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

Kuo D, Tan K, Zinman G, Ravasi T, Bar-Joseph Z, Ideker T - Genome Biol. (2010)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.

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.

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Differentially expressed genes. (a) Number of differentially expressed (up- and down-regulated) genes by species versus the number of cell doublings. (b) Venn diagram showing the overlap in the sets of differentially expressed genes selected in each species at a false discovery rate of q ≤ 0.1. The number of differentially expressed genes in each region of the Venn diagram is not identical across species, since the number of genes that a species contributes to an orthologous group (that is, number of paralogs) can vary. Ratios in parentheses indicate the number of differentially expressed orthologs by the total number of differentially expressed genes (not all genes possess orthologs).
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Figure 1: Differentially expressed genes. (a) Number of differentially expressed (up- and down-regulated) genes by species versus the number of cell doublings. (b) Venn diagram showing the overlap in the sets of differentially expressed genes selected in each species at a false discovery rate of q ≤ 0.1. The number of differentially expressed genes in each region of the Venn diagram is not identical across species, since the number of genes that a species contributes to an orthologous group (that is, number of paralogs) can vary. Ratios in parentheses indicate the number of differentially expressed orthologs by the total number of differentially expressed genes (not all genes possess orthologs).

Mentions: While it is possible that complementary strategies might be observed at different fluconazole dosages [20], we exposed each species to fluconazole at its 50% inhibitory concentration to facilitate direct comparison of the transcriptional response between species. We then monitored global mRNA expression levels at 1/3, 2/3, 1, 2, and 4 population doubling times (Figure 1a). We also found that sampling based on the doubling time of each species, as opposed to absolute time measurements, led to greater coherence in the expression profiles across species (Figure S2 in Additional file 1; Additional file 1: Analysis of Doubling Time Points vs. Absolute Time Points). Selected mRNA measurements were validated using quantitative RT-PCR against six genes (Figure S3 in Additional file 1). We also found significant overlap of the Sc differentially expressed genes with several previous microarray studies and some overlap with gene deletions conferring fluconazole sensitivity (Additional file 1: Microarray Design and Analysis).


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

Kuo D, Tan K, Zinman G, Ravasi T, Bar-Joseph Z, Ideker T - Genome Biol. (2010)

Differentially expressed genes. (a) Number of differentially expressed (up- and down-regulated) genes by species versus the number of cell doublings. (b) Venn diagram showing the overlap in the sets of differentially expressed genes selected in each species at a false discovery rate of q ≤ 0.1. The number of differentially expressed genes in each region of the Venn diagram is not identical across species, since the number of genes that a species contributes to an orthologous group (that is, number of paralogs) can vary. Ratios in parentheses indicate the number of differentially expressed orthologs by the total number of differentially expressed genes (not all genes possess orthologs).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2926788&req=5

Figure 1: Differentially expressed genes. (a) Number of differentially expressed (up- and down-regulated) genes by species versus the number of cell doublings. (b) Venn diagram showing the overlap in the sets of differentially expressed genes selected in each species at a false discovery rate of q ≤ 0.1. The number of differentially expressed genes in each region of the Venn diagram is not identical across species, since the number of genes that a species contributes to an orthologous group (that is, number of paralogs) can vary. Ratios in parentheses indicate the number of differentially expressed orthologs by the total number of differentially expressed genes (not all genes possess orthologs).
Mentions: While it is possible that complementary strategies might be observed at different fluconazole dosages [20], we exposed each species to fluconazole at its 50% inhibitory concentration to facilitate direct comparison of the transcriptional response between species. We then monitored global mRNA expression levels at 1/3, 2/3, 1, 2, and 4 population doubling times (Figure 1a). We also found that sampling based on the doubling time of each species, as opposed to absolute time measurements, led to greater coherence in the expression profiles across species (Figure S2 in Additional file 1; Additional file 1: Analysis of Doubling Time Points vs. Absolute Time Points). Selected mRNA measurements were validated using quantitative RT-PCR against six genes (Figure S3 in Additional file 1). We also found significant overlap of the Sc differentially expressed genes with several previous microarray studies and some overlap with gene deletions conferring fluconazole sensitivity (Additional file 1: Microarray Design and Analysis).

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.

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.

Show MeSH
Related in: MedlinePlus