The rumen microbial metagenome associated with high methane production in cattle
Open accessAbstract
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.
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Metagenomics has been used to study methane emissions in cattle and sheep, biomarkers to predict ruminal methanogenesis, effects of feed-conversion-ratio, breed and host genetics on rumen microbiome composition, nutrient acquisition, diet effects, and feed additives on antimicrobial-resistance gene abundance.
“Subsequently, metagenomics has been used to study many aspects of rumen microbiology, including methane emissions in cattle (Wallace et al., 2015) and sheep (Shi et al., 2014), biomarkers to predict ruminal methanogenesis (Auffret et al., 2017b), the effect of feed-conversion-ratio, and breed and host genetics on the composition of the rumen microbiome (Roehe et al., 2016), nutrient acquisition (M”
From Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present, and future (2018) · cites this paper as Wallace et al., 2015
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“Published by NRC Research Press Methanobrevibacter were 2.5 times more abundant in high than low CH4 producing beef cattle, whereas Succinivibrionaceaewere 75.9%more abundant in low than high CH4 producing cattle (Wallace et al. 2015).”
BackgroundInfluentialPasture feeding changes the bovine rumen and milk metabolome
O’Callaghan, T., Vázquez-Fresno, R., Serra-Cayuela, A., Dong, E., Mandal, R., Hennessy, D., McAuliffe, S., Dillon, P., Wishart, D., Stanton, C., et al. (2018) · Metabolites · doi:10.3390/metabo8020027 · Cited by 117 · see citing works
Auffret, M. D., Stewart, R., Dewhurst, R. J., Duthie, C.-A., Rooke, J. A., Wallace, R. J., Freeman, T. C., Snelling, T. J., Watson, M., & Roehe, R. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.02642 · Cited by 82 · see citing works
Self-citationMartinez-Fernandez, G., Denman, S. E., Cheung, J., & McSweeney, C. S. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.01871 · Cited by 59 · see citing works
Mi, L., Yang, B., Hu, X., Luo, Y., Liu, J., Yu, Z., & Wang, J. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.00575 · Cited by 50 · see citing works
Popova, M., McGovern, E., McCabe, M. S., Martin, C., Doreau, M., Arbre, M., Meale, S. J., Morgavi, D. P., & Waters, S. M. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.00937 · Cited by 48 · see citing works
- Authors
- Wallace, R. J., Rooke, J. A., McKain, N., Duthie, C.-A., Hyslop, J. J., Ross, D. W., Waterhouse, A., Watson, M., & Roehe, R.
- Affiliations
- University of Aberdeen (GB)
- Scotland's Rural College (GB)
- Roslin Institute (GB)
- Corresponding author
- Wallace, R. John
- Venue
- BMC Genomics
- Published
- 2015-10-23
- Volume
- 16
- Issue
- 1
- Pages
- 839-839
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC4619255
- OpenAlex
- W2164748414
- Cited by
- 416
- References
- 75
- Citations by year
- 2026: 37 · 2025: 41 · 2024: 51 · 2023: 44 · 2022: 41 · 2021: 39 · 2020: 47 · 2019: 22 · 2018: 44 · 2017: 37 · 2016: 13
- FWCI
- 69.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Genomics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Aberdeen University Research Archive (Aberdeen University) (submittedVersion)
- Funders
- Rural and Environment Science and Analytical Services Division
- Scottish Government
- Department for Environment, Food and Rural Affairs, UK Government
- Scotland’s Rural College
- Directorate for Biological Sciences
- Medical Research Council (MR/K001744/1)
- Biotechnology and Biological Sciences Research Council (BBS/E/D/05191132, BBS/E/D/20231761, BB/J004235/1, BBS/E/D/20211553, BB/J004235, BB/ J004243/1, BBS/E/D/20310000)
- Natural Environment Research Council (NBAF010003)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Ruminant Nutrition and Digestive Physiology
- Physical Sciences › Engineering › Building and Construction › Anaerobic Digestion and Biogas Production
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Keywords
- rumen microbiota, cattle, Methanobrevibacter, archaea, Succinivibrionaceae, Proteobacteria, acetate production, hydrogen production, beef cattle, rumen contents, greenhouse gas emissions reduction, 16S rRNA gene abundance
- MeSH terms
- Animals, Archaea, Australia, Bacteria, Cattle, Methane, RNA, Ribosomal, 16S, Rumen, Metagenome, Metagenomics, Microbiota
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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