Rumen metagenome and metatranscriptome analyses of low methane yield sheep reveals a Sharpea-enriched microbiome characterised by lactic acid formation and utilisation
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BACKGROUND: Enteric fermentation by farmed ruminant animals is a major source of methane and constitutes the second largest anthropogenic contributor to global warming. Reducing methane emissions from ruminants is needed to ensure sustainable animal production in the future. Methane yield varies naturally in sheep and is a heritable trait that can be used to select animals that yield less methane per unit of feed eaten. We previously demonstrated elevated expression of hydrogenotrophic methanogenesis pathway genes of methanogenic archaea in the rumens of high methane yield (HMY) sheep compared to their low methane yield (LMY) counterparts. Methane production in the rumen is strongly connected to microbial hydrogen production through fermentation processes. In this study, we investigate the contribution that rumen bacteria make to methane yield phenotypes in sheep. RESULTS: Using deep sequence metagenome and metatranscriptome datasets in combination with 16S rRNA gene amplicon sequencing from HMY and LMY sheep, we show enrichment of lactate-producing Sharpea spp. in LMY sheep bacterial communities. Increased gene and transcript abundances for sugar import and utilisation and production of lactate, propionate and butyrate were also observed in LMY animals. Sharpea azabuensis and Megasphaera spp. act as important drivers of lactate production and utilisation according to phylogenetic analysis and read mappings. CONCLUSIONS: Our findings show that the rumen microbiome in LMY animals supports a rapid heterofermentative growth, leading to lactate production. We postulate that lactate is subsequently metabolised mainly to butyrate in LMY animals, producing 2 mol of hydrogen and 0.5 mol of methane per mol hexose, which represents 24 % less than the 0.66 mol of methane formed from the 2.66 mol of hydrogen produced if hexose fermentation was directly to acetate and butyrate. These findings are consistent with the theory that a smaller rumen size with a higher turnover rate, where rapid heterofermentative growth would be an advantage, results in lower hydrogen production and lower methane formation. Together with previous methanogen gene expression data, this builds a strong concept of how animal traits and microbial communities shape the methane phenotype in sheep.
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In the rumens of high methane-emitting sheep, members of Ruminococcaceae and Lachnospiraceae were found at higher relative abundance, while the rumens of low methane-emitting sheep were enriched with Erysipelotrichaceae, especially Sharpea spp.
“In the rumens of high methane-emitting sheep, members of Ruminococcaceae and Lachnospiraceae were found at higher relative abundance, while the rumens of low methane-emitting sheep were enriched with Erysipelotrichaceae, especially Sharpea spp. (Kamke et al., 2016).”
From Comparative Analysis of the Microbiota Between Sheep Rumen and Rabbit Cecum Provides New Insight Into Their Differential Methane Production (2018) · cites this paper as Kamke et al., 2016
Density of Sharpea has been shown to be significantly lower in low methane emitting animals.
“When investigating the rumen bacterial associations with methane production, density of Sharpea has been shown to be significantly lower in low methane emitting animals (Kamke et al., 2016).”
From Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present, and future (2018) · cites this paper as Kamke et al., 2016
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R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
MUSCLE: multiple sequence alignment with high accuracy and high throughput
Edgar, R. C. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh340 · Cited by 44,662 · see citing works
RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies
Stamatakis, A. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu033 · Cited by 32,291 · see citing works
ARB: a software environment for sequence data
Ludwig, W. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh293 · Cited by 6,649 · see citing works
Hobson, P. N., & Stewart, C. S. (1997) · Springer eBooks · doi:10.1007/978-94-009-1453-7 · Cited by 2,146 · see citing works
GSEA-P : a desktop application for Gene Set Enrichment Analysis
Subramanian, A., Kuehn, H., Gould, J., Tamayo, P., & Mesirov, J. P. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm369 · Cited by 1,380 · see citing works
Herrmann, K. M., & Weaver, L. M. (1999) · Annual Review of Plant Physiology and Plant Molecular Biology · doi:10.1146/annurev.arplant.50.1.473 · Cited by 1,343 · see citing works
Reichardt, N., Duncan, S. H., Young, P., Belenguer, Á., Leitch, E. C. M., Scott, K. P., Flint, H. J., & Louis, P. (2014) · The ISME Journal · doi:10.1038/ismej.2014.14 · Cited by 1,302 · see citing works
Janssen, P. H. (2010) · Animal Feed Science and Technology · doi:10.1016/j.anifeedsci.2010.07.002 · Cited by 848 · see citing works
Self-citationSpecific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants.
Shabat, S. K. B., Sasson, G., Doron-Faigenboim, A., Durman, T., Yaacoby, S., Berg Miller, M. E., White, B. A., Shterzer, N., & Mizrahi, I. (2016) · The ISME Journal · doi:10.1038/ismej.2016.62 · Cited by 823 · see citing works
The genome portal of the Department of Energy Joint Genome Institute: 2014 updates
Nordberg, H. P., Cantor, M. N., Dusheyko, S., Hua, S., Poliakov, A., Shabalov, I., Smirnova, T., Grigoriev, I. V., & Dubchak, I. L. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1069 · Cited by 775 · see citing works
Methanogens: Methane Producers of the Rumen and Mitigation Strategies
Hook, S. E., Wright, A. G., & McBride, B. W. (2010) · Archaea · doi:10.1155/2010/945785 · Cited by 565 · see citing works
Dien, B. S., Jung, H.-J. G., Vogel, K. P., Casler, M. D., Lamb, J. F. S., Iten, L., Mitchell, R. B., & Sarath, G. (2006) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2006.02.004 · Cited by 491 · see citing works
The rumen microbial metagenome associated with high methane production in cattle
Wallace, R. J., Rooke, J. A., McKain, N., Duthie, C.-A., Hyslop, J. J., Ross, D. W., Waterhouse, A., Watson, M., & Roehe, R. (2015) · BMC Genomics · doi:10.1186/s12864-015-2032-0 · Cited by 416 · see citing works
Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome
Shi, W., Moon, C. D., Leahy, S. C., Kang, D., Froula, J., Kittelmann, S., Fan, C., Deutsch, S., Gagic, D., Seedorf, H., et al. (2014) · Genome Research · doi:10.1101/gr.168245.113 · Cited by 414 · see citing works
Self-citationRoehe, R., Dewhurst, R. J., Duthie, C.-A., Rooke, J. A., McKain, N., Ross, D. W., Hyslop, J. J., Waterhouse, A., Freeman, T. C., Watson, M., et al. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1005846 · Cited by 359 · see citing works
Sander, E. G., Warner, R. G., Harrison, H. N., & Loosli, J. K. (1959) · Journal of Dairy Science · doi:10.3168/jds.s0022-0302(59)90772-6 · Cited by 317 · see citing works
Evaluation of dietary strategies to reduce methane production in ruminants: A modelling approach
Benchaar, C., Pomar, C., & Chiquette, J. (2001) · Canadian Journal of Animal Science · doi:10.4141/a00-119 · Cited by 288 · see citing works
Two different bacterial community types are linked with the low-methane emission trait in sheep.
Kittelmann, S., Pinares-Patiño, C. S., Seedorf, H., Kirk, M. R., Ganesh, S., McEwan, J. C., & Janssen, P. H. (2014) · PLoS ONE · doi:10.1371/journal.pone.0103171 · Cited by 277 · see citing works
Self-citationCounotte, G. H. M., Prins, R. A., Janssen, R. H. A. M., & deBie, M. J. A. (1981) · Applied and Environmental Microbiology · doi:10.1128/aem.42.4.649-655.1981 · Cited by 276 · see citing works
Dijkstra, J., Boer, H., van Bruchem, J., Bruining, M., & Tamminga, S. (1993) · British Journal Of Nutrition · doi:10.1079/bjn19930041 · Cited by 271 · see citing works
Methane emission by sectors: A comprehensive review of emission sources and mitigation methods
Yusuf, R. O., Noor, Z. Z., Abba, A. H., Abu-Hassan, M. A., & Din, M. F. M. (2012) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.04.008 · Cited by 254 · see citing works
Klieve, A. V., Hennessy, D., Ouwerkerk, D., Forster, R. J., Mackie, R. I., & Attwood, G. T. (2003) · Journal of Applied Microbiology · doi:10.1046/j.1365-2672.2003.02024.x · Cited by 248 · see citing works
Self-citationLow-methane yield sheep have smaller rumens and shorter rumen retention time
Goopy, J. P., Donaldson, A., Hegarty, R., Vercoe, P. E., Haynes, F., Barnett, M., & Oddy, V. H. (2014) · British Journal Of Nutrition · doi:10.1017/s0007114513002936 · Cited by 230 · see citing works
Heritability estimates of methane emissions from sheep
Pinares-Patiño, C. S., Hickey, S. M., Young, E. A., Dodds, K. G., MacLean, S., Molano, G., Sandoval, E., Kjestrup, H., Harland, R., Hunt, C., et al. (2013) · animal · doi:10.1017/s1751731113000864 · Cited by 228 · see citing works
Formate as an Intermediate in the Bovine Rumen Fermentation
Hungate, R. E., Smith, W. W., Bauchop, T., Yu, I. K., & Rabinowitz, J. C. (1970) · Journal of Bacteriology · doi:10.1128/jb.102.2.389-397.1970 · Cited by 225 · see citing works
Mackie, R. I., & Gilchrist, F. M. C. (1979) · Applied and Environmental Microbiology · doi:10.1128/aem.38.3.422-430.1979 · Cited by 190 · see citing works
Lactate and Acrylate Metabolism by Megasphaera elsdenii under Batch and Steady-State Conditions
Prabhu, R., Altman, E., & Eiteman, M. A. (2012) · Applied and Environmental Microbiology · doi:10.1128/aem.02443-12 · Cited by 186 · see citing works
Influence of Purified Materials on the Development of the Ruminant Stomach
Flatt, W. P., Warner, R. G., & Loosli, J. K. (1958) · Journal of Dairy Science · doi:10.3168/jds.s0022-0302(58)91138-x · Cited by 156 · see citing works
Ross, E. M., Moate, P. J., Marett, L. C., Cocks, B., & Hayes, B. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073056 · Cited by 138 · see citing works
Sani, N. A., Hartantyo, S. H. P., & Forsythe, S. J. (2012) · Journal of Dairy Science · doi:10.3168/jds.2012-5409 · Cited by 109 · see citing works
Regulation of lactate metabolism in the rumen
Counotte, G. H. M., & Prins, R. A. (1981) · Veterinary Research Communications · doi:10.1007/bf02214975 · Cited by 106 · see citing works
Herd, R. M., Arthur, P. F., Donoghue, K. A., Bird, S. H., Bird-Gardiner, T. L., & Hegarty, R. S. (2014) · Journal of Animal Science · doi:10.2527/jas.2014-8273 · Cited by 90 · see citing works
Repeatability of methane emissions from sheep
Pinares-Patiño, C. S., McEwan, J. C., Dodds, K. G., Cárdenas, E. A., Hegarty, R. S., Koolaard, J. P., & Clark, H. (2011) · Animal Feed Science and Technology · doi:10.1016/j.anifeedsci.2011.04.068 · Cited by 78 · see citing works
Towards Enhanced Galactose Utilization by Lactococcus lactis
Neves, A. R., Pool, W. A., Solopova, A., Kok, J., Santos, H., & Kuipers, O. P. (2010) · Applied and Environmental Microbiology · doi:10.1128/aem.01195-10 · Cited by 74 · see citing works
Ross, E. M., Moate, P. J., Marett, L. C., Cocks, B., & Hayes, B. J. (2013) · Journal of Dairy Science · doi:10.3168/jds.2013-6766 · Cited by 69 · see citing works
Two Major Archaeal Pseudomurein Endoisopeptidases: PeiW and PeiP
Visweswaran, G. R. R., Dijkstra, B. W., & Kok, J. W. (2010) · Archaea · doi:10.1155/2010/480492 · Cited by 66 · see citing works
Tribe, D. E., Camakaris, H. M., & Pittard, J. (1976) · Journal of Bacteriology · doi:10.1128/jb.127.3.1085-1097.1976 · Cited by 65 · see citing works
Is rumen retention time implicated in sheep differences in methane emission?
Pinares-Patiño, C. S., Ebrahimi, S. H., McEwan, J. C., Dodds, K. G., Clark, H., & Luo, D. (2011) · doi:10.13140/2.1.3309.5043 · Cited by 62 · see citing works
Hungate, R. E. (1966) · Elsevier eBooks · doi:10.1016/b978-1-4832-3308-6.50005-x · Cited by 57 · see citing works
The diversity of allosteric controls at the gateway to aromatic amino acid biosynthesis
Light, S. H., & Anderson, W. F. (2013) · Protein Science · doi:10.1002/pro.2233 · Cited by 56 · see citing works
Salvetti, E., Felis, G. E., Dellaglio, F., Castioni, A., Torriani, S., & Lawson, P. A. (2010) · INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY · doi:10.1099/ijs.0.029231-0 · Cited by 53 · see citing works
Smuts, M. P., Meissner, H. H., & Cronjé, P. B. (1995) · Journal of Animal Science · doi:10.2527/1995.731206x · Cited by 52 · see citing works
I. MICROBIAL ECOLOGY OF THE RUMEN
Hungate, R. E. (1960) · Bacteriological Reviews · doi:10.1128/mmbr.24.4.353-364.1960 · Cited by 52 · see citing works
I. MICROBIAL ECOLOGY OF THE RUMEN
Hungate, R. E. (1960) · Bacteriological Reviews · doi:10.1128/br.24.4.353-364.1960 · Cited by 51 · see citing works
Morita, H., Shiratori, C., Murakami, M., et al. (2008) · INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY · doi:10.1099/ijs.0.65543-0 · Cited by 36 · see citing works
Two Gene Clusters Coordinate Galactose and Lactose Metabolism in Streptococcus gordonii
Zeng, L., Martino, N., & Burne, R. A. (2012) · Applied and Environmental Microbiology · doi:10.1128/aem.01393-12 · Cited by 36 · see citing works
Bourquin, L. D., & Fahey, G. C., Jr. (1994) · Journal of Animal Science · doi:10.2527/1994.7251362x · Cited by 32 · see citing works
Genetic and environmental variation in methane emissions of sheep at pasture1
Robinson, D. L., Goopy, J. P., Hegarty, R. S., et al. (2014) · Journal of Animal Science · doi:10.2527/jas.2014-8042 · Cited by 31 · see citing works
spls: Sparse Partial Least Squares (SPLS) Regression and Classification
Chung, D., Chun, H., & Keles, S. (2008) · doi:10.32614/cran.package.spls · Cited by 28 · see citing works
Remote Control by Inter-Enzyme Allostery: A Novel Paradigm for Regulation of the Shikimate Pathway
Munack, S., Würth-Roderer, K., Ökvist, M., Fahrig‐Kamarauskaitė, J., Sasso, S., van Eerde, A., Kast, P., & Krengel, U. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2016.01.001 · Cited by 27 · see citing works
Rumen Differences between Sheep Identified as being Low or High Methane Emitters
Bain, W. E., Bezuidenhout, L., Jopson, N. B., et al. (2014) · Figshare · Cited by 13 · see citing works
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Research works printed but not linked: 1
- [2]Yusuf RO Noor ZZ Abba AH Hassan MAA Din MFM Renewable and sustainable energy reviews. Renewable and sustainable energy reviews Elsevier 2012 16 5059 70
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Cultivation and sequencing of rumen microbiome members from the Hungate1000 Collection
Hungate1000 project collaborators, Seshadri, R., Leahy, S. C., Attwood, G. T., Teh, K. H., Lambie, S. C., Cookson, A. L., Eloe-Fadrosh, E. A., Pavlopoulos, G. A., Hadjithomas, M., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4110 · Cited by 624 · see citing works
Self-citationAssembly of 913 microbial genomes from metagenomic sequencing of the cow rumen
Stewart, R. D., Auffret, M. D., Warr, A., Wiser, A. H., Press, M. O., Langford, K. W., Liachko, I., Snelling, T. J., Dewhurst, R. J., Walker, A. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03317-6 · Cited by 559 · see citing works
The rumen microbiome: balancing food security and environmental impacts
Mizrahi, I., Wallace, R. J., & Moraïs, S. (2021) · Nature Reviews Microbiology · doi:10.1038/s41579-021-00543-6 · Cited by 490 · see citing works
Huws, S. A., Creevey, C. J., Oyama, L. B., Mizrahi, I., Denman, S. E., Popova, M., Muñoz-Tamayo, R., Forano, E., Waters, S. M., Hess, M., et al. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.02161 · Cited by 487 · see citing works
Self-citationSasson, G., Kruger Ben-Shabat, S., Seroussi, E., Doron-Faigenboim, A., Shterzer, N., Yaacoby, S., Berg Miller, M. E., White, B. A., Halperin, E., & Mizrahi, I. (2017) · mBio · doi:10.1128/mbio.00703-17 · Cited by 219 · see citing works
CowPI: a rumen microbiome focussed version of the PICRUSt functional inference software
Wilkinson, T. J., Huws, S. A., Edwards, J. E., Kingston-Smith, A. H., Siu-Ting, K., Hughes, M., Rubino, F., Friedersdorff, M., & Creevey, C. J. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.01095 · Cited by 160 · see citing works
Weaning age influences the severity of gastrointestinal microbiome shifts in dairy calves
Meale, S. J., Li, S. C., Azevedo, P., Derakhshani, H., DeVries, T. J., Plaizier, J. C., Steele, M. A., & Khafipour, E. (2017) · Scientific Reports · doi:10.1038/s41598-017-00223-7 · Cited by 133 · see citing works
Edwards, J. E., Forster, R. J., Callaghan, T. M., Dollhofer, V., Dagar, S. S., Cheng, Y., Chang, J., Kittelmann, S., Fliegerova, K., Puniya, A. K., et al. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.01657 · Cited by 130 · see citing works
Self-citationAuffret, 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
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
Elekwachi, C. O., Wang, Z., Wu, X., Rabee, A., & Forster, R. J. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.01814 · Cited by 48 · see citing works
- Authors
- Kamke, J., Kittelmann, S., Soni, P., Li, Y., Tavendale, M., Ganesh, S., Janssen, P. H., Shi, W., Froula, J., Rubin, E. M., & Attwood, G. T.
- Affiliations
- AgResearch (NZ)
- Lawrence Berkeley National Laboratory (US)
- Corresponding author
- Attwood, Graeme T.
- ORCID
- Venue
- Microbiome
- Published
- 2016-10-19
- Volume
- 4
- Issue
- 1
- Pages
- 56-56
- ISSN
- 2049-2618 (ISSN-L)
- PMC
- PMC5069950
- OpenAlex
- W2534284271
- Cited by
- 339
- References
- 52
- Citations by year
- 2026: 27 · 2025: 27 · 2024: 46 · 2023: 38 · 2022: 52 · 2021: 38 · 2020: 41 · 2019: 25 · 2018: 29 · 2017: 15
- FWCI
- 48.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Microbiome (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- New Zealand Fund for Global Partnerships in Livestock Emissions Research (15809)
- Office of Science of the U.S. Department of Energy (DE-AC02-05CH11231)
- 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 microbiome, Megasphaera, lactic acid production, hydrogen production, enteric methane, rumen bacteria, butyrate, propionate, sugar metabolism
- MeSH terms
- Animals, Bacteria, Base Sequence, Butyrates, Fatty Acids, Fermentation, Hexoses, Lactobacillaceae, Methane, Propionates, RNA, Ribosomal, 16S, Rumen, Sheep, Sequence Analysis, DNA, Lactic Acid, Metagenome, Global Warming, High-Throughput Nucleotide Sequencing, Microbiota
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