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.

Development and application of MALDI-TOF MS for identification of food spoilage fungi.

Filamentous fungi are frequently involved in food spoilage and cause important food losses and substantial economic damage. Their rapid and accurate identification is a key step to better manage food safety and quality. In recent years, MALDI-TOF MS has emerged as a powerful tool to identify microorganisms and has successfully been applied to the identification of filamentous fungi especially in the clinical context. The aim of this study was to implement a spectral database representative of food spoilage molds. To this end, after application of a standardized extraction protocol, 6477 spectra were acquired from 618 fungal strains belonging to 136 species and integrated in the VITEK MS database. The performances of this database were then evaluated by cross-validation and ∼95% of correct identification to the species level was achieved, independently of the cultivation medium and incubation time. The database was also challenged with external isolates belonging to 52 species claimed in the database and 90% were correctly identified to the species level. To our best knowledge, this is the most comprehensive database of food-relevant filamentous fungi developed to date. This study demonstrates that MALDI-TOF MS could be an alternative to conventional techniques for the rapid and reliable identification of spoilage fungi in food and industrial environments.

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.

Routine identification of Nocardia species by MALDI-TOF mass spectrometry.

We here show adequate species identification for bacterial isolates of the genus Nocardia spp. through VITEK mass spectrometry. Application of a specific sample preparation method in combination with a robust matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) database leads to 94% accurate identification to the species level on a set of 164 isolates. The possibility to identify Nocardia spp. using MALDI-TOF MS will be available in the next release of VITEK MS update (IVD Version 3.0).

Identification of mycobacterium spp. and nocardia spp. from solid and liquid cultures by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS).

Identification of microorganisms by MALDI-TOF MS has been widely accepted in clinical microbiology. However, for Mycobacterium spp. and Nocardia spp. such identification has not yet reached the optimal level of routine testing. Here we describe the development of an identification tool for 49 and 15 species of Mycobacterium spp. and Nocardia spp., respectively. During database construction, a number of ambiguous reference identifications were revealed and corrected via molecular analyses. Eventually, more than 2000 individual mass spectra acquired from 494 strains were included in a reference database and subjected to bio-statistical analyses. This led to correct species identification and correct combination of species into several complexes or groups, such as the Mycobacterium tuberculosis complex. With the Advanced Spectrum Classifier algorithm, class-specific bin weights were determined and tested by cross-validation experiments with good results. When challenged with independent isolates, overall identification performance was 90% for identification of Mycobacterium spp. and 88% for Nocardia spp. However, for a number of Mycobacterium sp. isolates, no identification could be achieved and in most cases, this could be attributed to the production of polymers that masked the species-specific protein peak patterns. For the species where >20 isolates were tested, correct identification reached 95% or higher. With the current spectral database, the identification of Mycobacterium spp. and Nocardia spp. by MALDI-TOF MS can be performed in routine clinical diagnostics although in some complicated cases verification by sequencing remains mandatory.

Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry using the Vitek MS system for rapid and accurate identification of dermatophytes on solid cultures.

The objective of this research was to extend the Vitek MS fungal knowledge base version 2.0.0 to allow the robust identification of clinically relevant dermatophytes, using a variety of strains, incubation times, and growth conditions. First, we established a quick and reliable method for sample preparation to obtain a reliable and reproducible identification independently of the growth conditions. The Vitek MS V2.0.0 fungal knowledge base was then expanded using 134 well-characterized strains belonging to 17 species in the genera Epidermophyton, Microsporum, and Trichophyton. Cluster analysis based on mass spectrum similarity indicated good species discrimination independently of the culture conditions. We achieved a good separation of the subpopulations of the Trichophyton anamorph of Arthroderma benhamiae and of anthropophilic and zoophilic strains of Trichophyton interdigitale. Overall, the 1,130 mass spectra obtained for dermatophytes gave an estimated identification performance of 98.4%. The expanded fungal knowledge base was then validated using 131 clinical isolates of dermatophytes belonging to 13 taxa. For 8 taxa all strains were correctly identified, and for 3 the rate of successful identification was >90%; 75% (6/8) of the M. gypseum strains were correctly identified, whereas only 47% (18/38) of the African T. rubrum population (also called T. soudanense) were recognized accurately, with a large quantity of strains misidentified as T. violaceum, demonstrating the close relationship of these two taxa. The method of sample preparation was fast and efficient and the expanded Vitek MS fungal knowledge base reliable and robust, allowing reproducible dermatophyte identifications in the routine laboratory.