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Updated: May 15, 2026

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
Published on: July 18, 2013
Approaching the functional annotation of fungal virulence factors using cross-species genetic interaction profiling
Jessica C S Brown1, Hiten D Madhani
1Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, United States of America.
Abstract:
In many human fungal pathogens, genes required for disease remain largely unannotated, limiting the impact of virulence gene discovery efforts. We tested the utility of a cross-species genetic interaction profiling approach to obtain clues to the molecular function of unannotated pathogenicity factors in the human pathogen Cryptococcus neoformans. This approach involves expression of C. neoformans genes of interest in each member of the Saccharomyces cerevisiae gene deletion library, quantification of their impact on growth, and calculation of the cross-species genetic interaction profiles. To develop functional predictions, we computed and analyzed the correlations of these profiles with existing genetic interaction profiles of S. cerevisiae deletion mutants. For C. neoformans LIV7, which has no S. cerevisiae ortholog, this profiling approach predicted an unanticipated role in the Golgi apparatus. Validation studies in C. neoformans demonstrated that Liv7 is a functional Golgi factor where it promotes the suppression of the exposure of a specific immunostimulatory molecule, mannose, on the cell surface, thereby inhibiting phagocytosis. The genetic interaction profile of another pathogenicity gene that lacks an S. cerevisiae ortholog, LIV6, strongly predicted a role in endosome function. This prediction was also supported by studies of the corresponding C. neoformans null mutant. Our results demonstrate the utility of quantitative cross-species genetic interaction profiling for the functional annotation of fungal pathogenicity proteins of unknown function including, surprisingly, those that are not conserved in sequence across fungi.
Insights
Cross-species genetic profiling reveals functions of unannotated fungal virulence genes. This method identified roles for Cryptococcus neoformans genes LIV7 and LIV6 in Golgi and endosome function, respectively, aiding phagocytosis and pathogenicity understanding.
Area of Science:
- Mycology
- Genetics
- Cell Biology
Background:
- Many fungal virulence genes lack functional annotation, hindering research into disease mechanisms.
- Understanding pathogenicity factors is crucial for developing antifungal therapies.
Purpose of the Study:
- To assess the utility of cross-species genetic interaction profiling for annotating uncharacterized virulence genes in Cryptococcus neoformans.
- To predict and validate the molecular functions of novel pathogenicity factors.
Main Methods:
- Expressed Cryptococcus neoformans genes in Saccharomyces cerevisiae deletion library to generate cross-species genetic interaction profiles.
- Correlated these profiles with existing Saccharomyces cerevisiae genetic interaction data for functional prediction.
- Validated predicted functions through experimental studies in Cryptococcus neoformans.
Main Results:
- The profiling approach successfully predicted functions for unannotated genes, including those without sequence homologs.
- Cryptococcus neoformans LIV7 was identified as a Golgi apparatus protein involved in suppressing mannose exposure and inhibiting phagocytosis.
- Cryptococcus neoformans LIV6 was predicted and validated to function in the endosome.
Conclusions:
- Quantitative cross-species genetic interaction profiling is a powerful tool for functional annotation of fungal pathogenicity proteins.
- This approach is effective even for proteins lacking sequence conservation across fungal species.
- The findings provide insights into virulence mechanisms of Cryptococcus neoformans and potential therapeutic targets.
Related Concept Videos
Regulation of Bacterial Virulence
Gene Regulation in Microbial Communities: Quorum Sensing
Microbial Interactions: Cooperation

