3.9 Article

Responses of pathogenic and nonpathogenic yeast species to steroids reveal the functioning and evolution of multidrug resistance transcriptional networks

期刊

EUKARYOTIC CELL
卷 7, 期 1, 页码 68-77

出版社

AMER SOC MICROBIOLOGY
DOI: 10.1128/EC.00256-07

关键词

-

资金

  1. Reseau National des Genopoles
  2. CNRS
  3. Department of Science and Technology (DST), India [SP/SO/BB-12/2004]
  4. Council of Scientific and Industrial Research (CSIR), India [38(1122)/06/EMR-II]
  5. University Grants Commission (UGC)

向作者/读者索取更多资源

Steroids are known to induce pleiotropic drug resistance states in hemiascomycetes, with tremendous potential consequences for human fungal infections. Our analysis of gene expression in Saccharomyces cerevisiae and Candida albicans cells subjected to three different concentrations of progesterone revealed that their pleiotropic drug resistance ( PDR) networks were strikingly sensitive to steroids. In S. cerevisiae, 20 of the Pdr1p/Pdr3p target genes, including PDR3 itself, were rapidly induced by progesterone, which mimics the effects of PDR1 gain-of-function alleles. This unique property allowed us to decipher the respective roles of Pdr1p and Pdr3p in PDR induction and to define functional modules among their target genes. Although the expression profiles of the major PDR transporters encoding genes ScPDR5 and CaCDR1 were similar, the S. cerevisiae global PDR response to progesterone was only partly conserved in C. albicans. In particular, the role of Tac1p, the main C. albicans PDR regulator, in the progesterone response was apparently restricted to five genes. These results suggest that the C. albicans and S. cerevisiae PDR networks, although sharing a conserved core regarding the regulation of membrane properties, have different structures and properties. Additionally, our data indicate that other as yet undiscovered regulators may second Tac1p in the C. albicans drug response.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

3.9
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据