YEASTRACT+: a portal for the exploitation of global transcription regulation and metabolic model data in yeast biotechnology and pathogenesis.

YEASTRACT+ (http://yeastract-plus.org/) is a tool for the analysis, prediction and modelling of transcription regulatory data at the gene and genomic levels in yeasts. It incorporates three integrated databases: YEASTRACT (http://yeastract-plus.org/yeastract/), PathoYeastract (http://yeastract-plus.org/pathoyeastract/) and NCYeastract (http://yeastract-plus.org/ncyeastract/), focused on Saccharomyces cerevisiae, pathogenic yeasts of the Candida genus, and non-conventional yeasts of biotechnological relevance. In this release, YEASTRACT+ offers upgraded information on transcription regulation for the ten previously incorporated yeast species, while extending the database to another pathogenic yeast, Candida auris. Since the last release of YEASTRACT+ (January 2020), a fourth database has been integrated. CommunityYeastract (http://yeastract-plus.org/community/) offers a platform for the creation, use, and future update of YEASTRACT-like databases for any yeast of the users' choice. CommunityYeastract currently provides information for two Saccharomyces boulardii strains, Rhodotorula toruloides NP11 oleaginous yeast, and Schizosaccharomyces pombe 972h-. In addition, YEASTRACT+ portal currently gathers 304 547 documented regulatory associations between transcription factors (TF) and target genes and 480 DNA binding sites, considering 2771 TFs from 11 yeast species. A new set of tools, currently implemented for S. cerevisiae and C. albicans, is further offered, combining regulatory information with genome-scale metabolic models to provide predictions on the most promising transcription factors to be exploited in cell factory optimisation or to be used as novel drug targets. The expansion of these new tools to the remaining YEASTRACT+ species is ongoing.

Prediction of Gene and Genomic Regulation in Candida Species, Using the PathoYeastract Database: A Comparative Genomics Approach.

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.