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      VENNTURE–A Novel Venn Diagram Investigational Tool for Multiple Pharmacological Dataset Analysis

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          Abstract

          As pharmacological data sets become increasingly large and complex, new visual analysis and filtering programs are needed to aid their appreciation. One of the most commonly used methods for visualizing biological data is the Venn diagram. Currently used Venn analysis software often presents multiple problems to biological scientists, in that only a limited number of simultaneous data sets can be analyzed. An improved appreciation of the connectivity between multiple, highly-complex datasets is crucial for the next generation of data analysis of genomic and proteomic data streams. We describe the development of VENNTURE, a program that facilitates visualization of up to six datasets in a user-friendly manner. This program includes versatile output features, where grouped data points can be easily exported into a spreadsheet. To demonstrate its unique experimental utility we applied VENNTURE to a highly complex parallel paradigm, i.e. comparison of multiple G protein-coupled receptor drug dose phosphoproteomic data, in multiple cellular physiological contexts. VENNTURE was able to reliably and simply dissect six complex data sets into easily identifiable groups for straightforward analysis and data output. Applied to complex pharmacological datasets, VENNTURE’s improved features and ease of analysis are much improved over currently available Venn diagram programs. VENNTURE enabled the delineation of highly complex patterns of dose-dependent G protein-coupled receptor activity and its dependence on physiological cellular contexts. This study highlights the potential for such a program in fields such as pharmacology, genomics, and bioinformatics.

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          Most cited references54

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          A ternary complex model explains the agonist-specific binding properties of the adenylate cyclase-coupled beta-adrenergic receptor.

          The unique properties of agonist binding to the frog erythrocyte beta-adrenergic receptor include the existence of two affinity forms of the receptor. The proportion and relative affinity of these two states of the receptor for ligands varies with the intrinsic activity of the agonist and the presence of guanine nucleotides. The simplest model for hormone-receptor interactions which can explain and reproduce the experimental data involves the interaction of the receptor R with an additional membrane component X, leading to the agonist-promoted formation of a high affinity ternary complex HRX. Computer modeling of agonist binding data with a ternary complex model indicates that the model can fit the data with high accuracy under conditions where the ligand used is either a full or a partial agonist and where the system is altered by the addition of guanine nucleotide or after treatment with group-specific reagents, e.g. p-hydroxymercuribenzoate. The parameter estimates obtained indicate that the intrinsic activity of the agonist is correlated significantly with the affinity constant L of the component X for the binary complex HR. The major effect of adding guanine nucleotides is to destabilize the ternary complex HRX from which both the hormone H and the component X can dissociate. The modulatory role of nucleotides on the affinity of agonists for the receptor is consistent with the assumption that the component X is the guanine nucleotide binding site. The ternary complex model was also applied successfully to the turkey erythrocyte receptor system. The model provides a general scheme for the activation by agonists of adenylate cyclase-coupled receptor systems and also of other systems where the effector might be different.
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            Differential signal transduction by five splice variants of the PACAP receptor.

            The two forms of pituitary adenylyl cyclase-activating polypeptide (PACAP-27 and -38) are neuropeptides of the secretin/glucagon/vasoactive intestinal polypeptide/growth-hormone-releasing hormone family and regulate hormone release from the pituitary and adrenal gland. They may also be involved in spermatogenesis, and PACAP-38 potently stimulates neuritogenesis and survival of cultured rat sympathetic neuroblast and promotes neurite outgrowth of PC-12 cells. The PACAP type-I receptor (found in hypothalamus, brain stem, pituitary, adrenal gland and testes), specific for PACAP, is positively coupled to adenylyl cyclase and phospholipase C. The recently cloned type II receptor does not discriminate between PACAP and vasoactive intestinal polypeptide and is coupled to only adenylyl cyclase. Here we have used a new expression cloning strategy, based on the induction of a reporter gene by cyclic AMP, to isolate a complementary DNA encoding the type-I PACAP receptor. On transfection of this cDNA, both PACAP-27 and -38 stimulate adenylyl cyclase with similar EC50 values (50% effective concentration, 0.1-0.4 nM), whereas only PACAP-38 stimulates phospholipase C with high potency (EC50 = 15 nM). Four other splice variants were isolated with insertions at the C-terminal end of the third intracellular loop. Expression of these cDNAs revealed altered patterns of adenylyl cyclase and phospholipase C stimulation, suggesting a novel mechanism for fine tuning of signal transduction.
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              Beta-arrestin-mediated activation of MAPK by inverse agonists reveals distinct active conformations for G protein-coupled receptors.

              It is becoming increasingly clear that signaling via G protein-coupled receptors is a diverse phenomenon involving receptor interaction with a variety of signaling partners. Despite this diversity, receptor ligands are commonly classified only according to their ability to modify G protein-dependent signaling. Here we show that beta2AR ligands like ICI118551 and propranolol, which are inverse agonists for Gs-stimulated adenylyl cyclase, induce partial agonist responses for the mitogen-activated protein kinases extracellular signal-regulated kinase (ERK) 1/2 thus behaving as dual efficacy ligands. ERK1/2 activation by dual efficacy ligands was not affected by ADP-ribosylation of Galphai and could be observed in S49-cyc- cells lacking Galphas indicating that, unlike the conventional agonist isoproterenol, these drugs induce ERK1/2 activation in a Gs/i-independent manner. In contrast, this activation was inhibited by a dominant negative mutant of beta-arrestin and was abolished in mouse embryonic fibroblasts lacking beta-arrestin 1 and 2. The role of beta-arrestin was further confirmed by showing that transfection of beta-arrestin 2 in these knockout cells restored ICI118551 promoted ERK1/2 activation. ICI118551 and propranolol also promoted beta-arrestin recruitment to the receptor. Taken together, these observations suggest that beta-arrestin recruitment is not an exclusive property of agonists, and that ligands classically classified as inverse agonists rely exclusively on beta-arrestin for their positive signaling activity. This phenomenon is not unique to beta2-adrenergic ligands because SR121463B, an inverse agonist on the V2 vasopressin receptor-stimulated adenylyl cyclase, recruited beta-arrestin and stimulated ERK1/2. These results point to a multistate model of receptor activation in which ligand-specific conformations are capable of differentially activating distinct signaling partners.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                14 May 2012
                : 7
                : 5
                : e36911
                Affiliations
                [1 ]Metabolism Unit, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America
                [2 ]Receptor Pharmacology Unit, Laboratory of Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America
                [3 ]Diabetes Section, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America
                [4 ]Gene Expression and Genomics Unit, Research Resources Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America
                University College Dublin, Ireland
                Author notes

                Conceived and designed the experiments: SM BM. Performed the experiments: BM WC TY SSP DL BN SG KF SM. Analyzed the data: BM WC SM. Contributed reagents/materials/analysis tools: KGB. Wrote the paper: SM BM.

                Article
                PONE-D-11-21759
                10.1371/journal.pone.0036911
                3351456
                22606307
                5b6e2d60-4c4b-4cf6-a2a4-a0c1c967884d
                This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
                History
                : 1 November 2011
                : 10 April 2012
                Page count
                Pages: 17
                Categories
                Research Article
                Biology
                Biochemistry
                Proteins
                Proteome
                Computational Biology
                Biological Data Management
                Molecular Cell Biology
                Signal Transduction
                Signaling in Cellular Processes
                G-Protein Signaling
                Proteomics
                Computer Science
                Information Technology
                Databases
                Software Engineering
                Software Design
                Engineering
                Software Engineering
                Software Design

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                Uncategorized

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