Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets

<p>Abstract</p> <p>Background</p> <p>Cancer is caused by genetic abnormalities, such as mutations of oncogenes or tumor suppressor genes, which alter downstream signal transduction pathways and protein-protein interactions. Comparisons of the interactions of proteins in cancerous and normal cells can shed light on the mechanisms of carcinogenesis.</p> <p>Results</p> <p>We constructed initial networks of protein-protein interactions involved in the apoptosis of cancerous and normal cells by use of two human yeast two-hybrid data sets and four online databases. Next, we applied a nonlinear stochastic model, maximum likelihood parameter estimation, and Akaike Information Criteria (AIC) to eliminate false-positive protein-protein interactions in our initial protein interaction networks by use of microarray data. Comparisons of the networks of apoptosis in HeLa (human cervical carcinoma) cells and in normal primary lung fibroblasts provided insight into the mechanism of apoptosis and allowed identification of potential drug targets. The potential targets include BCL2, caspase-3 and TP53. Our comparison of cancerous and normal cells also allowed derivation of several party hubs and date hubs in the human protein-protein interaction networks involved in caspase activation.</p> <p>Conclusion</p> <p>Our method allows identification of cancer-perturbed protein-protein interactions involved in apoptosis and identification of potential molecular targets for development of anti-cancer drugs.</p

Comparisons of Robustness and Sensitivity between Cancer and Normal Cells by Microarray Data

Robustness is defined as the ability to uphold performance in face of perturbations and uncertainties, and sensitivity is a measure of the system deviations generated by perturbations to the system. While cancer appears as a robust but fragile system, few computational and quantitative evidences demonstrate robustness tradeoffs in cancer. Microarrays have been widely applied to decipher gene expression signatures in human cancer research, and quantification of global gene expression profiles facilitates precise prediction and modeling of cancer in systems biology. We provide several efficient computational methods based on system and control theory to compare robustness and sensitivity between cancer and normal cells by microarray data. Measurement of robustness and sensitivity by linear stochastic model is introduced in this study, which shows oscillations in feedback loops of p53 and demonstrates robustness tradeoffs that cancer is a robust system with some extreme fragilities. In addition, we measure sensitivity of gene expression to perturbations in other gene expression and kinetic parameters, discuss nonlinear effects in feedback loops of p53 and extend our method to robustness-based cancer drug design

The Significance of the Lingual Nerve During Periodontal/Implant Surgery

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141105/1/jper0372.pd

Tissue Biotype and Its Relation to the Underlying Bone Morphology

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142069/1/jper0569.pd

Risk Assessment of Lingual Plate Perforation in Posterior Mandibular Region: A Virtual Implant Placement Study Using Coneâ Beam Computed Tomography

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141229/1/jper0129.pd

Quarantine Methods and Prevention of Secondary Outbreak of Pandemic (H1N1) 2009

Quarantine Methods and Secondary Outbreak Preventio

Effect of Coherence on Radiation Forces acting on a Rayleigh Dielectric Sphere

The radiation forces on a Rayleigh dielectric sphere induced by a partially coherent light beam are greatly affected by the coherence of the light beam. The magnitude of the radiation forces on a dielectric sphere near the focus point greatly decreases as the coherence decreases. For the light beam with good coherence, the radiation force may be used to trap a particle; and for the light beam with intermediate coherence, the radiation force may be used to guide and accelerate a particle.Comment: 12 pages, 3 figure

Rapamycin Upregulates Connective Tissue Growth Factor Expression in Hepatic Progenitor Cells Through TGF-β-Smad2 Dependent Signaling

Rapamycin (sirolimus) is a mTOR kinase inhibitor and is widely used as an immunosuppressive drug to prevent graft rejection in organ transplantation currently. However, some recent investigations have reported that it had profibrotic effect in the progression of organ fibrosis, and its precise role in the liver fibrosis is still poorly understood. Here we showed that rapamycin upregulated connective tissue growth factor (CTGF) expression at the transcriptional level in hepatic progenitor cells (HPCs). Using lentivirus-mediated small hairpin RNA (shRNA) we demonstrated that knockdown of mTOR, Raptor, or Rictor mimicked the effect of rapamycin treatment. Mechanistically, inhibition of mTOR activity with rapamycin resulted in a hyperactive PI3K-Akt pathway, whereas this activation inhibited the expression of CTGF in HPCs. Besides, rapamycin activated the TGF-β-Smad signaling, and TGF-β receptor type I (TGFβRI) serine/threonine kinase inhibitors completely blocked the effects of rapamycin on HPCs. Moreover, Smad2 was involved in the induction of CTGF through rapamycin-activated TGF-β-Smad signaling as knockdown completely blocked CTGF induction, while knockdown of Smad4 expression partially inhibited induction, whereas Smad3 knockdown had no effect. Rapamycin also induced ROS generation and latent TGF-β activation which contributed to TGF-β-Smad signaling. In conclusion, this study demonstrates that rapamycin upregulates CTGF in HPCs and suggests that rapamycin has potential fibrotic effect in liver