Identification, Comparison, and Validation of Robust Rumen Microbial Biomarkers for Methane Emissions Using Diverse Bos Taurus Breeds and Basal Diets

Abstract

Previous shotgun metagenomic analyses of ruminal digesta identified some microbial information that might be useful as biomarkers to select cattle that emit less methane (CH₄), which is a potent greenhouse gas. It is known that methane production (g/kgDMI) and to an extent the microbial community is heritable and therefore biomarkers can offer a method of selecting cattle for low methane emitting phenotypes. In this study a wider range of Bos Taurus cattle, varying in breed and diet, was investigated to determine microbial communities and genetic markers associated with high/low CH₄ emissions. Digesta samples were taken from 50 beef cattle, comprising four cattle breeds, receiving two basal diets containing different proportions of concentrate and also including feed additives (nitrate or lipid), that may influence methane emissions. A combination of partial least square analysis and network analysis enabled the identification of the most significant and robust biomarkers of CH₄ emissions (VIP > 0.8) across diets and breeds when comparing all potential biomarkers together. Genes associated with the hydrogenotrophic methanogenesis pathway converting carbon dioxide to methane, provided the dominant biomarkers of CH₄ emissions and methanogens were the microbial populations most closely correlated with CH₄ emissions and identified by metagenomics. Moreover, these genes grouped together as confirmed by network analysis for each independent experiment and when combined. Finally, the genes involved in the methane synthesis pathway explained a higher proportion of variation in CH₄ emissions by PLS analysis compared to phylogenetic parameters or functional genes. These results confirmed the reproducibility of the analysis and the advantage to use these genes as robust biomarkers of CH₄ emissions. Volatile fatty acid concentrations and ratios were significantly correlated with CH₄, but these factors were not identified as robust enough for predictive purposes. Moreover, the methanotrophic Methylomonas genus was found to be negatively correlated with CH₄. Finally, this study confirmed the importance of using robust and applicable biomarkers from the microbiome as a proxy of CH₄ emissions across diverse production systems and environments.

Keywords: biomarkers; diets; metagenomics; methane; rumen microbiome.

Figure 1

Boxplots representing methane emissions under different conditions. High, High methane emitters (n = 25); Low, Low methane emitters (n = 25); FOR, Forage (n = 34); CONC, Concentrate (n = 16); CONT, all controls (n = 20); NIT, all samples with nitrate (n = 12); RSC, all samples with supplementary lipid (n = 12); Comb, all samples with nitrate and supplementary lipid (n = 6); AAx, all samples from Aberdeen Angus (n = 13); CHx, all samples from Charolais (n = 12); LIMx, all samples from Limousin (n = 13); Luing: all samples from Luing (n = 12). ^**P < 0.01.

Figure 2

Diversity of methanogen genera using (A) Kraken database or (B) Greengenes database. A, Acetoclastic methanogens; H, Hydrogenotrophic methanogens; M, Methylotrophic methanogens. ^**P < 0.01, ^*P < 0.05 indicates different between low and high emitting groups.

Figure 3

Linear regression between Archaea:Bacteria ratio and CH₄ emissions. Black circle: all samples. Equation for the linear regression was included in figure when the difference was significant (P < 0.05).

Figure 4

Functional clusters of microbial genes identified using network analysis for (A) the 2011 experiment (n = 1424 genes), (B) the 2013 experiment (n = 1178 genes), (C) the 2014 experiment (n = 1224 genes). Correlation analysis of microbial gene abundance was used to construct networks, where nodes represent microbial genes and edges the correlation in their abundance.