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• W1972029046 abstract "Streptococcus pyogenes is a major bacterial pathogen and a potent inducer of inflammation causing plasma leakage at the site of infection. A combination of label-free quantitative mass spectrometry-based proteomics strategies were used to measure how the intracellular proteome homeostasis of S. pyogenes is influenced by the presence of human plasma, identifying and quantifying 842 proteins. In plasma the bacterium modifies its production of 213 proteins, and the most pronounced change was the complete down-regulation of proteins required for fatty acid biosynthesis. Fatty acids are transported by albumin (HSA) in plasma. S. pyogenes expresses HSA-binding surface proteins, and HSA carrying fatty acids reduced the amount of fatty acid biosynthesis proteins to the same extent as plasma. The results clarify the function of HSA-binding proteins in S. pyogenes and underline the power of the quantitative mass spectrometry strategy used here to investigate bacterial adaptation to a given environment. Streptococcus pyogenes is a major bacterial pathogen and a potent inducer of inflammation causing plasma leakage at the site of infection. A combination of label-free quantitative mass spectrometry-based proteomics strategies were used to measure how the intracellular proteome homeostasis of S. pyogenes is influenced by the presence of human plasma, identifying and quantifying 842 proteins. In plasma the bacterium modifies its production of 213 proteins, and the most pronounced change was the complete down-regulation of proteins required for fatty acid biosynthesis. Fatty acids are transported by albumin (HSA) in plasma. S. pyogenes expresses HSA-binding surface proteins, and HSA carrying fatty acids reduced the amount of fatty acid biosynthesis proteins to the same extent as plasma. The results clarify the function of HSA-binding proteins in S. pyogenes and underline the power of the quantitative mass spectrometry strategy used here to investigate bacterial adaptation to a given environment. Streptococcus pyogenes (or group A streptococcus), a Gram-positive bacterium infecting only humans, is a common colonizer of the upper respiratory tract and the skin where it causes relatively mild and superficial clinical conditions such as pharyngitis and impetigo (1Cunningham M.W. Pathogenesis of group A streptococcal infections.Clin. Microbiol. Rev. 2000; 13: 470-511Crossref PubMed Scopus (1769) Google Scholar). However, the morbidity and the costs for society of these infections are highly significant, and a study funded by the WHO in 2005 reported that S. pyogenes annually causes over 600 million cases of pharyngitis and 111 million cases of skin infections worldwide (2Carapetis J.R. Steer A.C. Mulholland E.K. Weber M. The global burden of group A streptococcal diseases.Lancet Infect. Dis. 2005; 5: 685-694Abstract Full Text Full Text PDF PubMed Scopus (2012) Google Scholar). Apart from these common and mostly uncomplicated infections, the bacterium is responsible for severe invasive and potentially life-threatening conditions such as necrotizing fasciitis and sepsis, and acute rheumatic fever following S. pyogenes throat or skin infections is the most frequent cause of heart disease in children. The study mentioned above reported that at least 517,000 deaths occur each year due to these conditions, emphasizing that S. pyogenes is one of the most significant bacterial pathogens in the human population. A better understanding of the biology of S. pyogenes and its interactions with the human host is required to identify novel prophylactic, diagnostic, and treatment opportunities to reduce the global burden of S. pyogenes diseases. Several virulence factors that promote S. pyogenes colonization, immune evasion, and spread have been identified (for reviews, see Refs. 1Cunningham M.W. Pathogenesis of group A streptococcal infections.Clin. Microbiol. Rev. 2000; 13: 470-511Crossref PubMed Scopus (1769) Google Scholar and 3Musser J.M. DeLeo F.R. Toward a genome-wide systems biology analysis of host-pathogen interactions in group A Streptococcus.Am. J. Pathol. 2005; 167: 1461-1472Abstract Full Text Full Text PDF PubMed Scopus (86) Google Scholar, 4Aziz R.K. Kansal R. Aronow B.J. Taylor W.L. Rowe S.L. Kubal M. Chhatwal G.S. Walker M.J. Kotb M. Microevolution of group A streptococci in vivo. Capturing regulatory networks engaged in sociomicrobiology, niche adaptation, and hypervirulence.PLoS One. 2010; 5: e9798Crossref PubMed Scopus (43) Google Scholar, 5Ashbaugh C.D. Warren H.B. Carey V.J. Wessels M.R. Molecular analysis of the role of the group A streptococcal cysteine protease, hyaluronic acid capsule, and M protein in a murine model of human invasive soft-tissue infection.J. Clin. Invest. 1998; 102: 550-560Crossref PubMed Scopus (164) Google Scholar), but a comprehensive view of the mechanisms operating during various phases of infection is still lacking. A characteristic property of S. pyogenes is its capacity to induce a powerful inflammatory response leading to vascular leakage at the site of infection. In this situation, and if the bacterium invades the vasculature, it will be exposed to plasma and its constituents. S. pyogenes expresses a number of surface proteins that bind several of the most abundant plasma proteins (albumin, fibrinogen, IgG, proteins of the complement and contact systems, etc.) with high affinity and specificity, underlining the intense molecular interplay between the pathogen and human plasma. These interactions could influence the gene expression in S. pyogenes with implications for bacterial adaptation and virulence, a notion supported by findings that a subset of S. pyogenes virulence proteins change their abundance levels upon contact with plasma (6Lange V. Malmström J.A. Didion J. King N.L. Johansson B.P. Schäfer J. Rameseder J. Wong C.H. Deutsch E.W. Brusniak M.Y. Bühlmann P. Björck L. Domon B. Aebersold R. Targeted quantitative analysis of Streptococcus pyogenes virulence factors by multiple reaction monitoring.Mol. Cell. Proteomics. 2008; 7: 1489-1500Abstract Full Text Full Text PDF PubMed Scopus (183) Google Scholar, 7Graham M.R. Virtaneva K. Porcella S.F. Barry W.T. Gowen B.B. Johnson C.R. Wright F.A. Musser J.M. Group A Streptococcus transcriptome dynamics during growth in human blood reveals bacterial adaptive and survival strategies.Am. J. Pathol. 2005; 166: 455-465Abstract Full Text Full Text PDF PubMed Scopus (121) Google Scholar). However, a full picture of how the proteome is influenced by plasma is lacking, and there is presently little knowledge concerning how specific plasma proteins that bind to the surface of S. pyogenes change the gene expression of the bacterium. Mass spectrometry-based proteomics methods have rapidly developed over the past decade to a point where almost comprehensive identification and quantification of complete bacterial proteomes is possible (8Malmström J. Beck M. Schmidt A. Lange V. Deutsch E.W. Aebersold R. Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans.Nature. 2009; 460: 762-765Crossref PubMed Scopus (344) Google Scholar). In the present work, a label-free quantitative shotgun proteomics workflow was adapted to study the homeostasis of the S. pyogenes proteome upon exposure to increasing concentrations of human plasma. The determined S. pyogenes protein abundance profile was grouped into specific functional protein categories allowing the investigation of transcriptional regulation, protein function, and pathway organization using a targeted selected reaction monitoring (SRM) 4The abbreviations used are: SRMselected reaction monitoringHSAhuman serum albuminFABfatty acid biosynthesisTHTodd-Hewitt brothFAfatty acid. proteomics workflow. To analyze how a specific protein from human plasma known to bind to the surface of S. pyogenes influences gene expression, we studied the effect of albumin (HSA), the most abundant human plasma protein. The results demonstrate a profound and specific influence on the proteins of the fatty acid biosynthesis (FAB), clarifying an important function of HSA-binding surface proteins of S. pyogenes. selected reaction monitoring human serum albumin fatty acid biosynthesis Todd-Hewitt broth fatty acid. S. pyogenes strains of the M1 serotype, SF370 (ATCC 700294) and AP1 (a covS truncated strain 40/58 from the WHO Collaborating Centre for Reference and Research on Streptococci, Prague, Czech Republic), and an mga mutant of AP1 expressing low amounts of M and M-like proteins (9Kihlberg B.M. Cooney J. Caparon M.G. Olsén A. Björck L. Biological properties of a Streptococcus pyogenes mutant generated by Tn916 insertion in mga.Microb. Pathog. 1995; 19: 299-315Crossref PubMed Scopus (86) Google Scholar), were grown (37 °C; 5% CO2) in Todd-Hewitt broth (TH) (Difco Laboratories). Supplements were added at the following concentrations: 1, 5, 10, or 20% (v/v) citrated human plasma (Lund University Hospital), 4 mg/ml of HSA (Sigma), 4 mg/ml of essentially fatty acid free (∼0.005%) HSA (Sigma), 0.3 mg/ml of human fibrinogen (Sigma), or 1.2 mg/ml of human IgG (Sigma). Cells were harvested at mid-exponential phase (A620 nm = 0.5) by centrifugation, washed three times in ice-cold PBS (this will remove secreted proteins), and re-suspended in sterile ice-cold water. To isolate intracellular bacterial proteins, the samples were homogenized in a cell disruptor (FastPrep FP120, Savant Machines Inc.) three times for 20 s at level 6. Cell debris was removed by centrifugation at 10,000 × g and the homogenates were diluted in 200 mm Tris, pH 8.3, containing 6 m urea, 5 mm EDTA, and 0.2% Triton X-100. With this procedure the homogenates will contain the intracellular protein pool but also the fraction of cell wall proteins and plasma proteins bound to the bacterial surface that is released during the homogenization procedure. The protein concentration was determined using the Bradford reagent (Sigma), and the homogenates were subjected to reversed phase LC-MS/MS analysis. The hybrid LTQ-FT-ICR mass spectrometer (Thermo Finnigan) was interfaced to a nanoelectrospray ion source. Chromatographic separation of peptides was achieved on an Agilent Series 1100 LC system (Agilent Technologies) equipped with a 11-cm fused silica emitter, 100-μm inner diameter (BGB Analytik), packed in-house with a Magic C18 AQ 5-μm resin (Michrom BioResources). Peptides were separated by a 65-min linear gradient of 5 to 40% acetonitrile in water, containing 0.1% formic acid, with a flow rate of 0.95 μl/min. Three MS/MS spectra were acquired in the linear ion trap per each FT-MS scan, which was acquired at 100,000 FWHM nominal resolution settings with an overall cycle time of ∼1 s. Charge state screening was employed to select for ions with at least two charges and rejecting ions with undetermined charge state. The hybrid Orbitrap-LTQ XL mass spectrometer (Thermo Electron, Bremen, Germany) was coupled online to a split-less Eksigent Two-dimensional NanoLC system (Eksigent Technologies, Dublin, CA). Peptides were loaded with a constant flow rate of 10 μl/min onto a pre-column (Zorbax 300SB-C18 5 × 0.3 mm, 5 μm, Agilent Technologies, Wilmington, DE) and subsequently separated on a RP-LC analytical column (Zorbax 300SB-C18 150 mm × 75 μm, 3.5 μm, Agilent Technologies) with a flow rate of 350 nl/min. The peptides were eluted with a linear gradient from 95% solvent A (0.1% formic acid in water) and 5% solvent B (0.1% formic acid in acetonitrile) to 40% solvent B over 55 min. The mass spectrometer was operated in data-dependent mode to automatically switch between Orbitrap-MS (from m/z 400 to 2000) and LTQ-MS/MS acquisition. Four MS/MS spectra were acquired in the linear ion trap per each FT-MS scan, which was acquired at 60,000 FWHM nominal resolution settings using the lock mass option (m/z 445.120025) for internal calibration. The dynamic exclusion list was restricted to 500 entries using a repeat count of two with a repeat duration of 20 s and with a maximum retention period of 120 s. Precursor ion charge state screening was enabled to select for ions with at least two charges and rejecting ions with undetermined charge state. The normalized collision energy was set to 30%, and one microscan was acquired for each spectrum. The resulting MS/MS data were deposited in the peptide atlas. Identified peptides with high probability from FAB proteins were synthesized (JPT Technologies, Berlin, Germany) and the SRM transition lists were generated using a previously published method (10Picotti P. Rinner O. Stallmach R. Dautel F. Farrah T. Domon B. Wenschuh H. Aebersold R. High-throughput generation of selected reaction-monitoring assays for proteins and proteomes.Nat. Methods. 2010; 7: 43-46Crossref PubMed Scopus (403) Google Scholar). The SRM measurements were performed on a TSQ Vantage triple quadropole mass spectrometer (Thermo Electron, Bremen, Germany) equipped with a nanoelectrospray ion source (Thermo Electron). Chromatographic separations of peptides were performed on an Eksigent One-dimensional NanoLC system (Eksigent Technologies) using the same chromatographic conditions as described above for the Eksigent Two-dimensional NanoLC system connected to the hybrid Orbitrap-LTQ XL mass spectrometer. The LC was operated with a flow rate of 400 nl/min. The mass spectrometer was operated in SRM mode, with both Q1 and Q3 settings at unit resolution (FWHM 0.7 Da). A spray voltage of +1700 V was used with a heated ion transfer setting of 270 °C for desolvation. Data were acquired using the Xcalibur software (version 2.1.0). The dwell time was set to 10 ms and the scan width to 0.01 m/z. All collision energies were calculated using the formula: CE = (parent m/z) × 0.034 + 3.314. The MS2 spectra were searched with the spectra identification pipeline. The pipeline combines the results of the search engines X! Tandem, version 2009.04.01.1 with k-score plugin (11Craig R. Beavis R.C. A method for reducing the time required to match protein sequences with tandem mass spectra.Rapid Commun. Mass Spectrom. 2003; 17: 2310-2316Crossref PubMed Scopus (403) Google Scholar), Mascot, version 2.3 (12Perkins D.N. Pappin D.J. Creasy D.M. Cottrell J.S. Probability-based protein identification by searching sequence databases using mass spectrometry data.Electrophoresis. 1999; 20: 3551-3567Crossref PubMed Scopus (6814) Google Scholar), and OMSSA, version 2.7.1 (13Geer L.Y. Markey S.P. Kowalak J.A. Wagner L. Xu M. Maynard D.M. Yang X. Shi W. Bryant S.H. Open mass spectrometry search algorithm.J. Proteome Res. 2004; 3: 958-964Crossref PubMed Scopus (1168) Google Scholar), and generates a common peptide and protein list using the Trans-Proteomic pipeline, version 4.4.0 (14Keller A. Eng J. Zhang N. Li X.J. Aebersold R. A uniform proteomics MS/MS analysis platform utilizing open XML file formats.Mol. Syst. Biol. 2005; 1 (2005 0017)Crossref PubMed Scopus (599) Google Scholar). All searches were performed with full tryptic cleavage specificity, up to 2 allowed missed cleavages, a precursor mass error of 15 ppm, and an error tolerance of 0.5 Da for the fragment ions. The sample preparation cysteine carbamidomethylation was defined as fixed modification in the search parameters. A protein data base with sequences for S. pyogenes (NC_002737 from NCBI) and human (Swiss-Prot, version 57.1 including known splice variants and isoforms) was used to match the individual spectra to certain peptides. The data base was extended by decoy sequences to validate the resulting peptide-spectrum matches (15Elias J.E. Gygi S.P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry.Nat. Methods. 2007; 4: 207-214Crossref PubMed Scopus (2873) Google Scholar). A value of 0.01 for the false-discovery rate was then used to generate the final protein list with ProteinProphet. Label-free quantification was performed with the OpenMS software framework (16Sturm M. Bertsch A. Gröpl C. Hildebrandt A. Hussong R. Lange E. Pfeifer N. Schulz-Trieglaff O. Zerck A. Reinert K. Kohlbacher O. OpenMS, an open-source software framework for mass spectrometry.BMC Bioinformatics. 2008; 9: 163Crossref PubMed Scopus (494) Google Scholar). Unless indicated otherwise, TOPP tools (17Kohlbacher O. Reinert K. Gröpl C. Lange E. Pfeifer N. Schulz-Trieglaff O. Sturm M. TOPP. The OpenMS proteomics pipeline.Bioinformatics. 2007; 23: e191-e197Crossref PubMed Scopus (215) Google Scholar), small applications provided by OpenMS, were used for the individual processing steps. The input to the analysis pipeline consisted of 10-mzXML files containing LC-MS/MS data acquired on the LTQ-FTICR instrument in profile mode. These files were first converted to mzML format with the FileConverter tool, then centroided using the PeakPicker (“high_res” algorithm). These two steps were carried out in OpenMS 1.6; for the following steps, an early (January 2010) development version of OpenMS 1.7 was used. On the centroided mzML files, feature detection was performed using the FeatureFinder (“centroided” algorithm), producing one feature map per sample. In parallel, results from the MS2 identification pipeline were preprocessed with a custom Python script and split into individual pepXML files (one per sample and search engine). Peptide identification data from these files was read, filtered to 1% FDR, and stored in OpenMS′ idXML format (one file per sample) by a custom C++ program built on top of the OpenMS library (release version of OpenMS 1.7). To annotate the feature maps with identified peptides, the IDMapper tool was applied to the pairs of feature maps/idXML files derived from the same sample. The resulting annotated feature maps were adjusted to a common retention time scale with MapAligner (“identification” algorithm), then combined into one consensus map by grouping features across samples with the FeatureLinker (“unlabeled” algorithm). The consensus map was converted to a table in CSV format using the TextExporter. A custom R script was used to read the quantification data from the CSV file, clean up annotation conflicts, and compute peptide abundances from the intensities of annotated features. To reduce the impact of feature detection differences in the individual samples, only the charge state with the highest intensity per peptide was used for quantification. The resulting peptide abundances were exported into a data base, where protein abundances were inferred by summing up the abundances for the peptides uniquely mapping to each protein (18Malmström L. Marko-Varga G. Westergren-Thorsson G. Laurell T. Malmström J. 2DDB. A bioinformatics solution for analysis of quantitative proteomics data.BMC Bioinformatics. 2006; 7: 158Crossref PubMed Scopus (22) Google Scholar). The quantitative data from openMS was normalized using the total ion current from each LC/MS experiment that could be assigned to S. pyogenes and then organized in a matrix with proteins as rows and conditions (e.g. % plasma) as columns. The three first principle components resulting from applying principle component analysis (19Venables W.N. Ripley B.D. Chambers J. Eddy W. Hārdle W. Sheater S. Tierney L. Modern Applied Statistics with S. 4 Ed. Springer, New York2002Crossref Google Scholar) to the matrix were used to cluster the proteins using k-mean clustering (20Hartigan J.A. Wong M.A. A k-means clustering algorithm.Appl. Statist. 1979; 28: 100-108Crossref Google Scholar) always performed in R (version 2.9.0). One vial of the BODIPY-FA probe from the QBT Fatty Acid Uptake Assay Kit (Molecular Devices) was mixed with 1 ml of 20 mg/ml of HSA-FA in 20 mm Tris-HCl, pH 7.4. The probe-HSA complex was added 1/10 (v/v) to S. pyogenes wild type (AP1) and mga mutant AP1 bacteria grown to A620 nm = 0.15 in TH broth. Triplicate samples of either living or heat-killed (80 °C, 5 min) bacteria containing probe-HSA complex were further incubated at 37 °C and 5% CO2. At time points 0, 40, 80, and 120 min, a small aliquot of the samples were removed and placed on ice. Bacteria were labeled using IVIG (polyclonal human therapeutic IgG, 5 min, 500 μg/ml) and DyLight 649-conjugated goat anti-human F(ab′)2 fragments (Jackson ImmunoResearch, 5 min, 1:100) on ice. Acquisition of images was performed using a fluorescence microscope (Nikon Eclipse TE300 equipped with a Hamamatsu C4742–95 cooled CCD camera, using a Plan Apochromat ×100 objective with NA 1.4). Images were acquired with identical exposure time (NIS Elements 3, Nikon) and have been processed with linear brightness and contrast adjustments (Adobe Photoshop CS5) to represent the samples as they were observed in the microscope. The flow cytometry measurements were performed using a FacsCalibur flow cytometer (BD Biosciences) equipped with lasers tuned to 488 and 633 nm. Bacteria were gated using forward and side scatter in the logarithmic mode and bound IgG (DyLight 649-labeled) was detected in the FL4 channel. Fluorescence from the BODIPY-FA probe was collected in the FL1 channel. Adapting to human plasma is a critical ability for S. pyogenes with implication for pathogenesis and virulence. Human plasma is a complex mixture of proteins and nutrients constituting on one hand a rich medium for the bacterium, and on the other hand a source of host defense systems. In the adaptation process, the type and amount of proteins synthesized is tightly regulated to create a composition that is optimized to cope with the new environment. To study this process, mass spectrometry-based quantitative proteomics was used to identify and quantify the intracellular protein constituent of the S. pyogenes-translated genome. S. pyogenes (strain SF370 of the M1 serotype) was grown in TH medium supplemented with different amounts of human plasma (0, 1, 5, 10, and 20%) in two biological replicates. Following centrifugation and washing, the bacteria were homogenized. Resulting debris was spun down and proteins of the supernatants were digested to peptides and analyzed using LC-MS/MS. To maximize the number of identified proteins we extensively annotated the detected peptides using the combined outputs of multiple search engines at a stringent false-discovery rate. An analysis pipeline based on the OpenMS software framework (16Sturm M. Bertsch A. Gröpl C. Hildebrandt A. Hussong R. Lange E. Pfeifer N. Schulz-Trieglaff O. Zerck A. Reinert K. Kohlbacher O. OpenMS, an open-source software framework for mass spectrometry.BMC Bioinformatics. 2008; 9: 163Crossref PubMed Scopus (494) Google Scholar) was then used to quantitatively compare the LC-MS peptide patterns from different samples resulting in an extensive quantification matrix for the S. pyogenes proteins. In this experiment, 523 S. pyogenes proteins (supplemental Table S2) were identified and quantified with an estimated false discovery rate of 5% using a reversed data base search strategy (15Elias J.E. Gygi S.P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry.Nat. Methods. 2007; 4: 207-214Crossref PubMed Scopus (2873) Google Scholar) and Protein and PeptideProphet (21Keller A. Nesvizhskii A.I. Kolker E. Aebersold R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.Anal. Chem. 2002; 74: 5383-5392Crossref PubMed Scopus (3912) Google Scholar, 22Nesvizhskii A.I. Keller A. Kolker E. Aebersold R. A statistical model for identifying proteins by tandem mass spectrometry.Anal. Chem. 2003; 75: 4646-4658Crossref PubMed Scopus (3655) Google Scholar). The data indicate that S. pyogenes regulates its proteome composition in a dose-dependent manner upon exposure to increasing amounts of plasma (Fig. 1), first demonstrated by using principle component analysis revealing the degree of proteome reorganization. This analysis suggests that there is a small difference in protein abundance between bacteria grown in 0 or 1% plasma, whereas the proteome of bacteria grown in 10 or 20% plasma was considerably different compared with bacteria grown in medium without plasma (Fig. 1F). Cultures grown in 5% plasma display an intermediate profile. In a second stage, the protein abundance profiles were split, using k-mean clustering, into five clusters containing the distinguishing proteins as shown in Fig. 1. The predominant cluster contains the proteins that remain constant at the plasma concentrations tested (Fig. 1A). Two clusters represent the proteins with decreasing protein abundances (Fig. 1, B and C), and two contain the proteins with increasing or fluctuating protein abundances (Fig. 1, D and E). Sixty-one proteins observed in less than four of the five growth conditions were excluded, as these proteins are present at a too low concentration to allow accurate clustering. The clustered data reveal that there are a large number of proteins that either increase or decrease in protein abundance. The largest cluster, 246 proteins (46%), contains the proteins that remain constant at the different plasma concentrations (Fig. 1G, label A), followed by the cluster containing 155 proteins (30%) where the protein abundances were decreased (Fig. 1G, labels B and C). A lower number of proteins, 58 proteins (11%), showed increased abundance (Fig. 1G, labels D and E) (supplemental Table S1 lists the proteins that were mostly influenced by plasma). However, the number of proteins that change their abundance does not necessarily reflect the degree of proteome adaptation. The disproportional amount of proteins of the cluster in Fig. 1A becomes more apparent when the total ion intensities for proteins in the respective cluster are summed up and compared with the protein count (Fig. 1H). Total ion intensities represent the recorded signal intensities for the peptides associated with a protein in the mass spectrometer, and provide a rough estimate of protein abundance (8Malmström J. Beck M. Schmidt A. Lange V. Deutsch E.W. Aebersold R. Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans.Nature. 2009; 460: 762-765Crossref PubMed Scopus (344) Google Scholar, 23Silva J.C. Gorenstein M.V. Li G.Z. Vissers J.P. Geromanos S.J. Absolute quantification of proteins by LCMSE. A virtue of parallel MS acquisition.Mol. Cell. Proteomics. 2006; 5: 144-156Abstract Full Text Full Text PDF PubMed Scopus (1155) Google Scholar, 24Vogel C. Marcotte E.M. Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data.Nat. Protoc. 2008; 3: 1444-1451Crossref PubMed Scopus (69) Google Scholar, 25Ishihama Y. Oda Y. Tabata T. Sato T. Nagasu T. Rappsilber J. Mann M. Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein.Mol. Cell. Proteomics. 2005; 4: 1265-1272Abstract Full Text Full Text PDF PubMed Scopus (1655) Google Scholar, 26Lu P. Vogel C. Wang R. Yao X. Marcotte E.M. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation.Nat. Biotechnol. 2007; 25: 117-124Crossref PubMed Scopus (916) Google Scholar). The cluster in Fig. 1A can be ascribed to 89% of the ion intensities, which indicates that the abundance level for the majority of the intracellular proteins remains constant. Only 9% of ion intensities represent the proteins with decreased protein abundances (Fig. 1H, labels A and B), and 2% of the ion intensities are ascribed to proteins that increase (Fig. 1H, labels D and E). The large over-representation of the proteins with unchanged abundance indicates that the overall proteome reorganization is relatively modest in response to human plasma. Even though a considerable number of proteins change in concentration they tend to be present at relatively low amounts compared with the proteins that remain constant. There are almost three times as many proteins that display decreased protein abundances compared with those that are increased (155 versus 58), and the difference in ion intensities is close to 5-fold (9 versus 2%). The results indicate that a number of proteins become dispensable for S. pyogenes growing in plasma. It should be noted that proteins secreted by the bacteria are lost during the washing step, and despite low intracellular abundance they could be present in high amounts in the growth medium. Hypothetically, the proteins that are repressed or induced belong to certain functional categories that could reflect how the bacteria adapt to the new environment. To allow visualization of the complicated expression profile data we used Cytoscape (27Shannon P. Markiel A. Ozier O. Baliga N.S. Wang J.T. Ramage D. Amin N. Schwikowski B. Ideker T. Cytoscape. A software environment for integrated models of biomolecular interaction networks.Genome Res. 2003; 13: 2498-2504Crossref PubMed Scopus (26615) Google Scholar). Cytoscape is a network viewer showing protein annotations together with protein-protein interaction databases or functional categories to provide context, in this case subsystem classification from the National Microbial Pathogen Data Resource (NMPDR) (28McNeil L.K. Reich C. Aziz R.K. Bartels D. Cohoon M. Disz T. Edwards R.A. Gerdes S. Hwang K. Kubal M. Margaryan G.R. Meyer F. Mihalo W. Olsen G.J. Olson R. Osterman A. Paarmann D. Paczian T. Parrello B. Pusch G.D. Rodionov D.A. Shi X. Vassieva O. Vonstein V. Zagnitko O. Xia F. Zinner J. Overbeek R. Stevens R. The National Microbial Pathogen Database Resource (NMPDR). A genomics platform based on subsystem annotation.Nucleic Acids Res. 2007; 35: D347-D353Crossref PubMed Scopus (83) Google Scholar). The network topology to the left in Fig. 2 outlines the overall network organization, where rectangles indicate proteins, clustered together if they belong to the same functional categories. Green rec" @default.
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• W1972029046 date "2012-01-01" @default.
• W1972029046 modified "2023-10-16" @default.
• W1972029046 title "Streptococcus pyogenes in Human Plasma" @default.
• W1972029046 cites W111924924 @default.
• W1972029046 cites W1607549514 @default.
• W1972029046 cites W1852325076 @default.
• W1972029046 cites W1873318425 @default.
• W1972029046 cites W1911082967 @default.
• W1972029046 cites W1945320323 @default.
• W1972029046 cites W1964697420 @default.
• W1972029046 cites W1970037234 @default.
• W1972029046 cites W1975194986 @default.
• W1972029046 cites W1977556410 @default.
• W1972029046 cites W1981593008 @default.
• W1972029046 cites W1986887325 @default.
• W1972029046 cites W1993562651 @default.
• W1972029046 cites W1996144148 @default.
• W1972029046 cites W2006951698 @default.
• W1972029046 cites W2015556811 @default.
• W1972029046 cites W2018491773 @default.
• W1972029046 cites W2032526817 @default.
• W1972029046 cites W2038193703 @default.
• W1972029046 cites W2041852635 @default.
• W1972029046 cites W2044390926 @default.
• W1972029046 cites W2044511581 @default.
• W1972029046 cites W2046869263 @default.
• W1972029046 cites W2047275456 @default.
• W1972029046 cites W2052388444 @default.
• W1972029046 cites W2077451921 @default.
• W1972029046 cites W2081550727 @default.
• W1972029046 cites W2085630110 @default.
• W1972029046 cites W2086208676 @default.
• W1972029046 cites W2087049575 @default.
• W1972029046 cites W2089960919 @default.
• W1972029046 cites W2090151674 @default.
• W1972029046 cites W2094557849 @default.
• W1972029046 cites W2096057003 @default.
• W1972029046 cites W2112078820 @default.
• W1972029046 cites W2126297361 @default.
• W1972029046 cites W2132899146 @default.
• W1972029046 cites W2137914924 @default.
• W1972029046 cites W2141626941 @default.
• W1972029046 cites W2142251632 @default.
• W1972029046 cites W2148342439 @default.
• W1972029046 cites W2148490163 @default.
• W1972029046 cites W2153370150 @default.
• W1972029046 cites W2159675211 @default.
• W1972029046 cites W2162140882 @default.
• W1972029046 cites W2162393208 @default.
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