In Silico Prediction of Prioritised Drug Targets in Candida Albicans and Nakaseomyces Glabratus

Candida is one of the most prevalent human fungal pathogens in nosocomial infections, mostly affecting immunocompromised patients and neonates. Most Candida species have been reported to develop resistance to existing antifungals, underscoring the need for novel therapeutics. Identification of drug targets is instrumental in new drug development. Many genes contribute to the virulence of Candida species. In this study, an integrated approach combining text mining with transcriptome-wide gene expression analysis has been employed in two major pathogenic species, Candida albicans and Nakaseomyces glabratus to identify novel drug targets. Computational text mining and RNA sequencing-based differential gene expression analysis were performed, and common genes were selected to narrow down the search. These common genes were then analysed using Gene Ontology and KEGG to unravel their biological function and pathways. The analyses identified 79 text-mining genes put and 6312 differentially expressed genes in C. albicans and 5508 differentially expressed genes in N. glabratus, revealing 49 genes common to TMGs (text mining genes) and DEGs (differentially expressed genes). Of the 49 common genes, 10 drug-gene interactions have been reported, indicating the druggability potential of these genes for the design of a novel drug against the human fungal pathogen. Following host non-homology analysis, products of ALS1, ALS3, ALS9, EPA6, ECE1, TPS1, MDR1, RPN4, and TAC1 were identified as potential drug targets.