Molecular Mechanisms Involved in Prokaryotic Cycling of Labile Dissolved Organic Matter in the Sea

Molecular mechanisms involved in prokaryotic cycling of labile dissolved organic matter in the sea

Linnaeus University Dissertations

No 412/2021

MOLECULAR MECHANISMS INVOLVED IN PROKARYOTIC CYCLING OF LABILE DISSOLVED ORGANIC MATTER IN THE SEA

BENJAMIN PONTILLER

LINNAEUS UNIVERSITY PRESS

Molecular mechanisms involved in prokaryotic cycling of labile dissolved organic matter in the sea Doctoral Dissertation, Department of Biology and Environmental Science, Linnaeus University, Kalmar, 2021

ISBN: 978-91-89283-65-7 (print), 978-91-89283-66-4 (pdf) Published by: Linnaeus University Press, 351 95 Växjö Printed by: Holmbergs, 2021

Abstract Pontiller, Benjamin (2021). Molecular mechanisms involved in prokaryotic cycling of labile dissolved organic matter in the sea, Linnaeus University Dissertations No 412/2021, ISBN: 978-91-89283-65-7 (print), 978-91-89283-66-4 (pdf) . Roughly half of the global primary production originates from microscopic phytoplankton in marine ecosystems, converting carbon dioxide into organic matter. This organic matter pool consists of a myriad of compounds that fuel heterotrophic bacterioplankton. However, knowledge of the molecular mechanisms – particularly the metabolic pathways involved in the degradation and utilization of dissolved organic matter (DOM) – and transcriptional dynamics over spatiotemporal gradients are still scarce. Therefore, we studied the molecular mechanisms of bacterioplankton communities, including archaea, involved in the cycling of DOM, over different spatiotemporal scales in experiments and through field observations. In seawater experiments, we found a divergence of bacterioplankton transcriptional responses to different organic matter compound classes (carbohydrates, nucleic acids, and proteins) and condensation states (monomers or polymers). These responses were associated with distinct bacterial taxa, suggesting pronounced functional partitioning of these compounds in the Sea. Baltic Proper mesocosms amended with two different river loadings (forest versus agriculture river water) revealed a divergence in gene expression patterns between treatments during bloom decay. This was particularly true for genes in phosphorus and nitrogen metabolism, highlighting the importance and sensitivity of interaction effects between river- and phytoplankton-derived DOM in regulating bacterial activity responses to changes in precipitation- induced riverine runoff. In shipboard mesocosms in an Atlantic coastal upwelling system, we found significant changes in bacterioplankton transcription of hydrolyzing enzymes and membrane transporters from phytoplankton bloom development to senescence, primarily driven by phytoplankton-derived DOM and dissolved organic carbon dynamics. These responses differed substantially between bacterial orders, suggesting that functional resource partitioning is dynamically structured by temporal changes in DOM quantity and quality. Further analysis of these gene systems in a stratified fjord revealed pronounced divergence in transcription with depth and between bacterial taxa; moreover, transcription was more variable in the surface waters. This highlights the interplay between functional and physical partitioning of biogeochemical cycles. Collectively, the findings in this thesis contribute novel insights into the interdependency between prokaryotes and DOM by shedding light on the mechanisms involved in DOM cycling over ecologically relevant spatiotemporal scales. Keywords: Bacteria and archaea, labile dissolved organic matter, metatranscriptomics, monomers, polymers, carbohydrate-active enzymes (CAZymes), peptidases, membrane transporters

“Dedicated to Felix Prahl for his friendship, inspiration, and integrity”

Table of Contents

List of included publications ...... 3 Author’s contributions ...... 4 Additional publications not included in this thesis ...... 5 Abbreviations ...... 6 Introduction ...... 9 Organic matter in the ocean ...... 10 High molecular weight DOM - sources, quantity, quality and vertical fluxes ...... 13 Gene systems with important roles in the degradation and the uptake of DOM ...... 15 Enzymes (carbohydrate-active enzymes and peptidases) ...... 15 Membrane transporters ...... 17 Considerations regarding bacterial foraging strategies and the micro-scale architecture in which bacteria interact with phytoplankton and organic matter ...... 18 The Ocean Microbiome ...... 20 Alphaproteobacteria ...... 21 Gammaproteobacteria ...... 23 Bacteroidetes, Flavobacteriales ...... 25 Planctomycetes ...... 26 Verrucomicrobia ...... 27 Nitrospinae ...... 27 Cyanobacteria ...... 27 Thaumarchaeota ...... 29 Aims of this thesis ...... 31 Methodology ...... 33 Sequencing technology, bioinformatics, advantages, and limitations ...... 33 Bioinformatics ...... 34 Sampling site characteristics ...... 37 The Baltic Sea ...... 37 The Northeast Atlantic Ocean ...... 39 The Gullmar Fjord ...... 41 Results and Discussion ...... 43 Bacterioplankton community responses to a priori selected labile organic matter compounds ...... 43

1 Community expression responses to allochthonous and autochthonous DOM ...... 46 Temporal and spatial variability of DOM cycling gene transcription ...... 48 Temporal responses ...... 48 Spatiotemporal responses ...... 52 Additional perspectives and directions of research on the interdependency between DOM and microbes ...... 55 Genomic features and transcriptional responses of polymer degraders .. 55 Other genomic features and complementary processes which drive resource partitioning ...... 55 Future perspectives ...... 58 Conclusions ...... 59 Acknowledgements ...... 60 References ...... 63

2 List of included publications

I. Pontiller B, Martínez-García S, Lundin D, Pinhassi J (2020) Labile dissolved organic matter compound characteristics select for divergence in marine bacterial activity and transcription. Frontiers in Microbiology. doi: 10.3389/fmicb.2020.588778

II. Osbeck CMG*, Pontiller B*, Teikari JE, Traving SJ, Happel EM, Henke B, Huchaiah V, Nilsson E, Alneberg J, Lundin D, Sivonen K, Andersson AF, Riemann L, Middelboe M, Kisand V, Pinhassi J (2021) Divergent responses of Baltic Sea bacteria to forest and agriculture river loadings. (Manuscript) * These authors contributed equally

III. Pontiller B, Martínez-García S, Joglar V, Amnebrink D, Pérez- Martínez C, González JM, Lundin D, Fernández E, Teira E, Pinhassi J (2021) Rapid bacterioplankton transcription cascades regulate organic matter utilization during phytoplankton bloom progression in a coastal upwelling system. (Under review)

IV. Pontiller B, Pérez-Martínez C, Bunse C, Osbeck CMG, González JM, Lundin D, Pinhassi J (2021) Taxon-specific shifts in bacterial and archaeal transcription of dissolved organic matter cycling genes in a stratified fjord. (Manuscript)

Paper I is a reprint from the final published article in Frontiers under the terms of the Creative Commons Attribution License (CC BY 4.0). Supplementary material of Paper I is accessible online from the publisher’s homepage. Additional Supplementary material (Figures and Tables) of Paper III and IV can be accessed via the links mentioned in the manuscripts.

3 Author’s contributions I contributed to the included publications and manuscripts as follows:

I. Concept and design: Pontiller B, Pinhassi J, Martínez-García S, Pinhassi J Sampling: Pontiller B, Månsson, A Laboratory work: Pontiller B, Arnautovic S Bioinformatics: Pontiller B, Lundin D Data analysis: Pontiller B, Lundin D Drafting manuscript: Pontiller B, Lundin D, Pinhassi J Proofreading and edit: Pontiller B, Lundin D, Martínez-García S, Pinhassi J

II. Concept and design: Pinhassi J, Middelboe, M Sampling: Osbeck CMG, Pontiller B, and all co-authors Laboratory work: Osbeck CMG, Pontiller B, Arnautovic S, Lautin S Bioinformatics: Pontiller B, Lundin D, Alneberg J, Andersson AF Data analysis: Pontiller B, Osbeck CMG, Lundin D Drafting manuscript: Osbeck CMG, Pontiller B, Pinhassi J Proofreading and edit: Osbeck CMG, Pontiller B, Lundin D, Pinhassi J, and all co-authors

III. Concept and design: Teira E, Fernández E, Martínez-García S, Pinhassi J Sampling: Pontiller B, Joglar, V, Pérez-Martínez C Laboratory work: Pontiller B, Arnautovic S, Karlsson, C Bioinformatics: Pontiller B, Lundin D, González JM Data analysis: Pontiller B, Lundin D Drafting manuscript: Pontiller B, Martínez-García S, Lundin D, Pinhassi J Proofreading and edit: Pontiller B, Lundin D, Martínez-García S, Pinhassi J, and all co-authors

IV. Concept and design: Pontiller B, Bunse C, Osbeck CMG, Pinhassi J Sampling: Pontiller B, Osbeck CMG, Bunse C, Pérez-Martínez C, Karlsson C Laboratory work: Pontiller B, Osbeck CMG, Arnautovic S, Karlsson C Bioinformatics: Pontiller B, Lundin D, González JM Data analysis: Pontiller B Drafting manuscript: Pontiller B, Pinhassi J Proofreading and edit: Pontiller B, Lundin D, Pinhassi J, and all co-authors

4 Additional publications not included in this thesis

Frank AH, Pontiller B, Herndl GJ, Reinthaler T (2016) Erythromycin and GC7 fail as domain-specific inhibitors for bacterial and archaeal activity in the open ocean. Aquatic Microbial Ecology. 77(2):99-110. doi: 10.3354/ame01792

Joglar V, Álvarez-Salgado XA, Gago-Martinez A, Leao JM, Pérez-Martínez C, Pontiller B, Lundin D, Pinhassi J, Fernández E, Teira E (2021) Cobalamin and microbial plankton dynamics along a coastal to offshore transect in the Eastern North Atlantic Ocean. Environ Microbiol. 23(3),1559-1583. doi:10.1111/1462- 2920.15367

5 Abbreviations

AGase: Alpha-glucosidase APase: Alkaline-phosphatase BGase: Beta-glucosidase cDNA: reverse-transcribed mRNA into complementary DNA Copiotrophs: Opportunists, or r-strategists: Microorganisms that typically have large genome sizes and fast growth rates under high nutrient concentrations EEA: Extracellular Enzymatic Activity Endo-acting enzymes: Enzymes that randomly cleave the polymers midchain Exo-acting enzymes: Enzymes that cleave the polymer chain from the terminal end (reducing or the non-reducing end) Fundamental niche: The theoretical space as defined by a set of environmental variables under which an organism can exist in isolation Gene: A sequence of nucleotides encoding a protein or RNA product. Glycoconjugates: Carbohydrates - glycans - that are covalently linked to other biological molecules (amino acids - peptidoglycans; proteins - glycopeptides and glycoproteins; lipids - glycolipids and lipopolysaccharides; other small molecules - glycosides) Isomer: Molecules with identical molecular formulas (number of atoms of each element) but a distinct arrangement of atoms, thus 3D structure Isozymes: Enzymes with a similar function but a different amino acid sequence in different organisms, thus, likely different structure, enzyme kinetics, substrate range, etc. LAPase: Leucine-aminopeptidase MCA: The fluorophore L-leucine-7-amido-4-methylcoumarin hydrochloride used to determine leucine aminopeptidase (LAPase) activity Metagenomics: The study of genetic information from the environment of many organisms Metatranscriptomics: The study of transcripts (actively transcribed genes) directly from the environment originating from many organisms Mineralization: Transformation of organic matter to its inorganic starting materials (“minerals”) MUF: The fluorophore 4-methylumbelliferyl used for measuring for instance α- and β-glucosidase, and alkaline-phosphatase activities (AGase, BGase, and APase) Oligotrophs: k-strategists: Characterized by streamlined genomes, limited gene regulation, and adapted to oligotrophic low nutrient environments Operon: Adjacent genes, which are transcribed at the same time by a single promoter

6 ORF: Open reading frame is a sequence of nucleotides that begins with an initiation codon (e.g., ATG), ends with a stop codon (TAA, TGA, or TAG), and most likely encodes a protein Ortholog: Two similar proteins in two different organisms assumed to have the same function Read: The sequence of bases of a single DNA or RNA molecule Realized niche: The actual space an organism occupies accounting for the presence of other organisms Sequencing: Determining the sequence of bases of DNA or RNA

7 “How inappropriate to call this planet Earth, when clearly it is Ocean.” Arthur C. Clarke

8 Introduction

A striking feature of the planet Earth is the vast amount of water covering ~71% of its surface (Shiklomanov and Rodda, 2003), making it the largest life-support system. The World Ocean is on average ~3700 m deep, reaching up to ~11 km (Shiklomanov and Rodda, 2003), comprising a volume of ~1338 × 106 km3 (Shiklomanov and Rodda, 2003). Given the extensive size of this ecosystem, it is unsurprising that the majority of it remains unexplored. Nevertheless, the oceans are the foundation of life on Earth (Pendleton et al., 2020). Life in the Sea is dominated by microbes, given that just 1 Liter of seawater harbors as many as 1 billion microbial cells (Whitman et al., 1998). Roughly half of the planet’s primary production (i.e., photosynthesis, ultimately producing oxygen and organic carbon) is carried out by microorganisms in the ocean, with important implications for life and the climate on Earth (Falkowski et al., 1998; Field et al., 1998). Microorganisms are the oldest living organisms on Earth and have, ever since their emergence ~4 billion years ago (Nutman et al., 2016; Dodd et al., 2017), been central to ecosystem functioning (Falkowski et al., 2008). They are the most diverse and widespread form of life (Hug et al., 2016; Locey and Lennon, 2016), capable of thriving in virtually all known habitats, including sea ice and hydrothermal vents in the deep sea (de Wit and Bouvier, 2006; Sogin et al., 2006; Koh et al., 2011). Although the minuscule carbon content of ~12 fg C (1.2 × 10-14 g C) per cell may appear insignificant at the first glance, collectively, bacteria (70 Gt C) and archaea (7 Gt C) contain orders of magnitude more carbon than multicellular organisms including humans (0.06 Gt C) (Fukuda et al., 1998; Bar-On et al., 2018). Microbial cells are small, typically cell volumes range between ~0.4 and ~3 µm3 (Levin and Angert, 2015). Yet, under nutrient-rich conditions, marine bacteria show fast growth rates. Estimated doubling times can range from minutes for bacterial model organisms in pure culture to days or even weeks for bacteria inhabiting the deep sea (Kirchman, 2008; 2016; Hagström et al., 2017). The combination of small size (efficient uptake of nutrients), short generation times (fast growth), numerical dominance, diverse metabolic strategies, and the long evolutionary history contributed to the striking phylogenetic and metabolic diversity of microbes that is present today (Hug et al., 2016). Ultimately, microorganisms are the engines driving planetary element cycles (e.g., C, N, and S) (Whitman et al., 1998; Falkowski et al., 2008; Kujawinski, 2011) and a central component of the ocean food web (microbial loop) (Azam, 1998) (Figure 1).

9 Figure 1 | Conceptual representation of the microbial loop. The majority of autochthonous organic matter originates from primary producers, which synthesize particulate organic matter (POM) and eventually dissolved organic matter (DOM). DOM, in turn, is readily respired by prokaryotes into carbon dioxide. However, a fraction of it re-enters the food web and becomes available to higher trophic levels (phytoplankton to fish). Modified and adapted from (Azam and Malfatti, 2007) and (Azam, 1998).

Organic matter in the ocean Organic matter is predominantly produced through primary production (the conversion of inorganic carbon dioxide to organic carbon) by photosynthetic algae and cyanobacteria (autochthonous sources), and to a smaller extent transported into the oceans via rivers and atmospheric deposition (allochthonous sources) (Carlson and Hansell, 2015; Mühlenbruch et al., 2018). As of March 22nd, 2021 the atmosphere contained 417.19 parts per million (ppm) of carbon dioxide (CO2) (CO2.earth, 2021). According to estimates ~48.5 Gt C are fixed annually by net primary production in the euphotic zone of the oceans (Benner and Amon, 2015). This results in an inventory of ~700 Gt of total organic carbon (TOC) (Hansell, 2013; Benner and Amon, 2015). Multiple biological, chemical and physical processes regulate both the magnitude and the quality of the released organic matter into the surrounding water. Accordingly, extracellular release by phytoplankton (Thornton, 2014), excretion by grazers, viral and bacterial lysis (Pernthaler, 2005), solubilization of particles, and release of extracellular polymeric substances by bacteria and archaea (Carlson, 2002; Carlson and Hansell, 2015) are shaping the organic matter pool. This pool, divided into two operationally defined size classes, consists of particulate organic matter (POM) that is retained on filters (filter pore sizes typically range

10 between 0.2-0.7 µm) and dissolved organic matter (DOM), which passes through these filters (Nagata, 2008). The DOM pool harbors approximately 700 Gt C (1 Gt = one gigaton or 1 × 1015 g), roughly half the amount of carbon currently present in the atmosphere (Hansell, 2013). DOM is a complex mixture of reduced carbon compounds, often bonded to heteroatoms such as oxygen, nitrogen (dissolved organic nitrogen; DON), phosphorus (dissolved organic phosphorus, DOP), and sulfur (dissolved organic sulfur, DOS).

Meters mm µm nm

POC DOC

Zooplankton Colloidal Phytoplankton Dissolved Prokaryotes ~600 Da Viruses

Macromolecules

Microgels and colloidal nanogels

Macrogels and TEP HMW-DOM LMW-DOM 106 105 104 103 102 101 Molecular weight Figure 2 | The size continuum of organic matter and microbes in the ocean. The marine organic matter pool consists of particulate organic carbon (POC) and dissolved organic carbon (DOC) and is typically divided based on filter membrane pore size cutoffs of 0.2 to 0.7 µm. Depicted is the size range of organic carbon in the ocean, ranging from chemically dissolved compounds such as monomers which can pass through the cell membrane (Weiss et al., 1991), polymers, colloids, transparent exopolymer particles (TEP), and gel organic matter (GOM). The DOC pool consists of low molecular weight DOM (LMW-DOM) < 1000 Da and high molecular weight DOM (HMW-DOM) > 1000 Da (Benner et al., 1992; Carlson and Hansell, 2015). Modified and adapted from (Azam and Malfatti, 2007) and (Verdugo et al., 2004).

Dissolved organic carbon (DOC) is the largest fraction of the DOM pool. DOC consists of a myriad of compounds, spanning over different compound classes and condensation states (i.e., monomers, oligomers, and polymers) (Hansell and Carlson, 2015; Moran et al., 2016), and is divided into two classes based on a molecular weight cut-off (Figure 2). Compounds exceeding 1 kDa are considered as high-molecular-weight (HMW-DOM) and these below as low- molecular-weight (LMW-DOM) (Benner et al., 1992; Carlson and Hansell, 2015).

11 The DOC pool is grouped into categories based on characteristics such as concentration, turnover times, and chemical identity of constituents (Carlson and Hansell, 2015). Compounds of the low-concentration high-flux pool are part of the labile DOC pool (LDOC). The LDOC pool has a relatively small inventory (~0.2 Gt C) compared to the other pools because marine microbes utilize compounds in steady-state over just hours to days. Comparisons of the estimated pool size and the turnover time of LDOC show a disproportionately high flux compared to its stock (Hansell, 2013). Hence, this labile fraction essentially fuels a significant amount of the heterotrophic production in the surface ocean (Hansell, 2013) (Figure 3). The LDOC pool consists of e.g., free sugars, dissolved free amino acids, labile proteins, nucleotides, DNA, and carboxylic acids (Biersmith and Benner, 1998; Geider and La Roche, 2002; Kirchman, 2003; Nagata, 2008; Bergauer et al., 2018; Vorobev et al., 2018). Importantly, low concentrations (or pool sizes) of specific compounds in seawater, often below the detection threshold of currently available instruments, can be a result of low production. Alternatively, concentrations can be low because the turnover and consumption rates by bacteria are extremely high. The latter seems to apply to many important organic compounds (e.g., sugars, amino acids, carboxylic acids, and vitamins) present in seawater (Moran et al., 2016). The semi-labile pool (SLDOC) consists of organic matter, which is less attractive and energetically less favorable for microbial growth and that is associated with a slower turnover (month to years, inventory ~6 Gt C) compared to the labile pool. The largest organic matter pool (~630 Gt C) in the ocean - primarily found in the bathy- and abyssopelagic - is refractory (RDOC). Curiously, the precise reasons for this massive amount of RDOC are still unknown. One hypothesis is that bacteria and archaea alter the DOM pool in a manner that prevents further degradation by microorganisms (microbial carbon pump concept), resulting in an estimated turnover time of RDOC between 4000- 6000 years (Jiao et al., 2010; Carlson and Hansell, 2015; Moran et al., 2016). Thus, the turnover of RDOC is comparably slow (Figure 3). Arrieta and colleagues (2015), in turn, hypothesized that single constituents are too diluted to support bacterial carbon and energy demands in the deep ocean. Irrespectively of the precise mechanisms, bacterial transformation alone is unlikely to account for the large pool of RDOC (Arrieta et al., 2015).

Figure 3 | Comparison of the inventories and fluxes of dissolved organic matter (DOM). The dissolved organic carbon pool (DOC) is a complex mixture of a myriad of organic compounds, grouped into different classes ranging from labile to refractory, based on characteristics such as inventories and turnover times by microorganisms. The labile dissolved organic carbon (LDOC) pool has a small pool size but a disproportionately high flux through marine microbes. The refractory dissolved organic matter pool (RDOC), in turn, is large but has a comparatively slow turnover time. Thus, the labile high-flux portion of the DOM pool supports most of the heterotrophic production in the Ocean but appears to be “invisible” (Moran et al., 2016). Modified and adapted from (Hansell, 2013) and (Jiao et al., 2010). High molecular weight DOM - sources, quantity, quality and vertical fluxes High molecular weight DOM (HMW-DOM) accounts for roughly one-third of the total DOM pool in the ocean (Benner et al., 1992), and it has been shown that HMW-DOM - in particular carbohydrates - is preferentially remineralized by marine microbes (Benner and Amon, 2015). Polysaccharides (glycans) contribute roughly half of the HMW-DOM pool in surface waters and up to one- fourth below the euphotic zone (Benner et al., 1992) and fulfill important roles as primary energy storage compounds in photo- and chemoautotrophic organisms (Lapebie et al., 2019; Becker et al., 2020b; Arnosti et al., 2021). Carbohydrates including polysaccharides derive from a multitude of sources such as plants (e.g., cellulose), algae including phytoplankton (e.g., laminarin, chrysolaminarin, pectin, and fucoidan) (Beattie et al., 1961; Lewis and McCourt, 2004; Bennke et al., 2016), and crustaceans (e.g., chitin). Cellulose is one of the most abundant glycans on our planet, with an estimated stock of ~9.2 × 1011 tons, produced by plants at ~0.85 × 1011 t y-1 (Duchesne and Larson,

13 1989; Leschine, 1995). Phytoplankton, in turn, produce every year ~12% (5-15 Gt y-1) of the annual primary production in the form of laminarin (Alderkamp et al., 2007; Becker et al., 2020b). Chitin, is a critical source of carbon and nitrogen and one of the most abundant polymers in the ocean (Souza et al., 2011). According to estimates, annually ~2.3 Mt of chitin is produced in the marine system by zooplankton (Jeuniaux and Voss-Foucart, 1991). Also, the cell walls of brown macroalgae such as Macrocystis and Sargassum are composed of proteins and polysaccharides, in particular the sulfated polysaccharide fucoidan and alginate, accounting for up to ~45% of their dry weight (Deniaud-Bouet et al., 2014; Deniaud-Bouet et al., 2017). While the precise diversity of glycans remain unknown, estimates range between several thousand to a theoretical maximum of up to 1012 isomers (hexasaccharides) (Lapebie et al., 2019). Phytoplankton- and bacterioplankton-derived organic matter can form three- dimensional networks of biopolymers referred to as gel organic matter (GOM) (Verdugo et al., 2004). GOM is composed of transparent exopolymer particles (TEP) that are abiotically assembled from dissolved extracellular polysaccharides released by phytoplankton (e.g., diatoms) and bacteria in large quantities (Alldredge et al., 1993), and globular, sheet, or string-like proteinaceous particles (Coomassie stained particles - CSP) (Long and Azam, 1996). Also, filter fluorescent particles (FFP) and DAPI yellow particles (DYP) have been identified that may overlap with TEP and CSP (Mostajir et al., 1995; Samo et al., 2008). Given that TEP is enriched in carbohydrates and CSP in proteins it is important to understand the influence that GOM quantity and quality have for determining successional patterns of bacterioplankton community composition and function during phytoplankton blooms, the role microbial enzymes play in the formation and degradation of GOM, and how these enzymatic activities influence export fluxes (Cho and Azam, 1988; Smith et al., 1995; Nagata, 2008). Collectively, these gel organic matter constituents are vital sources of carbon (C) and nitrogen (N) for heterotrophic microbes and aquatic food webs (Passow, 2002). These constituents can form nanogels that are buoyant but more importantly can aggregate into larger particles that sink from the photic zone to the ocean’s interior (Figure 4), thereby connecting microbial and classical food webs but also establishing a link between surface and deep waters (Verdugo et al., 2004; Herndl and Reinthaler, 2013). Ultimately this vertical particulate organic matter (POM) flux is the prime source of organic compounds for heterotrophic microbes in the deep sea (Ducklow and Steinberg, 2001; Aristegui et al., 2009).

14 Mucus production Aggregation Diatom with Diatom Diatom EH surface mucus EH aggregate

EH EH EH Euphotic zone Euphotic

Aggregation TEP/POM Free-living Dissolved mucus + bacteria Uptake Colloidal mucus EH attached bacteria

EH Sinking

POM/aggregate Free-living + bacteria DOM Uptake EH attached bacteria

Meso- and bathypelagic zone EH = Enzymatic Hydrolysis Figure 4 | Conceptual model depicting the crucial role of extracellular hydrolysis in the assembly and degradation of particulate organic matter (POM). Phytoplankton - particularly diatoms - excrete polymers that abiotically form larger aggregates such as marine snow and transparent exopolymers (TEP). Particle-attached and free-living bacteria produce a suite of hydrolytic enzymes that break these polymers into smaller oligo and monomers. Thereby, enzymatic activities prevent the aggregation of POM and hence the export. However, particle-attached bacteria hydrolyze POM during the sinking process, ultimately providing dissolved organic matter (DOM) for free-living bacteria in meso- and bathypelagic zones. Modified and adapted from (Cho and Azam, 1988; Smith et al., 1995; Nagata, 2008).

Gene systems with important roles in the degradation and the uptake of DOM Enzymes (carbohydrate-active enzymes and peptidases) Primary producers synthesize and excrete the majority of organic matter in the sea (Field et al., 1998). These exudates are rich in polymers and especially polysaccharides (Mühlenbruch et al., 2018). Since the uptake of large biopolymers by bacteria is limited to ~600 Da molecular weight (Figure 2) (Weiss et al., 1991), some bacteria invest in the production of a large variety of extracellular enzymes that are either cell-attached or secreted, enabling them to hydrolyze polymers into oligo- and monomers that are suitable for transport across the cell membrane (Hoppe et al., 2002; Arnosti, 2011; Traving et al.,

15 2015; Baltar et al., 2019). Among these enzymes are carbohydrate-active enzymes (CAZymes) that play essential roles in the synthesis, degradation, and modification of complex carbohydrates and glycoconjugates (Cantarel et al., 2009). CAZymes are ubiquitously distributed among all organisms (Grondin et al., 2017). Since the first classification attempt by Henrissat in 1991 (Henrissat, 1991), CAZymes have been particularly well studied in human gut microbiomes but also in an environmental context such as during phytoplankton blooms (Bauer et al., 2006; Teeling et al., 2012; El Kaoutari et al., 2013; Kappelmann et al., 2019). CAZymes can be divided into five modules: i) Glycoside Hydrolases (GHs) comprise catalytic enzymes which hydrolyze and rearrange glycosidic bonds, ii) Glycosyltransferases (GTs) are involved in the assembly of glycosidic bonds, iii) Polysaccharide Lyases (PLs) cleave polysaccharides using a non-hydrolytic mechanism, iv) Carbohydrate Esterases (CEs) catalyze the hydrolysis of carbohydrate esters, v) Auxiliary Activities (AAs) consist of enzymes carrying out redox reactions together with CAZymes, and vi) Carbohydrate-Binding Modules (CBMs) display a modular structure with non- catalytic modules appended to the enzymes mentioned above (i-v) (Cantarel et al., 2009; Lombard et al., 2014; Zhang et al., 2018). In March 2021, the database called CAZy contained 170 GH families, 114 GT families, 41 PL families, 18 CE, 16 AA families, and 88 families of non-catalytic CBM (see also www.cazy.org and www.cazypedia.org). In addition, some families are grouped into clans based on conserved three-dimensional structure, catalytic geometry and reaction stereochemistry (Davies and Sinnott, 2008). However, assignment of substrate specificity to certain GH families is hampered by the fact that multiple enzymes with different three-dimensional structures are catalyzing the same reaction. In fact, divergent evolution resulted in a diversification of GHs so that specific enzymes can be found in more than one GH family and a range of different specificities can be found within a GH family (Davies and Sinnott, 2008). Nevertheless, putative substrate specificities can be assigned to important GH families to provide a better understanding of their potential ecological roles and the range of hydrolyzable compounds that drive heterotrophic bacteria (Teeling et al., 2012; Kamke et al., 2013; Becker et al., 2017; Pelve et al., 2017; Becker et al., 2020b; Vera-Ponce de Leon et al., 2020). Peptidases (also called proteases) are enzymes that degrade proteins into polypeptides or single amino acids by cleaving the peptide bonds through hydrolysis (Rawlings, 2016). In comparison to the complexity of glycans, peptides are uniform, thus, a relatively small set of highly conserved families of peptidases suffice to degrade the majority of proteins (Rawlings, 2016; Lapebie et al., 2019). These proteolytic enzymes are currently classified based on i) the reaction that they catalyze, ii) the chemical properties of the catalytic site, and iii) sequence similarity taking into account evolutionary and structural relationships (Barrett, 1994) and are deposited in the MEROPS database (Rawlings et al., 2018). The database version 12.0 (September 2017) consists

16 of 3181 sequenced and experimentally characterized peptidases that are manually curated (Rawlings et al., 2018). The peptidases are hierarchically classified into protein species, families, and clans (Rawlings, 2016). A family is a set of homologous proteolytic enzymes that are grouped based on the catalytic type of the enzymes into Aspartic proteases (A), Cysteine (C), Glutamic (G), Metallo (M), Asparagine (N), Serine (S), Threonine (T), Mixed (P), and Unknown (U) (Rawlings, 2016). The catalytic types serine (942), metallo (633), and cysteine (615) are best represented in the MEROPS database (Rawlings et al., 2010). Clans, in turn, summarize one or more peptide families that share an evolutionary relationship e.g., similar tertiary structures, alternatively a similar order of catalytic-site residues in the polypeptide chain, and possibly common sequence motifs (sequence-specific binding sites for proteins) around the catalytic residues (Rawlings et al., 2018).

Membrane transporters Bacteria and archaea in the ocean are exposed to constantly changing environmental conditions, for example, varying nutrient concentrations over space and time (Stocker, 2012; Zehr et al., 2017). In order to be successful in this dynamic environment, microorganisms evolved various uptake systems which allow them to scavenge nutrients efficiently (Hosie and Poole, 2001; Davidson and Chen, 2004; Mulligan et al., 2011; Fernandez et al., 2019). Phytoplankton-derived organic matter (in particular POM and HWM-DOM) needs to be transformed into oligo and monomeric compounds (LMW-DOM) prior to uptake via diverse transport systems. Subsequently, compounds < 600 Da need to pass the outer cell membrane of gram-negative bacteria and make their way through the periplasmic space, and finally pass the inner membrane (Arnosti, 2011). This uptake is enabled and facilitated by a diverse set of membrane transport proteins. These proteins are hierarchically grouped into different transporter classification (TC) systems and collected in the transporter classification database (TCDB) (Saier et al., 2006). At the time of writing, the database consisted of 1557 families of transport proteins including for example, adenosine triphosphate-binding cassette transporters (ABC) (Davidson and Chen, 2004), tripartite ATP-independent periplasmic transporters (TRAP) (Mulligan et al., 2011), tripartite tricarboxylate transporters (TTT) (Winnen et al., 2003), TonB-dependent transporters (TBDT) (Noinaj et al., 2010), and ammonium transporters (Pantoja, 2012). These transporters are grouped based on TC categories into channels/pores (TC 1), electrochemical potential-driven transporters (TC 2), primary active transporters (TC 3), group translocators (TC 4), and electron carriers (TC 5).

17 Considerations regarding bacterial foraging strategies and the micro-scale architecture in which bacteria interact with phytoplankton and organic matter From a birds-eye view, the oceans are seemingly homogenous. However, at the microscale, chemical and physical gradients are exceptionally dynamic, heterogeneous and patchy (Stocker, 2012). The most important interface between phytoplankton and prokaryotes is the thin region surrounding a phytoplankton cell coined the “phycosphere”. This region is enriched in organic compounds, which become readily available upon bloom demise (Buchan et al., 2014; Mühlenbruch et al., 2018) (Smriga et al., 2016; Seymour et al., 2017). However, phytoplankton blooms are episodic events, and nutrient hotspots dissipate over very short timescales due to uptake and physical mixing (Stocker, 2012). In contrast to nutrient-rich systems that are characterized by intense phytoplankton blooms, open ocean areas like the big-gyres (Reintjes et al., 2019b) are typically defined by low inorganic nutrient concentrations and therefore have the lowest primary production (sometimes referred to as oceanic “deserts”). These differences in environmental conditions are broadly associated with the extremes of two distinct evolutionary strategies among bacteria. Hence, some bacteria are well adapted to thrive under high nutrient concentrations, while others evolved to thrive in the oligotrophic oceans (Koch, 2001; Lauro et al., 2009; Giovannoni, 2017). Microbes well adapted to respond rapidly to nutrient pulses, depict fast growth rates (> 1 h-1), and have typically large genome sizes (e.g., > 4.8 Mbp Photobacterium angustum S14) are known as copiotrophs (r-strategy) (Lauro et al., 2009; Kirchman, 2016). Copiotrophs regulate the expression of many genes compared to their oligotrophic counterparts (Cottrell and Kirchman, 2016). Also, many are motile, able to sense chemical gradients (chemotaxis), thereby prolonging the time spent in nutrient hotspots (Smriga et al., 2016). Motility is a common copiotrophic trait and is often linked with the expression of extracellular enzymes (Mühlenbruch et al., 2018). At a first glance, this lifestyle seems desirable in the competition for resources by enabling cells to quickly adapt to fluctuations or periodic pulses of nutrients (Stocker et al., 2008; Smriga et al., 2016; Fernandez et al., 2019). However, operating a complex motility machinery is associated with additional energetic costs (Stocker, 2012; Taylor and Stocker, 2012) and may increase the encounter rate with predators (Matz and Jurgens, 2005; Visser and Kiørboe, 2006). Contrary to the copiotrophs, which thrive under high nutrient concentrations as found in, e.g., coastal upwelling zones, oligotrophic microorganisms face nutrient scarcity in a relatively stable environment. Therefore, these organisms tend to have evolved smaller genomes. This process is referred to as genome

18 streamlining (Giovannoni et al., 2005b) and represents an evolutionary adaptation of oligotrophs to nutrient-poor conditions. Having smaller genomes reduces the energy and nutrient demand and contributes to the success of oligotrophs in the open ocean. Oligotrophs typically grow slower (< 0.2 h-1) compared to copiotrophs (Lauro et al., 2009; Kirchman, 2016). Examples of such bacteria are found within the Alphaproteobacteria (e.g., SAR11 clade), including Pelagibacter ubique the most abundant and ubiquitously distributed bacteria in the ocean (genome size ~1.3 Mbp, ~0.4 µm in diameter) (Giovannoni, 2017; Zehr et al., 2017). These bacteria regulate only a small fraction (< 0.1%) of their genes (Cottrell and Kirchman, 2016), although up to 10% have been reported elsewhere (Steindler et al., 2011). These two contrasting environmental settings and associated microbial adaptations (lifestyles) are critical for our understanding of microbial evolution (Koch, 2001) and ultimately for ecosystem processes. Besides the broadly used dichotomy between copiotrophs and oligotrophs, bacterial foraging strategies are certainly more diverse and nuanced in their natural habitats (Fernandez et al., 2019). However, knowledge of the various foraging strategies utilized by marine microbes and how they contribute to the partitioning of resources is crucial for understanding the complex dynamics of DOM cycling in the sea. For instance, Reintjes and colleagues (2019) proposed a three-player model consisting of selfish, sharing, and scavenging microbes that are collectively responsible for the transformation and respiration of organic matter (Reintjes et al., 2019a). The different foraging strategies highlight the diverse adaptations of microbes to distinct niches (Lauro et al., 2009). Bacterial and archaeal communities in the ocean are exceptionally diverse (Sunagawa et al., 2015). Understanding the mechanisms that allow the coexistence of a plethora of different species (e.g., phytoplankton, bacteria, and archaea) in a seemingly homogenous environment with limited resources has been a central question in microbial ecology (Hutchinson, 1961; Sunagawa et al., 2015). For example, resource (niche) partitioning among species provides one explanation for this observation (Salazar and Sunagawa, 2017). The ecological niche concept, as outlined by Hutchinson (1957), describes a niche as an n-dimensional hypervolume that corresponds to an environmental state which enables a species to exist indefinitely. This niche space is called an organism’s fundamental niche (Hutchinson, 1957). Thus, each organism in a population has a fundamental niche space. The genome contents of bacteria ultimately define their fundamental ecological niches (Marco, 2008; Lauro et al., 2009). More recently, it has been shown that ecological niches can be predicted from metagenomes (Suen et al., 2007; Alneberg et al., 2020). However, microorganisms rarely live isolated, thus are likely to compete for the same resources in nature. Assuming that two species have the same fundamental niche, they cannot stably coexist according to competition exclusion principles

19 (Hardin, 1960). Thus, a microorganism’s realized niche is the reduced space in which a cell can coexist and be competitive. Metatranscriptomics, in turn, allows detecting the portion of the genome that is actively transcribed as a consequence of, for example, an environmental stimulus such as exposure to organic matter as demonstrated in (Gifford et al., 2013) and Paper I. Thus, the analysis of transcribed genes enables us to estimate a microorganism’s realized niche at a given time. While this response does not necessarily indicate the full potential (fundamental niche), it allows hypothesizing about the actively used fraction of the realized niche space that the microorganism occupied under competition. Thus, we can estimate the particular role of these organisms in a biogeochemical process. In the light of the previously mentioned considerations, metatranscriptomic analyses potentially represent conservative estimates of bacterial phenotypes (functional traits).

The Ocean Microbiome Aquatic ecosystems are full of microbial life, collectively accounting for the largest living surface area on Earth (Whitman et al., 1998). Although the precise mechanisms involved in prokaryotic foraging in the ocean remain unknown, advances in molecular techniques and tools allow us to shed light on the microbial “black box” and to study the incredible diversity and complexity that is hidden in just one drop of water (Stocker, 2012; Moran et al., 2016). At a high taxonomic level, the world's oceans are essentially dominated by a few bacterial and archaeal clades (e.g., Alphaproteobacteria, Gammaproteobacteria, Bacteroidetes, Cyanobacteria, and Thaumarchaeota) (Glockner et al., 1999; Kirchman, 2002; Kirchman et al., 2003; Brown et al., 2014; Yilmaz et al., 2015; Giovannoni, 2017). Here, I am introducing a few of them with a focus on their contribution to the processing of DOM compounds (degradation, modification, and uptake) and their role in biogeochemical cycles. However, this is by no means an exhaustive overview of the exceptional diversity of microorganisms. Many more bacterial and archaeal groups with essential roles in global nutrient cycles exist, which go beyond the scope of this thesis (Figure 5).

Figure 5 | A current view of the tree of life. The tree is based on 16 concatenated ribosomal protein alignments comprising 3083 genomes. Relevant bacterial and archaeal lineages, of which a few are discussed in this thesis, are highlighted in color. Modified and adapted from (Hug et al., 2016).

Alphaproteobacteria The Alphaproteobacteria are a diverse and ancient bacterial group, displaying exceptionally diverse metabolic strategies (Giovannoni, 2017; Daniel et al., 2018). This class consists of two subgroups that are abundant in the oceans, oligotrophs that are capable of thriving and surviving in low-nutrient environments such as the SAR11 clade (e.g., Pelagibacter ubique) and members that are often associated with phytoplankton blooms, e.g., the Roseobacter clade (Buchan et al., 2014; Giovannoni, 2017).

21 The SAR11 clade (Pelagibacterales) is relatively old, branching near the root of the Alphaproteobacteria, and these bacteria are among the most abundant organisms in the world accounting for up to 50% of the total surface microbial community in the ocean (Morris et al., 2002). They are ubiquitously distributed, reaching the highest cell abundances in stratified, oligotrophic oceanic gyre systems (Giovannoni, 2017). Pelagibacterales are small, vibrioid, and free- living cells. An interesting feature of Pelagibacter ubique is the reduced genome size (Grote et al., 2012), resulting in one of the smallest genomes (~1.3 Mbp) of free-living bacteria sequenced so far (Giovannoni et al., 2005b). Interestingly, comparative genomics showed unprecedented conservation of the genome among the SAR11 clade (Grote et al., 2012). However, the authors noticed substantial variation among genomes (e.g., phosphorus metabolism, C1 metabolism, glycolysis), suggesting a high degree of adaptive specialization for resources resulting in ecotype divergence within the SAR11 clade (Grote et al., 2012). For instance, the SAR11 clade can generate energy by a light-driven proteorhodopsin proton pump (Giovannoni et al., 2005a) or respiration (Giovannoni, 2017). Moreover, they are exceptionally efficient in the uptake of labile compounds such as amino acids and dimethylsulfoniopropionate (DMSP) through high-affinity transporters such as ABC transporters (Malmstrom et al., 2004; Sowell et al., 2009; Zhao et al., 2017). Interestingly, SAR11 isolates from the open ocean lacked the ability to grow on glucose, whereas a coastal isolate could use glucose as a carbon source (Schwalbach et al., 2010). Taken together, SAR11 are exceptionally abundant, active, and specialized to thrive under low nutrient conditions in euphotic water layers. Therefore, they significantly impact element cycles such as carbon (C), nitrogen (N), sulfur (S) (Tripp et al., 2008; Giovannoni, 2017). The Roseobacter clade, a paraphyletic group within the Rhodobacteraceae (Simon et al., 2017), comprises more than 60 described genera and thousands of uncharacterized species and strains (Buchan et al., 2014; Pujalte et al., 2014). Within the Roseobacter clade, many taxa thrive in a broad variety of environments such as sediments, sea ice, and animal surfaces (Buchan et al., 2005; Buchan et al., 2014; Daniel et al., 2018). They comprise 20 to 30% of the bacterioplankton community in coastal surface waters but less than 1% below the euphotic zone (Buchan et al., 2005; Daniel et al., 2018). Roseobacters play an important role in the transformation of C, N, S, and phosphorus (P). Genome analysis of isolated and uncultured members suggests diverse adaptations to different environmental settings within this clade (Newton et al., 2010; Ottesen et al., 2011; Buchan et al., 2014). Accordingly, genomic studies revealed a relatively high abundance of transporters, in particular, ABC (ATP binding cassette), TRAP (Tripartite ATP-independent periplasmic), and MFS (Major facilitator superfamily) (Moran et al., 2007; Newton et al., 2010). Therefore, the Rosebacter seems to be exceptionally efficient in the uptake of phytoplankton- derived organic matter such as DMSP, urea, polyamines (spermidine and

22 putrescine), amino acids (taurine, glycine betaine), methylated amines, phosphoesters, and phosphonates (Gonzales et al., 1999; Newton et al., 2010; Chen, 2012).

Gammaproteobacteria The class Gammaproteobacteria consists of ~250 genera with broad ranges of trophic strategies, morphologies, and adaptations to a variety of environmental gradients (Williams et al., 2010). At large, this class consists of copiotrophs but also bacteria from the oligotrophic marine gammaproteobacteria (OMG) group, including the recently proposed order Cellvibrionales ord. nov. (Cho et al., 2007; Kang et al., 2011; Huggett and Rappe, 2012; Spring et al., 2015). The OMG contains for instance proteorhodopsin and ribulose bisphosphate carboxylase genes (Cho et al., 2007; Kang et al., 2011; Huggett and Rappe, 2012). Commonly known copiotrophic Gammaproteobacteria are Vibrio, Alteromonas, Pseudoalteromonas, and to some extent Oceanospirillum (Fuhrman and Hagström, 2008). Many members within the Gammaproteobacteria are responsive to organic matter additions and show exceptionally high activities and growth rates in enrichment experiments and field observations (McCarren et al., 2010; Mason et al., 2012; Sarmento and Gasol, 2012; Mason et al., 2014; Pedler et al., 2014; Pedler Sherwood et al., 2015) and (Paper I and III). They are among the major polysaccharide degraders and some members are even capable of degrading the structural polysaccharide cellulose found in plant cell walls (Edwards et al., 2010). Thus, this group consists of important players in the turnover of major element cycles, e.g., C and S (Dyksma et al., 2016).

Alteromonadales The Alteromonadales is an order of the class Gammaproteobacteria. Members of this order are motile copiotrophic heterotrophs, of which many are r- strategists (Koch, 2001; Pedler et al., 2014). Alteromonadales are globally distributed and thrive particularly well in temperate (Ivars-Martinez et al., 2008a) and artic regions (Williams et al., 2013). Members within the order are abundant in surface waters but also functionally active in the deep sea (Lopez- Perez et al., 2012; Bergauer et al., 2018) reflecting ecotype diversification (Garcia-Martinez et al., 2002; Lopez-Lopez et al., 2005; Ivars-Martinez et al., 2008b; Neumann et al., 2015). Alteromonas macleodii is one of the best-studied “model” species and regarded as a representative strain of the order Alteromonadales (Lopez-Perez et al., 2012). Single isolates within the order are exceptionally efficient in the utilization of DOC. Hence, they have the potential to significantly contribute to the cycling of carbon compounds (Pedler et al., 2014), and it is not surprising to find them among the dominant bacterial groups

23 associated with particles and increased abundance/activity during elevated concentration of HMW-DOM from seawater or phytoplankton-derived organic matter (McCarren et al., 2010; Sarmento and Gasol, 2012). Alteromonas spp. show an array of diverse metabolic strategies related to distinct carbohydrate utilization. For instance, A. macleodii encodes alginolytic systems consisting of alginate lyases, is capable of hydrolyzing laminarin, pullulan, and xylan, and encodes TonB-dependent receptors (TBDR) (Neumann et al., 2015; Wietz et al., 2015; Mitulla et al., 2016). Moreover, catabolite repression allows them to prioritize certain polysaccharides e.g., laminarin (GH16, GH3, GH1) over alginate and pectin (GH105, GH28, CE8, PL1, PL6, PL17) (Koch et al., 2019a; Koch et al., 2020). Hence, Alteromonadales are key players during phytoplankton blooms and are known to thrive in laboratory co-cultures with cyanobacteria (Tada et al., 2011; Aharonovich and Sher, 2016; Hou et al., 2018). They certainly fulfill an important ecological role as external polymer degraders in natural environments (Reintjes et al., 2019a) and may be crucial for exchanging metabolites with other bacteria (Aharonovich and Sher, 2016).

Pseudomonadales The genus Pseudomonas within the order Pseudomonadales is perhaps best known for its pathogenic representatives, e.g., Pseudomonas aeruginosa (Mougous et al., 2006). However, the genus is exceptionally diverse and globally distributed, found in a wide range of habitats (from freshwater to the open ocean and the deep sea), and often associated with plants and animals (Palleroni and Doudoroff, 1972; Spiers et al., 2000; Nelson et al., 2002; Timmis, 2002; Khan et al., 2007; Bravakos et al., 2021). Some species are efficient degraders and capable of utilizing toxic compounds, aromatic hydrocarbons, dipeptides, and proteins, as well as branched-chain amino acids (Griffith and Fletcher, 1991; Bartolome-Martin et al., 2004; Kazakov et al., 2009; Teufel et al., 2010; Wasi et al., 2013). Moreover, members within the Pseudomonadales were exceptionally competitive and active in enrichment experiments amended with amino acids (Paper I). Thus, this order fulfills vital contributions in biogeochemical element cycles in the global ocean.

Oceanospirillales Oceanospirillales are found in marine environments (Satomi and Fujii, 2014). Together with Alteromonadales, they contributed up to ~2% in metagenomes and ~20% of metaproteomes in the Arctic (Williams et al., 2013) and are known to be members of the surface water communities in the Atlantic Ocean (Joglar et al., 2020; Joglar et al., 2021). Oceanospirillales are capable of degrading hydrocarbons (near-complete pathways for non-gaseous n-alkane and cycloalkane degradation) and produce bactericidal compounds (Mason et al.,

24 2012; Redmond and Valentine, 2012; Mason et al., 2014; Satomi and Fujii, 2014). Also, they encode chemotaxis and motility genes besides a complete B12 vitamin synthesis pathway in conjunction with a suite of transporters for scavenging nutrients such as amino acids, fatty acids, carboxylic acids, ammonium, iron, sulfate, and phosphate (Mason et al., 2012; Delmont et al., 2015). A multi-omics study showed that the utilization of organic matter in the bathypelagic zone was dominated by Oceanospirillales (Zhao et al., 2020). Also, in a metatranscriptomics study, Oceanospirillales dominated most metabolic processes upon enrichment with a suite of monosaccharides (Paper I). The experimental evidence suggests that these bacteria are extraordinarily efficient in scavenging labile DOM compounds.

Cellvibrionales Recently, it has been suggested to assign members of the OMG group to the newly proposed order Cellvibrionales (Spring et al., 2015). The evolutionary relationship of the three major orders Cellvibrionales, Oceanospirillales, and Pseudomonadales is complex (Liao et al., 2020). Although knowledge of the ecophysiology of Cellvibrionales is limited, they seem to be globally distributed (Banerjee et al., 2018; Signori et al., 2018; Chenard et al., 2019; Pajares et al., 2020) and found to be important during phytoplankton blooms in the North Atlantic upwelling system (Joglar et al., 2020). Moreover, metatranscriptomics analyses during upwelling induced phytoplankton blooms and across a stratified depth profile showed that Cellvibrionales are exceptionally active in transcribing glycoside hydrolases (GHs) (Paper III and IV), suggesting a vital role as polymer degraders in the global ocean.

Bacteroidetes, Flavobacteriales The term Flavobacteria refers to bacteria in the class Flavobacteriia in the phylum Bacteroidetes. Currently, the order Flavobacteriales consist of only four families. The Flavobacteriaceae form a major and very diverse family in this order, with more than 100 genera (Buchan et al., 2014). Sequenced genomes indicate features that allow the organisms to attach to particles and to perform gliding motility (Fernandez-Gomez et al., 2013). Flavobacteriaceae are widespread across diverse habitats ranging from marine, freshwater to terrestrial habitats (Fuhrman and Hagström, 2008). Flavobacteria, like other relatives in the phylum, are, judged by their genome content and transcriptional response, specialized in the degradation of HMW-DOM compounds (Teeling et al., 2012; Fernandez-Gomez et al., 2013) and (Paper I and III). This is reflected in a relatively large number of glycoside hydrolases (GHs), peptidases, and glycosyltransferases (GTs), along with genes encoding for TonB-dependent receptors (e.g., SusC and RagA) and outer membrane-binding proteins (e.g.,

25 SusD and RagB) (Fernandez-Gomez et al., 2013; Mann et al., 2013; Williams et al., 2013). Noteworthy, these diverse genes are strictly regulated and arranged in clusters known as polysaccharide utilization loci (PULs) (Bjursell et al., 2006; Fernandez-Gomez et al., 2013; Grondin et al., 2017; Kappelmann et al., 2019; Lapebie et al., 2019). In laboratory experiments and field observations, including phytoplankton blooms, it was shown that they perform crucial ecological roles in the degradation of polysaccharides and proteins (Pinhassi et al., 1999; Cottrell and Kirchman, 2000; Riemann et al., 2000; Pinhassi et al., 2004; Teeling et al., 2012; Fernandez-Gomez et al., 2013; Bennke et al., 2016; Orsi et al., 2016) and (Paper I and III). Another interesting feature of some relatives discovered recently, is a ‘selfish’ uptake mechanism of molecules exceeding > 600 Da molecular weight into the periplasmic space through TonB- dependent outer membrane transporters and further hydrolysis in the periplasmic space to minimize the loss of hydrolysis products (Reintjes et al., 2017). These genomic adaptations ultimately enable Flavoacteriales to degrade a suite of organic polymers in aquatic environments.

Planctomycetes Planctomycetes are members of the PVC superphylum (Wagner and Horn, 2006). The PVC superphylum is a monophyletic group consisting of the phyla Planctomycetes, Verrucomicrobia, Chlamydiae, Lentisphaerae, and the candidate phyla Poribacter and OP3 (Wagner and Horn, 2006). Planctomycetes are globally distributed and abundant in various habitats including terrestrial and marine systems where they constitute up to 7% of the microbial community (Glockner et al., 1999). They are aerobic heterotrophs that are often associated with marine snow and phytodetritus (Glockner et al., 2003). In the Baltic Sea, they are not among the most abundant bacterial groups, but they increase in abundance during summer and typically show strong correlations with phytoplankton biomass and colored dissolved organic matter (cDOM) (Lindh et al., 2015; Bunse et al., 2016). Interestingly, the majority of Planctomycetes lack peptidoglycan in their cell walls (Glockner et al., 2003; van Teeseling et al., 2015) and possess additional features that are eukaryote-like such as a membrane-bounded nuclear body (Fuerst, 1995). Comparative genome analysis of a few members of the phylum Planctomycetes showed a versatile hydrolytic potential and growth on a variety of polysaccharides (e.g., xylan, pectin, starch, cellulose, chitin, and microbial-derived polysaccharides) (Ivanova et al., 2017; Dedysh and Ivanova, 2019). Also, some representatives of marine Planctomycetes are capable of gaining energy from the anaerobic oxidation of ammonia directly into dinitrogen gas (Anammox) (Strous et al., 1999) and may even be capable of fixing nitrogen (Delmont et al., 2018). Thus, they play crucial ecological roles by taking part in marine N and C cycles (Glockner et al., 2003; Wagner and Horn, 2006; Delmont et al., 2018).

26 Verrucomicrobia Verrucomicrobia (PVC superphylum) are best known for their abundance in soil but are also ubiquitous in the ocean. They constitute ~2% of the bacterial community, and depict pronounced community variability related to physiochemical gradients (Freitas et al., 2012). In the Baltic Sea, they become abundant during summer (Andersson et al., 2010). Despite their seemingly low abundance in marine microbial communities, Verrucomicrobia harbor an astounding set of carbohydrate-active enzymes (CAZymes) (Martinez-Garcia et al., 2012). In particular, they encode glycoside hydrolases (GH107, GH2); sulfatases, and carbohydrate esterases to hydrolyze the cell wall polysaccharide fucoidan in brown macroalgae (Sichert et al., 2020). Interestingly, the degradation of fucoidans is slower compared to other polysaccharides, likely because of the branched and highly sulfated structure of this polysaccharide. Curiously, during the degradation of fucoidans, toxic intermediates such as lactaldehyde are produced (Sichert et al., 2020). Therefore, the degradation of fucoidans is limited to highly specialized organisms which harbor genomic features and adaptations such as bacterial microcompartments (BMC) to cope with the toxic intermediates (Sichert et al., 2020). Thus, Verrucomicrobia may be more important in the cycling of carbon, especially of specific polysaccharides, than their abundances may suggest.

Nitrospinae The majority of inorganic fixed nitrogen in marine ecosystems is present in the form of nitrate, thus nitrite is rapidly oxidized to nitrate by nitrite-oxidizing bacteria (NOB) (Kuypers et al., 2018). NOB are chemolithoautotrophs that - catalyze the second step of nitrification, the aerobic oxidation of nitrite (NO2 ) - to nitrate (NO3 ) (Daims et al., 2016; Kuypers et al., 2018). For instance, Nitrospinae are the most abundant NOB in the global dark ocean where they substantially contribute to the oxidation of nitrite via nitrite oxidoreductase genes that are often encoded on a single operon (NxrABC) (Pachiadaki et al., 2017).

Cyanobacteria Marine picocyanobacteria are the most abundant organisms on Earth that possess chlorophyll a and perform oxygenic photosynthesis (Scanlan et al., 2009; Schirrmeister et al., 2011). The phylum Cyanobacteria consists of hundreds of genera, and two of them, Prochlorococcus (Chisholm et al., 1988) typically found under stable oligotrophic settings and Synechococcus (Waterbury et al., 1979) which are often found in dynamic environments with higher nutrient concentrations (Scanlan et al., 2009; Flombaum et al., 2013; Biller et al., 2015) occur in high abundances in oceanic waters, along with e.g.

27 larger genera like Trichodesmium. However, the two genera differ in their metabolic diversity and global distribution. For instance, Prochlorococcus shows pronounced ecotype diversification toward different light intensities (high- and low-light) (Moore et al., 1998), whereas Synechococcus is genetically exceptionally diverse and shows adaptations to gradients in nutrients and light quality through diverse pigments (Rocap et al., 2003; Dufresne et al., 2005; Palenik et al., 2006; Scanlan et al., 2009 and references therein). Prochlorococcus is typically found between 45ºN and 40ºS and more abundant offshore, whereas Synechococcus is ubiquitously distributed (Scanlan et al., 2009 and references therein). Interestingly, while photoautotrophs are known for their ability to produce carbohydrates, they also encode a variety of enzymes (e.g., cellulases, amylases, galactosidases, proteases, and lipases) to degrade them (Brasil et al., 2017). A search for Synechococcus and Prochlorococcus in the CAZy database (January 18th, 2020) resulted in 71 and 34 entries, respectively, including diverse sets of e.g., glycoside hydrolases and carbohydrate esterases. Although these groups utilize light energy to produce organic compounds, they also encode transporters, for example, ABC transporters (for amino acids, peptides and sugars), and ferrous iron transporters, suggesting that mixotrophy is an important feature of picocyanobacteria (Scanlan et al., 2009; Yelton et al., 2016).

Archaea

Archaea are often morphologically indistinguishable from Bacteria. However, they are a separate evolutionary branch fundamentally different from Bacteria and in many regards more similar to Eukaryotes (Spang et al., 2015). Consequently, they do share specific features with Eukaryotes, namely the same number of RNA polymerase subunits, DNA polymerase family B (Huet et al., 1983). Yet, another difference is that Archaea lack a murein cell wall and many taxa have only one membrane that is surrounded by a glycoprotein S-layer (Albers and Meyer, 2011). While it was once thought that Archaea are restricted to extreme habitats characterized by high temperature, pressure, or salinity (Santoro et al., 2019), our understanding of their global distribution, metabolic capabilities and importance in biogeochemical cycles has changed dramatically.

28 Thaumarchaeota Nitrification, the oxidation of ammonia via nitrite to nitrate, plays a central role in the oceanic nitrogen cycle. For decades, it was believed that ammonia- oxidizing bacteria (AOB) exclusively perform the first step of nitrification (the oxidation of ammonia to nitrite) (Kuypers et al., 2018). However, the discovery of Nitrosopumilus maritimus, an archaeon belonging to the phylum Thaumarchaeota (Brochier-Armanet et al., 2008; Spang et al., 2010), revealed for the first time that mesophilic ammonia-oxidizing archaea (AOA) may play a crucial role in the global nitrogen cycle (Könneke et al., 2005). N. maritimus oxidizes ammonia to nitrite with the key enzyme ammonia monooxygenase (alpha subunit - amoA) (Könneke et al., 2014; Kuypers et al., 2018). Archaeal ammonia oxidation is coupled with the inorganic fixation of carbon dioxide (Pratscher et al., 2011). Thaumarchaeota fix inorganic carbon via a modified version of the 3-hydroxypropionate/4-hydroxybutyrate cycle (Berg et al., 2007; Fuchs, 2011; Hugler and Sievert, 2011; Könneke et al., 2014). However, the low energy yield attainable by ammonia oxidation suggests that a large quantity of ammonia has to be oxidized to fix a small amount of carbon dioxide (Norman et al., 2015). Thaumarchaeota can account for up to ~40% of microbial communities (Karner et al., 2001; Herndl et al., 2005) and it has been shown that AOA are widespread in marine waters and sediments including hypoxic waters (Francis et al., 2005; Berg et al., 2015) to the extent that AOA may even outperform their bacterial counterparts (AOB) by orders of magnitude (Wuchter et al., 2006). Moreover, members of the Thaumarchaeota encode a variety of ABC type transporters (e.g., amino acids, oligopeptides, phosphonates), high-affinity ammonium transporters and urea metabolism genes (urea transporters and ureases), allowing them to outcompete bacteria under low ambient ammonium concentrations (nM range) (Martens-Habbena et al., 2009; Walker et al., 2010; Alonso-Saez et al., 2012; Bayer et al., 2016). Regarding their role in the degradation of polymers, a large scale meta-omics study recently reported only a minor contribution of Archaea encoding and expressing CAZymes and peptidases (Zhao et al., 2020). This is in agreement with our results from the Gullmar Fjord (Paper IV). Thus, Archaea, particularly Thaumarchaeota, seem to be more important in labile organic matter uptake than extracellular degradation of polymers. Recently, two genomic studies identified populations within the Thaumarchaeota that do not oxidize ammonium but rather have a fully heterotrophic lifestyle (Aylward and Santoro, 2020; Reji and Francis, 2020). This confirms early suggestions that ~17% of bulk of archaeal biomass production is fueled by heterotrophic consumption of organic carbon (Ingalls et al., 2006). Moreover, Thaumarchaeota may be an ecologically relevant source of labile organic matter (e.g., amino acids and cobalamin), as has been recently demonstrated for AOA in pure cultures (Doxey et al., 2015; Bayer et al., 2019). Assuming that AOA would release similar quantities of organic matter in their

29 natural environment as has been estimated in the laboratory, these exudates could indeed be important substrates for heterotrophs in the dark ocean. Thus, Thaumarchaeota are important constituents of the marine microbiome with crucial roles in oceanic C and N cycles.

30 Aims of this thesis

The ocean microbiome is exceptionally diverse regarding taxonomy and metabolic and ecological functions. Although our knowledge of microbial diversity and functional potential has advanced over the last decades, mechanistic understanding of the molecular mechanisms involved in microbial DOM cycling in aquatic systems is still limited. This thesis aims to extend our knowledge of the interdependency between DOM and prokaryotes using metatranscriptomics, laboratory experiments, and field studies over different spatiotemporal gradients. A first focus was on examining functional gene expression responses in natural microbial communities to ecologically relevant a priori defined organic matter compounds (Paper I). I thereafter focused on complex DOM mixtures (uncharacterized) derived from rivers rich in humic substances or inorganic nutrients (Paper II), and phytoplankton-derived organic matter produced during either induced (Paper II and III) or natural blooms (Paper III) across spatiotemporal gradients (Paper II, III, and IV). These studies were carried out with the following overarching aims:

• To determine functional gene expression responses to different ecologically relevant DOM compound classes (carbohydrates, nucleic acids, and protein) and condensation states (monomers or polymers), I conducted seawater enrichment cultures with natural bacterial communities from the Baltic Sea (Linnæus Microbial Observatory - LMO) (Paper I).

• To examine how allochthonous DOM characteristics influence natural bacterioplankton communities, I examined the influence of two climate change relevant river water additions (forest- versus agricultural-influenced DOM composition) and inorganic nutrients on Baltic Sea bacterioplankton transcription responses in a mesocosm experiment by applying metatranscriptomics (Paper II).

• To investigate the temporal gene expression dynamics of bacterioplankton to natural phytoplankton-derived DOM (autochthonous), I used an onboard mesocosm experiment in comparison to a field bloom. Bacteria were used as bioindicators to assess the importance of putative glycans and the role of particular genes in determining bacterioplankton succession during the phytoplankton blooms. To determine the responsiveness of glycoside hydrolases, peptidase, and transporters, and their role in driving functional cascades during the blooms, I conducted a comparative analysis between mesocosms and the field (Paper III).

31 • To elucidate the variability of prokaryotic gene transcription of carbohydrate degrading enzymes and peptidases along with transporters in a strongly stratified water column, I investigated prokaryotic transcription profiles in relation to physicochemical variables (e.g., DOC, nutrients, oxygen, temperature, and salinity) (Paper IV).

32 Methodology

The applied research strategies in this thesis relied on laboratory microcosms (Paper I) and mesocosms studies (Paper II and III) along with extensive field sampling (Paper IV). Given that most bacteria and archaea escape cultivation attempts under laboratory conditions (Staley and Konopka, 1985; Martiny, 2019), all included studies relied on state-of-the-art sequencing technology (i.e., metatranscriptomics - sequencing of messenger RNA (mRNA)). The gene expression data were analyzed in relation to standard measurements such as cell abundance, enzymatic activities, bacterial production, chlorophyll a, DOC, and nutrient concentrations. To obtain a broader view of bacterial responses, we conducted the studies in aquatic environments with differences in water chemistry (i.e., salinity, temperature, DOC, and nutrient concentrations). For detailed information on bioinformatic and statistical analyses, I refer the reader to the Material and Methods sections of the individual articles and references therein.

Sequencing technology, bioinformatics, advantages, and limitations A breakthrough in DNA sequencing technology in 2005 changed the fate of many scientific fields, including microbial ecology (Kchouk et al., 2017). The “Next Generation Sequencing (NGS) Technologies” replaced the relatively time-consuming and expensive Sanger sequencing, allowing to obtain a higher throughput and more sequences at a lower cost (Mardis, 2011). Rapid development and improvements in sequencing technology contributed to the widespread use of DNA and RNA sequencing as a standard tool for environmental screening and basic science (Shendure et al., 2017). This leap in technology opened up new possibilities of gathering unprecedented amount of genetic information from diverse systems in which microorganisms play pivotal roles. Concomitantly, the massive amount of generated sequence data poses additional challenges of data storage and computational capacities (Sboner et al., 2011; Muir et al., 2016), and often there are shortcomings regarding sequence data and metadata availability (Jurburg et al., 2020). Nowadays, most sequencing in microbial ecology, with a prime interest in the entire microbial community (microbiome), is performed on one of Illumina’s sequencing platforms (e.g., HiSeq and MiSeq) (Kchouk et al., 2017). The basics of Illumina sequencing are i) library preparation, ii) cluster generation, iii) sequencing and iv) data analysis (Slatko et al., 2018; Illumina, 2020). During library preparation the DNA or cDNA (mRNA reverse transcribed into complementary DNA) is

33 randomly fragmented and short adapters (known sequences) are ligated to the 5’ and 3’ end. The adapter-ligated fragments are then amplified by PCR (polymerase chain reaction) to generate multiple copies of identical fragments. Illumina consists of so-called flow cells which typically comprise eight lanes manufactured on a glass chip. These lanes contain surface-bound oligonucleotides that are complementary to the library adaptors. This ensures that each fragment is physically separated in the cluster generation step. Bridge amplification is used to increase the number of fragments to ensure the detection of bases during the sequencing step. For the sequencing step, Illumina uses a proprietary reversible terminator-based method that allows detecting single base incorporation into the DNA template strands in real-time with very low error rates. The output of such platforms is typically millions of short 100 to 300 base pairs (bp) long fragments called reads, which contain the information of specific gene fragments in the case of amplicon sequencing or fragments of mRNA for transcriptomics. Illumina can deliver up to 2 billion reads from a single flow-cell (Illumina, 2020).

Bioinformatics Untargeted meta-omics enable the culture-independent analysis of DNA or RNA derived from virtually all microorganisms from an environmental sample without the need for cultivation. In this thesis, I used metatranscriptomics to study the expression of functional genes in diverse prokaryotic communities. After sequencing of the messenger RNA (mRNA), the short reads can either be directly used to infer functional and taxonomic information (assembly-free approach), or de novo assembled into longer sequence fragments called contigs (assembly-based approach). Afterwards, potential genes (ORFs) can be predicted, and sequences aligned against references databases (e.g., NCBI (Pruitt et al., 2007), BARM (Alneberg et al., 2018), TARA Ocean (Pesant et al., 2015), or GORG (Pachiadaki et al., 2019)) to assign functional and taxonomic labels. Both approaches are valid to derive mechanistic understanding of microbial expression responses (Anwar et al., 2019). Metatranscriptomics is a powerful tool that revolutionized microbial ecology and many other disciplines because it allows obtaining functional snapshots in space and time by analyzing genes that are actually expressed in an environmental sample (Poretsky et al., 2005; Moran, 2009; Moran et al., 2013). For instance, metatranscriptomics can overcome some of the limitations of detecting compounds of the labile DOC pool that are typically below the detection limits of currently available analytical tools because of exceptionally rapid turnover by marine microbes (Moran et al., 2016). This has been shown in studies that analyzed transcribed functional genes involved in the assimilation or degradation of organic compounds and pollutants to deduce these vital compounds by using microbes

34 as biosensors (McCarren et al., 2010; Poretsky et al., 2010; Teeling et al., 2012; Gifford et al., 2013; Lidbury et al., 2014; Vorobev et al., 2018; Karlsson et al., 2019). Collectively, these studies demonstrate the power of metatranscriptomics for environmental science. However, there are many limitations to omics of which I want to discuss a few that are relevant to this thesis. The analysis of gene expression is hampered by the fact that the majority of RNA is ribosomal RNA (rRNA) accounting between 80-90% of the total RNA pool (Stewart et al., 2010; Merchant and Helmann, 2012). Although rRNA removal protocols and kits are available (Stewart et al., 2010) and remaining rRNA can be bioinformatically removed, the low proportion of mRNA and residual rRNA molecules reduce the intended sequencing depth of functional genes of interest (Filiatrault, 2011). Also, mRNA is inherently unstable and has typically short half-life times (< 10 min), which may bias in situ transcriptional responses between sample retrieval from the environment and the processing of mRNA to an unknown extent (Steiner et al., 2019). Given that reads or contigs need to be aligned to reference databases to assign useful functional and/or taxonomic information, novel functions remain unknown due to imbalances between generating sequences and experimental assignment of functions in the laboratory (Moran, 2008). Thus, the accuracy and completeness of processes and taxa expressing certain functions depend on the completeness of the databases that are used for annotating functional genes and taxonomy, which are typically biased toward fast-growing isolates and human pathogens. This limitation becomes obvious when genes with unknown functions have high abundances, and often only half of the genes can be taxonomically assigned (Shi et al., 2011; Alivisatos et al., 2015; Dupont et al., 2015). For instance, less than 5% of metagenomes and less than 10% of metatranscriptomes can be assigned at the species level (Salazar et al., 2019). Also, gene expression is under strict regulation, post-transcriptional modification through RNA-binding proteins (RBS) and post-translational control of translated proteins via modification and degradation (Mata et al., 2005), which impairs our knowledge of the relevance of these functions in the environment, and generally do not inform about the enzymatic kinetics of these enzymes (Louca et al., 2018). Despite the aforementioned limitations, metatranscriptomics, as a stand-alone approach, allows us to derive snapshots of functional gene expression responses in diverse microbial communities and has been proven a powerful tool in environmental microbiology. Significant progress has been made in sequencing and bioinformatic approaches, yet equivalent breakthroughs in our ability to measure relevant enzymatic activities in nature are lagging behind, given that these techniques remained essentially unchanged for decades (Arnosti, 2011). Moreover, widely used enzymatic activity assays use substrate analogs, such as the commonly used leucine-MCA, MUF-β-, and MUF-α-glucose proxies, which have the caveat of measuring exo-acting rather than endo-acting enzymatic activities.

35 These proxies do not represent the precise structure of HMW-DOM compounds, highlighting an important limitation regarding the enzyme- substrate fit (Arnosti, 2011). Given that mass spectrometry is limited by the fact that molecular formula generally do not allow resolving the structure of a compound of interest (Dittmar and Stubbins, 2014), and that an identified transcript that encodes for an enzyme prevents us to infer in situ substrate range and enzyme kinetics, our ability to derive knowledge about the substrate- enzyme fit in the environment is limited (Arnosti, 2011). Thus, linking enzymatic activity assays with meta-omics is inherently difficult. However, future research aiming at coupling enzyme kinetics with omics is required along with the development of alternative techniques to the widely used substrate proxies (Arnosti, 2003; Arnosti et al., 2005; Murray et al., 2007), in order to advance our understanding of the complex relationships between enzyme activities and their role in the turnover of specific HMW-DOM compounds (Arnosti, 2011).

36 Sampling site characteristics

Project Locations Paper I & II Paper III Paper IV

Figure 6 | Overview of the sampling locations. Paper I and II were conducted with water samples from the Linnæus Microbial Observatory (LMO) time series station located in the Baltic Sea (Sweden); Paper III was carried out in the NW Iberian upwelling system (Vigo, Spain) with water samples from a coastal station; Paper IV was conducted in the Gullmar Fjord (Kristineberg, Sweden) at station Alsbäck.

The Baltic Sea The Baltic Sea is a large semi-enclosed brackish water ecosystem consisting of three main basins (Gulf of Bothnia, Gulf of Finland, and the Baltic Proper) (Kullenberg and Jacobsen, 1981). The Baltic Sea is characterized by a relatively long residence time of the water (~35 years) due to limited water exchange with the North Sea through the Danish Straits (Kullenberg and Jacobsen, 1981). This is reflected in a horizontally pronounced but temporally relatively stable salinity

37 gradient ranging from essentially freshwater in the north (Gulf of Bothnia) to a high salinity of up to 35 PSU (Practical Salinity Unit) in the south (Skagerrak) (Hansson and Gustafsson, 2011). The catchment area surrounding the Baltic Sea is densely populated (~85 million people), and thus heavily influenced by forest, agriculture and industrial land use (Voss et al., 2011; Reusch et al., 2018). Consequently, the Baltic Sea receives increased loads of nutrients (nitrogen and phosphorus) and sewage water (Conley et al., 2011; Gustafsson et al., 2012) resulting in elevated nutrient concentrations compared to other coastal seas and the oligotrophic open ocean. For instance, ammonium concentrations typically vary between 1-2 µM, phosphate between ~0.6– 2.8 μM, and silicate between ~1.5 and 23 µM (Bunse et al., 2019). In addition to elevated nutrient concentrations, the geographical location and pronounced seasonal variation of environmental parameters (e.g., light intensity, water temperature, and stratification) result in yearly recurring and relatively well predictable phytoplankton blooms in the Baltic Sea. These massive blooms are typically dominated by diatoms (e.g., Chaetoceros, Thalassiosira, and Skeletonema) and dinoflagellates (e.g., Biecheleria, Apocalathium, and Gymnodinium) during spring, whereas filamentous cyanobacteria (e.g., Aphanizomenon/Dolichospermum, Nodularia, Pseudanabaena, Planktothrix, and Snowella) dominate the phytoplankton biomass during summer (Wasmund et al., 2011; Legrand et al., 2015; Fortis-Bertos et al., 2016; Spilling et al., 2018). Accordingly, chlorophyll a dynamics show pronounced seasonal variation ranging from ~0.8 µg L-1 in winter to ~4 µg L-1 in April but can reach up to ~13 µg L-1 during spring phytoplankton blooms, resulting in average dissolved organic carbon (DOC) concentrations of ~360 µM C (Bunse et al., 2019). Thus, these blooms have important ecological consequences for the entire ecosystem due to pronounced CO2 uptake and production of POM and DOM, which are subsequently degraded and respired by heterotrophic microbes that in turn produce CO2. In the light of projected climate change and increasing anthropogenic pressure, it is predicted that the Baltic Sea will be disproportionately affected until the end of this century (Andersson et al., 2015; Reusch et al., 2018). Thus, monitoring and research efforts aiming on teasing apart the response of this ecosystem to future stressors such as warming of surface waters, increased nutrient loading, ocean acidification, and oxygen depletion are imperative to formulate and implement required management strategies for a sustainable future “ocean health” (Franke et al., 2020). For Paper I and II, water samples were collected from the Linnæus Microbial Observatory (LMO). The sampling site LMO is situated in the Western Baltic Proper located roughly 10 km off the coast of Kårehamn, Öland, Sweden (56°55.851 N, 17°03.640 E) (Figure 6 and Figure 7). Surface water (2 m depth) was collected on February 16th and March 15th, 2016 (Paper I) and on May 30th, 2016 (Paper II) during the routine sampling effort at the LMO time series. In addition, ~120 L of river water from an agriculture-influenced

38 catchment area (Lielupe river, Latvia, 56°48'42" N, 23°35'5" E) and a boreal forest (humic) catchment area (Lapäarti river, Finland, 62°14'21”, N 21°34'38" E) were collected between May 26th and 28th, 2016 (Paper II).

Figure 7 | The Linnæus Microbial Observatory (LMO) time-series station. The color bar depicts the sea surface temperature (SST) derived from multi-scale ultra-high resolution (MUR) satellite measurements that were obtained from the National Oceanic and Atmospheric Administration (NOAA) ERDDAP during the time of sampling on February 16th, 2016 (Paper I). The figure is redrawn from Paper I - Figure S1.

The Northeast Atlantic Ocean The Atlantic Ocean is the second-largest water body in terms of surface area and plays a paramount role for regulating the Earth’s climate (Shiklomanov and Rodda, 2003). The northern part of the Atlantic Ocean is the origin of the North Atlantic Deep Water (NADW) that is essentially the driving force of the thermohaline circulation (ocean conveyor belt) (Broecker, 1991). This large- scale circulation of water masses, among other factors, determines the movement of surface water (gyres). The rotational movement of surface water, in clockwise (CW) direction in the northern hemisphere and counterclockwise (CCW) in the southern hemisphere together with local winds and wind-fetch induced transport of water is the basis for the vertical flow of water along the western side of the continents (along coastlines) resulting in upwelling of cold nutrient-rich subsurface water. The combination of the before mentioned processes contribute to the high primary production that is typically found in

39 coastal upwelling areas (Bakun, 1990; Capone and Hutchins, 2013; Bode et al., 2015). Along the west coast of Galicia (NW Spain) strong local northerly winds, especially during the summer months result in the upwelling of nutrient-rich subsurface water, whereas downwelling typically occurs between October and March (Figueiras et al., 2002; Arístegui et al., 2009; Álvarez-Salgado et al., 2011). These intermittent upwelling events are characterized by pronounced spring and summer phytoplankton blooms (Nogueira et al., 1997; Cermeño et al., 2006) typically dominated by diatoms (e.g., Thalassiosira and Chaetoceros) and dinoflagellates (e.g., Dinophyceae) (Villamaña et al., 2019). The upwelling periods induce substantial variability in the duration of sequential bloom cycles that can vary from a couple of days to weeks (Pitcher et al., 1991; Nogueira and Figueiras, 2005; Wilkerson et al., 2006; Smayda and Trainer, 2010; Broullón et al., 2020; Fraga, 1981). In Paper III, we carried out mesocosm experiments together with field observations in August 2016 at a coastal station (stn 3; 42° 7' 42.3984'' N, 8° 55' 44.9724'' W) in close proximity to the Ria de Vigo (Spain) (Figure 6 and Figure 8). Water samples were collected from the surface layer (5-20 m depth) and subsequently onboard mesocosm incubations were set up to study the temporal gene expression responses of bacterioplankton communities throughout a naturally induced phytoplankton bloom (Paper III).

Figure 8 | Geographical location of the study site in the northeast Atlantic Ocean off the northwest Iberian coastline (Spain). The color bar shows the sea surface temperature (SST) on August 6th, 2016 when water for mesocosms was sampled from the coastal station (STN 3). Satellite data was obtained from the National Oceanic and Atmospheric Administration (NOAA) ERDDAP. The figure is redrawn from Paper III - Figure S1.

40 The Gullmar Fjord The Gullmar Fjord, located about 100 km north of Gothenburg on the Swedish west coast, is a marine nature reserve, thus, relatively unaffected by pollution and industry (Lindahl et al., 1998). The fjord is 28 km long and between 1 and 2 km wide (Filipsson and Nordberg, 2004). The fjord has a maximum depth of ~120 m and a deep basin below 100 m depth that is roughly 1 km wide and 5 km long and situated ~15 km inwards from the inlet (Filipsson and Nordberg, 2004). Furthermore, a sill of 42 m depth is situated close to the inlet (Lindahl, 2009). Two major current systems shape the fjord water: i) a mixture of low saline Baltic and Kattegat/Skagerrak water flowing northwards parallel along the west coast and ii) a mixture of North Sea and North Atlantic water with a higher salinity flowing towards the coast (Belgrano et al., 1999). The first layer is approximately 15 m thick, made up of a mixture of Baltic Sea and Skagerrak water which is characterized by a salinity ranging between 24 and 27 PSU (Practical Salinity Units). The surface layer has an estimated turnover time of 16 to 26 days. The second layer is generally found at a depth of 15 to 50 m and is composed of surface water from the Skagerrak with a salinity typically ranging from 30 to 33 PSU. The average residence time has been estimated to be 40 days. The third layer is a mixture of relatively cold (4ºC to 8ºC) Skagerrak and North Sea water with a salinity higher than 33 PSU making up the water layer below 50 m depth (Arneborg and Liljebladh, 2001b; Arneborg, 2004; Arneborg et al., 2004). The bottom layer is relatively stagnant with minor seasonal variability and stratification is pronounced during summer through a strong thermocline (Lindahl et al., 1998; Arneborg, 2004; Filipsson and Nordberg, 2004). In addition, the fjord receives moderate (22 m3 s-1) freshwater input from the Örekil River in the inner end (Filipsson and Nordberg, 2004). The mesotrophic sill fjord supports phytoplankton blooms (Tiselius et al., 2016). The phytoplankton community during summer is typically dominated by flagellates, dinoflagellates (Gymnodinium and Prorocentrum) and diatoms (Leptocylindrus, Nitzschia and Chaetoceros) (Schollhorn and Graneli, 1996). It has been shown that during spring, diatoms contribute substantially to the vertical export of carbon (Waite et al., 2005). The before mentioned unique geological and hydrographic characteristics of the Gullmar Fjord make it a perfect model system that acts in many ways like a miniature ocean (Tobias- Hunefeldt et al., 2019). Thus, we can study microbial processes over ~100 m depth that typically occur over thousands of meters in the open ocean. In Paper IV, we investigated the functional responses of bacteria and archaea throughout a strongly stratified water column with pronounced shifts in environmental variables (e.g., salinity, temperature, and nutrient concentrations) to study the vertical variability of carbohydrate-degrading enzymes (CAZymes), peptidases and transporters.

Figure 9 | Overview of the study site in the Gullmar Fjord. In total, seven stations were sampled during the Kristineberg project (manuscript not included in this thesis), the sampling site Alsbäck (S4 -58°19'22.7" N; 11°32'49.0" E) that was studied in Paper IV is highlighted in blue. The figure is redrawn from Paper IV - Figure 1A and B.

42 Results and Discussion

Bacterioplankton community responses to a priori selected labile organic matter compounds Dissolved organic matter (DOM) consists of a myriad of organic compounds fueling the majority of heterotrophic production in the ocean (Hansell et al., 2009). However, we still lack crucial knowledge of the molecular mechanisms that natural bacterioplankton assemblages use to metabolize DOM, and, importantly, how these responses are shaped by different DOM characteristics such as compound classes (e.g., carbohydrates and protein) or condensation state (monomers or polymers). This knowledge is imperative, given that bacteria readily assimilate the majority of these compounds over very short time scales, thereby impacting planetary element cycles and the stoichiometry of the DOM pool (Moran et al., 2016). We carried out seawater culture regrowth experiments enriched with ecologically relevant labile DOM compounds, representing three compound classes (carbohydrates, nucleic acids, and proteins) and two distinct condensation states (monomers and polymers) (Paper I - Table 1) analyzed by metatranscriptomics (Paper I). We showed that microbial community gene expression responses differed between compound classes (carbohydrates, proteins, and nucleic acids) and condensation states. The responses at the level of condensation states varied between mono- versus polysaccharides and amino acids versus polypeptides, but marginally for DNA versus nucleotides (Figure 10A). These differences in expression were associated with substantial shifts in the relative contribution of different taxonomic groups. Alteromonadales outcompeted commensal bacteria in polysaccharide, DNA, and nucleic acid treatments. Flavobacteriales, in turn, dominated in polypeptide treatments, whereas Oceanospirillales were most active in monosaccharides and Pseudomonadales in amino acid treatments (Figure 10B).

Figure 10 | Functional gene expression responses upon enrichment with labile organic matter compounds. Panel (A) shows a non-metric multidimensional scaling (NMDS) plot based on pairwise Bray-Curtis distances of normalized read counts per million (cpm). Convex hulls group biological replicates within treatments. Experiment 1, E1 (conducted on February 16th, 2016); experiment 2, E2 (performed on March 15th, 2016); monoCH, monosaccharide mix; monoNUC, nucleotide mix; monoPR, amino acid mix; Cbx, carboxylic acid mix, polyCH, polysaccharide mix; polyNUC, DNA; polyPR, polypeptides; contOne and contTwo, controls from experiment 1 and 2, respectively. Panel (B) depicts the proportions of normalized read counts of the 10 most abundant orders grouped into the 15 most abundant metabolic SEED categories. The figure is redrawn from Paper I (Pontiller et al., 2020).

44 Our analysis of functional gene expression responses to DOM enrichments showed that a representative set of significantly more abundant genes was shared between monomers and polymers, referred to as “core” responses. However, these responses were relatively specific to each of the tested compound classes (Paper I - Figure 4A-C), suggesting a functional partitioning occurs at the compound class level (Paper I - Figure 5A and C). In addition, a substantial number of significant genes were specific to each treatment – except for nucleic acids – here referred to as “non-core” responses (Paper I - Figure 4A-C), indicating that another layer of partitioning is dependent on the condensation state (Paper I - Figure 5B and D). These findings suggest pronounced functional – and associated taxonomical – partitioning of the tested types of DOM compounds. This confirms recent suggestions of resource partitioning by Bryson and colleagues (2017), who used similar compound classes and condensation states coupled with stable isotope probing (SIP) and community composition analysis (Bryson et al., 2017). These authors reported distinct substrate-specific shifts in community composition and trends in the assimilation of the tested compounds among a few bacterial groups, with noticeable differences between bacterial lifestyles within these groups. However, observations of the substrate range and preference among bacterial populations are inconsistent. For instance, Mou and colleagues (2008) reported that microbial populations in coastal waters shared a representative set of carbohydrate-cycling genes to metabolize a wide range of substrates (generalists). Gomez-Consarnau and colleagues (2012), in turn, showed a generally distinct but variable spectrum of labile organic matter utilization among bacterial populations (range from specialists to generalists). Thus, complex bacterioplankton communities likely demonstrate a plethora of substrate ranges and capabilities in natural settings. Collectively, our results show that taxonomy and function were tightly linked, implying that a limited number of bacterial taxa utilize these DOM compounds at a given time in the sea. Indeed, typically a handful of bacterial groups, of which many are opportunists, contribute disproportionately to the turnover of DOM in the sea (Buchan et al.; Pedler et al., 2014). In fact, the turnover of labile DOM compounds (as tested in Paper I) by heterotrophic bacterioplankton into CO2 has been shown to be exceptionally fast (and thus “invisible”) (Moran et al., 2016). Thus, our findings in Paper I are vital for our understanding of the dynamics of biogeochemical nutrient cycling (Moran et al., 2016; Cavicchioli et al., 2019). Future research aiming at teasing apart the liaison between bacterioplankton and DOM, beyond single point experiments and over ecologically relevant time scales, is of utmost importance. These future research efforts, however, should incorporate global change-relevant environmental parameters along with flux measurements for obtaining a holistic understanding on biogeochemical cycles in the future ocean.

45 Community expression responses to allochthonous and autochthonous DOM Climate change is projected to increase precipitation and riverine runoff in the northern hemisphere by the end of the century and thereby is expected to affect the Baltic Sea in particular (Andersson et al., 2015). The Baltic Sea, surrounded by a large catchment area, is heavily influenced by extensive forest and agricultural landscapes (Voss et al., 2011; Reusch et al., 2018). If predictions are accurate, riverine water enriched in anthropogenically influenced DOM will increasingly influence the DOM pool in the Baltic Sea. However, the effects of this altered DOM input on primary producers and bacterial metabolic processes are mostly unknown. We therefore conducted a mesocosm experiment enriched with water from two different rivers from different catchment areas, one influenced by a boreal forest (DOMhum) and the other by agricultural land use (DOMagri). Additionally, we included a treatment consisting of inorganic nutrients with nitrogen, phosphorous, and silicon (N+P+Si) to induce a reference phytoplankton bloom. The experimental setup allowed us to compare bacterial gene expression patterns in response to allochthonous (river-derived) and autochthonous (phytoplankton-derived) DOM (Paper II). Although all treatments triggered pronounced phytoplankton growth which differed slightly in chlorophyll a concentrations and phytoplankton community composition (Paper II - Figure 1), bacterial growth (abundance and production) was remarkably similar in all treatments until bloom decay. From day 5 onwards bacterial growth increased, most notably in the N+P+Si treatment (Paper II - Figure 1 and Figure 2A and B). Noteworthy, the river water additions resulted in very similar DOC and phosphate concentrations but differed substantially in DON (Paper II - Figure 2C and D). These findings suggest that the balanced N:P:Si stoichiometry in this treatment resulted in a more bioavailable phytoplankton-derived DOM composition (autochthonous) compared to the allochthonous DOM. Although DOC concentrations were similar between the river water treatments, bacterial gene expression responses differed significantly in all treatments relative to controls (Figure 11A). These results suggest that bacterioplankton communities are sensitive to alterations in allochthonous and autochthonous DOM input. However, we noticed substantial differences in expression patterns between DOMhum, DOMagri, and N+P+Si, implying that initial DOM composition – particularly the different catchment areas versus the phytoplankton-derived DOM, triggered different responses in the microbial community (Figure 11A). The most pronounced shift in active bacterial taxa during the early time point (day 3), primarily representative for expression responses toward allochthonous DOM from river water enrichments, was related to Actinobacteria (Mycobacteriaceae). By contrast, we noticed a substantial increase of Bacteroidetes (Flavobacteriaceae) in the N+P+Si

46 treatment at a late time point (day 7) in response to autochthonous DOM (phytoplankton-derived but influenced by the stoichiometry of the initial river water) (Paper II – Figure 3B).

A 2D Stress: 0.076

0.2

0.0 NMDS2

-0.2

-0.75 -0.50 -0.25 0.00 0.25 0.50 NMDS1

Day 0.5 3 7 Treatment Control N+P+Si DOMagri DOMhum

37 Protein Metabolism Carbohydrates Virulence Amino Acids and Derivatives Cofactors, Vitamins, Prosthetic Groups, Pigments DNA Metabolism RNA Metabolism Respiration Membrane Transport Miscellaneous Stress Response Photosynthesis Phages and Transposable elements Cell Wall and Capsule Fatty Acids, Lipids, and Isoprenoids Iron acquisition and metabolism Nitrogen Metabolism Nucleosides and Nucleotides Virulence, Disease and Defense Sulfur Metabolism Mitochondrial electron transport system in plants Phosphorus Metabolism Predictions based on plant-prokaryote comparative analysis Cell Division and Cell Cycle Regulation and Cell signaling Unclassified Other SEED 10 11 12 13 14 10 11 12 13 14 Log CPM Figure 11 | Community expression responses to allochthonous and autochthonous DOM. Panel (A) non-metric multidimensional scaling (NMDS) plot depicting bacterioplankton gene expression profiles. Panel (B) shows an overview of the relative abundance of log- transformed read counts per million (CPM) grouped into top-level metabolic SEED categories on day 3 and 7. Abbreviation of treatments: Control - unamended seawater; N+P+Si - inorganic nutrient additions; DOMagri - agricultural-influenced river water; DOMhum - forest landscape-influenced river water. The figure is redrawn from Paper II.

47 Analysis of functional genes grouped into metabolic categories (SEED) further emphasized the divergence of bacterial expression responses toward river and inorganic nutrient additions, particularly substantial differences in nitrogen and phosphorus metabolism between treatments (Figure 11B). Detailed gene expression analysis of the DOMhum treatment showed significant enrichments of urea metabolism genes, including transporters, carboxylases, and ureases compared to controls (Paper II – Figure 6). Urea, although typically present in low concentrations, is a major constituent of the low molecular weight nitrogen pool in aquatic systems and an important N source for bacteria (Berman, 2003; Solomon et al., 2010). Given that DON concentrations in the DOMhum treatment were 3-fold lower compared to the DOMagri, the expression of urea and ammonium transporters together with ureases showcase the community + effort to acquire ammonium (NH4 ) (Paper II – Figure 7). In the DOMagri treatment, however, we noticed pronounced responses related to phosphorus metabolism, including phosphate transporters, porins, and alkaline phosphatases – the expression of these genes increased substantially from day 3 to day 7. These results were indicative of a microbial community struggling to maintain a balanced N:P stoichiometry, given the high concentration of DON in the DOMagri treatment. The N:P imbalance shifted the system toward P- limitation, as indicated by elevated expression levels of the phosphate starvation gene phoH, especially on day 7 (Paper II – Figure 8). Although inorganic nutrient limitation (N and P) affects microbial groups differently (Sebastian and Gasol, 2013), microorganisms generally increase the expression of gene systems related to P acquisition (e.g., phosphorus transporters and alkaline phosphatases) under P-limiting environmental conditions (Santos-Beneit, 2015; Alonso-Saez et al., 2020). Our results show that bacterial transformations of DOM and nutrient cycling in coastal waters and estuarine environments are sensitive to alterations in the precipitation-induced riverine runoff in a catchment area-dependent manner. Thereby, we provide new knowledge crucial for interpreting and modeling the effects of increased organic matter input in projected climate change scenarios. Finally, the projected increase of precipitation and allochthonous DOM input to coastal areas warrants research elucidating the enzyme systems and microbial taxa capable of degrading terrestrial-derived DOM such as humic substances (Colatriano et al., 2018; Santos-Junior et al., 2020).

Temporal and spatial variability of DOM cycling gene transcription Temporal responses Coastal upwelling systems are exceptionally productive, allowing pronounced phytoplankton growth (Capone and Hutchins, 2013; Bode et al., 2015).

48 However, the injection of nutrient-rich water into the upper surface layer can vary in intensity over just days (Pitcher et al., 1991; Nogueira and Figueiras, 2005; Wilkerson et al., 2006; Smayda and Trainer, 2010; Broullón et al., 2020; Fraga, 1981). These short-term temporal variations also affect phytoplankton growth, the subsequent release of organic matter and hence, bacterial community dynamics and function. However, surprisingly little is known about the microbially mediated processes involved in the degradation and uptake of phytoplankton-derived organic matter on the time scales of a few days to weeks that characterize these upwelling areas. We performed a high-resolution comparative metatranscriptomics analysis by linking experimental mesocosms with field observations in a coastal station in the Northwest Iberian upwelling system (Figure 8). The objective of Paper III was to elucidate the role glycoside hydrolases (GHs), peptidases (PEPs), and transporters (TPs) play during phytoplankton bloom development and decay. To derive crucial knowledge of bacterial responses to subtle shifts in phytoplankton-derived DOM, it was vital to conduct mesocosm incubations. Our analyses from experimental diatom/dinoflagellate bloom development to senescence phase revealed substantial differences between bacterial taxa in terms of growth, transcriptional activity and timing (Paper III - Figure 1F). These shifts were associated with significant alterations in community transcription of functional genes over just days (Paper III - Figure 2A) and likely driven by phytoplankton-derived DOM (i.e., chlorophyll a and dissolved organic carbon) (Paper III - Figure 2B). Our results are in line with studies showing that alterations in substrate availability can structure bacterioplankton community composition and functioning during phytoplankton blooms. (Riemann et al., 2000; Pinhassi et al., 2004; Teeling and Glockner, 2012). Accordingly, we found pronounced order-specific differences in transcriptional regulation of DOM cycling genes from bloom development to senescence (Paper III - Figure 3). This temporal partitioning was exceptionally pronounced for Alteromonadales responding to early decay and Flavobacteriales towards senescence (Figure 12A-C).

Figure 12 | Taxon-specific progression of transcriptional responses during an experimental phytoplankton bloom. Panel (A) shows normalized transcript abundances of glycoside hydrolases (GHs), (B) peptidases (PEPs), and (C) transporters (TPs). Number of transcribed PFAMs per order and gene system are shown in parentheses. Abbreviation of bloom phases, bloom development (DP), early decay (ED), and senescence phases (SP). The bubble size denotes the average normalized transcript abundance per order and bloom phase over all days in %. The location of each bubble in the plot indicates the time point of highest relative expression across the bloom phases. The top 12 most abundant GH families are shown in color, all others in grey. All PEPs are color-coded according to their proteolytic families in the MEROPS database. The top 12 most abundant TP families in addition to TC TTT, MR, and PCR are shown in color. Abbreviation of TC families: Mot/Exb - The H+- or Na+-translocating Bacterial Flagellar Motor/ExbBD Outer Membrane Transport Energizer; OMR - Outer Membrane Receptor; OOP - OmpA-OmpF Porin; TRAP-T - Tripartite ATP- independent Periplasmic Transporter; TTT - Tricarboxylate Transporter; ABC - ATP- binding Cassette; F-ATPase - H+- or Na+-translocating F-type, V-type and A-type ATPase; Sec - General Secretory Pathway; NaT-DC - Na+-transporting Carboxylic Acid Decarboxylase; QCR - Proton-translocating Quinol:Cytochrome c Reductase; COX - Proton-translocating Cytochrome Oxidase; Na-NDH - Na+-translocating NADH:Quinone Dehydrogenase; FeoB - Ferrous Iron Uptake; MR - Ion-translocating Microbial Rhodopsin, PCR - Photosynthetic Reaction Center. The figure is redrawn from Paper III.

These results suggest pronounced functional resource partitioning and different ecological roles of these taxa during phytoplankton blooms. However, Alteromonadales and Flavobacteriales transcribed a representative set of GHs and PEPs, indicating largely overlapping glycan and polypeptide niches. Both

50 showed a potential preference for the storage polysaccharide laminarin (GH3, GH16, and GH17) (Figure 12A-C). Thus, additional factors, such as growth rates (Kirchman, 2016), foraging strategies (Reintjes et al., 2019a), and enzyme kinetics, are likely contributing to the temporal partitioning between Aleromonadales and Flavobacteriales in the degradation of phytoplankton- derived DOM. For instance, isozymes are functionally similar (catalyze the same chemical reaction) in different organisms, but a few changes in the amino acid sequence can lead to differences in protein structure (Zhang et al., 2011) and enzyme kinetics (Bloch and Schlesinger, 1974; Baani and Liesack, 2008). Thus, one can expect that the contribution of Alteromonadales and Flavobacteriales to the degradation of high molecular weight DOM, particle aggregation (e.g., TEP dynamics), and phytoplankton bloom formation will differ in the ocean. The transcriptional GH responses of Cellvibrionales were interesting, considering the less diverse set of GHs (11 PFAMs) compared to the polymer degraders mentioned above (Alteromonadales and Flavobacteriales), which responded primarily to phytoplankton cell lysis. In contrast, Cellvibrionales displayed high transcriptional investments in GHs during bloom development, likely towards polysaccharides released from physiologically deteriorating phytoplankton. Our results suggest a crucial role of Cellvibrionales as polymer degraders ‘sharing’ hydrolysis products with other bacteria such as Pelagibacterales (‘scavengers’) or Rhodobacterales (Buchan et al., 2014; Reintjes et al., 2019a). The latter may prefer organic matter from ‘healthy’ phytoplankton cells (Alonso and Pernthaler, 2006; Allers et al., 2007; Sowell et al., 2009; Buchan et al., 2014; Giovannoni, 2017; Noell and Giovannoni, 2019) and our results (Paper III and IV) suggest that Rhodobacterales are likely not as competitive in the degradation of polymers as Alteromonadales or Flavobacteriales. Next, we validated the ecological importance of the mesocosm findings by comparison to the upwelling event occurring simultaneously in the field (Paper III - Figure 5 and S9). These analyses generally showed strikingly similar transcriptional investments in GH, PEP, and TP transcription in the field by the bacterial orders, except for Saprospirales, that were substantially more active in the mesocosms than in the field (Paper III - Figure 5). The temporal replacement of Alteromonadales by Flavobacteriales was also visible through the increasing investment in GH transcription by Alteromonadales in the field after the early bloom decay (increasing regression slopes). Flavobacteriales, in turn, increased these efforts during the same period in mesocosms (Paper III - Figure 5 and S9). We noticed a large degree of metabolic plasticity involved in the transcription of these gene systems, but GHs and PEPs were more responsive than TPs (Paper III - Figure S9). These findings imply that the enzymatic activities of Alteromonadales, Flavobacteriales, and Cellvibrionales during phytoplankton blooms have crucial implications on the vertical flux of

51 organic matter. These enzymes determine the transfer of carbon from particulate sinking to dissolved non-sinking pools (Smith et al., 1995). Collectively, our findings imply that transcriptional cascades in the utilization of organic matter and nutrients critically contribute to modulating the stoichiometry of the DOM pool. Given that GHs and PEPs determine the composition and concentration of organic matter exported from the euphotic zone, studying the functional responses of polymer degraders and their contribution to particle assembly and degradation are of utmost importance for upcoming research agendas (Figure 4).

Spatiotemporal responses As discussed in the previous section, gathering new knowledge of the functional responses of key taxa in the degradation and uptake of organic matter across depth gradients is important. The vertical export of particulate organic matter (POM) from the euphotic zone to the deep ocean is the prime source of organic matter for heterotrophs in deeper layers (Ducklow and Steinberg, 2001; Aristegui et al., 2009). Therefore, knowledge of the enzymatic repertoire, the key taxa that produce them, and the dynamics of their expression across vertical gradients is paramount for a better understanding of depth-layer-dependent DOM cycling. Water column stratification leads to alterations of nutrient fluxes, microbial community composition, and ultimately ecosystem functioning compared to mixed waters (Crump et al., 2004; Galand et al., 2009; Auguet et al., 2010; Agogue et al., 2011; Hamdan et al., 2013). Fjords can be regarded as small- scale oceans and are suitable model systems representative for other stratified systems such as the open ocean gyres (Gašparović et al., 2005; Storesund et al., 2015; Storesund et al., 2017; Tobias-Hunefeldt et al., 2019). We elucidated the prokaryotic transcription of DOM cycling genes (carbohydrate-active enzymes and peptidases together with transporters), over five depth layers during two different sampling dates (July and September) at station Alsbäck (S4) in the Gullmar Fjord (Figure 9). Strong physicochemical gradients characterized the water column of this fjord (Paper IV - Figure 1C- D), which resulted in a divergence of prokaryotic gene transcription with depth - - (Paper IV - Figure 2A). DOC and NO3 +NO2 concentrations were likely important variables in driving this divergence (Paper IV - Figure 2B). Transcriptional responses also differed between sampling dates, with important + contributions of NH4 and chlorophyll a in explaining variations (Paper IV - Figure 2B) but the influence of sampling date attenuated substantially from surface to 100 m depth. Vislova and colleagues (2019) showed the variability of genes and taxa depicting diel cycles decreased from surface to deep waters as a consequence of light attenuation (Vislova et al., 2019). Similarly, our results (Paper IV)

52 showed that carbohydrate-active enzymes (CAZymes) and transporters (TPs) are among the gene systems that differ in transcription with depth. Moreover, transcription patterns of these genes indicated pronounced taxon-specific partitioning with depth (Paper IV - Figure 3 and 5), suggesting a water parcel- dependent processing of carbohydrates and nutrients by a few key players in this system. The obtained carbohydrate-active enzyme (CAZyme) signatures implied crucial roles of the structural polysaccharides, chitin and peptidoglycan along the storage glycans laminarin and glycogen. In fact, chitin is one of the major polymers in the ocean and an essential source of C and N for marine microbes (Souza et al., 2011). This insoluble polymer is produced in large quantities in the marine system by zooplankton and fungi (Jeuniaux and Voss- Foucart, 1991). Thus, chitin might be particularly important in mesotrophic systems in which phytoplankton-derived storage polysaccharides such as laminarin or chrysolaminarin are less available. Important taxa that engaged in the transcription of GHs were, for instance, Cellvibrionales, Bacteroidetes, and Cyanobacteria (Figure 13A).

[GH] Glycoside hydrolases GH19 GH16 GH13 GH3 GH23 GH109 GH0 5_A 5_B 15_A 15_B 50_A

50_B July 75_A 75_B 100_A 100_B 2_A 2_B Depth [m] 25_A 25_B 55_A 55_B 75_A

75_B September 100_A 100_B 01010101010101 Proportion B

TRAP-T TTT ABC OprB OMR SSS Amt 5_A 5_B 15_A 15_B 50_A

50_B July 75_A 75_B 100_A 100_B 2_A 2_B Depth [m] 25_A 25_B 55_A 55_B 75_A

75_B September 100_A 100_B 01010101010101 Proportion

Alteromonadales Nitrosomonadales Pirellulales Rhodobacterales Cellvibrionales Nitrosopumilales Planctomycetales Rhodospirillales Order Flavobacteriales Nitrospinales Puniceicoccales Synechococcales Methylococcales Pelagibacterales Rhizobiales Other Order

Figure 13 | Taxon-specific transcription of DOM cycling genes across depth. Panel (A) shows the proportional transcript abundance of the most abundant glycoside hydrolases (GHs). Panel (B) depicts the proportional transcript abundance of selected transporter

53 families (TPs). Abbreviation of TC families: TRAP-T - Tripartite ATP-independent Periplasmic Transporter; TTT - Tricarboxylate Transporter; ABC - ATP-binding Cassette; OprB - Glucose-selective Porin; OMR - Outer Membrane Receptor; SSS - Solute:Sodium Symporter; Amt - Ammonium Transporter Channel. The figure is modified from Paper IV.

Interestingly, Cellvibrionales and Flavobacteriales were also active polymer degraders during experimental and natural phytoplankton blooms in the Northwest Iberian upwelling system (Paper III). Strikingly, Thaumarchaeota were exceptionally active in transcribing ammonium transporters below 25 m depth, accounting for up to 79% of transporter transcription. Moreover, strong transporter signals related to ABC type transporters by Pelagibacterales and Rhodobacterales below 15 m depth in July and the surface layer in September, and Outer Membrane Receptors (OMR) by Alteromonadales and Cellvibrionales in the surface layer, suggested essential roles of glycine betaine, DMSP, peptides, amino acids, and carbohydrates in this system (Figure 13B). Our results demonstrate that fjords can be valuable model systems mimicking vertical depth gradients found in other stratified systems, including the open ocean. We demonstrate that stratification promotes functional partitioning of CAZymes, peptidases, and transporters in a depth-layer-dependent manner by different taxonomic groups. Thereby, we highlight the potential influence of fine-tuned alteration of DOM components in shaping biogeochemical cycles across spatiotemporal gradients. Additionally, the exceptional activity of chemoautotrophs in deeper waters suggests a disproportionate contribution to nutrient uptake - in particular of ammonia. However, bacteria and archaea can also be a considerable source of organic compounds (Carlson, 2002), as has been recently shown for chemoautotrophic Thaumarchaeota strains of the Nitrosopumilus genus which released labile organic matter (e.g., amino acids), at least in pure culture and under laboratory conditions (Bayer et al., 2019). Thus, research aiming at quantifying the “priming” potential of chemoautotrophs to support heterotrophic production in the deep sea is of crucial importance for our understanding of DOM dynamics in bathy- and abyssopelagic zones. Moreover, only recently have we begun to gather knowledge on the mechanisms that are behind the transformation of labile DOM into RDOM (Jiao et al., 2010; Shi et al., 2012; Herndl and Reinthaler, 2013; Arrieta et al., 2015; Osterholz et al., 2015) and integrative research efforts will certainly shed more light on the key drivers of the biological carbon pump.

54 Additional perspectives and directions of research on the interdependency between DOM and microbes Genomic features and transcriptional responses of polymer degraders In Paper I, we found that well-known polymer degraders such as Alteromonadales and Flavobacteriales dominated in treatments that were amended with labile organic matter compounds, in Paper II we noticed a substantial increase of Bacteroidetes (Flavobacteriaceae) to phytoplankton- derived organic matter and in Paper III we observed that both groups overall displayed very similar functional profiles of glycoside hydrolases (GH) and peptidases (PEPs) but their transcriptional investment in these gene systems differed substantially over time. However, under mesotrophic conditions (Paper IV), Alteromonadales were less active than Flavobacteriales and showed an interesting shift towards PEPs and transporters (TPs) with depth. Curiously, both orders contributed only marginally to the transcription of carbohydrate-active enzymes (CAZymes) in the Gullmar Fjord (Paper IV) compared to in an upwelling system (Paper III). Judging by the genomic predisposition of both groups (Fernandez-Gomez et al., 2013; Koch et al., 2019a; Koch et al., 2019b; Koch et al., 2020) and our transcriptional responses (Paper III) they should occupy similar realized glycan and peptide niches, although with differentiation in temporal adjustments.

Other genomic features and complementary processes which drive resource partitioning As previously mentioned, Alteromonadales and Flavobacteriales seem capable of thriving on similar suites of organic compounds when in isolation. However, under direct competition, the two groups targeted different compounds (Bryson et al., 2017) and Paper I. We noticed a strong functional partitioning between Alteromonadales and Flavobacteriales to organic matter enrichments. Thus, other genomic features that determine for instance growth rates (Campbell et al., 2011; Kirchman, 2016; Sanchez et al., 2020) and enzyme kinetics may be important in determining successional patterns in the environment. Reintjes and colleagues (2019) proposed different foraging strategies for these two crucial polymer degraders (Reintjes et al., 2019a). Members of the Alteromonadales are considered external polymer degraders capable of utilizing most of their hydrolysis products due to exceptionally rapid growth (Pedler et al., 2014; Kirchman, 2016). Flavobacteriales, in turn, are likely ‘selfish’ by minimizing the loss of hydrolysis products to the environment through sophisticated genomic adaptations (Reintjes et al., 2019a). These include

55 surface-associated enzymes that partially degrade and bind polysaccharides. TonB-dependent outer membrane transporters enable the transport of oligosaccharides (> 600 Da) into the periplasm, where further enzymatic degradation occurs. Finally, membrane transporters convey monomers through the inner membrane into the cytosol (Reintjes et al., 2017). However, Flavobacteriales are typically slower growing than Alteromonadales; thus, these two differences can likely explain the patterns that we observed in our studies (Paper I and III). Moreover, we established a strong correlation between chlorophyll a, DOC, and inorganic nutrients and bacterioplankton community transcription as potential drivers of degradation cascades (Paper III - Figure 2B and IV - Figure 2B). This relationship was observed for temporal as well as vertical gradients and matched findings by Teeling and colleagues (2012) during North Sea phytoplankton blooms (2012) and by DeLong and colleagues (2006) in the North Pacific Subtropical Gyre (NPSG), respectively. Chemical composition and condensation state of DOM released by phytoplankton vary with species and over time (Becker et al., 2014; Thornton, 2014). Consequently, the phytoplankton bloom development phase is likely dominated by the release of low molecular weight DOM such as monomers, organic acids, and polysaccharides, whereas during the early bloom decay phase the massive release of phytoplankton cell material results in a shift toward high molecular weight DOM (HMW-DOM) (Buchan et al., 2014; Mühlenbruch et al., 2018). Thus, parts of the dynamics in functional gene expression observed in Paper II, III and IV may indeed reflect temporal variation in the structure of organic matter components. However, in Paper III and IV, the included environmental variables did not explain all of the variation we observed in transcription over depth and sampling date. Thus, other environmental variables that we did not measure and complementary processes - that transform the DOM pool and shape microbial interactions over time and depth - are likely equally if not more important as DOM characteristics in shaping transcription responses in prokaryotes. Interestingly, these processes are rarely explicitly discussed in relation to bacterioplankton succession over time and space (including vertical gradients). Bacteria and archaea release substantial amounts (1-9% of total produced DOC) of organic carbon compounds (Carlson, 2002; Bayer et al., 2019), that in turn act as drivers of microbial community assembly (Gralka et al., 2020). For example, extracellular polymeric substances (EPS) released by Alteromonas spp. can be an attractive organic matter source for Flavobacteria that are capable of utilizing EPS such as galacturonic acids (Zhang et al., 2015). Interestingly, flavobacterial transcription of GHs peaked following the Alteromonadales in our study (Paper III), which is in line with the general notion that Alteromonas affiliated populations often precede Flavobacteria in bacterial succession studies.

56 Antagonistic interactions between phytoplankton and bacteria or between different bacteria, mediated by algicidal compounds and diverse virulence factors, can either directly or indirectly influence bacterial utilization of the DOM pool (Mayali and Azam, 2004; Amin et al., 2012; Cirri and Pohnert, 2019). For example, among the taxa observed in this thesis, Alteromonadales and Rhodobacterales are known to produce inhibitory substances that effectively suppress susceptible bacteria such as Bacteroidetes (Long and Azam, 2001; Czaran et al., 2002; Pernthaler, 2005). Thus, the delay in flavobacterial response compared to Alteromonadales (Paper III) could be partly inflicted by inhibitory substances secreted by the latter. Finally, selective top-down control by protist grazing (Hahn and Hofle, 1999; Simek et al., 2001; Jurgens and Matz, 2002; Rocke et al., 2015; Baltar et al., 2016) and cell lysis by bacteriophages (Bouvier and del Giorgio, 2007; Holmfeldt et al., 2007; Winter et al., 2010) ultimately affect bacterial community structure and function. Thus, grazing may be a contributing factor resulting in temporal succession dynamics of bacterioplankton through selectively changing the abundance and composition of bacterial communities, and concomitantly by altering the DOM pool characteristics (Azam et al., 1983; Fuhrman, 1999; Sandaa et al., 2009; Chow et al., 2014; Rocke et al., 2015). Given that high grazing pressure can affect certain bacterial populations such as Alteromonas and Pseudoalteromonas more than others (Beardsley et al., 2003) it is likely that such grazing-induced mortality also shapes successional patterns that are often observed during phytoplankton blooms (Teeling et al., 2012) and Paper III. For example, protist grazing could be an additional factor for the reduced contribution of Alteromonadales in July compared to September in the Gullmar Fjord (Paper IV). However, the role of top-down control by bacteriophages/viruses and protist grazing in shaping both bacterioplankton community structure and phytoplankton dynamics is still far from understood, in part because of the inherent complexity of these processes. Hence, future research is required to disentangle these complex relationships and their role in biogeochemical element cycles in more detail.

57 Future perspectives With ongoing rapid developments of new instruments and techniques to quantitatively assess DOM composition, metabolites and microbial transcription (Kujawinski, 2011; Han, 2016; Moran et al., 2016; Merder et al., 2020), I foresee a rise in priority for research into how sequential changes in the DOM pool shape the interdependency of DOM and bacteria in the sea. Importantly, in order to move beyond mostly hypothesis generating approaches, integrative research efforts are required to test hypotheses. The power of integrative science has been demonstrated in exciting contemporary research that implement metagenomics, metaproteomics, metabolomics, and lipidomics (Helbert et al., 2019; Becker et al., 2020a; Ferrer-Gonzalez et al., 2020; Herold et al., 2020; Patriarca et al., 2020; Nowinski and Moran, 2021; Vidal-Melgosa et al., 2021). Ecosystem relevant processes are expected to change in the future ocean (Andersson et al., 2015; Reusch et al., 2018; Cavicchioli et al., 2019; Franke et al., 2020; Pendleton et al., 2020). Thus, combined research efforts are vital to elucidate how climate change relevant variables such as increased temperature, ocean acidification, eutrophication, and increased input of terrestrial DOM may alter the composition of the marine DOM pool, microbial community composition and function, and ultimately fluxes in future planetary biogeochemical cycles.

58 Conclusions

The research in Paper I, has shown that both compound class and condensation state of labile dissolved organic matter shape bacterioplankton community transcription. Our results suggest that a pronounced degree of resource partitioning and specialization of bacteria is involved in the turnover of these compounds in the Sea. In Paper II, we showed that both facets of DOM – allochthonous and autochthonous – determine phytoplankton bloom dynamics and bacterioplankton gene expression. These responses differed substantially in a catchment-area dependent manner, demonstrating that interaction effects between river- and phytoplankton-derived DOM are sensitive to alterations in the precipitation-induced riverine runoff. Paper III contributed important mechanistic understanding of the bacterial role in the degradation and processing of phytoplankton-derived organic matter in a coastal upwelling system by showing that enzymes (glycoside hydrolases and peptidases) and transporters are important genomic features that enable bacteria to fine-tune their realized niche space by avoiding direct competition. Also, these enzymes had a large potential to drive functional succession over very short time scales during a mixed phytoplankton bloom (diatoms/dinoflagellates) with important implications for the degradation of prototypical DOM compounds that play a key role in the “invisible” flux of organic matter. In Paper IV, we elucidated the vertical distribution and variability of carbohydrate active enzymes (CAZymes), peptidases, and transporters in a stratified water column, highlighting the important role of bacterial functional groups in a stratified ecosystem. In addition, we showed the importance of chemolithoautotrophic Thaumarchaeota (Archaea) in nutrient cycling, in particular their role in ammonia oxidation and exceptionally high transcriptional activity in ammonium transport, ultimately linking C- and N-cycles in oxygen minimum zones. Taken together, this thesis identified important bacterial and archaeal taxa that are actively involved in the cycling of DOM in aquatic ecosystems. Several studies in this thesis pointed toward a considerable degree of functional resource partitioning of labile DOM compounds between, and importantly also within, different bacterial groups. Moreover, we demonstrated that carbohydrate-active enzyme, peptidases and transporters are important gene systems that drive functional cascades over short spatiotemporal scales, and thereby we add novel mechanistic understanding of ecologically relevant genes that ultimately shape the interdependency between DOM and prokaryotes in the contemporary ocean.

59 Acknowledgements

This thesis would not have come to fruition without the contribution of those who mentored me during my scientific journey. Foremost, I want to express my deepest gratitude to my Ph.D. supervisors Prof. Jarone Pinhassi and Prof. Daniel Lundin, whose contributions to my personal and scientific development are invaluable. Jarone, I am grateful for the freedom I had over the last years as a member of your research group. You enabled me to attend many courses and conferences in Sweden and abroad. Also, you provided plenty of opportunities allowing me to take part in collaborative projects that brought me from Spain to Japan. All those things ultimately broadened my personal and scientific perspectives, fostered my independence as a scientist, and positively influenced my career trajectory. Daniel, you sparked my interest in bioinformatics and data analysis, shared your knowledge unconditionally, and have been an incredible resource during the last years. I particularly enjoyed the science talks during our run and bike sessions in the mesmerizing Swedish nature. Additionally, I would like to thank Prof. Catherine Legrand for her valuable feedback on my thesis and invaluable contribution as a role model inspiring the next generation of scientists. Finally, I want to express my deepest gratitude to my former master thesis supervisor Prof. Gerhard Herndl for being an inspiration, giving rise to my interest in biological oceanography, and paving the way to my future career as a scientist.

I owe a significant proportion of my thesis work to Prof. Eva Teira, Prof. Emilio Fernández, and Sandra Martinez Garcia, who welcomed me as a collaborator in the ENVISION project and during two research cruises onboard the R/V Ramon Margalef. Thank you for hosting me in Vigo during consecutive visits, your valuable feedback, support, and encouragement. Many more people contributed in various ways among Vanessa Joglar, Ester Barber-Lluch, Marta Hernández- Ruiz, Antero Prieto, and María Pérez-Lorenzo.

I want to thank all of my colleagues and friends among Dennis Amnebrink, Clara Perez Martinez, Emelie Nilsson, Laura Seidel, and Stephanie Turner, for valuable feedback on my thesis. Camilla Karlsson, Sabina Arnautovic, Anabella Aguilera, Evangelia Charalampous, Mellat Solomon, George Westmeijer, Javier Alegria, Eva Sörensson, Lina Mattsson, Anu Helin, Emil Fridolfsson, Johanna Sunde, Håkan Johansson, Henrik Flink, Daniela Polic, Elias Broman, Xiaofen Wu, and Mireia Bertos-Fortis, thank you all for your support in and outside the lab.

Also, I want to acknowledge my friends and former colleagues who finished before me but were exceptionally important to my personal and professional

60 progress. Christopher Osbeck for arranging my first and second apartment, your assistance upon arrival at the airport, and your contributions to my scientific progress. Carina Bunse, thank you for sharing your knowledge with me and the encouragement to apply for our funding. Jürg Brendon Logue, I am grateful for your kindness and generosity in sharing your expertise. Diego Brambilla, your Pizza is indeed one of the best pieces I have ever had. Josef Lautin, thank you for introducing me to the Swedish midsummer and for all your support and countless science and lab discussions. I want to acknowledge Stephan Christel and Gaofeng Ni, who I joined for my first scripting course, and in particular Stephan, it was an honor to be your Toastmaster. Domenico Simone and Margarita Lopez Fernandez, “Kalmarfornia” would not have been the same without you. I am grateful for the time we had together and miss the countless BBQs. I owe special thanks to Anders Månsson, Henrik Hallberg, Leon Norén, and Stefan Hagberg for their vital contributions to our research output. None of the presented studies would have been possible without your efforts in keeping the university, laboratories, and instruments running. Last but not least, I am utterly grateful for the professional support from Kristoffer Bergström and the crew of the Provider operated by EON, Norther Offshore Service (NOS). Your assistance enables us to conduct research projects that would have been impossible otherwise and allow us to push weather and seasonal constraints in favor of continuing sampling for the LMO time series.

I also want to thank the steering committee of the GENECO program Prof. Bengt Hansson, Ass. Prof. Helena Westerdahl, Ass. Prof. Dag Ahrén, Ass. Prof. Olof Hellgren, Emily O’Connor, Prof. Torbjörn Säll, and Prof. Christina Rengefors. Also, I want to acknowledge Prof. Johan Hollander, the organizer of the GENECO mentor program, and Johanna Steen from the MiL Institute for their fantastic work during the mentor program. I am very grateful that I could be part of the program that had an invaluable impact on my progress during my Ph.D. studies and personal growth. I also want to acknowledge Tina Lindh, who played a vital role in my personal development.

To my family, thank you for your encouragement and support during the last years. It was not easy to leave you, and I want to apologize to all of you for my temporary absence to follow my passion. I promise I will make up for the “lost” time caused by the move to Sweden. Little did I know back then that Sofia and I would run into each other, and I am glad we did. You truly changed my life for the better. Your support and encouragement kept me going and keeps inspiring me. I owe you much more than I can express with words. I have met numerous people in my life, of which many were transient, but I am glad that Julia Kesting is one of them who stayed. Thank you for the pleasant conversations during our walks in Vienna that truly inspired me, and I am looking forward to many more.

Finally, I want to acknowledge the indispensable role of Balck Coffee, OAS Cafe, and Cafe Triberg in sparking my creativity and motivation during the last years. I can say with great confidence that your well-brewed espressos are unmatched. I am utterly thankful for the countless moments I was fortunate enough to experience in great company in one of your establishments that brightened even the darkest day. A special shoutout goes to Ölands Choklad. Nothing compares to the outstanding Italian espresso, and I am grateful for the inspirational moments I experienced while gazing over the Baltic Sea from the other side of the bridge (Ölandsbron).

62 References

Agogue, H., Lamy, D., Neal, P.R., Sogin, M.L., and Herndl, G.J. (2011). Water mass-specificity of bacterial communities in the North Atlantic revealed by massively parallel sequencing. Mol Ecol 20(2), 258-274. doi: 10.1111/j.1365-294X.2010.04932.x. Aharonovich, D., and Sher, D. (2016). Transcriptional response of Prochlorococcus to co-culture with a marine Alteromonas: differences between strains and the involvement of putative infochemicals. ISME J 10(12), 2892-2906. doi: 10.1038/ismej.2016.70. Albers, S.V., and Meyer, B.H. (2011). The archaeal cell envelope. Nat Rev Microbiol 9(6), 414-426. doi: 10.1038/nrmicro2576. Alderkamp, A.C., van Rijssel, M., and Bolhuis, H. (2007). Characterization of marine bacteria and the activity of their enzyme systems involved in degradation of the algal storage glucan laminarin. FEMS Microbiol Ecol 59(1), 108-117. doi: 10.1111/j.1574-6941.2006.00219.x. Alivisatos, A.P., Blaser, M.J., Brodie, E.L., Chun, M., Dangl, J.L., Donohue, T.J., et al. (2015). A unified initiative to harness Earth's microbiomes. Science 350(6260), 507-508. doi: 10.1126/science.aac8480. Alldredge, A.L., Passow, U., and Logan, B.E. (1993). The abundance and significance of a class of large, transparent organic particles in the ocean. Deep-Sea Res Pt I Oceanogr Res Pap 40(6), 1131- 1140. doi: 10.1016/0967-0637(93)90129-q. Allers, E., Gomez-Consarnau, L., Pinhassi, J., Gasol, J.M., Simek, K., and Pernthaler, J. (2007). Response of Alteromonadaceae and Rhodobacteriaceae to glucose and phosphorus manipulation in marine mesocosms. Environ Microbiol 9(10), 2417-2429. doi: 10.1111/j.1462-2920.2007.01360.x. Alneberg, J., Bennke, C., Beier, S., Bunse, C., Quince, C., Ininbergs, K., et al. (2020). Ecosystem-wide metagenomic binning enables prediction of ecological niches from genomes. Commun Biol 3(1), 119. doi: 10.1038/s42003-020-0856-x. Alneberg, J., Sundh, J., Bennke, C., Beier, S., Lundin, D., Hugerth, L.W., et al. (2018). BARM and BalticMicrobeDB, a reference metagenome and interface to meta-omic data for the Baltic Sea. Sci Data 5, 180146. doi: 10.1038/sdata.2018.146. Alonso, C., and Pernthaler, J. (2006). Roseobacter and SAR11 dominate microbial glucose uptake in coastal North Sea waters. Environ Microbiol 8(11), 2022-2030. doi: 10.1111/j.1462- 2920.2006.01082.x. Alonso-Saez, L., Moran, X.A.G., and Gonzalez, J.M. (2020). Transcriptional patterns of biogeochemically relevant marker genes by temperate marine bacteria. Front Microbiol 11, 465. doi: 10.3389/fmicb.2020.00465. Alonso-Saez, L., Waller, A.S., Mende, D.R., Bakker, K., Farnelid, H., Yager, P.L., et al. (2012). Role for urea in nitrification by polar marine archaea. Proc Natl Acad Sci U S A 109(44), 17989- 17994. doi: 10.1073/pnas.1201914109. Álvarez-Salgado, X.A., Figueiras, F.G., Fernández-Reiriz, M.J., Labarta, U., Peteiro, L., and Piedracoba, S. (2011). Control of lipophilic shellfish poisoning outbreaks by seasonal upwelling and continental runoff. Harmful Algae 10(2), 121-129. doi: 10.1016/j.hal.2010.08.003. Amin, S.A., Parker, M.S., and Armbrust, E.V. (2012). Interactions between diatoms and bacteria. Microbiol Mol Biol Rev 76(3), 667-684. doi: 10.1128/MMBR.00007-12. Andersson, A., Meier, H.E., Ripszam, M., Rowe, O., Wikner, J., Haglund, P., et al. (2015). Projected future climate change and Baltic Sea ecosystem management. Ambio 44 Suppl 3, 345-356. doi: 10.1007/s13280-015-0654-8. Andersson, A.F., Riemann, L., and Bertilsson, S. (2010). Pyrosequencing reveals contrasting seasonal dynamics of taxa within Baltic Sea bacterioplankton communities. ISME J 4(2), 171-181. doi: 10.1038/ismej.2009.108. Anwar, M.Z., Lanzen, A., Bang-Andreasen, T., and Jacobsen, C.S. (2019). To assemble or not to resemble-A validated Comparative Metatranscriptomics Workflow (CoMW). Gigascience 8(8). doi: 10.1093/gigascience/giz096.

63 Arístegui, J., Barton, E.D., Álvarez-Salgado, X.A., Santos, A.M.P., Figueiras, F.G., Kifani, S., et al. (2009). Sub-regional ecosystem variability in the Canary Current upwelling. Prog Oceanogr 83(1-4), 33-48. doi: 10.1016/j.pocean.2009.07.031. Aristegui, J., Gasol, J.M., Duarte, C.M., and Herndl, G.J. (2009). Microbial oceanography of the dark ocean’s pelagic realm. Limnol Oceanogr 54(5), 1501-1529. Arneborg, L. (2004). Turnover times for the water above sill level in Gullmar Fjord. Cont Shelf Res 24(4- 5), 443-460. doi: 10.1016/j.csr.2003.12.005. Arneborg, L., Erlandsson, C.P., Liljebladh, B., and Stigebrandt, A. (2004). The rate of inflow and mixing during deep-water renewal in a sill fjord. Limnol Oceanogr 49(3), 768-777. doi: DOI 10.4319/lo.2004.49.3.0768. Arneborg, L., and Liljebladh, B. (2001b). The internal seiches in Gullmar Fjord. Part II: contribution to basin water mixing. J Phys Oceanogr 31. Arnosti, C. (2003). Fluorescent derivatization of polysaccharides and carbohydrate-containing biopolymers for measurement of enzyme activities in complex media. J Chromatogr B Analyt Technol Biomed Life Sci 793(1), 181-191. doi: 10.1016/s1570-0232(03)00375-1. Arnosti, C. (2011). Microbial extracellular enzymes and the marine carbon cycle. Ann Rev Mar Sci 3, 401-425. doi: 10.1146/annurev-marine-120709-142731. Arnosti, C., Finke, N., Larsen, O., and Ghobrial, S. (2005). Anoxic carbon degradation in Arctic sediments: Microbial transformations of complex substrates. Geochim Cosmochim Acta 69(9), 2309-2320. doi: 10.1016/j.gca.2004.11.011. Arnosti, C., Wietz, M., Brinkhoff, T., Hehemann, J.H., Probandt, D., Zeugner, L., et al. (2021). The biogeochemistry of marine polysaccharides: sources, inventories, and bacterial drivers of the carbohydrate cycle. Ann Rev Mar Sci 13, 81-108. doi: 10.1146/annurev-marine-032020- 012810. Arrieta, J.M., Mayol, E., Hansman, R.L., Herndl, G.J., Dittmar, T., and Duarte, C.M. (2015). Dilution limits dissolved organic carbon utilization in the deep ocean. Science 348(6232), 331-333. doi: 10.1126/science.1258955. Auguet, J.C., Barberan, A., and Casamayor, E.O. (2010). Global ecological patterns in uncultured Archaea. ISME J 4(2), 182-190. doi: 10.1038/ismej.2009.109. Aylward, F.O., and Santoro, A.E. (2020). Heterotrophic Thaumarchaea with small genomes are widespread in the dark ocea. mSystems 5(3), 1-20. doi: 10 .1128/mSystems.00415-20. Azam, F. (1998). Microbial control of oceanic carbon flux: the plot thickens. Science 280, 694-696. Azam, F., Fenchel, T., Field, J.G., Gray, J.S., Meyer-Reil, L.A., and Thingstad, F. (1983). The ecological eole of water-column microbes in the Sea. Mar Ecol Prog Ser 10, 257-263. Azam, F., and Malfatti, F. (2007). Microbial structuring of marine ecosystems. Nat Rev Microbiol 5(10), 782-791. doi: 10.1038/nrmicro1747. Baani, M., and Liesack, W. (2008). Two isozymes of particulate methane monooxygenase with different methane oxidation kinetics are found in Methylocystis sp. strain SC2. Proc Natl Acad Sci U S A 105(29), 10203-10208. doi: 10.1073/pnas.0702643105. Bakun, A. (1990). Global climate change and intensification of coastal ocean upwelling. Science 247(4939), 198-201. doi: 10.1126/science.247.4939.198. Baltar, F., Bayer, B., Bednarsek, N., Deppeler, S., Escribano, R., Gonzalez, C.E., et al. (2019). Towards integrating evolution, metabolism, and climate change studies of marine ecosystems. Trends Ecol Evol. doi: 10.1016/j.tree.2019.07.003. Baltar, F., Palovaara, J., Unrein, F., Catala, P., Hornak, K., Simek, K., et al. (2016). Marine bacterial community structure resilience to changes in protist predation under phytoplankton bloom conditions. ISME J 10(3), 568-581. doi: 10.1038/ismej.2015.135. Banerjee, S., Schlaeppi, K., and van der Heijden, M.G.A. (2018). Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol 16(9), 567-576. doi: 10.1038/s41579-018-0024- 1. Bar-On, Y.M., Phillips, R., and Milo, R. (2018). The biomass distribution on Earth. Proc Natl Acad Sci U S A 115(25), 6506-6511. doi: 10.1073/pnas.1711842115. Barrett, A.J. (1994). Classification of peptidases. Methods Enzymol 244, 1-15. doi: 10.1016/0076- 6879(94)44003-4. Bartolome-Martin, D., Martinez-Garcia, E., Mascaraque, V., Rubio, J., Perera, J., and Alonso, S. (2004). Characterization of a second functional gene cluster for the catabolism of phenylacetic acid in Pseudomonas sp. strain Y2. Gene 341, 167-179. doi: 10.1016/j.gene.2004.06.042.

64 Bauer, M., Kube, M., Teeling, H., Richter, M., Lombardot, T., Allers, E., et al. (2006). Whole genome analysis of the marine Bacteroidetes 'Gramella forsetii' reveals adaptations to degradation of polymeric organic matter. Environ Microbiol 8(12), 2201-2213. doi: 10.1111/j.1462- 2920.2006.01152.x. Bayer, B., Hansman, R.L., Bittner, M.J., Noriega-Ortega, B.E., Niggemann, J., Dittmar, T., et al. (2019). Ammonia-oxidizing archaea release a suite of organic compounds potentially fueling prokaryotic heterotrophy in the ocean. Environ Microbiol 21(11), 4062-4075. doi: 10.1111/1462-2920.14755. Bayer, B., Vojvoda, J., Offre, P., Alves, R.J., Elisabeth, N.H., Garcia, J.A., et al. (2016). Physiological and genomic characterization of two novel marine thaumarchaeal strains indicates niche differentiation. ISME J 10(5), 1051-1063. doi: 10.1038/ismej.2015.200. Beardsley, C., Pernthaler, J., Wosniok, W., and Amann, R. (2003). Are readily culturable bacteria in coastal North Sea waters suppressed by selective grazing mortality? Appl Environ Microb 69(5), 2624-2630. doi: 10.1128/aem.69.5.2624-2630.2003. Beattie, A., Hirst, E.L., and Percival, E. (1961). Studies on the metabolism of the Chrysophyceae. Comparative structural investigations on leucosin (chrysolaminarin) separated from diatoms and laminarin from the brown algae. Biochem J 79, 531-537. doi: 10.1042/bj0790531. Becker, J.W., Berube, P.M., Follett, C.L., Waterbury, J.B., Chisholm, S.W., Delong, E.F., et al. (2014). Closely related phytoplankton species produce similar suites of dissolved organic matter. Front Microbiol 5, 111. doi: 10.3389/fmicb.2014.00111. Becker, K.W., Harke, M.J., Mende, D.R., Muratore, D., Weitz, J.S., DeLong, E.F., et al. (2020a). Combined pigment and metatranscriptomic analysis reveals highly synchronized diel patterns of phenotypic light response across domains in the open oligotrophic ocean. ISME J. doi: 10.1038/s41396-020-00793-x. Becker, S., Scheffel, A., Polz, M.F., and Hehemann, J.H. (2017). Accurate quantification of laminarin in marine organic matter with enzymes from marine microbes. Appl Environ Microbiol 83(9). doi: 10.1128/AEM.03389-16. Becker, S., Tebben, J., Coffinet, S., Wiltshire, K., Iversen, M.H., Harder, T., et al. (2020b). Laminarin is a major molecule in the marine carbon cycle. Proc Natl Acad Sci U S A 117(12), 6599-6607. doi: 10.1073/pnas.1917001117. Belgrano, A., Lindahl, O., and Hernroth, B. (1999). North Atlantic Oscillation primary productivity and toxic phytoplankton in the Gullmar Fjord, Sweden (1985-1996). Proc Biol Sci 266(1418), 425-430. doi: DOI 10.1098/rspb.1999.0655. Benner, R., and Amon, R.M. (2015). The size-reactivity continuum of major bioelements in the ocean. Ann Rev Mar Sci 7, 185-205. doi: 10.1146/annurev-marine-010213-135126. Benner, R., Pakulski, J.D., McCarthy, M., Hedges, J.I., and Hatcher, P.G. (1992). Bulk chemical characteristics of dissolved organic matter in the ocean. Science 255(5051), 1561-1564. doi: 10.1126/science.255.5051.1561. Bennke, C.M., Kruger, K., Kappelmann, L., Huang, S., Gobet, A., Schuler, M., et al. (2016). Polysaccharide utilisation loci of Bacteroidetes from two contrasting open ocean sites in the North Atlantic. Environ Microbiol 18(12), 4456-4470. doi: 10.1111/1462-2920.13429. Berg, C., Vandieken, V., Thamdrup, B., and Jurgens, K. (2015). Significance of archaeal nitrification in hypoxic waters of the Baltic Sea. ISME J 9(6), 1319-1332. doi: 10.1038/ismej.2014.218. Berg, I.A., Kockelkorn, D., Buckel, W., and Fuchs, G. (2007). A 3-hydroxypropionate/4- hydroxybutyrate autotrophic carbon dioxide assimilation pathway in Archaea. Science 318(5857), 1782-1786. doi: 10.1126/science.1149976. Bergauer, K., Fernandez-Guerra, A., Garcia, J.A.L., Sprenger, R.R., Stepanauskas, R., Pachiadaki, M.G., et al. (2018). Organic matter processing by microbial communities throughout the Atlantic water column as revealed by metaproteomics. Proc Natl Acad Sci U S A 115(3), E400-E408. doi: 10.1073/pnas.1708779115. Berman, T. (2003). Dissolved organic nitrogen: a dynamic participant in aquatic ecosystems. Aquat Microb Ecol 31, 279-305. Biersmith, A., and Benner, R. (1998). Carbohydrates in phytoplankton and freshly produced dissolved organic matter. Mar Chem 63, 131-144. Biller, S.J., Berube, P.M., Lindell, D., and Chisholm, S.W. (2015). Prochlorococcus: the structure and function of collective diversity. Nat Rev Microbiol 13(1), 13-27. doi: 10.1038/nrmicro3378.

65 Bjursell, M.K., Martens, E.C., and Gordon, J.I. (2006). Functional genomic and metabolic studies of the adaptations of a prominent adult human gut symbiont, Bacteroides thetaiotaomicron, to the suckling period. J Biol Chem 281(47), 36269-36279. doi: 10.1074/jbc.M606509200. Bloch, W., and Schlesinger, M.J. (1974). Kinetics of substrate hydrolysis by molecular variants of Escherichia coli alkaline phosphatase. J Biol Chem 249(6), 1760-1768. Bode, A., Estevez, M.G., Varela, M., and Vilar, J.A. (2015). Annual trend patterns of phytoplankton species abundance belie homogeneous taxonomical group responses to climate in the NE Atlantic upwelling. Mar Environ Res 110, 81-91. doi: 10.1016/j.marenvres.2015.07.017. Bouvier, T., and del Giorgio, P.A. (2007). Key role of selective viral-induced mortality in determining marine bacterial community composition. Environ Microbiol 9(2), 287-297. doi: 10.1111/j.1462-2920.2006.01137.x. Brasil, B.d.S.A.F., de Siqueira, F.G., Salum, T.F.C., Zanette, C.M., and Spier, M.R. (2017). Microalgae and cyanobacteria as enzyme biofactories. Algal Res 25, 76-89. doi: 10.1016/j.algal.2017.04.035. Bravakos, P., Mandalakis, M., Nomikou, P., Anastasiou, T.I., Kristoffersen, J.B., Stavroulaki, M., et al. (2021). Genomic adaptation of Pseudomonas strains to acidity and antibiotics in hydrothermal vents at Kolumbo submarine volcano, Greece. Sci Rep 11(1), 1336. doi: 10.1038/s41598-020-79359-y. Brochier-Armanet, C., Boussau, B., Gribaldo, S., and Forterre, P. (2008). Mesophilic crenarchaeota: proposal for a third archaeal phylum, the Thaumarchaeota. Nat Rev Microbiol 6(3), 245-252. doi: 10.1038/nrmicro1852. Broecker, W.S. (1991). The great ocean conveyor. Oceanogr 4(2), 79-89. Broullón, E., López-Mozos, M., Reguera, B., Chouciño, P., Doval, M.D., Fernández-Castro, B., et al. (2020). Thin layers of phytoplankton and harmful algae events in a coastal upwelling system. Prog Oceanogr 189. doi: 10.1016/j.pocean.2020.102449. Brown, M.V., Ostrowski, M., Grzymski, J.J., and Lauro, F.M. (2014). A trait based perspective on the biogeography of common and abundant marine bacterioplankton clades. Mar Genomics 15, 17-28. doi: 10.1016/j.margen.2014.03.002. Bryson, S., Li, Z., Chavez, F., Weber, P.K., Pett-Ridge, J., Hettich, R.L., et al. (2017). Phylogenetically conserved resource partitioning in the coastal microbial loop. ISME J 11(12), 2781-2792. doi: 10.1038/ismej.2017.128. Buchan, A., Gonzalez, J.M., and Moran, M.A. (2005). Overview of the marine roseobacter lineage. Appl Environ Microbiol 71(10), 5665-5677. doi: 10.1128/AEM.71.10.5665-5677.2005. Buchan, A., LeCleir, G.R., Gulvik, C.A., and Gonzalez, J.M. (2014). Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat Rev Microbiol 12(10), 686- 698. doi: 10.1038/nrmicro3326. Bunse, C., Bertos-Fortis, M., Sassenhagen, I., Sildever, S., Sjoqvist, C., Godhe, A., et al. (2016). Spatio- temporal interdependence of bacteria and phytoplankton during a baltic sea spring bloom. Front Microbiol 7, 517. doi: 10.3389/fmicb.2016.00517. Bunse, C., Israelsson, S., Baltar, F., Bertos-Fortis, M., Fridolfsson, E., Legrand, C., et al. (2019). High frequency multi-year variability in Baltic Sea microbial plankton stocks and activities. Front Microbiol 9(3296), 1-18. doi: 10.3389/fmicb.2018.03296. Campbell, B.J., Yu, L., Heidelberg, J.F., and Kirchman, D.L. (2011). Activity of abundant and rare bacteria in a coastal ocean. Proc Natl Acad Sci U S A 108(31), 12776-12781. doi: 10.1073/pnas.1101405108. Cantarel, B.L., Coutinho, P.M., Rancurel, C., Bernard, T., Lombard, V., and Henrissat, B. (2009). The carbohydrate-active enzymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Res 37(Database issue), D233-238. doi: 10.1093/nar/gkn663. Capone, D.G., and Hutchins, D.A. (2013). Microbial biogeochemistry of coastal upwelling regimes in a changing ocean. Nat Geosci 6(9), 711-717. doi: 10.1038/Ngeo1916. Carlson, C.A. (2002). "Production and removal processes," in Biogeochemistry of marine dissolved organic matter, eds. D.A. Hansell & C.A. Carlson. (San Diego, CA: Elsevier), 91–152. Carlson, C.A., and Hansell, D.A. (2015). "DOM sources, sinks, reactivity, and budgets," in Biogeochemistry of marine dissolved organic matter, eds. D.A. Hansell & C.A. Carlson. Second ed: Elsevier), 66-94.

66 Cavicchioli, R., Ripple, W.J., Timmis, K.N., Azam, F., Bakken, L.R., Baylis, M., et al. (2019). Scientists' warning to humanity: microorganisms and climate change. Nat Rev Microbiol 17(9), 569- 586. doi: 10.1038/s41579-019-0222-5. Cermeño, P., Marañón, E., Pérez, V., Serret, P., Fernández, E., and Castro, C.G. (2006). Phytoplankton size structure and primary production in a highly dynamic coastal ecosystem (Ría de Vigo, NW-Spain): Seasonal and short-time scale variability. Estuar Coast Shelf Sci 67(1-2), 251- 266. doi: 10.1016/j.ecss.2005.11.027. Chen, Y. (2012). Comparative genomics of methylated amine utilization by marine Roseobacter clade bacteria and development of functional gene markers (tmm, gmaS). Environ Microbiol 14(9), 2308-2322. doi: 10.1111/j.1462-2920.2012.02765.x. Chenard, C., Wijaya, W., Vaulot, D., Lopes Dos Santos, A., Martin, P., Kaur, A., et al. (2019). Temporal and spatial dynamics of bacteria, archaea and protists in equatorial coastal waters. Sci Rep 9(1), 16390. doi: 10.1038/s41598-019-52648-x. Chisholm, S.W., Olson, R.J., Zettler, E.R., Goericke, R., Waterbury, J.B., and Welschmeyer, N.A. (1988). A novel free-living prochlorophyte abundant in the oceanic euphotic zone. Nature 334(6180), 340-343. doi: 10.1038/334340a0. Cho, B.C., and Azam, F. (1988). Major role of bacteria in biogeochemical fluxes in the ocean's interior. Nature 332(6163), 441-443. doi: 10.1038/332441a0. Cho, J.C., Stapels, M.D., Morris, R.M., Vergin, K.L., Schwalbach, M.S., Givan, S.A., et al. (2007). Polyphyletic photosynthetic reaction centre genes in oligotrophic marine gammaproteobacteria. Environ Microbiol 9(6), 1456-1463. doi: 10.1111/j.1462- 2920.2007.01264.x. Chow, C.E., Kim, D.Y., Sachdeva, R., Caron, D.A., and Fuhrman, J.A. (2014). Top-down controls on bacterial community structure: microbial network analysis of bacteria, T4-like viruses and protists. ISME J 8(4), 816-829. doi: 10.1038/ismej.2013.199. Cirri, E., and Pohnert, G. (2019). Algae-bacteria interactions that balance the planktonic microbiome. New Phytol 223(1), 100-106. doi: 10.1111/nph.15765. CO2.earth (2021). Available: https://www.co2.earth [Accessed 23.03.2021]. Colatriano, D., Tran, P.Q., Gueguen, C., Williams, W.J., Lovejoy, C., and Walsh, D.A. (2018). Genomic evidence for the degradation of terrestrial organic matter by pelagic Arctic Ocean Chloroflexi bacteria. Commun Biol 1, 90. doi: 10.1038/s42003-018-0086-7. Conley, D.J., Carstensen, J., Aigars, J., Axe, P., Bonsdorff, E., Eremina, T., et al. (2011). Hypoxia is increasing in the coastal zone of the Baltic Sea. Environ Sci Technol 45(16), 6777-6783. doi: 10.1021/es201212r. Cottrell, M.T., and Kirchman, D.L. (2000). Natural assemblages of marine proteobacteria and members of the Cytophaga-Flavobacter cluster consuming low- and high-molecular-weight dissolved organic matter. Appl Environ Microbiol 66(4), 1692-1697. Cottrell, M.T., and Kirchman, D.L. (2016). Transcriptional control in marine copiotrophic and oligotrophic bacteria with streamlined genomes. Appl Environ Microbiol 82(19), 6010-6018. doi: 10.1128/AEM.01299-16. Crump, B.C., Hopkinson, C.S., Sogin, M.L., and Hobbie, J.E. (2004). Microbial biogeography along an estuarine salinity gradient: combined influences of bacterial growth and residence time. Appl Environ Microbiol 70(3), 1494-1505. doi: 10.1128/aem.70.3.1494-1505.2004. Czaran, T.L., Hoekstra, R.F., and Pagie, L. (2002). Chemical warfare between microbes promotes biodiversity. Proc Natl Acad Sci U S A 99(2), 786-790. doi: 10.1073/pnas.012399899. Daims, H., Lucker, S., and Wagner, M. (2016). A new perspective on microbes formerly known as nitrite-oxidizing bacteria. Trends Microbiol 24(9), 699-712. doi: 10.1016/j.tim.2016.05.004. Daniel, R., Simon, M., and Wemheuer, B. (2018). Molecular ecology and genetic diversity of the Roseobacter clade. Front Microbiol 9, 1185. doi: 10.3389/fmicb.2018.01185. Davidson, A.L., and Chen, J. (2004). ATP-binding cassette transporters in bacteria. Annu Rev Biochem 73, 241-268. doi: 10.1146/annurev.biochem.73.011303.073626. Davies, G.J., and Sinnott, M.L. (2008). Sorting the diverse: the sequence-based classifications of carbohydrate-active enzymes. Biochem J. doi: 10.1042/bj20080382. de Wit, R., and Bouvier, T. (2006). 'Everything is everywhere, but, the environment selects'; what did Baas Becking and Beijerinck really say? Environ Microbiol 8(4), 755-758. doi: 10.1111/j.1462-2920.2006.01017.x.

67 Dedysh, S.N., and Ivanova, A.A. (2019). Planctomycetes in boreal and subarctic wetlands: diversity patterns and potential ecological functions. FEMS Microbiol Ecol 95(2). doi: 10.1093/femsec/fiy227. Delmont, T.O., Eren, A.M., Vineis, J.H., and Post, A.F. (2015). Genome reconstructions indicate the partitioning of ecological functions inside a phytoplankton bloom in the Amundsen Sea, Antarctica. Front Microbiol 6, 1090. doi: 10.3389/fmicb.2015.01090. Delmont, T.O., Quince, C., Shaiber, A., Esen, O.C., Lee, S.T., Rappe, M.S., et al. (2018). Nitrogen- fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes. Nat Microbiol 3(7), 804-813. doi: 10.1038/s41564-018-0176-9. Deniaud-Bouet, E., Hardouin, K., Potin, P., Kloareg, B., and Herve, C. (2017). A review about brown algal cell walls and fucose-containing sulfated polysaccharides: Cell wall context, biomedical properties and key research challenges. Carbohydr Polym 175, 395-408. doi: 10.1016/j.carbpol.2017.07.082. Deniaud-Bouet, E., Kervarec, N., Michel, G., Tonon, T., Kloareg, B., and Herve, C. (2014). Chemical and enzymatic fractionation of cell walls from Fucales: insights into the structure of the extracellular matrix of brown algae. Ann Bot 114(6), 1203-1216. doi: 10.1093/aob/mcu096. Dittmar, T., and Stubbins, A. (2014). "Dissolved organic matter in aquatic systems," in Treatise on Geochemistry.), 125-156. Dodd, M.S., Papineau, D., Grenne, T., Slack, J.F., Rittner, M., Pirajno, F., et al. (2017). Evidence for early life in Earth's oldest hydrothermal vent precipitates. Nature 543(7643), 60-64. doi: 10.1038/nature21377. Doxey, A.C., Kurtz, D.A., Lynch, M.D., Sauder, L.A., and Neufeld, J.D. (2015). Aquatic metagenomes implicate Thaumarchaeota in global cobalamin production. ISME J 9(2), 461-471. doi: 10.1038/ismej.2014.142. Duchesne, L.C., and Larson, D.W. (1989). Cellulose and the evolution of plant life. Bioscience 39(4), 238-241. doi: 10.2307/1311160. Ducklow, H., and Steinberg, D.K. (2001). Upper ocean carbon export and the biological pump. Oceanography 14(4). doi: DWH-AR0056008. Dufresne, A., Garczarek, L., and Partensky, F. (2005). Accelerated evolution associated with genome reduction in a free-living prokaryote. Genome Biol 6(2), R14. doi: 10.1186/gb-2005-6-2-r14. Dupont, C.L., McCrow, J.P., Valas, R., Moustafa, A., Walworth, N., Goodenough, U., et al. (2015). Genomes and gene expression across light and productivity gradients in eastern subtropical Pacific microbial communities. ISME J 9(5), 1076-1092. doi: 10.1038/ismej.2014.198. Dyksma, S., Bischof, K., Fuchs, B.M., Hoffmann, K., Meier, D., Meyerdierks, A., et al. (2016). Ubiquitous Gammaproteobacteria dominate dark carbon fixation in coastal sediments. ISME J 10(8), 1939-1953. doi: 10.1038/ismej.2015.257. Edwards, J.L., Smith, D.L., Connolly, J., McDonald, J.E., Cox, M.J., Joint, I., et al. (2010). Identification of carbohydrate metabolism genes in the metagenome of a marine biofilm community shown to be dominated by gammaproteobacteria and bacteroidetes. Genes 1(3), 371-384. doi: 10.3390/genes1030371. El Kaoutari, A., Armougom, F., Gordon, J.I., Raoult, D., and Henrissat, B. (2013). The abundance and variety of carbohydrate-active enzymes in the human gut microbiota. Nat Rev Microbiol 11(7), 497-504. doi: 10.1038/nrmicro3050. Falkowski, P.G., Barber, R.T., and Smetacek, V.V. (1998). Biogeochemical controls and feedbacks on ocean primary production. Science 281(5374), 200-207. Falkowski, P.G., Fenchel, T., and Delong, E.F. (2008). The microbial engines that drive Earth's biogeochemical cycles. Science 320(5879), 1034-1039. doi: 10.1126/science.1153213. Fernandez, V.I., Yawata, Y., and Stocker, R. (2019). A foraging mandala for aquatic microorganisms. ISME J 13(3), 563-575. doi: 10.1038/s41396-018-0309-4. Fernandez-Gomez, B., Richter, M., Schuler, M., Pinhassi, J., Acinas, S.G., Gonzalez, J.M., et al. (2013). Ecology of marine Bacteroidetes: a comparative genomics approach. ISME J 7(5), 1026- 1037. doi: 10.1038/ismej.2012.169. Ferrer-Gonzalez, F.X., Widner, B., Holderman, N.R., Glushka, J., Edison, A.S., Kujawinski, E.B., et al. (2020). Resource partitioning of phytoplankton metabolites that support bacterial heterotrophy. ISME J. doi: 10.1038/s41396-020-00811-y. Field, C.B., Behrenfeld, M.J., Randerson, J.T., and Falkowski, P. (1998). Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281(5374), 237-240.

68 Figueiras, F.G., Labarta, U., and Reiriz, M.J.F. (2002). "Coastal upwelling, primary production and mussel growth in the Rías Baixas of Galicia," in Sustainable Increase of Marine Harvesting: Fundamental Mechanisms and New Concepts: Proceedings of the 1st Maricult Conference held in Trondheim, Norway, 25–28 June 2000, eds. O. Vadstein & Y. Olsen. (Dordrecht: Springer Netherlands), 121-131. Filiatrault, M.J. (2011). Progress in prokaryotic transcriptomics. Curr Opin Microbiol 14(5), 579-586. doi: 10.1016/j.mib.2011.07.023. Filipsson, H.L., and Nordberg, K. (2004). Climate variations, an overlooked factor influencing the recent marine environment. An example from Gullmar Fjord, Sweden, illustrated by benthic foraminifera and hydrographic data. Estuaries 27(5), 867-881. doi: Doi 10.1007/Bf02912048. Flombaum, P., Gallegos, J.L., Gordillo, R.A., Rincon, J., Zabala, L.L., Jiao, N., et al. (2013). Present and future global distributions of the marine cyanobacteria Prochlorococcus and Synechococcus. Proc Natl Acad Sci U S A 110(24), 9824-9829. doi: 10.1073/pnas.1307701110. Fortis-Bertos, M., Farnelid, H., Lindh, M.V., Casini, M., Andersson, A., Pinhassi, J., et al. (2016). Unscrambling Cyanobacteria Community Dynamics Related to Environmental Factors. Front Microbiol 7(625), 1-13. doi: 10.3389/fmicb.2016.00625. Fraga, F. ( 1981). Upwelling off the Galician Coast, Northwest Spain. Francis, C.A., Roberts, K.J., Beman, J.M., Santoro, A.E., and Oakley, B.B. (2005). Ubiquity and diversity of ammonia-oxidizing archaea in water columns and sediments of the ocean. Proc Natl Acad Sci U S A 102(41), 14683-14688. doi: 10.1073/pnas.0506625102. Franke, A., Blenckner, T., Duarte, C.M., Ott, K., Fleming, L.E., Antia, A., et al. (2020). Operationalizing ocean health: Toward integrated research on ocean health and recovery to achieve ocean sustainability. One Earth 2(6), 557-565. doi: 10.1016/j.oneear.2020.05.013. Freitas, S., Hatosy, S., Fuhrman, J.A., Huse, S.M., Welch, D.B., Sogin, M.L., et al. (2012). Global distribution and diversity of marine Verrucomicrobia. ISME J 6(8), 1499-1505. doi: 10.1038/ismej.2012.3. Fuchs, G. (2011). Alternative pathways of carbon dioxide fixation: insights into the early evolution of life? Annu Rev Microbiol 65, 631-658. doi: 10.1146/annurev-micro-090110-102801. Fuerst, J.A. (1995). The planctomycetes: emerging models for microbial ecology, evolution and cell biology. Microbiology 141 ( Pt 7), 1493-1506. doi: 10.1099/13500872-141-7-1493. Fuhrman, J.A. (1999). Marine viruses and their biogeochemical and ecological effects. Nature 399(6736), 541-548. doi: 10.1038/21119. Fuhrman, J.A., and Hagström, Å. (2008). "Bacterial and archaeal community structure and its patterns," in Microbial ecology of the oceans, ed. D. Kirchman. 2 ed: Wiley Online Library), 45-90. Fukuda, R., Ogawa, H., Nagata, T., and Koike, I.I. (1998). Direct determination of carbon and nitrogen contents of natural bacterial assemblages in marine environments. Appl Environ Microbiol 64(9), 3352-3358. Galand, P.E., Casamayor, E.O., Kirchman, D.L., and Lovejoy, C. (2009). Ecology of the rare microbial biosphere of the Arctic Ocean. Proc Natl Acad Sci U S A 106(52), 22427-22432. doi: 10.1073/pnas.0908284106. Garcia-Martinez, J., Acinas, S.G., Massana, R., and Rodriguez-Valera, F. (2002). Prevalence and microdiversity of Alteromonas macleodii-like microorganisms in different oceanic regions. Environ Microbiol 4(1), 42-50. doi: 10.1046/j.1462-2920.2002.00255.x. Gašparović, B., Plavšić, M., Ćosović, B., and Reigstad, M. (2005). Organic matter characterization and fate in the sub-arctic Norwegian fjords during the late spring/summer period. Estuar Coast Shelf Sci 62(1-2), 95-107. doi: 10.1016/j.ecss.2004.08.008. Geider, R., and La Roche, J. (2002). Redfield revisited: variability of C:N:P in marine microalgae and its biochemical basis. Eur J Phycol 37(1), 1-17. doi: 10.1017/s0967026201003456. Gifford, S.M., Sharma, S., Booth, M., and Moran, M.A. (2013). Expression patterns reveal niche diversification in a marine microbial assemblage. ISME J 7(2), 281-298. doi: 10.1038/ismej.2012.96. Giovannoni, S.J. (2017). SAR11 bacteria: the most abundant plankton in the oceans. Ann Rev Mar Sci 9, 231-255. doi: 10.1146/annurev-marine-010814-015934. Giovannoni, S.J., Bibbs, L., Cho, J.C., Stapels, M.D., Desiderio, R., Vergin, K.L., et al. (2005a). Proteorhodopsin in the ubiquitous marine bacterium SAR11. Nature 438(7064), 82-85. doi: 10.1038/nature04032.

69 Giovannoni, S.J., Tripp, H.J., Givan, S., Podar, M., Vergin, K.L., Baptista, D., et al. (2005b). Genome streamlining in a cosmopolitan oceanic bacterium. Science 309(5738), 1242-1245. doi: 10.1126/science.1114057. Glockner, F.O., Fuchs, B.M., and Amann, R. (1999). Bacterioplankton compositions of lakes and oceans: a first comparison based on fluorescence in situ hybridization. Appl Environ Microbiol 65(8), 3721-3726. Glockner, F.O., Kube, M., Bauer, M., Teeling, H., Lombardot, T., Ludwig, W., et al. (2003). Complete genome sequence of the marine planctomycete Pirellula sp. strain 1. Proc Natl Acad Sci U S A 100(14), 8298-8303. doi: 10.1073/pnas.1431443100. Gonzales, M.J., Kiene, R., and Moran, M.A. (1999). Transformation of sulfur compounds by an abundant lineage of marine bacteria in the alpha-subclass of the class Proteobacteria. Appl Environ Microbiol 65(9), 3810–3819. Gralka, M., Szabo, R., Stocker, R., and Cordero, O.X. (2020). Trophic interactions and the drivers of microbial community assembly. Curr Biol 30(19), R1176-R1188. doi: 10.1016/j.cub.2020.08.007. Griffith, P.C., and Fletcher, M. (1991). Hydrolysis of protein and model dipeptide substrates by attached and nonattached marine Pseudomonas sp. Strain NCIMB 2021. Appl Environ Microbiol 57(8), 2186-2191. Grondin, J.M., Tamura, K., Dejean, G., Abbott, D.W., and Brumer, H. (2017). Polysaccharide utilization loci: fueling microbial communities. J Bacteriol 199(15). doi: 10.1128/JB.00860-16. Grote, J., Thrash, J.C., Huggett, M.J., Landry, Z.C., Carini, P., Giovannoni, S.J., et al. (2012). Streamlining and core genome conservation among highly divergent members of the SAR11 clade. mBio 3(5). doi: 10.1128/mBio.00252-12. Gustafsson, B.G., Schenk, F., Blenckner, T., Eilola, K., Meier, H.E., Muller-Karulis, B., et al. (2012). Reconstructing the development of Baltic sea eutrophication 1850-2006. Ambio 41(6), 534- 548. doi: 10.1007/s13280-012-0318-x. Hagström, Å., Azam, F., Berg, C., and Zweifel, U.L. (2017). Isolates as models to study bacterial ecophysiology and biogeochemistry. Aquat Microb Ecol 80(1), 15-27. doi: 10.3354/ame01838. Hahn, M.W., and Hofle, M.G. (1999). Flagellate predation on a bacterial model community: interplay of size-selective grazing, specific bacterial cell size, and bacterial community composition. Appl Environ Microbiol 65(11), 4863-4872. Hamdan, L.J., Coffin, R.B., Sikaroodi, M., Greinert, J., Treude, T., and Gillevet, P.M. (2013). Ocean currents shape the microbiome of Arctic marine sediments. ISME J 7(4), 685-696. doi: 10.1038/ismej.2012.143. Han, X. (2016). Lipidomics for studying metabolism. Nat Rev Endocrinol 12(11), 668-679. doi: 10.1038/nrendo.2016.98. Hansell, D.A. (2013). Recalcitrant dissolved organic carbon fractions. Ann Rev Mar Sci 5, 421-445. doi: 10.1146/annurev-marine-120710-100757. Hansell, D.A., and Carlson, C.A. (2015). Biogeochemistry of marine dissolved organic matter. New York: Elsevier. Hansell, D.A., Carlson, C.A., Repeta, D.J., and Schlitzer, R. (2009). Dissolved organic matter in the ocean. Oceanography 22(4), 202-211. Hansson, D., and Gustafsson, E. (2011). Salinity and hypoxia in the Baltic Sea since A.D. 1500. J Geophys Res 116(C3). doi: 10.1029/2010jc006676. Hardin, G. (1960). The Competition Exclusion Principle. Science 131(3409), 1292-1297. Helbert, W., Poulet, L., Drouillard, S., Mathieu, S., Loiodice, M., Couturier, M., et al. (2019). Discovery of novel carbohydrate-active enzymes through the rational exploration of the protein sequences space. Proc Natl Acad Sci U S A 116(13), 6063-6068. doi: 10.1073/pnas.1815791116. Henrissat, B. (1991). A classification of glycosyl hydrolases based on amino acid sequence similarities. Biochem J 280 ( Pt 2), 309-316. doi: 10.1042/bj2800309. Herndl, G.J., and Reinthaler, T. (2013). Microbial control of the dark end of the biological pump. Nat Geosci 6(9), 718-724. doi: 10.1038/ngeo1921. Herndl, G.J., Reinthaler, T., Teira, E., van Aken, H., Veth, C., Pernthaler, A., et al. (2005). Contribution of archaea to total prokaryotic production in the deep Atlantic Ocean. Appl Environ Microbiol 71(5), 2303-2309. doi: 10.1128/AEM.71.5.2303-2309.2005.

70 Herold, M., Martinez Arbas, S., Narayanasamy, S., Sheik, A.R., Kleine-Borgmann, L.A.K., Lebrun, L.A., et al. (2020). Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance. Nat Commun 11(1), 5281. doi: 10.1038/s41467-020- 19006-2. Holmfeldt, K., Middelboe, M., Nybroe, O., and Riemann, L. (2007). Large variabilities in host strain susceptibility and phage host range govern interactions between lytic marine phages and their Flavobacterium hosts. Appl Environ Microbiol 73(21), 6730-6739. doi: 10.1128/AEM.01399-07. Hoppe, H.G., Arnosti, C., and Herndl, G.J. (2002). "Ecological Significance of Bacterial Enzymes in the Marine Environment," in Enzymes in the environment, eds. R.G. Burns & R.P. Dick. (New York: Marcel Dekker), 73-97. Hosie, A.H., and Poole, P.S. (2001). Bacterial ABC transporters of amino acids. Res Microbiol 152(3- 4), 259-270. Hou, S., Lopez-Perez, M., Pfreundt, U., Belkin, N., Stuber, K., Huettel, B., et al. (2018). Benefit from decline: the primary transcriptome of Alteromonas macleodii str. Te101 during Trichodesmium demise. ISME J 12(4), 981-996. doi: 10.1038/s41396-017-0034-4. Huet, J., Schnabel, R., Sentenac, A., and Zillig, W. (1983). Archaebacteria and eukaryotes possess DNA- dependent RNA polymerases of a common type. EMBO J 2(8), 1291-1294. Hug, L.A., Baker, B.J., Anantharaman, K., Brown, C.T., Probst, A.J., Castelle, C.J., et al. (2016). A new view of the tree of life. Nat Microbiol 1(5), 16048. doi: 10.1038/nmicrobiol.2016.48. Huggett, M.J., and Rappe, M.S. (2012). Genome sequence of strain HIMB30, a novel member of the marine gammaproteobacteria. J Bacteriol 194(3), 732-733. doi: 10.1128/JB.06506-11. Hugler, M., and Sievert, S.M. (2011). Beyond the calvin cycle: autotrophic carbon fixation in the ocean. Ann Rev Mar Sci 3, 261-289. doi: 10.1146/annurev-marine-120709-142712. Hutchinson, G.E. (1957). Concluding Remarks. Cold Spring Harb Symp Quant Biol 22(0), 415-427. doi: 10.1101/sqb.1957.022.01.039. Hutchinson, G.E. (1961). The paradox of the plankton. Am Nat 95(882), 137-145. doi: 10.1086/282171. Illumina (2020). illumina HiSeq 2500 sequencing paltform [Online]. Available: https://www.illumina.com/systems/sequencing-platforms/hiseq-2500.html [Accessed 23.03.2021]. Ingalls, A.E., Shah, S.R., Hansman, R.L., Aluwihare, L.I., Santos, G.M., Druffel, E.R., et al. (2006). Quantifying archaeal community autotrophy in the mesopelagic ocean using natural radiocarbon. Proc Natl Acad Sci U S A 103(17), 6442-6447. doi: 10.1073/pnas.0510157103. Ivanova, A.A., Naumoff, D.G., Miroshnikov, K.K., Liesack, W., and Dedysh, S.N. (2017). Comparative genomics of four Isosphaeraceae Planctomycetes: a common pool of plasmids and glycoside hydrolase genes shared by Paludisphaera borealis PX4(T), Isosphaera pallida IS1B(T), Singulisphaera acidiphila DSM 18658(T), and Strain SH-PL62. Front Microbiol 8, 412. doi: 10.3389/fmicb.2017.00412. Ivars-Martinez, E., D'Auria, G., Rodriguez-Valera, F., Sanchez-Porro, C., Ventosa, A., Joint, I., et al. (2008a). Biogeography of the ubiquitous marine bacterium Alteromonas macleodii determined by multilocus sequence analysis. Mol Ecol 17(18), 4092-4106. doi: 10.1111/j.1365-294x.2008.03883.x. Ivars-Martinez, E., Martin-Cuadrado, A.B., D'Auria, G., Mira, A., Ferriera, S., Johnson, J., et al. (2008b). Comparative genomics of two ecotypes of the marine planktonic copiotroph Alteromonas macleodii suggests alternative lifestyles associated with different kinds of particulate organic matter. ISME J 2(12), 1194-1212. doi: 10.1038/ismej.2008.74. Jeuniaux, C., and Voss-Foucart, M.F. (1991). Chitin biomass and production in the marine environment. Biochem Syst Ecol 19(5), 347-356. doi: 10.1016/0305-1978(91)90051-z. Jiao, N., Herndl, G.J., Hansell, D.A., Benner, R., Kattner, G., Wilhelm, S.W., et al. (2010). Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean. Nat Rev Microbiol 8(8), 593-599. doi: 10.1038/nrmicro2386. Joglar, V., Alvarez-Salgado, X.A., Gago-Martinez, A., Leao, J.M., Perez-Martinez, C., Pontiller, B., et al. (2021). Cobalamin and microbial plankton dynamics along a coastal to offshore transect in the Eastern North Atlantic Ocean. Environ Microbiol 23(3), 1559-1583. doi: 10.1111/1462-2920.15367. Joglar, V., Prieto, A., Barber-Lluch, E., Hernández-Ruiz, M., Fernández, E., and Teira, E. (2020). Spatial and temporal variability in the response of phytoplankton and prokaryotes to B-vitamin

71 amendments in an upwelling system. Biogeosciences 17(10), 2807-2823. doi: 10.5194/bg- 17-2807-2020. Jurburg, S.D., Konzack, M., Eisenhauer, N., and Heintz-Buschart, A. (2020). The archives are half- empty: an assessment of the availability of microbial community sequencing data. Commun Biol 3(1), 474. doi: 10.1038/s42003-020-01204-9. Jurgens, K., and Matz, C. (2002). Predation as a shaping force for the phenotypic and genotypic composition of planktonic bacteria. Anton Leeuw Int J G 81(1-4), 413-434. doi: 10.1023/a:1020505204959. Kamke, J., Sczyrba, A., Ivanova, N., Schwientek, P., Rinke, C., Mavromatis, K., et al. (2013). Single- cell genomics reveals complex carbohydrate degradation patterns in poribacterial symbionts of marine sponges. ISME J 7(12), 2287-2300. doi: 10.1038/ismej.2013.111. Kang, I., Kang, D., Oh, H.M., Kim, H., Kim, H.J., Kang, T.W., et al. (2011). Genome sequence of strain IMCC2047, a novel marine member of the gammaproteobacteria. J Bacteriol 193(14), 3688- 3689. doi: 10.1128/JB.05226-11. Kappelmann, L., Kruger, K., Hehemann, J.H., Harder, J., Markert, S., Unfried, F., et al. (2019). Polysaccharide utilization loci of North Sea Flavobacteriia as basis for using SusC/D-protein expression for predicting major phytoplankton glycans. ISME J 13(1), 76-91. doi: 10.1038/s41396-018-0242-6. Karlsson, C.M.G., Cerro-Galvez, E., Lundin, D., Karlsson, C., Vila-Costa, M., and Pinhassi, J. (2019). Direct effects of organic pollutants on the growth and gene expression of the Baltic Sea model bacterium Rheinheimera sp. BAL341. Microb Biotechnol 12(5), 892-906. doi: 10.1111/1751- 7915.13441. Karner, M.B., Delong, E.F., and Karl, D.M. (2001). Archaeal dominance in the mesopelgaic zone of the Pacific Ocean. Nature 409, 507-510. Kazakov, A.E., Rodionov, D.A., Alm, E., Arkin, A.P., Dubchak, I., and Gelfand, M.S. (2009). Comparative genomics of regulation of fatty acid and branched-chain amino acid utilization in proteobacteria. J Bacteriol 191(1), 52-64. doi: 10.1128/JB.01175-08. Kchouk, M., Gibrat, J.F., and Elloumi, M. (2017). Generations of sequencing technologies: from first to next generation. Biol Med 09(03). doi: 10.4172/0974-8369.1000395. Khan, N.H., Ishii, Y., Kimata-Kino, N., Esaki, H., Nishino, T., Nishimura, M., et al. (2007). Isolation of Pseudomonas aeruginosa from open ocean and comparison with freshwater, clinical, and animal isolates. Microb Ecol 53(2), 173-186. doi: 10.1007/s00248-006-9059-3. Kirchman, D.L. (2002). The ecology of Cytophaga-Flavobacteria in aquatic environments. FEMS Microbiol Ecol 39(2), 91-100. doi: 10.1111/j.1574-6941.2002.tb00910.x. Kirchman, D.L. (2003). "9 - The Contribution of Monomers and other Low-Molecular Weight Compounds to the Flux of Dissolved Organic Material in Aquatic Ecosystems," in Aquatic Ecosystems, eds. S.E.G. Findlay & R.L. Sinsabaugh. (Burlington: Academic Press), 217- 241. Kirchman, D.L. (2008). Microbial ecology of the oceans. John Wiley & Sons, Inc. Kirchman, D.L. (2016). Growth rates of microbes in the oceans. Ann Rev Mar Sci 8, 285-309. doi: 10.1146/annurev-marine-122414-033938. Kirchman, D.L., Yu, L., and Cottrell, M.T. (2003). Diversity and abundance of uncultured cytophaga- like bacteria in the Delaware estuary. Appl Environ Microbiol 69(11), 6587-6596. doi: 10.1128/aem.69.11.6587-6596.2003. Koch, A.L. (2001). Oligotrophs versus copiotrophs. Bioessays 23(7), 657-661. doi: 10.1002/bies.1091. Koch, H., Durwald, A., Schweder, T., Noriega-Ortega, B., Vidal-Melgosa, S., Hehemann, J.H., et al. (2019a). Biphasic cellular adaptations and ecological implications of Alteromonas macleodii degrading a mixture of algal polysaccharides. ISME J 13(1), 92-103. doi: 10.1038/s41396- 018-0252-4. Koch, H., Freese, H.M., Hahnke, R.L., Simon, M., and Wietz, M. (2019b). Adaptations of Alteromonas sp. 76-1 to polysaccharide degradation: a CAZyme plasmid for ulvan degradation and two alginolytic systems. Front Microbiol 10, 504. doi: 10.3389/fmicb.2019.00504. Koch, H., Germscheid, N., Freese, H.M., Noriega-Ortega, B., Lucking, D., Berger, M., et al. (2020). Genomic, metabolic and phenotypic variability shapes ecological differentiation and intraspecies interactions of Alteromonas macleodii. Sci Rep 10(1), 809. doi: 10.1038/s41598- 020-57526-5.

72 Koh, E.Y., Phua, W., and Ryan, K.G. (2011). Aerobic anoxygenic phototrophic bacteria in Antarctic sea ice and seawater. Environ Microbiol Rep 3(6), 710-716. doi: 10.1111/j.1758- 2229.2011.00286.x. Könneke, M., Bernhard, A.E., de la Torre, J.R., Walker, C.B., Waterbury, J.B., and Stahl, D.A. (2005). Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature 437(7058), 543-546. doi: 10.1038/nature03911. Könneke, M., Schubert, D.M., Brown, P.C., Hugler, M., Standfest, S., Schwander, T., et al. (2014). Ammonia-oxidizing archaea use the most energy-efficient aerobic pathway for CO2 fixation. Proc Natl Acad Sci U S A 111(22), 8239-8244. doi: 10.1073/pnas.1402028111. Kujawinski, E.B. (2011). The impact of microbial metabolism on marine dissolved organic matter. Ann Rev Mar Sci 3, 567-599. doi: 10.1146/annurev-marine-120308-081003. Kullenberg, G., and Jacobsen, T.S. (1981). The Baltic Sea: an outline of its physical oceanography. Mar Pollut Bull 12(6), 183-186. doi: 10.1016/0025-326x(81)90168-5. Kuypers, M.M.M., Marchant, H.K., and Kartal, B. (2018). The microbial nitrogen-cycling network. Nat Rev Microbiol 16(5), 263-276. doi: 10.1038/nrmicro.2018.9. Lapebie, P., Lombard, V., Drula, E., Terrapon, N., and Henrissat, B. (2019). Bacteroidetes use thousands of enzyme combinations to break down glycans. Nat Commun 10(1), 2043. doi: 10.1038/s41467-019-10068-5. Lauro, F.M., McDougald, D., Thomas, T., Williams, T.J., Egan, S., Rice, S., et al. (2009). The genomic basis of trophic strategy in marine bacteria. Proc Natl Acad Sci U S A 106(37), 15527-15533. doi: 10.1073/pnas.0903507106. Legrand, C., Fridolfsson, E., Bertos-Fortis, M., Lindehoff, E., Larsson, P., Pinhassi, J., et al. (2015). Interannual variability of phyto-bacterioplankton biomass and production in coastal and offshore waters of the Baltic Sea. Ambio 44 Suppl 3, 427-438. doi: 10.1007/s13280-015- 0662-8. Leschine, S.B. (1995). Cellulose degradation in anaerobic environments. Annu Rev Microbiol 49, 399- 426. doi: 10.1146/annurev.mi.49.100195.002151. Levin, P.A., and Angert, E.R. (2015). Small but mighty: cell size and bacteria. Cold Spring Harb Perspect Biol 7(7), a019216. doi: 10.1101/cshperspect.a019216. Lewis, L.A., and McCourt, R.M. (2004). Green algae and the origin of land plants. Am J Bot 91(10), 1535-1556. doi: 10.3732/ajb.91.10.1535. Liao, H., Lin, X., Li, Y., Qu, M., and Tian, Y. (2020). Reclassification of the taxonomic framework of orders Cellvibrionales, Oceanospirillales, Pseudomonadales, and Alteromonadales in class Gammaproteobacteria through phylogenomic tree analysis. mSystems 5(5). doi: 10.1128/mSystems.00543-20. Lidbury, I., Murrell, J.C., and Chen, Y. (2014). Trimethylamine N-oxide metabolism by abundant marine heterotrophic bacteria. Proc Natl Acad Sci U S A 111(7), 2710-2715. doi: 10.1073/pnas.1317834111. Lindahl, O. (2009). Primary phytoplankton productivity in the Gullmar Fjord, Sweden. Naturvårdsverket 6306, 1-36. Lindahl, O., Belgrano, A., Davidsson, L., and Hernroth, B. (1998). Primary production, climatic oscillations, and physico-chemical processes: the Gullmar Fjord time-series data set (1985- 1996). ICES J Mar Sci 55(4), 723-729. doi: DOI 10.1006/jmsc.1998.0379. Lindh, M.V., Sjostedt, J., Andersson, A.F., Baltar, F., Hugerth, L.W., Lundin, D., et al. (2015). Disentangling seasonal bacterioplankton population dynamics by high-frequency sampling. Environ Microbiol 17(7), 2459-2476. doi: 10.1111/1462-2920.12720. Locey, K.J., and Lennon, J.T. (2016). Scaling laws predict global microbial diversity. Proc Natl Acad Sci U S A 113(21), 5970-5975. doi: 10.1073/pnas.1521291113. Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P.M., and Henrissat, B. (2014). The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res 42(D1), D490- D495. doi: 10.1093/nar/gkt1178. Long, R.A., and Azam, F. (1996). Abundant protein-containing particles in the sea. Aquat Microb Ecol 10(3), 213-221. doi: DOI 10.3354/ame010213. Long, R.A., and Azam, F. (2001). Antagonistic interactions among marine pelagic bacteria. Appl Environ Microbiol 67(11), 4975-4983. doi: 10.1128/AEM.67.11.4975-4983.2001. Lopez-Lopez, A., Bartual, S.G., Stal, L., Onyshchenko, O., and Rodriguez-Valera, F. (2005). Genetic analysis of housekeeping genes reveals a deep-sea ecotype of Alteromonas macleodii in the

73 Mediterranean Sea. Environ Microbiol 7(5), 649-659. doi: 10.1111/j.1462- 2920.2005.00733.x. Lopez-Perez, M., Gonzaga, A., Martin-Cuadrado, A.B., Onyshchenko, O., Ghavidel, A., Ghai, R., et al. (2012). Genomes of surface isolates of Alteromonas macleodii: the life of a widespread marine opportunistic copiotroph. Sci Rep 2, 696. doi: 10.1038/srep00696. Louca, S., Polz, M.F., Mazel, F., Albright, M.B.N., Huber, J.A., O'Connor, M.I., et al. (2018). Function and functional redundancy in microbial systems. Nat Ecol Evol 2(6), 936-943. doi: 10.1038/s41559-018-0519-1. Malmstrom, R.R., Kiene, R.P., Cottrell, M.T., and Kirchman, D.L. (2004). Contribution of SAR11 bacteria to dissolved dimethylsulfoniopropionate and amino acid uptake in the North Atlantic ocean. Appl Environ Microbiol 70(7), 4129-4135. doi: 10.1128/AEM.70.7.4129-4135.2004. Mann, A.J., Hahnke, R.L., Huang, S., Werner, J., Xing, P., Barbeyron, T., et al. (2013). The genome of the alga-associated marine flavobacterium Formosa agariphila KMM 3901T reveals a broad potential for degradation of algal polysaccharides. Appl Environ Microbiol 79(21), 6813- 6822. doi: 10.1128/AEM.01937-13. Marco, D. (2008). Metagenomics and the niche concept. Theory Biosci 127(3), 241-247. doi: 10.1007/s12064-008-0028-x. Mardis, E.R. (2011). A decade's perspective on DNA sequencing technology. Nature 470(7333), 198- 203. doi: 10.1038/nature09796. Martens-Habbena, W., Berube, P.M., Urakawa, H., de la Torre, J.R., and Stahl, D.A. (2009). Ammonia oxidation kinetics determine niche separation of nitrifying archaea and bacteria. Nature 461(7266), 976-979. doi: 10.1038/nature08465. Martinez-Garcia, M., Brazel, D.M., Swan, B.K., Arnosti, C., Chain, P.S., Reitenga, K.G., et al. (2012). Capturing single cell genomes of active polysaccharide degraders: an unexpected contribution of Verrucomicrobia. PLoS One 7(4), e35314. doi: 10.1371/journal.pone.0035314. Martiny, A.C. (2019). High proportions of bacteria are culturable across major biomes. ISME J 13(8), 2125-2128. doi: 10.1038/s41396-019-0410-3. Mason, O.U., Han, J., Woyke, T., and Jansson, J.K. (2014). Single-cell genomics reveals features of a Colwellia species that was dominant during the Deepwater Horizon oil spill. Front Microbiol 5, 332. doi: 10.3389/fmicb.2014.00332. Mason, O.U., Hazen, T.C., Borglin, S., Chain, P.S., Dubinsky, E.A., Fortney, J.L., et al. (2012). Metagenome, metatranscriptome and single-cell sequencing reveal microbial response to Deepwater Horizon oil spill. ISME J 6(9), 1715-1727. doi: 10.1038/ismej.2012.59. Mata, J., Marguerat, S., and Bahler, J. (2005). Post-transcriptional control of gene expression: a genome- wide perspective. Trends Biochem Sci 30(9), 506-514. doi: 10.1016/j.tibs.2005.07.005. Matz, C., and Jurgens, K. (2005). High motility reduces grazing mortality of planktonic bacteria. Appl Environ Microbiol 71(2), 921-929. doi: 10.1128/AEM.71.2.921-929.2005. Mayali, X., and Azam, F. (2004). Algicidal bacteria in the sea and their impact on algal blooms. J Eukaryot Microbiol 51(2), 139-144. doi: 10.1111/j.1550-7408.2004.tb00538.x. McCarren, J., Becker, J.W., Repeta, D.J., Shi, Y., Young, C.R., Malmstrom, R.R., et al. (2010). Microbial community transcriptomes reveal microbes and metabolic pathways associated with dissolved organic matter turnover in the sea. Proc Natl Acad Sci U S A 107(38), 16420- 16427. doi: 10.1073/pnas.1010732107. Merchant, S.S., and Helmann, J.D. (2012). Elemental economy: microbial strategies for optimizing growth in the face of nutrient limitation. Adv Microb Physiol 60, 91-210. doi: 10.1016/B978- 0-12-398264-3.00002-4. Merder, J., Freund, J.A., Feudel, U., Hansen, C.T., Hawkes, J.A., Jacob, B., et al. (2020). ICBM- OCEAN: Processing ultrahigh-resolution mass spectrometry data of complex molecular mixtures. Anal Chem 92(10), 6832-6838. doi: 10.1021/acs.analchem.9b05659. Mitulla, M., Dinasquet, J., Guillemette, R., Simon, M., Azam, F., and Wietz, M. (2016). Response of bacterial communities from California coastal waters to alginate particles and an alginolytic Alteromonas macleodii strain. Environ Microbiol 18(12), 4369-4377. doi: 10.1111/1462- 2920.13314. Moore, L.R., Rocap, G., and Chisholm, S.W. (1998). Physiology and molecular phylogeny of coexisting Prochlorococcus ecotypes. Nature 393(6684), 464-467. doi: 10.1038/30965.

74 Moran, M.A. (2008). "Genomics and metagenomics of marine prokaryotes," in Microbial ecology of the oceans, ed. D. Kirchman.), 91-129. Moran, M.A. (2009). Metatranscriptomics: eavesdropping on complex microbial communities. Microbe Magazine 4(7), 329-335. doi: 10.1128/microbe.4.329.1. Moran, M.A., Belas, R., Schell, M.A., Gonzalez, J.M., Sun, F., Sun, S., et al. (2007). Ecological genomics of marine Roseobacters. Appl Environ Microbiol 73(14), 4559-4569. doi: 10.1128/AEM.02580-06. Moran, M.A., Kujawinski, E.B., Stubbins, A., Fatland, R., Aluwihare, L.I., Buchan, A., et al. (2016). Deciphering ocean carbon in a changing world. Proc Natl Acad Sci U S A 113(12), 3143- 3151. doi: 10.1073/pnas.1514645113. Moran, M.A., Satinsky, B., Gifford, S.M., Luo, H., Rivers, A., Chan, L.K., et al. (2013). Sizing up metatranscriptomics. ISME J 7(2), 237-243. doi: 10.1038/ismej.2012.94. Morris, R.M., Rappe, M.S., Connon, S.A., Vergin, K.L., Siebold, W.A., Carlson, C.A., et al. (2002). SAR11 clade dominates ocean surface bacterioplankton communities. Nature 420(6917), 806-810. doi: 10.1038/nature01240. Mostajir, B., Dolan, J.R., and Rassoulzadegan, F. (1995). A simple method for the quantification of a class of labile marine pico- and nano-sized detritus: DAPI Yellow Particles (DYP). Aquat Microb Ecol 9(3), 259-266. doi: DOI 10.3354/ame009259. Mougous, J.D., Cuff, M.E., Raunser, S., Shen, A., Zhou, M., Gifford, C.A., et al. (2006). A virulence locus of Pseudomonas aeruginosa encodes a protein secretion apparatus. Science 312(5779), 1526-1530. doi: 10.1126/science.1128393. Mühlenbruch, M., Grossart, H.P., Eigemann, F., and Voss, M. (2018). Mini-review: phytoplankton- derived polysaccharides in the marine environment and their interactions with heterotrophic bacteria. Environ Microbiol 20(8), 2671-2685. doi: 10.1111/1462-2920.14302. Muir, P., Li, S., Lou, S., Wang, D., Spakowicz, D.J., Salichos, L., et al. (2016). The real cost of sequencing: scaling computation to keep pace with data generation. Genome Biol 17, 53. doi: 10.1186/s13059-016-0917-0. Mulligan, C., Fischer, M., and Thomas, G.H. (2011). Tripartite ATP-independent periplasmic (TRAP) transporters in bacteria and archaea. FEMS Microbiol Rev 35(1), 68-86. doi: 10.1111/j.1574- 6976.2010.00236.x. Murray, A.E., Arnosti, C., De La Rocha, C.L., Grossart, H.P., and Passow, U. (2007). Microbial dynamics in autotrophic and heterotrophic seawater mesocosms. II. Bacterioplankton community structure and hydrolytic enzyme activities. Aquat Microb Ecol 49, 123-141. doi: 10.3354/ame01139. Nagata, T. (2008). "Organic Matter–Bacteria Interactions in Seawater," in Microbial ecology of the oceans. (New York, NY, USA: John Wiley & Sons, Inc.), 207-241. Nelson, K.E., Weinel, C., Paulsen, I.T., Dodson, R.J., Hilbert, H., Martins dos Santos, V.A., et al. (2002). Complete genome sequence and comparative analysis of the metabolically versatile Pseudomonas putida KT2440. Environ Microbiol 4(12), 799-808. doi: 10.1046/j.1462- 2920.2002.00366.x. Neumann, A.M., Balmonte, J.P., Berger, M., Giebel, H.A., Arnosti, C., Voget, S., et al. (2015). Different utilization of alginate and other algal polysaccharides by marine Alteromonas macleodii ecotypes. Environ Microbiol 17(10), 3857-3868. doi: 10.1111/1462-2920.12862. Newton, R.J., Griffin, L.E., Bowles, K.M., Meile, C., Gifford, S., Givens, C.E., et al. (2010). Genome characteristics of a generalist marine bacterial lineage. ISME J 4(6), 784-798. doi: 10.1038/ismej.2009.150. Noell, S.E., and Giovannoni, S.J. (2019). SAR11 bacteria have a high affinity and multifunctional glycine betaine transporter. Environ Microbiol 21(7), 2559-2575. doi: 10.1111/1462- 2920.14649. Nogueira, E., and Figueiras, F.G. (2005). The microplankton succession in the Ria de Vigo revisited: species assemblages and the role of weather-induced, hydrodynamic variability. J Marine Syst 54(1-4), 139-155. doi: 10.1016/j.jmarsys.2004.07.009. Nogueira, E., Pérez, F.F., and Rı́os, A.F. (1997). Modelling thermohaline properties in an estuarine upwelling ecosystem (Rı́a de Vigo: NW Spain) using box-jenkins transfer function models. Estuar Coast Shelf Sci 44(6), 685-702. doi: 10.1006/ecss.1996.0143.

75 Noinaj, N., Guillier, M., Barnard, T.J., and Buchanan, S.K. (2010). TonB-dependent transporters: regulation, structure, and function. Annu Rev Microbiol 64, 43-60. doi: 10.1146/annurev.micro.112408.134247. Norman, J.S., Lin, L., and Barrett, J.E. (2015). Paired carbon and nitrogen metabolism by ammonia- oxidizing bacteria and archaea in temperate forest soils. Ecosphere 6(10). doi: 10.1890/es14- 00299.1. Nowinski, B., and Moran, M.A. (2021). Niche dimensions of a marine bacterium are identified using invasion studies in coastal seawater. Nat Microbiol. doi: 10.1038/s41564-020-00851-2. Nutman, A.P., Bennett, V.C., Friend, C.R., Van Kranendonk, M.J., and Chivas, A.R. (2016). Rapid emergence of life shown by discovery of 3,700-million-year-old microbial structures. Nature 537(7621), 535-538. doi: 10.1038/nature19355. Orsi, W.D., Smith, J.M., Liu, S., Liu, Z., Sakamoto, C.M., Wilken, S., et al. (2016). Diverse, uncultivated bacteria and archaea underlying the cycling of dissolved protein in the ocean. ISME J 10(9), 2158-2173. doi: 10.1038/ismej.2016.20. Osterholz, H., Niggemann, J., Giebel, H.A., Simon, M., and Dittmar, T. (2015). Inefficient microbial production of refractory dissolved organic matter in the ocean. Nat Commun 6(7422), 1-8. doi: 10.1038/ncomms8422. Ottesen, E.A., Marin, R.I., Preston, C.M., Young, C.R., Ryan, J.P., Scholin, C.A., et al. (2011). Metatranscriptomic analysis of autonomously collected and preserved marine bacterioplankton. ISME J 5(12), 1881-1895. doi: 10.1038/ismej.2011.70. Pachiadaki, M.G., Brown, J.M., Brown, J., Bezuidt, O., Berube, P.M., Biller, S.J., et al. (2019). Charting the complexity of the marine microbiome through single-cell genomics. Cell 179(7), 1623- 1635.e1611. doi: 10.1016/j.cell.2019.11.017. Pachiadaki, M.G., Sintes, E., Bergauer, K., Brown, J.M., Record, N.R., Swan, B.K., et al. (2017). Major role of nitrite-oxidizing bacteria in dark ocean carbon fixation. Science 358(6366), 1046- 1051. doi: 10.1126/science.aan8260. Pajares, S., Varona-Cordero, F., and Hernandez-Becerril, D.U. (2020). Spatial distribution patterns of bacterioplankton in the oxygen minimum zone of the tropical mexican pacific. Microb Ecol 80(3), 519-536. doi: 10.1007/s00248-020-01508-7. Palenik, B., Ren, Q., Dupont, C.L., Myers, G.S., Heidelberg, J.F., Badger, J.H., et al. (2006). Genome sequence of Synechococcus CC9311: Insights into adaptation to a coastal environment. Proc Natl Acad Sci U S A 103(36), 13555-13559. doi: 10.1073/pnas.0602963103. Palleroni, N.J., and Doudoroff, M. (1972). Some properties and taxonomic sub-divisions of the genus Pseudomonas. Annu Rev Phytopathol 10(1), 73-100. doi: 10.1146/annurev.py.10.090172.000445. Pantoja, O. (2012). High affinity ammonium transporters: molecular mechanism of action. Front Plant Sci 3, 34. doi: 10.3389/fpls.2012.00034. Passow, U. (2002). Transparent exopolymer particles (TEP) in aquatic environments. Prog Oceanogr 55, 287-333. Patriarca, C., Sedano‐Núñez, V.T., Garcia, S.L., Bergquist, J., Bertilsson, S., Sjöberg, P.J.R., et al. (2020). Character and environmental lability of cyanobacteria‐derived dissolved organic matter. Limnol Oceanogr. doi: 10.1002/lno.11619. Pedler, B.E., Aluwihare, L.I., and Azam, F. (2014). Single bacterial strain capable of significant contribution to carbon cycling in the surface ocean. Proc Natl Acad Sci U S A 111(20), 7202- 7207. doi: 10.1073/pnas.1401887111. Pedler Sherwood, B., Shaffer, E.A., Reyes, K., Longnecker, K., Aluwihare, L.I., and Azam, F. (2015). Metabolic characterization of a model heterotrophic bacterium capable of significant chemical alteration of marine dissolved organic matter. Mar Chem 177, 357-365. doi: 10.1016/j.marchem.2015.06.027. Pelve, E.A., Fontanez, K.M., and DeLong, E.F. (2017). Bacterial succession on sinking particles in the ocean's Interior. Front Microbiol 8, 2269. doi: 10.3389/fmicb.2017.02269. Pendleton, L., Evans, K., and Visbeck, M. (2020). We need a global movement to transform ocean science for a better world. Proc Natl Acad Sci U S A. doi: 10.1073/pnas.2005485117. Pernthaler, J. (2005). Predation on prokaryotes in the water column and its ecological implications. Nat Rev Microbiol 3(7), 537-546. doi: 10.1038/nrmicro1180.

76 Pesant, S., Not, F., Picheral, M., Kandels-Lewis, S., Le Bescot, N., Gorsky, G., et al. (2015). Open science resources for the discovery and analysis of Tara Oceans data. Sci Data 2, 150023. doi: 10.1038/sdata.2015.23. Pinhassi, J., Azam, F., Hemphala, J., Long, R.A., Martinez, J., Zweifel, U.L., et al. (1999). Coupling between bacterioplankton species composition, population dynamics, and organic matter degradation. Aquat Microb Ecol 17(1), 13-26. doi: DOI 10.3354/ame017013. Pinhassi, J., Sala, M.M., Havskum, H., Peters, F., Guadayol, O., Malits, A., et al. (2004). Changes in bacterioplankton composition under different phytoplankton regimens. Appl Environ Microbiol 70(11), 6753-6766. doi: 10.1128/AEM.70.11.6753-6766.2004. Pitcher, G.C., Walker, D.R., Mitchellinnes, B.A., and Moloney, C.L. (1991). Short-term variability during an anchor station study in the southern Benguela Upwelling System - phytoplankton dynamics. Prog Oceanogr 28(1-2), 39-64. doi: Doi 10.1016/0079-6611(91)90020-M. Pontiller, B., Martinez-Garcia, S., Lundin, D., and Pinhassi, J. (2020). Labile dissolved organic matter compound characteristics select for divergence in marine bacterial activity and transcription. Front Microbiol 11, 588778. doi: 10.3389/fmicb.2020.588778. Poretsky, R.S., Bano, N., Buchan, A., LeCleir, G., Kleikemper, J., Pickering, M., et al. (2005). Analysis of microbial gene transcripts in environmental samples. Appl Environ Microbiol 71(7), 4121- 4126. doi: 10.1128/AEM.71.7.4121-4126.2005. Poretsky, R.S., Sun, S., Mou, X., and Moran, M.A. (2010). Transporter genes expressed by coastal bacterioplankton in response to dissolved organic carbon. Environ Microbiol 12(3), 616-627. Pratscher, J., Dumont, M.G., and Conrad, R. (2011). Ammonia oxidation coupled to CO2 fixation by archaea and bacteria in an agricultural soil. Proc Natl Acad Sci U S A 108(10), 4170-4175. doi: 10.1073/pnas.1010981108. Pruitt, K.D., Tatusova, T., and Maglott, D.R. (2007). NCBI reference sequences (RefSeq): a curated non- redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 35(Database issue), D61-65. doi: 10.1093/nar/gkl842. Pujalte, M.J., Lucena, T., Ruvira, M.A., Arahal, D.R., and Macián, M.C. (2014). "The family Rhodobacteraceae," in The Prokaryotes: Alphaproteobacteria and Betaproteobacteria, eds. E. Rosenberg, E.F. DeLong, S. Lory, E. Stackebrandt & F. Thompson. (Berlin, Heidelberg: Springer Berlin Heidelberg), 439-512. Rawlings, N.D. (2016). Peptidase specificity from the substrate cleavage collection in the MEROPS database and a tool to measure cleavage site conservation. Biochimie 122, 5-30. doi: 10.1016/j.biochi.2015.10.003. Rawlings, N.D., Barrett, A.J., and Bateman, A. (2010). MEROPS: the peptidase database. Nucleic Acids Res 38(Database issue), D227-233. doi: 10.1093/nar/gkp971. Rawlings, N.D., Barrett, A.J., Thomas, P.D., Huang, X., Bateman, A., and Finn, R.D. (2018). The MEROPS database of proteolytic enzymes, their substrates and inhibitors in 2017 and a comparison with peptidases in the PANTHER database. Nucleic Acids Res 46(D1), D624- D632. doi: 10.1093/nar/gkx1134. Redmond, M.C., and Valentine, D.L. (2012). Natural gas and temperature structured a microbial community response to the Deepwater Horizon oil spill. Proc Natl Acad Sci U S A 109(50), 20292-20297. doi: 10.1073/pnas.1108756108. Reintjes, G., Arnosti, C., Fuchs, B., and Amann, R. (2019a). Selfish, sharing and scavenging bacteria in the Atlantic Ocean: a biogeographical study of bacterial substrate utilisation. ISME J 13(5), 1119-1132. doi: 10.1038/s41396-018-0326-3. Reintjes, G., Arnosti, C., Fuchs, B.M., and Amann, R. (2017). An alternative polysaccharide uptake mechanism of marine bacteria. ISME J 11(7), 1640-1650. doi: 10.1038/ismej.2017.26. Reintjes, G., Tegetmeyer, H.E., Burgisser, M., Orlic, S., Tews, I., Zubkov, M., et al. (2019b). On-Site Analysis of Bacterial Communities of the Ultraoligotrophic South Pacific Gyre. Appl Environ Microbiol 85(14). doi: 10.1128/AEM.00184-19. Reji, L., and Francis, C.A. (2020). Metagenome-assembled genomes reveal unique metabolic adaptations of a basal marine Thaumarchaeota lineage. ISME J 14(8), 2105-2115. doi: 10.1038/s41396-020-0675-6. Reusch, T.B.H., Dierking, J., Andersson, H.C., Bonsdorff, E., Carstensen, J., Casini, M., et al. (2018). The Baltic Sea as a time machine for the future coastal ocean. Sci Adv 4(5), 1-16. doi: 10.1126/sciadv.aar8195.

77 Riemann, L., Steward, G.F., and Azam, F. (2000). Dynamics of bacterial community composition and activity during a mesocosm diatom bloom. Appl Environ Microbiol 66(2), 578-587. Rocap, G., Larimer, F.W., Lamerdin, J., Malfatti, S., Chain, P., Ahlgren, N.A., et al. (2003). Genome divergence in two Prochlorococcus ecotypes reflects oceanic niche differentiation. Nature 424(6952), 1042-1047. doi: 10.1038/nature01947. Rocke, E., Pachiadaki, M.G., Cobban, A., Kujawinski, E.B., and Edgcomb, V.P. (2015). Protist community grazing on prokaryotic prey in deep ocean water masses. PLoS One 10(4), e0124505. doi: 10.1371/journal.pone.0124505. Saier, M.H., Jr., Tran, C.V., and Barabote, R.D. (2006). TCDB: the transporter classification database for membrane transport protein analyses and information. Nucleic Acids Res 34(Database issue), D181-186. doi: 10.1093/nar/gkj001. Salazar, G., Paoli, L., Alberti, A., Huerta-Cepas, J., Ruscheweyh, H.J., Cuenca, M., et al. (2019). Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179(5), 1068-1083 e1021. doi: 10.1016/j.cell.2019.10.014. Salazar, G., and Sunagawa, S. (2017). Marine microbial diversity. Curr Biol 27(11), R489-R494. doi: 10.1016/j.cub.2017.01.017. Samo, T.J., Malfatti, F., and Azam, F. (2008). A new class of transparent organic particles in seawater visualized by a novel fluorescence approach. Aquat Microb Ecol 53, 307-321. doi: 10.3354/ame01251. Sanchez, O., Ferrera, I., Mabrito, I., Gazulla, C.R., Sebastian, M., Auladell, A., et al. (2020). Seasonal impact of grazing, viral mortality, resource availability and light on the group-specific growth rates of coastal Mediterranean bacterioplankton. Sci Rep 10(1), 19773. doi: 10.1038/s41598- 020-76590-5. Sandaa, R.A., Gomez-Consarnau, L., Pinhassi, J., Riemann, L., Malits, A., Weinbauer, M.G., et al. (2009). Viral control of bacterial biodiversity - evidence from a nutrient-enriched marine mesocosm experiment. Environ Microbiol 11(10), 2585-2597. doi: 10.1111/j.1462- 2920.2009.01983.x. Santoro, A.E., Richter, R.A., and Dupont, C.L. (2019). Planktonic marine archaea. Ann Rev Mar Sci 11, 131-158. doi: 10.1146/annurev-marine-121916-063141. Santos-Beneit, F. (2015). The Pho regulon: a huge regulatory network in bacteria. Front Microbiol 6, 402. doi: 10.3389/fmicb.2015.00402. Santos-Junior, C.D., Sarmento, H., de Miranda, F.P., Henrique-Silva, F., and Logares, R. (2020). Uncovering the genomic potential of the Amazon River microbiome to degrade rainforest organic matter. Microbiome 8(1), 151. doi: 10.1186/s40168-020-00930-w. Sarmento, H., and Gasol, J.M. (2012). Use of phytoplankton-derived dissolved organic carbon by different types of bacterioplankton. Environ Microbiol 14(9), 2348-2360. doi: 10.1111/j.1462-2920.2012.02787.x. Satomi, M., and Fujii, T. (2014). "The Family Oceanospirillaceae," in The Prokaryotes: Gammaproteobacteria, eds. E. Rosenberg, E.F. DeLong, S. Lory, E. Stackebrandt & F. Thompson. (Berlin, Heidelberg: Springer Berlin Heidelberg), 491-527. Sboner, A., Mu, X.J., Greenbaum, D., Auerbach, R.K., and Gerstein, M.B. (2011). The real cost of sequencing: higher than you think! Genome Biol 12(8), 125. doi: 10.1186/gb-2011-12-8-125. Scanlan, D.J., Ostrowski, M., Mazard, S., Dufresne, A., Garczarek, L., Hess, W.R., et al. (2009). Ecological genomics of marine picocyanobacteria. Microbiol Mol Biol Rev 73(2), 249-299. doi: 10.1128/MMBR.00035-08. Schirrmeister, B.E., Antonelli, A., and Bagheri, H.C. (2011). The origin of multicellularity in cyanobacteria. BMC Evol Biol 11, 45. doi: 10.1186/1471-2148-11-45. Schollhorn, E., and Graneli, E. (1996). Influence of different nitrogen to silica ratios and artificial mixing on the structure of a summer phytoplankton community from the Swedish west coast (Gullmar fjord). J Sea Res 35(1-3), 159-167. doi: Doi 10.1016/S1385-1101(96)90743-1. Schwalbach, M.S., Tripp, H.J., Steindler, L., Smith, D.P., and Giovannoni, S.J. (2010). The presence of the glycolysis operon in SAR11 genomes is positively correlated with ocean productivity. Environ Microbiol 12(2), 490-500. doi: 10.1111/j.1462-2920.2009.02092.x. Sebastian, M., and Gasol, J.M. (2013). Heterogeneity in the nutrient limitation of different bacterioplankton groups in the Eastern Mediterranean Sea. ISME J 7(8), 1665-1668. doi: 10.1038/ismej.2013.42.

78 Seymour, J.R., Amin, S.A., Raina, J.B., and Stocker, R. (2017). Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships. Nat Microbiol 2, 17065. doi: 10.1038/nmicrobiol.2017.65. Shendure, J., Balasubramanian, S., Church, G.M., Gilbert, W., Rogers, J., Schloss, J.A., et al. (2017). DNA sequencing at 40: past, present and future. Nature 550(7676), 345-353. doi: 10.1038/nature24286. Shi, Y., McCarren, J., and DeLong, E.F. (2012). Transcriptional responses of surface water marine microbial assemblages to deep-sea water amendment. Environ Microbiol 14(1), 191-206. doi: 10.1111/j.1462-2920.2011.02598.x. Shi, Y., Tyson, G.W., Eppley, J.M., and DeLong, E.F. (2011). Integrated metatranscriptomic and metagenomic analyses of stratified microbial assemblages in the open ocean. ISME J 5(6), 999-1013. doi: 10.1038/ismej.2010.189. Shiklomanov, I.A., and Rodda, J.C. (2003). "World water resources at the beginning of the twenty-first century," in International Hydrology Series. (Cambridge: Cambridge University Press), 1- 18. Sichert, A., Corzett, C.H., Schechter, M.S., Unfried, F., Markert, S., Becher, D., et al. (2020). Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan. Nat Microbiol. doi: 10.1038/s41564-020-0720-2. Signori, C.N., Pellizari, V.H., Enrich-Prast, A., and Sievert, S.M. (2018). Spatiotemporal dynamics of marine bacterial and archaeal communities in surface waters off the northern Antarctic Peninsula. Deep-Sea Res Pt II 149, 150-160. doi: 10.1016/j.dsr2.2017.12.017. Simek, K., Pernthaler, J., Weinbauer, M.G., Hornak, K., Dolan, J.R., Nedoma, J., et al. (2001). Changes in bacterial community composition and dynamics and viral mortality rates associated with enhanced flagellate grazing in a mesoeutrophic reservoir. Appl Environ Microbiol 67(6), 2723-2733. doi: 10.1128/AEM.67.6.2723-2733.2001. Simon, M., Scheuner, C., Meier-Kolthoff, J.P., Brinkhoff, T., Wagner-Dobler, I., Ulbrich, M., et al. (2017). Phylogenomics of Rhodobacteraceae reveals evolutionary adaptation to marine and non-marine habitats. ISME J 11(6), 1483-1499. doi: 10.1038/ismej.2016.198. Slatko, B.E., Gardner, A.F., and Ausubel, F.M. (2018). Overview of next-generation sequencing technologies. Curr Protoc Mol Biol 122(1), e59. doi: 10.1002/cpmb.59. Smayda, T.J., and Trainer, V.L. (2010). Dinoflagellate blooms in upwelling systems: Seeding, variability, and contrasts with diatom bloom behaviour. Prog Oceanogr 85(1-2), 92-107. doi: 10.1016/j.pocean.2010.02.006. Smith, D.C., Steward, G.F., Long, R.A., and Azam, F. (1995). Bacterial mediation of carbon fluxes during a diatom bloom in a mesocosm. Deep-Sea Res Pt II 42(1), 75-97. doi: 10.1016/0967- 0645(95)00005-B. Smriga, S., Fernandez, V.I., Mitchell, J.G., and Stocker, R. (2016). Chemotaxis toward phytoplankton drives organic matter partitioning among marine bacteria. Proc Natl Acad Sci U S A 113(6), 1576-1581. doi: 10.1073/pnas.1512307113. Sogin, M.L., Morrison, H.G., Huber, J.A., Mark Welch, D., Huse, S.M., Neal, P.R., et al. (2006). Microbial diversity in the deep sea and the underexplored "rare biosphere". Proc Natl Acad Sci U S A 103(32), 12115-12120. doi: 10.1073/pnas.0605127103. Solomon, C.M., Collier, J.L., Berg, G.M., and Glibert, P.M. (2010). Role of urea in microbial metabolism in aquatic systems: a biochemical and molecular review. Aquat Microb Ecol 59(1), 67-88. doi: 10.3354/ame01390. Souza, C.P., Almeida, B.C., Colwell, R.R., and Rivera, I.N. (2011). The importance of chitin in the marine environment. Mar Biotechnol (NY) 13(5), 823-830. doi: 10.1007/s10126-011-9388- 1. Sowell, S.M., Wilhelm, L.J., Norbeck, A.D., Lipton, M.S., Nicora, C.D., Barofsky, D.F., et al. (2009). Transport functions dominate the SAR11 metaproteome at low-nutrient extremes in the Sargasso Sea. ISME J 3(1), 93-105. doi: 10.1038/ismej.2008.83. Spang, A., Hatzenpichler, R., Brochier-Armanet, C., Rattei, T., Tischler, P., Spieck, E., et al. (2010). Distinct gene set in two different lineages of ammonia-oxidizing archaea supports the phylum Thaumarchaeota. Trends Microbiol 18(8), 331-340. doi: 10.1016/j.tim.2010.06.003. Spang, A., Saw, J.H., Jorgensen, S.L., Zaremba-Niedzwiedzka, K., Martijn, J., Lind, A.E., et al. (2015). Complex archaea that bridge the gap between prokaryotes and eukaryotes. Nature 521(7551), 173-179. doi: 10.1038/nature14447.

79 Spiers, A.J., Buckling, A., and Rainey, P.B. (2000). The causes of Pseudomonas diversity. Microbiology 146 ( Pt 10), 2345-2350. doi: 10.1099/00221287-146-10-2345. Spilling, K., Olli, K., Lehtoranta, J., Kremp, A., Tedesco, L., Tamelander, T., et al. (2018). Shifting diatom—dinoflagellate dominance during spring bloom in the Baltic Sea and its potential effects on biogeochemical cycling. Front Mar Sci 5. doi: 10.3389/fmars.2018.00327. Spring, S., Scheuner, C., Goker, M., and Klenk, H.P. (2015). A taxonomic framework for emerging groups of ecologically important marine gammaproteobacteria based on the reconstruction of evolutionary relationships using genome-scale data. Front Microbiol 6, 281. doi: 10.3389/fmicb.2015.00281. Staley, J.T., and Konopka, A. (1985). Measurement of in situ activities of nonphotosynthetic microorganisms in aquatic and terrestrial habitats. Ann Rev Microbiol 39, 321-346. doi: 10.1146/annurev.mi.39.100185.001541. Steindler, L., Schwalbach, M.S., Smith, D.P., Chan, F., and Giovannoni, S.J. (2011). Energy starved Candidatus Pelagibacter ubique substitutes light-mediated ATP production for endogenous carbon respiration. PLoS One 6(5), e19725. doi: 10.1371/journal.pone.0019725. Steiner, P.A., De Corte, D., Geijo, J., Mena, C., Yokokawa, T., Rattei, T., et al. (2019). Highly variable mRNA half-life time within marine bacterial taxa and functional genes. Environ Microbiol. doi: 10.1111/1462-2920.14737. Stewart, F.J., Ottesen, E.A., and DeLong, E.F. (2010). Development and quantitative analyses of a universal rRNA-subtraction protocol for microbial metatranscriptomics. ISME J 4(7), 896- 907. doi: 10.1038/ismej.2010.18. Stocker, R. (2012). Marine microbes see a sea of gradients. Science 338(6107), 628-633. doi: 10.1126/science.1208929. Stocker, R., Seymour, J.R., Samadani, A., Hunt, D.E., and Polz, M.F. (2008). Rapid chemotactic response enables marine bacteria to exploit ephemeral microscale nutrient patches. Proc Natl Acad Sci U S A 105(11), 4209-4214. doi: 10.1073/pnas.0709765105. Storesund, J.E., Erga, S.R., Ray, J.L., Thingstad, T.F., and Sandaa, R.A. (2015). Top-down and bottom- up control on bacterial diversity in a western Norwegian deep-silled fjord. FEMS Microbiol Ecol 91(7). doi: 10.1093/femsec/fiv076. Storesund, J.E., Sandaa, R.A., Thingstad, T.F., Asplin, L., Albretsen, J., and Erga, S.R. (2017). Linking bacterial community structure to advection and environmental impact along a coast-fjord gradient of the Sognefjord, western Norway. Prog Oceanogr 159, 13-30. doi: 10.1016/j.pocean.2017.09.002. Strous, M., Fuerst, J.A., Kramer, E.H., Logemann, S., Muyzer, G., van de Pas-Schoonen, K.T., et al. (1999). Missing lithotroph identified as new planctomycete. Nature 400(6743), 446-449. doi: 10.1038/22749. Suen, G., Goldman, B.S., and Welch, R.D. (2007). Predicting prokaryotic ecological niches using genome sequence analysis. PLoS One 2(8), e743. doi: 10.1371/journal.pone.0000743. Sunagawa, S., Coelho, L.P., Chaffron, S., Kultima, J.R., Labadie, K., Salazar, G., et al. (2015). Structure and function of the global ocean microbiome. Science 348(6237), 1-10. doi: 10.1126/science.1261359. Tada, Y., Taniguchi, A., Nagao, I., Miki, T., Uematsu, M., Tsuda, A., et al. (2011). Differing growth responses of major phylogenetic groups of marine bacteria to natural phytoplankton blooms in the western North Pacific Ocean. Appl Environ Microbiol 77(12), 4055-4065. doi: 10.1128/AEM.02952-10. Taylor, J.R., and Stocker, R. (2012). Trade-offs of chemotactic foraging in turbulent water. Science 338(6107), 675-679. doi: 10.1126/science.1219417. Teeling, H., Fuchs, B.M., Becher, D., Klockow, C., Gardebrecht, A., Bennke, C.M., et al. (2012). Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science 336(6081), 608-611. doi: 10.1126/science.1218344. Teeling, H., and Glockner, F.O. (2012). Current opportunities and challenges in microbial metagenome analysis--a bioinformatic perspective. Brief Bioinform 13(6), 728-742. doi: 10.1093/bib/bbs039. Teufel, R., Mascaraque, V., Ismail, W., Voss, M., Perera, J., Eisenreich, W., et al. (2010). Bacterial phenylalanine and phenylacetate catabolic pathway revealed. Proc Natl Acad Sci U S A 107(32), 14390-14395. doi: 10.1073/pnas.1005399107.

80 Thornton, D.C.O. (2014). Dissolved organic matter (DOM) release by phytoplankton in the contemporary and future ocean. Eur J Phycol 49(1), 20-46. doi: 10.1080/09670262.2013.875596. Timmis, K.N. (2002). Pseudomonas putida: a cosmopolitan opportunist par excellence. Environ Microbiol 4(12), 779-781. doi: 10.1046/j.1462-2920.2002.00365.x. Tiselius, P., Belgrano, A., Andersson, L., and Lindahl, O. (2016). Primary productivity in a coastal ecosystem: a trophic perspective on a long-term time series. J Plankton Res 38(4), 1092-1102. doi: 10.1093/plankt/fbv094. Tobias-Hunefeldt, S.P., Wing, S.R., Espinel-Velasco, N., Baltar, F., and Morales, S.E. (2019). Depth and location influence prokaryotic and eukaryotic microbial community structure in New Zealand fjords. Sci Total Environ 693, 133507. doi: 10.1016/j.scitotenv.2019.07.313. Traving, S.J., Thygesen, U.H., Riemann, L., and Stedmon, C.A. (2015). A model of extracellular enzymes in free-living microbes: which strategy pays off? Appl Environ Microbiol 81(21), 7385-7393. doi: 10.1128/AEM.02070-15. Tripp, H.J., Kitner, J.B., Schwalbach, M.S., Dacey, J.W., Wilhelm, L.J., and Giovannoni, S.J. (2008). SAR11 marine bacteria require exogenous reduced sulphur for growth. Nature 452(7188), 741-744. doi: 10.1038/nature06776. van Teeseling, M.C., Mesman, R.J., Kuru, E., Espaillat, A., Cava, F., Brun, Y.V., et al. (2015). Anammox Planctomycetes have a peptidoglycan cell wall. Nat Commun 6, 6878. doi: 10.1038/ncomms7878. Vera-Ponce de Leon, A., Jahnes, B.C., Duan, J., Camuy-Velez, L.A., and Sabree, Z.L. (2020). Cultivable, host-specific Bacteroidetes symbionts exhibit diverse polysaccharolytic strategies. Appl Environ Microbiol 86(8). doi: 10.1128/AEM.00091-20. Verdugo, P., Alldredge, A.L., Azam, F., Kirchman, D.L., Passow, U., and Santschi, P.H. (2004). The oceanic gel phase: a bridge in the DOM-POM continuum. Mar Chem 92(1-4), 67-85. doi: 10.1016/j.marchem.2004.06.017. Vidal-Melgosa, S., Sichert, A., Francis, T.B., Bartosik, D., Niggemann, J., Wichels, A., et al. (2021). Diatom fucan polysaccharide precipitates carbon during algal blooms. Nat Commun 12(1), 1150. doi: 10.1038/s41467-021-21009-6. Villamaña, M., Marañón, E., Cermeño, P., Estrada, M., Fernández-Castro, B., Figueiras, F.G., et al. (2019). The role of mixing in controlling resource availability and phytoplankton community composition. Prog Oceanogr 178. doi: 10.1016/j.pocean.2019.102181. Vislova, A., Sosa, O.A., Eppley, J.M., Romano, A.E., and DeLong, E.F. (2019). Diel oscillation of microbial gene transcripts declines with depth in oligotrophic ocean waters. Front Microbiol 10. doi: 10.3389/fmicb.2019.02191. Visser, A.W., and Kiørboe, T. (2006). Plankton motility patterns and encounter rates. Oecologia 148(3), 538-546. doi: 10.1007/s00442-006-0385-4. Vorobev, A., Sharma, S., Yu, M., Lee, J., Washington, B.J., Whitman, W.B., et al. (2018). Identifying labile DOM components in a coastal ocean through depleted bacterial transcripts and chemical signals. Environ Microbiol 20(8), 3012-3030. doi: 10.1111/1462-2920.14344. Voss, M., Dippner, J.W., Humborg, C., Hürdler, J., Korth, F., Neumann, T., et al. (2011). History and scenarios of future development of Baltic Sea eutrophication. Estuar Coast Shelf Sci 92(3), 307-322. doi: 10.1016/j.ecss.2010.12.037. Wagner, M., and Horn, M. (2006). The Planctomycetes, Verrucomicrobia, Chlamydiae and sister phyla comprise a superphylum with biotechnological and medical relevance. Curr Opin Biotechnol 17(3), 241-249. doi: 10.1016/j.copbio.2006.05.005. Waite, A.M., Gustafsson, O., Lindahl, O., and Tiselius, P. (2005). Linking ecosystem dynamics and biogeochemistry: Sinking fractionation of organic carbon in a Swedish fjord. Limnol Oceanogr 50(2), 658-671. doi: DOI 10.4319/lo.2005.50.2.0658. Walker, C.B., de la Torre, J.R., Klotz, M.G., Urakawa, H., Pinel, N., Arp, D.J., et al. (2010). Nitrosopumilus maritimus genome reveals unique mechanisms for nitrification and autotrophy in globally distributed marine crenarchaea. Proc Natl Acad Sci U S A 107(19), 8818-8823. doi: 10.1073/pnas.0913533107. Wasi, S., Tabrez, S., and Ahmad, M. (2013). Use of Pseudomonas spp. for the bioremediation of environmental pollutants: a review. Environ Monit Assess 185(10), 8147-8155. doi: 10.1007/s10661-013-3163-x.

81 Wasmund, N., Tuimala, J., Suikkanen, S., Vandepitte, L., and Kraberg, A. (2011). Long-term trends in phytoplankton composition in the western and central Baltic Sea. J Mar Syst 87(2), 145-159. doi: 10.1016/j.jmarsys.2011.03.010. Waterbury, J.B., Watson, S.W., Guillard, R.R.L., and Brand, L.E. (1979). Widespread occurrence of a unicellular, marine, planktonic, cyanobacterium. Nature 277(5694), 293-294. doi: 10.1038/277293a0. Weiss, M.S., Abele, U., Weckesser, J., Welte, W., Schiltz, E., and Schulz, G.E. (1991). Molecular architecture and electrostatic properties of a bacterial porin. Science 254(5038), 1627-1630. Whitman, W.B., Coleman, D.C., and Wiebe, W.J. (1998). Prokaryotes: The unseen majority. Proc Natl Acad Sci U S A 95, 6578-6583. Wietz, M., Wemheuer, B., Simon, H., Giebel, H.A., Seibt, M.A., Daniel, R., et al. (2015). Bacterial community dynamics during polysaccharide degradation at contrasting sites in the Southern and Atlantic Oceans. Environ Microbiol 17(10), 3822-3831. doi: 10.1111/1462-2920.12842. Wilkerson, F.P., Lassiter, A.M., Dugdale, R.C., Marchi, A., and Hogue, V.E. (2006). The phytoplankton bloom response to wind events and upwelled nutrients during the CoOP WEST study. Deep- Sea Res Pt II 53(25-26), 3023-3048. doi: 10.1016/j.dsr2.2006.07.007. Williams, K.P., Gillespie, J.J., Sobral, B.W., Nordberg, E.K., Snyder, E.E., Shallom, J.M., et al. (2010). Phylogeny of Gammaproteobacteria. J Bacteriol 192(9), 2305-2314. doi: 10.1128/JB.01480- 09. Williams, T.J., Wilkins, D., Long, E., Evans, F., DeMaere, M.Z., Raftery, M.J., et al. (2013). The role of planktonic Flavobacteria in processing algal organic matter in coastal East Antarctica revealed using metagenomics and metaproteomics. Environ Microbiol 15(5), 1302-1317. doi: 10.1111/1462-2920.12017. Winnen, B., Hvorup, R.N., and Saier, M.H. (2003). The tripartite tricarboxylate transporter (TTT) family. Res Microbiol 154(7), 457-465. doi: 10.1016/s0923-2508(03)00126-8. Winter, C., Bouvier, T., Weinbauer, M.G., and Thingstad, T.F. (2010). Trade-offs between competition and defense specialists among unicellular planktonic organisms: the "killing the winner" hypothesis revisited. Microbiol Mol Biol Rev 74(1), 42-57. doi: 10.1128/MMBR.00034-09. Wuchter, C., Abbas, B., Coolen, M.J., Herfort, L., van Bleijswijk, J., Timmers, P., et al. (2006). Archaeal nitrification in the ocean. Proc Natl Acad Sci U S A 103(33), 12317-12322. doi: 10.1073/pnas.0600756103. Yelton, A.P., Acinas, S.G., Sunagawa, S., Bork, P., Pedros-Alio, C., and Chisholm, S.W. (2016). Global genetic capacity for mixotrophy in marine picocyanobacteria. ISME J. doi: 10.1038/ismej.2016.64. Yilmaz, P., Yarza, P., Rapp, J.Z., and Glockner, F.O. (2015). Expanding the World of Marine Bacterial and Archaeal Clades. Front Microbiol 6, 1524. doi: 10.3389/fmicb.2015.01524. Zehr, J.P., Weitz, J.S., and Joint, I. (2017). How microbes survive in the open ocean. Science 357(6352), 646-647. doi: 10.1126/science.aan5764. Zhang, H., Moon, Y.H., Watson, B.J., Suvorov, M., Santos, E., Sinnott, C.A., et al. (2011). Hydrolytic and phosphorolytic metabolism of cellobiose by the marine aerobic bacterium Saccharophagus degradans 2-40T. J Ind Microbiol Biotechnol 38(8), 1117-1125. doi: 10.1007/s10295-011-0945-4. Zhang, H., Yohe, T., Huang, L., Entwistle, S., Wu, P., Yang, Z., et al. (2018). dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res 46(W1), W95- W101. doi: 10.1093/nar/gky418. Zhang, Z., Chen, Y., Wang, R., Cai, R., Fu, Y., and Jiao, N. (2015). The fate of marine bacterial exopolysaccharide in natural marine microbial communities. PLoS One 10(11), 1-16. doi: 10.1371/journal.pone.0142690. Zhao, X., Schwartz, C.L., Pierson, J., Giovannoni, S.J., McIntosh, J.R., and Nicastro, D. (2017). Three- dimensional structure of the ultraoligotrophic marine bacterium "Candidatus Pelagibacter ubique". Appl Environ Microbiol 83(3). doi: 10.1128/AEM.02807-16. Zhao, Z., Baltar, F., and Herndl, G.J. (2020). Linking extracellular enzymes to phylogeny indicates a predominantly particle-associated lifestyle of deep-sea prokaryotes. Sci Adv 6(16). doi: 10.1126/sciadv.aaz4354.