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GPCR Endocytosis Confers Uniformity in Responses to Chemically Distinct Ligands

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MOL #106369 Molecular Pharmacology GPCR endocytosis confers uniformity in responses to chemically distinct ligands Nikoleta G. Tsvetanova, Michelle Trester-Zedlitz, Billy W. Newton, Daniel P. Riordan, Aparna B. Sundaram, Jeffrey R. Johnson, Nevan J. Krogan and Mark von Zastrow Department of Psychiatry, University of California, San Francisco, CA, USA (N.G.T., M.T-Z., M.v.Z.), Department of Cellular & Molecular Pharmacology, University of California, San Francisco, CA, USA (M.v.Z.), California Institute for Quantitative Biosciences, QB3, University of California, San Francisco, CA, USA (B.W.N., J.R.J., N.J.K.), J. David Gladstone Institute, San Francisco, CA, USA (N.J.K.), Department of Biochemistry, Stanford University, Stanford, CA, USA (D.P.R.), Lung Biology Center, Department of Medicine, University of California, San Francisco, CA, USA (A.B.S.) Supplementary Information Supplemental Table 1. Summary of phospho-proteomic SILAC analysis. Data are summary of the two media-swap experiments for each condition. High iso = 1 μM isoproterenol; low iso = 10 nM isoproterenol; sal = 50 nM salmeterol. High Iso peptides 4101 High Iso unique proteins 1270 Low Iso peptides 3796 Low Iso unique proteins 1196 Sal peptides 4303 Sal unique proteins 1318 Supplemental Table 2. Beta2-AR regulated phosphosites- position of phosphorylated residue(s) and fold-changes. Values are shown as average Log2 (drug/no drug) from a set of medium swap experiments treated as two independent replicates. High iso = 1 μM isoproterenol; low iso = 10 nM isoproterenol; sal = 50 nM salmeterol. NA = neither of the replicates had a value for the peptide. Uniprot Protein ID Name Phospho-site(s) High Iso Low Iso Sal P49792 NUP358 Ser1509 3.66 3.04 2.31 O75396 SEC22B Ser137 3.32 0.52 1.16 P46821 MAP1B Ser561 2.85 2.68 NA P67809 YBX1 Ser313 2.79 2.86 NA Q6ZNB6 NFXL1 Ser835 2.62 1.80 0.65 Q9H2J7 SLC6A15 Ser699, Ser701 2.26 1.84 1.87 Q7Z3C6 ATG9A Ser735 2.08 1.64 0.95 P13591 NCAM1 Ser783 2.04 1.58 1.22 Q96D71 REPS1 Ser272, Ser273 2.04 2.06 0.44 Q3KQU3 PARCC1 Ser113 1.93 1.27 1.11 Q96D71 REPS1 Ser272, Ser273 1.89 1.70 2.15 Q9NWW5 CLN6 Ser31 1.88 1.28 1.23 O43847 NRD1 Ser94 1.86 1.77 1.43 O43318 MAP3K7 Ser389 1.83 NA 0.89 Q4KMP7 FP2461 Ser687 1.75 1.38 0.43 O43847 NRD1 Ser94 1.75 1.79 1.56 O43847 NRD1 Ser96 1.74 1.70 1.19 P16949 STMN1 Ser63 1.70 1.22 0.78 Q92614 MYO18A Ser1067, Ser1069 1.57 1.48 0.44 P53602 MPD Ser96 1.55 1.12 0.64 Q92685 ALG3 Ser13 1.54 1.39 1.31 P62753 RPS6 Ser235, Ser236 1.54 1.01 0.82 P13591 NCAM1 Ser779 1.43 1.15 NA P35222 CTNNB1 Thr551 1.35 0.90 1.01 P16403 H1F2 Ser36 1.27 0.86 0.78 1 P62753 RPS6 Ser235, Ser 236, Ser 240 1.27 1.02 1.06 P35222 CTNNB1 Ser552 1.26 1.00 0.99 P35222 CTNNB1 Thr556 1.24 1.43 NA Q9UK61 C3orf63 Ser694, Ser699 1.11 0.55 0.99 Q96TC7 FAM82A2 Ser46 1.09 0.91 0.94 Q09161 CBP80 Ser22 1.05 0.62 0.19 Q14671 PUM1 Ser709 1.05 0.30 0.05 Q96TC7 FAM82A2 Ser44 1.04 1.00 0.83 O75152 ZC3H11A Ser290 1.04 0.06 0.32 P53396 ACLY Thr453 1.01 0.85 0.44 Q09161 CBP80 Thr21 1.00 0.47 0.18 P53396 ACLY Thr453 0.68 1.13 0.54 P53396 ACLY Ser455 0.61 0.77 0.47 Q9C0C9 UBE2O Thr834 -0.32 -0.40 -1.11 P35659 DEK Ser306, Ser307 -0.84 -1.44 -0.27 O75475 PSIP1 Ser271 -0.96 -0.90 -0.89 P17096 HMGA1 Ser103 -0.99 -1.00 -0.87 P35659 DEK Ser303, Ser306, Ser307 -1.00 -0.87 -0.89 P08240 SRPR Ser296 -1.01 -0.64 -0.29 O75448 TRAP100 Ser862 -1.06 NA -1.54 Q9H1E3 NUCKS1 Ser144 -1.13 -0.89 -0.72 Q7Z4V5 HDGF2 Ser366, Ser369 -1.14 -1.05 -0.48 Q7Z4V5 HDGF2 Ser366, Ser369 -1.18 -1.01 -0.10 Q7Z4V5 HDGF2 Ser366, Ser369 -1.25 -1.00 NA P19338 NCL Ser145 -1.25 -0.92 -0.46 Q14978 NOLC1 Ser264 -1.74 -2.44 -0.57 Q14978 NOLC1 Ser362 -2.62 NA -0.60 P35659 DEK Ser301, Ser303, Ser306 NA -1.06 NA Q14978 NOLC1 Ser361 NA -1.66 -0.78 2 Supplemental Table 3. Beta2-AR regulated phosphosites- modified sequence and predicted kinases targeting the peptide. Position(s) of phosphorylated residues are indicated in parentheses. Kinases were assigned using the NetPhorest1 algorithm. Uniprot Protein Modified Previously Predicted ID Name Sequence described Kinase1 P67809 YBX1 _AADPPAENS(ph)SAPEAEQGGA TGFbR2 E_ P13591 NCAM1 _AAFSKDES(ph)KEPIVEVR_ TGFbR2 Q96TC7 FAM82A2 _S(ph)QSLPNSLDYTQTSDPGR_ Gunaratne et TGFbR2 al. (2010)2 Q14978 NOLC1 _AAES(ph)SSDSSDSDSSEDDEA TGFbR2 PSKPAGTTK_ Q14978 NOLC1 _S(ph)SSSEDSSSDEEEEQKKPM TGFbR2 K_ Q14978 NOLC1 _AAESS(ph)SDSSDSDSSEDDEA TGFbR2 PSKPAGTTK_ O43318 MAP3K7 _RMS(ph)ADMSEIEAR_ PKCtheta Q92685 ALG3 _SGS(ph)AAQAEGLCK_ PKBgam ma P53602 MPD _RNS(ph)RDGDPLPSSLSCK_ PKBgam ma O75396 SEC22B _NLGS(ph)INTELQDVQR_ Gunaratne et PKBbeta al. (2010)2 P35222 CTNNB1 _RTS(ph)MGGTQQQFVEGVR_ Gunaratne et PKAbeta al. (2010)2 P46821 MAP1B _KES(ph)KEETPEVTK_ PKAalpha Q6ZNB6 NFXL1 _KAS(ph)EIKEAEAK_ PKAalpha P49792 NUP358 _KQS(ph)LPATSIPTPASFK_ PKAalpha Q3KQU3 PARCC1 _RSS(ph)QPSPTAVPASDSPPTK PKAalpha QEVK_ Q7Z3C6 ATG9A _RES(ph)DESGESAPDEGGEGAR PKAalpha _ O43847 NRD1 _RGS(ph)LSNAGDPEIVK_ PKAalpha O43847 NRD1 _LGADESEEEGRRGS(ph)LSNAG PKAalpha DPEIVK_ P16949 STMN1 _RKS(ph)HEAEVLK_ Yip et al. PKAalpha (2014)4 Q14671 PUM1 _RDS(ph)LTGSSDLYKR_ Gunaratne et PKAalpha al. (2010)2 Q09161 CBP80 _KTS(ph)DANETEDHLESLICK_ Pim3 O43847 NRD1 _LGADESEEEGRRGSLS(ph)NAG PAK1 DPEIVK_ P35222 CTNNB1 _RT(ph)SMGGTQQQFVEGVR_ Gunaratne et MRCKa al. (2010)2 Q9C0C9 UBE2O _NMTVEQLLTGSPT(ph)SPTVEPE MAPK3 KPTR_ 3 P53396 ACLY _T(ph)ASFSESRADEVAPAKK_ ICK O75152 ZC3H11A _KFS(ph)AGGDSDPPLKR_ ICK P53396 ACLY _T(ph)ASFSESRADEVAPAK_ ICK P53396 ACLY _TAS(ph)FSESRADEVAPAKK_ Berwick et al. ICK (2002)5 O75448 TRAP100 _LLS(ph)SNEDDANILSSPTDR_ DMPK2 P62753 RPS6 _RLS(ph)S(ph)LRASTSK_ Lundby et al. CLK4; (2013)3 PKCalpha P62753 RPS6 _RLS(ph)S(ph)LRAS(ph)TSK_ Lundby et al. CLK4; (2013)3 PKCalpha Q96D71 REPS1 _RQS(ph)S(ph)SYDDPWKITDEQ CLK1; ICK R_ Q96D71 REPS1 _RQS(ph)S(ph)SYDDPWK_ CLK1; ICK Q9UK61 C3orf63 _KKS(ph)VGGDS(ph)DTEDMR_ CLK1; CK2a2 Q9H2J7 SLC6A15 _KQS(ph)GS(ph)PTLDTAPNGR_ CLK1 Q9NWW5 CLN6 _HGS(ph)VSADEAAR_ CLK1 Q4KMP7 FP2461 _RAS(ph)AGPAPGPVVTAEGLHP CLK1 SLPSPTGNSTPLGSSK_ Q92614 MYO18A _RVS(ph)SS(ph)SELDLPSGDHCE CLK1 AGLLQLDVPLLR_ P16403 H1F2 _KAS(ph)GPPVSELITK_ CLK1 Q96TC7 FAM82A2 _SQS(ph)LPNSLDYTQTSDPGR_ Gunaratne et CLK1 al. (2010)2 Q09161 CBP80 _KT(ph)SDANETEDHLESLICK_ CLK1 P13591 NCAM1 _AAFS(ph)KDESKEPIVEVR_ CK2alpha P35659 DEK _KESES(ph)EDS(ph)S(ph)DDEPLI CK2alpha KK_ P17096 HMGA1 _KLEKEEEEGISQESS(ph)EEEQ_ CK2alpha P08240 SRPR _GTGSGGQLQDLDCS(ph)SSDDE CK2alpha GAAQNSTKPSATK_ P35659 DEK _KES(ph)ES(ph)EDS(ph)SDDEPLI CK2alpha KK_ P19338 NCL _KEDS(ph)DEEEDDDSEEDEEDD CK2alpha EDEDEDEDEIEPAAMK_ P35659 DEK _KESESEDS(ph)S(ph)DDEPLIKK_ CK2alpha O75475 PSIP1 _TGVTS(ph)TSDSEEEGDDQEGE CK2a2 KKR_ Q7Z4V5 HDGF2 _GS(ph)GGS(ph)SGDELREDDEP CK2a2 VKK_ Q9H1E3 NUCKS1 _DSGSDEDFLMEDDDDS(ph)DYG CK2a2 SSK_ Q7Z4V5 HDGF2 _GSGGS(ph)S(ph)GDELREDDEP CK2a2 VKK_ Q7Z4V5 HDGF2 _GS(ph)GGS(ph)SGDELREDDEP CK2a2 VK_ P35222 CTNNB1 _RTSMGGT(ph)QQQFVEGVR_ ATR 4 Supplemental Table 4. Statistical analysis of the abundance of target phosphopeptides in isoproterenol and salmeterol. Summarized are p-values from a two-sided comparison corrected for multiple hypothesis testing based on parameters from regression analysis of the β2-AR phosphotargets. High iso = 1 μM isoproterenol; low iso = 10 nM isoproterenol; sal = 50 nM salmeterol. Condition used for null distribution simulation High Iso Low Iso Sal Sal <0.0001 0.0036 N.S. Low Iso 0.014 N.S. <0.0001 High Iso N.S. N.S. <0.0001 Supplemental Table 5. B2-AR-dependent transcriptional targets from DNA microarray experiments. Averaged Log2(Drug/No Drug) values for each condition (10 nM isoproterenol or 50 nM salmeterol) from n=3 are shown. Previously known CREB targets are indicated. Gene Description Iso Sal CREB Target C6orf176 Chromosome 6 open reading frame 176 5.36 5.70 Yes CGA Glycoprotein hormones, alpha polypeptide 3.88 4.29 Yes PCK1 Phosphoenolpyruvate carboxykinase 1 3.76 4.18 Yes NR4A3 Nuclear receptor subfamily 4, group A, 3.18 3.58 Yes member 3 NR4A1 Nuclear receptor subfamily 4, group A, 2.83 3.17 Yes member 1 NR4A2 Nuclear receptor subfamily 4, group A, 2.17 2.35 Yes member 2 PDE4B Phosphodiesterase 4B, cAMP-specific 2.08 1.48 Yes CCK Cholecystokinin 1.94 2.18 Yes DUSP1 Dual specificity phosphatase 1 1.75 1.73 Yes CCL8 Chemokine (C-C motif) ligand 8 1.65 2.59 No HIST1H4C Histone cluster 1, H4c 1.64 0.97 Yes LOC387763 Hypothetical LOC387763 1.61 1.73 No mtRNA_COX-2 Mitochondrially encoded cytochrome c 1.59 1.51 No oxidase 2 PDE4D Phosphodiesterase 4D, cAMP-specific 1.52 1.12 Yes EST_AA48139 AA481397_Exon1_331 1.41 1.07 No 7 ENO1 Enolase 1 1.36 1.31 No FOSB FBJ murine osteosarcoma viral oncogene 1.36 2.02 Yes homolog B 5 NME2 Non-metastatic cells 2, protein 1.35 1.41 No SOD1 Superoxide dismutase 1, soluble 1.30 1.24 No GNB2L1 Guanine nucleotide binding protein (G 1.28 1.30 No protein), beta polypeptide 2-like 1 EEF1G Eukaryotic translation elongation factor 1 1.27 1.22 No gamma RPS9 Ribosomal protein S9 1.27 1.30 Yes KRTAP19-5 Keratin associated protein 19-5 1.27 1.15 No RPRM Eeprimo, TP53 dependent G2 arrest 1.26 1.21 No mediator candidate mtRNA_ND4 Mitochondrially encoded NADH 1.25 1.17 No dehydrogenase subunit 4 AVPI1 Arginine vasopressin-induced 1 1.24 1.34 No PRMT1 Protein arginine methyltransferase 1 1.22 1.07 No DYNLT1 Dynein, light chain, Tctex-type 1 1.22 1.06 No RPS15 Ribosomal protein S15 1.20 1.17 No H2AFX H2A histone family, member X 1.16 1.15 No AARS Alanyl-tRNA synthetase 1.16 0.99 No mtRNA_CYTB Mitochondrially encoded cytochrome b 1.16 0.91 No mtRNA_RNR2 Mitochondrially encoded 16S ribosomal 1.14 1.09 No RNA MYL6 Myosin, light polypeptide 6 1.14 1.14 Yes ARPC2 Actin related protein 2/3 complex, subunit
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  • Role and Regulation of the P53-Homolog P73 in the Transformation of Normal Human Fibroblasts

    Role and Regulation of the P53-Homolog P73 in the Transformation of Normal Human Fibroblasts

    Role and regulation of the p53-homolog p73 in the transformation of normal human fibroblasts Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Bayerischen Julius-Maximilians-Universität Würzburg vorgelegt von Lars Hofmann aus Aschaffenburg Würzburg 2007 Eingereicht am Mitglieder der Promotionskommission: Vorsitzender: Prof. Dr. Dr. Martin J. Müller Gutachter: Prof. Dr. Michael P. Schön Gutachter : Prof. Dr. Georg Krohne Tag des Promotionskolloquiums: Doktorurkunde ausgehändigt am Erklärung Hiermit erkläre ich, dass ich die vorliegende Arbeit selbständig angefertigt und keine anderen als die angegebenen Hilfsmittel und Quellen verwendet habe. Diese Arbeit wurde weder in gleicher noch in ähnlicher Form in einem anderen Prüfungsverfahren vorgelegt. Ich habe früher, außer den mit dem Zulassungsgesuch urkundlichen Graden, keine weiteren akademischen Grade erworben und zu erwerben gesucht. Würzburg, Lars Hofmann Content SUMMARY ................................................................................................................ IV ZUSAMMENFASSUNG ............................................................................................. V 1. INTRODUCTION ................................................................................................. 1 1.1. Molecular basics of cancer .......................................................................................... 1 1.2. Early research on tumorigenesis ................................................................................. 3 1.3. Developing
  • Measuring Gene Expression Part 3 Key Steps in Microarray Analysis

    Measuring Gene Expression Part 3 Key Steps in Microarray Analysis

    Measuring Gene Expression Part 3 David Wishart Bioinformatics 301 [email protected] Key Steps in Microarray Analysis • Quality Control (checking microarrays for errors or problems) • Image Processing – Gridding – Segmentation (peak picking) – Data Extraction (intensity, QC) • Data Analysis and Data Mining Comet Tailing • Often caused by insufficiently rapid immersion of the slides in the succinic anhydride blocking solution. Uneven Spotting/Blotting • Problems with print tips or with overly viscous solution • Problems with humidity in spottiing chamber High Background • Insufficient Blocking • Precipitation of labelled probe Gridding Errors Spotting errors Uneven hybridization Gridding errors Key Steps in Microarray Analysis • Quality Control (checking microarrays for errors or problems) • Image Processing – Gridding – Segmentation (spot picking) – Data Extraction (intensity, QC) • Data Analysis and Data Mining Microarray Scanning PMT Pinhole Detector lens Laser Beam-splitter Objective Lens Dye Glass Slide Microarray Principles Laser 1 Laser 2 Green channel Red channel Scan and detect with overlay images Image process confocal laser system and normalize and analyze Microarray Images • Resolution – standard 10µm [currently, max 5µm] – 100µm spot on chip = 10 pixels in diameter • Image format – TIFF (tagged image file format) 16 bit (64K grey levels) – 1cm x 1cm image at 16 bit = 2Mb (uncompressed) – other formats exist i.e. SCN (Stanford University) • Separate image for each fluorescent sample – channel 1, channel 2, etc. Image
  • 1471-2105-8-217.Pdf

    1471-2105-8-217.Pdf

    BMC Bioinformatics BioMed Central Software Open Access GenMAPP 2: new features and resources for pathway analysis Nathan Salomonis1,2, Kristina Hanspers1, Alexander C Zambon1, Karen Vranizan1,3, Steven C Lawlor1, Kam D Dahlquist4, Scott W Doniger5, Josh Stuart6, Bruce R Conklin1,2,7,8 and Alexander R Pico*1 Address: 1Gladstone Institute of Cardiovascular Disease, 1650 Owens Street, San Francisco, CA 94158 USA, 2Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, 513 Parnassus Avenue, San Francisco, CA 94143, USA, 3Functional Genomics Laboratory, University of California, Berkeley, CA 94720 USA, 4Department of Biology, Loyola Marymount University, 1 LMU Drive, MS 8220, Los Angeles, CA 90045 USA, 5Computational Biology Graduate Program, Washington University School of Medicine, St. Louis, MO 63108 USA, 6Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064 USA, 7Department of Medicine, University of California, San Francisco, CA 94143 USA and 8Department of Molecular and Cellular Pharmacology, University of California, San Francisco, CA 94143 USA Email: Nathan Salomonis - [email protected]; Kristina Hanspers - [email protected]; Alexander C Zambon - [email protected]; Karen Vranizan - [email protected]; Steven C Lawlor - [email protected]; Kam D Dahlquist - [email protected]; Scott W Doniger - [email protected]; Josh Stuart - [email protected]; Bruce R Conklin - [email protected]; Alexander R Pico* - [email protected] * Corresponding author Published: 24 June 2007 Received: 16 November 2006 Accepted: 24 June 2007 BMC Bioinformatics 2007, 8:217 doi:10.1186/1471-2105-8-217 This article is available from: http://www.biomedcentral.com/1471-2105/8/217 © 2007 Salomonis et al; licensee BioMed Central Ltd.