Occurrence of Escherichia coli, noroviruses, and F-specific coliphages in fresh market-ready produce.

Forty samples of fresh produce collected from retail food establishments were examined to determine the occurrence of Escherichia coli, F-specific coliphages, and noroviruses. An additional six samples were collected from a restaurant undergoing investigation for a norovirus outbreak. Nineteen (48%) of the retail samples and all outbreak samples were preprocessed (cut, shredded, chopped, or peeled) at or before the point of purchase. Reverse transcription-PCR, with the use of primers JV 12 and JV 13, failed to detect norovirus RNA in any of the samples. All six outbreak samples and 13 (33%) retail samples were positive for F-specific coliphages (odds ratio undefined, P = 0.003). Processed retail samples appeared more likely to contain F-specific coliphages than unprocessed samples (odds ratio 3.8; 95% confidence interval 0.8 to 20.0). Only two (5.0%) retail samples were positive for E. coli; outbreak samples were not tested for E. coli. The results of this preliminary survey suggest that F-specific coliphages could be useful conservative indicators of fecal contamination of produce and its associated virological risks. Large-scale surveys should be conducted to confirm these findings.

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.

Comparison of genotypic-based microbial source tracking methods requiring a host origin database.

Microbial source tracking (MST) results, obtained using identical sample sets and pulsed field gel electrophoresis (PFGE), repetitive element PCR (rep-PCR) and ribotyping techniques were compared. These methods were performed by six investigators in analysis of duplicate, blind sets of water samples spiked with feces from five possible sources (sewage, human, dog, cow and seagull). Investigators were provided with samples of the fecal material used to inoculate the water samples for host origin database construction. All methods correctly identified the dominant source in the majority of the samples. Modifications of some of these methods correctly identified the dominant sources in over 90% of the samples; however, false positive rates were as high as 57%. The high false positive rates appeared to be indirectly proportional to the levels of stringency applied in pattern analysis. All the methods produced useful data but the results highlighted the need to modify and optimize these methods in order to minimize sources of error.