STL-based analysis of TRAIL-induced apoptosis challenges the notion of type I/type II cell line classification

MicroRNA: a connecting road between apoptosis and cholesterol metabolism

Resistance to apoptosis leads to tumorigenesis and failure of anti-cancer therapy. Recent studies also highlight abrogated lipid/cholesterol metabolism as one of the root causes of cancer that can lead to metastatic transformations. Cancer cells are dependent on tremendous supply of cellular cholesterol for the formation of new membranes and continuation of cell signaling. Cholesterol homeostasis network tightly regulates this metabolic need of cancer cells on cholesterol and other lipids. Genetic landscape is also shared between apoptosis and cholesterol metabolism. MicroRNAs (miRNAs) are the new fine tuners of signaling pathways and cellular processes and are known for their ability to post-transcriptionally repress gene expression in a targeted manner. This review summarizes the current knowledge about the cross talk between apoptosis and cholesterol metabolism via miRNAs. In addition, we also emphasize herein recent therapeutic modulations of specific miRNAs and their promising potential for the treatment of deadly diseases including cancer and cholesterol related pathologies. Understanding of the impact of miRNA-based regulation of apoptosis and metabolic processes is still at its dawn and needs further research for the development of future miRNA-based therapies. As both these physiological processes affect cellular homeostasis, we believe that this comprehensive summary of miRNAs modulating both apoptosis and cholesterol metabolism will open uncharted territory for scientific exploration and will provide the foundation for discovering novel drug targets for cancer and metabolic diseases.

Modeling dynamics of cell-to-cell variability in TRAIL-induced apoptosis explains fractional killing and predicts reversible resistance

Isogenic cells sensing identical external signals can take markedly different decisions. Such decisions often correlate with pre-existing cell-to-cell differences in protein levels. When not neglected in signal transduction models, these differences are accounted for in a static manner, by assuming randomly distributed initial protein levels. However, this approach ignores the a priori non-trivial interplay between signal transduction and the source of this cell-to-cell variability: temporal fluctuations of protein levels in individual cells, driven by noisy synthesis and degradation. Thus, modeling protein fluctuations, rather than their consequences on the initial population heterogeneity, would set the quantitative analysis of signal transduction on firmer grounds. Adopting this dynamical view on cell-to-cell differences amounts to recast extrinsic variability into intrinsic noise. Here, we propose a generic approach to merge, in a systematic and principled manner, signal transduction models with stochastic protein turnover models. When applied to an established kinetic model of TRAIL-induced apoptosis, our approach markedly increased model prediction capabilities. One obtains a mechanistic explanation of yet-unexplained observations on fractional killing and non-trivial robust predictions of the temporal evolution of cell resistance to TRAIL in HeLa cells. Our results provide an alternative explanation to survival via induction of survival pathways since no TRAIL-induced regulations are needed and suggest that short-lived anti-apoptotic protein Mcl1 exhibit large and rare fluctuations. More generally, our results highlight the importance of accounting for stochastic protein turnover to quantitatively understand signal transduction over extended durations, and imply that fluctuations of short-lived proteins deserve particular attention.