Distribution and relationship of antibiotics, heavy metals and resistance genes in the upstream of Hanjiang River Basin in Shiyan, China

The upstream basin of Hanjiang River is an important water source for the middle route of China’s South-to-North Water Diversion Project. The quality of water and soil in the Hanjiang River have enormous biological and environmental impacts, and resistant genetic contamination has emerged, but only few studies are concerned the correlation between heavy metals and metal resistance genes (MRGs). In this study, 8 antibiotics and 19 heavy metals were analyzed, the results showed that the highest antibiotic content was tetracycline, with mean concentrations of 43.201 µg/kg and 0.022 µg/L. Mn was the highest heavy metal in soil with a content of 1408.284 µg/kg, and in water was Zn with a content of 10.611 µg/L. We found that the most abundant antibiotic resistance genes (ARGs) and metal resistance genes (MRGs) in the study area were bacA and arsT genes, coding for resistance mechanisms to bacitracin and arsenic, respectively. The data showed that heavy metals had a greater impact on antibiotic resistance genes than antibiotics, and the correlation between resistance genes was significantly positive. This work expands our understanding of the correlations of antibiotics, heavy metals, and resistance genes in the Hanjiang River, indicating that more attention should be paid to the effects of resistance genes and the quality of water.


Introduction
Since 2006 antibiotic resistance genes (ARGs) were named as a new pollutant, and resistance genes have attracted widespread attention (Pruden et al., 2006).In 1998, a study found that bacteria containing resistance genes were induced in the human gastrointestinal and urinary tracts (van den Braak et al., 1998).On the one hand, resistance genes can be transferred between human organs through horizontal gene transfer (HGT), and it poses a health risk to humans through the entry of resistance genes and resistant bacteria in animals directly from human-animal contact (Oppegaard et al., 2001).On the other hand, resistance genes in organisms are excreted into the environment, which has an impact on environmental health, and excreted resistance genes can then enter the soil, and visa infiltration function, resistance Vol:.( 1234567890) genes can enter groundwater and are transferred horizontally to crops, such as crop (Kay et al., 2002), during tillage, causing an impact on the health of the organism in the form of a food chain.
ARGs are caused by antibiotics and antibiotic residues.Since penicillin has been found in 1943, antibiotics are widely used in clinical therapy, veterinary, and agriculture (McManus et al., 2002;Zhu et al., 2013).However, the abuse and overuse of antibiotics caused contamination and pollution, such as broad-spectrum cephalosporins and fluoroquinolone (Diwan et al., 2012), klebsiella pneumoniae (Picão et al., 2013).Antibiotic residues can also cause irreversible harm to humans, and long-term accumulation of antibiotics through the food chain in the body can reduce the immunity of the body (Guo et al., 2021;Ma et al., 2019).Except to remain in the body, antibiotics can also be excreted into the environment through the digestive tract (Cheng et al., 2013;Zhang et al., 2013).Hu et al. demonstrated that 30-90% of antibiotics in living organisms will be excreted with feces into the environment (Hu et al., 2010), causing harmful issues, such as antibiotic residues in hospital wastewater (Brown et al., 2006).Although wastewater treatment plants (WWTPs) treat wastewater, existing technologies can not completely remove contaminants such as residual antibiotics and antibiotic resistant bacteria (ARB).ARGs are formed through antibiotics that persist in the environment, exerting selection pressure on microorganisms and affecting their composition (Fridman et al., 2014;Relman & Lipsitch, 2018).
Many studies (Zhang et al., 2022) have shown that not only antibiotics can induce ARGs, but also other environmental factors can, for example, heavy metals (Di Cesare et al., 2016).Heavy metals are widely found in nature and are the main material resource for the production and life of people.However, industrial, manufacturing, and agricultural activities contribute to the accumulation of heavy metals.Heavy metal contamination is widely present in the environment and can provide long-term stable selection pressure for bacteria (Xie et al., 2011).It was found that when the Cu content in soil reaches a certain value, it affects the abundance, community structure and function of soil microorganisms, induces a decrease in soil quality and damages the ecological structure (Chen et al., 2018;Lin et al., 2019).The selection and spread of metal resistance genes (MRGs) are also due to this longterm stable selection pressure on bacteria (Charlesworth et al., 2011;Chen et al., 2015).A study of Danish seaport waters by Rasmussen et al. found that strains isolated from heavy metal-contaminated waters contained more resistant plasmic particles than those isolated from non-heavy metal-contaminated water (Rasmussen & Sørensen, 1998).Klasen et al. researched that silver and sulfonamide resistance genes are located on the same replicon and also suggested a mediating effect of heavy metals on the production of ARGs (Klasen, 2000).
The Hanjiang River is a tributary of the Yangtze River and has occupied an important position in China since ancient times.At the same time, the Hanjiang River also participated in the composition of the Danjiangkou reservoir.Due to the uneven distribution of water resources between the north and south of China, the South-to-North Water Transfer Project (SNWTP) has greatly solved the problem of drinking water and using water for northern residents.The residential life and agricultural activities of the dense population have caused a significant impact on the Hanjiang river (Sun et al., 2017).Thirteen antibiotics were found in the surface water and sediment of the Hanjiang River, and the risk assessment was used to speculate that the combined effect of antibiotics would have adverse effects on aquatic organisms (Hu et al., 2018).Li et al. (2019) detected 61 and 54 target antibiotics in the water and sediment of Danjiangkou Reservoir, respectively, and found a positive correlation between antibiotic detection rates and concentrations, suggesting that this may be potentially harmful to aquatic organisms and human health.Song et al. (2021) found that cadmium (Cd) was the most common heavy metal pollutant in the sediments of the upper Hanjiang River.So monitoring the water quality of the Hanjiang River is a top priority.In addition to water quality, the condition of the surrounding soil should be monitored to determine whether it affects human health and environmental pollution.Metagenomics is an effective sequencing method to analyze the relationship between microbial communities and environment, including compositions, functions, even the link of the process (Simon & Daniel, 2011), so this study using this sequencing technology to (1) study the distribution and concentration of antibiotics, heavy metals in all samples; (2) investigate the distribution and abundance of microbes and resistance genes in DNA samples; (3) explore the correlation between heavy metals, microbes, and resistance genes.

Sample sites and sample collection
As shown in Fig. 1, 13 sample sites were chosen, collected soil and water samples.A total of 26 (13 soil samples and 13 water samples) environment samples were obtained from a section of Hanjiang River, Shiyan, China, which is the core water source of the South-to-North Water Diversion in China.The sampling site was located in the upper branch of the Hanjiang flowing through Shiyan, bordering the Danjiangkou reservoir area, and surrounded by dense population and many human activities.
All samples were collected at a distance of approximately 20 m from each other and with a depth of up to 15 cm.All samples were subsampled and distributed into clean resealable bags for antibiotic and heavy metal analysis.

Antibiotics analysis
The water samples from each sampling site were filtered through 0.45 µm pore size membrane with 500 ml, then 0.1 g of EDTA-2Na was added to increase the stability of the aqueous solution, and the pH of each sample was adjusted to about 3.0 by using 1% dilute hydrochloric acid.Finally add 100 ng of 13 C 3 -caffeine (aim to determine the recovery of the actual sample) internal standard to be used.The soil samples were dried naturally at room temperature, ground and passed through a 100 mesh sieve, then accurately weighed 5 g in a 50 mL centrifuge tube, added 0.5 g of EDTA-2Na and 30 mL of acetonitrilecitric acid buffer solution (v:v = 4:3), centrifuged at 3500 rpm for 5 min in a high-speed centrifuge, removed the supernatant to a clean 50 mL tube, and add another 20 mL of acetonitrile-citric acid buffer solution to the previous centrifuge tube, centrifuge again and transfer the supernatant, and finally combine the two supernatants for use.

Heavy metals analysis
Each water sample was filtered through a 0.45 μm pore size membrane, 0.2 ml was taken and diluted 20 times with 2% nitric acid.The soil sample was dried naturally, ground after 100 mesh sieve and baked in the oven at 60 °C for 24 h, 0.1 g of sample was added to 8 ml of aqua regia (V hydrochloric acid: V nitric acid = 3: 1), microwave digestion (Shanghai, China) and electric hot plate (Shanghai, China) to drive out acid, and after the solution reached about 1 ml, it was determined to 50 ml with deionized water, and 2-3 ml of liquid was filtered by a 0.22 μm needle filter in a centrifuge tube.Dilute another 0.04 ml of liquid to 4 ml and dilute 100 fold (set up an average of 2 sample blanks per 10 samples (Deng et al., 2022).The concentrations of heavy metals in all samples were analyzed using inductively coupled plasma mass spectrometry (ICP-MS, Thermo, USA) (Elias et al., 2018).A total of 19 metal elements were analyzed, scandium (Sc), vanadium (V), chromium (Cr), manganese (Mn), cobalt (Co), nickel (Ni), cuprum (Cu), zinc (Zn), arsenic (As), selenium (Se), molybdenum (Mo), cadmium (Cd), Indium (In), stannum (Sn), antimony (Sb), tungsten (W), thallium (Tl), lead (Pb), bismuth (Bi), separately.

DNA extraction and metagenomic sequencing
The soil and sediment DNA was extracted by DNeasy PowerSoil Pro Kit (Germany), and the water DNA was extracted by Bio Fastspin water genes DNA extraction Kit (Hangzhou, China).Every procedures were following the manufacturer's instructions.Extracted DNA was tested by Agarose gel electrophoresis and spectrophotometer (Nanodrop, China).The final DNA extracts were stored in a refrigerator at − 20 °C for further analysis.Due to limited funding and the quality of samples, we selected 9 DNA samples to Sangon Biotech Company (Shanghai, China) for macro-genome sequencing based on the quality and concentration of DNA in the 26 extracted DNAs (2 from soil and 7 from water).

Data analysis
Data collation and analysis used SPSS 23.0.The distribution of sampling sites in the Hanjiang River was conducted using ArcMap 10.7.1.The content and concentration of antibiotics, heavy metals and the distribution and abundance of microbes and resistance genes were drawn bar charts and dot-line plots using OriginLab 2021.The principal component analysis (PCA) plot was also drawn using OriginLab 2021.All Venn diagrams were drawed by using Draw Venn Diagram Universiteit Gent (https:// bioin forma tics.psb.ugent.be/ webto ols/ Venn).The "pheatmap" package in RStudio and "Correlation Plot" in Origin were used to make correlation heatmaps.

Result and discussion
The distribution of antibiotics in samples Figure 2 shows the total amount of 8 antibiotics in the soil and water samples and their distribution in the different sampling sites using dot-line plots.Among all analyzed antibiotics in the soil samples, TC was the most abundant antibiotic with a total value of 561.621 µg/kg, and the mean value was 43.201 µg/ kg, similar to Lyu's finding that tetracycline was the dominant antibiotic observed in soil (Lyu et al., 2020).The second highest abundant antibiotic was OTC with a total value of 148.596 µg/kg, and a mean value of 11.430 µg/kg.This result may be related to the excessive use of tetracycline.TC have been widely used in agricultural planting and animal husbandry because of low cost and good effect, and there are farmlands around the sampling point area.Our inspection data were consistent with the conclusion that tetracycline is one of the most commonly used antibiotics (Wang et al., 2017).OTC is the most common antibiotic used in animals, but the absorption and metabolism of OTC in livestock are poor, resulting in a large amount of OTC released into the soil, which may be the reason for the high concentration of OTC near the sampling sites (Chen et al., 2013;  1234567890) Tolls, 2001).As shown in Fig. 2c and d, except for AMOX and CFX, all antibiotics were detected in soil samples.The highest content of TC in soil-5, the lowest in soil-0, and the content was 68.611 µg/kg and 21.649 µg/kg, respectively.Although OTC was the second highest content of antibiotics in soil, the concentration varied widely between individual samples.The contents of SD, SMX and SMZ tended to zero.
As Fig. 2b, e, and f shown, all 8 antibiotics were detected in water samples.OTC was the highest antibiotic, with a total of 0.255 µg/L and an average of 0.020 µg/L, followed by AMOX with a total of 0.181 µg/L and a mean value of 0.014 µg/L.It may be that due to the fluidity of water, most antibiotics cannot accumulate in large quantities, but quinolone and tetracycline antibiotics can strongly adsorb suspended particles and deposits (Qiao et al., 2018), thus the abundance of TC, OTC was higher than other antibiotics in water samples.This result was found at the same level as rivers in Hong Kong (Deng et al., 2016), and other major rivers in China (Li et al., 2018).The detection results of Danjiangkou Reservoir by Li et al. (2019) showed that sulfonamides were the main antibiotics in the water quality, especially SMX.Although our test results showed that tetracycline antibiotics accounted for the main components, the content of SMX was very close to that determined by Li, and the content of sulfonamides was also the highest in the water quality monitoring of the Hanjiang River by Hu et al. (2018).This may be related to the widespread use, high water solubility, and persistence of sulfonamides in the treatment of pathogens (Stoob et al., 2007;Xu et al., 2014).

The distribution of heavy metals in samples
As shown in Fig. 3, the contents of different heavy metals varied greatly.Mn was the most abundant heavy metal in the soil, with a total content of 18,307.697μg/kg, and the highest content in soil-7 with a value of 1854.029μg/kg.Zn was the second highest heavy metal in the soil samples, with a total content of 2675.788μg/kg, and the highest content in soil-0 at 325.794 μg/kg.According to "Soil Element Background Values in China 1990," the background value of Mn element in soil was 86-2024 mg/kg, with the mean value was 798 mg/kg (Agency & Centre, 1990).All our samples' Mn content were much lower than the value.Our test results were lower than those for heavy metals in other areas in China (Liang et al., 2023;Wu et al., 2015), which was also lower than the modern flood SWD in the upper Hanjiang River (832.400 mg/kg) (Guo et al., 2014).In unfertilized and uncontaminated soils, Zn averaged content was 10-100 mg/kg, (Noulas et al., 2018), and "Soil Element Background Values in China 1990" detected Zn content in Hubei Province was 27.300-283 mg/kg, with the mean value was 83.600 mg/kg (Agency & Centre, 1990), which higher than our data.
In water samples, Zn content was the highest with a total of 137.948 μg/kg, followed by Mo content with a total of 121.690 μg/kg.Zinc is released into water and soil as a result of natural processes and human activities.Our results were less than those of song et al. (63.800 mg/kg) (Song et al., 2021).Studies have shown that when the zinc content of a water source is about 0.015 mg /L, it indicates that the water source is fresh and free of pollution (Noulas et al., 2018).The average Zn content detected by our study was 0.010 mg/L, indicating that the water of the Hanjiang River in Shiyan City were relatively clean.

The distribution and concentration of microorganisms
After the blastp homology comparison of the gene set protein sequence with the Nr (NCBI non-redundant protein sequences) database, the number of microorganisms at the phylum level was 104, with the highest content was Proteobacteria, followed by Actinobacteria and Bacteroidetes, and this result was consistent with previous studies (Hu et al., 2022;Xu et al., 2022).The number of microorganisms at the species level was 11,127, with the highest level was Actinomycetia Bacterium, followed by Betaproteobacteria Bacterium and Burkholderiaceae Bacterium.Our samples contained Proteobacteria accounting for 30.8%-66.5%.In a study on major rivers in Shenzhen, China, Proteobacteria was found to be the most abundant species in surface water, accounting for 37.4-51.7%(Qiu et al., 2019), which was similar to our experimental results.
As Fig. 4a, b shown, the distance among samples was small, except for soil-5.Among all samples, the content of Proteobacteria, Actinobacteria, Bacteroidetes, and Verrucomicrobia accounted for more than 80% of the total content.The sum of the abundance of Proteobacteria and Actinobacteria accounted for more than half of the abundance of all samples.
Similarly, this was similar to the results of the farmland microbial communities around the gold tailings, that Proteobacteria and Actinobacteria make up a large portion of the microbial community (Qiao et al., 2021).Compared to other samples, soil-5 had a higher content of Actinobacteria.
Figure 4c and d shows the absolute and relative abundance of ARGs.Except for soil-5, the distribution of ARGs in all samples did not differ significantly.Obviously, the content of vanR gene in soil-5 was much higher than other ARGs.The vanS/vanR two-component system is activated to express the van gene in response to extracellular glycopeptide antibiotics (Hong et al., 2008).So we analyzed the main distribution and concentration of van genes in soil-5 (Fig. 5g).The number of vanS genes was also high, and the reason for the much higher number of van genes at this locus than in the other samples remains to be explored.In soil and water samples, there were 469 shared ARGs (Fig. 5h), which were dominated by multidrug resistance genes (53.5%).
As shown by the absolute abundance of MRGs (Fig. 5e, f), the MRG distribution of soil-5 differed from the other sampling sites, with only five target genes, and the highest concentration was actP (encoding a resistance mechanism to Cu).The highest abundance of Cu resistance genes was found in the study of urban rivers on the Qinghai-Tibet Plateau, which was consistent with our results (Xu et al., 2022).Salam et al. detected resistance genes expressing resistance to Cu, Zn, As, Cd and other metals in chronically contaminated soils using macro-genome sequencing (Salam, 2020), that was similar to our sequencing results.In soil and water samples, there were 175 shared MRGs.Shared MRGs in the soil accounted for 79.2% of all MRGs in the soil.Due to the abundance of MRGs measured in water was greater than that in soil, we hypothesized that MRGs in water were transferred to soil.The correlations between antibiotics, heavy metals, ARGs and MRGs A correlation heat map was conducted to explore the relationship among the relative abundance of antibiotics, heavy metals, ARGs, and MRGs based on significant correlation analysis by Spearman correlation.The correlation heatmap consisted of 47 elements (8 antibiotics, 19 heavy metals, 10 ARGs, and 10 MRGs) (Fig. 6).The correlations between antibiotics were all positive, such as SD and SMZ (p < 0.05), while the correlations between antibiotics and heavy metals were mostly positive, except for Mo and Sb which were negatively correlated with antibiotics, such as Mo and SMZ (p < 0.05) and Sb and SD (p < 0.05).We speculated that the combined toxicity of heavy metals and antibiotics showed antagonistic and additive effects, and this result was also consistent with previous studies (Sun et al., 2009).
The correlation between metals was different from the correlation between antibiotics, which could be positive or negative, for example, Cu was positively correlated with Sc (p < 0.05), Cd was positively correlated with Mn (p < 0.05), Sb was negatively correlated with Cr and Zn (p < 0.05), Bi was negatively correlated with Mo (p < 0.05).
Figure 6 also shows that the correlations between antibiotics and ARGs were not significant, and the only significant correlations were between bacA and OTC, sul2 and CFX, with the significant negative correlation (p < 0.05).Our results differed from the conclusion that ARGs abundance was positively correlated with some antibiotic residues (Zarei-Baygi et al., 2019).This result also reinforced the idea that the presence of antibiotic selection pressures is not necessarily necessary for the selection of antibiotic resistance genes (Davison, 1999), and antibiotics are not the only influential factor in the production and spread of ARGs.We found that antibiotics were significantly negatively correlated with MRGs.
Interestingly, Fig. 6 shows that the correlation between heavy metals and ARGs was higher than that between antibiotics and ARGs, which was similar to the findings of others who found that the presence of heavy metals in the environment enhanced the widespread presence of ARGs (McKinney et al., 2010;Yang et al., 2018).In our data analysis, MRGs were negatively correlated with heavy metals, except for acrB was positively correlated with As (p < 0.05).It may be due to the low concentration of heavy metals.Berg J et al. found that extensive enrichment of Cu in agricultural soils could enhance not only the tolerance of indigenous bacteria to Cu but also their antibiotic resistance (Berg et al., 2005).Alonso et al. also found that the exposure specificity of Cu in soil could not only select for strains resistant to Cu but also synergistically select for antibiotic resistant strains (Alonso et al., 2001).All of these evidences pointed to the importance of heavy metals in ARGs production and transmission, so it may be possible to reduce ARGs pollution by controlling the content of heavy metals.The analysis of our data suggested that heavy metals may be more selective for ARGs.This result agreed with Mazhar et al. that may be due to the long-term bioavailability of heavy metals; whereas, antibiotics were not stable in the environment due to transformation and degradation (Mazhar et al., 2021;Wang et al., 2021), so antibiotics had a weaker selective effect on microorganisms than heavy metals (Sun et al., 2021).
The correlation between ARGs was positive, between MRGs also be positive, and between ARGs and MRGs was almost all positive, except for arsC with macB (p < 0.05).Our results are consistent with those of Li et al.'s (2023) water quality tests in the Wuhan section of the Yangtze River: sul1 in ARGs was significantly positively correlated with tet gene.Our results also showed that there was an antagonism between ARGs and MRGs.And this finding was consistent with Yuan et al.'s (2019) finding that the recent ARG profiles of different species of MRGs had a bias, indicating a significant association between ARGs and MRGs.Some studies had found that microorganisms exposed to environmental factors such as antibiotics and heavy metals for a long time can obtain more resistance genes during evolutionary adaptation, and because ARGs and MRGs are chained on movable elements (such as plasmids, transposons and integrated binding elements, etc.) (Baker-Austin et al., 2006;Seiler & Berendonk, 2012) resistance genes could be transmitted through horizontal gene transfer (HGT).

The correlation of microbes with ARGs and MRGs
As shown in Fig. 7a and b, the effects of antibiotics, heavy metals and microbes were not very significant, but the effects between resistance genes and microbes were obvious.The correlation of microbes with ARGs and MRGs could be positive or negative.Proteobacteria was positively correlated with ARGs and MRGs (p < 0.05).A study of antibiotic resistance genes in Chishui River, a tributary of the Yangtze River in China, showed significant positive correlations between ARGs and most microorganisms (p < 0.01) (Wu et al., 2022), which was partially similar to our results.Proteobacteria was the dominant bacterium in heavy metal contamination due to its good resistance to heavy metals (Qiao et al., 2021), the "take-back mechanism" for ARG transfer could lead to the transfer and spread of resistance genes (Jiang et al., 2017), and the synergistic effect of proteobacteria as part of this process with MRGs could put selection pressure on resistance genes, increase the co-existence of ARGs and MRGs in the same bacteria, and accelerate the spread and dissemination of resistance genes, which could allow the long-term presence of these genes in the environment (Pal et al., 2015).
In our study, Actinobacteria was negatively correlated with all resistance genes, such as bacA and copR (p < 0.05).Previous studies have suggested that some ARGs acquire resistance genes from Actinobacteria to protect themselves, and as microbes continue to evolve, new antibiotic resistance mechanisms may also emerge (Ji et al., 2012;Jiang et al., 2017).Although our data showed that there was no significant correlation between microorganisms and antibiotics or heavy metals, it had been reported that the combined pollution of heavy metals and antibiotics can increase the abundance of resistance genes in the environment, and affect microorganisms through mobile genetic elements (MGEs), ARGs and MRGs (Fard et al., 2011;Iglesia et al., 2010).Therefore, the distribution and concentration of antibiotics and heavy metals have potential effects on microorganism.

Conclusion
In this study, we analyzed the distribution and concentration of antibiotics, heavy metals, microorganisms, and resistance genes in the surrounding water of the Hanjiang River, and explored the relationships among them.The data showed that the most abundant target antibiotic in the samples was tetracycline, and the most abundant target heavy metals in the samples were Mn and Zn, so the monitoring of such pollutants should be strengthened.The effect of heavy metals on antibiotic genes was greater than that of antibiotics, and low concentrations of heavy metals were negatively correlated with some resistance genes.There was a significant positive correlation between antibiotics and heavy metals, and the correlation between microorganisms and resistance genes was also evident.And the different distributions and concentrations of van genes in soil-5 need to be further studied and explored.More research should be carried out

Fig. 1
Fig. 1 Sample sites near the Hanjiang Basin in the Shiyan Municipality, Hubei, China.And water and soil samples were collected at each sampling site.For the clarity of the sampling points

Fig. 2 a
Fig. 2 a Total value of antibiotics in soil samples.b Total value of antibiotics in water samples.c, d Spatial distribution of antibiotics in soil samples.e, f Spatial distribution of antibiotics in water samples

Fig. 3 a
Fig. 3 a Total content of heavy metals in soil samples.b The total content of heavy metals in water samples.c, d, e Spatial distribution of heavy metals in soil samples.f, g Spatial distribution of heavy metals in water samples

Fig. 4 a
Fig. 4 a Principal component analysis (PCA) was performed on nine samples using microbial abundance in samples classified by phylum.b The relative abundance and community composition of bacteria (phylum level)

Fig. 5 aFig. 6
Fig. 5 a Total copy numbers of ARGs.b The total copy numbers of MRGs.c The distribution of absolute abundance of ARGs.d The distribution of relative abundance of ARGs.e The distribution of absolute abundance of MRGs.f The distribution of relative abundance of MRGs.g The copy number of van genes in soil-5 (copies/g).h The number of specific or shared ARGs in soil and water samples.i The number of specific or shared MRGs in soil and water samples ◂

Fig. 7 a
Fig. 7 a Correlation of microbes with antibiotics and heavy metals.b The correlation of microbes with ARGs and MRGs