ARHGAP18 blocking peptide

The imaging genetics approach to studying the genetic basis of disease leverages the individual strengths of both neuroimaging and genetic studies by visualizing and quantifying the brain activation patterns in the context of genetic background. Brain imaging as an intermediate phenotype can help clarify the functional link among genes, the molecular networks in which they participate, and brain circuitry and function. Integrating genetic data from a genome-wide association study (GWAS) with brain imaging as a quantitative trait (QT) phenotype can increase the statistical power to identify risk genes. A QT analysis using brain imaging (DLPFC activation during a working memory task) as a quantitative trait has identified unanticipated risk genes for schizophrenia. Several of these genes (RSRC1, ARHGAP18, ROBO1-ROBO2, GPC1, TNIK, and CTXN3-SLC12A2) have functions related to progenitor cell proliferation, migration, and differentiation, cytoskeleton reorganization, axonal connectivity, and development of forebrain structures. These genes, however, do not function in isolation but rather through gene regulatory networks. To obtain a deeper understanding how the GWAS-identified genes participate in larger gene regulatory networks, we measured correlations among transcript levels in the mouse and human postmortem tissue and performed a gene set enrichment analysis (GSEA) that identified several microRNA associated with schizophrenia (448, 218, 137). The results of such computational approaches can be further validated in animal experiments in which the networks are experimentally studied and perturbed with specific compounds. Glypican 1 and FGF17 mouse models for example, can be used to study such gene regulatory networks. The model demonstrates epistatic interactions between FGF and glypican on brain development and may be a useful model of negative symptom schizophrenia. (C) 2010 Elsevier Inc. All rights reserved.

Objective: Cellular senescence has been thought to be an important barrier to tumor formation. Recent studies have shown that stress-induced premature senescence (SIPS) can promote partial tumor invasion, but how SIPS affects diffuse large B-cell lymphoma (DLBCL) remains inconclusive. This study aimed to address that issue. Materials and Methods: The immunophenotype of the LY8 cell line was measured with flow cytometry. SIPS induced by tert-butyl hydroperoxide (tBHP) was detected by senescence 13-galactosidase staining. Cell proliferation was analyzed with CCK8 and expression levels of ARHGAP18 (SENEX gene-encoding protein), p16/p21, and Rb/pRb were measured with western blot. LY8 cells were transfected with SENEX-SiRNA/NC and verified by western blot. Results: Our results suggested that the immunophenotype of the LY8 cell line is CD19-, CD20-, and CD10-positive and the immunoglobulin light chain is the kappa type. The cellular senescence model of DLBCL could be successfully induced by 30 pM tBHP. ARHGAP18, p21, p16, and Rb protein levels were significantly increased but the level of pRb expression was decreased in the SIPS group compared with other groups. Meanwhile, the proliferation rate was increased in the SIPS group more than other tBHP groups. Furthermore, the expressions of p21 and p16 were significantly decreased in the SENEX-SiRNA group compared with the negative control group. Conclusion: SIPS formation activates ARHGAP18 and the p16/Rb pathway and promotes DLBCL cell proliferation. Furthermore, SENEX activates the p16 pathway in DLBCL SIPS promotes proliferation by activating SENEX and the p16/Rb pathway in DLBCL SENEX-related SIPS may serve as an important target for relapsed/refractory DLBCL therapy.