Metagenomic approach to infer rumen microbiome derived traits of cattle.

Ruminants enable the conversion of indigestible plant material into animal consumables, including dairy products, meat, and valuable fibers. Microbiome research is gaining popularity in livestock species because it aids in the knowledge of illnesses and efficiency processes in animals. In this study, we use WGS metagenomic data to thoroughly characterize the ruminal ecosystem of cows to infer positive and negative livestock traits determined by the microbiome. The rumen of cows from Argentina were described by combining different gene biomarkers, pathways composition and taxonomic information. Taxonomic characterization indicated that the two major phyla were Bacteroidetes and Firmicutes; in third place, Proteobacteria was highly represented followed by Actinobacteria; Prevotella, and Bacteroides were the most abundant genera. Functional profiling of carbohydrate-active enzymes indicated that members of the Glycoside Hydrolase (GH) class accounted for 52.2 to 55.6% of the total CAZymes detected, among them the most abundant were the oligosaccharide degrading enzymes. The diversity of GH families found suggested efficient hydrolysis of complex biomass. Genes of multidrug, macrolides, polymyxins, beta-lactams, rifamycins, tetracyclines, and bacitracin resistance were found below 0.12% of relative abundance. Furthermore, the clustering analysis of genera and genes that correlated to methane emissions or feed efficiency, suggested that the cows analysed could be regarded as low methane emitters and clustered with high feed efficiency reference animals. Finally, the combination of bioinformatic analyses used in this study can be applied to assess cattle traits difficult to measure and guide enhanced nutrition and breeding methods.