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

Nature Chemical Biology (2024)Cite this article

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Abstract

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.

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Fig. 1: Varied coordination of growth and death masks high levels of non-apoptotic death activated by DNA damage in the absence of p53.
Fig. 2: Chemo-genetic profiles fail to identify death regulatory genes due to confounding effects of varied growth rates.
Fig. 3: MEDUSA, a method for inferring the death regulatory function of each gene in chemo-genetic profiling.
Fig. 4: MEDUSA accurately identifies death regulatory genes.
Fig. 5: MEDUSA identifies a respiration-dependent form of non-apoptotic death activated by DNA damage in p53-null cells.
Fig. 6: High NAD+ levels potentiate DNA damage sensitivity in p53-null cells.

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Data availability

Source data collected for a panel of nine DNA-damaging drugs at varied doses in 20 p53-WT and p53-null cell lines are included in Supplementary Table 1. Data for genome-wide chemo-genetic screens are available as FASTQ files from the Gene Expression Omnibus (GEO) (accession numbers 7356374, 7356376–7356385 and 7356387–7356393), as raw counts (Supplementary Table 2) and as computed values for the fold change, growth rate and death rate for each gene (Supplementary Tables 3 and 5). Meta-analysis of publicly available data from apoptotic CRISPR screens is included in Supplementary Table 4. MEDUSA validation data are included in Supplementary Table 6. Metabolomic data are available as normalized ion counts in Supplementary Table 7, and isotope labeling data are available in Supplementary Table 8. Raw data for metabolite profiling are also available at https://doi.org/10.5281/zenodo.7931639. RNA sequencing data are available from the GEO (accession numbers GSM7356394–7356398 and GSM7356400–7356404). All other data are available in the associated source data files. Source data are provided with this paper.

Code availability

Custom MATLAB code associated with this study is deposited on GitHub (https://github.com/MJLee-Lab) with the versions used in this study released through Zenodo, including functions for fitting cell growth (https://github.com/MJLee-Lab/fitGrowth; https://doi.org/10.5281/zenodo.10635978), fitting drug dose–response curves (https://github.com/MJLee-Lab/fit_via; https://doi.org/10.5281/zenodo.10635970), fitting lethal fraction kinetics (https://github.com/MJLee-Lab/fitLED; https://doi.org/10.5281/zenodo.10635968), computing drug GRADE and generating FV/GR plots (https://github.com/MJLee-Lab/GRADE; https://doi.org/10.5281/zenodo.10635965), simulating RV in the presence/absence of growth arrest and/or death activation (https://github.com/MJLee-Lab/RVsim; https://doi.org/10.5281/zenodo.10635795) and implementing MEDUSA (https://github.com/MJLee-Lab/MEDUSA; https://doi.org/10.5281/zenodo.10635729).

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Acknowledgements

We thank current and past members of the UMass Chan Medical School DSB community for their helpful comments and critiques during the design and execution of this study. Additionally, we thank T. Leete for assistance with training in an early stage of this project; C. Navarro for thoughtful comments and editing of this paper; C. Baer and the UMass Chan SCOPE Core for assistance with some microscopy experiments; A. Mitchell for providing an H2B-mCherry plasmid; T. Fazzio for providing the pX330 plasmid; T. Fortier, E. Baehrecke and the UMass Chan Electron Microscopy Core for assistance with transmission electron microscopy experiments; and M. Green and D. Kim for providing access to some of the cell lines used in this study. M.S.I. is supported by the Novo Nordisk Foundation Center for Basic Metabolic Research, an independent research center based at the University of Copenhagen, and partially funded by an unconditional donation from the Novo Nordisk Foundation (grant number NNF18CC0034900). This work was supported by grants from the National Institutes of Health/National Institute of General Medical Sciences (R01GM127559 to M.J.L.), the National Cancer Institute (F31CA268847 to M.E.H.), the JKTG Foundation (to M.J.L.) and the American Cancer Society (RSG-17-011-01 to M.J.L.).

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Authors and Affiliations

  1. Department of Systems Biology, UMass Chan Medical School, Worcester, MA, USA

    Megan E. Honeywell, Nicholas W. Harper, Rachel E. Fontana, Gavin A. Birdsall, Peter Cruz-Gordillo, Sydney A. Porto & Michael J. Lee

  2. Program in Molecular Medicine, UMass Chan Medical School, Worcester, MA, USA

    Marie S. Isidor, Madison Jerome, David A. Guertin, Jessica B. Spinelli & Michael J. Lee

  3. Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Marie S. Isidor

  4. John B. Little Center for Radiation Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Cameron S. Fraser & Kristopher A. Sarosiek

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Contributions

This project was conceived by M.E.H. and M.J.L. M.E.H. 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 execution and analysis of chemo-genetic profiling. MEDUSA was developed and validated by M.E.H. GRADE-based evaluation of growth and death rates was performed by M.E.H. and M.J.L. N.W.H. assisted with modeling error types in conventional chemo-genetic profiling. R.E.F. assisted with screen parameterization. G.A.B. assisted with profiling DNA damage signaling in U2OS and p53KO cells. P.C.-G. assisted with cell line selection and evaluation of DNA damage sensitivity. S.A.P. assisted with evaluating morphology and stability of non-apoptotic and apoptotic cells. BH3 profiling experiments were designed, executed and analyzed by C.S.F. and K.A.S. Seahorse experiments were performed by M.S.I. and M.E.H., with D.A.G. assisting with design, interpretation and analysis. Metabolomic profiling experiments were designed and analyzed by M.E.H. and J.B.S. and performed by M.E.H., J.B.S. and M.J. J.B.S. 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 M.J.L. All other experiments, statistical analyses and modeling were conducted by M.E.H. M.E.H. and M.J.L. wrote and edited the paper.

Corresponding author

Correspondence to Michael J. Lee.

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Extended data

Extended Data Fig. 1 Characterizing the DNA damage-induced coordination between growth and death using GRADE analysis. Related to Fig. 1.

(a) Schematic of the FLICK assay and equations for calculating relative viability (RV), fractional viability (FV), and normalized growth rate inhibition values (GR). (b) Sensitivity of WT and p53-null cell lines to DNA-damaging chemotherapeutics, measured using FLICK and analyzed using GRADE. Drug doses are colored in pseudocolor with higher doses in deeper blue shades. Measurements were made 72 hours after drug exposure. The grey area reflects the full set of possible GR/LF relationships given a comprehensive simulation of drug-induced changes to the growth rate and death rate. Cell doubling times (DT, time in hours for 1 population doubling) highlighted, as DT affects the zone of possible GR/LF relationships. Data are mean ± SD from n = 3 independent experiments.

Source data

Extended Data Fig. 2 Variation in drug-induced coordination between growth and death obscures the level of cell death in relative viability-based evaluation of drug sensitivity. Related to Fig. 1.

(a) Growth and death rates for HDAC inhibition (Vorinostat), BH3 mimetic (ABT-737), or a ferroptotic drug (RSL3), compared to 6 DNA-damaging chemotherapeutics. Data were generated using FLICK with GRADE analysis, with responses evaluated over the first 72 hours following drugging. Doses used were 1-10 µM for each compound which caused > 80% LF at assay endpoint. Data are the mean from n = 9-11 independent experiments, with each dot representing an individual replicate. (b-c) DNA damage signaling measured using phosphorylation of H2AX at serine 139 (pH2AX) in response to etoposide. (b) Response kinetics for 31.6 µM Etoposide over time. (c) pH2AX in U2OS (WT) and U2OSp53KO (KO) measured across doses of Etoposide, 2 hours after drug exposure. For (b-c), quantified values below the blots are the mean ± SD from 2 independent experiments. (d) Gating strategy used for cell cycle analysis in (e). (e) Cell cycle position evaluated using propidium iodide (PI) and phospho-histone H3 (pH-H3). (left) Examples of U2OS cells exposed to DMSO or 10 µM Nutlin for 24 hours. (right) Quantification of cell cycle phase. Data are mean ± SD from n = 3 independent experiments. (f) Live cell counts over time for U2OS and U2OSp53KO cells treated with a sub-lethal dose of etoposide. Data collected using a hemacytometer. Data are mean ± SD from n = 3 independent experiments. (g-i) Simulation of population dynamics following exposure to DNA damage. (g) Relative cell number over time for different growth and death models. Growth and death rates are parameterized from observed rates in p53WT and KO cells. (h) Relative viability dose-response function calculated from simulated data in (g). (i) Sensitivity of the RV metric to cell death. The expected change in RV at different points in time, for simulations of drugs with different death rates. Red curve is the average death rate observed for DNA-damaging agents.

Source data

Extended Data Fig. 3 p53 deletion switches the mechanism of DNA damage-induced cell death from apoptotic to non-apoptotic. Related to Fig. 1.

(a) Death onset times (DO) for data in Extended Data 1b. For each drug, data are shown for all doses that induce death across all 10 WT or p53-null cells. See also Supplementary Table 1. (b) Flow cytometry gating strategy for monitoring activation of apoptosis (cleaved-CASP3, cleaved-PARP double-positive cells). Untreated U2OS cells (top row) and U2OS cells treated with an apoptotic drug (31.6 µM Etoposide, bottom row) are shown for comparison. (c-e) Evaluation of inflammatory cell death in U2OS and U2OSp53KO cells. (c) Schematic for conditioned media experiment. Cells were exposed to 31.6 µM etoposide for 48 hours. Conditioned media (CM) was removed and filtered, then applied to untreated U2OS cells for 8 hours before cells were processed for RNA-sequencing. (d) RNA-seq for CM-induced gene expression changes. Volcano plot showing the -log10FDR p-values and log2-fold change (L2FC) for U2OS cells treated with conditioned media from either U2OS or U2OSp53KO cells (log2(U2OSp53KO/U2OS)). (e) Pathway-level enrichment using GSEA, highlighting enrichment for inflammatory signatures in cells treated with media conditioned by U2OSp53KO cells. NES = normalized enrichment score. FDR-adjusted p-value cut-off shown with dashed red line.

Source data

Extended Data Fig. 4 p53 deletion does not alter the capacity to activate apoptosis. Related to Fig. 1.

(a-b) Apoptotic priming evaluated using BH3 profiling. Numbers of the y-axis in (a) and (b) report the µM concentrations of the listed peptides. (a) Basal BH3 profiles in U2OS and U2OSp53KO cells. Data are the mean ± SD for n = 4 independent experiments. (b) Change in apoptotic priming level following exposure to the listed dose of Etoposide (Etop) for the listed time. (c) Sensitivity to BH3 mimetic, ABT-199 using FLICK assay. (left) dose-response profile 48 hours after drug exposure. (right) Death kinetics for the same doses of ABT-199 as shown on left, starting with 100 µM. Data are mean ± SD of n = 3 independent experiments.

Source data

Extended Data Fig. 5 Chemo-genetic screening analysis strategy and replicate correlation. Related to Fig. 2.

(a-b) U2OS cells treated with Etoposide for 12 days. (a) Live cells were counted to determine the growth defect of each dose. ED = ‘Effective Dose’ (for example, ED30 = effective dose for 30% reduction in population size after 12 days, compared to untreated). Data collected using hemacytometer-based cell counting. (b) Lethal fraction evaluated using using hemacytometer-based cell counting and trypan blue-exclusion following 12-day drug exposure. Data in (a-b) demonstrate that conventional ‘ED20’ effect sizes are non-lethal for DNA damage. Lethality is not observed until > ED99. For (a) and (b) data are mean of n = 2 independent experiments with individual replicates shown. (c) Analysis pipeline for calculating L2RV from chemo-genetic screens. (d) Example of correlation between counts for two replicates of the same screen condition. Pearson Correlation Coefficient (r) shown. (e) Example of correlation between gene-level L2RV values for two screen replicates. z-scored L2RV was calculated for each replicate. Non-targeting (Non-targ.) sgRNAs shown in black. All other genes shown in blue.

Source data

Extended Data Fig. 6 Validation of MEDUSA. Related to Figs. 3 and 4.

(a) Probability density function (PDF) for non-targeting sgRNAs or DNA repair genes in Untreated vs. T0 comparison. Knockout of DNA repair genes causes reduced growth rate. (b) Schematic of method used to validate hits from the whole-genome CRISPR screen. For (c), (e), and (f) validation conditions replicate the chemo-genetic profiling conditions. (c) Validation data generated using FLICK, grey = non-targeting sgRNA, blue = targeted gene. Data are mean ± SD for n = 4 independent experiments. (d-f) Validation of gene knockouts that cause reduced growth rates. (d) Phase diagram from MEDUSA highlighting 16 genes in the validation set whose knockout causes reduced growth rate. Purple are genes predicted by MEDUSA to increase death rate when knocked out; Green are genes predicted by MEDUSA to decrease death rate when knocked out. (e) Scatter plot comparing MEDUSA-inferred rates on x-axis versus FLICK validation on y-axis. p-values and odds ratios (OR) calculated using a one-tailed Fisher’s exact test. (f) as in (e) but for L2RV analysis on x-axis compared versus FLICK validation on y-axis. (g) MEDUSA-inferred growth and death rates in U2OS and U2OSp53KO. Non-targeting genes in black; apoptotic regulatory genes in blue; all other genes in grey.

Source data

Extended Data Fig. 7 Validation of respiration-dependent death in p53 KO cells. Related to Fig. 5.

(a-b) Validation of MEDUSA-based inferences for genes encoding subunits of ETC Complex I (NDUFB8 and NDUFC1) and Complex V (ATP5F1 and ATP5I), compared to non-targeting sgRNA (Non-targ.). Validation was performed using the FLICK assay, with conditions replicating the chemo-genetic profiling experiment (5 µM Etoposide, 4 days). (a) Death kinetics. (b) Lethal fraction (LF) at end point. In (b), lines represent the mean, with individual replicates shown (n = 3 for targeted sgRNAs, n = 6 for non-targeting). Statistical evaluation performed using pairwise t-tests (two-sided) with FDR correction for multiple hypotheses. FDR adjusted p-values for WT: NDUFB8: 0.1349; NDUFC1: 0.0864; ATPF51: 0.1349: ATPFI: 0.1236. FDR for KO: NDUFB8: 0.0004; NDUFC1: <0.0001; ATP5F1: <0.0001; ATP5I: 0.0003. (c) Basal respiration rates for WT and KO cells measured using the Seahorse Mitochondrial Stress Test. p values: basal: <0.0001; ATP-linked: <0.0001; Proton Leak: <0.0001; Max rate: <0.0001; Spare capacity: 0.0002, Non-mito: <0.0001. (d-e) Mitochondrial abundance in U2OS and U2OSp53KO. (d) qPCR of mitochondrial DNA. Delta CT (cycle time) used to quantify relative abundance. (e) MitoTracker Green fluorescence level. Data are mean ± SD from n = 3 independent experiments. No sequential gating used in the analysis. (f) MEDUSA-based chemo-genetic profiles for etoposide in U2OS and U2OSp53KO cells, highlighting MDM2. (g) FLICK-based validation of MDM2 knockout. Conditions replicate chemo-genetic profiling experiment. (h) Relative abundance of ETC Complexes I-V. (left) Representative immunoblot. (right) Quantification from n = 3 independent experiments. (i) Blue native PAGE of ETC complexes. Representative blot shown from n = 3 independent experiments that produced similar results. ETC protein complexes highlighted. (j) Death kinetics following 31.6 µM etoposide ±Rotenone (Rot.). Data for U2OS and 3 independent clones of p53KO. Clone 2 was used as ‘U2OSp53KO’ throughout the study. For (c), data are mean ± SD from n = 6 independent experiments. For all other panels with error bars, data are mean ± SD from n = 3 independent experiments. For panels (b-e) statistical evaluation was performed using pairwise t-tests (two-sided). p-values (c-e) are not corrected for multiple hypothesis testing. ns = not significant; *** p < 0.05.

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Extended Data Fig. 8 Steady state metabolite levels. Related to Fig. 6.

(a) Metabolite levels quantified using LC-MS, shown for vehicle-treated U2OS and U2OSp53KO cells at T0. (b) Metabolite levels shown for intermediate metabolites involved in glycolysis, PPP, or TCA cycle for U2OS and U2OSp53KO. Data are shown for vehicle and etoposide treated samples at 48 hours. Data are mean ± SD from n = 3 independent experiments. Statistical evaluation was performed using pairwise t-tests (two-sided) with FDR correction for multiple hypotheses. Exact FDR values shown in red if FDR < 0.05 and grey if FDR > 0.05. See also Supplementary Table 7.

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Extended Data Fig. 9 Proportional enrichment of glucose-derived metabolites is not altered by the loss of p53. Related to Fig. 6.

(a) Isotope tracing using 13C6-Glucose. Cells were labeled for 8 hours. Fractional enrichment for intermediate metabolites in glycolysis, PPP, and TCA cycle shown for U2OS and U2OSp53KO cells treated with etoposide for 48 hours. See also Supplementary Table 8. (b) Data collected as in (a) but analyzed to compare the fractional enrichment of glucose-derived metabolites for upstream and downstream components of glycolysis, PPP, or the TCA cycle. Abbreviations: glucose 6-phosphate (G6P), 2-phosphoglycerate (2PG), ribose 5-phosphate (R5P), uridine monophosphate (UMP). Data are mean ± SD of n = 3 independent experiments.

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Extended Data Fig. 10 Hyperactive respiration in p53-deficient cells promotes the lethality of DNA damage through production of NAD + . Related to Fig. 6.

(a) FLICK-based analysis of FK866 sensitivity in cells exposed to DNA damage. Data are the log2-scaled change in DNA damage-induced LF-max in 1 µM FK866 treated vs. DMSO treated samples. Data collected 48 hours after exposure to 0.1 µM Idarubicin or 3.16 µM Teniposide. (b) NAD+ levels evaluated using LC-MS after 4-hour exposure to DMSO, 1 µM FK866, or 1 µM Rotenone. (c) As in (b) but for NADH. (d) Seahorse Mitochondrial Stress Test in the presence and absence of 1 µM FK866. For (a-c), data are mean ± SD of n = 3 independent experiments. For (d), data are mean ± SD of n = 6 independent experiments.

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Supplementary information

Reporting Summary

Supplementary Tables 1–8

All supplementary tables, supplied as eight separate tabs. Legends for the eight supplementary tables are provided in a tab within the file.

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Honeywell, M.E., Isidor, M.S., Harper, N.W. et al. Functional genomic screens with death rate analyses reveal mechanisms of drug action. Nat Chem Biol (2024). https://doi.org/10.1038/s41589-024-01584-7

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