Distribution of Candida in the oral cavity and its differentiation based on the internally transcribed spacer (ITS) regions of rDNA.

This study aimed to determine the distribution of Candida species in the oral cavity and differentiate the species based on PCR amplification, including HinfI and MspI digestion, in order to assess the effectiveness of using the rDNA region for species identification. Samples from saliva as well as palate, tongue and cheek mucosa surfaces were collected from 45 individuals, consisting of three groups: periodontal disease patients; denture-wearers; and the control group. The samples were serially diluted, spread on BHI and YPD agar plates and scored for colony-forming units (CFUs). Fifteen random candidal colonies were isolated and subjected to genomic DNA extraction, based on glass beads disruption. Four primers were used to amplify regions in the rDNA, and the ITSI-5.8S-ITSII PCR product was digested by HinfI and MspI restriction enzymes. The microbial loads on all sites of the denture-wearers were found to be significantly higher than control, while in the periodontal disease group only the microbial loads on the tongue were significantly higher than control. Meanwhile, there was no significant difference at other sites. The restriction fragment lengths of the clinical samples were compared to those of seven control species, allowing the differentiation of all seven species and the identification of 14 species from the clinical samples. The MspI restriction digest was not able to distinguish between C. albicans and C. dubliniensis, whereas the HinfI digest could not distinguish between C. tropicalis and C. parapsilosis. It was concluded that PCR-RFLP of the candidal rDNA region has potential for species identification. This study demonstrates the potential use of candidal rDNA as a means for identifying Candida species, based on genotype. The results also indicate the possibility of constructing genetic probes that target specific restriction fragments in the ITSI-5.8S-ITSII region, enabling swift and precise identification of Candida species.