Effects of full-scale advanced water treatment on antibiotic resistance ...

FEMS Microbiology Ecology, 92, 2016, fiw065 doi: 10.1093/femsec/fiw065 Advance Access Publication Date: 27 March 2016 Research Article

RESEARCH ARTICLE

Effects of full-scale advanced water treatment on antibiotic resistance genes in the Yangtze Delta area in China Shuting Zhang1 , Wenfang Lin2 and Xin Yu2,∗ 1

Shenyang University of Chemical Technology, the Eleventh Street, Tiexi Area, Shenyang 110142, China and Safe Drinking Water and Related Microbiology, Institute of Urban Environment, China Academy of Sciences, Xiamen 361021, China 2

∗

Corresponding author: Safe Drinking Water and Related Microbiology, Institute Of Urban Environment, CAS, Xiamen 361021, China. Tel: 0592-6190780; E-mail: [email protected] One sentence summary: This manuscript describes the effects certain drinking water treatments have on the removal of antibiotic resistance genes. The resistance gene may even be increased due to the disinfection and distribution system. Editor: James Tiedje

ABSTRACT As emerging microbial contaminants, antibiotic resistance genes (ARGs) are widespread in the aquatic environment, including source water, which might enter water supply systems and endanger public health by enhancing the resistance of opportunistic pathogens to some antibiotics. In the present study, we investigated how water treatments affect the levels of ARGs in a full-scale drinking water treatment plant for one year using real-time PCR. The 16s rRNA gene and eleven ARG families, including tetA, tetG, aacC1, strA, ermB, cmlA5, vanA, dfrA1, sulII, blaTEM-1 and blaoxa-1 , in source water and the outlet of each treatment and tap water were monitored. The results showed that nine ARG families were detected at relatively high levels, for example, the sulII gene was detected at ∼104 copies mL−1 compared with 105 copies mL−1 in finished water and tap water in July, whose relative concentrations were consistently high. Treatments for the reduction of the absolute concentrations of ARGs included sand filtration, coagulation/sedimentation and two-stage O3 -BAC filtration, while distribution could increase ARGs an average of 0.50 log. Keywords: ARGs; drinking water treatment; real-time PCR; antibiotic resistance

INTRODUCTION The enhanced resistance of bacteria in clinical or water environments has frequently been reported (Heddini et al. 2009; Lu et al. 2015), including both wastewater treatment (Yang et al. 2014; Zhang et al. 2015) and drinking water treatment processes (Guo et al. 2014; Bai et al. 2015). Antibiotic resistance genes (ARGs) are considered major public health problems worldwide (Zhang et al. 2006; Oberle et al. 2012). Antibiotic resistance genes encoding antibiotic resistance are considered emerging contaminants in aquatic environments (Pruden et al. 2006; Machado and

Bordalo 2014; Lu et al. 2015). Bacterial resistance to one or more antibiotics in water can be enriched by effluents from animal agriculture (Chen et al. 2011; Vilacoba et al. 2013), pharmaceutical wastewater (Aydin, Ince and Ince 2015) and municipal wastewater treatments (Wang et al. 2013; Zhang et al. 2015). This resistance might even enter the drinking water supply system (Flores Ribeiro et al. 2014; Bai et al. 2015; Tanner et al. 2015), representing a pathway for human exposure to antibiotic-resistant pathogens (Machado and Bordalo 2014). Currently, hundreds of ARGs have been detected in the water environment. The expression of ARGs leads to antibiotic

Received: 31 August 2015; Accepted: 29 February 2016 C FEMS 2016. All rights reserved. For permissions, please e-mail: [email protected]

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resistance in bacteria through the following ways: (1) target bypass (Huovinen et al. 1995; Happi et al. 2005), (2) efflux pumps (Kumar and Schweizer 2005), (3) antibiotic inactivation (Wright 2005), and (4) target modification (Lambert 2005). This resistance can spread among bacteria via inheritance and horizontal gene transfer of ARGs (Summers 2006; Tenover 2006; Babic et al. 2008). With respect to the importance of the microbiological safety of drinking water for human health, this issue of bacterial antibiotic resistance in drinking water has been studied since the 1980s (Armstrong, Calomiris and Seidler 1982). In this field, different researchers have focused on different aspects of ARGs using culture-dependent methods to detect and quantify antibiotic resistance bacteria (ARB) in the water supply system. For example, Armstrong, Calomiris and Seidler (1982) tested bacteria for resistance to six antibiotics (Armstrong, Calomiris and Seidler 1982); Vaz-Moreira, Nunes and Manaia (2011) analysed the susceptibility of Sphingomonadaceae to five classes of antibiotics (Vaz-Moreira, Nunes and Manaia 2011); multidrug resistances were also analysed using drug sensitivity tests (Bai et al. 2015); and the presence of carbapenem-resistant bacteria was detected using a fluorogenic heterotrophic plate count (HPC) test (Tanner et al. 2015). Additional studies have employed molecular techniques, such as PCR and quantitative real-time PCR methods, to investigate ARB and ARGs. Using these techniques, Xi et al. (2009) summarized that water distribution systems might serve as reservoirs for the spread of antibiotic resistance. Shi et al. (2013) observed that the sulI gene had the highest abundance compared with other ARGs detected in drinking water, followed by tetA and tetG genes (Shi et al. 2013). Sulfonamide and tetracycline resistance genes (Guo et al. 2014) and vanA and ampC genes in drinking water biofilms (Schwartz et al. 2003) have also been examined using molecular techniques. High-throughput sequencing is also a promising tool for investigating the abundance and diversity of ARGs (Shi et al. 2013). In previous reports, studies of ARB and ARGs have typically been focused on source water (Vaz-Moreira, Nunes and Manaia 2012; Jiang et al. 2013), finished water (Xi et al. 2009; Guo et al. 2014; Bergeron et al. 2015) and tap water (Pavlov et al. 2004; Figueira et al. 2011; Shi et al. 2013; Bergeron et al. 2015). Only a few reports have concerned the effects of different treatment procedures in drinking water plants on ARGs (Guo et al. 2014) or ARB (Bai et al. 2015). In addition, previous results have shown that ARB in source water could not be entirely eliminated by common water treatment processes in drinking water plants, including some advanced treatment processes [e.g. ozone and biological activated carbon (BAC) filtration] (Guo et al. 2014; Bai et al. 2015). Although bacterial antibiotic resistance in drinking water has been well studied, some questions have not been

solved, and various aspects require further studies. For example, the effects and relevant mechanisms of different treatment processes on ARB and ARGs; the application of advanced molecular biology techniques to examine this issue; the influence of different temperatures and horizontal gene transfer mechanisms on the spread of ARGs in drinking water; and how to entirely eliminate ARB and ARGs in the water supply system. In the present study, we monitored the advanced treatment train in a full-scale drinking water plant in the Yangtze Delta area, which is representative of typical drinking water plants, located in the most developed area in China. We focused on the effects of different treatments such as biological pre-treatment, coagulation/sedimentation, sand filtration, twostage O3 -biological activated carbon (O3 -BAC) filtration and disinfection and distribution systems. Quantitative real-time PCR was used to quantify the eleven ARG families and the 16s rRNA gene.

MATERIALS AND METHODS Full-scale drinking water treatment plant The full-scale drinking water plant is located in the Yangtze Delta area, serving a city with a population of ∼670 000 people. The source water in this area was seriously polluted, where the concentrations of ammonia nitrogen were higher than 2 mg L−1 in most months, and the chemical oxygen demand (CODMn ) could reach 20 mg L−1 (according to the data from water plant monitoring). The advanced treatment train for drinking water was initiated in 2006. The production of the advanced treatment train is 50 000 m3 d−1 . The concentrations of the disinfectants, referring to the available chlorine, were 3–3.5 mg L−1 (ClO2 ). The water treatments employed in these plants are illustrated in Fig. 1.

Methods for water sampling and the determination of water parameters Different volumes of water samples were collected in four consecutive seasons (October, January, April and July) at the following eight sites (Fig. 1): SW: source water, BP: effluent of biological pre-treatment, CS: effluent of coagulation/sedimentation, SF: effluent of sand filtration, OB1: effluent of first-stage O3 -BAC filtration, OB2: effluent of second- stage O3 -BAC filtration, DF: effluent treated with ClO2 , TW: tap water. In addition, finished water, ‘DF’ was obtained from the clear-water reservoir. During the collection process, sterile plastic buckets were used, and the volumes of the collected water samples were ∼10 –15 L. Filtration was subsequently performed using 0.22 μm

Figure 1. Schematic drawing of the treatment train in the drinking water treatment plant Sampling sites are indicated with abbreviations: SW: source water, BP: effluent of biological pre-treatment, CS: effluent of coagulation/sedimentation, SF: effluent of sand filtration, OB1: effluent of the first-stage O3 -BAC, OB2: effluent of the second-stage O3 -BAC, DF: effluent treated with ClO2 and TW: tap water.

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SYBR Premix Ex Taq (TaKaRa Dalian, China), 0.4 μL of ROX Reference Dye II (50×) (TaKaRa Dalian), 0.2 μL of each primer (Invitrogen), 1 μL of template DNA and sterile distilled water. The PCR protocol was initiated at 95◦ C for 30 s, followed by 40 cycles at 95◦ C for 5 s, annealing at the appropriate temperatures for 20 s and 72◦ C for 32 s. A melting curve analysis was also performed to confirm the product specificity. In addition, the standard curves were produced using 10-fold serial dilutions of the plasmids carrying targeted genes whose concentrations were determined prior to amplification using a Qubit fluorometer. The 10-fold serial dilutions of the plasmids from 101 to 106 copies μL−1 (for the 16s rRNA gene, the concentrations of the plasmid were from104 to 109 copies μL−1 ) were tested in triplicate using real-time PCR under the conditions described above.

membrane filters (Millipore, Billerica, MA, USA), and the membrane samples were stored at –20◦ C until further molecular analysis. The volumes of the filtered water samples from different treatments were not the same because the contents of the impurities in the water were different. Thus, the volumes of the filtered water samples depended on the contents of the impurities in the water or the degree of membrane clogging. Each water sample was filtered three times in parallel. Some important water quality parameters were also determined in the field or in the laboratory. Dissolved oxygen (DO), pH level and temperature were determined in the field using a Hach HQ10d portable meter (LDO HQ10, Hach, Loveland, Colorado, USA). The total HPC was immediately determined after sampling using a nutrient agar medium following the national standard examination methods for drinking water (GB/T 5750.12-2006, China). Approximately 200 mL of filtered water from each site was stored at 4◦ C and transported to the laboratory to determine the total organic carbon (TOC) and total nitrogen (TN) based on a TOC analyser (TOC-Vcph, Shimadzu, Kyoto, Japan).

Data analysis The analysis of the real-time PCR results was performed using ABI 7500 Software v2.0.1, Excel v2003 and SPSS v13.0. After amplification, the copy number of each targeted gene was calculated depending on the volumes of the filtered water samples and the formulas generated using standard curves. The formulas of the standard curves were generated based on the copy numbers of the amplified plasmids and the corresponding CT values. The molecular weight of each plasmid was calculated based on the reported sequences of the vector and the inserted rRNA genes, and subsequently the copy numbers of the amplified plasmids were determined. After PCR amplification, the threshold fluorescence values were automatically adjusted, and the cycle threshold (CT ) of the standard curves was determined. The standard curves were shown using ABI 7500 software. The R2 values of the determination coefficients of the standard curves were all higher than 0.99 (Table S1, Supporting Information). Based on the formula for the amplification efficiency and the slopes of the standard curves, the amplification efficiencies of the amplicons were determined, and the results are shown in Table S2 (Supporting Information). All copy numbers were log-transformed. The relative concentrations of the ARGs at each site were shown as ratios of the copies of each ARG to the corresponding copy of the 16s rRNA gene (ARG/16s rRNA gene). The concentrations of ARGs after different treatments were compared using one-way analysis of variance (ANOVA), followed by Tukey’s post hoc analysis at P < 0.05 (SPSS v13.0) (Table 1), and the correlation analysis of the temperature and the relative concentration of the sulII gene was also performed using SPSS v13.0 (Table 2).

DNA extraction and purification Total DNA was extracted from the filtered membrane using a soil DNA kit (Omega, Norcross, GA, USA) and subsequently purified using classic phenol extraction and ethanol methods. For each sample, 30 μL of elution buffer supplied in the kit was used for DNA dissolution, and the solution was stored at –20◦ C until further analysis. Both the concentration and quality of the purified DNA were determined using a Qubit fluorometer (Invitrogen, Carlsbad, California, USA), and the availability of the total DNA was examined via electrophoresis on a 1.5% agarose gel. The DNA templates used in real-time PCR were 10-fold diluted from the total DNA.

Detection of the 16s rRNA gene and the ARGs using real-time PCR The names and references of the primers used in the realtime PCR experiments targeting eleven ARG families and the 16s rRNA gene are shown in Table S1 (Supporting Information). Real-time PCR (SYBR green) was performed in triplicate to detect and quantify the 12 targeted genes with the primers shown in Table S1 (Supporting Information), namely, 16s rRNA, tetA, tetG, aacC1, strA, ermB, cmlA5, vanA, dfrA1, sulII, blaTEM-1 , and blaoxa-1 genes, using the ABI 7500 Real-Time PCR System (Applied Biosystems, Carlsbad, CA, USA). The volume of each reaction mixture was 20 μL, and the reactions comprised 10 μL of

Table 1. Comparison of ARG concentrations after various treatments using one-way analysis of variance (ANOVA). Sig.

SW

SW BP CS SF OB1 OB2 DF TW

1.00 0.72 0.00 0.00 0.00 0.00 0.00

BP

CS

SF

OB1

OB2

DF

TW

1.00

0.72 0.98

0.00 0.00 0.01

0.00 0.00 0.00 0.94

0.00 0.00 0.00 0.26 0.93

0.00 0.00 0.00 0.16 0.84 1.00

0.00 0.00 0.00 0.91 1.00 0.96 0.89

0.98 0.00 0.00 0.00 0.00 0.00

0.01 0.00 0.00 0.00 0.00

0.94 0.26 0.16 0.91

Sig.: significance. The bold type: the mean difference is significant at the 0.05 level.


0.93 0.84 1.00

1.00 0.96

0.89

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Table 2. Correlation analysis of temperature and the relative concentration of sulII gene.

TEMP sulII

Pearson Correlation Sig. (2-tailed) Pearson Correlation Sig. (2-tailed)

TEMP

sulII

1.000 0.350a

0.350a 0.049 1.000

0.049

a

Correlation is significant at the 0.05 level (2-tailed). TEMP: temperature.

RESULTS Water quality parameters in the full-scale drinking water treatment plant Some water quality parameters, including the TOC, TN, DO, HPC and temperature, were monitored during the 4-month sampling period, and the data are shown in Table S3 (Supporting Information). In the advanced treatment train, the TOC declined in the finished water compared with the source water from 8.00 to 4.64 mg L−1 , but increased up to 5.15 mg L−1 in tap water after

distribution in the pipelines. The TN decreased from 5.53 to 5.07 mg L−1 . The DO value increased in the effluents of biological pretreatment, O3 -BAC filter and disinfection. The HPC decreased in the treatment trains from 103 –105 CFU mL−1 to ∼10 CFU mL−1 . However, in July, the HPC of the finished water was excessive, reaching up to 103 –104 CFU mL−1 .

The absolute 16s rRNA gene and ARG concentrations along the treatment train The decrease in the bacterial biomass (16s rRNA gene) along the treatment train was obvious, and the concentrations of the 16s rRNA gene were reduced from 2.88 × 108 to 1.41 × 109 copies per 100 mL water sample to 3.31 × 105 –2.34 × 108 copies per 100 mL water sample in the entire treatment train in 4 months, but these levels subsequently increased in tap water (Fig. 2). Variations in the amounts of the 16s rRNA gene in each specific treatment were observed. In the effluents of the biological pre-treatment, the concentrations of the 16s rRNA gene decreased slightly compared with the source water, remaining at ∼108 –109 copies per 100 mL water sample. After coagulation/sedimentation, a maximum reduction in the

Figure 2. The absolute concentrations of the 16s rRNA gene and the ARGs along the advanced treatment train in 4 months.


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concentration of the 16s rRNA gene was detected in January (0.76 log), whereas almost no change was observed in July. The 16s rRNA gene concentration showed a 0.53–2.25 log decrease after sand filtration. The two-stage O3 -BAC filtration also reduced the rRNA gene concentration 0.56 to 0.75 log, except in October. Subsequently, throughout the year, disinfection with ClO2 could reduce the16s rRNA gene in the finished water, except in July, in which the gene levels were increased 0.80 log. In tap water, the levels of the 16s rRNA gene increased 0.14–0.70 log compared with the levels in the finished water (Fig. 2). In terms of ARGs, nine ARG families were detected in the plant (i.e. tetA, tetG, strA, ermB, cmlA5, dfrA1, sulII, blaTEM-1 and blaoxa-1 ) and two families were not detected (aacC1 and vanA). Variations in the absolute ARG concentrations were nearly the same as those of the 16s rRNA gene (Fig. 2). ARGs with high absolute concentrations included the sulII gene (ranging from ∼1.51 × 103 copies per 100 mL to 4.79 × 107 copies per 100 mL; confers resistance to sulphonamide and trimethoprim) and the dfrA1 gene (ranging from ∼3.31 × 103 copies per 100 mL to 5.37 × 106 copies per 100 mL; confers resistance to sulphonamide and trimethoprim), except for some results, which were lower than the detection limit. The cmlA5 gene (ranging from ∼0 copies per 100 mL to 1.48 × 105 copies per 100 mL; confers resistance to chloramphenicol) and the ermB gene (ranging from ∼0 copies

per 100 mL to 2 × 106 copies per 100 mL; confers resistance to macrolide) showed low absolute concentrations. Notably, many of the obtained results were lower than the detection limit. With respect to the different months, the absolute ARG concentrations were relatively lower in October and January when the average temperatures were ∼15.5◦ C and 3.5◦ C, respectively, whereas the ARG concentrations were higher in April and July when the average temperatures were ∼22◦ C and 30◦ C, respectively (Table S3, Supporting Information).

Relative ARG concentrations along the treatment train From the source water to the tap water, the relative ARG concentrations (ARGs/16s rRNA gene ratio) varied along the treatment train during different seasons. Figure 3 shows variations in the relative ARG concentrations in January and July. In January, we detected nine different ARGs in the source water, arranged according to the proportions of their relative concentrations: sulII, strA, blaoxa-1 , dfrA1, strA, tetG, ermB, tetA, tetG, blaTEM-1 and cmlA5 gene. Among these genes, the sulII gene accounted for ∼39% of all ARGs, and the strA gene accounted for ∼24% of all ARGs. After biological pre-treatment, the proportion of sulII gene increased to ∼53%, and there were no obvious changes in the subsequent coagulation/sedimentation. However, after the

Figure 3. The relative concentrations of ARGs along the advanced treatment train in January and July (100% stacked column).


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coagulation/sedimentation treatment, the ermB gene was nearly undetectable. After sand filtration, the blaTEM-1 gene considerably increased to ∼47%. After O3 -BAC filtration, the proportion of sulII gene increased ∼80%–96%. After disinfection, strA and blaTEM-1 genes showed an additional increase of 25% and 11%, respectively. In tap water, sulII and blaTEM-1 genes showed a further increased, accounting for 72% and 19%, respectively. In July, ARGs from the source water were arranged according to the proportions of their relative concentrations: sulII, dfrA1, tetA, tetG, strA, blaoxa-1 , blaTEM-1 , ermB and cmlA5 (Fig. 3). Among these genes, the proportion of sulII gene was the highest, at ∼66%, and the proportion of the sulII gene remained high in subsequent treatments, showing more than 90%. DfrA1, tetA, tetG and strA genes were also detected at a certain proportion. In source water, the sum of the proportions of these genes was below 40% and subsequently decreased to lower than 10% after the water treatments; however, a slight rebound to ∼20% was observed in tap water.

DISCUSSION Selection and detection of ARGs We selected the target ARGs for the following reasons. According to the various antibiotics to which these ARGs confer resistance, the existing resistance genes can be classified into five classes (Zhang, Zhang and Fang 2009): tetracycline resistance genes; aminoglycoside resistance genes; macrolide, chloramphenicol and vancomycin resistance genes; sulphonamide and trimethoprim resistance genes; and β-Lactam/penicillin resistance genes (Zhang, Zhang and Fang 2009). We selected two to three ARGs belonging to each class mentioned above to achieve more comprehensive detection results. In addition, according to previous studies, these targeted ARGs have frequently been detected in the source water of this area. Thus, we selected the eleven ARGs listed above for examination in the present study. In recent years, other studies have also reported the detection results in drinking water supply systems, for example, ermB, sulI, tetA, tetW, tetX and mecA genes (Bergeron et al. 2015); blaTEM-1 , ampC, aphA2, sulI, ermA, ermB, drfA17 genes and tet resistance genes (Shi et al. 2013); and sul and tet resistance genes (Guo et al. 2014).

The absolute removal of ARGs In the present study, we observed eleven ARGs in the treatment train of a drinking water plant, and nine of these molecules were detected in the collected samples. The absolute removal of ARGs in the treatment train was obvious, as most treatments decreased the concentration of ARGs (Fig. 4a). However, the distribution system showed the greatest potential for producing ARG contamination (Xi et al. 2009). Trends in the variations of the absolute concentrations of ARGs were nearly the same as those of the bacterial biomass (16s rRNA gene), indicating that the decrease in ARGs generally followed a reduction in the bacterial biomass (Fig. 2). Obviously, in treatment plants, sand filtration plays a significant role in the reduction of the absolute ARG concentrations (Fig. 4a; Table 1). In addition, coagulation/sedimentation and two-stage O3 -BAC filtration also decreased the absolute ARG concentrations (Fig. 4a). Most resistance genes are carried by ARB, and as particulate solids in water, coagulate, settle, and become trapped by dense sand in the sand filter. Free ARGs can also be removed by coagulation/sedimentation treatment as dissolved organic compounds


Figure 4. The removal effects on the concentrations and the relative concentrations of ARGs after water treatments. (a) The removal effect on the absolute concentrations of ARGs was defined as: log10 (the absolute concentrations of ARGs in the effluents of the present treatment—the absolute concentrations of ARGs in the effluents of the former treatment). (b) The removal effect on the relative concentrations of the ARGs was defined as: log10 (the relative concentrations of the ARGs in the effluents of the present treatment—the relative concentrations of the ARGs in the effluents of the former treatment). OB1 and OB2 were considered as one treatment.

in water (Exall and Vanloon 2000; Liu et al. 2007). Moreover, ARB could also be killed by ozone sterilization or adsorbed by O3 -BAC filtration, together with free ARGs. Notably, in October, the absolute ARG concentration increased in the effluents of the O3 BAC filter, reflecting the fact that bacteria must penetrate the BAC filter (Camper et al. 1986) before entering the next water treatment. In the finished water, obtained from the clear-water reservoir, the changes in the concentrations of both the bacterial biomass and ARGs showed different trends during the 4-month observation period (Figs 2 and 4a). Although disinfectant kills bacteria and damages DNA molecules, the bacteria maintain a certain opportunity to regrow in a clear-water reservoir, hence, the combined effect of these two mechanisms makes these results irregular. In tap water, the absolute concentrations of ARGs increased in most months, likely reflecting the ability to regrow in the distribution system (Xi et al. 2009).

Variations in the relative ARG concentrations The relative concentrations of ARGs, or relative ARG abundance, show the proportion of ARB in the microbial community. The transfer or spread of ARGs is repressed when the relative concentrations of ARGs decrease. Thus, it is important to elucidate the impact of each treatment on the relative ARG concentrations to minimize ARG contamination. After various treatments, the

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relative concentrations of ARGs dramatically changed, although some regularity was observed. In January and July, in which temperatures greatly differed, the relative ARG concentrations were distinct (Fig. 3). In the source water, the diversity of ARGs in January was more abundant than that in July (Fig. 3). We speculated that the temperature affects the relative concentrations of certain ARGs. In January, because of the low temperature (3.5◦ C), the growth of bacteria carrying ARGs was slow, generating ARG diversity. However, the increased temperature (30◦ C) observed in July promoted bacterial growth, and certain types of bacteria could occupy the dominant position in the microbial population; consequently, those carrying ARGs, such as the sulII gene, were also detected in a large proportion. The relative concentration of the sulII gene was significantly correlated with temperature (Table 2), but this result must be confirmed with additional experiments. Nine ARGs were detected in the source water, but some were difficult to detect at the end of the treatment train. This result suggested that the bacteria carrying these ARGs did not adapt with the environment in the water treatment plant, and water treatments could effectively reduce the concentration of ARGs, such as blaoxa-1 , ermB and dfrA1, in January. However, some ARGs, such as sulII, tetA, tetG, blaTEM-1 and strA gene, would be sustained or re-emerge in subsequent treatments, indicating that the bacteria carrying these genes might gradually adapt to the environment of the water treatment plant or gain resistance to disinfection. The sulII gene showed the highest relative concentration in the effluents of all treatments, and this result was consistent with those of other studies, showing that sulfonamide resistance genes had the highest abundance among ARGs (Shi et al. 2013; Guo et al. 2014). The sulII gene was detected in Pseudomonas (Leesukon et al. 2013), Acinetobacter (Marti et al. 2006; Khorsi et al. 2015), Salmonella (Tuckman, Petersen and Projan 2000; Akiyama, Presedo and Khan 2013), Escherichia (Tas et al. 2015), Stenotrophomonas (He et al. 2015) and Aeromonas (Balassiano et al. 2007). In addition, sulfonamides, antibiotics corresponding to the sulII gene, have been popularly used on animal farms in the upstream watershed of this area (Lv et al. 2013). Except for sulII, several other ARGs, such as tetA/G, blaTEM-1 and strA, re-emerged in subsequent treatments. These genes were typically observed in Acinetobacter (Chen, Young and Huang 2006; Nigro, Post and Hall 2011; Khorsi et al. 2015), Pseudomonas (Dubois et al. 2002; Han et al. 2004; Auerbach, Seyfried and McMahon 2007; Leesukon et al. 2013), Escherichia (Anantham and Hall 2012; Lalzampuia et al. 2013; Tas et al. 2015) and Salmonella (Yau et al. 2010; Akiyama, Presedo and Khan 2013; Ahmed et al. 2014). Among these bacteria, many strains of Acinetobacter (Vidal et al. 1996; Tomaras et al. 2003; Goh et al. 2013) and Pseudomonas (Yang et al. 2005; Deligianni et al. 2010) form biofilms and demonstrate regrowth on the surface of the medium inside the BAC filter, thereby increasing the concentrations of the ARGs these bacteria carry. Based on these results, there are only a few treatments that consistently and effectively reduce the relative concentrations of ARGs, namely, sand filtration and two-stage O3 -BAC filtration (Fig. 4b). In contrast, some treatments increase the relative concentrations of ARGs (e.g. disinfection and distribution system) (Fig. 4b). Sand filtration and two-stage O3 -BAC filtration can lower the relative concentrations of ARGs, but the specific mechanism underlying this decrease remains unknown. However, a


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previous report indicated that the resistant plasmids carried by ARB might partially or completely degrade after several generations, particularly in oligotrophic water (Griffiths et al. 1990). Therefore, we speculated that in the sand and two-stage O3 -BAC filters of this treatment plant, ARB might lose resistant plasmids when growing on the biofilms of sand and carbon granules. The elevation of the relative concentrations of ARGs in the effluents of certain processes might enhance the antibiotic resistance of the microbial community. The relative concentrations of ARGs increase in the effluents of finished water and tap water (Fig. 4b). We propose that there are two conditions that elevate the relative concentrations of ARGs in water treatment plants. First, ‘selectable elements’, such as antibiotics and disinfectants, apply selective pressure on the microbial community (Alonso, Sanchez and Martinez 2001; Chiao et al. 2014). Secondly, there are adequate conditions for the regrowth of microorganisms. Indeed, these two conditions can promote co-selection mechanisms (Baker-Austin et al. 2006), enabling ARB to gain an ecological advantage in competitive growth. As types of biocides, chlorine dioxide, for the disinfection and maintenance of tap water, also provides selective pressure to residual microorganisms. In the clear-water reservoir and pipelines, the regrowth of bacteria was cleared in major months (Fig. 4a). Thus, the relative concentrations of ARGs were primarily increased in finished water and tap water (Fig. 4b). Several studies have also reported this consequence. Shrivastava et al. (2004) showed evidence that multidrug-resistant (MAR) bacteria could be selected by chlorine treatment. Armstrong, Calomiris and Seidler (1982) demonstrated that the proportion of MAR bacteria obviously increased after flash mixing with chlorine, and concluded that effect reflected the selective phenomenon. Because of the complexity of the origin of antibiotic resistance, all of the above effects might partially contribute to the eventual relative increase of ARGs.

CONCLUSIONS Eleven ARG families were monitored in a full-scale drinking water treatment plant in the Yangtze Delta area, and nine of these families were detected in the effluents of different water treatments and tap water during four sampling months. We concluded that the water treatment train and distribution systems could impact the absolute or relative ARGs concentrations. Among these methods, sand filtration, coagulation/sedimentation and two-stage O3 -BAC reduce the absolute concentrations of ARGs, but ARB might regrow in clean-water reservoirs and pipelines. Although the removal of the relative concentrations of ARGs is difficult, sand filtration and two-stage O3 -BAC filtration play key roles. The sulII gene showed the highest relative concentration in the effluents of the treatment train, while the tetA and tetG, blaTEM-1 , and strA genes were differently eliminated in this treatment train. Temperature had an effect on the relative concentration of the sulII gene. In addition, disinfection and the distribution system might occasionally increase the relative ARG concentrations. Therefore, we recommend limiting ARG contamination, strengthening sand filtration and two-stage O3 -BAC filtration, and reducing the settler, filter and reservoir nutrition and temperature.

SUPPLEMENTARY DATA Supplementary data are available at FEMSEC online.

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ACKNOWLEDGEMENTS We would like to acknowledge Antionette W. for proofreading this manuscript, and Zhuoying Wu, Lu lv, Chengsong Ye for practical support in the experiment. We are grateful for the financial support for this study provided by the National Hi-tech R&D Program of China (2012AA062607), National Natural Science Foundation of China (51408372, 51478450 and 51278482). Conflict of interest. None declared.

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