Dataset Information

Signaling efficiency of G?q through its effectors p63RhoGEF and GEFT depends on their subcellular location.

ABSTRACT: The p63RhoGEF and GEFT proteins are encoded by the same gene and both members of the Dbl family of guanine nucleotide exchange factors. These proteins can be activated by the heterotrimeric G-protein subunit G?q. We show that p63RhoGEF is located at the plasma membrane, whereas GEFT is confined to the cytoplasm. Live-cell imaging studies yielded quantitative information on diffusion coefficients, association rates and encounter times of GEFT and p63RhoGEF. Calcium signaling was examined as a measure of the signal transmission, revealing more efficient signaling through the membrane-associated p63RhoGEF. A rapamycin dependent recruitment system was used to dynamically alter the subcellular location and concentration of GEFT, showing efficient signaling through GEFT only upon membrane recruitment. Together, our results show efficient signal transmission through membrane located effectors, and highlight a role for increased concentration rather than increased encounter times due to membrane localization in the G?q mediated pathways to p63RhoGEF and PLC?.

SUBMITTER: Goedhart J

PROVIDER: S-EPMC3722567 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Signaling efficiency of Gαq through its effectors p63RhoGEF and GEFT depends on their subcellular location.

Goedhart Joachim J van Unen Jakobus J Adjobo-Hermans Merel J W MJ Gadella Theodorus W J TW

Scientific reports 20130101

The p63RhoGEF and GEFT proteins are encoded by the same gene and both members of the Dbl family of guanine nucleotide exchange factors. These proteins can be activated by the heterotrimeric G-protein subunit Gαq. We show that p63RhoGEF is located at the plasma membrane, whereas GEFT is confined to the cytoplasm. Live-cell imaging studies yielded quantitative information on diffusion coefficients, association rates and encounter times of GEFT and p63RhoGEF. Calcium signaling was examined as a mea ...[more]

PMID: 23884432

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Project description:BACKGROUND: Many biological processes are mediated by dynamic interactions between and among proteins. In order to interact, two proteins must co-occur spatially and temporally. As protein-protein interactions (PPIs) and subcellular location (SCL) are discovered via separate empirical approaches, PPI and SCL annotations are independent and might complement each other in helping us to understand the role of individual proteins in cellular networks. We expect reliable PPI annotations to show that proteins interacting in vivo are co-located in the same cellular compartment. Our goal here is to evaluate the potential of using PPI annotation in determining SCL of proteins in human, mouse, fly and yeast, and to identify and quantify the factors that contribute to this complementarity. RESULTS: Using publicly available data, we evaluate the hypothesis that interacting proteins must be co-located within the same subcellular compartment. Based on a large, manually curated PPI dataset, we demonstrate that a substantial proportion of interacting proteins are in fact co-located. We develop an approach to predict the SCL of a protein based on the SCL of its interaction partners, given sufficient confidence in the interaction itself. The frequency of false positive PPIs can be reduced by use of six lines of supporting evidence, three based on type of recorded evidence (empirical approach, multiplicity of databases, and multiplicity of literature citations) and three based on type of biological evidence (inferred biological process, domain-domain interactions, and orthology relationships), with biological evidence more-effective than recorded evidence. Our approach performs better than four existing prediction methods in identifying the SCL of membrane proteins, and as well as or better for soluble proteins. CONCLUSION: Understanding cellular systems requires knowledge of the SCL of interacting proteins. We show how PPI data can be used more effectively to yield reliable SCL predictions for both soluble and membrane proteins. Scope exists for further improvement in our understanding of cellular function through consideration of the biological context of molecular interactions.

Project description:BACKGROUND: In the past decades, various protein subcellular-location (SCL) predictors have been developed. Most of these predictors, like TMHMM 2.0, SignalP 3.0, PrediSi and Phobius, aim at the identification of one or a few SCLs, whereas others such as CELLO and Psortb.v.2.0 aim at a broader classification. Although these tools and pipelines can achieve a high precision in the accurate prediction of signal peptides and transmembrane helices, they have a much lower accuracy when other sequence characteristics are concerned. For instance, it proved notoriously difficult to identify the fate of proteins carrying a putative type I signal peptidase (SPIase) cleavage site, as many of those proteins are retained in the cell membrane as N-terminally anchored membrane proteins. Moreover, most of the SCL classifiers are based on the classification of the Swiss-Prot database and consequently inherited the inconsistency of that SCL classification. As accurate and detailed SCL prediction on a genome scale is highly desired by experimental researchers, we decided to construct a new SCL prediction pipeline: LocateP. RESULTS: LocateP combines many of the existing high-precision SCL identifiers with our own newly developed identifiers for specific SCLs. The LocateP pipeline was designed such that it mimics protein targeting and secretion processes. It distinguishes 7 different SCLs within Gram-positive bacteria: intracellular, multi-transmembrane, N-terminally membrane anchored, C-terminally membrane anchored, lipid-anchored, LPxTG-type cell-wall anchored, and secreted/released proteins. Moreover, it distinguishes pathways for Sec- or Tat-dependent secretion and alternative secretion of bacteriocin-like proteins. The pipeline was tested on data sets extracted from literature, including experimental proteomics studies. The tests showed that LocateP performs as well as, or even slightly better than other SCL predictors for some locations and outperforms current tools especially where the N-terminally anchored and the SPIase-cleaved secreted proteins are concerned. Overall, the accuracy of LocateP was always higher than 90%. LocateP was then used to predict the SCLs of all proteins encoded by completed Gram-positive bacterial genomes. The results are stored in the database LocateP-DB http://www.cmbi.ru.nl/locatep-db1. CONCLUSION: LocateP is by far the most accurate and detailed protein SCL predictor for Gram-positive bacteria currently available.

Dataset Information

Signaling efficiency of G?q through its effectors p63RhoGEF and GEFT depends on their subcellular location.

Publications

Signaling efficiency of Gαq through its effectors p63RhoGEF and GEFT depends on their subcellular location.

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