The Zygosaccharomyces bailii transcription factor Haa1 is required for acetic acid and copper stress responses suggesting subfunctionalization of the ancestral bifunctional protein Haa1/Cup2.

BACKGROUND: The food spoilage yeast species Zygosaccharomyces bailii exhibits an extraordinary capacity to tolerate weak acids, in particular acetic acid. In Saccharomyces cerevisiae, the transcription factor Haa1 (ScHaa1) is considered the main player in genomic expression reprogramming in response to acetic acid stress, but the role of its homologue in Z. bailii (ZbHaa1) is unknown. RESULTS: In this study it is demonstrated that ZbHaa1 is a ScHaa1 functional homologue by rescuing the acetic acid susceptibility phenotype of S. cerevisiae haa1Δ. The disruption of ZbHAA1 in Z. bailii IST302 and the expression of an extra ZbHAA1 copy confirmed ZbHAA1 as a determinant of acetic acid tolerance. ZbHaa1 was found to be required for acetic acid stress-induced transcriptional activation of Z. bailii genes homologous to ScHaa1-target genes. An evolutionary analysis of the Haa1 homologues identified in 28 Saccharomycetaceae species genome sequences, including Z bailii, was carried out using phylogenetic and gene neighbourhood approaches. Consistent with previous studies, this analysis revealed a group containing pre-whole genome duplication species Haa1/Cup2 single orthologues, including ZbHaa1, and two groups containing either Haa1 or Cup2 orthologues from post-whole genome duplication species. S. cerevisiae Cup2 (alias Ace1) is a transcription factor involved in response and tolerance to copper stress. Taken together, these observations led us to hypothesize and demonstrate that ZbHaa1 is also involved in copper-induced transcriptional regulation and copper tolerance. CONCLUSIONS: The transcription factor ZbHaa1 is required for adaptive response and tolerance to both acetic acid and copper stresses. The subfunctionalization of the single ancestral Haa1/Cup2 orthologue that originated Haa1 and Cup2 paralogues after whole genome duplication is proposed.

A fast method to distinguish between fermentative and respiratory metabolisms in single yeast cells.

Saccharomyces cerevisiae adjusts its metabolism based on nutrient availability, typically transitioning from glucose fermentation to ethanol respiration as glucose becomes limiting. However, our understanding of the regulation of metabolism is largely based on population averages, whereas nutrient transitions may cause heterogeneous responses. Here we introduce iCRAFT, a method that couples the ATP Förster resonance energy transfer (FRET)-based biosensor yAT1.03 with Antimycin A to differentiate fermentative and respiratory metabolisms in individual yeast cells. Upon Antimycin A addition, respiratory cells experienced a sharp decrease of the normalized FRET ratio, while respiro-fermentative cells showed no response. Next, we tracked changes in metabolism during the diauxic shift of a glucose pre-grown culture. Following glucose exhaustion, the entire cell population experienced a progressive rise in cytosolic ATP produced via respiration, suggesting a gradual increase in respiratory capacity. Overall, iCRAFT is a robust tool to distinguish fermentation from respiration, offering a new single-cell opportunity to study yeast metabolism.

The pH-dependent lactose metabolism of Lactobacillus delbrueckii subsp. bulgaricus: An integrative view through a mechanistic computational model.

The fermentation process of milk to yoghurt using Lactobacillus delbrueckii subsp. bulgaricus in co-culture with Streptococcus thermophilus is hallmarked by the breakdown of lactose to organic acids such as lactate. This leads to a substantial decrease in pH - both in the medium, as well as cytosolic. The latter impairs metabolic activities due to the pH-dependence of enzymes, which compromises microbial growth. To quantitatively elucidate the impact of the acidification on metabolism of L. bulgaricus in an integrated way, we have developed a proton-dependent computational model of lactose metabolism and casein degradation based on experimental data. The model accounts for the influence of pH on enzyme activities as well as cellular growth and proliferation of the bacterial population. We used a machine learning approach to quantify the cell volume throughout fermentation. Simulation results show a decrease in metabolic flux with acidification of the cytosol. Additionally, the validated model predicts a similar metabolic behaviour within a wide range of non-limiting substrate concentrations. This computational model provides a deeper understanding of the intricate relationships between metabolic activity and acidification and paves the way for further optimization of yoghurt production under industrial settings.

pH dependencies of glycolytic enzymes of yeast under in vivo-like assay conditions.

Under carbon source transitions, the intracellular pH of Saccharomyces cerevisiae is subject to change. Dynamics in pH modulate the activity of the glycolytic enzymes, resulting in a change in glycolytic flux and ultimately cell growth. To understand how pH affects the global behavior of glycolysis and ethanol fermentation, we measured the activity of the glycolytic and fermentative enzymes in S. cerevisiae under in vivo-like conditions at different pH. We demonstrate that glycolytic enzymes exhibit differential pH dependencies, and optima, in the pH range observed during carbon source transitions. The forward reaction of GAPDH shows the highest decrease in activity, 83%, during a simulated feast/famine regime upon glucose removal (cytosolic pH drop from 7.1 to 6.4). We complement our biochemical characterization of the glycolytic enzymes by fitting the Vmax to the progression curves of product formation or decay over time. The fitting analysis shows that the observed changes in enzyme activities require changes in Vmax , but changes in Km cannot be excluded. Our study highlights the relevance of pH as a key player in metabolic regulation and provides a large set of quantitative data that can be explored to improve our understanding of metabolism in dynamic environments.