The aim of this study was to analyze the enterobacterial repetitive intergenic consensus (ERIC)-types, phylo-groups and antimicrobial resistance (AMR) patterns of Escherichia coli and to investigate if these approaches are suitable for microbial source tracking (MST). E. coli strains were isolated from cattle faeces and Karaj River. For genetic diversity, AMR profile, and phylo-grouping, we applied ERIC-PCR, disk diffusion, and multiplex-PCR, respectively. Fifty isolates from each sample group were used in the study. ERIC fingerprinting produced ten different bands, demonstrating 64 unique and 36 repetitive profiles. Six isolates from the river showed the same ERIC pattern of the cattle, of which four expressed the same AMR profile. E. coli isolates from water were represented in A, B1, C, and D phylo-groups. Phylo-groups A, B1, and E were more prevalent in the cattle isolates and B2 was absent in both sources. Three of the water isolates with the same ERIC-type and AMR to cattle isolates showed the same phylo-groups. Genetic characteristics, AMR, and phylo-groups of the isolates from the river are diverse and complex. For accurate MST, complementary approaches should be applied together and a comprehensive library should be provided.

INTRODUCTION

Escherichia coli is a normal inhabitant of the lower intestinal tract of warm-blooded animals and humans, therefore the presence of E. coli in water is an implicit indicator of recent faecal contamination and the risk of enteric pathogens. Although the majority of E. coli strains are commensals, some are known to be pathogenic, causing intestinal and extra-intestinal diseases, such as diarrhea and urinary tract infections (Lyautey et al. 2010). The possible reservoir of faecal contamination includes surface runoff from manure-treated agricultural land or farm animal feedlots, failing or inadequate septic systems, sewer overflow, and wildlife, along with domestic sewage disposal and animal husbandries near the bank (Kon et al. 2009). Water pollution raises considerable public health concern as it can transport pathogenic parasites, bacteria and viruses Borges et al. (2003). Hence, understanding the origin of faecal pollution is paramount in assessing associated health risks as well as the actions necessary to solve the problem (Dombek et al. 2000). Tracing the origin of faecal pollution by using microbiological, genotypic, phenotypic, and chemical methods is termed microbial source tracking (MST). When applying MST, it is critical to introduce applicable methods by which the faecal contamination sources can be identified (Lu et al. 2004). Different approaches have been proposed for tracking the contamination sources. These approaches can be broadly divided into library-dependent and library-independent techniques. Antimicrobial resistance (AMR) profiling and DNA fingerprinting-based methods (such as ribotyping, pulse field gel electrophoresis, REP-PCR), and phylo-typing have been reported as candidate library-dependent methods (Foley et al. 2009). A range of DNA fingerprinting techniques exists, among which repetitive extra-genic palindromic elements–polymerase chain reaction (REP-PCR) provides high taxonomic resolution and may act as a rapid detector of diversity and evolution of the microbial genomes (Dombek et al. 2000). Among REP fingerprinting methods (REP/ERIC/BOX), enterobacterial repetitive intergenic consensus (ERIC) PCR is more preferred because of the simple protocol and discriminatory power similar to PFGE (Lin et al. 2008). In addition to REP typing, phylo-typing is also an applicable grouping technique in which combinations of specific genes can be used to cluster E. coli strains into phylo-groups. Multilocus sequence typing data improves the understanding of E. coli phylogenetic structure and allows strains to be classified in one of the seven phylo-groups A, B1, B2, C, D, E, and F (Clermont et al. 2013).

Although E. coli has diverse genotypic and phylotypic characteristics, some characteristics are shared among strains exposed to similar environments due to selection pressure. The level of selective pressure exerted in a mixed area may be a useful criterion for identifying the host sources of E. coli in the watershed. One such tool for examining the selection pressure of E. coli is assessing their antimicrobial sensitivities (Ishii & Sadowsky 2008). AMR in E. coli has been globally identified in isolates from environmental, animal and human sources. This is a consequence of the use of antimicrobials in medicine and their application in animal husbandries, which have brought about phenotypic changes (Cantas et al. 2013).

This aim of this study was to determine the genetic diversity and AMR patterns of E. coli isolated from Khozankala cattle husbandry and Karaj River and to evaluate the applicability of these typing methods for MST.

METHODS

Study area and sample collection

This descriptive cross-sectional study was performed from August 2015 to October 2015. The authors collected 106 water samples (one sample per day) from the Karaj River, which is located in the Central Alborz Mountains with an area of 850 km2 and length of 75 km. Water samples were collected from a given sampling site in Karaj River (with geographic coordinates of 35.9404423, 51.0742861) according to standard microbiological sampling protocols (APHA 2012). They were immediately placed in a lightproof insulated box containing ice-packs to ensure rapid cooling and shipped to the quality control office (central laboratory) of Alborz Province Water and Wastewater Company.

For enteric sampling, following autoclaving of swabs in the capped tubes, they were inserted into the rectum of cattle to insure the collection of faecal material. The swabs and adhering faecal material were then placed in the screw-capped tube, stored on ice in a cool box and shipped to the laboratory.

Bacterial isolates

To isolate E. coli, water samples were inoculated into 15 tubes of lauryl tryptose (LST) broth (Merck KGaA) followed by E. coli (EC) broth (Merck KGaA) at 44.5°C and streaked onto eosin methylene blue agar (Merck KGaA) (APHA 2012). Colonies showing metal sheen were considered as presumptive E. coli isolates and were subjected to IMViC (Merck KGaA), glucuronidase, and tryptophanase (Merck KGaA) tests for final confirmation (APHA 2012).

To isolate E. coli from faeces, following the breaking off of the top portion of the swab which was in contact with the hand, the swabs were inserted into one tube of LST broth and followed the abovementioned procedure for isolation.

Confirmed isolates were inoculated into sterile cryotube vials containing nutrient broth and were incubated overnight at 37 °C. Sterile glycerol (Merck KGaA) was then added to each vial at a final concentration of 15% (vol/vol), and the vials were stored at −70 °C. The bacterial stocks were revived in Brain Heart Infusion broth under optimal growth conditions and genomic DNA was extracted from the bacterial pellet applying the AccuPrep® Genomic DNA Extraction Kit (Bioneer, South Korea).

ERIC typing

The strains were fingerprinted by ERIC-PCR as described by Versalovic et al. (1991); however, to decrease the unspecified bands we modified the method by using HotStart Taq DNA polymerase. Amplification was performed with Veriti® 96-Well Thermal Cycler (Applied Biosystems) and PCR products were evaluated by horizontal electrophoresis in 1% agarose gel (Merck KGaA) containing SYBR green (Thermo Scientific). Finally the loaded gels were visualized by Gel DOCTM XR+ (BIORAD) and analyzed by Image LabTM 4.0 software.

Computer-assisted image analysis and cluster assignment

The positions of fingerprints on gels were normalized using GeneRuler 100 bp plus DNA Ladder (Thermo Scientific) as the external standard. Using Mesquite version 2.75 (Maddison & Maddison 2012) and PAUP 4.0 beta for Windows software (Swofford 2000), a dendrogram was generated using the unweighted-pair group method (UPGMA). Clusters were initially assigned on the basis of 90% similarity.

Diversity index

For diversity of ERIC profiles and phylo-groups in isolates from water and cattle, Shannon–Weaver, which is one of the most used diversity measures, was chosen because it accounts for both abundance and evenness of the samples and assumes that individuals are randomly sampled from an infinitely large community and all species are represented in the sample (Pielou 1975).

Phylo-typing

As previously described by Clermont et al. (2013), E. coli strains were tested for chuA, yjaA, TSPE4.C2, arpA, and trpA genes by PCR and the results characterized the phylo-groups including A, B1, B2, C, D, E, and F.

AMR profiling

AMR profiling was performed applying the Kirby-Bauer disk diffusion method on Mueller-Hinton agar (Merck KGaA) plates according to the Performance Standards for Antimicrobial Susceptibility Testing of the Clinical and Laboratory Standards Institute (CLSI 2012). Since we found no clear data for antibiotics prescription in villages around the Chalous River, we chose a complete panel (seven categories) of antibiotics to be used including cefotaxime (30 μg), cefalotin (30 μg), trimethoprim/sulfamethoxazole (25 μg), amoxicillin/clavulanic (20/10 μg), ampicillin (10 μg), streptomycin (10 μg), gentamycin (10 μg), nalidixic acid (30 μg), chloramphenicol (30 μg), and tetracycline (30 μg) were used.

Statistical analysis

The chi-squared test was used to find any association between the phylo-groups, ERIC clusters (E1–E15) and AMR profiles. Analysis was performed using IBM SPSS statistics software version 19.0 (USA).

MST library

To build the MST library, E. coli isolated from cattle was subjected to ERIC fingerprinting, phylo-grouping and AMR profile determination as described above (Kon et al. 2009).

RESULTS AND DISCUSSION

E. coli recovery

As described by Sakizadeh et al. (2015), during the sampling period, the water samples from Karaj River displayed intensive faecal contamination ranging from 70 to 1,600 × 102 MPN/100 ml for E. coli. From 50 water and 50 faecal samples, a total of 100 E. coli isolates (one isolate per water sample) were isolated and included in present study.

Genetic diversity of E. coli isolates

ERIC types

ERIC iPCR fingerprints showed ten different bands ranging from 542 bp to 3,300 bp (Figure 1), while the most common band of about 1,200 bp could be observed in 47 E. coli isolates from cattle and the most prevalent band of about 2,850 bp could be observed in 32 isolates from water. Fifty isolates from water produced 39 unique profiles, while 50 isolates from cattle formed 30 single profiles. Moreover, six E. coli isolates from water had the same profiles as the isolates from cattle (Table 1), indicating the probability of the same origin.
Table 1

Isolates of water and cattle with the same ERIC profiles

Cluster Isolates from cattle Isolates from water 
E3 C8, C24, C38 W33 
E9 C19, C21, C27, C39, C47, C50 W4, W7 
E12 C25, C26 W6, W17 
E13 C5, C11, C16 W37 
Cluster Isolates from cattle Isolates from water 
E3 C8, C24, C38 W33 
E9 C19, C21, C27, C39, C47, C50 W4, W7 
E12 C25, C26 W6, W17 
E13 C5, C11, C16 W37 
Figure 1

ERIC-PCR fingerprinting patterns of the isolates. Lane MW: GeneRuler 100 bp plus DNA Ladder (Thermo Scientific); Lane 1–18: ERIC fingerprints of E. coli isolates from cattle.

Figure 1

ERIC-PCR fingerprinting patterns of the isolates. Lane MW: GeneRuler 100 bp plus DNA Ladder (Thermo Scientific); Lane 1–18: ERIC fingerprints of E. coli isolates from cattle.

Cluster analysis at a coefficient of 90% similarity, grouped 100 isolates into 15 clusters, designated E1–E15 (Figure 2). The isolates of the two sources were not uniformly distributed along the dendrogram, which means most of the clusters cannot be definitely attributed to a given source (water or cattle). In other words, E1 cluster contains ten isolates, eight from water and two from cattle, E12 cluster contains 11 isolates, nine from cattle and two from water and E3 contains seven isolates from water and three from cattle. However, E2 contains eight members of water origin, and E10 contains four isolates of cattle origin.
Figure 2

Cluster analysis by ERIC-PCR fingerprint of 100 E. coli isolates. The bottom bar indicates the percentage of similarity. The letters on the right indicate the isolate number (C: E. coli isolated from cattle and W: E. coli isolated from water).

Figure 2

Cluster analysis by ERIC-PCR fingerprint of 100 E. coli isolates. The bottom bar indicates the percentage of similarity. The letters on the right indicate the isolate number (C: E. coli isolated from cattle and W: E. coli isolated from water).

Diversity index for ERIC profiles in water and cattle isolates

The degree of diversity calculated for the 100 isolates, using the Shannon–Weaver index, was 3.43. The diversity was also calculated for the isolates from water (n = 50) and cattle (n = 50) separately, for which the diversity indices were 3.65 and 2.39, respectively, indicating more genetic diversity for E. coli of water origin.

AMR profiles

AMR tests showed no extensive drug-resistant (XDR) isolate; however, 49 (98%) and 47 (94%) isolates were MDR (resistant to two or more categories) for water- and cattle-originated E. coli, respectively (Tables 2 and 3). The most common resistance was observed against cefalotin followed by tetracycline, while no resistant isolate was observed against trimethoprim/sulfamethoxazole (Figure 3).
Table 2

Phylo-groups, ERIC clusters and AMR profiles of isolates from cattle

Host Phylo-group Cluster AMR profile 
C1 B1 E02 CF,S 
C2 E03 CZ,CF,SXT,C,DOX 
C3 E01 CZ,AMP,AMC,SXT 
C4 B1 E09 CT,CF,S,TE 
C5 E05 CF,DOX 
C6 E12 CF,S,K,N,C 
C7 B1 E09 CF,S,TE 
C8 E02 CF,DOX 
C9 B1 E01 CF,SXT 
C10 E14 CZ,CF,S,TE 
C11 E07 CF,DOX 
C12 B1 E07 CZ,AMP,AMC,S 
C13 E07 CF,N,TE 
C14 B1 E15 CZ,AMP,AMC,S,DOX 
C15 E03 CF 
C16 E07 CF,S 
C17 B1 E12 CF,S,CF,S,K,N,C 
C18 E14 AMP,CF,S 
C19 E13 CZ,AMP,AMC,CF,KNCT,E 
C20 E15 CF,SXT 
C21 E11 CZ,AMP,AMC,CF,CRO,S,K,N 
C22 B1 E04 CF,DOX 
C23 B1 E03 CZ,AMP,CF 
C24 E03 CF,S,N,C 
C25 E11 CF,SXT,NA 
C26 B1 E04 CZ,AMP,AMC,CF,S,SXT,GM,K,TE 
C27 B1 E02 CZ,CF,N 
C28 E02 CF 
C29 B1 E04 CF,TE,DOX 
C30 E03 CF,N,C 
C31 E01 CZ,CF,S 
C32 B1 E06 CF 
C33 E03 CF,DOX 
C34 B1 E05 CF,S 
C35 E07 CZ,CF,SXT,DOX 
C36 B1 E06 CF 
C37 E13 CF 
C38 B1 E06 CF,S,SXT,CT,EDOX 
C39 E07 CF,DOX 
C40 E03 CF,TE 
C41 E02 CZ,CF,S 
C42 E03 CF,S 
C43 B1 E01 CF,S,CAZ,C 
C44 E01 CZ,CF,S 
C45 E01 CZ,CF,S 
C46 E02 CZ,CF 
C47 E02 CZ,AMP,CF 
C48 B1 E01 CZ,AMP,AMC,CT,CF,NOR,CRO,S,TOB,SXT,CIP,N,A,LEV,C 
C49 E01 CF,N,DOX 
C50 E02 AMP,AMC,CF 
Host Phylo-group Cluster AMR profile 
C1 B1 E02 CF,S 
C2 E03 CZ,CF,SXT,C,DOX 
C3 E01 CZ,AMP,AMC,SXT 
C4 B1 E09 CT,CF,S,TE 
C5 E05 CF,DOX 
C6 E12 CF,S,K,N,C 
C7 B1 E09 CF,S,TE 
C8 E02 CF,DOX 
C9 B1 E01 CF,SXT 
C10 E14 CZ,CF,S,TE 
C11 E07 CF,DOX 
C12 B1 E07 CZ,AMP,AMC,S 
C13 E07 CF,N,TE 
C14 B1 E15 CZ,AMP,AMC,S,DOX 
C15 E03 CF 
C16 E07 CF,S 
C17 B1 E12 CF,S,CF,S,K,N,C 
C18 E14 AMP,CF,S 
C19 E13 CZ,AMP,AMC,CF,KNCT,E 
C20 E15 CF,SXT 
C21 E11 CZ,AMP,AMC,CF,CRO,S,K,N 
C22 B1 E04 CF,DOX 
C23 B1 E03 CZ,AMP,CF 
C24 E03 CF,S,N,C 
C25 E11 CF,SXT,NA 
C26 B1 E04 CZ,AMP,AMC,CF,S,SXT,GM,K,TE 
C27 B1 E02 CZ,CF,N 
C28 E02 CF 
C29 B1 E04 CF,TE,DOX 
C30 E03 CF,N,C 
C31 E01 CZ,CF,S 
C32 B1 E06 CF 
C33 E03 CF,DOX 
C34 B1 E05 CF,S 
C35 E07 CZ,CF,SXT,DOX 
C36 B1 E06 CF 
C37 E13 CF 
C38 B1 E06 CF,S,SXT,CT,EDOX 
C39 E07 CF,DOX 
C40 E03 CF,TE 
C41 E02 CZ,CF,S 
C42 E03 CF,S 
C43 B1 E01 CF,S,CAZ,C 
C44 E01 CZ,CF,S 
C45 E01 CZ,CF,S 
C46 E02 CZ,CF 
C47 E02 CZ,AMP,CF 
C48 B1 E01 CZ,AMP,AMC,CT,CF,NOR,CRO,S,TOB,SXT,CIP,N,A,LEV,C 
C49 E01 CF,N,DOX 
C50 E02 AMP,AMC,CF 

AMC: amoxicillin/clavulanic, AMP: ampicillin C: chloramphenicol, CF: cefalotin, CTX: cefotaxime, GM: gentamycin, NA: nalidixic acid, S: streptomycin, SXT: trimethoprim/sulfamethoxazole, TE: tetracycline.

Table 3

Phylo-groups, ERIC clusters and AMR profiles of isolates from water

Host Phylo-group Cluster AMR profile 
W1 E01 CZ,AMP,AMC,CF,NOR,CTX,CRO,S,TOB,SXT,NA,GM,LEV,DOX 
W2 E10 CZ,AMP,AMC,NOR,S,SXT,CIP,NA,GM,LEV,TE 
W3 B1 E08 CT,CF,NOR,SXT,NA 
W4 E12 CF,NCT,E 
W5 E13 CF 
W6 E12 AMP,AMC,CF,S,TOB,SXT,NA,GM,N,C,TE 
W7 B1 E07 CZ,AMP,AMC,CF,NOR,S,SXT,NA,DOX 
W8 E03 CF,DOX 
W9 B1 E13 CZ,AMP,AMC,PRL,S,NA,TE 
W10 B1 E07 AMC,CF,NOR,NA,C,DOX 
W11 E13 CF 
W12 B1 E12 AMP,AMC,CF,S,TOB,SXT,NA,GM,C,TE,DOX 
W13 E04 CZ,AMP,AMC,CF,S,TOB,SXT,NA,N,C 
W14 B1 E03 AMC,CF,NOR,S,CIP,NA,LEV,C,TE,DOX 
W15 E13 CF,NOR,S,NA 
W16 B1 E13 CZ,AMP,AMC,CF,NOR,S,SXT,NA,K,N,CT,E 
W17 E10 AMC,CF,NOR,S,NA,LEV,DOX 
W18 E12 CZ,AMP,AMC,CT,CRO,S,SXT,C,TE 
W19 E09 CZ,AMP,AMC,CT,CF,CRO,CAZ,K,N 
W20 B1 E04 CF,NOR,NA,C,TE,DOX 
W21 E03 CF,DOX 
W22 E04 CF,S,SXT,NA,K,N,C,TE 
W23 B1 E04 CF,NA,DOX 
W24 E03 CF,DOX 
W25 E12 CF,S,K,N,C 
W26 E12 CF,S,K,N,C 
W27 E03 CF,DOX 
W28 B1 E13 CZ,AMP,AMC,CF,NA 
W29 B1 E04 AMP,AMC,CF,CTX,CRO,S,SXT,CAZ,C 
W30 E13 AMP,AMC,CF,NOR,SXT,NA,C,TE 
W31 B1 E12 CF,S,SXT,N,A 
W32 B1 E15 AMP,AMC,CF,S,SXT,NA,C,TE,DOX 
W33 E12 AMP,AMC,CF,NOR,S,SXT,CIP,NA,GM,LEV 
W34 E15 AMP,AMC,CF,NOR,S,SXT,CIP,NA,LEV,C,TE 
W35 E12 AMP,AMC,CF,NOR,S,CIP,NA,LEV,K,N,DOX 
W36 B1 E12 CZ,AMP,AMC,CF,NOR,S,NA,GM,K,N 
W37 E05 CF,NOR,S,SXT,NA,GM,LEV,DOX 
W38 E03 CF,DOX 
W39 B1 E09 CT,CF,S,TE 
W40 E05 CF,S,C 
W41 E05 AMP,AMC,CF,S,NA,TE,DOX 
W42 E04 AMP,CF,S,SXT,NA,C 
W43 B1 E09 AMP,AMC,CF,NOR,S,SXT,NA,TE,DOX 
W44 E09 AMP,AMC,CF,S,SXT,C,DOX 
W45 B1 E06 CZ,AMP,AMC,CF,CRO,S,SXT,N,TE,DOX 
W46 E05 AMP,CF,NA,C 
W47 B1 E09 AMP,AMC,CF,S,NA 
W48 B1 E01 AMP,AMC,CF,S,SXT,TE,DOX 
W49 E14 AMP,AMC,CF 
W50 E09 AMP,AMC,CF,S,NA,TE,DOX 
Host Phylo-group Cluster AMR profile 
W1 E01 CZ,AMP,AMC,CF,NOR,CTX,CRO,S,TOB,SXT,NA,GM,LEV,DOX 
W2 E10 CZ,AMP,AMC,NOR,S,SXT,CIP,NA,GM,LEV,TE 
W3 B1 E08 CT,CF,NOR,SXT,NA 
W4 E12 CF,NCT,E 
W5 E13 CF 
W6 E12 AMP,AMC,CF,S,TOB,SXT,NA,GM,N,C,TE 
W7 B1 E07 CZ,AMP,AMC,CF,NOR,S,SXT,NA,DOX 
W8 E03 CF,DOX 
W9 B1 E13 CZ,AMP,AMC,PRL,S,NA,TE 
W10 B1 E07 AMC,CF,NOR,NA,C,DOX 
W11 E13 CF 
W12 B1 E12 AMP,AMC,CF,S,TOB,SXT,NA,GM,C,TE,DOX 
W13 E04 CZ,AMP,AMC,CF,S,TOB,SXT,NA,N,C 
W14 B1 E03 AMC,CF,NOR,S,CIP,NA,LEV,C,TE,DOX 
W15 E13 CF,NOR,S,NA 
W16 B1 E13 CZ,AMP,AMC,CF,NOR,S,SXT,NA,K,N,CT,E 
W17 E10 AMC,CF,NOR,S,NA,LEV,DOX 
W18 E12 CZ,AMP,AMC,CT,CRO,S,SXT,C,TE 
W19 E09 CZ,AMP,AMC,CT,CF,CRO,CAZ,K,N 
W20 B1 E04 CF,NOR,NA,C,TE,DOX 
W21 E03 CF,DOX 
W22 E04 CF,S,SXT,NA,K,N,C,TE 
W23 B1 E04 CF,NA,DOX 
W24 E03 CF,DOX 
W25 E12 CF,S,K,N,C 
W26 E12 CF,S,K,N,C 
W27 E03 CF,DOX 
W28 B1 E13 CZ,AMP,AMC,CF,NA 
W29 B1 E04 AMP,AMC,CF,CTX,CRO,S,SXT,CAZ,C 
W30 E13 AMP,AMC,CF,NOR,SXT,NA,C,TE 
W31 B1 E12 CF,S,SXT,N,A 
W32 B1 E15 AMP,AMC,CF,S,SXT,NA,C,TE,DOX 
W33 E12 AMP,AMC,CF,NOR,S,SXT,CIP,NA,GM,LEV 
W34 E15 AMP,AMC,CF,NOR,S,SXT,CIP,NA,LEV,C,TE 
W35 E12 AMP,AMC,CF,NOR,S,CIP,NA,LEV,K,N,DOX 
W36 B1 E12 CZ,AMP,AMC,CF,NOR,S,NA,GM,K,N 
W37 E05 CF,NOR,S,SXT,NA,GM,LEV,DOX 
W38 E03 CF,DOX 
W39 B1 E09 CT,CF,S,TE 
W40 E05 CF,S,C 
W41 E05 AMP,AMC,CF,S,NA,TE,DOX 
W42 E04 AMP,CF,S,SXT,NA,C 
W43 B1 E09 AMP,AMC,CF,NOR,S,SXT,NA,TE,DOX 
W44 E09 AMP,AMC,CF,S,SXT,C,DOX 
W45 B1 E06 CZ,AMP,AMC,CF,CRO,S,SXT,N,TE,DOX 
W46 E05 AMP,CF,NA,C 
W47 B1 E09 AMP,AMC,CF,S,NA 
W48 B1 E01 AMP,AMC,CF,S,SXT,TE,DOX 
W49 E14 AMP,AMC,CF 
W50 E09 AMP,AMC,CF,S,NA,TE,DOX 
Figure 3

Number of isolates resistant to antimicrobial agents. E. coli from water and cattle are shown in blue and red bars, respectively (AMC: amoxicillin/clavulanic, AMP: ampicillin, C: chloramphenicol, CF: cefalotin, CTX: cefotaxime, GM: gentamycin, NA: nalidixic acid, S: streptomycin, SXT: trimethoprim/sulfamethoxazole, TE: tetracycline). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/ws.2017.051.

Figure 3

Number of isolates resistant to antimicrobial agents. E. coli from water and cattle are shown in blue and red bars, respectively (AMC: amoxicillin/clavulanic, AMP: ampicillin, C: chloramphenicol, CF: cefalotin, CTX: cefotaxime, GM: gentamycin, NA: nalidixic acid, S: streptomycin, SXT: trimethoprim/sulfamethoxazole, TE: tetracycline). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/ws.2017.051.

Among the isolates from water and cattle with the same ERIC profiles, four isolates from water showed the same AMR as the cattle isolates, increasing the probability of the same origin (Table 4).

Table 4

Isolates of water and cattle with the same ERIC and AMR profile

Cluster Isolates from cattle Isolates from water 
E3 C8, C24, C38 W33 
E9 C39 W4 
E12 C25, C26 W6 
E13 C5 W37 
Cluster Isolates from cattle Isolates from water 
E3 C8, C24, C38 W33 
E9 C39 W4 
E12 C25, C26 W6 
E13 C5 W37 

Phylo-groups

E. coli strains from water and cattle showed different distribution patterns of phylo-groups. E. coli isolates from water were mostly represented in A, B1, and E phylo-groups, with ten (20%), 19 (38%), and eight (16%) isolates, respectively, while no B2 phylo-group was found (Tables 5), as described previously by Unno et al. (2009). There was a greater number of isolates in phylo-groups A and D for cattle isolates with 15 (30%) and 15 (30%) isolates, respectively, while there was one isolate for C, E, and F phylo-groups.

Table 5

Hosts and phylo-groups of the isolates

Host Phylo-groups
 
Total Shannon–Weaver index 
B1 
No. of isolates from cattle 14 18 15 50 3.305 
No. of isolates from water 10 19 50 3.443 
Total 24 37 20 100  
Host Phylo-groups
 
Total Shannon–Weaver index 
B1 
No. of isolates from cattle 14 18 15 50 3.305 
No. of isolates from water 10 19 50 3.443 
Total 24 37 20 100  

Among the isolates from water and cattle with similar ERIC and AMR profiles, four isolates demonstrated the same phylo-group, confirming the same origin (Tables 4).

Diversity index of phylo-groups in water and cattle isolates

The Shannon–Weaver diversity index of phylo-groups in water and cattle isolates is shown in Table 5.

Statistical analysis

The Pearson chi-squared test did not show any significant association between ERIC types (E1–E15) and phylo-groups (p value = 0.578), ERIC types and AMR (Table 6), and phylo-groups and AMR (Table 6), confirming the independence of these characteristics. Furthermore, Pearson chi-square calculated for the water/cattle and the phylo-groups was 0.1 (p value = 0.1) which rejects any correlation between water/cattle and phylo-groups.

Table 6

Pearson chi-square between phylo-groups and antibiotic resistance

Antibiotic resistance ERIC types Phylo-groups 
Cefotaxime 0.6 0.518 
Cefalotin 0.136 0.781 
Trimethoprim/sulfamethoxazole 0.249 0.43 
Amoxicillin/clavulanic 0.13 0.09 
Ampicillin 0.144 0.41 
Streptomycin 0.377 0.3 
Gentamycin 0.824 0.1 
Nalidixic acid 0.1 0.12 
Chloramphenicol 0.28 0.09 
Tetracycline 0.25 0.03 
Antibiotic resistance ERIC types Phylo-groups 
Cefotaxime 0.6 0.518 
Cefalotin 0.136 0.781 
Trimethoprim/sulfamethoxazole 0.249 0.43 
Amoxicillin/clavulanic 0.13 0.09 
Ampicillin 0.144 0.41 
Streptomycin 0.377 0.3 
Gentamycin 0.824 0.1 
Nalidixic acid 0.1 0.12 
Chloramphenicol 0.28 0.09 
Tetracycline 0.25 0.03 

DISCUSSION

Faecal contamination is considered to be one of the most difficult challenges facing environmental scientists trying to protect water for drinking, recreation or other uses. Just a few decades ago, it was impossible to identify the sources of microbial pollution. Several methods based on phenotypic and genotypic characteristics have been developed for MST; however, it is difficult to find the most reliable one (Domingo & Edge 2010; Kinzelman et al. 2012). This work was designed to determine the genotypic and phenotypic properties of E. coli to introduce the best MST to identify the sources of contamination. To do so, E. coli was enumerated and isolated in the Karaj River along the Chalous Road, and to build the library, E. coli from cattle (Khozankala husbandry) faeces was isolated. The MPN/100 ml of E. coli in water samples ranged from 70 to 1,600 × 102, which is in agreement with the study by Sakizadeh et al. (2015) which demonstrated faecal pollution of the river. This is not surprising, as reported by Ponce-Terashima et al. (2014) in some Brazilian villages surface waters, especially rivers nearby the livestock husbandries and inhabited villages, can receive contaminant from different sources.

To develop a reliable MST tool, followed by isolating E. coli clones, we determined the ERIC types, phylo-groups (A, B1, B2, C, D, E, and F) and AMR profiles.

Genetic diversity of E. coli

As described by Ibekwe et al. (2011) and Kon et al. (2009) the genotyping results of this study indicated considerable genetic diversity among E. coli isolates, particularly those isolated from water samples, although multiple isolates were obtained from the same water sampling site, by the same protocol. Our results showed a possible hypothesis for such variation is the entirely different sources of water contamination, While the genotypes inhabiting the cattle in the same environment are much more limited. A second hypothesis, described by Lu et al. (2004), involves genomic rearrangement during survival as the result of mutation or recombination. This result is consistent with those reported by Campos et al. (2009), who observed an absence of an endemic clone and a high diversity of E. coli strains isolated from humans and foods.

Considering the number of bands produced for the isolates, to avoid any unspecified amplification and to increase the reproducibility of the assay, we optimized the PCR protocol by using HotStart Taq DNA polymerase. Based on our results, ERIC fingerprints revealed just ten different bands among 100 isolates, while Borges et al. (2003) reported the number of bands obtained via ERIC-PCR ranged from three to 20, with an average of 12.6 bands for each of the 98 isolates, as well as Oltramari et al. (2014) who generated patterns of four to 20 bands from E. coli. There is no doubt that the higher number of the bands will complicate interpretation and dendrogram generation, therefore, it is highly recommended by the authors to use HotStart Taq DNA polymerase for REP-PCR assays or more discriminating tools such as PFGE, which is based on a universal protocol for phylogenetic studies.

Our results revealed a Shannon–Weaver index of 3.65 among the 50 water isolates and 2.39 for cattle isolates confirming the higher heterogeneity of the water isolates, which is similar to those found by Borges et al. (2003) for E. coli isolates in water samples using the REP-PCR technique.

Pearson chi-square between ERIC types (E1–E15), host, AMR, and phylo-groups could not imply any significant association, suggesting no ERIC type can be attributed to a specific host, phylo-group or AMR profile, concluding that genetic diversity alone cannot be an appropriate MST tool because as shown, out of six isolates with the same ERIC fingerprints, just four isolates demonstrated a similar AMR pattern (Table 6).

AMR of E. coli

In this study we observed a high rate of antibiotic resistance, which is consistent with the report by Ayazi et al. (2015), who found an alarming increase mainly due to increases in the use of antibiotics for treatment of infections in IRI within a 5-year period. However we could not find any reliable information on the consumption of antibiotics.

Similar to our observations related to genetic diversity, we observed that the overall AMR of isolates from water is higher in comparison with isolates from cattle. The most AMR of E. coli isolates from both origins were against cefalotin followed by tetracycline. High AMR of the isolates from water can be attributed to the natural presence of such agents in rivers but a more possible explanation is that the isolates are excreted from animals or humans treated by these antibiotics causing overall resistance (Meireles et al. 2013), which may imply a high rate of tetracycline consumption in this region. Furthermore, high resistance to tetracycline is not surprising because tetracycline is often used as a growth promoter in animal food, and the corresponding resistance genes are located on mobile genetic elements that can be transmitted among bacteria (Marshal & Levy 2011). Resistance to other agents such as amoxicillin–clavulanic acid and ampicillin in E. coli isolates was proportionally found in both origins, possibly related to therapeutic end use for human and animal diseases (Landers et al. 2012). Based on our results, the high resistance of E. coli isolates for tetracycline and cephalothin agrees with the findings of Sayah et al. (2005), who found the highest levels of resistance were observed against tetracycline and cephalothin with isolates collected from domestic and wild animal faecal samples, human septage, and surface water in the Red Cedar watershed in Michigan.

In this study, E. coli isolates from cattle and water showed no resistance to SXT, contrary to the results reported by Schroeder et al. (2002), who reported a high prevalence of resistance to sulfamethoxazole among O157:H7 isolates recovered from humans and cattle (Schroeder et al. 2002). However, as in our study they observed a high prevalence of tetracycline resistance among the isolates.

In the current study, a statistically significant relation could not be observed between the AMR, ERIC types, host, and phylo-groups, suggesting that if AMR profile/profiling is applied alone, no accurate MST result can be gained. However, in combination with others (as a complementary tool), it can increase the possibility of correct tracking.

Phylo-grouping of E. coli

This report determined the prevalence of different phylo-groups of Escherichia coli and demonstrated that some phylo-groups may be dominant in some species. Our results confirmed the dominance of B1 and absence of B2 in cattle, which is in agreement with the studies of Unno et al. (2009) and Coura et al. (2015). Following B1 (n = 37), phylo-groups of A (n = 24), D (n = 20), E (n = 9) and C (n = 7) were the most prevalent, while F (n = 3) was rarely found in either origin. The distribution and dominance of B1 and A1 in cow, goat and sheep samples by Carlos et al. (2010) is consistent with our results.

As well as the ERIC diversity, a higher Shannon–Weaver index for phylo-groups of E. coli from water in comparison with cattle can be observed (Table 5). In other words, it can be inferred that in addition to Khozankala husbandry, E. coli can enter the Chalous River through various sources including wild and domestic animals, human (inhabitants and passengers), animal manure, etc., which all together necessitate the source tracking of E. coli origins.

Pearson chi-square did not attribute any phylo-group to any specific host (p value = 0.1) (Table 4). Taken together, as Coura et al. (2015) and Carlos et al. (2010) described, the analysis of E. coli population structure can be useful as a supplementary bacterial source tracking tool, but standing alone, would not result in reliable MST results.

CONCLUSION

Overall, genotypic and phenotypic analysis revealed that E. coli isolates were very diverse and there was no evidence that a given group of E. coli isolates with distinct origin might represent a dominant population from surface water. In conclusion our results confirmed that a combination of methods including DNA-based typing and AMR profiling can be promising tools for accurate MST. However, the authors believe that despite applying all MST tools, it may be difficult to trace all bacteria in surface water, since surface water can receive various kinds of new bacterial genotypes which would not be present in our libraries and the phenotypic and genotypic characteristics of a bacterium may alter during multiplication and survival.

COMPETING INTERESTS

No conflict of interest.

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