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Rapid discrimination of Pseudomonas aeruginosa ST175 isolates involved in a nosocomial outbreak using MALDI-TOF Mass Spectrometry and FTIR Spectroscopy coupled with Machine Learning
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  • Belén Rodríguez-Sánchez,
  • Ana Candela,
  • Manuel J. Arroyo,
  • María Sánchez-Cueto,
  • Mercedes Marín,
  • Emilia Cercenado,
  • Gema Méndez,
  • Patricia Muñoz,
  • Luis Mancera,
  • David Rodriguez-Temporal
Belén Rodríguez-Sánchez
Hospital General Universitario Gregorio Maranon
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Ana Candela
Hospital General Universitario Gregorio Maranon
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Manuel J. Arroyo
Clover Bioanalytical Software Av del Conocimiento 41 18016 Granada Spain
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María Sánchez-Cueto
Hospital General Universitario Gregorio Maranon
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Mercedes Marín
Hospital General Universitario Gregorio Maranon
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Emilia Cercenado
Hospital General Universitario Gregorio Maranon
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Gema Méndez
Clover Bioanalytical Software Av del Conocimiento 41 18016 Granada Spain
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Patricia Muñoz
Hospital General Universitario Gregorio Maranon
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Luis Mancera
Clover Bioanalytical Software Av del Conocimiento 41 18016 Granada Spain
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David Rodriguez-Temporal
Hospital General Universitario Gregorio Maranon
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Abstract

Objectives: Evaluation of Matrix-Assisted Laser Desorption Ionization-Time 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. Methods: Twenty-seven P. aeruginosa isolates from a 2014 outbreak in the Hematology department of our hospital were previously characterized by PFGE and WGS. Besides, 8 P. aeruginosa isolates were analyzed as unrelated controls. MALDI-TOF MS spectra were acquired by applying the colony on the MALDI target plate followed by 1 µl of formic acid 100% and 1 µl of HCCA matrix. For the analysis with FTIR-S, colonies were resuspended in 70% ethanol and sterile water according to the manufacturer instructions. Spectra from both methodologies were analyzed using Clover Biosoft® software, that allowed data modelling using different algorithms and validation of the classifying models. Results: Three outbreak-specific biomarkers were found at 5169, 6915 and 7236 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 whole-genome sequencing 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. Conclusions: 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.
28 Jul 2022Submitted to Transboundary and Emerging Diseases
29 Jul 2022Assigned to Editor
29 Jul 2022Submission Checks Completed
02 Aug 2022Reviewer(s) Assigned