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Research Article

Identification of the Trichophyton mentagrophytes complex species using MALDI-TOF mass spectrometry

, , , , &
Pages 580-585 | Received 17 Sep 2012, Accepted 10 Jan 2013, Published online: 19 Mar 2013
 

Abstract

Dermatophytes are fungi capable of invading keratinized tissues and are responsible for the most common fungal infection worldwide: dermatophytosis. Identification of these organisms to the species level is often necessary for the correct treatment of these infections, and is always recommended from an epidemiological point of view. Since the identification of dermatophytes is sometimes problematic, we assessed whether Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS) could provide a useful tool to identify dermatophytes of the Trichophyton mentagrophytes complex. A reference database was constructed with 17 strains of six different species belonging to this complex. A total of 54 dermatophyte strains of the Belgian co-ordinated collections of micro-organisms, Scientific Institute of Public Health, Brussels, Belgium (BCCM™/IHEM) collection were used to challenge this database; 89% of the tested strains (not used as reference strains in the database) could readily be identified. When incorrect identifications were encountered, the confusion was always between phylogenetically closely related taxa which indicates that observations made by MALDI-TOF MS correlate with phylogenetic data. To assess this observation, a dendrogram outlining the similarities between the obtained spectra was constructed. Strikingly, the relationships found in this dendrogram were highly similar to the ones observed in the phylogenetic tree recently reported by Beguin and co-workers. In conclusion, MALDI-TOF MS is a fast and reliable tool for the identification of dermatophytes, since it can even discriminate between the closely related species of the T. mentagrophytes complex. Moreover, our data indicate that the data obtained by MALDI-TOF MS correlate with phylogenetic data.

Acknowledgments

We thank Karin Goens, Caroline Verstraeten and Sam Roesems for their technical assistance.

Declaration of interest: The authors report no conflicts of interest. The authors alone are responsible for the content and the writing of the paper.

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