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Tetracyclines, sulfonamides and quinolones and their corresponding resistance genes in the Three Gorges Reservoir, China
Science of the Total Environment 631–632 (2018) 840–848
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Tetracyclines, sulfonamides and quinolones and their corresponding resistance genes in the Three Gorges Reservoir, China Muting Yan, Chen Xu, Yumei Huang, Huayue Nie, Jun Wang ⁎ Joint Laboratory of Guangdong Province and Hong Kong Region on Marine Bioresource Conservation and Exploitation, South China Agricultural University, Guangzhou 510642, China College of Marine Sciences, South China Agricultural University, Guangzhou 510642, China
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• Antibiotics and corresponding ARGs were determined in the Three Gorges Reservoir. • SAs were had higher total concentrations than TCs and FQs. • Gene sul was dominating of quantified ARGs. • IntI1 may facilitate the proliferation and propagation of seven ARGs. • Human activities might be relative to the distribution and magnitudes of ARGs.
a r t i c l e
i n f o
Article history: Received 16 January 2018 Received in revised form 8 March 2018 Accepted 8 March 2018 Available online xxxx Keywords: Antibiotic Antibiotic resistance genes Three Gorges Reservoir Engineered aquatic environment High-performance liquid chromatographytandem mass spectrometry Real-Time Quantitative PCR
a b s t r a c t The Three Gorges Project significantly impacted water quality and ecological balance in this area. The special engineered aquatic environment could be an important reservoir for antibiotic resistance genes (ARGs). Fifteen ARGs corresponding to three groups of antibiotics (tetracyclines, sulfonamides and quinolones) were determined in surface water, soil and sediment in this study. Total concentrations of antibiotics ranged from 21.55 to 536.86 ng/L, 3.69 to 438.76 ng/g, 15.78 to 213.84 ng/g in water, soil and sediment, respectively. Polymerase chain reaction (PCR) of ARGs revealed the presence of two sulfonamide resistance genes (sul1, sul2), five tetracycline resistance genes (tetA, tetB, tetM, tetQ, tetG) and class 1 integron gene (intI1) in all samples. And the relative abundance of sulfonamide resistance genes was generally higher than tetracycline resistance genes in three matrices. Significant correlations (p b 0.05) were found between the concentrations of intI1 and ARGs (tetA, tetB, tetM, tetQ, tetG, sul1, sul2), indicating intI1 may facilitate the proliferation and propagation of these genes. Redundancy analysis (RDA) showed distribution of ARGs was related to the certain antibiotics residues, which may exert selective pressure on bacteria and thus enrich the abundance of ARGs. The results of this study could provide useful information for both better understanding and management of the contamination caused by ARGs and related antibiotics in engineered aquatic environments. © 2018 Elsevier B.V. All rights reserved.
1. Introduction ⁎ Corresponding author at: College of Marine Sciences, South China Agricultural University, Guangzhou 510642, China. E-mail address: [email protected] (J. Wang).
https://doi.org/10.1016/j.scitotenv.2018.03.085 0048-9697/© 2018 Elsevier B.V. All rights reserved.
Antibiotics have played crucial roles in controlling human infectious diseases. Tetracyclines, sulfonamides and quinolones are widely used categories in livestock farming and healthcare.
M. Yan et al. / Science of the Total Environment 631–632 (2018) 840–848
Excessive use has resulted in large amounts of such compounds discharged directly into natural ecosystem, without an effective removal processing (Luo et al., 2010; Martinez, 2009; Pruden et al., 2006). In China, Zhang et al. (2015) reported that approximate 53,800 tons of antibiotics were released into the environment annually, and the three groups mentioned above accounted for 44% of the total in 2013. Under the selective pressure of antibiotic residues, susceptible microorganisms in the environment may become resistant to antibiotics through various mechanisms that can evolve and accelerate the propagation of antibiotic resistance genes (ARGs). Even at sub-inhibitory concentration, antibiotics in natural ecosystems could also induce bacteria to develop antibiotic resistance (Marti et al., 2014). Horizontal gene transfer (HGT), which capacitates the mobile genetic elements (MGEs) exchange between different microbes, is the primary mechanism for sharing ARGs between nonpathogens and pathogens (Davison, 1999; Marti et al., 2014; Pruden et al., 2006; Stoll et al., 2012). The prevalence of antibiotic resistance in bacteria not only enriches the abundance of ARGs, but also induces the generation of resistant pathogen, posing a risk to public health (Martínez, 2008). Furthermore, several reports showed that ARGs might exist and persist in the absence of selective pressure (Allen et al., 2009; Alonso et al., 2001; Manson et al., 2004). Once spread in natural microbial populations, ARGs may challenge the dynamic equilibrium of ecosystems and threat human health through food chains (Martinez, 2009). Aquatic environments are frequently interfered by anthropogenic activities, making them an ideal reservoir of ARGs (Marti et al., 2014; Taylor et al., 2011; Zhang et al., 2009). Previous studies have reported the presence of ARGs in various aquatic environments (Cheesanford et al., 2001; Gao et al., 2012a; Rizzo et al., 2013). Tetracycline and sulfonamide resistance genes are the most frequently detected ARGs in the aquatic environments (Jiang et al., 2013). Additionally, soil contains a large number of microbial communities impacted by anthropogenic activities, making it a likely reservoir of diverse antibiotic resistance determinants, thus may require special scientific consideration (Negreanu et al., 2012; Seiler and Berendonk, 2007; Torres-Cortés et al., 2011). The Three Gorges Reservoir has an overall length of 660 km, encompassing 26 cities in Chongqing municipality and Hubei province. The Three Gorges Dam, which is the largest hydroelectric project along the Yangtze River in China, exerts significant effects on economic and social benefits as well as the local ecological environment. The environmental pollution in this area may not only impact the water quality in the reservoir, but also threaten drinking water security of millions of people along the Yangtze River. Various antibiotics have been detected in water or soil from Yangtze River, as well as the Yangtze Estuary, caused by the great doses of antibiotics used in livestock and poultry breeding industry (Shi et al., 2014; Sun et al., 2017; Yan et al., 2013). ARGs including sul, tet and intI1 genes were widely distributed in the water and sediments of the Yangtze Estuary, raising great environmental risk to the ecosystem (Guo et al., 2014; Lin et al., 2015). However, comprehensive study about these antibiotics and corresponding ARGs in different matrices of the Three Gorges Reservoir is limited so far. And most studies focus on natural or artificial aquatic system. The impact on ARGs and antibiotics in large water conservancy project has been rarely explored. The objectives of this study were to (1) characterize the occurrence and concentrations of antibiotics residues in three matrices of the Three Gorges Reservoir; (2) document the abundance and distribution of fifteen antibiotic resistance genes (sul: sul1, sul2, sul3; tet: tetA, tetB, tetC, tetM, tetO, tetQ, tetW, tetG; qnr: qnrA, qnrB, qnrD, qnrS) and class 1 integron gene (intI1); (3) explore potential linkage between antibiotics and corresponding ARGs under the water project. The results would facilitate better understanding of these emerging contaminants in engineered environment and provide information for antibiotic control in the future.
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2. Materials and methods 2.1. Sampling sites and sample collection Three Gorges Reservoir is located in the section of the upper reaches of the Yangtze River, within East longitudes 106°20′–110°30′ and Northern latitude 29°00′–31°50′. From upstream to downstream, thirteen sites were selected in the investigated area, numbered M1 to M13, respectively. One or two sampling points were set in each town as well as the feasibility of the actual operation. Sites M1 and M2 were situated near a waste water treatment plant (WWTP) receiving municipal sewage. Sites (M2, M8, M12) were close to the hospitals and several husbandry (M5, M7, M10) scattered in these areas, while site M3 was located in an industrial area of machinery manufacturing. Site M13 was situated in the lower reach next to the Three Gorges Dam. All samples were collected during the wet season from August 18 to 24, 2015. Detailed information is described in Fig. 1. Water samples (1 L) were collected from the top 50 cm of the water surface, by sailing a boat in the cross sectional midpoint of the river. Meanwhile, sediments (1 kg) were taken from the top 5 cm of the river bed using a core sampler. While the topsoil samples (1 kg) were collected near the river bank within 1 m by using the stainless steel sediment sampler. Three identical sample replicates of each location were collected and then homogeneously mixed on the spot. The distance was around 1 km between each repetition. All samples were stored in pre-cleaned amber glass bottle, immediately moved into a 4 °C ice box, and then transported to the laboratory for pretreatment within 24 h. 2.2. HPLC-MS/MS analysis of antibiotics Water samples (1 L) were filtered through 0.22 μm glass fiber filters (Waters, USA) using a vacuum filter to remove suspended solids, and the membranes were stored at −80 °C for DNA extraction. Soil and sediment samples were freezing-dried before grinded, and then added three extraction buffers (15 mL methanol, 10 mL acetic acid buffer, 5 mL 0.1 mol/L Na2EDTA) to 5 g samples in sequence. Solid phase extraction was performed according to the previous literature (Luo et al., 2010), using the Oasis hydrophilic-lipophilic-balanced (HLB) cartridges (500 mg, 6 mL, Waters, USA). Thirteen antibiotics selected for this study based on their application in medical and livestock industry in China, including four tetracyclines (TCs: oxytetracycline (OTC), chlortetracycline (CTC), tetracycline (TC), doxycycline (DXC)), five sulfonamides (SAs: sulfamethazine (STZ), sulfadiazine (SDZ), sulfameter (ST), sulfamethoxazole (SMZ), trimethoprim (TMP)), four quinolones (FQs: norfloxacin (NOR), ofloxacin (OFL), ciprofloxacin (CIP), enrofloxacin (ENR)), were detected by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS, Thermo Fisher Scientific, USA). For MS detection, the positive mode (ESI+) and multiple reaction monitoring (MRM) mode were performed. The MS desolvation temperature was 350 °C, cone gas flow was 50 L/h, and the desolvation gas flow was 500 L/h. The capillary voltage was 4.0 kV, extractor voltage was 2.0 V, and multiplier was 650.00 V. The detailed parameters of operational processes and methods were conducted according to the published literature (Luo et al., 2010). The limits of quantitation (LOQs) for antibiotics under consideration were presented in Table S1 (Supplementary material). The LOQs ranged from not detected to 7.32 ng/L in water, from 0.11 to 2.18 ng/g in soil, from 0.20 to 2.34 ng/g in sediment, respectively. 2.3. Analysis of antibiotic resistance genes 2.3.1. DNA extraction and PCR The filter membranes of water samples were cut into small pieces and then placed in centrifuge tubes. DNA was extracted using the E.Z.N.A.™ water DNA Kit (OMEGA, USA) as per manufacturer's protocol. For soil and sediments (dry weight 500 mg), DNA extraction was
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Fig. 1. Location of the sampling sites.
conducted with the FastDNA™ SPIN Kit for Soil (MP Biomedicals, USA) according to the instructions provided by the manufacturer. To minimize PCR inhibition, DNA purification was achieved with the Geneclean Spin Kit (QBiogene, Carlsbad, CA). The concentration and quality of the purified DNA were verified by agarose gel electrophoresis and spectrophotometry (NanoDrop 2000, Theromo Fisher Scientific, USA). Polymerase chain reaction (PCR) detection assays were used to screen the presence of fifteen kinds of antibiotic resistance genes (ARGs) in three matrix samples, including three sulfonamide resistance genes (sul1, sul2, sul3), eight tetracycline resistance genes (tetA, tetB, tetC, tetM, tetO, tetQ, tetW, tetG), four quinolone resistance genes (qnrA, qnrB, qnrD, qnrS) and class 1 integron gene (intI1). Sequence of all target ARG primers and PCR reaction conditions were listed in Table S2 (Supplementary material), according to the published literatures. All qualitative PCR assays were performed in a 25 μL total volume, and the PCR mixtures consisted of 12.5 μL 2 × Utaq PCR Master Mix, 1 μL of each primer, 9.5 μL ddH2O, and 1 μL template DNA. The amplification procedure was as following: initial denaturation at 94 °C for 3 min, followed by 44 cycles of 94 °C for 30 s, 30 s at annealing temperature (Table S1), and 72 °C for 30 s, with a final extension step of 5 min at 72 °C. The PCR amplified products were analyzed by electrophoresis on a 1.5% agarose gel in 0.5 × Tris-Borate-EDTA (TBE) buffer with negative controls (sterile pure water) and positive controls, which consisting of sequenced PCR amplicons obtained from these samples from the Three Gorges Reservoir and then cloned into Escherichia coli DH5α (using the pEASY-T1 Simple Cloning Kit, Transgene, China), to confirm the existence of ARGs. Plasmids carrying target genes were extracted for further quantitative analysis by Plasmid Mini Preparation Kit (ZOMINBIO, China) according to the manufacturer's protocol.
variations in diverse bacterial abundance and extraction efficiency, 16S rRNA gene was quantified as the housekeeping gene to normalize ARGs in all samples. Each qPCR reaction mixture contained 10 μL of SYBR Green real time PCR Master Mix, 1 μL of each primer, 7.4 μL of ddH2O, and 1 μL of template DNA. Amplification was conducted with ABI7500 (Applied Biosystems, USA) as follows: initial cycle of 3 min at 95 °C, followed by 44 cycles of 1 min at 95 °C, 20 s at different annealing temperatures presented in Table S1, and 30 s at 72 °C followed by a final melt curve stage with temperature ramping from 60 to 95 °C. Each reaction was run in triplicate with negative controls (sterile pure water) and positive controls (plasmids DNA). Calibration curves were generated as described by Pei et al. (2006), using 10-fold serial dilutions of the plasmids as standard. Correlation coefficients (R2 N 0.99) were available for calibration curves, and the copy number of corresponding genes was calculated according to the curves.
2.3.2. Real-Time Quantitative PCR (qPCR) Real-Time qPCR was performed using a SYBR Green approach to quantification analyze identified ARGs with 100% detection rates from water, soil and sediment samples. To avoid the error caused by potential
In this study, HPLC-MS/MS analysis revealed that ten antibiotics (OTC, TC, CTC, STZ, SDZ, ST, SMZ, TMP, ENR, OFL) were detected in all samples, while relatively lower detection frequencies were observed for NOR (46.15%, 38.46%) and DXC (38.46%, 38.46%) in soil and
2.4. Statistical analysis ANOVA was used to assess the homogeneity of variance with a significance level of 5% (p b 0.05). Pearson correlation analysis was performed to analyze the relationship between ARGs in various environmental components using SPSS 19.0. Redundancy analysis (RDA) and heat map of genes abundance was conducted with R Statistical software version 3.3.0 using R-Studio. Averages and standard deviations of all data were determined using Microsoft Excel 2010. 3. Results and discussion 3.1. Profiles of antibiotics
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Table 1 The concentrations of antibiotics in water, soil, and sediments. Compounds
OTC TC CTC DXC TCs SDZ TMP STZ ST SMZ SAs OFL ENR NOR FQs
Water (ng/L)
Soil (ng/g)
Sediment (ng/g)
Max
Min
Mean
Total
Freq (100%)
Max
Min
Mean
Total
Freq (100%)
Max
Min
Mean
Total
Freq (100%)
15.62 26.29 39.69 nd 80.5 359.16 209.69 68.41 24.90 173.46 536.86 105.23 27.36 nd 126.8
6.14 20.28 12.35 nd 38.8 27.68 12.32 11.42 8.27 3.51 74.2 10.81 9.04 nd 21.55
10.62 22.91 30.06 nd 63.6 95.39 119.07 30.12 14.37 78.55 337.5 31.12 19.32 nd 50.4
138.1 297.8 390.7 nd 826.6 1240.0 1547.9 391.6 186.8 1021.1 4387.37 404.6 251.2 nd 655.8
100 100 100 0
22.14 81.81 127.28 2.45 143.2 24.52 38.99 7.37 192.34 198.44 438.76 74.76 145.13 19.83 237.9
0.74 4.02 1.76 nd 6.5 0.55 0.18 2.02 1.77 3.45 13.1 2.02 1.66 nd 3.69
6.64 25.87 20.47 0.30 53.3 4.09 15.33 2.90 38.98 37.86 99.2 28.83 43.25 3.85 75.9
86.3 336.3 266.0 3.8 692.5 53.1 199.3 37.7 506.8 492.1 1289.1 374.8 562.2 50.0 987.1
100 100 100 30.77
10.76 118.20 32.34 6.04 143.7 41.54 20.92 4.72 121.31 57.03 213.84 72.09 86.76 16.68 154.3
1.63 8.83 3.60 nd 15.78 1.16 4.00 2.13 10.75 6.16 37.9 3.92 14.18 nd 18.1
4.16 31.76 14.68 0.80 51.4 7.02 11.71 3.01 35.93 24.91 82.6 22.46 42.59 3.09 68.1
54.1 412.9 190.9 10.4 668.3 91.2 152.3 39.1 467.1 323.8 1073.6 291.9 553.6 40.1 885.6
100 100 100 38.46
100 100 100 100 100 100 100 0
100 100 100 100 100 100 100 46.15
100 100 100 100 100 100 100 38.46
Max - Maximum; Min - Minimum; Freq - detection frequency; nd - not detected, including levels below limits of quantification (LOQ); OTC - oxytetracycline; TC - tetracycline; CTC - chlortetracycline; DXC - doxycycline; TCs - total amount of four tetracyclines; SDZ - sulfadiazine; TMP - trimethoprim; STZ - sulfamethazine; ST - sulfameter; SMZ - sulfamethoxazole; SAs - total amount of five sulfonamides; OFL - ofloxacin; ENR - enrofloxacin; NOR - norfloxacin; FQs - total amount of four quinolones.
sediment, respectively. CIP was not detected in this region. Details of detection frequency and concentration level were shown in Table 1. In surface water, the total concentrations of antibiotic ranged from 21.55 to 536.86 ng/L. SAs were the predominant antibiotic with the highest concentration, reaching 4387.37 ng/L, indicating extensive sulfonamides pollution in this region. Due to the properties of hydrophilic and degradation resistance, sulfonamides tend to transport across long distance in the aquatic environment. The average value of SAs was (67.50 ng/L) higher than TCs (21.20 ng/L) and FQs (25.22 ng/L) in water. The levels of SAs in this study were higher than the concentration detected in other 6 tributaries of the Yangtze River, in which the highest concentration was 38.1 ng/L, while TCs and FQs were not detected (Feng et al., 2017). Among sulfonamides group, concentrations of SDZ was the highest, ranging from 27.68 to 359.16 ng/L, which were higher than those reported in Haihe River (210–385 ng/L) (Luo et al., 2011) and Wangyang River (up to 91.8 ng/L) (Jiang et al., 2014). For soil and sediments, concentrations of the three groups of antibiotics ranged from 3.69 to 438.76 ng/g, 15.78 to 213.84 ng/g, respectively. The average concentration followed the order of SAs N FQs N TCs. The peak concentrations were found for SMZ (198.44 ng/g) in soil and ST (121.31 ng/g) in sediments. The levels were significantly higher than those in Yongjiang River (nd–0.2 ng/g) (Xue et al., 2013) and Huangpu River (nd–0.6 μg/kg) (Chen and Zhou, 2013), respectively. In terms of individual antibiotic, ENR had the highest mean value reaching 43.25 ng/g in soil and 42.59 ng/g in sediment than other compounds, likely due to their strong adsorption of sediments and particles. Compared to previous studies (Liang et al., 2013; Shi et al., 2014), the high pollution level of ENR suggested serious usage and emission status in this region. The distribution characteristic was shown in Fig. 2. In surface water, sites that located nearby the hospitals (M2, M8, M12) and livestock farms (M5, M7, M10) exhibited higher levels of antibiotics than the others. The total concentration peaked at site M12 (623.42 ng/L), and SAs accounted for 86% of all detected antibiotics. The total environmental emission of antibiotics for the Yangtze River upstream and downstream was estimated N5000 tons per year, and SAs were one of the major categories (Zhang et al., 2015). For soil, antibiotics showed the highest concentration at sites M1 (438.76 ng/g), followed by M7 (137.43 ng/g) and M12 (130.76 ng/g). This suggests that the effluent from WWTP could be an important source of antibiotics, as well as the discharge from husbandry and hospitals. In 2014, the sewage from industry, municipal and agriculture was more than 1 billion tons (http:// www.zhb.gov.cn/hjzl/shj/sxgb/). However, among 107 sewage treatment plants, only 60.7% reached the standard of effluent quality in
2013 (Zou et al., 2014). Thus, this may be the important sources of antibiotic into environment. In sediments, site M13 was located in downstream beside the Three Gorges Dam and detected with the highest concentration (total 497.01 ng/g). This might due to that dam could intercept the sediment and some antibiotics would adsorb clay particles. To control the antibiotic contamination, strict regulation for pharmaceutical usage is an essential measure. Besides, exploring the novel process of wastewater treatment and alternative medicine is feasible reference. 3.2. Abundance of ARGs Among the fourteen ARGs, two sulfonamide resistance genes (sul1, sul2), five tetracycline resistance genes (tetA, tetB, tetM, tetQ, tetG) and class 1 integron gene (intI1) were detected in all samples, while tetC and one quinolone resistance gene qnrD simply occurred in surface water based on the conventional PCR analysis. The relative abundance (gene copy numbers of ARGs normalized to the gene copy numbers of 16S rRNA) of detected ARGs was shown in Fig. 3 and Table S3 (Supplementary material). The color of the spot in heatmap corresponds to the log-transformed abundance of the ARGs. The heatmap clearly illustrates that sul1 and sul2 genes were the most abundant and frequent genes detected in all samples, reflecting their widely distribution in the Three Gorges Reservoir. Sul1 and sul2 genes were plasmid-borne that encoded alternative drug-resistant variants of the dihydropteroate synthase (DHPS) enzymes in gram-negative bacteria (Sköld, 2000). It was observed that sul1 and sul2 were predominant in the Three Gorges Reservoir compared with tet genes (ANOVA, p b 0.05), with the highest relative abundance of 8.56 × 10−4 copies/16S rRNA to 7.04 × 10−1 copies/16S rRNA, 8.04 × 10−4copies/16S rRNA to 5.19 × 10−1 copies/ 16S rRNA in water and solid (soil and sediment) samples, respectively. The levels of sul were higher than those in Haihe River (Luo et al., 2010) and Poudre River (Pruden et al., 2006). For tetracycline resistance genes, the total relative abundances ranged from 3.93 × 10−5 to 4.64 × 10−2 copies/16S rRNA, 4.23 × 10−5 to 5.53 × 10−2 copies/16S rRNA, 4.49 × 10−5 to 2.31 × 10−2 copies/16S rRNA in water, soil and sediment samples, respectively. Of all tetracycline resistance genes, tetA was the most abundant in water, with the mean concentration of 1.04 × 10−2 copies/16S rRNA, while in soils and sediments, tetB had the highest mean concentrations up to 1.05 × 10−2 copies/16S rRNA and 3.30 × 10−3 copies/16S rRNA, respectively. Gene tetA and tetB could express efflux pump proteins on plasmids. They frequently appeared in various environmental compartments (Piotrowska and Popowska, 2014; Zhang et al., 2009). Gene tetB was the most commonly
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Fig. 2. The concentrations of oxytetracycline (OTC), chlortetracycline (CTC), tetracycline (TC), doxycycline (DXC), sulfamethazine (STZ), sulfadiazine (SDZ), sulfameter (ST), sulfamethoxazole (SMZ), trimethoprim (TMP), norfloxacin (NOR), ofloxacin (OFL) and enrofloxacin (ENR) in the Three Gorges Reservoir. Colors reflected the three different classes of antibiotics: tetracyclines (blue), sulfonamides (black) and quinolones (red). M1 to M13 indicate the thirteen sampling sites. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
carried Gram-negative efflux gene identified in Gram-negative bacteria, which were the dominant bacterial populations in aquatic environment (Jiang et al., 2013; Roberts, 2005). Furthermore, the overall trend of tetracycline resistance genes concentrations in soil and sediment samples was tetB N tetG N tetA N tetQ N tetM, with the lowest abundance for tetM in surface water as well. The positive correlation (Table 2) was observed in this study between efflux pump genes and ribosomal protection tetracycline genes (tetQ and tetM), which were generally associated with conjugative chromosomal elements that could transfer mobilizable plasmids to other species (Chopra and Roberts, 2001). Furthermore, class 1 integron gene (intI1) was found with a detection frequency of 100% in all samples, and the concentration ranged from 1.47 × 10−4copies/16S rRNA to 3.19 × 10−1 copies/16S rRNA, followed by sul1. As genetic platform for ARGs capture, intI1 showed positive correlation with seven ARGs (Table 2). In particular, genes sul1, sul2 exhibited the significant correlation (p b 0.01) with intI1, indicating that it could facilitate the propagation of sul in the Three Gorges
Reservoir. Another cause of the strong correlation between intI1 and sul1 may be that sul1 probably serve as partial structure in the 3′-conserved segment (CS) region of the class 1 integron (Su et al., 2012). The findings were consistent with previous studies found in Haihe River (Luo et al., 2010) and Wenyu River (Xu et al., 2016). Pearson correlation coefficient also revealed substantial connection among tet and intI1 (p b 0.05), suggesting that class 1 integron played a major role in the widespread of antibiotic resistance among microbial communities. Consist with the previous results, intI1 obviously exhibited a similar spatial pattern with sul and tet in sediments within the Yangtze Estuary and nearby coastal area (Lin et al., 2015). Class 1 integron could capture and express gene cassettes from a vast pool of diverse cassettes, thus ARGs could develop multiple drug resistances among microorganisms though horizontal gene transfer (Gillings et al., 2008). In addition, mobilization of the integron might facilitate their later transfer into a wide range of pathogens, posing a potential health hazard (Johnson et al., 2016; Wright et al., 2008).
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Fig. 3. Heat map of antibiotic resistance genes (ARGs) in all sampling sites. Each grid represented the relative abundance of the target ARGs for all samples normalized in a log 10 scale. Vertical axis represented the sequence of sampling sites, while the horizontal axis represented the target ARGs. The legend showed corresponding fold change values. (a) Water samples; (b) soil samples; (c) sediment samples.
3.3. Spatial distribution of ARGs Redundancy analysis (RDA) showed the spatial distribution characteristic based on the concentration of ARGs under the influence of antibiotics in three matrices (Fig. 4). The numbers in brackets described the percentage of variance explained by the first two axes. In the diagram (Fig. 4A) for water, sites M12, M9 and M8 were displayed to be a cluster that express a relatively high level of ARGs, where were found positive relations between SDZ, TC and corresponding resistance genes, indicating the emissions of pharmaceuticals from nearby hospitals might exert a selective pressure for environmental microorganism. Sites (M1–M6) from upstream lay close together, suggesting these locations had similar influences on antibiotics. Typically, site M3 was frequently impacted by industrial activities, resulting in heavy metals enrichment. Previous studies demonstrated that heavy metals contamination was correlated
to concentration of ARGs in general, because they could select indirectly for antibiotic resistance by co-selection with antibiotic (Mao et al., 2015; Seiler and Berendonk, 2007). Conversely, sites (M7, M10, M11, M13) from downstream were displayed to be a cluster, showing that the resembling contamination profiles of related genes. Anthropogenic sources in the watershed could alter the distribution and magnitudes of ARGs in different matrices (Pruden et al., 2012). Redundancy analysis of soils (Fig. 4B) showed distinct groups of the thirteen sampling sites with the first two axes explanation of 52.31% and 21.98%, respectively. It was observed that sites located or near hospitals gathered together with the relatively higher level of ARGs, which was significantly correlated with SMZ, DXC and CTC. Specifically, site M11 had the high relative abundance of ARGs compared with other regions in solid samples (ANOVA, p b 0.05). Different from water and soil, the distribution of ARGs in sediment exhibited a contamination trend in
Table 2 Correlation between class 1 integron gene (intI1), tetracycline resistance genes (tetA, tetB, tetC, tetM, tetQ, tetG) and sulfonamide resistance genes (sul1, sul2) in water, soil, and sediments. Water
sul1 sul2 tetA tetB tetG tetM tetQ tetC a b
intI1
sul1
0.946b 0.889b
0.922b
0.743b 0.694b 0.584a 0.796b
0.868b 0.825b 0.594a 0.676a 0.887b
sul2
0.887b 0.739b
0.741b
tetB
0.940b 0.666a 0.560a 0.814b
Correlation is significant at the 0.05 level. Correlation is significant at the 0.01 level.
tetG
0.818b 0.682a 0.823b
Soil tetM
tetQ
intI1
sul1
0.641a
0.792b
0.579a 0.871b 0.793b
0.909b
tetA
0.722b 0.788b 0.698b 0.966b
Sediment tetB
0.660a 0.852b
tetG
0.683a
tetM
0.709b
intI1
sul2
0.902b 0.626a 0.922b
0.642a 0.898b
0.912b 0.656a
0.914b 0.639a
tetA
tetB
tetG
tetM
0.790b 0.884b 0.770b 0.953b
0.994b 0.857b
0.888b
0.835b
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Fig. 4. Redundancy analysis (RDA) of the quantitative correlation between antibiotic resistance genes and relevant antibiotic in water (a), soil (b), sediment (c) samples. The percentage of the total variance explained by each axis was shown.
a number of locations, indicating these points might have the analogical environmental behaviors. The individual sites were separated from the cluster, such as M1, which was adjacent to the WWTP with maximum abundances, showing a positive correlation with CTC and TC. Additionally, biological treatment process in WWTP might facilitate the proliferation of antibiotic resistant bacteria. Several studies demonstrated WWTP was a potential reservoir for mobile antibiotic resistance elements to foster horizontal gene transfer and developed multi-drug resistance in bacteria (Schlüter et al., 2007). Spatially, no significant relationship was found in each identical category of ARGs among three matrices except gene tetM, which has the widest host range of many tetracycline resistance genes as a result of the connection with conjugative and broad-host-range transposons (Gao et al., 2012a). It could be inferred that the dissemination of ARGs in this area might be aggravated by antibiotics related to human activities. Although plentiful studies have been conducted concerning the relationships between ARGs and antibiotics, the conclusion remained inconsistent. For example, Gao et al. (2012b) demonstrated a correlation between certain sul and sulfonamides in WWTP effluent, while no significance was found between tet with concentration of tetracyclines. Generally, the relative
abundance of ARGs not only concerned with antibiotics, but also was related to the species of genes and other environmental factors, such as temperature and heavy metal concentrations (Mao et al., 2015; Pei et al., 2007). Given the complexity of aquatic environment, it was difficult to determine which impact factors accounted for the distribution characteristics of ARGs in natural condition due to limited information. In addition, antibiotic resistance was a natural phenomenon in microbial pangenome (Allen et al., 2009; D'Costa et al., 2011). As selective forces for bacterial evolution, antimicrobial together with multiple factors might induce an integrated effect on the emergence and persistence of multidrug-resistant microorganism and subsequent co-selection processes (Cantón and RuizGarbajosa, 2011). Hence, further studies are needed to establish a synthetic assessment model in the future for a better understanding of the behavior of antibiotics resistance genes in different medium, as well as the risks associated with the corresponding antibiotics. 4. Conclusions This study demonstrated that the Three Gorges Reservoir has been an important reservoir of three groups of antibiotics and related
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resistance genes. SAs were had higher total concentrations than TCs and FQs in water, soil and sediment. Gene sul was dominating of quantified ARGs. The co-enrichment of ARGs and intI1 would probably exacerbate the risks of transfer of ARGs from environmental microorganism to human-associated bacteria, and thus pose potential hazards to human health. Significant relationships were found between the relative abundance of ARGs and the corresponding residual antibiotic concentrations near hospitals and livestock, indicating human activities might play an important role in exacerbating the transmission of resistance determinants. Considering seasonal fluctuations in ARGs, the above results could only provide basic data of those emerging contaminants in investigated region in summer, not all through the year. Nonetheless, the heavy pollution level of antibiotics should be paid attention and appropriate models would be established to assess the risk. Strict regulations and exploring new treatment technology will contribute to contamination prevention and control. Acknowledgments This project was supported by High Level Talents Program of South China Agricultural University (0A293-8000/217047 and K17021), National Natural Science Foundation of China (41706186); Cooperative Project of Joint Laboratory of Guangdong Province and Hong Kong Region on Marine Bioresource Conservation and Exploitation (MBCE201702). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2018.03.085. References Allen, H.K., Moe, L.A., Rodbumrer, J., Gaarder, A., Handelsman, J., 2009. Functional metagenomics reveals diverse β-lactamases in a remote Alaskan soil. ISME J. 3, 243–251. Alonso, A., Sanchez, P., Martinez, J.L., 2001. Environmental selection of antibiotic resistance genes. Environ. Microbiol. 3, 1–9. Cantón, R., Ruiz-Garbajosa, P., 2011. Co-resistance: an opportunity for the bacteria and resistance genes. Curr. Opin. Pharmacol. 11, 477–485. Cheesanford, J.C., Aminov, R.I., Krapac, I.J., Garriguesjeanjean, N., Mackie, R.I., 2001. Occurrence and diversity of tetracycline resistance genes in lagoons and groundwater underlying two swine production facilities. Appl. Environ. Microbiol. 67, 1494–1502. Chen, K., Zhou, J.L., 2013. Occurrence and behavior of antibiotics in water and sediments from the Huangpu River, Shanghai, China. Chemosphere 95, 604–612. Chopra, I., Roberts, M., 2001. Tetracycline antibiotics: mode of action, applications, molecular biology, and epidemiology of bacterial resistance. Microbiol. Mol. Biol. Rev. 65, 232–260. Davison, J., 1999. Genetic exchange between bacteria in the environment. Plasmid 42, 73–91. D'Costa, V.M., King, C.E., Kalan, L., Morar, M., Sung, W.W., Schwarz, C., Froese, D., Zazula, G., Calmels, F., Debruyne, R., 2011. Antibiotic resistance is ancient. Nature 477, 457–461. Feng, L., Cheng, Y., Feng, L., Zhang, S., Liu, Y., 2017. Distribution of typical antibiotics and ecological risk assessment in main waters of Three Gorges Reservoir area. Res. Environ. Sci. 30, 1031–1040. Gao, P., Mao, D., Luo, Y., Wang, L., Xu, B., Xu, L., 2012a. Occurrence of sulfonamide and tetracycline-resistant bacteria and resistance genes in aquaculture environment. Water Res. 46, 2355–2364. Gao, P., Munir, M., Xagoraraki, I., 2012b. Correlation of tetracycline and sulfonamide antibiotics with corresponding resistance genes and resistant bacteria in a conventional municipal wastewater treatment plant. Sci. Total Environ. 421, 173–183. Gillings, M., Boucher, Y., Labbate, M., Holmes, A., Krishnan, S., Holley, M., Stokes, H., 2008. The evolution of class 1 integrons and the rise of antibiotic resistance. J. Bacteriol. 190, 5095–5100. Guo, X., Li, J., Yang, F., Yang, J., Yin, D., 2014. Prevalence of sulfonamide and tetracycline resistance genes in drinking water treatment plants in the Yangtze River Delta, China. Sci. Total Environ. 493, 626–631. Jiang, L., Hu, X., Xu, T., Zhang, H., Sheng, D., Yin, D., 2013. Prevalence of antibiotic resistance genes and their relationship with antibiotics in the Huangpu River and the drinking water sources, Shanghai, China. Sci. Total Environ. 458, 267–272. Jiang, Y., Li, M., Guo, C., An, D., Xu, J., Zhang, Y., Xi, B., 2014. Distribution and ecological risk of antibiotics in a typical effluent-receiving river (Wangyang River) in North China. Chemosphere 112, 267–274.
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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv
Tetracyclines, sulfonamides and quinolones and their corresponding resistance genes in the Three Gorges Reservoir, China Muting Yan, Chen Xu, Yumei Huang, Huayue Nie, Jun Wang ⁎ Joint Laboratory of Guangdong Province and Hong Kong Region on Marine Bioresource Conservation and Exploitation, South China Agricultural University, Guangzhou 510642, China College of Marine Sciences, South China Agricultural University, Guangzhou 510642, China
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• Antibiotics and corresponding ARGs were determined in the Three Gorges Reservoir. • SAs were had higher total concentrations than TCs and FQs. • Gene sul was dominating of quantified ARGs. • IntI1 may facilitate the proliferation and propagation of seven ARGs. • Human activities might be relative to the distribution and magnitudes of ARGs.
a r t i c l e
i n f o
Article history: Received 16 January 2018 Received in revised form 8 March 2018 Accepted 8 March 2018 Available online xxxx Keywords: Antibiotic Antibiotic resistance genes Three Gorges Reservoir Engineered aquatic environment High-performance liquid chromatographytandem mass spectrometry Real-Time Quantitative PCR
a b s t r a c t The Three Gorges Project significantly impacted water quality and ecological balance in this area. The special engineered aquatic environment could be an important reservoir for antibiotic resistance genes (ARGs). Fifteen ARGs corresponding to three groups of antibiotics (tetracyclines, sulfonamides and quinolones) were determined in surface water, soil and sediment in this study. Total concentrations of antibiotics ranged from 21.55 to 536.86 ng/L, 3.69 to 438.76 ng/g, 15.78 to 213.84 ng/g in water, soil and sediment, respectively. Polymerase chain reaction (PCR) of ARGs revealed the presence of two sulfonamide resistance genes (sul1, sul2), five tetracycline resistance genes (tetA, tetB, tetM, tetQ, tetG) and class 1 integron gene (intI1) in all samples. And the relative abundance of sulfonamide resistance genes was generally higher than tetracycline resistance genes in three matrices. Significant correlations (p b 0.05) were found between the concentrations of intI1 and ARGs (tetA, tetB, tetM, tetQ, tetG, sul1, sul2), indicating intI1 may facilitate the proliferation and propagation of these genes. Redundancy analysis (RDA) showed distribution of ARGs was related to the certain antibiotics residues, which may exert selective pressure on bacteria and thus enrich the abundance of ARGs. The results of this study could provide useful information for both better understanding and management of the contamination caused by ARGs and related antibiotics in engineered aquatic environments. © 2018 Elsevier B.V. All rights reserved.
1. Introduction ⁎ Corresponding author at: College of Marine Sciences, South China Agricultural University, Guangzhou 510642, China. E-mail address: [email protected] (J. Wang).
https://doi.org/10.1016/j.scitotenv.2018.03.085 0048-9697/© 2018 Elsevier B.V. All rights reserved.
Antibiotics have played crucial roles in controlling human infectious diseases. Tetracyclines, sulfonamides and quinolones are widely used categories in livestock farming and healthcare.
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Excessive use has resulted in large amounts of such compounds discharged directly into natural ecosystem, without an effective removal processing (Luo et al., 2010; Martinez, 2009; Pruden et al., 2006). In China, Zhang et al. (2015) reported that approximate 53,800 tons of antibiotics were released into the environment annually, and the three groups mentioned above accounted for 44% of the total in 2013. Under the selective pressure of antibiotic residues, susceptible microorganisms in the environment may become resistant to antibiotics through various mechanisms that can evolve and accelerate the propagation of antibiotic resistance genes (ARGs). Even at sub-inhibitory concentration, antibiotics in natural ecosystems could also induce bacteria to develop antibiotic resistance (Marti et al., 2014). Horizontal gene transfer (HGT), which capacitates the mobile genetic elements (MGEs) exchange between different microbes, is the primary mechanism for sharing ARGs between nonpathogens and pathogens (Davison, 1999; Marti et al., 2014; Pruden et al., 2006; Stoll et al., 2012). The prevalence of antibiotic resistance in bacteria not only enriches the abundance of ARGs, but also induces the generation of resistant pathogen, posing a risk to public health (Martínez, 2008). Furthermore, several reports showed that ARGs might exist and persist in the absence of selective pressure (Allen et al., 2009; Alonso et al., 2001; Manson et al., 2004). Once spread in natural microbial populations, ARGs may challenge the dynamic equilibrium of ecosystems and threat human health through food chains (Martinez, 2009). Aquatic environments are frequently interfered by anthropogenic activities, making them an ideal reservoir of ARGs (Marti et al., 2014; Taylor et al., 2011; Zhang et al., 2009). Previous studies have reported the presence of ARGs in various aquatic environments (Cheesanford et al., 2001; Gao et al., 2012a; Rizzo et al., 2013). Tetracycline and sulfonamide resistance genes are the most frequently detected ARGs in the aquatic environments (Jiang et al., 2013). Additionally, soil contains a large number of microbial communities impacted by anthropogenic activities, making it a likely reservoir of diverse antibiotic resistance determinants, thus may require special scientific consideration (Negreanu et al., 2012; Seiler and Berendonk, 2007; Torres-Cortés et al., 2011). The Three Gorges Reservoir has an overall length of 660 km, encompassing 26 cities in Chongqing municipality and Hubei province. The Three Gorges Dam, which is the largest hydroelectric project along the Yangtze River in China, exerts significant effects on economic and social benefits as well as the local ecological environment. The environmental pollution in this area may not only impact the water quality in the reservoir, but also threaten drinking water security of millions of people along the Yangtze River. Various antibiotics have been detected in water or soil from Yangtze River, as well as the Yangtze Estuary, caused by the great doses of antibiotics used in livestock and poultry breeding industry (Shi et al., 2014; Sun et al., 2017; Yan et al., 2013). ARGs including sul, tet and intI1 genes were widely distributed in the water and sediments of the Yangtze Estuary, raising great environmental risk to the ecosystem (Guo et al., 2014; Lin et al., 2015). However, comprehensive study about these antibiotics and corresponding ARGs in different matrices of the Three Gorges Reservoir is limited so far. And most studies focus on natural or artificial aquatic system. The impact on ARGs and antibiotics in large water conservancy project has been rarely explored. The objectives of this study were to (1) characterize the occurrence and concentrations of antibiotics residues in three matrices of the Three Gorges Reservoir; (2) document the abundance and distribution of fifteen antibiotic resistance genes (sul: sul1, sul2, sul3; tet: tetA, tetB, tetC, tetM, tetO, tetQ, tetW, tetG; qnr: qnrA, qnrB, qnrD, qnrS) and class 1 integron gene (intI1); (3) explore potential linkage between antibiotics and corresponding ARGs under the water project. The results would facilitate better understanding of these emerging contaminants in engineered environment and provide information for antibiotic control in the future.
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2. Materials and methods 2.1. Sampling sites and sample collection Three Gorges Reservoir is located in the section of the upper reaches of the Yangtze River, within East longitudes 106°20′–110°30′ and Northern latitude 29°00′–31°50′. From upstream to downstream, thirteen sites were selected in the investigated area, numbered M1 to M13, respectively. One or two sampling points were set in each town as well as the feasibility of the actual operation. Sites M1 and M2 were situated near a waste water treatment plant (WWTP) receiving municipal sewage. Sites (M2, M8, M12) were close to the hospitals and several husbandry (M5, M7, M10) scattered in these areas, while site M3 was located in an industrial area of machinery manufacturing. Site M13 was situated in the lower reach next to the Three Gorges Dam. All samples were collected during the wet season from August 18 to 24, 2015. Detailed information is described in Fig. 1. Water samples (1 L) were collected from the top 50 cm of the water surface, by sailing a boat in the cross sectional midpoint of the river. Meanwhile, sediments (1 kg) were taken from the top 5 cm of the river bed using a core sampler. While the topsoil samples (1 kg) were collected near the river bank within 1 m by using the stainless steel sediment sampler. Three identical sample replicates of each location were collected and then homogeneously mixed on the spot. The distance was around 1 km between each repetition. All samples were stored in pre-cleaned amber glass bottle, immediately moved into a 4 °C ice box, and then transported to the laboratory for pretreatment within 24 h. 2.2. HPLC-MS/MS analysis of antibiotics Water samples (1 L) were filtered through 0.22 μm glass fiber filters (Waters, USA) using a vacuum filter to remove suspended solids, and the membranes were stored at −80 °C for DNA extraction. Soil and sediment samples were freezing-dried before grinded, and then added three extraction buffers (15 mL methanol, 10 mL acetic acid buffer, 5 mL 0.1 mol/L Na2EDTA) to 5 g samples in sequence. Solid phase extraction was performed according to the previous literature (Luo et al., 2010), using the Oasis hydrophilic-lipophilic-balanced (HLB) cartridges (500 mg, 6 mL, Waters, USA). Thirteen antibiotics selected for this study based on their application in medical and livestock industry in China, including four tetracyclines (TCs: oxytetracycline (OTC), chlortetracycline (CTC), tetracycline (TC), doxycycline (DXC)), five sulfonamides (SAs: sulfamethazine (STZ), sulfadiazine (SDZ), sulfameter (ST), sulfamethoxazole (SMZ), trimethoprim (TMP)), four quinolones (FQs: norfloxacin (NOR), ofloxacin (OFL), ciprofloxacin (CIP), enrofloxacin (ENR)), were detected by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS, Thermo Fisher Scientific, USA). For MS detection, the positive mode (ESI+) and multiple reaction monitoring (MRM) mode were performed. The MS desolvation temperature was 350 °C, cone gas flow was 50 L/h, and the desolvation gas flow was 500 L/h. The capillary voltage was 4.0 kV, extractor voltage was 2.0 V, and multiplier was 650.00 V. The detailed parameters of operational processes and methods were conducted according to the published literature (Luo et al., 2010). The limits of quantitation (LOQs) for antibiotics under consideration were presented in Table S1 (Supplementary material). The LOQs ranged from not detected to 7.32 ng/L in water, from 0.11 to 2.18 ng/g in soil, from 0.20 to 2.34 ng/g in sediment, respectively. 2.3. Analysis of antibiotic resistance genes 2.3.1. DNA extraction and PCR The filter membranes of water samples were cut into small pieces and then placed in centrifuge tubes. DNA was extracted using the E.Z.N.A.™ water DNA Kit (OMEGA, USA) as per manufacturer's protocol. For soil and sediments (dry weight 500 mg), DNA extraction was
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Fig. 1. Location of the sampling sites.
conducted with the FastDNA™ SPIN Kit for Soil (MP Biomedicals, USA) according to the instructions provided by the manufacturer. To minimize PCR inhibition, DNA purification was achieved with the Geneclean Spin Kit (QBiogene, Carlsbad, CA). The concentration and quality of the purified DNA were verified by agarose gel electrophoresis and spectrophotometry (NanoDrop 2000, Theromo Fisher Scientific, USA). Polymerase chain reaction (PCR) detection assays were used to screen the presence of fifteen kinds of antibiotic resistance genes (ARGs) in three matrix samples, including three sulfonamide resistance genes (sul1, sul2, sul3), eight tetracycline resistance genes (tetA, tetB, tetC, tetM, tetO, tetQ, tetW, tetG), four quinolone resistance genes (qnrA, qnrB, qnrD, qnrS) and class 1 integron gene (intI1). Sequence of all target ARG primers and PCR reaction conditions were listed in Table S2 (Supplementary material), according to the published literatures. All qualitative PCR assays were performed in a 25 μL total volume, and the PCR mixtures consisted of 12.5 μL 2 × Utaq PCR Master Mix, 1 μL of each primer, 9.5 μL ddH2O, and 1 μL template DNA. The amplification procedure was as following: initial denaturation at 94 °C for 3 min, followed by 44 cycles of 94 °C for 30 s, 30 s at annealing temperature (Table S1), and 72 °C for 30 s, with a final extension step of 5 min at 72 °C. The PCR amplified products were analyzed by electrophoresis on a 1.5% agarose gel in 0.5 × Tris-Borate-EDTA (TBE) buffer with negative controls (sterile pure water) and positive controls, which consisting of sequenced PCR amplicons obtained from these samples from the Three Gorges Reservoir and then cloned into Escherichia coli DH5α (using the pEASY-T1 Simple Cloning Kit, Transgene, China), to confirm the existence of ARGs. Plasmids carrying target genes were extracted for further quantitative analysis by Plasmid Mini Preparation Kit (ZOMINBIO, China) according to the manufacturer's protocol.
variations in diverse bacterial abundance and extraction efficiency, 16S rRNA gene was quantified as the housekeeping gene to normalize ARGs in all samples. Each qPCR reaction mixture contained 10 μL of SYBR Green real time PCR Master Mix, 1 μL of each primer, 7.4 μL of ddH2O, and 1 μL of template DNA. Amplification was conducted with ABI7500 (Applied Biosystems, USA) as follows: initial cycle of 3 min at 95 °C, followed by 44 cycles of 1 min at 95 °C, 20 s at different annealing temperatures presented in Table S1, and 30 s at 72 °C followed by a final melt curve stage with temperature ramping from 60 to 95 °C. Each reaction was run in triplicate with negative controls (sterile pure water) and positive controls (plasmids DNA). Calibration curves were generated as described by Pei et al. (2006), using 10-fold serial dilutions of the plasmids as standard. Correlation coefficients (R2 N 0.99) were available for calibration curves, and the copy number of corresponding genes was calculated according to the curves.
2.3.2. Real-Time Quantitative PCR (qPCR) Real-Time qPCR was performed using a SYBR Green approach to quantification analyze identified ARGs with 100% detection rates from water, soil and sediment samples. To avoid the error caused by potential
In this study, HPLC-MS/MS analysis revealed that ten antibiotics (OTC, TC, CTC, STZ, SDZ, ST, SMZ, TMP, ENR, OFL) were detected in all samples, while relatively lower detection frequencies were observed for NOR (46.15%, 38.46%) and DXC (38.46%, 38.46%) in soil and
2.4. Statistical analysis ANOVA was used to assess the homogeneity of variance with a significance level of 5% (p b 0.05). Pearson correlation analysis was performed to analyze the relationship between ARGs in various environmental components using SPSS 19.0. Redundancy analysis (RDA) and heat map of genes abundance was conducted with R Statistical software version 3.3.0 using R-Studio. Averages and standard deviations of all data were determined using Microsoft Excel 2010. 3. Results and discussion 3.1. Profiles of antibiotics
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Table 1 The concentrations of antibiotics in water, soil, and sediments. Compounds
OTC TC CTC DXC TCs SDZ TMP STZ ST SMZ SAs OFL ENR NOR FQs
Water (ng/L)
Soil (ng/g)
Sediment (ng/g)
Max
Min
Mean
Total
Freq (100%)
Max
Min
Mean
Total
Freq (100%)
Max
Min
Mean
Total
Freq (100%)
15.62 26.29 39.69 nd 80.5 359.16 209.69 68.41 24.90 173.46 536.86 105.23 27.36 nd 126.8
6.14 20.28 12.35 nd 38.8 27.68 12.32 11.42 8.27 3.51 74.2 10.81 9.04 nd 21.55
10.62 22.91 30.06 nd 63.6 95.39 119.07 30.12 14.37 78.55 337.5 31.12 19.32 nd 50.4
138.1 297.8 390.7 nd 826.6 1240.0 1547.9 391.6 186.8 1021.1 4387.37 404.6 251.2 nd 655.8
100 100 100 0
22.14 81.81 127.28 2.45 143.2 24.52 38.99 7.37 192.34 198.44 438.76 74.76 145.13 19.83 237.9
0.74 4.02 1.76 nd 6.5 0.55 0.18 2.02 1.77 3.45 13.1 2.02 1.66 nd 3.69
6.64 25.87 20.47 0.30 53.3 4.09 15.33 2.90 38.98 37.86 99.2 28.83 43.25 3.85 75.9
86.3 336.3 266.0 3.8 692.5 53.1 199.3 37.7 506.8 492.1 1289.1 374.8 562.2 50.0 987.1
100 100 100 30.77
10.76 118.20 32.34 6.04 143.7 41.54 20.92 4.72 121.31 57.03 213.84 72.09 86.76 16.68 154.3
1.63 8.83 3.60 nd 15.78 1.16 4.00 2.13 10.75 6.16 37.9 3.92 14.18 nd 18.1
4.16 31.76 14.68 0.80 51.4 7.02 11.71 3.01 35.93 24.91 82.6 22.46 42.59 3.09 68.1
54.1 412.9 190.9 10.4 668.3 91.2 152.3 39.1 467.1 323.8 1073.6 291.9 553.6 40.1 885.6
100 100 100 38.46
100 100 100 100 100 100 100 0
100 100 100 100 100 100 100 46.15
100 100 100 100 100 100 100 38.46
Max - Maximum; Min - Minimum; Freq - detection frequency; nd - not detected, including levels below limits of quantification (LOQ); OTC - oxytetracycline; TC - tetracycline; CTC - chlortetracycline; DXC - doxycycline; TCs - total amount of four tetracyclines; SDZ - sulfadiazine; TMP - trimethoprim; STZ - sulfamethazine; ST - sulfameter; SMZ - sulfamethoxazole; SAs - total amount of five sulfonamides; OFL - ofloxacin; ENR - enrofloxacin; NOR - norfloxacin; FQs - total amount of four quinolones.
sediment, respectively. CIP was not detected in this region. Details of detection frequency and concentration level were shown in Table 1. In surface water, the total concentrations of antibiotic ranged from 21.55 to 536.86 ng/L. SAs were the predominant antibiotic with the highest concentration, reaching 4387.37 ng/L, indicating extensive sulfonamides pollution in this region. Due to the properties of hydrophilic and degradation resistance, sulfonamides tend to transport across long distance in the aquatic environment. The average value of SAs was (67.50 ng/L) higher than TCs (21.20 ng/L) and FQs (25.22 ng/L) in water. The levels of SAs in this study were higher than the concentration detected in other 6 tributaries of the Yangtze River, in which the highest concentration was 38.1 ng/L, while TCs and FQs were not detected (Feng et al., 2017). Among sulfonamides group, concentrations of SDZ was the highest, ranging from 27.68 to 359.16 ng/L, which were higher than those reported in Haihe River (210–385 ng/L) (Luo et al., 2011) and Wangyang River (up to 91.8 ng/L) (Jiang et al., 2014). For soil and sediments, concentrations of the three groups of antibiotics ranged from 3.69 to 438.76 ng/g, 15.78 to 213.84 ng/g, respectively. The average concentration followed the order of SAs N FQs N TCs. The peak concentrations were found for SMZ (198.44 ng/g) in soil and ST (121.31 ng/g) in sediments. The levels were significantly higher than those in Yongjiang River (nd–0.2 ng/g) (Xue et al., 2013) and Huangpu River (nd–0.6 μg/kg) (Chen and Zhou, 2013), respectively. In terms of individual antibiotic, ENR had the highest mean value reaching 43.25 ng/g in soil and 42.59 ng/g in sediment than other compounds, likely due to their strong adsorption of sediments and particles. Compared to previous studies (Liang et al., 2013; Shi et al., 2014), the high pollution level of ENR suggested serious usage and emission status in this region. The distribution characteristic was shown in Fig. 2. In surface water, sites that located nearby the hospitals (M2, M8, M12) and livestock farms (M5, M7, M10) exhibited higher levels of antibiotics than the others. The total concentration peaked at site M12 (623.42 ng/L), and SAs accounted for 86% of all detected antibiotics. The total environmental emission of antibiotics for the Yangtze River upstream and downstream was estimated N5000 tons per year, and SAs were one of the major categories (Zhang et al., 2015). For soil, antibiotics showed the highest concentration at sites M1 (438.76 ng/g), followed by M7 (137.43 ng/g) and M12 (130.76 ng/g). This suggests that the effluent from WWTP could be an important source of antibiotics, as well as the discharge from husbandry and hospitals. In 2014, the sewage from industry, municipal and agriculture was more than 1 billion tons (http:// www.zhb.gov.cn/hjzl/shj/sxgb/). However, among 107 sewage treatment plants, only 60.7% reached the standard of effluent quality in
2013 (Zou et al., 2014). Thus, this may be the important sources of antibiotic into environment. In sediments, site M13 was located in downstream beside the Three Gorges Dam and detected with the highest concentration (total 497.01 ng/g). This might due to that dam could intercept the sediment and some antibiotics would adsorb clay particles. To control the antibiotic contamination, strict regulation for pharmaceutical usage is an essential measure. Besides, exploring the novel process of wastewater treatment and alternative medicine is feasible reference. 3.2. Abundance of ARGs Among the fourteen ARGs, two sulfonamide resistance genes (sul1, sul2), five tetracycline resistance genes (tetA, tetB, tetM, tetQ, tetG) and class 1 integron gene (intI1) were detected in all samples, while tetC and one quinolone resistance gene qnrD simply occurred in surface water based on the conventional PCR analysis. The relative abundance (gene copy numbers of ARGs normalized to the gene copy numbers of 16S rRNA) of detected ARGs was shown in Fig. 3 and Table S3 (Supplementary material). The color of the spot in heatmap corresponds to the log-transformed abundance of the ARGs. The heatmap clearly illustrates that sul1 and sul2 genes were the most abundant and frequent genes detected in all samples, reflecting their widely distribution in the Three Gorges Reservoir. Sul1 and sul2 genes were plasmid-borne that encoded alternative drug-resistant variants of the dihydropteroate synthase (DHPS) enzymes in gram-negative bacteria (Sköld, 2000). It was observed that sul1 and sul2 were predominant in the Three Gorges Reservoir compared with tet genes (ANOVA, p b 0.05), with the highest relative abundance of 8.56 × 10−4 copies/16S rRNA to 7.04 × 10−1 copies/16S rRNA, 8.04 × 10−4copies/16S rRNA to 5.19 × 10−1 copies/ 16S rRNA in water and solid (soil and sediment) samples, respectively. The levels of sul were higher than those in Haihe River (Luo et al., 2010) and Poudre River (Pruden et al., 2006). For tetracycline resistance genes, the total relative abundances ranged from 3.93 × 10−5 to 4.64 × 10−2 copies/16S rRNA, 4.23 × 10−5 to 5.53 × 10−2 copies/16S rRNA, 4.49 × 10−5 to 2.31 × 10−2 copies/16S rRNA in water, soil and sediment samples, respectively. Of all tetracycline resistance genes, tetA was the most abundant in water, with the mean concentration of 1.04 × 10−2 copies/16S rRNA, while in soils and sediments, tetB had the highest mean concentrations up to 1.05 × 10−2 copies/16S rRNA and 3.30 × 10−3 copies/16S rRNA, respectively. Gene tetA and tetB could express efflux pump proteins on plasmids. They frequently appeared in various environmental compartments (Piotrowska and Popowska, 2014; Zhang et al., 2009). Gene tetB was the most commonly
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Fig. 2. The concentrations of oxytetracycline (OTC), chlortetracycline (CTC), tetracycline (TC), doxycycline (DXC), sulfamethazine (STZ), sulfadiazine (SDZ), sulfameter (ST), sulfamethoxazole (SMZ), trimethoprim (TMP), norfloxacin (NOR), ofloxacin (OFL) and enrofloxacin (ENR) in the Three Gorges Reservoir. Colors reflected the three different classes of antibiotics: tetracyclines (blue), sulfonamides (black) and quinolones (red). M1 to M13 indicate the thirteen sampling sites. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
carried Gram-negative efflux gene identified in Gram-negative bacteria, which were the dominant bacterial populations in aquatic environment (Jiang et al., 2013; Roberts, 2005). Furthermore, the overall trend of tetracycline resistance genes concentrations in soil and sediment samples was tetB N tetG N tetA N tetQ N tetM, with the lowest abundance for tetM in surface water as well. The positive correlation (Table 2) was observed in this study between efflux pump genes and ribosomal protection tetracycline genes (tetQ and tetM), which were generally associated with conjugative chromosomal elements that could transfer mobilizable plasmids to other species (Chopra and Roberts, 2001). Furthermore, class 1 integron gene (intI1) was found with a detection frequency of 100% in all samples, and the concentration ranged from 1.47 × 10−4copies/16S rRNA to 3.19 × 10−1 copies/16S rRNA, followed by sul1. As genetic platform for ARGs capture, intI1 showed positive correlation with seven ARGs (Table 2). In particular, genes sul1, sul2 exhibited the significant correlation (p b 0.01) with intI1, indicating that it could facilitate the propagation of sul in the Three Gorges
Reservoir. Another cause of the strong correlation between intI1 and sul1 may be that sul1 probably serve as partial structure in the 3′-conserved segment (CS) region of the class 1 integron (Su et al., 2012). The findings were consistent with previous studies found in Haihe River (Luo et al., 2010) and Wenyu River (Xu et al., 2016). Pearson correlation coefficient also revealed substantial connection among tet and intI1 (p b 0.05), suggesting that class 1 integron played a major role in the widespread of antibiotic resistance among microbial communities. Consist with the previous results, intI1 obviously exhibited a similar spatial pattern with sul and tet in sediments within the Yangtze Estuary and nearby coastal area (Lin et al., 2015). Class 1 integron could capture and express gene cassettes from a vast pool of diverse cassettes, thus ARGs could develop multiple drug resistances among microorganisms though horizontal gene transfer (Gillings et al., 2008). In addition, mobilization of the integron might facilitate their later transfer into a wide range of pathogens, posing a potential health hazard (Johnson et al., 2016; Wright et al., 2008).
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Fig. 3. Heat map of antibiotic resistance genes (ARGs) in all sampling sites. Each grid represented the relative abundance of the target ARGs for all samples normalized in a log 10 scale. Vertical axis represented the sequence of sampling sites, while the horizontal axis represented the target ARGs. The legend showed corresponding fold change values. (a) Water samples; (b) soil samples; (c) sediment samples.
3.3. Spatial distribution of ARGs Redundancy analysis (RDA) showed the spatial distribution characteristic based on the concentration of ARGs under the influence of antibiotics in three matrices (Fig. 4). The numbers in brackets described the percentage of variance explained by the first two axes. In the diagram (Fig. 4A) for water, sites M12, M9 and M8 were displayed to be a cluster that express a relatively high level of ARGs, where were found positive relations between SDZ, TC and corresponding resistance genes, indicating the emissions of pharmaceuticals from nearby hospitals might exert a selective pressure for environmental microorganism. Sites (M1–M6) from upstream lay close together, suggesting these locations had similar influences on antibiotics. Typically, site M3 was frequently impacted by industrial activities, resulting in heavy metals enrichment. Previous studies demonstrated that heavy metals contamination was correlated
to concentration of ARGs in general, because they could select indirectly for antibiotic resistance by co-selection with antibiotic (Mao et al., 2015; Seiler and Berendonk, 2007). Conversely, sites (M7, M10, M11, M13) from downstream were displayed to be a cluster, showing that the resembling contamination profiles of related genes. Anthropogenic sources in the watershed could alter the distribution and magnitudes of ARGs in different matrices (Pruden et al., 2012). Redundancy analysis of soils (Fig. 4B) showed distinct groups of the thirteen sampling sites with the first two axes explanation of 52.31% and 21.98%, respectively. It was observed that sites located or near hospitals gathered together with the relatively higher level of ARGs, which was significantly correlated with SMZ, DXC and CTC. Specifically, site M11 had the high relative abundance of ARGs compared with other regions in solid samples (ANOVA, p b 0.05). Different from water and soil, the distribution of ARGs in sediment exhibited a contamination trend in
Table 2 Correlation between class 1 integron gene (intI1), tetracycline resistance genes (tetA, tetB, tetC, tetM, tetQ, tetG) and sulfonamide resistance genes (sul1, sul2) in water, soil, and sediments. Water
sul1 sul2 tetA tetB tetG tetM tetQ tetC a b
intI1
sul1
0.946b 0.889b
0.922b
0.743b 0.694b 0.584a 0.796b
0.868b 0.825b 0.594a 0.676a 0.887b
sul2
0.887b 0.739b
0.741b
tetB
0.940b 0.666a 0.560a 0.814b
Correlation is significant at the 0.05 level. Correlation is significant at the 0.01 level.
tetG
0.818b 0.682a 0.823b
Soil tetM
tetQ
intI1
sul1
0.641a
0.792b
0.579a 0.871b 0.793b
0.909b
tetA
0.722b 0.788b 0.698b 0.966b
Sediment tetB
0.660a 0.852b
tetG
0.683a
tetM
0.709b
intI1
sul2
0.902b 0.626a 0.922b
0.642a 0.898b
0.912b 0.656a
0.914b 0.639a
tetA
tetB
tetG
tetM
0.790b 0.884b 0.770b 0.953b
0.994b 0.857b
0.888b
0.835b
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Fig. 4. Redundancy analysis (RDA) of the quantitative correlation between antibiotic resistance genes and relevant antibiotic in water (a), soil (b), sediment (c) samples. The percentage of the total variance explained by each axis was shown.
a number of locations, indicating these points might have the analogical environmental behaviors. The individual sites were separated from the cluster, such as M1, which was adjacent to the WWTP with maximum abundances, showing a positive correlation with CTC and TC. Additionally, biological treatment process in WWTP might facilitate the proliferation of antibiotic resistant bacteria. Several studies demonstrated WWTP was a potential reservoir for mobile antibiotic resistance elements to foster horizontal gene transfer and developed multi-drug resistance in bacteria (Schlüter et al., 2007). Spatially, no significant relationship was found in each identical category of ARGs among three matrices except gene tetM, which has the widest host range of many tetracycline resistance genes as a result of the connection with conjugative and broad-host-range transposons (Gao et al., 2012a). It could be inferred that the dissemination of ARGs in this area might be aggravated by antibiotics related to human activities. Although plentiful studies have been conducted concerning the relationships between ARGs and antibiotics, the conclusion remained inconsistent. For example, Gao et al. (2012b) demonstrated a correlation between certain sul and sulfonamides in WWTP effluent, while no significance was found between tet with concentration of tetracyclines. Generally, the relative
abundance of ARGs not only concerned with antibiotics, but also was related to the species of genes and other environmental factors, such as temperature and heavy metal concentrations (Mao et al., 2015; Pei et al., 2007). Given the complexity of aquatic environment, it was difficult to determine which impact factors accounted for the distribution characteristics of ARGs in natural condition due to limited information. In addition, antibiotic resistance was a natural phenomenon in microbial pangenome (Allen et al., 2009; D'Costa et al., 2011). As selective forces for bacterial evolution, antimicrobial together with multiple factors might induce an integrated effect on the emergence and persistence of multidrug-resistant microorganism and subsequent co-selection processes (Cantón and RuizGarbajosa, 2011). Hence, further studies are needed to establish a synthetic assessment model in the future for a better understanding of the behavior of antibiotics resistance genes in different medium, as well as the risks associated with the corresponding antibiotics. 4. Conclusions This study demonstrated that the Three Gorges Reservoir has been an important reservoir of three groups of antibiotics and related
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resistance genes. SAs were had higher total concentrations than TCs and FQs in water, soil and sediment. Gene sul was dominating of quantified ARGs. The co-enrichment of ARGs and intI1 would probably exacerbate the risks of transfer of ARGs from environmental microorganism to human-associated bacteria, and thus pose potential hazards to human health. Significant relationships were found between the relative abundance of ARGs and the corresponding residual antibiotic concentrations near hospitals and livestock, indicating human activities might play an important role in exacerbating the transmission of resistance determinants. Considering seasonal fluctuations in ARGs, the above results could only provide basic data of those emerging contaminants in investigated region in summer, not all through the year. Nonetheless, the heavy pollution level of antibiotics should be paid attention and appropriate models would be established to assess the risk. Strict regulations and exploring new treatment technology will contribute to contamination prevention and control. Acknowledgments This project was supported by High Level Talents Program of South China Agricultural University (0A293-8000/217047 and K17021), National Natural Science Foundation of China (41706186); Cooperative Project of Joint Laboratory of Guangdong Province and Hong Kong Region on Marine Bioresource Conservation and Exploitation (MBCE201702). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2018.03.085. References Allen, H.K., Moe, L.A., Rodbumrer, J., Gaarder, A., Handelsman, J., 2009. Functional metagenomics reveals diverse β-lactamases in a remote Alaskan soil. ISME J. 3, 243–251. Alonso, A., Sanchez, P., Martinez, J.L., 2001. Environmental selection of antibiotic resistance genes. Environ. Microbiol. 3, 1–9. Cantón, R., Ruiz-Garbajosa, P., 2011. Co-resistance: an opportunity for the bacteria and resistance genes. Curr. Opin. Pharmacol. 11, 477–485. Cheesanford, J.C., Aminov, R.I., Krapac, I.J., Garriguesjeanjean, N., Mackie, R.I., 2001. Occurrence and diversity of tetracycline resistance genes in lagoons and groundwater underlying two swine production facilities. Appl. Environ. Microbiol. 67, 1494–1502. Chen, K., Zhou, J.L., 2013. Occurrence and behavior of antibiotics in water and sediments from the Huangpu River, Shanghai, China. Chemosphere 95, 604–612. Chopra, I., Roberts, M., 2001. Tetracycline antibiotics: mode of action, applications, molecular biology, and epidemiology of bacterial resistance. Microbiol. Mol. Biol. Rev. 65, 232–260. Davison, J., 1999. Genetic exchange between bacteria in the environment. Plasmid 42, 73–91. D'Costa, V.M., King, C.E., Kalan, L., Morar, M., Sung, W.W., Schwarz, C., Froese, D., Zazula, G., Calmels, F., Debruyne, R., 2011. Antibiotic resistance is ancient. Nature 477, 457–461. Feng, L., Cheng, Y., Feng, L., Zhang, S., Liu, Y., 2017. Distribution of typical antibiotics and ecological risk assessment in main waters of Three Gorges Reservoir area. Res. Environ. Sci. 30, 1031–1040. Gao, P., Mao, D., Luo, Y., Wang, L., Xu, B., Xu, L., 2012a. Occurrence of sulfonamide and tetracycline-resistant bacteria and resistance genes in aquaculture environment. Water Res. 46, 2355–2364. Gao, P., Munir, M., Xagoraraki, I., 2012b. Correlation of tetracycline and sulfonamide antibiotics with corresponding resistance genes and resistant bacteria in a conventional municipal wastewater treatment plant. Sci. Total Environ. 421, 173–183. Gillings, M., Boucher, Y., Labbate, M., Holmes, A., Krishnan, S., Holley, M., Stokes, H., 2008. The evolution of class 1 integrons and the rise of antibiotic resistance. J. Bacteriol. 190, 5095–5100. Guo, X., Li, J., Yang, F., Yang, J., Yin, D., 2014. Prevalence of sulfonamide and tetracycline resistance genes in drinking water treatment plants in the Yangtze River Delta, China. Sci. Total Environ. 493, 626–631. Jiang, L., Hu, X., Xu, T., Zhang, H., Sheng, D., Yin, D., 2013. Prevalence of antibiotic resistance genes and their relationship with antibiotics in the Huangpu River and the drinking water sources, Shanghai, China. Sci. Total Environ. 458, 267–272. Jiang, Y., Li, M., Guo, C., An, D., Xu, J., Zhang, Y., Xi, B., 2014. Distribution and ecological risk of antibiotics in a typical effluent-receiving river (Wangyang River) in North China. Chemosphere 112, 267–274.
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