Skip to main content
NCBI home page
BMC Microbiology logoLink to BMC Microbiology
. 2025 Jul 3;25:406. doi: 10.1186/s12866-025-04115-z

Microbiome, resistome, and potential transfer of antibiotic resistance genes in Chinese wet market under One Health sectors

Juan Yang 1,2, Le Wang 3, Qian Liang 2, Yang Wang 3, Xiaorong Yang 2,✉, Xianping Wu 4,✉, Xiaofang Pei 1,✉
PMCID: PMC12224677  PMID: 40604389

Abstract

Background

Antibiotic resistance has become a serious challenge to global public health. The spread of antibiotic resistance genes (ARGs) among humans, animals, and the environment has become a critical issue within the “One Health” framework. Chinese wet market with live poultry trade provides an interface for close interaction between humans and chickens, and is considered as potential source for disease dissemination. However, the understanding of ARGs in this kind of market, including their shared profiles, influencing factors, and potential horizontal transfer subtypes and directions, remains limited.

Results

In this study, we explored the microbiome, resistome, and mobility of ARGs, and identified putative horizontal gene transfer (HGT) events in the Chinese wet market system by utilizing metagenomic assembly and binning. Consequently, a total of 1080 ARG subtypes were identified from 36 metagenomes, and 221 subtypes were shared among human feces, chicken feces, chicken carcasses, and the environment. The composition of ARGs was influenced by mobile genetic elements (MGEs) and bacterial communities. As for the host of ARGs, 89 ARG-carrying genomes (ACGs) were identified, with 18 of them carrying multiple ARGs and MGEs, indicating the potential mobility of ARGs. Notably, six ACGs were identified as opportunistic pathogens carrying multiple ARGs and MGEs, which were annotated as Escherichia coli, Acinetobacter johnsonii, Klebsiella variicola, Klebsiella pneumoniae, and Citrobacter freundii. In addition, 164 potential HGT events were identified based on ACGs, and ParS, vanB, ugd, and macB were annotated as potentially transferred ARG subtypes in humans and the wet market.

Conclusions

This study offers new insights into the potential for HGT of ARGs within a Chinese wet market setting, highlighting putative transmission patterns among humans, poultry, and the environment. To our knowledge, few studies have explored ARG transfer potential in this context using metagenome-assembled genomes, making this a valuable contribution to One Health surveillance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-025-04115-z.

Keywords: Antibiotic resistance genes, Metagenomics, Horizontal gene transfer, One health, Chinese wet market

Background

Antibiotic resistance poses huge threats to human health, which has become a major challenge to global public health. The wide use of antibiotics in animal husbandry, agriculture, and healthcare has accelerated the emergence and spread of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) [1, 2]. ARB and ARGs are widely present in various animals and foods, which can enter the food chain and spread to humans at any time from farm to fork [3], and can transmit to the relevant environment through animal feces and wastewater [1]. Similar livestock-associated ARB and ARGs have been identified in humans, animals, and the environment along the poultry-producing chain [4, 5]. These resistance elements can spread along the chain and contaminate the terminal meat products [6], resulting in the widespread distribution of ARB and ARGs across multiple ecological compartments [7].

Chinese wet market with live poultry trade and slaughter provides an opportunity for close interaction between customers and live birds, where birds from various sources are gathered together for selling and slaughtering [8, 9]. Live poultry trade is often concerned regarding the dissemination of zoonotic diseases including bacterial pathogens and avian influenza viruses etc [10, 11]. In addition, gut microbes of poultry are regarded as ARG reservoirs [12]. Live poultry trade speeds up the transfer of ARGs among humans, birds, and the environment through horizontal gene transfer (HGT), and may eventually transmit to human pathogens [9]. Although conventional cooking practices were generally sufficient to inactivate ARB, recent evidence suggested that functional ARGs in plasmids may persist in heat-treated food products [13]. These residual ARGs may be taken up by opportunistic pathogens in the human gastrointestinal tract, where they can persist, colonize, and disseminate through HGT within the gut microbiota [14, 15]. The emergence of ARGs has been studied in wet markets or live poultry markets along the live poultry trade chain [10, 16]. Moreover, previous studies have explored the prevalence and transmission of ARGs among farmed animals, slaughtered meats, and workers [5, 17–19]. However, the influencing factors of the resistome and their potential mobility in the Chinese wet market with live poultry trade are poorly understood. Besides, these previous researches were mainly concerned about pathogens carrying ARGs with high risks, and mainly utilized bacteria culturing and whole genome sequencing methods, which greatly limited the analysis of ARGs dissemination in the whole system, as most of the microorganisms were unculturable. Metagenomic sequencing allows direct analysis of entire genomic information in a sample without species culturing, which enables us to analyze all ARG subtypes present in the environment. In this study, combined with MetaChip [20], a pipeline for HGT identification at the community level, we explored the potential transfer of ARGs in humans, chickens, and the environment in the Chinese wet market with live poultry trade. Although this method has been applied to investigate the putative transfer of cyanophycin synthase-encoding genes and ARGs in wastewater [21, 22], its application in studying ARG transmission in Chinese wet market with live poultry trade remains limited. To the best of our knowledge, few studies have utilized this approach in such settings, despite the wet market being a major interface for human-animal-environment interactions and a potential hotspot for the spread of antibiotic resistance.

Antibiotic resistance is a critical “One Health” issue underpinned by integrating efforts from human, animal, and environmental health, which has received widespread attention from various dimensions [23, 24]. Although the Chinese wet market with live poultry trade provides an opportunity for close contact between humans and chickens [9, 16, 25], there is little understanding of the resistome and potential HGT of ARGs among humans, chickens, and the environment in the wet market system. Thus, the purpose of this study was to (1) characterize the profiles and explore the shared and unique ARGs in Chinese wet market with live poultry trade; (2) reveal the influencing factors for ARGs composition; (3) identify the potential community-level horizontal ARGs transfer among humans, chickens, and the environment.

Methods

Sample collection

Sampling activities were conducted at a Chinese wet market with live poultry trade and slaughter in Sichuan province in western China. The local chickens were fed with natural grains and then sold from local farmers to vendors in the market. However, we lacked antibiotic treatment records due to the decentralized source of these chickens. Samples were collected from two adjacent live poultry trade and slaughter stalls, where the local chickens were kept in 12 different cages, with five to eight chickens in each cage. For the chicken feces, a fresh mixture of chicken feces from each cage was collected in triplicate, resulting in a total of 12 fresh, still-warm mixed droppings (at least 5 g of feces for each sample). Four wastewater samples were collected from the discharge for slaughter and processing activities at the stalls, and 300 mL of wastewater per sample was filtered by 0.22 μm microporous membranes (Millipore, USA). Environmental swab samples were collected from two stalls. In each stall, separate sterile swabs were used for each site, i.e., cage, floor, chopping board, and equipment, with each site swabbed twice. Swabs from four sites in each stall were subsequently pooled to generate a mixed sample, and at last four composite environmental swab samples were collected. Six local chicken carcass samples were collected from the same location as the fecal samples, with each sample composed of approximately 900 g of carcass meat trimmings. Upon arrival at the laboratory, carcass trimmings were processed immediately by adding 180 mL of sterile phosphate-buffered saline (PBS) to each sample bag and then hand-massaged the bag [26]. After massaging, all supernatant was centrifuged (10,000 × g for 10 min at 4 °C) to pellet the cells [26], which were then used for DNA extraction. Triplicate negative controls consisting of PBS solution from the same batch, without carcass input, were processed identically to the samples for DNA extraction. However, DNA concentrations obtained from all three negative controls were below the limit required for metagenomic library preparation. To further analyze the unique and shared ARGs among humans, chickens, and the environment, fecal samples were collected from 10 residents aged 36–60 years, who lived within 3 km of the market and reported visiting the market at least once per week. All participants were regular market visitors but were not engaged in poultry slaughtering and have not taken antibiotics in the past three months. The sampling of human feces was approved by the Ethics Commission of the Sichuan Center for Disease Control and Prevention. All samples were collected on the continuous 2-day period and stored on ice during transportation to the laboratory and pretreatment within 24 h after sampling. Detailed sample information was presented in Table S1.

DNA extraction and metagenomic sequencing

DNA was extracted from all samples (n = 36), and different extraction kits were selected according to sample types. For chicken feces, human feces, environmental swabs, and wastewater membrane samples, DNeasy PowerSoil Pro Kit (QIAGEN, Hilden, Germany) was used for DNA extraction according to the manufacturer’s protocol. For chicken carcass samples, QIAamp DNA Microbiome kit (QIAGEN, Hilden, Germany) was used to first deplete host DNA and then enrich the bacterial microbiome. After extraction, the concentration of DNA was measured by Qubit 2.0 (Thermo Fisher Scientific, Waltham, USA). Then, the extracted DNA was used to construct the library using the Nextera XT DNA library preparation kit (Illumina, San Diego, USA) and sequenced by the Illumina Nextseq2000 platform with 2 × 150 bp paired reads in our laboratory. The raw data have been submitted to the Genome Sequence Archive of the National Genomics Data Center under BioProject accession number PRJCA032661.

Taxonomic classification, annotation, and quantification of ARGs and MGEs

Fastp (v0.23.4) [27] was used to trim the raw metagenomic reads with quality value < 30 or ambiguous nucleotides > 10. Reads that matched to the chicken reference genome assembly (GRCg.7b) and human reference genome assembly (GRCh38.p14) were filtered using Bowtie2 (v2.5.1) [28] and Samtools (v1.16.1) [29]. Then, Kraken2 (v2.1.3) [30] and Bracken (v2.9) [31] were used for taxonomic classification and abundance estimation. ARGs-OAP (v3.0) [32] pipeline was used for the annotation of ARGs by aligning the clean reads with the structured ARG database (SARG v3.0) using Blast + (v2.12.0) [33], with e value 1e – 7, similarity > 80%, and query coverage > 70%. The abundance of ARGs was normalized to copy numbers of 16 S ribosomal RNA according to ARGs-OAP [32]. The identification and quantification of MGEs followed a similar approach as ARGs, however, the SARG database was replaced with the Mobile genetic elements (MGEs) database [34], which contains 278 different genes and more than 2000 unique sequences.

Metagenomic assembly and binning

Clean reads were de novo assembled using MEGAHIT (v1.2.9) [35]. Totally, 13,942 to 155,455 contigs for each sample were generated with N50 values from 1,276 to 10,898 bp (Table S2). The assembled contigs longer than 1000 bp were then processed by Prodigal (v2.6.3, set as -p meta) [36] for open reading frames (ORFs) prediction. Predicted ORFs that were longer than 100 bp were pooled, de-replicated by cd-hit (v 4.8.1) [37], and clustered to produce non-redundant meta-gene catalog.

Then, the assembly results were used to recover metagenomic-assembled genomes (MAG) using metaWRAP (v1.3.2) [38]. Binning was firstly conducted by Initial_binning module with the default parameters by three metagenomic binning software- MaxBin2 [39], MetaBAT2 [40], and CONCOCT [41]. Results from initial binning were consolidated by Bin_refinement module in the metaWRAP to produce an improved bin quality, and CheckM (v1.0.12) [42] was employed for completeness and contamination evaluation with completeness > 50% and contamination < 10%. These MAGs were treated as medium- and high-quality genomes in accordance with the Minimum Information about a Metagenome-Assembled Genome (MIMAG) standards proposed by the Genome Standards Consortium [43]. The draft genomes were dereplicated by dRep [44], and the MAGs were clustered into the same species at a threshold of 95% average nucleotide identity (ANI). Then CoverM (v 0.7.0) was used for abundance estimation of the MAGs. Gene prediction of MAGs was performed using Prodigal (-p meta) [36] and then annotation of ARGs and MGEs was conducted against SARG v3.0 and MGEs database using diamond [45] with an e-value of 10−5, alignment identity > 80%. GTDB-Tk (v 2.3.2) [46] was used to annotate the taxonomy classification of MAGs based on the Genome Taxonomy Database with default parameters [47]. Besides, MAGs which were clustered into species-level genome bins were then mapped against a published list of opportunistic pathogenic species to identify opportunistic pathogenic MAGs [48].

Community-level horizontal gene transfers among humans, chickens, and the environment

Community-level HGT events among all dereplicated ARG-carrying genomes (ACGs) were inferred using MetaCHIP (v.1.10.13) [20]. The dereplicated genome data, along with their taxonomic classification from GTDB-Tk tool were provided as input files. PI and BP modules were used to detect HGTs among customized groups. The detection of HGT was carried out based on best-match and phylogenetic approaches with high sequence similarity (> 95%). For further validation of the likely candidates for HGT events, BLASTN was conducted between each pair, and then the putative gene transfer of flanking regions was plotted.

Statistical analysis and visualization

Statistical analyses were conducted using R 4.4.1 with a significant level of p at 0.05. Wilcoxon rank sum test was used for comparisons between the two sample groups. Principal coordinate analysis (PCoA) based on the Bray-Curtis distance was performed to compare the antibiotic resistome in different sample groups. Upset analysis was conducted using the UpSetR package to identify shared and unique ARGs. The visualization of ARGs carried by MAGs was performed by circlize package. Adonis, Procrustes analysis, and variation partitioning analysis (VPA) were conducted with the vegan package. The abundance of ARGs was visualized using pheatmap package in R. Network analysis was employed to explore the co-occurrence pattern between ARGs and MGEs, as well as ARGs and bacterial taxa based on strong and significant correlation matrixes (Spearman’s r > 0.8; p < 0.01), and visualized using Gephi (v0.10).

Results

ARGs profile in humans and the Chinese wet market system

A total of 2,088,671,524 high-quality reads with an average of 58,018,653 reads per sample were sequenced from human feces, chicken feces, chicken carcasses, and the environment (Table S2). In total, 1080 ARG subtypes conferring resistance to 26 types were identified in 36 metagenomes, and the distribution varied among different sample groups (Fig. 1a). Overall, tetracycline resistance genes were observed consistently the most abundant type in wet market system and human feces. ARGs conferring resistance to tetracycline and macrolide-lincosamide-streptogramin (MLS) were dominated in human feces samples, accounting for 58% of the total ARG abundance. In contrast, the top two types were ARGs conferring resistance to tetracycline and multidrug in chicken feces and carcass samples, accounting for 52% and 42%, respectively. While aminoglycoside was the most abundant ARG type in the environmental samples from the wet market.

Fig. 1.

Fig. 1

Open in a new tab

Profiles of ARGs in human feces and the Chinese wet market system (chicken feces, chicken carcasses, and the environment). a The relative abundance (log-transformed) of ARGs from four sample groups. Different annotation colors on the top represented various sample groups. b The boxplot showed the abundance difference in the four groups. Significance was indicated by the Wilcoxon rank sum test at p < 0.05 (**). c Principal coordinate analysis (PCoA) based on Bray-Curtis distance showed the difference of ARG abundance in various sample groups. MLS: Macrolide-lincosamide-streptogramin

The average abundance of ARGs detected in various origins was shown in Fig. 1b. Obviously, the average abundance of ARGs in the human feces was significantly different from chicken feces, chicken carcasses, and the environment samples (Wilcoxon rank sum test, all p < 0.05) (Fig. 1b). Furthermore, PCoA based on Bray-Curtis distance was performed to compare the composition of resistome in different sample groups. As shown, PCoA axis1 and PCoA axis2 explained over 60% of the total ARGs abundance variation (Fig. 1c), and different ARGs composition was observed (adonis R2 = 0.46, p < 0.001) (Fig. 1c). The human feces samples were clustered with chicken feces samples, and ARGs from chicken carcasses and the environment in wet market were clustered, indicating these samples may possess similar composition of ARGs.

Shared and unique resistome in humans, chickens, and the environment

The shared and unique ARGs in samples from four groups were investigated to better understand the connection between ARGs in human feces and the wet market system. We found that a total of 221 ARG subtypes were shared among chicken feces, chicken carcasses, the environment, and human feces, accounting for 32.7, 37.4, 32.5, and 52.9% of the total ARG subtypes in each group, respectively (Fig. 2a). Interestingly, 135 extra subtypes were found shared among samples from chickens, carcasses, and the environment (Fig. 2a), suggesting that the live poultry trade and slaughter in the wet market may contribute to the transfer of ARGs. Among these shared ARG subtypes, ARGs conferring resistance to aminoglycoside, bacitracin, and multidrug were dominant in chickens and carcasses from the wet market, while those for MLS and mupirocin were more abundant in human fecal samples (Fig. S1). This result may be caused by the different usage practices of antibiotics in chickens and humans, which led to distinct antibiotic resistance types. Of special interest was the tetX gene, which was identified in 26 of the 36 metagenomes. Moreover, to determine dominant ARGs shared among humans, chickens, carcasses, and the environment, we identified a common set of 45 ARG subtypes which shared by all groups and predominated 90% of ARG abundance (Fig. 2b), suggesting core resistome in the wet market and human gut microbiomes.

Fig. 2.

Fig. 2

Open in a new tab

Shared and unique ARGs in human feces and the Chinese wet market system. a Upset diagram displayed the shared and unique ARGs between different sample groups. The blue bar represented the total number of ARG subtypes detected in each group, and the solid black points indicated the occurrence of ARGs in that group. The points linked by lines demonstrated the shared ARGs in different groups, and the orange bar on the top showed the shared and unique ARG numbers. b Relative abundance of 45 dominant ARG subtypes shared by all groups and predominated 90% of ARG abundance

The unique ARG subtypes were identified to characterize the resistome discrepancy. There were over 100 unique ARG subtypes in chickens, carcasses, and the environment samples, while only 82 were in human feces (Fig. 2a), suggesting the diversity difference between the wet market system and humans. Among the detected unique ARGs, we found blaCMY like genes were mainly detected in samples from humans, while blaTEM like genes were mainly identified in chicken feces and chicken carcasses (Fig. S2). This maybe resulted from the different resistance mechanisms of these genes, as blaCMY like genes were considered to co-select with MGEs like insertion sequences (IS) and plasmid [49], while blaTEM like genes enable resistance by producing enzymes that break down these drugs [50]. Moreover, it should be noted that mcr genes were found in chicken feces, the environment, and chicken carcasses (Fig. S3), with their detection rate being 16.7%, 62.5%, and 100%, respectively, suggesting that mcr genes have been widespread in the market and the potential dissemination of ARGs from chickens to the environment.

Influencing factors of ARGs in humans and the Chinese wet market system

In general, the variation of ARGs was related to the shift of bacterial community composition and MGEs. Firstly, we conducted spearman correlation and found that both the number and total abundance of ARGs demonstrated significant correlations with the number (Fig. 3a) and abundance (Fig. 3b) of MGEs, respectively. Furthermore, Procrustes analysis was conducted based on the PCoA matrix to assess the association between the distribution patterns of ARG subtypes and MGE profiles. A significant correlation between ARG subtypes and MGEs was observed (Fig. S4) (M2 = 0.049, p < 0.001, 999 permutations), which indicated the potential transfer role of MGEs in the dissemination of ARGs. A similar Procrustes analysis was conducted to identify the correlation between ARG subtypes and the bacterial community at the genus level, and a significant correlation was observed (Fig. S5) (M2 = 0.038, p < 0.001, 999 permutations), which provided insights into microbial dynamics and the spread of resistance genes.

Fig. 3.

Fig. 3

Open in a new tab

Influencing factors of ARGs in human feces and the Chinese wet market. a Spearman correlation between the detected number of ARGs and the number of MGEs. b Spearman correlation between the abundance of ARGs and the abundance of MGEs. c Co-occurrence patterns between ARG subtypes and MGEs. d Co-occurrence patterns between ARG subtypes and the bacterial community at the genus level. The nodes of the network were colored according to modularity. A connection represented a strong (Spearman’s correlation coefficient r > 0.8) and significant (p < 0.01) correlation. The size of each node was proportional to the number of connections

Moreover, network analysis based on strong and significant correlations (r > 0.8, p < 0.01) was used to further explore the specific interactions between certain ARG subtypes and MGEs. The network (Fig. 3c) consisted of 166 nodes and 333 edges, and a modularity index of 0.58 which suggested the network had a modular structure. The connections in the network analysis were used to depict the complex co-occurrence patterns between ARG subtypes and MGEs, and the detailed network analysis information was demonstrated in Table S3. In general, the IS was the most predominant MGE type connected to ARGs. The entire network could be divided into six major modules (Module I – VI), and the most frequently connected gene in each module was istA, IS1166, qacEdelta, istB3, istA3, and ISBthe5, respectively (Fig. 3c). The co-occurrence of MGEs with multiple ARGs in the network suggested that these genes may share the same transfer mechanism and MGEs may promote the rapid spread of resistance genes in different environments. The network demonstrated that istA, tniA, tniB, and IS1166 significantly co-occurred with over 26 ARGs each (Table S3). In addition, qacEdelta associated with 21 ARG subtypes (e.g., aac(6’)-Ib’, aadA, blaVIM−1, and sul1) was also observed in module IV (Fig. 3c). Besides, the network analysis was conducted to explore the co-occurrence patterns between ARG subtypes and bacterial taxa at the genus level (Fig. 3d) with the same parameters as MGEs. The network comprised 239 nodes and 615 edges, with a modularity index of 0.576. The details of the co-occurrence pattern between ARG subtypes and bacterial genera can be found in Table S4. Then VPA analysis was further used to understand how much of the variation in ARG composition was explained by MGEs and bacteria taxa. The result demonstrated bacterial taxa at the genus level and MGEs could explain 35% and 12% of ARGs variation, respectively (Fig. S6). All the evidence indicated that the distribution of ARGs could be affected by MGEs and microbial communities.

Taxonomic profiles of MAGs and their co-occurrence with ARGs and MGEs

To better understand the host of ARGs and identify the co-occurrence pattern of ARGs and MGEs on draft genomes, 523 medium- and high-quality MAGs were recovered at a threshold of > 50% completeness and < 5% contamination (Table S5). Of these, 194 (37.1%) MAGs were evaluated as near with > 90% completeness, 163 (31.2%) were substantial, and 166 (31.7%) were moderate with 50–70% completeness (Fig. S7). Among them, 89 MAGs carrying 119 ARG subtypes were identified as ACGs, of which 41 met the criteria for high-quality MAGs based on MIMAG guidelines (Table S5). The Circos diagram demonstrated the distribution of ARGs on MAGs, revealing these MAGs span across 19 ARG types, with multidrug (35 ARG subtypes), bacitracin (1 ARG subtype), and tetracycline (17 ARG subtypes) resistance genes being the predominant ARG types carried by ACGs (Fig. 4). Taxonomic classification showed these ACGs were assigned to 7 phyla, and the most frequent bacteria in the phylum level were Pseudomonadata, Actinomycetota, and Bacillota (Fig. 4).

Fig. 4.

Fig. 4

Open in a new tab

The Circos diagram showed the relationship between MAGs and their carrying ARGs. The circle represented the taxonomic annotation of MAGs at the phylum level and their carrying ARG types, and the length of the bars on the circle represented the total number of ARGs carried by the MAGs or the number of MAGs that carrying the ARG type

To further assess the coexistence pattern and distribution of ARGs and MGEs on MAGs, and to investigate their potential roles in the ARGs dissemination, ARGs and MGEs carried by ACGs were annotated. The heatmap showed detailed information about the ACGs, including their taxonomic classification at genus or species level, average relative abundance in each group, and their carried ARGs and MGEs (Fig. 5). As shown, 72 ACGs were annotated to species-level genome bins using GTDB-Tk database (Fig. 5a). The distribution of ARGs across the ACGs revealed that 28 (31.5%) of the genomes carried multiple ARGs, with multidrug resistance genes exhibited the highest occurrence frequency (Fig. 5b). Besides, 39 ACGs carrying MGEs were identified, and transposase was the most predominant element, presenting in over 32.6% of the analyzed ACGs (Fig. 5c). It is noteworthy that 18 ACGs from all four groups were found carrying both multiple ARGs and MGEs (Table S6), suggesting the potential mobility for HGT, which could facilitate the spread of ARGs in human-animal-environment microbial communities. In addition, 11 ACGs were identified as opportunistic pathogens, and 6 of them carried multiple ARGs and MGEs (Fig. 5a, Table S7), which posed a significant challenge for both clinical treatment and public health efforts to control the spread of antibiotic resistance. These 6 opportunistic pathogens were annotated to Escherichia coli, Acinetobacter johnsonii, Klebsiella variicola, Klebsiella pneumoniae, and Citrobacter freundii, and mainly carried multidrug resistance genes and transposase MGEs (Fig. 5, Table S7). It should be noted that JF11_bin.69 from chicken feces which was annotated as Escherichia coli carried 42 ARGs and 23 MGEs, which suggested potential risk for spreading of multidrug-resistant bacteria within the farming environment and the food chain. Moreover, four of the six ACGs annotated as opportunistic pathogens carried ARGs with current threats, namely Rank I ARGs [51], including YP_002847905-bacA, CP001918.1.gene3442.p01-mdtE, HQ451074.1.gene4.p01-blaTEM−1, ZP_03029343-tolC, and YP_002295271-mdtL(Table S8). Apart from the Rank I ARGs, these ACGs all carried MGEs and other low-risk ARGs on their genome simultaneously, which indicated the possible HGT of high- and low-risk ARGs within the same MAG, potentially increasing the transmission of ARGs.

Fig. 5.

Fig. 5

Open in a new tab

The taxonomic annotation, abundance, and distribution of ARGs on ACGs. a The average abundance (log-transformed) of 89 ACGs recovered from different sample groups. ACG name labels with parentheses demonstrated the taxonomic annotation of ACGs at the genus or species level, and the label with a red asterisk indicated opportunistic pathogenic bacteria. b and c ARGs (MGEs) identified in ACGs. The color indicated the number of carried ARGs (MGEs)

Identification of horizontal ARG transfer at the community level

The high presence of MGEs on the ACGs could be related to the transfer of ARGs between microbes. Thus, we analyzed the possible HGT transfer on all ACGs to better understand the transmission pathways of antibiotic resistance and to identify how ARGs were mobilized. Results from the metaCHIP pipeline revealed 164 potential HGT events across all ACGs in the wet market system and human samples, including 7 phyla and 69 species (Fig. 6a). Pseudomonadota exhibited the highest occurrence of the putative HGT events, and most of the HGT assumed to transfer to members within this phylum, representing 21.9% of all events. However, Bacillota_A tended to transfer to other phyla, including Bacteroidota, Actinomycetota, and Bacillota, accounting for 64.2% of its HGT events (Fig. 6a). The frequent putative HGT events occurred in Bacillota_A indicated extensive transfer potential either among species of this phylum or between them and other organisms. At the species level, Phocaeicola coprocola exhibited the highest frequency of these HGTs, with 13 donors and 7 recipients observed (Fig. S8).

Fig. 6.

Fig. 6

Open in a new tab

Potential HGT events in human feces and the wet market system. a The putative HGT among the recovered 89 ACGs at the phylum level. b Potential HGT events for ARGs involved in ACGs (The matching gene IDs were colored blue and the annotated ARGs were shown on the top. The upper part was the donor gene sequence, and the lower part was the acceptor gene sequence)

Moreover, ARGs annotation was conducted for the putative genes in potential HGT events, which was shown in Fig. 6b. Notably, H7_bin.29 (Agathobacter rectalis) was likely to transfer ParS and vanB to H6_bin.42 (Barnesiella intestinihominis) (Fig. 6b), enhancing the latter’s capability for antibiotic resistance. The presence and transfer of vanB in common people indicated a serious public health concern. In addition, the subtype ugd, conferring resistance to polymyxin, seemed to move from JF3_bin41 to JF11_bin5 within species from Bacteroidota (Fig. 6b). The detection of the ugd indicated that the bacteria may be resistant to polymyxin, a last-resort antibiotic. Interestingly, we found macB appeared to transfer from JF8_bin2 to ET4_bin.1, and from JF3_bin.4 to H4_bin.6 among different MAGs in Bacillota_A, which was inferred as both donors and recipients (Fig. 6b). These findings indicated the transmission of ARGs among humans, chickens, and the environment.

Discussion

The Chinese wet market with live poultry trade is an important interface for gathering, trading, and slaughtering animals, and provides pathways for the transmission of ARGs among humans, animals, and the environment through direct contact or by food chain [52, 53]. However, limited data are available on the influencing factors of ARGs in human and Chinese wet markets, and their potential dissemination to other ecological niches. Thus, by using metagenomic assembly and binning, we identified shared ARGs in humans and the Chinese wet market system, analyzed their affecting factors, and revealed the potential HGT of ARGs in humans, chickens, and the environment.

Although the chickens in this study were fed with natural cereals and corn, we still found ARGs’ presence in the feces of these chickens. It has been reported that chicken gut was a reservoir for ARGs [12, 54], with tetracycline resistance genes as the predominant ARG type [12, 55], which was similar to our findings (Fig. 1). The dominance of tetracycline resistance genes might due to the historically frequent use of tetracyclines in clinical and agricultural settings, as well as their incomplete metabolism, resulting in environmental accumulation and selective pressure to humans and chickens [56, 57]. Despite the average abundance of ARGs in chicken feces (0.01 copies/16S rRNA gene) (Fig. 1b) was lower than that in non-intensive aquaculture (0.06 copies/16S rRNA gene) [58], we still found the transmission of ARGs between chicken feces and the environment in the wet market. This revealed horizontal ARG transfer in the wet market system, which suggested more attention should be paid as the high animal density and complex environment in the market provided an ideal condition for the dissemination and evolution of ARGs.

Furthermore, we found the presence of tetX and its variants tet(X3/X4) genes in human feces, chicken feces, chicken carcasses, and environmental microbiomes. Similar to our findings, the tet(X)-like genes were also identified in samples from healthy workers, animals, and the environment in the live poultry market system [16, 59]. Since the discovery of tet(X3/X4) genes identified from bacterial isolates of animal and human sources, plasmid-encoded tetX3 and tetX4 were widely detected in species isolated from various origins including humans, animals, and the environment microbiomes [59, 60]. Moreover, mcr genes (including mcr-3, mcr-4, mcr-5, mcr-7, mcr-8, mcr-9, and mcr-10) were identified in samples from chickens, carcasses, and the environment in the wet market system, which suggested their widespread distribution. Since the first discovery of mrc-1 in China [61], mcr-1 and its variants have been widely detected in animals and environments in the live poultry market [16], increasing the transmission rate of mcr genes from chickens to the environment. Briefly, mcr and tetX-like genes are ARGs both conferring resistance to the last-resort antibiotics and the continuous emergence and dissemination of these genes along the live poultry trade warrants worldwide attention, as these genes could transfer to healthy humans via direct contact with poultry or by the food chain.

Recently, network analysis has been widely used to reveal the co-occurrence patterns by analyzing the nodes (entities) and edges (connections), which has been used for exploring the connections among ARGs [62, 63], between ARGs and bacterial taxa [4, 64], and between ARGs and MGEs [58, 65] in various origins. In this study, we observed that qacEdelta was correlated with ARGs including aadA, blaVIM−1, sul1, and qacE, with strong and significant correlation (Fig. 3c). Similar to our research, Li et al. also found the co-occurrence pattern of qacEdelta and sul1 [58], which was then validated in the annotation of MAGs in this study. qacEdelta and qacE are genes conferring resistance to quaternary ammonium compounds. qacEdelta is a mutant version of qacE which may evolve by inserting a DNA segment carrying sul1 [66]. qacEdelta is located on the 3’ conserved region of class 1 integrons and widely distributes throughout gram-negative bacteria [66, 67], suggesting the high transfer potential of qacEdelta. The coexistence of qacEdelta and Rank I ARGs with current threats (aadA and blaVIM−1) [51], indicates the potential transfer of genes between high- and low-risk ARGs, which may accelerate the development of multidrug-resistant pathogens. Besides, network analysis also found the co-occurrence of tnpA and ARGs including bacA and macB, which was also confirmed by the results of MAGs. Overall, the co-occurrence results revealed by network analysis and genome annotation provided the evidence for potential transfer of ARGs across bacterial communities through HGT.

Moreover, metaCHIP was used to identify the putative HGT events on ACGs. We found the transmission of ARGs between the same medium, for example, the spread of ParS and vanB in human gut microbiome (Fig. 6b). The same ARG has been detected simultaneously on surfaces and in patients [68], and the likely gene transfer from one patient to another in a single hospital has been found [69], suggesting that sharing environment and touching the equipment surface might facilitate the spread of resistance genes. In addition, we discovered the potential transfer of ARGs between chicken feces and human feces, as well as between chicken feces and the environment of the wet market, which may spread to the surrounding air through aerosols [70]. Airborne transmission was considered one of the pathways for the dissemination of ARGs from animals to workers [10, 70], implicating the inhalational exposure and the spread of ARGs through the respiratory route [71]. ARGs in chicken feces can spread to the environment and contaminate door handles, public seats, or market entrances and exits. Workers and healthy market visitors might transfer ARGs by directly touching objects surfaces or contaminated food animals without gloves, increasing the risk of ARGs spread to hands and clothing, which could further contaminate the home environment [72]. These above results demonstrated the transmission of ARGs among humans, chickens, and the wet market environment, which highlighted the urgent need for monitoring and controlling ARG dissemination across humans, animals, and the environment.

However, limitations were acknowledged in this study. Firstly, human fecal samples were collected from nearby residents instead of workers at the market, which hindered our analysis for the direct transmission of ARGs between chickens and workers who were consistently contact with the chickens. Thus, in the future, research can be conducted on the transmission of ARGs in interconnected human-animal-environment settings. Another limitation of this study was that only short-read metagenomic sequencing was utilized to analyze potential ARG transfer, which makes it difficult to resolve full MAGs or confirm the physical linkage between ARGs and MGEs. In future research, complementary approaches such as culturing or long-read sequencing could be incorporated to validate metagenomic findings, as these methods generate longer reads and enable the identification of ARGs in the context of neighboring genes, thereby offering insights into their potential mobility and co-selection with MGEs. Additionally, more sampling activities can be conducted on potential transmission mediums, such as door handles, floors, surfaces, human hands, and air, to further identify the medium responsible for the spread of ARGs.

Conclusions

This study comprehensively investigated the resistome, influencing factors, host, and potential HGT of ARGs in humans and Chinese wet market with live poultry trade and slaughter. Our findings provided evidence for putative HGT events and revealed the potential transmission of ARGs among humans, chickens, and the environment in the wet market. Our results also emphasized the important role of the Chinese wet market with live poultry trade for the dissemination of ARGs, which suggested the urgent need for surveillance and establishing alternative practices to mitigate the spread of ARGs. This study provided insights for the potential HGT of ARGs across humans, chickens, and the environment, and emphasized the importance for monitoring to reduce the dissemination of ARGs in the Chinese wet market with live poultry trade.

Supplementary Information

Supplementary Material 1. (5.7MB, docx)
Supplementary Material 2. (99.9KB, xlsx)

Acknowledgements

Not applicable.

Abbreviations

ARGs

Antibiotic resistance genes

HGT

Horizontal gene transfer

MGEs

Mobile genetic elements

ORFs

open reading frames

MAGs

Metagenomic-assembled genomes

ACGs

ARG-carrying genomes

PCoA

Principal coordinate analysis

VPA

Variation partitioning analysis

MLS

Macrolide-lincosamide-streptogramin

Authors' contributions

Juan Yang: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Software, Visualization, Writing - origial draft, Writing - review & editing. Le Wang: Investigation, Methodology, Writing - origial draft. Qian Liang: Investigation, Methodology, Writing - origial draft. Yang Wang: Investigation, Methodology, Writing - origial draft. Xiaorong Yang: Conceptualization, Resources, Methodology, Data curation, Writing - review & editing. Xianping Wu: Project administration, Funding acquisition, Resources, Writing - review & editing. Xiaofang Pei: Conceptualization, Data curation, Validation, Supervision, Writing - review & editing. All authors have read and approved the final manuscript.

Funding

This work was supported by the Department of Science and Technology of Sichuan Province (Major Science and Technology Projects) (grant numbers [2022ZDZX0017]) and the Special Funding for Postdoctoral Research Projects in Sichuan Province [TB2024045].

Data availability

The datasets generated and analyzed during the current study are available in the Genome Sequence Archive of the National Genomics Data Center under BioProject accession number PRJCA032661, which is publicly accessible at https://ngdc.cncb.ac.cn/gsa/search? searchTerm=%22PRJCA032661%22.

Declarations

Ethics approval and consent to participate

This study was performed in accordance with protocols approved by the Ethics Commission of the Sichuan Center for Disease Control and Prevention. The ethical approval number was SCCDCIRB 2023-001. All participants provided informed consent before their involvement in the study. The informed consent process ensured that participants were fully informed about the nature of the study, their voluntary participation, and their right to withdraw at any time without any consequences.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Xiaorong Yang, Email: yangyangxr@163.com.

Xianping Wu, Email: wwwuxp@163.com.

Xiaofang Pei, Email: xxpei@scu.edu.cn.

References

  • 1.Zhu Y-G, Johnson TA, Su J-Q, Qiao M, Guo G-X, Stedtfeld RD, et al. Diverse and abundant antibiotic resistance genes in Chinese swine farms. Proc Natl Acad Sci. 2013;110:3435–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Xu C, Kong L, Gao H, Cheng X, Wang X. A review of current bacterial resistance to antibiotics in food animals. Front Microbiol. 2022;13:822689. [DOI] [PMC free article] [PubMed]
  • 3.Abdalla SE, Abia ALK, Amoako DG, Perrett K, Bester LA, Essack SY. From farm-to-fork: E. Coli from an intensive pig production system in South Africa shows high resistance to critically important antibiotics for human and animal use. Antibiotics. 2021;10:178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Baker M, Zhang X, Maciel-Guerra A, Dong Y, Wang W, Hu Y, et al. Machine learning and metagenomics reveal shared antimicrobial resistance profiles across multiple chicken farms and abattoirs in China. Nat Food. 2023;4:707–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Wang Y, Zhang R, Li J, Wu Z, Yin W, Schwarz S, et al. Comprehensive resistome analysis reveals the prevalence of NDM and MCR-1 in Chinese poultry production. Nat Microbiol. 2017;2:1–7. [DOI] [PubMed] [Google Scholar]
  • 6.Ahmed AM, Shimamoto T. Molecular analysis of multidrug resistance in Shiga toxin-producing escherichia coli O157:H7 isolated from meat and dairy products. Int J Food Microbiol. 2015;193:68–73. [DOI] [PubMed] [Google Scholar]
  • 7.Subbiah M, Caudell MA, Mair C, Davis MA, Matthews L, Quinlan RJ, et al. Antimicrobial resistant enteric bacteria are widely distributed amongst people, animals and the environment in tanzania. Nat Commun. 2020;11:228. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Zhong S, Crang M, Zeng G. Constructing freshness: the vitality of wet markets in urban China. Agric Hum Values. 2020;37:175–85. [Google Scholar]
  • 9.Wang Y, Hu Y, Cao J, Bi Y, Lv N, Liu F, et al. Antibiotic resistance gene reservoir in live poultry markets. J Infect. 2019;78:445–53. [DOI] [PubMed] [Google Scholar]
  • 10.Gao X-L, Shao M-F, Luo Y, Dong Y-F, Ouyang F, Dong W-Y, et al. Airborne bacterial contaminations in typical Chinese wet market with live poultry trade. Sci Total Environ. 2016;572:681–7. [DOI] [PubMed] [Google Scholar]
  • 11.Gao GF. Influenza and the live poultry trade. Science. 2014;344:235–235. [DOI] [PubMed] [Google Scholar]
  • 12.Yang J, Tong C, Xiao D, Xie L, Zhao R, Huo Z, et al. Metagenomic insights into chicken gut antibiotic resistomes and microbiomes. Microbiol Spectr. 2022;10:e01907–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.James C, Dixon R, Talbot L, James SJ, Williams N, Onarinde BA. Assessing the impact of heat treatment of food on antimicrobial resistance genes and their potential uptake by other bacteria—a critical review. Antibiotics. 2021;10:1440. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Tan R, Jin M, Li J, Yang D. The dissemination, health risks, and mitigation approaches of antibiotic resistance genes in the gut Microbiome. J Hazard Mater Adv. 2025;17:100634. [Google Scholar]
  • 15.Wang Q, Liu C, Sun Y, Li X, Gu W, Wang N, et al. Dietary intake of Enrofloxacin promotes the spread of antibiotic resistance from food to simulated human gut. ISME J. 2025;19:wraf045. 10.1093/ismejo/wraf045. [DOI] [PMC free article] [PubMed]
  • 16.Wang Y, Lyu N, Liu F, Liu WJ, Bi Y, Zhang Z, et al. More diversified antibiotic resistance genes in chickens and workers of the live poultry markets. Environ Int. 2021;153:106534. [DOI] [PubMed] [Google Scholar]
  • 17.Guo C-H, Chu M-J, Liu T, Wang J, Zou M, Liu B-T. High prevalence and transmission of blaNDM-positive escherichia coli between farmed ducks and slaughtered meats: an increasing threat to food safety. Int J Food Microbiol. 2024;424:110850. [DOI] [PubMed] [Google Scholar]
  • 18.Liu X, Li R, Dong N, Ye L, Chan EW-C, Chen S. Complete genetic analysis of plasmids carried by two nonclonal blaNDM-5- and mcr-1-bearing escherichia coli strains: insight into plasmid transmission among foodborne bacteria. Microbiol Spectr. 2021;9:e0021721. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Gan D, Lin Z, Zeng L, Deng H, Walsh TR, Zhou S, et al. Housefly gut microbiomes as a reservoir and facilitator for the spread of antibiotic resistance. ISME J. 2024;18:wrae128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Song W, Wemheuer B, Zhang S, Steensen K, Thomas T, MetaCHIP. Community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches. Microbiome. 2019;7:36. [DOI] [PMC free article] [PubMed]
  • 21.Ma Y, Wu N, Zhang T, Li Y, Cao L, Zhang P, et al. The microbiome, resistome, and their co-evolution in sewage at a hospital for infectious diseases in shanghai, China. Microbiol Spectr. 2023;12:e03900–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Liu H. Toward nitrogen recovery: Co-cultivation of microalgae and bacteria enhances the production of high-value nitrogen-rich cyanophycin. Water Res. 2024;256:121624. [DOI] [PubMed]
  • 23.Jin L, Pruden A, Boehm AB, Alvarez PJJ, Raskin L, Kohn T, et al. Integrating environmental dimensions of one health to combat antimicrobial resistance: essential research needs. Environ Sci Technol. 2022;56:14871–4. [DOI] [PubMed] [Google Scholar]
  • 24.Hu Y, Gao GF, Zhu B. The antibiotic resistome: gene flow in environments, animals and human beings. Front Med. 2017;11:161–8. [DOI] [PubMed] [Google Scholar]
  • 25.Maciel-Guerra A, Baker M, Hu Y, Wang W, Zhang X, Rong J, et al. Dissecting microbial communities and resistomes for interconnected humans, soil, and livestock. ISME J. 2023;17:21–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Weinroth MD, Thomas KM, Doster E, Vikram A, Schmidt JW, Arthur TM, et al. Resistomes and Microbiome of meat trimmings and colon content from culled cows Raised in conventional and organic production systems. Anim Microbiome. 2022;4:21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Chen S. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using Fastp. iMeta. 2023;2:e107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012;9:357. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, et al. Twelve years of samtools and BCFtools. GigaScience. 2021;10:giab008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Wood DE, Lu J, Langmead B. Improved metagenomic analysis with kraken 2. Genome Biol. 2019;20:257. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. PeerJ Comput Sci. 2017;3:e104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Yin X, Zheng X, Li L, Zhang A-N, Jiang X-T, Zhang T. ARGs-OAP v3.0: Antibiotic-resistance gene database curation and analysis pipeline optimization. Engineering. 2023;27:234–41. [Google Scholar]
  • 33.Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Pärnänen K, Karkman A, Hultman J, Lyra C, Bengtsson-Palme J, Larsson DGJ, et al. Maternal gut and breast milk microbiota affect infant gut antibiotic resistome and mobile genetic elements. Nat Commun. 2018;9:3891. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinforma Oxf Engl. 2015;31:1674–6. [DOI] [PubMed] [Google Scholar]
  • 36.Hyatt D, LoCascio PF, Hauser LJ, Uberbacher EC. Gene and translation initiation site prediction in metagenomic sequences. Bioinforma Oxf Engl. 2012;28:2223–30. [DOI] [PubMed] [Google Scholar]
  • 37.Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinforma Oxf Engl. 2012;28:3150–2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018;6:158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated Binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7. [DOI] [PubMed] [Google Scholar]
  • 40.Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive Binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6. [DOI] [PubMed] [Google Scholar]
  • 42.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol. 2017;35:725–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Olm MR, Brown CT, Brooks B, Banfield JF. dRep: A tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Buchfink B, Reuter K, Drost H-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods. 2021;18:366–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics. 2022;38:5315–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Parks DH, Chuvochina M, Rinke C, Mussig AJ, Chaumeil P-A, Hugenholtz P. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res. 2022;50:D785–94. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Yi X, Liang J-L, Su J-Q, Jia P, Lu J, Zheng J, et al. Globally distributed mining-impacted environments are underexplored hotspots of multidrug resistance genes. ISME J. 2022;16:2099–113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Kurpiel PM, Hanson ND. Point mutations in the inc antisense RNA gene are associated with increased plasmid copy number, expression of blaCMY-2 and resistance to piperacillin/tazobactam in escherichia coli. J Antimicrob Chemother. 2012;67:339–45. [DOI] [PubMed] [Google Scholar]
  • 50.Mroczkowska JE, Barlow M. Fitness trade-offs in BlaTEM evolution. Antimicrob Agents Chemother. 2008. 10.1128/aac.00018-08. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Zhang A-N, Gaston JM, Dai CL, Zhao S, Poyet M, Groussin M, et al. An omics-based framework for assessing the health risk of antimicrobial resistance genes. Nat Commun. 2021;12:4765. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.van den Bogaard AE, Willems R, London N, Top J, Stobberingh EE. Antibiotic resistance of faecal enterococci in poultry, poultry farmers and poultry slaughterers. J Antimicrob Chemother. 2002;49:497–505. [DOI] [PubMed] [Google Scholar]
  • 53.Van Gompel L, Luiken REC, Hansen RB, Munk P, Bouwknegt M, Heres L, et al. Description and determinants of the faecal resistome and Microbiome of farmers and slaughterhouse workers: A metagenome-wide cross-sectional study. Environ Int. 2020;143:105939. [DOI] [PubMed] [Google Scholar]
  • 54.Xu Y, Huang Y, Guo L, Zhang S, Wu R, Fang X et al. Metagenomic analysis reveals the Microbiome and antibiotic resistance genes in Indigenous Chinese yellow-feathered chickens. Front Microbiol. 2022;13:930289. [DOI] [PMC free article] [PubMed]
  • 55.Ma L, Xia Y, Li B, Yang Y, Li L-G, Tiedje JM, et al. Metagenomic assembly reveals hosts of antibiotic resistance genes and the shared resistome in pig, chicken, and human feces. Environ Sci Technol. 2016;50:420–7. [DOI] [PubMed] [Google Scholar]
  • 56.Su Z, Cui S, Wen D, Chen L. Metagenomic insights into resistome, mobilome and virulome in different fecal waste. Environ Res. 2024;262:119861. [DOI] [PubMed] [Google Scholar]
  • 57.Chang D, Mao Y, Qiu W, Wu Y, Cai B. The source and distribution of Tetracycline antibiotics in china: A review. Toxics. 2023;11:214. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Tian L, Fang G, Li G, Li L, Zhang T, Mao Y. Metagenomic approach revealed the mobility and co-occurrence of antibiotic resistomes between non-intensive aquaculture environment and human. Microbiome. 2024;12:107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Sun J, Chen C, Cui C-Y, Zhang Y, Liu X, Cui Z-H, et al. Plasmid-encoded tet(X) genes that confer high-level Tigecycline resistance in escherichia coli. Nat Microbiol. 2019;4:1457–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.He T, Wang R, Liu D, Walsh TR, Zhang R, Lv Y, et al. Emergence of plasmid-mediated high-level Tigecycline resistance genes in animals and humans. Nat Microbiol. 2019;4:1450–6. [DOI] [PubMed] [Google Scholar]
  • 61.Liu Y-Y, Wang Y, Walsh TR, Yi L-X, Zhang R, Spencer J, et al. Emergence of plasmid-mediated colistin resistance mechanism MCR-1 in animals and human beings in china: A Microbiological and molecular biological study. Lancet Infect Dis. 2016;16:161–8. [DOI] [PubMed] [Google Scholar]
  • 62.Li B, Yang Y, Ma L, Ju F, Guo F, Tiedje JM, et al. Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes. ISME J. 2015;9:2490–502. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Chen H, Li Y, Sun W, Song L, Zuo R, Teng Y. Characterization and source identification of antibiotic resistance genes in the sediments of an interconnected river-lake system. Environ Int. 2020;137:105538. [DOI] [PubMed] [Google Scholar]
  • 64.Ma L, Yang H, Guan L, Liu X, Zhang T. Risks of antibiotic resistance genes and antimicrobial resistance under chlorination disinfection with public health concerns. Environ Int. 2022;158:106978. [DOI] [PubMed] [Google Scholar]
  • 65.Zhao R, Feng J, Huang J, Li X, Li B. Reponses of microbial community and antibiotic resistance genes to the selection pressures of ampicillin, cephalexin and Chloramphenicol in activated sludge reactors. Sci Total Environ. 2021;755:142632. [DOI] [PubMed] [Google Scholar]
  • 66.Paulsen IT, Littlejohn TG, Rådström P, Sundström L, Sköld O, Swedberg G, et al. The 3’ conserved segment of integrons contains a gene associated with multidrug resistance to antiseptics and disinfectants. Antimicrob Agents Chemother. 1993;37:761–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Chuanchuen R, Khemtong S, Padungtod P. Occurrence of qacE/qacEDelta1 genes and their correlation with class 1 integrons in salmonella enterica isolates from poultry and swine. Southeast Asian J Trop Med Public Health. 2007;38:855–62. [PubMed] [Google Scholar]
  • 68.Lerminiaux NA, Cameron ADS. Horizontal transfer of antibiotic resistance genes in clinical environments. Can J Microbiol. 2019;65:34–44. [DOI] [PubMed] [Google Scholar]
  • 69.Evans DR, Griffith MP, Sundermann AJ, Shutt KA, Saul MI, Mustapha MM, et al. Systematic detection of horizontal gene transfer across genera among multidrug-resistant bacteria in a single hospital. eLife. 2020;9:e53886. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Yang F, Gao Y, Zhao H, Li J, Cheng X, Meng L, et al. Revealing the distribution characteristics of antibiotic resistance genes and bacterial communities in animal-aerosol-human in a chicken farm: from one-health perspective. Ecotoxicol Environ Saf. 2021;224:112687. [DOI] [PubMed] [Google Scholar]
  • 71.Xie J, Jin L, He T, Chen B, Luo X, Feng B, et al. Bacteria and antibiotic resistance genes (ARGs) in PM2.5 from china: implications for human exposure. Environ Sci Technol. 2019;53:963–72. [DOI] [PubMed] [Google Scholar]
  • 72.Alam M-U, Rahman M, Abdullah-Al-Masud, Islam MA, Asaduzzaman M, Sarker S, et al. Human exposure to antimicrobial resistance from poultry production: assessing hygiene and waste-disposal practices in Bangladesh. Int J Hyg Environ Health. 2019;222:1068–76. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (5.7MB, docx)
Supplementary Material 2. (99.9KB, xlsx)

Data Availability Statement

The datasets generated and analyzed during the current study are available in the Genome Sequence Archive of the National Genomics Data Center under BioProject accession number PRJCA032661, which is publicly accessible at https://ngdc.cncb.ac.cn/gsa/search? searchTerm=%22PRJCA032661%22.

Articles from BMC Microbiology are provided here courtesy of BMC

RESOURCES