Dataset Information

Methods for Detecting Microbial Methane Production and Consumption by Gas Chromatography.

ABSTRACT: Methane is an energy-dense fuel but is also a greenhouse gas 25 times more detrimental to the environment than CO2. Methane can be produced abiotically by serpentinization, chemically by Sabatier or Fisher-Tropsh chemistry, or biotically by microbes (Berndt et al., 1996; Horita and Berndt, 1999; Dry, 2002; Wolfe, 1982; Thauer, 1998; Metcalf et al., 2002). Methanogens are anaerobic archaea that grow by producing methane gas as a metabolic byproduct (Wolfe, 1982; Thauer, 1998). Our lab has developed and optimized three different gas chromatograph-utilizing assays to characterize methanogen metabolism (Catlett et al., 2015). Here we describe the end point and kinetic assays that can be used to measure methane production by methanogens or methane consumption by methanotrophic microbes. The protocols can be used for measuring methane production or consumption by microbial pure cultures or by enrichment cultures.

SUBMITTER: Aldridge JT

PROVIDER: S-EPMC4993457 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Methods for Detecting Microbial Methane Production and Consumption by Gas Chromatography.

Aldridge Jared T JT Catlett Jennie L JL Smith Megan L ML Buan Nicole R NR

Bio-protocol 20160401 7

Methane is an energy-dense fuel but is also a greenhouse gas 25 times more detrimental to the environment than CO2. Methane can be produced abiotically by serpentinization, chemically by Sabatier or Fisher-Tropsh chemistry, or biotically by microbes (Berndt et al., 1996; Horita and Berndt, 1999; Dry, 2002; Wolfe, 1982; Thauer, 1998; Metcalf et al., 2002). Methanogens are anaerobic archaea that grow by producing methane gas as a metabolic byproduct (Wolfe, 1982; Thauer, 1 ...[more]

PMID: 27559541

Similar Datasets

Project description:Farmed ruminants are the largest source of anthropogenic methane emissions globally. The methanogenic archaea responsible for these emissions use molecular hydrogen (H2), produced during bacterial and eukaryotic carbohydrate fermentation, as their primary energy source. In this work, we used comparative genomic, metatranscriptomic and co-culture-based approaches to gain a system-wide understanding of the organisms and pathways responsible for ruminal H2 metabolism. Two-thirds of sequenced rumen bacterial and archaeal genomes encode enzymes that catalyse H2 production or consumption, including 26 distinct hydrogenase subgroups. Metatranscriptomic analysis confirmed that these hydrogenases are differentially expressed in sheep rumen. Electron-bifurcating [FeFe]-hydrogenases from carbohydrate-fermenting Clostridia (e.g., Ruminococcus) accounted for half of all hydrogenase transcripts. Various H2 uptake pathways were also expressed, including methanogenesis (Methanobrevibacter), fumarate and nitrite reduction (Selenomonas), and acetogenesis (Blautia). Whereas methanogenesis-related transcripts predominated in high methane yield sheep, alternative uptake pathways were significantly upregulated in low methane yield sheep. Complementing these findings, we observed significant differential expression and activity of the hydrogenases of the hydrogenogenic cellulose fermenter Ruminococcus albus and the hydrogenotrophic fumarate reducer Wolinella succinogenes in co-culture compared with pure culture. We conclude that H2 metabolism is a more complex and widespread trait among rumen microorganisms than previously recognised. There is evidence that alternative hydrogenotrophs, including acetogenic and respiratory bacteria, can prosper in the rumen and effectively compete with methanogens for H2. These findings may help to inform ongoing strategies to mitigate methane emissions by increasing flux through alternative H2 uptake pathways, including through animal selection, dietary supplementation and methanogenesis inhibitors.

Project description:BACKGROUND:Methane 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. METHODS:Four 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. RESULTS:Deep 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. CONCLUSIONS:The 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

Methods for Detecting Microbial Methane Production and Consumption by Gas Chromatography.

Publications

Methods for Detecting Microbial Methane Production and Consumption by Gas Chromatography.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets