Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in Silico Peptide Mass Libraries
2020; Elsevier BV; Volume: 19; Issue: 12 Linguagem: Inglês
10.1074/mcp.tir120.002061
ISSN1535-9484
AutoresPeter Lasch, Andy Schneider, Christian Blumenscheit, Joerg Doellinger,
Tópico(s)Genomics and Phylogenetic Studies
ResumoOver the past decade, modern methods of MS (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. Although MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem MS (LC-MS2) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be laborious and time-consuming, especially if the experimental LC-MS2 data are tested against sequence databases covering a broad panel of different microbiological taxa. In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC–MS measurements. Peptide masses are then extracted from MS1 data and systematically tested against an in silico library of all possible peptide mass data compiled in-house. The library has been computed from the UniProt Knowledgebase covering Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from correlation coefficients between experimental and strain-specific in silico peptide mass profiles and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient – less than 2 mins per sample - and has been successfully tested by a test set of 39 LC-MS1 peak lists obtained from 19 different microbial pathogens. The proposed method is rapid, simple and automatable and we foresee wide application potential for future microbiological applications. Over the past decade, modern methods of MS (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. Although MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem MS (LC-MS2) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be laborious and time-consuming, especially if the experimental LC-MS2 data are tested against sequence databases covering a broad panel of different microbiological taxa. In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC–MS measurements. Peptide masses are then extracted from MS1 data and systematically tested against an in silico library of all possible peptide mass data compiled in-house. The library has been computed from the UniProt Knowledgebase covering Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from correlation coefficients between experimental and strain-specific in silico peptide mass profiles and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient – less than 2 mins per sample - and has been successfully tested by a test set of 39 LC-MS1 peak lists obtained from 19 different microbial pathogens. The proposed method is rapid, simple and automatable and we foresee wide application potential for future microbiological applications. Rapid and reliable identification of pathogenic bacteria is of vital importance in many areas of public health and is relevant also in the food industry and for biodefense. In the context of clinical microbiology, a large variety of very different techniques, among them biochemical, serological, chemotaxonomic, and more recently spectroscopic, spectrometric and genomic tools are routinely used. For example, MS-based techniques, such as MALDI-TOF MS have emerged as invaluable tools for accurate and cost-effective identification of microorganisms in today's routine clinical microbiology (1Seng P. Drancourt M. Gouriet F. La Scola B. Fournier P.E. Rolain J.M. Raoult D. Ongoing revolution in bacteriology: routine identification of bacteria by matrix-assisted laser desorption ionization time-of-flight mass spectrometry.Clin. Infect. DIS. 2009; 49: 543-551Crossref PubMed Scopus (1324) Google Scholar, 2Nomura F. Proteome-based bacterial identification using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS): A revolutionary shift in clinical diagnostic microbiology.Biochim. Biophys. Acta. 2015; 1854: 528-537Crossref PubMed Scopus (77) Google Scholar, 3Schubert S. Kostrzewa M. MALDI-TOF MS in the Microbiology Laboratory: Current Trends.Curr. Issues Mol. Biol. 2017; 23: 17-20Crossref PubMed Scopus (41) Google Scholar, 4Welker M. Van Belkum A. Girard V. Charrier J.P. Pincus D. An update on the routine application of MALDI-TOF MS in clinical microbiology.Expert Rev. Proteomics. 2019; 16: 695-710Crossref PubMed Scopus (17) Google Scholar). The MALDI-TOF MS approach allows obtaining the genus and species identity of unknown samples by matching microbial mass spectra against spectral libraries collected from microorganisms with a known taxonomic identity. Although identification is most reliably achieved at the species level, the question of whether MALDI-TOF MS is suitable for identification and discrimination below the species level is still controversially discussed by the scientific community (4Welker M. Van Belkum A. Girard V. Charrier J.P. Pincus D. An update on the routine application of MALDI-TOF MS in clinical microbiology.Expert Rev. Proteomics. 2019; 16: 695-710Crossref PubMed Scopus (17) Google Scholar, 5Sandrin T.R. Goldstein J.E. Schumaker S. MALDI TOF MS profiling of bacteria at the strain level: a review.Mass Spectrom. Rev. 2013; 32: 188-217Crossref PubMed Scopus (207) Google Scholar, 6Demirev P. Sandrin T.R. Applications of Mass Spectrometry in Microbiology - From Strain Characterization to Rapid Screening for Antibiotic Resistance. Springer International Publishing, Switzerland2016Crossref Scopus (13) Google Scholar, 7Sauget M. Valot B. Bertrand X. Hocquet D. Can MALDI-TOF Mass Spectrometry Reasonably Type Bacteria?.Trends Microbiol. 2017; 25: 447-455Abstract Full Text Full Text PDF PubMed Scopus (94) Google Scholar). Although a large number of studies convincingly demonstrate successful discrimination and identification of pathogenic bacteria by MALDI-TOF MS at the species level, there is also ample evidence for limitations of the taxonomic resolution, particularly at the infraspecies level and when dealing with differentiation of genetically closely related species (8Rodrigues C. Novais A. Sousa C. Ramos H. Coque T.M. Canton R. Lopes J.A. Peixe L. Elucidating constraints for differentiation of major human Klebsiella pneumoniae clones using MALDI-TOF MS.Eur. J. Clin. Microbiol. Infect. Dis. 2016; 36: 379-386Crossref PubMed Scopus (10) Google Scholar, 9Sousa C. Botelho J. Grosso F. Silva L. Lopes J. Peixe L. Unsuitability of MALDI-TOF MS to discriminate Acinetobacter baumannii clones under routine experimental conditions.Front. Microbiol. 2015; 6: 481Crossref PubMed Scopus (22) Google Scholar, 10Lasch P. Fleige C. Stammler M. Layer F. Nubel U. Witte W. Werner G. Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates.J. Microbiol. Methods. 2014; 100: 58-69Crossref PubMed Scopus (78) Google Scholar, 11Grenga L. Pible O. Armengaud J. Pathogen proteotyping: A rapidly developing application of mass spectrometry to address clinical concerns.Clin. Mass Spectrom. 2019; 14: 9-17Crossref Scopus (18) Google Scholar). For example, differentiation between Escherichia coli and Shigella (12Paauw A. Jonker D. Roeselers G. Heng J.M. Mars-Groenendijk R.H. Trip H. Molhoek E.M. Jansen H.J. van der Plas J. de Jong A.L. Majchrzykiewicz-Koehorst J.A. Speksnijder A.G. Rapid and reliable discrimination between Shigella species and Escherichia coli using MALDI-TOF mass spectrometry.Int. J. Med. Microbiol. 2015; 305: 446-452Crossref PubMed Scopus (29) Google Scholar, 13He Y. Li H. Lu X. Stratton C.W. Tang Y.W. Mass spectrometry biotyper system identifies enteric bacterial pathogens directly from colonies grown on selective stool culture media.J. Clin. Microbiol. 2010; 48: 3888-3892Crossref PubMed Scopus (53) Google Scholar) or of Bacillus cereus and Bacillus anthracis (14Dybwad M. van der Laaken A.L. Blatny J.M. Paauw A. Rapid identification of bacillus anthracis spores in suspicious powder samples by using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS).Appl. Environ. Microbiol. 2013; 79: 5372-5383Crossref PubMed Scopus (28) Google Scholar, 15Lasch P. Wahab T. Weil S. Palyi B. Tomaso H. Zange S. Kiland Granerud B. Drevinek M. Kokotovic B. Wittwer M. Pfluger V. Di Caro A. Stammler M. Grunow R. Jacob D. Identification of highly pathogenic microorganisms by matrix-assisted laser desorption ionization-time of flight mass spectrometry: results of an interlaboratory ring trial.J. Clin. Microbiol. 2015; 53: 2632-2640Crossref PubMed Scopus (46) Google Scholar) requires additional measures beyond the standard microbial identification workflow, such as custom reference libraries, higher levels of standardization, and/or sophisticated data analysis concepts (machine learning, etc.). In these studies the reduced discriminatory power of MALDI-TOF MS has been attributed to the restricted m/z range (m/z 2000–20,000) and the dependence from mass patterns produced by a sub-proteome of small, abundant and basic proteins, mainly ribosomal subunit proteins (8Rodrigues C. Novais A. Sousa C. Ramos H. Coque T.M. Canton R. Lopes J.A. Peixe L. Elucidating constraints for differentiation of major human Klebsiella pneumoniae clones using MALDI-TOF MS.Eur. J. Clin. Microbiol. Infect. Dis. 2016; 36: 379-386Crossref PubMed Scopus (10) Google Scholar). Because the molecular evolution of these proteins is rather slow, ribosomal proteins are supposed to offer only limited taxonomic specificity. Further limiting factors of the MALDI-TOF MS method are the relatively low resolution, resulting in decreased selectivity and a reduced dynamic sensitivity, i.e. a lowered detectability of protein signals over a wide concentration range (11Grenga L. Pible O. Armengaud J. Pathogen proteotyping: A rapidly developing application of mass spectrometry to address clinical concerns.Clin. Mass Spectrom. 2019; 14: 9-17Crossref Scopus (18) Google Scholar). In contrast to MALDI-TOF MS, liquid chromatography-tandem MS (LC-MS2) generally detects large numbers of signals at very high resolution with very high mass accuracy in a single run (11Grenga L. Pible O. Armengaud J. Pathogen proteotyping: A rapidly developing application of mass spectrometry to address clinical concerns.Clin. Mass Spectrom. 2019; 14: 9-17Crossref Scopus (18) Google Scholar). Shotgun proteomic methods observe proteolytic cleavage products, often tryptic peptides, instead of intact proteins. This enables MS data collection with high analytical sensitivity. Moreover, coupling of MS with chromatographic separation (LC) has shown to increase the dynamic sensitivity and allows sensitive detection also of low abundant peptides. Finally, LC-MS2 is much less restricted to classes of proteins with specific physicochemical properties. Even though proteomic techniques are still complex, rather cost-intensive and limited for use by well-equipped laboratories, the many advantages of LC–MS have led to an increasing number of activities aiming at evaluating potential applications of LC–MS in microbiology (16Jabbour R.E. Deshpande S.V. Wade M.M. Stanford M.F. Wick C.H. Zulich A.W. Skowronski E.W. Snyder A.P. Double-blind characterization of non-genome-sequenced bacteria by mass spectrometry-based proteomics.Appl. Environ. Microbiol. 2010; 76: 3637-3644Crossref PubMed Scopus (39) Google Scholar, 17Jabbour R.E. Deshpande S.V. Stanford M.F. Wick C.H. Zulich A.W. Snyder A.P. A protein processing filter method for bacterial identification by mass spectrometry-based proteomics.J. Proteome Res. 2011; 10: 907-912Crossref PubMed Scopus (14) Google Scholar, 18Berendsen E.M. Levin E. Braakman R. von der Riet-van Oeveren D. Sedee N.J.A. Paauw A. Identification of microorganisms grown in blood culture flasks using liquid chromatography–tandem mass spectrometry.Future Microbiol. 2017; 18: 1135-1145Crossref Scopus (7) Google Scholar, 19Tracz D.M. McCorrister S.J. Chong P.M. Lee D.M. Corbett C.R. Westmacott G.R. A simple shotgun proteomics method for rapid bacterial identification.J. Microbiol. Methods. 2013; 94: 54-57Crossref PubMed Scopus (22) Google Scholar, 20Berendsen E.M. Levin E. Braakman R. Prodan A. van Leeuwen H.C. Paauw A. Untargeted accurate identification of highly pathogenic bacteria directly from blood culture flasks.Int. J. Med. Microbiol. 2020; 310151376 Crossref PubMed Scopus (7) Google Scholar). Various groups have used shotgun proteomics for the classification and identification of pathogenic microorganisms. For example, a proteomics-based workflow for bacterial identification has been suggested by Dworzanski, which involved construction of a bacterial proteome database from bacterial genomes, LC-MS2 data acquisition from digested bacterial cell extracts, identification of tryptic peptides and sequence-to-bacterium assignments (21Dworzanski J.P. Deshpande S.V. Chen R. Jabbour R.E. Snyder A.P. Wick C.H. Li L. Mass spectrometry-based proteomics combined with bioinformatic tools for bacterial classification.J. Proteome Res. 2006; 5: 76-87Crossref PubMed Scopus (62) Google Scholar). The approach has been later utilized to determine the relatedness among strains of B. cereus sensu stricto, B. thuringiensis and B. anthracis by estimating fractions of shared peptides derived from a prototype database (22Dworzanski J.P. Dickinson D.N. Deshpande S.V. Snyder A.P. Eckenrode B.A. Discrimination and phylogenomic classification of Bacillus anthracis-cereus-thuringiensis strains based on LC-MS/MS analysis of whole cell protein digests.Anal. Chem. 2010; 82: 145-155Crossref PubMed Scopus (24) Google Scholar). LC-MS2 has been also used by Tracz and coworkers to identify Biosafety Level 3 bacteria (19Tracz D.M. McCorrister S.J. Chong P.M. Lee D.M. Corbett C.R. Westmacott G.R. A simple shotgun proteomics method for rapid bacterial identification.J. Microbiol. Methods. 2013; 94: 54-57Crossref PubMed Scopus (22) Google Scholar). Sequence data from tryptic microbial peptides were obtained and employed for Mascot searches against a database containing concatenated protein sequences derived from microbial genomes. Identification of bacterial species was carried out by summing up matches from unique and degenerated (shared) peptides found per concatenated sequence; a post-culture analysis time of less than 8 h has been reported. Another alternative based on LC–MS has been proposed by Jabbour. Bacterial samples were lysed and subjected to tryptic digestion followed by LC-MS2 (16Jabbour R.E. Deshpande S.V. Wade M.M. Stanford M.F. Wick C.H. Zulich A.W. Skowronski E.W. Snyder A.P. Double-blind characterization of non-genome-sequenced bacteria by mass spectrometry-based proteomics.Appl. Environ. Microbiol. 2010; 76: 3637-3644Crossref PubMed Scopus (39) Google Scholar). Subsequently, peptides were identified and matched against databases. Bacteria were then identified based on the assessment of unique peptides obtained by an algorithm called BACid. Comparison between microbial protein sequence data obtained by bottom-up tandem MS and reference databases was also performed by Boulund and colleagues. The proposed analysis pipeline (TCUP) not only returned specific genes of reference genomes that matched with peptide sequences determined by LC-MS2, but provided also the relative abundances of individual bacteria identified in a given mixed culture (23Boulund F. Karlsson R. Gonzales-Siles L. Johnning A. Karami N. Al-Bayati O. Åhrén C. Moore E.R.B. Kristiansson E. Typing and characterization of bacteria using bottom-up tandem mass spectrometry proteomics.Mol. Cell. Proteomics. 2017; 16: 1052-1063Abstract Full Text Full Text PDF PubMed Scopus (35) Google Scholar). In this way, TCUP allowed typing and characterizing pathogenic bacteria from pure cultures and to estimate the relative abundances of individual microbial species from mixed microbial samples (23Boulund F. Karlsson R. Gonzales-Siles L. Johnning A. Karami N. Al-Bayati O. Åhrén C. Moore E.R.B. Kristiansson E. Typing and characterization of bacteria using bottom-up tandem mass spectrometry proteomics.Mol. Cell. Proteomics. 2017; 16: 1052-1063Abstract Full Text Full Text PDF PubMed Scopus (35) Google Scholar). In the same year Berendsen and coworkers suggested a generic LC-MS2 method for the identification of microorganisms from positive blood cultures (18Berendsen E.M. Levin E. Braakman R. von der Riet-van Oeveren D. Sedee N.J.A. Paauw A. Identification of microorganisms grown in blood culture flasks using liquid chromatography–tandem mass spectrometry.Future Microbiol. 2017; 18: 1135-1145Crossref Scopus (7) Google Scholar). A LC–MS compatible sample preparation method was developed that enabled accurate identification of bacteria grown in blood culture flasks to species level based on LC-MS2 bottom-up proteomics, database searches and matching with taxon-specific discriminative peptides. Advantages of the LC-MS2-based approaches for bacterial identification outlined above are the excellent accuracy of identification, high taxonomic resolution, universal applicability to the ever-growing numbers of known microbes and the ability to identify bacteria from mixtures, e.g. in polymicrobial infections. On the other hand, the comparatively high computational requirements have to be mentioned. Because the time required for peptide identification correlates with the number of entries contained in sequence databases, computation time can be saved by restricting the size of the database, for example by using genus-specific databases. However, database restrictions contradict the use of shotgun proteomics as an unbiased approach for microbial identification. Another important limitation of LC-MS2-based approaches is the severely reduced accuracy and sensitivity of common search algorithms when extensive protein sequence databases are used. The large search space impedes the identification of true peptide matches within large numbers of similar sequences. With this proof-of-concept study, we introduce an alternative, easy-to use and computational less demanding approach for microbial identification. The proposed method is based on bottom-up proteomics as the analytical technique and involves acquisition of LC–MS data from pure microbial cultures. MS1 data are extracted and tested against a database compiled in-house using public protein databases (UniProtKB) with currently more than 12,000 strain-specific in silico mass profiles. We demonstrate that the MS1 information can be used for rapid and accurate taxonomic identification, at least at the species level, and discuss possibilities to combine the suggested analysis pipeline with known MS2-based analysis methods in microbiology. The performance and accuracy of the proposed method for microbial identification was tested using 19 well-characterized bacterial strains that were predominantly obtained from established strain collections such as DSM (Deutsche Sammlung von Mikroorganismen), ATCC (American Type Culture Collection) and NCTC (National Collection of Type Cultures). Strains E 125, E 131 and E153 of Burkholderia thailandensis originated from the strain collection at the Robert Koch-Institute (RKI) (24Lasch P. Stammler M. Zhang M. Baranska M. Bosch A. Majzner K. FT-IR hyperspectral imaging and artificial neural network analysis for identification of pathogenic bacteria.Anal. Chem. 2018; 90: 8896-8904Crossref PubMed Scopus (36) Google Scholar). An overview of the microbial strains and species utilized is given in Table I. Abbreviations: DSM – Deutsche Sammlung von Mikroorganismen; ATCC – American Type Culture Collection; NCTC – National Collection of Type Cultures; LMG – Belgian Coordinated Collections of Microorganisms, Universiteit Gent – Laboratorium voor Microbiologie, NIH – National Institute of Health, * (24Lasch P. Stammler M. Zhang M. Baranska M. Bosch A. Majzner K. FT-IR hyperspectral imaging and artificial neural network analysis for identification of pathogenic bacteria.Anal. Chem. 2018; 90: 8896-8904Crossref PubMed Scopus (36) Google Scholar). Bacteria were streaked under sterile conditions on solid culture media by an inoculation loop and incubated for 48 h. Strains prepared by the STrap protocol (see below and Table I) were cultivated according to species-specific cultivation requirements on tryptic soy agar (TSA, ReadyPlate TSA ISO, Merck Life Science) or Middlebrook agar produced in-house. Cells from these cultures were harvested by scraping; approximately 10 µL of microbial material was then washed three times by 1 ml of ice-cold phosphate buffered salt solution and centrifuged at 4000 g at 4 °C for 5 min. All other bacteria were cultured on Casein-Soy-Peptone (Caso, Oxoid, Wesel, Germany) agar plates under aerobic conditions for 24 h at 37 °C and further processed according to the protocol Sample Preparation by Easy Extraction and Digestion (SPEED, see below and Table I for details). In this proof of concept study we tested the proposed MS1-based identification workflow by proteomic data from 19 different bacterial strains from which 39 RAW files were collected. The Burkholderia subset (see below and supporting information) included biological and technical replicate spectra. LC–MS measurements were shuffled in most cases in such a way, that technical replicates of the same sample were not measured consecutively. Cells were suspended in 5% SDS (SDS), 20 mm DTT (DTT), 50 mm Tris/HCl buffer, pH 7.6 (sample/buffer 1:10 (v/v)), incubated at 95 °C for 10 min and further sonicated for 15 cycles à 30 s at high intensity level and 4 °C using Bioruptor®Plus (Diagenode, Liege, Belgium). Protein concentrations were determined by measuring the tryptophan fluorescence at an emission wavelength of 350 nm using 295 nm for excitation with an Infinite® M1000 PRO microplate reader (Tecan, Männedorf, Switzerland) according to (25Wiśniewski J.R. Gaugaz F.Z. Fast and sensitive total protein and Peptide assays for proteomic analysis.Anal. Chem. 2015; 87: 4110-4116Crossref PubMed Scopus (105) Google Scholar). Samples were further processed using S-Trap™ mini filters according to the STrap sample preparation protocol (26Zougman A. Selby P.J. Banks R.E. Suspension trapping (STrap) sample preparation method for bottom-up proteomics analysis.Proteomics. 2014; 14: 1006-1010Crossref PubMed Scopus (106) Google Scholar) and the manufacturer's instructions (Protifi, Huntington NY). SPEED protocol-based preparation of microbial cells was carried as outlined in (27Doellinger J. Schneider A. Hoeller M. Lasch P. Sample preparation by easy extraction and digestion (SPEED) - a universal, rapid, and detergent-free protocol for proteomics based on acid extraction.Mol. Cell. Proteomics. 2020; 19: 209-222Abstract Full Text Full Text PDF PubMed Scopus (21) Google Scholar). Briefly, bacterial cells were suspended in trifluoroacetic acid (TFA, Uvasol® for spectroscopy, Merck, Darmstadt, Germany) in a sample/TFA ratio of 1:4 (v/v) and heated at 70 °C for 3 min. Acid extracts were then neutralized with 2M TrisBase using the 10-fold volume of TFA solution and further incubated at 95 °C for 5 min after adding tris (2-carboxyethyl)phosphine (TCEP) to a final concentration of 10 mm and 2-chloroacetamide (CAA) to a final concentration of 40 mm. Protein concentrations were determined by turbidity measurements at 360 nm (1 AU = 0.79 µg/µL) using a GENESYS™ 10S UV-Vis Spectrophotometer (Thermo Fisher Scientific, Waltham, MA) and adjusted to 0.25 µg/µL using a 10:1 (v/v) mixture of 2M TrisBase and TFA. The solution was afterward diluted 1:5 (v/v) with water. Proteins were digested for 20 h at 37 °C using Trypsin Gold, Mass Spectrometry grade (Promega, Fitchburg, WI) at an enzyme/protein ratio of 1:50 (w/w). Peptides were desalted using 200 µL StageTips™ packed with three Empore™ SPE Disks C18 (3M Purification, Inc., Lexington, MN) according to (28Ishihama Y. Rappsilber J. Mann M. Modular stop and go extraction tips with stacked disks for parallel and multidimensional Peptide fractionation in proteomics.J. Proteome Res. 2006; 5: 988-994Crossref PubMed Scopus (206) Google Scholar) and concentrated using a vacuum concentrator. Samples were resuspended in 20 µL 0.1% formic acid (FA) and peptides were quantified by measuring the absorbance at 280 nm using a Nanodrop 1000 device (Thermo Fisher Scientific, Rockford, IL). Nano-LC tandem MS (nLC-MS2) measurements of pathogenic bacteria were carried out within the scope of different proteomics projects. Desalted digests were analyzed on an EASY-nanoLC 1200 device (Thermo Fisher Scientific, Bremen, Germany) coupled online to a Q Exactive™ Plus mass spectrometer (Thermo Fisher Scientific). Two different LC setups were used. 1 µg peptides of samples #1 – 13 were loaded on a Acclaim™ PepMap™ trap column (20 mm × 75 μm i.d., 100 Å, C18, 3 μm; Thermo Fisher Scientific, Bremen, Germany) at a flow rate of 3 µL/min and were subsequently separated on a 200 cm μPAC™ column (PharmaFluidics, Ghent, Belgium) using a 160 min gradient of 3 to 28% acetonitrile (ACN) in 0.1% FA at 300 nL/min flow rate. Column temperature was kept at 50 °C using a butterfly heater (Phoenix S&T, Chester, PA). In contrast, 1 µg peptides of samples #14 – 21 were directly loaded and separated on a 50 cm Acclaim™ PepMap™ column (75 μm inner diameter, i.d., 100 Å C18, 2 μm; Thermo Fisher Scientific, Bremen, Germany) using a linear 120 min gradient of 3 to 28% ACN in 0.1% FA at 200 nL/min flow rate using a column temperature of 40 °C. The mass spectrometer was operated in a data-dependent manner in the m/z range of 300 – 1,650. Full scan spectra were recorded with a resolution of 70,000 using an automatic gain control (AGC) target value of 3 × 106 with a maximum injection time of 20 ms. Up to the 10 most intense 2+ – 5+ charged ions were selected for higher-energy c-trap dissociation (HCD) with a normalized collision energy (NCE) of 25%. Fragment spectra were recorded at an isolation width of 2 Th and a resolution of 17,[email protected] m/z using an AGC target value of 1 × 105 with a maximum injection time of 50 ms. The minimum AGC MS2 target value was set to 1 × 104. Once fragmented, peaks were dynamically excluded from precursor selection for 30 s within a 10 ppm window. Peptides were ionized using electrospray with a stainless-steel emitter, i.d. 30 μm, (Proxeon, Odense, Denmark) at a spray voltage of 2.0 kV and a heated capillary temperature of 275 °C. This study entirely relied on MS1 data. Peptide feature detection was carried out using the Minora algorithm of the Proteome Discoverer software v. 2.2.0388 (Thermo-Fisher Scientific) with default settings. Peptide feature text files were then processed by LCMS-Biotyping.vvf, an in house-written method compiled for obtaining the following parameters from the peptide features: MS1 peak positions (in m/z units) with the respective ion charge state, normalized abundance and signal-to-noise ratio (SNR). These parameters were stored in tab-separated text files and imported by a custom-designed Matlab function readlcmstxtfile (Matlab, The Mathworks, Natick, MA). As part of the parseuniprot toolbox (see below) this function supports import of peptide feature text files obtained from LC-MS1 data and performs data pre-processing, including molecular weight (MW) determination by considering charge states, detecting and removing peak entries originating from oxidized peptides (mass shift +15.99491 Da) as well as from peptides with deamidated glutamine or asparagines residues (mass shift +0.98402 Da). Spectral pre-processing involved furthermore partially removing (underweighting) of low intensity and low MW peaks; based on the principle that the relevance of a specific peak for subsequent identification analysis co-varies with its intensity and MW values (see below). As the result of pre-processing, experimental LC-MS1 data of a given sample is collapsed into a single MS1 mass peak list, which contains the filtered peptide mass data. Such data are in the following referred to as experimental LC-MS1 peak lists, or – after conversion into continuous spectra - as LC-MS1 test spectra. Assembly of the in silico library of microbial peptide mass data were done utilizing information from the UniProt Knowledgebase (UniProtKB). In particular, UniProtKB/Swiss-Prot and UniProtKB/TrEMBL text files, uniprot_sprot_bacteria.dat.gz (size: 211,465,997 byte, date: Dec 10, 2018) and uniprot_trembl_bacteria.dat.gz (60,987,643,246 byte, Dec 09, 2018) were downloaded from
Referência(s)