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Single-cell Analysis Reveals Quantitative Patterns of DNA Damage Checkpoint Adaptation

Thursday, September 16, 2021 - 11:00
Sahand Jamal Rahi
Address: ID de réunion : 892 640 6558 Code secret : 7mdSKr

Why biological quality-control systems fail is often mysterious. Checkpoints in yeast and animals are overridden (adapt) after prolonged arrests allowing self-replication to proceed despite the continued presence of errors. Although critical for biological systems, checkpoint adaptation is not understood quantitatively or at the system level by experiment or theory. To uncover potential patterns in checkpoint adaptation, we derived the mathematically optimal checkpoint strategy, balancing the trade-off between risk and opportunities for growth. The theory predicts the optimal override time based on two inputs, the statistics i) of error correction and ii) of survival. We applied the theory experimentally to the DNA damage checkpoint in budding yeast whose override is not understood quantitatively, functionally, or at the system level. Using a fluorescent construct which allowed cells with DNA breaks to be isolated by flow cytometry, we quantified i) the probability distribution of repair for a double-strand DNA break (DSB), including for rare events deep in the tail of the distribution, as well as ii) the survival probability after override. Based on these two measurements, the optimal checkpoint theory predicted remarkably accurately the DNA damage checkpoint override times as a function of DSB numbers, which we also measured for the first time precisely. Thus, a first-principles calculation uncovered hitherto hidden patterns underlying the highly noisy checkpoint override process. Our multi-DSB results revise well-known bulk culture measurements and show that override is a more general phenomenon than previously thought. Further, we show that override is an advantageous strategy in cells with wild-type DNA repair genes. The universal nature of the balance between risk and self-replication opportunity ought to be relevant to other biological systems as well.

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