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Sample-based modeling reveals bidirectional interplay between cell cycle progression and extrinsic apoptosis

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  • Dirke Imig
  • Nadine Pollak
  • Frank Allgöwer
  • Markus Rehm

Abstract

Apoptotic cell death can be initiated through the extrinsic and intrinsic signaling pathways. While cell cycle progression promotes the responsiveness to intrinsic apoptosis induced by genotoxic stress or spindle poisons, this has not yet been studied conclusively for extrinsic apoptosis. Here, we combined fluorescence-based time-lapse monitoring of cell cycle progression and cell death execution by long-term time-lapse microscopy with sampling-based mathematical modeling to study cell cycle dependency of TRAIL-induced extrinsic apoptosis in NCI-H460/geminin cells. In particular, we investigated the interaction of cell death timing and progression of cell cycle states. We not only found that TRAIL prolongs cycle progression, but in reverse also that cell cycle progression affects the kinetics of TRAIL-induced apoptosis: Cells exposed to TRAIL in G1 died significantly faster than cells stimulated in S/G2/M. The connection between cell cycle state and apoptosis progression was captured by developing a mathematical model, for which parameter estimation revealed that apoptosis progression decelerates in the second half of the cell cycle. Similar results were also obtained when studying HCT-116 cells. Our results therefore reject the null hypothesis of independence between cell cycle progression and extrinsic apoptosis and, supported by simulations and experiments of synchronized cell populations, suggest that unwanted escape from TRAIL-induced apoptosis can be reduced by enriching the fraction of cells in G1 phase. Besides novel insight into the interrelation of cell cycle progression and extrinsic apoptosis signaling kinetics, our findings are therefore also relevant for optimizing future TRAIL-based treatment strategies.Author summary: TRAIL (TNF-related apoptosis-inducing ligand) induces cell death preferentially in cancer cells. Whether cell cycle progression notably affects extrinsic apoptosis has remained unclear, since systematic experimental and mathematical studies to quantitatively understand such interdependencies remained challenging. Here, we applied statistical and mechanistic modeling, linked with experimental analyses, to determine if cell cycle and apoptosis progression are interconnected. Using sample-based modeling, we demonstrate that times required to commit apoptotic cell death depend on the cell cycle position at the time of TRAIL exposure, with delays manifesting during S phase, around a time that can be defined as a point of apoptosis deceleration (PAD). Overall, cells receiving TRAIL in the G1 phase were more likely to die, providing scope to optimize TRAIL-based treatment strategies with respect to cell cycle dynamics.

Suggested Citation

  • Dirke Imig & Nadine Pollak & Frank Allgöwer & Markus Rehm, 2020. "Sample-based modeling reveals bidirectional interplay between cell cycle progression and extrinsic apoptosis," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-17, June.
  • Handle: RePEc:plo:pcbi00:1007812
    DOI: 10.1371/journal.pcbi.1007812
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    1. Fengzhi Li & Grazia Ambrosini & Emily Y. Chu & Janet Plescia & Simona Tognin & Pier Carlo Marchisio & Dario C. Altieri, 1998. "Control of apoptosis and mitotic spindle checkpoint by survivin," Nature, Nature, vol. 396(6711), pages 580-584, December.
    2. Sabrina L. Spencer & Suzanne Gaudet & John G. Albeck & John M. Burke & Peter K. Sorger, 2009. "Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis," Nature, Nature, vol. 459(7245), pages 428-432, May.
    3. Silvia Márquez-Jurado & Juan Díaz-Colunga & Ricardo Pires Neves & Antonio Martinez-Lorente & Fernando Almazán & Raúl Guantes & Francisco J. Iborra, 2018. "Mitochondrial levels determine variability in cell death by modulating apoptotic gene expression," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    4. Ernst Wit & Edwin van den Heuvel & Jan-Willem Romeijn, 2012. "‘All models are wrong...’: an introduction to model uncertainty," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 217-236, August.
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