Dataset Information

Defining the transcriptome landscape of Candida glabrata during pH change and nitrosative stress by RNA-Seq

ABSTRACT: The yeast Candida glabrata is one of the most important human fungal pathogens and has emerged as a leading cause of nosocomial bloodstream infections. Its resistance to many routinely used antimycotics and its close relationship to the model organism Saccharomyces cerevisiae make this fungus an interesting target of biomedical research. Although the genome sequence was published a decade ago, little is known about the transcriptional dynamics within this pathogen, especially compared to other pathogenic and nonpathogenic yeasts.We provide a detailed RNA-Seq-based analysis of the transcriptomic landscape of C. glabrata in nutrient-rich media as well as under stress conditions and weak acidic and alkaline environments. Using state of the art bioinformatics tools, we were able to refine the annotation of the C. glabrata genome up to 5288 transcriptional active protein-coding ORFs. Introns were identified in 175 of these genes. Based on our data we were able to increase the number of noncoding RNAs to 68. Additionally, we could detect the expression of 50 novel ORFs. This comprehensive analysis of C. glabrata’s transcriptomic landscape significantly enhances the annotation of current genome annotation and contributes to a further understanding of the molecular mechanisms of the pathogenicity of this medical important yeast.

ORGANISM(S): Nakaseomyces glabratus

PROVIDER: GSE61606 | GEO | 2015/01/27

SECONDARY ACCESSION(S): PRJNA261678

REPOSITORIES: GEO

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Project description:Compared to other model organisms and despite the clinical relevance of the pathogenic yeast Candida albicans, no comprehensive analysis has been done to provide experimental support of its in silico-based genome annotation. Here we have undertaken a genome-wide experimental annotation to accurately uncover the transcriptional landscape of the pathogenic yeast C. albicans using strand-specific high-density tiling arrays. RNAs were purified from cells growing under conditions relevant to C. albicans pathogenicity, including biofilm, lab-grown yeast and serum-induced hyphae as well as cells isolated from the mouse caecum. This work provides a genome-wide experimental validation for a large number of predicted ORFs for which transcription had not been detected by other approaches. Additionally, we identified more than 2000 novel transcriptional segments, including new ORFs and exons, non-coding RNAs (ncRNA) as well as convincing cases of antisense gene transcription. We also characterized the 5’- and 3’-untranslated regions (UTR) of expressed ORFs, and established that genes with long 5’UTRs are significantly enriched in regulatory functions controlling filamentous growth. Furthermore, we found that genomic regions adjacent to telomeres harbor a cluster of expressed ncRNAs. To validate and confirm new ncRNA candidates, we adapted an iterative strategy combining both genome-wide occupancy of the different subunits of RNA polymerases I, II and III, and expression data. This comprehensive approach allowed the identification of different families of ncRNA. In summary, we provide a comprehensive expression atlas that covers relevant C. albicans pathogenic developmental stages in addition to a discovery of new ORF and non-coding genetic elements. We have undertaken a genome-wide experimental annotation to accurately uncover the transcriptional landscape of the pathogenic yeast C. albicans using strand-specific high-density tiling arrays. RNAs were purified from cells growing under conditions relevant to Candida albicans pathogenicity, including biofilm, lab-grown yeast and serum-induced hyphae as well as cells isolated from the mouse caecum. We also adapted a strategy in which genome-wide occupancy of different subunits of RNA polymerases (RNAP) I, II and III, is combined with expression data to annotate ncRNAs resulting from real transcriptional events. For this purpose we have performed ChIP-chip of subunits that represent the three RNAP machines in C. albicans cells growing in rich media (YPD) at 30°C. In this study, we performed peak detection only for RNA Polymerase III (Rpc82p). All detected peaks and their genomic features are included as a supplementary file on the Sample record (GSM561024).

Dataset Information

Defining the transcriptome landscape of Candida glabrata during pH change and nitrosative stress by RNA-Seq

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