Metabolic and transcriptomic response of the wine yeast Saccharomyces cerevisiae strain EC1118 after an oxygen impulse under carbon-sufficient, nitrogen-limited fermentative conditions

Metabolic Modeling of Wine Fermentation at Genome Scale

Wine fermentation is an ancient biotechnological process mediated by different microorganisms such as yeast and bacteria. Understanding of the metabolic and physiological phenomena taking place during this process can be now attained at a genome scale with the help of metabolic models. In this chapter, we present a detailed protocol for modeling wine fermentation using genome-scale metabolic models. In particular, we illustrate how metabolic fluxes can be computed, optimized and interpreted, for both yeast and bacteria under winemaking conditions. We also show how nutritional requirements can be determined and simulated using these models in relevant test cases. This chapter introduces fundamental concepts and practical steps for applying flux balance analysis in wine fermentation, and as such, it is intended for a broad microbiology audience as well as for practitioners in the metabolic modeling field.

Landscape of the Dark Transcriptome Revealed Through Re-mining Massive RNA-Seq Data

The "dark transcriptome" can be considered the multitude of sequences that are transcribed but not annotated as genes. We evaluated expression of 6,692 annotated genes and 29,354 unannotated open reading frames (ORFs) in the Saccharomyces cerevisiae genome across diverse environmental, genetic and developmental conditions (3,457 RNA-Seq samples). Over 30% of the highly transcribed ORFs have translation evidence. Phylostratigraphic analysis infers most of these transcribed ORFs would encode species-specific proteins ("orphan-ORFs"); hundreds have mean expression comparable to annotated genes. These data reveal unannotated ORFs most likely to be protein-coding genes. We partitioned a co-expression matrix by Markov Chain Clustering; the resultant clusters contain 2,468 orphan-ORFs. We provide the aggregated RNA-Seq yeast data with extensive metadata as a project in MetaOmGraph (MOG), a tool designed for interactive analysis and visualization. This approach enables reuse of public RNA-Seq data for exploratory discovery, providing a rich context for experimentalists to make novel, experimentally testable hypotheses about candidate genes.

Saccharomyces cerevisiae Gene Expression during Fermentation of Pinot Noir Wines at an Industrially Relevant Scale

This study characterized Saccharomyces cerevisiae RC212 gene expression during Pinot noir fermentation at pilot scale (150 liters) using industry-relevant conditions. The reported gene expression patterns of RC212 are generally similar to those observed under laboratory fermentation conditions but also contain gene expression signatures related to yeast-environment interactions found in a production setting (e.g., the presence of non-Saccharomyces microorganisms).

Saccharomyces cerevisiae metabolism produces ethanol and other compounds during the fermentation of grape must into wine. Thousands of genes change expression over the course of a wine fermentation, allowing S. cerevisiae to adapt to and dominate the fermentation environment. Investigations into these gene expression patterns previously revealed genes that underlie cellular adaptation to the grape must and wine environments, involving metabolic specialization and ethanol tolerance. However, the majority of studies detailing gene expression patterns have occurred in controlled environments that may not recapitulate the biological and chemical complexity of fermentations performed at production scale. Here, an analysis of the S. cerevisiae RC212 gene expression program is presented, drawing from 40 pilot-scale fermentations (150 liters) using Pinot noir grapes from 10 California vineyards across two vintages. A core gene expression program was observed across all fermentations irrespective of vintage, similar to that of laboratory fermentations, in addition to novel gene expression patterns likely related to the presence of non-Saccharomyces microorganisms and oxygen availability during fermentation. These gene expression patterns, both common and diverse, provide insight into Saccharomyces cerevisiae biology critical to fermentation outcomes under industry-relevant conditions.

IMPORTANCE This study characterized Saccharomyces cerevisiae RC212 gene expression during Pinot noir fermentation at pilot scale (150 liters) using industry-relevant conditions. The reported gene expression patterns of RC212 are generally similar to those observed under laboratory fermentation conditions but also contain gene expression signatures related to yeast-environment interactions found in a production setting (e.g., the presence of non-Saccharomyces microorganisms). Key genes and pathways highlighted by this work remain undercharacterized, indicating the need for further research to understand the roles of these genes and their impact on industrial wine fermentation outcomes.

Application of Protein Hydrolysates from Defatted Walnut Meal in High-Gravity Brewing to Improve Fermentation Performance of Lager Yeast

Protein hydrolysates were prepared from an industrially defatted walnut meal (DWMPH) by enzymolysis employing Neutrase, Protamex, and Flavorzyme, respectively, with/without ultrasonic treatment. The effects of DWMPH supplementations on fermentation performance of lager yeast in high-gravity brewing were investigated. Results showed that ultrasonic-assisted enzymolysis simultaneous treatment (UAE) and ultrasonic pretreatment followed by enzymolysis (UPE) significantly increased degree of hydrolysis (DH) by 1.43 times and 0.71 times of traditional enzymolysis (TE) at least, respectively, Protamex treatment exhibited higher DH (13.3-32.8%) than Neutrase (9.2-25.3%) or Flavorzyme (11.8-28.7%). Compared with control, DWMPH supplementations prepared by UAE using Protamex (UAE-P), Neutrase (UAE-N), or Flavorzyme (UAE-F) significantly improved fermentation performance of lager yeast, especially for UAE-P with the highest major fractions of Mw < 1 kDa, increased wort fermentability and ethanol production by 15% and 17%, respectively, while UAE-F with the highest major fractions of Mw > 3 kDa obviously improved the foam stability of final beers. Furthermore, DWMPH supplementations significantly increased yeast growth and cell viability, promoted glycogen and trehalose accumulation, upregulated stress markers HSP12 and SSA3 expression in yeast cells, improved the formation of higher alcohols and esters, and increased the ratio of higher alcohol to ester indicating a better balanced taste of final beers.

Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches

Chemical analysis of grape juice and wine has been performed for over 50 years in a targeted manner to determine a limited number of compounds using Gas Chromatography, Mass-Spectrometry (GC-MS) and High Pressure Liquid Chromatography (HPLC). Therefore, it only allowed the determination of metabolites that are present in high concentration, including major sugars, amino acids and some important carboxylic acids. Thus, the roles of many significant but less concentrated metabolites during wine making process are still not known. This is where metabolomics shows its enormous potential, mainly because of its capability in analyzing over 1000 metabolites in a single run due to the recent advancements of high resolution and sensitive analytical instruments. Metabolomics has predominantly been adopted by many wine scientists as a hypothesis-generating tool in an unbiased and non-targeted way to address various issues, including characterization of geographical origin (terroir) and wine yeast metabolic traits, determination of biomarkers for aroma compounds, and the monitoring of growth developments of grape vines and grapes. The aim of this review is to explore the published literature that made use of both targeted and untargeted metabolomics to study grapes and wines and also the fermentation process. In addition, insights are also provided into many other possible avenues where metabolomics shows tremendous potential as a question-driven approach in grape and wine research.

Systems-based approaches enable identification of gene targets which improve the flavour profile of low-ethanol wine yeast strains

Metabolic engineering has been vital to the development of industrial microbes such as the yeast Saccharomyces cerevisiae. However, sequential rounds of modification are often needed to achieve particular industrial design targets. Systems biology approaches can aid in identifying genetic targets for modification through providing an integrated view of cellular physiology. Recently, research into the generation of commercial yeasts that can produce reduced-ethanol wines has resulted in metabolically-engineered strains of S. cerevisiae that are less efficient at producing ethanol from sugar. However, these modifications led to the concomitant production of off-flavour by-products. A combination of transcriptomics, proteomics and metabolomics was therefore used to investigate the physiological changes occurring in an engineered low-ethanol yeast strain during alcoholic fermentation. Integration of 'omics data identified several metabolic reactions, including those related to the pyruvate node and redox homeostasis, as being significantly affected by the low-ethanol engineering methodology, and highlighted acetaldehyde and 2,4,5-trimethyl-1,3-dioxolane as the main off-flavour compounds. Gene remediation strategies were then successfully applied to decrease the formation of these by-products, while maintaining the 'low-alcohol' phenotype. The data generated from this comprehensive systems-based study will inform wine yeast strain development programmes, which, in turn, could potentially play an important role in assisting winemakers in their endeavour to produce low-alcohol wines with desirable flavour profiles.

Transcriptome landscape of Synechococcus elongatus PCC 7942 for nitrogen starvation responses using RNA-seq

The development of high-throughput technology using RNA-seq has allowed understanding of cellular mechanisms and regulations of bacterial transcription. In addition, transcriptome analysis with RNA-seq has been used to accelerate strain improvement through systems metabolic engineering. Synechococcus elongatus PCC 7942, a photosynthetic bacterium, has remarkable potential for biochemical and biofuel production due to photoautotrophic cell growth and direct CO2 conversion. Here, we performed a transcriptome analysis of S. elongatus PCC 7942 using RNA-seq to understand the changes of cellular metabolism and regulation for nitrogen starvation responses. As a result, differentially expressed genes (DEGs) were identified and functionally categorized. With mapping onto metabolic pathways, we probed transcriptional perturbation and regulation of carbon and nitrogen metabolisms relating to nitrogen starvation responses. Experimental evidence such as chlorophyll a and phycobilisome content and the measurement of CO2 uptake rate validated the transcriptome analysis. The analysis suggests that S. elongatus PCC 7942 reacts to nitrogen starvation by not only rearranging the cellular transport capacity involved in carbon and nitrogen assimilation pathways but also by reducing protein synthesis and photosynthesis activities.