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12621 Targeted Chemical-Genetic Screen Platform for Identifying Drug Modes-of-Action
- Kevin Lin, Maximilian Billmann, Henry Ward, Ya-Chu Chang, Anja-Katrin Bielinsky, Chad L. Myers
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- Journal:
- Journal of Clinical and Translational Science / Volume 5 / Issue s1 / March 2021
- Published online by Cambridge University Press:
- 30 March 2021, pp. 101-102
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- Article
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ABSTRACT IMPACT: The key to advancing precision medicine is to deepen our understanding of drug modes-of-action (MOA). This project aims to develop a novel method for predicting MOA of potential drug compounds, providing an experimental and computational platform for more efficient drug discovery. OBJECTIVES/GOALS: To develop (1) a targeted CRISPR-Cas9 chemical-genetic screen approach, and (2) a computational method to predict drug mode-of-action from chemical-genetic interaction profiles. METHODS/STUDY POPULATION: Screening drugs against a gene deletion library can identify knockouts that modulate drug sensitivity. These chemical-genetic interaction (CGI) screens can be performed in human cell lines using a pooled lentiviral CRISPR-Cas9 approach to assess drug sensitivity/resistance of single-gene knockouts across the human genome. A targeted, rather than genome-wide, library can enable scaling these screens across many drugs.
CGI profiles can be derived from phenotypic screen readouts. These profiles are analogous to genetic interaction (GI) profiles, which represent sensitivity/resistance of gene knockouts to a second gene knockout rather than a drug. To computationally predict a drug’s genetic target, we leverage the property that a drug’s CGI profile will be similar to its target’s GI profile. RESULTS/ANTICIPATED RESULTS: Five proof-of-principle screens will be conducted with compounds that have existing genome-wide profiles and well-characterized MOA. I will generate CGI profiles for these five compounds and identify genes that are drug-sensitizers or drug-suppressors. I will then evaluate whether targeted library screens can recapitulate the CGIs found in genome-wide screens. Finally, I will develop a computational tool to integrate these CGI profiles with GI profiles (derived from another project) to predict gene-level and bioprocess-level drug targets. These predictions (from both targeted and genome-wide profiles) will be benchmarked against a drug-target and drug-bioprocess standard. DISCUSSION/SIGNIFICANCE OF FINDINGS: This work will develop a scalable, targeted chemical-genetic screen approach to discovering how putative therapeutics work. The targeted screen workflow provides a method for higher-throughput drug screening. The computational pipeline provides a powerful tool for exploring the MOA of uncharacterized drugs or repurposing FDA-approved drugs.
2 - Computational paradigms for analyzing genetic interaction networks
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- By Carles Pons, University of Minnesota-Twin Cities, Michael Costanzo, University of Toronto, Charles Boone, University of Toronto, Chad L. Myers, University of Minnesota-Twin Cities
- Edited by Florian Markowetz, Michael Boutros
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- Book:
- Systems Genetics
- Published online:
- 05 July 2015
- Print publication:
- 02 July 2015, pp 12-35
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Summary
The advent of sequencing technologies has revolutionized our understanding and approach to studying biological systems. Indeed, whole-genome sequencing projects have already targeted many different species, enabling the identification of most genes in those organisms. However, observed phenotypes cannot be explained by genes alone, but rather by the interactions that their products establish under some environmental conditions (Waddington 1957). Thus, it is through the analysis of these interaction net-works (e.g. regulatory, metabolic, molecular, or genetic) that we can better understand the genotype-to-phenotype relationship, the complexity and evolution of organisms, or the differences among individuals of the same species. The topology and dynamics of these biological networks can be unveiled by systematic perturbation of their nodes (i.e. genes). For instance, upon single-gene deletions in Saccharomyces cerevisiae under standard laboratory conditions, most genes (∼80%) were not found to be essential for cell viability (Giaever et al. 2002). Though many of these genes may be required for growth in other environments (Hillenmeyer et al. 2008), this result suggests extensive functional redundancy among genes. Such functional buffering confers robustness to biological networks and shields the cellular machinery from genetic perturbations (Hartman et al. 2001). Additionally, the small effect on phenotype that many gene deletions exhibit (see Figure 2.1) evidences that single perturbations alone cannot capture the complexity of the genotype-to-phenotype relationship. Therefore, a combinatorial approach to gene perturbations is best suited to elucidate biological systems and can enable a better characterization of genes and cellular functioning.
Definition of genetic interaction
Genetic interactions reveal functional relations between genes that contribute to a pheno-typic trait. William Bateson first introduced the term, formerly known as epistasis (see Phillips [1998] for a description on the origin and evolution of the definition), to refer to an allele at one locus preventing a variant at another from manifesting its effect (Bateson 1909).