Reduction of oxidative cellular damage by overexpression of the thioredoxin TRX2 gene improves yield and quality of wine yeast dry active biomass

Abstract

BackgroundWine Saccharomyces cerevisiae strains, adapted to anaerobic must fermentations, suffer oxidative stress when they are grown under aerobic conditions for biomass propagation in the industrial process of active dry yeast production. Oxidative metabolism of sugars favors high biomass yields but also causes increased oxidation damage of cell components. The overexpression of the TRX2 gene, coding for a thioredoxin, enhances oxidative stress resistance in a wine yeast strain model. The thioredoxin and also the glutathione/glutaredoxin system constitute the most important defense against oxidation. Trx2p is also involved in the regulation of Yap1p-driven transcriptional response against some reactive oxygen species.ResultsLaboratory scale simulations of the industrial active dry biomass production process demonstrate that TRX2 overexpression increases the wine yeast final biomass yield and also its fermentative capacity both after the batch and fed-batch phases. Microvinifications carried out with the modified strain show a fast start phenotype derived from its enhanced fermentative capacity and also increased content of beneficial aroma compounds. The modified strain displays an increased transcriptional response of Yap1p regulated genes and other oxidative stress related genes. Activities of antioxidant enzymes like Sod1p, Sod2p and catalase are also enhanced. Consequently, diminished oxidation of lipids and proteins is observed in the modified strain, which can explain the improved performance of the thioredoxin overexpressing strain.ConclusionsWe report several beneficial effects of overexpressing the thioredoxin gene TRX2 in a wine yeast strain. We show that this strain presents an enhanced redox defense. Increased yield of biomass production process in TRX2 overexpressing strain can be of special interest for several industrial applications.

Modification of the TRX2 gene dose in Saccharomyces cerevisiae affects hexokinase 2 gene regulation during wine yeast biomass production

“…All the upregulated genes were grouped into five functional categories: ribosomal subunit (p<1.39E-5), DNA metabolic process (p<1.45E-4), intracellular transport (p<1.95E-4), stress response (p<5.23E-3) and sulphur metabolism (p<8.10E-3) ( Table 4). A strong down-regulation of the cytoplasmic ribosomal proteins codified by the RPLs and RPSs genes has been described during the metabolic transition and the diauxic shift, indicating repression of protein synthesis to reprogram the gene expression profile for adaptation to new environmental conditions (Ashe et al 2000;DeRisi et al 1997;Gómez-Pastor et al 2010b). However, in the trx2Δ strain, we observed an up-regulation of several genes encoding ribosomal proteins for both the 60S and 40S ribosomal subunits.…”

“…The global gene expression experiments were done with three representative samples taken at 14 h (early-batch phase), 18 h (late-batch) and 80 h (late-fed batch) under the identical fermentation conditions and sampling stages to those previously reported by Gómez-Pastor et al (2010b). mRNA was obtained from each sample, and microarray hybridizations were performed using three biological replicates for each time point.…”

“…In addition, the hexose transportrelated genes also displayed a specific repression during the metabolic transition in the absence of thioredoxin, even for the high-affinity glucose transporters (HXT2 and HXT4), unlike the observed behaviour displayed by control strain T73 (Gómez-Pastor et al 2010b). …”

“…The microarrays hybridization conditions were identical to those previously described (Gómez-Pastor et al 2010b). The obtained data were analyzed using the GenePix Pro 6.1 software package (Axon Instruments, Foster City, CA, USA).…”

“…A 5% false discovery rate and Bonferroni correction were used to select the statistically significant data. Only the log 2 ratios showing a variation greater than a 1.5-fold in comparison to the previous T 73 data (Gómez-Pastor et al 2010b) were considered. A functional group analysis was done using the GOstat and GO term finder (SGD database; http://www.yeastgenome.org) online applications.…”