Regional antimicrobial resistance gene flow among the One Health sectors in China

Abstract

Background: Antimicrobial resistance poses a significant threat to global health, with its spread intricately linked across human, animal, and environmental sectors. Revealing the antimicrobial resistance gene (ARG) flow among the One Health sectors is essential for better control of antimicrobial resistance.

Results: In this study, we investigated regional ARG transmission among humans, food, and the environment in Dengfeng, Henan Province, China by combining large-scale metagenomic sequencing with culturing of resistant bacterial isolates in 592 samples. A total of 40 ARG types and 743 ARG subtypes were identified, with a predominance of multidrug resistance genes. Compared with microbes from human fecal samples, those from food and environmental samples showed a significantly higher load of ARGs. We revealed that dietary habits and occupational exposure significantly affect ARG abundance. Pseudomonadota, particularly Enterobacteriaceae, were identified as the main ARG carriers shaping the resistome. The resistome in food samples was found more affected by mobile genetic elements (MGEs), whereas in environmental samples, it was more associated with the microbial composition. We evidenced that horizontal gene transfer (HGT) mediated by plasmids and phages, together with strain transmission, particularly those associated with the Enterobacteriaceae members, drive regional ARG flow. Lifestyle, dietary habits, and occupational exposure are all correlated with ARG dissemination and flies and food are important potential sources of ARGs to humans. The widespread mobile carbapenemase gene, OXA-347, carried by non-Enterobacteriaceae bacteria in the human gut microbiota, requires particular attention. Finally, we showed that machine learning models based on microbiome profiles were effective in predicting the presence of carbapenem-resistant strains, suggesting a valuable approach for AMR surveillance.

Conclusions: Our study provides a full picture of regional ARG transmission among the One Health sectors in a county-level city in China, which facilitates a better understanding of the complex routes of ARG transmission and highlights new points of focus for AMR surveillance and control. Video Abstract.

Keywords: Antimicrobial resistance gene; Carbapenemase; Mobile genetic element; One Health; Transmission pathway.

Fig. 1

The resistome profiles of the 592 samples. a Design of the sample collection in Dengfeng City, Henan Province, China, involving three groups, 18 subgroups, and 592 samples. b The proportion of antimicrobial resistance gene (ARG) types (top panel) and the number of ARG subtypes in each ARG type (bottom panel). c The ARG abundance in the three groups. The abundance of ARGs was transformed as a “copy of ARG per copy of 16S rRNA gene”. d Biomarker ARGs in samples from various subgroups, were identified using the extremely randomized tree algorithm. Heatmap visualizes the Z score distribution according to the abundance of each ARG. Clustering was performed using K-means clustering via the R package “ComplexHeatmap”. The right annotation shows the type of each ARG. e The Shannon indices of ARGs in the three groups. f Principal coordinate analysis (PCoA) of the resistome profiles for each sample in the three groups (PERMANOVA, P < 2.2 × 10⁻¹⁶, and R² = 10.9%). g The abundance of the bacteriophages B40-8 and crAssphage in samples from various subgroups (measured in terms of coverage per Gb)

Fig. 2

Relationship between the microbiome and the resistome. a The relative abundance of taxa at the phylum level within the microbial communities of samples from various subgroups. b The Shannon diversity of the microbial communities in samples from various groups. c PCoA of samples from various groups. d The correlation between the microbiome and the resistome revealed by Procrustes analysis. Different colored nodes represent different groups; squares and circles represent the microbial profiles and the resistome profiles, respectively. e The variable importance of the top 20 taxa was determined by the random forest model using the mean decrease Gini value. A higher value indicates a stronger connection between the taxon and the resistome profile. f The relative abundance of six taxa in samples from the three groups. g Spearman’s correlations between the abundance of the six taxa and the abundance (left panel) and Shannon index (right panel) of the resistome profiles (P < 0.05). The length of each bar represents the correlation coefficient

Fig. 3

ARGs carried by plasmids and prophages/phages. a The source of ACCs (chromosome, plasmid, or phage). b Proportion of plasmids and prophages/phages in all ACCs. c Proportion of plasmid sequences in all ACCs of samples from the three groups. d Comparisons of the plasmid replicon-related ARGs discovered in our study with those discovered in the PLSDB database. RC, an abbreviation of rep_cluster; different colors represent the outcomes of the comparison (shared, appeared in both our study and the PLSDB database; present, appeared only in our study; database, appeared only in the PLSDB database). e Spearman’s correlation between the ratios of ARGs carried by prophages/phages in the human samples of this study and in the Gut Phage Database. f Co-occurrence network of the ARGs carried by the prophages/phages sequences and the predicted phage hosts. The blue squares represent the families of bacteria; the orange circles represent the ARGs; and the gray lines represent the associations between the ARGs and the phage hosts

Fig. 4

ARG-sharing network and clonal spread. a The ratio of ARGs that are shared between human subgroups and food/environmental subgroups, relative to the total shared ARGs between human fecal samples and food/environmental samples. b The proportion of ARGs that are shared with different food/environmental subgroups in different human subgroups. c ARG subtypes are shared between samples from different human subgroups and samples from vegetables, fruits, pork, and flies. Only ARG subtypes from the risk rank Q1 were considered. d Strain-level phylogenies of seven species of the family Enterobacteriaceae. Each node represents a sample containing the corresponding strain. Different colors represent samples from different sources. Blue lines indicate that these samples possess the same strain (normalized phylogenetic distance, nGD < 0.1). The annotations are the number of samples containing the corresponding strain

Fig. 5

Distribution and transmission of carbapenemase genes. a Phylogenetic tree of 45 carbapenem-resistant isolates was collected in this study. The right panel shows the presence of specific carbapenemase genes. b Clusters of MAGs and the isolate genomes in the seven species. We clustered each species by including the corresponding genomes of the isolates and the MAGs with a cut-off of 99.0% ANI. Only the species found both in MAGs and isolates are present. Different colored circles represent the various sources of the MAGs. Blue squares represent the source of the chromosomes. The gray line indicates that the connected MAG and isolate were obtained from the same sample. The rounded blue rectangles indicate groups containing the same strain as the origin of the genomes (ANI ≥ 99.0%). The carbapenemase genes found in the isolate genomes are labeled. c The co-occurrence network depicts the relationships between the OXA-347 gene and the associated microbes, plasmids, and insertion sequences. The blue rectangles represent the microbes; the green hexagon represents the OXA-347 gene; the orange triangles represent the plasmid replicons; and the red circles represent the insertion sequences. d Prevalence of the potential OXA-347-carrying microbial species revealed in c. Only six species were detected in our data

Fig. 6

Random forest model to predict the presence of carbapenem-resistant isolates. Performance of the random forest models used to predict the presence of carbapenem-resistant isolates, utilizing either microbiome profiles, resistome profiles, or a combination of both. The performance indicators, including the area under the curve (a), sensitivity (b), and specificity (c), were computed based on the results obtained from a tenfold cross-validation. The numbers displayed at the top represent the mean values of these three indicators. d The importance of the top 20 taxa was determined by the random forest model built on the microbiome profiles