Comprehensive molecular characterization of anti-EFG1 2'OMe effects on C. albicans: A bioinformatics study

Candidiasis is a fungal infection primarily caused by Candida albicans. It affects millions of people worldwide and poses significant clinical challenges, resulting in high morbidity and mortality rates, largely due to increasing resistance to conventional antifungal therapies resulting from their widespread use [1]. Innovative therapies, such as antisense oligonucleotides (ASOs), are being explored to address this issue. One promising candidate is anti-EFG1 2'OMe, which control C. albicans filamentation, through Efg1 inhibition [2,3]. This study aimed to perform a comprehensive characterization of the molecular effects of anti-EFG1 2'OMe on C. albicans using a bioinformatics approach. For this, various bioinformatics tools were employed to process and analyze proteomic datasets of C. albicans cells exposed to the ASO, obtained by LC/MS-MS. Initial data processing involved decoding protein identifiers via Uniprot database and identifying ASO-targeted proteins based on abundance ratios and utilizing the BioinfoGP Venny tool. The results indicated that the presence of the ASO led to the repression and induction of 282 proteins, highlighting significant modulation of the proteome. Further analysis using the FungiFun tool revealed that the most common functions of both the induced and repressed proteins were related to protein binding and metabolism, including carbohydrate metabolism. This suggests that while the ASO may impair critical functions, C. albicans cells potentially induce others to adapt to stress. Additionally, using PathoYeastract and Candida Genome Database tools, 44 Efg1 targets and 26 proteins with filamentation-related phenotype were identified among proteins repressed by the ASO, demonstrating a direct relationship with Efg1 inhibition. However, 63 Efg1 targets and 31 proteins with filamentation-related phenotypes were induced by the ASO, indicating a potential compensatory response to the ASO's effects. Among these, 9 proteins represent potential targets for the design of new ASOs to combine with anti-EFG1 2'OMe, creating an optimal therapeutic cocktail. Overall, these bioinformatics analyses enhanced the understanding of the molecular mechanisms underlying anti-EFG1 2'OMe effects, paving the way for more effective therapies against candidiasis.