Failure of cells to respond to DNA damage is a key mechanism of toxicity by environmental agents and a primary step in the onset of cancer. In this research program, we are using systematic approaches to map and model the genetic networks underlying the DNA damage response (DDR). Since DDR pathways are widely conserved, our studies bridge between Homo sapiens and the budding yeast Saccharomyces cerevisiae? impacting our knowledge of how genotoxic agents lead to pathogenesis in humans, coupled to a classic model organism for which new genetic technologies are readily developed and deployed. In the past five years of funding (NIH grant R01-ES14811), we made significant progress in identifying changes in yeast genetic, transcriptional, and signaling networks in response to DNA damage stress. We also developed innovative new technologies, including the experimental technique of ?differential? genetic interaction mapping and a computational approach to translate interaction networks into hierarchical, data-driven gene ontologies. In the next period of support, we will: (1) Significantly expand the yeast genetic interaction maps to include dynamic growth curves and specific DDR pathway readouts at high-throughput; (2) Develop and apply CRISPR technology to create parallel genetic network maps in human cell lines; and (3) Integrate all new and prior data to build comprehensive ontologies of DDR subsystems in yeast and human, which we will compare to systematically identify areas of conservation and divergence and validate specific DDR phenotypic predictions in mechanistic assays. This work moves us closer to a comprehensive structure/function model of the DDR. A growing set of DNA- damage-induced genetic networks and ontologies in model species and humans are important resources for understanding genetic polymorphisms that predispose an individual to environmental DNA damage and DDR- related diseases.
We propose to develop comprehensive maps and models of signal transduction networks in response to DNA damage. These maps are a major biomedical resource which will be used to identify and target chemotherapeutic agents and their modulators.
|Shen, John Paul; Ideker, Trey (2018) Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls. J Mol Biol 430:2900-2912|
|Neal, Sonya; Jaeger, Philipp A; Duttke, Sascha H et al. (2018) The Dfm1 Derlin Is Required for ERAD Retrotranslocation of Integral Membrane Proteins. Mol Cell 69:306-320.e4|
|Bui, Nam; Huang, Justin K; Bojorquez-Gomez, Ana et al. (2018) Disruption of NSD1 in Head and Neck Cancer Promotes Favorable Chemotherapeutic Responses Linked to Hypomethylation. Mol Cancer Ther 17:1585-1594|
|Ma, Jianzhu; Yu, Michael Ku; Fong, Samson et al. (2018) Using deep learning to model the hierarchical structure and function of a cell. Nat Methods 15:290-298|
|Jaeger, Philipp A; Ornelas, Lilia; McElfresh, Cameron et al. (2018) Systematic Gene-to-Phenotype Arrays: A High-Throughput Technique for Molecular Phenotyping. Mol Cell 69:321-333.e3|
|Shen, John Paul; Zhao, Dongxin; Sasik, Roman et al. (2017) Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions. Nat Methods 14:573-576|
|Moder, Martin; Velimezi, Georgia; Owusu, Michel et al. (2017) Parallel genome-wide screens identify synthetic viable interactions between the BLM helicase complex and Fanconi anemia. Nat Commun 8:1238|
|Kramer, Michael H; Farré, Jean-Claude; Mitra, Koyel et al. (2017) Active Interaction Mapping Reveals the Hierarchical Organization of Autophagy. Mol Cell 65:761-774.e5|
|Hofree, Matan; Carter, Hannah; Kreisberg, Jason F et al. (2016) Challenges in identifying cancer genes by analysis of exome sequencing data. Nat Commun 7:12096|
|Yu, Michael Ku; Kramer, Michael; Dutkowski, Janusz et al. (2016) Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems. Cell Syst 2:77-88|
Showing the most recent 10 out of 47 publications