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Unveiling new features of the human pathogen Cryptococcus neoformans through the reconstruction and exploitation of a dedicated genome-scale metabolic model

Summary

Scientists have created a detailed computer model of how the dangerous fungus Cryptococcus neoformans works at the metabolic level. By studying 890 genes and thousands of chemical reactions in this pathogen, they identified new weak points that could be targeted with future antifungal drugs. The model reveals why this fungus is particularly good at causing brain infections and shows several unique metabolic features not found in other pathogenic yeasts, offering hope for more effective treatments.

Background

Cryptococcus neoformans is a major human pathogen causing severe pulmonary and central nervous system infections with high mortality rates, particularly in immunocompromised patients. The pathogen’s tropism to the brain and drug resistance profile make it a WHO priority. Limited understanding of its metabolism related to CNS adaptation and host interactions necessitates advanced computational approaches.

Objective

To reconstruct and validate the first genome-scale metabolic model (iRV890) for C. neoformans var. grubii and identify unique metabolic features and potential antifungal drug targets. The study aims to elucidate the metabolic basis of pathogenicity and CNS tropism through comparative analysis with other pathogenic yeasts.

Results

The model achieved 85% prediction accuracy for carbon and nitrogen source utilization. Unique metabolic features were identified including arabinitol and rhamnose assimilation, ascorbate biosynthesis from inositol, and specialized melanin production via the L-DOPA pathway. Ninety-six predicted essential genes were identified as potential drug targets, with Erg4, Chs1, Fol1, and Fas1 being particularly promising due to absence of human orthologs.

Conclusion

The iRV890 model provides the first comprehensive systems-level understanding of C. neoformans metabolism and represents a valuable platform for identifying novel antifungal drug targets and understanding pathogenic mechanisms related to CNS adaptation. Key findings suggest unique metabolic pathways contribute to brain tropism and virulence, offering new directions for targeted drug development.
  • Published in:Computational and Structural Biotechnology Journal,
  • Study Type:Computational Model Development and Validation Study,
  • Source: PMC12167027, PMID: 40520592, doi: 10.1016/j.csbj.2025.05.034
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