# ANTIBIOTIC RESISTANCE IN AQUATIC SYSTEMS

EDITED BY: Satoru Suzuki, Amy Pruden, Marko Virta and Tong Zhang PUBLISHED IN: Frontiers in Microbiology

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ISSN 1664-8714 ISBN 978-2-88945-131-9 DOI 10.3389/978-2-88945-131-9

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# **ANTIBIOTIC RESISTANCE IN AQUATIC SYSTEMS**

Topic Editors: **Satoru Suzuki,** Ehime University, Japan **Amy Pruden,** Virginia Tech, USA **Marko Virta,** University of Helsinki, Finland **Tong Zhang,** The University of Hong Kong, Hong Kong

Cover image by Dr. Satoru Suzuki, Ehime University.

Rivers, lakes and the ocean receive antibiotic resistance genes from human environments. The aquatic environments are a huge reservoir and exchange stage of antibiotic resistance genes.

**Citation:** Suzuki, S., Pruden, A., Virta, M., Zhang, T., eds. (2017). Antibiotic Resistance in Aquatic Systems. Lausanne: Frontiers Media. doi: 10.3389/978-2-88945-131-9

# Table of Contents

#### *04 Editorial: Antibiotic resistance in aquatic systems* Satoru Suzuki, Amy Pruden, Marko Virta and Tong Zhang

#### **Chapter 1. Reservoirs**

*07 Detection of multi-drug resistant* **Escherichia coli** *in the urban waterways of Milwaukee, WI*

Anthony D. Kappell, Maxwell S. DeNies, Neha H. Ahuja, Nathan A. Ledeboer, Ryan J. Newton and Krassimira R. Hristova

*19 Diverse and abundant multi-drug resistant* **E. coli** *in Matang mangrove estuaries, Malaysia*

Aziz Ghaderpour, Wing Sze Ho, Li-Lee Chew, Chui Wei Bong, Ving Ching Chong, Kwai-Lin Thong and Lay Ching Chai

*32 Seawater is a reservoir of multi-resistant* **Escherichia coli***, including strains hosting plasmid-mediated quinolones resistance and extended-spectrum beta-lactamases genes*

Marta S. Alves, Anabela Pereira, Susana M. Araújo, Bruno B. Castro, António C. M. Correia and Isabel Henriques

*42 Sulfonamide and tetracycline resistance genes in total- and culturable-bacterial assemblages in South African aquatic environments*

Satoru Suzuki, Mitsuko Ogo, Tatsuya Koike, Hideshige Takada and Brent Newman

#### **Chapter 2. Horizontal gene transfer**

#### **Chapter 3. Resistance enhancement**

Daniel E. Carey and Patrick J. McNamara

## Editorial: Antibiotic Resistance in Aquatic Systems

#### Satoru Suzuki 1 \*, Amy Pruden2 , Marko Virta3 and Tong Zhang4

*1 Center for Marine Environmental Studies, Ehime University, Matsuyama, Japan, 2 Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, USA, 3 Department of Food and Environmental Sciences, University of Helsinki, Helsinki, Finland, 4 Department of Civil Engineering, The University of Hong Kong, Hong Kong, Hong Kong*

Keywords: antibiotic resistance, aquatic systems, wastewater treatment plant, aquaculture, horizontal gene transfer

#### **Editorial on the Research Topic**

#### **Antibiotic Resistance in Aquatic Systems**

Edited by:

*David W. Graham, Newcastle University, UK*

#### Reviewed by:

*Jose Luis Balcazar, Catalan Institute for Water Research, Spain Eddie Cytryn, Agricultural Research Organization, Volcani Center, Israel Maurizio Labbate, University of Technology Sydney, Australia*

#### \*Correspondence:

*Satoru Suzuki<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Antimicrobials, Resistance and Chemotherapy, a section of the journal Frontiers in Microbiology*

Received: *07 October 2016* Accepted: *04 January 2017* Published: *25 January 2017*

#### Citation:

*Suzuki S, Pruden A, Virta M and Zhang T (2017) Editorial: Antibiotic Resistance in Aquatic Systems. Front. Microbiol. 8:14. doi: 10.3389/fmicb.2017.00014* The spread of antibiotic-resistant pathogens and their resistance traits is an epic global challenge, as recognized by various international bodies, including the G8 Science Ministry in 2013 and the Elmau summit in 2015 (https://www.g7germany.de/Webs/G7/EN/Home\_en/home\_node.html). While most attention continues to be devoted to the clinic and the need to develop new drugs, there is growing recognition of the need to understand the origin and ecology of antibiotic resistance in order to slow its spread and maximize the lifespan of our antibiotic arsenal (Wright, 2010). In particular, the aquatic environment can serve both as a natural reservoir of antibiotic resistance and a conduit for the spread of clinical resistance traits of major concern (Michael et al., 2013). Aquatic bodies, including lakes, rivers, streams and even coastlines, receive effluent from wastewater treatment plants (WWTP), runoff from agricultural activity, and other human inputs and influences that may either serve to elevate natural background levels of antibiotic resistance genes (ARGs) and stimulate their transfer into pathogens or other organisms, or as a conduit for the propagation of antibiotic resistant pathogens and clinical ARGs of concern (Michael et al., 2013). Current pathogen risk models are not equipped to factor in unique challenges that antibiotic resistance poses, including the potential that non-pathogenic antibiotic resistant bacteria (ARB) can serve as a reservoir to transfer their ARGs to pathogens or the role of selective agents, such as antibiotics and metals, in amplifying this potential. The challenge of controlling the spread of antibiotic resistance can have a different face in developed or developing countries, depending on local policies, practices, technologies and constraints.

The articles in this e-book include new evidences of the origin, spread, and fate of ARB and ARGs in aquatic systems, focusing on water systems from wastewater, freshwater to seawater. These various media have gained attention as potential sources, sinks, or conduits in the potential to spread antibiotic resistance.

It is well-known that heavy use of antibiotics and synthetic antimicrobial agents contribute to the selection pressure. The fate and impact of antibiotics used both in humans and livestock are of particular concern. Municipal and on-farm wastewater treatment is critical for controlling pollution and pathogens, but is not tailored specifically to the control of antibiotic resistance. WWTPs are mainly aimed at reducing solid and nutrient loads to surface waters, but this does not guarantee biodegradation of trace chemical pollutants or genetic elements. Although advanced disinfection facilities can greatly reduce the danger of waterborne diseases (United States Environmental Protection and Agency, 2004), antibiotics and ARGs can still be released to the environment in disinfected effluents (Michael et al., 2013; Rizzo et al., 2013; Berkner et al., 2014; Carey and McNamara). Recent works report the improvement of disinfection in terms of ARG removal (Munir et al., 2011; McKinney and Pruden, 2012; Guo et al., 2013; Yuan et al., 2015; Zhuang et al., 2015).

The potential for horizontal gene transfer (HGT) in WWTPs is a matter of importance, and there is debate regarding the favorable conditions and the actual HGT rates. In this ebook, Miller et al. explore the extent to which influent ARB and ARG composition in raw sludge influences the fate of ARB in the digested sludge community and potential for ARG transfer. Enhancement of HGT by the ionic liquid (IL) 1 butyl-3-methylimidazolium hexafluorophosphate showed that longer carbon chain enhanced HGT (Wang et al.). IL has been thought as an environmentally-friendly solvent, but recently water solubility, environmental toxicity and stability of IL are considered to be risk (Pham et al., 2010). The enhancement of HGT is a new effect of IL. Vanadium is also reported to have enhancement effect of HGT in environment (Suzuki et al., 2012). Such studies demonstrate that, besides antibiotics, other chemicals enhancing HGT could play a role to spread ARGs in water environments, including between pathogenic bacteria and environmental bacteria. In case of coastal sea, although the contaminated waters are significantly diluted, fecalderived bacteria, such as E. coli, remain viable ARG reservoirs of concern (Alves et al.; Kappell et al.; Ghaderpour et al.). ARB and ARGs are known to occur in aquatic environments without antibiotic contaminations (Port et al., 2014). ARBs and ARGs flow into rivers, groundwater and marine environments by influx of WWTP effluent as mentioned above. Additionally, there is marked potential aquaculture practices (Tamminen et al., 2011) and open ocean conditions (Rahman et al., 2008) to contribute to the selection and spread of ARBs and ARGs. Mobile genetic elements (MGEs) of aquatic species conveying multidrug resistance genes are reported in this book (Nonaka et al.). These elements might pose a risk to human health if the MGEs transfer to other bacteria (Piotrowska and Popowska), especially human pathogens. Thus, this topic highlights insight obtained from various water environments and their interfaces.

The role of antibiotics and antimicrobials in ARB selection is of keen interest and evidence suggests that even very low concentration of antibiotics can be effective to select and maintain resistance traits in bacteria (Gullberg et al., 2011). Marine bacterial plasmids pAQUs (Nonaka et al.) appear to disseminate ARGs among marine organisms, which can be stably retained in the bacterial community, even after the antibiotics are removed (Bien et al., 2015). Exogenous ARGs retained in the environment can potentially be horizontally transferred among the native bacterial community.

It is well-known that the majority of aquatic environmental bacteria are unculturable or yet-to-be cultured (Amann et al., 1995; Takami et al., 1997; Bloomfield et al., 1998). This

#### REFERENCES

characteristic is distinct from that of human and animal pathogens, which have been the subject of the development of standard methods for isolation and monitoring. Consequently, conventional culture-dependent methods for monitoring ARBs and ARGs only reveal 0.1% or less of the true aquatic bacterial community (Amann et al., 1995; Takami et al., 1997; Bloomfield et al., 1998). Suzuki et al. (2013) and Suzuki et al. found that the defining features of ARGs tend to be distinct between culturable and yet-to-be cultured bacteria. The recent advent of next-generation DNA sequencing and metagenomics poises the scientific community on the verge of a major advance in understanding the behavior of ARGs in the environment (D'Costa et al., 2006; Port et al., 2014). However, deep sequencing and the capacity to perform high-throughput bioinformatics analyses are required to match ARGs with their bacterial hosts. Thus, there is still a long way to go before we have full understanding of the factors driving the behavior of ARGs in

aquatic realm. Articles in this e-book provide new insight into the dynamics of ARGs in a diverse range of aquatic environments and their interfaces, including distribution and diversity of ARGs in various countries, horizontal gene transfer, and the dynamic processes involved in governing ARB and ARG fate. These papers should expand our knowledge and understanding of connections between resistance traits in human- and animal-related bacteria and aquatic ecosystems.

Although this e-book does not include, gene flux among various aquatic compartments, such as sediments, biofilms and periphyton, should be paid attention. ARGs are exchanged between organisms, but it does not drive mass movement and local exposures. As an example, movement of ARGs is reported between water column and biofilms (Engemann et al., 2008). The geological wide flux of ARGs is occurred by natural water movement (Knapp et al., 2012), and water use and food are suspected also as ARGs transport factors (Suzuki and Hoa, 2012). The study from global ecological viewpoint is needed to reveal the ARGs fate and dynamics in environments.

#### AUTHOR CONTRIBUTIONS

SS: Planning, manuscript editing and making editorial manuscript. AP: Manuscript editing and making editorial manuscript. MV: Manuscript editing. TZ: Manuscript editing and reviewing editorial manuscript.

#### FUNDING

This work is partly supported by Grant-in-Aid KAKENHI 25257402 and 22241014.

Bien, T. L., Sato-Takabe, Y., Ogo, M., Usui, M., and Suzuki, S. (2015). Persistence of multi-drug resistance plasmids in sterile water under very low concentrations of tetracycline. Microbes Environ. 30, 339–343. doi: 10.1264/jsme2.ME15122

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Suzuki, Pruden, Virta and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

## Detection of multi-drug resistant *Escherichia coli* in the urban waterways of Milwaukee, WI

*Anthony D. Kappell1, Maxwell S. DeNies1, Neha H. Ahuja1, Nathan A. Ledeboer2,3, Ryan J. Newton4 and Krassimira R. Hristova1\**

*1 Department of Biological Sciences, Marquette University, Milwaukee, WI, USA, 2 Department of Pathology, Medical College of Wisconsin, Milwaukee, WI, USA, 3 Dynacare Laboratories, Milwaukee, WI, USA, 4 School of Freshwater Sciences, Great Lakes WATER Institute, University of Wisconsin–Milwaukee, Milwaukee, WI, USA*

#### *Edited by:*

*Amy Pruden, Virginia Tech, USA*

*Reviewed by: Celia Manaia, Universidade Católica Portuguesa, Portugal Ying Yang, The University of Hong Kong, China*

#### *\*Correspondence:*

*Krassimira R. Hristova, Department of Biological Sciences, Marquette University, 530 N. 15th Street, Wehr Life Sciences Building, 208, P. O. Box 1881, Milwaukee, WI 53201-1881, USA<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Antimicrobials, Resistance and Chemotherapy, a section of the journal Frontiers in Microbiology*

*Received: 17 December 2014 Accepted: 05 April 2015 Published: 29 April 2015*

#### *Citation:*

*Kappell AD, DeNies MS, Ahuja NH, Ledeboer NA, Newton RJ and Hristova KR (2015) Detection of multi-drug resistant Escherichia coli in the urban waterways of Milwaukee, WI. Front. Microbiol. 6:336. doi: 10.3389/fmicb.2015.00336* Urban waterways represent a natural reservoir of antibiotic resistance which may provide a source of transferable genetic elements to human commensal bacteria and pathogens. The objective of this study was to evaluate antibiotic resistance of *Escherichia coli* isolated from the urban waterways of Milwaukee, WI compared to those from Milwaukee sewage and a clinical setting in Milwaukee. Antibiotics covering 10 different families were utilized to determine the phenotypic antibiotic resistance for all 259 *E. coli* isolates. All obtained isolates were determined to be multi-drug resistant. The *E. coli* isolates were also screened for the presence of the genetic determinants of resistance including *ermB* (macrolide resistance), *tet*(M) (tetracycline resistance), and β-lactamases (*bla*OXA, *bla*SHV, and *bla*PSE). *E. coli* from urban waterways showed a greater incidence of antibiotic resistance to 8 of 17 antibiotics tested compared to human derived sources. These *E. coli* isolates also demonstrated a greater incidence of resistance to higher numbers of antibiotics compared to the human derived isolates. The urban waterways demonstrated a greater abundance of isolates with co-occurrence of antibiotic resistance than human derived sources. When screened for five different antibiotic resistance genes conferring macrolide, tetracycline, and β-lactam resistance, clinical *E. coli* isolates were more likely to harbor *ermB* and *bla*OXA than isolates from urban waterway. These results indicate that Milwaukee's urban waterways may select or allow for a greater incidence of multiple antibiotic resistance organisms and likely harbor a different antibiotic resistance gene pool than clinical sources. The implications of this study are significant to understanding the presence of resistance in urban freshwater environments by supporting the idea that sediment from urban waterways serves as a reservoir of antibiotic resistance.

Keywords: antibiotic resistance genes, antibiotic resistance bacteria, Great Lakes, sediment, environment, *E. coli*

#### Introduction

The increasing number of multiple-antibiotic resistant pathogens has become a serious threat to human health (Centers for Disease Control and Prevention [CDC], 2013; Review on Antimicrobial Resistance [RAR], 2014; World Health Organization [WHO], 2014). Over the past two decades researchers have expanded their focus from the clinical settings to also include the natural environment as a reservoir of antibiotic resistance (Kümmerer, 2004; Baquero et al., 2008; Martinez et al., 2009; Allen et al., 2010; Galán et al., 2013; Michael et al., 2013; Wellington et al., 2013). The resistome of fecal bacteria from human and animal sources released into the environment impart antibiotic resistances genes to the non-resistant indigenous microorganisms (Aminov, 2011; Tacão et al., 2014). The subsequent transfer of antibiotic resistance genes from the indigenous microorganisms to human-associated bacteria may take place (Devirgiliis et al., 2011; Figueira et al., 2011). Simultaneously, positive selective pressure for antibiotic resistance genes in the environment may be stimulated by the presence of antibiotics or other contaminants (Silveira et al., 2014; Varela et al., 2014; Gao et al., 2015). These facts highlight the need to identify the potential sources of antibiotic resistant bacteria in environments used by human populations (Rosewarne et al., 2010; Gomez-Alvarez et al., 2012).

*Escherichia coli* are currently used by the Environmental Protection Agency (EPA, USA) as an indicator organism for fecal contamination and bacterial impairment for watersheds. *E. coli* is a natural member of intestinal microbiome of humans and other animals (reviewed in Harwood et al., 2014). The major sources of fecal contamination in various watersheds include human (Bernhard and Field, 2000), agricultural animals (Shanks et al., 2008), pets (Ervin et al., 2014), and wild animals (Somarelli et al., 2007; Guber et al., 2015), such as gulls (Alves et al., 2014; Araújo et al., 2014). The major source of fecal contamination of urban waterways of Milwaukee, WI was determined to be human (Newton et al., 2011, 2013).

Multi-drug resistant (MDR) *E. coli* and other *Enterobacteriaceae* isolates are characterized by non-susceptibility (or non-sensitivity) to at least one agent in three or more antibiotic categories (Magiorakos et al., 2012). Antibiotic resistance surveillance data show that *E. coli* has high resistance for older generation human and veterinary antibiotics including ampicillin, streptomycin, and tetracycline and the increasing resistance to newer antibiotics such as fluoroquinolones and cephalosporins (Tadesse et al., 2012).

*E. coli* had been recognized as a contributor to the dissemination of antibiotic resistance genes in natural environments (Henriques et al., 2006; Zhao and Dang, 2012; Alm et al., 2014; Alves et al., 2014). The gene encoding resistance to tetracycline class antibiotics, *tet*(M), which is predominately found on transposons within enterococci have also been found on plasmids within *E. coli* (Jurado-Rabadán et al., 2014), and possibly in *E. coli* from a natural river basin (Hu et al., 2008). The *ermB* gene encoding resistance to macrolides, lincosamides, and streptogramin have been identified on transposons and plasmids within or transferable to *E. coli* (Poyart et al., 1995; Poirel et al., 2011). The genes *bla*OXA, *bla*SHV, and *bla*PSE are grouped in the most common types of β-lactamases belonging to *Enterobacteriaceae* (Bush and Jacoby, 2010).

Freshwater environments are recognized as reactors for the evolution and dissemination of antibiotic resistance (Alm et al., 2014; Czekalski et al., 2014; Marti et al., 2014; Vaz-Moreira et al., 2014), however, processes occurring in urban freshwater environments are less understood. In addition, the presence of antibiotic resistance *E. coli* in urban waterways represents a health issue in areas that are used for recreation activities.

The Milwaukee Harbor, an urbanized estuary, has a documented history of contamination from human activities. Located within the harbor is a wastewater treatment plant discharging the treated effluent within the outer harbor into the Lake. High incidence of antibiotic resistance in *E. coli*, an indication of sewage contamination also was detected in storm water from the Menomonee River, which flows through the city of Milwaukee and into the Lake Michigan harbor (Salmore et al., 2006). Human fecal pollution is constant within the contributing rivers and the Milwaukee Harbor and increased contamination during heavy rain events has been reported (Newton et al., 2013). There is also evidence of high levels of personal care products and pharmaceuticals, including antibiotics present within the Milwaukee Harbor (Blair et al., 2013a). A study by LaPara et al. (2011) found that the presence of tetracycline resistance determinants *tet*(A), *tet*(X), and *tet*(W) in Lake Superior surface waters receiving Waste Water Treatment Plant (WWTP) effluent near urban environments were correlated with the presence of fecal bacteria.

The objective of this study was to evaluate the abundance of multiple-antibiotic resistant bacteria present in the Milwaukee's urban waterways compared to the human derived bacterial community from Milwaukee, WI. Since the urban waterways of Milwaukee have the potential for positive selection of antibiotic resistance due to history of antibiotics present (Blair et al., 2013a), we hypothesize that the *E. coli* isolated from urban waterways in Milwaukee maintain a similar or greater incidence of antibiotic resistance compared to the human derived *E. coli* isolates. The relationship of the resistances identified in the microbial community of the urban waterways and the human derived microbial community were explored in order to test this hypothesis. *E. coli* were isolated from sediment within the inner and outer Milwaukee harbor, human derived sewage, and from a clinical laboratory servicing Milwaukee. A broad range of antibiotics covering different families (β-lactams, aminoglycosides, tetracyclines, quinolones/fluoroquinolones, sulfonamides, dihydrofolate reductase inhibitors, UDP-*N*-acetylglucosamine enolpyruvyl transferase inhibitor, rifampicin, and chloramphenicol) were used to determine the resistance of the *E. coli* isolates. The presence of genetic determinates of resistance for *ermB* (macrolide resistance), *tet*(M), and β-lactamases (*bla*OXA, *bla*SHV, and *bla*PSE), were screened in the same *E. coli* isolates.

### Materials and Methods

#### Study Site and Sample Collection

On March 22, 2012, four sediment grab samples at different locations were collected with a box corer from the Milwaukee Harbor Estuary. (A sampling map is shown in Supplementary Figure S1.) The Milwaukee Harbor Estuary is defined as the confluence of the Milwaukee, the Kinnickinnic, and the Menomonee Rivers into the Milwaukee Harbor. A subsample from each grab sample was collected in two sterile 50 mL conical centrifuge tubes and held at 4◦C until filtering and plating. Strains isolated from the Kinnickinnic River and the junction of the Kinnickinnic River with the Milwaukee and Menomonee Rivers in the inner Milwaukee Harbor are referred to as the inner harbor isolates (*n* = 36). The strains referred to as the outer harbor isolates (*n* = 58) are further downstream of the junction of the rivers and were from sediment collected near the effluent pipe of the Jones Island WWTP in the outer Milwaukee Harbor. Human derived sewage (*n* = 66) from the influent of the Jones Island WWTP was used for assessing microbial resistance of the human population. The influent WWTP water was placed in a pre-chilled 1 L bottle and kept at 4◦C until filtering and plating. Disassociation of bacteria from sediment particles in the grab samples were performed as by Boehm et al. (2009). Briefly, grab samples (3 g) were diluted 1:10 in sterile buffered (pH 7.0) water and shook vigorously by hand for 2 min. The undiluted and serial diluted eluents from the sediment samples and the water samples from the influent of the WWTP were subjected to filtration through a 0.45 μm filter and the filter was placed on a modified membranethermotolerant *E. coli* Agar (modified mTEC) plate as in EPA Method 1603 (Environmental Protection Agency [EPA], 2002). As the EPA Method indicates the plates were initially incubated for 2 h at 35◦C for recovery of injured cells followed by incubation at 44.5◦C for 22 h. The colonies identified as *E. coli* based on pigmentation on the modified mTEC were recovered on Tryptic Soy Agar (TSA) media and incubated at 37◦C for 18 h. *E. coli* strains of clinical consequence (*n* = 99) were obtained from Dynacare Laboratories (Milwaukee, WI, USA) on TSA slants. The clinical *E. coli* isolates were collected from various patient populations including outpatients and hospitalized patients from throughout the Milwaukee area and identity was confirmed by MALDI-TOF (Anderson et al., 2012; Buchan et al., 2012). All *E. coli* isolates were then grown in Tryptic Soy Broth (TSB) at 37◦C for 18 h and stored at −20◦C after the addition of glycerol to a final concentration of 10%. An additional 1 mL of culture was pelleted by centrifugation and stored at −20◦C for DNA extraction.

#### Antibiotic Susceptibility Testing

The *E. coli* isolates were tested for susceptibility to 17 antibiotics by a 96-well broth dilution method in Muller–Hinton broth utilizing three antibiotic concentrations based on the Clinical and Laboratory Standards Institute (Clinical and Laboratory Standards Institute [CLSI], 2011) guidelines. Four wells were used for each *E. coli* isolate consisting of three wells containing a serial half concentration dilution of the antibiotic and one well with no antibiotic as a positive control for growth. The 96-well plates containing 200 μL per well were incubated at 35◦C for 18 h. The following antibiotics were used: ampicillin (AMP; Sigma, A0166; 8–32 μg mL−1), gentamicin (GEN; Sigma, G1264; 4–16 μg mL−1), streptomycin (STR; Sigma, S9137; 8–32 μg mL−1), neomycin (NEO; Sigma, N6386; 16– 64 μg mL−1), tetracycline (TET; Sigma, T7660; 4–16 μg mL−1), ciprofloxacin (CPR; Sigma, 17850; 1–4 μg mL−1), chloramphenicol (CHL; Sigma, C1919; 8–32 μg mL−1), trimethoprim (TRM; Sigma, 92131; 8–32 μg mL−1), sulfamethoxazole (SFM; Sigma, S7507; 256–1024 μg mL−1), fosfomycin (FOS; Sigma, P5396; 64–256 μg mL−1), erythromycin (ERY; Sigma, E5389; 2–8 μg mL−1), aztreonam (AZT; Sigma, A6848; 4–16 μg mL−1), cefuroxime (CFX; Sigma, C4417; 8–32 μg mL−1), meropenem (MER; Sigma, M2574; 1–4 μg mL−1), moxifloxacin (MOX; Selleckchem, S1465; 1–4 μg mL−1), rifampicin (RIF; Sigma, R7382; 1–4 μg mL−1), and cefepime (CFP; US Pharmacopeial, 1097636; 8–32 μg mL−1). Isolates were classified as sensitive when growth was sequestered to the well without antibiotic and resistant when growth was observed in all four wells. Intermediate level of resistance was concluded when growth was observed in wells containing antibiotic concentrations diluted from the maximum. Where appropriate the classification of nonsensitive was used to denote any strain with any level of resistance to an antibiotic which includes Resistant or Intermediate levels determined by the Clinical and Laboratory Standards Institute (Clinical and Laboratory Standards Institute [CLSI], 2011).

#### Antibiotic Resistance Gene Detection

*Escherichia coli* strains regardless of resistances were screened by real-time PCR to detect genes conferring resistance comprized of *tet(M)*, *ermB*, *bla*OXA, *bla*SHV, and *bla*PSE. Extractions of the total DNA of the isolates were performed with the Wizard Genomic DNA Purification Kit (Promega, A1120) on 1 mL of culture in TSB incubated at 37◦C for 18 h. The PCR reactions were performed in a MyiQ or CFX Connect Real-Time PCR Detection Systems (Bio-rad, USA). Primers used are presented in Supplementary Table S1. The reaction mixture of 20 μL consisted of Standard *Taq* Buffer (10 mM Tris-HCl, 50 mM KCl, 1.5 mM MgCl2, pH 8.3; NEB, USA), 500 nM of each primer, 500 μM dNTPs, 0.5 U of *Taq* DNA Polymerase (NEB, USA), 0.1x SYBR Green I Nucleic Acid Gel Stain (Lonza, Cat#50513) and 25 ng of genomic DNA. PCR thermocycling conditions consisted of an initial denaturation step of 5 min at 95◦C followed by 40 cycles of 95◦C for 30 s, the gene specific annealing temperature (Supplementary Table S1) for 30 s, and 72◦C for 30 s concluding with a melt curve analysis. Presence of the gene was determined by the cycle threshold and melt curve analysis of the real-time PCR results compared to a positive standard and no template controls. Amplifiable DNA was determined utilizing the 1369F and 1492R Bacterial 16S Primers (Suzuki et al., 2000).

Positive standards for PCR were generated by utilizing the PCR reaction mixture and conditions as used for screening without SYBR Green. DNA template for the PCR reactions to create standards was pooled DNA from clinical strains, not used in this study, with documented multiple resistances. PCR products at the expected product size determined by 2% agarose gel electrophoresis were excised from the gel, purified utilizing a QIAquick Gel Extraction Kit (Qiagen, USA), and cloned into the plasmid pUC19 (Yanisch-Perron et al., 1985). For cloning into pUC19, the plasmid was fully digested with SmaI endonuclease enzyme (NEB, USA) and the enzyme heat inactivated at 65◦C for 30 min. The SmaI digested pUC19 was treated in a 50 μL reaction with Standard Taq Buffer, 0.5 U of Taq DNA Polymerase (NEB, USA), and 100 μM dTTP to generate 3 T overhangs to aid in cloning the PCR products with 3 A overhangs. The PCR products for the generation of standards were ligated into pUC19 utilizing T4 Ligase by manufacture's recommended conditions (NEB, USA). Chemically competent TOP10 cells (Invitrogen, USA) were transformed with the ligation reaction and plated onto TSA augmented with 200 μg mL−1 ampicillin. Colony PCR was utilized to screen for isolates containing pUC19 with the insertions utilizing the M13 forward and reverse primers. Isolates demonstrating successful insertion of the PCR product into pUC19 were propagated and plasmids utilized for positive standards were isolated by I-Blue Mini Plasmid Kit (IBI, USA) and confirmed by Sanger sequencing.

Of the 295 positive amplification from the screening PCRs, 146 (49%) were purified utilizing the QIAquick PCR Purification Kit (Qiagen, USA) and sequenced utilizing their respective primers. All sequenced amplicons were confirmed to be the targeted genes (Results summarized in Supplementary Table S2).

#### Statistical Analysis

Antibiotic resistance phenotypic profiles and gene presence was converted into numerical code. For each antibiotic: 1 signified susceptibility, 2 for intermediate resistance, and 3 represented resistance. The presence of a gene was signified as 1 and absence as 0. Principal component analysis (PCA) was performed using the package FactoMineR (Lê et al., 2008) from the open source statistical program R (R Core Team, 2012). Multivariate statistical analyses were performed using routines in the R package 'vegan' (Oksanen et al., 2013). Bray–Curtis similarities were calculated for antibiotic resistance levels and gene presence or absence using vegdist function in vegan package as input for multivariate ANOVA (MANOVA) analyses using the permutational alternative (Anderson, 2001) to standard parametric MANOVA provided by vegan's 'adonis' function. Multiple pairwise tests were conducted using Tukey's honestly significant differences (HSD) at the 5% family wise level of significance. The 'glm' function with the Poisson distribution and Analysis of Deviance for Generalized Liner Model utilizing the Chi-square test to determine significant differences between the antibiotic variable vectors and the source vectors.

Proportional *Z* test was utilized to identify significant differences between count data which is represented as percentages such as antibiotic resistances and presence of genes by location. Mann–Whitney *U* test was used for comparison of number of antibiotic resistance in the isolates from the locations.

#### Results

#### Antibiotic Resistance Profiles

The antibiotic resistance patterns of the *E. coli* isolates to 17 antibiotics are shown in **Figure 1**. Duplicate isolates were determined and eliminated based on phenotypic and genotypic results; however, no duplication was observed. Resistant-level phenotype, growth at the highest concentrations of antibiotic within the phenotypic assay, was detected for all antibiotics tested. All isolates (*n* = 259) showed a resistant-level phenotype for at least one antibiotic and non-sensitive level phenotype, growth at any concentration of antibiotic tested within the phenotypic assay, for at least six antibiotics tested (**Figure 2**; Supplementary Figure S2). All *E. coli* isolates were non-sensitive to at least one agent in three or more antibiotic categories as defined for multi-drug resistant (MDR) bacteria in Magiorakos et al. (2012). Three isolates, two from the inner harbor and one from the outer harbor were non-sensitive (intermediate or resistant level of phenotypic antibiotic resistance) to all 17 antibiotics tested. These three strains resistant to all antibiotics tested are potential extensively

FIGURE 1 | Percentage of *Escherichia coli* isolates from sewage (S, *n* **=** 66), outer harbor (O, *n* **=** 58), inner harbor (I, *n* **=** 36), and clinical setting (C, *n* **=** 99) indicating resistance (solid) or intermediate

resistance (transparent) to 17 different antibiotics. Table indicates significant difference in antibiotic resistance (not including intermediate resistance) between locations (*p <* 0.01) by proportional *Z* test.

drug-resistant (XDR) bacteria showing non-sensitivity to 11 of the required 15 out of 17 antibiotic categories (Magiorakos et al., 2012). Antibiotic resistance was most prevalent for erythromycin, sulfamethoxazole, aztreonam, and ampicillin occurring in 72– 88% of all isolates. The least prevalent antibiotic resistance in all isolates was for chloramphenicol, observed in less than 7%.

*E. coli* isolates collected in the inner or outer harbor of Milwaukee demonstrated significantly greater frequencies of resistances to eight antibiotics compared to the *E. coli* isolates collected from human derived sewage and of clinical concern (*p <* 0.01; **Figure 1**). These eight antibiotics included ampicillin, neomycin, tetracycline, ciprofloxacin, fosfomycin, rifampicin, cefuroxime, and cefepime. The isolates from the urban waterways additionally showed significantly greater frequencies of resistance to erythromycin compared to isolates from human derived sewage (*p <* 0.01) and aztreonam compared to clinical isolates (*p <* 0.01). Of the antibiotics tested, the isolates in the outer harbor collected near the WWTP effluent showed significantly greater frequencies of tetracycline, fosfomycin, and rifampicin resistance compared to the inner harbor isolates (*p <* 0.01).

Since all *E. coli* isolates tested were determined to be MDR bacteria, the distribution of the number of antibiotics the isolates were resistant was used to explore the differences between the urban waterway and human derived isolates. The number of antibiotic resistances in the *E. coli* isolates was greater from the inner harbor (median = 9, range: 6–16 antibiotics) and outer harbor (median = 10, range: 4–14) of the urban waterways compared to the clinical isolates (median = 6, range: 2–12) and isolates from human derived sewage (median = 6.5, range: 1– 13; **Figure 2**, *p <* 0.001). (The number of antibiotic resistance in the *E. coli* isolates demonstrating non-sensitive phenotypes similar to determine MDR are in Supplementary Figure S2.) There was no significant difference in the number of antibiotics an isolate was resistant between inner and outer harbor isolates (*p* = 0.52) nor human derived sewage and clinical isolates (*p* = 0.17).

Principal component analysis of the combined genotype and phenotypic resistance profiles for each isolate was used to explore the difference in the environmental resistome from which the *E. coli* isolates were derived (**Figure 3**). (**Figure 3** contains genotype and phenotype in separate PCA analysis as well.) PCA analysis demonstrated a gradient related to number of antibiotics the *E. coli* isolates were resistant to (**Figure 3**, dotted arrow). Permutational MANOVA analysis showed significant differences between isolates from the different sources (*p <* 0.001) and follow up pairwise comparison showed significant differences between all sources (*p <* 0.01) with the exception of the human derived sources (clinical and human derived sewage, *p* = 0.74).

Isolates collected from human derived sewage were positively correlated with moxifloxacin resistance (*p <* 0.001). The gene *bla*OXA encoding a β-lactamase showed significant (*p <* 0.001) positive correlation with the clinical isolates. The vectors related to the phenotypic antibiotic resistance for meropenem, a mono-β-lactam (*p <* 0.001) were also significantly correlated with the clinical isolates. The inner harbor isolates positively correlated with antibiotic resistance for fosfomycin, UDP-*N*acetylglucosamine enolpyruvyl transferase inhibitor (*p <* 0.01), cefuroxime, a second generation cephalosporin (*p <* 0.001), and the presence of the *tet*(M) (*p <* 0.01) gene encoding tetracycline resistance. The outer harbor isolates were positively correlated with resistance to tetracycline (*p <* 0.001), fosfomycin (*p <* 0.001) and cefuroxime (second generation cephalosporin; *p <* 0.01).

The PCA analysis suggested putative incidences of cooccurrences of resistances across sources. The co-occurrences of non-sensitive phenotypes (resistance and intermediate levels) for combinations of antibiotics were recurrent. **Table 1** shows the co-occurrences of non-sensitive phenotypes to 3–5 antibiotics demonstrated within 20% or more of the isolates from a location. The majority of co-occurrences of resistances were identified within the urban waterways. The presence of the co-occurrences in the urban waterways is expected due to the high incidences of resistance in the isolates (**Figure 1**) and the number of resistance

FIGURE 3 | Principal Component Analysis (PCA) biplots of *E. coli* isolates in terms of their phenotypic antibiotic resistance profiles and/or their genotype based on the presence of antibiotic resistance genes. Separate PCA were performed on data containing both the phenotypic and genotypic profiles (Left) and the genotypic (Middle) and phenotypic (Right) profiles separately. The green points represent the *E. coli* isolates from the inner Milwaukee Harbor, black points from the outer harbor, red points from human derived sewage, and blue from clinical isolates. Points in the upper panels are individual isolates and the points in the bottom

panels represent the group center (average of individuals). Dotted line in the upper panels shows the gradient of number of resistance (multiple antibiotic resistances) in the isolates. The ellipses represent a 95% confidence interval of the respective points in the same color. In the right panel: Black arrows represent antibiotics (see Materials and Methods for abbreviations) utilized in the phenotypic antibiotic resistance screening. Erythromycin (ERY), Sulfamethoxazole (SFM), and Moxifloxacin (MOX) respective arrows may not be perceptible due to little influence in the ordination. Red arrows represent antibiotic resistance genes screened by PCR.

within the isolates (**Figure 2**). The co-occurrences of ampicillin, fosfomycin, rifampicin, and cefuroxime resistance were of the greatest abundance in urban waterway isolates compared to human derived sewage and clinical *E. coli* isolates. The cooccurrences of neomycin, sulfamethoxazole, erythromycin, and meropenem resistance were not significantly different between the urban waterways and clinical isolates indicating a potentially common resistance mechanism between the environmental and the clinical *E. coli* isolates.

In summary, a higher incidence of antibiotic resistance, prevalence of resistance to a greater number of antibiotics and a greater incidence of co-occurrences of resistance was identified within the urban waterway (inner and outer Milwaukee Harbor) *E. coli* isolates compared to the human derived isolates (sewage and clinical isolates) from Milwaukee.

#### Detection of Antibiotic Resistance Genes

The presences of five antibiotic resistance genes: *ermB* (macrolide resistance), *tet*(M), and β-lactamases (*bla*OXA, *bla*SHV, and *bla*PSE) were screened for in the *E. coli* isolates (**Figure 4**). More than 65% of all the *E. coli* isolates (*n* = 259) harbored at least one of the five resistance genes chosen for analysis. The presence of *ermB* was the most prevalent represented in 38% of the isolates. The β-lactamase encoding genes *bla*OXA and *bla*SHV were present in 26 and 25% of all strains, respectively. The resistance gene *bla*PSE and *tet*(M) were the least prevalent at 10 and 14%, respectively. The clinical *E. coli* isolates showed significantly greater frequencies of the *bla*OXA compared to the urban waterway isolates of the inner and outer harbor and the human derived sewage (**Figure 4**). The greater incidence of resistance to meropenem in the clinical *E. coli* isolates can be partially (58.2%) explained by the greater frequency of the *bla*OXA gene in the isolates compared to less than 10% in the meropenem resistant *E. coli* isolates from urban waterways and human derived sewage (**Table 2**). The presence of any β-lactamase genes could explain 73% of the ampicillin resistant clinical *E. coli* isolates compared to only 21–40% of the ampicillin resistant *E. coli* isolates from the urban waterways and human derived sewage.

TABLE 1 | The percentage of *Escherichia coli* isolates from the urban waterways of the inner and outer Milwaukee Harbor and of human derived sources (sewage and clinical) with co-occurrence of antibiotic resistance.

1*Criteria for inclusion was non-sensitive resistance (resistance or intermediate resistance level) for three or more antibiotics and presence in greater than 20% in one or more locations.*

2*Reported in percentage of isolates from location and number of isolates in parenthesis.*

3*Statistical significance was determined between locations by proportional Z test.*

The *E. coli* isolates of the inner harbor demonstrated greater frequencies of the *tet*(M) gene compared to the isolates of the outer harbor and derived from human sources (clinical and human derived sewage isolates). The presence of the *tet*(M) gene explained greater than 38.9% of the tetracycline resistant isolates detected in the inner harbor and less than 16.1% in the other tetracycline isolates from other sources.

The *ermB* gene was present in significantly greater frequency in clinical *E. coli* isolates than the *E. coli* isolates from the outer harbor and human derived sewage. The presence of *ermB* may explain 52% of the erythromycin resistance observed in the

clinical isolates, while less than 40% of erythromycin resistance could be explained in the urban waterways and human derived sewage isolates.

The few genetic determinants detected in this study had little to no effect on the PCA analysis in the presence of the phenotypic data (**Figure 3**). Utilizing only the genetic determinations in PCA analysis and MANOVA did not show any significant difference between sources of *E. coli* isolates.

While the genetic determinates were able to explain some of the high incidences of resistance within the clinical *E. coli* isolates, direct phenotypic determination of antibiotic resistance showed

TABLE 2 | Percentage of antibiotic resistance in *E. coli* isolates from different locations explained by the presence of a gene able to confer the resistance.

1*Genes encoding* β*-lactamases are abbreviated to their types (i.e., blaSHV as SHV).* 2*Percentages represent the number of strains from a location that have a non-sensitive resistance (resistance or intermediate resistance level) for an antibiotic and the presence of at least one gene listed in the table. In parenthesis: (number of isolates with gene/total with non-sensitive resistance).*

greater incidences of antibiotic resistance and greater number of resistance within the *E. coli* isolates from urban waterways.

#### Discussion

The emergence and dispersion of antibiotic resistance has reduced the susceptibility of pathogens to antibiotics in medical treatment. A detailed examination of the origin and role of antibiotic resistance in natural environments is necessary to understand the evolution and dissemination of antibiotic resistance genes in pathogens (Allen et al., 2010). The urban rivers merging into Milwaukee Harbor and ultimately Lake Michigan allowed for a representative assessment of the distribution of antibiotic resistance within these urban-influenced environments.

Prevalence of resistance to erythromycin, sulfamethoxazole, aztreonam, and ampicillin was detected in all studied sources. Occurrence of resistance and resistant determinates related to β-lactams, sulfonamides, and macrolides are not uncommon in urban settings (Hu et al., 2008; Munir et al., 2011; Rodríguez-Mozaz et al., 2014; Yang et al., 2014). The detection of macrolides and sulfonamides within the influent and effluent of a municipal wastewater treatment plant servicing the greater Milwaukee, WI area (Blair et al., 2013b) may indicate high historical usage of these antibiotics which may have led to establishment of resistant *E. coli* within the bacterial communities observed in this study. Similar levels of antibiotic resistance in the sediment of the urban waterways of Milwaukee compared to raw sewage have been detected previously (Salmore et al., 2006). The low prevalence of chloramphenicol resistance in this study is consistent with published data within the U. S. (Johnson et al., 2013) and supports the idea that this old-generation antibiotic has the potential to address the current need for new antibiotics despite the potential of negative side-effects (Falagas et al., 2008).

The high rate of MDR classification within the isolates used in this study may be due to choice of older individual antibiotics within the antibiotic classes as representatives to distinguish individual isolates based on phenotypic resistance. Previous studies have indicated lower percentages of MDR compared to this study (Baquero et al., 2008; Figueira et al., 2011; Ham et al., 2012). However, these studies used more recently deployed or fewer antibiotics compared to this study making it less likely to establish MDR.

The *E. coli* isolates from urban waterways demonstrated greater incidence of resistance to single antibiotics and multiple antibiotic resistance within a single isolate compared to human derived sources (sewage and clinical isolates). The observation of greater incidence of antibiotic resistance in *E. coli* from natural waters compared to a possible sources of fecal contamination is consistent with the observations of Alves et al. (2014), which showed a higher incidence of multi-resistant *E. coli* in coastal waters of an uninhabited island than the source of fecal contamination by seagull feces and human sewage from tourist. The higher level of resistance within the urban waterway *E. coli* isolates may be explained by the historically high abundance of antibiotics, in particular of macrolides, fluoroquinolones, and sulfonamides, quantified within the water and sediments of the Milwaukee Harbor (Blair et al., 2013a). These antibiotics may have synergistic selective effect on mechanisms of antibiotic resistance leading to increased selective pressure for antibiotic resistance within the microbial population (Davies et al., 2006; Andersson and Hughes, 2014). In other urban waterway environments receiving treated wastewaters a higher incidence and chronic presence of antibiotics and antibiotic resistance genes were detected downstream of released effluent (LaPara et al., 2011; Munir et al., 2011; Korzeniewska et al., 2013; Alves et al., 2014; Czekalski et al., 2014; Rodríguez-Mozaz et al., 2014).

The greater prevalence of the antibiotic resistance cooccurrences in urban waterways may point to a mobile resistome within the microbial population of urban waterway sediment. Previous studies tend to use a strict criteria for multiple resistance shared between isolates when exploring phenotypic similarities between locations and sources (Hu et al., 2008; Alves et al., 2014). For example, isolates showing resistance to four antibiotics would be grouped, while an isolate resistant to the same four antibiotics with additional resistances would be excluded from the grouping. This restriction may led to misinterpretation in the prevalence of specific multiple resistances shared between isolates. The prevalence of resistance to specific antibiotics or antibiotic classes within a microbial population may be explained by the presence of mobile genetic elements within the population encoding that antibiotic resistance (Thavasi et al., 2007). The existence of plasmids bearing extended spectrum β-lactamase (ESBL) and fosfomycin resistance determinants that can spread effectively in *Enterobacteriaceae* have been discovered and are of great clinical concern (Zhao et al., 2015). A high prevalence of co-occurrence of resistance to fosfomycin, ampicillin, cefuroxime, and rifampicin and other patterns within the urban waterways of Milwaukee may be explained by the existence of mobile genetic elements encoding the resistances for these antibiotics within the microbial community. The detection of the antibiotic resistance genes *ermB*, *tet*(M), *bla*OXA, *bla*SHV, and *bla*PSE (**Figure 1**) which are associated with natural plasmids (Brisson-Noel et al., 1988; Carattoli, 2013) and transposons (Bryan et al., 2004) within the *E. coli* isolates of the urban waterways supports the possible presence of these type of mobile genetic elements.

The greater incidence of resistance observed in the urban waterways (**Figure 1**) was not fully explained by the presence of the resistance genes tested (**Table 2**). The genes *ermB* and *bla*OXA were present in a greater percentage of isolates in the clinical *E. coli* isolates. While the transfer of the resistance gene *ermB* from Gram-positive cocci to *E. coli* (Brisson-Noel et al., 1988) was previously detected, very few studies attempt to identify and quantify this gene in non-Gram-positives despite the ability to confer resistance (Goetting-Minesky and Fenno, 2010). The *ermB* gene has also been identified in *Bacteroides* (Shoemaker et al., 2001) and *Campylobacter coli* (Qin et al., 2013). To our knowledge, this study is the first to attempt to quantify the presence of *ermB* in *E. coli* from environmental waters. The emergence of *bla*OXA carbapenemase producing *Enterobacteriaceae* is difficult to detect because of their relative low MICs (Seiffert et al., 2014). The detection of *bla*OXA is consistent with expectations in the environment (Ojer-Usoz et al., 2014) and clinical isolates (Kaftandzieva et al., 2014). One possible explanation for the lower presence of the genes tested in *E. coli* from the urban waterways to that of the human derived sources may be related to survival in non-human environments. The selective pressure on the *E. coli* in the sediment of the urban waterways from various physical conditions including nutrient availability, temperature, and predation (Beversdorf et al., 2007) and chemicals including antibiotics, personal care products, and metals may select for phenotypes that have co-selection for mechanisms of antibiotic resistance other than the ones encoded by the studied genes (Graham et al., 2010). Co-selection can also be related to cross-resistance, such as multi-drug resistance pumps where the primary roles is thought to provide tolerance to toxic compounds in the environment (Poole, 2005; Martinez et al., 2009; Allen et al., 2010). An additional explanation of the higher incidence of antibiotic resistance within the urban waterways is the selection of antibiotic resistance within the bacteria prior to entering the environment. The sources of the antibiotic resistant *E. coli* in this study can include the WWTP (Korzeniewska et al., 2013) as the major contaminating source was previously determined to be human in the study area (Newton et al., 2013). Determining the resistances from the contaminating bacterial sources may yield

#### References

Allen, H. K., Donato, J., Wang, H. H., Cloud-Hansen, K. A., Davies, J., and Handelsman, J. (2010). Call of the wild: antibiotic resistance genes in natural environments. *Nat. Rev. Microbiol.* 8, 251–259. doi: 10.1038/nrmicro 2312

information to determine if the resistances observed in this study are due to depositing of resistance or selection of resistance.

#### Conclusion

This study demonstrates urban waterway sediment as a relevant reservoir of *E. coli* strains containing multiple resistances to antibiotics and antibiotic resistance genes. The presence of known antibiotic resistance genes harbored on mobile genetic elements and a greater level of resistance compared to the local human derived *E. coli* suggests that sediment of the urban waterways may harbor an extensive mobile resistome. Further work is needed to explore the presence of a putative mobile resistome, its contribution to the reservoir of antibiotic resistance, and the potential of transfer to human commensal bacteria and pathogens.

#### Author Contributions

AK and KH conceived and designed the experiments. AK, MD, and NA performed the experiments. NL, RN, and KH contributed materials, reagents, strains, and tools for analysis. The manuscript was prepared by AK, RN, and KH.

#### Acknowledgments

This work was funded by the University of Wisconsin Sea Grant Institute under grants from the National Sea Grant College Program, National Oceanic and Atmospheric Administration, U.S. Department of Commerce, and from the State of Wisconsin. Federal grant number (NA14OAR4170092), project number (R/HCE-20). Partial financial support for this work was from Marquette University. Financial support was provided also to NA by the Biological Sciences Department, MU, and to MD by the Summer Research Program (NSF REU site DBI-0755047). The authors would like to thank Amanda Skowronski and Nicholas Callard, undergraduate students, and Meghan Fealey, Brittney Wyatt, and Michael J. Mashock, graduate students, from Marquette University for their contribution in gathering some of the data.

#### Supplementary Material

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb.2015.00336/ abstract

extended-spectrum beta-lactamases genes. *Front. Microbiol.* 5:426. doi: 10.3389/fmicb.2014.00426

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2015 Kappell, DeNies, Ahuja, Ledeboer, Newton and Hristova. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## **Diverse and abundant multi-drug resistant** *E. coli* **in Matang mangrove estuaries, Malaysia**

*Aziz Ghaderpour 1, 2, Wing Sze Ho1, Li-Lee Chew1, 2, Chui Wei Bong1, 2, Ving Ching Chong1, 2, Kwai-Lin Thong1, 2 and Lay Ching Chai 1, 2\**

*1 Faculty of Science, Institute of Biological Science, University of Malaya, Kuala Lumpur, Malaysia, 2 Institute of Ocean and Earth Sciences, University of Malaya, Kuala Lumpur, Malaysia*

*E.*coli, an important vector distributing antimicrobial resistance in the environment, was found to be multi-drug resistant, abundant, and genetically diverse in the Matang mangrove estuaries, Malaysia. One-third (34%) of the estuarine *E. coli* was multi-drug resistant. The highest antibiotic resistance prevalence was observed for aminoglycosides (83%) and beta-lactams (37%). Phylogenetic groups A and B1, being the most predominant *E. coli*, demonstrated the highest antibiotic resistant level and prevalence of integrons (integron I, 21%; integron II, 3%). Detection of phylogenetic group B23 downstream of fishing villages indicates human fecal contamination as a source of *E. coli* pollution. Enteroaggregative *E. coli* (1%) were also detected immediately downstream of the fishing village. The results indicated multi-drug resistance among *E. coli* circulating in Matang estuaries, which could be reflective of anthropogenic activities and aggravated by bacterial and antibiotic discharges from village lack of a sewerage system, aquaculture farms and upstream animal husbandry.

#### *Edited by:*

*Rustam Aminov, Technical University of Denmark, Denmark*

#### *Reviewed by:*

*Veljo Kisand, University of Tartu, Estonia Daniela Ceccarelli, University of Maryland, USA*

#### *\*Correspondence:*

*Lay Ching Chai, Faculty of Science, Institute of Biological Sciences, University of Malaya, 50603 Kuala Lumpur, Malaysia<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Antimicrobials, Resistance and Chemotherapy, a section of the journal Frontiers in Microbiology*

*Received: 10 April 2015 Accepted: 02 September 2015 Published: 29 September 2015*

#### *Citation:*

*Ghaderpour A, Ho WS, Chew L-L, Bong CW, Chong VC, Thong K-L and Chai LC (2015) Diverse and abundant multi-drug resistant E. coli in Matang mangrove estuaries, Malaysia. Front. Microbiol. 6:977. doi: 10.3389/fmicb.2015.00977* **Keywords:** *E. coli***, Matang mangrove estuaries, antibiotic resistance, phylogenetic groups**

#### **Introduction**

*Escherichia coli* (*E. coli*), a component of the common intestinal microbiota in humans and warm-blooded animals, is an important indicator of fecal contamination in aquatic environments and food. Certain strains of *E. coli* are able to cause gastrointestinal and extraintestinal infections in humans. The pathogenic *E. coli* that cause gastrointestinal infections include enterohemorrhagic *E. coli* (EHEC), also referred to as Shiga toxin-producing *E. coli* (STEC) or verocytotoxic *E. coli* (VTEC), enteropathogenic *E. coli* (EPEC), enteroaggregative *E. coli* (EAEC), enterotoxigenic *E. coli* (ETEC), enteroinvasive *E. coli* (EIEC), and diffusely adherent *E. coli* (DAEC) (Galvin et al., 2010; Koczura et al., 2013).These pathogenic *E. coli*, together with other virulent strains of *E. coli* could cause extraintestinal infections in humans such as pyelonephritis, urinary tract infection, cystitis, neonatal meningitis, and bacteremia (Johnson and Stell, 2000; Galvin et al., 2010; Koczura et al., 2013).

*E. coli* is divided into four main phylogenetic groups A, B1, B2, and D, based on the presence and absence of, *chu*A, a gene that is responsible for heme transport in enterohemorrhagic O157:H7 *E. coli*; *yja*A, an unknown functional gene which is identified in the recent complete genome sequence of *E. coli* K-12; and TSPE4.C2, an anonymously designated DNA fragment which is the non-coding region in *E. coli* strains (Clermont et al., 2000). These four phylogenetic groups of *E. coli* appear to have distinctive genetic and phenotypic characteristics that are associated with different ecological niches (Bergthorsson and Ochman, 1998; Johnson et al., 2001; Gordon and Cowling, 2003; Gordon, 2004; Walk et al., 2007). It is interesting to note that most of the *E. coli* strains that are able to endure environmental stresses in the aquatic environment belong to the B1 phylogenetic group (Walk et al., 2007). In addition, *E. coli* strains that belong to the phylogenetic groups B2 and D harbor more virulent genes than those belonging to A and B1 (Johnson et al., 2001). The extraintestinal pathogenic *E. coli* strains are mostly phylogenetic group B2 and to a lesser extent of group D; while the commensal *E. coli* strains are commonly categorized into phylogenetic group A and B1 (Maynard et al., 2004; Vignaroli et al., 2012; Koczura et al., 2013; Pereira et al., 2013). Escobar-Páramo et al. (2004) found that most ETEC, EHEC, and EIEC belong to the phylogenetic groups A and B1; while EPEC, EAEC, and DAEC are usually not linked to any phylogenetic grouping.

The occurrence of *E. coli* in the environment, particularly in aquatic systems, is a global public health concern, especially when the water is used for human activities. The problem is recently exacerbated by the emergence of antimicrobial resistant *E. coli* strains in aquatic environments worldwide (Koczura et al., 2013; Pereira et al., 2013). Antibiotic resistant *E. coli* and other enteric bacteria, that survived the extensive antibiotic treatments in the gut of humans or animals, can enter aquatic systems through discharge from poultry and livestock production, and hospital and municipal wastewaters (Pruden et al., 2006; Pereira et al., 2013). Therefore, the rivers that are used for recreational activities, irrigation, and other purposes, can be efficient vehicles that disseminate the antibiotic resistant bacteria (Pruden et al., 2006; Su et al., 2012; Pereira et al., 2013).

Antibiotic resistance is mostly transferred among bacteria by mobile genetic elements such as integrons, plasmids and transposons. Integrons are well-organized gene expression systems that can integrate one or more non-functional gene cassettes and convert them into expressed genes (Hall and Collis, 1995; Recchia and Hall, 1995; Su et al., 2012). Integrons can easily spread the antibiotic resistance among bacterial species due to their association with plasmids. There are three types of integrons, class 1, 2, and 3 integrons and their classification is according to the integrase gene (*intI*) (Carattoli, 2003; Cambray et al., 2010; Su et al., 2012).

In addition to their use in human medicine, antibiotics are also widely used as therapeutics, prophylactics and metaphylactics in animals and aquaculture farms (Jaime et al., 2012). Twenty different antibiotics (e.g., erythromycin, tetracycline, oxalinic acid, oxytetracycline, amplicillin, norfloxacine, trimethoprim, sulfudimethoxine) and chemicals (e.g., calcium carbonate, potassium permanganate, formalin, and thiodane) are commonly used in aquaculture farms in Malaysia (Keh, 2003). Generally, antibiotics are mixed with feeds, or dissolved directly in water. However, fish are not able to metabolize the antibiotics, which are largely returned to the environment via faces that pose a potential threat to public health (Burridge et al., 2010; Jaime et al., 2012).

Several estuaries within Matang Mangrove Forest Reserve (MMFR), Perak were selected as study sites in this study. The MMFR bears the distinctive features of being one of the best managed mangrove forests in the world, an important nursery area sustaining the country's largest fisheries landings and site to the state's largest brackish water aquaculture farms (Ariffin and Nik Mustafa, 2013). The MMFR exemplifies significant economic benefits and ecological services that could be obtained through sustained forest management. However, the Matang area also demonstrates the increasing dilemma of multiple-use conflicts of land and water resources (Chong et al., 2010). Twenty eight of the 34 permanent settlements in the MMFR are fishing villages sited along the mangrove estuaries with a total population of 31,800 people (Lim and Mohamad Parit, 2001).

A recent study shows that the Matang mangrove estuaries may be facing a great challenge of anthropogenic pollution due to contamination by various potentially pathogenic bacteria including high counts of *E. coli* in both water and sediment (Ghaderpour et al., 2014). This kind of anthropogenic pollution could become a more significant public health issue if these bacteria are resistant to antibiotics. Therefore, in this study, we aimed to (i) investigate the genetic diversity of *E. coli* in Matang estuaries, (ii) determine the virulence factors of the estuarine *E. coli* isolates, and (iii) detect antibiotic resistant *E. coli* and their resistance mechanisms in Matang mangrove estuaries.

### **Materials and Methods**

#### **Study Sites and Sample Collection**

The MMFR is located in the northwest coast of peninsular Malaysia (**Figure 1**). It is a silviculture production forest of 41,000 ha managed on a sustainable basis since 1902. Within the sheltered waterways of the forest reserve, nearly 8000 floating fish cages are present, while 385 shrimp and fish ponds cover a total area of 223 ha on the landward margin of the mangrove forests (Ariffin and Nik Mustafa, 2013). Cockle culture covers an extensive area of 4726 ha within and outside the Matang estuaries.

Water and sediment samplings were carried out in the various estuaries, particularly the Sangga Besar, Sepetang and Selinsing Rivers. Sangga Besar River is the main waterway (8 km) traversed by fishing boats between the fishing village at Kuala Sepetang and the coastal fishing grounds. Unlike the Sangga Besar, the Selinsing River is a long waterway (18 km) relatively less used by boats as well as having few floating fish farms at its river mouth. Eight sampling sites (A–H) were set up, from upstream to downstream of the estuary (**Figure 1**). Station A is situated at the upper estuary of the Sepetang River, while stations B to F are located along the Sangga Besar River. Two further sampling sites, G and H were set up at the upstream and downstream of the Selinsing River, respectively. Station A is located upstream of the Kuala Sepetang village, while Station B, C, and G are situated 1– 7 km downstream of the village, receiving the untreated sewage and other anthropogenic pollution from the village. Station D and E are located near to cockle culture beds and fish cages, respectively. Station F is situated at the river mouth downstream of the fish cages close to other cockle culture areas on the coastal mudflat. Station H is located 15 km downstream from the Kuala Sepetang village on Selinsing River, with very little human activity (**Figure 1**).

Four water and sediment samples were collected at each sampling station in October 2011 (wet season) and May 2012

(dry season). Water samples (0.5 m depths) were collected in acid washed bottles. A 15 cm × 15 cm Ekman grab (Wildco, USA) was used to collect bottom sediment. The top 5 cm surface of the collected sediment samples was sampled using a spatula and kept in sterile bottles. All samples were stored in ice, and immediately transferred into the laboratory freezer kept at 4◦C until the analyses were carried out within 24 h of sampling.

#### **Isolation of** *E. coli* **and Detection of Their Housekeeping and Virulence Genes**

Membrane filtration technique was used to isolate *E. coli*. Water (5 ml) and sediment (1 g in 9 ml of sterile 0.85% saline) samples were filtered through 0.45 mm nitrocellulose filters (47 mm diameter). The filters were then transferred on CHROMagar ECC (CHROMagar Inc., Paris, France) and incubated at 42 ± 0.5◦C for 24 h. Blue colonies as presumptive *E. coli* were randomly picked and purified on Luria Bertani medium. After purification, presumptive *E. coli* isolates were kept in stab and in glycerol forms (Luria Bertani Broth with 50% glycerol) at 37◦C and −20◦C, respectively for the future analysis. The identity of the presumptive *E. coli* isolates was confirmed by PCR targeting the *pho*A gene, an *E. coli* housekeeping gene (Yu and Thong, 2009).

Two different multiplex PCR assays were carried out for all confirmed *E. coli* to identify the pathogenic *E. coli* (EHEC, EPEC, EAEC, ETEC, EIEC and DAEC). For both multiplex PCR, reaction mixture was carried out in 25μL volume consisting of 1X green buffer (5X green GoTaq reaction buffer, pH 8.5), 0.5U of *Taq* DNA polymerase (Promega, Madison,Wis, USA), 1.65 mM MgCl2, 0.3μM of the selected primers, 220μM of each deoxynucleoside triphosphate (dNTP), and 5μL of DNA template. PCR amplification and primers used in this study were previously described by Gómez-Duarte et al. (2009). Primers sequences are listed in Supplementary Table 1. Six positive control strains (*E. coli* 2060–004, E2348/69, JM221, E9034A, C1845, and EC-12) were used in this study and sterile water was included as negative control. The six positive strains were provided by Oscar G. Gomez-Duarte, International Enteric Vaccines Research Program [IEVRP], University of Iowa Children's Hospital, Iowa City, IA (Gómez-Duarte et al., 2009).

#### **Phylogenetic Grouping**

A triplex PCR assay was used to determine the phylogenetic groups (A, B1, B2, and D) of *E. coli*. PCR conditions for presence or absence of *chu*A, *yja*A, and TSPE4-C2 were used as described by Clermont et al. (2000) and Carlos et al. (2010) (**Table 1**). Each reaction was performed in 25μL volume containing 5μL (appr 10 ng) DNA template, 120μM each dNTP, 1X PCR buffer, 0.5 U *Taq* polymerase, 1.5 mM MgCl2, 0.4μM of yjaA primers, 0.24μM each chuA, and TSPE4-C2 primers (Supplementary Table 1). Combination of presence or absence of *chu*A, *yja*A, and TSPE4-C2 was used for identification of phylogenetic grouping as follows: phylogenetic group A (A0, −*/*−*/*−; A1, −*/*+*/*−), phylogenetic group B (B1, −*/*−*/*+), phylogenetic group B2 (B22, +*/*+*/*−; B23, +*/*+*/*+) and phylogenetic group D (D1, +*/*−*/*−; D2, +*/*−*/*+) (Carlos et al., 2010).

#### **Antibiotic Susceptibility Testing**

Agar disc diffusion method on Mueller-Hinton Agar (Oxoid, Basingstoke, UK) was used to determine the antibiotic resistance patterns among *E. coli* isolates. A total of 15 antibiotics belonging to 7 classes were tested, including aminoglycosidase: gentamicin (CN, 10μg), kanamycin (K, 30μg), streptomycin (S, 25μg), and neomycin (N, 30μg); β-lactams: amoxicillin/clavulanic acid (AMC, 30μg), ampicillin (AMP, 10μg), ceftriaxone (CRO, 30μg), ceftiofur (EFT, 30μg); quinolones: nalidixic acid (NA, 30μg), oxolinic acid (OA, μg); fluoroquinolone: enrofloxacin (ENR, 5μg), ciprofloxacin (CIP, 5μg); tetracycline: tetracycline (TE, 30μg); phenicols: chloramphenicol (C, 30μg), and sulfamethoxazole/trimethoprim (SXT, 25μg) (Oxoid Ltd.,Basingtoke, Hampshire, England). The bacteria were designated as resistant, intermediate and sensitive according to the Clinical Laboratory Standards Institute (CLSI)'s recommendations (CLSI, 2012). Bacteria strains that were resistant and intermediate were categorized as nonsusceptible while the sensitive strains were categorized as susceptible.

#### **Detection of Class 1, 2, and 3 Integrons**

All *E. coli* isolates were screened for integrase genes. Three different sets of primers were used to detect class 1, 2, and 3 integrase genes (Supplementary Table 1). Primers IntI1-F, IntI1- R and 5 CS and 3 CS were used to detect *intI*1 gene and class 1 integron gene cassettes, respectively. For class two integrons, IntI2-F, IntI2-R, and attI2-F/orfX-R primers were used for amplifying *intI*2 and class 2 integron gene cassettes, respectively. The primer IntI3-F/IntI3-R was used to amplify *intI*3 gene.

The reaction mixture for all PCR assays was performed in 25μL volume which consisted of 1X PCR green buffer, 1.4 mM MgCl2, 120μM of each dNTP, 0.06μM of each primers, and 0.5 U of *Taq* DNA polymerase (Promega, Madison, Wisconsin, USA). The multiplex PCR amplification of integrase type 1 and 2, and class 1 integron gene cassettes was performed as previously described by Lim et al. (2009).

#### **Confirmation of PCR Products by DNA Sequencing**

PCR products were purified and submitted to a commercial company (First BASE Laboratories, Malaysia) for sequencing using BigDyeR Terminator v3.1 cycle sequencing kit chemistry. Homologs search against the GenBank databases of nr/nt using BLASTN (http://blast.ncbi.nlm.nih.gov/blast) were carried out for all the DNA sequences obtained.

#### **Genetic Diversity of** *E. coli* **Isolates Using REP-PCR**

Genetic diversity of 148 *E. coli* isolates was analyzed by Repetitive Extragenic Palindromic-PCR (REP-PCR) using REP primer (Supplementary Table 1). Bacterial isolates were grown in 1 ml of sterile Luria Bertani broth overnight at 37◦C. Bacterial suspension in 1.5 ml microfuge tubes were centrifuged and washed with sterile Phosphate buffered saline (PBS) and 1X TE (Tris-EDTA) buffer. Bacterial pellets were suspended in sterile water and were then heated at 99◦C for 10 min and chilled on ice for 20 min. The extracted crude DNA was used for amplification with REP oligonucleotides (Operon Biotechnologies GmBH, Germany) as previously reported (Lim et al., 2009). REP-PCR assay was carried out in 25μL volume which included of 1X buffer (5X buffer GoTaq reaction buffer, pH 8.5), 1U of *Taq* DNA polymerase (Promega, Madison, Wisconsin, USA), 2.5 mM MgCl2, 200μM of each dNTP 0.5μM of the primer and 4μL of DNA template. Temperature program for this assay consisted of initial denaturing at 94◦C for 7 min, followed by 30 cycles of 30 s at 94◦C, 1 min at 44◦C, 8 min at 72◦C and a final extension step 16 min at 65◦C. PCR products were loaded in 1.5% agarose gels and stained in Gel red nucleic acid stain (Biotium Inc, USA). Banding patterns were analyzed by GelCompar II, version 2.5 (Applied Maths, Kortrijk, Belgium).

#### **Statistical Analysis**

Prevalence of the antibiotic resistance was determined using x/y 100% equation, where x is defined as number of resistant *E. coli* isolates to antibiotics, and y is total isolated number of *E. coli* from each sample. Principle component analysis (PCA) was used to depict the distribution of *E. coli* with respect to phylogenetic groups, integron genes, and antibiotics resistance among eight sampling stations in Matang mangrove estuary. To perform the PCA procedure, the number of isolates of *E. coli* containing the phylogenetic groups, integron genes and antibiotics resistance was square root transformed to meet the parametric assumptions. PCA was performed using CANOCO 4.5 software (Ter Braak and Smilauer, 2002). Pearson's chi-squared goodness-of-fit test was applied to evaluate the significant difference among phylogenetic groups based on the frequency data. Also, comparisons of associations between phylogroups and antibiotic resistance were done using the chi- square test when no more than 20% of the expected cell frequencies were *<*5 and all individual expected counts were ≥1, otherwise Fisher's exact test (two-tailed) was used. The degree of association between the presence of antibiotic resistance and presence of integron genes were tested using the correlation (Rn) of Ives and Gibbons for dichotomous normal-scale data in Zar (2010). Significance was tested using the

*tetracycline; C, chloramphenicol; SXT, sulfamethoxazole/trimetroprim.* chi-square contingency test. All tests were performed at the 5% level of significance.

#### **Results**

#### **Distribution of Phylogenetic Groups and Pathotypes of** *E. coli* **in Matang Mangrove Estuaries**

A total of 175 presumptive *E. coli* bacteria were isolated from 64 water and sediment samples, during dry and wet seasons. Out of 175 presumptive *E. coli* isolates, 148 (85) were confirmed as *E. coli*, of which 17 were from station A, 28 from station B, 8 from station C, 24 from station D, 20 from station E, 8 from station F, 19 from station G and 24 from station H.

Among the 148 *E. coli* isolates, phylogenetic group B1 was the most prevalent (*n* = 65, 44%) followed by phylogenetic group A and D which had similar prevalence (*n* = 38; 26%). The phylogenetic group B2 was the least prevalent (5%) in Matang mangrove estuaries, and was only detected during the dry season. Phylogenetic groups B1 and D were present at all the stations located along Sangga Besar and Selinsing Rivers. Phylogenetic group B1 was the most frequent isolate from all stations except station E and H, where phylogenetic groups A and D predominated, respectively (**Figure 2**). Phylogenetic group D was more frequently isolated in the wet season (32%) than in the dry season (22%). The prevalence of group A was significantly higher at station E, located in Sangga Besar River (χ2 = 16.526, *df* = 6, *p* = 0.011); while station E recorded a significantly lower occurrence of *E. coli* subset D. Station H located downstream of Selinsing River had significantly higher occurrence of *E. coli* subset D (χ2 = 20.105, *df* = 6, *p* = 0.005) (**Figures 1**, **2**).

The genotypes of each phylogenetic group (A0/A1, B22/B23, and D1/D2) were not distributed homogenously among all stations. Genotype A0 comprised almost two thirds of

phylogenetic group A and could be detected in almost all stations, while genotype A1 was only present in the upstream of Sangga Besar River (station A and B) and Selinsing River (station G and H). Genotype B23 was only detected from station D, E, F, and G which are all located downstream of a fishing village (**Figure 2b**).

None of the 148 isolates was EHEC, EPEC, ETEC, DAEC, or EIEC. However, two isolates originated from station D located upstream of Sangga Besar River and station G located upstream of Selinsing River were identified to be EAEC.

#### **Antibiotic Resistance among the** *E. coli* **Isolates**

Among the 148 *E. coli* isolates from Matang mangrove estuaries, 128 (87%) were non-susceptible to at least one of 15 antibiotics tested (**Table 2**). Only 20 (14%) *E. coli* isolates were sensitive to all the 15 tested antibiotics. The antibiotic resistance percentages were: N (74%), S (31%), K (19%), CN (3%), AMC (11%), AMP (33%), CRO (1%), EFT (3%), ENR (22%), CIP (10%), OA (18%), NA (22%), TE (28%), C (16%), and SXT (19%) (**Table 1** and Supplementary Table 2).

In comparisons among stations, Principle Component Analysis (PCA) showed that high antibiotic resistance among the *E. coli* isolates was detected at the stations (B and D) near to the fishing village (positive direction of PCA1, **Figure 3**). Most *E. coli* isolated from these stations were phylogenetic group B1 and carried class 1 integron. Moreover, high antibiotic resistances especially to aminoglycosides and beta-lactams were also detected in *E. coli* isolates from station F located on the cockle mud flat. On the other hand, low antibiotic resistance of *E. coli* isolates was detected at the stations with less anthropogenic influence (station H) and close to the fish cages (station E) (**Table 1** and **Figure 3**). Overall, *E. coli* isolates from Matang mangrove estuaries demonstrated high prevalence of multidrug resistance (MDR). One third of the tested *E. coli* isolates (34%) were found to be non-susceptible to three or more antibiotics (3R); and about one in every five isolates (20%) were non-susceptible to six or more antibiotics (6R) tested in this study (**Table 1** and Supplementary Table 2). Further, four out of 148 isolates (3%) were non-susceptible to all of the 15 antibiotics examined in this work (**Table 1** and Supplementary Table 2). Fifty out of 148 (34%) *E. coli* samples isolated from Matang mangrove estuaries demonstrated resistance to at least three antibiotic classes and 10% were non-susceptible to all the seven antibiotic classes tested (**Table 1** and Supplementary Table 2).

The analysis of the phylogenetic groups with respect to their antibiotic resistance indicated that all groups showed some level of resistance to antibiotics. However, *E. coli* isolates in A and B1 phylogenetic groups (positive direction of PCA1) generally shows increasing resistance to most antibiotics as compared to the B2 and D phylogenetic groups (negative direction of PCA1). The phylogenetic group A and B1 demonstrated higher non-susceptibility level toward betalactams, (fluoro) quinolones, tetracycline, phenicols, and sulfamethoxazole/trimethoprim. Additionally, multi-drug resistance phenotype was more frequently identified in groups A and B1 (**Table 1** and Supplementary Table 2). Chi-square independence test found only significant association between phylogroups with enrofloxacin (ENR; χ2 = 12.27; *df* = 3; *p* = 0.007), oxolinic acid (OA; χ2 = 11.71; *df* = 3; *p* = 0.008) and nalidixic acid (NA; χ2 = 12.04; *df* = 3; *p* = 0.001), which are all (fluoro) quinolones.

**FIGURE 3 | PCA ordination biplot showing the distribution of** *E. coli* **phylogenetic groups by sampling stations with respect to their resistance to various antibiotics and the integras genes they carried.** The first (PCA1) and second (PCA2) principal components respectively explained 70.7 and 8.6% of the total variance of the explanatory variables. Phylogroups are denoted by various symbols: black circle A, pink square B1, empty square B2 and empty diamond D. Bold letter beside symbol denotes sampling stations located in Sepetang River (A), Sangga Besar River (B–E), Selinsing River (G and H) and adjacent coastal water (F). Black solid arrows indicate isolates resistant to 15 antibiotics: Class I (black box), CN, aminoglycosidase: gentamicin; K, kanamycin; S, streptomycin; N, neomycin; Class II (gray box); AMC, β-lactams: amoxicillin/clavulanic acid; AMP, ampicillin; CRO, ceftriaxone; EFT, ceftiofur; Class III (blue box); ENR, fluoroquinolone: enrofloxacin; CIP, ciprofloxacin; Class IV (yellow box); NA, quinolones: nalidixic acid; OA, oxolinic acid; Class V; TE, tetracycline: tetracycline; Class VI; C, phenicols: chloramphenicol and Class VII; SXT, sulfamethoxazole/trimethoprim. Red dotted arrows indicate *E. coli* integron gene (s).

#### **Prevalence of the Class 1, 2, and 3 integrons**

All 148 *E. coli* isolates were screened for class I, II and III integrons. Class I and II integrons were detected in 31 (21%) and 4 (3%) isolates, respectively (**Table 2**). Class III integron was absent in the *E. coli* isolates in Matang mangrove estuaries. Four isolates (3%) harbored both class I and II integrons. Of the 31 *intI*1-positive isolates, only 7 (23%) harbored gene cassettes; while none of the *intI*2 positive isolates carried any gene cassette. The size of the gene cassette in class 1 integron ranged from 700 to 1200 bp. Only one out of 148 isolates was found to carry more than one gene cassette, and this particular isolate was obtained from downstream of the Selinsing River (station H). The DNA sequences of all class I integrons have been deposited in GenBank under accession nos. KR952338-KR952345.

Five types of the resistance gene cassettes were detected in this study (**Table 2**). Three types of *dfr*A (*dfr*A 1, *dfr*A 5, and *dfr*A 17) were identified, which encoded the dihydrofolate reductase enzyme, mediating resistance to trimethoprim. Additionally, two types of the *aad*A (*aad*A 1 and *aad*A 22) were detected in class 1 integron that confers resistance to spectinomycin and streptomycin. The *aad*A and *dfr*A genes can be present alone or in combination with each other and other resistance genes as only one cassette combination (*dfr*A1 + *aad*A1) was recovered in this study (**Table 2**). All *E. coli* isolates that carried the *aad*A and *dfr*A genes were resistant to aminoglycosides and SXT, respectively (**Table 2**).

Half of the *intI* positive *E.coli* belonged to group B1 (*n* = 15, 48%) followed by nine isolates (29%) in group A and six isolates (19%) in group D. Only one isolate in group B2 was detected as *intI*1 positive. This integron gene was closely associated with most of the antibiotic resistance. In the PCA biplot, the gradients of increasing number of *IntI* positive isolates were in the same direction as the gradients of antibiotic resistance (**Figure 3**). Resistance to all antibiotics (except N, AMC, CN, and CRO), i.e., 73.3% of the 15 tested antibiotics, was significantly correlated to *intI*1-positive isolates (*p <* 0.001), with correlation (Rn) values that ranged from 0.50 (S) to 0.81 (SXT) (Supplementary Table 3). Of the eleven antibiotic resistant isolates that were *intI*1 positive, seven were also significantly correlated to *intI*2-positive isolates (*p <* 0.03), with Rn values that ranged from 0.49 (TE) to 0.73 (C). Among four *intI*2 positive isolates, three and one isolates belonged to phylogenetic group A and B1, respectively. Moreover, four out of seven isolates (57%) harbored gene cassette class 1 belonged to phylogenetic group D (**Table 2**).

#### **Genetic Diversity of** *E. coli*

All the confirmed 148 *E. coli* isolates that were recovered in October 2011 and May 2012 were subjected to REP-PCR. REP-PCR was carried out to determine the diversity of *E. coli* using UPGMA (unweighted pair-group method using arithmetic averages). REP-PCR subtyped the 148 *E. coli* isolates into 118 different REP profiles comprising of 200 to 2000 bp DNA fragments. At 80% similarity, the coefficient of similarity (*F*value) ranged from 0.69 to 1.0 and the 148 isolates were clustered into seven groups (Supplementary Table 4). Cluster I and V included 76 and 27 *E. coli* isolates from different stations, respectively. Another 45 *E. coli* isolates were grouped into five clusters with each comprising of two to 18 isolates. Most of resistant isolates including MDR phenotypes were grouped into cluster, I, V, and III of the REP-PCR profile (Supplementary Table 4).

### **Discussion**

#### **Multi-Drug Resistant (MDR)** *E. coli* **in Matang Mangrove Estuaries**

In the present study, the Matang mangrove estuaries harbor a high prevalence of antibiotic resistant *E. coli* in surface waters and sediments., the results indicated that four in every five *E. coli* isolates from Matang estuaries were non-susceptible to one or more antibiotics tested (1R); and one out of four 1R Isolates was non-susceptible to six or more antibiotics tested (**Table 1**). About one-third of the *E. coli* isolated from Matang estuaries were MDR and this figure is relatively higher compared to Tagus estuary in Portugal, in which 19% of the *E. coli* isolates were MDR (Pereira et al., 2013). However, the antibiotic resistant level was lower compared to the 88% found in Dongjiang River, China (Su et al., 2012). Laroche et al. (2009) also reported a relatively high level of MDR *E. coli* (39%) in Seine River, France. The high level of MDR *E. coli* observed in all of these studies from different parts of the world, including the current work highlighted the potential risk of dissemination of antibiotic resistance traits via the aquatic system.

PCA analysis showed that the prevalence of antibiotic resistance in *E. coli* was related to the sampling stations, in which antibiotic resistant *E. coli* was more frequently isolated from the upstream of both Sangga Besar and Selinsing Rivers. Station B was one of the stations with high numbers of MDR *E. coli* isolates recovered. This is likely due to its proximity to Kuala Sepetang fishing village where there is open sewage disposal and poor sanitation (Ghaderpour et al., 2014). Majority of *E. coli* isolates that demonstrated 100% non-susceptibility to all 15 antibiotics tested were isolated from station B. Also, *E. coli* isolates that showed non-susceptibility toward aminoglycosides and beta-lactams were more frequently isolated from stations located downstream of Kuala Sepetang or between the latter and Sangga Besar fishing village at the river mouth (**Table 1**). The findings suggested anthropogenic sources as the major contributor to the presence of antibiotic resistant *E. coli* in Matang mangrove estuaries. As the results show, beta-lactams resistant *E. coli* (i.e., non-susceptible to amocixillin-clavulanic acid, ampicillin, ceftriazone, or ceftiofur) was least frequently isolated from station H, but more frequently isolated from station F (75%) and station B (57%) located downstream of the villages. The highest antibiotic resistance prevalence was detected for neomycin (74%), ampicillin (33%), streptomycin (31%), and tetracycline (28%). The major antibiotic resistance found among *E. coli* isolates was in agreement with previous studies in aquatic environments (Hamelin et al., 2007; Laroche et al., 2009; Pereira et al., 2013). This resistance may reflect the abuse of antibiotics in human and veterinary medicine and aquaculture farms in the study area. The direct discharge of untreated sewage from the villages into the estuarine system, active fish and shrimp farming, as well as the presence of upstream swine farms are the main contributing factors to the high prevalence of MDR *E. coli* in Matang estuaries. Aquaculture could be a significant contributor to antibiotic resistance since the use of antibiotics, pesticides, and other chemotherapeutic agents in feed additives and treatment baths is as yet unregulated in Malaysia (Majusha et al., 2005; Ibrahim et al., 2010). Sulfonamides, tetracyclines, oxytetracycline, ampicillin, chloramphenicol, and quinoline are common antibiotics used in aquaculture farms in Perak as well as in the country (Mohamed et al., 2000; Keh, 2003). Nevertheless, reservoir sources of *E.coli* may include shorebirds and other feral animals such as monkeys, flying foxes and bats, as well as domestic animals, especially dogs (Frenzel and Couvillion, 2002).

It is interesting to note that even though the *E. coli* count at station F was low as reported by Ghaderpour et al. (2014), which resulted in lower isolation rate, a high level of antibiotic resistance was found in *E. coli* isolates from station F (**Table 1** and **Figure 2**). A high bacteria count is expected at the river mouth due to bacterial contamination from the upstream, but due to the fact that *E. coli* is sensitive to high salinity (Gao et al., 2013), at station F, most of the *E. coli* would have been inactivated except antibiotic resistant strains that are also tolerant to high salinity. On the other hand, low antibiotic resistance was found in station H, downstream of Selinsing River, despite the high count of *E. coli* reported previously (Ghaderpour et al., 2014). Since station H is far away from obvious anthropogenic sources, its high count of *E. coli* could originate from wildlife. It has been shown in other studies that the prevalence of antibiotic resistance was low in wild animals compared to human and agricultural sources (Sayah et al., 2005; Edge and Hill, 2007).

#### **Distribution of Class I and II Integrons among** *E. coli* **from Matang Mangrove Estuaries**

Integron is an important factor in developing MDR resistance phenotypes among *E. coli* and other bacterial species since they are able to transfer different resistance genes simultaneously (Martinez, 2009). In the present study, 21% of *E. coli* isolates were integron positive. It was higher than those values reported in previous studies, 11% in France and Portugal (Laroche et al., 2009; Pereira et al., 2013), and 15% in Czech Republic (Dolejská et al., 2009). The frequency of the class 2 integron (3%) among *E. coli* isolates in Matang estuaries concurred with earlier studies in estuary (Laroche et al., 2009), in animal and human *E. coli* isolates (Kang et al., 2005; Skurnik et al., 2005; Cocchi et al., 2007). No class 3 integron was found in this study since integron class 3 is rare even among the human and animal *E. coli.* This finding is consistent with other reports (Laroche et al., 2009; Su et al., 2012; Pereira et al., 2013).

Class Integron 1 can be considered as a marker for antibiotic resistance even without the presence of the gene cassette as they are usually located on the transposons or plasmids that carry other resistance genes (Leverstein-van Hall et al., 2003; Mokracka et al., 2011). This might explain why 90% of the integron class 1 positive isolates were found as multi-drug resistance (**Tables 1**, **2**).

Among the 31 integron positive *E. coli* isolates, only seven (22%) were detected carrying gene cassettes. All the gene cassettes were detected in class 1 integron positive *E. coli* isolates. Five different gene cassettes were found in this study (**Table 2**), and these have been previously reported among clinical *E. coli* (Lim et al., 2009; Ho et al., 2012; Su et al., 2012; Koczura et al., 2013). It is interesting to note that the gene cassettes found in class 1 integron among *E. coli* isolates in Matang estuaries were also reported in *E. coli* isolates from swine (Lapierre et al., 2010), poultry (Soufi et al., 2009), and humans (Lim et al., 2009; Ho et al., 2012).

#### **Predominant Phylogenetic Groups in Matang Mangrove Estuaries**

The *E. coli* phylogenetic group B1 was the most prevalent (44%) in Matang mangrove estuaries, followed by phylogenetic groups A and D. Phylogenetic groups A and B1 comprised more than half (70%) of the total *E. coli* isolated from Matang mangrove estuaries. MLST genotyping has grouped phylogenetic groups A and B1 together, implying groups A and B1 to be sister groups (Gordon et al., 2008). Commensal *E. coli* was found to be predominantly in phylogenetic groups A and B1; whilst extraintestinal *E. coli* strains usually belong to groups B2 and D (Bingen et al., 1998; Picard et al., 1999; Johnson and Stell, 2000). Other studies have also reported that groups A and B1 were usually less pathogenic compared to groups B2 and D (Bingen et al., 1998; Picard et al., 1999; Johnson et al., 2001). Strains of A and B1 groups were recovered more frequently from aquatic environments than group B2 and D strains (Figueira et al., 2011). Walk et al. (2007) demonstrated that the majority of the *E. coli* strains that are able to persist in the environment belong to the B1 phylogenetic group. The findings from the present study are in agreement with previous studies in that A and B1 groups are predominant in the aquatic system, although phylogenetic group B1 comprised the most frequently isolated group in the present study. However, the distribution of these common phylogenetic groups did not hold true at station H, where phylogenetic group D was predomiant. Although, many studies have demonstrated groups B2 and D are usually more virulent (Bingen et al., 1998; Picard et al., 1999; Johnson et al., 2001), *E. coli* O157:H7 and other EHEC are in group D (Bidet et al., 2005), where cattle and birds (Escobar-Paìramo et al., 2006) are the main reservoirs. The findings obtained from the present study are realistic since station H was less influenced by human activity compared to the other stations where human activity was the main factor affecting the quality of the aquatic environment. Station H was least impacted by human activity where wild animals are thought to be the main source of *E. coli* at station H. However, due to the fact that pathogenic strains usually belong to group D, fecal contamination by feral animals can present a potential risk of pathogenic *E. coli*. Therefore, the identification of this kind of fecal contamination is necessary for appropriate management practices and remediation strategies to improve sustainable water resources.

Low prevalence of phylogenetic group B2 was found in our study which is in agreement with previous studies (Pereira et al., 2013). These isolates were detected in stations that were influenced more by human activities. It may show that this group is more associated with anthropogenic contamination as has been previously reported (Carlos et al., 2010). However, the percentage of *E. coli* in each group differs according to the sites of infection, geographical location, and level of antibiotic resistance (Duriez et al., 2001; Bukh et al., 2009). Carlos et al. (2010) reported that genotype B23 was found only in human feces and could be a good indicator for human fecal contamination in water. In the present study, genotype B23 was only detected from stations D, E, F, and G which were all located downstream of the fishing villages.

Many studies have reported the association of *E. coli* phylogenetic groups to their virulence, antibiotic resistance, and antibiotic resistant genes (Skurnik et al., 2005; Rijavec et al., 2006; Bukh et al., 2009; Garcia-Aljaro et al., 2009; Mataseje et al., 2009; Pereira et al., 2013; Mosquito et al., 2015). However, variable observations were reported from different studies. For instance, Mosquito et al. (2015) and Bukh et al. (2009) both reported that strains belonging to phylogenetic group D presented significantly higher percentages of multidrug resistance than the rest of the groups; while Skurnik et al. (2005), Rijavec et al. (2006), Garcia-Aljaro et al. (2009), Mataseje et al. (2009) and Pereira et al. (2013) had all found antibiotic resistance to be lower in *E. coli* phylogenetic group D and B2. In this study, we observed a lower percentage of antibiotic resistance in phylogenetic groups B2 (0%) and D (8%) as compared to group A (29%) and B1 (23%) (**Table 1**). However, the variation in observations from different studies could be due to the different origin of *E. coli.* Both studies (Bukh et al., 2009; Mosquito et al., 2015) reported an association of multidrug resistance with *E. coli* phylogenetic group D was of clinical origins but not environmental. Therefore, in our study, the higher percentage of multidrug resistance observed in group A and B1 was plausible. However, both *E. coli* phylogenetic group B2 and D demonstrated higher percentage of non-susceptibility to aminoglycosides, but not quinolones/fluoroquinolones. On the other hand, group A and B1 appear to be associated with resistance toward aminoglycosides and (fluoro) quinolones (**Table 1** and **Figure 3**). The low non-susceptibility percentage in group D is out of our expectation as most studies have reported on its high resistance toward (fluoro) quinolones (Sabaté et al., 2008; Bukh et al., 2009; Mosquito et al., 2015). Probably, a study into the origins of *E. coli* phylogenetic group D isolated in this study will yield an explanation for this variation.

#### **Isolation of EAEC Pathotype from Matang Mangrove Estuaries**

Our present finding also indicated that two isolates (1%) were pathogenic and identified as EAEC. Both EAEC isolates (SD4 and WGO3) belonged to phylogenetic group D and they were grouped in different clusters (Supplementary Table 4). Low percentage of the pathotypes in aquatic systems was also reported in a previous study (Hamelin et al., 2007). EAEC is known as an emergent pathogen causing diarrhea in developing countries and gastroenteritis outbreaks in industrialized countries, especially among children (Keskimäki et al., 2000; Villaseca et al., 2005). In Japan in 1993, a massive outbreak due to EAEC infection occurred among children (Keskimäki et al., 2000).

The source of EAEC remains unknown. The isolated EAEC were from stations D and G, and likely came from municipal wastewater as a potential source of pathogenic *E. coli*. The contaminated water may contaminate aquaculture products, while it is conceivable that their consumption could cause foodborne illness in humans. Water and food have been reported as the possible sources of EAEC infection in developing counties (Villaseca et al., 2005). This risk could become more significant if such bacteria are resistant to antibiotics.

Both EAEC isolates, SD4 and WGO3, contains class 1 integron with gene cassettes *aadA1* (1000 bp) and *dfrA5* (700 bp), respectively. The same gene cassettes with same length have been reported among clinical isolates of *E. coli* in the stools of infected persons in Malaysia (Lim et al., 2009; Ho et al., 2012). In addition, both EAEC isolates were MDR but were sensitive to fluoroquinolones. Fluoroquinolones has been found to be negatively associated with the presence of the virulence factors among the clinical *E. coli* strain (Johnson et al., 2003; Kawamura-Sato et al., 2010), but has not been observed among isolates from animals (Johnson et al., 2003; Kawamura-Sato et al., 2010). This may indicate that *E. coli* population in Matang estuaries is contributed by different sources of contamination.

#### **Genetic Diversity Using REP-PCR of** *E. coli* **isolates from Matang Mangrove Estuaries**

REP-PCR is known as a fast and discriminatory technique to characterize the genomic diversity of *E. coli* isolates (Rademaker et al., 2000). Although the present results indicate a high diversity of *E. coli* isolates, the cluster analysis showed that the 148 isolates could be grouped into seven clusters at 80% similarity.

Overall, 76 (51%) and 27 (18%) *E. coli* isolates from eight different sampling sites were grouped into cluster I and V, respectively. The close similarity between some of the *E. coli* isolates over large spatial scale reflects both the hydrodynamics and interconnected waterways that characterize the Matang estuarine system. However, the diverse and variable nature of the *E. coli* population could be due to their survival rate, growth and inputs from different sources in the aquatic environment and, which differ according to the strain characteristic (Anderson et al., 2006; Kon et al., 2007, 2009). This explains why eight *E. coli* isolates in station F were grouped into four different clusters in the present study, while diversity was less in station C (*n* = 8) with two clusters, and station D (*n* = 24) with three clusters.

Although Selinsing River (stations G and H) was less used for aquaculture production compared to Sangga Besar River, the diversity of *E. coli* isolates was slightly higher. Besides the prolonged persistence and adaptation of *E. coli* in the water, heterogeneity among isolates could be due to erosion of the river banks and the presence of more wildlife that act as vectors of *E. coli* transmission (Solo-Gabriele et al., 2000; Kon et al., 2009). The high diversity was also found at stations A, B, and E as these stations were located next to human settlements and fish cage farms (E). However, there is no a single source that contributes to *E.coli* diversity and multiple sources are more likely (Kon et al., 2009).

Several studies reported different sources of *E. coli* isolates such as bird droppings (Edge and Hill, 2005), wildlife (Somarelli

#### **References**

et al., 2007), and humans (Ram et al., 2004). However, it was not the main aim of this study to trace the sources of *E. coli* in the estuarine system. Perhaps, future work should compare the REP-PCR of *E. coli* isolates with those from humans and known animals in order to determine the sources of *E. coli* isolates in the Matang area.

#### **Conclusion**

Our study shows a high prevalence of the MDR (34%) among *E. coli* isolated from Matang estuaries. Phylogenetic group B1 has high resistance to antibiotics, thus playing a major role in the transmission of antimicrobial resistance via the aquatic environment. In particular, a high level of antibiotic resistance was observed at the upstream of the estuaries, which could be attributable to the adjacent human settlement and fish farms. Resistance to aminoglycosides, beta-lactams and tetracycline were most frequently detected in this study. Presence of integrons (21%) is another interesting result reflecting the contaminated MMFR estuaries that could act as a reservoir or source of MDR *E. coli* for the transmission of antibiotic resistance to the environment.

#### **Acknowledgments**

This research was financially supported by University of Malaya High Impact Research Grant UM.C/625/1/HIR/MOHE/SC/20 (UM.S/P/HIR/ MOHE/24) and IOES HiCoE Research Grant (IOES-2014E) from Ministry of Education Malaysia. Six strains (2060-004, E2348/69, JM221, E9034A, C1845, EC-12) were included as positive control for *E. coli* pathotyping, a gift from Oscar G. Gomez-Duarte, International Enteric Vaccines Research Program [IEVRP], University of Iowa Children's Hospital, Iowa City, IA.

#### **Supplementary Material**

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb. 2015.00977

(Canada) Beach. *Environ. Microbiol*. 73, 1961–1967. doi: 10.1128/AEM.02 437-06

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2015 Ghaderpour, Ho, Chew, Bong, Chong, Thong and Chai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

### Seawater is a reservoir of multi-resistant *Escherichia coli*, including strains hosting plasmid-mediated quinolones resistance and extended-spectrum beta-lactamases genes

#### *Marta S. Alves , Anabela Pereira , Susana M. Araújo , Bruno B. Castro , António C. M. Correia and Isabel Henriques\**

*Department of Biology and CESAM, University of Aveiro, Aveiro, Portugal*

#### *Edited by:*

*Satoru Suzuki, Ehime University, Japan*

#### *Reviewed by:*

*Daniela Ceccarelli, University of Maryland, USA Etinosa Igbinosa, University of Benin, Nigeria Masaru Usui, Rakuno Gakuen, Japan*

#### *\*Correspondence:*

*Isabel Henriques, Department of Biology and CESAM, University of Aveiro, Campus Universitário de Santiago, Aveiro, 3810-193 Aveiro, Portugal e-mail<EMAIL_ADDRESS>
The aim of this study was to examine antibiotic resistance (AR) dissemination in coastal water, considering the contribution of different sources of fecal contamination. Samples were collected in Berlenga, an uninhabited island classified as Natural Reserve and visited by tourists for aquatic recreational activities. To achieve our aim, AR in *Escherichia coli* isolates from coastal water was compared to AR in isolates from two sources of fecal contamination: human-derived sewage and seagull feces. Isolation of *E. coli* was done on Chromocult agar. Based on genetic typing 414 strains were established. Distribution of *E. coli* phylogenetic groups was similar among isolates of all sources. Resistances to streptomycin, tetracycline, cephalothin, and amoxicillin were the most frequent. Higher rates of AR were found among seawater and feces isolates, except for last-line antibiotics used in human medicine. Multi-resistance rates in isolates from sewage and seagull feces (29 and 32%) were lower than in isolates from seawater (39%). Seawater AR profiles were similar to those from seagull feces and differed significantly from sewage AR profiles. Nucleotide sequences matching resistance genes *blaTEM*, *sul1*, *sul2*, *tet*(A), and *tet*(B), were present in isolates of all sources. Genes conferring resistance to 3rd generation cephalosporins were detected in seawater (*bla*CTX-M-1 and *bla*SHV-12) an seagull feces (*bla*CMY-2). Plasmid-mediated determinants of resistance to quinolones were found: *qnrS1* in all sources and *qnrB19* in seawater and seagull feces. Our results show that seawater is a relevant reservoir of AR and that seagulls are an efficient vehicle to spread human-associated bacteria and resistance genes. The *E. coli* resistome recaptured from Berlenga coastal water was mainly modulated by seagulls-derived fecal pollution. The repertoire of resistance genes covers antibiotics critically important for humans, a potential risk for human health. d

**Keywords:** *Escherichia coli***, antibiotic resistance, fecal pollution, water quality, Microbial source tracking**

#### **INTRODUCTION**

Fecal contamination in aquatic environments contributes to the spread of human pathogens along with the dissemination of antibiotic-resistant bacteria. Overexposure to antibiotics is leading to increasing levels of resistance in the human (and other animals) commensal microbiota (Austin et al., 1999). The resistomes of fecal bacteria, once in environmental settings, contribute with antibiotic resistance (AR) genes to non-resistant indigenous microorganisms (Aminov, 2011; Tacão et al., 2014). In aquatic systems the cycle may include subsequent transmission of AR to human-associated bacteria (Devirgiliis et al., 2011; Figueira et al., 2011). At the same time, enrichment in AR bacteria is promoted by the presence of antimicrobials or other contaminants in the environment. This facts reinforce the need to identify the sources of antibiotic-resistant bacteria in aquatic environments of human usage (Rosewarne et al., 2010; Gomez-Alvarez et al., 2012).

*Escherichia coli* is a natural member of the intestinal microbiota of human and other homeothermic animals and is a fecal indicator bacteria of election (see review by Harwood et al., 2014). Multi-resistant strains are found in environments such as coastal waters. Although *E. coli* is considered harmless and commensal, several pathotypes have been recognized as significant human pathogens, comprising strains implicated in gastro-intestinal, urinary, or respiratory infections (Kaper et al., 2004). *E. coli* strains can be assigned to four main phylogenetic groups: A, B1, B2 and D (Clermont et al., 2000). Extraintestinal pathogenic bacteria usually belong to groups B2 and D, precisely those that include strains with a larger diversity of virulence factors. The commensal strains belong to groups A and B1 (Picard et al., 1999; Johnson et al., 2001).

In recent years, *E. coli* has been recognized as a major player in the dissemination of AR (Henriques et al., 2006a; Zhao and Dang, 2012). Resistance genotypes so far characterized include genes for resistance to last-line antibiotics such as 3rd generation cephalosporins and fluoroquinolones (Alouache et al., 2012; Wellington et al., 2013; Tacão et al., 2014). To some extent, similar resistance phenotypes and genotypes are shared by strains exposed to similar environmental pressures (Wicki et al., 2011). Considering this fact, some animal hosts may favor the preferential development of certain genotypes.

Seagulls readily utilize food sources of human origin, especially garbage, which may contribute to the high levels of AR associated to their microbiota (Bonnedahl et al., 2009; Poirel et al., 2012). Multi-resistant phenotypes are found in *E. coli* strains isolated from seagull droppings in different geographic areas (Gionechetti et al., 2008; Bonnedahl et al., 2009; Dolejska et al., 2009; Poirel et al., 2012). Genes encoding ESBLs (extended spectrum betalactamases), such as *bla*CTX-M-1 and *bla*CTX-M-15 (Bonnedahl et al., 2009; Dolejska et al., 2009; Simões et al., 2010), and resistance to quinolones (like the plasmid-mediated *qnrS*) (Literak et al., 2010; Vredenburg et al., 2014) integrate the repertoire of seagull's resistome.

Freshwater environments are well-recognized reactors for the dissemination and evolution of AR (Baquero et al., 2008; Figueira et al., 2011; Tacão et al., 2012) but processes occurring in seawater are less understood. The presence of AR strains of *E. coli* in coastal water represents a health issue in areas that are used for recreational activities.

Berlenga is an uninhabited island, recently added to the World Network of Biosphere Reserves due to its ecological relevance. Episodes of fecal pollution have been recorded in its coastal water (Araújo et al., 2014) with seagulls confirmed as the main source of fecal contamination. Human-derived sewage (from visiting tourists) was also suggested as a secondary source (Araújo et al., 2014).

Because it is a well-delimited ecosystem, the island constitutes a privileged location to study pathways implicated in dissemination of AR in natural environments. Resistance to 3rd generation cephalosporins or fluoroquinolones, used as front-line drugs for treatment of Gram-negative infections (Cantón and Coque, 2006; Cattoir et al., 2007), deserved special attention.

In this study the focus was put in coastal water and in the specific contribution of the two previously identified sources of fecal pollution. To accomplish our aims, *E. coli* strains were characterized at the phenotypic level with respect to their resistances profiles; at the genotypic level, specific genes were used as indicators for the resistance to last-line use antibiotics. Phenotypic and genotypic data from coastal water strains was compared with data obtained from the strains of each of the two sources of fecal contamination. Main contributions to the water resistome are hypothesized and discussed.

#### **MATERIALS AND METHODS**

#### **STUDY SITE AND SAMPLE COLLECTION**

Berlenga Island is the main island of the Berlengas archipelago, located in the Portuguese continental shelf, 5.7 miles northwest of Cape Carvoeiro. The island has a great diversity of habitats, marine species, and avifauna, including a large population of the yellow-legged gull (*Larus* [*cachinnans*] *michahellis*). Recently, Berlengas was added to the UNESCO'S World Biosphere Reserve. Although uninhabited, many tourists visit the island in the summer mainly for recreational activities such as swimming and diving (Araújo et al., 2014). Human-derived sewage is collected on a settlement tank and discharged near the coastline of the island.

Samples of seagull feces, human-derived sewage and seawater from the Berlenga beach were collected as described in Araújo et al. (2014). Sampling was performed every 2 weeks between May and September 2011. Briefly, samples of seagull feces, human-derived sewage and seawater were collected in each sampling date and consisted in: (i) 5 composite samples of seagull feces scattered on the beach; (ii) 250 mL of raw human-derived sewage taken from the effluent of the sanitary infrastructures; and (iii) 2 L of seawater collected about 30 cm below the surface. Samples were collected in sterile containers and kept on ice until processing.

#### *E. COLI* **STRAINS**

A total of 939 *E. coli* isolates were retrieved in Chromocult Coliform Agar plates (Merck, Germany) from seagull feces (*n* = 427), sewage (*n* = 170) and seawater (*n* = 342) as described by Araújo et al. (2014). Genetic diversity of the *E. coli* isolates was inspected by BOX-PCR with primers and conditions previously described (Araújo et al., 2014). Fingerprinting analysis allowed the selection of 414 non-clonal isolates, which were included in the present study. From those, 179 isolates were from seagull feces, 69 isolates were from sewage and 166 were from seawater.

#### **DETERMINATION OF** *E. COLI* **PHYLOGENETIC GROUPS**

The triplex PCR developed by Clermont et al. (2000), with primers for genes *chuA* and *yjaA* and for the DNA fragment TSPE4-C2, was used to determine the phylogenetic groups of the *E. coli* strains. Template DNA was obtained by suspending 2 bacterial colonies in 100μL of sterile water. For each PCR reaction, 1μL of this suspension was used as template. Appropriate positive and negative controls were included in the assay.

#### **ANTIBIOTIC SUSCEPTIBILITY TESTING**

The *E. coli* isolates were tested for susceptibility to 16 antibiotics by disk diffusion method on Mueller-Hinton Agar (Oxoid, Basingstoke, UK) according to the Clinical and Laboratory Standards Institute guidelines (CLSI, 2012). The following disks (Oxoid, UK) were used: ampicillin (10 μg), amoxicillin (10μg), amoxicillin/clavulanic acid (20/10μg), piperacillin (100μg), piperacillin/tazobactam (100/10μg), cephalothin (30μg), ceftazidime (30μg), cefotaxime (30μg), gentamicin (10μg), streptomycin (10μg), imipenem (10μg), nalidixic acid (30μg), ciprofloxacin (5μg), tetracycline (30μg), chloramphenicol (30μg) and trimethoprim/sulfamethoxazole (1.25/23.75μg). *E. coli* ATCC 25922 was used as quality control. Isolates were classified as sensitive, intermediate or resistant according to the CLSI recommendations after 24 h incubation at 37◦C. To determine the percentage of resistant strains to each antibiotic and to define resistance phenotypes, intermediate and resistant isolates were subsequently grouped in a same resistant class.

#### **ANTIBIOTIC RESISTANCE GENE DETECTION**

*Escherichia coli* strains displaying resistance and intermediate resistance phenotypes were screened by PCR to detect genes conferring resistance to β-lactams (*bla*TEM, *bla*SHV, *bla*CTX-M, *bla*IMP, *bla*VIM, *bla*KPC, *bla*OXA-48, *bla*GES, *bla*AmpC-like), tetracycline [*tet*(A), *tet*(B)], quinolones (*qnrA*, *qnrB*, *qnrS*) and sulfonamides (*sul1*, *sul2*). The genes tested were chosen among the most prevalent in clinical and environmental *E. coli* isolates. The PCR reactions were performed in a MyCycler Thermal cycler (Bio-Rad, USA). Primers and PCR conditions are presented in **Table 1** and Table S1 (Supporting Information). The reaction mixtures (25 μL total volume) consisted of 6.25μL NZYTaq 2x Green Master Mix (2.5 mM MgCl2; 200μM dNTPs; 0.2 U/μL DNA polymerase) (NZYtech, Portugal), 16.25μL of ultrapure water, 0.75μL of each primer (reverse and forward), and 1μL of cell suspension prepared as described above. The presence of *bla*AmpC-like genes was inspected using a multiplex PCR as described by Dallenne et al. (2010). Negative and positive controls were included in each PCR experiment. The negative control differed from other reaction mixtures by substituting the cell suspension for the same volume of dH2O; positive control strains are indicated in **Table 1**. PCR products were analyzed by electrophoresis on a 1.5% agarose gel and stained with ethidium bromide. All amplicons obtained using primers for *bla*SHV, *bla*CTX-M*, bla*AmpC-like, *qnrA*, *qnrB*, and *qnrS* and PCR products obtained using primers for *bla*TEM from 3rd generation cephalosporins-resistant isolates were sequenced (accession numbers KM094197 to KM094211). For this PCR products were purified with DNA Clean & Concentrator (Zymo Research, USA) following manufacturer's instructions, and used as template in the sequencing reactions. Online similarity searches were performed with the BLAST software at the National Center for Biotechnology Information website.

The genomic contexts of both genes were determined by PCR using previously described primers and conditions (Saladin et al., 2002; Eckert et al., 2006; Tacão et al., 2012). Primers targeted either *bla*CTX-M-1 or *bla*CMY-2 and genes previously found in the vicinity of these resistance genes (i.e., IS*ECP1* for *bla*CTX-M-1 and *bla*CMY-2; *ORF477* for *bla*CTX-M-1) (Saladin et al., 2002; Eckert et al., 2006; Tacão et al., 2012). PCR fragments were sequenced as described above.

*aA, B, C, D, and E represent different PCR programs (see Table S1 for details).*

#### **STATISTICAL ANALYSIS**

AR phenotypes for each isolate were converted into a numeric code for each antibiotic: 0 signifying susceptibility, 1 representing intermediate resistance and 2 representing resistance. Principal Component Analysis (PCA, using a covariance matrix model) was used to explore AR patterns, reducing the multidimensional data matrix to a bidimensional biplot fit for interpretation, as performed in suchlike studies (Parveen et al., 1997; Su et al., 2012; Pereira et al., 2013). One-way analyses of variance (ANOVA) were performed on the PCA sample scores (principal components 1 and 2—PC1 and PC2) to assess significant differences (*p* ≤ 0*.*05) in the AR profiles of *E. coli* isolates among sampling sources or phylogenetic groups. When applicable, *post-hoc* Tukey tests followed ANOVA tests. ANOVAs and PCA were conducted using Minitab and CANOCO for Windows version 4.5 (Scientia Ltd., UK), respectively.

#### **RESULTS**

#### **PHYLOGENETIC DIVERSITY OF** *E. COLI* **ISOLATES**

The set of 414 *E. coli* isolates were assigned to the main phylogenetic groups: A, B1, B2, and D (**Figure 1**). Overall, group A was the most prevalent (45% of the total number of isolates) followed by group B1 (31%), group D (21%) and group B2 (3%). Considering the different sampling sources (e.g., seawater, seagull feces and human-derived sewage) distribution among phylogenetic groups was rather similar, except for group B2 isolates that were only detected in sewage (10% of the total number of isolates retrieved from this source) and in seagull feces (4%).

#### **ANTIBIOTIC RESISTANCE PROFILES**

The susceptibility patterns of the isolates to 16 antibiotics are shown in **Figure 2**. Resistance (or intermediate resistance) was detected to all antibiotics tested and 94% of the isolates were resistant to at least one of the antibiotics. The most prevalent AR was toward streptomycin (83–100%), followed by tetracycline and cephalothin in seawater (48 and 42% respectively), and tetracycline and amoxicillin in the fecal sources (35 and 34% in seagull feces and 23% for both antibiotics in sewage). Isolates were

more susceptible to imipenem, 3rd generation cephalosporins, gentamicin and the combination piperacillin/tazobactam.

For the majority of the antibiotics tested (e.g., penicillins, cephalothin, tetracycline), resistance rates were higher in isolates from seawater and seagull feces (**Figure 2**). Resistance to imipenem, 3rd generation cephalosporins and ciprofloxacin was most common in sewage. Multi-resistance levels (i.e., resistance to antibiotics of at least three different classes) were higher among isolates recovered from seawater (39%), followed by isolates from seagull feces (32%) and sewage (29%).