Functional genomic screens with death rate analyses reveal mechanisms of drug action

A common approach for understanding how drugs induce their therapeutic effects is to identify the genetic determinants of drug sensitivity. Because ‘chemo-genetic profiles’ are performed in a pooled format, inference of gene function is subject to several confounding influences related to variation...

Full description

Saved in:
Bibliographic Details
Published inNature chemical biology Vol. 20; no. 11; pp. 1443 - 1452
Main Authors Honeywell, Megan E., Isidor, Marie S., Harper, Nicholas W., Fontana, Rachel E., Birdsall, Gavin A., Cruz-Gordillo, Peter, Porto, Sydney A., Jerome, Madison, Fraser, Cameron S., Sarosiek, Kristopher A., Guertin, David A., Spinelli, Jessica B., Lee, Michael J.
Format Journal Article
LanguageEnglish
Published New York Nature Publishing Group US 01.11.2024
Nature Publishing Group
Subjects
631/67/1059
631/80/82
631/92/360
631/92/609
631/92/93
Antineoplastic Agents - pharmacology
Apoptosis
Apoptosis - drug effects
Apoptosis - genetics
Biochemical Engineering
Biochemistry
Bioorganic Chemistry
Cell Biology
Cell death
Cell Death - drug effects
Cell Death - genetics
Cell Line, Tumor
Chemistry
Chemistry and Materials Science
Chemistry/Food Science
Damage detection
Deoxyribonucleic acid
DNA
DNA damage
DNA Damage - drug effects
DNA fingerprinting
Effectiveness
Genes
Genetic analysis
Genetic diversity
Genomics - methods
Humans
Lethality
Mortality
p53 Protein
Tumor Suppressor Protein p53 - genetics
Tumor Suppressor Protein p53 - metabolism
Online AccessGet full text

Cover

More Information
Summary:A common approach for understanding how drugs induce their therapeutic effects is to identify the genetic determinants of drug sensitivity. Because ‘chemo-genetic profiles’ are performed in a pooled format, inference of gene function is subject to several confounding influences related to variation in growth rates between clones. In this study, we developed Method for Evaluating Death Using a Simulation-assisted Approach (MEDUSA), which uses time-resolved measurements, along with model-driven constraints, to reveal the combination of growth and death rates that generated the observed drug response. MEDUSA is uniquely effective at identifying death regulatory genes. We apply MEDUSA to characterize DNA damage-induced lethality in the presence and absence of p53. Loss of p53 switches the mechanism of DNA damage-induced death from apoptosis to a non-apoptotic death that requires high respiration. These findings demonstrate the utility of MEDUSA both for determining the genetic dependencies of lethality and for revealing opportunities to potentiate chemo-efficacy in a cancer-specific manner. A method called MEDUSA was developed for identifying death regulatory genes in chemo-genetic profiling data, which enables characterization of a previously unappreciated mechanism of death induced by DNA damage in p53-deficient cells.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
AUTHOR CONTRIBUTIONS
This project was conceived by MEH and MJL. MEH performed all experiments and analyses in this study, including all drug sensitivity studies, cell and reagent development, biochemical evaluation of mitochondrial function, genetic evaluation of mechanisms of cell death, and the execution and analysis of chemo-genetic profiling. MEDUSA was developed and validated by MEH. GRADE-based evaluation of growth and death rates was performed by MEH and MJL. NWH assisted with modeling error types in conventional chemo-genetic profiling. REF assisted with screen parameterization. GAB assisted with profiling DNA damage signaling in U2OS and p53KO cells. PCG assisted with cell line selection and evaluation of DNA damage sensitivity. SAP assisted with evaluating morphology and stability of non-apoptotic and apoptotic cells. BH3 profiling experiments were designed, executed, and analyzed by CSF and KAS. Seahorse experiments were performed by MSI and MEH, with DAG assisting with design, interpretation, and analysis. Metabolomic profiling experiments were designed and analyzed by MEH and JBS, and performed by MEH, JBS, and MJ. JBS assisted with the design, interpretation, and analysis of assays to assess mitochondrial abundance and ETC protein composition. Meta-analysis of published screens was performed by MJL. All other experiments, statistical analyses, and modeling were conducted by MEH. MEH and MJL wrote and edited the manuscript.
ISSN:1552-4450
1552-4469
1552-4469
DOI:10.1038/s41589-024-01584-7