Dataset Information

Tang2010_PolyGlutamate

ABSTRACT: This a model from the article: Experimental and computational analysis of polyglutamine-mediated cytotoxicity. Tang MY, Proctor CJ, Woulfe J, Gray DA. PLoS Comput Biol.2010 Sep 23;6(9). 20885783, Abstract: Expanded polyglutamine (polyQ) proteins are known to be the causative agents of a number of human neurodegenerative diseases but the molecular basis of their cytoxicity is still poorly understood. PolyQ tracts may impede the activity of the proteasome, and evidence from single cell imaging suggests that the sequestration of polyQ into inclusion bodies can reduce the proteasomal burden and promote cell survival, at least in the short term. The presence of misfolded protein also leads to activation of stress kinases such as p38MAPK, which can be cytotoxic. The relationships of these systems are not well understood. We have used fluorescent reporter systems imaged in living cells, and stochastic computer modeling to explore the relationships of polyQ, p38MAPK activation, generation of reactive oxygen species (ROS), proteasome inhibition, and inclusion body formation. In cells expressing a polyQ protein inclusion, body formation was preceded by proteasome inhibition but cytotoxicity was greatly reduced by administration of a p38MAPK inhibitor. Computer simulations suggested that without the generation of ROS, the proteasome inhibition and activation of p38MAPK would have significantly reduced toxicity. Our data suggest a vicious cycle of stress kinase activation and proteasome inhibition that is ultimately lethal to cells. There was close agreement between experimental data and the predictions of a stochastic computer model, supporting a central role for proteasome inhibition and p38MAPK activation in inclusion body formation and ROS-mediated cell death. This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team. For more information see the terms of use. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

DISEASE(S): Neurodegenerative Disease

SUBMITTER: Carole Proctor

PROVIDER: BIOMD0000000285 | BioModels | 2010-09-27

REPOSITORIES: BioModels

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Publications

Experimental and computational analysis of polyglutamine-mediated cytotoxicity.

Tang Matthew Y MY Proctor Carole J CJ Woulfe John J Gray Douglas A DA

PLoS computational biology 20100923 9

Expanded polyglutamine (polyQ) proteins are known to be the causative agents of a number of human neurodegenerative diseases but the molecular basis of their cytoxicity is still poorly understood. PolyQ tracts may impede the activity of the proteasome, and evidence from single cell imaging suggests that the sequestration of polyQ into inclusion bodies can reduce the proteasomal burden and promote cell survival, at least in the short term. The presence of misfolded protein also leads to activatio ...[more]

PMID: 20885783

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Project description:This a model from the article: Modelling the Role of UCH-L1 on Protein Aggregation in Age-Related Neurodegeneration. Proctor CJ, Tangeman PJ, Ardley HC. PLoS One. 2010 Oct 6;5(10):e13175 20949132 , Abstract: Overexpression of the de-ubiquitinating enzyme UCH-L1 leads to inclusion formation in response to proteasome impairment. These inclusions contain components of the ubiquitin-proteasome system and α-synuclein confirming that the ubiquitin-proteasome system plays an important role in protein aggregation. The processes involved are very complex and so we have chosen to take a systems biology approach to examine the system whereby we combine mathematical modelling with experiments in an iterative process. The experiments show that cells are very heterogeneous with respect to inclusion formation and so we use stochastic simulation. The model shows that the variability is partly due to stochastic effects but also depends on protein expression levels of UCH-L1 within cells. The model also indicates that the aggregation process can start even before any proteasome inhibition is present, but that proteasome inhibition greatly accelerates aggregation progression. This leads to less efficient protein degradation and hence more aggregation suggesting that there is a vicious cycle. However, proteasome inhibition may not necessarily be the initiating event. Our combined modelling and experimental approach show that stochastic effects play an important role in the aggregation process and could explain the variability in the age of disease onset. Furthermore, our model provides a valuable tool, as it can be easily modified and extended to incorporate new experimental data, test hypotheses and make testable predictions. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Project description:Proctor2007 - Age related decline of proteolysis, ubiquitin-proteome system This is a stochastic model of the ubiquitin-proteasome system for a generic pool of native proteins (NatP), which have a half-life of about 10 hours under normal conditions. It is assumed that these proteins are only degraded after they have lost their native structure due to a damage event. This is represented in the model by the misfolding reaction which depends on the level of reactive oxygen species (ROS) in the cell. Misfolded proteins (MisP) are first bound by an E3 ubiquitin ligase. Ubiquitin (Ub) is activated by E1 (ubiquitin-activating enzyme) and then passed to E2 (ubiquitin-conjugating enzyme). The E2 enzyme then passes the ubiquitin molecule to the E3/MisP complex with the net effect that the misfolded protein is monoubiquitinated and both E2 and E3 are released. Further ubiquitin molecules are added in a step-wise manner. When the chain of ubiquitin molecules is of length 4 or more, the polyubiquitinated misfolded protein may bind to the proteasome. The model also includes de-ubiquitinating enzymes (DUB) which cleave ubiquitin molecules from the chain in a step-wise manner. They work on chains attached to misfolded proteins both unbound and bound to the proteasomes. Misfolded proteins bound to the proteasome may be degraded releasing ubiquitin. Misfolded proteins including ubiquitinated proteins may also aggregate. Aggregates (AggP) may be sequestered (Seq_AggP) which takes them out of harm's way or they may bind to the proteasome (AggP_Proteasome). Proteasomes bound by aggregates are no longer available for protein degradation. Figure 2 and Figure 3 has been simulated using Gillespie2. This model is described in the article: An in silico model of the ubiquitin-proteasome system that incorporates normal homeostasis and age-related decline. Proctor CJ, Tsirigotis M, Gray DA. BMC Syst Biol 2007; 1: 17 Abstract: BACKGROUND: The ubiquitin-proteasome system is responsible for homeostatic degradation of intact protein substrates as well as the elimination of damaged or misfolded proteins that might otherwise aggregate. During ageing there is a decline in proteasome activity and an increase in aggregated proteins. Many neurodegenerative diseases are characterised by the presence of distinctive ubiquitin-positive inclusion bodies in affected regions of the brain. These inclusions consist of insoluble, unfolded, ubiquitinated polypeptides that fail to be targeted and degraded by the proteasome. We are using a systems biology approach to try and determine the primary event in the decline in proteolytic capacity with age and whether there is in fact a vicious cycle of inhibition, with accumulating aggregates further inhibiting proteolysis, prompting accumulation of aggregates and so on. A stochastic model of the ubiquitin-proteasome system has been developed using the Systems Biology Mark-up Language (SBML). Simulations are carried out on the BASIS (Biology of Ageing e-Science Integration and Simulation) system and the model output is compared to experimental data wherein levels of ubiquitin and ubiquitinated substrates are monitored in cultured cells under various conditions. The model can be used to predict the effects of different experimental procedures such as inhibition of the proteasome or shutting down the enzyme cascade responsible for ubiquitin conjugation. RESULTS: The model output shows good agreement with experimental data under a number of different conditions. However, our model predicts that monomeric ubiquitin pools are always depleted under conditions of proteasome inhibition, whereas experimental data show that monomeric pools were depleted in IMR-90 cells but not in ts20 cells, suggesting that cell lines vary in their ability to replenish ubiquitin pools and there is the need to incorporate ubiquitin turnover into the model. Sensitivity analysis of the model revealed which parameters have an important effect on protein turnover and aggregation kinetics. CONCLUSION: We have developed a model of the ubiquitin-proteasome system using an iterative approach of model building and validation against experimental data. Using SBML to encode the model ensures that it can be easily modified and extended as more data become available. Important aspects to be included in subsequent models are details of ubiquitin turnover, models of autophagy, the inclusion of a pool of short-lived proteins and further details of the aggregation process. This model is hosted on BioModels Database and identified by: BIOMD0000000105. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Dataset Information

Tang2010_PolyGlutamate

Publications

Experimental and computational analysis of polyglutamine-mediated cytotoxicity.

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