Anti-CLK3 monoclonal antibody, clone 8E7

Recent progress in the understanding of seven-transmembrane receptor (7TMR) signalling has promoted the development of a new generation of pathway selective ligands. The angiotensin II type I receptor (AT(1a)R) is one of the most studied 7TMRs with respect to selective activation of the beta-arrestin dependent signalling. Two complimentary global phosphoproteomics studies have analyzed the complex signalling induced by the AT(1a)R. Here we integrate the data sets from these studies and perform a joint analysis using a novel method for prediction of differential kinase activity from phosphoproteomics data. The method builds upon NetworKIN, which applies sophisticated linear motif analysis in combination with contextual network modelling to predict kinase-substrate associations with high accuracy and sensitivity. These predictions form the basis for subsequently nonparametric statistical analysis to identify likely activated kinases. This suggested that AT(1a)R-dependent signalling activates 48 of the 285 kinases detected in HEK293 cells. Of these, Aurora B, CLK3 and PKG1 have not previously been described in the pathway whereas others, such as PKA, PKB and PKC, are well known. In summary, we have developed a new method for kinase-centric analysis of phosphoproteomes to pinpoint differential kinase activity in large-scale data sets.

Background: Hundreds of genetic variants are thought to contribute to variation in asthma risk by modulating gene expression. Methods that increase the power of genome-wide association studies (GWASs) to identify risk-associated variants are needed. Objective: We sought to develop a method that aggregates the evidence for association with disease risk across expression quantitative trait loci (eQTLs) of a gene and use this approach to identify asthma risk genes. Methods: We developed a gene-based test and software package called EUGENE that (1) is applicable to GWAS summary statistics; (2) considers both cis- and trans-eQTLs; (3) incorporates eQTLs identified in different tissues; and (4) uses simulations to account for multiple testing. We applied this approach to 2 published asthma GWASs (combined n = 46,044) and used mouse studies to provide initial functional insights into 2 genes with novel genetic associations. Results: We tested the association between asthma and 17,190 genes that were found to have cis- and/or trans-eQTLs across 16 published eQTL studies. At an empirical FDR of 5%, 48 genes were associated with asthma risk. Of these, for 37, the association was driven by eQTLs located in established risk loci for allergic disease, including 6 genes not previously implicated in disease cause (eg, LIMS1, TINF2, and SAFB). The remaining 11 significant genes represent potential novel genetic associations with asthma. The association with 4 of these replicated in an independent GWAS: B4GALT3, USMG5, P2RY13, and P2RY14, which are genes involved in nucleotide synthesis or nucleotide-dependent cell activation. In mouse studies, P2ry13 and P2ry14-purinergic receptors activated by adenosine 5-diphosphate and UDP-sugars, respectively-were upregulated after allergen challenge, notably in airway epithelial cells, eosinophils, and neutrophils. Intranasal exposure with receptor agonists induced the release of IL-33 and subsequent eosinophil infiltration into the lungs. Conclusion: We identified novel associations between asthma and eQTLs for 4 genes related to nucleotide synthesis/signaling and demonstrated the power of gene-based analyses of GWASs.