The prevalence of infections due to molds has been increasing, particularly in immunocompromised individuals. Successful treatment of these infections requires rapid, accurate identification of the pathogen. We developed an algorithm incorporating multi-locus DNA sequence analyses and traditional phenotype-based testing for the identification of molds. Traditional methods of identification rely on the production of specific morphological structures, and, in some cases, the results of biochemical tests. These methods are very useful for identifying molds that display expected phenotypes in a clinically useful time frame. However, these methods do not provide definitive identification for phenotypic variants and previously undescribed molds. In addition, some organisms can take weeks to produce identifying structures. DNA sequence analyses are performed if mold isolates cannot be identified phenotypically within 10 days or if the organism is suspected to be a danger to technologists if manipulated, e.g. Coccidioides immitis. Sequence-based identification necessitates comparison of sequences from the unknown organism to a database of sequences from identified organisms. We have developed a phenotypically-verified database of sequences from 603 mold isolates representing 179 species. The identity of each mold was verified by phenotypic analyses and DNA analyses using multiple loci. The database includes sequences from clinical isolates identified in our laboratory and type strains. For each mold, we analyzed three genetic loci, the internal transcribed spacer regions 1 and 2 (ITS 1 and 2) of the ribosomal RNA operon and the D1/D2 region of the 28S ribosomal RNA gene. ITS and 28S sequences from nonspecific strains are >99% identical. Identification by ITS sequence analysis, 28S sequence analysis and phenotypic methods are 100% concordant. We have previously shown that ITS sequences, like 28S sequences, are phylogenetically informative, however, ITS sequences can provide subspecies-level identification of some molds. Because these sequences are phylogenetically informative, comparing ITS and 28S sequences from an unknown organism to our database provides an identification in cases of >99% sequence identity. If there is not an identical match in the database, sequence analysis provides information about the closest relatives of the unknown organism, thereby facilitating the recognition of previously unidentified and emerging pathogens. Phenotypic variants, previously undescribed molds, and molds slow to produce recognizable structures can be identified within 2 to 3 days using DNA-based methods. The addition of sequence analyses to traditional phenotype-based methods results in a powerful algorithm for the identification of clinically important molds.
Full conference title:
The 15 th Congress of the International Society for Human and Animal Mycology
- ISHAM 15th (2003)