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Genome-Scale Metabolic Model of the Human Pathogen Candida albicans: A Promising Platform for Drug Target Prediction
Romeu Viana1,2, Oscar Dias3, Davide Lagoa3
1Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal.
Abstract:
Candida albicans is one of the most impactful fungal pathogens and the most common cause of invasive candidiasis, which is associated with very high mortality rates. With the rise in the frequency of multidrug-resistant clinical isolates, the identification of new drug targets and new drugs is crucial in overcoming the increase in therapeutic failure. In this study, the first validated genome-scale metabolic model for Candida albicans, iRV781, is presented. The model consists of 1221 reactions, 926 metabolites, 781 genes, and four compartments. This model was reconstructed using the open-source software tool merlin 4.0.2. It is provided in the well-established systems biology markup language (SBML) format, thus, being usable in most metabolic engineering platforms, such as OptFlux or COBRA. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. Finally, this genome-scale metabolic reconstruction was tested as a platform for the identification of drug targets, through the comparison between known drug targets and the prediction of gene essentiality in conditions mimicking the human host. Altogether, this model provides a promising platform for global elucidation of the metabolic potential of C. albicans, possibly guiding the identification of new drug targets to tackle human candidiasis.
Insights
A new genome-scale metabolic model for Candida albicans, iRV781, has been developed. This validated model aids in identifying novel drug targets to combat multidrug-resistant fungal infections and high mortality rates.
Area of Science:
- Microbiology
- Systems Biology
- Computational Biology
Background:
- Candida albicans is a major fungal pathogen causing invasive candidiasis with high mortality.
- Increasing multidrug-resistant strains necessitate new therapeutic strategies and drug targets.
- Understanding C. albicans metabolism is key to developing effective treatments.
Purpose of the Study:
- To present the first validated genome-scale metabolic model for Candida albicans (iRV781).
- To validate the model's accuracy in predicting metabolic capabilities.
- To utilize the model as a platform for identifying potential drug targets.
Main Methods:
- Reconstruction of the iRV781 model using the merlin 4.0.2 software.
- Model validation by comparing predicted carbon and nitrogen source utilization with experimental data.
- Application of the model for gene essentiality prediction under host-mimicking conditions to identify drug targets.
Main Results:
- The iRV781 model comprises 1221 reactions, 926 metabolites, and 781 genes across four compartments.
- The model accurately predicted C. albicans' metabolic capabilities based on experimental data.
- Gene essentiality analysis identified potential drug targets relevant to host conditions.
Conclusions:
- The iRV781 model is a validated resource for studying Candida albicans metabolism.
- This model serves as a promising platform for discovering new drug targets against C. albicans.
- The findings can guide the development of novel therapies to combat candidiasis.
