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Genomic and transcriptomic analysis of Saccharomyces cerevisiae isolates with focus in succinic acid production

Publication . Franco-Duarte, Ricardo; Bessa, Daniela; Gonçalves, Filipa; Martins, Rosa; Silva-Ferreira, António César; Schuller, Dorit; Sampaio, Paula; Pais, Célia

Succinic acid is a platform chemical that plays an important role as precursor for the synthesis of many valuable bio-based chemicals. Its microbial production from renewable resources has seen great developments, specially exploring the use of yeasts to overcome the limitations of using bacteria. The objective of the present work was to screen for succinate-producing isolates, using a yeast collection with different origins and characteristics. Four strains were chosen, two as promising succinic acid producers, in comparison with two low producers. Genome of these isolates was analysed, and differences were found mainly in genes SDH1, SDH3, MDH1 and the transcription factor HAP4, regarding the number of single nucleotide polymorphisms and the gene copy-number profile. Real-time PCR was used to study gene expression of 10 selected genes involved in the metabolic pathway of succinic acid production. Results show that for the non-producing strain, higher expression of genes SDH1, SDH2, ADH1, ADH3, IDH1 and HAP4 was detected, together with lower expression of ADR1 transcription factor, in comparison with the best producer strain. This is the first study showing the capacity of natural yeast isolates to produce high amounts of succinic acid, together with the understanding of the key factors associated, giving clues for strain improvement.

2017Journal article

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New integrative computational approaches unveil the Saccharomyces cerevisiae pheno-metabolomic fermentative profile and allow strain selection for winemaking

Publication . Franco-Duarte, Ricardo; Umek, Lan; Mendes, Inês; Castro, Cristiana C.; Fonseca, Nuno; Martins, Rosa; Ferreira, António César Silva; Sampaio, Paula; Pais, Célia; Schuller, Dorit

During must fermentation by Saccharomyces cerevisiae strains thousands of volatile aroma compounds are formed. The objective of the present work was to adapt computational approaches to analyze pheno-metabolomic diversity of a S. cerevisiae strain collection with different origins. Phenotypic and genetic characterization together with individual must fermentations were performed, and metabolites relevant to aromatic profiles were determined. Experimental results were projected onto a common coordinates system, revealing 17 statistical-relevant multi-dimensional modules, combining sets of mostcorrelated features of noteworthy biological importance. The present method allowed, as a breakthrough, to combine genetic, phenotypic and metabolomic data, which has not been possible so far due to difficulties in comparing different types of data. Therefore, the proposed computational approach revealed as successful to shed light into the holistic characterization of S. cerevisiae pheno-metabolome in must fermentative conditions. This will allow the identification of combined relevant features with application in selection of good winemaking strains.

2016Journal article

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