Catalogue of antibiotic resistome and host-tracking in drinking water deciphered by a large scale survey
- Liping Ma†1,
- Bing Li†2,
- Xiao-Tao Jiang1,
- Yu-Lin Wang1,
- Yu Xia1,
- An-Dong Li1 and
- Tong Zhang1Email authorView ORCID ID profile
© The Author(s). 2017
Received: 27 April 2017
Accepted: 2 November 2017
Published: 28 November 2017
Excesses of antibiotic resistance genes (ARGs), which are regarded as emerging environmental pollutants, have been observed in various environments. The incidence of ARGs in drinking water causes potential risks to human health and receives more attention from the public. However, ARGs harbored in drinking water remain largely unexplored. In this study, we aimed at establishing an antibiotic resistome catalogue in drinking water samples from a wide range of regions and to explore the potential hosts of ARGs.
A catalogue of antibiotic resistome in drinking water was established, and the host-tracking of ARGs was conducted through a large-scale survey using metagenomic approach. The drinking water samples were collected at the point of use in 25 cities in mainland China, Hong Kong, Macau, Taiwan, South Africa, Singapore and the USA. In total, 181 ARG subtypes belonging to 16 ARG types were detected with an abundance range of 2.8 × 10−2 to 4.2 × 10−1 copies of ARG per cell. The highest abundance was found in northern China (Henan Province). Bacitracin, multidrug, aminoglycoside, sulfonamide, and beta-lactam resistance genes were dominant in drinking water. Of the drinking water samples tested, 84% had a higher ARG abundance than typical environmental ecosystems of sediment and soil. Metagenomic assembly-based host-tracking analysis identified Acidovorax, Acinetobacter, Aeromonas, Methylobacterium, Methyloversatilis, Mycobacterium, Polaromonas, and Pseudomonas as the hosts of ARGs. Moreover, potential horizontal transfer of ARGs in drinking water systems was proposed by network and Procrustes analyses.
The antibiotic resistome catalogue compiled using a large-scale survey provides a useful reference for future studies on the global surveillance and risk management of ARGs in drinking water.
The overuse and misuse of antibiotics, not only for human therapy, but also for livestock breeding around the world over the past decades have led to the emergence and excess of antibiotic resistance genes (ARGs) and antibiotic resistant bacteria (ARB) in a diverse range of environments [1–4]. Among the various ARB and ARG reservoirs, aquatic ecosystems are considered to be the most important due to the high mobility of organisms and genetic elements . ARGs have been reported to rapidly spread, conducted by mobile genetic elements in ecosystems . The rapid transfer and spread of ARGs among bacterial cells  could be facilitated by mobile genetic elements, including plasmids, transposons and integrons, etc. Recently, findings on ARGs in drinking water distribution systems, especially in treated drinking water that may have direct contact with human beings, have given rise to medical concerns from both researchers and the public [8–10]. Although most microorganisms can be effectively removed after the treatment process, disinfection resistant microbes can proliferate in the drinking water distribution system . Previous studies have reported that drinking water chlorination could contribute to the enrichment of ARGs, likely induced by the underlying mechanisms of cross- or co-resistance to disinfectants and antibiotics [9, 11]. Thus, disinfection resistant microorganisms may carry more ARGs after drinking water treatment, causing potential risks and deserving more public attention.
To date, the molecular study of the microbial community and ARGs in drinking water at the user end has been mainly restricted by the following difficulties: (1) low biomass concentration for DNA extraction, (2) sample collection logistics, and (3) sampling standardization. Because of these difficulties, few studies have been conducted regarding the spatial variations of ARGs and microbial communities in drinking water at the point of use. However, the occurrence of ARGs in the point of use of drinking water may pose direct threats to human health and deserves more attentions from the public. Forsberg et al. found that soil bacteria structures resistomes across habitats, indicating the horizontal gene transfer (HGT) of ARGs between soil bacteria was in low frequency, in contrast to human pathogens . Whether drinking water bacteria drive resistomes and the potential risks of HGT both remain largely unknown. The in-depth investigation of ARGs and bacterial community profiles in large-scale drinking water samples is central to understanding the overall picture, which is essential for decision-making about water management to control antibiotic resistance in drinking water systems.
In the present study, we collected drinking water samples at the point of use from 25 cities in seven countries and regions, including mainland China, Hong Kong, Macau, Taiwan, South Africa, Singapore, and the USA. We applied a metagenomic approach to achieve the following goals: (1) detect the antibiotic resistome in drinking water samples over a wide range of regions, (2) investigate the correlation between bacteria and resistomes, and (3) explore the potential hosts of ARGs. This large sequencing data set for a wide scope survey on the microbial community and ARGs in drinking water reflects the comprehensive resistome profiles and reveals the potential risks to human health caused by ARGs in drinking water.
Tap water sampling, pretreatment, and DNA extraction
Drinking water samples were collected from the point of use of 25 cities in mainland China (n = 20), Hong Kong (n = 1), Macau (n = 1), South Africa (n = 1), Singapore (n = 1), and the USA (n = 1). Additional sample descriptions can be found in supporting information, Additional file 1 Table S1 and Additional file 1 Figure S1. High-performance cartridge-type water purifiers (Torayvino, Toray Industries Inc., Japan) were installed on taps according to a protocol described elsewhere , to collect microorganisms by filtering tap water. Approximately 2000 L of tap water were filtered, controlling the flow rate of ~ 40 L/h, for about 48 h. After filtration, the purifiers were immersed into 50% ethanol solution to fix the captured microorganisms. The filters were delivered back to the laboratory within 72 h using an ice box. Upon arrival, the hollow fiber filter within purifiers was taken out and immersed into 100 mL ultrapure water and then treated using ultrasonication (Branson Ultrasonics Corp., USA) for 15 min to detach the microbial cells. The cells in water were subsequently collected by the filtration using a 0.45-μm cellulose ester membrane (Millipore Corp., USA). The effectiveness of capturing microorganisms by this approach had been evaluated before . The membranes were stored at − 20 °C before DNA extraction. Additionally, three water purifiers with no tap water filtration were used as blank samples. Genomic DNA was extracted using FastDNA SPIN Kit for Soil (MP Biomedicals, France) following the standard protocol. DNA concentration was measured by Qubit® 2.0 Fluorometer (Invitrogen, Life techniques). The DNA concentration of the three blank samples was below the detection limit.
DNA for each sample (5 μg) was used for 350 bp library construction (Nextera® DNA Library Preparation Kit), and paired-end (2 × 100 bp reads) metagenomic sequencing was performed on an Illumina HiSeq 4000 platform in the Beijing Genomics Institute (BGI). Data filtration was performed to guarantee the quality of the downstream analysis (Additional file 1 S1). Filtered data obtained from tap water samples was 120 Gb (giga base pairs) in total, which is the largest sequence data set reported to date on the study of ARGs in drinking water samples. The metagenomics data was deposited into the National Center for Biotechnology Information Short Reads Archive database (NCBI SRA) under the BioProject PRJNA305188.
Illumina MiSeq sequencing for 16S rRNA genes
The V4 region (~ 265 nucleotides) of the 16S rRNA gene sequences was amplified using F515 (5′-GTGCCAGCMGCCGCGGTAA-3′) and R806 (5′-GGACTACHVGGGTWTCTAAT-3′) primers (Additional file 1 Table S2). Dual-index sequencing strategy for primers (adapter + barcode + pad + linker + primer; Additional file 1 Table S3) and reaction conditions used in this study have been described elsewhere . PCR assays were performed in triplicate to avoid the variations during amplification, and purified PCR amplicons were pooled together and sequenced on Illumina MiSeq PE250 (BGI). All the drinking water samples were successfully amplified and generated 32,525–147,248 sequencing reads. Additionally, three water purifiers with no tap water filtration were used as blank samples. Two samples were selected to evaluate the biases of using primers with different barcodes. One sample was selected using different DNA extraction kits to assess the effects of the kits on DNA extractions (Additional file 1 S2). All 16S rRNA gene sequences generated from tap water samples were deposited into NCBI SRA under the BioProject PRJNA305188. Sequences were analyzed using Mothur software as described previously . Sequencing depth was normalized to 30,000 sequences for each sample. 16S rRNA gene sequences were clustered into OTUs based on the similarity threshold of 0.97.
Identification of ARG-like sequences
Metagenomic assembly and identification of ARG-carrying contigs
After quality control, the metagenomic sequences were assembled using IDBA algorithm (version 1.1.1) . The identification of ARG-carrying contigs (ACCs) was conducted following the strategy proposed in our previous study . In brief, contigs were assembled using IDBA with default parameters. The open reading frames (ORFs) prediction was conducted using Prodigal (version 2) . Then, the predicted ORF sequences were searched against structured non-redundant ARDB database for ARG-like ORFs identification using BLASTX under an E-value ≤ 10−10 . An ORF sequence was considered to be an ARG-like ORF if its best BLASTX hit alignment to ARG sequences was under a cutoff of ≥ 80% similarity and ≥ 70% query coverage . The identified ARG-like ORFs were then classified according to the structured non-redundant ARDB database.
Taxonomic annotation of ARG-carrying contigs
To perform the taxonomic annotation of the identified ACCs, the ORF sequences of each ACC were compared to the local NCBI NR database using BLASTP with an E-value ≤ 10−5  and then were parsed and annotated using MEGAN (MEtaGenome ANalyzer, version 5) . An in house R script was used to assign taxa to contigs. In short, if more than 50% of the ORFs on a contig were attributed to the same kingdom/phylum/class/order/family/genus, then the contig was assigned to that taxon .
Previously, network analysis was extensively used for the exploration of the underlying associations among genes, proteins, and microorganisms in complex microbial communities [25, 26]. In the present study, a correlation matrix was constructed with ARG and 16S rRNA data to explore the potential correlations of ARG–ARG, ARG–bacteria, and bacteria–bacteria by calculating all pairwise Spearman’s correlation coefficients (ρ) among ARG subtypes that occurred in at least 40% of the tap water samples. A correlation between two nodes was regarded as statistically significant for ρ ≥ 0.6 and P value ≤ 0.01. ρ and P value were generated via R-function “rcorr” (Hmisc package). To reduce the frequency of false-positive results, the P values were then adjusted using Benjamini–Hochberg method . The strong pairwise correlations among ARGs and species abundances formed correlation networks. The network analysis was performed in R environment using igraph, VEGAN, and Hmisc packages, and was visualized by the interactive platform of Gephi (version 0.9.0).
To assess the potential for HGT of ARGs in drinking water, Procrustes analysis was performed based on the previously published hypothesis that bacterial phylogeny may structure the resistome (HGT in low frequency) if there were a strong correlation between the ARG profile and bacterial composition . In brief, the difference between groups (ARG-bacteria) was analyzed using a one-way ANOVA test with Tukey post-hoc tests. A P value of < 0.05 (two-sided) was considered as statistically significant. Procrustes transformations were performed using two Bray-Curtis distances plots (PCoA) as input based on the matrix of microbial community and ARGs at subtype level. The measure of fit M 2 (the sum of squared distances between matched sample pairs) and P values were determined from 10,000 labeled permutations .
Results and discussion
Broad-spectrum profile of ARG abundance in tap water
To further explore the potential correlation among ARGs, network analysis was used. It revealed the ARG combinations of mexE-mexF-oprN and sul1-aadA-aadB in drinking water (modularity = 0.493; Additional file 1 Figure S4, Additional file 1 S3). These gene combinations were previously discovered on the whole genome of Pseudomonas aeruginosa and Salmonella enterica, respectively [37, 38]. Thus, the metagenomic based approach largely facilitated ARGs investigation over a larger spectrum without PCR bias and captured a more comprehensive picture of the correlation among ARG profiles in drinking water.
Comparison of ARG profiles from drinking water and other environmental samples
The host of ARGs in tap water samples
The spatial distribution of bacterial community
Totally, 26,862 bacterial OTUs were observed in at least one of these tap water samples. Among them, the highest bacterial diversity across all samples, 5018 OTUs, was observed in Tibet tap water of mainland China, followed by 4302 OTUs in water from Xinjiang, and 4262 OTUs in water from Inner Mongolia in northwestern China. The lowest bacterial diversity was observed in Macau (122 OTUs), followed by California of the USA (210 OTUs), and Sichuan province of China (226 OTUs). Based on the OTUs and corresponding abundances, PCoA was performed to compare the spatial variations of bacterial community using weighted Unifrac distance, which considered both species abundance and phylogeny (Additional file 1 Figure S6). Overall, differences in phylogenetic diversity and abundance of OTUs were obvious across drinking water samples. Notably, the composition of bacterial communities was more similar among the triplicated samples collected from the same tap, triplicated DNA extractions from the same sample, DNA extractions using different kits, and triplicated PCRs using primers with different barcodes. Thus, in the present study, DNA extraction strategy and PCR amplifications using primers with different barcodes are not expected to be factors influencing the detection of microbial compositions in tap water samples.
The horizontal gene transfer potential for ARGs among bacterial population
In this study, a catalog of antibiotic resistome in drinking water was established and the host-tracking of ARGs was conducted via a large-scale survey using metagenomic approach. In total, 181 ARG subtypes belonging to 16 ARG types were detected with an abundance range from 2.8 × 10−2 to 4.2 × 10−1 capc. The highest abundance was observed in northern China. The dominant ARGs in the drinking water samples include bacitracin, multidrug, aminoglycoside, sulfonamide, and beta-lactam resistance genes. Moreover, metagenomic assembly based host-tracking revealed that 80% of the ARG-carrying contigs originating from Pseudomonas spp. carried multidrug resistance genes. The findings of this study should propel the global surveillance and risk assessment of ARGs in drinking water onto the agendas of water supply authorities. This will aid to prevent both the proliferation of ARGs in drinking water and their horizontal transfer to pathogenic microbes that might cause more cases of antibiotic ineffectiveness and threat to public health.
We thank Yuanqing Chao, Ke Yu, Yanping Mao, Anni Zhang, Yuchen Pang, Yu Deng, Wenjun Sun, Jun Yang, Yuanyuan Wei, and Ying Yang for their help with sample collection.
We thank the Hong Kong General Research Fund (172057/15E) and the Shenzhen Knowledge Innovation Program-Basic Research Project (JCYJ20150831192847649) for financially supporting this study. Dr. Liping Ma and Dr. Yu Xia thank the University of Hong Kong for the postdoctoral fellowship. Mr. Xiao-Tao Jiang, Mr. Yu-Lin Wang, and Mr. An-Dong Li thank the University of Hong Kong for the postgraduate studentships. The authors also would like to thank the reviewers of this manuscript for their valuable comments and suggestions.
Availability of data and materials
The sequence data sets generated from the tap water samples were deposited into the National Center for Biotechnology Information Short Reads Archive database (NCBI SRA) under the BioProject of PRJNA305188, https://0-www-ncbi-nlm-nih-gov.brum.beds.ac.uk/sra/. The non-redundant ARG database and all the customized scripts used for sequencing analysis in this study are available via https://github.com/cherrymaryma/ARGs-in-drinking-water.
MLP and LB contributed equally to this work. MLP, LB, and ZT designed this study. All co-author joined the tap water collection. MLP, LB, and ZT contributed to drafting the initial manuscript, and all co-authors revised, read, and approved the final manuscript.
Ethics approval and consent to participate
The manuscript does not report data collected from humans or animals.
Consent for publication
The manuscript does not contain any individual person’s data in any form.
The authors declare that they have no competing interests.
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- Hatosy SM, Martiny AC. The ocean as a global reservoir of antibiotic resistance genes. Appl Environ Microb. 2015;81(21):7593–9.View ArticleGoogle Scholar
- Frey SK, Topp E, Khan IUH, Ball BR, Edwards M, Gottschall N, Sunohara M, Lapen DR. Quantitative Campylobacter spp., antibiotic resistance genes, and veterinary antibiotics in surface and ground water following manure application: influence of tile drainage control. Sci Total Environ. 2015;532:138–53.View ArticlePubMedGoogle Scholar
- Bergeron S, Boopathy R, Nathaniel R, Corbin A, LaFleur G. Presence of antibiotic resistant bacteria and antibiotic resistance genes in raw source water and treated drinking water. Int Biodeter Biodegr. 2015;102:370–4.View ArticleGoogle Scholar
- Segawa T, Takeuchi N, Rivera A, Yamada A, Yoshimura Y, Barcaza G, Shinbori K, Motoyama H, Kohshima S, Ushida K. Distribution of antibiotic resistance genes in glacier environments. Env Microbiol Rep. 2013;5(1):127–34.View ArticleGoogle Scholar
- Zhang XX, Zhang T, Fang H. Antibiotic resistance genes in water environment. Appl Microbiol Biot. 2009;82(3):397–414.View ArticleGoogle Scholar
- Chen BW, Yuan K, Chen X, Yang Y, Zhang T, Wang YW, Luan TG, Zou SC, Li XD. Metagenomic analysis revealing antibiotic resistance genes (ARGs) and their genetic compartments in the Tibetan environment. Environ Sci Technol. 2016;50(13):6670–9.View ArticlePubMedGoogle Scholar
- Ma LP, Xia Y, Li B, Yang Y, Li LG, Tiedje JM, Zhang T. Metagenomic assembly reveals hosts of antibiotic resistance genes and the shared resistome in pig, chicken and human feces. Environ Sci Technol. 2016;50(1):420–7.View ArticlePubMedGoogle Scholar
- Chao YQ, Ma LP, Yang Y, Ju F, Zhang XX, Wu WM, Zhang T. Metagenomic analysis reveals significant changes of microbial compositions and protective functions during drinking water treatment. Sci Rep-UK. 2013;3:3550.View ArticleGoogle Scholar
- Shi P, Jia SY, Zhang XX, Zhang T, Cheng SP, Li AM. Metagenomic insights into chlorination effects on microbial antibiotic resistance in drinking water. Water Res. 2013;47(1):111–20.View ArticlePubMedGoogle Scholar
- Jia SY, Shi P, Hu Q, Li B, Zhang T, Zhang XX. Bacterial community shift drives antibiotic resistance promotion during drinking water chlorination. Environ Sci Technol. 2015;49(20):12271–9.View ArticlePubMedGoogle Scholar
- Xi CW, Zhang YL, Marrs CF, Ye W, Simon C, Foxman B, Nriagu J. Prevalence of antibiotic resistance in drinking water treatment and distribution systems. Appl Environ Microb. 2009;75(17):5714–8.View ArticleGoogle Scholar
- Forsberg KJ, Patel S, Gibson MK, Lauber CL, Knight R, Fierer N, Dantas G. Bacterial phylogeny structures soil resistomes across habitats. Nature. 2014;509(7502):612–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol. 2013;79(17):5112–20.View ArticlePubMedPubMed CentralGoogle Scholar
- Yang Y, Li B, Ju F, Zhang T. Exploring variation of antibiotic resistance genes in activated sludge over a four-year period through a metagenomic approach. Environ Sci Technol. 2013;47(18):10197–205.View ArticlePubMedGoogle Scholar
- Liu B, Pop M. ARDB-antibiotic resistance genes database. Nucleic Acids Res. 2009;37:D443–7.View ArticlePubMedGoogle Scholar
- Li B, Yang Y, Ma LP, Ju F, Guo F, Tiedje JM, Zhang T. Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes. ISME J. 2015;9(11):2490–502.View ArticlePubMedPubMed CentralGoogle Scholar
- Yang Y, Jiang XT, Chai BL, Ma LP, Li B, Zhang AN, Cole JR, Tiedje JM, Zhang T. ARGs-OAP: online analysis pipeline for antibiotic resistance genes detection from metagenomic data using an integrated structured ARG-database. Bioinformatics. 2016;32(15):2346–51.View ArticlePubMedGoogle Scholar
- DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72(7):5069–72.View ArticlePubMedPubMed CentralGoogle Scholar
- Angly FE, Dennis PG, Skarshewski A, Vanwonterghem I, Hegenholtz P, Tyson GW. CopyRighter: a rapid tool for improving the accuracy of microbial community profiles through lineage-specific gene copy number correction. Microbiome. 2014;2(1):11.View ArticlePubMedPubMed CentralGoogle Scholar
- Peng Y, Leung HCM, Yiu SM, Chin FYL. IDBA—a practical iterative de Bruijn graph de novo assembler. Lect N Bioinformat. 2010;6044:426–40.Google Scholar
- Hu YF, Yang X, Qin JJ, Lu N, Cheng G, Wu N, Pan YL, Li J, Zhu LY, Wang X, Meng ZQ, Zhao FQ, Liu D, Ma JC, Qin N, Xiang CS, Xiao YH, Li LJ, Yang HM, Wang J, Yang RF, Gao GF, Wang J, Zhu B. Metagenome-wide analysis of antibiotic resistance genes in a large cohort of human gut microbiota. Nat Commun. 2013;4:2151.PubMedGoogle Scholar
- Mao YP, Xia Y, Wang ZP, Zhang T. Reconstructing a Thauera genome from a hydrogenotrophic-denitrifying consortium using metagenomic sequence data. Appl Microbiol Biot. 2014;98(15):6885–95.View ArticleGoogle Scholar
- Huson DH, Mitra S, Ruscheweyh HJ, Weber N, Schuster SC. Integrative analysis of environmental sequences using MEGAN4. Genome Res. 2011;21(9):1552–60.View ArticlePubMedPubMed CentralGoogle Scholar
- Ishii S, Suzuki S, Norden-Krichmar TM, Tenney A, Chain PSG, Scholz MB, Nealson KH, Bretschger O. A novel metatranscriptomic approach to identify gene expression dynamics during extracellular electron transfer. Nat Commun. 2013;4:1601.View ArticlePubMedGoogle Scholar
- Ju F, Xia Y, Guo F, Wang ZP, Zhang T. Taxonomic relatedness shapes bacterial assembly in activated sludge of globally distributed wastewater treatment plants. Environ Microbiol. 2014;16(8):2421–32.View ArticlePubMedGoogle Scholar
- Barberan A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012;6(2):343–51.View ArticlePubMedGoogle Scholar
- Benjamini Y, Hochberg Y. Controlling the false discovery rate—a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B-Methodological. 1995;57(1):289–300.Google Scholar
- Zhang QQ, Ying GG, Pan CG, Liu YS, Zhao JL. Comprehensive evaluation of antibiotics emission and fate in the river basins of China: source analysis, multimedia modeling, and linkage to bacterial resistance. Environ Sci Technol. 2015;49(11):6772–82.View ArticlePubMedGoogle Scholar
- Andersson DI, Hughes D. Persistence of antibiotic resistance in bacterial populations. FEMS Microbiol Rev. 2011;35(5):901–11.View ArticlePubMedGoogle Scholar
- Loveland-Curtze J, Miteva VI, Brenchley JE. Herminiimonas glaciei sp nov., a novel ultramicrobacterium from 3042 m deep Greenland glacial ice. Int J Syst Evol Micr. 2009;59:1272–7.View ArticleGoogle Scholar
- Chaturvedi P, Reddy GSN, Shivaji S. Dyadobacter hamtensis sp. nov., from Hamta glacier, located in the Himalayas, India. Int J Syst Evol Micr. 2005;55:2113–7.View ArticleGoogle Scholar
- Sreedharan K, Philip R, Singh ISB. Virulence potential and antibiotic susceptibility pattern of motile Aeromonads associated with freshwater ornamental fish culture systems: a possible threat to public health. Braz J Microbiol. 2012;43(2):754–65.View ArticlePubMedPubMed CentralGoogle Scholar
- Hu Q, Zhang XX, Jia SY, Huang KL, Tang JY, Shi P, Ye L, Ren HQ. Metagenomic insights into ultraviolet disinfection effects on antibiotic resistome in biologically treated wastewater. Water Res. 2016;101:309–17.View ArticlePubMedGoogle Scholar
- Volkmann H, Schwartz T, Bischoff P, Kirchen S, Obst U. Detection of clinically relevant antibiotic-resistance genes in municipal wastewater using real-time PCR (TaqMan). J Microbiol Meth. 2004;56(2):277–86.View ArticleGoogle Scholar
- Smith MS, Yang RK, Knapp CW, Niu YF, Peak N, Hanfelt MM, Galland JC, Graham DW. Quantification of tetracycline resistance genes in feedlot lagoons by real-time PCR. Appl Environ Microb. 2004;70(12):7372–7.View ArticleGoogle Scholar
- Jiang L, Hu XL, Xu T, Zhang HC, Sheng D, Yin DQ. Prevalence of antibiotic resistance genes and their relationship with antibiotics in the Huangpu River and the drinking water sources, shanghai, China. Sci Total Environ. 2013;458:267–72.View ArticlePubMedGoogle Scholar
- Kohler T, MicheaHamzehpour M, Henze U, Gotoh N, Curty LK, Pechere JC. Characterization of MexE-MexF-OprN, a positively regulated multidrug efflux system of Pseudomonas aeruginosa. Mol Microbiol. 1997;23(2):345–54.View ArticlePubMedGoogle Scholar
- Tosini F, Visca P, Luzzi I, Dionisi AM, Pezzella C, Petrucca A, Carattoli A. Class 1 integron-borne multiple-antibiotic resistance carried by IncFI and IncL/M plasmids in Salmonella enterica serotype typhimurium. Antimicrob Agents Ch. 1998;42(12):3053–8.Google Scholar
- Miranda CC, de Filippis I, Pinto LH, Coelho-Souza T, Bianco K, Cacci LC, Picao RC, Clementino MM. Genotypic characteristics of multidrug-resistant Pseudomonas aeruginosa from hospital wastewater treatment plant in Rio de Janeiro, Brazil. J Appl Microbiol. 2015;118(6):1276–86.View ArticlePubMedGoogle Scholar
- Winkler ML, Papp-Wallace KM, Hujer AM, Domitrovic TN, Hujer KM, Hurless KN, Tuohy M, Hall G, Bonomo RA. Unexpected challenges in treating multidrug-resistant gram-negative bacteria: resistance to Ceftazidime-Avibactam in archived isolates of Pseudomonas aeruginosa. Antimicrob Agents Ch. 2015;59(2):1020–9.View ArticleGoogle Scholar
- Glaeser SP, Kampfer P. The prokaryotes. Berlin, Heidelberg: Springer; 2014.Google Scholar
- Koskinen R, Ali-Vehmas T, Kampfer P, Laurikkala M, Tsitko I, Kostyal E, Atroshi F, Salkinoja-Salonen M. Characterization of Sphingomonas isolates from Finnish and Swedish drinking water distribution systems. J Appl Microbiol. 2000;89(4):687–96.View ArticlePubMedGoogle Scholar
- Ling F, Hwang C, LeChevallier WM, Andersen GL, Liu W-T. Core-satellite populations and seasonality of water meter biofilms in a metropolitan drinking water distribution system. The ISME journal. 2015;1751-7362(15):1–14.Google Scholar
- Forsberg KJ, Reyes A, Wang B, Selleck EM, Sommer MO, Dantas G. The shared antibiotic resistome of soil bacteria and human pathogens. Science. 2012;337(6098):1107–11.View ArticlePubMedPubMed CentralGoogle Scholar
- Newman MEJ. Modularity and community structure in networks. P Natl Acad Sci USA. 2006;103(23):8577–82.View ArticleGoogle Scholar