Substrate Type Determines Metagenomic Profiles from Diverse Chemical Habitats

Environmental parameters drive phenotypic and genotypic frequency variations in microbial communities and thus control the extent and structure of microbial diversity. We tested the extent to which microbial community composition changes are controlled by shifting physiochemical properties within a hypersaline lagoon. We sequenced four sediment metagenomes from the Coorong, South Australia from samples which varied in salinity by 99 Practical Salinity Units (PSU), an order of magnitude in ammonia concentration and two orders of magnitude in microbial abundance. Despite the marked divergence in environmental parameters observed between samples, hierarchical clustering of taxonomic and metabolic profiles of these metagenomes showed striking similarity between the samples (>89%). Comparison of these profiles to those derived from a wide variety of publically available datasets demonstrated that the Coorong sediment metagenomes were similar to other sediment, soil, biofilm and microbial mat samples regardless of salinity (>85% similarity). Overall, clustering of solid substrate and water metagenomes into discrete similarity groups based on functional potential indicated that the dichotomy between water and solid matrices is a fundamental determinant of community microbial metabolism that is not masked by salinity, nutrient concentration or microbial abundance.</p

Substrate Type Determines Metagenomic Profiles from Diverse Chemical Habitats

Environmental parameters drive phenotypic and genotypic frequency variations in microbial communities and thus control the extent and structure of microbial diversity. We tested the extent to which microbial community composition changes are controlled by shifting physiochemical properties within a hypersaline lagoon. We sequenced four sediment metagenomes from the Coorong, South Australia from samples which varied in salinity by 99 Practical Salinity Units (PSU), an order of magnitude in ammonia concentration and two orders of magnitude in microbial abundance. Despite the marked divergence in environmental parameters observed between samples, hierarchical clustering of taxonomic and metabolic profiles of these metagenomes showed striking similarity between the samples (>89%). Comparison of these profiles to those derived from a wide variety of publically available datasets demonstrated that the Coorong sediment metagenomes were similar to other sediment, soil, biofilm and microbial mat samples regardless of salinity (>85% similarity). Overall, clustering of solid substrate and water metagenomes into discrete similarity groups based on functional potential indicated that the dichotomy between water and solid matrices is a fundamental determinant of community microbial metabolism that is not masked by salinity, nutrient concentration or microbial abundance

Metabolic Subsystems Over-represented and Under-represented in the Australian Sea Lion Faecal Microbiome compared to two Antarctic Seawater Microbiomes.

<p>A: The metabolic subsystems that are over-represented in the Australian sea lion faecal microbiome compared to two Antarctic seawater microbiomes. B: The metabolic subsystems that are under-represented in the Australian sea lion faecal microbiome compared to two Antarctic seawater microbiomes.</p

Taxonomic Diversity of Australian Sea Lion Gut Microbiome.

<p>A: The Australian sea lion gut microbiome was dominated by <i>Firmicutes</i> and <i>Proteobacteria</i>. The following phyla were also present in the ASL gut microbiome but had <10 sequences and thus are not shown on the graph: <i>Aquificae</i>, <i>Viridiplantae</i>, <i>Korarchaeota</i>, Bacteriophage ROSA, <i>Englenozoa</i>, <i>Lactobacillus plantarum</i> bacteriophage phiJL-1, Plasmid PCD4, Plasmid pIP404, Environmental samples, ssRNA negative strand viruses. B: <i>Firmicutes</i> were in turn dominated by <i>Clostridia</i> and <i>Bacilli</i>.</p

Multi-Dimensional Scaling Plot Comparing Australian Sea Lion Microbiome Metabolic Potential with several other Gut, Seawater and Whale Fall Microbiomes.

<p>Metabolic potential of the Australian sea lion gut microbiome is compared to publicly available seawater samples (Antarctic, North Pacific, South Pacific and Indian Oceans), gut microbiomes (human, fish, cow and chicken), and whale fall microbiomes from the MG-RAST server.</p

Metabolic Subsystems Over-represented and Under-represented in the Australian Sea Lion Faecal Microbiome compared to both Human A and Fish A Gut Microbiomes.

<p>A: The metabolic subsystems that are over-represented in the Australian sea lion faecal microbiome compared to Human A and Fish A gut microbiomes. B: The metabolic subsystems that are under-represented in the Australian sea lion faecal microbiome compared to Human A and Fish A gut microbiomes.</p

Metabolic Potential of Australian Sea Lion Gut Microbiome.

<p>A: The metabolic potential of the Australian sea lion gut microbiome is dominated by clustering-based subsystems and carbohydrates. Protein metabolism and DNA metabolism are also highly represented. Sequences coding for prophage, secondary metabolism, macromolecular synthesis and dormancy and sporulation were also present but were represented by <10 sequences each and hence are not shown here. B: The metabolic potential of the clustering based subsystems in the Australian sea lion gut microbiome are dominated by clustering based subsystems, cell division and protein export. The following metabolic functions were also present but had <10 sequences and are not shown here: hypothetical associated with RecF, carotenoid biosynthesis, tricarboxylate transporter, probably organic hydroperoxide resistance related hypothetical, protein, pigment biosynthesis, related to N-acetylglucosamine utilization subsystem, TldD cluster, tRNA sulfuration, chemotaxis, response regulators, cluster of unknown function, DNA polymerase III episolon cluster, lipoprotein B cluster, putrescine/GABA utilization cluster, D-tyrosyl-tRNA (Tyr) deacylase (EC′3.1.-.-) cluster, metaylamine utilisation, putative GGDEF doman protein related to agglutinin secretion, and siderophore biosynthesis. C: The clustering-based subsystems were further dominated by clustering-based systems (hierarchical level 3), di- and oligosaccharides, central carbohydrate metabolism, monosaccharides.</p

Synergetic impacts of turbulence and fishing reduce ocean biomass

gathered the data and helped with the analysis JCM developed the model, gathered data, and did the analysis MK contributed to model development MT helped with analysis and contributed to writing, interpretation and insight LS helped with analysis, and contributed to writing and interpretation and insigh