Rapid and reliable discrimination between Shigella species and Escherichia coli using MALDI-TOF mass spectrometry

Paauw, Armand; Jonker, Debby; Roeselers, Guus; Heng, Jonathan M.E.; Mars-Groenendijk, Roos H.; Trip, Hein; Molhoek, E. Margo; Jansen, Hugo-Jan; Plas, J. van der; Jong, A.L. de; Majchrzykiewicz-Koehorst, J.A.; Speksnijder, A.G.C.L.

doi:10.1016/j.ijmm.2015.04.001

Cited by 65 publications

(48 citation statements)

References 24 publications

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“…Resource 2), indicating that a more evenly distribution of species in the database does not improve 303 identification of E. coli. Although percentages of correct species assignments to S. flexneri and S. 304 sonnei were high, other species were falsely assigned to them, both in our study as in a previous study 305 [11]. In the latter study, correct species identification was based on the majority rule that three out of 306 four spots should indicate the same species.…”

supporting

confidence: 41%

“…With the custom-made database with or without combination with the Bruker databases, >91% of S. 292 flexneri and S. sonnei were correctly identified at the species level. For S. boydii and S. dysenteriae, 293 our custom-made database performed poor as has been shown before [11]. However, compared to a 294 previous study our custom-made database assigned less E. coli isolates correctly [11].…”

mentioning

confidence: 61%

“…For S. boydii and S. dysenteriae, 293 our custom-made database performed poor as has been shown before [11]. However, compared to a 294 previous study our custom-made database assigned less E. coli isolates correctly [11]. This indicates 295 that the inclusion of EIEC isolates in the custom-made database as well as in the test set complicates 296 the identification.…”

mentioning

confidence: 70%

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Matrix-Assisted Laser Desorption-Ionization Time-of-Flight mass spectrometry using a custom-made database, biomarker assignment, or mathematical classifiers does not differentiate Shigella spp. and Escherichia coli

Beld

Rossen

Evers

et al. 2019

Preprint

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PurposeShigella spp. and E. coli are closely related, and cannot be distinguished using Matrix-Assisted Laser Desorption-Ionization Time-of-Flight mass spectrometry (MALDI-TOF MS) with commercially available databases. Here, three alternative approaches using MALDI-TOF for identification and distinction of Shigella spp., E. coli and its pathotype EIEC were explored.MethodsA custom-made database was developed, biomarkers were assigned and classification models using machine learning were designed and evaluated using spectra of 456 Shigella spp., 42 E. coli and 61 EIEC isolates, obtained by the direct smear method and the ethanol-formic acid extraction method.ResultsIdentification with a custom-made database resulted in >94% Shigella identified at the genus level, and >91% S. sonnei and S. flexneri at the species level, but distinction of S. dysenteriae, S. boydii and E. coli was poor. Moreover, 10-15% of duplicates rendered discrepant results. With biomarker assignment, 98% S. sonnei isolates were correctly identified, although the S. sonnei biomarkers were not specific as other species were also identified as S. sonnei. Discriminating markers for S. dysenteriae, S. boydii, and E. coli were not assigned at all. Classifiers identified Shigella in 96% of isolates correctly, but most E. coli isolates were also assigned to Shigella.ConclusionNone of the proposed alternative approaches is suitable for use in clinical diagnostics for the identification of Shigella spp., E. coli and EIEC because of their poor distinctive properties. We suggest the use of MALDI-TOF MS for identification of the Shigella spp./E. coli complex, but other tests should be used for distinction.

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supporting

confidence: 41%

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confidence: 61%

mentioning

confidence: 70%

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Matrix-Assisted Laser Desorption-Ionization Time-of-Flight mass spectrometry using a custom-made database, biomarker assignment, or mathematical classifiers does not differentiate Shigella spp. and Escherichia coli

Beld

Rossen

Evers

et al. 2019

Preprint

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“…At present, both systems are unable to distinguish between Shigella and E . coli , which, in the future, might be overcome by more advances databases [59]. Other enteropathogens such as Yersinia , Campylobacter , Vibrio , Aeromonas , and Plesiomonas were correctly identified by both systems with the above mentioned limitations of species differentiation among Aeromomas and the database-dependent limitation of Microflex LT to differentiate V .…”

Section: Discussionmentioning

confidence: 99%

Head-to-head comparison of Microflex LT and Vitek MS systems for routine identification of microorganisms by MALDI-TOF mass spectrometry in Chile

et al. 2017

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BackgroundMatrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry (MS) is a new and revolutionary identification method for microorganisms and has recently been introduced into clinical microbiology in many industrialized countries in Europe and North America.ObjectivesOur study aimed to compare the performance and practicality of two commercial MALDI-TOF MS platforms in a head-to head manner at a routine laboratory in Chile.MethodsDuring a five-month period in 2012–13, the diagnostic efficiency (correct identification rate) and agreement between Microflex LT (Bruker Daltonics) and Vitek MS (bioMérieux) was compared in a parallel manner to conventional identification including genotypic analysis for difficult-to-identify strains. The study included 804 microbial isolates: 252 Enterobacteriaceae, 126 non-fermenters, 36 other gram-negative rods, 279 gram-positive cocci, 32 gram-positive rods, 32 anaerobes, and 47 yeasts. Other relevant factors of the two devices such as user friendliness and connectivity were also evaluated and compared.ResultsBoth systems correctly identified the vast majority (98%) of the isolates to the genus level. Vitek MS reached higher rates of identification to species and species complex level than Microflex LT (81% vs. 85% and 87% vs. 93%, respectively), which was mainly based on the higher performance among coagulase negative staphylococci and Candida isolates. The evaluation of user friendliness and other technical aspects showed only marginal differences, which slightly favored Vitek MS, mainly due to its ready-to-use supplies, easier connectivity and workflow integration, and availability of local technical support.ConclusionsBoth MALDI-TOF MS systems permitted fast and accurate identification of most microbial strains and showed a high level of user-friendliness. The observed differences were marginal and slightly favored Vitek MS, mainly due to practicality and connectivity issues within our setting.

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“…While 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 (Lasch et al, 2014;Sousa et al, 2015;Rodrigues et al, 2016;Grenga et al, 2019). For example, differentiation between Escherichia coli and Shigella (He et al, 2010;Paauw et al, 2015) or of Bacillus cereus and Bacillus anthracis (Dybwad et al, 2013;Lasch et al, 2015) required 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 (Rodrigues et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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

Schneider

Blumenscheit

Doellinger

2019

Preprint

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1.ABSTRACTOver 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.

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Rapid and reliable discrimination between Shigella species and Escherichia coli using MALDI-TOF mass spectrometry

Cited by 65 publications

References 24 publications

Matrix-Assisted Laser Desorption-Ionization Time-of-Flight mass spectrometry using a custom-made database, biomarker assignment, or mathematical classifiers does not differentiate Shigella spp. and Escherichia coli

Matrix-Assisted Laser Desorption-Ionization Time-of-Flight mass spectrometry using a custom-made database, biomarker assignment, or mathematical classifiers does not differentiate Shigella spp. and Escherichia coli

Head-to-head comparison of Microflex LT and Vitek MS systems for routine identification of microorganisms by MALDI-TOF mass spectrometry in Chile

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

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Rapid and reliable discrimination between Shigella species and Escherichia coli using MALDI-TOF mass spectrometry

Cited by 65 publications

References 24 publications

Matrix-Assisted Laser Desorption-Ionization Time-of-Flight mass spectrometry using a custom-made database, biomarker assignment, or mathematical classifiers does not differentiate Shigella spp. and Escherichia coli

Matrix-Assisted Laser Desorption-Ionization Time-of-Flight mass spectrometry using a custom-made database, biomarker assignment, or mathematical classifiers does not differentiate Shigella spp. and Escherichia coli

Head-to-head comparison of Microflex LT and Vitek MS systems for routine identification of microorganisms by MALDI-TOF mass spectrometry in Chile

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

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