Correct Interpretation of Comprehensive Phosphorylation Dynamics Requires Normalization by Protein Expression Changes*

The interpretation of quantitative phosphoproteomics studies is complicated because each differential phosphorylation event integrates both changes in protein expression and phosphorylation. Here we investigated this phenomenon by performing parallel comparisons of protein expression and phosphorylation in S. cerevisiae. In each of two experiments comparing yeast mutants bearing deletions in FUS3 or STE7 with their wild-type counterparts, we quantified over 4100 proteins, including all members of the yeast mating pathway. We also identified 12,499 unique phosphorylation sites in this work. We demonstrate the critical importance of controlling the protein-level false-discovery rate and provide a novel method to assess the accuracy of protein false-discovery rate estimates. For the first time, 96% of nonredundant phosphopeptide ratios could be calibrated by protein levels, allowing truly differential phosphorylation to be distinguished from altered protein expression. This revealed a starkly different view, with 25% of seemingly differential phosphopeptides now attributed to changes in protein expression. Combined protein expression and phosphorylation surveys uncovered both independent and concerted changes in protein expression and phosphorylation, while highlighting the partially redundant role of a second MAPK (Kss1) in the mating pathway.

[1]  F. White Quantitative phosphoproteomic analysis of signaling network dynamics. , 2008, Current opinion in biotechnology.

[2]  G. Fink,et al.  Elements of a single MAP kinase cascade in Saccharomyces cerevisiae mediate two developmental programs in the same cell type: mating and invasive growth. , 1994, Genes & development.

[3]  J. W. Gloor,et al.  Mitogen-activated protein (MAP) kinase phosphorylation of MAP kinase kinase: determination of phosphorylation sites by mass spectrometry and site-directed mutagenesis. , 1994, Journal of biochemistry.

[4]  E. O’Shea,et al.  Global analysis of protein expression in yeast , 2003, Nature.

[5]  Marcus B Smolka,et al.  Proteome-wide identification of in vivo targets of DNA damage checkpoint kinases , 2007, Proceedings of the National Academy of Sciences.

[6]  M. Mann,et al.  Global and site-specific quantitative phosphoproteomics: principles and applications. , 2009, Annual review of pharmacology and toxicology.

[7]  I. Herskowitz MAP kinase pathways in yeast: For mating and more , 1995, Cell.

[8]  J. Shabanowitz,et al.  Phosphoproteome analysis by mass spectrometry and its application to Saccharomyces cerevisiae , 2002, Nature Biotechnology.

[9]  Steven P Gygi,et al.  A probability-based approach for high-throughput protein phosphorylation analysis and site localization , 2006, Nature Biotechnology.

[10]  Steven P Gygi,et al.  Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry , 2007, Nature Methods.

[11]  Steven P. Gygi,et al.  Large-scale phosphorylation analysis of mouse liver , 2007, Proceedings of the National Academy of Sciences.

[12]  S. Elledge,et al.  A quantitative atlas of mitotic phosphorylation , 2008, Proceedings of the National Academy of Sciences.

[13]  C. Widmann,et al.  Mitogen-activated protein kinase: conservation of a three-kinase module from yeast to human. , 1999, Physiological reviews.

[14]  M. Mann,et al.  Brain phosphoproteome obtained by a FASP-based method reveals plasma membrane protein topology. , 2010, Journal of proteome research.

[15]  S. Brunak,et al.  Quantitative Phosphoproteomics Reveals Widespread Full Phosphorylation Site Occupancy During Mitosis , 2010, Science Signaling.

[16]  E. O’Shea,et al.  Global analysis of protein localization in budding yeast , 2003, Nature.

[17]  Edward L. Huttlin,et al.  A Tissue-Specific Atlas of Mouse Protein Phosphorylation and Expression , 2010, Cell.

[18]  John R Yates,et al.  Proteomics by mass spectrometry: approaches, advances, and applications. , 2009, Annual review of biomedical engineering.

[19]  Jie Ma,et al.  Bayesian Nonparametric Model for the Validation of Peptide Identification in Shotgun Proteomics*S , 2009, Molecular & Cellular Proteomics.

[20]  Steven P Gygi,et al.  The SCX/IMAC enrichment approach for global phosphorylation analysis by mass spectrometry , 2008, Nature Protocols.

[21]  L. Bardwell,et al.  Mechanisms of MAPK signalling specificity. , 2006, Biochemical Society transactions.

[22]  Brendan K Faherty,et al.  Optimization and Use of Peptide Mass Measurement Accuracy in Shotgun Proteomics*S , 2006, Molecular & Cellular Proteomics.

[23]  M. Mann,et al.  Mislocalized activation of oncogenic RTKs switches downstream signaling outcomes. , 2009, Molecular cell.

[24]  Daniel B. McClatchy,et al.  Quantitative analysis of brain nuclear phosphoproteins identifies developmentally regulated phosphorylation events. , 2008, Journal of proteome research.

[25]  M. Mann,et al.  Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast , 2008, Nature.

[26]  M. Waterfield,et al.  Quantification of Gel-separated Proteins and Their Phosphorylation Sites by LC-MS Using Unlabeled Internal Standards , 2005, Molecular & Cellular Proteomics.

[27]  E. Elion,et al.  FUS3 phosphorylates multiple components of the mating signal transduction cascade: evidence for STE12 and FAR1. , 1993, Molecular biology of the cell.

[28]  William Stafford Noble,et al.  Semi-supervised learning for peptide identification from shotgun proteomics datasets , 2007, Nature Methods.

[29]  H. Madhani,et al.  Principles of MAP kinase signaling specificity in Saccharomyces cerevisiae. , 2004, Annual review of genetics.

[30]  K. Resing,et al.  Mapping protein post-translational modifications with mass spectrometry , 2007, Nature Methods.

[31]  G. Fink,et al.  FUS3 represses CLN1 and CLN2 and in concert with KSS1 promotes signal transduction. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[32]  Craig D Wenger,et al.  Phosphoproteomics for the masses. , 2010, ACS chemical biology.

[33]  J. Yates,et al.  An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database , 1994, Journal of the American Society for Mass Spectrometry.

[34]  S. Gygi,et al.  Global Analysis of Cdk1 Substrate Phosphorylation Sites Provides Insights into Evolution , 2009, Science.

[35]  Samuel Kaplan,et al.  A computational strategy to analyze label-free temporal bottom-up proteomics data. , 2008, Journal of proteome research.

[36]  Steven P Gygi,et al.  The impact of peptide abundance and dynamic range on stable-isotope-based quantitative proteomic analyses. , 2008, Journal of proteome research.

[37]  Matthias Mann,et al.  A Dual Pressure Linear Ion Trap Orbitrap Instrument with Very High Sequencing Speed* , 2009, Molecular & Cellular Proteomics.

[38]  Michael J MacCoss,et al.  Dual-pressure linear ion trap mass spectrometer improving the analysis of complex protein mixtures. , 2009, Analytical chemistry.

[39]  Richard D. Smith,et al.  Quantitative proteomic approaches for studying phosphotyrosine signaling , 2007, Expert review of proteomics.

[40]  G. Fink,et al.  Combinatorial Control Required for the Specificity of Yeast MAPK Signaling , 1997, Science.

[41]  Peter R. Baker,et al.  Quantitative Analysis of Synaptic Phosphorylation and Protein Expression*S , 2008, Molecular & Cellular Proteomics.