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

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 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 two minutes 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.

Unbiased antimicrobial resistance detection from clinical bacterial isolates using proteomics

“…n. s. o. r. t. i. u. m. UniProt, 2019). Therefore, we utilized a LC-MS 1 based method for microbial species identification, which has been adopted from MALDI-ToF MS biotyping and relies on LC-MS 1 pattern analysis (Lasch et al , 2020). The proteome of the identified species is then employed for predicting a sample-specific spectral library, which are in turn used as a background for peptide identification from the Comprehensive Antibiotic Resistance Database (CARD) (Alcock et al , 2020).…”

“…This enables the use of DIA-based proteomics, which records the entity of peptide fragments in a sample above its sensitivity threshold in an unbiased manner with deep proteome coverage. The prediction of sample-specific background libraries for AMR detection is based on the results of a recently introduced strategy for biotyping bacteria using LC-MS 1 data, whose recording can easily be integrated into DIA measurements (Lasch et al , 2020). This approach resulted in an identification accuracy of 93 % with a runtime of ~ 1 min on a decent desktop PC using a database with 8540 bacterial entries.…”

“…Identification of microbial species was carried out as outlined by (Lasch et al , 2020) with slight modifications. Briefly, peptide (MS 1 ) feature lists were extracted from raw DIA data using the Minora algorithm of the Proteome Discoverer ™ software (v2.2.0388, Thermo-Fisher Scientific).…”

The Use of MALDI-TOF Mass Spectrometry to Analyze Commensal Oral Yeasts in Nursing Home Residents

“…Recently, mix-microbial identification by MALDI-TOF MS has used specific algorithms to determine the mass spectra biomarkers for microorganisms [ 47 ]. Meanwhile, liquid chromatography–tandem MS is also used to identify mix-microbial samples [ 48 , 49 ]. As the mass spectral database grows significantly, yeast identification using MS, including identification of mix-culture, would be expected in the near future.…”

Emerging Options for the Diagnosis of Bacterial Infections and the Characterization of Antimicrobial Resistance

“…As one example, results from the study of Roux–Dalvai and colleagues [ 38 ] revealed that an HPLC-MS-based assessment of peptides, followed by the establishment of a set of unique peptide signatures using machine learning methods (i.e., top-down approach) resulted in the direct identification of fifteen distinct pathogenic bacterial species in urine specimens. Moreover, if the acquired peptide signatures are systematically evaluated using an in silico library generated from public databases (e.g., Swiss–Prot and Translated EMBL Nucleotide Sequence Data Library (TrEMBL); a bottom-up approach), the number of identifiable isolates can be increased to include more than 12,000 bacterial strains [ 59 ]. However, the transfer of these approaches to routine clinical laboratories remains limited and MALDI-TOF MS devices remain the most prominent at this time [ 38 ].…”