Anti-GNA13 (full length) polyclonal antibody

Among the 10 Frizzled (FZD) isoforms belonging to the Class F of G protein-coupled receptors (GPCRs), FZD(10) remains the most enigmatic. FZD10 shows homology to FZD(4) and FZD(9) and was previously implicated in both beta-catenin-dependent and-independent signalling. In normal tissue, FZD(10) levels are generally very low; however, its upregulation in synovial carcinoma has attracted some attention for therapy. Our findings identify FZD(10), as a receptor interacting with and signalling through the heterotrimeric G protein G alpha(13) but not G alpha(12), G alpha(i1,) G alpha(oA), G alpha(s), or G alpha(q). Stimulation with the FZD agonist WNT induced the dissociation of the G alpha(13) protein from FZD(10), and led to global G alpha(12/13)-dependent cell changes assessed by dynamic mass redistribution measurements. Furthermore, we show that FZD(10) mediates G alpha(12/13) activation-dependent induction of YAP/TAZ transcriptional activity. In addition, we show a distinct expression of FZD(10) in embryonic CNS endothelial cells at E11.5-E14.5. Given the well-known importance of G alpha(13) signalling for the development of the vascular system, the selective expression of FZD(10) in brain vascular endothelial cells points at a potential role of FZD(10)-G alpha(13) signalling in CNS angiogenesis. (C) 2017 Elsevier Inc. All rights reserved.

With the recent success of genome-wide association studies (GWAS), a wealth of association data has been accomplished for more than 200 complex diseases/traits, proposing a strong demand for data integration and interpretation. A combinatory analysis of multiple GWAS datasets, or an integrative analysis of GWAS data and other high-throughput data, has been particularly promising. In this study, we proposed an integrative analysis framework of multiple GWAS datasets by overlaying association signals onto the protein-protein interaction network, and demonstrated it using schizophrenia datasets. Building on a dense module search algorithm, we first searched for significantly enriched subnetworks for schizophrenia in each single GWAS dataset and then implemented a discovery-evaluation strategy to identify module genes with consistent association signals. We validated the module genes in an independent dataset, and also examined them through meta-analysis of the related SNPs using multiple GWAS datasets. As a result, we identified 205 module genes with a joint effect significantly associated with schizophrenia; these module genes included a number of well-studied candidate genes such as DISC1, GNA12, GNA13, GNAI1, GPR17, and GRIN2B. Further functional analysis suggested these genes are involved in neuronal related processes. Additionally, meta-analysis found that 18 SNPs in 9 module genes had P-meta <1x10(-4), including the gene HLA-DQA1 located in the MHC region on chromosome 6, which was reported in previous studies using the largest cohort of schizophrenia patients to date. These results demonstrated our bi-directional network-based strategy is efficient for identifying disease-associated genes with modest signals in GWAS datasets. This approach can be applied to any other complex diseases/traits where multiple GWAS datasets are available.