Enhanced identification of spp. using MALDI-TOF mass spectrometry.

INTRODUCTION

The genus including clinically isolated species and , has a still underexplored role in clinical microbiology. Despite the clinical relevance of spp., current MALDI-TOF commercial systems fail to differentiate these species. Whole genome sequencing (WGS) remains the most effective method to distinguish species. However, this method is not adapted for routine lab workflow. Enhancing MALDI-TOF's accuracy could make it a rapid and effective approach for distinguishing species in routine laboratory diagnostics.

OBJECTIVES

This study aims to improve the performance of MALDI-TOF for identifying spp. using WGS as the gold-standard reference method.

METHODS

We applied Machine Learning (ML) algorithms to a collection of 235 clinicial  spp. strains to develop an optimized identification model. Whole genome sequencing was used to characterize these strains and perform phylogenetic analysis, categorizing 209 strains as and 26 as .

RESULTS

The ML-based classifiers showed improved identification accuracy (44 of the 160 designed with accuracy at 1). Also, MS analysis identified 11 peaks able to discriminate between and .

CONCLUSION

Through development of a publicly-available online ML-based classifier, this study has improved the capacity of MALDI-TOF for distinguishing spp, providing a reliable, user-friendly solution suited to routine clinical diagnostics and supporting a better understanding of the roles of and in human pathology.