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C Andrew Carson

Publications and source records attributed to C Andrew Carson.

4 recordsLinked to original sources

Specificity of a Bacteroides thetaiotaomicron marker for human feces.

A bacterial primer set, known to produce a 542-bp amplicon specific for Bacteroides thetaiotaomicron, generated this product in PCR with 1 ng of extracted DNA from 92% of 25 human fecal samples, 100% of 20 sewage samples, and 16% of 31 dog fecal samples. The marker was not detected in 1 ng of fecal DNA from 61 cows, 35 horses, 44 pigs, 24 chickens, 29 turkeys, and 17 geese.

Animals↗

Comparison of ribotyping and repetitive extragenic palindromic-PCR for identification of fecal Escherichia coli from humans and animals.

This report compares the performances of two popular genotypic methods used for tracking the sources of fecal pollution in water, ribotyping and repetitive extragenic palindromic-PCR (rep-PCR). The rep-PCR was more accurate, reproducible, and efficient in associating DNA fingerprints of fecal Escherichia coli with human and animal hosts of origin.

Animals↗

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.

Animals↗

Assessment of statistical methods used in library-based approaches to microbial source tracking.

Several commonly used statistical methods for fingerprint identification in microbial source tracking (MST) were examined to assess the effectiveness of pattern-matching algorithms to correctly identify sources. Although numerous statistical methods have been employed for source identification, no widespread consensus exists as to which is most appropriate. A large-scale comparison of several MST methods, using identical fecal sources, presented a unique opportunity to assess the utility of several popular statistical methods. These included discriminant analysis, nearest neighbour analysis, maximum similarity and average similarity, along with several measures of distance or similarity. Threshold criteria for excluding uncertain or poorly matched isolates from final analysis were also examined for their ability to reduce false positives and increase prediction success. Six independent libraries used in the study were constructed from indicator bacteria isolated from fecal materials of humans, seagulls, cows and dogs. Three of these libraries were constructed using the rep-PCR technique and three relied on antibiotic resistance analysis (ARA). Five of the libraries were constructed using Escherichia coli and one using Enterococcus spp. (ARA). Overall, the outcome of this study suggests a high degree of variability across statistical methods. Despite large differences in correct classification rates among the statistical methods, no single statistical approach emerged as superior. Thresholds failed to consistently increase rates of correct classification and improvement was often associated with substantial effective sample size reduction. Recommendations are provided to aid in selecting appropriate analyses for these types of data.

Animals↗