Novel Repair Replicase

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PROJECT SUMMARY Cytosine deamination of DNA prior to and during amplification, library preparation, and sequencing is the largest source of errors (C to A) in next-generation sequencing (NGS) data. Thermocycling conditions during PCR sig- nificantly accelerates cytosine deamination. These errors impede the detection of low-abundance variants such as driver mutations for small primary and secondary tumors and lower fidelity for complex samples such as microbiomes and are particularly acute for damaged samples such as formalin-fixed, paraffin-embedded (FFPE) tissue samples. The NGS-based molecular diagnostics market alone is estimated to reach $2.3B by 2025 and needs better enzymes. We have discovered a new group of thermostable proofreading DNA polymerase A enzymes with uracil-DNA glycosylase (UDG) activity (UDG-DNAP). Preliminary data shows that one of these enzymes, UP19, possesses very strong 3’ exonuclease activity, UDG activity for removing uracil from DNA, and is capable of robust PCR amplification under normal thermocycling conditions. In this proposal, we will further develop this enzyme with a goal of commercializing a first-in-class, thermostable proofreading DNA polymerase A with intrinsic UDG activity for correcting uracil mistakes in DNA. Specific Aim 1 seeks to characterize and optimize UP19 in standard PCR reactions against industry standard control enzymes. Specific Aim 2 seeks to measure the fidelity of UP19 against industry standard enzymes using an Illumina platform and high-quality DNA from flash-frozen tumor samples as the template. Specific Aim 3 seeks to measure uracil error correction of UP19 using FFPE treated samples and the resulting sequence error rate against industry standard enzymes and a standalone mesophilic UDG enzyme. As a result of this project, we will have a better understanding of this novel enzyme family and better understand the applicability of UP19 for NGS applications. The ultimate goal will be to develop a better enzyme for the NGS market that will reduce error rates and misdiagnoses. 1