RESUMO
BACKGROUND: Rumen microorganisms carry antimicrobial resistance genes which pose a threaten to animals and humans in a One Health context. In order to tackle the emergence of antimicrobial resistance it is vital to understand how they appear, their relationship with the host, how they behave as a whole in the ruminal ecosystem or how they spread to the environment or humans. We sequenced ruminal samples from 416 Holstein dairy cows in 14 Spanish farms using nanopore technology, to uncover the presence of resistance genes and their potential effect on human, animal and environmental health. RESULTS: We found 998 antimicrobial resistance genes (ARGs) in the cow rumen and studied the 25 most prevalent genes in the 14 dairy cattle farms. The most abundant ARGs were related to the use of antibiotics to treat mastitis, metritis and lameness, the most common diseases in dairy cattle. The relative abundance (RA) of bacteriophages was positively correlated to the ARGs RA. The heritability of the RA of the more abundant ARGs ranged between 0.10 (mupA) and 0.49 (tetW), similar to the heritability of the RA of microbes that carried those ARGs. Even though these genes are carried by the microorganisms, the host is partially controlling their RA by having a more suitable rumen pH, folds, or other physiological traits that promote the growth of those microorganisms. CONCLUSIONS: We were able to determine the most prevalent ARGs (macB, msbA, parY, rpoB2, tetQ and TaeA) in the ruminal bacteria ecosystem. The rumen is a reservoir of ARGs, and strategies to reduce the ARG load from livestock must be pursued.
RESUMO
The advent of metagenomics in animal breeding poses the challenge of statistically modelling the relationship between the microbiome, the host genetics and relevant complex traits. A set of structural equation models (SEMs) of a recursive type within a Markov chain Monte Carlo (MCMC) framework was proposed here to jointly analyse the host-metagenome-phenotype relationship. A non-recursive bivariate model was set as benchmark to compare the recursive model. The relative abundance of rumen microbes (RA), methane concentration (CH4 ) and the host genetics was used as a case of study. Data were from 337 Holstein cows from 12 herds in the north and north-west of Spain. Microbial composition from each cow was obtained from whole metagenome sequencing of ruminal content samples using a MinION device from Oxford Nanopore Technologies. Methane concentration was measured with Guardian® NG infrared gas monitor from Edinburgh Sensors during cow's visits to the milking automated system. A quarterly average from the methane eructation peaks for each cow was computed and used as phenotype for CH4 . Heritability of CH4 was estimated at 0.12 ± 0.01 in both the recursive and bivariate models. Likewise, heritability estimates for the relative abundance of the taxa overlapped between models and ranged between 0.08 and 0.48. Genetic correlations between the microbial composition and CH4 ranged from -0.76 to 0.65 in the non-recursive bivariate model and from -0.68 to 0.69 in the recursive model. Regardless of the statistical model used, positive genetic correlations with methane were estimated consistently for the seven genera pertaining to the Ciliophora phylum, as well as for those genera belonging to the Euryarchaeota (Methanobrevibacter sp.), Chytridiomycota (Neocallimastix sp.) and Fibrobacteres (Fibrobacter sp.) phyla. These results suggest that rumen's whole metagenome recursively regulates methane emissions in dairy cows and that both CH4 and the microbiota compositions are partially controlled by the host genotype.
