Microbial and viral communities transform the chemistry of Earth's ecosystems, yet the specific reactions catalyzed by these biological engines are hard to decode due to the absence of a scalable, metabolically resolved, annotation software. Here, we present DRAM (Distilled and Refined Annotation of Metabolism), a framework to translate the deluge of microbiome-based genomic information into a catalog of microbial traits. To demonstrate the applicability of DRAM across metabolically diverse genomes, we evaluated DRAM performance on a defined, in silico soil community and previously published human gut metagenomes. We show that DRAM accurately assigned microbial contributions to geochemical cycles and automated the partitioning of gut microbial carbohydrate metabolism at substrate levels. DRAM-v, the viral mode of DRAM, established rules to identify virally-encoded auxiliary metabolic genes (AMGs), resulting in the metabolic categorization of thousands of putative AMGs from soils and guts. Together DRAM and DRAM-v provide critical metabolic profiling capabilities that decipher mechanisms underpinning microbiome function.

The current paradigm, widely incorporated in soil biogeochemical models, is that microbial methanogenesis can only occur in anoxic habitats. In contrast, here we show clear geochemical and biological evidence for methane production in well-oxygenated soils of a freshwater wetland. A comparison of oxic to anoxic soils reveal up to ten times greater methane production and nine times more methanogenesis activity in oxygenated soils. Metagenomic and metatranscriptomic sequencing recover the first near-complete genomes for a novel methanogen species, and show acetoclastic production from this organism was the dominant methanogenesis pathway in oxygenated soils. This organism, Candidatus Methanothrix paradoxum, is prevalent across methane emitting ecosystems, suggesting a global significance. Moreover, in this wetland, we estimate that up to 80% of methane fluxes could be attributed to methanogenesis in oxygenated soils. Together, our findings challenge a widely held assumption about methanogenesis, with significant ramifications for global methane estimates and Earth system modeling.

ABSTRACTMicrobial and viral communities transform the chemistry of Earth’s ecosystems, yet the specific reactions catalyzed by these biological engines are hard to decode due to the absence of a scalable, metabolically resolved, annotation software. Here, we present DRAM (Distilled and Refined Annotation of Metabolism), a framework to translate the deluge of microbiome-based genomic information into a catalog of microbial traits. To demonstrate the applicability of DRAM across metabolically diverse genomes, we evaluated DRAM performance on a defined, in silico soil community and previously published human gut metagenomes. We show that DRAM accurately assigned microbial contributions to geochemical cycles, and automated the partitioning of gut microbial carbohydrate metabolism at substrate levels. DRAM-v, the viral mode of DRAM, established rules to identify virally-encoded auxiliary metabolic genes (AMGs), resulting in the metabolic categorization of thousands of putative AMGs from soils and guts. Together DRAM and DRAM-v provide critical metabolic profiling capabilities that decipher mechanisms underpinning microbiome function.

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BackgroundTriclosan is a widely used antimicrobial compound and emerging environmental contaminant. Although the role of the gut microbiome in health and disease is increasingly well established, the interaction between environmental contaminants and host microbiome is largely unexplored, with unknown consequences for host health. This study examined the effects of low, environmentally relevant levels of triclosan exposure on the fish gut microbiome. Developing fathead minnows (Pimephales promelas) were exposed to two low levels of triclosan over a 7-day exposure. Fish gastrointestinal tracts from exposed and control fish were harvested at four time points: immediately preceding and following the 7-day exposure and after 1 and 2 weeks of depuration.ResultsA total of 103 fish gut bacterial communities were characterized by high-throughput sequencing and analysis of the V3-V4 region of the 16S rRNA gene. By measures of both alpha and beta diversity, gut microbial communities were significantly differentiated by exposure history immediately following triclosan exposure. After 2 weeks of depuration, these differences disappear. Independent of exposure history, communities were also significantly structured by time. This first detailed census of the fathead minnow gut microbiome shows a bacterial community that is similar in composition to those of zebrafish and other freshwater fish. Among the triclosan-resilient members of this host-associated community are taxa associated with denitrification in wastewater treatment, taxa potentially able to degrade triclosan, and taxa from an unstudied host-associated candidate division.ConclusionsThe fathead minnow gut microbiome is rapidly and significantly altered by exposure to low, environmentally relevant levels of triclosan, yet largely recovers from this short-term perturbation over an equivalently brief time span. These results suggest that even low-level environmental exposure to a common antimicrobial compound can induce significant short-term changes to the gut microbiome, followed by restoration, demonstrating both the sensitivity and resilience of the gut flora to challenges by environmental toxicants. This short-term disruption in a developing organism may have important long-term consequences for host health. The identification of multiple taxa not often reported in the fish gut suggests that microbial nitrogen metabolism in the fish gut may be more complex than previously appreciated.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-015-0069-6) contains supplementary material, which is available to authorized users.