2 results
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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- Open access
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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.
3 - Mapping genetic interactions across many phenotypes in metazoan cells
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- By Christina Laufer, Heidelberg University, Maximilian Billmann, Heidelberg University, Michael Boutros, German Cancer Research Center, Heidelberg
- Edited by Florian Markowetz, Michael Boutros
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- Book:
- Systems Genetics
- Published online:
- 05 July 2015
- Print publication:
- 02 July 2015, pp 36-50
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Summary
Interactions between genes can be experimentally determined by combining multiple mutations and identifying combinations where the resulting phenotype differs from the expected one. Such genetic interactions, for example measured in yeast for cell proliferation and growth phenotypes, provided intricate insights into the genetic architecture and interplay of pathways. Due to the lack of comprehensive deletion libraries, similar experiments in higher eukaryotic cells have been challenging. Recently, we and others described methods to perform systematic, comprehensive double-perturbation analyses in Drosophila and human cells using RNA interference. We also introduced methods to use multiple phenotypes to map genetic interactions across a broad spectrum of processes.
This chapter focuses on the systematic mapping of genetic interactions and the use of image-based phenotypes to improve genetic interaction calling. It also describes experimental approaches for the analysis of genetic interactions in human cells and discusses concepts to expand genetic interaction mapping towards a genomic scale.
A short history of genetic interaction analysis
Using quantitative traits to map genetic interactions has a long tradition in Drosophila. In the 1960s and 1970s, Dobzhansky, Rendel, and others used externally visible pheno-types or overall fitness to study non-mendelian inheritance and dissect the heritability of complex traits (Fig. 3.1a). One of the underlying assumptions was that genetic loci in the Drosophila genome interact to shape complex phenotypes or buffer detrimental alleles.
In 1965, Dobzhansky and colleagues analyzed epistatic interactions between the components of genetic variants in Drosophila. They crossed flies carrying mutant alleles into a wild-type background obtained from a natural habitat and found that the combination of particular chromosomes showed synthetic sick phenotypes, whereas both chromosomes alone did not. This for the first time demonstrated the presence of bi-chromosomal synthetic interactions in Drosophila populations. Similarly, Rendel and colleagues demonstrated the existence of epistatic modifiers in Drosophila by analysis of scute alleles, which reduce the number of scutellar bristles on the dorsal thorax from four to an average of one.