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. 2008 Aug;7(8):1489-500.
doi: 10.1074/mcp.M800032-MCP200. Epub 2008 Apr 13.

Targeted Quantitative Analysis of Streptococcus Pyogenes Virulence Factors by Multiple Reaction Monitoring

Free PMC article

Targeted Quantitative Analysis of Streptococcus Pyogenes Virulence Factors by Multiple Reaction Monitoring

Vinzenz Lange et al. Mol Cell Proteomics. .
Free PMC article


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.


F<sc>ig</sc>. 1.
Fig. 1.
Characterization of the S. pyogenes proteome. Multidimensional peptide separation was coupled to MS identification to characterize the S. pyogenes proteome. a, coverage of the hypothetical, confirmed, or total predicted proteins by the proteome map. b, distribution of the detected proteins across the cellular component ontology groups.
F<sc>ig</sc>. 2.
Fig. 2.
The TIQAM work flow. Starting with a protein list of interest, tryptic peptides are generated by TIQAM. Several sources of information might be integrated to prioritize peptides for which specific transitions are generated. Upon performing MRM-triggered MS/MS experiments, the data are displayed in a protein/peptide-centered view to validate the transitions. The user may then decide to perform additional rounds of validation or export a final list of validated transitions for quantitative experiments.
F<sc>ig</sc>. 3.
Fig. 3.
Validation and quantitative measurements. Validation using TIQAM is shown. The MRM traces (a) and an MS/MS spectrum (b) of a peptide (AGEQAIFVR) of the virulence factor protein “surface lipoprotein” are displayed. c, quantitative analysis. Upon successful validation each sample was analyzed by MRM. The use of prototype “scheduled MRM” software enabled the acquisition of 282 transition traces in one LC-MS run without compromising sensitivity or sampling rate.
F<sc>ig</sc>. 4.
Fig. 4.
Quantification of potential virulence factors by MRM analysis. S. pyogenes was incubated with medium containing 0, 1, 5, 10, or 20% human plasma. Two independent experiments were performed for each concentration, resulting in a total of 10 samples. Every sample was quantitatively analyzed twice by MRM. Each peptide was targeted by three (or two) specific transitions, resulting in six (or four) measurements per peptide and sample (heavy and light). A, peptide averages and S.D. (error bars) of the six (or four) measurements. The replicate experiments at each concentration are distinguished by color. The one to four peptides per protein are plotted next to each other. Therefore, a protein with three proteotypic peptides is plotted with three red and three blue values at each concentration. B, 95% confidence intervals (error bars) for the mean relative protein abundance with dependence on plasma concentration (derived using linear mixed-effects models). n.s., not significant.

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