Spatio-temporal characteristics of the gastrointestinal resistome in a cow-to-calf model and its environmental dissemination in a dairy production system

Abstract

Microbiome and resistome transmission from mother to child, as well as from animal to environment, has been widely discussed in recent years. Dairy cows mainly provide milk and meat. However, in the dairy production system, the characteristics and transmission trends of resistome assembly and the microbiome in the gastrointestinal tract (GIT) remain unclear. In this study, we sequenced the GIT (rumen fluid and feces) microbiome of dairy cow populations from two provinces in China (136 cows and 36 calves), determined the characteristics of their resistome profiles and the distribution of antibiotics resistance genes (ARGs) across bacteria and further tracked the temporal dynamics of the resistome in offspring during early life using multi-omics technologies (16S ribosomal RNA [rRNA] sequencing, metagenome, and metatranscriptome). We characterized the GIT resistome in cows, distinguished by gut sites and regions. The abundance of ARGs in calves peaked within the first 3 days after birth, with Enterobacteriaceae as the dominant microbial host. As calves aged, resistome composition stabilized, and overall ARG abundance gradually decreased. Both diet and age influenced carbohydrate-active enzymes and ARG profiles. Resistance profiles in ecological niches (meconium, colostrum, soil, and wastewater) were unique, resembling maternal sources. Mobile genetic elements (MGEs), mainly found in soil and wastewater, played an important role in mediating these interactions. Multidrug resistance consistently emerged as the most significant form of resistance at the both the metagenome and metatranscriptome levels. Several antibiotic classes showed higher proportions at the RNA level than at the DNA level, indicating that even low-abundance gene groups can have a considerable influence through high expression. This study broadens our understanding of ARG dissemination in livestock production systems, providing a foundation for developing future preventive and control strategies.

Keywords: antimicrobial resistance genes; dairy production system; gastrointestinal tract; microbiome transmission; multi‐omics analysis.

Figure 1

The rumen and hindgut microbiota of cows in Gansu and Shaanxi province. (A) Experimental design and sampling. D0–D84 represented that each cow and related environmental samples are sampled at their corresponding age points. These newborn calves were fed with colostrum from their dam within half an hour after birth and then transferred to the clean and tidy separate calf hutches. Calves were fed milk from the age of 1–3 weeks, fed milk replacer (Table S2) from the age of 3–8 weeks, and weaned at 56 days of age. After weaning, the calves were maintained in calf hutches until the end of the experiment at 84 days of age. The calves had unrestricted access to water and starters (Table S3) from birth to 84 days of age. (B) Principal coordinates analysis (PCoA) based on the Bray–Curtis distance of microbiota in the rumen and feces of cows from Gansu and Shaanxi. (C) The α diversity of microbiota in the rumen and feces of cows from Gansu and Shaanxi. The histogram indicated the expression levels of these genes at different ages (*p < 0.05, **p < 0.01, ***p < 0.001). (D) The microbial composition of rumen and feces at the genus level in cows from Gansu and Shaanxi.

Figure 2

The gut resistome profile of cows in Gansu and Shaanxi. (A) The total abundance of antibiotics resistance genes (ARG) resistance to 21 drug classes in the rumen and feces. (B) The α diversity of resistome in the rumen and feces of cows from Gansu and Shaanxi. The boxplot indicated the expression levels of these genes at different ages. (*p < 0.05, **p < 0.01, ***p < 0.001). (C) Principal coordinates analysis (PCoA) based on the Bray–Curtis distance of resistome in the rumen and feces of cows from Gansu and Shaanxi. (D) The composition of resistance mechanisms in the rumen and feces. (E) The identification of signature ARGs in the four groups. The abundance values were log‐transformed (log [transcripts per million (TPM) + 1, 10]) for better visualization. (F) Fit of neutral model determined the contribution of deterministic and stochastic processes on ARGs. (G) The correlation analysis (feces ARG richness vs. rumen ARG richness; ARG richness vs. microbial richness).

Figure 3

Characteristics of three resistome clusters associated with feces and rumen. (A) The heatmap depicting sample clustering based on the first 50 abundance values of antibiotics resistance genes (ARG) abundances. The ARGs were standardized by z‐score, and z = (x–µ)/σ. The transcripts per million (TPM) of ARGs (x) in each sample is subtracted from the mean (µ) and then divided by the standard deviation (σ). (B) Density plot of ARG richness in the cow cohort. (C) ARGs were clustered with partitioning around medoids clustering based on Euclidean distance. (D) The proportion of different clustering samples in the four groups. (E) The significant difference of the top 10 microbial families among the four clusters. The bar graph indicated the expression levels of these genes at different ages (*p < 0.05, **p < 0.01, ***p < 0.001). (F) Sankey diagram connecting the predicted bacterial hosts (2nd column) from the three clusters (1st column) to the top 10 ARGs (3rd column) in the six drug classes (4th column). (G) Spearman correlation analysis between ARGs and bacteria (*p < 0.05, **p < 0.01, ***p < 0.001).

Figure 4

The temporal dynamics of fecal resistome in calves. (A and B) The Chao1 index (A) and Shannon index (B) of fecal antibiotics resistance genes (ARG) in calves on Day 0, 1, 3, 35, 56, and 84. The histogram indicates the expression levels of these genes at different ages. (*p < 0.05, **p < 0.01, ***p < 0.001). (C) Principal coordinates analysis (PCoA) based on the Bray–Curtis distance of resistome in the feces of calves. (D) The relative abundance of fecal ARGs at different ages and the absolute abundance (Transcripts Per Million [TPM]) of fecal ARGs at different ages. (E) The predicted bacterial hosts of fecal ARGs at different ages.

Figure 5

Functional capacity of gut microbiome in the calves. (A and B) The Chao1 index (A) and Shannon index (B) of fecal carbohydrate‐active enzymes (CAZyme) in calves on Days 0, 1, 3, 35, 56, and 84. The histogram indicates the expression levels of these genes at different ages (*p < 0.05, **p < 0.01, ***p < 0.001). (C) The relative abundance of fecal CAZymes at different ages. (D) The absolute abundance (transcripts per million [TPM]) of fecal CAZymes at different ages. AA, auxiliary activities; CBM, carbohydrate‐binding modules; CE, carbohydrate esterases; GH, glycoside hydrolases; GT, glycosyl transferases; PL, polysaccharide lyases; SLH, cellulosome modules. (E) The identification of signature CAZymes in the four groups. The abundance values were log‐transformed (log [TPM + 1, 10]) for better visualization. (F) The predicted bacterial hosts of fecal CAZymes at different ages. (G) The correlation analysis (ARG abundance vs. CAZyme abundance; ARG richness vs. CAZyme richness; ARG diversity vs. CAZyme diversity).

Figure 6

The resistome characteristics of meconium, colostrum, wastewater, and soil. (A) The microbial composition of four groups at the genus level. ME = Meconium; CL = Colostrum; ES = Soil; WT = Wastewater. (B) The SourceTracker analysis of microbiome in the four groups. (C) Procrustes analysis of the association between the microbial composition and ARG profile at the four groups. (D) The α diversity of microbiota in the four groups. (E) The abundance of antibiotics resistance genes (ARGs) in the four groups. The histogram indicated the expression levels of these genes at different ages (*p < 0.05, **p < 0.01, ***p < 0.001). (F) The composition of drug class of ARGs in the four groups. (G) Wayne diagram and upset plot showing the intersection of feces, rumen, and meconium/colostrum/soil/wastewater. (H) The Co‐occurrence network between ARGs and mobile genetic elements (MGE). (I) Partial Least Squares Path Modeling (PLS‐PM) to investigate direct and indirect effects of the microbiome, MGEs, and ARGs. The solid line indicated a significant causal relationship between the two factors, while the dashed line indicated no significant relationship between the two factors.

Figure 7

The comparison of antibiotics resistance genes (ARG) at DNA‐based and RNA‐based level. (A) Principal coordinates analysis (PCoA) based on the Bray–Curtis distance of ARG transcriptional abundance in the groups of feces, rumen, soil, and water. (B) The Chao1 index and Shannon index of four groups at the metatranscriptome level (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). (C) The Venn diagram of ARG identification between the metagenome and metatranscriptome level. (D) The comparison of antibiotic class of ARGs between metagenome and metatranscriptome. (E) The comparison of ARG abundance between metagenome and metatranscriptome.