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J Anim Breed Genet. 2019 Aug 16. doi: 10.1111/jbg.12427. [Epub ahead of print]

Identification of rumen microbial biomarkers linked to methane emission in Holstein dairy cows.

Author information

1
UMR 1313 GABI, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France.
2
Animal Breeding and Genetics Program, IRTA Torre Marimon, Caldes de Montbui, Spain.
3
Department of Animal Genetics, CRAG, UAB, Bellaterra, Spain.
4
VetAgro Sup, UMR 1213 Herbivores, INRA, Université Clermont Auvergne, Saint-Genès-Champanelle, France.
5
INRA METAGENOPOLIS Unit, Jouy-en-Josas, France.
6
UMR 1348 PEGASE, INRA, Agrocampus-Ouest, Saint-Gilles, France.

Abstract

Mitigation of greenhouse gas emissions is relevant for reducing the environmental impact of ruminant production. In this study, the rumen microbiome from Holstein cows was characterized through a combination of 16S rRNA gene and shotgun metagenomic sequencing. Methane production (CH4 ) and dry matter intake (DMI) were individually measured over 4-6 weeks to calculate the CH4 yield (CH4 y = CH4 /DMI) per cow. We implemented a combination of clustering, multivariate and mixed model analyses to identify a set of operational taxonomic unit (OTU) jointly associated with CH4 y and the structure of ruminal microbial communities. Three ruminotype clusters (R1, R2 and R3) were identified, and R2 was associated with higher CH4 y. The taxonomic composition on R2 had lower abundance of Succinivibrionaceae and Methanosphaera, and higher abundance of Ruminococcaceae, Christensenellaceae and Lachnospiraceae. Metagenomic data confirmed the lower abundance of Succinivibrionaceae and Methanosphaera in R2 and identified genera (Fibrobacter and unclassified Bacteroidales) not highlighted by metataxonomic analysis. In addition, the functional metagenomic analysis revealed that samples classified in cluster R2 were overrepresented by genes coding for KEGG modules associated with methanogenesis, including a significant relative abundance of the methyl-coenzyme M reductase enzyme. Based on the cluster assignment, we applied a sparse partial least-squares discriminant analysis at the taxonomic and functional levels. In addition, we implemented a sPLS regression model using the phenotypic variation of CH4 y. By combining these two approaches, we identified 86 discriminant bacterial OTUs, notably including families linked to CH4 emission such as Succinivibrionaceae, Ruminococcaceae, Christensenellaceae, Lachnospiraceae and Rikenellaceae. These selected OTUs explained 24% of the CH4 y phenotypic variance, whereas the host genome contribution was ~14%. In summary, we identified rumen microbial biomarkers associated with the methane production of dairy cows; these biomarkers could be used for targeted methane-reduction selection programmes in the dairy cattle industry provided they are heritable.

KEYWORDS:

metagenomics; metataxonomics; methane emission; microbial biomarker

PMID:
31418488
DOI:
10.1111/jbg.12427

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