Dynamic Modelling of Pathways to Cellular Senescence Reveals Strategies for Targeted Interventions

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Abstract

Cellular senescence, a state of irreversible cell cycle arrest, is thought to help protect an organism from cancer, yet also contributes to ageing. The changes which occur in senescence are controlled by networks of multiple signalling and feedback pathways at the cellular level, and the interplay between these is difficult to predict and understand. To unravel the intrinsic challenges of understanding such a highly networked system, we have taken a systems biology approach to cellular senescence. We report a detailed analysis of senescence signalling via DNA damage, insulin-TOR, FoxO3a transcription factors, oxidative stress response, mitochondrial regulation and mitophagy. We show in silico and in vitro that inhibition of reactive oxygen species can prevent loss of mitochondrial membrane potential, whilst inhibition of mTOR shows a partial rescue of mitochondrial mass changes during establishment of senescence. Dual inhibition of ROS and mTOR in vitro confirmed computational model predictions that it was possible to further reduce senescence-induced mitochondrial dysfunction and DNA double-strand breaks. However, these interventions were unable to abrogate the senescence-induced mitochondrial dysfunction completely, and we identified decreased mitochondrial fission as the potential driving force for increased mitochondrial mass via prevention of mitophagy. Dynamic sensitivity analysis of the model showed the network stabilised at a new late state of cellular senescence. This was characterised by poor network sensitivity, high signalling noise, low cellular energy, high inflammation and permanent cell cycle arrest suggesting an unsatisfactory outcome for treatments aiming to delay or reverse cellular senescence at late time points. Combinatorial targeted interventions are therefore possible for intervening in the cellular pathway to senescence, but in the cases identified here, are only capable of delaying senescence onset.

Figures

  • Figure 2. ROS inhibition increased mitochondrial membrane potential. (A) Simulated time-courses for mitochondrial membrane potential (ym). Gradual ROS inhibition from 0% (black, control) to 90% predicted an increase in mitochondrial ym due to perturbation of the new mitochondrial population. (B) The model prediction was confirmed by measuring mitochondrial membrane potential by live cell imaging and quantifying the fluorescence intensities (n = 3). Exogenous addition of SOD and catalase significantly increased the average ym (Mann-Whitney test, * P,0.05) in vitro. In silico inhibition of ROS levels also partially reactivated mitochondrial ym in a dose dependent manner, with between 15 and 30% levels giving equivalent restoration of ym to the in vitro data. In vitro mitochondrial ym was determined in MRC5 cells 15, 18 and 21 days post IR using live cell imaging of cells loaded with the mitochondrial ym dependent dye TMRM and non-potential dependent mitotracker green. (C) Example images of data used in (B) for control cells (upper panel) and cells treated with SOD and catalase (100 U each) in the medium (lower panel) for 15 days post IR, stained with the mitochondrial ym dependent dye TMRM, the non-potential dependent dye mitotracker green, and the nuclear counterstain Hoechst 33342. Scale bar is 10 mm. doi:10.1371/journal.pcbi.1003728.g002
  • Figure 3. mTOR inhibition decreased mitochondrial mass. (A) Simulated time-courses for mitochondrial mass. Gradual mTOR specific inhibition from 0% (black, control) to 90% predicted a decrease in mitochondrial mass due to the perturbation of the population of old mitochondria. (B) The model prediction was confirmed by measuring mitochondrial mass using mitotracker green and quantifying the fluorescence intensities (n = 3). Exogenous addition of 10 nM Torin1 significantly decreased the average mass (Mann-Whitney test, * P,0.05) in vitro. In silico inhibition of mTOR levels also partially decreased mitochondrial mass in a dose dependent manner, with between 15 and 30% levels giving equivalent restoration of ym to the in vitro data. (C) Example images of data used in (B) for control cells (upper panel) and cells treated with Torin in the medium (lower panel) for 15 days post IR, stained with the mitochondrial ym dependent dye TMRM, the non-potential dependent dye mitotracker green, and the nuclear counterstain Hoechst 33342. Scale bar is 10 mm. doi:10.1371/journal.pcbi.1003728.g003
  • Figure 4. Combined mTOR-ROS inhibition increased mitochondrial membrane potential. (A) Model predictions were obtained by plotting the intensity for each readout (membrane potential (ym), mitochondrial mass and DNA damage, respectively) with inhibition of mTOR (x axis) and ROS (y axis). The control (no inhibition) is represented as the point (0, 0). Prediction data are shown at days 12, 15, 18 and 21 post-irradiation. (B) In vitro ym, mitochondrial mass and DNA damage foci number upon inhibition of ROS, TOR or combined TOR-ROS at days 12, 15, 18 and 21, or 18 and 21 (for DNA damage foci). Cells were treated with 10 nM Torin1 (TOR inhibitor), or Torin1 with SOD and catalase (100 U each) (n = 3, ANOVA with Dunn’s post-hoc test, * P,0.05 within time points). (C) Example images of data used in (B) for control cells (left panels) and cells treated with Torin1, SOD and catalase in the medium (right panel) stained for ym and mitochondrial mass at day 21 (above panel) or for DNA damage foci at day 18 (lower panel). doi:10.1371/journal.pcbi.1003728.g004
  • Figure 5. AMPK, FoxO3a, or Mitophagy simulated activations consistently improve mitochondrial function. (A) Model single perturbation of AMPK, FoxO3a, or mitophagy from 0% (control, black) to 150% gradual over-activation. (B) Model double perturbations were obtained by plotting the intensity for each readout (left, mitochondrial mass and on the right, membrane potential (ym)) with over-activation of AMPK (x axis) and FoxO3a (y axis) (above), or AMPK (x axis) and mitophagy (y axis) (below). The control (no over-activation) is represented as the point (0, 0). Prediction data are shown at 15 days post-irradiation. doi:10.1371/journal.pcbi.1003728.g005
  • Figure 7. Dynamic sensitivity analysis. Sensitivity analysis at 1, 10 and 20 days post-irradiation indicated three states of cellular senescence: early, middle and late senescence. Sensitivity analysis was computed for the model species (y axis) upon perturbation of the kinetic rate constant parameters (x axis). The parameters k1–k11 are involved in the IIS-TOR signalling sub-network, whereas the groups of parameters k12–k32 and k33– k41 regulate the DDR-ROS signalling response and mitochondrial dynamics, respectively. Model species sensitivities changed over time, highlighting a dysfunction for mitophagy and mitochondria, and DDR-ROS stress-response at the later time points. These dysregulations consolidated over time
  • Table 1. Model Lyapunov exponents.

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Dalle Pezze, P., Nelson, G., Otten, E. G., Korolchuk, V. I., Kirkwood, T. B. L., von Zglinicki, T., & Shanley, D. P. (2014). Dynamic Modelling of Pathways to Cellular Senescence Reveals Strategies for Targeted Interventions. PLoS Computational Biology, 10(8). https://doi.org/10.1371/journal.pcbi.1003728

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