ArticleMethods in molecular biology (Clifton, N.J.)2022
Prediction of Gene and Genomic Regulation in Candida Species, Using the PathoYeastract Database: A Comparative Genomics Approach.
Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors at 4 institutions in 1 country.
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Abstract
The ability of living organisms to survive changing environmental conditions is dependent on the implementation of gene expression programs underlying adaptation and fitness. Transcriptional networks can be exceptionally complex: a single transcription factor (TF) may regulate hundreds of genes, and multiple TFs may regulate a single gene-depending on the environmental conditions. Moreover, the same TF may act as an activator or repressor in distinct conditions. In turn, the activity of regulators themselves may be dependent on other TFs, as well as posttranscriptional and posttranslational regulation. These traits greatly contribute to the intricate networks governing gene expression programs.In this chapter, a step-by-step guide of how to use PathoYeastract, one of several interconnecting databases within the YEASTRACT+ portal, to predict gene and genomic regulation in Candida spp. is provided. PathoYeastract contains a set of analysis tools to study regulatory associations in human pathogenic yeasts, enabling: (1) the prediction and ranking of TFs that contribute to the regulation of individual genes; (2) the prediction of the genes regulated by a given TF; and (3) the prediction and ranking of TFs that regulate a genome-wide transcriptional response. These capabilities are illustrated, respectively, with the analysis of: (1) the TF network controlling the C. glabrata QDR2 gene; (2) the regulon controlled by the C. glabrata TF Rpn4; and (3) the regulatory network controlling the C. glabrata transcriptome-wide changes induced upon exposure to the antifungal drug fluconazole. The newest potentialities of this information system are explored, including cross-species network comparison. The results are discussed considering the performed queries and integrated with the current knowledge on the biological data for each case-study.
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