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

Yuliaxis Ramayo-Caldas, Laura Zingaretti, Milka Popova, Jordi Estellé, Aurelien Bernard, Nicolas Pons, Pau Bellot, Núria Mach, Andrea Rau, Hugo Roume, Miguel Perez-Enciso, Philippe Faverdin, Nadège Edouard, Dusko Ehrlich, Diego P. Morgavi, Gilles Renand

Research output: Contribution to journalArticleResearch

9 Citations (Scopus)

Abstract

© 2019 The Authors. Journal of Animal Breeding and Genetics published by Blackwell Verlag GmbH 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 (CH4y = 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 CH4y and the structure of ruminal microbial communities. Three ruminotype clusters (R1, R2 and R3) were identified, and R2 was associated with higher CH4y. 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 CH4y. 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 CH4y 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.
Original languageEnglish
JournalJournal of Animal Breeding and Genetics
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • metagenomics
  • metataxonomics
  • methane emission
  • microbial biomarker

Fingerprint Dive into the research topics of 'Identification of rumen microbial biomarkers linked to methane emission in Holstein dairy cows'. Together they form a unique fingerprint.

Cite this