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Biology subjects

Fontana, R. E.

Publications and source records attributed to Fontana, R. E..

2 recordsLinked to original sources

p53 controls choice between apoptotic and non-apoptotic death following DNA damage

DNA damage can activate apoptotic and non-apoptotic forms of cell death; however, it remains unclear what features dictate which type of cell death is activated. We report that p53 controls the choice between apoptotic and non-apoptotic death following exposure to DNA damage. In contrast to the conventional model, which suggests that p53-deficient cells should be resistant to DNA damage-induced cell death, we find that p53-deficient cells die at high rates following DNA damage, but exclusively using non-apoptotic mechanisms. Our experimental data and computational modeling reveal that non-apoptotic death in p53-deficient cells has not been observed due to use of assays that are either insensitive to cell death, or that specifically score apoptotic cells. Using functional genetic screening - with an analysis that enables computational inference of the drug-induced death rate - we find in p53-deficient cells that DNA damage activates a mitochondrial respiration-dependent form of cell death, called MPT-driven necrosis. Cells deficient for p53 have high basal respiration, which primes MPT-driven necrosis. Finally, using metabolite profiling, we identified mitochondrial activity-dependent metabolic vulnerabilities that can be targeted to potentiate the lethality of DNA damage specifically in p53-deficient cells. Our findings reveal how the dual functions of p53 in regulating mitochondrial activity and the DNA damage response combine to facilitate the choice between apoptotic and non-apoptotic death.

cell biology↗

Drug GRADE: an integrated analysis of population growth and cell death reveals drug- specific and cancer subtype-specific response profiles

In the pre-clinical evaluation of anti-cancer drugs, two different measurement approaches are used: relative viability, which scores an amalgam of growth arrest and cell death, and fractional viability, which more specifically scores the degree of cell killing. In this study, we directly quantify relationships between drug-induced growth inhibition and drug-induced cell death by counting live and dead cells over time using quantitative microscopy. We find that most drugs affect both growth and death, but with different proportions and with different relative timing. These features lead to a non-uniform and unpredictable relationship between the canonical relative and fractional drug response measurements. To unify these disparate measurements, we create a new data visualization and data analysis platform, called drug GRADE, which characterizes the degree to which cell death contributes to an observed reduction in population size for any given drug. Our new method reveals both drug- and genotype-specific drug responses, which are not captured using traditional pharmaco-metrics. Taken together, this study highlights the extremely idiosyncratic nature of drug-induced growth and cell death and provides a new analysis tool for quantitatively evaluating these behaviors.

systems biology↗