Targeted Quantitative Analysis of Streptococcus pyogenes Virulence Factors by Multiple Reaction Monitoring*□S

Abstract

In many studies, particularly in the field of systems biology, it is essential that identical protein sets are precisely quantified in multiple samples such as those representing differentially perturbed cell states. The high degree of reproducibility required for such experiments has not been achieved by classical mass spectrometry-based proteomics methods. In this study we describe the implementation of a targeted quantitative approach by which predetermined protein sets are first identified and subsequently quantified at high sensitivity reliably in multiple samples. This approach consists of three steps. First, the proteome is extensively mapped out by multidimensional fractionation and tandem mass spectrometry, and the data generated are assembled in the PeptideAtlas database. Second, based on this proteome map, peptides uniquely identifying the proteins of interest, proteotypic peptides, are selected, and multiple reaction monitoring (MRM) transitions are established and validated by MS2 spectrum acquisition. This process of peptide selection, transition selection, and validation is supported by a suite of software tools, TIQAM (Targeted Identification for Quantitative Analysis by MRM), described in this study. Third, the selected target protein set is quantified in multiple samples by MRM. Applying this approach we were able to reliably quantify low abundance virulence factors from cultures of the human pathogen Streptococcus pyogenes exposed to increasing amounts of plasma. The resulting quantitative protein patterns enabled us to clearly define the subset of virulence proteins that is regulated upon plasma exposure. Molecular & Cellular Proteomics 7:1489–1500, 2008. A key element of the experimental framework for systems biology is the comprehensive, quantitative measurement of whole biological systems in differentially perturbed states (1). Among the different types of measurements possible, protein quantification is particularly informative because proteins catalyze or control the majority of cellular functions. Currently the most widely applied quantitative proteome analysis technologies consist of the labeling of the samples by stable isotopes, the reproducible separation of complex peptide mixtures, usually by capillary LC, and the identification and quantification of selected peptides by tandem mass spectrometry and sequence database searching (2, 3). Relative quantitative values are generated by these methods if two or more samples are being compared, and absolute quantification can be achieved if suitable, calibrated reference samples are available (4). Using such shotgun methods, in each measurement only a fraction of the analytes present in a complex sample is identified and quantified. Peptide ions are selected by the mass spectrometer automatically based on precursor ion signal intensities. Due to a multitude of factors, including interference between analytes and variations in precursor ion spectra, the selection of peptides is not reproducible in consecutive runs in particular for peptides of lower signal intensities. As a critical consequence of this undersampling effect comprehensive analyses of whole systems are not supported by these technologies rendering them poorly suitable for systems biology and other experiments that depend on the comparison of complete or at least reproducible data sets. To overcome these fundamental technical limitations confronting proteomics we have suggested in the past a substantially different approach that emulates successful genomics strategies (5–7). It depends on the generation of deep, ideally complete proteome maps followed by the targeted analysis of peptides that collectively represent the proteins that constitute the system under investigation. We have termed peptides that are typically observed in a mass spectrometer and that uniquely identify a particular protein “proteotypic peptides” (PTPs) (8). From the Institute of Molecular Systems Biology, ETH Zurich, Zurich 8093, Switzerland, Institute for Systems Biology, Seattle, Washington 98103, Department of Clinical Sciences, Lund University, Lund 22184, Sweden, Seminar für Statistik, ETH Zurich, Zurich 8092, Switzerland, Bioinformatics Institute, A*STAR (Agency for Science, Technology and Research), Biopolis, Singapore 138671, Singapore, Competence Center for Systems Physiology and Metabolic Diseases, Zurich 8093, Switzerland, and Faculty of Science, University of Zurich, Zurich 8092, Switzerland Received, January 23, 2008, and in revised form, April 11, 2008 Published, MCP Papers in Press, April 13, 2008, DOI 10.1074/ mcp.M800032-MCP200 1 The abbreviations used are: PTP, proteotypic peptide; ICPL, isotope-coded protein labeling; MRM, multiple reaction monitoring; TIQAM, Targeted Identification for Quantitative Analysis by MRM. Research

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@inproceedings{Lange2008TargetedQA, title={Targeted Quantitative Analysis of Streptococcus pyogenes Virulence Factors by Multiple Reaction Monitoring*□S}, author={V Lange and Johan Malmstr{\"{o}m and John P Didion and Nichole L. King and Bj{\"{o}rn Johansson and Juliane Sch{\"a}fer and Jonathan Rameseder and Chee-Hong Wong and Eric W. Deutsch and Mi-Youn K. Brusniak and Peter B{\"{u}hlmann and Lars Bj{\"{o}rck and Bruno Domon and Ruedi Aebersold}, year={2008} }