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• W2015039564 abstract "G protein-coupled receptors (GPCRs) regulate diverse physiological processes, and many human diseases are due to defects in GPCR signaling. To identify the dynamic response of a signaling network downstream from a prototypical Gs-coupled GPCR, the vasopressin V2 receptor, we have carried out multireplicate, quantitative phosphoproteomics with iTRAQ labeling at four time points following vasopressin exposure at a physiological concentration in cells isolated from rat kidney. A total of 12,167 phosphopeptides were identified from 2,783 proteins, with 273 changing significantly in abundance with vasopressin. Two-dimensional clustering of phosphopeptide time courses and Gene Ontology terms revealed that ligand binding to the V2 receptor affects more than simply the canonical cyclic adenosine monophosphate-protein kinase A and arrestin pathways under physiological conditions. The regulated proteins included key components of actin cytoskeleton remodeling, cell-cell adhesion, mitogen-activated protein kinase signaling, Wnt/β-catenin signaling, and apoptosis pathways. These data suggest that vasopressin can regulate an array of cellular functions well beyond its classical role in regulating water and solute transport. These results greatly expand the current view of GPCR signaling in a physiological context and shed new light on potential roles for this signaling network in disorders such as polycystic kidney disease. Finally, we provide an online resource of physiologically regulated phosphorylation sites with dynamic quantitative data (http://helixweb.nih.gov/ESBL/Database/TiPD/index.html). G protein-coupled receptors (GPCRs) regulate diverse physiological processes, and many human diseases are due to defects in GPCR signaling. To identify the dynamic response of a signaling network downstream from a prototypical Gs-coupled GPCR, the vasopressin V2 receptor, we have carried out multireplicate, quantitative phosphoproteomics with iTRAQ labeling at four time points following vasopressin exposure at a physiological concentration in cells isolated from rat kidney. A total of 12,167 phosphopeptides were identified from 2,783 proteins, with 273 changing significantly in abundance with vasopressin. Two-dimensional clustering of phosphopeptide time courses and Gene Ontology terms revealed that ligand binding to the V2 receptor affects more than simply the canonical cyclic adenosine monophosphate-protein kinase A and arrestin pathways under physiological conditions. The regulated proteins included key components of actin cytoskeleton remodeling, cell-cell adhesion, mitogen-activated protein kinase signaling, Wnt/β-catenin signaling, and apoptosis pathways. These data suggest that vasopressin can regulate an array of cellular functions well beyond its classical role in regulating water and solute transport. These results greatly expand the current view of GPCR signaling in a physiological context and shed new light on potential roles for this signaling network in disorders such as polycystic kidney disease. Finally, we provide an online resource of physiologically regulated phosphorylation sites with dynamic quantitative data (http://helixweb.nih.gov/ESBL/Database/TiPD/index.html). G protein-coupled receptors (GPCRs) 1The abbreviations used are:GPCRG protein-coupled receptorAqp2aquaporin-2cAMPcyclic adenosine monophosphatedDAVP[deamino-Cys1,D-Arg8]vasopressinGOGene OntologyiTRAQisobaric tag for relative and absolute quantitationMAPmitogen-activated proteinPKAprotein kinase AV2Rtype 2 vasopressin receptorIMCDinner medullary collecting ductTPMtemporal pattern miningERKextracellular signal-regulated kinaseLPAlysophosphatidic acid. mediate physiological regulation in a multiplicity of organisms and in practically every mammalian tissue. The human genome contains ∼1,000 genes that code for GPCRs, reflecting the broad importance of this receptor superfamily in physiological regulation (1Shenoy S.K. Lefkowitz R.J. Seven-transmembrane receptor signaling through β-arrestin.Sci. STKE. 2005; 2005: cm10Crossref PubMed Google Scholar). These receptors contain seven transmembrane domains and signal through two classically defined pathways: heterotrimeric G protein activation and arrestin binding (2Luttrell L.M. Lefkowitz R.J. The role of β-arrestins in the termination and transduction of G-protein-coupled receptor signals.J. Cell Sci. 2002; 115: 455-465Crossref PubMed Google Scholar). Ligand binding to the receptor triggers downstream changes in phosphorylation of intermediate signaling proteins and regulatory targets. An important question is whether GPCR signaling only occurs through those two classically defined pathways or involves cross-talk with other pathways. One way to address this question is through large scale analysis of protein phosphorylation (i.e. phosphoproteomics). To do this in a physiological setting, we have undertaken a dynamic, quantitative phosphoproteomic analysis of type 2 vasopressin receptor (V2R; gene Avpr2) signaling in a native rat kidney collecting duct epithelium. G protein-coupled receptor aquaporin-2 cyclic adenosine monophosphate [deamino-Cys1,D-Arg8]vasopressin Gene Ontology isobaric tag for relative and absolute quantitation mitogen-activated protein protein kinase A type 2 vasopressin receptor inner medullary collecting duct temporal pattern mining extracellular signal-regulated kinase lysophosphatidic acid. Vasopressin is a nine-amino acid peptide hormone that regulates water and solute transport in the mammalian kidney but also has important physiological effects in other tissues. Through its actions in the kidney, vasopressin allows an organism to maintain its serum osmolality within a very narrow range (290–294 mosmol/kg of H2O in human) despite varying degrees of water intake. Dysfunctions in vasopressin signaling occur in a number of clinical disorders including syndrome of inappropriate anti-diuretic hormone hypersecretion (seen in many forms of cancer), congestive heart failure, hepatic cirrhosis, and nephrogenic diabetes insipidus (3Elhassan E.A. Schrier R.W. The use of vasopressin receptor antagonists in hyponatremia.Expert. Opin. Investig. Drugs. 2011; 20: 373-380Crossref PubMed Scopus (11) Google Scholar). Vasopressin signaling is also recognized to be an important factor in the progression of autosomal dominant polycystic kidney disease, one of the most common life-threatening genetic diseases with prevalence estimated to be as high as 1 in 400 individuals (4Torres V.E. Harris P.C. Mechanisms of disease: Autosomal dominant and recessive polycystic kidney diseases.Nat. Clin. Pract. Nephrol. 2006; 2: 40-55Crossref PubMed Scopus (239) Google Scholar). One of the main targets of vasopressin in the kidney is the renal collecting duct cell, which expresses the vasopressin receptor V2R. In these cells, vasopressin regulates the water channel aquaporin-2 (Aqp2) (reviewed in Ref. 5Moeller H.B. Olesen E.T. Fenton R.A. Regulation of the water channel aquaporin-2 by post-translational modifications.Am. J. Physiol. Renal. Physiol. 2011; 300: F1062-F1073Crossref PubMed Scopus (91) Google Scholar) and urea channel Slc14a2 (6Blount M.A. Mistry A.C. Fröhlich O. Price S.R. Chen G. Sands J.M. Klein J.D. Phosphorylation of UT-A1 urea transporter at serines 486 and 499 is important for vasopressin-regulated activity and membrane accumulation.Am. J. Physiol. Renal Physiol. 2008; 295: F295-F299Crossref PubMed Scopus (71) Google Scholar) through the heterotrimeric G protein Gs and subsequent activation of adenylyl cyclases that mediate a rise in intracellular cAMP. Vasopressin also increases intracellular calcium through V2R and cAMP (7Chou C.L. Yip K.P. Michea L. Kador K. Ferraris J.D. Wade J.B. Knepper M.A. Regulation of aquaporin-2 trafficking by vasopressin in the renal collecting duct. Roles of ryanodine-sensitive Ca2+ stores and calmodulin.J. Biol. Chem. 2000; 275: 36839-36846Abstract Full Text Full Text PDF PubMed Scopus (185) Google Scholar). Despite a number of recent studies that have explored the steady state response of cells to vasopressin through phosphoproteomic methodologies (8Rinschen M.M. Yu M.J. Wang G. Boja E.S. Hoffert J.D. Pisitkun T. Knepper M.A. Quantitative phosphoproteomic analysis reveals vasopressin V2-receptor-dependent signaling pathways in renal collecting duct cells.Proc. Natl. Acad. Sci. U.S.A. 2010; 107: 3882-3887Crossref PubMed Scopus (147) Google Scholar, 9Bansal A.D. Hoffert J.D. Pisitkun T. Hwang S. Chou C.L. Boja E.S. Wang G. Knepper M.A. Phosphoproteomic profiling reveals vasopressin-regulated phosphorylation sites in collecting duct.J. Am. Soc. Nephrol. 2010; 21: 303-315Crossref PubMed Scopus (51) Google Scholar, 10Hoffert J.D. Pisitkun T. Wang G. Shen R.F. Knepper M.A. Quantitative phosphoproteomics of vasopressin-sensitive renal cells: Regulation of aquaporin-2 phosphorylation at two sites.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 7159-7164Crossref PubMed Scopus (301) Google Scholar, 11Gunaratne R. Braucht D.W. Rinschen M.M. Chou C.L. Hoffert J.D. Pisitkun T. Knepper M.A. Quantitative phosphoproteomic analysis reveals cAMP/vasopressin-dependent signaling pathways in native renal thick ascending limb cells.Proc. Natl. Acad. Sci. U.S.A. 2010; 107: 15653-15658Crossref PubMed Scopus (92) Google Scholar), a comprehensive, dynamic profile of vasopressin signaling has yet to emerge. To carry out a dynamic phosphoproteomic analysis, we utilized a multiplexed labeling strategy (iTRAQ) allowing analysis of four distinct time points following the addition of vasopressin. Other studies have utilized various phosphoproteomic strategies to quantify biological responses (12Gruhler A. Olsen J.V. Mohammed S. Mortensen P. Faergeman N.J. Mann M. Jensen O.N. Quantitative phosphoproteomics applied to the yeast pheromone signaling pathway.Mol. Cell. Proteomics. 2005; 4: 310-327Abstract Full Text Full Text PDF PubMed Scopus (698) Google Scholar, 13Olsen J.V. Blagoev B. Gnad F. Macek B. Kumar C. Mortensen P. Mann M. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks.Cell. 2006; 127: 635-648Abstract Full Text Full Text PDF PubMed Scopus (2833) Google Scholar, 14Bose R. Molina H. Patterson A.S. Bitok J.K. Periaswamy B. Bader J.S. Pandey A. Cole P.A. Phosphoproteomic analysis of Her2/neu signaling and inhibition.Proc. Natl. Acad. Sci. U.S.A. 2006; 103: 9773-9778Crossref PubMed Scopus (181) Google Scholar, 15Iwai L.K. Benoist C. Mathis D. White F.M. Quantitative phosphoproteomic analysis of T cell receptor signaling in diabetes prone and resistant mice.J. Proteome Res. 2010; 9: 3135-3145Crossref PubMed Scopus (42) Google Scholar, 16Rigbolt K.T. Prokhorova T.A. Akimov V. Henningsen J. Johansen P.T. Kratchmarova I. Kassem M. Mann M. Olsen J.V. Blagoev B. System-wide temporal characterization of the proteome and phosphoproteome of human embryonic stem cell differentiation.Sci. Signal. 2011; 4: rs3Crossref PubMed Scopus (366) Google Scholar, 17Xiao K. Sun J. Kim J. Rajagopal S. Zhai B. Villén J. Haas W. Kovacs J.J. Shukla A.K. Hara M.R. Hernandez M. Lachmann A. Zhao S. Lin Y. Cheng Y. Mizuno K. Ma’ayan A. Gygi S.P. Lefkowitz R.J. Global phosphorylation analysis of β-arrestin-mediated signaling downstream of a seven transmembrane receptor (7TMR).Proc. Natl. Acad. Sci. U.S.A. 2010; 107: 15299-15304Crossref PubMed Scopus (164) Google Scholar, 18Schreiber T.B. Mäusbacher N. Kéri G. Cox J. Daub H. An integrated phosphoproteomics work flow reveals extensive network regulation in early lysophosphatidic acid signaling.Mol. Cell. Proteomics. 2010; 9: 1047-1062Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar). To our knowledge, the present study is the first temporal quantitative phosphoproteomic analysis of a physiological response using a native mammalian model. It also represents the largest collection of quantitative phosphorylation data on vasopressin signaling to date. These new data implicate a number of previously unidentified pathways that are regulated downstream of V2R, including the Wnt/β-catenin signaling and apoptosis pathways, which are relevant to the pathogenesis of polycystic kidney disease, as well as a variety of water balance disorders. Brief descriptions of key experimental procedures are provided below. For complete details, see supplemental materials online. IMCD suspensions were prepared from rat kidney inner medullas (150–250 μg of protein/inner medulla) using the method of Stokes et al. (19Stokes J.B. Grupp C. Kinne R.K. Purification of rat papillary collecting duct cells: Functional and metabolic assessment.Am. J. Physiol. 1987; 253: F251-F262PubMed Google Scholar) with modifications (20Pisitkun T. Bieniek J. Tchapyjnikov D. Wang G. Wu W.W. Shen R.F. Knepper M.A. High-throughput identification of IMCD proteins using LC-MS/MS.Physiol. Genomics. 2006; 25: 263-276Crossref PubMed Scopus (59) Google Scholar). After isolation, IMCD suspensions were incubated for 0.5, 2, 5, and 15 min at 37 °C in the presence or absence of 1 nm [deamino-Cys1,d-Arg8]vasopressin (dDAVP), a V2 receptor-specific analog of vasopressin, followed by centrifugation at >10,000 × g for 10 s. Pelleted IMCD tubules were lysed in 150 μl of lysis buffer containing 8 m urea, 50 mm Tris-HCl, 75 mm NaCl with 1× HALTTM protease and phosphatase inhibitor (Pierce). (The four time points are labeled based on the time of incubation with dDAVP and do not include the 30 s of additional preparation time for centrifugation and lysis.) Protein samples were sonicated for 1 min with 0.5-s pulses on ice. The samples were spun at 10,000 × g for 10 min to pellet the debris, and the supernatant was saved for further analysis. Five microliters of each sample was saved for analysis of protein concentration by the BCA method (Pierce). Another 15 μl was removed for protein immunoblotting. The remainder of each sample (500 μg of protein) was reduced with 10 mm DTT for 1 h at 37 °C, alkylated with 40 mm iodoacetamide for 1 h at room temperature in the dark, and then quenched with 40 mm DTT for 15 min. The samples were diluted to <1 m urea with 50 mm ammonium bicarbonate buffer and then digested with trypsin overnight at 37 °C using an enzyme-to-protein ratio of 1:25 (w:w). After acidification with 0.5% formic acid, the samples were desalted on a 1-cc Oasis HLB cartridge (Waters, Milford, MA) prior to iTRAQ labeling. This entire sample preparation process was repeated two additional times on separate days to produce a total of three biological replicates. iTRAQ labeling was performed according to the manufacturer’s protocol (Applied Biosystems, Foster City, CA). Briefly, the peptide samples were resuspended in 150 μl of iTRAQ dissolution buffer (0.5 m triethylammonium bicarbonate, pH 8.5). Each peptide sample was labeled with 5 units of iTRAQ 8-plex reagent for 2 h at room temperature according to the experimental design shown in Fig. 1. The reaction was quenched by adding 0.5% formic acid. All eight iTRAQ-labeled samples were combined into a single sample, then desalted (HLB cartridges; Waters), and dried in vacuo. iTRAQ-labeled peptide samples were fractionated by strong cation exchange chromatography, and phosphopeptides were enriched by Ga+3 IMAC. The samples were analyzed on an LTQ Orbitrap Velos mass spectrometer (Thermo Scientific, San Jose, CA). The average precursor isolation window was 3 m/z. MS data files are available via the Proteomic Commons Tranche Repository (https://proteomecommons.org/tranche/). (See the supplemental data online.) MS2 spectra were searched with Proteome Discoverer software (version 1.1.0.263; Thermo Scientific) running the Sequest algorithm on a concatenated database containing both the forward and reversed complement of the Rat Refseq Database (National Center for Biotechnology Information, March 3, 2010, 30,734 entries), which included a list of common contaminating proteins from other species. Precursor ion tolerance was 25 ppm, whereas fragment ion tolerance was 0.05 Da. Three missed trypsin cleavage sites were allowed. Static modifications included carbamidomethylation of cysteine (+57.021 Da) and iTRAQ 8-plex modification of lysine and peptide N termini (+304.205 Da). Variable modifications included oxidation of methionine (+15.995 Da); phosphorylation of serine, threonine, and tyrosine (+79.966 Da); and iTRAQ 8-plex modification of tyrosine (+304.205 Da). Known contaminant ions were excluded. The data sets were filtered to include <1% false positive hits (estimated based on target decoy analysis (21Elias J.E. Gygi S.P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry.Nat. Methods. 2007; 4: 207-214Crossref PubMed Scopus (2873) Google Scholar)) based on the following Xcorr threshold values for each charge state for the each replicate: +2 (2.14, 1.965, 2.355); +3 (2.38, 2.315, 2.81); +4 (2.66, 2.4, 3.06); and +5 (2.7, 2.405, 3.065). Phosphorylation site assignment was performed using a dynamic programming algorithm (false localization rate < 1%) that is currently under review. PubMed (http://www.ncbi.nlm.nih.gov/pubmed/), PhosphoSitePlus (http://www.phosphosite.org), and HPRD (http://www.hprd.org) (22Prasad T.S. Kandasamy K. Pandey A. Human Protein Reference Database and Human Proteinpedia as discovery tools for systems biology.Methods Mol. Biol. 2009; 577: 67-79Crossref PubMed Scopus (223) Google Scholar) were used to search for known phosphorylation sites. Phosphopeptides matching to multiple protein isoforms were identified using ProMatch software (23Tchapyjnikov D. Li Y. Pisitkun T. Hoffert J.D. Yu M.J. Knepper M.A. Proteomic profiling of nuclei from native renal inner medullary collecting duct cells using LC-MS/MS.Physiol. Genomics. 2010; 40: 167-183Crossref PubMed Scopus (42) Google Scholar). Reporter ion intensities for redundant peptides were summed for each iTRAQ channel. (We defined peptides as redundant if the charge states, site(s) of modification, and amino acid sequences were identical.) Abundance ratios (dDAVP/control) for all four time points were calculated, and each was normalized using a correction factor that was based on the ratio of the summed reporter ion intensities for the corresponding dDAVP and vehicle control channels. The log2 of the normalized ratio was used as the basis for calculation of the mean and standard deviation for each peptide across all three biological replicates. Unpaired t tests determined whether changes in phosphopeptide abundance were significant. Background variability across the three replicate time courses was assessed for all phosphopeptides (mean log2 (0.5 min control/5 min control) = −0.02 ± 0.22 (S.E.)) (Fig. 1C). In addition, average nonphosphorylated peptide abundance ratios for each protein were obtained by analyzing the IMAC flow-through fractions (included in supplemental Table S1). The majority of proteins did not change abundance with vasopressin (average log2(dDAVP/control) = −0.025 ± 0.14 (S.E.)), indicating that changes in phosphopeptide abundance are not likely due to changes in total protein abundance. Average precursor ion isolation purity (i.e. the percentage of target ion intensity compared with the complete ion intensity in the precursor isolation window) for the three replicate data sets was 83%. Because precursor isolation purity is not always a reliable indicator of accurate iTRAQ quantitation (24Ting L. Rad R. Gygi S.P. Haas W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics.Nat. Methods. 2011; 8: 937-940Crossref PubMed Scopus (718) Google Scholar), data sets were not filtered for a particular isolation purity threshold. The isolation purities for individual phosphopeptides are included in supplemental Table S1. Cluster analysis of phosphopeptides with similar temporal profiles was performed using a temporal pattern mining (TPM) algorithm (http://helixweb.nih.gov/TPM/) (25Saeed F. Pisitkun T. Knepper M.A. Hoffert J.D. Mining temporal patterns from iTRAQ mass spectrometry (LC-MS/MS) data.The Proceedings of the ISCA 3rd International Conference on Bioinformatics and Computational Biology (BiCoB). 2011; 1: 152-159Google Scholar). To be included for cluster analysis, a phosphopeptide had to be present in at least two of the three time courses, and all eight iTRAQ reporter ions needed to be present in each spectrum to obtain quantifiable iTRAQ ratios at each of the four time points. This analysis produced 30 distinct temporal clusters. Phosphorylation motif analysis was then performed on each of these clusters by using the motif-x algorithm (26Schwartz D. Gygi S.P. An iterative statistical approach to the identification of protein phosphorylation motifs from large-scale data sets.Nat. Biotechnol. 2005; 23: 1391-1398Crossref PubMed Scopus (722) Google Scholar) (supplemental Table S5). Some of the clusters contained too few phosphopeptides to produce a motif. In house software (ABE; see “Bioinformatics”) was then used to extract Gene Ontology (GO) terms for each phosphopeptide in these clusters, as well as for all identified phosphopeptides (i.e. background). The proportion of each GO biological process term in each cluster was then calculated, and this value divided by the proportion of that particular GO Biological Process term in the background data (supplemental Table S6). The values for the 100 most abundant GO biological process terms for all clusters were then hierarchically clustered using Gene Cluster software (27Eisen M.B. Spellman P.T. Brown P.O. Botstein D. Cluster analysis and display of genome-wide expression patterns.Proc. Natl. Acad. Sci. U.S.A. 1998; 95: 14863-14868Crossref PubMed Scopus (13268) Google Scholar) to detect patterns in GO term enrichment between the various temporal phosphopeptide clusters. Automated Bioinformatics Extractor, ABE (http://helixweb.nih.gov/ESBL/ABE/), was used to extract GO terms and conserved protein domains through NCBI Entrez Programming Utilities (http://eutils.ncbi.nlm.nih.gov). The DAVID bioinformatic tool (Database for Annotation, Visualization and Integrated Discovery, NIAID, http://david.abcc.ncifcrf.gov/) (28Huang da W. Sherman B.T. Lempicki R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.Nat. Protoc. 2009; 4: 44-57Crossref PubMed Scopus (25869) Google Scholar) was used to extract the list of PANTHER pathway terms (http://www.pantherdb.org) (29Mi H. Lazareva-Ulitsky B. Loo R. Kejariwal A. Vandergriff J. Rabkin S. Guo N. Muruganujan A. Doremieux O. Campbell M.J. Kitano H. Thomas P.D. The PANTHER database of protein families, subfamilies, functions and pathways.Nucleic Acids Res. 2005; 33: D284-D288Crossref PubMed Scopus (633) Google Scholar) associated with each phosphopeptide. Three replicate 8-plex iTRAQ time course experiments were performed as indicated in Fig. 1A to quantify the effects of vasopressin at four separate time points (0.5, 2, 5, and 15 min), each with its own time point control. Immunoblotting for Aqp2 phosphorylated at Ser-256 confirmed the response of the IMCD cells to the vasopressin analog dDAVP (supplemental Fig. S1). Aqp2 phosphorylated at Ser-256 was significantly increased during all four time points, whereas the total protein abundance of Aqp2 did not change with vasopressin. A combined total of 12,167 phosphopeptides corresponding to 2,783 proteins were identified, although not all of the phosphopeptides were present in all three replicate time courses (Fig. 1B and supplemental Table S1; nonphosphorylated peptide data in supplemental Table S2). A database of all phosphopeptide identifications including dynamic, quantitative data for both phosphopeptide and corresponding protein level abundances is available online (http://helixweb.nih.gov/ESBL/Database/TiPD/index.html). Background variability across the three replicate time courses was assessed for all phosphopeptides (mean log2 (0.5 min control/5 min control) = −0.02 ± 0.22 (S.E.)) (Fig. 1C). The majority of phosphopeptides (90% of the total) were singly phosphorylated, and serine residues were the most commonly modified amino acid (83% of the total). 4,202 nonredundant phosphorylation sites (50% of the total) were previously unidentified based on information from various online phosphorylation site databases (see “Experimental Procedures”). Approximately 28% of phosphorylation sites were present in known “regions of interest” (based on the current NCBI Reference Sequence record), which include binding sites, enzyme active sites, regions of local secondary structure, and conserved protein domains. (Full results are provided in supplemental Table S3.) Of the phosphopeptides that were present in all three replicate time courses, 273 changed significantly during at least one time point, and 40% of these changes occurred within 2 min of exposure to vasopressin. Analysis of PANTHER Pathway terms for these 273 phosphopeptides revealed that a number of signaling pathways are regulated by vasopressin (Fig. 1D). These included pathways previously implicated in vasopressin signaling (the phosphatidylinositol 3-kinase pathway, the protein kinase B/Akt pathway, various MAP kinase pathways (30Pisitkun T. Jacob V. Schleicher S.M. Chou C.L. Yu M.J. Knepper M.A. Akt and ERK1/2 pathways are components of the vasopressin signaling network in rat native IMCD.Am. J. Physiol. Renal. Physiol. 2008; 295: F1030-F1043Crossref PubMed Scopus (64) Google Scholar), and signaling through Rho kinases (31Klussmann E. Tamma G. Lorenz D. Wiesner B. Maric K. Hofmann F. Aktories K. Valenti G. Rosenthal W. An inhibitory role of Rho in the vasopressin-mediated translocation of aquaporin-2 into cell membranes of renal principal cells.J. Biol. Chem. 2001; 276: 20451-20457Abstract Full Text Full Text PDF PubMed Scopus (149) Google Scholar, 32Tamma G. Klussmann E. Maric K. Aktories K. Svelto M. Rosenthal W. Valenti G. Rho inhibits cAMP-induced translocation of aquaporin-2 into the apical membrane of renal cells.Am. J. Physiol. Renal. Physiol. 2001; 281: F1092-F1101Crossref PubMed Scopus (114) Google Scholar)), as well as pathways not known to be regulated downstream from the V2 receptor (angiogenesis, Wnt signaling pathway, and heterotrimeric G protein Gαq- and Gαo-mediated pathway). Many of the phosphoproteins that changed with vasopressin were shared among multiple signaling pathways, suggesting that there may be considerable interpathway connectivity. The full results of the PANTHER Pathway analysis are available in supplemental Table S4. Forty-six phosphopeptides changed significantly (p < 0.05) in abundance during at least one time point by at least 40% (−0.5 ≤ log2(dDAVP/control) ≥ 0.5) (value is based on 2× S.E. of control:control quantitation; see “Experimental Procedures”). Phosphopeptides that increased or decreased in the presence of dDAVP based on these criteria are presented in Table I. This table includes numerous membrane channels (Aqp2 and Slc14a2), trafficking proteins (Lrba, Sec22b, Agfg1, and Sept9), and protein kinases (Camkk2, Prkar1a, Ptk2, Map3k7, Map4k6, and Pak2). There were also a number of phosphoproteins involved in actin binding and cytoskeletal reorganization (Eps8l1, Lcp1, Ctnna1, and Kif13b), a result consistent with prior studies showing that vasopressin regulates cytoskeletal dynamics (33Simon H. Gao Y. Franki N. Hays R.M. Vasopressin depolymerizes apical F-actin in rat inner medullary collecting duct.Am. J. Physiol. 1993; 265: C757-C762Crossref PubMed Google Scholar) and that depolymerization of the cortical actin network promotes Aqp2 trafficking (31Klussmann E. Tamma G. Lorenz D. Wiesner B. Maric K. Hofmann F. Aktories K. Valenti G. Rosenthal W. An inhibitory role of Rho in the vasopressin-mediated translocation of aquaporin-2 into cell membranes of renal principal cells.J. Biol. Chem. 2001; 276: 20451-20457Abstract Full Text Full Text PDF PubMed Scopus (149) Google Scholar, 34Noda Y. Horikawa S. Kanda E. Yamashita M. Meng H. Eto K. Li Y. Kuwahara M. Hirai K. Pack C. Kinjo M. Okabe S. Sasaki S. Reciprocal interaction with G-actin and tropomyosin is essential for aquaporin-2 trafficking.J. Cell Biol. 2008; 182: 587-601Crossref PubMed Scopus (79) Google Scholar). Many of the proteins listed in Table I have not been implicated previously in vasopressin signaling.Table IPhosphopeptides that significantly changed in abundance in response to dDAVPProtein nameRefSeqGene symbolPeptide sequencePhosphositeAverage log2(dDAVP/control)0.5 min2 min5 min15 minAverage log2(dDAVP/control) ≥ 0.5 1-Phosphatidylinositol 4,5-bisphosphate phosphodiesterase β-3NP_203501Plcb3NNS#ISEAKSer-11070.410.68bp < 0.05.0.65bp < 0.05.0.67 2-Oxoisovalerate dehydrogenase subunit α, mitochondrialNP_036914BckdhaIGHHS#TSDDSSAYRSer-338aAmbiguous phosphorylation site.−0.16−0.26−0.071.00bp < 0.05. 6-Phosphofructo-2-kinase/fructose-2,6-biphosphatase 3NP_476476Pfkfb3NSVTPLAS#PEPTKSer-496−0.260.76bp < 0.05.−0.080.40 α1-SyntrophinNP_001094371Snta1NSAGGTSVGWDS#PPASPLQRSer-183aAmbiguous phosphorylation site.−0.090.56−0.331.12bp < 0.05. Ankyrin repeat and SOCS box protein 4NP_001019489Asb4S#LPLSLKSer-4081.40bp < 0.05.1.96bp < 0.05.1.201.24bp < 0.05. Aquaporin-2NP_037041Aqp2RQS#VELHS#PQSLPRSer-256, Ser-2610.720.66bp < 0.05.0.83−0.15 Aquaporin-2NP_037041Aqp2RQS#VELHSPQSLPRSer-2560.890.83bp < 0.05.1.091.13 Bcl-2-related ovarian killer proteinNP_059008BokRSS#VFAAEIMDAFDRSer-81.991.212.92bp < 0.05.2.28bp < 0.05. Calcium/calmodulin-dependent protein kinase kinase 2NP_112628Camkk2S#FGNPFEGSRSer-4940.260.22bp < 0.05.0.46bp < 0.05.0.76bp < 0.05. cAMP-dependent protein kinase type I-α regulatory subunitNP_037313Prkar1aTDS#REDEISPPPPNPVVKSer-77aAmbiguous phosphorylation site.0.65bp < 0.05.−0.160.160.32 Cytokine receptor-like factor 2 precursorNP_604460Crlf2GS#FPGLFEKSer-278−0.10.410.260.59bp < 0.05. Focal adhesion kinase 1NP_037213Ptk2LQPQEIS#PPPT" @default.
• W2015039564 created "2016-06-24" @default.
• W2015039564 creator A5003847045 @default.
• W2015039564 creator A5006125328 @default.
• W2015039564 creator A5010454251 @default.
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• W2015039564 date "2012-02-01" @default.
• W2015039564 modified "2023-09-29" @default.
• W2015039564 title "Dynamics of the G Protein-coupled Vasopressin V2 Receptor Signaling Network Revealed by Quantitative Phosphoproteomics" @default.
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