Dataset Information

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 (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.

SUBMITTER: Ramayo-Caldas Y

PROVIDER: S-EPMC6972549 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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

Ramayo-Caldas Yuliaxis Y Zingaretti Laura L Popova Milka M Estellé Jordi J Bernard Aurelien A Pons Nicolas N Bellot Pau P Mach Núria N Rau Andrea A Roume Hugo H Perez-Enciso Miguel M Faverdin Philippe P Edouard Nadège N Ehrlich Dusko D Morgavi Diego P DP Renand Gilles G

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie 20190816 1

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, ...[more]

PMID: 31418488

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Project description:Methane (CH4) is produced as an end product from feed fermentation in the rumen. Yield of CH4 varies between individuals despite identical feeding conditions. To get a better understanding of factors behind the individual variation, 73 dairy cows given the same feed but differing in CH4 emissions were investigated with focus on fiber digestion, fermentation end products and bacterial and archaeal composition. In total 21 cows (12 Holstein, 9 Swedish Red) identified as persistent low, medium or high CH4 emitters over a 3 month period were furthermore chosen for analysis of microbial community structure in rumen fluid. This was assessed by sequencing the V4 region of 16S rRNA gene and by quantitative qPCR of targeted Methanobrevibacter groups. The results showed a positive correlation between low CH4 emitters and higher abundance of Methanobrevibacter ruminantium clade. Principal coordinate analysis (PCoA) on operational taxonomic unit (OTU) level of bacteria showed two distinct clusters (P < 0.01) that were related to CH4 production. One cluster was associated with low CH4 production (referred to as cluster L) whereas the other cluster was associated with high CH4 production (cluster H) and the medium emitters occurred in both clusters. The differences between clusters were primarily linked to differential abundances of certain OTUs belonging to Prevotella. Moreover, several OTUs belonging to the family Succinivibrionaceae were dominant in samples belonging to cluster L. Fermentation pattern of volatile fatty acids showed that proportion of propionate was higher in cluster L, while proportion of butyrate was higher in cluster H. No difference was found in milk production or organic matter digestibility between cows. Cows in cluster L had lower CH4/kg energy corrected milk (ECM) compared to cows in cluster H, 8.3 compared to 9.7 g CH4/kg ECM, showing that low CH4 cows utilized the feed more efficient for milk production which might indicate a more efficient microbial population or host genetic differences that is reflected in bacterial and archaeal (or methanogens) populations.

Dataset Information

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

Publications

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

Similar Datasets

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