Rapid identification and discrimination of methicillin‐resistant Staphylococcus aureus strains via matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry

Abstract

Rationale Methicillin‐resistant Staphylococcus aureus (MRSA) is one of major clinical pathogens responsible for both hospital‐ and community‐acquired infections worldwide. A delay in targeted antibiotic treatment contributes to longer hospitalization stay, higher costs, and increasing in‐hospital mortality. Matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS) has been integrated into the routine workflow for microbial identification over the past decade, and it has also shown promising functions in the detection of bacterial resistance. Therefore, we describe a rapid MALDI‐TOF MS‐based methodology for MRSA screening with machine‐learning algorithms. Methods A total of 452 clinical S. aureus isolates were included in this study, of which 194 were MRSA and 258 were methicillin‐sensitive S. aureus (MSSA). The mass‐to‐charge ratio (m/z) features from MRSA and MSSA strains were binned and selected through Lasso regression. These features were then used to train a non‐linear support vector machine (SVM) with radial basis function (RBF) kernels to evaluate the discrimination performance. The classifiers' accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC) were evaluated and compared with those from the random forest (RF) model. Results A total of 2601 unique spectral peaks of all isolates were identified and 38 m/z features were selected for the classifying model. The AUCs of the non‐linear RBF‐SVM model and the RF model were 0.89 and 0.87, respectively, and the accuracy ranged between 0.86 (RBF‐SVM) and 0.82 (RF). Conclusions Our study demonstrates that MALDI‐TOF MS coupled with machine‐learning algorithms could be used to develop a rapid and easy‐to‐use method to discriminate MRSA from MSSA. Considering that this method is easy to implement in routine microbiology laboratories, it suggests a cost‐effective and time‐efficient alternative to conventional resistance detection in the future to improve clinical treatment.

Rapid Identification of Methicillin-Resistant Staphylococcus aureus Using MALDI-TOF MS and Machine Learning from over 20,000 Clinical Isolates

“…Tang et al discovered nine characteristic peaks of MRSA among 214 S. aureus isolates ( 9 ). Liu et al discovered 38 characteristics peaks for a classification model from 452 clinical S. aureus isolates ( 10 ). Wang et al processed 4,858 mass spectra using various ML methods and identified 200 peaks as marker attributes for a predictive model.…”

MDRSA: A Web Based-Tool for Rapid Identification of Multidrug Resistant Staphylococcus aureus Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry

“…Previous studies were mainly devoted to identifying methicillin-resistant Staphylococcus aureus (MRSA), and figuring out their informative peaks ( Wang et al, 2013 , 2020 ; Josten et al, 2014 ; Østergaard et al, 2015 ; Camoez et al, 2016 ; Rhoads et al, 2016 ; Bai et al, 2017 ; Sogawa et al, 2017 ; Kim et al, 2019 ; Tang et al, 2019 ; Liu et al, 2021 ). Bai et al (2017) proposed a genetic algorithm with a t -test based population seeding for wrapper feature selection on 727 Staphylococcus aureus clinical isolates’ mass spectra derived from Vitek MS, and their accuracy based on support vector machine classifier was 0.72.…”

“…Even though their prediction accuracy was over 90%, only 20 isolates were used. Liu et al (2021) used R to analyze 452 Staphylococcus aureus clinical isolates’ mass spectra derived from Vitek MS, and the best area under the receiver operating characteristic curve was 0.89 by support vector machine. Compared with previous studies, our study used much clinical data and considered three antibiotics.…”

Recent Developments in Phenotypic and Molecular Diagnostic Methods for Antimicrobial Resistance Detection in Staphylococcus aureus: A Narrative Review

“…The peak seems to be connected with the 50S ribosomal subunit and could be further investigated as a biomarker for MRSA [ 67 ]. Liu applied a machine learning approach (support vector machine algorithm) to identify peaks that could discriminate between MRSA and MSSA isolates They analyzed 194 MRSA and 258 MSSA and found 38 features that could discriminate MRSA from MSSA with a sensitivity of 84.0% and a specificity of 88.0% [ 68 ]. For an overview of studies on the identification of specific MRSA peaks, please refer to the works of Burckhardt and Liu [ 15 , 68 ].…”