APPLIED AND ENVIRONMENTAL MICROBIOLOGY, Nov. 2010, p. 7509–7513 0099-2240/10/$12.00 doi:10.1128/AEM.00726-10 Copyright © 2010, American Society for Microbiology. All Rights Reserved.
Vol. 76, No. 22
Evaluation of Virulence Factor Profiling in the Characterization of Veterinary Escherichia coli Isolates䌤† Donna E. David,1 Aaron M. Lynne,1,2 Jing Han,3 and Steven L. Foley1,2* Marshfield Clinic Research Foundation, Marshfield, Wisconsin 544491; Department of Biological Sciences, Sam Houston State University, Huntsville, Texas 7734122; and Division of Microbiology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 720793 Received 23 March 2010/Accepted 21 September 2010
Escherichia coli has been used as an indicator organism for fecal contamination of water and other environments and is often a commensal organism in healthy animals, yet a number of strains can cause disease in young or immunocompromised animals. In this study, 281 E. coli isolates from bovine, porcine, chicken, canine, equine, feline, and other veterinary sources were analyzed by BOXA1R PCR and by virulence factor profiling of 35 factors to determine whether they had utility in identifying the animal source of the isolates. The results of BOXA1R PCR analysis demonstrated a high degree of diversity; less than half of the isolates fell into one of 27 clusters with at least three isolates (based on 90% similarity). Nearly 60% of these clusters contained isolates from more than one animal source. Conversely, the results of virulence factor profiling demonstrated clustering by animal source. Three clusters, named Bovine, Chicken, and Porcine, based on discriminant components analysis, were represented by 90% or more of the respective isolates. A fourth group, termed Companion, was the most diverse, containing at least 84% of isolates from canine, feline, equine, and other animal sources. Based on these results, it appears that virulence factor profiling may have utility, helping identify the likely animal host species sources of certain E. coli isolates. located on transmissible elements, such as plasmids, which can facilitate their dissemination through the bacteria in a specific environment (15). In poultry, for example, E. coli isolates from multiple different serogroups can share common plasmid-associated virulence factors, which allows for avian pathogenic E. coli to be identified among a diverse population of strains (15, 21). Thus, isolates from different environments are likely to have different cohorts of genes that can potentially be exploited to distinguish bacteria acquired from different sources. There have been a high number of studies characterizing virulence factors of E. coli from a particular animal species (20–22, 28, 30). In each of these studies, the goals were to identify which genes are important for virulence in the respective animal species. Based on the results of these studies, it appeared that for the different animal species there were different sets of virulence factors that likely played a role in disease. Typically E. coli-associated disease occurs in very young or immunocompromised animals, without being a major problem in adult animals, even though a percentage of adult animals has harbored E. coli with identified virulence factors (20, 22, 30). Because a proportion of virulence factors persist in E. coli from adult animals and different virulence factors appear to be important in different animal species, these factors may aid in the determination of the animal source of bacteria. Therefore, this paper describes the potential utility of virulence factor profiling to distinguish E. coli isolates taken from different animal sources, which may have utility as part of an MST scheme.
Escherichia coli is a common inhabitant of the intestinal tracts of many animal species and is used as an indicator that other pathogenic bacteria, such as Salmonella species, may be present in a particular environment. Animal feces containing pathogenic organisms can enter watersheds through runoff or improper manure handling practices and enter the food supply through fecal contamination, serving as a potential source of human infections (2, 10, 17). Therefore, it is important to find improved ways of identifying sources of contamination to limit exposures to pathogenic microorganisms. A common limitation of microbial source tracking (MST) methods, such as ribotyping and repetitive element PCR, is that these techniques derive genotypes based on the assumption that specific genetic sequences are present in different locations in the genomes of nonclonal strains. Thus, these techniques provide little information beyond separating apparently nonclonal isolates (19). These methods also require a pure culture to be effective, thereby increasing the time needed to genotype the isolates (19). An alternative approach to traditional genotyping methods for the initial identification of potential sources of fecal contamination is a virulence factor-based method that in addition to MST could provide information about potential pathogenicity in humans or other animal species. Virulence genes are likely under selective pressure and subject to natural selection processes, allowing genes that are important in a specific niche to persist. Additionally, many virulence genes are known to be * Corresponding author. Mailing address: National Center for Toxicological Research, 3900 NCTR Road, Jefferson, AR 72079. Phone: (870) 543-7547. Fax: (870) 543-7307. E-mail:
[email protected]. † Supplemental material for this article may be found at http://aem .asm.org/. 䌤 Published ahead of print on 1 October 2010.
MATERIALS AND METHODS Bacterial strains. In this study, 281 E. coli isolates from bovine (n ⫽ 57), canine (n ⫽ 46), chicken (n ⫽ 63), equine (n ⫽ 9), feline (n ⫽ 8), swine (n ⫽ 85), and other (e.g., zoo, sheep, rodents; n ⫽ 13) clinical/diagnostic samples
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FIG. 1. Results of BOXA1R primer and virulence factor PCR analysis for 281 E. coli isolates used in this study. The dendrogram to the left is based on the BOXA1R results. Red bars in the gel images indicate the bands present in each of the gels. For virulence factor PCR results, a blue box indicates detection of the virulence gene, and a golden box indicates the absence of a detectable gene. The columns to the right of the virulence genes indicate the animal source, state where the organism was isolated, and the year isolated, respectively. For the animal source, green isolates are porcine, red are bovine, yellow are chicken, light blue are canine, pink are feline, brown are equine, and purple are from other animal sources. For the states, green represents isolates from Arkansas, red are from Georgia, dark blue are from Missouri, yellow are from North Dakota, and turquoise are from Oklahoma. For the year of isolation, those marked in green are from 1992, red are from 1996, pink are from 1997, yellow are from 1998, turquoise are from 1999, and purple are from 2004.
from Arkansas, Georgia, Missouri, North Dakota, and Oklahoma were evaluated (Fig. 1). BOXA1R PCR. Isolates were genotyped using BOXA1R PCR analysis as described by Dombek et al. (9). The banding pattern results were analyzed with BioNumerics software (Applied Maths, Kortrijk, Belgium) using Dice coefficients with 1% tolerance and 1.5% optimization coefficients to determine isolate similarity and generate a dendrogram (Fig. 1). Virulence factor profiling. For virulence factor profiling using PCR, supernatant from boiled preparations was combined with specific virulence factor primers, H2O, and 2⫻ Master Mix (Promega, Madison, WI) and amplified using optimized conditions for each primer set (see Table S1 in the supplemental material) (4, 7, 8, 11–14, 26, 27). The PCR analysis was carried out as previously described using the optimized annealing temperature shown in Table S1 in the
supplemental material (18). PCR products were analyzed using 2% E-Gels (Invitrogen, Carlsbad, CA). Positive control strains were included with each set of reactions, and each set was repeated to confirm the results. PCR data were imported into BioNumerics and analyzed based on the presence or absence of virulence genes. Statistical analysis. Data were summarized as percent positive by animal host (see Table S2 in the supplemental material) and analyzed with BioNumerics using discriminant analysis with variance to determine whether isolates from the defined animal source groups could be separated from each other based upon their virulence gene profiles (16). Additionally, jackknife analysis was carried out using BioNumerics with the results of both BOXA1R PCR and virulence factor profiling using the maximum similarities setting to determine the ability of the methods to correctly identify an isolate in its respective animal source group.
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TABLE 1. Jackknife similarity results for BOXA1R PCR analysis % of isolates classified asa: Isolate group
Porcine Bovine Chicken Equine Canine Other Feline a
Porcine
Bovine
Chicken
Equine
Canine
Other
Feline
81.2 3.5 11.1 33.3 19.6 30.8 25
4.7 75.4 6.4 11.1 10.9 15.4 12.5
9.4 14.0 71.4 22.2 10.9 7.7 0
0 0 3.2 22.2 4.4 7.7 0
2.4 5.3 6.4 11.1 39.1 7.7 62.5
2.4 1.8 0 0 2.2 30.8 0
0 0 1.6 0 13.0 0 0
Bold values indicate percent of isolates correctly classified.
RESULTS AND DISCUSSION The results of BOXA1R PCR analysis demonstrated a high degree of diversity. Based on 90% similarity, only 125 of 281 (44%) isolates fell into one of 27 clusters with at least three isolates. Of those clusters, 16 (59%) contained isolates from more than one animal source, indicating a lack of source and common BOXA1R PCR profile agreement in most instances. To gain a better understanding of how well the method worked to separate isolates by their particular source, jackknife analysis was carried out. With jackknife analysis, a series of analyses are done in which each individual isolate profile is removed from the phylogenetic analysis, the analysis is repeated without the isolate, and the isolate profile is compared to the results of the analysis of the different species groups. The analysis determined whether the removed isolate was correctly grouped with its original animal source group or misclassified as another animal source. The process was repeated for all of the isolates in the original analysis and a cumulative percentage of classification results was determined. Results of the jackknife analysis for the BOXA1R PCR are shown in Table 1. Overall, the percentage of correct classification ranges from 0% for feline isolates to 81.2% for porcine isolates. The feline isolates were most often misclassified as canine isolates (62.5% of the time), which is likely due to the overall similarity of the isolates from the two sources, coupled with the much larger sample size of canine isolates. Overall, the results of virulence factor genotyping appeared to demonstrate clustering based on the original animal source. Distinct clusters with isolates from predominant sources were identified (Fig. 2). Three clusters, referred to as Bovine, Chicken, and Porcine, were represented by 90% or more of isolates from their respective sources. The Companion group was the most diverse, containing at least 84% of isolates from canine and feline sources as well as those from equine and other animal sources. The result of the jackknife analysis for the virulence factor genotyping is shown in Table 2. Overall, the correct classification percentage ranged from 37.5% for feline isolates to 96.5% for porcine isolates. Again, the primary misclassification for the feline isolates was as canine isolates. When the overall virulence factor profiling results were compared to the BOXA1R PCR results, virulence factor typing correctly classified the isolates by their respective sources more often than BOXA1R PCR did (Tables 1 and 2). This initial study focused on determining whether virulence factor-based methods would have potential utility in an MST scheme for E. coli isolated from veterinary sources. Because
disease pathogenesis varies among animal species, E. coli strains originating from different sources may acquire a unique cohort of genes for survival (5). Veterinary E. coli infections are generally associated with immunonaïve young animals in cattle and swine and with preexisting disease or immunocompromised animals due to environmental stress in poultry (1, 3). Thus, potential pathogens may persist in immunocompetent animals and in production environments, even in the absence of disease. In poultry, for example, nearly 20% of E. coli isolates from apparently healthy birds contained genes associated with avian E. coli virulence (21). Likewise, in swine, Rosengren et al. (22) found that in a study of 151 isolates from healthy animals during growth and finishing stages, over half of the isolates contained at least two factors associated with por-
FIG. 2. Cumulative virulence factor PCR data were also analyzed by discriminant component analysis with variance in BioNumerics to determine grouping of isolates based on their cumulative virulence gene profiles. The isolates are represented by a symbol corresponding to the animal source group as follows: Bovine (多), Chicken (J), Swine (F), Canine ( ), Feline ( ), Equine (W), and Other ( ). Four major clusters were identified, delineated by ovals on the figure, and defined by the predominant source (Bovine, Chicken, Swine, and Companion). Adjacent to the ovals are the number of isolates from different animal species present in the respective clusters.
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APPL. ENVIRON. MICROBIOL. TABLE 2. Jackknife similarity results for virulence factor profiling % of isolates classified asa:
Isolate group
Porcine Bovine Chicken Equine Canine Other Feline a
Porcine
Bovine
Chicken
Equine
Canine
Other
Feline
96.5 0 0 11.1 2.2 7.7 12.5
1.2 89.5 0 22.2 4.4 0 0
2.4 3.5 95.2 0 4.4 15.4 0
0 3.5 0 44.4 2.2 0 0
0 3.5 4.8 22.2 82.6 15.4 37.5
0 0 0 0 2.2 61.5 12.5
0 0 0 0 2.2 0 37.5
Bold values indicate percent of isolates correctly classified.
cine virulence (22). Additionally, virulence factors associated with disease in calves can be detected in healthy adult cows (6, 25, 29, 30). Based on these studies of E. coli from different healthy animal species from various geographical locations, it appeared that virulence factors were found in healthy animals regardless of geographical origin. Therefore, the selection of isolates from clinical/diagnostic sources likely provided a good starting point for the evaluation and determination of virulence factors that would have the most potential in a source tracking scheme. Virulence associated with E. coli from different animal species likely varies due to differences in pathogenesis and host systems. Poultry disease is typified by septicemia following respiratory exposure to pathogenic E. coli, while mammalian infections are generally associated with diarrhea or extraintestinal infections following gastrointestinal colonization. Thus, virulence factors associated with poultry disease include those for respiratory colonization, serum resistance (iss), and iron acquisition (iut and iuc), while those associated with mammalian hosts include factors for attachment to the gut of the different animal species (K88, K99, F18, F41) and production of diarrheal toxins (estA and estB) (1, 20). Indeed, iss along with tsh and cvaC were preferentially found in chicken isolates, while K99, F41, espP, and afaE were associated with bovine isolates and K88, F18, and estB with swine isolates. There were few individual factors that were distinctly associated with companion animal, equine, or other animal isolates; it was not until the cumulative profiles were examined that these isolates (Companion cluster) were largely separated from the food animal isolates (Fig. 2). Therefore, future work will be necessary to identify whether there are specific differences associated with the Companion animal cluster isolates, and if there are differences, how we will distinguish them from isolates of other various sources. Certain factors do not appear overly useful in an MST scheme; for example, 987P and katP were not detected in any of the isolates, while fimA and ompT were widely distributed among the different sources. There was also an attempt to minimize the potential impact of spatial and temporal differences in the results. There were isolates collected in Arkansas for each of the animal species, and there were cattle, swine, and chicken isolates from North Dakota. Overall, these isolates continued to cluster with the isolates from the other states based on source, rather than geography (data not shown). It is important to note that there are potential limitations in this study, one of which being that the number of isolates used
in the study was somewhat limited. Because of available resources, the isolates were chosen based on availability of clinical samples. As described above, the focus of this work was on veterinary clinical isolates because they are more likely to have divergent virulence profiles, yet a percentage of these “virulent” organisms may remain in the healthy population as commensals. The number of clinical isolates available from equine and feline sources was especially limited, as there were fewer than 10 isolates each. This limited our ability to discriminate them from the canine isolates, as indicated in the Companion cluster of Fig. 2. Future studies will need to be done to collect a larger set of isolates from these sources to better identify discriminatory factors that will separate the isolates from different sources in the Companion cluster. Likewise, additional isolates, such as those from humans, turkeys, and wildlife sources, will need to be tested to determine whether the current panel is effective in discriminating these isolates based on their sources or whether additional host-specific factors need to be identified. If additional factors are required, they could potentially be genes that are from other enteric organisms. For human fecal contamination, organisms like Bacteroides spp. and Bifidobacterium spp. have been evaluated as marker organisms for human fecal contamination (28); thus, specific gene targets from these organisms could potentially be utilized to identify human fecal contamination. A potential advantage of the virulence factor PCR genotyping approach is that it could potentially be deployed in a culture-independent fashion, since a pure culture is not necessarily required to identify source-associated virulence signatures. The PCR-based approach will likely allow for the detection of virulence factors in a population of E. coli. Numerous studies have found that E. coli isolates from healthy animals contain virulence genes; however, the prevalence is typically lower than that in diseased animals (20, 21, 30). Therefore, the ability to screen populations of E. coli for virulence genes will likely be beneficial when strains containing the source-associated virulence factors are present in the animal population at a relatively low frequency. When collecting samples for genotyping, if high bacterial loads are detected in the water, the bacteria could be concentrated and their DNA extracted for use as template DNA for PCR screening of virulence genes, without the need for enrichment. Conversely, if the levels are too low for direct sample analysis, the concentrated bacteria could be inoculated into a nonspecific enrichment broth to allow the bacterial numbers to increase prior to DNA isolation and PCR testing.
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The PCR results could potentially be interpreted in several different ways to identify the likely animal source. These include the use of a library-matching algorithm in which the composite virulence factor profile is compared to an existing library of profiles from E. coli of known sources to identify its likely origin (23). Alternatively, a decision tree can be developed such that isolates from different sources are separated in a stepwise approach based on the presence or absence of specific virulence factors (24). For example, iss could be used to separate chicken isolates from the majority of isolates from mammalian sources, and then afaE-8 primers could separate many cattle isolates from the remaining mammalian isolates, and so on until an optimum decision tree is built. The ability to rapidly detect potentially host-specific signatures could allow investigators to more rapidly focus on potential sources of contamination associated with a particular animal source prior to the isolation of the contaminating E. coli. Then as investigators hone in on the source of contamination, they can use more definitive genotyping tools, such as pulsed-field gel electrophoresis, ribotyping, or sequencing-based methods, which are more time consuming and expensive and require a pure culture to link the contamination source to the contamination event. Overall, associating bacteria causing contamination to a specific animal source should allow regulators to focus their search on farms producing that particular animal species and mitigate circumstances which led to the contamination. Based on the results of this study, it appears that the use of virulence factor profiling may be a useful part of an MST scheme for E. coli isolates of veterinary origin. The identification of a cohort of genes associated with a particular animal source would potentially allow investigators to narrow the search for the contamination source, allowing for a more efficient use of resources. ACKNOWLEDGMENTS We thank David White and the Arkansas Livestock and Poultry Commission for providing isolates for the study and Rajesh Nayak and Huizhong Chen for their assistance in reviewing the manuscript. We thank the Marshfield Clinic Research Foundation and the U.S. Food and Drug Administration for financial support of the research. Jing Han is supported through the Oak Ridge Institute for Science and Education. Views presented in this paper do not necessarily reflect those of the U.S. Food and Drug Administration. REFERENCES 1. Aiello, S. E., and A. Mays (ed.). 1998. The Merck veterinary manual, 8th ed. Merck and Company, Whitehouse Station, NJ. 2. Auld, H., D. MacIver, and J. Klaassen. 2004. Heavy rainfall and waterborne disease outbreaks: the Walkerton example. J. Toxicol. Environ. Health A 67:1879–1887. 3. Barnes, H. J., J. P. Vaillancourt, and W. B. Gross. 2003. Colibacillosis, p. 631–656. In Y. M. Saif, H. J. Barnes, J. R. Glisson, A. M. Fadly, L. R. McDougald, and D. E. Swayne (ed.), Diseases of poultry, 11th ed. Iowa State University Press, Ames, IA. 4. Boerlin, P., R. Travis, C. L. Gyles, R. Reid-Smith, N. Janecko, H. Lim, V. Nicholson, S. A. McEwen, R. Friendship, and M. Archambault. 2005. Antimicrobial resistance and virulence genes of Escherichia coli isolates from swine in Ontario. Appl. Environ. Microbiol. 71:6753–6761. 5. Conner, C. P., D. M. Heithoff, S. M. Julio, R. L. Sinsheimer, and M. J. Mahan. 1998. Differential patterns of acquired virulence genes distinguish Salmonella strains. Proc. Natl. Acad. Sci. U. S. A. 95:4641–4645. 6. Cookson, A. L., M. Cao, J. Bennett, C. Nicol, F. Thomson-Carter, and G. T. Attwood. 2010. Relationship between virulence gene profiles of atypical enteropathogenic Escherichia coli and Shiga toxin-producing E. coli isolates from cattle and sheep in New Zealand. Appl. Environ. Microbiol. 76:3744– 3747. 7. Deszo, E. L., S. M. Steenbergen, D. I. Freedberg, and E. R. Vimr. 2005. Escherichia coli K1 polysialic acid O-acetyltransferase gene, neuO, and the
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