Abstract
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.
Highlights
Over the past decade, modern methods of mass spectrometry (MS) (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms
Overview of the Identification Analysis Workflow—In this proof of concept study, we present a computational pipeline that is suitable for identification of pathogenic microorganisms from bottom-up MS (MS) data
First steps of the proposed approach include cultivation of pathogenic bacteria under standardized conditions followed by sample preparation using validated protocols
Summary
Modern methods of MS (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. 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 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. Differentiation between Escherichia coli and Shigella [12, 13] or of Bacillus cereus and Bacillus anthracis [14, 15] 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 MALDITOF 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 [8]. 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 [16,17,18,19,20]
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