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

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 (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.

Methane and Carbon Dioxide Emission of Beef Heifers in Relation with Growth and Feed Efficiency

“…Recent sequencing possibilities of the metagenome or at least the 16S and 18S rRNA genes have broadened our knowledge on the extremely variable and abundant microbial communities in the rumen fluid. Analyses of the microbiome have been conducted in beef and dairy cattle for the characterization of the communities linked to methane emission or feed efficiency [50][51][52][53][54]. In the first four studies, the abundance of a number of archea and bacteria communities was actually identified in higher emitters.…”

Stochasticity constrained by deterministic effects of diet and age drive rumen microbiome assembly dynamics

“…Moreover the rumen microbiome composition of the adult cow was found to be connected with many of its host attributes and performance [8][9][10][11] . Recent studies, using metagenomic approaches, show that the rumen microbiome genes are associated with feed conversion processes, growth rate, and appetite [12][13][14] . Despite decades of research of the rumen microbiome, fundamental questions regarding its composition are still standing.…”

Opportunities and limits of combining microbiome and genome data for complex trait prediction

“…Although authors focused on inference and not so much in prediction, Difford et al reported that no bacteria genera were significantly associated with methane emissions [4]. Other authors in turn have reported polymicrobial associations, including members of bacterial, archaeal, fungal, and protozoan communities, with methane emissions, e.g., [11,25,37–39].…”

“…These microbiome genome-wide association studies (mGWAS) suggest that microbiome abundances can be treated as any other complex trait in humans or livestock [22]. For instance, Crespo-Piazuelo et al [24] or Ramayo-Caldas et al ., [25,26] identified several quantitative trait loci (QTL) that modulate gut bacterial and eukaryotic communities. In general, although the ‘heritability’ of each genera or OTU (Operational Taxonomic Unit) is typically weak, considering the whole microbiome simultaneously should increase power [27].…”