Next Article in Journal
Meteorological Drought Monitoring in Northeastern China Using Multiple Indices
Previous Article in Journal
Generation of Monthly Precipitation Climatologies for Costa Rica Using Irregular Rain-Gauge Observational Networks
Previous Article in Special Issue
Microbial Community Composition and Antibiotic Resistance Genes within a North Carolina Urban Water System
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Editorial

Monitoring Antimicrobial Resistance Dissemination in Aquatic Systems

by
Charmaine Ng
1 and
Karina Yew-Hoong Gin
2,3,*
1
Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117411, Singapore
2
Department of Civil and Environmental Engineering, National University of Singapore, Singapore 138602, Singapore
3
NUS Environmental Research Institute (NERI), Singapore 138602, Singapore
*
Author to whom correspondence should be addressed.
Water 2019, 11(1), 71; https://doi.org/10.3390/w11010071
Submission received: 13 December 2018 / Revised: 28 December 2018 / Accepted: 28 December 2018 / Published: 3 January 2019
(This article belongs to the Special Issue Antimicrobial Resistance in Environmental Waters)

Abstract

:
This special issue on Antimicrobial Resistance in Environmental Waters features 11 articles on monitoring and surveillance of antimicrobial resistance (AMR) in natural aquatic systems (reservoirs, rivers), and effluent discharge from water treatment plants to assess the effectiveness of AMR removal and resulting loads in treated waters. The occurrence and distribution of antimicrobials, antibiotic resistant bacteria (ARB), antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) was determined by utilizing a variety of techniques including liquid chromatography—mass spectrometry in tandem (LC-MS/MS), traditional culturing, antibiotic susceptibility testing (AST), molecular and OMIC approaches. Some of the key elements of AMR studies presented in this special issue highlight the underlying drivers of AMR contamination in the environment and evaluation of the hazard imposed on aquatic organisms in receiving environments through ecological risk assessments. As described in this issue, screening antimicrobial peptide (AMP) libraries for biofilm disruption and antimicrobial candidates are promising avenues for the development of new treatment options to eradicate resistance. This editorial puts into perspective the current AMR problem in the environment and potential new methods which could be applied to surveillance and monitoring efforts.

1. Introduction

The release of antimicrobials, antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) originating from human and animal waste to the environment is a global problem which has serious ramifications on public health. In response to this growing health threat, the World Health Organization (WHO) launched a global action plan on Antimicrobial Resistance (AMR) in 2015 with 5 strategic objectives, one of which was to strengthen knowledge of the spread of AMR through surveillance and research [1]. As a guide, the WHO has drawn up a priority list of AMR pathogens based on the threat they pose on human infections, response to antibiotic treatment, transmissibility between humans and animals, and whether there are antibiotics in current research and development pipelines to treat infections caused by these pathogens. Those of highest priority are carbapenem-resistant Acinetobacter baumannii, carbapenem-resistant Pseudomonas aeruginosa, and carbapenem-resistant, extended spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae [2]. The WHO’s Global Antimicrobial Surveillance System (GLASS) report for 2018 revealed widespread occurrence of antibiotic resistance among half a million people with suspected bacterial infections across 22 countries [3]. An AMR risk assessment of the South East Asian Region by Chereau et al. [4] concluded a high likelihood of emergence and dissemination among humans. Low stewardship on antibiotic prescription in treatment of human infections and the absence of legal frameworks for antibiotic use in animal husbandry and aquaculture are the main drivers for the selection of ARB in South East Asia [5]. The direct release or insufficient treatment of wastewater effluents from healthcare, livestock, aquaculture, and agriculture sites into receiving environments also poses a significant risk. In 2013, China alone produced 92,700 tonnes of antibiotics, 48% of which were consumed by humans and 52% by animals [6]. It was reported that almost half of all antibiotics were released in rivers through wastewater effluents and the practice of manure and sludge land spreading [6].
In South East Asia, AMR risk ranking across humans, animals, and environmental compartments show that human to human transmission in community and hospital settings represent the highest risk of the emergence and selection of AMR vectors (ARB, ARGs), followed by food- and waterborne transmission to humans through ingestion of contaminated sources [4]. Although transmission via contact with contaminated environments (through soil, water, and air), and livestock/animals is regarded as low risk in comparison to human to human transmission, it is still considered a valid route of exposure, particularly in countries that are water-scarce and reliant on water reuse. Hence, it is crucial to address the impact of antimicrobials, ARB and ARGs on AMR prevalence in receiving environments, specifically in countries where data availability is scant and guidelines for AMR stewardship frameworks have yet to be established. It is only through identifying and tracking sources and sinks of AMR in the environment, where intervention strategies can be devised to prevent and control the spread of the problem.

2. Measuring Vectors of AMR

2.1. Antimicrobials

Currently, there are various practices and methods of monitoring AMR dissemination and the fate of ARB and ARGs in aquatic environmental systems. Highly sensitive analytical protocols have been developed to detect antimicrobials using liquid chromatography-tandem mass spectrometry in environmental water samples and effluents from wastewater treatment plants (WWTPs) [7,8], while culture-based techniques are used to enumerate ARB.

2.2. ARB

Most environmental and wastewater treatment AMR surveys use methods applied in clinical settings, where media is supplemented with antibiotics at concentrations above the recommended minimum inhibitory concentration (MIC) breakpoints implemented by the Clinical and Laboratory Standards Institute [9] or the European Committee on antimicrobial Susceptibility Testing [10,11,12,13,14,15,16,17]. For non-clinically relevant bacteria (such as environmental bacteria), epidemiological cut-off values (ECOFF) are an alternative method used to gauge non-wild-type bacteria that display reduced susceptibility to certain antimicrobials or biocides [18]. Further testing of multidrug resistance (MDR) of ARB isolates are performed using broth dilution assays, Kirby-Bauer disk diffusion test on Muller-Hinton agar, or high-throughput platforms such as the VITEK system by bioMerieux (France).

2.3. ARGs, MGEs

To detect the prevalence of ARGs and vectors such as mobile genetic elements (MGE) that may facilitate horizontal gene transfer of ARGs, traditional quantitative polymerase chain reaction (qPCR), and the more recent high-throughput qPCR (HT-qPCR) platform with capabilities of detection of ~200 different ARGs and mobile genetic elements (MGE), has been used to compare relative concentrations of AMR contamination across a variety of aquatic environments including water treatment plants [19,20,21,22,23,24]. OMIC approaches such as metagenomics are able to provide a holistic picture of the diversity of ARGs, MGE and vectors (e.g., integrons, plasmids) that assist horizontal gene transfer, and the overall microbial community structure (bacteria, viruses) in environmental systems and wastewaters [25,26,27,28,29]. ResCap, a targeted capture platform (TCP) designed to analyze ~78,000 ARGs, metal resistance and plasmid markers is a targeted metagenomics approach for qualitative and quantitative resistome analysis [30]. Other OMIC approaches, such as metatranscriptomics, enable the identification of active microbial members within a community and, in the context of AMR, enables the measurement of transcription activity of ARB through ARG expression [31].

3. Key Outcomes of this Special Issue

This special issue comprises of 11 research articles that fall within the scope of AMR. Broadly, topics extend from AMR monitoring and surveillance in environmental resources and effluents from water/wastewater treatment plants [32,33,34,35,36,37,38,39], antimicrobial ecological risk assessments of two river reservoir systems in China that are sources of drinking water supply [40,41], and exploring novel strategies of using engineered antimicrobial peptides (AMP) to target specific bacteria to disrupt biofilms that are major causes of chronic and persistent infections [42].

AMR Monitoring and Surveillance

Knowledge of removal efficiencies of technology employed at WWTPs is essential in AMR surveillance, whether effluents are intended for discharge into the environment or reuse for irrigation purposes.
To determine the presence of antibiotic resistant E. coli in a conventional WWTP in Georgia, Aslan et al. [36] isolated E. coli from post-secondary, post-UV and post-chlorination effluent and performed antibiotic susceptibility tests on the isolates. They reported that ~5.2 log removal of E. coli and an additional 1.1 log reduction post chlorination was obtained. However, the MICs of E. coli isolated in the finished water were higher than those at the other treatment stages. The selection of more resistant organisms in the finished water underscores the urgent need to evaluate the health risks of using reclaimed water for downstream irrigation. In a qPCR assessment of ARGs of a full-scale tertiary water reclamation plant, Quach-Cu et al. [39] showed that tertiary-stage WWTPs with disinfection had superior removal of ARGs (sul1, blaSHV/TEM), ~3–4 logs compared to reliance of secondary treatment alone where the removal was only ~1–3 logs. To assess the impact of treated effluent on receiving environments, Lambirth et al. [35] measured the removal efficiency of ten antibiotics and assayed resistomes upstream, downstream and within various treatment steps of two urban WWTPs (secondary and disinfection treatments). The authors found elevated concentration of all 10 antibiotics surveyed in downstream receiving waters compared to waters upstream of the WWTP. The relative abundance of ARG signatures encoding for resistance to carbapenems and ESBL antibiotics were much lower than those detected upstream and sampling points within the WWTP, which debunks the notion that the wastewater treatment process selects for ARG resistance. Instead, the authors hypothesized that antibiotics discharged from treated wastewater effluent into the downstream environment may have an effect on natural microbial communities.
Jumat et al. [37] conducted an ESBL study of diversity and transcriptional activity of bacteria in a WWTP in Saudi Arabia using metagenomics, metatranscriptomics and real-time qPCR. They found an increase in the relative abundance of Acinetobacter junii in MBR- and chlorinated treated effluent. Survival and predominance of A. junii was explained by metatranscriptomics data that showed an upregulation of gene associated with active cell repair, resistance, virulence (efflux transporters involved in metal and antibiotic resistance) and cell signaling. These adaptive cellular mechanisms enable A. junii to withstand depletion of nutrients and counter the effects of chlorination. However, the authors indicate that the low concentrations of viable A. junii isolated from MBR effluents may not present that huge a risk. The varying results from WWTP studies covered in this issue makes it challenging to establish whether WWTPs are indeed hotspots of AMR dissemination. Rather, it is likely that differences in global and plant operating process contribute to variation in antibiotic resistance elements detection.
In countries with high agricultural productivity, water reuse is commonly practiced to meet the high water demands of the industry [43]. On a global scale, there are no clear guidelines implemented on assessment of water quality for reuse purposes, although a few countries have drawn up recommended microbiological parameters to monitor the quality of recycled water [44]. A recent review by Hong et al. [44] highlights the urgency of understanding the risks of microbiological and ARG contamination linked to water reuse. In the Philippines, surface waters contaminated with fecal coliform are frequently used for irrigating urban farms in densely populated cities [45]. Vital et al. [33] evaluated the antibiotic resistance profiles of 212 E. coli strains isolated from irrigation water, soil, and vegetables from six urban agricultural farms. Of the total isolates, 36.5% were resistant to more than three antibiotics tested, with the most multidrug-resistant (MDR) isolates being detected in irrigation water, followed by soil and vegetables. Of the MDR E. coli isolated from irrigation water, 7 of them were ESBL producers that carried either blaTEM or blaCTX-M genes, which raises public health concerns in primary production environments such as agricultural soils and fresh produce grown in these areas.
The use of antimicrobials in food animals is widespread, and runoffs originating from animal waste may carry unmetabolized antibiotics or ARB and ARGs that have direct impact on surrounding water bodies [46]. Tsai et al. [32] linked significantly higher concentrations of A. baumannii along the Puzi River in China to sampling sites of livestock wastewater channels and tributaries adjacent to livestock farms. Further testing of 20 A. baumannii isolates against 7 antibiotics (ciprofloxacin, cefepime, gentamicin, imipenem, ampicillin-sulbactam, sulfamethoxazole/trimethoprim, tetracycline) by the Kirby-Bauer disk diffusion test showed that only 10% had resistance to sulfamethoxazole/trimethoprim and 5% had resistance to tetracycline. Although the A. baumannii isolated by Tsai et al. [32] did not display MDR patterns which are regarded a serious AMR threat by WHO, their epidemiological potential warrants further studies on prevalence and AMR developmental trends in the environment.
In China, the Yellow River in the North serves as an important source of drinking water and is flanked by cities along its banks and watersheds. Previous studies have reported high turbidity and concentrations of antimicrobials in the Yellow River Catchment [47]. This prompted Lu et al. [38] to investigate the distribution and abundance of ARGs in Sand Settling Reservoirs (SSR) and Drinking Water Treatment Plants (DWTP) along the Yellow River. By targeting 17 ARGs as a proxy for AMR removal through the treatment process, the total concentrations of ARGs decreased from 104 copies/mL in influent river waters to 103 copies/mL in SSR effluent to 102 copies/mL in finished water. The 2 MGE targets decreased by at least an order of magnitude, from 106 copies/mL in influent river waters to 105 copies/mL in finished waters.
Horizontal gene transfer (HGT) of ARGs in the mammalian gut through the ingestion of contaminated food or water generally poses a low risk due to harsh conditions in the gut. However, this may present a greater risk for propagation in the environment through transformation and transduction [44]. In another study by Xu et al. [34], the authors studied the relationship of 16 antibiotics with environmental water quality parameters and the impact of antibiotic concentrations on the microbial community structure along Qingcaosha Reservoir, the largest estuary reservoir in China. This reservoir has a similar function to the Yellow River, in that it compensates for drinking water shortages in Shanghai. From the study, the authors concluded that upstream runoffs and anthropogenic activity along the river contributed to the concentrations of antibiotics measured within the reservoir, and that tylosin, penicillin G and erythromycin-H2O showed significant correlations with variations in bacterial community structure. Further to this study, Jiang et al. [40] studied seasonal variations of antibiotics in surface waters of Qingcaosha Reservoir. By using risk quotients (RQs) based on the European technical guidance documentation (TGD) on risk assessment, they showed that out of the 17 antibiotics monitored, four antibiotics (doxycycline, penicillinV, norfloxacin, ciprofloxacin) posed a high risk to relevant sensitive aquatic organisms, as well as imposed selective stress on microbial communities. In another study, of a subtropical river-reservoir system located in the Headwater Region of the Dongjiang River which supplies drinking water to three major cities in China, Chen et al. [41] conducted an ecological risk assessment which showed that concentrations of ciprofloxacin and norfloxacin posed a moderate risk, while tetracycline posed a higher risk to the aquatic ecosystem.
Finally, through screening a local antimicrobial peptide (AMP) library, Chin et al. [42] identified LG21, an AMP that specifically binds to exopolysaccharide PsI of P. aeruginosa that has a functional role of biofilm formation, which provides a protective environment for tolerance and resistance towards antibiotic treatment. This strategy of exploring AMP to target specific biofilm matrix components to disrupt formation and development of biofilms is a promising line of treatment to eradicate antibiotic resistant biofilms in both environmental and clinical settings.

Acknowledgments

The guest editors are grateful to the authors that contributed their work to this special issue. We would also like to thank the reviewers and journal editors who dedicated their time and expertise towards evaluating the articles.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. World Health Organization. Global Action Plan on Antimicrobial Resistance. Available online: https://www.who.int/antimicrobial-resistance/global-action-plan/en/ (accessed on 29 December 2018).
  2. World Health Organization. WHO Publishes List of Bacteria for Which New Antibiotics are Urgently Needed. Available online: http://www.who.int/mediacentre/news/releases/2017/bacteria-antibiotics-needed/en/ (accessed on 29 December 2018).
  3. World Health Organization. Global Action Plan on Antimicrobial Resistance (GLASS) Report. Available online: https://www.who.int/glass/resources/publications/early-implementation-report/en/ (accessed on 29 December 2018).
  4. Chereau, F.; Opatowski, L.; Tourdjman, M.; Vong, S. Risk assessment for antibiotic resistance in South East Asia. BMJ 2017, 358. [Google Scholar] [CrossRef] [PubMed]
  5. Holloway, K.A.; Kotwani, A.; Batmanabane, G.; Puri, M.; Tisocki, K. Antibiotic use in South East Asia and policies to promote appropriate use: Reports from county situational analyses. BMJ 2017, 358. [Google Scholar] [CrossRef] [PubMed]
  6. Zhang, Q.Q.; Ying, G.G.; Pan, C.G.; Liu, Y.S.; Zhao, J.L. Comprehensive evaluation of antibiotics emission and fate in the river basins of China: Source analysis, multimedia modelling, and linkage to bacterial resistance. Environ. Sci. Technol. 2015, 49, 6772–6782. [Google Scholar] [CrossRef] [PubMed]
  7. Tran, N.H.; Chen, H.; Do, T.V.; Van Do, T.; Reinhard, M.; Ngo, H.H.; He, Y.; Gin, K.Y.H. Simultaneous analysis of multiple classes of antimicrobials in environmental water samples using SPE coupled with UHPLC-ESI-MS/MS and isotope dilution. Talanta 2016, 159, 163–173. [Google Scholar] [CrossRef] [PubMed]
  8. Tran, N.H.; Chen, H.; Reinhard, M.; Mao, F.; Gin, K.Y.H. Occurrence and removal of multiple classes of antibiotic snad antimicrobial agents in biological wastewater treatment processes. Water Rese. 2016, 104, 461–472. [Google Scholar] [CrossRef] [PubMed]
  9. CLSI. Clinical and Laboratory Standards Institute. Available online: https://clsi.org/meetings/microbiology/ (accessed on 29 December 2018).
  10. EUCAST. European Committee on Antimicrobial Susceptibility Testing. Available online: http://www.eucast.org/clinical_breakpoints/ (accessed on 29 December 2018).
  11. Le, T.H.; Ng, C.; Tran, N.H.; Chen, H.; Gin, K.Y.H. Removal of antibiotic residues, antibiotic resistant bacteria and antibiotic resistance genes in municipal wastewater by membrane bioreactor systems. Water Res. 2018, 145, 498–508. [Google Scholar] [CrossRef] [PubMed]
  12. Ng, C.; Chen, H.; Goh, S.G.; Haller, L.; Charles, F.R.; Wu, Z.; Trottet, A.; Gin, K.Y.H. Microbial water quality and the detection of multidrug resistant E. coli and antibiotic resistance genes in aquaculture sites of Singapore. Mar. Pollut. Bull. 2018, 135, 475–480. [Google Scholar] [CrossRef] [PubMed]
  13. Ng, C.; Goh, S.G.; Saeidi, N.; Gerhard, W.A.; Gunsch, C.K.; Gin, K.Y.H. Occurrence of Vibrio species, beta-lactam resistant Vibrio species, and indicator bacteria in ballast and port waters of a tropical harbour. Sci. Total Environ. 2018, 610, 651–656. [Google Scholar] [CrossRef] [PubMed]
  14. Haller, L.; Chen, H.; Ng, C.; Le, T.H.; Koh, T.H.; Barkham, T.; Sobsey, M.; Gin, K.Y.H. Occurrence and characteristics of extended-spectrum ß-lactamase-and carbapenemase-producing bacteria from hospital effluents in Singapore. Sci. Total Environ. 2018, 615, 1119–1125. [Google Scholar] [CrossRef] [PubMed]
  15. Low, A.; Ng, C.; He, J. Identification of antibiotic resistant bacteria community and a GeoChip based study of resistome in urban watersheds. Water Res. 2016, 106, 330–338. [Google Scholar] [CrossRef] [PubMed]
  16. Le, T.H.; Ng, C.; Chen, H.; Yi, X.Z.; Koh, T.H.; Barkham, T.M.S.; Zhou, Z.; Gin, K.Y.H. Occurrences and Characterization of Antibiotic Resistant Bacteria and Genetic Determinants of Hospital Wastewaters in a Tropical Country. Antimicrob. Agents Chemother. 2016, 21, 7449–7456. [Google Scholar] [CrossRef] [PubMed]
  17. Huang, Y.; Zhang, L.; Tiu, L.; Wang, H.H. Characterization of antibiotic resistance in commensal bacteria from an aquaculture ecosystem. Front. Microbiol. 2015. [Google Scholar] [CrossRef] [PubMed]
  18. Morrissey, I.; Oggioni, M.R.; Knight, D.; Curiao, T.; Coque, T.; Kalkanci, A.; Martinez, J.L. Evaluation of epidemiological cut-off values indicates that biocide resistant subpopulations are uncommon in nature isolates of clinically-relevant microoriganisms. PLoS ONE 2014. [Google Scholar] [CrossRef] [PubMed]
  19. Liu, L.; Su, J.Q.; Guo, Y.; Wilkinson, D.M.; Liu, Z.; Zhu, Y.G.; Yang, J. Large-scale biogeographical patterns of bacterial antibiotic resistome in the waterbodies of China. Environ. Int. 2018, 117, 292–299. [Google Scholar] [CrossRef] [PubMed]
  20. 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. Continental-scale pollution of estuaries with antibiotic resistance genes. Nat. Microbiol. 2017, 2, 16270. [Google Scholar] [CrossRef]
  21. Muziasari, W.I.; Pitkanen, L.K.; Sorum, H.; Stedtfeld, R.D.; Tiedje, J.M.; Virta, M. The Resistome of Farmed Fish Feces Contributes to the Enrichment of Antibiotic Resistance Genes in Sediments below Baltic Sea Fish Farms. Front. Microbiol. 2017. [Google Scholar] [CrossRef]
  22. Xu, L.; Ouyang, W.; Qian, Y.; Su, C.; Su, J.; Chen, H. High-throughput profiling of antibiotic resistance genes in drinking water treatment plants and distribution systems. Environ. Pollut. 2016, 213, 119–126. [Google Scholar] [CrossRef]
  23. An, X.L.; Su, J.Q.; Li, B.; Ouyang, W.Y.; Zhao, Y.; Chen, Q.L.; Cui, L.; Chen, H.; Gillings, M.R.; Zhang, T.; et al. Tracking antibiotic reistome during wastewater treatment using high throughput quantitative PCR. Environ. Int. 2018, 117, 146–153. [Google Scholar] [CrossRef]
  24. Karkman, A.; Johnson, T.A.; Lyra, C.; Stedtfeld, R.D.; Tamminen, M.; Tiedje, J.M.; Virta, M. High-throughput quantification of antibiotic resistance genes from an urban wastewater treatment plant. FEMS Microbiol. Ecol. 2016, 92. [Google Scholar] [CrossRef] [Green Version]
  25. Bondarczuk, K.; Piotrowska-Segat, Z. Microbial diversity and antibiotic resistance in a final effluent-receiving lake. Sci. Total Environ. 2018, 650, 2951–2961. [Google Scholar] [CrossRef]
  26. Chu, B.T.T.; Petrovich, M.L.; Chaudhary, A.; Wright, D.; Murphy, B.; Wells, G.; Poretsky, R. Metagenomics Reveals the Impact of Wastewater Treatment Plants on the Dispersal of Microorganisms and Genes in Aquatic Sediments. Appl. Environ. Microbiol. 2018. [Google Scholar] [CrossRef] [PubMed]
  27. Gupta, S.K.; Shin, H.; Han, D.; Hur, H.G.; Unno, T. Metagenomic analysis reveals the prevalence and persistence of antibiotic- and heavy metal-resistance genes in wastewater treatment plant. J. Microbiol. 2018, 56, 408–415. [Google Scholar] [CrossRef] [PubMed]
  28. Ng, C.; Tay, M.; Tan, B.; Le, T.H.; Haller, L.; Chen, H.; Koh, T.H.; Barkham, T.; Thompson, J.R.; Gin, K.Y.H. Characterization of Metagenomes in Urban Aquatic Compartments Reveals High Prevalence of Clinically Relevant Antibiotic Resistance Genes in Wastewaters. Front. Microbiol. 2017. [Google Scholar] [CrossRef] [PubMed]
  29. Guo, J.; Li, J.; Chen, H.; Bond, P.L.; Yuan, Z. Metagenomic analysis reveals wastewater treatment plants as hotspots of antibiotic resistance genes and mobile genetic elements. Water Res. 2017, 123, 468–478. [Google Scholar] [CrossRef] [PubMed]
  30. Lanza, V.F.; Baquero, F.; Martinez, J.L.; Ramos-Ruiz, R.; Gonzalez-Zorn, B.; Andremont, A.; Sanchez-Valenzuela, A.; Ehrlich, S.D.; Kennedy, S.; Ruppe, E.; et al. In-depth resistome analysis by targeted metagenomics. Microbiome 2018. [Google Scholar] [CrossRef] [PubMed]
  31. Rowe, W.P.M.; Baker-Austin, C.; Verner-Jeffreys, D.W.; Ryan, J.J.; Micallef, C.; Maskell, D.J.; Pearce, G.P. Overexpression of antibiotic resistance genes in hospital effluents over time. J. Antimicrob. Chemother. 2017, 72, 1617–1623. [Google Scholar] [CrossRef]
  32. Tsai, H.-C.; Chou, M.-Y.; Shih, Y.-J.; Huang, T.-Y.; Yang, P.-Y.; Chiu, Y.-C.; Chen, J.-S.; Hsu, B.-M. Distribution and Genotyping of Aquatic Acinetobacter baumannii Strains Isolated from the Puzi River and Its Tributaries Near Areas of Livestock Farming. Water 2018, 10, 1374. [Google Scholar] [CrossRef]
  33. Vital, P.G.; Zara, E.S.; Paraoan, C.E.M.; Dimasupil, M.A.Z.; Abello, J.J.M.; Santos, I.T.G.; Rivera, W.L. Antibiotic Resistance and Extended-Spectrum Beta-Lactamase Production of Escherichia coli Isolated from Irrigation Waters in Selected Urban Farms in Metro Manila, Philippines. Water 2018, 10, 548. [Google Scholar] [CrossRef]
  34. Xu, Z.; Jiang, Y.; Te, S.H.; He, Y.; Gin, K.Y.H. The Effects of Antibiotics on Microbial Community Composition in an Estuary Reservoir during Spring and Summer Sessions. Water 2018, 10, 154. [Google Scholar] [CrossRef]
  35. Lambirth, K.; Tsilimigrae, M.; Lulla, A.; Johnson, J.; Al-Shaer Am Wynblatt, O.; Sypolt, S.; Brouwer, C.; Clinton, S.; Keen, O.; Redmond, M.; et al. Microbial Community Composition and Antibiotic Resistance Genes within a North Carolina Urban Water System. Water 2018, 10, 1539. [Google Scholar] [CrossRef]
  36. Aslan, A.; Cole, Z.; Bhattacharya, A.; Oyibo, O. Presence of Antibiotic-Resistant Escherichia coli in Wastewater Treatment Plant Effluents Utilized as Water Reuse for Irrigation. Water 2018, 10, 805. [Google Scholar] [CrossRef]
  37. Jumat, M.R.; Haroon, M.F.; Al-Jassim, N.; Cheng, H.; Hong, P.-Y. An Increase of Abundance and Transcriptional Activity for Acinetobacter junii Post Wastewater Treatment. Water 2018, 10, 436. [Google Scholar] [CrossRef]
  38. Lu, J.; Tian, Z.; Yu, J.; Yang, M.; Zhang, Y. Distribution and Abundance of Antibiotic Resistance Genes in Sand Settling Reservoirs and Drinking Water Treatment Plants across the Yellow River, China. Water 2018, 10, 246. [Google Scholar] [CrossRef]
  39. Quach-Cu, J.; Herrera-Lynch, B.; Marciniak, C.; Adams, S.; Simmerman, A.; Reinke, R.A. The Effect of Primary, Secondary, and Tertiary Wastewater Treatment Processes on Antibiotic Resistance Gene (ARG) Concentrations in Solid and Dissolved Wastewater Fractions. Water 2018, 10, 37. [Google Scholar] [CrossRef]
  40. Jiang, Y.; Xu, C.; Wu, X.; Chen, Y.; Han, W.; Gin, K.Y.H.; He, Y. Occurrence, Seasonal Variation and Risk Assessment of Antibiotics in Qingcaosha Reservoir. Water 2018, 10, 115. [Google Scholar] [CrossRef]
  41. Chen, Y.; Chen, H.; Zhang, L.; Jiang, Y.; Gin, K.Y.H.; He, Y. Occurrence, Distribution, and Risk Assessment of Antibiotics in a Subtropical River-Reservoir System. Water 2018, 10, 104. [Google Scholar] [CrossRef]
  42. Chin, J.S.F.; Sinha, S.; Nalaparaju, A.; Yam, J.K.H.; Qin, Z.; Ma, L.; Liang, Z.-X.; Lu, L.; Bhattacharjya, S.; Yang, L. Pseudomonas aeruginosa PsI Exopolysaccharide Interacts with the Antimicrobial Peptide LG21. Water 2018, 9, 681. [Google Scholar] [CrossRef]
  43. Hong, P.-Y.; Julian, T.R.; Pype, M.-L.; Jiang, S.C.; Nelson, K.L.; Graham, D.; Pruden, A.; Manaia, C.M. Resing Treated Wastewater: Consideration of the Safety Aspects Associated with Antibiotic-Resistant Bacteria and Antibiotic Resistance Genes. Water 2018, 10, 244. [Google Scholar] [CrossRef]
  44. Jaramillo, M.F.; Restrepo, I. Wastewater Reuse in Agriculture: A Review about Its Limitations and Benefits. Sustainability 2017, 9, 1734. [Google Scholar] [CrossRef]
  45. Garcia, B.C.B.; Dimasupil, M.A.Z.; Vital, P.G.; Widmer, K.W.; Rivera, W.L. Fecal contamination in irrigation water and microbial quality of vegetable primary production in urban farms of Metro Manila, Philippines. J. Environ. Sci. Health 2015, 50, 734–743. [Google Scholar] [CrossRef]
  46. Landers, T.F.; Cohen, B.; Wittum, T.E.; Larson, E.L. A Review of Antibiotic Use in Food Animals: Perspective, Policy, and Potential. Public Health Rep. 2012, 127, 4–22. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Xu, W.; Zhang, G.; Zou, S.; Ling, Z.; Wang, G.; Yan, W. A preliminary investigation on the occurrence and distribution of antibiotics in the Yellow River and its tributaries, China. Water Environ. Res. 2009, 81, 248–254. [Google Scholar] [CrossRef] [PubMed]

Share and Cite

MDPI and ACS Style

Ng, C.; Gin, K.Y.-H. Monitoring Antimicrobial Resistance Dissemination in Aquatic Systems. Water 2019, 11, 71. https://doi.org/10.3390/w11010071

AMA Style

Ng C, Gin KY-H. Monitoring Antimicrobial Resistance Dissemination in Aquatic Systems. Water. 2019; 11(1):71. https://doi.org/10.3390/w11010071

Chicago/Turabian Style

Ng, Charmaine, and Karina Yew-Hoong Gin. 2019. "Monitoring Antimicrobial Resistance Dissemination in Aquatic Systems" Water 11, no. 1: 71. https://doi.org/10.3390/w11010071

APA Style

Ng, C., & Gin, K. Y. -H. (2019). Monitoring Antimicrobial Resistance Dissemination in Aquatic Systems. Water, 11(1), 71. https://doi.org/10.3390/w11010071

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop