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Identifying chemogenetic interactions from CRISPR screens with drugZ

BACKGROUND: Chemogenetic profiling enables the identification of gene mutations that enhance or suppress the activity of chemical compounds. This knowledge provides insights into drug mechanism of action, genetic vulnerabilities, and resistance mechanisms, all of which may help stratify patient popu...

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Autores principales: Colic, Medina, Wang, Gang, Zimmermann, Michal, Mascall, Keith, McLaughlin, Megan, Bertolet, Lori, Lenoir, W. Frank, Moffat, Jason, Angers, Stephane, Durocher, Daniel, Hart, Traver
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6706933/
https://www.ncbi.nlm.nih.gov/pubmed/31439014
http://dx.doi.org/10.1186/s13073-019-0665-3
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author Colic, Medina
Wang, Gang
Zimmermann, Michal
Mascall, Keith
McLaughlin, Megan
Bertolet, Lori
Lenoir, W. Frank
Moffat, Jason
Angers, Stephane
Durocher, Daniel
Hart, Traver
author_facet Colic, Medina
Wang, Gang
Zimmermann, Michal
Mascall, Keith
McLaughlin, Megan
Bertolet, Lori
Lenoir, W. Frank
Moffat, Jason
Angers, Stephane
Durocher, Daniel
Hart, Traver
author_sort Colic, Medina
collection PubMed
description BACKGROUND: Chemogenetic profiling enables the identification of gene mutations that enhance or suppress the activity of chemical compounds. This knowledge provides insights into drug mechanism of action, genetic vulnerabilities, and resistance mechanisms, all of which may help stratify patient populations and improve drug efficacy. CRISPR-based screening enables sensitive detection of drug-gene interactions directly in human cells, but until recently has primarily been used to screen only for resistance mechanisms. RESULTS: We present drugZ, an algorithm for identifying both synergistic and suppressor chemogenetic interactions from CRISPR screens. DrugZ identifies synthetic lethal interactions between PARP inhibitors and both known and novel members of the DNA damage repair pathway, confirms KEAP1 loss as a resistance factor for ERK inhibitors in oncogenic KRAS backgrounds, and defines the genetic context for temozolomide activity. CONCLUSIONS: DrugZ is an open-source Python software for the analysis of genome-scale drug modifier screens. The software accurately identifies genetic perturbations that enhance or suppress drug activity. Interestingly, analysis of new and previously published data reveals tumor suppressor genes are drug-agnostic resistance genes in drug modifier screens. The software is available at github.com/hart-lab/drugz. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13073-019-0665-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-67069332019-08-28 Identifying chemogenetic interactions from CRISPR screens with drugZ Colic, Medina Wang, Gang Zimmermann, Michal Mascall, Keith McLaughlin, Megan Bertolet, Lori Lenoir, W. Frank Moffat, Jason Angers, Stephane Durocher, Daniel Hart, Traver Genome Med Software BACKGROUND: Chemogenetic profiling enables the identification of gene mutations that enhance or suppress the activity of chemical compounds. This knowledge provides insights into drug mechanism of action, genetic vulnerabilities, and resistance mechanisms, all of which may help stratify patient populations and improve drug efficacy. CRISPR-based screening enables sensitive detection of drug-gene interactions directly in human cells, but until recently has primarily been used to screen only for resistance mechanisms. RESULTS: We present drugZ, an algorithm for identifying both synergistic and suppressor chemogenetic interactions from CRISPR screens. DrugZ identifies synthetic lethal interactions between PARP inhibitors and both known and novel members of the DNA damage repair pathway, confirms KEAP1 loss as a resistance factor for ERK inhibitors in oncogenic KRAS backgrounds, and defines the genetic context for temozolomide activity. CONCLUSIONS: DrugZ is an open-source Python software for the analysis of genome-scale drug modifier screens. The software accurately identifies genetic perturbations that enhance or suppress drug activity. Interestingly, analysis of new and previously published data reveals tumor suppressor genes are drug-agnostic resistance genes in drug modifier screens. The software is available at github.com/hart-lab/drugz. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13073-019-0665-3) contains supplementary material, which is available to authorized users. BioMed Central 2019-08-22 /pmc/articles/PMC6706933/ /pubmed/31439014 http://dx.doi.org/10.1186/s13073-019-0665-3 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Colic, Medina
Wang, Gang
Zimmermann, Michal
Mascall, Keith
McLaughlin, Megan
Bertolet, Lori
Lenoir, W. Frank
Moffat, Jason
Angers, Stephane
Durocher, Daniel
Hart, Traver
Identifying chemogenetic interactions from CRISPR screens with drugZ
title Identifying chemogenetic interactions from CRISPR screens with drugZ
title_full Identifying chemogenetic interactions from CRISPR screens with drugZ
title_fullStr Identifying chemogenetic interactions from CRISPR screens with drugZ
title_full_unstemmed Identifying chemogenetic interactions from CRISPR screens with drugZ
title_short Identifying chemogenetic interactions from CRISPR screens with drugZ
title_sort identifying chemogenetic interactions from crispr screens with drugz
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6706933/
https://www.ncbi.nlm.nih.gov/pubmed/31439014
http://dx.doi.org/10.1186/s13073-019-0665-3
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