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Systematic Analysis of Protein Phosphorylation Networks From Phosphoproteomic Data*

  • Chunxia Song
    Footnotes
    Affiliations
    CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic RandA Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China;
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  • Mingliang Ye
    Footnotes
    Affiliations
    CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic RandA Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China;
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  • Zexian Liu
    Affiliations
    Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China;
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  • Han Cheng
    Affiliations
    Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China;
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  • Xinning Jiang
    Affiliations
    CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic RandA Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China;
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  • Guanghui Han
    Affiliations
    CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic RandA Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China;
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  • Zhou Songyang
    Affiliations
    State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China;
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  • Yexiong Tan
    Affiliations
    The International Cooperation Laboratory on Signal Transduction of Eastern Hepatobiliary Surgery Institute, Second Military Medical University, Shanghai, 200438, China
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  • Hongyang Wang
    Affiliations
    The International Cooperation Laboratory on Signal Transduction of Eastern Hepatobiliary Surgery Institute, Second Military Medical University, Shanghai, 200438, China
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  • Jian Ren
    Affiliations
    State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China;
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  • Yu Xue
    Correspondence
    To whom correspondence should be addressed: Tel.: +86-411-84379610, Fax: +86-411-84379620, Tel.: +86-27-87793903, Fax: +86-27-87793172, or Tel./Fax: +86-20-39943788.
    Affiliations
    Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China;
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  • Hanfa Zou
    Affiliations
    CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic RandA Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China;
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  • Author Footnotes
    * This work was partly funded by the National Basic Research Program (973 project) (2012CB910101, 2010CB945401, 2012CB911201), the Creative Research Group Project by NSFC (21021004), the National Key Special Program on Infection diseases (2012ZX10002009-011), the Analytical Method Innovation Program of MOST (2009IM031800, 2010IM030500), National Natural Sciences Foundation of China (31171263, 20735004, 30830036, 30900835, 31071154, 91019020).
    This article contains supplemental Procedures, Results, Tables S1 to S20, and Figs. S1 to S6.
    ‡‡ Both authors contributed equally to this work.
Open AccessPublished:July 13, 2012DOI:https://doi.org/10.1074/mcp.M111.012625
      In eukaryotes, hundreds of protein kinases (PKs) specifically and precisely modify thousands of substrates at specific amino acid residues to faithfully orchestrate numerous biological processes, and reversibly determine the cellular dynamics and plasticity. Although over 100,000 phosphorylation sites (p-sites) have been experimentally identified from phosphoproteomic studies, the regulatory PKs for most of these sites still remain to be characterized. Here, we present a novel software package of iGPS for the prediction of in vivo site-specific kinase-substrate relations mainly from the phosphoproteomic data. By critical evaluations and comparisons, the performance of iGPS is satisfying and better than other existed tools. Based on the prediction results, we modeled protein phosphorylation networks and observed that the eukaryotic phospho-regulation is poorly conserved at the site and substrate levels. With an integrative procedure, we conducted a large-scale phosphorylation analysis of human liver and experimentally identified 9719 p-sites in 2998 proteins. Using iGPS, we predicted a human liver protein phosphorylation networks containing 12,819 potential site-specific kinase-substrate relations among 350 PKs and 962 substrates for 2633 p-sites. Further statistical analysis and comparison revealed that 127 PKs significantly modify more or fewer p-sites in the liver protein phosphorylation networks against the whole human protein phosphorylation network. The largest data set of the human liver phosphoproteome together with computational analyses can be useful for further experimental consideration. This work contributes to the understanding of phosphorylation mechanisms at the systemic level, and provides a powerful methodology for the general analysis of in vivo post-translational modifications regulating sub-proteomes.
      Protein kinase (PK)
      The abbreviations used are:
      PK
      protein kinase
      PTM
      post-translational modification
      SLM
      short linear motif
      p-site
      phosphorylation site
      ssKSR
      site-specific kinase-substrate relation
      KSR
      kinase-substrate relation
      HTP-MS
      high-throughput mass spectrometry
      GPS
      group-based prediction system
      HPN
      human phosphorylation network
      iGPS
      GPS algorithm with the interaction filter, or in vivo GPS
      PPI
      protein-protein interaction
      PPN
      protein phosphorylation network
      RP-RPLC
      reversed-phase-reversed-phase liquid chromatography
      P
      positive control
      N
      negative control
      Sn
      sensitivity
      Sp
      specificity
      Ac
      accuracy
      MCC
      Mathew correlation coefficient
      Kpr
      kinase precision
      Lpr
      large-scale precision
      FPR
      false positive rate
      FDR
      false discovery rate
      STK
      serine/threonine kinase
      TK
      tyrosine kinase
      KTF
      kiss-then-farewell
      No PPI
      without PPI
      Exp. PPI
      experimental PPI
      KOW
      Kyprides, Ouzounis, Woese
      PAF
      polymerase-associated factor
      CTD
      C-terminal repeat domain
      HLPP
      Human Liver Proteome Project
      MPSS
      massively parallel signature sequencing
      CNHLPP
      Chinese human liver proteome project
      pS
      phosphoserine
      pT
      phosphothreonine
      pY
      phosphotyrosine.
      1The abbreviations used are:PK
      protein kinase
      PTM
      post-translational modification
      SLM
      short linear motif
      p-site
      phosphorylation site
      ssKSR
      site-specific kinase-substrate relation
      KSR
      kinase-substrate relation
      HTP-MS
      high-throughput mass spectrometry
      GPS
      group-based prediction system
      HPN
      human phosphorylation network
      iGPS
      GPS algorithm with the interaction filter, or in vivo GPS
      PPI
      protein-protein interaction
      PPN
      protein phosphorylation network
      RP-RPLC
      reversed-phase-reversed-phase liquid chromatography
      P
      positive control
      N
      negative control
      Sn
      sensitivity
      Sp
      specificity
      Ac
      accuracy
      MCC
      Mathew correlation coefficient
      Kpr
      kinase precision
      Lpr
      large-scale precision
      FPR
      false positive rate
      FDR
      false discovery rate
      STK
      serine/threonine kinase
      TK
      tyrosine kinase
      KTF
      kiss-then-farewell
      No PPI
      without PPI
      Exp. PPI
      experimental PPI
      KOW
      Kyprides, Ouzounis, Woese
      PAF
      polymerase-associated factor
      CTD
      C-terminal repeat domain
      HLPP
      Human Liver Proteome Project
      MPSS
      massively parallel signature sequencing
      CNHLPP
      Chinese human liver proteome project
      pS
      phosphoserine
      pT
      phosphothreonine
      pY
      phosphotyrosine.
      -catalyzed phosphorylation is one of the most important and ubiquitous post-translational modifications (PTMs) of proteins. This process temporally and spatially modifies ∼30% of all cellular proteins and plays a crucial role in regulating a variety of biological processes such as signal transduction and the cell cycle (
      • Olsen 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.
      ,
      • Ubersax J.A.
      • Ferrell Jr., J.E.
      Mechanisms of specificity in protein phosphorylation.
      ,
      • Manning G.
      • Whyte D.B.
      • Martinez R.
      • Hunter T.
      • Sudarsanam S.
      The protein kinase complement of the human genome.
      ). The human genome encodes 518 PK genes (∼2% of the genome), with different PKs showing distinct recognition specificities; each PK modifies only a limited subset of substrates, thereby guaranteeing the fidelity of cell signaling (
      • Olsen 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.
      ,
      • Ubersax J.A.
      • Ferrell Jr., J.E.
      Mechanisms of specificity in protein phosphorylation.
      ,
      • Manning G.
      • Whyte D.B.
      • Martinez R.
      • Hunter T.
      • Sudarsanam S.
      The protein kinase complement of the human genome.
      ). It is accepted that short linear motifs (SLMs) around the phosphorylation sites (p-sites) provide primary specificity (
      • Ubersax J.A.
      • Ferrell Jr., J.E.
      Mechanisms of specificity in protein phosphorylation.
      ,
      • Kobe B.
      • Kampmann T.
      • Forwood J.K.
      • Listwan P.
      • Brinkworth R.I.
      Substrate specificity of protein kinases and computational prediction of substrates.
      ,
      • Kreegipuu A.
      • Blom N.
      • Brunak S.
      • Järv J.
      Statistical analysis of protein kinase specificity determinants.
      ,
      • Songyang Z.
      • Lu K.P.
      • Kwon Y.T.
      • Tsai L.H.
      • Filhol O.
      • Cochet C.
      • Brickey D.A.
      • Soderling T.R.
      • Bartleson C.
      • Graves D.J.
      • DeMaggio A.J.
      • Hoekstra M.F.
      • Blenis J.
      • Hunter T.
      • Cantley L.C.
      A structural basis for substrate specificities of protein Ser/Thr kinases: primary sequence preference of casein kinases I and II, NIMA, phosphorylase kinase, calmodulin-dependent kinase II, CDK5, and Erk1.
      ), and a variety of additional contextual factors, including co-localization, co-expression, co-complex, and physical interaction of the PKs with their targets, contribute additional specificity in vivo (
      • Yaffe M.B.
      • Leparc G.G.
      • Lai J.
      • Obata T.
      • Volinia S.
      • Cantley L.C.
      A motif-based profile scanning approach for genome-wide prediction of signaling pathways.
      ,
      • Linding R.
      • Jensen L.J.
      • Ostheimer G.J.
      • van Vugt M.A.
      • Jorgensen C.
      • Miron I.M.
      • Diella F.
      • Colwill K.
      • Taylor L.
      • Elder K.
      • Metalnikov P.
      • Nguyen V.
      • Pasculescu A.
      • Jin J.
      • Park J.G.
      • Samson L.D.
      • Woodgett J.R.
      • Russell R.B.
      • Bork P.
      • Yaffe M.B.
      • Pawson T.
      Systematic discovery of in vivo phosphorylation networks.
      ,
      • Linding R.
      • Jensen L.J.
      • Pasculescu A.
      • Olhovsky M.
      • Colwill K.
      • Bork P.
      • Yaffe M.B.
      • Pawson T.
      NetworKIN: a resource for exploring cellular phosphorylation networks.
      ,
      • Tan C.S.
      • Linding R.
      Experimental and computational tools useful for (re)construction of dynamic kinase-substrate networks.
      ). Aberrances of PKs or key substrates disrupt normal function, rewire signaling pathways, and are implicated in various diseases and cancers (
      • Manning G.
      • Whyte D.B.
      • Martinez R.
      • Hunter T.
      • Sudarsanam S.
      The protein kinase complement of the human genome.
      ,
      • Lahiry P.
      • Torkamani A.
      • Schork N.J.
      • Hegele R.A.
      Kinase mutations in human disease: interpreting genotype-phenotype relationships.
      ). In this regard, the identification of kinase-specific p-sites and the systematic elucidation of site-specific kinase-substrate relations (ssKSRs) would provide a fundamental basis for understanding cell plasticity and dynamics and for dissecting the molecular mechanisms of various diseases, whereas the ultimate progress could suggest potential drug targets for future biomedical design (
      • Linding R.
      • Jensen L.J.
      • Ostheimer G.J.
      • van Vugt M.A.
      • Jorgensen C.
      • Miron I.M.
      • Diella F.
      • Colwill K.
      • Taylor L.
      • Elder K.
      • Metalnikov P.
      • Nguyen V.
      • Pasculescu A.
      • Jin J.
      • Park J.G.
      • Samson L.D.
      • Woodgett J.R.
      • Russell R.B.
      • Bork P.
      • Yaffe M.B.
      • Pawson T.
      Systematic discovery of in vivo phosphorylation networks.
      ,
      • Linding R.
      • Jensen L.J.
      • Pasculescu A.
      • Olhovsky M.
      • Colwill K.
      • Bork P.
      • Yaffe M.B.
      • Pawson T.
      NetworKIN: a resource for exploring cellular phosphorylation networks.
      ,
      • Tan C.S.
      • Linding R.
      Experimental and computational tools useful for (re)construction of dynamic kinase-substrate networks.
      ).
      Conventional experimental identification of ssKSRs, performed in a one-by-one manner, is labor-intensive, time-consuming and expensive. There are only 3508 known kinase-specific p-sites in the 1390 proteins collected in the Phospho.ELM 8.2 database (released in April 2009) (
      • Diella F.
      • Gould C.M.
      • Chica C.
      • Via A.
      • Gibson T.J.
      Phospho.ELM: a database of phosphorylation sites–update 2008.
      ). In 2005, Ptacek et al. detected more than 4000 in vitro kinase-substrate relations (KSRs) in Saccharomyces cerevisiae using protein chip technology, although the exact p-sites were not determined (
      • Ptacek J.
      • Devgan G.
      • Michaud G.
      • Zhu H.
      • Zhu X.
      • Fasolo J.
      • Guo H.
      • Jona G.
      • Breitkreutz A.
      • Sopko R.
      • McCartney R.R.
      • Schmidt M.C.
      • Rachidi N.
      • Lee S.J.
      • Mah A.S.
      • Meng L.
      • Stark M.J.
      • Stern D.F.
      • De Virgilio C.
      • Tyers M.
      • Andrews B.
      • Gerstein M.
      • Schweitzer B.
      • Predki P.F.
      • Snyder M.
      Global analysis of protein phosphorylation in yeast.
      ). Recently, rapid advances in phosphoproteomics have provided a great opportunity to systematically assess phosphorylation (
      • Olsen 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.
      ,
      • Villén J.
      • Beausoleil S.A.
      • Gerber S.A.
      • Gygi S.P.
      Large-scale phosphorylation analysis of mouse liver.
      ,
      • Han G.
      • Ye M.
      • Zhou H.
      • Jiang X.
      • Feng S.
      • Jiang X.
      • Tian R.
      • Wan D.
      • Zou H.
      • Gu J.
      Large-scale phosphoproteome analysis of human liver tissue by enrichment and fractionation of phosphopeptides with strong anion exchange chromatography.
      ,
      • Han G.
      • Ye M.
      • Liu H.
      • Song C.
      • Sun D.
      • Wu Y.
      • Jiang X.
      • Chen R.
      • Wang C.
      • Wang L.
      • Zou H.
      Phosphoproteome analysis of human liver tissue by long-gradient nanoflow LC coupled with multiple stage MS analysis.
      ,
      • Tan C.S.
      • Bodenmiller B.
      • Pasculescu A.
      • Jovanovic M.
      • Hengartner M.O.
      • Jørgensen C.
      • Bader G.D.
      • Aebersold R.
      • Pawson T.
      • Linding R.
      Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases.
      ,
      • Xu C.F.
      • Lu Y.
      • Ma J.
      • Mohammadi M.
      • Neubert T.A.
      Identification of phosphopeptides by MALDI Q-TOF MS in positive and negative ion modes after methyl esterification.
      ,
      • Steen H.
      • Jebanathirajah J.A.
      • Rush J.
      • Morrice N.
      • Kirschner M.W.
      Phosphorylation analysis by mass spectrometry: myths, facts, and the consequences for qualitative and quantitative measurements.
      ,
      • Li X.
      • Gerber S.A.
      • Rudner A.D.
      • Beausoleil S.A.
      • Haas W.
      • Villén J.
      • Elias J.E.
      • Gygi S.P.
      Large-scale phosphorylation analysis of alpha-factor-arrested Saccharomyces cerevisiae.
      ,
      • Matsuoka S.
      • Ballif B.A.
      • Smogorzewska A.
      • McDonald 3rd, E.R.
      • Hurov K.E.
      • Luo J.
      • Bakalarski C.E.
      • Zhao Z.
      • Solimini N.
      • Lerenthal Y.
      • Shiloh Y.
      • Gygi S.P.
      • Elledge S.J.
      ATM and ATR substrate analysis reveals extensive protein networks responsive to DNA damage.
      ). State-of-the-art high-throughput mass spectrometry (HTP-MS) techniques have the ability to detect thousands of p-sites in cells or tissues in a single experiment (
      • Olsen 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.
      ,
      • Villén J.
      • Beausoleil S.A.
      • Gerber S.A.
      • Gygi S.P.
      Large-scale phosphorylation analysis of mouse liver.
      ,
      • Han G.
      • Ye M.
      • Liu H.
      • Song C.
      • Sun D.
      • Wu Y.
      • Jiang X.
      • Chen R.
      • Wang C.
      • Wang L.
      • Zou H.
      Phosphoproteome analysis of human liver tissue by long-gradient nanoflow LC coupled with multiple stage MS analysis.
      ,
      • Song C.
      • Ye M.
      • Han G.
      • Jiang X.
      • Wang F.
      • Yu Z.
      • Chen R.
      • Zou H.
      Reversed-phase-reversed-phase liquid chromatography approach with high orthogonality for multidimensional separation of phosphopeptides.
      ). We have collected 145,646 eukaryotic p-sites, primarily from these large-scale assays (supplemental Table S1); the regulatory PKs for 97.6% of these sites remain to be characterized.
      Alternatively, the in silico prediction of ssKSRs can generate useful information for subsequent experimental manipulation. In 2001, Yaffe et al. developed the SLM-based software Scansite for the prediction of ssKSRs directly from protein primary sequences (
      • Yaffe M.B.
      • Leparc G.G.
      • Lai J.
      • Obata T.
      • Volinia S.
      • Cantley L.C.
      A motif-based profile scanning approach for genome-wide prediction of signaling pathways.
      ). Later, the strategy was employed in a variety of kinase-specific predictors (
      • Xue Y.
      • Gao X.
      • Cao J.
      • Liu Z.
      • Jin C.
      • Wen L.
      • Yao X.
      • Ren J.
      A summary of computational resources for protein phosphorylation.
      ), including our group-based prediction system (GPS) program (
      • Xue Y.
      • Ren J.
      • Gao X.
      • Jin C.
      • Wen L.
      • Yao X.
      GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy.
      ). These tools may guarantee partially correct predictions for in vitro phosphorylation, but they are far from being adequate for in vivo hits because the contributions of various contextual factors cannot be neglected. To address this problem, Linding et al. developed a predictor of NetworKIN by combining an SLM-based approach with network contextual information to predict in vivo ssKSRs, and a potential in vivo human phosphorylation network (HPN) was modeled by annotating the phosphoproteomic data (
      • Linding R.
      • Jensen L.J.
      • Ostheimer G.J.
      • van Vugt M.A.
      • Jorgensen C.
      • Miron I.M.
      • Diella F.
      • Colwill K.
      • Taylor L.
      • Elder K.
      • Metalnikov P.
      • Nguyen V.
      • Pasculescu A.
      • Jin J.
      • Park J.G.
      • Samson L.D.
      • Woodgett J.R.
      • Russell R.B.
      • Bork P.
      • Yaffe M.B.
      • Pawson T.
      Systematic discovery of in vivo phosphorylation networks.
      ,
      • Linding R.
      • Jensen L.J.
      • Pasculescu A.
      • Olhovsky M.
      • Colwill K.
      • Bork P.
      • Yaffe M.B.
      • Pawson T.
      NetworKIN: a resource for exploring cellular phosphorylation networks.
      ).
      In this work, we developed a software package of iGPS (GPS algorithm with the interaction filter, or in vivo GPS) mainly for the prediction of in vivo ssKSRs. Eukaryotic PKs were classified into a hierarchy with four levels: group, family, subfamily, and single PK (
      • Manning G.
      • Whyte D.B.
      • Martinez R.
      • Hunter T.
      • Sudarsanam S.
      The protein kinase complement of the human genome.
      ). Based on the hypothesis that similar PKs recognize similar SLMs, we selected a predictor in GPS 2.0 (
      • Xue Y.
      • Ren J.
      • Gao X.
      • Jin C.
      • Wen L.
      • Yao X.
      GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy.
      ) for each PK and directly predicted the potential PKs for the un-annotated p-sites from the phosphoproteomic studies. Consequently, protein–protein interaction (PPI) information was used as the major contextual factor to reduce over 95% potentially false-positive hits. The performance of iGPS was shown by critical evaluations and comparisons to be promising for the accurate prediction of in vivo ssKSRs. Based on the prediction results of iGPS, we modeled eukaryotic protein phosphorylation networks (PPNs) and observed that phosphorylation regulation changes dramatically over the course of evolution, with poor conservation at both the site and substrate levels. This observation is consistent with previous studies (
      • Tan C.S.
      • Bodenmiller B.
      • Pasculescu A.
      • Jovanovic M.
      • Hengartner M.O.
      • Jørgensen C.
      • Bader G.D.
      • Aebersold R.
      • Pawson T.
      • Linding R.
      Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases.
      ,
      • Boekhorst J.
      • van Breukelen B.
      • Heck Jr., A.
      • Snel B.
      Comparative phosphoproteomics reveals evolutionary and functional conservation of phosphorylation across eukaryotes.
      ). Furthermore, we combined a new multidimensional separation approach using reversed-phase-reversed-phase liquid chromatography (RP-RPLC) (
      • Song C.
      • Ye M.
      • Han G.
      • Jiang X.
      • Wang F.
      • Yu Z.
      • Chen R.
      • Zou H.
      Reversed-phase-reversed-phase liquid chromatography approach with high orthogonality for multidimensional separation of phosphopeptides.
      ), with HTP-MS and a new data process platform of ArMone (
      • Jiang X.
      • Ye M.
      • Cheng K.
      • Zou H.
      ArMone: a software suite specially designed for processing and analysis of phosphoproteome data.
      ) to conduct a large-scale phosphorylation analysis of the human liver. Totally, 9719 p-sites of 2998 substrates were identified from 10,644 non-redundant phosphopeptides. The potential ssKSRs were predicted for the human liver phosphoproteome, whereas further statistical analysis suggested that 60 and 67 PKs preferentially regulate more or fewer p-sites in the human liver PPN (p value<0.01). A number of results are consistent with previous observations, whereas other predictions can be useful for further experimental manipulation.

      DISCUSSION

      In the post-genomic era, the dissection of the functional complexity and diversity of the proteome has emerged as an urgent challenge. In particular, proteins are transiently and dynamically regulated by hundreds of PTMs in vivo, which adds a dimension of functional complexity. As one of the most essential PTMs, phosphorylation has attracted considerable attention for its functional importance (
      • Olsen 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.
      ,
      • Ubersax J.A.
      • Ferrell Jr., J.E.
      Mechanisms of specificity in protein phosphorylation.
      ,
      • Manning G.
      • Whyte D.B.
      • Martinez R.
      • Hunter T.
      • Sudarsanam S.
      The protein kinase complement of the human genome.
      ). Investigating phosphorylation at the systemic level can help in the understanding of its molecular mechanisms and regulatory activity (
      • Linding R.
      • Jensen L.J.
      • Ostheimer G.J.
      • van Vugt M.A.
      • Jorgensen C.
      • Miron I.M.
      • Diella F.
      • Colwill K.
      • Taylor L.
      • Elder K.
      • Metalnikov P.
      • Nguyen V.
      • Pasculescu A.
      • Jin J.
      • Park J.G.
      • Samson L.D.
      • Woodgett J.R.
      • Russell R.B.
      • Bork P.
      • Yaffe M.B.
      • Pawson T.
      Systematic discovery of in vivo phosphorylation networks.
      ,
      • Linding R.
      • Jensen L.J.
      • Pasculescu A.
      • Olhovsky M.
      • Colwill K.
      • Bork P.
      • Yaffe M.B.
      • Pawson T.
      NetworKIN: a resource for exploring cellular phosphorylation networks.
      ,
      • Tan C.S.
      • Linding R.
      Experimental and computational tools useful for (re)construction of dynamic kinase-substrate networks.
      ). Rapid progresses in phosphoproteomics using phosphopeptide enrichment and HTP-MS techniques have detected tens of thousands of potential in vivo p-sites with high confidence (
      • Olsen 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.
      ,
      • Villén J.
      • Beausoleil S.A.
      • Gerber S.A.
      • Gygi S.P.
      Large-scale phosphorylation analysis of mouse liver.
      ,
      • Song C.
      • Ye M.
      • Han G.
      • Jiang X.
      • Wang F.
      • Yu Z.
      • Chen R.
      • Zou H.
      Reversed-phase-reversed-phase liquid chromatography approach with high orthogonality for multidimensional separation of phosphopeptides.
      ). However, deeper analysis of these un-annotated p-sites to allow elucidation of ssKSRs in eukaryotes is lacking and at present is hampered by limited computational methods. In contrast with previous studies that focused exclusively on humans (
      • Linding R.
      • Jensen L.J.
      • Ostheimer G.J.
      • van Vugt M.A.
      • Jorgensen C.
      • Miron I.M.
      • Diella F.
      • Colwill K.
      • Taylor L.
      • Elder K.
      • Metalnikov P.
      • Nguyen V.
      • Pasculescu A.
      • Jin J.
      • Park J.G.
      • Samson L.D.
      • Woodgett J.R.
      • Russell R.B.
      • Bork P.
      • Yaffe M.B.
      • Pawson T.
      Systematic discovery of in vivo phosphorylation networks.
      ,
      • Linding R.
      • Jensen L.J.
      • Pasculescu A.
      • Olhovsky M.
      • Colwill K.
      • Bork P.
      • Yaffe M.B.
      • Pawson T.
      NetworKIN: a resource for exploring cellular phosphorylation networks.
      ,
      • Tan C.S.
      • Bodenmiller B.
      • Pasculescu A.
      • Jovanovic M.
      • Hengartner M.O.
      • Jørgensen C.
      • Bader G.D.
      • Aebersold R.
      • Pawson T.
      • Linding R.
      Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases.
      ), here we designed a general and integrative approach to predict in vivo ssKSRs in five eukaryotic species. In iGPS 1.0, the GPS 2.0 algorithm was used to predict potential PKs for un-annotated p-sites (
      • Xue Y.
      • Ren J.
      • Gao X.
      • Jin C.
      • Wen L.
      • Yao X.
      GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy.
      ), and both experimentally identified and pre-predicted PPI information was adopted for further filtering of false-positive hits. Extensive evaluations and comparisons suggest the prediction performance to be promisingly accurate and better than NetworKIN (
      • Linding R.
      • Jensen L.J.
      • Ostheimer G.J.
      • van Vugt M.A.
      • Jorgensen C.
      • Miron I.M.
      • Diella F.
      • Colwill K.
      • Taylor L.
      • Elder K.
      • Metalnikov P.
      • Nguyen V.
      • Pasculescu A.
      • Jin J.
      • Park J.G.
      • Samson L.D.
      • Woodgett J.R.
      • Russell R.B.
      • Bork P.
      • Yaffe M.B.
      • Pawson T.
      Systematic discovery of in vivo phosphorylation networks.
      ,
      • Linding R.
      • Jensen L.J.
      • Pasculescu A.
      • Olhovsky M.
      • Colwill K.
      • Bork P.
      • Yaffe M.B.
      • Pawson T.
      NetworKIN: a resource for exploring cellular phosphorylation networks.
      ) (Table IV).
      With this powerful tool, we systematically predicted potentially ssKSRs and modeled PPNs from eukaryotic phosphoproteomic data. The total predictive coverage is 30.4% (44,290/145,646), which is a great amount of information for experimentalists. Among the top 10 PKs with the most p-sites in the five eukaryotic phosphoproteomes, we observed up to 33 PKs (66.7%) that belong to the CMGC group (supplemental Table S12). The PKs in the CMGC group are implicated in the cell cycle/cell division (e.g. CMGC/CDK) and signal transduction (e.g. CMGC/MAPK and CMGC/GSK) pathways (from the GO annotations in the UniProt database), which is consistent with the major roles of phosphorylation (
      • Olsen 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.
      ,
      • Ubersax J.A.
      • Ferrell Jr., J.E.
      Mechanisms of specificity in protein phosphorylation.
      ,
      • Manning G.
      • Whyte D.B.
      • Martinez R.
      • Hunter T.
      • Sudarsanam S.
      The protein kinase complement of the human genome.
      ,
      • Tan C.S.
      • Bodenmiller B.
      • Pasculescu A.
      • Jovanovic M.
      • Hengartner M.O.
      • Jørgensen C.
      • Bader G.D.
      • Aebersold R.
      • Pawson T.
      • Linding R.
      Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases.
      ). Three potential hypotheses may be offered to interpret this observation. First, the prediction might be influenced by the GPS 2.0 algorithm at the p-site level such that better performance can generate more kinase-specific p-sites (
      • Xue Y.
      • Ren J.
      • Gao X.
      • Jin C.
      • Wen L.
      • Yao X.
      GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy.
      ). In GPS 2.0, the Ac, Sn, Sp, and MCC of CMGC/MAPK are 86.05%, 91.21%, 85.94%, and 0.2950, respectively, whereas the performance of AGC/GRK is 92.46% (Ac), 94.05% (Sn), 92.37% (Sp), and 0.5999 (MCC) (
      • Xue Y.
      • Ren J.
      • Gao X.
      • Jin C.
      • Wen L.
      • Yao X.
      GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy.
      ). Although the accuracy of AGC/GRK is much better than CMGC/MAPK, no GRK members are included in the top 10 PKs with the most p-sites in any organism. Also, although the Ac, Sn, Sp, and MCC of Atypical/PIKK/ATM are 94.47%, 100.00%, 94.38%, and 0.4451, respectively, the human ATM is not contained in the top 10 PKs with the most p-sites. Thus, this result is not caused by a bias from the GPS 2.0 prediction. Second, the number of PPIs might influence the prediction at the substrate level such that more PPIs lead to a greater number of predicted substrates. We counted the number of PPIs for each PK, and only 14 CMGC PKs (28%) belong to the top 10 PKs with the most PPIs in the five species (supplemental Table S16). Again, although the number of GSK3A interacting proteins in humans is only the 28th in rank (supplemental Table S16), it is one of the top 10 PKs with the most p-sites in this study (supplemental Table S12). Although the number of ATM binding proteins ranks 8th in humans (supplemental Table S16), it is not included in the top 10 PKs with the most p-sites (supplemental Table S12). In this regard, this observation is not caused by a bias from the PPI filter. Finally, this prediction might reflect the bona fide status that most of the p-sites were phosphorylated and regulated by the CMGC group PKs. In addition, by analyzing the human liver phosphoproteome, we observed a similar result that 6 of the top 10 PKs with the most p-sites belong to CMGC PKs (supplemental Table S14). Taken together, it is proposed that CMGC PKs play a predominant role in regulating cellular phosphorylation.
      Although CMGC PKs play a general role for the phosphorylation, several PKs in a distinct sample might preferentially modify more or fewer p-sites to ensure the precise regulation. By the statistical analysis and comparison of predicted results of human liver and whole PPNs, we observed that a considerable number of PKs significantly regulate more or fewer p-sites in human liver PPN (supplemental Table S15). Beyond the results that are consistent with previous analyses, our study suggested that a number of PKs, such as CLK1, PKN2, and CK1d, also play a potentially important role in the human liver PPN (Table V). In 2007, Villen et al. experimentally identified thousands of p-sites from a 21-day-old mouse liver (
      • Villén J.
      • Beausoleil S.A.
      • Gerber S.A.
      • Gygi S.P.
      Large-scale phosphorylation analysis of mouse liver.
      ). By collecting 6089 p-sites in 2209 mouse liver proteins (
      • Villén J.
      • Beausoleil S.A.
      • Gerber S.A.
      • Gygi S.P.
      Large-scale phosphorylation analysis of mouse liver.
      ), we predicted 4502 ssKSRs among the 308 PKs and 543 proteins for 1176 p-sites, with a coverage rate of 19.3% (supplemental Table S17). However, we only detected 13 and 9 PKs with significantly over- or under-represented p-sites with the Yates’ chi-squared (χ2) test (p value < 0.01, supplemental Table S18) (
      • Liu Z.
      • Cao J.
      • Ma Q.
      • Gao X.
      • Ren J.
      • Xue Y.
      GPS-YNO2: computational prediction of tyrosine nitration sites in proteins.
      ). And the statistical significance is much lower against the result in the human liver PPN (supplemental Table S15). In this regard, we proposed that our results might be more useful for further studying hepatic functions in H. sapiens.
      Our approach can be generally used to identify potential in vivo ssKSRs in eukaryotes. The total predictive coverage is 30.4% (44,290/145,646) (Table III), which is a great amount of information for experimentalists. We anticipate that more efficient contextual filters will be integrated into this method over time to improve its prediction ability. This study can serve as a starting point for the general analysis of the various PTM-regulating proteomes, not limited to phosphorylation.

      Acknowledgments

      We thank Dr. Francesca Diella and Dr. Toby J. Gibson (EMBL) for always providing the new data set of the Phospho.ELM database during the past seven years. We thank Dr. Peter Hornbeck (Cell Signaling Technology, USA) for providing the PhosphoSitePlus data set on July 14, 2009. We thank Dr. Ralf Mrowka (Charité, Germany) for providing a Java applet for visualizing protein-protein interaction. We thank Drs. Hong Li and Guohui Ding (SIBS, China) for providing the SysPTM data set. We thank Dr. Rune Linding (ICR, UK) and Dr. Martin Lee Miller (Univ. of Copenhagen) for personal communications on computational phosphorylation. We thank Dr. Jing-Dong Jackie Han (IGDB), Dr. Edwin Wang (NRC, Canada), Dr. Xuegong Zhang (Tsinghua Univ.), Dr. Dong Li (BPRC), and Dr. Houjiang Zhou (The Heck Lab, Netherlands) for their helpful comments on network analysis. We thank Dr. Felix Cheung (Nature China) for his encouragement and helpful suggestions on presentation. Nature Publishing Group Language Editing (NPG Language Editing) and Pacific Edit reviewed the manuscript prior to submission. We also thank the anonymous reviewer, whose suggestions have greatly improved the presentation of this manuscript.

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