Trichophyton species: use of volatile fingerprints for rapid identification and discrimination.

BACKGROUND: Fungal infection of the skin is a common clinical problem, and laboratory confirmation of the diagnosis is important to ensure appropriate treatment. The identification of the species of fungus is also important, because different fungal species have different modes of transmission, and this may be of importance both in preventing re-infection and in avoidance of infection of others. OBJECTIVES: This study examined the potential of using volatile production patterns for the detection and discrimination between four Trichophyton species (T. mentagrophytes, T. rubrum, T. verrucosum and T. violaceum) in vitro on solid media and in broth culture. METHODS: Two different sensor array systems (conducting polymer and metal oxide sensors) were examined for comparing the qualitative volatile fingerprints produced in the headspace by these species over periods of 24-120 h. The relative sensitivity of detection of two of the species (T. mentagrophytes, T. rubrum) was determined for log 1 to log 7 inoculum levels over the same time period. RESULTS: The conducting polymer-based system was unable to differentiate between species based on volatile fingerprints over the experimental period. However, metal oxide-based sensor arrays were found to be able to differentiate between the four species within 96 h of growth using principal component analysis which accounted for approximately 94% of the data in principal components 1 and 2 based on the qualitative volatile production patterns. This differentiation was confirmed by cluster analysis of the data using Euclidean distance and Ward's linkage. Studies of the sensitivity of detection showed that for T. mentagrophytes and T. rubrum it was possible to differentiate between log 3, log 5 and log 7 inoculum levels within 96 h. CONCLUSIONS: This is the first detailed study of the use of qualitative volatile fingerprints for identification and discrimination of dermatophytes. This approach could have potential for rapid identification of patient samples, reducing significantly the time to treatment