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. 2024 Jul 30;14:17490. doi: 10.1038/s41598-024-68699-8

High-throughput profiling of antibiotic resistance genes in the Yellow River of Henan Province, China

Shuhong Zhang 1,✉, Guangli Yang 1, Yiyun Zhang 1, Chao Yang 1
PMCID: PMC11289115  PMID: 39080455

Abstract

Profiling antibiotic resistance genes (ARGs) in the Yellow River of China’s Henan Province is essential for understanding the health risks of antibiotic resistance. The profiling of ARGs was investigated using high-throughput qPCR from water samples in seven representative regions of the Yellow River. The absolute and relative abundances of ARGs and moble genetic elements (MGEs) were higher in summer than in winter (ANOVA, p < 0.001). The diversity and abundance of ARGs were higher in the Yellow River samples from PY and KF than the other sites. Temperature (r = 0.470 ~ 0.805, p < 0.05) and precipitation (r = 0.492 ~ 0.815, p < 0.05) positively influenced the ARGs, while pH had a negative effect (r = − 0.462 ~ − 0.849, p < 0.05). Network analysis indicated that the pathogenic bacteria Rahnella, Bacillus, and Shewanella were the possible hub hosts of ARGs, and tnpA1 was the potential MGE hub. These findings provide insights into the factors influencing ARG dynamics and the complex interaction among the MGEs, pathogenic bacteria and environmental parameters in enriching ARGs in the Yellow River of Henan Province.

Keywords: Antibiotic resistance genes, Mobile genetic elements, Pathogenic bacteria, Environmental factors, Yellow River

Subject terms: Ecology, Microbiology, Risk factors

Introduction

Antibiotic resistance represents a serious and growing human health threat worldwide1. By 2050, 10 million people are estimated to die annually due to antibiotic-resistant bacterial infections, which would be higher than the death toll from cancer and diabetes combined2. Research has proven that antibiotic resistance is a natural and ancient phenomenon3,4. Primarily, the extensive use of antibiotics in livestock and poultry breeding and agricultural production contributes to antibiotic resistance5, evidenced by antibiotic residues, antibiotic resistance genes (ARGs), and antibiotic resistance-carrying bacteria. These components responsible for antibiotic resistance pass through various systems, such as sewage, wastewater, livestock manure, compost, lagoons, and antibiotic manufacturing facility effluents and are ultimately released into rivers6.

ARGs can migrate among different microorganisms through horizontal gene transfer (HGT) mediated by mobile genetic elements (MGEs) (such as plasmids, transposons, integrons and phage)7,8. ARGs and metal resistance genes (MRGs) cooccur due to shared mode of action, which could be co-selected and spread together9. The original motive for generating ARGs and MRGs is the selection pressure by antibiotics and heavy metals, and their dissemination through horizontal gene transfer is facilitated by MGEs10,11. The fate of ARGs is determined by ARGs, MGEs, and MRGs. Therefore, to understand and find an effective strategy to manage antibiotic resistance, it is necessary to analyze ARGs, MGEs, and MRGs.

ARGs in the river system has become an important issue in recent years due to their potential threat to aquatic ecosystems and public health12. In China, the second longest river is the Yellow River, which supplies water to nearly 50 large- and medium-sized cities and 12% of the agricultural fields13. Antibiotics known to enhance the abundance of ARGs, such as the tetracyclines, fluoroquinolones, sulfonamides, and macrolides, have been detected in the Yellow River since 200914,15. Recently, scientists analyzed the levels of ARGs specifically in the Yellow River samples, with the absolute abundances of total ARGs varied from 1.49 × 104 copies/L to 8.35 × 108 copies/L13,16. The appearance of ARGs in pathogenic bacteria would aid in the understanding of environmental risk assessment of ARGs17. However, only a few researchers investigated the driving factors of ARGs in the Yellow River, such as MGEs, MRGs, pathogens, and environmental and socioeconomic parameters. Therefore, the present study analyzed the ARGs and their driving factors in the Yellow River of Henan Province. We used high throughput qPCR (HT-qPCR) to quantify ARGs, MGEs, and MRGs, high-throughput sequencing (16S rRNA gene) to assess the pathogenic bacteria, and correlation analysis to determine the association between ARGs and its driving factors in seven representative sections of the Yellow River near human-dominated regions. The objectives of the study were to comprehensively analyze the temporal (winter and summer) and spatial (sampling sites) variations in the diversity and abundance of ARGs and MGEs and delineate the key factors (MGEs, MRGs, pathogenic bacteria, environmental and socioeconomic parameters) shaping the ARG profiles along the Yellow River. The study’s findings will provide novel insights into the factors affecting ARG profiles and help develop effective strategies to prevent antibiotic resistance in the Yellow River.

Materials and methods

Study area and sampling strategy

Water samples were collected from the following seven sites along the Yellow River of Henan Province (Fig. 1): Luoyang (LY, 34° 38′ 15.90″ N, 115° 25′ 34.32″ E; altitude 81.62 m), Xinxiang (XX, 34° 56′ 52.15″ N, 114° 35′ 5.28″ E; 70.91 m), Hebi (HB, 35° 46′ 33.82″ N, 114° 13′ 11.28″ E; 108.28 m), Zhengzhou (ZZ, 34° 54′ 28.84″ N, 113° 37′ 3.36″ E; 92.34 m), Kaifeng (KF, 34° 55′ 31.80″ N, 114° 46′ 7.32″ E; 74.90 m), Puyang (PY, 35° 22′ 47.21″ N, 115° 22′ 47.28″ E; 65.90 m), and Shangqiu (SQ, 34° 40′ 17.98″ N, 115° 18′ 25.92″ E; 72.94 m). Water in SQ site comes from Wutun reservoir (located in the eastern part of Yellow River), which is the source of drinking water in Shangqiu City. On the other hand, source of LY water is Xiaolangdi reservoir. XX site receives water from Changyuan River, the lower reaches of the Yellow River. The floodplain of Changyuan River is wide, and the cultivated land in the floodplain is fertile and flat. Water in HB site originates from the Jindi River of Jun County, while the source of PY water is the Water Conservancy Scenic Area in the lower reaches of the Yellow River. Water at ZZ site comes from Zhengzhou Yellow River Cultural Park, while the Yellow River Tourist Area is the source of water in KF site. The 7 sampling sites were surrounded by trees and grasses.

Figure 1.

Figure 1

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Seven sampling sites of the Yellow River in Henan Province. The map was revised from the work by Zhang et al.18, with it as template under Arc Gis. The longitude and latitude of each sampling sites were imported into the software. LY, ZZ, KF, SQ, XX, PY, and HB indicate Luoyang, Zhengzhou, Kaifeng, Shangqiu, Xinxiang, Puyang, and Hebi, respectively.

In this study, two samplings were carried out, one in December 2021 (winter) and the other in July 2022 (summer). At each time point, three samples (2 L each) were collected from each site: the first sample was collected from 0.1 m depth as surface water, the second from about 1 m depth as middle water, and the third from about 2 m depth as bottom water. These three samples were combined to form a composite sample (6 L), which was transported to the laboratory in the dark on an ice bath and stored at − 20 °C. Then, 1.6 L of the composite sample was used to capture bacteria, and the remaining 0.4 L was used to determine the physicochemical properties, such as ammonia–nitrogen (NH4+-N), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP). Three replicates were maintained for each sampling site.

Analysis of environmental factors

The temperature and pH of the water samples were determined from the field using a Hach pH meter equipped with a pH probe and a temperature probe (PT-10, SARTORIUS, Germany). The levels of NH4+-N, TN, and TP in the collected water samples were analyzed using an automated discrete analyzer (SmartChem 200, Analyzer Medical System Inc., Guidonia, Rome, Italy)19, and the chemical oxygen demand (COD) was determined following the potassium dichromate method (GB 11914-89)20. The meteorological data (precipitation) were obtained from the National Meteorological Science Data Center (http://data.cma.cn/, accessed on 20 June 2023, Table S1).

Extraction of DNA

The composite water sample (1.6 L per replicate) was filtered under vacuum through a sterile steel filter (T-50.1l, JINTENG, China) equipped with a 0.22 μm cellulose filter membrane (JINTENG, China) to capture the bacteria21. This membrane was cut into pieces and immersed in phosphate buffer saline in a 2 mL tube to remove chemical impurities. Then, a FastDNA SPIN Kit (MP Bio, USA) was used to extract genomic DNA from the bacteria on the membrane following the manufacturer’s instructions. The quality of the extracted DNA was assessed by agarose gel electrophoresis, and the quantity was measured using a microvolume spectrophotometer (Thermo Fisher Scientific Inc. USA).

Quantification of ARGs, MGEs, and MRGs

HT-qPCR was performed using a total of 377 primer sets22 (Table S3) to quantify all major ARG subtypes (308 primer sets), MGE subtypes (57 primer sets), MRG subtypes (10 primer sets), and Hmt (1 primer set)23 in the water samples, maintaining the 16S rRNA gene (1 primer set) as the reference. The assay was performed on a SmartChip Real-time PCR system (WaferGen Biosystems Inc, Fremont, USA), using the following thermal cycle program: 10 min at 95 °C, followed by 40 cycles of denaturation at 95 °C for 30 s and annealing at 60 °C for 30 s24. Three technical replicates were maintained per sample, and a reaction mixture without the template was included as the negative control per primer set. The WaferGen software (WaferGen Biosystems Inc., Fremont, California, USA) automatically generated the melting curve. The HT-qPCR data with efficiencies beyond 1.7–2.3 or an r2 under 0.99 were discarded, and the data from samples that generated amplicons for all three technical replicates were considered positive and used for subsequent analysis. The relative copy number of each gene was finally calculated using the following equation24:

Relative gene copy number=10(31-CT)(10/3),

where CT value represents the threshold cycle (< 31 was accepted)24.

The relative abundance (ARG, MGE, and MRG subtypes) determined by HT-qPCR was then transformed into absolute abundance by normalizing it to the 16S rRNA gene copy number25. Thus, the relative abundance of each target gene was represented as the absolute copy number of the gene per copy number of 16S rRNA26.

High-throughput sequencing, data processing, and pathogenic bacterial identification

The DNA extracted from the bacteria in the river samples was used to amplify the V3–V4 hypervariable region of the 16S rRNA gene by PCR (GeneAmp 9700, ABI, USA), using 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) primers27. The amplicon was then sequenced on an Illumina NovaSeq PE250 platform (San Diego, CA, United States) at Guangdong Magigene Biotechnology Co., Ltd (106 Xingdao Huannan Road, Haizhu District, Guangzhou City, China).

The raw sequencing data were quality-filtered using TRIMMOMATIC and merged using FLASH with the following criteria: (i) the reads were truncated at any site with an average quality score < 20 over a 50 bp sliding window; (ii) the primers were allowed a mismatch of 2 nucleotides, and the reads containing ambiguous bases were removed; (iii) finally, sequences with an overlap longer than 10 bp were merged according to their overlap sequence28. The raw reads of all samples have been deposited into the GSA database of the National Data Centre for Genome Sciences (Project No. PRJCA018113).

Further, the operational units (OTUs) were picked from the 16S rRNA gene sequences with USEARCH (v7.1, http://drive5.com/uparse/)29. After mapping onto the reference sequences, the sequences with a similarity of over 97% were clustered into OTUs using UPARSE (v7.1)30, and the chimeric sequences were removed by UCHIME31. The taxonomy for each OTU from each sample was then annotated using the RDP classifier Bayes algorithm against the SILVA database28, and the abundance of each taxon was represented as the number of sequences from each OTU.

We further selected pathogenic bacterial genera from the annotation results based on the list obtained after compiling the genera mentioned in the existing publications32,33. A total of 37 potentially pathogenic genera were selected from the annotation results (Table S4). Finally, the absolute abundance of the pathogenic bacteria was calculated using the number of sequences in each OTU and illustrated as a heatmap based on multiple sample comparisons with TBtools.

Statistical analysis

One-way analysis of variance (ANOVA) or Kruskal–Wallis test was used to assess the significance in the differences in ARGs, MGEs, MRGs, pathogens, and 16S rRNA between seasons or among sampling sites; these analyses were performed using the Statistical Package for the Social Sciences (SPSS) (v23; IBM, Neconductivity York, NY, USA). Pair-wise Spearman’s correlation coefficients (r) between the abundance of ARG subtypes and the abundance of MGE subtypes, MRG subtypes, and pathogenic bacteria and the values of the environmental parameters were calculated using IBM SPSS Statistics 23 (IBM, Neconductivity York, NY, USA). The correlation was considered statistically significant at p < 0.05. Then, a heatmap was generated based on the log2-transformed absolute abundance of the ARG subtypes using TBtools. Principal component analysis (PCA) was also performed with TBtools to identify the spatial difference in ARG subtypes, and network analysis was carried out to explore the co-occurrence patterns among ARG subtypes, MGE subtypes, and pathogenic bacteria. The correlation in the network was considered robust at r > 0.4 and p < 0.05. Finally, the network was visualized using the circular layout algorithm in Cytoscape (v3.3.0)34,35.

Results

Diversity of ARGs, MGEs, and MRGs in the water samples of Yellow River

Further evaluation of samples collected during different seasons showed 96 ARG subtypes, 26 MGE subtypes, and 2 MRG subtypes in the water samples collected in winter and 106 ARG subtypes, 26 MGE subtypes, and 4 MRG subtypes in the water samples collected in summer (Fig. 2). The number of ARG subtypes detected in summer was significantly higher than that in winter (ANOVA, p = 0.006). However, the MGE subtypes (p = 0.194) showed no difference and MRG subtypes (p = 0.179) showed little difference in number between the two seasons.

Figure 2.

Figure 2

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Venn diagrams show the number of shared and unique (A) ARG subtypes, (B) MGE subtypes, and (C) MRG subtypes in the Yellow River water samples of Henan Province collected in summer and winter.

In addition, the number of ARG subtypes differed significantly among the sampling sites (Kruskal–Wallis test, p = 0.003). PY and KF had the highest number of ARG subtypes in winter, while LY and SQ had the least. In summer, PY had the highest number of ARG subtypes, while LY had the least (Fig. 3A). Then, we combined the data on two seasons to assess the spatial differences in the detected ARG subtypes. Venn diagram demonstrated that 34 ARG subtypes (24.11% of the total) were common among the seven sampling sites. On the other hand, PY and KF had the maximum unique ARG subtypes, and SQ and LY had the minimum (Fig. 3B).

Figure 3.

Figure 3

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Distribution of ARGs and MGEs in the Yellow River across seven sampling sites. (A) The number of ARG subtypes and MGE subtypes. ARGs were classified based on the antibiotics to which they conferred resistance. S refers to summer, and W refers to winter. The Venn diagrams show the number of shared and unique (B) ARG subtypes and (C) MGE subtypes of the seven sampling sites. LY, XX, HB, ZZ, KF, PY, and SQ refer to Luoyang, Xinxiang, Hebi, Zhengzhou, Kaifeng, Puyang, and Shangqiu, respectively.

The number of MGE subtypes also differed significantly among the sampling sites (Non-parametric Kruskal–Wallis test, p = 0.005). PY and KF had the highest number of MGE subtypes in winter, while HB and ZZ had the least. In summer, PY had the highest number of MGE subtypes while LY had the least (Fig. 3A). Then, we combined the data on two seasons to assess the spatial differences in MGE subtypes. Venn diagram demonstrated that 19 MGE subtypes (33.33% of the total) were shared among the seven sampling sites. PY and KF had one unique MGE subtype (Fig. 3C). These observations suggested more variations in ARG subtypes among the seven sampling sites than in MGE subtypes.

Abundance of ARGs, MGEs, and MRGs in the water samples of Yellow River

Further, HT-qPCR was performed to investigate the absolute abundances of ARGs, MGEs, MRGs, and 16S rRNA in the Yellow River water samples collected from seven sampling sites. Here, the absolute abundance of ARGs, MRGs, MGEs, and 16S rRNA ranged from 0 to 3.6 × 106 copies/mL, 0 to 9.0 × 107 copies/mL, 0 to 8.2 × 105 copies/mL, 1.9 × 106 to 9.0 × 106 copies/mL, respectively (Fig. 4A). Besides, the absolute abundance of nine ARG types showed significant positive correlations with the absolute abundance of the total number of MGEs (Spearman’s r = 0.60 ~ 0.90, p = 0.000 ~ 0.025; Table S5). The absolute abundance of ARGs (ANOVA, p < 0.001) and MGEs (p < 0.00000001) in summer was significantly higher than that in winter (Fig. 4A). In contrast, no noticeable difference was detected in the absolute abundance of 16S rRNA (p = 0.494) and MRGs (p = 0.067) between the seasons (Fig. 4A). Additionally, the absolute abundance of ARGs and MGEs across the sampling sites correlated significantly with that of the 16S rRNA (Spearman’s r = 0.679, p = 0.01; Spearman’s r = 0.659, p = 0.000003, respectively) (Fig. 4B).

Figure 4.

Figure 4

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Absolute abundance of ARGs, MRGs, MGEs, and 16S rRNA in the Yellow River water samples collected from seven sites during winter and summer (A). Linear regression models reveal the correlation between the absolute abundance of 16S rRNA and the total absolute abundance of ARGs and MGEs (B). LY, XX, HB, ZZ, KF, PY, and SQ indicate Luoyang, Xinxiang, Hebi, Zhengzhou, Kaifeng, Puyang, and Shangqiu, respectively. S refers to summer, and W refers to winter. The color band at the right shows the log10-transformed number of reads.

Further, we assessed the seasonal variations in ARGs and MGEs based on the relative abundance values to avoid the potential influence of bacterial community size (Fig. 5). This approach revealed that the relative abundance of ARGs and MGEs in summer was significantly higher than that in winter (ANOVA, p = 0.0000013, p = 0.000000002, respectively), consistent with the trend based on absolute abundance.

Figure 5.

Figure 5

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Relative abundance of ARGs and MGEs in the Yellow River water samples collected from seven sampling sites during winter and summer. The relative abundance was determined by dividing the absolute abundance of ARGs and MGEs by the copy number of 16S rRNA gene. LY, XX, HB, ZZ, KF, PY, and SQ represent Luoyang, Xinxiang, Hebi, Zhengzhou, Kaifeng, Puyang, and Shangqiu, respectively. S refers to summer, and W refers to winter.

Furthermore, PCA was conducted based on ARG subtypes' diversity and absolute abundance to confirm the differences between seasons and among sampling sites. This analysis showed that the ARG subtypes from ZZ, XX, SQ, HB, and LY in winter and summmer clustered together, while those from KF and PY remained separate from the rest in both seasons (Fig. 6).

Figure 6.

Figure 6

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Principal component analysis (PCA) based on the diversity and the absolute abundance of ARG subtypes in the water samples. LY, XX, HB, ZZ, KF, PY, and SQ represent Luoyang, Xinxiang, Hebi, Zhengzhou, Kaifeng, Puyang, and Shangqiu, respectively. S refers to summer, and W refers to winter.

We then assessed the correlation between the absolute abundance of ARG subtypes and that of MGE subtypes across the seven sampling sites based on the average of the winter and summer values. This analysis showed that tnpA1, IS26, and IS630 MGEs correlated with 41, 43, and 39 ARG subtypes, respectively, suggesting these as the hub MGEs connected to several ARG subtypes. Similarly, the arsA MRG was correlated with 43 ARG subtypes (Fig. 7).

Figure 7.

Figure 7

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Heatmap shows the correlation between the ARG subtypes and MGEs in the Yellow River water samples of Henan Province. The correlation coefficients were calculated using the absolute abundance values. The color gradient in the rectangular box at the top right represents the Spearman correlation coefficient. **r = 0.613 ~ 0.963, p < 0.01; *r = 0.458 ~ 0.611, p < 0.05.

Characterization of pathogenic bacteria and their association with ARG subtypes and MGEs in the water samples of Yellow River

A total of 37 pathogenic bacteria at the genus level were identified from the collected water samples based on high-throughput sequencing of 16S rRNA (Table S4) and subsequent comparison with the compiled list of reported pathogenic genera. Figure 8 shows the abundance of the 23 predominant pathogenic bacteria in the Yellow river water samples. Among these, seven pathogenic bacteria (Arcobacter, Comamonas, Exiguobacterium, Flavobacterium, Pseudomonas, Rahnella, and Shewanella) showed a significant difference in absolute abundance between the seasons; the absolute abundance in summer was higher than that in winter (ANOVA, p = 0.000001 ~ 0.041; Fig. 8).

Figure 8.

Figure 8

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Heatmap shows the absolute abundance of 23 pathogenic bacteria predominant at the genus level in the Yellow River water samples of Henan Province during winter and summer. The absolute abundance of the pathogenic bacteria was calculated using the number of sequences from each OTU. The color band at the top right shows the log10-transformed number of reads. LY, XX, HB, ZZ, KF, PY, and SQ represent Luoyang, Xinxiang, Hebi, Zhengzhou, Kaifeng, Puyang, and Shangqiu, respectively. S refers to summer, and W refers to winter.

Based on the absolute abundance, the top 20 ARG subtypes (aadA7, aadA17, catIII, MCR1.1, MaxE, QnrS2, AAC4Via, aadA2, ttgA, cefaqaclta, QnrB4, AAC6IIa, ANT2Ia, aadA5, blaSFO, OXY11, msrE, CMYmoxbetalac, qacFH, Aac3iidiiaiie), the top 7 MGE subtypes (IS630, ISCR1, TN5403, tnpA2, IS26, tnpA1, ISAba3Acineto), and the top 6 pathogenic bacteria (Rahnella, Bacillus, Pantoea, Acinetobacter, Chryseobacterium, Bacillus) were selected from both seasons to analyze the co-occurrence patterns using network analysis (Fig. 9). The results showed that the tnpA1 MGE correlated with Bacillus (r = 0.54, p = 0.04 < 0.05), Shewanella (r = 0.58, p = 0.02 < 0.05), and Rahnella (r = 0.53, p = 0.03 < 0.05). In addition, tnpA1 correlated with two ARG subtypes blaSFO (r = 0.53, p = 0.04 < 0.05) and msrE (r = 0.65, p = 0.02 < 0.05). These observations suggest the ability of tnpA1 to carry blaSFO and msrE into Bacillus, Shewanella, and Rahnella, proving it as the hub MGE responsible for the transfer of ARGs into pathogens.

Figure 9.

Figure 9

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Network analysis reveals the co-occurrence patterns among ARG subtypes, MGEs, and pathogenic bacteria. The network is based on Spearman's correlation analysis.

Environmental factors influencing ARGs and MGEs dynamics in Yellow River

Finally, spearman correlation coefficients were calculated to assess the relationships between ARG and MGE subtypes and various environmental factors, such as temperature, pH, NH4+-N, TP, COD, TN, latitude, longitude, altitude, and precipitation. The heatmap generated based on these correlation coefficients showed that precipitation (r = 0.492 ~ 0.815, p < 0.05) and temperature (r = 0.470 ~ 0.805, p < 0.05) were positively associated with the absolute abundance of 36 out of 63 ARG subtypes, and TP was positively associated with the absolute abundance of 3 ARG subtypes (r = 0.507 ~ 0.663, p < 0.05). Meanwhile, pH was negatively correlated with the absolute abundance of 33 ARG subtypes (r = − 0.462 ~ − 0.849, p < 0.05), and altitude was negatively correlated with the absolute abundance of 8 ARG subtypes (r = − 0.470 ~ − 0.785, p < 0.05). However, NH4+-N (r = − 0.381 ~ 0.378, p > 0.05), TN (r = − 0.340 ~ 0.368, p > 0.05), COD (r = − 0.378 ~ 0.383, p > 0.05), latitude (r = − 0.152 ~ 0.381, p > 0.05), and longitude (r = − 0.291 ~ 0.350, p > 0.05) showed no correlation with the absolute abundance of any ARG subtypes (Fig. S3A).

The heatmap, generated based on these correlation coefficients, showed positive correlations of precipitation (r = 0.616 ~ 0.827, p < 0.05) and temperature (r = 0.567 ~ 0.753, p < 0.05) with the absolute abundance of 13 out of 22 MGE subtypes. Meanwhile, pH was found to be negatively correlated with the absolute abundance of 13 MGE subtypes (r = − 0.686 ~ − 0.843, p < 0.01). Similarly, TP (r = − 0.545 and r = − 0.565, p < 0.05) and altitude (r = − 0.605 and r = − 0.613, p < 0.05) exhibited negative correlations with the absolute abundance of 2 MGE subtypes. Interestingly, NH4+-N (r = − 0.296 ~ 0.242, p > 0.05), TN (r = − 0.362 ~ 0.428, p > 0.05), COD (r = − 0.298 ~ 0.377, p > 0.05), latitude (r = − 0.178 ~ 0.305, p > 0.05), and longitude (r = − 0.246 ~ 0.267, p > 0.05) showed no correlation with any MGE subtypes (Fig. S3B).

Furthermore, precipitation (r = 0.546 ~ 0.814, p < 0.05) and temperature (r = 0.567 ~ 0.871, p < 0.05) were observed to be positively correlated with the abundance of 16 out of 23 pathogens. Longitude showed positive correlation with the abundance of only 2 pathogens (r = 0.608 and r = 0.604, p < 0.05). On the contrary, pH was found to be negatively correlated with the abundance of 15 pathogens (r = − 0.559 ~ − 0.882, p < 0.05). Similarly, TP showed negative correlation with the abundance of 8 pathogens (r = − 0.556 ~ − 0.678, p < 0.05), while latitude exhibited negative correlation with the abundance of only 1 pathogen (r = -0.643, p < 0.05). Meanwhile, NH4+-N (r = − 0.085 ~ 0.408, p > 0.05), TN (r = − 0.492 ~ 0.327, p > 0.05), COD (r = − 0.289 ~ 0.086, p > 0.05), and altitude (r = − 0.380 ~ 0.375, p > 0.05) showed no correlation with the abundance of any pathogens (Fig. S3C).

Hmt gene abundance and its association with ARG subtypes in the water samples of Yellow River

The abundance of Hmt gene was in the range of 4.27 × 105 ~ 2.04 × 109 gene copies mL−1. The present study used Hmt gene abundance to determine the influence of human activity on ARG distribution among the seven cities. This approach showed no correlation of Hmt (r = − 0.362 ~ 0.180, p > 0.05) with any ARG subtype, indicating that human activities did not affect the accumulation and proliferation of ARGs in the Yellow River of Henan Province.

Discussion

Variations in ARGs in the Yellow River between seasons and among sampling sites

The present study analyzed the variations in antibiotic resistance-associated factors in the Yellow River between two seasons (winter and summer) and among seven sites. The absolute and relative abundances of ARGs and MGEs were significantly higher in summer than in winter. These observations suggest a significant influence of season on ARGs and MGEs in the Yellow River, consistent with Wu et al.36 and Ke et al.37. However, the absolute abundances of 16S rRNA and MRGs did not vary between the seasons, which is in agreement with Xu et al.38.

The study further revealed the spatial similarities and differences in ARG diversity and abundance in the Yellow River. The seven sampling sites shared 34 ARG subtypes, indicating their prevalence along the Yellow River. Besides, numerous unique ARG subtypes were detected from KF, ZZ, XX, HB, and PY. The unique ARG subtypes were APH(3')-Ib, ErmQ, and tetL in KF and AAC(6')-Iic in PY. The detection of these ARGs, generally disseminated from clinical and animal sources39–41, indicates their flow via anthropogenic activities42. These observations confirm that the ARGs of KF and PY were different from the ARGs of other 5 sampling sites.

Further, we investigated whether season or region is more important in determining the ARG abundance and diversity. The Chi-square (χ2) values based on the ARG and MGE absolute and relative abundances were higher for the season than the sampling sites (Table S6). On the other hand, the χ2 values based on the ARG and MRG diversity were higher for the sampling sites than the season. These observations confirmed that season significantly influenced the absolute and relative abundances of ARGs and MGEs, while the sampling sites considerably affected their diversity.

Furthermore, absolute and relative abundances of ARGs and MGEs were higher in summer than in winter. This trend was consistent with the observations of a previous study43. These seasonal variations in the abundance of ARGs may be caused by the variations in temperature and precipitation. The positive correlation observed between water temperature and the absolute abundance of ARGs in this study was consistent with the earlier studies, which reported increase in the absolute abundance of ARGs with the rising temperature38,44,45. These findings suggest that high temperature is beneficial for the growth of pathogens45, which further leads to increase in the abundance of ARGs. In addition, high temperature is conducive to exchange of ARGs or absorption of free genetic material, which promote the horizontal transfer of ARGs. The positive correlations observed between temperature and the abundance of most pathogens and MGEs further support these two theories. Thus, at high temperature, ARGs enriched due to increase in MGEs co-occurring with ARGs and host pathogens46.

The positive correlation observed between precipitation and absolute abundance of ARGs in this study was also consistent with the earlier studies, which reported that the absolute abundance of ARGs was higher in the wet season, as compared to dry season26,47,48. Furthermore, precipitation was found to be positively correlated with most pathogens and MGEs in this study. Thus, the high levels of rainfall probably promoted the introduction of point and nonpoint ARGs, MGEs, and pathogen into the Yellow River of Henan province47,49.

Sampling sites had considerable effect on the diversity of ARGs and MGEs in this study, as suggested by the highest diversity of ARGs and MGEs subtypes in KF and PY sites than the other sampling sites. PCA showed the cluster of ZZ, XX, SQ, HB, and LY, and their clear seperation from KF and PY, which suggested that the ARGs subtypes from ZZ, XX, SQ, HB, and LY were more similar than KF and PY. Furthermore, IncN_korA, a unique MGE in PY, was reported to be associated with the genes related to resistance against extended-spectrum beta-lactamases (ESBLs)50. ACT beta-lac was also a unique ARG in PY, which was related to beta-lactamase. These findings confirmed that the unique ARG of ACT beta-lac was transported by the unique MGE of IncN_korA in PY site. On the other hand, IncHI2-smr0018 was a unique MGE in KF. IncHI2 plasmids have been reported to carry numerous classes of resistance genes, including the genes related to β-lactams and aminoglycosides51. In the present study, AAC(3)-Iic, ANT(6)-Ib and APH(3')-Ib related to beta-lactamase and class C beta and TEM beta-lac related to β-lactams were observed as the unique ARGs in KF. These findings suggested that these unique ARGs were transported by the unique MGE IncHI2-smr0018 in KF.

Overall, the above findings indicated that temperature and rainfall were the key factors controlling the abundance of ARGs and MGEs. Furthermore, sampling sites also considerably affected the diversity of ARGs, as indicated by the unique MGEs and ARGs observed in PY and KF.

Factors influencing the distribution of ARGs in the Yellow River water samples of Henan Province

The primary mechanism promoting ARG spread is HGT among various microorganisms via MGEs52. In this study, we found that all ARGs and nine ARG subtypes positively correlated with the absolute abundance of MGEs, indicating that the widespread prevalence of MGEs played an important role in accelerating ARG proliferation in the Yellow River53. In addition, tnpA1, which connected with most ARG subtypes (Fig. 6) and pathogenic bacteria (Fig. 8), was the hub MGE. Research has proven that tnpA plays an important role in the co-occurrence of ARGs54 and is correlated with ARGs across multiple water environments, such as rivers48,53 and drinking water sources55. Considering these facts, we suggest that reducing tnpA, the major MGE, is crucial for managing ARGs in the Yellow River of Henan province.

Pathogens also play a significant role in ARG spread; they acquire and spread ARGs across various environments56–58. Network analysis of this study showed that Bacillus, Rahnella, and Shewanella act as hosts for the spread of ARG subtypes, such as qacFH, OXY11, QnrS2, ANT2Ia, and MCR1.1 (Fig. 8). Bacillus, a potential host of ARGs, has been reported in the drinking water sources across China55. The present study also identified Bacillus, Rahnella, and Shewanella as potential hosts of multiple ARG subtypes, which is consistent with the reports in Yongjiang Estuary59. These findings collectively indicate that Bacillus, Rahnella, and Shewanella with multiple ARG subtypes confer multi-drug resistance, as the integration of these genes across the genome determines the multi-drug resistant characteristics for these microorganisms60. However, further investigations are needed to validate whether pathogenic bacteria carrying multiple ARG subtypes express multi-drug resistance by screening antibiotic-resistant bacteria under multi-drug selective pressure and analyzing the complete genome of the screened strains.The absolute abundance of 16S rRNA showed insignificant difference between summer and winter, while 7 pathogens showed a significant difference in absolute abundance between the seasons. Thus, season affects ARGs by affecting the abundance of some pathogenic bacteria, rather than by affecting the total biomass of microorganisms.

Abundance and distribution of ARGs displayed close relationships with the environmental parameters, including pH, water temperature, and precipitation. The correlations of water temperature and precipitation with the abundance of ARGs have been discussed in 4.1. For pH, ARG abundance decreased with an increase in pH in the surface sediments from commercial public squares around Nanjing City61. Low pH is optimal for leaching of heavy metals62, which showed positive correlation with ARG subtypes in a previous study63. For instance, Liu et al.64 reported a decrease in heavy metal content from upstream to down stream in the water-sediments collected from Yellow River in Henan province. This reported trend of heavy metal level was consistent with the negative correlation observed between altitude and a few ARG subtypes in this study, especially in PY (lowest altitude among the 7 sites). In addition, most pathogens were found to be negatively correlated with pH. A similar trend has also been reported for Baiyangdian Lake sediments65. Zhang et al.66 reported higher abundance of most soil bacteria in the near-neutral soil, as compared to acidic and alkaline soils. Most bacteria require an intracellular pH between 6.0 and 8.067,68, which is suitable for the functionality of most proteins69. In this study, weakly alkaline environment of sampling sites influenced the ARGs by affecting the abundance of pathogens. This finding was supported by the negative correlation observed between pH and pathogens. Overall, pH of sampling site affected the ARG profiles of river water by influencing heavy metal content and abundance of pathogens.

A positive correlation between the relative abundance of ARGs and Hmt was detected in the sub-Arctic and Arctic regions23 and proved the role of human activities in the accumulation and dissemination of ARGs70. However, high diversity of ARGs and relatively low concentrations of indicator genes (Hmt, intI) have been reported in the soil samples of southwestern highlands of Saudi Arabia, which suggested that anthropogenic activities were not the only source of observed ARGs71. In the present study, there was no connection between the abundance of ARGs and Hmt, which indicated that anthropogenic activities did not affect the abundance of ARGs.

Conclusion

This study revealed the profiles of ARGs in the Yellow River of Henan Province. Analysis of multiple samples from the river indicated that seasonal runoff induced by rainfall and temperature affected the absolute and relative abundances of ARGs and MGEs, while features of the sampling sites influenced their diversity. Sampling sites significantly affected the diversity of ARGs, with unique MGEs transporting unique ARGs in PY and KF. Besides, specific environmental factors (pH, temperature, and precipitation), MGEs (tnpA), and pathogens (Bacillus, Rahnella, and Shewanella) played significant roles in enhancing the abundance and distribution of ARGs. However, no evidence indicates that long-term water consumption from these regions harms human health. Further research is required to identify ARG targets and levels posing health risks.

Supplementary Information

Supplementary Information. (3.4MB, docx)

Acknowledgements

This work was financially supported by the Science and Technology Research Project of Henan Province (No. 222102320010).

Author contributions

Shuhong Zhang: validation, formal analysis, investigation, data curation, writing—original draft, review and editing, visualization. Guangli Yang: data curation, review and editing, visualization. Yiyun Zhang: methodology, writing—original draft. Chao Yang: methodology, writing—original draft.

Data availability

The raw sequence data from 16S rRNA high-throughput sequencing were submitted to the GSA database of the National Data Centre for Genome Sciences (Project No. PRJCA018113) and could be available with the website of https://ngdc.cncb.ac.cn/gsub/submit/gsa/list.

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.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-68699-8.

References

  • 1.Pruden, A., Arabi, M. & Storteboom, H. N. Correlation between upstream human activities and riverine antibiotic resistance genes. Environ. Sci. Technol.46, 11541–11549 (2012). 10.1021/es302657r [DOI] [PubMed] [Google Scholar]
  • 2.O’Neill, J. Antimicrobial resistance: Tackling a crisis for the health and wealth of nations. Rev. Antimicrob. Resist.20, 1–16 (2014). [Google Scholar]
  • 3.Dcosta, V. M. et al. Antibiotic resistance is ancient. Nature477, 457–461. 10.1038/nature10388 (2011). 10.1038/nature10388 [DOI] [PubMed] [Google Scholar]
  • 4.Martínez, J. L. Antibiotics and antibiotic resistance genes in natural environments. Science321, 365–367 (2008). 10.1126/science.1159483 [DOI] [PubMed] [Google Scholar]
  • 5.Liao, K. et al. Integrating microbial biomass, composition and function to discern the level of anthropogenic activity in a river ecosystem. Environ. Int.116, 147–155. 10.1016/j.envint.2018.04.003 (2018). 10.1016/j.envint.2018.04.003 [DOI] [PubMed] [Google Scholar]
  • 6.Vikesland, P. J. et al. Toward a comprehensive strategy to mitigate dissemination of environmental sources of antibiotic resistance. Environ. Sci. Technol.51(22), 13061–13069. 10.1021/acs.est.7b03623 (2017). 10.1021/acs.est.7b03623 [DOI] [PubMed] [Google Scholar]
  • 7.Forster, S. C. et al. Strain-level characterization of broad host range mobile genetic elements transferring antibiotic resistance from the human microbiome. Nat. Commun.13, 1445 (2022). 10.1038/s41467-022-29096-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Zhao, Y. et al. Evidence for co-selection of antibiotic resistance genes and mobile genetic elementsin metal polluted urban soils. Sci. Total Environ.656, 512–520 (2019). 10.1016/j.scitotenv.2018.11.372 [DOI] [PubMed] [Google Scholar]
  • 9.Li, L. G., Xia, Y. & Zhang, T. Co-occurrence of antibiotic and metal resistance genes revealed in complete genome collection. ISME J.11, 651–662 (2017). 10.1038/ismej.2016.155 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Xu, S., Liu, Y., Wang, R., Zhang, T. & Lu, W. Behaviors of antibiotic resistance genes (ARGs) and metal resistance genes (MRGs) during the pilot-scale biophysical drying treatment of sewage sludge: Reduction of ARGs and enrichment of MRGs. Sci. Total Environ.809, 152221. 10.1016/j.scitotenv.2021.152221 (2022). 10.1016/j.scitotenv.2021.152221 [DOI] [PubMed] [Google Scholar]
  • 11.Xie, W. Y. et al. Prevalent and highly mobile antibiotic resistance genes in commercial organic fertilizers. Environ. Int.162, 107157. 10.1016/j.envint.2022.107157 (2022). 10.1016/j.envint.2022.107157 [DOI] [PubMed] [Google Scholar]
  • 12.Chen, H., Jing, L., Teng, Y. & Wang, J. Characterization of antibiotics in a large-scale river system of China: Occurrence pattern, spatiotemporal distribution and environmental risks. Sci. Total Environ.618, 409–418 (2018). 10.1016/j.scitotenv.2017.11.054 [DOI] [PubMed] [Google Scholar]
  • 13.Yu, Q. et al. Seasonal distribution of antibiotic resistance genes in the Yellow River water and tap water, and their potential transmission from water to human. Environ. Pollut.292, 118304. 10.1016/j.envpol.2021.118304 (2022). 10.1016/j.envpol.2021.118304 [DOI] [PubMed] [Google Scholar]
  • 14.Zhao, S. et al. Temporal–spatial variation and partitioning prediction of antibiotics in surface water and sediments from the intertidal zones of the Yellow River Delta. China. Sci. Total Environ.569, 1350–1358. 10.1016/j.scitotenv.2016.06.216 (2016). 10.1016/j.scitotenv.2016.06.216 [DOI] [PubMed] [Google Scholar]
  • 15.Su, Z., Wang, K., Yang, F. & Zhuang, T. Antibiotic pollution of the Yellow River in China and its relationship with dissolved organic matter: Distribution and Source identification. Water Res.235, 119867. 10.1016/j.watres.2023.119867 (2023). 10.1016/j.watres.2023.119867 [DOI] [PubMed] [Google Scholar]
  • 16.Lu, J., Tian, Z., Yu, J., Yang, M. & Zhang, Y. Distribution and abundance of antibiotic resistance genes in sand settling reservoirs and drinking water treatment plants across the Yellow River China. Water10, 246. 10.3390/w10030246 (2018). 10.3390/w10030246 [DOI] [Google Scholar]
  • 17.Bengtsson-Palme, J., Jonsson, V. & Hess, S. What is the role of the environment in the emergence of novel antibiotic resistance genes? A modeling approach. Environ. Sci. Technol.55, 15734–15743. 10.1101/2021.04.04.438392 (2021). 10.1101/2021.04.04.438392 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Zhang, K. et al. The sources and potential hosts identification of antibiotic resistance genes in the Yellow River, revealed by metagenomic analysis. Int. J. Environ. Res. Public Health19(16), 10420. 10.3390/ijerph191610420 (2022). 10.3390/ijerph191610420 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Wu, D., Wang, B. H. & Xie, B. Validated predictive modelling of sulfonamide and beta-lactam resistance genes in landfill leachates. J. Environ. Manage241, 123–130. 10.1016/j.jenvman.2019.04.026 (2019). 10.1016/j.jenvman.2019.04.026 [DOI] [PubMed] [Google Scholar]
  • 20.Chen, M. X. et al. Occurrence of antibiotic resistance genes in a small township wastewater treatment plant and the receiving river. IOP Conf. Ser. Earth Environ. Sci.435, 012012. 10.1088/1755-1315/435/1/012012 (2020). 10.1088/1755-1315/435/1/012012 [DOI] [Google Scholar]
  • 21.Hao, H. et al. Profiling of intracellular and extracellular antibiotic resistance genes in tap water. J. Hazard Mater.365, 340–345. 10.1016/j.jhazmat.2018.11.004 (2019). 10.1016/j.jhazmat.2018.11.004 [DOI] [PubMed] [Google Scholar]
  • 22.Li, Z. et al. Transmission mechanisms of antibiotic resistance genes in arsenic-contaminated soil under sulfamethoxazole stress. Environ. Pollut.326, 121488. 10.1016/j.envpol.2023.121488 (2023). 10.1016/j.envpol.2023.121488 [DOI] [PubMed] [Google Scholar]
  • 23.Tan, L. et al. Arctic antibiotic resistance gene contamination, a result of anthropogenic activities and natural origin. Sci. Total Environ.621, 1176–1184. 10.1016/j.scitotenv.2017.10.110 (2018). 10.1016/j.scitotenv.2017.10.110 [DOI] [PubMed] [Google Scholar]
  • 24.Chen, Q. et al. Long-term field application of sewage sludge increases the abundance of antibiotic resistance genes in soil. Environ. Int.92–93, 1–10. 10.1016/j.envint.2016.03.026 (2016). 10.1016/j.envint.2016.03.026 [DOI] [PubMed] [Google Scholar]
  • 25.Zheng, F. et al. Fates of antibiotic resistance genes in the gut microbiome from different soil fauna under long-term fertilization. Environ. Sci. Technol.55(1), 423–432. 10.1021/acs.est.0c03893 (2021). 10.1021/acs.est.0c03893 [DOI] [PubMed] [Google Scholar]
  • 26.Li, H. et al. A continuous flow MFC-CW coupled with a biofilm electrode reactor tosimultaneously attenuate sulfamethoxazole and its corresponding resistancegenes. Sci. Total Environ.637–638, 295–305. 10.1016/j.scitotenv.2018.04.359 (2018). 10.1016/j.scitotenv.2018.04.359 [DOI] [PubMed] [Google Scholar]
  • 27.Li, H., Li, Z., Qu, J. & Wang, J. Bacterial diversity in traditional Jiaozi and sourdough revealed by high-throughput sequencing of 16S rRNA amplicons. LWT-Food Sci. Technol.81, 319–325. 10.1016/j.lwt.2017.04.007 (2017). 10.1016/j.lwt.2017.04.007 [DOI] [Google Scholar]
  • 28.Wang, X. et al. Alleviating effects of walnut green husk extract on disorders of lipid levels and gut bacteria flora in high fat diet-induced obesity rats. J. Funct. Foods52, 576–586. 10.1016/j.jff.2018.11.022 (2019). 10.1016/j.jff.2018.11.022 [DOI] [Google Scholar]
  • 29.He, Y. et al. Effect of Xiaoning liquid on gut microbiota in asthmatic mice by 16S rDNA high-throughput sequencing. Food Sci. Technol.42, e40721. 10.1590/fst.40721 (2021). 10.1590/fst.40721 [DOI] [Google Scholar]
  • 30.Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial ampliconreads. Nat. Methods10, 996–998. 10.1038/nmeth.2604 (2013). 10.1038/nmeth.2604 [DOI] [PubMed] [Google Scholar]
  • 31.Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improvessensitivity and speed of chimera detection. Bioinformatics27, 2194–2200. 10.1093/bioinformatics/btr381 (2011). 10.1093/bioinformatics/btr381 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Su, W. et al. Multi-omics methods reveal that putrescine and cadaverine cause different degrees of enrichment of high-risk resistomes and opportunistic pathogens in the water and sediment of the Yellow River. Environ. Res.219, 115069. 10.1016/j.envres.2022.115069 (2023). 10.1016/j.envres.2022.115069 [DOI] [PubMed] [Google Scholar]
  • 33.Cui, Q., Huang, Y., Wang, H. & Fang, T. Diversity and abundance of bacterial pathogens in urban rivers impacted by domestic sewage. Environ. Pollut.249, 24–35. 10.1016/j.envpol.2019.02.094 (2019). 10.1016/j.envpol.2019.02.094 [DOI] [PubMed] [Google Scholar]
  • 34.Halary, S., Leigh, J. W., Cheaib, B., Lopez, P. & Bapteste, E. Network analyses structure genetic diversity in independent genetic worlds. Proc. Natl. Acad. Sci. USA107, 127–132. 10.1073/pnas.0908978107 (2010). 10.1073/pnas.0908978107 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Yin, H. Q. et al. An integrated insight into the response of sedimentary microbial communities to heavy metal contamination. Sci. Rep.5, 14266–14277. 10.1038/srep14266 (2015). 10.1038/srep14266 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Wu, D. et al. Contamination profile of antibiotic resistance genes in ground water in comparison with surface water. Sci Total Environ715, 136975. 10.1016/J.SCITOTENV.2020.136975 (2020). 10.1016/J.SCITOTENV.2020.136975 [DOI] [PubMed] [Google Scholar]
  • 37.Ke, Y. et al. Antibiotic resistome alteration along a full-scale drinking water supply system deciphered by metagenome assembly: Regulated by seasonality, mobile gene elements and antibiotic resistant gene hosts. Sci. Total Environ.862, 160887. 10.1016/j.scitotenv.2022.160887 (2023). 10.1016/j.scitotenv.2022.160887 [DOI] [PubMed] [Google Scholar]
  • 38.Xu, S. et al. Environmental filtering dominated the antibiotic resistome assembly in river networks. Sci. Total Environ.834, 155293. 10.1016/j.scitotenv.2022.155293 (2022). 10.1016/j.scitotenv.2022.155293 [DOI] [PubMed] [Google Scholar]
  • 39.Miró, E. et al. Characterization of aminoglycoside-modifying enzymes in Enterobacteriaceae clinical strains and characterization of the plasmids implicated in their diffusion. Microb. Drug Resist.19(2), 94–99. 10.1089/mdr.2012.0125 (2013). 10.1089/mdr.2012.0125 [DOI] [PubMed] [Google Scholar]
  • 40.Kather, E. J., Marks, S. L. & Foley, J. E. Determination of the prevalence of antimicrobial resistance genes in canine Clostridium perfringens isolates. Vet. Microbiol.113(1–2), 97–101. 10.1016/j.vetmic.2005.10.021 (2006). 10.1016/j.vetmic.2005.10.021 [DOI] [PubMed] [Google Scholar]
  • 41.Galani, I., Souli, M., Chryssouli, Z., Orlandou, K. & Giamarellou, H. Characterization of a new integron containing blaVIM-1 and aac(6’)-IIc in an Enterobacter cloacae clinical isolate from Greece. J. Antimicrob. Chemoth.55, 634–638. 10.1093/jac/dki073 (2005). 10.1093/jac/dki073 [DOI] [PubMed] [Google Scholar]
  • 42.Chen, Y. et al. High-throughput profiling of antibiotic resistance gene dynamic in a drinking water river-reservoir system. Water Res.149, 179–189. 10.1016/j.watres.2018.11.007 (2019). 10.1016/j.watres.2018.11.007 [DOI] [PubMed] [Google Scholar]
  • 43.Zhang, L., Yan, C., Wang, D. & Zhen, Z. Spatiotemporal dynamic changes of antibiotic resistance genes in constructed wetlands and associated influencing factors. Environ. Pollut.303, 119176. 10.1016/j.envpol.2022.119176 (2022). 10.1016/j.envpol.2022.119176 [DOI] [PubMed] [Google Scholar]
  • 44.Zheng, J. et al. High-throughput profiling of seasonal variations of antibiotic resistance gene transport in a peri-urban river. Environ. Int.114, 87–94. 10.1016/j.envint.2018.02.039 (2018). 10.1016/j.envint.2018.02.039 [DOI] [PubMed] [Google Scholar]
  • 45.Yang, J. et al. Persistence of antibiotic resistance genes from river water to tap water in the Yangtze River Delta. Sci. Total Environ.742, 140592. 10.1016/j.scitotenv.2020.140592 (2020). 10.1016/j.scitotenv.2020.140592 [DOI] [PubMed] [Google Scholar]
  • 46.Ke, Y. et al. Seasonality determines the variations of biofilm microbiome and antibiotic resistome in a pilot-scale chlorinated drinking water distribution system deciphered by metagenome assembly. Environ. Sci. Technol.57(31), 11430–11441. 10.1021/acs.est.3c01980 (2023). 10.1021/acs.est.3c01980 [DOI] [PubMed] [Google Scholar]
  • 47.Huang, L. et al. Dissemination of antibiotic resistance genes (ARGs) by rainfall on a cyclic economic breeding livestock farm. Int. Biodeter. Biodegr.138, 114–121. 10.1016/j.ibiod.2019.01.009 (2019). 10.1016/j.ibiod.2019.01.009 [DOI] [Google Scholar]
  • 48.Nguyen, S. G. et al. Metagenomic investigation of the seasonal distribution of bacterial community and antibiotic-resistant genes in Day River Downstream, Ninh Binh. Vietnam. Appl. Biol. Chem.65(1), 26. 10.1186/s13765-022-00687-w (2022). 10.1186/s13765-022-00687-w [DOI] [Google Scholar]
  • 49.Wang, Y., Liu, G., Zhao, Z., Wu, C. & Yu, B. Using soil erosion to locate nonpoint source pollution risks in coastal zones: A case study in the Yellow River Delta. China. Environ. Pollut.283, 117117. 10.1016/j.envpol.2021.117117 (2021). 10.1016/j.envpol.2021.117117 [DOI] [PubMed] [Google Scholar]
  • 50.García-Fernández, A. et al. Multilocus sequence typing of IncN plasmids. J. Antimicrobial Chemoth.66(9), 1987–1991 (2011). 10.1093/jac/dkr225 [DOI] [PubMed] [Google Scholar]
  • 51.Fang, L. et al. Co-spread of metal and antibiotic resistance within ST3-IncHI2 plasmids from E coli isolates of food-producing animals. Sci. Rep.6(1), 25312. 10.1038/srep25312 (2016). 10.1038/srep25312 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Pazda, M. & Kumirska, J. Antibiotic resistance genes identified in wastewater treatment plantsystems—A review. Sci. Total Environ.697, 134023. 10.1016/j.scitotenv.2019.134023 (2019). 10.1016/j.scitotenv.2019.134023 [DOI] [PubMed] [Google Scholar]
  • 53.Peng, F., Isabwe, A., Guo, Y., Chen, H. & Yang, J. An extensively shared antibiotic resistome among four seasons suggests management prioritization in a subtropical riverine ecosystem. Sci. Total Environ.693, 533–540. 10.1016/j.scitotenv.2019.04.031 (2019). 10.1016/j.scitotenv.2019.04.031 [DOI] [PubMed] [Google Scholar]
  • 54.Na, G. et al. Occurrence and distribution characteristics of antibiotic resistance genes in sediments between urban and rural of the Liaohe River Basin. China. Environ. Sci. Pollut. Res.28(38), 54002–54014. 10.1007/s11356-021-13560-y (2021). 10.1007/s11356-021-13560-y [DOI] [PubMed] [Google Scholar]
  • 55.Han, Z. et al. Antibiotic resistomes in drinking water sources across a large geographical scale: multiple drivers and co-occurrence with opportunistic bacterial pathogens. Water Res.183, 116088. 10.1016/j.watres.2020.116088 (2020). 10.1016/j.watres.2020.116088 [DOI] [PubMed] [Google Scholar]
  • 56.Forsberg, K. J. et al. Bacterial phylogeny structures soil resistomes across habitats. Nature509, 612–616. 10.1038/nature13377 (2014). 10.1038/nature13377 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Wu, D., Huang, X. H., Sun, J. Z., Graham, D. W. & Xie, B. Antibiotic resistance genes and associated microbial community conditions in aging landfill systems. Environ. Sci. Technol.51(21), 12859–12867. 10.1021/acs.est.7b03797 (2017). 10.1021/acs.est.7b03797 [DOI] [PubMed] [Google Scholar]
  • 58.Zhou, Z. et al. Antibiotic resistance genes in an urban river as impacted by bacterial community and physicochemical parameters. Environ. Sci. Pollut. Res. Int.24(30), 23753–23762. 10.1007/s11356-017-0032-0 (2017). 10.1007/s11356-017-0032-0 [DOI] [PubMed] [Google Scholar]
  • 59.Zheng, C. L., Zhu, D. & Xu, Y. Y. Co-driving factors of tidal effect on the abundance and distribution of antibiotic resistance genes in the Yongjiang Estuary. China. Environ. Res.213, 113649. 10.1016/j.envres.2022.113649 (2022). 10.1016/j.envres.2022.113649 [DOI] [PubMed] [Google Scholar]
  • 60.Wan, C. et al. Illustration on phenotypic and genotypic characteristics of typical multi-antibiotic resistant bacteria in aquatic environments through complete genomes and comparative genomics. Chemosphere337, 139386. 10.1016/j.chemosphere.2023.139386 (2023). 10.1016/j.chemosphere.2023.139386 [DOI] [PubMed] [Google Scholar]
  • 61.Zuo, X., Chen, S., Wang, T., Zhang, S. & Li, T. Leaching risks of antibiotic resistance genes in urban underlying surface sediments during the simulated stormwater runoff and its controls. Water Res.221, 118735. 10.1016/j.watres.2022.118735 (2022). 10.1016/j.watres.2022.118735 [DOI] [PubMed] [Google Scholar]
  • 62.Wang, M. Y., Shen, W. T., Yan, L., Wang, X. H. & Xu, H. Stepwise impact of urban wastewater treatment on the bacterial community structure, antibiotic contents, and prevalence of antimicrobial resistance. Environ. Pollut.231, 1578–1585. 10.1016/j.envpol.2017.09.055 (2017). 10.1016/j.envpol.2017.09.055 [DOI] [PubMed] [Google Scholar]
  • 63.Xu, Y., Xu, J., Mao, D. & Luo, Y. Effect of the selective pressure of sub-lethal level of heavy metals on the fate and distribution of ARGs in the catchment scale. Environ. Pollut.220, 900–908. 10.1016/j.envpol.2016.10.074 (2017). 10.1016/j.envpol.2016.10.074 [DOI] [PubMed] [Google Scholar]
  • 64.Liu, S. et al. Spatial distribution of heavy metal contaminants: The effects of water-sediment regulation in the Henan section of the Yellow River. Sci. Total Environ.892, 164568. 10.1016/j.scitotenv.2023.164568 (2023). 10.1016/j.scitotenv.2023.164568 [DOI] [PubMed] [Google Scholar]
  • 65.Xiao, R. et al. Pathogen profile of Baiyangdian Lake sediments using metagenomic analysis and their correlation with environmental factors. Sci. Total Environ.913(2024), 169628. 10.1016/j.scitotenv.2023.169628 (2024). 10.1016/j.scitotenv.2023.169628 [DOI] [PubMed] [Google Scholar]
  • 66.Zhang, Y. et al. Fertilization shapes bacterial community structure by alteration of soil pH. Front. Microbiol.8, 1325. 10.3389/fmicb.2017.01325 (2017). 10.3389/fmicb.2017.01325 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. A Sci. India B103, 626–631. 10.1073/pnas.0507535103 (2006). 10.1073/pnas.0507535103 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Zhang, X., Liu, W., Zhang, G., Jiang, L. & Han, X. Mechanisms of soil acidification reducing bacterial diversity. Soil Biol. Biochem.81, 275–281. 10.1016/j.soilbio.2014.11.004 (2015). 10.1016/j.soilbio.2014.11.004 [DOI] [Google Scholar]
  • 69.Krulwich, T. A., Sachs, G. & Padan, E. Molecular aspects of bacterial pH sensing and homeostasis. Nat. Rev. Microbiol.9, 330–343. 10.1038/nrmicro2549 (2011). 10.1038/nrmicro2549 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Williams, M. C., Wenner, J. R., Rouzina, I. & Bloomfield, V. A. Effect of pH on the overstretching transition of double-stranded DNA: evidence of force-induced DNA melting. Biophys. J.80(2), 874–881. 10.1016/S0006-3495(01)76066-3 (2001). 10.1016/S0006-3495(01)76066-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Yasir, M. et al. Bacterial diversity and the antimicrobial resistome in the southwestern highlands of Saudi Arabia. Saudi J. Biol. Sci.29(4), 2138–2147. 10.1016/j.sjbs.2021.11.047 (2022). 10.1016/j.sjbs.2021.11.047 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supplementary Information. (3.4MB, docx)

Data Availability Statement

The raw sequence data from 16S rRNA high-throughput sequencing were submitted to the GSA database of the National Data Centre for Genome Sciences (Project No. PRJCA018113) and could be available with the website of https://ngdc.cncb.ac.cn/gsub/submit/gsa/list.

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