Dynamic Regulation of Caveolin-1 Phosphorylation and Caveolae Formation by Mammalian Target of Rapamycin Complex 2 in Bladder Cancer Cells.

The mammalian target of rapamycin (mTOR) and associated phosphatidylinositol 3-kinase/AKT/mTOR signaling pathway is commonly up-regulated in cancer, including bladder cancer. mTOR complex 2 (mTORC2) is a major regulator of bladder cancer cell migration and invasion, but the mechanisms by which mTORC2 regulates these processes are unclear. A discovery mass spectrometry and reverse-phase protein array-based proteomics dual approach was used to identify novel mTORC2 phosphoprotein targets in actively invading cancer cells. mTORC2 targets included focal adhesion kinase, proto-oncogene tyrosine-protein kinase Src, and caveolin-1 (Cav-1), among others. Functional testing shows that mTORC2 regulates Cav-1 localization and dynamic phosphorylation of Cav-1 on Y14. Regulation of Cav-1 activity by mTORC2 also alters the abundance of caveolae, which are specialized lipid raft invaginations of the plasma membrane associated with cell signaling and membrane compartmentalization. Our results demonstrate a unique role for mTORC2-mediated regulation of caveolae formation in actively migrating cancer cells.

POPSTR: Inference of Admixed Population Structure Based on Single-Nucleotide Polymorphisms and Copy Number Variations.

Statistical approaches for population structure estimation have been predominantly driven by a particular data type, single-nucleotide polymorphisms (SNPs). However, in the presence of weak identifiability in SNPs, population structure estimation can suffer from undesirable accuracy loss. Copy number variations (CNVs) are genomic structural variants with loci that are commonly shared within a specific population and thus provide valuable information for estimation of the ancestry of sampled populations. We develop a Bayesian joint modeling framework of SNPs and CNVs, called POPSTR, to better understand population structure than approaches that use SNPs solely. To deal with the increased data volume, we use the Metropolis Adjusted Langevin algorithm (MALA) that guides the target distribution in a computationally efficient way. We illustrate applications of our approach using the HapMap 2005 project data. We carry out simulation studies and show that the performance of our approach is comparable or better than that of popular benchmarks, STRUCTURE and ADMIXTURE. We also observe that using only CNVs can be remarkably efficient if SNP data are not available.

In silico analysis of autoimmune diseases and genetic relationships to vaccination against infectious diseases.

BACKGROUND: Near universal administration of vaccines mandates intense pharmacovigilance for vaccine safety and a stringently low tolerance for adverse events. Reports of autoimmune diseases (AID) following vaccination have been challenging to evaluate given the high rates of vaccination, background incidence of autoimmunity, and low incidence and variable times for onset of AID after vaccinations. In order to identify biologically plausible pathways to adverse autoimmune events of vaccine-related AID, we used a systems biology approach to create a matrix of innate and adaptive immune mechanisms active in specific diseases, responses to vaccine antigens, adjuvants, preservatives and stabilizers, for the most common vaccine-associated AID found in the Vaccine Adverse Event Reporting System. RESULTS: This report focuses on Guillain-Barre Syndrome (GBS), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Idiopathic (or immune) Thrombocytopenic Purpura (ITP). Multiple curated databases and automated text mining of PubMed literature identified 667 genes associated with RA, 448 with SLE, 49 with ITP and 73 with GBS. While all data sources provided valuable and unique gene associations, text mining using natural language processing (NLP) algorithms provided the most information but required curation to remove incorrect associations. Six genes were associated with all four AIDs. Thirty-three pathways were shared by the four AIDs. Classification of genes into twelve immune system related categories identified more "Th17 T-cell subtype" genes in RA than the other AIDs, and more "Chemokine plus Receptors" genes associated with RA than SLE. Gene networks were visualized and clustered into interconnected modules with specific gene clusters for each AID, including one in RA with ten C-X-C motif chemokines. The intersection of genes associated with GBS, GBS peptide auto-antigens, influenza A infection, and influenza vaccination created a subnetwork of genes that inferred a possible role for the MAPK signaling pathway in influenza vaccine related GBS. CONCLUSIONS: Results showing unique and common gene sets, pathways, immune system categories and functional clusters of genes in four autoimmune diseases suggest it is possible to develop molecular classifications of autoimmune and inflammatory events. Combining this information with cellular and other disease responses should greatly aid in the assessment of potential immune-mediated adverse events following vaccination.