RIN1 Is an ABL Tyrosine Kinase Activator and a Regulator of Epithelial-Cell Adhesion and Migration

SummaryBackground: ABL tyrosine kinases control actin remodeling in development and in response to environmental stimuli. These changes affect cell adhesion, cell migration, and cell-cell contact. Little is known, however, about upstream mechanisms regulating ABL protein activation.Results: We report that the RAS effector RIN1 is an activator of ABL tyrosine kinases. RIN1 expression in fibroblasts promotes the formation of membrane spikes; similar effects have been reported for ABL overexpression. RIN1 binds to the ABL SH3 and SH2 domains, and these interactions stimulate ABL2 catalytic activity. This leads to increased phosphorylation of CRK and CRKL, inhibiting these cytoskeletal regulators by promoting intramolecular over intermolecular associations. Activated RAS participates in a stable RAS-RIN1-ABL2 complex and stimulates the tyrosine kinase-activation function of RIN1. Deletion of the RAS binding domain (RBD) strongly stimulated the ABL2 activation function of RIN1, suggesting that RAS activation results from the relief of RIN1 autoinhibition. The ABL binding domain of RIN1 (RIN1-ABD) increased the activity of ABL2 immune complexes and purified RIN1-ABD-stimulated ABL2 kinase activity toward CRK. Mammary epithelial cells (MECs) from Rin1−/− mice showed accelerated cell adhesion and increased motility in comparison to wild-type cells. Knockdown of RIN1 in epithelial-cell lines blocked the induction of CRKL phosphorylation, confirming that RIN1 normally functions as an inhibitor of cell motility.Conclusions: RIN1 is a directly binding ABL tyrosine kinase activator in cells as well as in a defined-component assay. In response to environmental changes, this novel signal pathway mediates actin remodeling associated with adhesion and migration of epithelial cells

Where Have All the Interactions Gone? Estimating the Coverage of Two-Hybrid Protein Interaction Maps

Yeast two-hybrid screens are an important method for mapping pairwise physical interactions between proteins. The fraction of interactions detected in independent screens can be very small, and an outstanding challenge is to determine the reason for the low overlap. Low overlap can arise from either a high false-discovery rate (interaction sets have low overlap because each set is contaminated by a large number of stochastic false-positive interactions) or a high false-negative rate (interaction sets have low overlap because each misses many true interactions). We extend capture–recapture theory to provide the first unified model for false-positive and false-negative rates for two-hybrid screens. Analysis of yeast, worm, and fly data indicates that 25% to 45% of the reported interactions are likely false positives. Membrane proteins have higher false-discovery rates on average, and signal transduction proteins have lower rates. The overall false-negative rate ranges from 75% for worm to 90% for fly, which arises from a roughly 50% false-negative rate due to statistical undersampling and a 55% to 85% false-negative rate due to proteins that appear to be systematically lost from the assays. Finally, statistical model selection conclusively rejects the Erdös-Rényi network model in favor of the power law model for yeast and the truncated power law for worm and fly degree distributions. Much as genome sequencing coverage estimates were essential for planning the human genome sequencing project, the coverage estimates developed here will be valuable for guiding future proteomic screens. All software and datasets are available in Datasets S1 and S2, Figures S1–S5, and Tables S1−S6, and are also available from our Web site, http://www.baderzone.org

Benchmark Comparison of Cloud Analytics Methods Applied to Earth Observations

Earth Observation data are a vital resource for studying long term changes, but the large data volumes can be challenging to analyze. Time series analysis in particular is hampered by the typical thin-time-slice file organization. We examine several potential solutions inspired in large part by the data-parallel methods that have arisen with cloud computing. These solutions include various combinations of data re-organization, spatial indexing, distributed storage and pre-computation that we term "Analytics Optimized Data Stores" (AODS). We find that even simple solutions (such as a data cube) produce more than an order of magnitude improvement; the best provide two to three orders of magnitude improvement. The most performant solutions have tradeoffs in terms of generality or storage footprint, but may nonetheless be useful components in data analytics frameworks where performance is critical

Whole Genome Sequencing in Psychiatric Disorders: the WGSPD Consortium

As technology advances, whole genome sequencing (WGS) is likely to supersede other genotyping technologies. The rate of this change depends on its relative cost and utility. Variants identified uniquely through WGS may reveal novel biological pathways underlying complex disorders and provide high-resolution insight into when, where, and in which cell type these pathways are affected. Alternatively, cheaper and less computationally intensive approaches may yield equivalent insights. Understanding the role of rare variants in the noncoding gene-regulating genome, through pilot WGS projects, will be critical to determine which of these two extremes best represents reality. With large cohorts, well-defined risk loci, and a compelling need to understand the underlying biology, psychiatric disorders have a role to play in this preliminary WGS assessment. The WGSPD consortium will integrate data for 18,000 individuals with psychiatric disorders, beginning with autism spectrum disorder, schizophrenia, bipolar disorder, and major depressive disorder, along with over 150,000 controls