Transcription analysis of S. cerevisiae in VHG fermentation: The genome-wide transcriptional response of Saccharomyces cerevisiae during very high gravity ethanol fermentations is highly affected by the stationary phase

Yeast responses to stresses associated with industrial brewery handling: Figure 1

During brewery handling, production strains of yeast must respond to fluctuations in dissolved oxygen concentration, pH, osmolarity, ethanol concentration, nutrient supply and temperature. Fermentation performance of brewing yeast strains is dependent on their ability to adapt to these changes, particularly during batch brewery fermentation which involves the recycling (repitching) of a single yeast culture (slurry) over a number of fermentations (generations). Modern practices, such as the use of high-gravity worts and preparation of dried yeast for use as an inoculum, have increased the magnitude of the stresses to which the cell is subjected. The ability of yeast to respond effectively to these conditions is essential not only for beer production but also for maintaining the fermentation fitness of yeast for use in subsequent fermentations. During brewery handling, cells inhabit a complex environment and our understanding of stress responses under such conditions is limited. The advent of techniques capable of determining genomic and proteomic changes within the cell is likely vastly to improve our knowledge of yeast stress responses during industrial brewery handling.

Brewing yeast genomes and genome-wide expression and proteome profiling during fermentation

The genome structure, ancestry and instability of the brewing yeast strains have received considerable attention. The hybrid nature of brewing lager yeast strains provides adaptive potential but yields genome instability which can adversely affect fermentation performance. The requirement to differentiate between production strains and assess master cultures for genomic instability has led to significant adoption of specialized molecular tool kits by the industry. Furthermore, the development of genome-wide transcriptional and protein expression technologies has generated significant interest from brewers. The opportunity presented to explore, and the concurrent requirement to understand both, the constraints and potential of their strains to generate existing and new products during fermentation is discussed.

Genomic adaptation of ethanologenic yeast to biomass conversion inhibitors

One major barrier to the economic conversion of biomass to ethanol is inhibitory compounds generated during biomass pretreatment using dilute acid hydrolysis. Major inhibitors such as furfural and 5-hydroxymethylfurfural (HMF) inhibit yeast growth and subsequent fermentation. The ethanologenic yeast Saccharomyces cerevisiae demonstrated a dose-dependant inhibition by the inhibitors and has the potential to transform furfural and HMF into less toxic compounds of furfuryl alcohol and 2,5-bis-hydroxymethylfuran (also termed as furan-2,5-dimethanol (FDM)), respectively. For a sustainable and cost-competitive biomass-to-ethanol industry, it is important to develop more tolerant yeast strains that can, in situ, detoxify the inhibitors and produce ethanol. This study summarizes current knowledge and our understanding of the inhibitors furfural and HMF and discusses metabolic conversion pathways of the inhibitors and the yeast genomic expression response to inhibitor stress. Unlike laboratory strains, gene expression response of the ethanologenic yeast to furfural and HMF was not transient, but a continued dynamic process involving multiple genes at the genome level. This suggests that during the lag phase, ethanologenic yeasts undergo a genomic adaptation process in response to the inhibitors. The findings to date provide a strong foundation for future studies on genomic adaptation and manipulation of yeast to aid more robust strain design and development.

Integration of metabolome data with metabolic networks reveals reporter reactions

Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from Saccharomyces cerevisiae. The algorithm includes preprocessing of a genome-scale yeast model such that the fraction of measured metabolites within the model is enhanced, and thus it is possible to map significant alterations associated with a perturbation even though a small fraction of the complete metabolome is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through combination of metabolome and transcriptome data.