The effect of multiple primer-template mismatches on quantitative PCR accuracy and development of a multi-primer set assay for accurate quantification of pcrA gene sequence variants.

Quantitative PCR (qPCR) is a critical tool for quantifying the abundance of specific organisms and the level or expression of target genes in medically and environmentally relevant systems. However, often the power of this tool has been limited because primer-template mismatches, due to sequence variations of targeted genes, can lead to inaccuracies in measured gene quantities, detection failures, and spurious conclusions. Currently available primer design guidelines for qPCR were developed for pure culture applications, and available primer design strategies for mixed cultures were developed for detection rather than accurate quantification. Furthermore, past studies examining the impact of mismatches have focused only on single mismatches while instances of multiple mismatches are common. There are currently no appropriate solutions to overcome the challenges posed by sequence variations. Here, we report results that provide a comprehensive, quantitative understanding of the impact of multiple primer-template mismatches on qPCR accuracy and demonstrate a multi-primer set approach to accurately quantify a model gene pcrA (encoding perchlorate reductase) that has substantial sequence variation. Results showed that for multiple mismatches (up to 3 mismatches) in primer regions where mismatches were previously considered tolerable (middle and 5' end), quantification accuracies could be as low as ~0.1%. Furthermore, tests were run using a published pcrA primer set with mixtures of genomic DNA from strains known to harbor the target gene, and for some mixtures quantification accuracy was as low as ~0.8% or was non-detect. To overcome these limitations, a multiple primer set assay including minimal degeneracies was developed for pcrA genes. This assay resulted in nearly 100% accurate detection for all mixed microbial communities tested. The multi-primer set approach demonstrated herein can be broadly applied to other genes with known sequences.