Application of MALDI-TOF MS to species complex differentiation and strain typing of food related fungi: Case studies with Aspergillus section Flavi species and Penicillium roqueforti isolates.

Filamentous fungi are one of the main causes of food losses worldwide and their ability to produce mycotoxins represents a hazard for human health. Their correct and rapid identification is thus crucial to manage food safety. In recent years, MALDI-TOF emerged as a rapid and reliable tool for fungi identification and was applied to typing of bacteria and yeasts, but few studies focused on filamentous fungal species complex differentiation and typing. Therefore, the aim of this study was to evaluate the use of MALDI-TOF to identify species of the Aspergillus section Flavi, and to differentiate Penicillium roqueforti isolates from three distinct genetic populations. Spectra were acquired from 23 Aspergillus species and integrated into a database for which cross-validation led to more than 99% of correctly attributed spectra. For P. roqueforti, spectra were acquired from 63 strains and a two-step calibration procedure was applied before database construction. Cross-validation and external validation respectively led to 94% and 95% of spectra attributed to the right population. Results obtained here suggested very good agreement between spectral and genetic data analysis for both Aspergillus species and P. roqueforti, demonstrating MALDI-TOF applicability as a fast and easy alternative to molecular techniques for species complex differentiation and strain typing of filamentous fungi.

A MALDI-TOF MS database with broad genus coverage for species-level identification of Brucella.

Brucella are highly infectious bacterial pathogens responsible for a severely debilitating zoonosis called brucellosis. Half of the human population worldwide is considered to live at risk of exposure, mostly in the poorest rural areas of the world. Prompt diagnosis of brucellosis is essential to prevent complications and to control epidemiology outbreaks, but identification of Brucella isolates may be hampered by the lack of rapid and cost-effective methods. Nowadays, many clinical microbiology laboratories use Matrix-Assisted Laser Desorption Ionization-Time Of Flight mass spectrometry (MALDI-TOF MS) for routine identification. However, lack of reference spectra in the currently commercialized databases does not allow the identification of Brucella isolates. In this work, we constructed a Brucella MALDI-TOF MS reference database using VITEK MS. We generated 590 spectra from 84 different strains (including rare or atypical isolates) to cover this bacterial genus. We then applied a novel biomathematical approach to discriminate different species. This allowed accurate identification of Brucella isolates at the genus level with no misidentifications, in particular as the closely related and less pathogenic Ochrobactrum genus. The main zoonotic species (B. melitensis, B. abortus and B. suis) could also be identified at the species level with an accuracy of 100%, 92.9% and 100%, respectively. This MALDI-TOF reference database will be the first Brucella database validated for diagnostic and accessible to all VITEK MS users in routine. This will improve the diagnosis and control of brucellosis by allowing a rapid identification of these pathogens.