The rumen microbiome as a reservoir of antimicrobial resistance and pathogenicity genes is directly affected by diet in beef cattle

Abstract

Background: The emergence and spread of antimicrobial resistance is the most urgent current threat to human and animal health. An improved understanding of the abundance of antimicrobial resistance genes and genes associated with microbial colonisation and pathogenicity in the animal gut will have a major role in reducing the contribution of animal production to this problem. Here, the influence of diet on the ruminal resistome and abundance of pathogenicity genes was assessed in ruminal digesta samples taken from 50 antibiotic-free beef cattle, comprising four cattle breeds receiving two diets containing different proportions of concentrate.

Results: Two hundred and four genes associated with antimicrobial resistance (AMR), colonisation, communication or pathogenicity functions were identified from 4966 metagenomic genes using KEGG identification. Both the diversity and abundance of these genes were higher in concentrate-fed animals. Chloramphenicol and microcin resistance genes were dominant in samples from forage-fed animals (P < 0.001), while aminoglycoside and streptomycin resistances were enriched in concentrate-fed animals. The concentrate-based diet also increased the relative abundance of Proteobacteria, which includes many animal and zoonotic pathogens. A high ratio of Proteobacteria to (Firmicutes + Bacteroidetes) was confirmed as a good indicator for rumen dysbiosis, with eight cases all from concentrate-fed animals. Finally, network analysis demonstrated that the resistance/pathogenicity genes are potentially useful as biomarkers for health risk assessment of the ruminal microbiome.

Conclusions: Diet has important effects on the complement of AMR genes in the rumen microbial community, with potential implications for human and animal health.

Keywords: AMR; Diets; Metagenomics; Proteobacteria ratio; Rumen microbiome.

Fig. 1

a Principal Coordinates analysis (PCoA) of the structure of 204 selected genes (number of animals, n = 50 samples). Black cross: concentrate samples from 2011 experiment, white cross: concentrate samples from 2013 experiment, black triangle: forage samples from 2011 experiment, dark grey triangle: forage samples from 2013 experiment, grey triangle: forage samples from 2014 experiment. b Canonical variate analysis (CVA) of the structure of 204 selected genes (n = 50) based on diet. Black cross: samples from concentrate-fed animals (all years), grey triangle: samples from forage-fed animals (all years). Circle: 95% confidence range

Fig. 2

Relative abundance of genes significantly different between diet treatments (n = 50). Mean values with standard error are presented. Grey: samples from forage-fed animals, dark grey: samples from concentrate-fed animals. Arrow indicates the genes that are also detected in the network analysis

Fig. 3

Diversity of AMR genes between diets (n = 50). AMR genes with similar antibiotic resistance are grouped together into a final number of 8 groups

Fig. 4

Relative abundance of microbial phyla between diet treatments (n = 50). Grey: samples from forage-fed animals, dark grey: samples from concentrate-fed animals. **P value < 0.01, *P value < 0.05, °P value < 0.1

Fig. 5

Calculated Proteobacteria ratio over the three experiments (n = 50). Cutoff: values above 0.19 are considered as an indicator of rumen dysbiosis. Grey: samples from forage-fed animals, dark grey: samples from concentrate-fed animals

Fig. 6

Linear regression for studying the impact of acetate to propionate ratio to Proteobacteria ratio. a All samples (n = 50). b Samples from concentrate-fed animals (n = 16). c Samples from forage-fed animals (n = 34). Equation for the linear regression was included in figure when the difference was significant (P value < 0.05)

Fig. 7

Functional clusters of AMR genes identified using network analysis combining the three independent experiments. Correlation analysis of microbial gene abundance was used to construct networks, where nodes represent microbial genes and edges the correlation in their abundance