Abstract
Staphylococcus haemolyticus is one of the most significant coagulase-negative staphylococci, and it often causes severe infections. Rapid strain typing of pathogenic S. haemolyticus is indispensable in modern public health infectious disease control, facilitating the identification of the origin of infections to prevent further infectious outbreak. Rapid identification enables the effective control of pathogenic infections, which is tremendously beneficial to critically ill patients. However, the existing strain typing methods, such as multi-locus sequencing, are of relatively high cost and comparatively time-consuming. A practical method for the rapid strain typing of pathogens, suitable for routine use in clinics and hospitals, is still not available. Matrix-assisted laser desorption ionization-time of flight mass spectrometry combined with machine learning approaches is a promising method to carry out rapid strain typing. In this study, we developed a statistical test-based method to determine the reference spectrum when dealing with alignment of mass spectra datasets, and constructed machine learning-based classifiers for categorizing different strains of S. haemolyticus. The area under the receiver operating characteristic curve and accuracy of multi-class predictions were 0.848 and 0.866, respectively. Additionally, we employed a variety of statistical tests and feature-selection strategies to identify the discriminative peaks that can substantially contribute to strain typing. This study not only incorporates statistical test-based methods to manage the alignment of mass spectra datasets but also provides a practical means to accomplish rapid strain typing of S. haemolyticus.
Highlights
Staphylococcus haemolyticus is one of the most significant species among the coagulase-negative staphylococci (CoNS), whose main ecological niches are skin and the human and animal mucous membranes (Becker et al, 2014)
The selected peaks were found to be highly correlated with S. haemolyticus and were able to distinguish between the three groups of ST strains
This is a study that focused on the strain typing of S. haemolyticus based on the MALDI-TOF MS utilizing statistical tests and machine learning methods simultaneously
Summary
Staphylococcus haemolyticus is one of the most significant species among the coagulase-negative staphylococci (CoNS), whose main ecological niches are skin and the human and animal mucous membranes (Becker et al, 2014). They are often the causative agents of septicemia, peritonitis, otitis, and urinary tract infections. Strain typing of pathogenic S. haemolyticus forms an important part of the response to modern public health infectious disease outbreaks (MacCannell, 2013). Rapid typing of S. haemolyticus facilitates the identification of the origin of infection, and allows rapid infection control when patients are critically ill. A cost effective and rapid identification strategy that targets strain typing issues is essential and needs to be incorporated in routine clinical microbiology laboratory practices
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