Metagenomic profiles of antibiotic resistance genes in paddy soils ...

FEMS Microbiology Ecology, 92, 2016, fiw023 doi: 10.1093/femsec/fiw023 Advance Access Publication Date: 4 February 2016 Research Article

RESEARCH ARTICLE

Metagenomic profiles of antibiotic resistance genes in paddy soils from South China Ke-Qing Xiao1,2,†,‡ , Bing Li3,4,‡ , Liping Ma3 , Peng Bao1 , Xue Zhou1,2 , Tong Zhang3 and Yong-Guan Zhu1,5,∗ 1

State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Beijing 100085, China, 2 University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China, 3 Environmental Biotechnology Laboratory, Department of Civil Engineering, The University of Hong Kong, Hong Kong SAR, China, 4 Key Laboratory of Microorganism Application and Risk Control of Shenzhen, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China and 5 Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021, China ∗

Corresponding author: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Beijing, 100085, China. Tel: +86-10-62936940; Fax: +86-10-62936940; E-mail: [email protected] † Present address: Center for Geomicrobiology, Department of Bioscience, Aarhus University, Ny Munkegade 114, 8000 Aarhus C, Denmark. ‡ These authors contributed equally to this work. One sentence summary: Our results provided a wide spectrum profile of ARGs in paddy soil for the first time based on metagenomics. Editor: Pascal Simonet

ABSTRACT Overuse and arbitrary discarding of antibiotics have expanded antibiotic resistance reservoirs, from gut, waste water and activated sludge, to soil, freshwater and even the ocean. Based on the structured Antibiotic Resistance Genes Database and next generation sequencing, metagenomic analysis was used for the first time to detect and quantify antibiotic resistance genes (ARGs) in paddy soils from South China. A total of 16 types of ARGs were identified, corresponding to 110 ARG subtypes. The abundances and distribution pattern of ARGs in paddy soil were distinctively different from those in activated sludge and pristine deep ocean sediment, but close to those of sediment from human-impacted estuaries. Multidrug resistance genes were the most dominant type (38–47.5%) in all samples, and the ARGs detected encompassed the three major resistance mechanisms, among which extrusion by efflux pumps was predominant. Redundancy analysis (RDA) showed that pH was significantly correlated with the distribution of ARG subtypes (P < 0.05). Our results provided a broad spectrum profile of ARGs in paddy soil, indicating that ARGs are widespread in paddy soils of South China. Keywords: ARGs; paddy soil; metagenomic analysis

INTRODUCTION In the past 70 years, hundreds of antibiotics have been discovered or developed, initiating a climax of drug innovation and implementation in human and animal health and agriculture (Guidos 2011; Dias, Urban and Roessner 2012; Rex 2014).

Unfortunately, accelerated development of newer antibiotics is being overtaken by the pace of bacterial resistance (Edgar et al. 2012). Due to unmonitored use of antibiotics and the release of residuals into the environment, widespread antibiotic resistance genes (ARGs) and emerging antibiotic-resistant bacteria,

Received: 15 September 2015; Accepted: 3 February 2016 C FEMS 2016. All rights reserved. For permissions, please e-mail: [email protected]

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FEMS Microbiology Ecology, 2016, Vol. 92, No. 3

even ‘super-resistant bacteria’ like NDM-1 (Walsh et al. 2011; Ahammad et al. 2014), have become a great public concern. Globally, action is being taken by international and regional agencies, such as the World Organization for Animal Health and the World Health Organization, to improve regulation of antibiotics and to preserve the efficacy of antibiotics (Gilbert 2012). Recently, the European Commission and the European Federation of Pharmaceutical Industries and Associations (EFPIA) began their groundbreaking and ambitious New Drugs For Bad Bugs (ND4BB) campaign to tackle the shortage of effective antimicrobial drugs for Gram-negative pathogens (Rex 2014). ARGs are recognized as emerging environmental pollutants (Pruden et al. 2006; Zhu et al. 2013). Although antibiotic resistance is accepted as a natural and ancient phenomenon that predates the modern selective pressure of clinical antibiotic use (D’Costa et al. 2011), the high levels and prevalence of antibiotic resistance found to date are generally considered a modern phenomenon resulting from human activities (Davies and Davies 2010). On one hand, microbes predating the antibiotic era are highly susceptible to antibiotics, and also mobile genetic elements (MGEs) are mostly devoid of resistance genes (Davis and Anandan 1970; Hughes and Datta 1983). On the other, the presence of antibiotics has provided important selective stress in driving the evolution, proliferation and spread of ARGs, and can significantly contribute to the elevated levels of antibiotic resistance in the modern environment (Knapp et al. 2009). Some studies found a significantly positive correlation between the copy numbers of ARGs and total concentration of antibiotics in environments exposed to a high level of antibiotics (Wu et al. 2010; Gao, Munir and Xagoraraki 2012; Ji et al. 2012). Various methods have been applied to study the origin and dissemination of ARGs, including isolation and culture, PCR, quantitative PCR (qPCR), DNA microarray and metagenomic approaches (D’Costa et al. 2006; Ghosh and LaPara 2007; Zhang et al. 2009; Chen et al. 2013; Yang et al. 2013; Zhu et al. 2013; Su et al. 2014). Some antibiotic-resistant bacteria can be isolated from the soil (D’Costa et al. 2006; Ghosh and LaPara 2007), but this method only covers a small portion of microbes that are culturable. Uncultured soil bacteria are a reservoir of new antibiotic resistance genes (Riesenfeld, Goodman and Handelsman 2004), and ARGs of these populations may contribute greatly to the resistance reservoir and could be horizontally transferred. High-capacity qPCR may to some extent overcome the drawback of traditional PCR and qPCR largely caused by the limitation of primers (Zhu et al. 2013), but the problem of bias in the amplification process remains unsolved. A metagenomic approach combined with next generation sequencing (NGS) can simultaneously explore a broad-spectrum profile of ARGs without PCR bias in environmental samples (Yang et al. 2013), and it has been successfully used in detecting various ARGs in activated sludge and coastal estuary and deep ocean sediments (Chen et al. 2013; Yang et al. 2013; Ma, Li and Zhang 2014; Christgen et al. 2015). China ranks first in the production and use of antibiotics worldwide (Zhu et al. 2013), and it also has the second largest area of paddy fields in the world. Nevertheless, to date, knowledge of the diversity and abundance of ARGs in paddy soil is poor. Previous studies based on PCR and a small number of marker genes have shown elevated antibiotic level and dissemination of ARGs in farmlands of China (Wu et al. 2010; Zhu et al. 2013). The aim of this study was to characterize ARGs in five paddy soils from South China, and to understand the occurrence, abundance and variation of ARGs by a comparison with samples from other environments.

MATERIALS AND METHODS Sample collection and analysis Surface soils (0–20 cm) were collected from five distinct sites in South China: Leizhou in Guangdong Province (LZ), Jiaxing in Zhejiang Provicne (JX), Yingtan in Jiangxi Province (YT), Gushi of Taoyuan in Hunan Province (TY-G), and Baodongyu of Taoyuan in Hunan Province (TY-B). Samples for molecular analysis were preserved in a can with liquid nitrogen right away and then transported back to the lab and stored at −80◦ C before further use. Other samples were stored at 4◦ C for analysis of soil characteristics. Soil physiochemical properties (including pH, moisture content, total carbon and nitrogen, soil organic carbon, dissolved organic carbon, ammonium/nitrate, cation exchange capacity and particle size) were analysed using different methods, and the data showed that these soils differed in soil type, parent material and many other physiochemical properties, as reported in our previous studies (Xiao et al. 2014a,b). Both animal manure and chemical fertilizers were applied in the cultivation of rice in these sampling areas, and sometimes reclaimed water was introduced into agriculture lands due to lack of water. Pesticides were also used to prevent harmful insects and pests, while there was no recording of antibiotic use. Solid phase extraction combined with liquid chromatography– tandem mass spectrometry (LC-MS/MS) (Wu et al. 2011; Zhang et al. 2011) was used to separate and detect common antibiotics like tetracyclines, sulfonamides and quinolones, and proved to be a very efficient method with a detection limit range of 0.02–0.65 μg kg−1 for different antibiotics and the ability to detect five tetracyclines, nine tetracycline degradation products, four sulfonamides and six fluoroquinolones at the same time in both park soil and manure compost (Wu et al. 2011; Wang et al. 2014). However, none of these antibiotics could be detected in our paddy soil samples using the same method.

DNA extraction, next generation sequencing and bioinformatics analysis DNA was extracted from the soil samples (two replicates per soil) using the MoBio PowerSoil kit (MOBIO) according to the manufacturer’s protocol. DNA yields of 10 samples ranged from 1.0 to 2.5 μg, as quantified using the Quant-iT Picogreen dsDNA HS assay kit (Invitrogen) according to the manufacturer’s manual. Sequencing was performed at Majorbio, Inc., Shanghai, China using an Illumina Hiseq 2000 sequencing system (Illumina), generating 2 × 101 bp paired end reads. Raw reads containing three or more ambiguous nucleotides, or with an average quality score below 20, or with length less than 100 bp (101 bp in length) were removed to guarantee the quality of downstream analysis (Chen et al. 2013; Yang et al. 2013). A total of 750 385 006 clean reads were generated across all 10 samples with an average of 75 038 500 reads per sample (Supplementary Table S1). Data are available at the NCBI Sequence Read Archive under project no. SRP039858. MetaPhlAn (Version 2.0) was used for bacterial taxonomic classification and abundance quantification with the default parameter settings (Segata et al. 2012). The local BLASTX programs were used to align clean reads of the data set obtained against the Antibiotic Resistance Genes Database (ARDB) (Liu and Pop 2009). A read was annotated as an ARG-like sequence when the best BLASTX hit had a sequence identity of ≥90% (Kristiansson et al. 2011) and an alignment length of ≥25 amino acids (Chen et al. 2013; Yang et al. 2013). Although such a high similarity threshold most probably excluded some divergent ARGs from

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the analysis, a more conservative strategy was still used here and we only focus on those that are highly similar to the known ARGs.

Statistical analysis The proportions of different types or subtypes of ARG-like sequences in ‘total ARG-like sequences’ and ‘total metagenome sequences’ were defined as ‘percentage’ (%) and ‘abundance’ (ppm, 1 read in 1 million reads), respectively (Yang et al. 2013). ARG diversity was defined at reference sequence level and represents the number of reference sequences in each ARG subtype identified in our dataset (Chen et al. 2013). All data are presented as averages (n = 2). A Procrustes test for correlation analysis between ARGs and bacterial communities was performed in R3.2.3 (https://www.r-project.org/). Principal component analysis (PCA) was performed based on the abundances of the ARG types, and the Mann–Whitney test was implemented to compare whether ARG abundances were significantly different among various environments using PAleontological STatistics software (version 3) (Hu et al. 2013; Li et al. 2015). According to the result of a detrended correspondence analysis (DCA), redundancy analysis (RDA) was chosen to find the relationship between the soil parameters and the abundances of the ARG subtypes using CANOCO 4.5 for Windows (Microcomputer Power) (Xiao et al. 2014a), and significance was determined with a Monte Carlo permutation test (P < 0.05).

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RESULTS AND DISCUSSION Abundance and composition of ARGs Among the 25 ARG types in the structured database, 16 were detected in the five paddy soils studied with 11 types shared in all samples, conferring resistance to most major classes of antibiotics (Supplementary Fig. S1a). A summary of the resistance type, subtype and sequence diversity of ARGs in these five paddy soils is given in Fig. 1a. The whole ARG abundances (7–10 ppm) (Fig. 1b) were higher than those from the relatively pristine South China Sea sediment (SCS) (generally below 1.5 ppm) (Mann–Whitney test, P < 0.01), similar to those from the human-impacted Pearl River Estuary sediment (PRE) (Mann–Whitney test, P > 0.05) (Chen et al. 2013), but much lower than those from the activated sludge (generally above 20 ppm) (Mann–Whitney test P < 0.01) (Yang et al. 2013). Multidrug resistance genes (38–47.5% of detected ARG-like sequences) were the most dominant type in the five samples, followed by resistance genes for acriflavine (16.4–21%), MLS (macrolide– lincosamide–streptogramin) (13.2–20.7%), bacitracin (5.4–12.5%) and others (7.4–8.4%) (Fig. 1c). A total of 110 ARG subtypes were identified (Supplementary Figs S1b and S2) and the top 10 ARG subtypes accounted for over 85% of the identified ARG sequences. Among them there were one acriflavine resistance gene, two bacitracin resistance genes, one macrolide resistance gene, four subtypes of multidrug resistance genes, and two other subtypes (RND protein and bifunctional UDP-glucuronic

Figure 1. Composition and abundance of ARGs in five paddy soils. (a) ARG detection statistics at type, subtype and reference sequence level. They were based on (b) percentages at ARGs type level (%), (c) abundances at type level (ppm, reads per million reads), and (d) top 10 ARG subtypes. ARG diversity at reference sequence level represents the number of reference sequences identified in each subtype. Abbreviation: MLS, macrolide–lincosamide–streptogramin resistance genes; LZ, Leizhou in Guangdong Province; JX, Jiaxing in Zhejiang Provicne; YT, Yingtan in Jiangxi Province; TY-G, Gushi of Taoyuan in Hunan Province; TY-B, Baodongyu of Taoyuan in Hunan Province.


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Figure 2. Mechanism of antibiotic resistance. The resistance genes detected in all samples were classified based on the mechanism of antibiotic resistance using (a) number and (b) abundance of ARG subtypes.

acid) (Fig. 1d). These profiles of ARGs also showed similarity with those from PRE, but were distinct from those of SCS and activated sludge (Chen et al. 2013; Yang et al. 2013).

Resistance mechanism Microbial resistance to antibiotics currently spans almost all known classes of natural and synthetic compounds (D’Costa et al. 2006), while the underlying mechanisms vary, and sometimes two or three mechanisms coexist for one kind of antibiotic. The ARGs detected in these paddy soils encompassed the three major resistance mechanisms – extrusion by efflux pumps, antibiotic deactivation and cellular protection (Zhu et al. 2013). Extrusion by efflux pumps was the predominant resistance mechanism in all of the samples in terms of both subtype numbers (Fig. 2a) and subtype abundances (Fig. 2b), partly owing to the high abundance/percentage of multidrug ARG types. Multidrug efflux pumps are ubiquitous in bacteria and work efficiently by reducing the concentration of antibiotics (Lubelski, Konings and Driessen 2007; Mart´ınez 2008), while they have been found to be also involved in other processes such as detoxification of metabolic intermediates, virulence and signal trafficking (Mart´ınez 2008). Apart from efflux pumps, a significant fraction of ARGs act by the mechanism of antibiotic inactivation (13.09%), which is often considered to be mainly associated with resistance to common anthropogenic antibiotics, such as aminoglycosides, β-lactams and macrolides (Chen et al. 2013). Higher portions of antibiotic inactivation were found in samples containing high antibiotics concentrations, such as estuary sediments (20–25%) (Chen et al. 2013) and in manure and compost (>40%) (Zhu et al. 2013), implying there is a selection effect of antibiotics on ARGs. Cellular protection accounted for the least part (Fig. 2a and b), and focuses not only on removal or destruction of the antibiotic but also on reprogramming or camouflaging of the target in the now resistant bacteria (Walsh 2000). It could be found in the erythromycin-resistance manifolds, vancomycin-resistant enterococci and penicillin resistance, and works independently or by facilitating resistance with other mechanisms (Song et al. 1987; Bugg et al. 1991; Spratt 1994; Bussiere et al. 1998).

Figure 3. Statistical analysis of ARGs in paddy soil. (a) Redundancy analysis of soil parameters and top 10 selected ARG subtypes. (b) Principal component analysis of 16 ARG types; data from the present study, Yang et al. (2013) and Chen et al. (2013). Abbreviation: MLS, macrolide–lincosamide–streptogramin resistance genes.

Statistical analysis and implication To further study the impact of environmental factors on the composition of ARGs, RDA was performed using 10 soil parameters (pH, total carbon/nitrogen, etc.; see ‘Materials and methods’), all 16 ARG types, the top 10 selected ARG subtypes (Fig. 3) and 110 detected subtypes (not shown). Soil pH exerted a strong selection pressure on soil microbes by affecting nutrient availability or physiological activity, and appeared to have a pervasive effect on the abundance and diversity of various microorganisms (Rousk et al. 2010; Zhang et al. 2012; Wang et al. 2014; Xiao et al. 2014a). Although the relationship between ARG types and most environmental parameters was not significant (P > 0.05), pH was found to be significantly correlated with the distribution of 10 selected subtypes (P = 0.032) (Fig. 3a) as well as all subtypes (data not shown). This could be a consequence of pH difference in these soils, which affected the soil microbial communities,


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and then influenced the dissemination of ARGs in the paddy soil environment. Actually, microbial community structures showed remarkable differences among the five samples, which was consistent with differences in pH values (Xiao et al. 2014a); TY-G (6.11) and LZ (6.29) cluster together, while YT (4.24) and JX (5.32) formed another cluster, and TY-B (4.09) was closer to the latter (Supplementary Fig. S3; only data at class level presented). The Procrustes test was applied for correlation analysis between ARG types/subtyes and bacterial communities classified at different levels (phylum to species). No significant correlation existed (P > 0.05, 999 permutations), showing no significant effect of microbial community on distribution of ARGs in our samples. Such a contradiction might be due to the fact that among the total bacterial population only a tiny fraction of the species were antibiotic resistant (MacLean and Vogwill 2015) and also the small (only 5) sample size in our study. PCA was conducted based on the abundances of ARG types in three different ecosystems, including five paddy soil samples in the present study, eight activated sludge samples from Shatin WWTP of Hong Kong (Yang et al. 2013) and three sediment samples from Pearl River Estuary (Chen et al. 2013). Although most ARG types were shared among these environments (Fig. 1, and cf. Fig. 3 in Chen et al. (2013) and Fig. 2 in Yang et al. (2013)), their abundances were generally much higher in activated sludge than in the other two environments, especially for those associated with aminoglycoside, sulfonamide, tetracycline and quinolone, which are all antibiotics extensively used in human medicine or as veterinary promoters (Li et al. 2015). The PCA profile of the ARG subtypes of these metagenomic data sets clustered separately in accordance with their sources (Fig. 3b), and distribution of ARGs in paddy soils was distinctively different from those of activated sludge while closer to sediment from human-impacted estuary (Chen et al. 2013), in accordance with the relatively lower level of antibiotic exposure of our paddy soil samples, implying enrichment of ARGs in highly human-impacted environments (Li et al. 2015). However, the differences between these three environments could possibly be explained solely by the different microbial communities that live under the three different environmental conditions, and stronger analyses should be done to test this hypothesis. To the best of our knowledge, this is the first documented broad-spectrum investigation of different ARGs in paddy soils using the metagenomic approach combined with next generation sequencing. Our results clearly demonstrated there is a great diversity of ARGs in paddy soils, and the distribution of ARGs in paddy soil is significantly different from in other environments, such as activated sludge and sediments.

SUPPLEMENTARY DATA Supplementary data are available at FEMSEC online.

ACKNOWLEDGEMENTS This work was supported by Natural Science Foundation of China Grant 41090282 and Key Projects of Natural Science Foundation of China Grant 41990280 and Grant 21210008. Conflict of interest. None declared.

REFERENCES Ahammad ZS, Sreekrishnan TR, Hands CL et al. Increased waterborne blaNDM-1 resistance gene abundances associated with

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seasonal human pilgrimages to the upper Ganges River. Environ Sci Technol 2014;48:3014–20. Bugg TDH, Wright GD, Dutka-Malen S et al. Molecular basis for vancomycin resistance in Enterococcus faecium BM4147: biosynthesis of a depsipeptide peptidoglycan precursor by vancomycin resistance proteins VanH and VanA. Biochemistry 1991;30:10408–15. Bussiere DE, Muchmore SW, Dealwis CG et al. Crystal structure of ErmC , an rRNA methyltransferase which mediates antibiotic resistance in bacteria. Biochemistry 1998;37:7103–12. Chen B, Yang Y, Liang X et al. Metagenomic profiles of antibiotic resistance genes (ARGs) between human impacted estuary and deep ocean sediments. Environ Sci Technol 2013;47: 12753–60. Christgen B, Yang Y, Ahammad SZ et al. Metagenomics shows that low-energy anaerobic−aerobic treatment reactors reduce antibiotic resistance gene levels from domestic wastewater. Environ Sci Technol 2015;49:2577–84. D’Costa VM, King CE, Kalan L et al. Antibiotic resistance is ancient. Nature 2011;477:457–61. D’Costa VM, McGrann KM, Hughes DW et al. Sampling the antibiotic resistome. Science 2006;311:374–7. Davies J, Davies D. Origins and evolution of antibiotic resistance. Microbiol Mol Biol R 2010;74:417–33. Davis CE, Anandan J. The evolution of R factor-A study of a preantibiotic community in Borneo. New Engl J Med 1970;282:117–22. Dias DA, Urban S, Roessner U. A historical overview of natural products in drug discovery. Metabolites 2012;2:303. Edgar R, Friedman N, Molshanski-Mor S et al. Reversing bacterial resistance to antibiotics by phage-mediated delivery of dominant sensitive genes. Appl Environ Microbiol 2012;78:744–51. Gao P, Munir M, Xagoraraki I. Correlation of tetracycline and sulfonamide antibiotics with corresponding resistance genes and resistant bacteria in a conventional municipal wastewater treatment plant. Sci Total Environ 2012;421–2:173–83. Ghosh S, LaPara TM. The effects of subtherapeutic antibiotic use in farm animals on the proliferation and persistence of antibiotic resistance among soil bacteria. ISME J 2007;1:191–203. Gilbert N. Rules tighten on use of antibiotics on farms. Nature 2012;481:125. Guidos RJ. Combating antimicrobial resistance: policy recommendations to save lives. Clin Infect Dis 2011;52:S397–428. Hu Y, Yang X, Qin J et al. Metagenome-wide analysis of antibiotic resistance genes in a large cohort of human gut microbiota. Nat Commun 2013;4:2151. Hughes VM, Datta N. Conjugative plasmids in bacteria of the ‘pre-antibiotic’ era. Nature 1983;302:725–6. Ji X, Shen Q, Liu F et al. Antibiotic resistance gene abundances associated with antibiotics and heavy metals in animal manures and agricultural soils adjacent to feedlots in Shanghai; China. J Hazard Mater 2012;235–6:178–85. Knapp CW, Dolfing J, Ehlert PAI et al. Evidence of increasing antibiotic resistance gene abundances in archived soils since 1940. Environ Sci Technol 2009;44:580–7. Kristiansson E, Fick J, Janzon A et al. Pyrosequencing of antibiotic-contaminated river sediments reveals high levels of resistance and gene transfer elements. PLoS One 2011;6:e17038. Li B, Yang Y, Ma L et al. Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes. ISME J 2015;9:2490–502. Liu B, Pop M. ARDB—Antibiotic resistance genes database. Nucl Acids Res 2009;37:D443–7.


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Lubelski J, Konings WN, Driessen AJM. Distribution and physiology of ABC-type transporters contributing to multidrug resistance in bacteria. Microbiol Mol Biol R 2007;71: 463–76. Ma L, Li B, Zhang T. Abundant rifampin resistance genes and significant correlations of antibiotic resistance genes and plasmids in various environments revealed by metagenomic analysis. Appl Microbiol Biol 2014;98:5195–204. MacLean RC, Vogwill T. Limits to compensatory adaptation and the persistence of antibiotic resistance in pathogenic bacteria. Evol Med Public Health 2015;2015:4–12. Mart´ınez JL. Antibiotics and antibiotic resistance genes in natural environments. Science 2008;321:365–7. Pruden A, Pei R, Storteboom H et al. Antibiotic resistance genes as emerging contaminants: Studies in Northern Colorado. Environ Sci Technol 2006;40:7445–50. Rex JH. ND4BB: addressing the antimicrobial resistance crisis. Nat Rev Microbiol 2014;12:231–2. Riesenfeld CS, Goodman RM, Handelsman J. Uncultured soil bacteria are a reservoir of new antibiotic resistance genes. Environ Microbiol 2004;6:981–9. Rousk J, Baath E, Brookes PC et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J 2010;4:1340–51. Segata N, Waldron L, Ballarini A et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat Methods 2012;9:811–4. Song MD, Wachi M, Doi M et al. Evolution of an inducible penicillin-target protein in methicillin-resistant Staphylococcus aureus by gene fusion. FEBS Letters 1987;221: 167–71. Spratt B. Resistance to antibiotics mediated by target alterations. Science 1994;264:388–93. Su JQ, Wei B, Xu CY et al. Functional metagenomic characterization of antibiotic resistance genes in agricultural soils from China. Environ Int 2014;65:9–15. Walsh C. Molecular mechanisms that confer antibacterial drug resistance. Nature 2000;406:775–81.

Walsh TR, Weeks J, Livermore DM et al. Dissemination of NDM-1 positive bacteria in the New Delhi environment and its implications for human health: an environmental point prevalence study. Lancet Infect Dis 2011;11:355–62. Wang F-H, Qiao M, Lv Z-E et al. Impact of reclaimed water irrigation on antibiotic resistance in public parks, Beijing, China. Environ Pollut 2014;184:247–53. Wu N, Qiao M, Zhang B et al. Abundance and diversity of tetracycline resistance genes in soils adjacent to representative swine feedlots in China. Environ Sci Technol 2010;44:6933–9. Wu X, Wei Y, Zheng J et al. The behavior of tetracyclines and their degradation products during swine manure composting. Bioresource Technol 2011;102:5924–31. Xiao K-Q, Bao P, Bao Q-L et al. Quantitative analyses of ribulose1,5-bisphosphate carboxylase/oxygenase (RubisCO) largesubunit genes (cbbL) in typical paddy soils. FEMS Microbiol Ecol 2014a;87:89–101. Xiao K-Q, Nie S-A, Bao P et al. Rhizosphere effect has no effect on marker genes related to autotrophic CO2 fixation in paddy soils? J Soils Sediments 2014b;14:1082–7. Yang Y, Li B, Ju F et al. Exploring variation of antibiotic resistance genes in activated sludge over a four-year period through a metagenomic approach. Environ Sci Technol 2013;47:10197– 205. Zhang D, Lin L, Luo Z et al. Occurrence of selected antibiotics in Jiulongjiang River in various seasons, South China. J Environ Monitor 2011;13:1953–60. Zhang L-M, Hu H-W, Shen J-P et al. Ammonia-oxidizing archaea have more important role than ammonia-oxidizing bacteria in ammonia oxidation of strongly acidic soils. ISME J 2012;6:1032–45. Zhang T, Zhang M, Zhang X et al. Tetracycline resistance genes and tetracycline resistant lactose-fermenting Enterobacteriaceae in activated sludge of sewage treatment plants. Environ Sci Technol 2009;43:3455–60. Zhu YG, Johnson TA, Su JQ et al. Diverse and abundant antibiotic resistance genes in Chinese swine farms. Proc Natl Acad Sci U S A 2013;110:3435–40.
