Rapid Discrimination of Pseudomonas aeruginosa ST175 Isolates Involved in a Nosocomial Outbreak Using MALDI-TOF Mass Spectrometry and FTIR Spectroscopy Coupled with Machine Learning
A. Candela, Manuel J. Arroyo, María Sánchez-Cueto, Mercedes Marín, E. Cercenado, Gema Méndez, Patricia Muñoz, Luis Mancera, D. Rodríguez-Temporal, B. Rodríguez-Sánchez
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引用次数: 1
Abstract
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.
期刊介绍：
Transboundary and Emerging Diseases brings together in one place the latest research on infectious diseases considered to hold the greatest economic threat to animals and humans worldwide. The journal provides a venue for global research on their diagnosis, prevention and management, and for papers on public health, pathogenesis, epidemiology, statistical modeling, diagnostics, biosecurity issues, genomics, vaccine development and rapid communication of new outbreaks. Papers should include timely research approaches using state-of-the-art technologies. The editors encourage papers adopting a science-based approach on socio-economic and environmental factors influencing the management of the bio-security threat posed by these diseases, including risk analysis and disease spread modeling. Preference will be given to communications focusing on novel science-based approaches to controlling transboundary and emerging diseases. The following topics are generally considered out-of-scope, but decisions are made on a case-by-case basis (for example, studies on cryptic wildlife populations, and those on potential species extinctions):
Pathogen discovery: a common pathogen newly recognised in a specific country, or a new pathogen or genetic sequence for which there is little context about — or insights regarding — its emergence or spread.
Prevalence estimation surveys and risk factor studies based on survey (rather than longitudinal) methodology, except when such studies are unique. Surveys of knowledge, attitudes and practices are within scope.
Diagnostic test development if not accompanied by robust sensitivity and specificity estimation from field studies.
Studies focused only on laboratory methods in which relevance to disease emergence and spread is not obvious or can not be inferred (“pure research” type studies).
Narrative literature reviews which do not generate new knowledge. Systematic and scoping reviews, and meta-analyses are within scope.
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