Evaluation and Optimization of a PCR Assay and Multiple Regression Model for the Detection of Rhodococcus Corprophilus
Sharon C. Long
Rhodococcus coprophilus is an emerging tool in the microbial source tracking "tool-box". The actinomycete is a source specific indicator for grazing animal fecal contamination. Once thought of as less harmful to human health, concern over animal fecal contamination has increased with the greater understanding of zoonoses. Zoonotic organisms that have the ability to cause disease in both humans and animals. Major outbreaks from these pathogens have increased their awareness and created a need for better source water protection. The presence of Rhodococcus coprophilus has been shown to have the ability to indicate grazing animal fecal contamination but it's current detection methods are time consuming and labor intensive. PCR-based detection methods can provide results in as little as two to three days with greater accuracy than conventional enumeration procedures. PCR is not a practical detection method for all water testing facilities due to its high cost and specialized training requirements. Multiple linear regression models can be used to predict MST organisms densities using more easily obtained surrogates. The goal of this study was to develop a PCR-based assay and construct a multiple linear regression model for the detection of Rhodococcus coprophilus. The PCR-based assay development involved testing multiple concentration and extraction procedures to optimize detection. Two concentration methods and four extraction procedures were evaluated with mixed results. The Epicentre® WaterMasterTM extraction kit showed the best potential for extracting DNA from environmental water. The multiple linear regression model was constructed and tested using a historical data set. The model was able to explain 57.7% of the variance in Rhodococcus coprophilus densities and cOlTectly identified 82% of the historical data set and 83% ofthe Madison, WI data set as either contaminated with grazing animal feces or having background levels of the indicator. The model has shown to be useful in predicting grazing animal fecal contamination in two states. Rhodococcus coprophilus is a valuable tool in the MST "tool-box", but it's full potential cannot be realized until the detection procedure is optimized. Both the peRbased assay and multiple linear regression model investigated in this study has shown the ability to accurately and efficiently detect grazing animal fecal contamination.