Multi-domain analysis of the fecal microbiome of dairy heifers, beef heifers, bred beef heifers, and adult beef cows in the central United States using paired 16S rRNA and 18S rRNA metabarcoding
The growth and performance of cattle is dependent on efficient feed conversion, facilitated by the gut microbiome, including both prokaryotic and eukaryotic organisms. Extrinsic factors such as management practices and intrinsic factors like age and genetics can shape these communities. To characterize the relative effects of such factors on the prokaryotic and eukaryotic microbiomes, fresh fecal samples from dairy heifers (n = 8), beef heifers (n = 8), bred beef heifers (n = 8), and adult beef cows (n = 8) were subjected to paired 16S rRNA (V4) and a novel 18S rRNA (V4-V5) metabarcoding assay, and 18S rRNA amplicons were annotated using two databases in parallel. Taxonomic resolution of 18S rRNA amplicons was excellent, with SILVA and EUKARYOME databases each providing better resolution of different groups of organisms. While no differences in eukaryotic richness or alpha diversity were detected, beta diversity differed in all pairwise comparisons except between beef heifers and bred beef heifers. Prokaryotic richness was lower in adult beef cows than the other groups, and beta diversity differed in all pairwise comparisons. Adult beef cows were the most distinct, characterized by replacement of fungal organisms with protozoa and loss of thermophilic bacteria in the eukaryotic and prokaryotic compartments, respectively. Among numerous differentially abundant features, dairy heifers harbored greater abundance of Cooperia oncophora while beef heifers contained greater abundance of Buxtonella sulcata and Mucor. Correlation network analysis revealed multiple significant positive and negative associations between bacterial, fungal, and protozoal features during maturation of the fecal microbiome. Collectively, these data highlight the richness and diversity of both the prokaryotic and eukaryotic compartments of the bovine fecal microbiome, and the influence of management practices and age on both communities. From a technical standpoint, these data demonstrate the ability to resolve fungal, protozoal, and metazoan organisms, including potential parasites and pathogens, with a single metabarcoding assay. They also highlight the nuanced differences between databases and the value of parallel analyses using more than one database.