A New Approach for the Automated Categorization of Clinical Isolates of Staphylococcus aureus

A study led by Dr. Ignasi Roca, researcher at ISGlobal and Dr. Ma. Angeles Domínguez, at the Hospital Universitari de Bellvitge, describes an automated, simple and quick approach to discriminate between the major lineages of methicillin-resistant Staphylococcus aureus. The protocol, published in the journal Clinical Microbiology and Infection, is based on a particular type of mass spectrometry and could be implemented in routine laboratories. 

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the leading causes of hospital-acquired infections, which are produced by a limited number of genetic variants. For therapeutic and epidemiological purposes, it is important to identify the local dominant MRSA lineages. However, the current molecular methods used for their identification are expensive, time-consuming and require highly qualified personnel.  

A particular type of mass spectrometry called MALDI-TOF/MS was recently introduced into the clinics for the identification of bacterial species. This technique has the potential of further discriminating between different subspecies, identifying genetic variants and tracking outbreaks. However, all published MALDI-TOF/MS approaches to identify bacteria beyond the species level require a manual interpretation of the acquired spectra by trained personnel. 

In this study, the authors evaluated the possibility of using an automated MALDI-TOF/MS protocol to discriminate between the main MRSA lineages that circulate in the Hospital Universitari de Bellvitge. Using a collection of 82 MRSA clinical isolates belonging to the four main lineages (or clonal complexes, CC), the authors analyzed the spectra for each isolate and identified specific peaks for each CC. They then created a software algorithm that examines the presence or absence of each peak in order to automate the process. Even though the automated interpretation was slightly less sensitive than the supervised one, it still managed to classify MRSA isolates into their corresponding CC with a predictive value of 98.9%. The authors conclude that this  automated approach represents a powerful and reliable tool for S. aureus typing, which will allow the initiation of adequate control measures to limit MRSA transmission as well as the study of MRSA population dynamics. Importantly, the technique can be used in routine laboratories that already use mass spectrometry for bacterial identification.  

Reference

Camoez M, Sierra JM, Dominguez MA, Ferrer-Navarro M, Vila J, Roca I. Automated categorization of methicillin-resistant Staphylococcus aureus clinical isolates into different clonal complexes by MALDI-TOF mass spectrometry.Clin Microbiol Infect. 2015 Oct 23.