4.7 Article

A large-scale investigation and identification of methicillin-resistant Staphylococcus aureus based on peaks binning of matrix-assisted laser desorption ionization-time of flight MS spectra

Journal

BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 3, Pages -

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/bib/bbaa138

Keywords

Methicillin-resistant Staphylococcus aureus; MRSA; mass spectrometry; MALDI-TOF; binning method; machine learning; feature selection

Funding

  1. Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China, Chang Gung Memorial Hospital [CMRPG3G1722]
  2. Ministry of Science and Technology, Taiwan [108-2221-E-008s-043-MY3]

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Recent studies have shown the potential of using MALDI-TOF MS to detect superbugs like MRSA, leading to the development of a systematic pipeline based on a large-scale dataset. By clustering shifted MS peaks using a binning method and employing machine learning methods for classification, this study aims to improve the efficiency of distinguishing between MRSA and MSSA.
Recent studies have demonstrated that the matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) could be used to detect superbugs, such as methicillin-resistant Staphylococcus aureus (MRSA). Due to an increasingly clinical need to classify between MRSA and methicillin-sensitive Staphylococcus aureus (MSSA) efficiently and effectively, we were motivated to develop a systematic pipeline based on a large-scale dataset of MS spectra. However, the shifting problem of peaks in MS spectra induced a low effectiveness in the classification between MRSA and MSSA isolates. Unlike previous works emphasizing on specific peaks, this study employs a binning method to cluster MS shifting ions into several representative peaks. A variety of bin sizes were evaluated to coalesce drifted or shifted MS peaks to a well-defined structured data. Then, various machine learning methods were performed to carry out the classification between MRSA and MSSA samples. Totally 4858 MS spectra of unique S. aureus isolates, including 2500 MRSA and 2358 MSSA instances, were collected by Chang Gung Memorial Hospitals, at Linkou and Kaohsiung branches, Taiwan. Based on the evaluation of Pearson correlation coefficients and the strategy of forward feature selection, a total of 200 peaks (with the bin size of 10 Da) were identified as the marker attributes for the construction of predictive models. These selected peaks, such as bins 2410-2419, 2450-2459 and 6590-6599 Da, have indicated remarkable differences between MRSA and MSSA, which were effective in the prediction of MRSA. The independent testing has revealed that the random forest model can provide a promising prediction with the area under the receiver operating characteristic curve (AUC) at 0.8450. When comparing to previous works conducted with hundreds of MS spectra, the proposed scheme demonstrates that incorporating machine learning method with a large-scale dataset of clinical MS spectra may be a feasible means for clinical physicians on the administration of correct antibiotics in shorter turn-around-time, which could reduce mortality, avoid drug resistance and shorten length of stay in hospital in the future.

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