Dataset Information

Modeling the TNF?-induced apoptosis pathway in hepatocytes.

ABSTRACT: The proinflammatory cytokine TNF? fails to provoke cell death in isolated hepatocytes but has been implicated in hepatocyte apoptosis during liver diseases associated with chronic inflammation. Recently, we showed that TNF? is able to sensitize primary murine hepatocytes cultured on collagen to Fas ligand-induced apoptosis and presented a mathematical model of the sensitizing effect. Here, we analyze how TNF? induces apoptosis in combination with the transcriptional inhibitor actinomycin D (ActD). Accumulation of reactive oxygen species (ROS) in response to TNFR activation turns out to be critical for sustained activation of JNK which then triggers mitochondrial pathway-dependent apoptosis. In addition, the amount of JNK is strongly upregulated in a ROS-dependent way. In contrast to TNF? plus cycloheximide no cFLIP degradation is observed suggesting a different apoptosis pathway in which the Itch-mediated cFLIP degradation and predominantly caspase-8 activation is not involved. Time-resolved data of the respective pro- and antiapoptotic factors are obtained and subjected to mathematical modeling. On the basis of these data we developed a mathematical model which reproduces the complex interplay regulating the phosphorylation status of JNK and generation of ROS. This model was fully integrated with our model of TNF?/Fas ligand sensitizing as well as with a published NF-?B-model. The resulting comprehensive model delivers insight in the dynamical interplay between the TNF? and FasL pathways, NF-?B and ROS and gives an example for successful model integration.

SUBMITTER: Schlatter R

PROVIDER: S-EPMC3080376 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Modeling the TNFα-induced apoptosis pathway in hepatocytes.

Schlatter Rebekka R Schmich Kathrin K Lutz Anna A Trefzger Judith J Sawodny Oliver O Ederer Michael M Merfort Irmgard I

PloS one 20110420 4

The proinflammatory cytokine TNFα fails to provoke cell death in isolated hepatocytes but has been implicated in hepatocyte apoptosis during liver diseases associated with chronic inflammation. Recently, we showed that TNFα is able to sensitize primary murine hepatocytes cultured on collagen to Fas ligand-induced apoptosis and presented a mathematical model of the sensitizing effect. Here, we analyze how TNFα induces apoptosis in combination with the transcriptional inhibitor actinomycin D (ActD ...[more]

PMID: 21533085

Similar Datasets

Project description:In the liver tumor necrosis factor (TNF)-induced signaling critically regulates the immune response of non-parenchymal cells as well as proliferation and apoptosis of hepatocytes via activation of the NF-?B and JNK pathways. Especially, the induction of negative feedback regulators, such as I?B? and A20 is responsible for the dynamic and time-restricted response of these important pathways. However, the precise mechanisms responsible for different TNF-induced phenotypes under physiological stimulation conditions are not completely understood so far. In addition, it is not known if varying TNF concentrations may differentially affect the desensitization properties of both pathways. By using computational modeling, we first showed that TNF-induced activation and downstream signaling is qualitatively comparable between primary mouse hepatocytes and immortalized hepatocellular carcinoma (HCC) cells. In order to define physiologically relevant TNF levels, which allow for an adjustable and dynamic NF-?B/JNK pathway response in parenchymal liver cells, a range of cytokine concentrations was defined that led to gradual pathway responses in HCC cells (1-5 ng/ml). Repeated stimulations with low (1 ng/ml), medium (2.5 ng/ml) and high (5 ng/ml) TNF amounts demonstrated that JNK signaling was still active at cytokine concentrations, which led to dampened NF-?B signaling illustrating differential pathway responsiveness depending on TNF input dynamics. SiRNA-mediated inhibition of the negative feedback regulator A20 (syn. TNFAIP3) or its overexpression did not significantly affect the NF-?B response. In contrast, A20 silencing increased the JNK response, while its overexpression dampened JNK phosphorylation. In addition, the A20 knockdown sensitized hepatocellular cells to TNF-induced cleavage and activity of the effector caspase-3. In conclusion, a mathematical model-based approach shows that the TNF-induced pathway responses are qualitatively comparable in primary and immortalized mouse hepatocytes. The cytokine amount defines the pathway responsiveness under repeated treatment conditions with NF-?B signaling being dampened 'earlier' than JNK. A20 appears to be the molecular switch discriminating between NF-?B and JNK signaling when stimulating with varying physiological cytokine concentrations.

Project description:BACKGROUND: Apoptosis is a cell suicide mechanism that enables multicellular organisms to maintain homeostasis and to eliminate individual cells that threaten the organism's survival. Dependent on the type of stimulus, apoptosis can be propagated by extrinsic pathway or intrinsic pathway. The comprehensive understanding of the molecular mechanism of apoptotic signaling allows for development of mathematical models, aiming to elucidate dynamical and systems properties of apoptotic signaling networks. There have been extensive efforts in modeling deterministic apoptosis network accounting for average behavior of a population of cells. Cellular networks, however, are inherently stochastic and significant cell-to-cell variability in apoptosis response has been observed at single cell level. RESULTS: To address the inevitable randomness in the intrinsic apoptosis mechanism, we develop a theoretical and computational modeling framework of intrinsic apoptosis pathway at single-cell level, accounting for both deterministic and stochastic behavior. Our deterministic model, adapted from the well-accepted Fussenegger model, shows that an additional positive feedback between the executioner caspase and the initiator caspase plays a fundamental role in yielding the desired property of bistability. We then examine the impact of intrinsic fluctuations of biochemical reactions, viewed as intrinsic noise, and natural variation of protein concentrations, viewed as extrinsic noise, on behavior of the intrinsic apoptosis network. Histograms of the steady-state output at varying input levels show that the intrinsic noise could elicit a wider region of bistability over that of the deterministic model. However, the system stochasticity due to intrinsic fluctuations, such as the noise of steady-state response and the randomness of response delay, shows that the intrinsic noise in general is insufficient to produce significant cell-to-cell variations at physiologically relevant level of molecular numbers. Furthermore, the extrinsic noise represented by random variations of two key apoptotic proteins, namely Cytochrome C and inhibitor of apoptosis proteins (IAP), is modeled separately or in combination with intrinsic noise. The resultant stochasticity in the timing of intrinsic apoptosis response shows that the fluctuating protein variations can induce cell-to-cell stochastic variability at a quantitative level agreeing with experiments. Finally, simulations illustrate that the mean abundance of fluctuating IAP protein is positively correlated with the degree of cellular stochasticity of the intrinsic apoptosis pathway. CONCLUSIONS: Our theoretical and computational study shows that the pronounced non-genetic heterogeneity in intrinsic apoptosis responses among individual cells plausibly arises from extrinsic rather than intrinsic origin of fluctuations. In addition, it predicts that the IAP protein could serve as a potential therapeutic target for suppression of the cell-to-cell variation in the intrinsic apoptosis responsiveness.

Dataset Information

Modeling the TNF?-induced apoptosis pathway in hepatocytes.

Publications

Modeling the TNFα-induced apoptosis pathway in hepatocytes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure