Comparative whole-genome approach to identify traits underlying microbial interactions

Abstract

BackgroundInteractions among microorganisms affect the structure and function of microbial communities, potentially affecting ecosystem health and biogeochemical cycles. The functional traits mediating microbial interactions are known for several model organisms, but the prevalence of these traits across microbial diversity is unknown.ResultsWe developed a new genomic approach to systematically explore the occurrence of metabolic functions and specific interaction traits, and applied it to 473 sequenced genomes from marine bacteria. The bacteria could be divided into coherent genome functional clusters (GFCs), some of which are consistent with known bacterial ecotypes (e.g. within pico-Cyanobacteria and Vibrio taxa) while others suggest new ecological units (e.g. Marinobacter, Alteromonas and Pseudoalteromonas). Some traits important for microbial interactions, such as the production of and resistance towards antimicrobial compounds and the production of phytohormones, are widely distributed among the GFCs. Other traits, such as the production of siderophores and secretion systems, as well as the production and export of specific B vitamins, are less common. Linked Trait Clusters (LTCs) include traits that may have evolved together, for example chemotaxis, motility and adhesion are linked with regulatory systems involved in virulence and biofilm formation.ConclusionsOur results highlight specific GFCs, such as those comprising Alpha- and Gammaproteobacteria, as particularly poised to interact both synergistically and antagonistically with co-occurring bacteria and phytoplankton. Similar efficient processing of multidimensional microbial information will be increasingly essential for translating genomes into ecosystem understanding across biomes, and identifying the fundamental rules that govern community dynamics and assembly.

Metabolic phenotyping of marine heterotrophs on refactored media reveals diverse metabolic adaptations and lifestyle strategies

“…This agrees with our observation that these strains did not grow in the medium lacking carbon sources, although it is interesting to speculate about the possibility that the Ethylmalonyl-CoA pathway could play a dual role in organic acid assimilation (as supported by the enrichment mentioned above) and CO2 fixation, as documented in other photoheterotrophs (63). Taken together, these cryptic associations can be interpreted as correlated adaptations with a potential ecological significance, similar to recently identified linked trait clusters (46).…”

“…Linear regression revealed an improved and significant but still minor association between growth profile distance and genome distance (Figure 3B; adjusted R-squared = 0.2004, p-value < 2.2x10-16). The same was true when we compared the growth profile distance to the distance between KEGG modules, which better approximates the metabolic distance between strains (Figure 3C) (46). Taken together, global genomic data, at least with the metrics used so far, do not seem to capture the differences in metabolic phenotypes observed between strains.…”

“…In order to generate a collection of strains representative of major marine lineages common to both the global oligotrophic and temperate oceans, we collected 63 heterotrophic isolates from different sources (Supplementary table S1). Strains were selected via a comprehensive genomic analysis conducted in a recent effort (46), in which over 400 high-quality reference genomes were clustered into functional groups using a trait-based approach focusing on metabolism and microbial interactions. Representative strains were chosen from each functional cluster, applying the additional criteria that they be culturable in standard laboratory conditions (at 26 o C in Marine Broth), and have a BSL1 rating.…”

“…The gene content distance (Gab) and the metabolic trait distance (KEGG module distance, Mab) for all pairs of strains were calculated as described in Zoccarato et al (46), using the Jaccard distance. For each pair of strains, the Euclidean distance between growth profiles was plotted against the cophenetic distance, gene content distance, and KEGG module distance.…”

Phototroph-heterotroph interactions during growth and long-term starvation across Prochlorococcus and Alteromonas diversity

“…Finally, both the coarse-grained ecological classification of microbial interactions (e.g. positive/negative) and the high-resolution mechanistic view obtained using advanced physiology and omics approaches are difficult to translate into quantitative, predictive models of organismal growth and decline (Antoniewicz, 2020; Gralka et al, 2020; Zoccarato et al, 2020). Therefore, we still face fundamental challenges in interpreting and understanding the increasingly diverse and mechanistic view of microbial interactions and translating these insights into conceptual and mathematical models of cell growth and death.…”