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

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.

Keywords: Bacteria; LC-MS1; bioinformatics software; diagnostic; diagnostics; identification of microorganisms; mass spectrometry; microbiology.

Graphical abstract

Fig. 1.

Overview of the proposed LC-MS¹ based microbial identification workflow. Pure microbial cultures are prepared and colony material is processed using established sample preparation protocols for shotgun proteomics. Mass spectrometry data are then obtained using LC–MS. MS¹ data are extracted and pre-processed for subsequent comparison against a library of in silico mass profiles obtained from UniProtKB/Swiss-Prot and UniProtKB/TrEMBL protein sequence data. This library is composed of MW pattern, or profiles, each representing a characteristic strain-specific combination of peptide masses whereby peptides may be specific or nonspecific in a MS² context. A ranking list of correlation, or inter-spectral distance values (i.e. of scores) is established, which provides information on the taxonomic identity of the organism studied.

Fig. 2.

Schematic workflow for generating an in silico database from UniProtKB/Swiss-Prot and/or UniProtKB/TrEMBL protein sequence data. The Matlab toolbox parseuniprot represents a proteomic pipeline in which three main internal functions, readdat, resort and modfeat are consecutively executed. The function readdat converts the content from structured text files available from ftp://ftp.uniprot.org into Matlab structure arrays that contain the complete information required to compile the in silico databases. Such arrays are subsequently processed by the functions resort and modfeat; the output of the parseuniprot pipeline is a collection of strain-specific in silico peptide mass profiles suitable for computer-based comparison (pattern matching) with experimental LC-MS¹ test spectra.

Fig. 3.

Pre-processing and feature selection of LC-MS¹ data. MS¹ peak data were acquired from a culture of Enterococcus faecalis DSM 20371; sample preparation has been carried according to the SPEED sample preparation protocol (27). Top row: histogram bar chart of log₁₀ scaled MS¹ peak intensities (left) and the molecular weight (MW) distribution (right) of peaks after feature detection by the Minora algorithm (=original* data, blue bars) and after pre-processing and feature selection by readlcmstxtfile (processed data, red bars). Total number of peaks in original/processed MS¹ data: 82843/42559. Number of oxidized/deamidated peptides found and removed: 389/329. Lower row: ratio between the number of peaks present in processed and in original MS¹ data as a function of peak intensity (log₁₀ scaled, left), or of the MW (right). Pre-processing was carried out by readlcmstxtfile, a Matlab function developed in house. This function has been designed to preferentially remove low intensity signals in the low MW region (< 2000 Da). The blue shaded area between 2000 – 5500 Da indicates the MW range used for correlation analysis by MicrobeMS.

Fig. 4.

Data analysis workflow for microbial identification based on experimental LC-MS¹ data and in silico databases comprising strain-specific peptide mass profiles derived from microbial genomes.