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Comparative Whole-Genome Approach to Identify Traits bioRxiv preprint doi: https://doi.org/10.1101/2020.06.30.179929; this version posted August 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Comparative whole-genome approach to identify traits 2 underlying microbial interactions 3 4 Luca Zoccarato*a, Daniel Sher*b, Takeshi Mikic, Daniel Segrèd,e, Hans-Peter Grossart*a,f,g 5 6 (a) Department Experimental Limnology, Leibniz Institute of Freshwater Ecology and 7 Inland Fisheries (IGB), 16775 Stechlin, Germany 8 (b) Department of Marine Biology, Leon H. Charney School of Marine Sciences, 9 University of Haifa, 3498838 Haifa, Israel 10 (c) Department of Environmental Solution Technology, Ryukoku University, 612-8577 11 Kyoto, Japan 12 (d) Departments of Biology, Biomedical Engineering, Physics, Boston University, 02215 13 Boston, MA 14 (e) Bioinformatics Program & Biological Design Center, Boston University, 02215 15 Boston, MA 16 (f) Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), 14195 17 Berlin, Germany 18 (g) Institute of Biochemistry and Biology, Potsdam University, 14476 Potsdam, 19 Germany 20 21 Corresponding authors(*) 22 Luca Zoccarato, [email protected]; Daniel Sher, [email protected]; Hans- 23 Peter Grossart, [email protected] 24 25 Classification 26 BIOLOGICAL SCIENCES - Microbiology 27 28 Keywords 29 marine bacteria, functional clusters, functional traits, B vitamins, siderophore, 30 phytohormones, motility and chemotaxis, secretion systems, antimicrobial compounds 31 32 Author Contributions 33 L.Z., D.S. and H.-P.G. designed research; L.Z. and D.S. analyzed data; T.M. contributed 34 analytic tools; L.Z., D.S., D.Segrè and H.-P.G. wrote the paper. 35 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.30.179929; this version posted August 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 36 ABSTRACT 37 Interactions among microorganisms affect the structure and function of 38 microbial communities, potentially affecting ecosystem health and 39 biogeochemical cycles. The functional traits mediating microbial interactions 40 are known for several model organisms, but the prevalence of these traits 41 across microbial diversity is unknown. We developed a new genomic approach 42 to systematically explore the occurrence of metabolic functions and specific 43 interaction traits, and applied it to 473 sequenced genomes from marine 44 bacteria. The bacteria could be divided into coherent genome functional 45 clusters (GFCs), some of which are consistent with known bacterial ecotypes 46 (e.g. within pico-Cyanobacteria and Vibrio taxa) while others suggest new 47 ecological units (e.g. Marinobacter, Alteromonas and Pseudoalteromonas). 48 Some traits important for microbial interactions, such as the production of and 49 resistance towards antimicrobial compounds and the production of 50 phytohormones, are widely distributed among the GFCs. Other traits, such as 51 the production of siderophores and secretion systems, as well as the production 52 and export of specific B vitamins, are less common. Linked Trait Clusters (LTCs) 53 include traits that may have evolved together, for example chemotaxis, 54 motility and adhesion are linked with regulatory systems involved in virulence 55 and biofilm formation. Our results highlight specific GFCs, such as those 56 comprising Alpha- and Gammaproteobacteria, as particularly poised to interact 57 both synergistically and antagonistically with co-occurring bacteria and 58 phytoplankton. Similar efficient processing of multidimensional microbial 59 information will be increasingly essential for translating genomes into 60 ecosystem understanding across biomes, and identifying the fundamental rules 61 that govern community dynamics and assembly. 62 2 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.30.179929; this version posted August 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 63 Introduction 64 Interactions between microorganisms, such as symbiosis, competition and allelopathy, 65 are a central feature of microbial communities (1). In aquatic environments, 66 heterotrophic bacteria interact with microbial primary producers (phytoplankton) in 67 many ways, potentially affecting the growth of both organisms (2, 3) with 68 consequences for ecosystem functioning and biogeochemical cycles (4, 5). For 69 instance, heterotrophic bacteria consume up to 50% of the organic matter released by 70 phytoplankton, significantly affecting the dynamics of the huge pool of dissolved 71 organic carbon in the oceans (6). Thus, if and how a bacterium can interact with other 72 bacteria and eukaryotes may have important consequences for the biological carbon 73 pump in the current and future oceans (7, 8). 74 Recent studies, using specific model organisms in binary co-cultures, have started to 75 elucidate mechanisms underlying marine microbial interactions (mostly between 76 bacteria and phytoplankton). Many of these interactions are mediated by the 77 exchange of metabolites used for growth or respiration. For example, bacteria 78 associated with phytoplankton (i.e. within the phycosphere, (3, 9)), gain access to 79 labile organic carbon released by the primary producers, e.g. amino acids and small 80 sulfur-containing compounds (10–15). In return, phytoplankton benefit from an 81 increased accessibility to nutrients via bacteria-mediated processes, e.g. nitrogen and 82 phosphorus remineralization (16), vitamin supply (11, 17) and iron scavenging via 83 formation of siderophores (18, 19). In addition to such metabolic interactions, direct 84 signaling may also occur between bacteria and phytoplankton, with heterotrophic 85 bacteria directly controlling the phytoplankton cell cycle through phytohormones (10, 86 20) or harming it using toxins (15, 21). Through such specific infochemical-mediated 87 interactions, bacteria may also directly affect the rate of release of organic carbon 88 from phytoplankton, as well as rates of mortality and aggregation (15, 20, 22). 89 While much is known about microbial interactions involving model organisms such as 90 specific strains of Roseobacter (10, 15–17, 21), Alteromonas (23–25), Cyanobacteria 91 (26) or Vibrio (27, 28), little is known about the potential for such interactions to occur 92 in other species or microbial lineages. The few experimental studies that measure 93 microbial interactions across diversity (e.g. (29–31)) are usually limited in their 94 phylogenetic scope and are performed under conditions which are very different from 95 those occurring in the natural marine environment. However, the knowledge obtained 96 from model organisms on the molecular mechanisms underlying microbial interactions 97 and the increasing availability of high-quality genomes present an opportunity to map 3 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.30.179929; this version posted August 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 98 known interaction mechanisms to a large set of bacterial species from various taxa. 99 Here, we re-analyze the previously published genomes of 421 diverse marine bacteria, 100 providing an “atlas” of their functional metabolic capacity. The atlas includes also 52 101 bacteria isolated from extreme marine habitats, human and plant roots that are meant 102 to serve as functional out-groups and represent well known symbiotic plant bacteria 103 (i.e. Rhizobacteria). In particular, we focus on genomic traits likely to be involved in 104 mediating interactions between heterotrophic bacteria and other organisms. These 105 traits are estimated based on the presence of KEGG modules or of genes encoding for 106 transporters, phytohormones and secondary metabolite production. Trait-based 107 approaches offer a new perspective to investigating microbial diversity with a more 108 mechanistic understanding (32) and have been used in some specific cases to 109 highlight putative bacterial interactions (e.g., (33)). As shown below, our results 110 identify clusters of organisms whose genomes encode similar functional capacity 111 (defined as genome functional clusters: GFC). We propose that organisms belonging to 112 the same GFC are likely to interact in similar ways with other microorganisms. We also 113 identify clusters of traits that are statistically linked, and propose that these linked 114 trait clusters (LTCs) may have evolved to function together, potentially during 115 microbial interactions. GFCs and LTCs provide a framework to extend the knowledge 116 on microbial interactions gained from specific model systems, leading to testable 117 hypotheses as to the prevalence of microbial interactions across bacterial diversity. 118 119 Results & Discussion 120 Genome functional clusters, a framework to capture potential new ecotypes 121
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