Distribution and relationship of antibiotics, heavy metals and resistance genes in the Hanjiang Reservoir, Shiyan, China

doi:10.21203/rs.3.rs-2746807/v1

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Distribution and relationship of antibiotics, heavy metals and resistance genes in the Hanjiang Reservoir, Shiyan, China

https://doi.org/10.21203/rs.3.rs-2746807/v1

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published 14 Jul, 2023

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The Hanjiang Reservoir is an important water source for the middle route of China's South-to-North Water Diversion Project. The problems of water and soil quality in the Hanjiang reservoir have enormous biological and environmental impacts, 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 43.2 µg/kg and 0.0221 µg/L. Mn was the highest heavy metal in soil with a content of 1,408.28 µg/kg, and in water was Zn with a content of 10.61 µg/L. We found that the most abundant antibiotic resistance genes (ARGs) and 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 genes than antibiotics, and antagonism between resistance genes was more significant. This work expands our understanding of the correlations of antibiotics, heavy metals, and resistance genes in the Hanjiang reservoir, indicating that more attention should be paid to the effects of resistance genes and the quality of water.

antibiotic

heavy metal

antibiotic resistance genes

metal resistance genes

correlation

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 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 and Lipsitch, 2018). WWTPs thus play an important role in the selection and promotion of ARGs (Karkman et al., 2018).

Many studies (Di Cesare et al., 2016) have shown that not only antibiotics can induce ARGs, but also other environmental factors can, for example, heavy metals. 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 long-term 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 and 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. 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 reservoir (Sun et al., 2017), so monitoring the water quality of the Hanjiang reservoir 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 and 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.

2.1 Sample sites and sample collection

As Fig. 1. shown, 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 Reservoir, Shiyan, China, which is the core water source of the South-to-North Water Diversion in China. All samples were collected at a distance of approximately 20 meters 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.

2.2 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 C₃-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 acetonitrile-citric acid buffer solution (v: v = 4 : 3), centrifuged at 3,500 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.

Pre-treated water and soil samples were analyzed using solid phase extraction (SPE) and triple quadrupole liquid chromatography tandem mass spectrometry (HPLC-MS/MS, Thermo TSQ Quantiva, USA) to analyze the antibiotic species and content in the samples (Deng et al., 2022; Li et al., 2019). In this study, a total of 8 types of antibiotics were detected, sulfadiazine (SD), sulfamethoxazole (SMX), sulfamethazine (SMZ), enrofloxacin (ENR), cefoxitin (CFX), amoxicillin (AMOX), tetracycline (TC) and oxytetracycline (OTC), respectively.

2.3 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 hours, 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 2-3mL of liquid was filtered by a 0.22µm 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.

2.4 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 Bio for macro-genome sequencing based on the quality and concentration of DNA in the 26 extracted DNAs (2 from soil and 7 from water).

2.5 Data analysis

Data collation and analysis used SPSS 23.0. The distribution of sampling sites in the Hanjiang Reservoir 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. The “ pheatmap ” package in RStudio and “ Correlation Plot ” in Origin were used to make correlation heatmaps.

3.1 The distribution of antibiotics in samples

Figure 2. showed 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 561.62 µg/kg, and the mean value was 43.20 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 148.60 µg/kg, and a mean value of 11.43 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 was consistent with the conclusion that tetracycline is one of the most commonly used antibiotics (Wang et al., 2017). As shown in Fig. 2. (cd), 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 68.61 µg/kg and 21.65 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, SMZ tended to be close to zero.

As Fig. 2. (bef) shown, all 8 antibiotics were detected in water samples. OTC was the highest antibiotic, with a total of 0.26 µg/L and an average of 0.0221 µg/L, followed by AMOX with a total of 0.18 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).

3.2 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 18,307.7 µg/kg, and the highest content in soil7 with a value of 1,854.03 µg/kg. Zn was the second-highest heavy metal in the soil samples, with a total content of 2,675.79 µg/kg, and the highest content in soil0 at 325.79 µg/kg. because Mn is mainly present in soil solution, mines and smelters are the main sources of Mn pollution, so we speculate that the high Mn content in the soil samples may be related to the abundance of manganese tantalite in Shiyan. In our water samples, Zn content was the highest with a total of 137.95 µg/kg, followed by Mo content with a total of 121.69 µg/kg. wat3 had the highest Mn content, surpassing the content of all types of heavy metals in other samples with 37.42 µg/L. Our test results were lower than those for heavy metals in soils of other areas in China (Liang et al., 2023; Wu et al., 2015).

3.3 The distribution and concentration of microorganisms

After the blastp homology comparison of the gene set protein sequence with the Nr database, the number of microorganisms at the phylum level was 104, with the highest content was Proteobacteria, followed by Actinobacteria and Bacteroidetes. The number of microorganisms at the species level was 11127, with the highest level was Actinomycetia Bacterium, followed by Betaproteobacteria Bacterium and Burkholderiaceae Bacterium.

As Fig. 4. (a 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.

3.4 The distribution of ARGs in samples

A total of 1327 genes, 173 unique ARGs and MGEs (2 transposase genes, 1 transporter and 4 transcriptional regulatory protein) were detected from 2 soil and 7 water samples. Figure 5. (a) summarized the gene numbers of ARGs for each sampling site. We selected 10 genes as target genes for analysis, bacA, macB, sul1, sul2, tetA, rosB, arnA, acrB, mexF, and vanR, which accounted for 74.05%, 51.29%, 69.98%, 70.49%, 70.96%, 67.86%, 71.69%, 72.86% and 76.84% of the total ARG in soil-0, 5, and water-0, 2, 3, 4, 7, 8, and 10, respectively.

Figure 4. (cd) showed 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. 5.g). The number of vanS gene also high,and the reason that the number of van genes at this site was much higher than at other samples needed to be explored.

3.5 The distribution of MRGs in samples

Based on Bactmet database, a total of 1085 genes, 79 types of MRGs were detected. Figure 4. (b) showed the gene counts of all MRGs, with the highest number of MRGs in water-0 and the lowest in water-3. We selected czcC, arsT, actP, arsC, pstB, copR, modB, sodB, chrA, and acn as target genes, which accounted for 62.37%, 33.45%, 46.67%, 50.88%, 55.98%, 61.12%, 65.05%, 63.22% and 57.30% of the total MRGs in soil-0, 5, and water-0, 2, 3, 4, 7, 8, and 10, respectively. As shown by the absolute abundance of MRGs (Fig. 5. ef), 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). Salam et al. detected resistance genes expressing resistance to Cu, Zn, As, Cd and other metals in chronically contaminated soils using macrogenome sequencing (Salam, 2020), that was similar to our sequencing results.

3.6 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).

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 showed 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. showed 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 maybe 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 almostly all positive, except for arsC with macB (p < 0.05). This result showed that there was an antagonism between ARGs and MRGs. And this finding was consistent with Yuan et al.'s finding that the recent ARG profiles of different species of MRGs had a bias, indicating a significant association between ARGs and MRGs (Yuan et al., 2019). 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 and Berendonk, 2012)resistance genes could be transmitted through HGT.

3.7 The correlation of microbes with ARGs and MRGs

As Fig. 7. (ab) shown, 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). Proteobacteria is 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). Actinobacteria was negatively correlated with ARGs and MRGs, such as copR (p < 0.05). The vast majority of antibiotics used for treatment come from soil microbial communities, such as Streptomyces, which can produce many clinically important antibiotics, and other ARGs will obtain ARGs from actinomycete resistance genes to protect themselves, and as microorganisms continue to evolve, new antibiotic resistance mechanisms may also arise (Ji et al., 2012; Jiang et al., 2017). Therefore, we should be vigilant and strengthen research on the production and spread of resistance genes.

This study showed a better understanding of the distribution and concentration of antibiotics, heavy metals, microbes, resistance genes and the association between them around the Hanjiang reservoir. We found that the most abundant antibiotic contaminant in samples was tetracycline, and the highest content of target heavy metals in samples was Mn and Zn. Heavy metals have a greater impact on antibiotic genes than antibiotics, low concentrations of heavy metals were negatively correlated with some resistance genes. Different types of antibiotics and heavy metals can induce different degrees of resistance gene contamination, and the antagonism between ARGs and MRGs could make resistance genes widely spread. The different distribution and concentration of resistance genes in soil-5 need to be further studied and explored, and more research should be conducted in this area and more advanced testing and treatment measures developed to ensure public health.

Author Contributions

JL participated in experiments, conducted data analysis, and wrote major manuscripts. LJL conducted soil and water samples and experimentally obtained relevant experimental data. YZ was responsible for project management and capital acquisition. QL, FW, YHS, YL, JW, and YRZ provide many recommendations for experimental design and implementation. All authors reviewed the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (41807409), foundation for Innovative Research Team of Hubei Provincial Department of Education (JYT2021024), Advantages Discipline Group (Public health) Project in Higher Education of Hubei Province (2021-2025) and Clinical nutrition discipline construction project of Hubei University of Medicine (X1203625).

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No competing interests reported.

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Distribution and relationship of antibiotics, heavy metals and resistance genes in the Hanjiang Reservoir, Shiyan, China

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Sample sites and sample collection

2.2 Antibiotics analysis

2.3 Heavy metals analysis

2.4 DNA extraction and metagenomic sequencing

2.5 Data analysis

3. Result And Discussion

3.1 The distribution of antibiotics in samples

3.2 The distribution of heavy metals in samples

3.3 The distribution and concentration of microorganisms

3.4 The distribution of ARGs in samples

3.5 The distribution of MRGs in samples

3.6 The correlations between antibiotics, heavy metals, ARGs and MRGs

3.7 The correlation of microbes with ARGs and MRGs

4. Conclusion

Declarations

Author Contributions

Funding

References

Additional Declarations

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