Kesip çıkarmalı DNA onarım etkinliğinin epigenetik belirleyicileri

Passing through the atmosphere, UV components of sunlight reach the earth's surface. Long exposures of cells to UV-A and UV-B result in cellular dysfunctionalities by causing DNA damage. Nucleotide excision repair (NER) is a mechanism that identifies and removes bulky DNA adducts such as UV-induced dipyrimidines. NER consists of two sub-pathways with respect to its damage recognition step: global (G-NER) and transcription-coupled repair (TC-NER). TC-NER takes place on the transcribed strand of the genes, whereas G-NER is globally active throughout the genome. It has been reported some chromatin states affect the efficiency of NER which consists of G-NER and TC-NER, in combination. TC-NER is associated with transcription and related genomic features. However, epigenetic factors affecting the G-NER efficiency has been underexplored. Here, we processed the genome-wide datasets derived from DNA damage and repair maps as well as histone modification maps of the three cell lines. With the genomic DNA damage, repair, and histone modification datasets, we built machine learning models to reveal epigenetic factors that can be predictive of NER and particularly G-NER efficacy. Our models resulted in high accuracy prediction of DNA repair potential of the genomic regions. We suggest that cells' epigenetic architecture is likely the key determinant of global DNA repair bias, therefore, mutagenesis in cancer.