Statistical evaluation of bacterial source tracking data obtained by rep-PCR DNA fingerprinting of Escherichia coli

Alternative estimate of source distribution in microbial source tracking using posterior probabilities

Microbial source tracking (MST) is a procedure used to determine the relative contributions of humans and animals to fecal microbial contamination of surface waters in a given watershed. Studies of MST methodology have focused on optimizing sampling, laboratory, and statistical analysis methods in order to improve the reliability of determining which sources contributed most to surface water fecal contaminant. The usual approach for estimating a source distribution of microbial contamination is to classify water sample microbial isolates into discrete source categories and calculate the proportion of these isolates in each source category. The set of proportions is an estimate of the contaminant source distribution. In this paper we propose and compare an alternative method for estimating a source distribution-averaging posterior probabilities of source identity across isolates. We conducted a Monte Carlo simulation covering a wide variety of watershed scenarios to compare the two methods. The results show that averaging source posterior probabilities across isolates leads to more accurate source distribution estimates than proportions that follow classification.

Optimization and validation of rep-PCR genotypic libraries for microbial source tracking of environmental Escherichia coli isolates

Escherichia coli can be used to help identify sources of fecal contamination in the environment. Escherichia coli genotypic fecal libraries and pattern-matching algorithms were assessed for their effectiveness in correctly identifying sources. Fecal samples (n = 172) were collected from various sources from three agricultural landscapes in Canada. Escherichia coli isolates were fingerprinted using BOX- and enterobacterial repetitive intergenic consensus (ERIC) - polymerase chain reaction primers, revealing 769 and 1 057 distinct genotypes, respectively, for the 9 047 isolates collected in 2004 in Ontario. The average rate of correct classification (ARCC) was comparable for BOX- (48%) and ERIC-based (62%) libraries and between libraries with clones removed per sample (55%) and clones removed per unit (54%). ARCC increased with fewer classification units (from 44% to 65%). ARCC for k-nearest neighbour (64%) and maximum similarity (60%) algorithms were comparable, but maximum similarity had better sensitivity and specificity than k-nearest neighbour. Geographical and temporal shifts in community composition resulted in loss of accuracy. Several ERIC genotypes (n = 112) were common between sources and were removed from the library, improving ARCC (77%). The latter library proved to be more accurate, but its accuracy with respect to sourcing environmental isolates remains to be tested.

Assessment of the 16S-23S rDNA intergenic spacer region in Enterococcus spp. for microbial source tracking

A new library-based microbial source tracking (MST) approach intended for initial application in the coastal waters of Virginia was evaluated. Host-origin isolates of Enterococcus spp. were collected from beaches and the surrounding tidewater region of Virginia and used to construct a library based on the pattern of DNA band lengths produced by the amplification of the 16S-23S rDNA intergenic spacer (IGS) region, and subsequent digestion with MboI. Initial results from small host-origin libraries (64 and 200 total isolates) with discriminant analysis (DA) and logistic regression (LR) yielded high average rates of correct classification (ARCC) for a four-source classification split (birds, dogs, sewage, and wildlife), with ARCCs ranging from 83 to 100%. However, the poor results obtained when classification was attempted on a non-library validation set (VS, ARCCs of 47 and 48%, respectively, using DA and LR) demonstrated that a library of 200 isolates was insufficient to adequately represent the diversity of the enterococci in the sampled region. An increase in the library size to 1029 total isolates was accompanied by a reduction in the ARCC of the library to 42.7% with DA and 45.7% with LR, plus similarly poor results obtained from the VS. The low correct classification rates generated by the larger known-source library were unsuitable for field application. Many reported MST methods have been based on results obtained using small host-origin libraries without external validation. Our results indicate that such an approach can be very misleading, and that larger libraries and external validation is essential for the confirmation of preliminary results.

Fecal source tracking, the indicator paradigm, and managing water quality

Fecal source tracking is used because standard methods of measuring fecal contamination in water by enumerating fecal indicator bacteria (FIB) do not identify the sources of the contamination. This paper presents a critical review of source tracking with emphasis on the extent to which methods have been tested (especially in comparison with other methods and/or with blind samples), when methods are applicable, their shortcomings, and their usefulness in predicting public health risk or pathogen occurrence. In addition, the paper discusses the broader question of whether fecal source tracking and fecal indicator monitoring is the best approach to regulate water quality and protect human health. Many fecal source-tracking methods have only been tested against sewage or fecal samples or isolates in laboratory studies (proof of concept testing) and/or applied in field studies where the "real" answer is not known, so their comparative performance and accuracy cannot be assessed. For source tracking to be quantitative, stability of ratios between host-specific markers in the environment must be established. In addition, research is needed on the correlation between host-specific markers and pathogens, and survival of markers after waste treatments. As a result of the exclusive emphasis on FIB in legislation, monitoring has concentrated on FIB and lost sight of pathogens. A more rational approach to regulating water quality would start with available epidemiological data to identify pathogens of concern in a particular water body, and then use targeted pathogen monitoring coupled with targeted fecal source tracking to control them. Baseline monitoring of indicators would become just one tool among many.

A comparison of ARA and DNA data for microbial source tracking based on source-classification models developed using classification trees

The literature on microbial source tracking (MST) suggests that DNA analysis of fecal samples leads to more reliable determinations of bacterial sources of surface water contamination than antibiotic resistance analysis (ARA). Our goal is to determine whether the increased reliability, if any, in library-based MST developed with DNA data is sufficient to justify its higher cost, where the bacteria source predictions are used in TMDL surface water management programs. We describe an application of classification trees for MST applied to ARA and DNA data from samples collected in the Potomac River Watershed in Maryland. Conclusions concerning the comparison of ARA and DNA data, although preliminary at the current time, suggest that the added cost of obtaining DNA data in comparison to the cost of ARA data may not be justified, where MST is applied in TMDL surface water management programs.

Direct comparison of four bacterial source tracking methods and use of composite data sets

AIMS

Four bacterial source tracking (BST) methods, enterobacterial repetitive intergenic consensus sequence polymerase chain reaction (ERIC-PCR), automated ribotyping using HindIII, Kirby-Bauer antibiotic resistance analysis (KB-ARA) and pulsed-field gel electrophoresis (PFGE) were directly compared using the same collection of Escherichia coli isolates. The data sets from each BST method and from composite methods were compared for library accuracy and their ability to identify water isolates.

METHODS AND RESULTS

Potential sources of faecal pollution were identified by watershed sanitary surveys. Domestic sewage and faecal samples from pets, cattle, avian livestock, other nonavian livestock, avian wildlife and nonavian wildlife sources were collected for isolation of E. coli. A total of 2275 E. coli isolates from 813 source samples were screened using ERIC-PCR to exclude clones and to maximize library diversity, resulting in 883 isolates from 745 samples selected for the library. The selected isolates were further analysed using automated ribotyping with HindIII, KB-ARA and PFGE. A total of 555 E. coli isolates obtained from 412 water samples were analysed by the four BST methods. A composite data set of the four BST methods gave the highest rates of correct classification (RCCs) with the fewest unidentified isolates than any single method alone. RCCs for the four-method composite data set and a seven-way split of source classes ranged from 22% for avian livestock to 83% for domestic sewage. Two-method composite data sets were also found to be better than individual methods, having RCCs similar to the four-method composite and identification of the same major sources of faecal pollution.

CONCLUSIONS

The use of BST composite data sets may be more beneficial than the use of single methods.

SIGNIFICANCE AND IMPACT OF THE STUDY

This is one of the first comprehensive comparisons using composite data from several BST methods. While the four-method approach provided the most desirable BST results, the use of two-method composite data sets may yield comparable BST results while providing for cost, labour and time savings.

Differentiation of fecal Escherichia coli from poultry and free-living birds by (GTG)5-PCR genomic fingerprinting

Determination of the non-point sources of fecal pollution is essential for the assessment of potential public health risk and development of appropriate management practices for prevention of further contamination. Repetitive extragenic palindromic-PCR coupled with (GTG)(5) primer [(GTG)(5)-PCR] was performed on 573 Escherichia coli isolates obtained from the feces of poultry (chicken, duck and turkey) and free-living (Canada goose, hawk, magpie, seagull and songbird) birds to evaluate the efficacy of (GTG)(5)-PCR genomic fingerprinting in the prediction of the correct source of fecal pollution. A discriminant analysis with the jack-knife algorithm of (GTG)(5)-PCR DNA fingerprints revealed that 95%, 94.1%, 93.2%, 84.6%, 79.7%, 76.7%, 75.3% and 70.7% of magpie, hawk, turkey, seagull, Canada goose, chicken, duck and songbird fecal E. coli isolates classified into the correct host source, respectively. The results of this study indicate that (GTG)(5)-PCR can be considered to be a complementary molecular tool for the rapid determination of E. coli isolates identity and tracking the non-point sources of fecal pollution.

Determining sources of fecal bacteria in waterways

The microbiological contamination of waterways by pathogenic microbes has been, and is still, a persistent public safety concern in the United States and in most countries of the world. As most enteric pathogens are transmitted through the fecal-oral route, fecal pollution is generally regarded as the major contributor of pathogens to waterways. Fecal pollution of waterways can originate from wastewater treatment facilities, septic tanks, domestic- and wild-animal feces, and pets. Because enteric pathogens are derived from human or animal sources, techniques capable of identifying and apportioning fecal sources have been intensively investigated for use in remediation efforts and to satisfy regulatory concerns. Pollution of human origin is of the most concern, since human feces is more likely to contain human-specific enteric pathogens. Fecal indicator bacteria have been used successfully as the primary tool for microbiologically based risk assessment. However measurement of fecal indicator bacteria does not define what pathogens are present, or define the sources of these bacteria. Microbial source tracking (MST) methods that have the ability to differentiate among sources of fecal pollution are currently under development. These methods will ultimately be useful for risk assessment purposes and to aid regulatory agencies in developing strategies to remediate microbiologically impaired waterways.

Multiplex approach for screening genetic markers of microbial indicators

Genetic markers are expected to provide better specificity in epidemiological studies and potentially serve as better indicators of waterborne pathogens. Methods used to assess genetic markers of emerging microbial indicators include pulsed field gel electrophoresis, polymerase chain reaction (PCR), and microarrays. This paper outlines a high-throughput approach to screen for such genetic markers using a set of theoretical and experimental screening tools. The theoretical screening involves evaluating genes related to the ribosomal RNA and specific functions from emerging indicator groups, followed by experimental validation with appropriate sampling schemes and high-throughput and economical screening methods, such as microarrays, real time PCR, and on-chip PCR. Analysis of a wide range of samples covering temporal variability in location, host, and waterborne disease outbreaks is essential. The proposed approach is expected to shorten the time and cost associated with searching for new genetic markers of emerging indicators by at least 10-fold.

Evaluation of denaturing gradient gel electrophoresis to differentiate Escherichia coli populations in secondary environments

The development of methodology to differentiate mixed populations of Escherichia coli in the secondary habitat might improve monitoring of fecal pollution indicators and facilitate the development of strategies to mitigate bacterial pollution. The objective of this study was to determine the ability of denaturing gradient gel electrophoresis (DGGE) to differentiate mixed assemblages of E. coli in the natural environment. After confirming the identity of 184 environmental bacterial isolates as E. coli, each was subjected to polymerase chain reaction (PCR) of the beta-glucuronidase gene (uidA) followed by DGGE fingerprinting. The ability of DGGE to discriminate individual isolates at the strain level was determined by comparing fingerprints to those resulting from a standard, library-dependent fingerprinting method, BOX-PCR. Computerized analysis of fingerprints indicated that DGGE and BOX-PCR identified 15 and 21 unique phylotypes respectively. Rank-abundance plots comparing the numerical distribution of unique E. coli phylotypes detected by both methods revealed no difference in resolution at the population level. In water and sediment samples from two beaches, DGGE effectively distinguished indigenous E. coli populations with an average rate of correct classification (site-based) of 83%. Denaturing gradient gel electrophoresis of uidA genes isolated and PCR-amplified from environmental samples appears to be an effective tool to differentiate unique E. coli populations and should be useful to characterize E. coli dynamics in the secondary environment.

Chemical markers of human waste contamination: Analysis of urobilin and pharmaceuticals in source waters

Giving public water authorities another tool to monitor and measure levels of human waste contamination of waters simply and rapidly would enhance public protection. Most of the methods used today detect such contamination by quantifying microbes occurring in feces in high enough densities that they can be measured easily. However, most of these microbes, for example E. coli, do not serve as specific markers for any one host species and many can have origins other than feces. As an alternative, chemicals shed in feces and urine might be used to detect human waste contamination of environmental waters. One potential chemical marker of human waste is the compound urobilin. Urobilin is one of the final by-products of hemoglobin breakdown. Urobilin is excreted in both the urine and feces from many mammals, particularly humans. Source waters from 21 sites in New England, Nevada, and Michigan were extracted using hydrophilic-lipophilic balance (HLB) cartridges and then analyzed by high performance liquid chromatography-electrospray mass spectrometry (HPLC-ES-MS). As a marker of human waste, urobilin was detected in many of the source waters at concentrations ranging from not detectable to 300 ng L(-1). Besides urobilin, azithromycin, an antibiotic widely prescribed for human use only in the US, was also detected in many of these waters, with concentrations ranging from not detectable to 77 ng L(-1). This methodology, using both urobilin and azithromycin (or any other human-use pharmaceutical) could be used to give public water authorities a definitive method for tracing the sources of human waste contamination. The analysis and detection of urobilin in surface waters by HPLC-ES-MS has not been previously reported in the peer-reviewed literature.

Identifying host sources of fecal pollution: diversity of Escherichia coli in confined dairy and swine production systems

Repetitive extragenic palindromic PCR fingerprinting of Escherichia coli is one microbial source tracking approach for identifying the host source origin of fecal pollution in aquatic systems. The construction of robust known-source libraries is expensive and requires an informed sampling strategy. In many types of farming systems, waste is stored for several months before being released into the environment. In this study we analyzed, by means of repetitive extragenic palindromic PCR using the enterobacterial repetitive intergenic consensus primers and comparative analysis using the Bionumerics software, collections of E. coli obtained from a dairy farm and from a swine farm, both of which stored their waste as a slurry in holding tanks. In all fecal samples, obtained from either barns or holding tanks, the diversity of the E. coli populations was underrepresented by collections of 500 isolates. In both the dairy and the swine farms, the diversity of the E. coli community was greater in the manure holding tank than in the barn, when they were sampled on the same date. In both farms, a comparison of stored manure samples collected several months apart suggested that the community composition changed substantially in terms of the detected number, absolute identity, and relative abundance of genotypes. Comparison of E. coli populations obtained from 10 different locations in either holding tank suggested that spatial variability in the E. coli community should be accounted for when sampling. Overall, the diversity in E. coli populations in manure slurry storage facilities is significant and likely is problematic with respect to library construction for microbial source tracking applications.

Methods to increase fidelity of repetitive extragenic palindromic PCR fingerprint-based bacterial source tracking efforts

The goal of the study was to determine which similarity coefficient and statistical method to use to produce the highest rate of correct assignment (RCA) in repetitive extragenic palindromic PCR-based bacterial source tracking. In addition, the use of standards for deciding whether to accept or reject source assignments was investigated. The use of curve-based coefficients Cosine Coefficient and Pearson's Product Moment Correlation yielded higher RCAs than the use of band-based coefficients Jaccard, Dice, Jeffrey's x, and Ochiai. When enterococcal and Escherichia coli isolates from known sources were used in a blind test, the use of maximum similarity produced consistently higher RCAs than the use of average similarity. We also found that the use of a similarity value threshold and/or a quality factor threshold (the ratio of the average fingerprint similarity within a source to the average similarity of this source's isolates to an unknown) to decide whether to accept source assignments of unknowns increases the reliability of source assignments. Applying a similarity value threshold improved the overall RCA (ORCA) by 15 to 27% when enterococcal fingerprints were used and 8 to 29% when E. coli fingerprints were used. Applying the quality factor threshold resulted in a 22 to 32% improvement in the ORCA, depending on the fingerprinting technique used. This increase in reliability was, however, achieved at the expense of decreased numbers of isolates that were assigned a source.

Sample size, library composition, and genotypic diversity among natural populations of Escherichia coli from different animals influence accuracy of determining sources of fecal pollution

A horizontal, fluorophore-enhanced, repetitive extragenic palindromic-PCR (rep-PCR) DNA fingerprinting technique (HFERP) was developed and evaluated as a means to differentiate human from animal sources of Escherichia coli. Box A1R primers and PCR were used to generate 2,466 rep-PCR and 1,531 HFERP DNA fingerprints from E. coli strains isolated from fecal material from known human and 12 animal sources: dogs, cats, horses, deer, geese, ducks, chickens, turkeys, cows, pigs, goats, and sheep. HFERP DNA fingerprinting reduced within-gel grouping of DNA fingerprints and improved alignment of DNA fingerprints between gels, relative to that achieved using rep-PCR DNA fingerprinting. Jackknife analysis of the complete rep-PCR DNA fingerprint library, done using Pearson's product-moment correlation coefficient, indicated that animal and human isolates were assigned to the correct source groups with an 82.2% average rate of correct classification. However, when only unique isolates were examined, isolates from a single animal having a unique DNA fingerprint, Jackknife analysis showed that isolates were assigned to the correct source groups with a 60.5% average rate of correct classification. The percentages of correctly classified isolates were about 15 and 17% greater for rep-PCR and HFERP, respectively, when analyses were done using the curve-based Pearson's product-moment correlation coefficient, rather than the band-based Jaccard algorithm. Rarefaction analysis indicated that, despite the relatively large size of the known-source database, genetic diversity in E. coli was very great and is most likely accounting for our inability to correctly classify many environmental E. coli isolates. Our data indicate that removal of duplicate genotypes within DNA fingerprint libraries, increased database size, proper methods of statistical analysis, and correct alignment of band data within and between gels improve the accuracy of microbial source tracking methods.