 min for cultures at medium rate (∼50 µL/min). To quantify prokaryotes, samples were diluted 1:10 to 1:100 in TE (10 mM Tris, 1 mM EDTA [pH 7.5]), stained with SYBR Green (1/10,000 final concentration)

and incubated for 15 min in the dark. The discriminator was set on green fluorescence, and the samples were analyzed as before.

DNA extraction and sequencing

DNA from environmental samples was extracted using the method described by [START_REF] Alberti | Data Descriptor : Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition[END_REF]. Prokaryotic DNA extraction from cultures and PCR amplification were performed as previously described (Chapter 2). Pooled amplicons were sent to Fasteris SA (Plan-les-Ouates, Switzerland) for Illumina high throughput sequencing. The first 84 DNA samples (first 7 time points) were sequenced together using two independent Illumina runs (technical replicates). The last 12 DNA samples (day 392) were sequenced in another Illumina run without sequencing replicates.

Bioinformatics

The first steps of the bioinformatic treatment and quality control were performed by Fasteris SA. First, in order to separate the libraries, the base calling was done based on a 6 nt unique index, using the softwares MiSeq Control Software 2.6.2.1, RTA 1.18.54 and bcl2fastq2.17 v2.17.1.14 and allowing 1 mismatch. Then, Illumina standard adapters removal and quality trimming were done using the Trimmomatic package (version 0.32) (Bolger et al., 2014). Briefly paired-end reads were globally aligned to ensure an end-to-end match. The adapters sequences were identified allowing a maximum of 2 mismatches and removed if the quality score was higher than 30. Then, bases were filtered by quality using a 4-base sliding window scan and trimming was done when the average quality per base dropped below 5.

Reads without insert and with ambiguities were removed. All the scripts used for the further bioinformatic analyses can be downloaded from: https://github.com/mcamarareis/. Briefly, raw reads from each sequencing run were demultiplexed based on the 8 nucleotide tag sequences with cutadapt (version 2.8.1) using anchored mode and allowing no mismatches, insertions or deletions (Martin, 2011). Since R1 and R2 files (corresponding to each cycle of paired-end sequencing) contained reads with forward and reverse primers (further called forward and reverse reads), the demultiplexing was run two times. The first and second demultiplexing searched for adapters in the R1 file and in the R2 file, respectively, which separated forward and reverse reads for each sample corresponding to each cycle. The primer sequences were removed using the same software allowing mismatches at a rate of 0.1 (insertions and deletions were not allowed) (Martin, 2011). The demultiplexed primer-free sequences were processed to obtain an amplicon sequence variant (ASV) table using the DADA2 pipeline (version 1.14.0 in R 3.6.1) (Callahan et al., 2016;R Core Team, 2017). Since R1 and R2 cycles can have different error rates, they were analyzed independently during all the DADA2 processing. The same was done for the different sequencing runs. First, the sequences were filtered using the function filterAndTrim with default values and trimmed according to the sequence quality (see scripts for details). Then, a random subset of the reads was used to learn the error models from forward and reverse reads using the function learnerrors. The ASVs were inferred in pooled mode. Following ASV inference, the forward and reverse reads of R1 were merged. The reads coming from the R2 file were also merged but in an inverse position (reverse + forward). The sequences tables were built using the function makeSequenceTable and the sequence table obtained for R2 file was reverse complemented. To correct the mixed orientation, the reverse reads of the R2 were reverse complemented before merging with the forward reads. Before chimera removal, the sequence tables obtained independently for R1 and R2 were merged.

Sequences from the last time point were processed independently using the same methods. Then, all the independent processed data were included in the sequence table for chimera removal, also performed in pooled mode. The sequences were filtered according to the range of length of the primers (from 366 to 376 bp) and the resulting fasta file was used to assign taxonomy using the Silva database v138 by IDtaxa (using a confidence threshold of 50) (Quast et al., 2013;Murali et al., 2018). Chloroplasts and mitochondrial sequences were removed. Sequences not classified at the domain level by IDtaxa were classified using vsearch global alignment using Silva v138 (Rognes et al., 2016). These sequences were removed if they could not be classified and/or were classified as chloroplasts or mitochondrial sequences by vsearch. In addition, because IDtaxa failed in assigning taxonomy to many Rhodobacteraceae sequences at the genus level we used vsearch taxonomy (at 94.5% threshold (Yarza et al., 2014)) for the taxonomic diversity plots. The resultant ASV table was filtered to remove ASVs accounting for less than 0.001% (61 reads) of the total number of reads.

Consistency of technical replicates was evaluated by hierarchical cluster analysis (HCA) using Bray-Curtis distance of relative abundance data. After consistency was confirmed, independent technical replicates of each culture were merged by the sum of the number of reads of the ASVs present in the two replicates of the same culture. The abundance and prevalence filters applied removed about 68% of the total number of ASVs while keeping 99% of the number of reads.

Community composition and statistical analyzes

(i) Environmental samples. All the analyses were conducted in R version 4.0.2 in Rstudio

(1.1.442) and the plots were produced with ggplot2 (RStudio Team, 2016; Wickham, 2016; R Core Team, 2017). Taxonomy pie charts of environmental samples were produced at the genus level. To facilitate visualization, low abundant genus (accounting to less than 3% of relative abundance at each sample) present at the raw community table were grouped and named as "others". In order to compare the alpha diversity indexes in environmental samples and cultures, the ASV table was rarefied 100 times at the minimum number of reads (3,465) using the function rtk (seed=1,000) (Saary et al., 2017). Richness and Shannon indexes were obtained using the function get.mean.diversity. HCA was used to identify differences among sampling sites using the Euclidean distance of the Hellinger-transformed data using method "ward.D2" Chapter 3 165 (function hclust) (Legendre and Gallagher, 2001;Oksanen et al., 2015). Heatmap was produced using the function pheatmap [START_REF] Kolde | Package 'pheatmap[END_REF].

(ii) Experiment. To analyze alpha diversity dynamics of the cultures, rarefaction was performed at a reads depth of 5,957 reads as previously mentioned. The rarefied matrix was transformed in presence-absence (decostand function) and the Jaccard dissimilarity was calculated (vegdist function) for beta-diversity analysis (Oksanen et al., 2015). Hellinger distance (Euclidean distance of Hellinger-transformed matrix) was calculated from the non rarefied table (Legendre and Gallagher, 2001;Oksanen et al., 2015). To test the influence of the treatments, replicates and time on the microbiome beta diversity, we performed permutational analysis of variance (PERMANOVA) [START_REF] Anderson | PERMANOVA. Permutational multivariate analysis of variance[END_REF]. Before running the analysis, the functions betadisper and anova-like permutation test were used to identify significant deviations on the multivariate beta dispersion of the data for treatments, replicates, and time (Oksanen et al., 2015). The effect of treatments and replicates (nested within treatments) was tested using the function nested.npmanova [START_REF] Kindt | Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies[END_REF], which calculates the correct statistics for two factors in a nested design. To test the effect of time and the interaction between treatments and time, we used the function adonis (Oksanen et al., 2015) including treatments, replicates, and time (number of days) as fixed variables in the model. The permutations of adonis were restricted to the replicates level using strata. To identify pairwise differences among treatments (including replicates), nested.npmanova function was used to test combinations of each pair (total of six combinations). In this case, the number of permutations for treatments was not enough to estimate the p-value, so we focused the interpretation on the R2 values. HCA was done using the Hellinger distance (Euclidean distance of Hellinger-transformed data) and "ward.D2" method using the function hclust (Oksanen et al., 2015). Taxonomy barplots were produced by showing the three most abundant genera, while the less abundant were merged as "others". In order to identify the shared and specific ASVs in each treatment, we used the rarefied community table and considered all ASVs. IndVal analyses were run to identify indicative species of each treatment using the function multipatt (package indicspecies v1.7.9) with 10,000 permutations (De Cáceres and Legendre, 2009). P-values were adjusted for multiple comparisons using false discovery rate method (function p.adjust) (Oksanen et al., 2015).

Results

The samples used in the community assembly experiment were collected during the Tara BreizhBloom oceanographic cruise that sampled an E. huxleyi bloom during 5 days in the Celtic Sea (Figure 1A and 1B). Although these samples were obtained only at the end of the cruise, we present here the ancillary and prokaryotic diversity data to contextualize the results gathered from the laboratory experiment.

Abiotic and biotic parameters characterizing the E. huxleyi bloom in the Celtic sea

Coccolithophore blooms occur seasonally from April to June in the Bay of Biscay along the continental shelf to the Celtic Sea in June and the North-Sea in July and August [START_REF] Holligan | Satellite and ship studies of coccolithophore production along a continental shelf edge[END_REF][START_REF] Van Oostende | Phytoplankton community dynamics during late spring coccolithophore blooms at the continental margin of the Celtic Sea (North East Atlantic, 2006-2008)[END_REF][START_REF] Poulton | Coccolithophores on the north-west European shelf: calcification rates and environmental controls[END_REF][START_REF] Perrot | Coccolith-derived turbidity and hydrological conditions in May in the Bay of Biscay[END_REF]. We followed the bloom patch in end May-June 2019 in the Celtic Sea using near-real time interpolated images of non-algal suspended particulate matter (SPM) derived from satellite reflectance data [START_REF] Perrot | Coccolith-derived turbidity and hydrological conditions in May in the Bay of Biscay[END_REF] as provided by Ifremer at http://marc.ifremer.fr/en/results/turbidite.

Over the duration of the sampling period, temperature and salinity ranged from 12.4°C to 15.4°C and from 35.4 to 35.5 PSU, respectively (Supplementary Table 1). Nutrient concentrations were low with NO2 + NO3 and PO4 ranging from the detection threshold to 1.25 µmol and 0.05 to 0.2 μmol/L, respectively. These low values were typical of a bloom event where cells consume most of the nutrients. E. huxleyi cell densities ranged from 1.6 x 10 3 to 5.6 x 10 3 cells/mL over the sampling period (Figure 1C). Total prokaryotic abundance ranged from 8.1 × 10 5 to 2.0 × 10 6 cells/mL.

FCM data evidenced an average of total photosynthetic eukaryotic cell concentration of 2.47 x 10 4 cells/mL (min: 9.57 x 10 3 cells/mL and max: 3.68 x 10 4 cells/mL). The most abundant photosynthetic organisms identified by FCM were Synechococcus with cell concentrations ranging from 2.1 x 10 4 to 1.1 x 10 5 cells/mL. On the other hand, cell concentrations of heterotrophic bacteria ranged from 8.1 x 10 5 to 2.0 x 10 6 cells/mL. The lowest cell concentration was observed in the outside bloom samples (Supplementary Table 1).

Overall, the environmental samples displayed a prokaryotic richness of 146 ± 26 ASVs (mean ± SD, n=19) (Supplementary Figure 1A). Richness increased over the course of the bloom and reached a maximum at day 4 (174 ± 11 ASVs, n=4) and decreased the fifth day.

The samples collected for the community assembly experiments (0.2-3 µm fractions of day 5

PM inside bloom and day 5 PM outside bloom) contained 158 and 139 ASVs, respectively.

Note that the Shannon index, which is less affected by sampling effort, displayed homogeneous values (mean 3.9 ± 0.4 SD, n=19) across the samples (Supplementary Figure 1B). Hierarchical clustering revealed three groups of samples that clearly delineated the evolution of the prokaryotic communities across the bloom in a time-dependent manner (Supplementary Figure 2). Prokaryotic community collected from days 2 to 4 where the richness increased clustered together and were distinct from that sampled on the fifth day. Surprisingly, prokaryotic communities outside the bloom grouped with those collected a few hours before in the bloom area.

We found a diverse prokaryotic community at the genus level over the course of the bloom at AM and PM sites (Supplementary Figure 3). SAR11 clade (11% of the total reads, n=11) and Synechococcus sp. (7%) were present in all samples. Members of the genus Pseudoalteromonas emerged at day 2 and became the most abundant (12%), dominating over SAR11 on the fifth day in and outside the E. huxleyi bloom. Both members of the Roseobacter clade, assigned to Ascidiaceihabitans and Sulfitobacter, prevailed in the periods where richness increased. While Sulfitobacter was not observed after richness peaked, proportions of Ascidiaceihabitans remained stable. The Flavobacteriaceae were moderately abundant (11% of the total reads), the NS4 group being the most prevalent while NS5 group and Polaribacter were occasionally found. Thermoplasmata (4%) emerged at day 3 and were present until the end of the sampling. Some genera were detected in only one sample in which they predominated like Vibrio (12% in day 2 PM sample) and Nitrosopumilus (18% in day 3 AM sample) or were substantial such as the members of OCS116 clade (4% in day 2 PM sample).

Regarding the samples that served as inocula for the community assembly experiment, the main observed differences were the presence of Lentimonas (5.5%) in the bloom site sample and the presence of the OM60 (NOR5) clade (3.6%) and Thermoplasmata (4%) in the outside bloom sample. Ellipses represent 95% confidence. (B) R2 of permutational multivariate analysis of variance (PERMANOVA) and nested PERMANOVA using two metrics (see material and methods for details). (C) Hierarchical clustering produced with the Hellinger distance matrix using "ward.D2" method. Codes of each microbiome are experiment sampling day_treatment_replicate. Bar plots indicate the taxonomy of the 3 most abundant genera. The other genera were merged as "others".

Statistical significance of the effect of treatments, replicates and time on the diversity of the microbiomes was assessed by PERMANOVA and nested PERMANOVA. Before performing PERMANOVA analysis we tested the beta-dispersion of the microbiomes grouped by treatments, time and replicates. We observed significant beta-dispersion across treatments for both metrics tested (p = 0.001) while beta-dispersion of replicates was only significant using Hellinger metric (p = 0.007). Beta-dispersion was not significant over time for both metrics.

Still PERMANOVA results are robust to dispersion for balanced designs like ours (Anderson and Walsh, 2013). PERMANOVA results of Hellinger and Jaccard dissimilarity showed that treatments, replicates and time had a significant influence on the microbiomes beta diversity (Supplementary Tables 2 and3). Treatments explained from 26% (FJaccard = 4.43; pJaccard = 0.001) to 31% (FHellinger = 2.67; pHellinger = 0.002) of the patterns observed, while replicates were responsible for 15% (FJaccard = 2.72; pJaccard = 0.001) to 31% (FHellinger= 8.56; pHellinger = 0.001) (Figure 3B and Supplementary Tables 2 and3). Time was responsible for explaining only a small proportion of the variance, ranging from 6% (FHellinger = 14.76; pHellinger = 0.001) to 7% (FJaccard = 10.31; pJaccard = 0.001) (Figure 3B and Tables 2 and3). The interaction between treatments and time was significant using Hellinger distance (F = 2.23; p = 0.019) however it explained a small proportion of the variance (R2 = 0.025). In addition, pairwise comparisons of treatments (including replicates) using nested PERMANOVA (Supplementary Table 4) indicated that treatments explained a higher proportion of the variance between bloom DCM and bloom surface and/outside bloom treatments (R2 from 0.19 to 0.25) (Supplementary Table 4). In comparisons between surfaces (bloom and outside bloom) treatments, a larger proportion of the variance was explained by replicates (0.14) rather than treatments (0.07) (Supplementary Table 4).

Clustering using Hellinger distance revealed that the prokaryotic community composition of all the cultures clustered into three main groups according to the inoculum origin (Figure 3C). The outside bloom DCM treatments formed the two clusters (a and b) (Figure 3C), highlighting the compositional differences between the 3 replicates, which were dominated by different bacterial genera. Replicate 1 (cluster a) was dominated by Alcanivorax (77%, n=8), while replicates 2 (cluster a) and 3 (cluster a) were dominated by two different Erythrobacter (83% and 41%, respectively). The second main cluster (c) mainly consisted in treatments inoculated by both surface water samples (Figure 3C). Surface treatments were dominated by OM43 clade (20% and 35 % in the inside and outside bloom sample, respectively), KI89A clade (35% and 24% in the inside and outside bloom sample, respectively). The composition of both surface treatments was differentiated by the presence of SAR92 clade members in the outside bloom (17%) and Luminiphilus in the inside bloom (18%). Finally, the third cluster (d) formed by microbiomes from the inside bloom DCM treatment was dominated by members of the OM43 clade (25%), the KI89A clade (23%), and Polaribacter (10.5%) (Figure 3C). ASVs that dominate in cultures amended with bloom DCM and outside bloom DCM samples were in low abundance or were not detected in the original water mass (Figure 3C and Supplementary Figure 3). Besides the differences among treatments, we observed a somehow cyclic pattern of the beta-diversity over time using Hellinger distance (Figure 4). Bacterial communities from days 9, 21 and 32 clearly differed from each other. However, from days 49 to 392 their composition gradually tended to become similar to their initial status. This trend was consistently observed in all treatments, except in the outside bloom DCM cultures where it happened at the replicate level (replicates 1 and 3). This dynamic was mainly driven by the transient dominance OM43 clade during the alga crash (Supplementary Figure 5) and the increased abundance of Luminiphilus after the algal recovery.

Shared, specific ASVs and indicative ASVs among treatments

In total, 46 ASVs were shared between the microbiomes of the four treatments. Among the most prevalent (> 70%), Luminiphilus, OM43 and KI89A clades were the most abundant while, an Oxalobacteraceae assigned to Janthinobacterium by vsearch and Pseudomonas were lower (Supplementary Table 5). Among the shared ASVs, one (ASV2, KI89A clade) was identified as indicative (i.e., indicator value -IndVal -which measures specificity and fidelity of an ASV) of both surface treatments (Supplementary Table 6) (De Cáceres and Legendre, 2009). ASVs assigned to SAR92 and OM43 clades, were indicative of inside bloom treatments and outside bloom surface (IndVal from 0.7 to 1). 6). Nine of the specific ASVs belonged to the family Flavobacteriaceae, four being assigned to the genus Polaribacter (Supplementary Table 5). The most prevalent among the indicative flavobacterial ASVs, assigned to Aurantivirga and Polaribacter, were present in all and in 18 out of 24 'bloom-DCM' microbiomes, respectively, and displayed (Indval 1 and 0.87 respectively) (Supplementary Table 6). Other specific bloom DCM indicative ASVs belonged to Rhodobacteraceae (Sulfitobacter as classified by vsearch and Yoonia-Loktanella) and Tenderiaceae (Supplementary Table 6). In addition, some indicative ASVs with Indval higher than 0.86 (Polaribacter, KI89 clade and Aurantivirga) were shared with other treatments (Supplementary Table 6).

Outside bloom surface and DCM treatments displayed the lower number of specific ASVs. Alcanivorax, Herinciella and Joostela were significantly indicative of outside bloom DCM treatment (Supplementary Table 6) and the most prevalent. Outside bloom surface treatments harbored only a single specific ASV (Porticoccus) in a unique sample (1 out of 24 microbiomes).

Discussion

In this study, we explored the 2019 E. huxleyi bloom in the Celtic Sea and collected bacterioplankton samples for conducting a microbiome selection experiment in E. huxleyi axenic cultures. Satellite images and biological data we obtained indicate that the bloom was already in its decaying phase when we started the sampling.

First, the high reflectance patch visible on the satellite images (Figure 1B) and the daily vanishing of the coccolith-derived turbidity signal observed from the interpolated images of non-algal suspended particulate matter as provided by Ifremer (see Results section) were both indicative of detached coccoliths from dead E. huxleyi cells [START_REF] Neukermans | Optical modeling of spectral backscattering and remote sensing reflectance from Emiliania huxleyi Blooms[END_REF][START_REF] Perrot | Coccolith-derived turbidity and hydrological conditions in May in the Bay of Biscay[END_REF]. This assumption was confirmed by the complete disappearance of the coccolith-derived turbidity signal a couple of days after we left the sampling area. Second, a suite of ongoing experiments on the bloom samples using diagnostic lipid-and gene-based molecular biomarkers [START_REF] Vardi | Viral glycosphingolipids induce lytic infection and cell death in marine phytoplankton[END_REF][START_REF] Hunter | Targeted and untargeted lipidomics of Emiliania huxleyi viral infection and life cycle phases highlights molecular biomarkers of infection, susceptibility, and ploidy[END_REF][START_REF] Ziv | Viral serine palmitoyltransferase induces metabolic switch in sphingolipid biosynthesis and is required for infection of a marine alga[END_REF][START_REF] Vincent | Visualizing active viral infection reveals diverse cell fates in synchronized algal bloom demise[END_REF] revealed the detection of specific viral polar lipids and visualized E. huxleyi infected cells during bloom succession, suggesting that the demise of the E. huxleyi bloom was mediated by Coccothovirus infections (Vardi et al., pers. comm.) as often proposed [START_REF] Bratbak | Viral mortality of the marine alga Emiliania huxleyi (Haptophyceae) and termination of algal blooms[END_REF]Vardi et al., 2012;Laber et al., 2018). Third, the composition of the bacterial community, e.g. the presence of Flavobacteriaceae and Pseudoalteromonadaceae, is another indicator of the bloom demise (Lovejoy et al., 1998;Buchan et al., 2014). Flavobacteriia, are reported amongst the main bacteria present in the declining phase of phytoplankton blooms (Teeling et al., 2012(Teeling et al., , 2016;;Landa et al., 2016), which seems linked to their capacity to degrade high molecular weight substrates such as proteins and polysaccharides [START_REF] Cottrell | Natural assemblages of marine proteobacteria and members of the Cytophaga-Flavobacter cluster consuming low-and high-molecularweight dissolved organic matter[END_REF][START_REF] Kirchman | The ecology of Cytophaga-Flavobacteria in aquatic environments[END_REF][START_REF] Fernández-Gomez | Ecology of marine Bacteroidetes: a comparative genomics approach[END_REF][START_REF] Kappelmann | Polysaccharide utilization loci of North Sea Flavobacteriia as basis for using SusC/Dprotein expression for predicting major phytoplankton glycans[END_REF]. TonB-dependent transporters, often specific for polysaccharide uptake, were recently identified as the most highly expressed protein class, split approximately evenly between the Gammaproteobacteria and Bacteroidetes, during a spring phytoplankton bloom in the southern North Sea [START_REF] Francis | Changing expression patterns of TonB-dependent transporters suggest shifts in polysaccharide consumption over the course of a spring phytoplankton bloom[END_REF]. Finally, the algicidal effects of Pseudoalteromonas strains and species have been documented in many microalgae [START_REF] Holmström | Marine Pseudoalteromonas species are associated with higher organisms and produce biologically active extracellular agents[END_REF]Mayali and Azam, 2004;Li et al., 2018), which calls attention to their potential role in the E. huxleyi bloom termination (Lovejoy et al., 1998).

Besides the presence of Flavobacteriaceae and Pseudomonadaceae, our results are in agreement with the only two past studies on the diversity of the bacterioplankton associated with E. huxleyi blooms [START_REF] Gonzalez | Bacterial community structure associated with a dimethylsulfoniopropionate-producing North Atlantic algal bloom[END_REF]Zubkov et al., 2001). These molecular surveys also reported the dominance of SAR11, roseobacters, and SAR86 as main contributors among active prokaryotes [START_REF] Gonzalez | Bacterial community structure associated with a dimethylsulfoniopropionate-producing North Atlantic algal bloom[END_REF]Zubkov et al., 2001). Concomitantly to the predominance of SAR11, the most abundant organism in the oceans (Morris et al., 2002;Giovannoni, 2017) and common inhabitant of blooms [START_REF] Gonzalez | Bacterial community structure associated with a dimethylsulfoniopropionate-producing North Atlantic algal bloom[END_REF]Teeling et al., 2012Teeling et al., , 2016;;Landa et al., 2016), we found abundant members of the Roseobacter clade, most notably assigned to the genera Sulfitobacter and Ascidiaceihabitans (formerly Roseobacter OCT lineage), whose relative abundances typically fluctuate during phytoplankton blooms (Hahnke et al., 2015;[START_REF] Lucas | Shortterm dynamics of North Sea bacterioplankton-dissolved organic matter coherence on molecular level[END_REF][START_REF] Chafee | Recurrent patterns of microdiversity in a temperate coastal marine environment[END_REF][START_REF] Choi | Daily variation in the prokaryotic community during a spring bloom in shelf waters of the East China Sea[END_REF][START_REF] Kappelmann | Polysaccharide utilization loci of North Sea Flavobacteriia as basis for using SusC/Dprotein expression for predicting major phytoplankton glycans[END_REF]. Interestingly, a Sulfitobacter ASV was present at all sites. This is in agreement with the cooccurrence of a Sulfitobacter strain and E. huxleyi in the water column during the demise phase of an E. huxleyi bloom in the North Atlantic (Barak-Gavish et al., 2018). Since this Sulfitobacter strain was found to display a strong DMSP-mediated virulence when co-cultured with E. huxleyi, its possible contribution to the bloom termination as a complementary mortality agent to viral infection was suggested by these authors.

The experimental selection of E. huxleyi-associated microbiomes allowed us to capture the temporal dynamics of simultaneous crashes of all cultures followed by their growth recovery. During the first month of culture, the opposite dynamics of E. huxleyi and bacteria coupled to the sharp decrease of the bacterial alpha diversity indicated that a few bacterial taxa were outcompeting the others. A first possibility could be that inoculation with natural bacterioplankton communities propagated a virus infection in the cultures since diagnostic signatures of the presence of coccolithoviruses were found in the samples of the bloom site.

However, cultures inoculated with samples from the outside bloom showed the same pattern, although no E. huxleyi bloom was detected in this area during the entire sampling period. A second possibility could be the overgrowth of algicidal bacteria because (i) several bacteria of the Roseobacter clade and the genus Pseudomonas are known to kill phytoplankton, including E. huxleyi (Mayali and Azam, 2004;Seyedsayamdost et al., 2011;Segev et al., 2016;Barak-Gavish et al., 2018) and (ii) Roseobacter-like members and Pseudoalteromonas sp. were abundant during the bloom. This possibility is however unlikely because we did not find any Rhodobacteraceae and Pseudoalteromonaceace sequences in most of the cultures collected at the lowest algal cell abundance (day 32). All cultures were systematically dominated by members of clades OM43 (38-81% of the reads) and KI89A (15-45%), and both clades were associated with Erythrobacter (44%) in cultures inoculated with the outside bloom DCM sample. Almost systematically dominant, members of the OM43 clade are methylotrophic bacteria which can utilize methanol and methylated compounds as sole growth substrates [START_REF] Anthony | Methanol oxidation and growth yields in methylotrophic bacteria: a review[END_REF]. This clade is commonly found at low levels in coastal ecosystems [START_REF] Rappé | Phylogenetic comparisons of a coastal bacterioplankton community with its counterparts in open ocean and freshwater systems[END_REF][START_REF] Sekar | Flow sorting of marine bacterioplankton after fluorescence in situ hybridization[END_REF][START_REF] Song | Improved culturability of SAR11 strains in dilutionto-extinction culturing from the East Sea, West Pacific Ocean[END_REF] and has been observed to increase in abundance to ∼2% of bacterial cells during phytoplankton blooms (Morris et al., 2006). In the field, its abundance was found to increase during phytoplankton blooms [START_REF] Rich | Time-series analyses of Monterey Bay coastal microbial picoplankton using a 'genome proxy'microarray[END_REF][START_REF] Yang | Illumina sequencingbased analysis of free-living bacterial community dynamics during an Akashiwo sanguine bloom in Xiamen sea, China[END_REF] and to correlate with diatom abundance (Morris et al., 2006). To explain the simultaneous crashes of all the cultures, we favor a severe abiotic stress experienced by E. huxleyi in the first weeks of culture that could be due to growth-limiting light conditions since growth recovery was immediately observed after increasing light intensity. However, we cannot exclude that the inoculated diversified bacterial communities may have also contributed to a stress for the axenic alga. While this remains speculative, a likely hypothesis is however that algal stress/death induced the release of methylated compounds by the alga providing a selective advantage to members of the OM43 clade over bacteria in the phycosphere. We suspect that the unfavorable light conditions might have induced algal cell death and lysis promoting the release of methylated compounds further used as a carbon source by the specialist OM43 clade methylotrophs, similarly as other marine methylotrophs [START_REF] Vila-Costa | Phylogenetic identification and metabolism of marine dimethylsulfide-consuming bacteria[END_REF][START_REF] Schäfer | Isolation of Methylophaga spp. from marine dimethylsulfide-degrading enrichment cultures and identification of polypeptides induced during growth on dimethylsulfide[END_REF][START_REF] Neufeld | Substratespecific clades of active marine methylotrophs associated with a phytoplankton bloom in a temperate coastal environment[END_REF]. Among the possible produced substrates, methylated sugars and uronic acid methyl esters prevalent in polysaccharides were suggested as candidates [START_REF] Sosa | High molecular weight dissolved organic matter enrichment selects for methylotrophs in dilution to extinction cultures[END_REF]. In addition, dimethyl sulfide [START_REF] Wolfe | Grazing-activated production of dimethyl sulfide (DMS) by two clones of Emiliania huxleyi[END_REF] and methylated volatile organic compounds [START_REF] Reese | Chemical profiling of volatile organic compounds in the headspace of algal cultures as early biomarkers of algal pond crashes[END_REF][START_REF] Fisher | Low Molecular Weight Volatile Organic Compounds Indicate Grazing by the Marine Rotifer Brachionus plicatilis on the Microalgae Microchloropsis salina[END_REF] have been identified as indicators of algal stress/death. Upon culture recovery, we observed that the bacterial community composition followed a cyclic pattern suggesting that it reverted back to a composition similar to its initial community at day 9 of culture. Such cyclic pattern has been shown in the mucus microbiome of the coral Porites astreoides [START_REF] Glasl | The microbiome of coral surface mucus has a key role in mediating holobiont health and survival upon disturbance[END_REF] and the surface microbiome of the seaweed Delisea pulchra [START_REF] Longford | Interactions within the microbiome alter microbial interactions with host chemical defences and affect disease in a marine holobiont[END_REF] after experimental disturbances. This cyclic pattern of recovery was consistently observed in the four separate treatments, with some variability (e.g. in the outside bloom DCM treatment, cyclic pattern was observed at the replicate level). With the exception of the outside bloom DCM treatment, in general we observed low levels of betweenreplicate variability at all time points, indicating a high degree of uniformity among cultures at corresponding times in the survey. The microbiomes resulting from both surface treatments were remarkable in this respect. It is surprising that community composition remained so uniform across replicates at corresponding times, and that succession patterns were indeed reproducible. Although these samples were collected 34 km apart, they were both composed of Luminiphilus, SAR92 and KI89A clades as main components, suggesting the similarity of the initial bacterioplankton composition of both epipelagic surface waters and the functional complementarity between microbiome species.

Our results also illustrate the importance of niche differentiation in natural communities. In particular, we showed that bacterial species or phylogenetic groups from surface and bloom depth did not respond equally to the organic or inorganic nutrients provided in E. huxleyi cultures. Although no major differences were observed in the bacterioplankton samples from inside and outside bloom DCM communities, the resulting E. huxleyi microbiomes differed. Other microbiome studies of phytoplankton cultures have highlighted the impact of the initial community composition on microbiomes after short (Ajani et al., 2018;Sörenson et al., 2019;Jackrel et al., 2020), and long-term selection (Chapter 2). We cannot explain the variability of outside DCM treatment, although patchiness of bacterioplankton communities can be argued. However, remarkable features were found in the microbiomes resulting from inside bloom DCM waters where several specific flavobacterial ASVs, mainly assigned to Polaribacter and Aurantivirga, were initially selected and were amongst the most prevalent and abundant ASVs after the host crash. Both genera were identified as the main degraders of diatom polysaccharides during spring blooms in the Southern North Sea [START_REF] Krüger | In marine Bacteroidetes the bulk of glycan degradation during algae blooms is mediated by few clades using a restricted set of genes[END_REF]. Aurantivirga was found more abundant in early stages of diatom blooms [START_REF] Krüger | In marine Bacteroidetes the bulk of glycan degradation during algae blooms is mediated by few clades using a restricted set of genes[END_REF][START_REF] Liu | Seasonal dynamics of prokaryotes and their associations with diatoms in the Southern Ocean as revealed by an autonomous sampler[END_REF] while Polaribacter abundance was higher in late bloom stages (Teeling et al., 2012;Landa et al., 2016). This may be related to the differential capacity of these bacteria to degrade phytoplankton-derived polysaccharides during blooms (Teeling et al., 2012;[START_REF] Krüger | In marine Bacteroidetes the bulk of glycan degradation during algae blooms is mediated by few clades using a restricted set of genes[END_REF]. Indeed, expression profiles of TonB-dependent transporters have recently predicted that Aurantivirga produced the most abundant putative alpha-glucan transporter during the early phase of a bloom [START_REF] Francis | Changing expression patterns of TonB-dependent transporters suggest shifts in polysaccharide consumption over the course of a spring phytoplankton bloom[END_REF]. Interestingly, in the same bloom, SAR92 and Luminiphilus, which are often viewed as oligotrophic bacteria (Spring and Riedel, 2013;Cottrell and Kirchman, 2016), were important degraders of algal polysaccharides [START_REF] Francis | Changing expression patterns of TonB-dependent transporters suggest shifts in polysaccharide consumption over the course of a spring phytoplankton bloom[END_REF], suggesting their potential role in our cultures. Another important trait of the inside bloom DCM treatment was the presence of many indicative ASVs, including several flavobacterial ASVs and members of the Roseobacter clade (Sulfitobacter and Yoonia-Loktanella). These last genera are known to be part of the bacterial community associated with phytoplankton blooms (Zubkov et al., 2001;Barak-Gavish et al., 2018).

Based on previous reports of the E. huxleyi microbiomes diversity, we expected to find a dominance of Marinobacter in the cultured microbiomes (Chapter 1; Green et al., 2015).

Since Marinobacter species display relatively large cell sizes [START_REF] Bowman | Order X. Alteromonadales ord. nov. Bergey's[END_REF], a plausible explanation for their very low representation might be due to the 0.45 µm filtration step in our experimental design. However, we detected a few Marinobacter sequences in the cultures, suggesting that the filtration step does not fully explain their abundance. We acknowledge that the filtration step might have induced other modifications in the initial composition of the inocula. Many studies have indeed demonstrated that communities of freeliving and particle-associated bacteria (including those associated with algae) are different (e.g. [START_REF] Delong | Phylogenetic diversity of aggregateattached vs. free-living marine bacterial assemblages[END_REF]Simon et al., 2002;[START_REF] Grossart | Marine diatom species harbour distinct bacterial communities[END_REF][START_REF] Bachmann | Environmental drivers of free-living vs. particle-attached bacterial community composition in the Mauritania upwelling system[END_REF]. This situation was probably the case in our samples because E. huxleyi bloom demises are coupled with particle aggregation of organic molecules from dying phytoplankton. However, as our primary objective was to study the role of the host in bacterial community selection and assembly, we opted to remove other autotrophic phytoplankton cells, mainly Synechococcus and picoeukaryotes found in high abundance at both the sampling sites (see Supplementary Table 1). Despite these limitations, we can draw several conclusions from this work.

We assume that exopolysaccharides/exudates of axenic E. huxleyi cultures have strongly influenced which bacteria emerged first (day 9), which has crucial effects on the resulting community and its long-term stability. Our results suggest that the decrease of OM43 clade abundance after day 32 provided an open niche, which was recolonized by opportunistic bacteria that remained stable (SAR92, Alcanivorax, Erythrobacter, Yoonia, and Idiomarina) or increased with time (Luminiphilus) (data not shown). Since this situation was however limited to few ASVs, we hypothesize that the complete re-establishment of the initially grown prokaryotic community after disturbance depends on the degree of disruption of the microbiome and the initial microbiome diversity. The later factor, which may facilitate stability, was exemplified by the contrasting results obtained with the inside and outside bloom DCM samples. The bacterioplankton communities of the inside bloom DCM sample were the more diverse from the two samples and they resulted in more diverse microbiomes after 9 days of culture. The microbiomes grown from the inside bloom DCM sample displayed also the highest number of specific ASVs and indicative species. More importantly, they were composed of 13 ASVs with relative abundance higher than 2% whereas microbiomes from other treatments contained 3 to 5 ASVs displaying abundance higher than this threshold (data not shown). This is also the likely explanation of the almost complete cyclic pattern followed by the microbiomes resulting from inside bloom DCM samples (Figure 4A). Contrary to the other treatments, this bacterial community was collected in the E. huxleyi bloom and we hypothesize that pre-adaptation of the community to the different substrates produced by the future host promoted microbiomes of larger diversity. Some of the bacterial groups that we found in the treatments were not reported previously in phytoplankton cultures or in low abundance, notably Luminiphilus, SAR92, KI89A and OM43 clades (Chapter 1, Green et al., 2015). This is easily understandable because Luminiphilus, SAR92 and KI89A clades belong to an important group of oligotrophic marine Gammaproteobacteria that do not grow in the high nutrient concentrations provided by phytoplankton cultures [START_REF] Cho | Cultivation and growth characteristics of a diverse group of oligotrophic marine Gammaproteobacteria[END_REF]Spring and Riedel, 2013). Although they are widely distributed in the oceans, only a few OMG and clade OM43 isolates exist [START_REF] Cho | Cultivation and growth characteristics of a diverse group of oligotrophic marine Gammaproteobacteria[END_REF][START_REF] Yang | Expansion of cultured bacterial diversity by largescale dilution-to-extinction culturing from a single seawater sample[END_REF]. Our experimental approach combining prefiltration of natural samples and co-culture with E. huxleyi demonstrates that these bacteria are free-living in a bloom context and are stimulated by E. huxleyi-derived organic substrates. We believe that this experimental approach could give valuable conditions to isolate them and expand the collection of marine bacterial isolates resistant to traditional cultivation techniques. This approach could also be used to isolate Janthinobacterium whose low abundance but prevalence and specific presence in E. huxleyi cultures when compared with other studies (Chapter 1) was questionable. Sequences assigned to Janthinobacterium also prevailed in the new cultures and they emerged in the first microbiomes of the survey at day 9. Since we carefully checked the axenic status of the E. huxleyi culture before the experiment, we hypothesize that Janthinobacterium is a member of the rare biosphere particularly adapted to coccolithophorederived organic matter. Of note, an isolate of this genus has been shown to produce siderophores and auxins triggering an increase in Chlorella vulgaris growth by 163% (Krug et al., 2020).

Conclusions

In this work, we showed that the source of the initial bacterioplankton communities influences the resulting composition of E. huxleyi microbiomes. Our experimental approach demonstrated the stability of microalgal microbiomes to stressful conditions as it has also been reported for other phytoplankton microbiomes (Geng et al., 2016;[START_REF] Camp | Revealing changes in the microbiome of Symbiodiniaceae under thermal stress[END_REF]. Although species losses still occurred in the last sampled microbiomes of the survey, they were associated with low abundance taxa and did not induce major restructuring of the community, as previously shown in long-term experiments by Behringer et al., (2018). Overall, we bring evidence that microbiomes associated with phytoplankton collection cultures represent a valuable resource to explore phytoplankton-bacteria interactions. Isolation of several bacteria that correspond to specific and shared ASV will be necessary to investigate the role and functions of stable core bacterial members interacting with E. huxleyi. Future co-culture experiments coupled with transcriptomic and metabolomic analyses will provide valuable information about the genes and molecules involved in these ecologically key interactions.

DISCUSSION AND PERSPECTIVES

Discussion and Perspectives

The overall objective of this thesis was to characterize the bacterial diversity associated with coccolithophores in cultures and to investigate the patterns and processes of microbiome selection. In this last part, I will discuss the pitfalls of the approaches used here and the strategies applied to overcome the challenges. I also discuss the major contributions of this thesis to the current state of the art on phytoplankton microbiomes. Perspectives based on preliminary results obtained from co-culture experiments were proposed.

High-throughput sequencing technologies and the microbiomes studies: advances and challenges

In the past decade, the advances in molecular tools such as Illumina Miseq sequencing allowed exploration of the microbiomes of several phytoplankton (Ajani et al., 2018;Behringer et al., 2018;Lawson et al., 2018;Sörenson et al., 2019). Still, one of the main challenges of microbiome studies using amplicon data is how to get biologically meaningful information and separate true diversity from errors [START_REF] Prodan | Comparing bioinformatic pipelines for microbial 16S rRNA amplicon sequencing[END_REF]. Although this thesis did not focus on the development of methods to assess microbiomes, I carefully tuned the protocols used, starting with the choice of primers. I used primers that show lower bias for important bacterial groups (Alpha and Gammaproteobacteria), giving results very close to the ones expected in mock communities and natural samples (Parada et al., 2016). However, we could observe that the length of the amplicons and the region sequenced is not resolutive enough to perform differentiation within some genera, such as Marinobacter (See Chapter 1 -Supplementary Figure 8). New technologies involving long-reads sequencing (e.g., Nanopore MinION)

propose a solution for increasing taxonomic resolution. Association of both sequencing technologies have been applied to investigate microbiomes of dinoflagellates and showed promising results, with a better evaluation of the abundance at the species level (Shin et al., 2018). However, the limitation of this technology (accuracy and low throughput) still favors the use of Illumina short reads in microbiome studies [START_REF] Pollock | The Madness of Microbiome: Attempting To Find Consensus "Best Practice" for 16S Microbiome Studies[END_REF].

Furthermore, each step from the DNA collection until the DNA sequencing are subject to the introduction of contaminations and sequencing errors, which potentially introduce falsepositive and inflate diversity indexes if not corrected (Bokulich et al., 2013;Nearing et al., 2018). There are different strategies developed for correction and minimization of errors on microbiome data from amplicon sequencing. Different studies have shown that the use of mock communities and negative controls can be effective for controlling the various technical, biological and human errors that arise at the processing and sequencing, in addition to control contaminations (see Bokulich et al., 2020 and references therein). However, their laborious preparation and cost, make that they are not routinely applied. In the absence of mock communities, I applied other practices in order to decrease biases associated with errors. First, I compared different commonly used workflows to obtain a diversity table (Mothur [START_REF] Schloss | Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities[END_REF], QIIME2 using vsearch OTU clustering (Bolyen et al., 2019), Amplitools workflow (Kahlke, 2018) and FROGS [START_REF] Escudié | FROGS: find, rapidly, OTUs with galaxy solution[END_REF]) and the DADA2 denoising (Callahan et al., 2016). In general, workflows disagreed in number OTUs/ASVs, however the beta-diversity patterns were highly correlated. Based on this preliminary work, I selected DADA2 because it performs a good control of errors while allowing the easy comparison between different studies (Callahan et al., 2016).

Here, the sequencing of biological (Chapter 1) and technical replicates (Chapters 2

and 3) allowed to inspect variations in the diversity that could be associated with errors. By carefully inspecting the biological replicates, I could observe that they were consistent regarding the most abundant ASVs (in presence-absence and number of reads) although low abundance transient ASVs were also present. Considering a phytoplankton culture as a closed system, with no migration processes, these ASVs can be: (i) ASVs close to the detection limit of the methods and thus they are not consistently detected or (ii) errors and/or contaminations.

Either situation is difficult to pinpoint and thus after comparing abundance filters, I removed ASVs representing less than 0.001% of the total of reads and considered only those that were found in all replicates for each strain. The same reasoning was used for the technical replicates of chapters 2 and 3. Although this choice might decrease the power to detect the natural variability in the communities, it increases the certainty of the sequences investigated [START_REF] Alberdi | Scrutinizing key steps for reliable metabarcoding of environmental samples[END_REF]. I acknowledge that even though abundance filters can be effective at removing low abundance contamination and spurious diversity, they are also criticized by removing true diversity [START_REF] Prodan | Comparing bioinformatic pipelines for microbial 16S rRNA amplicon sequencing[END_REF]. Here, the choice was at most guided by limits on drawing conclusions about the rare biosphere based solely on sequencing methods. To put these in numbers, the filters used in the different chapters (prevalence in replicates and abundance) removed from 57% to 74% of the total number of ASVs, while keeping 99% of the reads. This demonstrates the very high number of singletons and low abundance ASVs which are very difficult to distinguish from errors and add rather low information for beta diversity analyses.

In addition to that, it has been shown that statistical methods and ordinations can perform better when low abundance features are removed because it decreases sparsity (zero inflated estimation due to rare species appearing only in one sample) [START_REF] Alberdi | A guide to the application of Hill numbers to DNA-based diversity analyses[END_REF].

Although it might be argued that the application of abundance filters can influence the alphadiversity measurements based on total number of observed and unobserved species (e.g richness and Chao1 indexes), their use on molecular data is also controversial [START_REF] Alberdi | A guide to the application of Hill numbers to DNA-based diversity analyses[END_REF]. Alpha diversity indexes in molecular data are highly affected by sequence depth, number of copies of the gene and other issues, which make it very difficult to get a real estimation of diversity. Moreover, not performing any filtering can be more damaging than filtering low abundance ASVs (e.g., increasing dissimilarity among replicates, overestimation of diversity) [START_REF] Alberdi | Scrutinizing key steps for reliable metabarcoding of environmental samples[END_REF]. Here, alpha diversity indexes were carefully used to compare samples from the same study which received the same bioinformatic treatment.

A last aspect that can be considered in microbiome studies is the association of strategies to validate the ecological relevance of the observations. Here I looked for the dominant ASVs from cultures in global oceans surveys and compared coccolithophore microbiomes with published datasets from other microalgae by applying the same methods (Chapter 1).

Altogether, these approaches helped to draw a better connection between culture-based in vitro studies and surveys of natural in situ samples and to get a general view of phytoplanktonassociated bacterial diversity. I still acknowledge that even the most careful treatment is not free of errors. For this reason, I believe that more than choosing the methods that better allow investigating the scientific question, sharing is a crucial step to guarantee fully reproducibility, comparison and improvement of the methods. For this reason, all the codes I have generated in this work are available online for further studies (https://github.com/mcamarareis).

Investigating microalgal microbiomes in culture: when simplified communities bring valuable insights into phytoplankton-bacteria interactions

One of the greatest challenges on investigating phycosphere-associated communities is on how to precisely tackle the diversity at such a small scale (Fu et al., 2020). In marine environmental surveys, biomass from a seawater sample is concentrated on a filter before being processed for metabarcoding analysis. This approach has allowed identification of co-occurring organisms and major driving forces at a global scale (Sunagawa et al., 2015;[START_REF] Logares | Disentangling the mechanisms shaping the surface ocean microbiota[END_REF]. However, it is difficult to precisely resolve how the microbial diversity is shaped within the phycosphere. In this scenario, the association of different approaches is a promising solution. Investigating simplified systems such as cultured microbiomes allows to draw hypotheses that can then be addressed in a large-scale perspective. However, studying the diversity of microbiomes that have been kept for years in laboratory conditions is still a matter of debate. In this thesis, I responded to fundamental questions regarding the study of microbiomes of clonal phytoplankton cultures that can be used as pavement for future studies on phytoplankton-bacteria interactions.

First, an important gap regarding the bacterial diversity associated with two coccolithophores (E. huxleyi and G. oceanica) in cultures was filled out by our results (Chapter 1). We found a striking dominance of Gammaproteobacteria in the coccolithophore microbiomes, which differs from other microalgae (e.g. diatoms) microbiomes (Ajani et al., 2018;Behringer et al., 2018). Surprisingly coccolithophore microbiomes have common points with that of cyanobacterial and Alexandrium sp. cultures with about 52% and 42% of the taxa shared respectively, including the most dominant one (Sörenson et al., 2019;Kearney et al., 2021). A considerable diversity of Marinobacter was found associated with coccolithophore cultures, most of which could not be assigned to known species by phylogenetic placement.

This indicates a high diversity of yet unknown Marinobacter species associated with coccolithophores which deserves further attention. Marinobacter spp. are considered 'opportunitrophs' because they take advantage of nutrient pulses to grow (Handley and Lloyd, 2013). However, their high dominance in the microbiomes of different phytoplankton cannot be reduced only to an opportunistic presence. Marinobacter isolates are shown to increase the growth of different phytoplankton in co-cultures (Amin et al., 2009;[START_REF] Bolch | Bacterial associates modify growth dynamics of the dinoflagellate Gymnodinium catenatum[END_REF]. This enhancement has been attributed to their capacity to produce modified siderophores which increase iron uptake by phytoplankton (Amin et al., 2009). Still, the metabolic exchanges among Marinobacter and phytoplankton other than diatoms are largely unexplored and might not be restricted to siderophores production.

I also showed that the dominant ASVs from the coccolithophore phycosphere are globally distributed in the world ocean, agreeing with their presence in cultures isolated from different oceanic regions, although they are found in low abundance in the surface ocean.

Furthermore, our results indicate the possible association of these taxa to large sinking particles in the oceans. Importantly, their higher abundance in deep waters suggest that, through the sinking of particles, these taxa are inoculated the deep realm, where they thrive, and contribute to its function and structure [START_REF] Mestre | Sinking particles promote vertical connectivity in the ocean microbiome[END_REF]. Altogether, our results point out to an important role of the phycosphere as a 'seed bank' of bacterial taxa displaying variable functions depending on their habitat. These taxa can bloom when conditions are favorable, following a 'bloom and burst' dynamic (Alonso-Sáez et al., 2014[START_REF] Kolde | Package 'pheatmap[END_REF]. Large blooms of phycosphere-associated taxa have been observed (Morris et al., 2006;Alonso-Sáez et al., 2014). Their transient behavior in the oceans seems mainly related to the seasonality of favorable conditions, however the precise conditions are still not known and may vary for each taxon [START_REF] Alonso-Sáez | The hidden seasonality of the rare biosphere in coastal marine bacterioplankton[END_REF]. In cultures, homogeneous conditions, concentrated organic matter, and reduced competition might be decisive for the dominance and long-term persistence of these taxa.

Short-to-long time scale persistency and robustness/resilience of microbiomes were other key findings from this thesis (Chapters 1 and 3). I confirmed the hypothesis that phytoplankton microbiomes in culture represent long-lasting and stable relationships (Krohn-Molt et al., 2017). Importantly, these microbiomes are robust and coupled to changes in the health state of the host (Chapter 3). Among microalgae, robustness/resilience of microbiomes to environmental perturbations were shown for N. salina and Symbiodinium (Geng et al., 2016;[START_REF] Camp | Revealing changes in the microbiome of Symbiodiniaceae under thermal stress[END_REF]. This coupling can have important evolutionary implications in phytoplankton-bacteria interactions. While the host is in a healthy state, interactions are stable, however, stress can trigger changes in these interactions and modify the microbiome dominant taxa. The coupling between host and microbiome health state has been demonstrated in corals, which experienced parasitism by its Symbiodinium partners under thermal stress (Baker et al., 2018). Concerning E. huxleyi, studies have shown that an associated bacterium can switch from mutualistic to parasitic according to senescence signals released by the host (Seyedsayamdost, Case, et al., 2011;Segev, Wyche, et al., 2016). We cannot characterize the patterns observed here as a switch in the interactions however they demonstrate that stable conditions are necessary for the maintenance of these associations. Further studies involving different levels of disturbance would help to better understand the relationships between the diversity and taxonomic structure of the microbiomes and their capacity to recover from disturbance, and ultimately the consequences of a failure to recover. These questions are of crucial importance to understand the impacts of environmental global changes on major processes driven by symbiotic interactions in the oceans.

Another key result from this work regards the processes of microbiome assembly. I showed that even if the most dominant taxa are shared among cultures, we can still identify patterns related to the assembly of microbiomes (Chapters 2 and 3). The local conditions of the place of isolation were shown to display an important role in the microbiome assembly.

Both abiotic parameters at the time of sampling and culture conditions (Chapter 2) and biotic factors (e.g. pre-exposition to E. huxleyi in the bloom) (Chapter 3) were detected as drivers of microbiome composition. These results reinforce the influence of deterministic processes on the assembly of phytoplankton microbiomes (Ajani et al., 2018;Sörenson et al., 2019). These forces might act at different scales. While the environmental factors select the bacterial community at a larger scale, the effect of the host is more pronounced at the phycosphere level where the organic matter released is more concentrated (Landa et al., 2016;Fu et al., 2020).

This local effect of the phycosphere is clear from the results of the Chapter 3, showing that the bacterial community sampled at the E. huxleyi bloom depth favored the presence of groups not found in the other treatments (even from the same site at upper depth). Altogether these results point out to a high complexity of the processes involved in microbiome assembly at multiple scales.

To conclude with the contributions of this thesis to the knowledge on phytoplanktonbacteria interactions, an important catalogue of the bacterial diversity found in microalgal cultures that were isolated from different places in the world, with different metabolic requirements (specially E. huxleyi, but not only) was also provided (Chapters 1 and 2). This unique dataset is being included in a global analysis of the Tara Oceans 16S rRNA metabarcodes as a reference for phytoplankton-associated bacteria. This reinforces the already mentioned importance of using microbiomes in culture to obtain insights and draw hypotheses that can be tested in other contexts.

Towards a functional exploitation of microalgal microbiomes and beyond

Many fundamental questions regarding phytoplankton microbiomes are yet to be answered. Especially for coccolithophores, the metabolic exchanges with mutualistic bacteria are still largely unexplored. Studying the interactions between bacterial isolates and E. huxleyi was one of the initial objectives of my thesis. During the last three years, in parallel to the experiments I detailed before, I also isolated many bacterial taxa associated with E. huxleyi and established axenic cultures that were both used in co-culture experiments. Together, the results presented here, and preliminary co-culture experiments opened new avenues in the study of phytoplankton-bacteria interactions at multiple scales.

One of the main paradigms of microbiomes is that they provide to the host conditions to grow better (Amin et al., 2012), to fight pathogens (Seyedsayamdost et al., 2014), to thrive in different environments (Amin et al., 2009), and to co-exist with competitors (Jackrel et al., 2020). Different studies propose that genes, or clusters of genes, might be more important than species for understanding the roles and assembly processes of microbiomes associated with various systems (Burke, Steinberg, et al., 2011;[START_REF] Fan | Functional equivalence and evolutionary convergence in complex communities of microbial sponge symbionts[END_REF]Louca et al., 2016). Macroalgal investigations have shown that although disagreements can be found regarding the microbiome diversity of a host grown in different environments, much more agreement is found if we investigate the functional diversity (Burke et al., 2011). Furthermore, a study on the global ocean microbiome evidenced a high functional redundancy across oceanic regions which harbored different taxonomic compositions (Sunagawa et al., 2015). Thus, it is expected that functional redundancy is also present in phytoplankton microbiomes. Investigation of the phycosphere of Microcystis aeruginosa from different lakes agrees with this hypothesis by showing functional convergence and coupling between host and microbiome function (Jackrel et al., 2019). Still, functional diversity in phytoplankton microbiomes remains largely unexplored.

Here, results of chapters 1 and 3 raised the question about the functional diversity in E.

huxleyi microbiomes. In both chapters the communities were dominated by gammaproteobacterial members, however the dominant taxa varied. In Chapter 1

Marinobacter and Alteromonas were dominant while in Chapter 3 dominance among the treatments and specific replicates was displayed by other taxa, such as OM43 clade, Luminiphilus, SAR92 clade, KI89 clade and Alcanivorax. The hypothesis for these differences is discussed in Chapter 3. The remaining question is, do these microbiomes present the same or complementary functional potential?

Functional assignment of the ASVs accounting for more than 1% of relative abundance per sample in each study revealed microbiomes highly similar regarding dominance and low abundance functions (see Annex 2 for the methods used). Although a considerable fraction of the ASVs (42% and 44% in chapters 1 and 3, respectively) of each study could not be assigned to a function, the remaining ones were assigned to 12 functions from which 10 were shared between both studies (Figure 1). et al., 2016). Grey boxes represent shared functions while red boxes are functions specific to ASVs from Chapter 1.

Among the identified functions, chemoheterotrophy and aerobic chemoheterotrophy displayed mainly by copiotrophic bacteria were the most dominant in both studies. These functions were assigned mainly to Rhodobacteraceae, Alteromonadaceae and Marinobacteraceae in Chapter 1, and to Flavobacteriaceae and Pseudomonadaceae in Chapter 3. Aerobic chemoheterotrophy is the function comprising the highest diversity of taxa in the oceans (Louca et al., 2016). Both incorporate activities involved in the degradation of various organic compounds and for this reason they are spread in most of bacterial taxa (Louca et al., 2016).

In addition, we identified interesting patterns regarding more specific functions. venustensis) succeeded in one of the replicates in Chapter 3 (see Chapter 3 -Figure 3C). This calls attention to the important role of hydrocarbon-degrading bacteria in E. huxleyi microbiomes and shows that the function can be filled by other taxa in the absence of Marinobacter. Recent studies pointed out the phycosphere as a niche for hydrocarbondegrading bacteria (Chernikova et al., 2020;Thompson et al., 2020;Kearney et al., 2021). The main compounds hypothesized to be degraded by these bacteria in phytoplankton cultures are polycyclic aromatic hydrocarbons (PAHs) and long-chain hydrocarbon-like compounds [START_REF] Mishamandani | Response of the bacterial community associated with a cosmopolitan marine diatom to crude oil shows a preference for the biodegradation of aromatic hydrocarbons[END_REF]. Genomic evidence highlights the potential of E. huxleyi to produce lipidic compounds, however metabolic pathways involved in the production of PAHs are yet not established [START_REF] Read | Pan genome of the phytoplankton Emiliania underpins its global distribution[END_REF]. Moreover, bacteria have been shown to modulate E.

huxleyi production of lipidic compounds, which might be a mechanism involved in their interactions [START_REF] Segev | Bacterial influence on alkenones in live microalgae[END_REF]. Alcanivorax is considered as the most cosmopolite obligate hydrocarbon-degrading genus [START_REF] Yakimov | Obligate oildegrading marine bacteria[END_REF]. However, members of this genus are much less dominant in phytoplankton cultures than Marinobacter (Lupette et al., 2016;Kearney et al., 2021). For example, in the Chapter 1 Alcanivorax accounted to 0.42% of the reads, while Marinobacter to 61%. This might be attributed to the versatility of Marinobacter, which can explore diverse types of substrates while most Alcanivorax members specifically degrade hydrocarbons [START_REF] Yakimov | Obligate oildegrading marine bacteria[END_REF]Handley and Lloyd, 2013).

A similar pattern was observed for methylotrophs, such as OM43 clade and

Methylophaga. In coccolithophore-associated microbiomes (Chapter 1), methylotrophs mainly represented by Methylophaga were present in low abundance. In Chapter 3, OM43 clade dominated the community composition of the four treatments at days 32 and 46 (Chapter 3 -Figure 3C). Methylotrophic bacteria metabolize and assimilate one-carbon (C1) compounds and have been identified in E. huxleyi blooms [START_REF] Neufeld | Substratespecific clades of active marine methylotrophs associated with a phytoplankton bloom in a temperate coastal environment[END_REF]. Methylophaga is also considered as putative hydrocarbon-degrading bacteria although the database that we used did not classify them as such [START_REF] Mishamandani | Response of the bacterial community associated with a cosmopolitan marine diatom to crude oil shows a preference for the biodegradation of aromatic hydrocarbons[END_REF]. In phytoplankton blooms, methylotrophic bacteria seem to rely on phytoplankton for carbon and energy sources in addition to compete for cobalamin (Bertrand et al., 2015). However, their ecological role and interactions with phytoplankton are not well investigated. Genomic analysis of an OM43 isolate evidenced a narrow substrate range and a streamlined genome [START_REF] Giovannoni | The small genome of an abundant coastal ocean methylotroph[END_REF]. Their presence and overdominance under certain conditions indicate that E. huxleyi can produce a specific suite of compounds which stimulate their growth.

Finally, metabolism of sulfur compounds was consistently represented by Sulfitobacter in both studies. Sulfitobacter members are important actors in the phycosphere, displaying interactions that range from mutualism to parasitism (Amin et al., 2015;Barak-Gavish et al., 2018). Draft genomes of Sulfitobacter isolates from naked and coccolith-bearing E. huxleyi cultures showed that they encode genes involved in symbiotic relationships (type IV secretion system) besides degradation of lignin and DMSP (Orata et al., 2016;Rosana et al., 2016).

However, symbiotic interactions of E. huxleyi and Sulfitobacter have not been described up to now. On the other hand, parasitic interactions are known and hypothesized to contribute to E.

huxleyi bloom demise (Barak-Gavish et al., 2018).

Although preliminary, this functional screening allowed to identify interesting patterns in E. huxleyi microbiomes. Further investigations focusing on interactions between E. huxleyi and isolates of different bacterial groups supposedly displaying the same functions will help to understand how functional redundancy is important in phytoplankton microbiomes. Moreover, a functional approach will help to disentangle the influence of dominant taxa and of the rare biosphere on the host. Fundamental opened questions are: Do dominant taxa contribute more to the host because of their high abundance? Are interactions between host and dominant groups stronger because their metabolic exchanges are more pronounced? What is the role of the rare biosphere in phytoplankton microbiomes? How rare taxa display important roles for the host when they are constantly in low abundance? Which conditions can promote the increase of their relative abundance? Do they wait for the hosts' weakness for blooming and killing it?

Exploring Emiliania huxleyi aggregates

From my experience with axenic E. huxleyi cultures I could observe that, contrary to other microalgae, E. huxleyi is able to grow steadily in axenic conditions (Bolch et al., 2011) (see Annex 3 for the methods used). However, growth rates of the axenic cultures are significantly lower than when compared to the xenic ones (host with its associated microbiome) (Figure 2A). resulting in the formation of large aggregates [START_REF] Passow | Transparent exopolymer particles (TEP) in aquatic environments[END_REF]. I hypothesize two reasons for this formation of aggregates. One could be a response of E. huxleyi to the stress in the absence of its microbiome. This phenomena has been observed when E. huxleyi is submitted to different stressors (Engel et al., 2004;Vardi et al., 2012). The TEP production dramatically increased during viral infection and it has been hypothesized as a defense strategy (Vardi et al., 2012;Nissimov et al., 2018). Moreover, higher CO2 concentrations induce the TEP production by E.

huxleyi as a response when nutrients are exhausted (Engel et al., 2004). Considering that bacteria in cultures can provide better conditions to the microalgal growth, such as the release of vitamins and growth promoters, their absence can represent a stressor (Amin et al., 2012). A second hypothesis is that bacteria consumes the TEP produced by E. huxleyi, which accumulates in their absence. Bacteria have been shown to affect the aggregation of different microalgae (Grossart, Czub, et al., 2006;[START_REF] Gärdes | Diatom-associated bacteria are required for aggregation of Thalassiosira weissflogii[END_REF]Cruz and Neuer, 2019). For some (e.g. Thalassiosira rotula and Synechococcus sp.), bacteria stimulate the aggregation (Grossart, Czub, et al., 2006;[START_REF] Gärdes | Diatom-associated bacteria are required for aggregation of Thalassiosira weissflogii[END_REF]Cruz and Neuer, 2019), but for others (Skeletonema costatum), they prevent microalgal aggregation by consuming the TEP and protein-containing particles (Grossart et al., 2006).

During my PhD, I co-cultured several bacterial isolates from E. huxleyi cultures with an axenic E. huxleyi culture (Figure 3). The bacterial candidates were shown to enhance E. huxleyi growth and decrease cell aggregation. These isolates belong to Marinobacter, Nioella, Halomonas and Sulfitobacter (Figure 3) (see Annex 3 for the methods used). Investigation using transcriptomics in co-cultures and individual partners associated with quantification of exopolysaccharides under manipulated conditions will help to understand the involvement of these bacteria in TEP degradation. Differentially expressed genes can be explored in data available from global surveys (such as the 'Ocean gene atlas') in order to understand the distribution of these genes in the global oceans and their potential large-scale impacts in global processes [START_REF] Villar | The Ocean Gene Atlas: Exploring the biogeography of plankton genes online[END_REF]. Furthermore, such hypotheses can be explored at larger scales, in E. huxleyi blooms surveys, by the quantification and characterization of the bacterial community associated with sinking particles. Association of satellite images, lipidomic-and gene-based molecular approaches, in situ optical sensors and sediment traps has been used to investigate the influence of viral infection on carbon export during an E. huxleyi bloom (Laber et al., 2018). Similar approach applied to the investigation of bacterial communities will bring valuable information on the role of phycosphere-associated bacteria on aggregate formation in a large-scale perspective.

Concluding remarks

Using molecular approaches, this thesis shed light on fundamental questions regarding the diversity and assembly processes in the phycosphere. Still, 49 years after the publication of the pioneer work of [START_REF] Bell | Chemotactic and growth responses of marine bacteria to algal extracellular products[END_REF] on the phycosphere, we are far from understanding all the processes underlying the complex interactions in this microscale environment.

Multiscale studies involving multiple hosts, culture-based experiments, meta-and single cell 'omics' techniques, field observations and systems biology modelling, will provide the knowledge necessary to decipher the complexity and dynamics of these interactions and their impact on major ecological processes in the oceans. Axenization and culture maintenance E. huxleyi strain RCC1212 was isolated from the Atlantic Ocean near to South Africa (Latitude: -34.47, Longitude: 17.30) on September 1st, 2000. This strain was axenized using the protocol described at the Chapter 3. Cultures were kept in K/2 medium prepared (Keller et al., 1987;Probert, 2019) with reconstituted red sea water (https://www.redseafish.com/red-seasalts/red-sea-salt/) and filter-sterilized at 0.1 µm and were regularly checked for bacterial contamination by flow cytometry as above mentioned. Axenization of cultures used for the experiments was confirmed by fixing 5 mL of culture with glutaraldehyde 25% (0.25% final concentration), staining with SYBR green (1/10,000 final concentration) and filtering in 0.2 µm polycarbonate black membrane (Millipore Isopore) for observation by epifluorescence microscopy. Axenic cultures were grown in 50 mL vented tissue culture flasks at the same light and temperature conditions as the bacterial isolates.

Experimental set-up

Prior to experiments, axenic cultures were daily diluted by inoculating the same volume of fresh media. This allowed to quickly increase the volume of culture while keeping them in exponential growth. For bacteria, four days before the experiment unique colonies of each targeted bacterium were streaked in fresh solid medium. At the day of the experiment single colonies were diluted in axenic K/2 medium.

For each test, I inoculated axenic E. huxleyi at a cell concentration of ~ 5 x 10 4 cells/mL with a bacterium partner at the cell concentration of ~ 1 x 10 6 cells/mL. All the bacterial treatments were performed in triplicates and axenic controls in 4 replicates. Cultures were kept at the same light and temperature conditions as for maintenance.

Sampling and flow cytometry analysis

For 11 days 490 μL of each culture was fixed with glutaraldehyde 25% (0.25% final concentration) and Polaxamer 188 solution 10% (Sigma-Aldrich) (0.1% final concentration) incubated for 20 min in the dark, flash frozen in liquid nitrogen and stored at -80°C for flow

Résumé

Structure et assemblage des microbiomes bactériens dans la phycosphère d'Emiliania huxleyi

Les interactions entre le phytoplancton marin et les bactéries sont une symbiose fondamentale dans les océans, affectant les cycles du carbone et des nutriments. Au cours de la dernière décennie, l'introduction de nouveaux outils de séquençage environnemental à haut débit a dévoilé une diversité inattendue de bactéries associées au phytoplancton dans l'océan ou en culture. Cependant, les modèles et les processus de sélection et d'assemblage du microbiome dans la phycosphère, c'est-à-dire la région environnant la cellule phytoplanctonique, restent mal connus. Dans cette thèse, Emiliania huxleyi, un phytoplancton-clé pour le cycle global du carbone et l'une des microalgues les plus étudiées de nos jours, a été sélectionnée comme espèce modèle.

La première partie du travail visait à étudier la diversité microbiologique associée à 73 souches d'E. huxleyi et de son taxon frère Gephyrocapsa oceanica, isolées des océans mondiaux entre 1959 et 2015. Leurs microbiomes étaient dominés par des Gammprotéobactéries du genre Marinobacter, indépendamment de la variation inter et intra-espèce de l'hôte, du lieu d'isolement et de l'âge des cultures. L'abondance et la distribution des principaux taxons bactériens associés aux cultures d'Emiliania/Gephyrocapsa dans les jeux de données de métabarcodes d'ADNr 16S issus des océans du monde, ont montré qu'ils sont préférentiellement associés à des fractions planctoniques de grande taille et enrichis dans les couches bathypélagiques, attirant l'attention sur le rôle potentiellement important des bactéries associées au phytoplancton dans le flux des agrégats organiques et du plancton de grande taille vers la couche océanique profonde.

La deuxième partie était consacrée à l'étude des moteurs de la sélection et de l'assemblage du microbiome dans les cultures. Les microbiomes de souches appartenant à 7 phyla de phytoplancton et résultant de millions d'années d'évolution, isolées des mêmes échantillons d'eau de mer lors de campagnes océanographiques ont été comparés. Nos résultats indiquent que la diversité des microbiomes dans les phycosphères cultivées est régie par des processus déterministes à plusieurs échelles, dont le lieu d'isolement et le milieu de culture.

Dans la troisième et dernière partie de ce travail, nous avons étudié les facteurs de sélection du microbiome par E. huxleyi. Des échantillons d'eau de mer collectés à l'intérieur et à l'extérieur d'une efflorescence d'E. huxleyi au large de la Bretagne (2019) ont servi d'inoculums à une culture axénique d'E. huxleyi et les microbiomes assemblés ont été suivis pendant un an. Nous avons observé que les microbiomes assemblés étaient dépendants de la composition de la communauté bactérienne initiale et résistants aux perturbations. Dans l'ensemble, les résultats des trois parties mettent en évidence la stabilité des microbiomes du phytoplancton dans le temps et sa résilience aux changements d'état de santé de l'hôte. Dans son ensemble, ce travail fournit une ressource de base originale sur la diversité bactérienne des cultures de microalgues et une collection d'isolats bactériens qui peuvent être utilisés dans de futures études des interactions phytoplancton-bactéries.

Mots-clés: Emiliania huxleyi, phycosphère, bactéries, diversité du microbiome, processus de sélection, metabarcoding.

Figure 1 .

 1 Figure 1. Classical view of marine microorganisms' participation in the carbon cycle. Figure and legend (adapted) fromBuchan et al., (2014). Key processes of the marine carbon cycle

Figure 4 .

 4 Figure 4. The rhizosphere and the phycosphere are analogous microenvironments. The phycosphere defines the region surrounding a phytoplankton cell which is enriched in organic substrates exuded by the cell. The phycosphere is an important microenvironment for planktonic bacteria. Figure and legend (adapted) from Seymour et al., (2017).

Figure 5 .

 5 Figure 5. The continuum of symbiotic relationships. Symbiotic relationships encompass multiple dimensions of effects represented on two axes. If a symbiosis has a positive (blue)

  Figure and legend (adapted) from Tipton et al., (2019).

Figure 6 .

 6 Figure 6. Coccolithophores bloom in the English Channel (June 24, 2020). Source: https://eoimages.gsfc.nasa.gov/images/imagerecords/146000/146897/englishchannel_tmo_20

Figure 7 .

 7 Figure 7. Difference in heterococcolith architecture between a) Emiliania huxleyi and b) Gephyrocapsa oceanica. E. huxleyi lacks the bridge in the central area which is present in G. oceanica. Adapted from Bendif et al., (2016). Scale bar = 2 microns.

Figure 8 .

 8 Figure 8. Main morphotypes described in Emiliania huxleyi. The classification is based on morphometric characteristics of the coccoliths and structural features of the central area (Table

Figure 9 .

 9 Figure 9. Phylogeography of G. huxleyi populations (Bendif et al in prep). a. Phylogenetic tree of 61 isolates based on the concatenation of 1,000 supergenes (10 kbp) randomly selected, forming a matrix of 10 Mbp. The delineation of species A1, A2 and B is congruent with a principal component analysis (PCA) based on wide-genomic single nucleotide polymorphism (~ 2.6 million snps), genetic structure inferred from a discriminant analysis in principal component (DAPC) and coalescent phylogenetic reconstruction. Sub-clades correspond to a mixture of geographical and morphotype delineation. b, Biogeography of phylogenetic groups.

Figure 10 .

 10 Figure 10. Schematic representation of the 'Cheshire Cat' mechanism in E. huxleyi in response to viral infection. As currently described, the haplodiplontic life cycle of E. huxleyi comprises a calcified diploid and noncalcified scale-bearing biflagellate haploid stage that is resistant to specific viruses (EhV). Both diploid and haploid cells likely interconnect through meiosis and syngamy. In a new study, the authors show that in response to infection by EhV, E. huxleyi can

Figure 1 .

 1 Figure 1. Composition of the coccolithophore microbiomes as defined by class and family of ASVs. The bacterial communities are grouped by hierarchical clustering of the ASVs accounting for more than 5% of the reads in each culture. Only the three most abundant families are represented to improve visualization.

Figure 2 .

 2 Figure 2. A) Principal Component Analysis (PCA) of the microbiome composition including all ASVs. B) Heatmap including the ASVs with more than 5% of the reads in each culture.

Figure 3 .

 3 Figure 3. Venn diagram showing the number of OTUs shared by coccolithophore microbiomes

  , H), both were enriched in the bathypelagic samples of Malaspina (Figure 4E, J).

Figure 4 .

 4 Figure 4. Relative abundance (proportion) and distribution of dominant coccolithophoresassociated Marinobacter ASVs in the Tara Oceans and Malaspina expeditions. (A) Log10transformed relative abundance of ASV2 in Tara Oceans dataset. (B) Relative abundance of ASV2 in Tara Oceans dataset by plankton size fraction. (C) by depth. (D) Log10-transformed relative abundance of ASV2 in the Malaspina dataset. (E) Relative abundance of ASV2 in Malaspina dataset by depth. (F) Log10-transformed relative abundance of ASV3 in Tara Oceans dataset. (G) Relative abundance of ASV3 in Tara Oceans dataset by size fraction. (H) by depth. (I) Log10-transformed relative abundance of ASV3 in the Malaspina dataset. (J) Relative abundance of ASV3 in Malaspina dataset by depth. SRF -surface; DCM -deep chlorophyll maximum; MES -mesopelagic. Relative abundance was calculated as the number

  ) Microbial community structure and function on sinking particles in the North Pacific Subtropical Gyre. Front Microbiol 6: 1-14.Foster, R.A., Carpenter, E.J., and Bergman, B. (2006) Unicellular cyanobionts in open ocean dinoflagellates, radiolarians, and tintinnids: Ultrastructural characterization and immunolocalization of phycoerythrin and nitrogenase 1. J Phycol 42: 453-463. Gärdes, A., Iversen, M.H., Grossart, H.P., Passow, U., and Ullrich, M.S. (2011) Diatomassociated bacteria are required for aggregation of Thalassiosira weissflogii. ISME J 5: 436-445. Gärdes, A., Ramaye, Y., Grossart, H.-P., Passow, U., and Ullrich, M.S. (2012) Effects of Marinobacter adhaerens HP15 on polymer exudation by Thalassiosira weissflogii at different N: P ratios. Mar Ecol Prog Ser 461: 1-14. Gauthier, M.J., Lafay, B., Christen, R., Fernandez, L., Acquaviva, M., Bonin, P., and Bertrand, J.-C. (1992) Marinobacter hydrocarbonoclasticus gen. nov., sp. nov., a new, extremely halotolerant, hydrocarbon-degrading marine bacterium. Int J Syst Evol Microbiol 42: 568-576. Gonzalez, J.M., Simó, R., Massana, R., Covert, J.S., Casamayor, E.O., Pedrós-Alió, C., and Moran, M.A. (2000) Bacterial community structure associated with a dimethylsulfoniopropionate-producing North Atlantic algal bloom. Appl Environ Microbiol 66: 4237-4246. Green, D.H., Echavarri-Bravo, V., Brennan, D., and Hart, M.C. (2015) Bacterial diversity associated with the coccolithophorid algae Emiliania huxleyi and Coccolithus pelagicus f. braarudii. Biomed Res Int 2015:. Gutierrez, T., Green, D.H., Nichols, P.D., Whitman, W.B., Semple, K.T., and Aitken, M.D. (2013) Polycyclovorans algicola gen. nov., sp. nov., an aromatic-hydrocarbon-degrading marine bacterium found associated with laboratory cultures of marine phytoplankton.Appl Environ Microbiol 79: 205-214. Hagino, K., Bendif, E.M., Young, J.R., Kogame, K.,Probert, I., Takano, Y., et al. (2011) New evidence for morphological and genetic variation in the cosmopolitan coccolithophore Emiliania huxleyi (prymnesiophyceae) from the cox1b-atp4 genes. J Phycol 47: 1164-1176.Harvey, E.L., Deering, R.W.,Rowley, D.C., Gamal, A. El, Schorn, M., Moore, B.S., et al. (2016) A bacterial quorum-sensing precursor induces mortality in the marine coccolithophore, Emiliania huxleyi. Front Microbiol 7:. heterotrophic bacterial assemblages within Synechococcus cultures. Appl Environ Microbiol 84: e01517-17. Zubkov, M. V., Fuchs, B.M., Archer, S.D.,Kiene, R.P., Amann, R., and Burkill, P.H. (2001) Linking the composition of bacterioplankton to rapid turnover of dissolved dimethylsulphoniopropionate in an algal bloom in the North Sea. Environ Microbiol 3: 304-311.

(

  163 samples), 3-20 µm (36 samples), 5-20 µm (94 samples), 20-180 µm (188 samples), and 180-2,000 μm (192 samples)]. The second dataset comprised a total of 124 surface samples and 41 bathypelagic samples collected during the Malaspina 2010 expedition across the world's

Supplementary figure 10 .

 10 Relative abundance (proportion) and distribution of dominant coccolithophores-associated Marinobacter ASVs in the Tara Oceans and Malaspina expeditions. (A) Log10-transformed relative abundance of ASV4 in Tara Oceans dataset. (B) Relative abundance of ASV4 in Tara Oceans dataset by plankton size fraction. (C) by depth. (D) Log10-transformed relative abundance of ASV4 in the Malaspina dataset. (E) Relative abundance of ASV4 in Malaspina dataset by depth. (F) Log10-transformed relative abundance of ASV5 in Tara Oceans dataset. (G) Relative abundance of ASV5 in Tara Oceans dataset by size fraction. (H) by depth. (I) Log10-transformed relative abundance of ASV5 in the Malaspina dataset. (J) Relative abundance of ASV5 in Malaspina dataset by depth. Relative abundance was calculated as the number of reads of the ASV/zOTU by station/size fraction/depth divided by the total number of reads at the corresponding station/size fraction/depth. Relative abundance (proportion) and distribution of dominant coccolithophores-associated ASVs in the Tara Oceans and Malaspina expeditions. (A) Log10transformed relative abundance of ASV6 in Tara Oceans dataset. (B) Relative abundance of ASV6 in Tara Oceans dataset by plankton size fraction. (C) by depth. (D) Log10-transformed relative abundance of ASV6 in the Malaspina dataset. (E) Relative abundance of ASV6 in Malaspina dataset by depth. (F) Log10-transformed relative abundance of ASV7 in Tara Oceans dataset. (G) Relative abundance of ASV7 in Tara Oceans dataset by size fraction. (H) by depth. (I) Log10-transformed relative abundance of ASV7 in the Malaspina dataset. (J) Relative abundance of ASV7 in Malaspina dataset by depth. Relative abundance was calculated as the number of reads of the ASV/zOTU by station/size fraction/depth divided by the total number of reads at the corresponding size station/fraction/depth. Relative abundance (proportion) and distribution of dominant coccolithophores-associated ASVs in the Tara Oceans and Malaspina expeditions. (A) Log10transformed relative abundance of ASV8 in Tara Oceans dataset. (B) Relative abundance of ASV8 in Tara Oceans dataset by plankton size fraction. (C) by depth. (D) Log10-transformed relative abundance of ASV8 in the Malaspina dataset. (E) Relative abundance of ASV8 in Malaspina dataset by depth. (F) Log10-transformed relative abundance of ASV9 in Tara Oceans dataset. (G) Relative abundance of ASV9 in Tara Oceans dataset by size fraction. (H) by depth. (I) Log10-transformed relative abundance of ASV9 in the Malaspina dataset. (J) Relative abundance of ASV9 in Malaspina dataset by depth. Relative abundance was calculated as the number of reads of the ASV/zOTU by station/size fraction/depth divided by the total number of reads at the corresponding station/size fraction/depth. Relative abundance (proportion) and distribution of dominant coccolithophores-associated ASVs in the Tara Oceans and Malaspina expeditions. (A) Log10transformed relative abundance of ASV10 in Tara Oceans dataset. (B) Relative abundance of ASV10 in Tara Oceans dataset by plankton size fraction. (C) by depth. (D) Log10-transformed relative abundance of ASV10 in the Malaspina dataset. (E) Relative abundance of ASV10 in Malaspina dataset by depth. (F) Log10-transformed relative abundance of ASV13 in Tara Oceans dataset. (G) Relative abundance of ASV13 in Tara Oceans dataset by size fraction. (H) by depth. (I) Log10-transformed relative abundance of ASV13 in the Malaspina dataset. (J) Relative abundance of ASV13 in Malaspina dataset by depth. Relative abundance was calculated as the number of reads of the ASV/zOTU by station/size fraction/depth divided by the total number of reads at the corresponding station/size fraction/depth.This second chapter is dedicated to the investigation of the processes of microbiome assembly in phytoplankton cultures. The study was guided by the results obtained in Chapter 1, where I did not find a significant influence of the parameters tested (host species, E. huxleyi morphogroups, age of the cultures and place of isolation) on the diversity of coccolithophoresassociated bacteria. These results raised the question about the selection of microbiomes in cultures. Do they result from deterministic processes or are they randomly selected? To answer this question, I screened the Roscoff Culture Collection to extract phytoplankton species, including E. huxleyi, that were isolated from the same seawater samples and for which environmental parameters were available. Then, I compared their associated microbiomes with the objective of identifying and quantifying the influence of environmental filters, host specificity, culture media and random selection on their diversity.

3°58' 6

 6 ''W)(Brittany, France). All the media recipes are available at: http://roscoff-culturecollection.org/culture-media. Cultures (20 mL) were grown in 50 mL vented tissue culture flasks on a 12h light and 12h dark cycle under a white light irradiance (Philips Master TL_D 18W/865). Prior to experiments, cryopreserved cultures were acclimated to the growth conditions for at least two growth cycles. Cultures were established by transferring aliquots (200 µL) into 20 mL of medium.DNA sampling, extraction, and 16S rRNA sequencingOnce the cultures reached mid-late log phase (about 7 days after transfer), 2 mL was transferred to a sterile Eppendorf tube and centrifuged at 2,000 g for 30 sec to reduce the microalgal load. The supernatants were transferred into new tubes containing 2 μL of Poloxamer 188 solution 10% (Sigma-Aldrich) and centrifuged at 5,600 g for 5 min. Pelleted strains were extracted using a modified protocol from NucleoSpin PlantII® DNA Mini kit (Macherey-Nagel). First, cells were incubated 1 hour at 55°C with 400 µL of lysis buffer PL1, 25µL proteinase K (20mg/mL) and 100 µL lysozyme (20mg/mL). Nucleic acids were then extracted following the recommendations of the manufacturer and eluted in 100 µL of the buffer provided by the kit. Polymerase Chain Reaction (PCR) was conducted to amplify 16S ribosomal prokaryotic gene with the universal prokaryote primers 515F-Y 5'-GTGYCAGCMGCCGCGGTAA-3' and 926R 5'-CCGYCAATTYMTTTRAGTTT-3'(Parada et al., 2016). The forward primer was 5'-labeled with an eight-nucleotide tag unique to each sample. Triplicate PCR reactions (30 µL) contained 10 ng of DNA, 0.625 units of GoTaq G2 Flexi polymerase (Promega), 1X of enzyme buffer, 1.5 mM of MgCl2, 0.4 mM of dNTPs, and 0.32 µM of each primer. The PCR program consisted in an initial denaturation step at 95°C during 10 min, 32 cycles of denaturation at 95°C for 30 sec, annealing at 50°C for 1 min and elongation at 72°C for 1 min, and a final step at 72°C for 10 min. PCR reactions were purified using the NucleoSpin Gel and PCR Clean-Up kit (Macherey-Nagel), and quantified with the Quant-It PicoGreen double stranded DNA Assay kit (ThermoFisher). The purified PCR products were pooled in equal concentrations. Library preparation and high-throughput sequencing using Illumina technology were performed at Fasteris SA (Plan-les-Ouates, Switzerland). The DNA pool was sequenced using two independent Illumina runs (technical replicates).

  Prior to CLR transformation, we replaced zero abundance values using the simple multiplicative model of zCompositions package v1.3.4 (function cmultRepl) (Palarea-Albaladejo and Martín-

  Distance-based redundancy analysis (db-RDA) using Bray-Curtis dissimilarity. (B) Redundancy analysis (RDA) using Hellinger-transformed data, (C) CLR-transformed data (Aitchinson distance), and (D) PhILR-transformed data.

Figure 1 :

 1 Figure 1: Sampling area and characteristics of the E. huxleyi bloom in the Celtic sea. A) Map showing the bloom area (black rectangle). The sampling strategy covered daily sampling for five days (shape coded) in the morning (AM, grey symbol) and the afternoon (PM, black symbol). The red square indicates the location of the sampling performed outside the bloom, 34 km apart from the last bloom station sampled. B) Satellite image showing the bloom area. Source: https://www.star.nesdis.noaa.gov/sod/mecb/color/ocview/ocview.html; May 21, 2019. C) E. huxleyi cell concentrations at AM bloom sites during the survey. Cell concentrations were calculated from duplicate filters using scanning electron microscopy (20 images per filter were analyzed).

Figure 2 .

 2 Figure 2. Dynamics of (A) E. huxleyi and (B) bacterial cell concentration (cell/mL) over time (mean ± SD, n=3 for the four first time points and n=6 for the last four). (C) Richness, expressed as number of prokaryotic ASVs over time for each treatment (mean ± SD, n=3). Colors of the curves indicated the different water samples used as inoculum: D5_IN_PM_DCM (DCM sample inside the bloom); D5_IN_PM_SRF (surface sample inside the bloom); D5_OUT_PM_DCM (DCM sample outside the bloom); D5_OTU_PM _SRF (surface sample outside the bloom). The red dotted line represents the moment following the increase of the inoculum at the culture transfer to recover E. huxleyi cell concentration.

Figure 3 .

 3 Figure 3. Beta-diversity patterns of E. huxleyi microbiomes across treatments and time. (A) Principal coordinates analysis (PCoA) using Jaccard dissimilarity matrix of the presenceabsence transformed rarefied table. Colors correspond to each treatment that received prokaryotic communities from different water samples: D5_IN_PM_DCM (DCM sample inside the bloom); D5_IN_PM_SRF (surface sample inside the bloom); D5_OUT_PM_DCM (DCM sample outside the bloom); D5_ OTU_PM _SRF (surface sample outside the bloom).

Figure 4 .

 4 Figure 4. Principal components analysis (PCA) showing the cyclic patterns of the microbiome beta diversity. Community distances (Euclidian distances of Hellinger-transformed data) are shown for microbiomes from bloom DCM (D5_IN_PM_DCM) (A), bloom surface (D5_IN_PM_SRF) (B), outside bloom DCM (D5_OUT_PM_DCM) (C), and outside bloom surface (D5_OUT_PM_SRF) (D). In C., the cyclic pattern is observed at the replicate level for replicates 1 and 3. The polygons link replicates (shape coded) at each time point (color coded).

Figure 5 .

 5 Figure 5. Venn diagram showing the number of ASVs shared by the different treatments. Acronym of different treatments: D5_IN_PM_DCM, bloom DCM sample; D5_IN_PM_SRF, bloom surface sample; D5_OTU_PM_DCM, outside bloom DCM sample; D5_ OTU_PM _SRF, outside bloom site SRF.

Figure 1 .

 1 Figure 1. Functional classification of ASVs, found in chapters 1 and 3, having more than 1% of relative abundance. Classification was done based in FAPROTAX database v1.2.4 (Louca

  Regarding the bacterial diversity, our results evidenced a dominance of Marinobacter and Alteromonas, well known hydrocarbon degraders, in the Chapter 1 one while in Chapter 3 they were absent. Functional assignment demonstrated that in the absence of Marinobacter and Halomonas, another hydrocarbon degrader, Alcanivorax (100% similar to Alcanivorax

Figure 2 .

 2 Figure 2. Growth curves comparing axenic (antibiotic-treated culture) and xenic (culture with its associated microbiome) Emiliania huxleyi cultures.

Figure 3 .

 3 Figure 3. Growth curves of co-culture experiments comparing the axenic E. huxleyi culture alone (control) and with a bacterial isolate. The curves shown start at the beginning of exponential phase (day 3) and stop at the end of exponential growth (days 6/7).

  photosynthetic calcifying cell. Genome Biol 10:. Ducklow, H.W., Steinberg, D.K., and Buesseler, K.O. (2001) Upper ocean carbon export and the biological pump. Oceanogr DC-OCEANOGRAPHY Soc 14: 50-58. Durham, B.P., Dearth, S.P., Sharma, S., Amin, S.A., Smith, C.B., Campagna, S.R., et al. (2017) Recognition cascade and metabolite transfer in a marine bacteria-phytoplankton model system. Environ Microbiol 19: 3500-3513. Durham, B.P., Sharma, S., Luo, H., Smith, C.B., Amin, S.A., Bender, S.J., et al.(2015) Cryptic carbon and sulfur cycling between surface ocean plankton. Proc Natl Acad Sci U S A 112: 453-457. Dziallas, C. and Grossart, H.P. (2011) Temperature and biotic factors influence bacterial communities associated with the cyanobacterium Microcystis sp. Environ Microbiol 13: 1632-1641.

Table

  ASV2 and ASV3) from our study, were shared with other phytoplankton. Interestingly, the OTU that contained Marinobacter ASV2 was shared with cyanobacteria only and was present in 53% of cyanobacterial strains (39/74). Other prevalent OTUs shared with cyanobacteria belonged to Thalassobius (38/74), Alcanivorax (44/74) and to a member of the Rhodobacteraceae (19/74). The OTU that contained ASV3, was shared with cyanobacteria and also Alexandrium 49/100 samples. Other coccolithophores OTUs shared with both microalgae were Hyphomonas, Thalassospira, Methylophaga, and Balneola. OTUs specific to coccolithophores were mainly members of Gammaproteobacteria (52%, 24/46) and Bacteroidia (28%, 13/46) classes. The most prevalent belong to the genera Pseudomonas and Janthinobacterium and were found in 81% and 69% of the coccolithophore strains,

Alteromonas, a ubiquitous genus from our study. Other shared OTUs, that comprised alphaproteobacterial genera (Hyphomonas, Roseivirga, SAR 11 clade 1, and Nautella), Pseudohongiella, and an unclassified Flavobacteriaceae were not prevalent in coccolithophores. The two most prevalent OTUs that included the two most abundant ASVs Chapter 1 66 (respectively. One OTU belonging to Nioella genus (Rhodobacteraceae) was found in 16 strains and a Polycyclovorans (Gutierrez et al., 2013) was present in 10 strains. Other OTUs specific to coccolithophores belong to Cyclobacteriaceae, Flavobacteriaceae, Rhodobacteraceae, Alteromonadaceae, and Cryomorphaceae families.

table 1 .

 1 Characteristics of strains sequenced at the study. Coccolithophore taxa, E. huxleyi cellular morphotype and morphogroups, place and date of isolation and temperature of maintenance. E. huxleyi cellular morphotypes and morphogroups were determined based on scanning electron microscopy. Mean and standard deviation of photosynthetic quantum yield over. Consecutive culture transfers for each culture. Cultures with asterisk (*) were sampled two times.

	Culture	Coccolithophore	Cellular	Cellular			Place of isolation		Date of Isolation	Temperature of
	code		morphotype	morphogroup	Lat	Long	Oceanic Region	Ocean/Sea of Origin	(DD/MM/YY)	maintenance (°C)
	ARC30-1	E. huxleyi	A	A	79.00	8.00	Western Coast of Svalbard	Arctic Ocean	01/07/12
	ARC63-5	E. huxleyi	O	B	71.75	8.44	Norwegian Sea	Arctic Ocean	01/07/12
	ARC68-3	E. huxleyi	O	B	67.83	-12.20	Northern Icelandic Sea	Arctic Ocean	01/07/12
	CHC347	E. huxleyi	A	A	-30.25 -71.70	South Pacific	Pacific Ocean	01/11/12
	NIES1311	E. huxleyi	O	B			Bering sea	Pacific Ocean	01/10/02
	NIES1313	E. huxleyi	Non-calcifying	Non-calcifying	29.59 128.41	East China Sea	Pacific Ocean	09/09/03
	NIES837	E. huxleyi	A	A			Great barrier reef australia	Pacific Ocean	01/11/90
	PLY848	E. huxleyi	A	A	46.20	-7.21	Bay of Biscay	Atlantic Ocean	25/06/11
	PLY850	E. huxleyi	A	A	45.70	-7.16	Bay of Biscay	Atlantic Ocean	20/06/11
	RCC1210	E. huxleyi	A	A	59.77	20.64	Baltic Sea	Atlantic Ocean	03/07/98
	RCC1212	E. huxleyi	B	B	-34.47	17.30	South Atlantic	Atlantic Ocean	01/09/00
	RCC1213	E. huxleyi	A overcalcified	A	40.80	14.25	Tyrrhenian Sea	Mediterranean Sea	01/12/00
	RCC1216	E. huxleyi	R	A	-42.30 169.83	Tasman Sea	Pacific Ocean	01/09/98
	RCC1217	E. huxleyi	Haploid	Haploid	-42.30 169.83	Tasman Sea	Pacific Ocean	01/09/98
	RCC1239	E. huxleyi	O	B	43.22 141.02		Pacific Ocean	22/04/02
	RCC1240	E. huxleyi	A	A	41.50 141.25		Pacific Ocean	26/10/02
	RCC1242	E. huxleyi	Non-calcifying	Non-calcifying	-2.67	-82.72	South Pacific	Pacific Ocean	07/05/91
	RCC1245	E. huxleyi	A	A	45.00	-1.08	French coast	Atlantic Ocean	01/02/99
	RCC1253	E. huxleyi	Non-calcifying	Non-calcifying	43.22 141.02		Pacific Ocean	22/04/02
	RCC1255	E. huxleyi	Non-calcifying	Non-calcifying	59.50	10.60		Atlantic Ocean	12/05/59

Chapter 1 94 Supplementary figure 3. Mean and standard deviation of bacterial cell concentration over consecutive culture transfers. Cultures with asterisk (*) were sampled two times.

Supplementary figure 4.

Mean Bacteria/coccolithophores ratio for each culture. Cultures with asterisk (*) were sampled two times.

Table

					*
		2			*
		1
	CAP2 (3.24%)	0			PO 4 * *	Sampling cruise
		-1	Temperature	Salinity *		AMT24 cruise 2014
						BIOSOPE cruise 2004
						DYMAPHY cruise 2011
		-2				Total_Chla	PROSOPE cruise 1999 TAN1702 2017
			p=0.001			TAN1810 2018
			-2	-1	0	1	2
				CAP1 (8.01%)

Table 2 .

 2 Disentangling the influence of cultivation media and environmental parameters on the beta diversity patterns of microbiomes. Adjusted R2 of variation partitioning analysis using different beta diversity metrics. Aitchison distance was produced as the Euclidean distance of the centered log-ratio transformed data. Bray-Curtis dissimilarity was calculated on the relative abundance data. Hellinger was calculated as the Euclidean distance of the Hellinger transformed data. Jaccard dissimilarity was calculated with presence/absence of ASVs of the rarefied table. PhILR was produced as the Euclidean distance of the phylogenetic isometric logratio transformed data. For the all the metrics (except Jaccard) the raw table was used.

	Significance of the partitions was tested by partial db-RDA (Jaccard and Bray-Curtis) and
	partial RDA (PhILR, Aitchison and Hellinger). Significance code: *** p < 0.001; ** p < 0.01;
	* p < 0.05. -indicates adjusted R2 close to 0.
	Metric tested	Medium	Intersection	Environmental	Medium
				parameters	+
					Environmental
					parameters
	Aitchison	0.05	0.07	0.07	0.20**
	Bray-Curtis	0.13**	0.07	0.09	0.29***
	Hellinger	0.11**	0.06	0.09*	0.26***
	Jaccard	0.05*	0.07	0.05*	0.17***
	PhilR	0.05	0.17	-	0.20*

Marine phytoplankton and bacteria are submitted to variable physicochemical and biological conditions which are known to influence their abundance and distribution (De

Table 1 .

 1 Depth, temperature, salinity, phosphate (PO4) and chlorophyll a concentration, Latitude and Longitude (decimal degrees) of each sample from where the microalgae used in this study were isolated.

	Sampling	Sampling	Sampling	Temperature	Salinity	PO4	Chlorophyll	Latitude Longitude
	cruise/Year	station	depth	(°C)	(PSU)	(µM)	a
			(m)				(mg/m3)
	AMT24	9	2	19.900	35.770 0.030	0.047	41.770	-18.750
	AMT24	48	2	25.354	36.648 0.120	0.026	-11.480	-25.050
	BIOSOPE	stMAR4	10	27.788	35.563 0.351	0.163	-8.330	-141.250
	BIOSOPE	stEGY2	5	18.083	34.699 0.177	0.064	-31.820	-91.470
	BIOSOPE	STB20	5	17.565	33.947 0.095	0.311	-33.350	-78.100
	BIOSOPE	STB20	40	14.801	33.993 3.302	0.524	-33.350	-78.100
	DYMAPHY	namfield	5	11.997	34.385 0.120	0.436	53.820	3.420
	PROSOPE	9	5	23.931	38.195 0.003	0.082	41.900	10.430
	PROSOPE	8	30	18.113	37.743 0.007	0.046	39.120	14.080
	TAN1702	20	10	9.853	34.435 0.835	0.199	-51.537	167.482
	TAN1702	31	75	9.877	34.346 0.770	0.453	-51.643	171.119
	TAN1702	31	5	9.892	34.345 0.740	0.515	-51.643	171.119
	TAN1810	227	40	13.276	34.987 0.242	1.811	-42.771	178.346

Table 2 .

 2 Detailed taxonomic information, source of isolation and culturing conditions of the phytoplankton strains. * indicate strains recovered from cryopreservation. NA indicates that no sequences are available in GenBank and that corresponding cultures were identified by microscopy visualization.

	Strain		Taxonomic information			Source of isolation	Culture conditions
		Phylum	Taxonomy	GenBank	Gene	Cruise (Year)	Station of	Growth	Growth	Light
				accession			isolation	temperature	medium	intensity
								(°C)	(μmol s -1 m -2 )
	RCC4548	Ochrophyta	Pelagomonas calceolata KX014627	18S rRNA	AMT24 (2014)	station 9		K	100
	RCC4549	Haptophyta	Emiliania huxleyi	KX014628	18S rRNA	AMT24 (2014)	station 9		K/2	60
	RCC4566	Ochrophyta	Pelagomonas calceolata KX014639	18S rRNA	AMT24 (2014)	station 48		K	100
	RCC4567	Haptophyta	Emiliania huxleyi	KX014640	18S rRNA	AMT24 (2014)	station 48		K	100
	RCC909	Haptophyta	Emiliania huxleyi	KT861255	18S rRNA	BIOSOPE (2004)	STB20_5m		K	100
	RCC917	Chlorophyta	Chloropicon roscoffensis EU106769	18S rRNA	BIOSOPE (2004)	STB20_5m		K	100
	RCC918	Haptophyta	Dicrateria sp.	KF899845	18S rRNA	BIOSOPE (2004)	STB20_5m		K	100
	RCC919	Ochrophyta	Pelagomonas calceolata KT861049	18S rRNA	BIOSOPE (2004)	STB20_5m		K	100
	RCC928	Chlorophyta	Prasinoderma singularis KT860930	18S rRNA	BIOSOPE (2004)	STB20_40m		K	100
	RCC948	Haptophyta	Emiliania huxleyi	JN098172	cox3	BIOSOPE (2004)	STB20_40m		K/2	25
	RCC868*	Haptophyta	Emiliania huxleyi	EU106749	18S rRNA	BIOSOPE (2004)	EGY2		K/2	100
	RCC869	Ochrophyta	Pelagomonas calceolata LN735476	16S rRNA	BIOSOPE (2004)	EGY2		K	100
	RCC940	Haptophyta	Phaeocystis globosa	EU106787	18S rRNA	BIOSOPE (2004)	EGY2		K/2ET	100
	RCC1000	Haptophyta	Phaeocystis sp.	JX660991	18S rRNA	BIOSOPE (2004)	stMAR4		K/2ET	100
	RCC1007	Ochrophyta	Florenciella sp.	KT861110	18S rRNA	BIOSOPE (2004)	stMAR4		K	100
	RCC854*	Chlorophyta	Prasinoderma coloniale KT860905	18S rRNA	BIOSOPE (2004)	stMAR4		K	100
	RCC856	Chlorophyta	Chloropicon laureae	MK086001	genome	BIOSOPE (2004)	stMAR4		K	100
	RCC954	Ochrophyta	Pelagomonas calceolata KT861051	18S rRNA	BIOSOPE (2004)	stMAR4		K	100
	RCC955	Haptophyta	Emiliania huxleyi	KT861256	18S rRNA	BIOSOPE (2004)	stMAR4		K/2	60
	RCC2612	Myzozoa	Scrippsiella sp.	KT860944	18S rRNA DYMAPHY (2011)	nam field		F/2	100

Table 3 .

 3 Individual statistics of parameters that drive significantly the microbiome diversity in the models including hosts and environmental parameters. Anova-like permutation test Type III (margin) was used. -Not significant. Total chl a: Total chlorophyll a

	Metric	Host	Temperature	Salinity	PO4	Total chl a
	Aitchison	-	F= 3.12	F= 1.84	-	-
			p=0.001	p=0.033
	Bray-Curtis	F= 1.62	F= 4.31	-	F=1.90	-
		p=0.045	p=0.002		p=0.045
	Hellinger	F= 1.79	F= 3.89	F= 1.70	-	-
		p=0.009	p=0.002	p=0.046
	Jaccard	F= 1.39	F= 2.62	F= 1.47	-	F= 1.67
		p=0.026	p=0.001	p=0.035		p=0.023
	PhILR	-	F=10.56	-	-	-
			p=0.001
	Supplementary

Table 4 .

 4 Individual statistics of parameters that drive significantly microbiome diversity in the models including media and environmental parameters. Anova-like permutation test Type III (margin) was used. -Not significant. Total chl a: Total chlorophyll a

	Metric	Medium	Temperature	Salinity	PO4	Total chl a
	Aitchson	-	-	F= 2.27	-	-
				p=0.021
	Bray-Curtis	F= 2.19	F= 2.71	-	F=2.07	-
		p=0.005	p=0.005		p=0.036
	Hellinger	F= 1.97	F= 2.30	-	F= 1.85	-
		p=0.001	p=0.002		p=0.022
	Jaccard	F= 1.39	-	F= 1.74	F= 1.50	-
		p=0.017		p=0.011	p=0.031
	PhILR	-	-	-	-	-
	Supplementary

Table 5 .

 5 Significant indicator ASVs and their indicator values. The p-values were adjusted to multiple comparison using false discovery rate (FRD) correction.

	ASV Id	Component A	Component B	Indicator value	p.value	Corrected
		Specificity	Fidelity	(IndVal)		p.value
	ASV_8	0.9999	0.6842	0.8271	0.0001	0.0116
	ASV_16	0.732	0.8947	0.8093	0.0003	0.0140
	ASV_25	0.8527	0.6316	0.7338	0.0002	0.0140
	ASV_57	1	0.5263	0.7255	0.0001	0.0116
	ASV_76	1	0.4737	0.6882	0.0003	0.0140
	ASV_42	1	0.4211	0.6489	0.0012	0.0349
	ASV_168	1	0.4211	0.6489	0.0007	0.0272
	ASV_7	1	0.3684	0.6070	0.0012	0.0349

Table 2 .

 2 Permutational multivariate analysis of variance (PERMANOVA) and nested PERMANOVA results (nested.npmanova) using Eulidean distance of Hellingertransformed data. 'Treatments' is a fixed factor with four levels (D5_IN_PM_SRF, D5_IN_PM_DCM, D5_OUT_PM_SRF and D5_OUT_PM_DCM). 'Replicates' is a fixed factor with 12 levels (3 replicates x 4 treatments) nested within treatments. 'Time' is a fixed continuous factor (days after the starting of the experiment). Permutations were restricted to the replicates in traditional adonis (strata). •F.model and P-value correctly calculated using nested.npmanova. * Significant results.

	Site code	Date	Lat	Long	Time	Sampling	Nitrite	Nitrate	Phosphate	Silicate	Temperature	Salinity	Par	Fluorescence	Turbidity	Oxygen	Oxygen	Total	Synechococcus	Bacteria
			N	W	UTC	depth	µmol/L	+	µmol/L	µmol/L	°C	PSU			WETntu0	Volts	mL/L	Eukaryotes	cells/mL	cells/mL
						m		nitrite										cells/mL
								µmol/L
	D1_IN_AM_DCM	20190529	48°21.000	7°01.114	09:21	20	0.159	0.989	0.162	1.245	13.308	35.5042	6.55E+00	2.0457	1.8356	2.1988	5.3034	2.43E+04	4.20E+04	1.01E+06
	D1_IN_PM_DCM	20190529	48°20.543	7°02.520	16:10	20	0.017	DL	0.078	0.986	14.2163	35.4004	2.01E+01	0.9833	2.0452	2.3406	5.56827	2.95E+04	6.40E+04	1.62E+06
	D2_IN_AM_DCM	20190530	48°20.794	7°02.301	06:50	15	0.0119	1.246	0.197	1.515	14.1133	35.3901	1.27E+01	0.7179	2.1609	2.1143	4.89932	2.46E+04	6.30E+04	1.39E+06
	D2_IN_PM_DCM	20190530	48°20.817	7°00.384	16:31	20	0.021	DL	0.076	1.138	14.264	35.3993	3.42E+01	1.0677	2.0305	2.3299	5.52171	2.71E+04	6.25E+04	1.35E+06
	D3_IN_AM_DCM	20190531	48°20.441	7°00.976	10:18	20	0.028	0.103	0.084	1.202	13.4539	35.376	5.85E+01	2.4072	1.9514	2.3633	5.73291	9.57E+03	2.08E+04	1.11E+06
	D3_IN_PM_DCM	20190531	48°20.658	7°02.183	18:32	25	0.026	DL	0.068	1.117	13.2655	35.3729	1.44E+01	3.1488	2.048	2.335	5.6121	3.05E+04	7.48E+04	1.54E+06
	D4_IN_AM_DCM	20190601	48°21.568	6°59.241	09:41	20	0.02	DL	0.083	1.067	12.4174	35.4077	1.51E+01	2.6518	1.8822	2.2428	5.39943	3.68E+04	1.10E+05	2.03E+06
	D4_IN_PM_DCM	20190601	48°20.705	7°01.985	17:07	20	0.027	0.053	0.102	1.111	ND	ND	ND	ND	ND	ND	ND	3.21E+04	7.16E+04	1.45E+06
	D5_IN_AM_DCM	20190602	48°21.427	6°59.496	09:40	15	0.035	0.04	0.061	0.922	13.2725	35.3667	8.69E+00	2.0143	1.9281	2.3174	5.58092	2.19E+04	1.01E+05	1.33E+06
	D5_IN_PM_SRF	20190602	48°19.160	6°58.717	14:16	3	0.028	0.033	0.048	1.13	ND	ND	ND	ND	ND	ND	ND	ND	ND	ND
	D5_IN_PM_DCM	20190602	48°19.081	6°57.090	13:45	25	0.03	0.035	0.094	0.276	ND	ND	ND	ND	ND	ND	ND	2.51E+04	9.15E+04	1.16E+06
	D5_OUT_PM_SRF	20190602	48°21.961	6°28.519	18:00	3	0.034	0.039	0.073	0.197	15.3573	35.375	2.59E+02	0.4863	1.6543	2.3532	5.46434	ND	ND	ND
	D5_OUT_PM_DCM	20190602	48°22.026	6°29.658	17:12	15	0.031	0.037	0.086	0.158	14.3971	35.3501	4.21E+01	1.3364	1.5285	2.3578	5.62631	1.05E+04	3.40E+04	8.06E+05

Table 3 .

 3 Permutational multivariate analysis of variance (PERMANOVA) and nested PERMANOVA results (nested.npmanova) using Jaccard dissimilarity. 'Treatments' is a fixed factor with four levels (D5_IN_PM_SRF, D5_IN_PM_DCM, D5_OUT_PM_SRF and D5_OUT_PM_DCM). 'Replicates' is a fixed factor with 12 levels (3 replicates x 4 treatments) nested within treatments. 'Time' is a fixed continuous factor (days after the starting of the experiment). Permutations were restricted to the replicates in traditional adonis. •F.model and P-value correctly calculated using nested.npmanova. * Significant results.

	Jaccard	Df	Sum of	Mean	F.Model	R2	P-value
			Squares	sum of
				squares
	Treatments•	3	5.917	1.972	4.430	0.255	0.001*
	Replicates•	8	3.562	0.445	2.722	0.153	0.001*
	Time	1	1.508	1.508	10.316	0.065	0.001*
	Treatments x Time	3	0.536	0.179	1.222	0.023	0.180
	Residuals	80	11.693	0.146	NA	0.504	NA
	Total	95	23.215	NA	NA	1	NA

Table 5 .

 5 Shared and specific ASVs among the treatments. Prevalence corresponds to the number of samples where the ASV was found. The percentage of prevalence is equal to the prevalence divided by the total of samples. The percentage of reads corresponds to the number of reads of the ASV divided by the total number of reads of the rarefied table. N corresponds to the number of samples used to calculate the prevalence. Prevalence of ASVs that were shared among all treatments was calculated based on the total number of samples (96). For specific ASVs, the number of samples considered was the total of each treatment (24). The taxonomy was assigned by vsearch using 94.5% identity to identify genus level. Status indicates if the ASV is specific to the treatment or shared among all treatments.

	ASVId	Prevalence Prevalence	N	% of	Taxonomy
		in N		total
		samples		of
				reads

Acknowledgements

I would like first to acknowledge my advisors, Christian and Colomban, for giving me the opportunity to work on this project. Thank you for your advices and for the enriching discussions. Thank you for stimulating me to think 'out of the box'. Having done this PhD is one of the greatest experiences of my life, and for sure the most intense. Really thank you for this opportunity to come in France! It was an honor to work with you both.

Acknowledgments

This work was supported by Sorbonne University, the Région Bretagne, the Centre National de la Recherche Scientifique (CNRS, France), and the French Government ''Investissements d'Avenir'' programmes OCEANOMICS (ANR-11-BTBR-0008). The authors are thankful to Cédric Berney for helping in the improvement of the phylogenetic analysis. We are thankful to the ABiMS bioinformatic platform (http://abims.sb-roscoff.fr) for the computational resources and to the RCC and NIES culture collections for the cultures provided. This article is contribution number XX of Tara Oceans.

Acknowledgements

This work was supported by the Sorbonne University, the Région Bretagne, the Centre National de la Recherche Scientifique (CNRS, France), and the French Government ''Investissements d'Avenir'' programmes OCEANOMICS (ANR-11-BTBR-0008). The DYMAPHY cruise was financed by INTERREG IVA "2 Mers Seas Zeeën" European crossborder project DYMAPHY (Development of a DYnamic observation system for the assessment of MArine water quality, based on PHYtoplankton analysis), and the EU FP7 PROTOOL (PROductivity TOOLs: Automated Tools to Measure Primary Productivity in European Seas). We are also thankful to Veronique Creach, Andres Gutierrez-Rodriguez, and Monica Hanley for providing the ancillary data from DYPMAPHY, TAN1702/1810, and AMT24 cruises, respectively. Finally, we would like to thank the ABiMS bioinformatic platform (http://abims.sb-roscoff.fr) for the computational resources, the RCC team for the phytoplankton resources, and Miguel Méndez-Sandín for his contribution in improving the manuscript.

Acknowledgments

This work was supported by Sorbonne University, the Région Bretagne, the Centre National de la Recherche Scientifique (CNRS, France), and the French Government ''Investissements d'Avenir'' programmes OCEANOMICS (ANR-11-BTBR-0008). We are grateful to the Tara Ocean Foundation, led by Romain Trouble and Etienne Bourgois, for the sampling opportunity and facilities onboard Tara, and to all the scientific and logistic team involved in the Tara Breizh Bloom cruise, notably captain Martin Herteau and his crew. We are thankful to the ABiMS bioinformatic platform (http://abims.sb-roscoff.fr) for the computational resources, and to Nicolas Henry and Lydia White for their help in statistical analysis. This article is contribution number XX of Tara Oceans.

Cosmopolitan Oceanic Bacterium.

Science (80-) 309: 1242-1245.

Giovannoni, S.J., Tripp, H J, Givan, S., Podar, M., Vergin, K.L., Baptista, D., et al. (2005) Cultivation and Growth

Relative abundance Supplementary figure 9. Phylogenetic placement of Marinobacter ASVs in the Marinobacter reference tree (sequences 1382 bp).

Disentangling the influence of environment, hosts, and culture medium

Exploratory HCA using Jaccard dissimilarity separated the microbiomes associated with the 43 phytoplankton cultures into two main clusters, group A containing mainly strains isolated from warm waters (>15°C) and group B that consisted in most of strains isolated from cold waters (Supplementary Figure 2). Constrained analyses (db-RDA and RDA) that included physicochemical parameters as explanatory variables revealed the significant effect of environmental parameters on the beta diversity patterns of phytoplankton-associated bacterial assemblages (p = 0.01) (Figure 2 and Supplementary Figure 3). The axis 1 of the constrained analysis plot, negatively correlated with temperature and salinity, discriminated microbiomes from cold and warm samples (Figure 2), confirming the clustering results (Supplementary Figure 2). Two TAN1702 microbiomes (RCC5673 and RCC5675), that both clustered into group A (warm samples) in the hierarchical clustering (Supplementary Figure 2), were closer to cold samples in the constrained analysis (Figure 2). This trend was also observed with three BIOSOPE samples that clustered in the cold group B in the hierarchical clustering (Figure 2 and Supplementary Figure 2). Similar conclusions regarding cold and warm waters groups could be drawn from the other distance metrics tested, although the importance of environmental parameters varied (Supplementary Figure 3). Axis 2 discriminated microbiomes associated with phytoplankton cultures from TAN1810 which are linked to relatively high Chl a concentrations (Figure 2).

SUPPLEMENTARY MATERIAL

Microbiome assembly in phytoplankton cultures is partially driven by deterministic processes

Supplementary Methods

Origin of phytoplankton cultures

The 43 strains used in this study were isolated from 6 oceanographic cruises that covered a variety of oceanic regions. The PROSOPE (PROductivity of Oceanic PElagic Systems) cruise onboard the RV Thalassa (9 September -3 October 1999) explored the biogeochemical processes in waters of different trophic status in the Mediterranean Sea (Claustre et al., 2020).

The Thyssen, M., Alvain, S., Lefèbvre, A., Dessailly, D., Rijkeboer, M., Guiselin, N., et al. (2015) High-resolution analysis of a North Sea phytoplankton community structure based on in situ flow cytometry observations and potential implication for remote sensing.

Biogeosciences 12: 4051-4066.

Supplementary Figure 2.

Dendrogram showing clustering of phytoplankton-associated bacteria composition using Jaccard dissimilarity matrix and method "ward.D". The dashed line represents the threshold that separates the two groups of samples used in the indicator species analysis (IndVal). The two groups are consistent with the ordination produced by the db-RDA using Jaccard dissimilarity.

Abstract

Phytoplankton and heterotrophic bacteria are the major primary producers and decomposers in the oceans. Understanding their interactions is critical to assess the individual contribution of the associated bacterial community and phytoplankton to their success in ocean ecosystems. Questions remain regarding how the phytoplankton microbiomes are selected, and the role of the host in the process of selection. Here, we used the globally spread coccolithophore Emiliania huxleyi to investigate these questions. During a 5 days cruise following an E. huxleyi bloom patch in the Celtic Sea in Spring 2019, we collected bacterial communities from surface and deep chlorophyll maximum depth samples within and outside the bloom and inoculated them in axenic E. huxleyi cultures. The cultures were then followed over one year in order to evaluate the short-to-long term changes in microbiomes composition.

Although the culture conditions led to the severe reduction of the initial bacterioplankton diversity, the resulting microbiomes were dependent on the composition of the initial bacterioplankton community. The inoculum involving bacterial populations collected at the E.

huxleyi bloom depth resulted in a more diverse microbiome that maintained over time several specific Flavobacteriaceae members typically associated with the degradation of polysaccharides produced during bloom demises. The microbiomes selected were stable over time and resilient to disturbance. Overall, this work sheds new light on the importance of the initial inoculum composition in microbiome recruitment and elucidates the temporal dynamics of its composition and resilience.

Community assembly experiment (i) Dynamics of cell concentrations and alpha-diversity patterns in the cultures

Seawater samples used to inoculate axenic E. huxleyi cultures were filtered through 0.45 µm membranes to remove the abundant autotrophic eukaryotes and Synecochoccus populations they contained. A loss of about 40% of the initial bacterial cell concentration was observed after this filtration step (data not shown). A clear decrease of E. huxleyi cell concentrations (80% ± 0.04 SD, n=12) was noticed in all the treatments between the inoculation and the 3 first culture transfers (from day 9 to day 32) (Figure 2A). To prevent culture death, light intensity was increased at day 32 to 70 ± 20 µmol photons s -1 m -2 and a larger microalgal inoculum (10% of the final culture volume instead of 1%) was transferred. Cultures recovered growth. Their cell densities gradually increased at each next transfer until day 70 where they reached highest cell concentration (9.9 x 10 5 ± 1.2 x 10 5 cells/mL) and remained stable up to the end of the experiment (Figure 2A).

Bacterial cell concentrations increased during the crash period and decreased once algal cultures recovered (Figure 2B). Regarding the structure of the bacterial community, a severe loss of richness between the natural and culture samples was observed (Supplementary Figure 1A). At day 9, the bacterial richness in the cultures was about one fourth of the richness in the environmental samples (33 ± 8 SD, n=12) (Supplementary figure 1A). This reflected also a parallel decrease in the Shannon index, which at day 9, was about one third the values recorded in environmental samples (1.4 ± 0.6 SD, n=12, Supplementary figure 1B). Over the course experiment, we could observe a decrease in richness along the first five weeks (mean decrease 24 ± 7 SD, n=12, until day 46) (Figure 2C). After an increase at day 58 that corresponded to the period of culture recovery, the richness values decreased again and remained stable until day 392 (11 ASVs ± 3). The decrease of richness was mainly associated with the loss of low abundance ASVs, while the dominant ones remained over the course of the experiment (Supplementary Figure 4). In general, the Shannon index also decreased over the first three time points (mean decrease 0.55 ± 0.63 SD, n=12) and then gradually increased to values similar to that from day 21 (day 392: 1.21 ± 0.4, n=12) (data not shown). The highest richness and Shannon indexes were obtained in the treatments amended with the bloom DCM sample (richness: 26 ± 11; Shannon 1.6 ± 0.4, n=24).

Beta diversity patterns among treatments and over time

In order to identify the influence of the different initial prokaryotic communities and to follow the changes in the microbiome beta diversity with time, we used two metrics, Hellinger distance (Euclidean distance of Hellinger-transformed data) and Jaccard dissimilarity.

Principal component analysis using Jaccard dissimilarity demonstrated that E. huxleyi cultures inoculated with surface samples grouped together (Figure 3A), while those inoculated with inside and outside bloom DCM samples formed two other independent clusters.

Supplementary Material

Microbiome assembly in axenic Emiliania huxleyi cultures is influenced by the source community composition and is resilient to disturbance Supplementary Table 1. Environmental parameters sampled over the course of the bloom. The codes correspond to the day of sampling, the place where it was collected (inside or outside the bloom), the time of the day (morning -AM or afternoon -PM) and the depth (surface -SRF and deep chlorophyll maximum -DCM). Supplementary Figure 2. Heatmap representing the hierarchical clustering of free-living (0.2-3 µm) prokaryotic communities at the sampling sites. The hierarchical clustering was built using the method "ward.D2" and Euclidean distances of the Hellinger-transformed community data.

Supplementary

Taxomy is represented by phylum and genus level.

Coccolith morphometrics partially delineate morphotypes in worldwide isolates of

Gephyrocapsa huxleyi

In preparation

Abstract

The haptophyte Gephyrocapsa huxleyi is by far the most widespread and abundant coccolithophore in modern ocean. It plays a fundamental role in the marine ecosystem since it produces calcium carbonate coccoliths responsible of around 30% of carbonate precipitation in the global ocean. Gephyrocapsa huxleyi is also well recognized for its wide phenotypic variability, mainly with morphological variants distinguished by fine morphometric variations in the coccoliths, coined as morphotypes. Although, this morphometric variability has been usually assessed in the environment, sometimes on cultured strains from a locality but rarely at a wide scale on cultured strains. In here, we measured coccolith variability in a worldwide set of 120 cultured strains of G. huxleyi in order to test whether this variability relates to the environmental context of isolation. We collected coccoliths of cultured strains from North Atlantic, Indian Ocean and coastal Chile growing under similar conditions, and for which we acquired scanning electron micrographs to measure morphometric features. We found significant differences between each population associated with different patterns of distribution for each measured parameter and a partial concordance between morphotypes and clusters inferred from measurements. Moreover, a significant positive correlation was found between the latitude of isolation and the width of elements forming the shield of the coccoliths, while a correlation between sea surface temperature and the length of the coccoliths was significantly negative. These preliminary results demonstrated a wide phenotypic diversity with potential sign of endemism, suggesting extending further morphometric investigations in this ecologically relevant species.

Annex 2 Functional assignation of abundant taxa

In order to get insights into the functional diversity found in microbiomes of E. huxleyi in cultures (chapters 1 and 3), ASVs accounting to more than 1% of relative abundance at each sample were selected (Chapter 1: 180 and Chapter 3: 32). Functional assignment was performed using FAPROTAX database (v1.2.4, 2020) (Louca et al., 2016). This database maps prokaryotic clades (e.g. genera or species) to established metabolic or ecologically relevant functions using the published data on cultured strains (Louca et al., 2016). Functional contribution was normalized by the number of reads of assigned ASVs. Functional plots were produced in R version 4.0.2 using ggplot2 (Wickham, 2016;R Core Team, 2017).

Annex 3

Methods of co-culture experiments

Heterotrophic bacteria isolation and maintenance

Heterotrophic bacteria were isolated from coccolithophore cultures by serial dilution or by plating using two different media prepared with aged seawater collected at SOMLIT-Astan observatory site (48°46'18''N-3°58' 6''W) (Brittany, France) and stored in the dark. A lownutrient heterotrophic medium (LNHM) sterilized by filtration using 0.1µm pore size PES membrane filter units (Nalgene™ Rapid-Flow™) was amended with NH4Cl (10 µM), K2HPO4 (1 µM), the mixture of carbon compounds used in [START_REF] Rappé | Cultivation of the ubiquitous SAR11 marine bacterioplankton clade[END_REF] (1/10 strength), a mixture of the 20 aminoacids (1 µM each), the trace metal and vitamin solutions used in [START_REF] Carini | Nutrient requirements for growth of the extreme oligotroph 'Candidatus Pelagibacter ubique' HTCC1062 on a defined medium[END_REF] and DMSP (100 nM). In the second medium (YEP), the mixture of carbon compounds was replaced by yeast extract (100 mg/L) and peptone (500 mg/L). For serial dilutions, the coccolithophores cultures were diluted into the media to obtain 1 to 2 cells ml -1 and 350 µl were dispensed into several 96-well deepwell microplates (Nunc). Diluted aliquots of the coccolithophore cultures were also plated on LNHM medium solidified with Phytagel (0.7 g/L) and on YEP medium solidified with agar (15g/L). Cultures were incubated at 20°C in the dark for 4-6weeks and microbial growth in liquid media was screened by flow cytometry.

Purification of positive liquid cultures and colonies was achieved by streaking on the solidified media. Bacteria were maintained in solidified BD Difco marine broth medium (1/10 strength)

Annex 4

First page of master's thesis paper published during my PhD.

RESEARCH ARTICLE

Spatial heterogeneity and hydrological fluctuations drive bacterioplankton community composition in an Amazon floodplain system

Abstract

Structure and assembly of bacterial microbiomes in the Emiliania huxleyi phycosphere

Interactions between marine phytoplankton and bacteria are a critical symbiosis in the oceans, affecting carbon and nutrient cycles. Over the last decade, the introduction of novel tools for highthroughput environmental sequencing has unveiled an unexpected diversity of bacteria associated with phytoplankton in the ocean or in culture. However, the drivers of microbiome assembly in the phycosphere, i.e. the surrounding region of the phytoplankton cell, remain poorly known. In this thesis, Emiliania huxleyi, a key phytoplankton for global carbon cycle and one of the most studied microalgae, was selected, as a model species.

The first part of the work aimed at studying the microbiome diversity associated with 73 cultured strains of E. huxleyi and its sister taxon Gephyrocapsa oceanica, isolated from the world ocean between 1959 and 2015. Their microbiomes were consistently dominated by members of the gammaproteobacterial genus Marinobacter, independent of the inter-and intra-specific variation of the host, the place of isolation, and the age of the cultures. Abundance and distribution of the main bacterial taxa associated with Emiliania/Gephyrocapsa cultured strains in existing 16S rDNA metabarcoding datasets from the world ocean, provided evidence that they are preferentially associated with large plankton size fractions and enriched in the bathy-pelagic waters. These results call attention to the putative crucial role of phytoplankton-associated bacteria in the flux organic aggregates and of larger plankton to deeper ocean layer.

The second part of this thesis was dedicated to the investigation of the drivers of microbiome assembly in cultures. Microbiomes of strains belonging to 7 phytoplankton phyla resulting from millions of years of evolution and isolated from the same seawater samples during oceanographic expeditions were compared. Our results indicate that the diversity of the microbiomes in cultured phycospheres is driven by deterministic processes at multiple scales, including the location of isolation and the cultivation medium.

In the third and final part of this work, we investigated drivers of the microbiome selection by E. huxleyi. Filtered seawater samples collected inside and outside an E. huxleyi bloom offshore Brittany (2019) served as inocula into axenic E. huxleyi, and the resulting microbiomes were followed over one year. We observed that the resulting microbiomes were dependent on the composition of the initial bacterioplankton community, as well as resilient to disturbance.

Altogether, the results from the three parts highlight the stability of phytoplankton microbiomes over time and its resilience to changes in the health status of the host. Ultimately, this work provides a new baseline resource of bacterial diversity from microalgal cultures and a collection of bacterial isolates which can be used in future studies to investigate phytoplankton-bacteria interactions. Key-words: Emiliania huxleyi, phycosphere, bacteria, microbiome diversity, selection processes, metabarcoding.