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Individuals accumulate many mutations in their somatic genomes throughout their lifetime. Behind this simple idea is a complex mechanism shaped by both environmental and genetics factors. Such somatic mutation accumulation can be seen in loads of diseases including cancers, and are known to contribute to other biological phenomena such as aging. To understand how somatic mutation loads contribute to disease states and carcinogenesis, we need to first check what happens in the pre-disease, healthy state which could then give us clues about how one goes from healthy to diseased.
To do this we map and define the variation in mutation loads in yeast (our model organism), healthy individuals, and across different individuals with varying genetic makeup and a history of exposure to various agents that can damage DNA, for example, UV light, toxic chemicals, or even misregulated genes! We can then look at whether similar mutation patterns are also seen in cancers from individuals with a history of exposure to the same DNA damaging agents.
Yeast is an excellent model to measure the mutagenic effects of diverse environmental agents in a rapid, scalable, and genetically-tractable manner (Chan et al. (2012), PLoS Genetics; Saini et al., (2017), DNA Repair; Saini et al.(2020), Nucleic Acids Res.) Mutation signatures for various environmental mutagens identified in yeast can be found in cancer genomes, allowing better detection of the activity of these damaging agents in sequenced tumors.
Using the awesome power of yeast genetics, we will test the involvement of various DNA repair genes in avoidance of mutagenesis by the DNA adducts formed by the environmental carcinogens. We can also investigate the mutation spectra and signatures associated with multiple other environmental DNA-damaging agents.
Overall, in my lab, we are interested in connecting the dots between human somatic mutation load and landscape, the history of exposure to DNA damaging agents and the DNA repair capacity of the individual. Our lab studies the impact of somatic mutations using a unique blend of molecular biology, genetics and bioinformatics.
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