Background: Computational analyses have shown great potentials for providing tools for the rapid detection and identification of fungi for medical, scientific and commercial purposes. Various bioinformatics tools have been developed for finding the specific regions within the ribosomal RNA (rRNA) gene complex. Candida is a genus of yeast that includes about 150 different species and is the most common cause of human ocular infections. In the present study, rapid detection method of Candida, based on specific regions (18S, 5.8S and 28S) of ribosomal RNA (rRNA) genes of eight (8) species e.g. C. albicans, C. krusei, C. parapsilosis, C. glabrata, C. guilliermondii, C. kefyr, C. lusitaniae and C. tropicalis has been developed. Rapid diagnosis and early identification of causative agent through computational based methods with high accuracy will result in effective treatment.
Objective: Development of rapid detection method and assay for Candida species based on bioinformatics tools.
Methodology: Ribosomal RNA (18S, 5.8S and 28S) sequences of eight Candida species were retrieved from GenBank/EMBL databases. A set of unique primers were designed based on the conserved region in the given yeast species. To verify the in-silico specificity of the designed primers, the NCBI-BLAST program was employed to search the primers in short, near exact sequences. The primers were further analyzed by the AmplifX tool to determine their specificity and sensitivity against Candida species.
Conclusions: The study resulted in the development of rapid and reproducible detection strategy of Candida species on the basis of computational PCR that will be very helpful for the doctors/practitioners to prescribe targeted medicine against Candida and related causative agents.
Published on: Mar 24, 2017 Pages: 4-9