Núria Mach, Milka Popova, Hugo Roume, Pau Bellot, Laura M. Zingaretti, Gilles Renand, Dusko Ehrlich, Philippe Faverdin, Nadège Edouard, Andrea Rau, Jordi Estellé, Yuliaxis Ramayo-Caldas, Diego P. Morgavi, Miguel Pérez-Enciso, Nicolas Pons, Aurélien Bernard, Producció Animal, Genètica i Millora Animal, Génétique Animale et Biologie Intégrative (GABI), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institute of Agrifood Research and Technology (IRTA), Centre for Research in Agricultural Genomics (CRAG), Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), Institut National de la Recherche Agronomique (INRA)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, MetaGenoPolis, Institut National de la Recherche Agronomique (INRA), Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Apis-Gene, ANR-15-CE20-0014,Deffilait,Améliorer l'efficacité alimentaire des vaches laitières : comprendre les déterminants grace à de nouveaux outils de phénotypage pour mieux l'évaluer et élaborer des stratégies de sélection génétique en fonction des conditions d'élevage(2015), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centro Nacional de Análisi Genómico (CNAG), Centro Nacional de Análisis Genómico, MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Department of Statistics [West Lafayette], Purdue University [West Lafayette], AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Unité Mixte de Recherches sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique (INRA), European Commission, Ministerio de Economía y Competitividad (España), and Agence Nationale de la Recherche (France)
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., Y. Ramayo‐Caldas was funded by the European Union H2020 Research and Innovation programme under Marie Skłodowska‐Curie grant (P‐Sphere) agreement no 6655919. L. Zingareti is recipient of a FPI grant to achieve the PhD research from Ministry of Economy and Science (MINECO, Spain), associated with the “Centro de Excelencia Severo Ochoa 2016–2019” award SEV‐2015‐0533 to CRAG. The authors warmly thank all technical staff at the INRA Méjusseaume farm for providing high‐quality measurements. This work was funded by APIS‐GENE and used production data obtained in the Deffilait project cofounded by APIS‐GENE and the Agence Nationale de la Recherche (ANR‐15‐CE20‐0014).