An oligotrophic deep-subsurface community dependent on syntrophy is dominated by sulfur-driven autotrophic denitrifiers.

Subsurface lithoautotrophic microbial ecosystems (SLiMEs) under oligotrophic conditions are typically supported by H2 Methanogens and sulfate reducers, and the respective energy processes, are thought to be the dominant players and have been the research foci. Recent investigations showed that, in some deep, fluid-filled fractures in the Witwatersrand Basin, South Africa, methanogens contribute <5% of the total DNA and appear to produce sufficient CH4 to support the rest of the diverse community. This paradoxical situation reflects our lack of knowledge about the in situ metabolic diversity and the overall ecological trophic structure of SLiMEs. Here, we show the active metabolic processes and interactions in one of these communities by combining metatranscriptomic assemblies, metaproteomic and stable isotopic data, and thermodynamic modeling. Dominating the active community are four autotrophic ß-proteobacterial genera that are capable of oxidizing sulfur by denitrification, a process that was previously unnoticed in the deep subsurface. They co-occur with sulfate reducers, anaerobic methane oxidizers, and methanogens, which each comprise <5% of the total community. Syntrophic interactions between these microbial groups remove thermodynamic bottlenecks and enable diverse metabolic reactions to occur under the oligotrophic conditions that dominate in the subsurface. The dominance of sulfur oxidizers is explained by the availability of electron donors and acceptors to these microorganisms and the ability of sulfur-oxidizing denitrifiers to gain energy through concomitant S and H2 oxidation. We demonstrate that SLiMEs support taxonomically and metabolically diverse microorganisms, which, through developing syntrophic partnerships, overcome thermodynamic barriers imposed by the environmental conditions in the deep subsurface.

Soil organic carbon is a key determinant of CH4 sink in global forest soils.

Soil organic carbon (SOC) is a primary regulator of the forest-climate feedback. However, its indicative capability for the soil CH4 sink is poorly understood due to the incomplete knowledge of the underlying mechanisms. Therefore, SOC is not explicitly included in the current model estimation of the global forest CH4 sink. Here, using in-situ observations, global meta-analysis, and process-based modeling, we provide evidence that SOC constitutes an important variable that governs the forest CH4 sink. We find that a CH4 sink is enhanced with increasing SOC content on regional and global scales. The revised model with SOC function better reproduces the field observation and estimates a 39% larger global forest CH4 sink (24.27 Tg CH4 yr-1) than the model without considering SOC effects (17.46 Tg CH4 yr-1). This study highlights the role of SOC in the forest CH4 sink, which shall be factored into future global CH4 budget quantification.

Taxonomic and Functional Compositions Impacted by the Quality of Metatranscriptomic Assemblies.

Metatranscriptomics has recently been applied to investigate the active biogeochemical processes and elemental cycles, and in situ responses of microbiomes to environmental stimuli and stress factors. De novo assembly of RNA-Sequencing (RNA-Seq) data can reveal a more detailed description of the metabolic interactions amongst the active microbial communities. However, the quality of the assemblies and the depiction of the metabolic network provided by various de novo assemblers have not yet been thoroughly assessed. In this study, we compared 15 de novo metatranscriptomic assemblies for a fracture fluid sample collected from a borehole located at 1.34 km below land surface in a South African gold mine. These assemblies were constructed from total, non-coding, and coding reads using five de novo transcriptomic assemblers (Trans-ABySS, Trinity, Oases, IDBA-tran, and Rockhopper). They were evaluated based on the number of transcripts, transcript length, range of transcript coverage, continuity, percentage of transcripts with confident annotation assignments, as well as taxonomic and functional diversity patterns. The results showed that these parameters varied considerably among the assemblies, with Trans-ABySS and Trinity generating the best assemblies for non-coding and coding RNA reads, respectively, because the high number of transcripts assembled covered a wide expression range, and captured extensively the taxonomic and metabolic gene diversity, respectively. We concluded that the choice of de novo transcriptomic assemblers impacts substantially the taxonomic and functional compositions. Care should be taken to obtain high-quality assemblies for informing the in situ metabolic landscape.