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Genome-resolved correlation mapping links microbial community structure to metabolic interactions driving methane production from wastewater.

ABSTRACT: Anaerobic digestion of municipal mixed sludge produces methane that can be converted into renewable natural gas. To improve economics of this microbial mediated process, metabolic interactions catalyzing biomass conversion to energy need to be identified. Here, we present a two-year time series associating microbial metabolism and physicochemistry in a full-scale wastewater treatment plant. By creating a co-occurrence network with thousands of time-resolved microbial populations from over 100 samples spanning four operating configurations, known and novel microbial consortia with potential to drive methane production were identified. Interactions between these populations were further resolved in relation to specific process configurations by mapping metagenome assembled genomes and cognate gene expression data onto the network. Prominent interactions included transcriptionally active Methanolinea methanogens and syntrophic benzoate oxidizing Syntrophorhabdus, as well as a Methanoregulaceae population and putative syntrophic acetate oxidizing bacteria affiliated with Bateroidetes (Tenuifilaceae) expressing the glycine cleavage bypass of the Wood-Ljungdahl pathway.

SUBMITTER: Kieft B

PROVIDER: S-EPMC10477309 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Genome-resolved correlation mapping links microbial community structure to metabolic interactions driving methane production from wastewater.

Kieft Brandon B Finke Niko N McLaughlin Ryan J RJ Nallan Aditi N AN Krzywinski Martin M Crowe Sean A SA Hallam Steven J SJ

Nature communications 20230904 1

Anaerobic digestion of municipal mixed sludge produces methane that can be converted into renewable natural gas. To improve economics of this microbial mediated process, metabolic interactions catalyzing biomass conversion to energy need to be identified. Here, we present a two-year time series associating microbial metabolism and physicochemistry in a full-scale wastewater treatment plant. By creating a co-occurrence network with thousands of time-resolved microbial populations from over 100 sa ...[more]

PMID: 37666802

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Project description:Trace elements are associated with the microbial degradation of organic matter and methanogenesis, as enzymes in metabolic pathways often employ trace elements as essential cofactors. However, only a few studies investigated the effects of trace elements on the metabolic activity of microbial communities associated with biogenic coalbed methane production. We aimed to determine the effects of strategically selected trace elements on structure and function of active bacterial and methanogenic communities to stimulate methane production in subsurface coalbeds. Microcosms were established with produced water and coal from coalbed methane wells located in the Powder River Basin, Wyoming, USA. In initial pilot experiments with eight different trace elements, individual amendments of Co, Cu, and Mo lead to significantly higher methane production. Transcript levels of mcrA, the key marker gene for methanogenesis, positively correlated with increased methane production. Phylogenetic analysis of the mcrA cDNA library demonstrated compositional shifts of the active methanogenic community and increase of their diversity, particularly of hydrogenotrophic methanogens. High-throughput sequencing of cDNA obtained from 16S rRNA demonstrated active and abundant bacterial groups in response to trace element amendments. Active Acetobacterium members increased in response to Co, Cu, and Mo additions. The findings of this study yield new insights into the importance of essential trace elements on the metabolic activity of microbial communities involved in subsurface coalbed methane and provide a better understanding of how microbial community composition is shaped by trace elements.IMPORTANCEMicrobial life in the deep subsurface of coal beds is limited by nutrient replenishment. While coal bed microbial communities are surrounded by carbon sources, we hypothesized that other nutrients such as trace elements needed as cofactors for enzymes are missing. Amendment of selected trace elements resulted in compositional shifts of the active methanogenic and bacterial communities and correlated with higher transcript levels of mcrA. The findings of this study yield new insights to not only identify possible limitations of microbes by replenishment of trace elements within their specific hydrological placement but also into the importance of essential trace elements for the metabolic activity of microbial communities involved in subsurface coalbed methane production and provides a better understanding of how microbial community composition is shaped by trace elements. Furthermore, this finding might help to revive already spent coal bed methane well systems with the ultimate goal to stimulate methane production.

Project description:BackgroundMethane represents 16 % of total anthropogenic greenhouse gas emissions. It has been estimated that ruminant livestock produce ca. 29 % of this methane. As individual animals produce consistently different quantities of methane, understanding the basis for these differences may lead to new opportunities for mitigating ruminal methane emissions. Metagenomics is a powerful new tool for understanding the composition and function of complex microbial communities. Here we have applied metagenomics to the rumen microbial community to identify differences in the microbiota and metagenome that lead to high- and low-methane-emitting cattle phenotypes.MethodsFour pairs of beef cattle were selected for extreme high and low methane emissions from 72 animals, matched for breed (Aberdeen-Angus or Limousin cross) and diet (high or medium concentrate). Community analysis was carried out by qPCR of 16S and 18S rRNA genes and by alignment of Illumina HiSeq reads to the GREENGENES database. Total genomic reads were aligned to the KEGG genes databasefor functional analysis.ResultsDeep sequencing produced on average 11.3 Gb per sample. 16S rRNA gene abundances indicated that archaea, predominantly Methanobrevibacter, were 2.5× more numerous (P = 0.026) in high emitters, whereas among bacteria Proteobacteria, predominantly Succinivibrionaceae, were 4-fold less abundant (2.7 vs. 11.2 %; P = 0.002). KEGG analysis revealed that archaeal genes leading directly or indirectly to methane production were 2.7-fold more abundant in high emitters. Genes less abundant in high emitters included acetate kinase, electron transport complex proteins RnfC and RnfD and glucose-6-phosphate isomerase. Sequence data were assembled de novo and over 1.5 million proteins were annotated on the subsequent metagenome scaffolds. Less than half of the predicted genes matched matched a domain within Pfam. Amongst 2774 identified proteins of the 20 KEGG orthologues that correlated with methane emissions, only 16 showed 100 % identity with a publicly available protein sequence.ConclusionsThe abundance of archaeal genes in ruminal digesta correlated strongly with differing methane emissions from individual animals, a finding useful for genetic screening purposes. Lower emissions were accompanied by higher Succinovibrionaceae abundance and changes in acetate and hydrogen production leading to less methanogenesis, as similarly postulated for Australian macropods. Large numbers of predicted protein sequences differed between high- and low-methane-emitting cattle. Ninety-nine percent were unknown, indicating a fertile area for future exploitation.

Dataset Information

Genome-resolved correlation mapping links microbial community structure to metabolic interactions driving methane production from wastewater.

Publications

Genome-resolved correlation mapping links microbial community structure to metabolic interactions driving methane production from wastewater.

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