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

Abstract

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 MS to identify and distinguish 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. The isolates were identified using ipaH PCR and phenotypic and serological typing. 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, for instance S. boydii and E. coli 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, reflecting their relatedness and problematic taxonomical classification. We suggest the use of MALDI-TOF MS for the identification of the Shigella spp./E. coli complex, but other tests should be used for distinction.