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Hydroxymethylated Cytosines Are Associated with Elevated C to G Transversion Rates

Supek, Fran; Lehner, Ben; Hajkova, Petra; Warnecke, Tobias (2014) Hydroxymethylated Cytosines Are Associated with Elevated C to G Transversion Rates. PLoS Genetics, 10 (9). e1004585. ISSN 1553-7390

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It has long been known that methylated cytosines deaminate at higher rates than unmodified cytosines and constitute mutational hotspots in mammalian genomes. The repertoire of naturally occurring cytosine modifications, however, extends beyond 5-methylcytosine to include its oxidation derivatives, notably 5-hydroxymethylcytosine. The effects of these modifications on sequence evolution are unknown. Here, we combine base-resolution maps of methyl- and hydroxymethylcytosine in human and mouse with population genomic, divergence and somatic mutation data to show that hydroxymethylated and methylated cytosines show distinct patterns of variation and evolution. Surprisingly, hydroxymethylated sites are consistently associated with elevated C to G transversion rates at the level of segregating polymorphisms, fixed substitutions, and somatic mutations in tumors. Controlling for multiple potential confounders, we find derived C to G SNPs to be 1.43-fold (1.22-fold) more common at hydroxymethylated sites compared to methylated sites in human (mouse). Increased C to G rates are evident across diverse functional and sequence contexts and, in cancer genomes, correlate with the expression of Tet enzymes and specific components of the mismatch repair pathway (MSH2, MSH6, and MBD4). Based on these and other observations we suggest that hydroxymethylation is associated with a distinct mutational burden and that the mismatch repair pathway is implicated in causing elevated transversion rates at hydroxymethylated cytosines.

Item Type: Article
Uncontrolled Keywords: modified nucleotides; 5-hydroxymethylcytosine; mutational bias
Subjects: NATURAL SCIENCES > Biology
Divisions: Division of Electronics
Project titleProject leaderProject codeProject type
Strojno učenje prediktivnih modela u računalnoj biologiji[136501] Tomislav Šmuc098-0000000-3168MZOS
Depositing User: Fran Supek
Date Deposited: 13 Apr 2015 13:16
DOI: 10.1371/journal.pgen.1004585

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