Understanding the relationships between rumen microbiome genes and metabolites to be used for prediction of cattle phenotypes

The growing world population is facing increased future nutritional needs for meat and milk which need to be produced with minimal environmental impact, e.g. reduced methane emissions from ruminants. The combination of metagenomics and metabolomics can be effectively applied to understand rumen microbial gene expression, metabolic mechanisms that affect methane emissions and to address the challenges of ruminant production. Using 36 rumen samples derived from two omics studies, we conducted an in-depth analysis of the differences in diets and methane emissions from rumen metabolites and microbial genes. The top five integrals with significant (P<0.0001) differences in terms of their intensity measured across sample groups were found to be the same when samples were divided based on diet treatments and methane emissions. Based on the combination of statistical analysis and network approaches, this paper investigates the relationships between rumen microbial genes and integrals associated with metabolites which could be used for prediction of cattle phenotypes. Up to 98% of microbial genes and metabolites have no significant (P>0.05) linear correlation. The sample correlation network constructed using both integrals associated with metabolites and relative abundances of 20 microbial genes associated with methane emission exhibited a highly modular structure, which forms well-separated clusters according to different diet treatments. The evidence from this research confirmed the response of rumen microbes to different basal diets, and these activities subsequently affect methane emissions.