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Comparison of microbial source tracking efficacy for detection of cattle fecal contamination by quantitative PCR
- Xue, Jia, Feng, Yucheng
- The Science of the total environment 2019 v.686 pp. 1104-1112
- Bacteroidales, best management practices, cattle, detection limit, feces, genes, genetic markers, indicator species, plasmids, pollution, pollution control, quantitative polymerase chain reaction, regression analysis, standard deviation, surface water
- Identification of fecal contamination sources in surface water has become heavily dependent on quantitative PCR (qPCR) because this technique allows for the rapid enumeration of fecal indicator bacteria as well as the detection and quantification of fecal source-associated genetic markers in the environment. Identification of contamination sources in impaired waters is a prerequisite for developing best management practices to reduce future pollution. Proper management decisions rely on the quality and interpretation of qPCR data. In this study, we developed a method to determine analytical and process lower limits of detection (LLOD) and quantification (LLOQ) using two cattle-associated genetic markers targeting Bacteroidales. Analytical LLOD (ALLOD) for both CowM2 and CowM3 genetic markers in the qPCR assay were five gene copies per reaction. Using composite fecal DNA, the analytical LLOQ (ALLOQ) determined for CowM2 and CowM3 were 78 and 195 gene copies/reaction, respectively. When plasmid DNA was used, the ALLOQ for CowM2 and CowM3 were 46 and 20 gene copies/reaction, respectively. The process LLOD (PLLOD) for CowM2 and CowM3 were 0.4 and 0.02 mg feces/filter (wet weight), respectively. Using the standard deviation value of 0.25 as a cut-off point for LLOQ in regression analysis, the process LLOQ (PLLOQ) for CowM2 and CowM3 were 3.2 and 0.3 mg feces/filter, respectively. These results indicate that CowM3 exhibited superior performance characteristics compared with CowM2 for fecal samples collected from our geographical region. Moreover, the method for calculating LLOD and LLOQ developed here can be applied to other microbial source tracking studies.