Transcriptomic meta-analysis to identify potential antifungal targets in Candida albicans

Area of Science:

• Mycology
• Genomics
• Bioinformatics

Background:

• Candida albicans is a significant fungal pathogen responsible for human infections.
• Understanding differential gene expression is crucial for developing effective treatments.

Approach:

• A meta-analysis of seven selected Candida albicans datasets from the GEO and ENA databases was performed.
• Differential gene expression analysis and Gene Ontology (GO) enrichment were conducted using nf-core pipeline, DESeq2, and topGO.
• Genes were prioritized based on frequency analysis within GO clusters related to metabolism, ion homeostasis, and pathogenicity.

Key Points:

• A substantial number of differentially expressed genes were identified, with thousands of up-regulated and down-regulated Gene Ontology (GO) IDs.
• Frequency analysis highlighted specific genes, including TPS2, TPS1, RIM21, PRA1, SAP4, SAP6, RSP5, GLC7, and various SOD genes, as having high potential.
• These identified genes are implicated in central metabolism, ion homeostasis, and pathogenicity pathways.

Conclusions:

• This study provides a comprehensive understanding of gene expression dynamics in Candida albicans.
• Several genes, such as TPS2, SOD2, and RIM101, show significant promise as targets for novel antifungal drug development.
• The findings support the development of targeted antifungal therapies to combat C. albicans infections.