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. 2024 Dec 30;14(1):19.
doi: 10.3390/pathogens14010019.

Differentiation of Escherichia coli and Shigella flexneri by Metabolite Profiles Obtained Using Gold Nanoparticles-Based Surface-Assisted Laser Desorption/Ionization Mass Spectrometry

Affiliations

Differentiation of Escherichia coli and Shigella flexneri by Metabolite Profiles Obtained Using Gold Nanoparticles-Based Surface-Assisted Laser Desorption/Ionization Mass Spectrometry

Adrian Arendowski. Pathogens. .

Abstract

Escherichia coli and Shigella flexneri are challenging to differentiate using methods such as phenotyping, 16S rRNA sequencing, or protein profiling through matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) due to their close relatedness. This study explores the potential for identifying E. coli and S. flexneri by incorporating reference spectra of metabolite profiles, obtained via surface-assisted laser desorption/ionization mass spectrometry (SALDI MS) employing gold nanoparticles (AuNPs), into the Bruker Biotyper database. Metabolite extracts from E. coli and S. flexneri cells were prepared using liquid-liquid extraction in a chloroform-methanol-water system. The extracts were analyzed using Au-SALDI MS in positive ion mode, and reference spectra, compiled from 30 spectra for each bacterium, were added to the database. Identification of bacteria based on metabolite fingerprints in the Biotyper database produced correct results with scores exceeding 2.75. The results of Partial Least Squares-Discriminant Analysis (PLS-DA) demonstrated that the metabolomic approach could accurately differentiate the microorganisms under study. A panel of nine m/z values was also identified, each with an area under the ROC curve of above 0.8, enabling accurate identification of E. coli and S. flexneri. A search of metabolite databases allowed the following compounds to be assigned to the selected m/z values: N-acetylputrescine, arginine, 2-maleylacetate, benzoyl phosphate, N8-acetylspermidine, alanyl-glutamate, 4-hydroxy-2,3,4,5-tetrahydrodipicolinate, and sucrose. The analyses showed that identification of bacteria based on metabolite profiles obtained by the Au-SALDI MS method is feasible and can be useful for distinguishing closely related microorganisms that are difficult to differentiate by other techniques.

Keywords: gold nanoparticles; identification; mass spectrometry; metabolites; microorganisms; surface-assisted laser desorption/ionization.

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Conflict of interest statement

The author declares no conflicts of interest.

Figures

Figure 1
Figure 1
Gold nanoparticle-based SALDI MS spectrum of metabolite extract from E. coli (top) and S. flexneri (bottom).
Figure 2
Figure 2
The matching test (top) and reference (bottom) spectra obtained by AuNPs-SALDI MS for metabolite extracts from E. coli (A) and S. flexneri (B) in the Bruker Biotyper software (MBT Compass Library Revision H). The top spectrum—the spectrum of the test sample, the bottom spectrum—the reference spectrum.
Figure 3
Figure 3
Dendrogram for Au-SALDI MS data from E. coli and S. flexneri metabolite extracts.
Figure 4
Figure 4
The graphical representation of the statistical analysis of AuNPs-SALDI MS data from metabolite extracts of E. coli and S. flexneri includes the following: PLS-DA component 1 versus component 2 (A), PLS-DA VIP scores (B), multivariate ROC analysis (C), and predicted class probabilities derived from cross-validation (D).
Figure 5
Figure 5
Box plots and ROC analysis of nine m/z values most differentiating between E. coli and S. flexneri: 153.104 (A), 175.126 (B), 181.015 (C), 203.003 (D) 210.174 (E), 219.093 (F), 222.177 (G), 226.002 (H), and 343.093 (I).

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