Origins of cell-to-cell variability in apoptosis

Abstract

Diversity within a population of organisms is typically ascribed to genetic differences. However, even members of a genetically identical group of cells or organisms in identical environments can exhibit variability in state and phenotype. One striking example of such heterogeneity is revealed when a genetically identical population of human cells is exposed to saturating doses of a death-inducing drug called TRAIL - many cells in the population will undergo apoptosis, a form of controlled cell death, but a fraction of cells always survives the treatment. The goal of this thesis was to understand the origins of variability in both the timing and the probability of death in TRAIL-induced apoptosis. To this end, both experimental and computational methods were implemented. Experiments examining the response of sister cells to TRAIL provided strong evidence that variability in initial conditions played a key role, and ruled out genetic, stochastic, and cell cycle effects as possible causes of heterogeneity in response. A detailed analysis of the relative contributions of three segments of the TRAIL pathway revealed that the majority of the variability in time-to-death arose upstream of mitochondrial outer membrane permeabilization (MOMP), with little contribution from downstream reactions. More specifically, the rate of cleavage of initiator caspase substrates was highly predictive of a cell's death time. However, to determine whether (as opposed to when) a cell will die, variation in the MOMP threshold became critical.

(cont.) This dependency was indicated by observation of the height of the MOMP threshold in surviving and dying cells and by modulation of this threshold via overexpression of anti-apoptotic regulators of MOMP. Simulations of cell-to-cell variability in TRAIL-induced apoptosis confirmed that the endogenous variability in apoptotic regulators was sufficient to produce the observed variability in death time. However, knowledge of the concentration of individual proteins did not allow prediction of death time because variation in other proteins masked the underlying trends. The ability to simulate heterogeneity in cellular response also led to the development of novel, biologically intuitive methods of sensitivity analysis, which revealed that sensitivities shift depending on whether knowledge of covariance in initial conditions is included. The ability to predict sensitivity and resistance of tumors to TRAIL would be clinically valuable, as TRAIL is currently in clinical trials as an anti-cancer therapy. The results described here represent progress toward understanding the "fractional killing" of tumor cells following exposure to chemotherapy, and for understanding variability in mammalian signaling pathways in general.