Targeted Proteomics and Absolute Protein Quantification for the Construction of a Stoichiometric Host-Pathogen Surface Density Model
2017; Elsevier BV; Volume: 16; Issue: 4 Linguagem: Inglês
10.1074/mcp.m116.063966
ISSN1535-9484
AutoresKristoffer Sjöholm, Ola Kilsgård, Johan Teleman, Lotta Happonen, Lars Malmström, Johan Malmström,
Tópico(s)Antimicrobial Resistance in Staphylococcus
ResumoSepsis is a systemic immune response responsible for considerable morbidity and mortality. Molecular modeling of host-pathogen interactions in the disease state represents a promising strategy to define molecular events of importance for the transition from superficial to invasive infectious diseases. Here we used the Gram-positive bacterium Streptococcus pyogenes as a model system to establish a mass spectrometry based workflow for the construction of a stoichiometric surface density model between the S. pyogenes surface, the surface virulence factor M-protein, and adhered human blood plasma proteins. The workflow relies on stable isotope labeled reference peptides and selected reaction monitoring mass spectrometry analysis of a wild-type strain and an M-protein deficient mutant strain, to generate absolutely quantified protein stoichiometry ratios between S. pyogenes and interacting plasma proteins. The stoichiometry ratios in combination with a novel targeted mass spectrometry method to measure cell numbers enabled the construction of a stoichiometric surface density model using protein structures available from the protein data bank. The model outlines the topology and density of the host-pathogen protein interaction network on the S. pyogenes bacterial surface, revealing a dense and highly organized protein interaction network. Removal of the M-protein from S. pyogenes introduces a drastic change in the network topology, validated by electron microscopy. We propose that the stoichiometric surface density model of S. pyogenes in human blood plasma represents a scalable framework that can continuously be refined with the emergence of new results. Future integration of new results will improve the understanding of protein-protein interactions and their importance for bacterial virulence. Furthermore, we anticipate that the general properties of the developed workflow will facilitate the production of stoichiometric surface density models for other types of host-pathogen interactions. Sepsis is a systemic immune response responsible for considerable morbidity and mortality. Molecular modeling of host-pathogen interactions in the disease state represents a promising strategy to define molecular events of importance for the transition from superficial to invasive infectious diseases. Here we used the Gram-positive bacterium Streptococcus pyogenes as a model system to establish a mass spectrometry based workflow for the construction of a stoichiometric surface density model between the S. pyogenes surface, the surface virulence factor M-protein, and adhered human blood plasma proteins. The workflow relies on stable isotope labeled reference peptides and selected reaction monitoring mass spectrometry analysis of a wild-type strain and an M-protein deficient mutant strain, to generate absolutely quantified protein stoichiometry ratios between S. pyogenes and interacting plasma proteins. The stoichiometry ratios in combination with a novel targeted mass spectrometry method to measure cell numbers enabled the construction of a stoichiometric surface density model using protein structures available from the protein data bank. The model outlines the topology and density of the host-pathogen protein interaction network on the S. pyogenes bacterial surface, revealing a dense and highly organized protein interaction network. Removal of the M-protein from S. pyogenes introduces a drastic change in the network topology, validated by electron microscopy. We propose that the stoichiometric surface density model of S. pyogenes in human blood plasma represents a scalable framework that can continuously be refined with the emergence of new results. Future integration of new results will improve the understanding of protein-protein interactions and their importance for bacterial virulence. Furthermore, we anticipate that the general properties of the developed workflow will facilitate the production of stoichiometric surface density models for other types of host-pathogen interactions. Bacterial infections present an important healthcare challenge with a wide spectrum of disease severity. One of the most severe bacterial diseases is sepsis, a systemic immune response mainly caused by bacteria entering the bloodstream (1.Martin G.S. Mannino D.M. Eaton S. Moss M. The epidemiology of sepsis in the United States from 1979 through 2000.N. Engl. J. Med. 2003; 348: 1546-1554Crossref PubMed Scopus (4823) Google Scholar). 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The complex nature of the host-pathogen interaction network calls for new strategies to integrate and visualize these interactions at a molecular level preferably using systems wide analysis with as accurate measurements as possible. In particular, there is an unmet need to determine stoichiometric relationships between interacting proteins at the level of a biological system and visualize these interactions in an accurate model to support the development of new preventive, diagnostic and treatment strategies for severe infectious disease. The accurate analysis of host-pathogen interactions to determine the stoichiometric relationship between pathogen, surface proteins and interacting host proteins is an important step toward understanding bacterial immune evasion strategies. In the work presented here, we used the Gram-positive bacterium Streptococcus pyogenes as a model pathogen and human blood plasma as a model for interacting host proteins. 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Scoring proteomes with proteotypic peptide probes.Nat. Rev. Mol. Cell Biol. 2005; 6: 577-583Crossref PubMed Scopus (304) Google Scholar). In this article, we have performed a comprehensive study of the stoichiometric relationship between adhered host proteins and a bacterial surface. The stoichiometric relationship in combination with a new technical improvement for counting the number of bacteria using SRM enabled the construction of the first model of S. pyogenes surface interaction network in a host environment. This model includes the stoichiometric relations between host proteins, surface proteins, and the surface of the pathogen and it visualizes the density of this plethora of interactions in a stoichiometric surface density model. The S. pyogenes strains that were used for the construction of the stoichiometric density model are: SF370, a clinical isolate of the M1 serotype; and an isogenic M1-mutant of SF370, ΔM1 (38.Abbot E.L. Smith W.D. Siou G.P. Chiriboga C. Smith R.J. Wilson J.A. Hirst B.H. Kehoe M.A. Pili mediate specific adhesion of Streptococcus pyogenes to human tonsil and skin.Cell. Microbiol. 2007; 9: 1822-1833Crossref PubMed Scopus (157) Google Scholar). Strains used for cell counting of S. pyogenes are: JM50, JM57, JM59, JM62, JM67, JM72, JM81, and JM85 are all of the M1 serotype and isolated from healthcare clinics in Sweden during 2012 (39.Malmstrom L. Bakochi A. Svensson G. Kilsgard O. Lantz H. Petersson A.C. Hauri S. Karlsson C. Malmstrom J. Quantitative proteogenomics of human pathogens using DIA-MS.J. Proteomics. 2015; 129: 98-107Crossref PubMed Scopus (21) Google Scholar). Single colonies were grown at 37 °C and 5% CO2 to exponential and stationary phase (only the strains used for cell counting) in 30g/L Todd-Hewitt broth (BD, Sparks, MD) and 6g/L yeast extract (BD). The cells were then harvested by centrifugation and resuspended in 20 mm Tris-HCl, 150 mm NaCl, pH 7.6 (TBS tablet, Medicago, Uppsala, Sweden), to an approximate concentration of 2 × 109 CFU/ml. Four commonly used methods for counting bacteria, optical density, colony forming units (CFU), microscopy and flow cytometry, were evaluated. The first two methods are simple well-established methods of estimating cell count in a dilution series, optical density at 620 nm and counting the number of colonies (CFU) on an agar plate after 24-hour incubation. The third method, light field microscopy with phase contrast was performed using an Olympus CKX41 (Olympus Corporation, Tokyo, Japan) equipped with an INFINITY1 camera (Lumenera Corporation, Ottawa, ON, Canada), together with a Petroff-Hausser counting chamber and tryphan blue staining (Sigma-Aldrich, Steinheim, Germany). Microscopy images were analyzed automatically using a custom Adobe Photoshop CS6 procedure, in which the area of the dark bacteria is calculated in a selected area of the counting chamber, after removing the counting chamber reference lines. The method has an estimated Pearson's product-moment correlation of 0.989 (p < 3e-11), compared with manual counting. The fourth method, flow cytometry was performed using a BD Accuri C6 Flow Cytometer with a 488 nm filter, gating in FL1-A (488 nm) versus FSC-A, with a flow rate of 35 μl/min and a core size of 16 μm. Samples were stained using a fluorescent dye (SYTO™ BC, Thermo Fisher). Samples were collected until 30 μl or 30000 bacterial events. The bacterial population in FSC-A/FL1-A was moved toward higher FL1-A in the more concentrated dilutions. This higher dye absorbance could indicate aggregated bacteria, but no compensation was made for this. Human blood plasma (pooled healthy human plasma [Na-citrate], Innovative Research, Novi, MI) was mixed with bacteria, in a ratio 3:1, and the samples were incubated for 30 min at 37 °C, allowing plasma proteins to adsorb to the bacterial surface. This bacterial to plasma ratio is 10 times lower than the original plasma adsorption protocols (40.Janulczyk R. Iannelli F. Sjoholm A.G. Pozzi G. Bjorck L. Hic, a novel surface protein of Streptococcus pneumoniae that interferes with complement function.J. Biol. Chem. 2000; 275: 37257-37263Abstract Full Text Full Text PDF PubMed Scopus (178) Google Scholar, 41.Pandiripally V. Gregory E. Cue D. Acquisition of regulators of complement activation by Streptococcus pyogenes serotype M1.Infection Immunity. 2002; 70: 6206-6214Crossref PubMed Scopus (81) Google Scholar), but because the detection techniques have improved and to better replicate the disease state, the bacterial ratio was kept as low as possible. Bacteria were harvested after several washes in 20 mm Tris-HCl, 150 mm NaCl, and pH 7.6 through centrifugation (5000 × g). All samples were prepared in eight biological replicates. To elute the adsorbed proteins, the final cell pellets were prepared using two protocols. The first, glycine elution, has been described (12.Nordenfelt P. Waldemarson S. Linder A. Mörgelin M. Karlsson C. Malmström J. Björck L. Antibody orientation at bacterial surfaces is related to invasive infection.J. Exp. Med. 2012; 209: 2367-2381Crossref PubMed Scopus (75) Google Scholar). Briefly, the bacterial pellet was resuspended in 0.1 m glycine (Sigma-Aldrich), pH 2.0, followed by incubation for 10 min, and the supernatants were neutralized to pH 7–8 with 1 m Tris (Ultrapure, Saveen Werner AB, Limhamn, Sweden). In the second protocol, bacteria digest, the pellet was resuspended in water (HPLC-graded, Sigma-Aldrich) and transferred to tube containing 0.1 mm Silica beads (Lysing Matrix tubes, Nordic Biolabs #6911100, Täby, Sweden). The bacteria were lysed with a cell disruptor (FastPrep96, MP Biomedicals, Santa Ana, CA). The in solution digestion (see below) was performed without transferring the liquid to new tube to minimize sample loss and enhance surface protein yield. Samples were dried completely through vacuum evaporation (miVac Duo, Genevac, Ipswich, UK) and resuspended in 8 m urea (Sigma-Aldrich) to denature the sample. The protein sample was reduced using tris (2-carboxyethyl)-phosphine (Sigma-Aldrich), at a final concentration of 10 mm and the samples were incubated at 37 °C for 60 min. The samples were incubated for 30 min in the dark at room temperature with 2-iodoacetamide (Sigma-Aldrich) at a concentration of 20 mm as the alkylating agent. Samples were diluted with 100 mm ammonium bicarbonate (Sigma-Aldrich) to a urea concentration of 0.73 m and digested with a final concentration of 3.6 ng/μl trypsin (Sequence grade modified trypsin Porcin, Promega, Madison, WI) over night (18 h). The reaction was stopped by adding formic acid (Sigma-Aldrich) to a final sample pH of 2–3. Vydac UltraMicroSpin® silica C18 300Å columns (#SUM SS18V, The Nest Group, Inc., Southborough, MA) was used for sample desalting, clean-up and concentrating peptides according to the manufacturer's instructions. To remove silica beads and cell debris, the samples prepared by bacteria digest were centrifuged at 15,000 × g for 10 min, and supernatant was used for C18 peptide clean-up. Chromatographic separations of peptides were performed on an Easy-nLC II system (Thermo Fisher Scientific, San Jose, CA) with a nonlinear 30-min gradient of 5–10% acetonitrile (ACN, with 0.1% formic acid, UHPLC-graded, Fluka Analytical, Sigma-Aldrich) over 5 min, 10–20% ACN over 20 min, and 20–30% ACN over 5 min, using 15 cm 3 μm columns (Thermo Fisher Scientific). The SRM measurements were performed on a TSQ Quantiva triple quadrupole mass spectrometer (Thermo Fisher Scientific) equipped with a nano electrospray ion source (EASY-spray, Thermo Fisher Scientific). TSQ global parameters were: polarity: positive; spray voltage: 2000V; and transfer tube temperature: 325 °C. TSQ scan parameters were: quadrupole resolution: 0.7 FWHM in both Q1 and Q3; cycle time: 1.7 s; CID gas (argon) 2mTorr; source fragmentation: 10V; Chrom filter: 3 s; and 788 transitions were measured simultaneously. The 788 transition consist of 76 peptides, using 4–6 assays per peptide, with a complementary labeled peptide making up a total of 768 transitions and with 20 additional transitions for retention time calibration peptides. The human proteins monitored by SRM are based on previous publications (14.Sjöholm K. Karlsson C. Linder A. Malmström J. A comprehensive analysis of the Streptococcus pyogenes and human plasma protein interaction network.Mol. bioSystems. 2014; 10: 1698-1708Crossref PubMed Google Scholar). A full list of proteins, peptides, transitions selected for monitoring, charge, and collision energy is listed in supplemental Table S1. Peptides selected for targeted analysis was selected for the enriched binding, shown in a previous investigation (14.Sjöholm K. Karlsson C. Linder A. Malmström J. A comprehensive analysis of the Streptococcus pyogenes and human plasma protein interaction network.Mol. bioSystems. 2014; 10: 1698-1708Crossref PubMed Google Scholar), to the surface of S. pyogenes. Stable isotope labeled peptides (SpikeTides, JPT, Berlin, Germany; and AQUA QuantPro, Thermo Fisher Scientific) was used for absolute quantification of peptides. The linear range of the isotope labeled peptides were determined including the detection limit and the coefficient of determination (R2), listed in supplemental Table S1. The R2 was calculated between the log10 intensity and log10 peptide concentration. The log10 transform was used because of the large concentration range of the analyzed peptides, which would otherwise cause the correlation to be dominated by the high concentration peptides. The maximum measured value was 1 pmol/injection, which is wi
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