Mutational biases associated with potential iron-binding DNA motifs in rodent lacI and human p53 mutational databases

The role of Fenton oxidants in DNA damage, aging, and cancer is appreciated, but not well understood. Six potential iron-binding (PIB) DNA motifs were previously identified as sites of preferential strand cleavage. Since DNA-metal binding domains are a known determinant of oxidative DNA damage, and the location of strand breaks explains where oxidant attack occurs, we sought to determine whether the likelihood of base change mutations is a function of neighboring PIB motifs. We developed a sliding window function that computes the density of PIB motifs on both strands, within 4-12bp, for each location along a target gene. This range of window sizes reflects known diffusion distances of Fenton reaction products. Using mutational databases, odds of mutation at each base were calculated relative to PIB motif density, for all PIB motif types in aggregate, or for individual PIB motifs. Using mutational data from lacI transgenic animals, we observed a non-random distribution of PIB motifs, associated with increased odds of mutation, showing a strand bias. Sensitivity analysis confirmed that the optimum association between PIB motif density and mutations occurs when a 7bp radius is used for the window size. Randomly simulated mutations showed no association with PIB motif density. When the method was applied to human TP53 mutation data, we saw similar results, but no strand bias. As PIB motif density rises, linear trends are observed for increasing odds of mutation. Sensitivity analysis revealed associations between PIB motifs and GC --> AT transitions and GC --> TA transversions-the most commonly observed types of mutations arising from oxidative DNA damage. DNA-metal binding motifs are found in a wide variety of biological contexts, including many where conformational sensitivity to redox state is important. These techniques can help elucidate how DNA-iron-binding may affect lesions and subsequent mutations from multiple agents.