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Open Access Research

Evolutionary divergence in the fungal response to fluconazole revealed by soft clustering

Dwight Kuo1, Kai Tan2, Guy Zinman3, Timothy Ravasi14, Ziv Bar-Joseph3* and Trey Ideker1*

Author affiliations

1 Departments of Bioengineering and Medicine, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA

2 Departments of Internal Medicine and Biomedical Engineering, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA

3 Department of Computer Science, Carnegie Mellon University, 500 Forbes Avenue, Pittsburgh, PA 15213, USA

4 Red Sea Laboratory of Integrative Systems Biology, Division of Chemical and Life Sciences and Engineering, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia

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Citation and License

Genome Biology 2010, 11:R77  doi:10.1186/gb-2010-11-7-r77

Published: 23 July 2010

Abstract

Background

Fungal infections are an emerging health risk, especially those involving yeast that are resistant to antifungal agents. To understand the range of mechanisms by which yeasts can respond to anti-fungals, we compared gene expression patterns across three evolutionarily distant species - Saccharomyces cerevisiae, Candida glabrata and Kluyveromyces lactis - over time following fluconazole exposure.

Results

Conserved and diverged expression patterns were identified using a novel soft clustering algorithm that concurrently clusters data from all species while incorporating sequence orthology. The analysis suggests complementary strategies for coping with ergosterol depletion by azoles - Saccharomyces imports exogenous ergosterol, Candida exports fluconazole, while Kluyveromyces does neither, leading to extreme sensitivity. In support of this hypothesis we find that only Saccharomyces becomes more azole resistant in ergosterol-supplemented media; that this depends on sterol importers Aus1 and Pdr11; and that transgenic expression of sterol importers in Kluyveromyces alleviates its drug sensitivity.

Conclusions

We have compared the dynamic transcriptional responses of three diverse yeast species to fluconazole treatment using a novel clustering algorithm. This approach revealed significant divergence among regulatory programs associated with fluconazole sensitivity. In future, such approaches might be used to survey a wider range of species, drug concentrations and stimuli to reveal conserved and divergent molecular response pathways.