Heterogeneous polymerase fidelity and mismatch repair bias genome variation and composition

  1. Thomas A. Kunkel1,2
  1. 1Laboratory of Molecular Genetics, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina 27709, USA;
  2. 2Laboratory of Structural Biology, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina 27709, USA;
  3. 3Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, North Carolina 27710, USA;
  4. 4Department of Genetics, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina 27599, USA;
  5. 5Integrative Bioinformatics, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina 27709, USA
  1. Corresponding author: kunkel{at}niehs.nih.gov

Abstract

Mutational heterogeneity must be taken into account when reconstructing evolutionary histories, calibrating molecular clocks, and predicting links between genes and disease. Selective pressures and various DNA transactions have been invoked to explain the heterogeneous distribution of genetic variation between species, within populations, and in tissue-specific tumors. To examine relationships between such heterogeneity and variations in leading- and lagging-strand replication fidelity and mismatch repair, we accumulated 40,000 spontaneous mutations in eight diploid yeast strains in the absence of selective pressure. We found that replicase error rates vary by fork direction, coding state, nucleosome proximity, and sequence context. Further, error rates and DNA mismatch repair efficiency both vary by mismatch type, responsible polymerase, replication time, and replication origin proximity. Mutation patterns implicate replication infidelity as one driver of variation in somatic and germline evolution, suggest mechanisms of mutual modulation of genome stability and composition, and predict future observations in specific cancers.

Footnotes

  • Received May 13, 2014.
  • Accepted September 5, 2014.

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