Contamination profile of antibiotic resistance genes in ground water in comparison with surface water

Science of the Total Environment 715 (2020) 136975

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Contamination profile of antibiotic resistance genes in ground water in comparison with surface water Dai-Ling Wu a,b, Min Zhang a,b, Lu-Xi He a,b, Hai-Yan Zou a,b, You-Sheng Liu a,b, Bei-Bei Li a,b, Yuan-Yuan Yang a,b, Chongxuan Liu c, Liang-Ying He a,b, Guang-Guo Ying a,b,⁎ a SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China b School of Environment, South China Normal University, University Town, Guangzhou 510006, China c School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, 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

• High-throughput qPCR was applied to screen ARGs and MGEs in groundwater. • Total Coliform and wastewater indicators were found in the groundwater. • The groundwater was an important reservoir of ARGs. • A large numbers of ARGs were shared among groundwater, river water and sediment. • Anthropogenic activities had a significant impact on groundwater.

a r t i c l e

i n f o

Article history: Received 4 November 2019 Received in revised form 26 January 2020 Accepted 26 January 2020 Available online 28 January 2020 Editor: Fang Wang Keywords: Groundwater Antibiotic resistance ARGs MGEs Surface water

a b s t r a c t Dissemination of antibiotic resistance genes (ARGs) in the water environment has become an increasing concern. There have been many reports on ARGs in surface water, but little is known about ARGs in groundwater. In this study, we investigated the profiles and abundance of ARGs in groundwater in comparison with those in surface water of Maozhou River using high-throughput quantitative PCR (HT-qPCR). Totally 127 ARGs and 10 MGEs were detected by HT-qPCR, and among them the sulfonamides, multidrug and aminoglycosides resistance genes were the dominant ARG types. According to the results of HT-qPCR, 18 frequently detected ARGs conferring resistance to 6 classes of antibiotics and 3 MGEs were further quantified by qPCR in the wet season and dry season. The absolute abundance ranged from 1.23 × 105 to 8.89 × 106 copies/mL in wet season and from 8.50 × 102 to 2.65 × 106 copies/mL in the dry season, with sul1 and sul2 being the most abundant ARGs. The absolute abundance of ARGs and MGEs has no significant difference between the wet season and dry season while the diversity of ARGs in the dry season was higher than that in the wet season (p b 0.05). Totally 141 and 150 ARGs were detected in the water and sediments of Maozhou River, respectively. A total of 116 ARGs were shared among the groundwater, river water, and sediment, which accounted for 67.1% of all detected genes. Redundancy analysis further demonstrated that the environmental factors contributed 70.7% of the total ARG variations. The findings of large shared ARGs, abundant Total Coliforms and large wastewater burden in the groundwater provide a clear evidence that anthropogenic activities had a significant impact on groundwater. © 2020 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China. E-mail address: [email protected] (G.-G. Ying).

https://doi.org/10.1016/j.scitotenv.2020.136975 0048-9697/© 2020 Elsevier B.V. All rights reserved.

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1. Introduction Antibiotic resistance has been recognized as a global health issue (Allen et al., 2010; WHO, 2014; Sui et al., 2015; Wang et al., 2017; Ying et al., 2017). Intensive use of antibiotics in humans and animals has accelerated dissemination of antibiotic resistance determinants in the environment (Pruden et al., 2006; Zaffiri et al., 2012; Zhang et al., 2015). Antibiotic resistance genes (ARGs) or antibiotic resistance bacteria (ARB) can be transported and distributed from domestic wastewater, medical wastewater, livestock, and agriculture effluents to air, surface water, soil, sediment and even groundwater (Chang et al., 2008; Hou et al., 2015; Sui et al., 2015; Balzer et al., 2016; Chen et al., 2017a, 2017b). ARGs can pose risks to human and animal health due to their horizontal gene transfer (CheeSanford et al., 2009; Gillings, 2017; Qiao et al., 2018; Szekeres et al., 2018). Therefore, it is important to assess contamination of ARGs in various aquatic environments. Previous studies have mainly focused on the occurrence of antibiotic resistance genes in rivers such as Minjiang River (Chen et al., 2011), Dongjiang River (Su et al., 2012; Su et al., 2014), and Jiulongjiang River (Ou et al., 2015). Isolates resistant to tetracycline, sulfonamide, fluoroquinolone, ampicillin, extended-spectrum beta-lactamase, and chloramphenicol were frequently detected in surface water (Dang et al., 2007; Li et al., 2010; Tao et al., 2010; Sun et al., 2012; Zou et al., 2012). ARGs conferring resistance to sulfonamides and tetracyclines were detected and quantified in water samples from the Beijiang River of Guangdong (Ling et al., 2013), Huangpu River in Shanghai (Jiang et al., 2013), the urban rivers in Beijing (Xu et al., 2016), and Haihe River in Tianjin in China (Luo et al., 2010). ARGs in lake water samples from the northern part of Taihu Lake (Zhang et al., 2009a, 2009b) and from the Northern Yellow Sea were also reported (Na et al., 2014). The frequently detected ARGs and their abundance in different environmental compartments showed that antibiotic resistance has been aggravated by intensive human activities, such as aquaculture, livestock animal farming and municipal sewage discharge. The ARG burden in the environment has serious implications for human health owing to the potential dissemination and transfer of ARGs from environmental bacteria to human pathogens, thereby impairing the efficacy of antibiotic treatment and compromising public health (Qiao et al., 2018). In many countries, groundwater is one of the most important public water resources and usually serves as drinking water. However, due to the complexity of groundwater, ARGs in groundwater are rarely studied and their abundance and profile are largely unknown (Sui et al., 2015; Szekeres et al., 2018). In recent years, the emergence of ARGs in groundwater has become a concern (WHO, 2014; Sui et al., 2015). The presence of ARGs in groundwater from some contaminated locations have been reported, such as the groundwater near the municipal solid waste landfill (Chen et al., 2017a, 2017b), the groundwater below leaking sewers (Gallert et al., 2005), and groundwater underlying livestock facilities (Chee-Sanford et al., 2001; Koike et al., 2007). All of these studies mainly focused on ARGs types in groundwater below or near the specific pollution sources; however, a relatively regional characterization of resistome in groundwater under highly urbanized area is still unknown. Furthermore, the changes in the abundance and diversity of ARGs and MGEs in different depths of aquifers in groundwater and the seasonal changes are also unknown. Maozhou River basin is located in Shenzhen, a megacity in southern China, with a population of around 11,908,400. The river Maozhou has been seriously polluted with urban wastewater containing antibiotics (Qiu et al., 2019). The groundwater there is heavily exploited and consumed for various purposes. Along the river, there are two groundwater monitoring sites with wells of different depths. The objectives of this study were: (1) to provide a relatively integral characterization of resistome in groundwater of Maozhou River Basin in different seasons; (2) to determine the prevalence and diversity of ARGs and mobile

genetic elements (MGEs) in different depths of groundwater; and (3) to compare the ARGs and MGEs in groundwater to those in the river. 2. Materials and methods 2.1. Study area Maozhou River is located in the northwest of Shenzhen, and it originates from Yangtai Mountain Forest Park and belongs to the Pearl River Estuary water system. Two groundwater monitoring sites were set up along the Maozhou River (named as site 1 and site 2) (Fig. 1). Five different pumping wells with different depths were installed at each site. The depths of wells G1 to G5 at site 1 ranged from 6.3 m to 21 m, while those for wells G6 to G10 at site 2, which is near the Lingdingyang estuary, were from 7.0 m to 21 m. Spring water (YTM) in Yangtai Mountain was collected as the reference site, as we presumed it is the pristine groundwater. Considering the shallow groundwater table and permeable aquifer in the Pearl River Delta region, the ARGs in Maozhou River have the potential to spread into groundwater. To understand the possible source of ARGs in groundwater, river water samples (R1–R6) and corresponding 6 sediment samples (S1–S6) were collected from the upper reach to lower reach of the Maozhou River (Fig. 1). Notably, R4, which is under Yangchong Bridge, and R5, which is located near the Gonghe Village, are two long-term monitoring sites of Maozhaou River by the Environmental Protection Bureau of Guangdong province. R2 is under Loucun Bridge, which also has been a concern in the past years. R6 is situated in the vicinity of Lingding Sea estuary. The locations of river and sediment sampling sites are shown in the sampling map (Fig. 1). In addition, the water sample from Shiyan Reservoir (SYR) was collected as control of river water. The sediment samples in Shiyan Reservoir and Yangtai Mountain (S-SYR and S-YTM) were also collected as control sediments. 2.2. Sample collection All samples were collected in June 2018 and December 2018, which represented the wet season and dry season in Southern China. Groundwater samples were collected from the monitoring wells with clean and sterilized opaque polyethylene bottles after pumping water 10 min when the temperature, pH, DO, and conductivity were stable. River water samples were collected 0.5 m below the water surface. Sediment samples were collected from the top 5 cm layer of the riverbed. Three replicate samples were collected for each type of samples. All samples were transported ice-cooled and then stored in a cold room at 4 °C before being processed within 24 h. Water quality parameters included temperature, dissolved oxygen (DO), pH, EC, COD and TOC are provided in the Supporting Information (Tables S1 and S2). 2.3. Total DNA extraction Water samples were filtered through 0.22-μm-pore-size mixed cellulose ester membrane filters. Three filters were obtained for each sample. Sediment samples were weighed after freeze-dried from each sampling site. Every individual sample was subjected to DNA extraction using the PowerSoil DNA Isolation Kit (QIAGEN, USA), according to manufacturer's instruction. The quantity and quality of the extract DNA were assessed through spectrophotometer analysis (NanoDrop ND-1000, Thermo Scientific, Waltham, MA) and 1.0% agar gel electrophoresis. DNA was stored at −80 °C until use. 2.4. High-throughput quantitative PCR To indicate the resistance profiles of ARGs, high-throughput quantitative PCR (HT-qPCR) was used to detect and screen the occurrence of ARGs conferring resistance to all major classes of antibiotics. Prepared

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Fig. 1. Map showing sampling sites of monitoring wells and Maozhou River. Gx (x = 1–10) represents groundwater; Rx (1–6) represents river water, with Sx (1–6) indicating sediment; SYR and YTM represent Shiyan Reservoir and Yangtai Mountain (used as reference sites).

DNA samples from the wet season were sent to Microanaly Genetech Co. Limited (Anhui, China). HT-qPCR was performed by Wafergen SmartChip Real-time PCR system (Wafergen, Fremont, CA). A total 248 primer sets were used: 237 ARGs conferring resistance to all nine major classes of antibiotics (aminoglycosides, beta-lactams, chloramphenicols, MLSB, multidrug, sulfonamides, tetracyclines, vancomycin, and others); 8 transposases; the class 1 integron-integrase gene (intl1); the clinical class 1 integron-integrase gene (cintl1); and the 16S rRAN gene. Primers used to amplify each gene are listed in Table S3 (Zhu et al., 2013). DNA template was diluted to 10 ng/μL before being loaded onto the SmartChip Multisample Nanodispenser (MSND) using a 248 (assays) × 19 (samples) array. 100 nL PCR mixtures of each well consisted of 2 ng/μL DNA template, 1 × LightCycler 480 SYBR Green I Master Mix (Roche Applied Sciences, Indianapolis, IN), 1 ng/μL bovine serum albumin and 500 nM each primer. Amplification for each primer set was conducted by performing an initial denaturation at 95 °C for 10 min, followed by 40 cycles of denaturation at 95 °C for 30 s and annealing at 60 °C for 30 s, with an additional non-template control in each plate. A melting curve analysis was finally autogenerated by SmartChip qPCR software (V 2.7.0.1) (Zhu et al., 2013). The wells with multiple melting peaks and/or amplification efficiencies beyond the range (1.8–2.2) were defined as unqualified. A threshold cycle (CT) of 31 was used as the detection limit, and only ARGs with amplification in more than two replicates were regarded as positive ones. Gene copy number was calculated according to the equation described in previous studies (Zhu et al., 2013; Chen et al., 2016). 2.5. Quantitative PCR To verify seasonal variation of ARGs in groundwater, qPCR by standard curves was used to quantify specific ARGs in both wet season and dry season. ARGs conferring resistance to six antibiotic classes

were chosen according to HT-qPCR, including two sulfonamide resistance genes (sul genes: sul1 and sul2), six tetracycline resistance genes (tet genes: tetC, tetG, tetH, tetO, tetW, and tetB/P), four chloramphenicol resistance genes (cml genes: cmlA, floR, fexA, and cfr), two fluoroquinolone resistance genes (qnrD and qnrS), one aminoglycoside resistance gene (aadA1), and two erythromycin resistance genes conferring resistances to macrolide–lincosamides–streptogramin B (MLSB genes: ermA and ereA). The class 1 and class 2 integron genes (int1 and int2) and Tn 916/1545 were also quantified as indicators of potential horizontal gene transfer for multiple ARGs. The 16S ribosomal RNA gene was quantified as a measure of total bacterial load. The uidA gene was used as a proxy to estimate the concentration of E. coli, which was used as an indicator of fecal pollution. The specific primers, annealing temperatures and expected amplicon sizes for all gene targets are listed in Table S4. Positive controls and negative controls (Milli-Q water) were included in every run. In qPCR, each gene was conducted 40 cycles to improve the chances of product formation from the low initial concentrations. The reaction solution for qPCR was 20 μL, which contained: 10 μL of 2 × SYBR® Premix Ex Taq™ (Tli RNaseH Plus), 0.08 μL of each 0.05 mM primer, 0.04 μL of 50 × ROX reference dye, 2 μL of template DNA (DNA b 80 ng), and 7.8 μL of distilled water (DNase I treated) (Su et al., 2018). The qPCR assays were conducted on QuantStudio™ 7 Flex System (ABI, USA). The temperature program for the quantification of ARGs was as follows: initial denaturing for 1 min at 95 °C, followed by 40 cycles of 15 s at 95 °C, annealing temperatures of each gene (Table S4) for 30 s, 72 °C for 30 s, and a final step for a melting curve (Su et al., 2018). The standard curve of each gene was generated by 10-fold dilution of plasmids carrying the target gene, ranging from 101 copies to 107 copies, with three replicates. A threshold cycle (CT) of 31 was used as the detection limit, and only ARGs with amplification in more than two replicates and without multi-peak were regarded as positive ones (Su et al., 2018). The copy number of each ARG and 16S rRNA gene was calculated from the

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corresponding standard curve using the CT value of each gene in the qPCR runs. The square of the related coefficient (r2) of the standard curve ranged from 0.99 to 0.998, and the amplification efficiency ranged from 95% to 110%. 2.6. Abundance of total bacteria and Total Coliforms To indicate the total bacteria and the Total Coliforms in all samples, bacterial counting was conducted by plate count. For total bacterial counting, serial 10-fold dilutions of water samples were prepared in physiological saline; 1 mL aliquots were added into 20 mL LB Agar. The plates were incubated aerobically at 30 °C for 14–16 h and then counted. For Coliforms counting, serial 10-fold dilutions of water samples were prepared in physiological saline; 0.1 mL aliquots were inoculated onto MacConkey Agar. The pink and red bacterial colonies incubated after 14–16 h at 37 °C were counted. 2.7. Wastewater burden estimation and basic water quality parameters The artificial sweeteners (ASs) can be used as wastewater indicators, through which the wastewater burden (WB) can be evaluated (Yang et al., 2018). The WB in surface water or groundwater was calculated using the following formula (Yang et al., 2018): WB = Cci/Ce × 100%, where Cci is the concentration of a conservative indicator in surface water or groundwater at each sampling site; Ce is the average concentration of a conservative indicator in the effluent of nine WWTPs in the region. In this study, sucralose was used as the conservative indicator in surface water and groundwater and the average concentration of sucralose in the effluent of WWTPs was 3740 ng/L (L. Yang et al., 2017; Y.Y. Yang et al., 2017). Sucralose was extracted and analyzed according to our previous study (L. Yang et al., 2017; Y.Y. Yang et al., 2017). Chemical oxygen demand (COD), ammonia nitrogen (NH4-N), total nitrogen (TN) and total phosphorus (TP) were determined in the laboratory according to the standard methods (Clesceri et al., 1998). The basic quality parameters of surface water and groundwater in the wet season and dry season are listed in Tables S1 and S2. 2.8. Data analysis Averages and standard deviations were calculated using Excel 2010 (Microsoft, USA). Statistical analysis (Hotelling's T2 and Pearson correlation analysis) were performed by SPSS 24.0 software (IBM, USA). All statistical tests were considered significant at P-value b 0.05. Heatmap was performed in R 3.3.3 (R, A language and environment for statistical computing; Vienna, Austria; The R Foundation for Statistical Computing. ISBN: 3-900051-07-0. Retrieved from http://www.R-project.org/) with package “pheatmap”. Shannon diversity index was calculated by MATLAB R2014 (Mathworks, USA), then plotted by Origin 2018. Venn plot was drawn in website of http://bioinfogp.cnb.csic.es/tools/venny/ index.html. Redundancy analysis (RDA) and variation partitioning analysis (VPA) was performed by Canoco version 5.0 software (Chen et al., 2016). 3. Results 3.1. General properties of groundwater and river water The results of physical and chemical parameters of surface water and groundwater from wet season and dry season are presented in Tables S1 and S2. According to the Quality Standard for Ground Water (GB/T 14848-2017), the groundwater with high values of COD and NH4-N, and high concentrations of total bacteria could be classified into IV or V category (Tables S1, S2 and S5), indicating serious pollution. The results of the abundance of total bacteria and Total Coliforms are compiled in Table S5. The Total Coliform counts were conducted in

sampling water in the dry season while the concentrations of total bacteria in surface water and groundwater were cultivated in two seasons. What is worth noticing is that the Total Coliforms in some wells reached up to 4.53 × 104 CFU/mL. The E. coli gene uidA was not detected in groundwater in the two seasons, probably due to the limitation of the detection of qPCR. The abundance of uidA in surface water in two seasons reached a maximum of 1.43 × 103 copies/mL. Considering that the Shiyan Reservoir (SYR) is located in the upper reach of Maozhou River, the samples collected there were initially assumed to be pristine, but the present analysis showed that we underestimated the local pollution. Additionally, the concentrations of sucralose and the corresponding wastewater burden in each site are shown in Table S6. The WBs in groundwater ranged from 2.5% to 33.0% in the wet season while it ranged from 0.0% to 29.1% in the dry season. No WB was detected from groundwater monitoring site 2 in the dry season. In river, the WB ranged from 23.6% to 40.8% in the wet season and ranged from 7.8% to 45.1% in the dry season, with the highest value in R1 (45.1%). The WBs from the controls samples (SYR and YTM) in both seasons were 5.7% below.

3.2. Occurrence of ARGs in groundwater Resistance profiles of ARGs in groundwater were revealed by HTqPCR. The results showed that a total of 127 ARGs and 10 MGEs (8 transposase genes, and 2 integron-integrase genes) were detected in the groundwater from the 10 monitoring wells. The abundance of each detected gene with its corresponding sampling site is shown in Heatmap (Fig. 2A). The total numbers of ARGs detected in each site ranged from 69 to 90, with an average of 79. These ARGs confer resistance to nine classes of antibiotics, including aminoglycosides, betalactams, chloramphenicols, MLSB, multidrug, sulfonamides, tetracyclines, vancomycin, and others. The abundance of these ARGs in each groundwater sampling site varied as shown in Fig. 3. In the five groundwater wells from monitoring site 1 (G1 to G5), the main ARG types were sulfonamides, multidrug and aminoglycosides. Multidrug and sulfonamides were the main ARG types in the other five wells from monitoring site 2 (G6 to G10). The main MGEs in groundwater were cintl-1 and tnpA-04, which presented different proportions in individual wells. In the control sample (YTM), the main ARG type was multidrug and the main MGE was cintl-1. According to the results of HT-qPCR, 18 frequently detected ARGs representing 6 resistance types and 3 MGEs (int1, int2 and Tn916) were selected for further investigation. Quantitative PCR (qPCR) was used to quantify the abundance of ARGs and MGEs in groundwater from both wet and dry seasons (Fig. S1). The total absolute abundance of these ARGs in wet season ranged from 1.23 × 105 to 8.89 × 106 copies/mL while it ranged from 8.50 × 102 to 2.65 × 106 copies/mL in the dry season. The absolute abundances of ARGs from the wells at monitoring site 1 (G1 to G5) were higher than those wells at monitoring site 2 (G6 to G10) in general. The highest abundance of ARGs was observed in wells G3 and G10. Only 1.23 × 103 copies/mL and 8.70 × 103 copies/mL of these genes were detected from the reference site in the wet season and dry season, respectively. Sulfonamides (sul1 and sul2), aminoglycosides (aadA1), MLSB (ereA), chloramphenicols (cmlA), and tetracyclines (tetC and tetO) resistance genes occurred in groundwater of all ten wells in the wet season (Fig. S1). Sul1 had the highest absolute abundance in all wells except G10. Only sulfonamides (sul1 and sul2) and tetracyclines (tetO) genes occurred in all ten sampling wells in the dry season, with sul1 presented the highest abundance, followed by sul2 and aadA1. Generally, int1 was the most frequently detected MGEs and had the highest abundance in all detected MGEs. By the absolute concentration and relative abundance (copies of genes/copies of 16S rRNA gene) of each gene, the Hotelling's T2 test showed that there was no significant difference

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Fig. 2. Heatmap for the detected ARGs and MGEs in groundwater (A), river water (B) and sediment (C). The data were used after log10 (copies/mL).

Fig. 3. Profiles of ARGs and MGEs in groundwater. Left column shows ARGs and right column shows MGEs.

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between dry season and wet season (Figs. S5 & S6) (Hotelling's Trace = 0.838, p N 0.05). Shannon diversity index in groundwater (Fig. S2) showed that the diversity of ARGs was influenced by seasonal changes in groundwater (p b 0.05). The diversity of ARGs in the dry season was higher than that in the wet season. Notably, the diversity of ARGs decreased with well depth at site 1 in the wet season. However, at site 2, the diversity of ARGs had a decreasing trend in wells G6 and G7 but increased dramatically in the following three wells, which was considered to be probably influenced by the intrusion of seawater. 3.3. Occurrence of ARGs in riverine environment In the 6 sampling sites on Maozhou River in the wet season, a total number of 141 and 150 ARGs and 10 MGEs were detected in river water and sediments, respectively (Fig. 2B; C). The total numbers of ARGs detected in each river water sample ranged from 84 to 109. And 111 to 135 ARGs were detected in sediments. However, only 75 ARGs were found in the control river water (SYR), and 101 and 91 ARGs were detected in the control sediments (S-YTM and S-SYR). Generally, the abundance of mainly detected ARG types in river water samples had the following order: sulfonamides N multidrug N aminoglycosides N beta-lactamase N MLSB (Fig. 4A). The mainly detected MGEs in surface water were tnpA-05 and cintl-1 (Fig. 4A). The dominant ARG types in sediments were multidrug and sulfonamides, followed by aminoglycoside and beta-lactamase and the dominant MGEs in sediment were cintl-1, tnpA-04 and tnpA-05 (Fig. 4B). According to the results of HT-qPCR in the wet season, 18 frequently detected ARGs representing 6 resistance types and 3 MGEs (int1, int2 and Tn916) were selected as target genes for further investigation of surface water and sediment. Absolute concentrations of these genes in the wet season and dry season were analyzed by qPCR. As showed in Figs. S3 and S4, the absolute abundance of ARGs in R4 from the dry season and S6 from the wet season got the maximum value in these two sectors, with the highest abundance of ARGs of 1.91 × 107 copies/mL and 1.10 × 1010 copies/g, respectively. The total abundance of selected genes from lower reach sites (R4–R6) was significantly higher than from the upper reach sites (R1–R3) in both two seasons (p b 0.05). Sulfonamides (sul1 and sul2), aminoglycosides (aadA1), MLSB (ereA), chloramphenicols (cmlA and floR), beta-lactamase (blaTEM), fluoroquinolones (qnrD), tetracyclines (tetC, tetG, tetB/P, tetH, tetW and tetO) resistance genes, and MGEs (int1 and Tn916) were found in all six river water samples in the wet season (Fig. S3). Int2 was detected in all surface water in the dry season. Sul1 had the highest absolute abundance in surface water in both seasons, followed by sul2. In

sediments (Fig. S4), sul1, sul2, aadA1, cmlA, floR, blaTEM, qnrD, tetC, tetG, tetB/P, tetW and tetO, int1, int2 and Tn916 were found in all sites in the wet season. The most abundant gene in the sediments was sul1, followed by sul2. Among the ARGs, sul1 and tetH were predominant in S-SYR and S-YTM, respectively. By running Hotelling's T2 tests according to the absolute abundances of selected genes, the results indicated that there was no significant seasonal difference in river water and sediments. The univariate test showed that the abundances of tetH, blaTEM and uidA in river water from the dry season were significantly higher than those from the wet season (p b 0.01). Additionally, the Shannon diversity index indicated that the ARG diversities had seasonal variations in river water (p b 0.01) but did not appear in sediment (Fig. S2). Moreover, the diversities of ARGs in river water and sediments were much higher than those in groundwater (p b 0.01). 3.4. Share of ARGs and MGEs between groundwater and river water According to the detected genes by HT-qPCR, Venn diagrams showed the number of ARGs shared between the studied environments (groundwater, river water, and river sediments) (Fig. 5A). In total, there were 127genes, 151 genes and 160 genes detected from groundwater, river water and sediments, respectively. And 120 genes which accounted for 69.4% shared between groundwater and river water, and the same percentage of shared genes were present in groundwater and sediments. The 141 genes which accounted for 81.6% were shared between river water and sediments. And 116 genes which accounted for 67.1% in all detected genes were presented in all these three types of samples, which provide a potential that ARGs in these three environments may transfer from one sector to another. In addition, the number of genes detected in sediments was more than those in river water and groundwater. RDA (Redundancy analysis) was conducted to investigate the relationship between ARGs, MGEs and water quality parameters. MGEs and WQPs (water quality parameters) were analyzed as the environmental factors for ARGs (Fig. 5B). The results showed that the total explanatory variables account for 70.7% and the first two principal components accounted for 66.3% of the ARGs variation. The concentration of DO (dissolved oxygen) exhibited a significant positive relationship with the abundance of ARGs in river water and groundwater, while the NH4-N, TOC and TN showed negative correlations with the abundance of ARGs. TP had a positive impact on the abundance of 16S rRNA gene. Additionally, MGEs showed a strong positive correlation with ARGs, especially with floR, sul1, sul2 and aadA1. To determine the key contributor to the explanatory variation as whole and separate

Fig. 4. Profiles of ARGs and MGEs in water (A) and sediment (B) of Maozhou River. Left column shows ARGs and right column shows MGEs.

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Fig. 5. Share of ARGs and MGEs in groundwater and river water. A. Share of ARGs and MGEs among river water, sediment and groundwater; B. Redundancy analysis (RDA) of the quantitative correlation between water quality parameter (WQM), mobile genetic elements (MGEs) and selected ARGs from wet season and dry season. C. Variation partitioning analysis (VPA) differentiating effects of WQP and MGEs on the variation of ARGs.

influences of ARGs, VPA (variation partitioning analysis) was conducted by designating the explanatory variables and covariates. A total of 100% variations could be explained by selected variables. It was conducted that the WQPs explained 66.0% of the ARGs variation, the interaction between WQPs and MGEs explained 28.0% of the variation, while MGEs only contributed 6.0% of the ARGs variation (Fig. 5C). Furthermore, the results from RDA indicated that the abundance of ARGs had no significant difference between wet season and dry season, but the abundances of ARGs showed differences between river water and groundwater. (Fig. 5B). According to Pearson correlation analysis (Table S7), strong correlations were observed between aadA1, ereA, sul1, sul2, tetC, tetG, tetW, tetO, blaTEM, floR and cmlA. 4. Discussion 4.1. Characteristics of ARGs in groundwater In this study, by using HT-qPCR we provided a relatively integral characterization of resistome in groundwater, river water and sediment in the wet season, and found that ARGs were diverse and prevalent in the groundwater environment. This is consistent with a previous study by Chen et al. (2017a, 2017b). 171 ARGs and 8 MGEs were detected in groundwater near the municipal solid waste landfill. The absolute abundance of ARGs in groundwater was ranged from 2.5 × 109 to 1.27 × 1011 copies/L with genes conferring resistance to multidrug and beta-lactams were the most abundant (Chen et al., 2017a, 2017b). By qPCR, the relative abundance of detected ARGs was varied between1.8 × 10−3 to 3.9 × 100 copies/16S rRNA gene copies, which was higher than those of Szekeres et al. (2018). In the study of Szekeres et al. (2018), 11 ARGs measured by qPCR in groundwater to the proximity of urban area with a following order of ARG type frequencies: β-lactam N sulfonamide N tetracycline N other/ associated N MLSB N chloramphenicol N aminoglycoside. According to Hotelling's T2 test, the distributions of ARGs in groundwater, surface water and sediment between two seasons had no significant difference. Only the abundance of tetH and blaTEM in surface water from the dry season were significantly higher than those in the wet season. In the study of Du et al. (2015), it was found that the distributions of most ARGs in the influent of WWTPs were quite similar over the seasonal investigation while the abundances of tetW, tetX, and sul1 were higher in spring than they were in other seasons (Du et al., 2015). Seasonal variation of ARGs may be partly due to the changed antibiotics concentration. The concentration of antibiotics in municipal wastewater presented obvious seasonal variation: higher in winter and lower in

summer, which is positive and is associated with the usage of antibiotics in different seasons (Goossens et al., 2005; Sui et al., 2011). In the present study, sul1and sul2 were the most abundant ARGs in groundwater and river water and sediment, followed by aadA1, ereA, and tetC, while Int1 was the most abundant MGE. Qiu et al. (2019) also reported that sul1 had the highest relative abundance level in the Maozhou River with the highest value of 6.204 × 10−4 (Qiu et al., 2019). Sul1 gene is generally linked to other resistance genes of class 1 integrons, whereas sul2 is generally located on small non-conjugative plasmids (Sköld, 2000) or large transmissible multi-resistance plasmids (Enne et al., 2001), which exactly elucidated that sul1, sul2, floR and aadA had strong positive correlations with MGEs in RDA diagram (Fig. 5B). Besides, the int1 and tnpA genes are often used as markers for mobile genetic elements (Zhu et al., 2013), due to the rapid response of int1 genes to different environmental pressures (Gillings et al., 2015). These results were consistent with the investigation of Su et al. (2014) that tetracycline, sulfonamide, and macrolide resistance genes, as well as integrons in the sediments were detected in Dongjiang River basin which also forms Pearl River Delta (Su et al., 2014). These frequently detected ARGs in Pearl River Estuary were related to commonly used antibiotics including sulfonamides, fluoroquinolones, and aminoglycosides, indicating the significant anthropogenic impact on the dissemination of ARGs in this region (Chen et al., 2013). What is noteworthy is that the floR resistance gene was abundant and specific to wells G5 and G10 which were the deepest wells in site 1 and site 2, indicating it may originate from a certain source but not yet spread into other layers of aquifer. Their presence can be a health risk as this gene combination has been identified in the central region of integrons in Salmonella genomic island (SGI) 1 described in Salmonella enterica and Proteus mirabilis (Szekeres et al., 2018). Also, it was documented that a Salmonella virulence plasmid can recombine with an antibiotic resistance plasmid and subsequently spread both antibiotic resistance and virulence genes to the recipients (Hradecka et al., 2008). Additionally, Enterococcus is an important pathogen of nosocomial infection, which can cause urinary system infection, abdominal infection, and pulmonary infection. Enterococcus resistance to vancomycin is mainly mediated by vanA, vanB, vanC, vanD and other genes, among which vanA and vanB are the main genes that mediate high concentration resistance to vancomycin (Chow and Kak, 2002). Apart from that, Wang et al. (2019) detected that 50% of the 14 strains of carbapenem-resistant Escherichia coli (CREC) strains isolated from 12 patients from intensive care unit (ICU) and neurology unit of hospital were carrying the genes of blaTEM and blaCTX, with a positive detected rate of blaCTX as high as 64.29% (Wang et al., 2019). Carbapenem

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antibiotics are an important class of antibiotics in clinic medicine, which are considered to be the last ditch against strains producing extendedspectrum beta-lactamase (Abbas et al., 2019). According to the annual report suggested by Shenzhen Water Resources Bulletin 2018, the groundwater in Shenzhen was mainly used for domestic water, agriculture, mineral water and industry. The existence of these clinical related genes in groundwater indicated a health risk to human health. 4.2. Potential linkage to wastewater contamination and surface water filtration

were identified in groundwater, suggesting that the groundwater is an important reservoir of ARGs. A large numbers of ARGs which accounted 67.1% in all detected genes were shared among the groundwater, river water and sediment. Based on the large proportion of shared ARGs between the groundwater and river, the high abundance of the Total Coliforms, the large wastewater burden in groundwater, and the ARGs variation largely contributed by water quality, it can be concluded that the groundwater has been affected by direct or indirect local anthropogenic activities. Declaration of competing interest

Pharmaceuticals and personal care products (PPCPs) and artificial sweeteners (ASs) have been proposed as definitive wastewater indicators (Hagedorn and Weisberg, 2009; Dickenson et al., 2011). According to our previous study by Yang et al. (2018), sucralose was found to be a suitable wastewater indicator to reflect domestic wastewater contamination in surface water and groundwater qualitatively and quantitatively in the region (Yang et al., 2018). In present study, sucralose as artificial sweetener was detected from both groundwater and surface water (Table S6). By calculating, the wastewater burden data (Table S6) from the present study suggested the groundwater was contaminated by wastewater (Yang et al., 2018). Wastewater can impact groundwater by several plausible ways, such as discharge from domestic sewage systems (Katz et al., 2011), leakage from sewers (Wolf et al., 2012) and recharge of contaminated river water to groundwater (Buerge et al., 2009). This is confirmed further by the presence of the Total Coliforms in groundwater in the present study. Previous studies have reported groundwater contamination by sewage, which could even increase the abundance of antibiotic resistance in groundwater through direct bacterial transport or resistance genes transfer (Wolf et al., 2004; Gallert et al., 2005; Xi et al., 2009; Chen et al., 2017a, 2017b). However, in return, the structure of microbial communities might be affected when contaminated groundwater was supplied for landscape water or irrigation, which caused the disappearance of some original species and the occurrence of pathogenic bacteria or conditional pathogenic bacteria in recipient environment (Wang, 2015). By using environmental isotope (hydrogen and oxygen isotopes) and hydrochemistry, it has been proved that the interactions exist between surface water and groundwater (Song et al., 2006; Hu et al., 2007). Detection of various PPCPs showed that there exist the interactions between river and groundwater (L. Yang et al., 2017; Y.Y. Yang et al., 2017). It was found that ARGs in surface water and soils can leach to groundwater close to agricultural areas of animal production or aquaculture (Zhang et al., 2009a, 2009b). For example, tetracycline resistance genes of animal source have been detected in groundwater as far as 250 m downstream from waste lagoons of swine farms (Chee-Sanford et al., 2001) and in groundwater downstream from manure lagoons (Mackie et al., 2006). Similarly, ARGs in landfill could also leach into groundwater (Chen et al., 2017a, 2017b). Considering the shallow groundwater table and permeable aquifer, there is a high potential of interaction between surface water and groundwater in the Pearl River Delta region. By Venn plot, a total of 116 ARGs shared between groundwater, river water and sediment, which accounted for a large proportion (67.1%) (Fig. 5A). This finding together with bacterial indicator and wastewater burden data provides an evidence that the ARGs in groundwater have a close linkage to wastewater contamination and surface water filtration. The results from RDA and VPA indicated that ARGs in surface water and groundwater were significantly correlated with the water quality parameters such as DO, NH4-N and TOC, which may be greatly affected by human activities. 5. Conclusion The present study revealed diverse resistome in groundwater and riverine environment of the Maozhou River Basin. Totally 127 ARGs belong to 9 ARGs types and 10 MGEs including integrases and transposase

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