| Title: |
Rapid Discrimination of Pseudomonas aeruginosa ST175 Isolates Involved in a Nosocomial Outbreak Using MALDI-TOF Mass Spectrometry and FTIR Spectroscopy Coupled with Machine Learning |
| Authors: |
Candela, Ana; Arroyo, Manuel J.; Sánchez-Cueto, María; Marín, Mercedes; Cercenado, Emilia; Méndez, Gema; Muñoz, Patricia; Mancera, Luis; Rodríguez-Temporal, David; Rodríguez-Sánchez, Belén |
| Contributors: |
Khurshid, Mohsin; European Regional Development Fund; FIS; IiSGM |
| Source: |
Transboundary and Emerging Diseases ; volume 2023, page 1-11 ; ISSN 1865-1682 1865-1674 |
| Publisher Information: |
Wiley |
| Publication Year: |
2023 |
| Collection: |
Wiley Online Library (Open Access Articles via Crossref) |
| Description: |
The goal of this study was to evaluate matrix-assisted laser desorption ionization–iime of flight mass spectrometry (MALDI-TOF MS) and Fourier-transform infrared spectroscopy (FTIR-S) as diagnostic alternatives to DNA-based methods for the detection of Pseudomonas aeruginosa sequence type (ST) 175 isolates involved in a hospital outbreak. For this purpose, 27 P. aeruginosa isolates from an outbreak detected in the Hematology department of our hospital were analyzed by the above-mentioned methodologies. Previously, these isolates had been characterized by pulse-field gel electrophoresis (PFGE) and whole-genome sequencing (WGS). Besides, eight P. aeruginosa isolates were analyzed as unrelated controls. MALDI-TOF MS spectra were acquired by transferring several colonies onto the MALDI target and covering them with 1 µl of formic acid 100% and 1 µl of α-ciano-3,4-hidroxicinamic acid matrix. For the analysis with FTIR-S, colonies were resuspended in 70% ethanol and sterile water according to the manufacturer’s instructions. Spectra from both methodologies were analyzed using Clover Biosoft Software, which allowed data modeling using different algorithms and validation of the classifying models. Three outbreak-specific biomarkers were found at 5,169, 6,915, and 7,236 m/z in MALDI-TOF MS spectra. Classification models based on these three biomarkers showed the same discrimination power displayed by PFGE. Besides, K-nearest neighbor algorithm allowed the discrimination of the same clusters provided by WGS and the validation of this model achieved 97.0% correct classification. On the other hand, FTIR-S showed a discrimination power similar to PFGE and reached correct discrimination of the different STs analyzed. In conclusion, the combination of both technologies evaluated, paired with machine learning tools, may represent a powerful tool for real-time monitoring of high-risk clones and isolates involved in nosocomial outbreaks. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1155/2023/8649429 |
| Availability: |
https://doi.org/10.1155/2023/8649429; http://downloads.hindawi.com/journals/tbed/2023/8649429.pdf; http://downloads.hindawi.com/journals/tbed/2023/8649429.xml |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.992299E5 |
| Database: |
BASE |