PCR OPTIMIZATION PROTOCOL ALGORITHM FOR IDENTIFICATION OF MICROORGANISMS USING Pasteurella multocida

Abstract

Polymerase chain reaction (PCR) is a multifunctional tool used extensively in molecular biology. PCR is modified in numerous ways for specific purposes, all of which rely on proper efficiency, specificity and sensitivity. Despite the general acceptance of the "classical" principle, there is no unified optimization scheme that takes into account the diversity of PCR applications. Such an optimization scheme would guide researchers toward the best optimization approaches for their specific PCR application. Currently, researchers are guided only by data from previously published authors who submitted their own PCR optimization schemes. Here, we describe a PCR protocol optimization algorithm for the detection of microorganisms using Pasteurella multocida as an example. P. multocida, which we have use dextensively in the past, allows us to achieve the necessary PCR specificity and sensitivity to demonstrate the application of our algorithm. Our scheme differs from others as it uses an inductive method to learn the specificity of the protocol being developed. Our approach uses real-time PCR, with SYBR Green I, to monitor amplification during the optimization process. Furthermore, the simplicity of this approach means that it can be used to develop and optimize diagnostic PCR for a wide range of researchers and applications. As a proof of concept, PCR conditions were optimized for two pairs of primers which were tested on a collection of samples comprising DNA from 92 species of bacteria, three eukaryotic species, and with the sensitivity of at least five P. multocida genomic copy equivalents.