Metagenomics reveals the temporal dynamics of the rumen resistome and microbiome in goat kids

Abstract

Background: The gut microbiome of domestic animals carries antibiotic resistance genes (ARGs) which can be transmitted to the environment and humans, resulting in challenges of antibiotic resistance. Although it has been reported that the rumen microbiome of ruminants may be a reservoir of ARGs, the factors affecting the temporal dynamics of the rumen resistome are still unclear. Here, we collected rumen content samples of goats at 1, 7, 14, 28, 42, 56, 70, and 84 days of age, analyzed their microbiome and resistome profiles using metagenomics, and assessed the temporal dynamics of the rumen resistome in goats at the early stage of life under a conventional feeding system.

Results: In our results, the rumen resistome of goat kids contained ARGs to 41 classes, and the richness of ARGs decreased with age. Four antibiotic compound types of ARGs, including drugs, biocides, metals, and multi-compounds, were found during milk feeding, while only drug types of ARGs were observed after supplementation with starter feed. The specific ARGs for each age and their temporal dynamics were characterized, and the network inference model revealed that the interactions among ARGs were related to age. A strong correlation between the profiles of rumen resistome and microbiome was found using Procrustes analysis. Ruminal Escherichia coli within Proteobacteria phylum was the main carrier of ARGs in goats consuming colostrum, while Prevotella ruminicola and Fibrobacter succinogenes associated with cellulose degradation were the carriers of ARGs after starter supplementation. Milk consumption was likely a source of rumen ARGs, and the changes in the rumen resistome with age were correlated with the microbiome modulation by starter supplementation.

Conclusions: Our data revealed that the temporal dynamics of the rumen resistome are associated with the microbiome, and the reservoir of ARGs in the rumen during early life is likely related to age and diet. It may be a feasible strategy to reduce the rumen and its downstream dissemination of ARGs in ruminants through early-life dietary intervention. Video Abstract.

Keywords: Ages; Diet; Horizontal transfer; Metagenomics; Microbiome; Rumen resistome; Ruminants; Temporal dynamics.

Fig. 1

Rumen microbiome changes with ages in the early life of goats. A The ages, sampling day, and diet regime for goat kids in this study. B The number of observed families in rumen. The line inside the box denotes the median, and the boxes denote the interquartile (IQR) between the first and third quartiles (25th and 75th percentiles, respectively). The observed families on d1 were significantly lower than other ages (Wilcoxon rank-sum test, p < 0.05). C Principle Coordinate Analysis (PCoA) of Bray–Curtis distances between microbiota. The R2 and P-value of PERMONOVA to test the differences of beta diversity was labeled. The cluster of d1 samples was distinct compared with other ages, while d7 and d14 were clustered separately compared with the ages when goat kids accessed the starter diet (d42 to d84). D Bacterial abundances at the family level. Each bar represents a bacterial family and each column represents one sample

Fig. 2

The rumen resistome structure and composition in goat kids. A Principle Coordinate Analysis (PCoA) of Bray–Curtis distances for the rumen resistome, showing changes in resistome structure over time as assessed by PERMANOVA test. Samples collected on d1 formed a separate cluster while later samples were more similar. B Relative abundance of antibiotic resistance genes (ARGs) at the class level of MEGARes 2.0. Each column represents a sample, and each bar represents an ARG class

Fig. 3

The temporal dynamics of antibiotic resistance genes (ARGs) and their interactions. A Heatmap depicted the age-associated ARGs identified by the LEfSe algorithm. The heat map shows the average relative abundances of ARGs on a log scale. The color of cells from purple to red corresponds to the relative abundance of ARGs from low to high. B A network analysis of the interactions among ARGs at different ages. The SparCC algorithm was employed for network analysis. The nodes (resistance genes, ARGs) were colored by antibiotics at the group level of MEGARes 2.0, and the font color of ARGs represents the age-associated signatures identified by the LEfSe

Fig. 4

Rumen resistome associated with its bacterial community. A The most abundant host bacterial families of ARGs. Dots’ size represents the average relative abundance of bacteria at a certain age. B Procrustes analysis of the association between the composition of the resistome and that of bacterial community among different ages. The correlation coefficient, r, and P-value were generated by the ‘protest’ function, with p < 0.05 as the significant threshold. C A network analysis of the co-occurrence patterns between ARG and microbial taxa. The SparCC algorithm was used to calculate the relationships between bacterial taxa and ARGs. High abundances of families of Proteobacteria, such as Enterobacteriaceae, Pasteurellaceae, and Pseudomonadaceae, on d1, were strongly correlated with ARGs (ROB, GYRA, and RPOB) enriched. The abundance of bacterial families (Xanthomonadaceae, Prevotellaceae, and Fibrobacteraceae) increased with age and were associated with the ARGs as signatures during the starter supplementation period (d42 to d84)

Fig. 5

The temporal dynamics of majorhost bacterial species and phyla of antibiotic resistance genes (ARGs) in rumen resistome. The size of cycles represents the relative abundance of bacterial species at a certain age. To facilitate viewing, only those dominant species carrying ARGs were shown, and the diet regime was labeled with different colors for each microbial taxa. As observed, abundances and species of the bacteria carrying ARGs changed with age from d1 to d84

Fig. 6

Changes in enzymatic activity over age. A Principle Coordinate Analysis (PCoA) of Bray–Curtis distance of CAZy content. B PCoA of Bray–Curtis distance of COG pathways. Each point in (A, B) represents a unique sample. The P-value of the PERMONOVA test was labeled. C Total count of CAZy enzyme families in goat kids from day 1 to day 84. D Stacked bar plot of the relative abundances of CAZy families per class of enzymes with age. Each column represent a sample. Carbohydrate-Active EnZymes database (CAZy); Clusters of Orthologous Groups of proteins database (COG); Glycoside Hydrolases (GH); GlycosylTransferases (GT); Polysaccharide Lyases (PL); Carbohydrate Esterases (CE); Carbohydrate-Binding Modules (CBMs); Auxiliary Activities (AA)

Fig. 7

The abundances of age-associated CAZy families and their predicted bacterial families. A Heatmap depicting the age-associated CAZy enzymes. The color of cells from purple to red corresponds to the relative abundance of ARGs on a log scale from low to high. GH24 included lysozyme (EC 3.2.1.17) which is the major enzyme in colostrum. GH5 and GH6 contained cellulase (EC 3.2.1.4) and cellobiohydrolase (EC 3.2.1.91), GH5 included β-glucosidase (EC 3.2.1.21), and GH57 had α-amylase (EC 3.2.1.1). B The most abundant host bacterial families predicted to produce the CAZy are shown in the heatmap. The predicted bacteria, including Pasteurellaceae, Enterobacteriaceae, and Clostridiaceae, were abundant on d1, matching the abundances of the ARGs bacterial origins. The contributions of Prevotellaceae, Fibrobacteraceae, and Xanthomonadaceae to CAZy enzymes were high in later days, likely resulting from the increased intake of the solid diet