Micron-scale plasma membrane curvature is recognized by the septin cytoskeleton

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Cell Biology 213 (2016): 23-32, doi: 10.1083/jcb.201512029.Cells change shape in response to diverse environmental and developmental conditions, creating topologies with micron-scale features. Although individual proteins can sense nanometer-scale membrane curvature, it is unclear if a cell could also use nanometer-scale components to sense micron-scale contours, such as the cytokinetic furrow and base of neuronal branches. Septins are filament-forming proteins that serve as signaling platforms and are frequently associated with areas of the plasma membrane where there is micron-scale curvature, including the cytokinetic furrow and the base of cell protrusions. We report here that fungal and human septins are able to distinguish between different degrees of micron-scale curvature in cells. By preparing supported lipid bilayers on beads of different curvature, we reconstitute and measure the intrinsic septin curvature preference. We conclude that micron-scale curvature recognition is a fundamental property of the septin cytoskeleton that provides the cell with a mechanism to know its local shape.This work was supported by grants from the National Science Foundation (MCB-507511 to A.S. Gladfelter) and the National Institutes of Health (NIGMS-T32GM008704 to A.A. Bridges)

A novel SNP analysis method to detect copy number alterations with an unbiased reference signal directly from tumor samples

<p>Abstract</p> <p>Background</p> <p>Genomic instability in cancer leads to abnormal genome copy number alterations (CNA) as a mechanism underlying tumorigenesis. Using microarrays and other technologies, tumor CNA are detected by comparing tumor sample CN to normal reference sample CN. While advances in microarray technology have improved detection of copy number alterations, the increase in the number of measured signals, noise from array probes, variations in signal-to-noise ratio across batches and disparity across laboratories leads to significant limitations for the accurate identification of CNA regions when comparing tumor and normal samples.</p> <p>Methods</p> <p>To address these limitations, we designed a novel "Virtual Normal" algorithm (VN), which allowed for construction of an unbiased reference signal directly from test samples within an experiment using any publicly available normal reference set as a baseline thus eliminating the need for an in-lab normal reference set.</p> <p>Results</p> <p>The algorithm was tested using an optimal, paired tumor/normal data set as well as previously uncharacterized pediatric malignant gliomas for which a normal reference set was not available. Using Affymetrix 250K Sty microarrays, we demonstrated improved signal-to-noise ratio and detected significant copy number alterations using the VN algorithm that were validated by independent PCR analysis of the target CNA regions.</p> <p>Conclusions</p> <p>We developed and validated an algorithm to provide a virtual normal reference signal directly from tumor samples and minimize noise in the derivation of the raw CN signal. The algorithm reduces the variability of assays performed across different reagent and array batches, methods of sample preservation, multiple personnel, and among different laboratories. This approach may be valuable when matched normal samples are unavailable or the paired normal specimens have been subjected to variations in methods of preservation.</p

Identifier mapping performance for integrating transcriptomics and proteomics experimental results

Background\ud Studies integrating transcriptomic data with proteomic data can illuminate the proteome more clearly than either separately. Integromic studies can deepen understanding of the dynamic complex regulatory relationship between the transcriptome and the proteome. Integrating these data dictates a reliable mapping between the identifier nomenclature resultant from the two high-throughput platforms. However, this kind of analysis is well known to be hampered by lack of standardization of identifier nomenclature among proteins, genes, and microarray probe sets. Therefore data integration may also play a role in critiquing the fallible gene identifications that both platforms emit.\ud \ud Results\ud We compared three freely available internet-based identifier mapping resources for mapping UniProt accessions (ACCs) to Affymetrix probesets identifications (IDs): DAVID, EnVision, and NetAffx. Liquid chromatography-tandem mass spectrometry analyses of 91 endometrial cancer and 7 noncancer samples generated 11,879 distinct ACCs. For each ACC, we compared the retrieval sets of probeset IDs from each mapping resource. We confirmed a high level of discrepancy among the mapping resources. On the same samples, mRNA expression was available. Therefore, to evaluate the quality of each ACC-to-probeset match, we calculated proteome-transcriptome correlations, and compared the resources presuming that better mapping of identifiers should generate a higher proportion of mapped pairs with strong inter-platform correlations. A mixture model for the correlations fitted well and supported regression analysis, providing a window into the performance of the mapping resources. The resources have added and dropped matches over two years, but their overall performance has not changed.\ud \ud Conclusions\ud The methods presented here serve to achieve concrete context-specific insight, to support well-informed decisions in choosing an ID mapping strategy for "omic" data merging

Mobility And Privacy: Exploring Technical And Social Issues In Emerging Pervasive Sensor Networks

This dissertation considers two important topics related to advancing wireless sensor network (WSN) technology: the privacy concerns raised by ever increasing collection personally identifying data by sensing systems, and the opportunities for synergetic function created by imbuing sensor platforms with mobility. On the privacy side, the dissertation focuses on the collection of power consumption data in current and future demand-response systems. We build a data-gathering and behavior extraction system and conduct a small-scale monitoring experiment on a private residence. Our results show that certain personal information may be estimated with a high degree of accuracy. On the mobility side, we consider two difficult problems in multi-agent coordination: the Multiple Path Consensus (MPC) problem, and the Multiple Sensing Region Field of Interest (MSRF) problem. We characterize both problems as NP-complete, then proceed to develop computationally tractable formulations for each. We then develop algorithms which are able to solve practically-sized instances of these problems to optimality. Finally, we develop a practical real-world platform upon which to test multi-agent coordination algorithms, and give an example "iteratively-deployed WSN" application

The ENVISIONQuery package in BioConductor: Retrieving data through the Enfin-Encore annotation portal.

The Enfin-Encore (EnCore) is the integration platform for the ENFIN European Network of Excellence [1], which provides a portal to various database resources with a special focus on systems biology

The IdMappingAnalysis package in Bioconductor: Critically comparing identifier maps retrieved from bioinformatics annotation resources

With increasing frequency, studies of biological samples include processing on two (or more) high-throughput platforms. Each platform produces a large set of features, each labeled by an identifier. It is one thing to merge the data by sample, simply combining the features on both platforms into a single data set. However, exploiting the full biological significance of the data depends on linking a feature from one platform with a feature on the other, where the pair of features ar