The YEASTRACT+ information system (http://YEASTRACT-PLUS.org/) is a wide-scope tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in yeasts of biotechnological or human health relevance. YEASTRACT+ is a new portal that integrates the previously existing YEASTRACT (http://www.yeastract.com/) and PathoYeastract (http://pathoyeastract.org/) databases and introduces the NCYeastract (Non-Conventional Yeastract) database (http://ncyeastract.org/), focused on the so-called non-conventional yeasts. The information in the YEASTRACT database, focused on Saccharomyces cerevisiae, was updated. PathoYeastract was extended to include two additional pathogenic yeast species: Candida parapsilosis and Candida tropicalis. Furthermore, the NCYeastract database was created, including five biotechnologically relevant yeast species: Zygosaccharomyces baillii, Kluyveromyces lactis, Kluyveromyces marxianus, Yarrowia lipolytica and Komagataella phaffii. The YEASTRACT+ portal gathers 289 706 unique documented regulatory associations between transcription factors (TF) and target genes and 420 DNA binding sites, considering 247 TFs from 10 yeast species. YEASTRACT+ continues to make available tools for the prediction of the TFs involved in the regulation of gene/genomic expression. In this release, these tools were upgraded to enable predictions based on orthologous regulatory associations described for other yeast species, including two new tools for cross-species transcription regulation comparison, based on multi-species promoter and TF regulatory network analyses.

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.

Methanol is a promising feedstock for metabolically competent yeast strains-based biorefineries. However, methanol toxicity can limit the productivity of these bioprocesses. Therefore, the identification of genes whose expression is required for maximum methanol tolerance is important for mechanistic insights and rational genomic manipulation to obtain more robust methylotrophic yeast strains. The present chemogenomic analysis was performed with this objective based on the screening of the Euroscarf Saccharomyces cerevisiae haploid deletion mutant collection to search for susceptibility phenotypes in YPD medium supplemented with 8% (v/v) methanol, at 35 °C, compared with an equivalent ethanol concentration (5.5% (v/v)). Around 400 methanol tolerance determinants were identified, 81 showing a marked phenotype. The clustering of the identified tolerance genes indicates an enrichment of functional categories in the methanol dataset not enriched in the ethanol dataset, such as chromatin remodeling, DNA repair and fatty acid biosynthesis. Several genes involved in DNA repair (eight RAD genes), identified as specific for methanol toxicity, were previously reported as tolerance determinants for formaldehyde, a methanol detoxification pathway intermediate. This study provides new valuable information on genes and potential regulatory networks involved in overcoming methanol toxicity. This knowledge is an important starting point for the improvement of methanol tolerance in yeasts capable of catabolizing and copying with methanol concentrations present in promising bioeconomy feedstocks, including industrial residues.