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.

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.