Luiz-prevalence And Transmission Of Antibiotic Resistance And Microbiota Betweenhumans And Water Environments.pdf

Environment International 121 (2018) 1155–1161

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Prevalence and transmission of antibiotic resistance and microbiota between humans and water environments

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Zhen-Chao Zhoua, Wan-Qiu Fenga, Yue Hana, Ji Zhenga, Tao Chena, Yuan-Yuan Weia, ⁎ Michael Gillingsb, Yong-Guan Zhuc, Hong Chena, a

Institute of Environmental Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China Department of Biological Sciences, Macquarie University, Sydney, NSW 2019, Australia c Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China b

A R T I C LE I N FO

A B S T R A C T

Handling Editor: Olga-Ioanna Kalantzi

The transmission routes for antibiotic resistance genes (ARGs) and microbiota between humans and water environments is poorly characterized. Here, we used high-throughput qPCR analyses and 16S rRNA gene sequencing to examine the occurrence and abundance of antibiotic resistance genes and microbiota in both healthy humans and associated water environments from a Chinese village. Humans carried the most diverse assemblage of ARGs, with 234 different ARGs being detected. The total abundance of ARGs in feces, on skin, and in the effluent from domestic sewage treatment systems were approximately 23, 2, and 7 times higher than their abundance in river samples. In total, 53 ARGs and 28 bacteria genera that were present in human feces could also be found in the influent and effluent of rural sewage treatment systems, and also downstream of the effluent release point. We identified the bacterial taxa that showed a significant association with ARGs (P < 0.01, r > 0.8) by network analysis, supporting the idea that these bacteria could carry some ARGs and transfer between humans and the environment. Analysis of ARGs and microbiota in humans and in water environments helps to define the transmission routes and dynamics of antibiotic resistance within these environments. This study highlights human contribution to the load of ARGs into the environment and suggests means to prevent such dissemination.

Keywords: Peri-urban Aquatic Wastewater Drinking water Human Resistance

1. Introduction Antibiotic resistance is a serious global threat to human health, causing hundreds of thousands of fatalities each year (Pal et al., 2016; Pehrsson et al., 2016). The spread of resistant bacteria and their antibiotic resistance genes (ARGs) is exacerbated by exchange of resistance genes between human and environmental microbiota (Bhutani et al., 2014; Forsberg et al., 2012). Horizontal gene transfer (HGT) can facilitate the dissemination of ARGs, via mobile genetic elements (MGEs) such as integrons which are associated to insertion elements, transposons and plasmids (Aminov, 2011; Stevenson et al., 2017; von Wintersdorff et al., 2016; Zhu et al., 2017b). Genes that could potentially confer antibiotic resistance can be found in remote and pristine settings (D'Costa et al., 2011; Pawlowski et al., 2016; Segawa et al., 2013), but generally some of these are not clinically relevant. In contrast, environmental compartments with increased levels of anthropogenic pressure are often highly contaminated with ARGs of concern for human and animal health (Berendonk et al., 2015). Identifying the

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microbiota and ARGs that pose the greatest threat to public health requires an understanding of the distribution and dissemination of ARGs between various environments. Populations in rural areas interact directly with the environment, and often have impacts on water resources. However, little is known about the diversity and distribution of ARGs in rural drinking water treatment plants (DWTP), the residents that rely on this water, and the ARGs that flow through rural domestic sewage treatment systems (RDSTS). Previous studies have demonstrated ARGs in urban drinking water (Bai et al., 2015; Shi et al., 2013; Xu et al., 2016), wastewater treatment systems (Di Cesare et al., 2016; Chen and Zhang, 2013; Mao et al., 2015), and receiving rivers (Luo et al., 2010; Rodriguez-Mozaz et al., 2015; Xu et al., 2015; Zheng et al., 2017), highlighting the effect of different anthropogenic activities on ARGs distribution. The diversity of human-associated ARGs (Foster et al., 2017; Ma et al., 2016; Sommer et al., 2010; Zhu et al., 2017a) in human feces (Li et al., 2015; Pal et al., 2016; Yatsunenko et al., 2012), and on skin (Junker and Schreiber, 2008; Schloissnig et al., 2013) also revealed the prevalence of ARGs.

Corresponding author. E-mail address: [email protected] (H. Chen).

https://doi.org/10.1016/j.envint.2018.10.032 Received 14 July 2018; Received in revised form 12 September 2018; Accepted 2 October 2018 Available online 09 November 2018 0160-4120/ © 2018 Elsevier Ltd. All rights reserved.

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These findings emphasize the role of human/environment interaction in the dissemination of clinically important resistance genes and provide fundamental information on the ARGs in these different systems. Most studies of ARGs in water focus on municipal water treatment systems or natural water environments. However, the transmission of ARGs between environments is poorly understood, and important knowledge gaps remain. Studying microbial communities and ARGs in humans can contribute to management options for controlling antibiotic resistance. The objective of this work was to investigate the distribution and characteristics of ARGs in the water transmission network of a periurban village. We examined water treatment plant influent and effluent, tap water, human feces and skin, sewage treatment influent and effluent, and the connecting river, both upstream and downstream, using high-throughput quantitative PCR (HT-qPCR) (Zhu et al., 2013; Zhu et al., 2017c). Our results provide insights into the fate of ARGs in rural water supplies and during sewage treatment, and expand our understanding of ARG prevalence in humans and water environments in rural areas.

transformed into absolute copy numbers by normalization to the absolute 16S rRNA gene copy numbers (Zhu et al., 2013). Absolute 16S rRNA copy numbers were determined by the standard curve (SC) method of quantification using the StepOnePlus™ real-time PCR system (Applied Biosystems, Foster City, CA, USA) as described in a previous study (Chen and Zhang, 2013). 2.3. Bacterial 16S rRNA gene sequencing

2. Materials and methods

Microbial community compositions were determined by performing 16S rRNA gene sequencing on an Ion Torrent platform. The V4 to V5 regions of the bacterial 16S rRNA were amplified using the universal primer set 515F/907R (Turner et al., 1999). In order to identify individual samples in a mixture within a single sequencing run the primer set was tagged with unique barcodes (7-nucleotide barcodes) for each sample (Cui et al., 2016). All sequences of each sample were screened by filtering adaptor sequences and removing low-quality reads, ambiguous nucleotides and barcodes. High-quality sequences were analyzed using Quantitative Insights Into Microbial Ecology (QIIME). The sequences were clustered into operational taxonomic units (OTUs) at the 97% similarity level using UCLUST.

2.1. Subject recruitment and sampling

2.4. Statistical analysis

Healthy male and female volunteers of 23 to 50 years of age were recruited from a peri-urban village (29°78′N, 121°38′E) located in Ningbo, Zhejiang Province, China, in May 2017. Briefly, feces (marked as F1–F10) and skin (marked as S1–S10) microbiota samples of 6 male and 4 female adults were sampled. Sample collection was approved by the ethics committee of Zhejiang University School of Medicine, and all subjects provided written informed consent before participation. Further, nine water samples (marked as W1–W9) representing different types of water from the village were collected. W1, DWTP influent; W2, DWTP effluent; W3–W5, tap water; W6, RDSTS influent; W7, RDSTS effluent; W8, upstream; W9, downstream. To exclude temporal heterogeneity, water samples were collected three times over three days. Triplicate water samples were collected in sterile polyethylene bottles which were washed twice with each water sample in advance. All samples were transported to the laboratory on ice and stored at 4 °C until processing. Details of sample collection procedures and DNA extraction from feces, skin and water samples are described in Supplementary Information (S1).

Shannon-Weiner and Simpson indices were calculated for each sample. Pearson and Spearman correlations coefficients were determined by SPSS 20 (IBM, Armonk, NY, USA). Comparisons of ARG abundances among different samples were carried out using Kruskal Wallis tests with Bonferroni correction. Bar charts and Ternary plots were generated by OriginPro 9.0 (Origin Lab Corporation, USA). The principal coordinate analysis (PCoA) based on Bray-Curtis distance was evaluated for the ARG and bacterial community profiles between different samples, and redundancy analysis (RDA) was applied to search for potential links between ARGs and bacterial communities by using Canoco software (V 5.0). Graphs were generated using R3.4.0 (R Foundation for Statistical Computing) with the pheatmap and ggplot2 package, respectively. ANOSIM analyses were performed in R (vegan package) with 999 permutations. Network visualization was conducted using Frucherman Reingold algorithms in the Gephi platform (Bastian et al., 2009; Hu et al., 2017). Only statistically robust correlations with Spearman's correlation coefficient (ρ) > 0.7 and significance level (P) < 0.01 were used to form the final networks (Junker and Schreiber, 2008).

2.2. High-throughput qPCR

3. Results and discussion

A total of 296 primer sets were used to target the ARGs (285 primer sets targeted resistance genes for all major classes of antibiotics), MGEs (9 primer sets targeted transposase genes), and one primer set targeted the clinical class 1 integron-integrase gene and 16S rRNA genes (Table S1) (Zhu et al., 2017c). High-throughput qPCR was performed using the Wafergen SmartChip Real-time PCR system. The thermal cycle amplification conditions consisted of initial denaturation (10 min at 95 °C), followed by 40 cycles of denaturation (30 s at 95 °C) and annealing (30 s at 60 °C). Melting curve analyses were automatically generated by Wafergen software. A non-template negative control was used for each primer set and all quantitative PCRs were performed in technical triplicates. Exclusion criteria included wells with efficiencies beyond the range 1.7–2.3 or an r2 under 0.99 and amplicons with multiple melting curves. Only samples with more than two replicates within the detection limit (a threshold cycle (Ct) of 31) were regarded as positive quantification and used in further data analysis (Zhu et al., 2017c). Gene copy numbers were calculated using the following formula.

3.1. The persistent bacteria in humans and water environments The composition of bacterial communities was different among feces, skin and water (Anosim statistic R = 0.7579, P < 0.001). Bacteroidetes and Firmicutes were the dominant phyla in human feces accounting for 89.5% of total bacterial 16S rRNA gene sequences, Actinobacteria and Proteobacteria were the dominant phyla (69.3%) on human skin, and Bacteroidetes and Proteobacteria were the dominant phyla (70.2% to 86.1%) in water environments (Fig. S1). To further explore ARG transfer in water environments, we tracked the circulation of bacteria between feces and various water samples (Fig. S2). A total of 28 bacteria genera were all found in feces, influent, effluent and downstream, these bacteria were also persistent, here called type-I bacteria. A total of 9 bacteria were not found in the river water, and only persist into the effluent, here called type-II bacteria. Some 37 bacteria were successfully removed by treatment, since they only moved as far as the influent samples, here called type-III bacteria. Details of type-I, II, and III bacteria were shown in Table S2. Type-I bacteria were hardly removed by treatment, and these bacteria showed high abundance in feces, influent, effluent and the river.

Relative gene copy number = 10((31 − Ct)/(10/3)) The relative gene copy numbers of ARGs and MGEs were 1156

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we normalized the abundance of ARGs to copies per bacterial cell, using the abundance of the 16S rRNA gene as a proxy for cell number (Fig. 1b). Fecal samples exhibited the highest number of ARGs per cell, and these ARGs were dominated by genes conferring resistance to tetracycline, beta-lactams and MLSB. The only other sample to show similarly high normalized abundance score was the influent (W6) to the sewage treatment system (Fig. 1b), reflecting the abundance in feces. Some individual ARGs were consistently found at high abundance in all feces samples. These included the genes aacA_aphD, cfxA, ermB, ermF, tet(Q), tet(X), and tet(32) (Fig. S3). On skin, the resistance genes cphA-01, fox5, mepA, strB, and ttgB were consistently detected at high abundance. This demonstrated the different distribution of ARGs in human bodies, probably driven by the different ecological niches of the bacterial species in which they reside (Gibson et al., 2015; Schloissnig et al., 2013; Smillie et al., 2011). The similarities of ARGs within sample types was confirmed by PCoA (Fig. 2), which showed strong clustering of feces samples, well separated from skin samples. Samples from the drinking water treatment plant clustered together, consistent with their low abundance and diversity of ARGs (Fig. 1), while the remaining water samples form a separate cluster. From the PCoA analysis, drinking water and human skins were clustered together such as W3 and W4, indicating the relationship between drinking water and human skins. Previous study used animals to demonstrated the effected of drinking water on mice gut bacterial community (Dias et al., 2017), but the impacts that antibiotic resistance genes in drinking water may have on human health were still unclear (Vaz-Moreira et al., 2014). The diversity of ARGs detected in most feces and skin samples were

They may be the hosts of the persistent ARGs that subsequently transfer into aquatic environment. In contrast, type-II bacteria were not found in downstream water samples. These bacteria were generally anaerobic or facultative anaerobic bacteria that might not survive in well-oxygenated river water. Type-III bacteria were removed by treatment. These were often enteric bacteria and may not tolerate sewage treatment. Some Type-III bacteria require specific environmental condition, for instance species of the genus Aquabacterium that were commonly isolated from drinking water biofilms (Kalmbach et al., 1999). The change bacterial species composition could affect the distribution of ARGs (Zhu et al., 2017c), especially if these ARGs were located on chromosomes rather than mobile genetic elements.

3.2. The characterization of ARGs in various environments Using the HT-qPCR method, the total numbers of distinct resistance genes detected in all the combined samples from feces, skin and water were 234, 150 and 226, respectively. Individual samples exhibited lower diversity than these totals, with the highest diversity occurring in a fecal sample (149 ARGs) and the lowest in a skin sample (21 ARGs). In general, the highest diversity occurred in fecal samples, river water, sewage influent and effluent. Lower diversity characterized samples from the drinking water treatment plant, tap water and skin (Fig. 1a). Genes encoding resistance to beta-lactams, MLSB, tetracycline, aminoglycosides, vancomycin, and genes for efflux pumps were present in almost every sample, although their relative proportions varied (Fig. 1). Because the bacterial load in each sample type varied considerably,

Fig. 1. Diversity and abundance of antibiotic resistance genes in human feces (F1 to F10), on human skin (S1 to S10) and in water (W1 to W9). Number of distinct ARGs detected (a) and abundance of resistance genes normalized as copies per cell, based on 16S rRNA gene abundance (b). Error bars represent standard deviation. Resistance genes were classified based on the antibiotics to which they conferred resistance, these being: aminoglycosides, beta-lactams, fluoroquinolone/quinolone/ florfenicol/chlora phenicol/amphenicol (FCA), macrolide-lincosamide-streptogramin B (MLSB), sulfonamide, tetracycline, vancomycin and ‘others’, largely comprising efflux pumps. 1157

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a higher absolute abundance of ARGs (Fig. S4f), indicating the removal of ARGs was poor in RDSTS compared with municipal sewage treatment systems (Chen and Zhang, 2013), and that RDSTS may be a hotspot for ARG replication and propagation. The rural domestic sewage treatment system harbored diverse and abundant ARGs that conferred resistance to almost all antibiotics, showing that it could be a major conduit for transferring ARGs into the environment.

3.3. The persistent ARGs in humans and water environments To understand the transfer of ARGs in water environments, we tracked the circulation of ARGs between feces and various water samples (Fig. 3). A total of 53 ARGs were found in feces, sewage influent, effluent and the river downstream from the effluent point. These ARGs were persistent, and were not removed by treatment, here called type-I ARGs. A total of 32 ARGs did not survive in the river water, and only persist into the effluent, here called type-II ARGs. A total of 43 ARGs only move as far as the influent samples, and appeared to be successfully removed by treatment, here called type-III ARGs. Details and identities of type-I, II, III ARGs were shown in Table S3. Persistent ARGs may reflect a high level of risk for public health, since persistent ARGs were inefficiently removed by treatment systems. Genes for resistance to beta-lactams were persistent in both humans and water environments such as blaCTX, blaOXA, blaTEM, blaVEB and blaVIM, and beta-lactams were among the top three most consumed antibiotics worldwide (You et al., 2018). Among the persistent ARGs some of those were undoubtedly widespread in different bacterial species such as ermB, tetM, sul2 and so on. Large numbers of ARGs found in feces were also present in treatment plant influent, where the lower absolute abundance was presumably a result of dilution. A significant proportion of these ARGs was not removed by treatment, and were also found in effluent. The abundance of some ARGs does decline during treatment, and this may correspond to the removal of the organisms they reside in. Chromosomally located resistance determinants could be prone to removal if the cells they reside in are preferentially removed by treatment, while ARGs that can move between species by lateral gene transfer could survive by moving between organisms. Some ARGs present in the effluent were not found in the river water. Perhaps this is because the organisms they reside in do not survive in the river water environment. The analysis presented in Fig. 3 demonstrates the differential survival of ARGs, showing transmission from human, to treatment plant, to effluent, and then back into the receiving river.

Fig. 2. ARGs distribution among human feces, human skins and water. Principal coordinate analysis (PCoA) based on Bray-Curtis distance, showing the overall distribution pattern of ARG assemblages in each of the different samples. F, fecal samples; S, skin samples; W1, DWTP influent; W2, DWTP effluent; W3–W5, tap water; W6, RDSTS influent; W7, RDSTS effluent; W8, upstream; W9, downstream.

similar (Fig. S4a, b). Inverse Simpson and Shannon indices calculations consistently indicated that ARG diversity in water generally increased along the water transmission pathway from the drinking water treatment plant to tap water, and then to the sewage treatment influent (Fig. S4c). Treatment of sewage did not significantly reduce ARG diversity, and effluent exhibited much higher diversity than upstream river samples (W8). Further, downstream samples (W9) exhibited significantly higher ARG diversity than upstream (P < 0.05), indicating that the sewage effluent significantly increased the ARG diversity in the river (Guo et al., 2017; Pruden et al., 2012; Yang et al., 2014). The absolute abundance of ARGs ranged from about 5 × 105 to 4.5 × 1012 copies per gram in feces, and about 1.3 × 103 to 1.3 × 106 copies per cm2 on skin (Fig. S4). Abundance of these genes in water samples was much lower, with ARGs being some 3.9 × 103 to 8.7 × 1010 copies per L and MGEs ranging between 1 × 105 to 3 × 1010 copies per L (Fig. S4). Among water samples, RDSTS effluent contained

Fig. 3. Absolute abundance of ARGs in humans and water samples from various environmental compartments. Red points (n = 53) represent the ARGs that appear in feces, influent, effluent and the downstream river. These ARGs are persistent, and are not removed by treatment, here called Type-I ARGs. Some ARGs do not survive in the river water, and only persist into the effluent - green points (n = 32), here called Type-II ARGs, while some ARGs appear to be successfully removed by treatment, only moving as far as the influent samples - blue points (n = 43), here called Type-III ARGs. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 4. The Network analysis depicting the co-occurrence patterns between types of ARGs and bacteria. The nodes were coloured according to types of ARGs and genus. A connection represents a strong (Spearman's correlation coefficient (ρ) > 0.8) and significant (P < 0.01) correlation. Node size was weighted according to the number of connections (that is the degree) and edges weighted according to the correlation coefficient. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

3.4. Co-occurrence of ARGs and bacteria between humans and water environments To address the question of whether specific bacterial species might host particular ARGs, the co-occurrence patterns of ARGs and bacterial types were explored by network analysis (Fig. 4) (Junker and Schreiber, 2008). The visualization of the network consisted of 137 nodes (nodes represented type-I, II, III ARGs or bacteria) and 256 edges. The modularity index of network was 0.741 (values > 0.4) suggested that the network had a significantly modular structure (Zhang et al., 2017). Genes in the same module may interact more frequently among themselves than with genes in other modules. These genes were potentially carried by some specific MGEs or bacteria, and could then share similar dynamics and transmission between environmental compartments. Determining the co-occurrence of ARGs and bacteria would help to track transmission between humans and water environments and identify emerging ARG subtypes, these could then be targeted to controlling ARG spread in village communities. As shown in Fig. 4, Type-I ARGs and Type-I bacteria clustered

Fig. 5. Correlation of absolute abundance of ARGs with absolute abundance of MGEs.

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different cells and species during treatment. Pollution of river water with these bacteria and their resident ARGs closes the transmission circle, since downstream settlements can acquire these resistance genes via consumption of river water. Understanding the mechanisms of movement between different environmental compartments is a first step towards better control of antibiotic resistance. According to the results of this study, we suggested possible options for antibiotic resistance control: 1) control antibiotic use in rural and urban clinics especially for personal antibiotic use and raise the awareness of ARGs among rural residents; 2) focus on the ARGs elimination in rural DWTP to provide drinking water with low ARGs abundance; 3) set more RDSTS in rural area to increase the sewage collection rate and strengthen the tail water treatment.

together and had more interconnections within the network. This clustering strongly suggested that these ARGs were transferred from feces through influent, effluent and into the river by their residence within similarly persistent bacteria. Since the network was based on correlation, it was not really possible to assign residence of particular resistance genes to particular Bacterial genera. However, it was likely that the cloud of Type-1 ARGS associated with Type-1 Bacteria was collectively housed within these Bacteria, and it was possible that the ARGs were shared among these genera by lateral gene transfer. Some Type-1 resistance genes do show network patterns that were consistent with survival through the treatment process by lateral gene transfer. For instance, the network cluster containing ARGs of the aadA family were strongly cross connected, but not significantly connected to any named bacterial genera. This family of ARGs and the similarly unconnected blaOXA10 were borne on integron gene cassettes (Partridge et al., 2009). Gene cassettes are DNA elements which can be captured by integrons (Gillings, 2014). Consequently, these ARGs might persist through water treatment by lateral gene transfer between diverse hosts, or by survival as extracellular DNA. For some Type-II and Type-III ARGs, the network analysis demonstrated few relationships with particular bacterial genera, such as the module containing the acr-family and yce-family. Among Type-III bacteria and Type-III ARGs, Clostridium was correlated with ARG subtypes of MLSB resistance genes (mefA), as was Enterococcus (ermK-02), which also showed correlation with tetracycline resistance genes (tetS). These associations have been reported in previous studies (Li et al., 2015; Martins et al., 2006; Oravcova et al., 2014). The network analysis (Fig. 4) allowed predictions to be made about the fates of ARGs as they flow through various compartments. In some cases, ARGs might be moving between different cell hosts during the treatment process, in other cases ARGs were eliminated when their host cells die. Analyses like these will help identify critical points during sewage treatment that can be addressed to improve the removal of ARGs.

Acknowledgements This work was supported by Natural Science Foundation of China (21876147, 41571130064 and 21677121). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.envint.2018.10.032. References Aminov, R.I., 2011. Horizontal gene exchange in environmental microbiota. Front. Microbiol. 2, 158. Bai, X., Ma, X., Xu, F., Li, J., Zhang, H., Xiao, X., 2015. The drinking water treatment process as a potential source of affecting the bacterial antibiotic resistance. Sci. Total Environ. 533, 24–31. Bastian, M., Heymann, S., Jacomy, M., 2009. Gephi: an open source software for exploring and manipulating networks. In: International Conference on Weblogs and Social Media. 8. ICWSM, pp. 361–362 2009. Berendonk, T.U., Manaia, C.M., Merlin, C., Fattakassinos, D., Cytryn, E., Walsh, F., Bürgmann, H., Sørum, H., Norström, M., Pons, M.N., 2015. Tackling antibiotic resistance: the environmental framework. Nat. Rev. Microbiol. 13, 310. Bhutani, N., Muraleedharan, C., Talreja, D., Rana, S.W., Walia, S., Kumar, A., Walia, S.K., 2014. Occurrence of multidrug resistant extended spectrum beta-lactamase-producing bacteria on iceberg lettuce retailed for human consumption. Biomed. Res. Int. 2015, 547547. Chen, H., Zhang, M., 2013. Occurrence and removal of antibiotic resistance genes in municipal wastewater and rural domestic sewage treatment systems in eastern China. Environ. Int. 55, 9–14. Cui, E., Wu, Y., Zuo, Y., Chen, H., 2016. Effect of different biochars on antibiotic resistance genes and bacterial community during chicken manure composting. Bioresour. Technol. 203, 11–17. 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. Di Cesare, A., Eckert, E.M., D'Urso, S., Bertoni, R., Gillan, D.C., Wattiez, R., Corno, G., 2016. Co-occurrence of integrase 1, antibiotic and heavy metal resistance genes in municipal wastewater treatment plants. Water Res. 94, 208–214. Dias, M.F., Reis, M.P., Acurcio, L.B., Carmo, A.O., Diamantino, C.F., Motta, A.M., Kalapothakis, M., Nicoli, J.R., Nascimento, A.M.A., 2017. Changes in mouse gut bacterial community in response to different types of drinking water. Water Res. 132, 79. Forsberg, K.J., Reyes, A., Wang, B., Selleck, E.M., Sommer, M.O., Dantas, G., 2012. The shared antibiotic resistome of soil bacteria and human pathogens. Science 337, 1107–1111. Foster, K.R., Schluter, J., Coyte, K.Z., Rakoffnahoum, S., 2017. The evolution of the host microbiome as an ecosystem on a leash. Nature 548, 43–51. Gibson, M.K., Forsberg, K.J., Dantas, G., 2015. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. ISME J. 9, 207–216. Gillings, M.R., 2014. Integrons: past, present, and future. Microbiol. Mol. Biol. Rev. 78, 257–277. Guo, J., Li, J., Chen, H., Bond, P.L., Yuan, Z., 2017. Metagenomic analysis reveals wastewater treatment plants as hotspots of antibiotic resistance genes and mobile genetic elements. Water Res. 123, 468–478. Hu, H.W., Wang, J.T., Li, J., Shi, X.Z., Ma, Y.B., Chen, D., He, J.Z., 2017. Long-term nickel contamination increases the occurrence of antibiotic resistance genes in agricultural soils. Environ. Sci. Technol. 51, 790. Junker, B.H., Schreiber, F., 2008. Analysis of Biological Networks (Wiley Series in Bioinformatics). Wiley-Interscience. Kalmbach, S., Manz, W., Wecke, J., Szewzyk, U., 1999. Aquabacterium gen. nov., with description of Aquabacterium citratiphilum sp. nov., Aquabacterium parvum sp. nov. and Aquabacterium commune sp. nov., three in situ dominant bacterial species from

3.5. The influence of bacteria and MGEs on ARGs distribution Redundancy analysis was conducted to examine the relationships between entire ARG profiles and bacterial communities (Fig. S5). The two primary axes explained 70.5% of total variance in ARG profiles. Bacteroidetes, Firmicutes and Fusobacteria were significantly correlated with Axis 1 and genes conferring resistance to aminoglycoside, beta-lactams, MLSB, other/efflux and tetracycline. The total absolute abundance of ARGs was significantly correlated with MGEs (P < 0.01, r = 0.989) (Fig. 5). This strongly suggested cooccurrence and of ARGs and MGEs in the samples examined. In turn, this suggested that much of the survival of persistent ARGs could depend on their residence on MGEs, where they had the ability to move between different bacterial species. Our results and previous studies (Zheng et al., 2017; Zhou et al., 2017; Zhu et al., 2017c) consistently indicated the potential effect of MGEs on ARG distribution and transmission. 4. Conclusion In general, this work identified humans as a major contributor to the load of ARGs entering the environment. In this scenario, ARGs could be acquired from water, food, domestic animals, or other village members. ARGs then take up residence in the gut and skin microbiota, where their abundance will increase under any antibiotic therapy. Such microbiota, with their cargo of ARGs, enter waste water via washing or sewage. The domestic sewage treatment systems examined here do eliminate some bacteria and ARGs, but significant numbers survive the treatment process to emerge as effluent for disposal into the river. Survival through the system might occur via cell types that resist elimination, but is also likely to be driven by ARGs being able to transfer between 1160

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reservoir of antibiotic resistance genes. Virulence 1, 299–303. Stevenson, C., Hall, J.P., Harrison, E., Wood, A.J., Brockhurst, M.A., 2017. Gene mobility promotes the spread of resistance in bacterial populations. ISME J. 11 (8), 1930–1932. Turner, S., Pryer, K.M., Miao, V.P., Palmer, J.D., 1999. Investigating deep phylogenetic relationships among Cyanobacteria and plastids by small subunit rRNA sequence analysis. J. Eukaryot. Microbiol. 46, 327–338. Vaz-Moreira, I., Nunes, O.C., Manaia, C.M., 2014. Bacterial diversity and antibiotic resistance in water habitats: searching the links with the human microbiome. FEMS Microbiol. Rev. 38 (4), 761–778. von Wintersdorff, C.J., Penders, J., van Niekerk, J.M., Mills, N.D., Majumder, S., van Alphen, L.B., Savelkoul, P.H., Wolffs, P.F., 2016. Dissemination of antimicrobial resistance in microbial ecosystems through horizontal gene transfer. Front. Microbiol. 7, 173. Xu, J., Xu, Y., Wang, H., Guo, C., Qiu, H., He, Y., Zhang, Y., Li, X., Wei, M., 2015. Occurrence of antibiotics and antibiotic resistance genes in a sewage treatment plant and its effluent-receiving river. Chemosphere 119, 1379–1385. Xu, L., Ouyang, W., Qian, Y., Su, C., Su, J., Chen, H., 2016. High-throughput profiling of antibiotic resistance genes in drinking water treatment plants and distribution systems. Environ. Pollut. 213, 119–126. Yang, Y., Li, B., Zou, S., Fang, H.H., Zhang, T., 2014. Fate of antibiotic resistance genes in sewage treatment plant revealed by metagenomic approach. Water Res. 62, 97–106. Yatsunenko, T., Rey, F.E., Manary, M.J., Trehan, I., Dominguez-Bello, M.G., Contreras, M., Magris, M., Hidalgo, G., Baldassano, R.N., Anokhin, A.P., 2012. Human gut microbiome viewed across age and geography. Nature 486, 222. You, X., Wu, D., Wei, H., Xie, B., Lu, J., 2018. Fluoroquinolones and β-lactam antibiotics and antibiotic resistance genes in autumn leachates of seven major municipal solid waste landfills in China. Environ. Int. 113, 162–169. Zhang, S.Y., Su, J.Q., Sun, G.X., Yang, Y., Zhao, Y., Ding, J., Chen, Y.S., Shen, Y., Zhu, G., Rensing, C., 2017. Land scale biogeography of arsenic biotransformation genes in estuarine wetland. Environ. Microbiol. 19 (6). Zheng, J., Gao, R., Wei, Y., Chen, T., Fan, J., Zhou, Z., Makimilua, T.B., Jiao, Y., Chen, H., 2017. High-throughput profiling and analysis of antibiotic resistance genes in East Tiaoxi River, China. Environ. Pollut. 230, 648–654. Zhou, Z.C., Zheng, J., Wei, Y.Y., Chen, T., Dahlgren, R.A., Shang, X., Chen, H., 2017. Antibiotic resistance genes in an urban river as impacted by bacterial community and physicochemical parameters. Environ. Sci. Pollut. Res. 24, 1–10. Zhu, Y.G., Johnson, T.A., Su, J.Q., Qiao, M., Guo, G.X., Stedtfeld, R.D., Hashsham, S.A., Tiedje, J.M., 2013. Diverse and abundant antibiotic resistance genes in Chinese swine farms. Proc. Natl. Acad. Sci. U. S. A. 110, 3435–3440. Zhu, Y.G., Gillings, M., Simonet, P., Stekel, D., Banwart, S., Penuelas, J., 2017a. Human dissemination of genes and microorganisms in Earth's Critical Zone. Glob. Chang. Biol. 1–12. Zhu, Y.G., Gillings, M., Simonet, P., Stekel, D., Banwart, S., Penuelas, J., 2017b. Microbial mass movements. Science 357, 1099–1100. Zhu, Y.G., Zhao, Y., Li, B., Huang, C.L., Zhang, S.Y., Yu, S., Chen, Y.S., Zhang, T., Gillings, M.R., Su, J.Q., 2017c. Continental-scale pollution of estuaries with antibiotic resistance genes. Nat. Microbiol. 2, 16270.

the Berlin drinking water system. Int. J. Syst. Bacteriol. 2 (49 Pt), 769–777. Li, B., Yang, Y., Ma, L., Ju, F., Guo, F., Tiedje, J.M., Zhang, T., 2015. Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes. ISME J. 9 (11), 2490–2502. Luo, Y., Mao, D., Rysz, M., Zhou, Q., Zhang, H., Xu, L., P, J.J.A., 2010. Trends in antibiotic resistance genes occurrence in the Haihe River, China. Environ. Sci. Technol. 44, 7220–7225. Ma, L., Xia, Y., Li, B., Yang, Y., Li, L.G., Tiedje, J.M., Zhang, T., 2016. Metagenomic assembly reveals hosts of antibiotic resistance genes and the shared resistome in pig, chicken and human feces. Environ. Sci. Technol. 50 (1), 420–427. Mao, D., Yu, S., Rysz, M., Luo, Y., Yang, F., Li, F., Hou, J., Mu, Q., Alvarez, P.J., 2015. Prevalence and proliferation of antibiotic resistance genes in two municipal wastewater treatment plants. Water Res. 85, 458–466. Martins, d.C.P., Vazpires, P., Bernardo, F., 2006. Antimicrobial resistance in Enterococcus spp. isolated in inflow, effluent and sludge from municipal sewage water treatment plants. Water Res. 40, 1735–1740. Oravcova, V., Zurek, L., Townsend, A., Clark, A.B., Ellis, J.C., Cizek, A., Literak, I., 2014. American crows as carriers of vancomycin-resistant enterococci with vanA gene. Environ. Microbiol. 16, 939–949. Pal, C., Bengtsson-Palme, J., Kristiansson, E., Larsson, D.G.J., 2016. The structure and diversity of human, animal and environmental resistomes. Microbiome 4, 54. Partridge, S.R., Tsafnat, G., Coiera, E., Iredell, J.R., 2009. Gene cassettes and cassette arrays in mobile resistance integrons. FEMS Microbiol. Rev. 33, 757–784. Pawlowski, A.C., Wang, W., Koteva, K., Barton, H.A., McArthur, A.G., Wright, G.D., 2016. A diverse intrinsic antibiotic resistome from a cave bacterium. Nat. Commun. 7, 13803. Pehrsson, E.C., Tsukayama, P., Patel, S., Mejíabautista, M., Sosasoto, G., Navarrete, K.M., Calderon, M., Cabrera, L., Hoyosarango, W., Bertoli, M.T., 2016. Interconnected microbiomes and resistomes in low-income human habitats. Nature 533, 212–216. Pruden, A., Arabi, M., Storteboom, H.N., 2012. Correlation between upstream human activities and riverine antibiotic resistance genes. Environ. Sci. Technol. 46, 11541–11549. Rodriguez-Mozaz, S., Chamorro, S., Marti, E., Huerta, B., Gros, M., Sànchez-Melsió, A., Borrego, C.M., Barceló, D., Balcázar, J.L., 2015. Occurrence of antibiotics and antibiotic resistance genes in hospital and urban wastewaters and their impact on the receiving river. Water Res. 69, 234–242. Schloissnig, S., Arumugam, M., Sunagawa, S., Mitreva, M., Tap, J., Zhu, A., Waller, A., Mende, D.R., Schommer, N.N., Gallo, R.L., 2013. Structure and function of the human skin microbiome. Trends Microbiol. 21, 660–668. Segawa, T., Takeuchi, N., Rivera, A., Yamada, A., Yoshimura, Y., Barcaza, G., Shinbori, K., Motoyama, H., Kohshima, S., Ushida, K., 2013. Distribution of antibiotic resistance genes in glacier environments. Environ. Microbiol. Rep. 5, 127–134. Shi, P., Jia, S., Zhang, X.X., Zhang, T., Cheng, S., Li, A., 2013. Metagenomic insights into chlorination effects on microbial antibiotic resistance in drinking water. Water Res. 47, 111. Smillie, C.S., Smith, M.B., Friedman, J., Cordero, O.X., David, L.A., Alm, E.J., 2011. Ecology drives a global network of gene exchange connecting the human microbiome. Nature 480, 241–244. Sommer, M.O., Church, G.M., Dantas, G., 2010. The human microbiome harbors a diverse

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