PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-3 (3)
 

Clipboard (0)
None

Select a Filter Below

Journals
Authors
Year of Publication
Document Types
1.  KPNA6 (Importin α7)-Mediated Nuclear Import of Keap1 Represses the Nrf2-Dependent Antioxidant Response ▿  
Molecular and Cellular Biology  2011;31(9):1800-1811.
The transcription factor Nrf2 has emerged as a master regulator of cellular redox homeostasis. As an adaptive response to oxidative stress, Nrf2 activates the transcription of a battery of genes encoding antioxidants, detoxification enzymes, and xenobiotic transporters by binding the cis-antioxidant response element in the promoter regions of genes. The magnitude and duration of inducible Nrf2 signaling is delicately controlled at multiple levels by Keap1, which targets Nrf2 for redox-sensitive ubiquitin-mediated degradation in the cytoplasm and exports Nrf2 from the nucleus. However, it is not clear how Keap1 gains access to the nucleus. In this study, we show that Keap1 is constantly shuttling between the nucleus and the cytoplasm under physiological conditions. The nuclear import of Keap1 requires its C-terminal Kelch domain and is independent of Nrf1 and Nrf2. We have determined that importin α7, also known as karyopherin α6 (KPNA6), directly interacts with the Kelch domain of Keap1. Overexpression of KPNA6 facilitates Keap1 nuclear import and attenuates Nrf2 signaling, whereas knockdown of KPNA6 slows down Keap1 nuclear import and enhances the Nrf2-mediated adaptive response induced by oxidative stress. Furthermore, KPNA6 accelerates the clearance of Nrf2 protein from the nucleus during the postinduction phase, therefore promoting restoration of the Nrf2 protein to basal levels. These findings demonstrate that KPNA6-mediated Keap1 nuclear import plays an essential role in modulating the Nrf2-dependent antioxidant response and maintaining cellular redox homeostasis.
doi:10.1128/MCB.05036-11
PMCID: PMC3133232  PMID: 21383067
2.  Cross-scale, cross-pathway evaluation using an agent-based non-small cell lung cancer model 
Bioinformatics  2009;25(18):2389-2396.
We present a multiscale agent-based non-small cell lung cancer model that consists of a 3D environment with which cancer cells interact while processing phenotypic changes. At the molecular level, transforming growth factor β (TGFβ) has been integrated into our previously developed in silico model as a second extrinsic input in addition to epidermal growth factor (EGF). The main aim of this study is to investigate how the effects of individual and combinatorial change in EGF and TGFβ concentrations at the molecular level alter tumor growth dynamics on the multi-cellular level, specifically tumor volume and expansion rate. Our simulation results show that separate EGF and TGFβ fluctuations trigger competing multi-cellular phenotypes, yet synchronous EGF and TGFβ signaling yields a spatially more aggressive tumor that overall exhibits an EGF-driven phenotype. By altering EGF and TGFβ concentration levels simultaneously and asynchronously, we discovered a particular region of EGF-TGFβ profiles that ensures phenotypic stability of the tumor system. Within this region, concentration changes in EGF and TGFβ do not impact the resulting multi-cellular response substantially, while outside these concentration ranges, a change at the molecular level will substantially alter either tumor volume or tumor expansion rate, or both. By evaluating tumor growth dynamics across different scales, we show that, under certain conditions, therapeutic targeting of only one signaling pathway may be insufficient. Potential implications of these in silico results for future clinico-pharmacological applications are discussed.
Contact: deisboec@helix.mgh.harvard.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btp416
PMCID: PMC2735669  PMID: 19578172
3.  Cross-Scale Sensitivity Analysis of a Non-Small Cell Lung Cancer Model: Linking Molecular Signaling Properties to Cellular Behavior 
Bio Systems  2008;92(3):249-258.
Sensitivity analysis is an effective tool for systematically identifying specific perturbations in parameters that have significant effects on the behavior of a given biosystem, at the scale investigated. In this work, using a two-dimensional, multiscale non-small cell lung cancer (NSCLC) model, we examine the effects of perturbations in system parameters which span both molecular and cellular levels, i.e. across scales of interest. This is achieved by first linking molecular and cellular activities and then assessing the influence of parameters at the molecular level on the tumor’s spatio-temporal expansion rate, which serves as the output behavior at the cellular level. Overall, the algorithm operated reliably over relatively large variations of most parameters, hence confirming the robustness of the model. However, three pathway components (proteins PKC, MEK, and ERK) and eleven reaction steps were determined to be of critical importance by employing a sensitivity coefficient as an evaluation index. Each of these sensitive parameters exhibited a similar changing pattern in that a relatively larger increase or decrease in its value resulted in a lesser influence on the system’s cellular performance. This study provides a novel cross-scaled approach to analyzing sensitivities of computational model parameters and proposes its application to interdisciplinary biomarker studies.
doi:10.1016/j.biosystems.2008.03.002
PMCID: PMC2430419  PMID: 18448237
agent-based model; cellular behavior; epidermal growth factor; expansion rate; non-small cell lung cancer; sensitivity analysis

Results 1-3 (3)