Objective  To predict new compounds against Candida albicans based on transcriptome data and validate their biological activity.

Methods  Obtained transcriptome data of Candida albicans treated with Fluconazole from the GEO database and screened for differentially expressed genes. Metascape was used for KEGG and GO enrichment analysis, and the String database was used to construct the protein-protein interaction (PPI) network. The differentially expressed genes were predicted for anti-Candida albicans compounds using the CMap platform. The minimum inhibitory concentration of the predicted compounds was determined, and scanning electron microscopy was used to observe the morphological changes of Candida albicans. Molecular docking between the compounds and core genes was performed to predict potential antimicrobial mechanisms of action.

Results  The GSE159545 dataset was selected for differential gene analysis, resulting in a total of 448 differentially expressed genes, with 222 upregulated genes and 224 downregulated genes. The top 10 genes with the highest degree value in the PPI network were TRP5, HIS4, TRP3, PGI1, HIS5, URA7, DIM1, RPL7, ILV2, and GUA1. Among the top 10 compounds ranked by CMap, Colchicine A3, Cycloheximide, Sirolimus, and Doxorubicin were known compounds with anti-Candida albicans activity. Bioactivity experiments showed that GSK-1059615 possesses anti-Candida albicans activity, with a minimum inhibitory concentration of 32 μg/mL. GSK-1059615 has strong binding energy with Tryptophan synthase, CTP synthase, 60 S ribosomal protein, and Acetohydroxyacid synthase, suggesting they are potential antimicrobial targets.

Conclusion  The combination of transcriptomic data and CMap provides a novel approach and new ideas for predicting new anti-Candida albicans compounds.

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