Rapid Identification ofCandidaSpecies by Using Nuclear Magnetic Resonance Spectroscopy and a Statistical Classification Strategy

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Abstract
Nuclear magnetic resonance (NMR) spectra were acquired from suspensions of clinically important yeast species of the genusCandidato characterize the relationship between metabolite profiles and species identification. Major metabolites were identified by using two-dimensional correlation NMR spectroscopy. One-dimensional proton NMR spectra were analyzed by using a staged statistical classification strategy. Analysis of NMR spectra from 442 isolates ofCandida albicans,C. glabrata,C. krusei,C. parapsilosis, andC. tropicalisresulted in rapid, accurate identification when compared with conventional and DNA-based identification. Spectral regions used for the classification of the five yeast species revealed species-specific differences in relative amounts of lipids, trehalose, polyols, and other metabolites. Isolates ofC. parapsilosisandC. glabratawith unusual PCR fingerprinting patterns also generated atypical NMR spectra, suggesting the possibility of intraspecies discontinuity. We conclude that NMR spectroscopy combined with a statistical classification strategy is a rapid, nondestructive, and potentially valuable method for identification and chemotaxonomic characterization that may be broadly applicable to fungi and other microorganisms.

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