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

Abstract

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.

FIG. 1.

Frequency of occurrence of genotypes among rep-PCR DNA fingerprints from unique E. coli isolates. Analysis was limited to the 657 genotypes identified among the 1,535 unique E. coli isolates with rep-PCR DNA fingerprint similarities of 90% or greater.

FIG. 2.

Representative examples of HFERP DNA fingerprint images. Genomic DNAs from 24 E. coli strains were subjected to HFERP DNA fingerprint analysis with a mixture of unlabeled Box A1R and 6-FAM fluorescently labeled Box A1R primers. Each lane contained Genescan-2500 ROX internal lane standards and HFERP DNA fingerprints. The combined, dual-colored HFERP image (A) was captured using a Typhoon Imager and two emission filters. Values at right are sizes in base pairs. Individual images of the HFERP DNA fingerprints (B) and Genescan-2500 ROX internal lane standards (C) were acquired using one filter at a time.

FIG. 3.

Comparison of DNA fingerprint patterns of a reference E. coli strain generated by rep-PCR and by HFERP. (A) rep-PCR DNA fingerprint patterns were assembled from 29 individual PCRs, each of which was run on a separate agarose gel. Fingerprints were generated using E. coli isolate P294 as template DNA and the Box A1R primer. (B) HFERP DNA fingerprint patterns were assembled from 29 individual PCRs, each of which was run on a separate agarose gel. Fingerprints were generated using E. coli isolate P294 as template DNA and a mixture of unlabeled Box A1R and 6-FAM fluorescently labeled Box A1R primers. Bands were aligned using Genescan-2500 ROX internal standards, which were present in each lane. Similarities were determined using the cosine algorithm of BioNumerics, and dendrograms were generated with UPGMA.