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Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data

View ORCID ProfilePeter Lasch, Andy Schneider, Christian Blumenscheit, Joerg Doellinger
doi: https://doi.org/10.1101/870089
Peter Lasch
1Robert Koch-Institute, ZBS6 - Proteomics and Spectroscopy, Seestraße 10, Berlin, D-13353, Germany
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  • ORCID record for Peter Lasch
  • For correspondence: LaschP@rki.de
Andy Schneider
1Robert Koch-Institute, ZBS6 - Proteomics and Spectroscopy, Seestraße 10, Berlin, D-13353, Germany
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Christian Blumenscheit
1Robert Koch-Institute, ZBS6 - Proteomics and Spectroscopy, Seestraße 10, Berlin, D-13353, Germany
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Joerg Doellinger
1Robert Koch-Institute, ZBS6 - Proteomics and Spectroscopy, Seestraße 10, Berlin, D-13353, Germany
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1. ABSTRACT

Over the past decade, modern methods of mass spectrometry (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. While 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 mass spectrometry (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 very 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. MS1 data are then extracted and systematically tested against an in silico library of peptide mass data compiled in house. The library has been computed from the UniProt Knowledgebase 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 spectral distances between experimental and in silico peptide mass data 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 two minutes per sample - and has been successfully tested by a set of 19 different microbial pathogens. The approach is rapid, accurate and automatable and holds great potential for future microbiological applications.

  • Abbreviations

    ACN
    acetonitrile;
    AGC
    automatic gain control;
    DTT
    dithiothreitol;
    CAA
    2-chloroacetamide;
    FA
    formic acid;
    ID
    identifier;
    MALDI-TOF
    matrix-assisted laser desorption/ionization - time–of–flight;
    MBT
    MALDI Biotyper;
    MS
    mass spectrometry;
    MW
    molecular weight;
    NCE
    normalized collision energy;
    ppm
    parts per million;
    RKI
    Robert Koch-Institute;
    SDS
    sodium dodecyl sulfate;
    SNR
    signal-to-noise ratio;
    SPEED
    sample preparation by easy extraction and digestion;
    STrap
    suspension trapping;
    TFA
    trifluoroacetic acid;
    TSA
    tryptic soy agar;
    TCEP
    Tris(2-carboxyethyl)phosphine;
    UniProtKB
    UniProt Knowledgebase
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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    Posted December 10, 2019.
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    Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data
    Peter Lasch, Andy Schneider, Christian Blumenscheit, Joerg Doellinger
    bioRxiv 870089; doi: https://doi.org/10.1101/870089
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    Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data
    Peter Lasch, Andy Schneider, Christian Blumenscheit, Joerg Doellinger
    bioRxiv 870089; doi: https://doi.org/10.1101/870089

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