Figures
Abstract
With 3,480 E. coli strains isolated from the Yeongsan River basin, South Korea, correlations between phylogenetic groups and horizontal fluorophore enhanced rep-PCR (HFERP) genotypes were examined, and environmental factors affecting E. coli phylogenetic groups in the river water were determined. Interestingly, multidimentional scaling (MDS) analyses based on HFERP DNA fingerprint data indicated that E. coli in phylogenetic groups A and B1 were uniquely clustered. Results of self-organized maps (SOMs) analyses also indicated that E. coli phylogenetic groups were seasonally affected by water temperature, with greater occurrences of phylogenetic groups A and B1 in low and high temperature seasons, respectively. The presence of E. coli in phylogenetic groups A and B1 were inversely related. Furthermore, redundancy analysis (RDA) revealed that phylogenetic group B1 correlated positively with temperature, strain diversity, and biochemical oxygen demand (BOD) but negatively with phylogenetic group A. Results of this study indicated that while E. coli strains could be clustered based on their genotypes and environment conditions, their phylogenetic groups did not change in relation to the same conditions. The distributional differences of phylogenetic groups among E. coli populations in different environments may be caused by different genomic adaptability and plasticity of E. coli strains belonging to each phylogenetic group. Although several previous studies have reported different E. coli ecological structures depending on their origins, this study is a first description of the specific environmental factors affecting E. coli phylogenetic groups in river water.
Citation: Jang J, Di DYW, Lee A, Unno T, Sadowsky MJ, Hur H-G (2014) Seasonal and Genotypic Changes in Escherichia coli Phylogenetic Groups in the Yeongsan River Basin of South Korea. PLoS ONE 9(7): e100585. https://doi.org/10.1371/journal.pone.0100585
Editor: A. Mark Ibekwe, U. S. Salinity Lab, United States of America
Received: February 6, 2014; Accepted: May 28, 2014; Published: July 7, 2014
Copyright: © 2014 Jang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (MEST) (NRF: 2011-0029860) and the GSR(GIST Specialized Research) Project through a grant provided by GIST in 2014. The authors also wish to acknowledge a grant-in-aid for research from the Gwangju Regional Environmental Technology Development Center (10-2-70-73).
Competing interests: The authors have declared that no competing interests exist.
Introduction
Escherichia coli is generally considered to be a commensal bacterium found in the intestinal tracts of warm-blooded animals. Although this bacterium has been adopted as one of the best indicator organisms to examine fecal pollution in freshwater environments [1], its effectiveness as a fecal indicator has been questioned since several recent studies have reported naturalized E. coli populations which can survive for long period of time and reproduce in the environments outside the host bodies such as sand, soil and sediments [2]–[5]. Surrounding environmental conditions have been suggested to influence the genotypic traits of E. coli in the extraintestinal environments [6]. In a previous study, it was observed that genotypic richness of E. coli strains obtained from surface water of the Yeongsan River of South Korea was fluctuated with environmental conditions, especially water temperature, and season-specific E. coli genotypes were found in cold months [7]. Taken together, in order to fully understand ecology of this bacterium and its use as a fecal indicator, ecological approaches of E. coli community dynamics in the environments seem to be essential.
E. coli strains can be mainly assigned to four major phylogenetic groups: A, B1, B2 and D [8]. Recently, the extensive multi-locus sequence typing (MLST) and genome data sets for E. coli strains have refined our knowledge of E. coli phylogenetic group structure, and new phylogenetic groups of E. coli are now recognized such as phylogenetic groups C, E, F, and Escherichia clade I, which are comparatively rare to the major phylogenetic groups [9]–[14].
Strains belonging to each phylogenetic group possess different phenotypic and genotypic traits relative to each other [15], [16]. Furthermore, the four major phylogenetic groups have been reported to differ in their ecological habitats. While phylogenetic groups B2 and D have been less frequently found than A or B1 in the environment [17], strains in group B2 have been observed to persist longer than the other groups in infants [18]. Moreover, E. coli strains in phylogenetic group B2 or D were more frequently isolated from extraintestinal sites within host bodies than group A or B1 strains [16]. Some strains belonging to phylogenetic group B1 were reported to persist in water environments [17], [19]. Many studies have also reported the relationship between virulence and phylogenetic groups of E. coli. Phylogenetic group B2 strains tend to be more virulent than other groups [20]–[23], and virulence genes were most frequently present in phylogenetic group B1 strains in the absence of B2 strains [24]. Thus, phylogenetic group identification of unknown E. coli strains may provide important information of their physiological and ecological aspects.
While initial studies used multilocus enzyme electrophoresis (MLEE) to classify E. coli strains into phylogenetic groups [25], Clermont and colleagues (2000) developed a multiplex PCR-based method to determine the four major phylogenetic groups of E. coli strains [26]. This method was further validated by Gordon et al. (2008) and showed that the PCR-based and multi-locus sequence typing (MLST) methods classified 80–85% of 662 E. coli isolates into the same phylogenetic groups [27]. These methods have been often used for the phylogenetic grouping of E. coli strains from various sources [22], [24], [28], [29] since it reduces the time and cost of experimental procedures needed to examine a large number of strains. Recently, a new quadruplex PCR method enables an E. coli strain to be assigned to one of the seven phylogenetic groups (A, B1, B2, C, D, E, and F) and Escherichia clade I, which is also considered to be a phylogenetic group of E. coli [30].
The horizontal fluorophore-enhanced rep-PCR (HFERP) DNA fingerprinting technique enables accurate genotyping of E. coli strains [31], [32]. However, no direct correlation between HFERP DNA fingerprint patterns and phylogenetic groups of E. coli strains was described previously [24], [31]. Nevertheless, since rep-PCR DNA fingerprint patterns are susceptible to alteration of microbial genome structure, the technique can be used to study plasticity, molecular phylogeny, and evolution of microbial genomes [31]. The HFERP DNA fingerprinting technique, which revealed 1749 genotypes among 3480 E. coli isolates in a previous study [7], provides the much higher discriminatory power than the MLST or multiplex PCR methods assigning a E. coli strain to one of the eight phylogenetic groups as described above.
The aim of this study was to: 1) examine the relationship between phylogenetic groups and the HFERP DNA fingerprints of 3,480 E. coli strains isolated from the Yeongsan River basin, 2) determine correlations between E. coli phylogenetic groups and environmental factors in the environment, and 3) relations between virulence gene profiles of shiga toxigenic E. coli (STEC) and the phylogenetic groups of E. coli in the environment.
Materials and Methods
Environmental samples and bacterial strains
The sampling approach used and the isolation of E. coli strains from surface water samples was done as described previously [7]. A total of 3,480 E. coli strains were the same as those used in a previous study [7], and were subjected to further experiments here. Briefly, surface water samples were collected monthly from the 7 sites of the Yeongsan River basin (Table 1), including 3 agricultural sites, 3 urban sites, and 1 site affected by agricultural and urban, from April to December 2009, then sixty E. coli strains were isolated from each water sample but samples less than 60 isolates were excluded from further analyses. The membrane filtration technique with mTEC agar (Difco, Detroit, MI) was employed to obtain E. coli strains from freshwater samples [1]. ChromAgar ECC (Chromagar Microbiology, Paris, France) was used to reconfirm the phenotypical identity of potential E. coli isolates [6]. As practical point of view considering the large number of E. coli isolates, no further confirm tests for E. coli identification were proceeded since 97.3% of presumptive E. coli colonies obtained from the membrane filtration method with mTEC agar medium have been identified as E. coli strains in a previous study [33]. All E. coli isolates were preserved in LB freezing medium at −70°C [34], and their genomic DNA was prepared by boiling in 0.05 N NaOH. Colonies formed on LB agar plates were suspended in 100 µl of 0.05 N NaOH. The cells were lysed at 95°C for 15 min, and cell debris was precipitated by brief centrifugation. A dilution (1∶10) of the supernatant was used as a DNA template of the further PCR assays.
Moreover, water quality parameters such as temperature, pH, biochemical oxygen demand (BOD), and E. coli MPN values were also obtained from all water samples by using the YSI 6600 Sonde (YSI Incorporated, Yellow Springs, OH) and the Colilert (IDEXX Laboratories, Inc., Westbrook, MN) system as described previously [7].
All the sampling sites were located in the Yeongsan River basin which is one of public rivers managed by Korean government. While the Youngsanriver Environmental Management Office is the government organization which manages the study area, no specific permission was required for any research activities performed in this study.
Horizontal fluorophore-enhanced rep-PCR (HFERP) DNA fingerprinting technique and defining HFERP genotypes of E. coli strains.
Horizontal fluorophore-enhanced rep-PCR (HFERP) genotyping of the E. coli strains used in this study was done previously as described in our previous study [7]. Briefly, the BOX A1R (5′-CTACGGCAAGGCGACGCTGACG-3′) primer labeled with 6-FAM (6-carboxyfluorescein; Genotech Co. Ltd., Korea) was employed for the rep-PCR genotyping, then amplified DNA fragments and the Genescan-2500 ROX (6-carboxy-X-rhodamine) (Applied Biosystems, Foster City, CA) internal standard were separated by gel electrophoresis (70 V for 16 hours at 4°C) in 0.5 x TAE buffer using 1% Seachem LE agarose gels (FMC Bioproducts, Rockland, ME). Gel images were obtained by using by using a Typhoon 9400 variable mode imager (Molecular Dynamics/Amersham Biosciences, Sunnyvale, CA), and the image files were analyzed using BioNumerics v.6.01 software (Applied Maths, Sint-Martens-Latem, Belgium). Detailed protocols for HFERP DNA fingerprinting are accessible at http://www.ecolirep.umn.edu.
Phylogenetic grouping and virulence detection of E. coli strains.
The phylogenetic grouping of the 3,480 E. coli strains was done using the Clermont multiplex PCR methods as described previously [26]. Briefly, the multiplex PCR assay was performed in 20 µl total volume containing 2 µl of 10X PCR buffer supplied with Top DNA polymerase (Bioneer, Korea), 1.5 µl of 20 mM MgCl2, 20 pmol of each primer shown in top part of Table 2, 2 µM of each deoxynucleoside triphosphate, 2.5 U of Top DNA polymerase (Bioneer, Korea), and 3 µl of bacterial lysate. The PCR steps were composed of denaturation for 4 min at 94°C, 30 cycles at 94°C for 5 s and 10 s at 59°C, and a final extension step of 5 min at 72°C.
The presence of virulence genes among the E. coli strains related to shiga toxin producing E. coli (STEC), enterohemorrhagic E. coli (EHEC), and enteropathogenic E. coli (EPEC) (eaeA, stx1, and stx2) was determined by using multiplex PCR as previously described [35]. Briefly, the target genes were amplified in 25 µl reaction mixture containing 200 µM concentrations of deoxynuceloside triphosphates, 250 nM concentration of each primer shown in bottom part of Table 2, 1 U of Top DNA polymerase (Bioneer, Korea), 2.5 µl of 10X PCR buffer supplied with Top DNA polymerase (Bioneer, Korea), 2 mM MgCl2, and 2 µl of bacterial lysate. The reaction mixtures were subjected to first 10 cycles of 1 min of denaturation at 95°C, 2 min of annealing at 65°C, and 1.5 min of elongation at 72°C; a second 15 cycles of 1 min of denaturation at 95°C, 2 min of annealing at 60°C, and 1.5 min of elongation at 72°C; and a third 10 cycles of 1 min of denaturation at 95°C, 2 min of annealing at 65°C, and 2.5 min of elongation at 72°C. All of the PCR products were assessed by using an automated capillary electrophoresis system, QIAxcel (Qiagen, Hilden, Germany).
Statistical methods
Multidimentional scaling (MDS) analyses were done using BioNumerics v.6.01 software (Applied Maths, Sint-Martens-Latem, Belgium) and provided a three dimentional representation of E. coli phylogenetic groups based on similarities between HFERP DNA fingerprints. The correct assignment of E. coli strains to phylogenetic group was evaluated by using Jackknife analysis, with maximum similarities [32]. Self-organizing maps (SOMs) analyses using the SOM Toolbox implements (Laboratory of Computer and Information Science, Helsinki, Norway) of Matlab 2009b (MathWorks, Natick, MA) were used to determine if there were correlations between E. coli phylogenetic groups and environmental factors, including temperature, pH, BOD, and E. coli MPN values. Raw data for the SOMs analyses are organized as presented in Table S1 (supplementary file). The occurrence of four E. coli phylogenetic groups and the five water quality parameters described previously [7] were used as input variables for the SOMs analyses. The visualized map images, which were automatically optimized at 42 SOM cells, were generated with minimum quantization error (1.58) and the least topographic errors (0.034). Quantization error (QE) and topographic error (TE) were estimated to confirm reliable resolution and topology conservation of the SOMs analyses, and these are meanings of the statistical significance. QE is the average distance between each data vector and its best matching unit (BMU), measures map resolution. TE measures map quality, which represents the proportion of all data vectors for which 1st and 2nd BMUs are not adjacent, and is thus used for the measurement of topology preservation. The results of the SOMs analyses are based on statistical procedures, and map size and structure (n×m) are automatically optimized and determined based on the least QE and TE values among numerous cases. Borders between clusters existing in the map were defined by using a hierarchical cluster analysis. Features and analytic procedures of SOMs analyses have been described previously [7], [36]–[41].
Redundancy analysis (RDA), one of multivariate analyses included in software packages CANOCO v5.0 (Microcomputer Power, Ithaca, New York), was performed to test which environmental factors significantly correlated with the variation in the E. coli phylogenetic groups [42]. Five parameters including temperature, pH, BOD, E. coli MPN values, and strain diversity were used for the explanatory variables constraining the occurrences of the E. coli phylogenetic groups A and B1, which occupied over 80% of total E. coli strains tested in this study. Response data in the analysis have a gradient 0.8 standard deviation (SD) units, suggesting that E. coli phylogenetic group response to the gradient of the environmental factors is linear. The forward selection method was used to determine significant environmental variables affecting the E. coli phylogenetic groups using the 999 Monte Carlo permutations at P<0.05 [43].
Results
Genotypic separation of E. coli strains in different phylogenetic groups
The 3,480 E. coli strains isolated from the Yeongsan River basin in Korea were comprised of 1,724 (49.5%), 1,192 (34.3%), 188 (5.4%), and 376 (10.8%) strains in phylogenetic groups A, B1, B2, and D, respectively. The MDS analyses were performed to investigate a correlation between HFERP DNA fingerprints and phylogenetic groups of E. coli strains (Fig. 1). As shown in Figure 1, each dot in the three dimensional space indicates HFERP DNA fingerprint of each E. coli strain, and it was given a color according to one of the phylogenetic groups the E. coli strain belongs to. Distances between dots simply reflect similarities between HFERP DNA fingerprints of E. coli strains. Interestingly, phylogenetic groups A and B1 showed a tendency to cluster along with genotype distributions (indicated by blue circles). Moreover, the both phylogenetic groups were separated even within the tightly clustered genotypic group (indicated by black circle) obtained from the cold months as described previously [7]. In contrast, phylogenetic groups B2 and D showed no significant clustering along with genotypic distributions in the MDS analyses. As shown in Table 3, the clustering trend of the MDS analyses was reconfirmed by using Jackknife analysis, in which strains were removed from the group one at a time and treated as unknowns for classification. Phylogenetic groups A and B1 showed significantly higher percentages of E. coli strains correctly assigned to their own group (90.1% and 89.4%) than phylogenetic groups B2 and D (65.4% and 77.7%) (p<0.05, two-sample t-test assuming equal variances using Microsoft Excel).
Legend: A (red), B1 (green), B2 (sky-blue), and D (yellow).
SOMs analyses for trends of E. coli phylogenetic groups
Surface water samples were classified into self-organized map (SOM) cells based on their water quality parameters and prevalence of E. coli phylogenetic groups. These are used as input variables for the SOMs analyses, and water samples were assigned into SOM cells depending on the similarity among their variables (Fig. 2A). A hierarchical cluster analysis (Fig. 2B) revealed that clusters existed in the map. According to the similarities of the variables in the output cells, clusters were defined as combined cell units of the map (Fig. 2A). As shown in Figure 2, two main clusters (C1 and C2) were further subdivided into six sub-clusters (C1a, C1b1, C1b2, C2a, C2b1, and C2b2). Sub-clusters C2b1 and C2b2 in Cluster C2 were mainly comprised of E. coli obtained surface water samples collected from the cold months, October, November, and December. This suggested season-specificity of SOMs. Site-specificity was also observed in sub-cluster C2a since it includes only isolates from surface water samples collected from the urban-affected locations (sites GJ1, GJ2, GJ3, and YS3). Results in Figure 3A and 3B show the contribution of each input variable to the classification of SOMs cells by displaying the distribution of the variable intensities on the map. The intensities are expressed by colors between white and black, with the units of each variable. Dark and light colors indicate high and low values of each variable, respectively. The divisions of SOMs cells shown in Figure 3A can be explained by classification factors which are input variables showing intensity maps in Figure 3B. For instance, a sub-cluster C2a was mainly defined by E. coli MPN showing high values of the variable in the cluster. By the same token, a sub-cluster C2b2 was determined by phylogenetic group B2. Furthermore, these results indicated that the relationships between input variables can be observed by comparing the intensity maps to each other. Based on the analyses, the following interpretation could be deduced: (1) Elevated E. coli MPN counts determined cluster C2a, which showed specificity for urban-affected sites, and did not directly correlate with other variables; (2) Strain diversity and temperature were directly correlated, which was also described in a previous study[7], and these two variables are considered to be major contributors determining the two main clusters C1 and C2; (3) BOD was also correlated with strain diversity and temperature, and high BOD values appeared to influence of cluster C1b1; (4) No correlations were observed between pH and the other variables; (5) The high frequency of phylogenetic group A strains appeared to be correlated with low values of strain diversity and temperature. This correlation was weak and was not as significantly correlated as was the relationship between strain diversity and temperature. The high frequency of occurrence of phylogenetic group A strains with low temperature is consistent with the increasing ratio of phylogenetic group A in the cold months as described above. Strains in phylogenetic group A are also considered to be a determining factor for the main clusters C1 and C2; (6) Phylogenetic group B1 strains were mostly present in high temperatures and showed a frequency distribution contrary to phylogenetic group A strains; (7) Phylogenetic group B2 strains did not directly correlate with any other variables and appeared to affect cluster C2b2; and (8) The high frequency of phylogenetic group D strains correlated with high temperatures and contributed to assignment of cluster C1a. Overall, temperature affected strain diversity and the occurrence of phylogenetic groups A, B1, and D. In contrast, phylogenetic group B2 was not affected by any other variables. However, the lack of any patterns in B2 strains and to a lesser extent D strains probably reflects their rarity.
(A) Six clusters were determined by the SOMs analyses: two main clusters (C1 and C2) including six sub-clusters (C1a, C1b1, C1b2, C2a, C2b1 and C2b2), and (B) dendrogram created by clustering input variables based on the similarity among SOM cells.
Dark and light colors indicate high and low values of each variable, respectively. The intensity of strain diversity was defined as percentage of number of E. coli genotypes for total isolates.
Correlation between distribution of E. coli phylogenetic groups and environmental factors
Redundancy analysis (RDA) was performed to determine correlations between prevalence of E. coli phylogenetic groups and environmental factors in surface water of the Yeongsan River basin. Five parameters such as BOD, temperature, pH, E. coli MPN, and strain diversity were used for the possible explanatory variables for constraining the occurrence of E. coli phylogenetic groups A and B1, which occupied more than 80% of total E. coli strains. The RDA explained 49.3% of the total variation in the phylogenetic groups A and B1.
The correlations between the two E. coli phylogenetic groups and the environmental data were shown in an ordination diagram (Fig. 4) showing the results which are similar to ones observed from the SOMs analyses. Temperature, strain diversity, and BOD were positively correlated to each other. The pH values were not likely to correlate with the other environmental factors and phylogenetic groups. Temperature, strain diversity, BOD and E. coli MPN were determined to be significant explanatory variables affecting the occurrences of the two E. coli phylogenetic groups (P<0.05) by using a forward selection method. High values of temperature, strain diversity, and BOD were correlated with high number of phylogenetic group B1, but with low number of phylogenetic group A. E. coli MPN, on the other hand, were positively correlated with phylogenetic group A, but negatively with B1. Furthermore, negative correlation was shown between phylogenetic group A and B1.
The correlations of the environmental variables (explanatory variables) with the two phylognetic groups (response variables) can be predicted from angles between the arrows of the variables. The biplot projection of the phylogenetic group arrow tips onto the arrow of an environmental variable provides a more precise approximation.
The correlation between E. coli MPN and phylogenetic group A may be affected by the fact that group A was most frequently found among total E. coli strains, suggesting that larger number of E. coli isolates provides more chance to obtain phylogenetic group A strains.
Distribution of potential pathogenic E. coli strains among phylogenetic groups
Based on previously accepted definitions of E. coli pathotypes [24], [44], E. coli strains carrying virulence genes were assigned to the pathotypes: stx1, stx2 or stx1/stx2, shiga toxigenic E. coli (STEC); eaeA with stx1 or/and stx2, enterohemorrhagic E. coli (EHEC); eaeA without stx1 or stx2, enteropathogenic E. coli (EPEC). Among the 3,480 E. coli strains examined, 217 (6.2%), 64(1.8%), and 9 (0.3%) were assigned to STEC, EPEC, and EHEC groups, respectively. Results in Figure 5 show that the greatest percentage of STEC strains was observed among phylogenetic group D (9.3%, 35 of 376). In contrast, phylogenetic group B2 included the greatest percentage of EPEC strains (6.4%, 12 of 188).
Discussion
The separation of strains into phylogenetic groups A and B1 was also supported by genotypes defined by HFERP DNA fingerprint analyses as observed from MDS and jackknife analysis. MDS analyses indicated that although environmental factors such as temperature did affect the pattern of HFERP genotypes of E. coli strains [7], their phylogenetic groups that was correspondent with genotypic separation did not show any seasonal pattern of grouping (Fig. 1). This result is likely due to the fact that while phylogenetic grouping is based on indirect gross changes in enzyme mobility or stable genetic markers, rep-PCR technique directly detects changes in nucleotide base composition to identify possible rearrangements in bacterial genome structure. This suggests that the HFERP DNA fingerprinting method is more sensitive to genomic variations for adaption and plasticity in the environment than is phylogenetic grouping [31]. Furthermore, the seasonal specificity of E. coli HFERP genotypes, which was seen as a clustering of strains obtained from cold months, did not directly coincide with the distribution of E. coli phylogenetic groups as seen by the MDS analyses (Fig. 1). This also suggested that HFERP DNA fingerprint-defined genotypes of E. coli are more seasonally influenced by environmental factors, such as temperature than E. coli phylogenetic groups.
It is interesting that the SOMs analyses revealed that there was an inverse relationship between the occurrence of E. coli strains belonging to phylogenetic groups A and B1. Although the fact that phylogenetic groups A and B1 are inversely related to each other may simply reflect that they account for about 85% of the isolates, it is still remarkable since the inverse relation was correlated with the environmental factors. While E. coli strains belonging to phylogenetic group A was found to increase in the conditions such as low temperature and genotypical diversity, phylogenetic group B1 strains increased in the contrary conditions in the Yeongsan River basin, revealing that water temperature tends to be associated with the occurrence of specific E. coli phylogenetic groups. These correlations between E. coli phylogenetic groups and environmental factors were more clearly observed from the RDA. However, since phylogenetic groups were not strictly correlated with temperature as much as genotypic strain diversity did from the SOMs analyses, this suggests that other environmental factors may also cause differences between E. coli phylogenetic groups. In addition, the MDS analyses (Fig. 1) showed that phylogenetic group A was not comprised of strains obtained from only cold months, suggesting temperature is not only the factor affecting E. coli phylogeny.
Previously, Tenaillon et al. (2010) reported that the population structure of E. coli is predominantly clonal in spite of the occurrence of recombination events, allowing the delineation of major phylogenetic groups [14]. Based on the results reported here, our data suggest that E. coli phylogenetic groups are adaptable and genotypically-influenced by changes in environmental conditions. This conclusion is in large part supported by our observation that phylogenetic group A strains appear to be more adaptable to low temperature than the other groups examined in this study. While several previous studies already have reported different E. coli ecological structures depending on their origins, few studies described the specific environmental factors affecting E. coli phylogenetic groups in river water.
As described previously [7], no site-specificity of E. coli genotypes and phylogenetic groups was observed in this study as well. However, the representativeness of the E. coli strains for each surface water sample is quite variable because of the same number of the strains obtained from each sample, irrespective of how much total population of E. coli is in there. Particularly the representativeness of the E. coli isolates in urban sites showing generally high MPN values is likely to be lower than the other sites. Thus, it would be improper to discuss on site-specificity of the results in this study.
Although the total number of E. coli strains belonging to phylogenetic groups B2 and D occurred less frequently than ones belonging to the other phylogenetic groups, the percentage of potential E. coli pathotypes comprising EPEC and STEC strains were highest among members of groups B2 and D. This result is consistent with previous studies that phylogenetic groups A and B1 members were often comprised of commensal stains and that phylogenetic groups B2 and D were observed to contain more virulent strains than other groups [18], [45]–[47]. Thus, the previously reported relationship between phylogeny and virulence of E. coli strains can similarly be applied to the E. coli population existing in surface waters of the Yeongsan River basin. However, it should be noted that typical strains of EHEC O157:H7 possessing no β-d-glucuronidase were excluded from the results since E. coli strains examined in this study were initially obtained by using mTEC agar plates which has selectivity for the bacterial species based on β-d-glucuronidase activity [48].
In conclusion, we show that there is a relationship between genotypic groupings of E. coli based on their HFERP DNA fingerprints and strains in phylogenetic groups A and B1. Moreover, we report here that the SOMs and RDA analyses indicate that the distributional changes of these groups are affected by the environmental factors. The compositional differences of phylogenetic groups among E. coli populations in different environments may be caused by differences in adaptability and plasticity of E. coli strains belonging to each phylogenetic group. Environmentally-adapted genotypes and changes in phylogenetic groups according to specific environmental conditions probably are not related to temporal deposition of fecal pollutant since they were observed regardless of sampling locations. The genetic bases for these changes are unknown, and further investigations of genomic DNA sequence variation among the E. coli strains examined in the current study will likely provide in-depth information about the genotypic changes imparted by environmental conditions of the river water. Moreover, it would be worthwhile to perform a further repetitive monitoring of E. coli populations in the environment to confirm the reproducibility of the study results during a longer sampling period since a river environment is not a static ecosystem.
Supporting Information
Table S1.
Raw data of water parameters and E. coli phylogenetic groups for each surface water sample.
https://doi.org/10.1371/journal.pone.0100585.s001
(XLS)
Acknowledgments
We thank the members of the Environmental Systems Engineering Laboratory in the Gwangju Institute of Science and Technology who assisted with surface water sample collections from the Yeongsan River basin.
Author Contributions
Conceived and designed the experiments: JJ HH. Performed the experiments: JJ DYWD AL. Analyzed the data: JJ. Contributed reagents/materials/analysis tools: TU HH. Wrote the paper: JJ MJS HH.
References
- 1.
USEPA (2006) Method 1603: Escherichia coli (E. coli) in Water by Membrane Filtration Using Modified membrane-Thermotolerant Escherichia coli Agar. U.S. Environmental Protection Agency, Washington, DC.
- 2. Byappanahalli MN, Fujioka RS (1998) Evidence that tropical soil environment can support the growth of Escherichia coli. Water Sci Technol 38: 171–174.
- 3.
Sadowsky MJ, Whitman RL (2010) Fecal Bacteria. ASM Press: Washington, DC.
- 4. Byappanahalli MN, Whitman RL, Shively DA, Sadowsky MJ, Ishii S (2006) Population structure, persistence, and seasonality of autochthonous Escherichia coli in temperate, coastal forest soil from a Great Lakes watershed. Environ Microbiol 8: 504–513.
- 5. Ishii S, Ksoll WB, Hicks RE, Sadowsky MJ (2006) Presence and growth of naturalized Escherichia coli in temperate soils from lake superior watersheds. Appl Environ Microbiol 72: 612–621.
- 6. Dombek PE, Johnson LK, Zimmerley ST, Sadowsky MJ (2000) Use of repetitive DNA sequences and the PCR to differentiate Escherichia coli isolates from human and animal sources. Appl Environ Microbiol 66: 2572–2577.
- 7. Jang J, Unno T, Lee SW, Cho KH, Sadowsky MJ, et al. (2011) Prevalence of season-specific Escherichia coli strains in the Yeongsan River Basin of South Korea. Environ Microbiol 13: 3103–3113.
- 8. Herzer PJ, Inouye S, Inouye M, Whittam TS (1990) Phylogenetic distribution of branched RNA-linked multicopy single-stranded DNA among natural isolates of Escherichia coli. J Bacteriol 172: 6175–6181.
- 9. Clermont O, Olier M, Hoede C, Diancourt L, Brisse S, et al. (2011) Animal and human pathogenic Escherichia coli strains share common genetic backgrounds. Infect Genet Evol 11: 654–662.
- 10. Walk ST, Alm EW, Gordon DM, Ram JL, Toranzos GA, et al. (2009) Cryptic lineages of the genus Escherichia. Appl Environ Microbiol 75: 6534–6544.
- 11. Luo C, Walk ST, Gordon DM, Feldgarden M, Tiedje JM, et al. (2011) Genome sequencing of environmental Escherichia coli expands understanding of the ecology and speciation of the model bacterial species. Proc Natl Acad Sci USA 108: 7200–7205.
- 12. Moissenet D, Salauze B, Clermont O, Bingen E, Arlet G, et al. (2010) Meningitis caused by Escherichia coli producing TEM-52 extended-spectrum β-lactamase within an extensive outbreak in a neonatal ward: Epidemiological investigation and characterization of the strain. J Clin Microbiol 48: 2459–2463.
- 13. Jaureguy F, Landraud L, Passet V, Diancourt L, Frapy E, et al. (2008) Phylogenetic and genomic diversity of human bacteremic Escherichia coli strains. BMC Genomics 9: 560.
- 14. Tenaillon O, Skurnik D, Picard B, Denamur E (2010) The population genetics of commensal Escherichia coli. Nat Rev Microbiol 8: 207–217.
- 15. Bergthorsson U, Ochman H (1998) Distribution of chromosome length variation in natural isolates of Escherichia coli. Mol Biol Evol 15: 6–16.
- 16.
Gordon DM (2004) The influence of ecological factors on the distribution and genetic structure of Escherichia coli. In Escherichia coli and Salmonella typhimurium: Cellular and Molecular Biology. Neidhardt, F., et al. (eds) ASM press: Washington, DC.
- 17. Walk ST, Alm EW, Calhoun LM, Mladonicky JM, Whittam TS (2007) Genetic diversity and population structure of Escherichia coli isolated from freshwater beaches. Environ Microbiol 9: 2274–2288.
- 18. Nowrouzian FL, Adlerberth I, Wold AE (2006) Enhanced persistence in the colonic microbiota of Escherichia coli strains belonging to phylogenetic group B2: role of virulence factors and adherence to colonic cells. Microbes Infect 8: 834–840.
- 19. Ratajczak M, Laroche E, Berthe T, Clermont O, Pawlak B, et al. (2010) Influence of hydrological conditions on the Escherichia coli population structure in the water of a creek on a rural watershed. BMC Microbiol 10: 222.
- 20. Johnson JR, Stell AL (2000) Extended virulence genotypes of Escherichia coli strains from patients with urosepsis in relation to phylogeny and host compromise. J Infect Dis 181: 261–272.
- 21. Le Gall T, Clermont O, Gouriou S, Picard B, Nassif X, et al. (2007) Extraintestinal virulence is a coincidental by-product of commensalism in B2 phylogenetic group Escherichia coli strains. Mol Biol Evol 24: 2373–2384.
- 22. Escobar-Páramo P, Grenet K, Le Menac'h A, Rode L, Salgado E, et al. (2004) Large-scale population structure of human commensal Escherichia coli isolates. Appl Environ Microbiol 5698–5700.
- 23. Picard B, Garcia JS, Gouriou S, Duriez P, Brahimi N, et al. (1999) The Link between phylogeny and virulence in Escherichia coli extraintestinal infection. Infect Immun 67: 546–553.
- 24. Unno T, Han D, Jang J, Lee S-N, Ko G, et al. (2009) Absence of Escherichia coli phylogenetic group B2 strains in humans and domesticated animals from Jeonnam province, Republic of Korea. Appl Environ Microbiol 75: 5659–5666.
- 25. Goullet P, Picard B (1989) Comparative electrophoretic polymorphism of esterases and other enzymes in Escherichia coli. J Gen Microbiol 135: 135–143.
- 26. Clermont O, Bonacorsi S, Bingen E (2000) Rapid and simple determination of the Escherichia coli phylogenetic group. Appl Environ Microbiol 66: 4555–4558.
- 27. Gordon DM, Clermont O, Tolley H, Denamur E (2008) Assigning Escherichia coli strains to phylogenetic groups: multi-locus sequence typing versus the PCR triplex method. Environ Microbiol 10: 2484–2496.
- 28. Duriez P, Clermont O, Bonacorsi S, Bingen E, Chaventré A, et al. (2001) Commensal Escherichia coli isolates are phylogenetically distributed among geographically distinct human populations. Microbiol-SGM 147: 1671–1676.
- 29. Johnson JR, Kuskowski MA, Owens K, Gajewski A, Winokur PL (2003) Phylogenetic origin and oirulence genotype in relation to resistance to fluoroquinolones and/or extended-spectrum cephalosporins and cephamycins among Escherichia coli isolates from animals and humans. J Infect Dis 188: 759–768.
- 30. Clermont O, Christenson JK, Denamur E, Gordon DM (2013) The clermont Escherichia coli phylo-typing method revisited: improvement of specificity and detection of new phylo-groups. Environ Microbiol Rep 5: 58–65.
- 31. Ishii S, Sadowsky MJ (2009) Applications of the rep-PCR DNA fingerprinting technique to study microbial diversity, ecology and evolution. Environ Microbiol 11: 733–740.
- 32. Johnson LK, Brown MB, Carruthers EA, Ferguson JA, Dombek PE, et al. (2004) Sample size, library composition, and genotypic diversity among natural populations of Escherichia coli from different animals influence accuracy of determining sources of fecal pollution. Appl. Environ Microbiol 70: 4478–4485.
- 33. Ciebin BW, Brodsky MH, Eddington R, Horsnell G, Choney A, et al. (1995) Comparative evaluation of modified m-FC and m-TEC media for membrane filter enumeration of Escherichia coli in water. Appl Environ Microbiol 61: 3940–3942.
- 34.
Sambrook J, Russell WD (2001) Molecular cloning: a laboratory manual, 3rd ed. Cold Spring Harbor Laboratory Press: Cold Spring Harbor, NY.
- 35. Paton AW, Paton JC (1998) Detection and characterization of shiga Toxigenic Escherichia coli by using multiplex PCR assays for stx1, stx2,eaeA, Enterohemorrhagic E. coli hlyA, rfb O111, and rfb O157. J Clin Microbiol 36: 598–602.
- 36. Park Y-S, Céréghino R, Compin A, Lek S (2003) Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecol Model 160: 265–280.
- 37. Park Y-S, Chang J, Lek S, Cao W, Brosse S (2003) Conservation strategies for endemic fish species threatened by the three gorges dam. Conserv Biol 17: 1748–1758.
- 38. Tuang FN, Rademaker JLW, Alocilja EC, Louws FJ, de Bruijn FJ (1999) Identification of bacterial rep-PCR genomic fingerprints using a backpropagation neural network. FEMS Microbiol Lett 177: 249–256.
- 39. Tudesque L, Gevrey M, Grenouillet G, Lek S (2008) Long-term changes in water physicochemistry in the Adour-Garonne hydrographic network during the last three decades. Water Res 42: 732–742.
- 40. Weber M, Teeling H, Huang S, Waldmann J, Kassabgy M, et al. (2010) Practical application of self-organizing maps to interrelate biodiversity and functional data in NGS-based metagenomics. ISME J 5: 918–928.
- 41. Gevrey M, Rimet F, Park YS, Giraudel J-L, Ector L, et al. (2004) Water quality assessment using diatom assemblages and advanced modelling techniques. Freshwater Biol 49: 208–220.
- 42.
Smilauer JLaP Multivariate Analysis of Ecological Data using CANOCO. Cambridge University Press: Cambridge, UK.
- 43. Boer SI, Hedtkamp SIC, van Beusekom JEE, Fuhrman JA, Boetius A, et al. (2009) Time- and sediment depth-related variations in bacterial diversity and community structure in subtidal sands. ISME J 3: 780–791.
- 44. Tarr PI, Gordon CA, Chandler WL (2005) Shiga-toxin-producing Escherichia coli and haemolytic uraemic syndrome. The Lancet 365: 1073–1086.
- 45. Nowrouzian F, Hesselmar B, Saalman R, Strannegard I-L, Aberg N, et al. (2003) Escherichia coli in infants' intestinal microflora: Colonization rate, strain turnover, and virulence gene carriage. Pediatr Res 54: 8–14.
- 46. Gordon DM, Stern SE, Collignon PJ (2005) Influence of the age and sex of human hosts on the distribution of Escherichia coli ECOR groups and virulence traits. Microbiology-SGM 151: 15–23.
- 47. Johnson JR, O'Bryan TT, Kuskowski M, Maslow JN (2001) Ongoing horizontal and vertical transmission of virulence genes and papA alleles among Escherichia coli blood isolates from patients with diverse-source bacteremia. Infect Immun 69: 5363–5374.
- 48. Feng P, Lampel KA, Karch H, Whittam TS (1998) Genotypic and phenotypic changes in the emergence of Escherichia coli O157:H7. J Infect Dis 177: 1750–1753.