Targeted transcriptomic and metabolic profiling reveals temporal bottlenecks in the maize carotenoid pathway that may be addressed by multigene engineering

Carotenoids are a diverse group of tetraterpenoid pigments found in plants, fungi, bacteria and some animals. They play vital roles in plants and provide important health benefits to mammals, including humans. We previously reported the creation of a diverse population of transgenic maize plants expressing various carotenogenic gene combinations and exhibiting distinct metabolic phenotypes. Here we performed an in‐depth targeted mRNA and metabolomic analysis of the pathway to characterize the specific impact of five carotenogenic transgenes and their interactions with 12 endogenous genes in four transgenic lines representing distinct genotypes and phenotypes. We reconstructed the temporal profile of the carotenoid pathway during endosperm development at the mRNA and metabolic levels (for total and individual carotenoids), and investigated the impact of transgene expression on the endogenous pathway. These studies enabled us to investigate the extent of any interactions between the introduced transgenic and native partial carotenoid pathways during maize endosperm development. Importantly, we developed a theoretical model that explains these interactions, and our results suggest genetic intervention points that may allow the maize endosperm carotenoid pathway to be engineered in a more effective and predictable manner.     

Influence of grape treatment on the wine yeast populations isolated from spontaneous fermentations

AIM: To study the influence of different methods of grape treatment in wineries on the diversity of the yeast species in spontaneous fermentations. METHODS AND RESULTS: Grapes were crushed and pressed in three different ways followed by spontaneous fermentation. The same grape material picked and crushed aseptically directly in the vineyard served as control. Yeasts isolated at different stages of the fermentation were characterized by 5.8S-ITS-RFLP. Yeasts of the Saccharomyces sensu stricto complex were additionally analysed by microsatellite polymerase chain reaction fingerprinting. The diversity of yeast species isolated from winery fermentations was much greater than from the vineyard fermentation in respect to yeasts of the genus Saccharomyces as well as non-Saccharomyces. CONCLUSIONS: Oenonogical methods alter significantly the yeast diversity in spontaneous fermentations of grape juice. SIGNIFICANCE AND IMPACT OF THE STUDY: Managing spontaneous fermentations successfully depends not only on choosing the suitable grapes but also on the crushing and pressing techniques leading to different yeast populations.     

Design and evaluation of control strategies for high cell density fermentations

A methodology for the design and evalution of bioprocess control strategies is presented. The strategies manage nutrient supply with demand and vary with the metabolic condition and phase of fermentation operation. Six carbon source addition strategies are based on different combinations of available measurements; they are described and evaluated under different operating conditions for yeast cultivation. It is concluded that a single control strategy is not the most appropriate under all possible operating conditions. An oxygen uptake rate-based control strategy performs better with a mean respiratory quotient (RQ) value less than 1.1 during an oxygen limitation than an ethanol control strategy which had a mean RQ of 14. The designed strategies and an approach of applying the strategy that best matches fermentation conditions consistently enables achievement of high cell densities 78.7 g DCW/L and yields 0.50 g DCW/g glucose as the mean values for three fermentations.     

Creation and validation of a reactor engineering model for multiphase red wine fermentations

Red wine fermentations are performed in the presence of grape skins and seeds to ensure the extraction of color and other phenolics. The presence of these solids results in two distinct phases in the fermentor, as the solids float to the top to form a “cap.” Modeling of red wine fermentation is, therefore, complex and must consider spatial heterogeneity to predict fermentation kinetics. We have developed a reactor-engineering model for red wine fermentations that includes the fundamentals of fermentation kinetics, heat transfer, diffusion, and compressible fluid flow. To develop the heat transfer component of the model, the heat transfer properties of grapes were experimentally determined as a function of fermentation progression. COMSOL was used to solve all components of the model simultaneously utilizing a finite element analysis approach. Predictions from this model were validated using prior experimental work. Model prediction and experimental data showed excellent agreement. The model was then used to predict spatial profiles of active yeast cell concentration and ethanol productivity, as well as liquid velocity profiles. Finally, the model was used to predict how these gradients would change with differences in initial bioavailable nitrogen concentration, a key parameter in predicting fermentation outcome in nitrogen-limited wine fermentations.     

谷物蛋白对白酒发酵过程中微生物群落及其代谢多样性的调控

[背景] 在白酒发酵过程中,原料中的谷物蛋白可为微生物的生长提供氮源等营养物质,进而形成多种代谢产物。谷物蛋白可分为清蛋白、球蛋白、醇溶蛋白和谷蛋白。然而,谷物蛋白对微生物多样性及其代谢产物多样性的调控尚不明确。[目的] 揭示白酒发酵过程中与微生物多样性及其代谢产物多样性显著相关的关键谷物蛋白种类及其调控作用。[方法] 通过Osborne法测定不同品种高粱中谷物蛋白的组成;采用多组学联用技术解析4种高粱在发酵过程中的微生物菌群多样性及代谢产物多样性;通过模拟发酵揭示原料中影响微生物群落及其代谢多样性的关键蛋白。[结果] 4种高粱中的谷物蛋白组成存在显著差异（ANOSIM：R=0.85,P=0.001）;4种高粱在发酵第5天时,S4高粱的细菌多样性显著（P<0.05）高于其他3种高粱,S3高粱中微生物的代谢产物多样性显著（P<0.05）高于其他3种高粱;清蛋白和球蛋白含量与发酵第5天的优势细菌多样性（R²=0.34,P<0.05;R²=0.58,P<0.05）和代谢产物多样性呈显著正相关（R²=0.58,P<0.05;R²=0.36,P<0.05）,被定义为关键蛋白;模拟发酵实验验证了优势细菌多样性和代谢产物多样性可随着2种关键蛋白即清蛋白和球蛋白含量的升高而升高。当清蛋白含量在3.0 g/L时,优势细菌多样性及代谢产物多样性可分别达到0.72和0.65;当球蛋白含量在3.0 g/L时,优势细菌多样性及代谢产物多样性可分别达到0.66和0.81。[结论] 研究揭示了酿造原料中的清蛋白和球蛋白对发酵过程中细菌多样性及代谢产物多样性的调控作用,为提高白酒发酵的可控性及质量提供了依据。     

Continuous modeling of metabolic networks with gene regulation in yeast and in vivo determination of rate parameters

A continuous model of a metabolic network including gene regulation to simulate metabolic fluxes during batch cultivation of yeast Saccharomyces cerevisiae was developed. The metabolic network includes reactions of glycolysis, gluconeogenesis, glycerol and ethanol synthesis and consumption, the tricarboxylic acid cycle, and protein synthesis. Carbon sources considered were glucose and then ethanol synthesized during growth on glucose. The metabolic network has 39 fluxes, which represent the action of 50 enzymes and 64 genes and it is coupled with a gene regulation network which defines enzyme synthesis (activities) and incorporates regulation by glucose (enzyme induction and repression), modeled using ordinary differential equations. The model includes enzyme kinetics, equations that follow both mass-action law and transport as well as inducible, repressible, and constitutive enzymes of metabolism. The model was able to simulate a fermentation of S. cerevisiae during the exponential growth phase on glucose and the exponential growth phase on ethanol using only one set of kinetic parameters. All fluxes in the continuous model followed the behavior shown by the metabolic flux analysis (MFA) obtained from experimental results. The differences obtained between the fluxes given by the model and the fluxes determined by the MFA do not exceed 25% in 75% of the cases during exponential growth on glucose, and 20% in 90% of the cases during exponential growth on ethanol. Furthermore, the adjustment of the fermentation profiles of biomass, glucose, and ethanol were 95%, 95%, and 79%, respectively. With these results the simulation was considered successful. A comparison between the simulation of the continuous model and the experimental data of the diauxic yeast fermentation for glucose, biomass, and ethanol, shows an extremely good match using the parameters found. The small discrepancies between the fluxes obtained through MFA and those predicted by the differential equations, as well as the good match between the profiles of glucose, biomass, and ethanol, and our simulation, show that this simple model, that does not rely on complex kinetic expressions, is able to capture the global behavior of the experimental data. Also, the determination of parameters using a straightforward minimization technique using data at only two points in time was sufficient to produce a relatively accurate model. Thus, even with a small amount of experimental data (rates and not concentrations) it was possible to estimate the parameters minimizing a simple objective function. The method proposed allows the obtention of reasonable parameters and concentrations in a system with a much larger number of unknowns than equations. Hence a contribution of this study is to present a convenient way to find in vivo rate parameters to model metabolic and genetic networks under different conditions.     

Metabolic profiling as a tool for revealing Saccharomyces interactions during wine fermentation

The multi-yeast strain composition of wine fermentations has been well established. However, the effect of multiple strains of Saccharomyces spp. on wine flavour is unknown. Here, we demonstrate that multiple strains of Saccharomyces grown together in grape juice can affect the profile of aroma compounds that accumulate during fermentation. A metabolic footprint of each yeast in monoculture, mixed cultures or blended wines was derived by gas chromatography - mass spectrometry measurement of volatiles accumulated during fermentation. The resultant ion spectrograms were transformed and compared by principal-component analysis. The principal-component analysis showed that the profiles of compounds present in wines made by mixed-culture fermentation were different from those where yeasts were grown in monoculture fermentation, and these differences could not be produced by blending wines. Blending of monoculture wines to mimic the population composition of mixed-culture wines showed that yeast metabolic interactions could account for these differences. Additionally, the yeast strain contribution of volatiles to a mixed fermentation cannot be predicted by the population of that yeast. This study provides a novel way to measure the population status of wine fermentations by metabolic footprinting.     

Small molecule-sensing strategy and techniques for understanding the functionality of green tea

Various low-molecular-weight phytochemicals in green tea (Camellia sinensis L.), especially (–)-epigallocatechin-3-O-gallate (EGCG), are known to be involved in health promotion and disease risk reduction. However, the underlying mechanism has remained elusive because of the absence of an analytical technique that can easily detect the precise behavior of such a small molecule. Recently, we have identified a cell-surface EGCG-sensing receptor and the related signaling molecules that control the physiological functions of EGCG. We also developed a novel in situ label-free imaging technique for visualizing spatially resolved biotransformations based on simultaneous mapping of EGCG and its phase II metabolites. Furthermore, we established a chemometric method capable of evaluating the functionality of multicomponent green tea extracts by focusing on their compositional balances. This review highlights our proposed small molecule-sensing techniques for detecting the complex behavior of green tea components and linking such information to an enhanced understanding of green tea functionality.     

Study of the coumarate decarboxylase and vinylphenol reductase activities of Dekkera bruxellensis (anamorph Brettanomyces bruxellensis) isolates

Aim:  To evaluate the coumarate descarboxylase (CD) and vinylphenol reductase (VR) activities in Dekkera bruxellensis isolates and study their relationship to the growth rate, protein profile and random amplified polymorphic DNA (RAPD) molecular pattern.
Methods and Results:  CD and VR activities were quantified, as well, the growth rate, intracellular protein profile and molecular analysis (RAPD) were determined in 12 isolates of D. bruxellensis . All the isolates studied showed CD activity, but only some showed VR activity. Those isolates with the greatest growth rate did not present a different protein profile from the others. The FASC showed a relationship between RAPD molecular patterns and VR activity.
Conclusion:  CD activity is common to all of the D. bruxellensis isolates. This was not the case with VR activity, which was detected at a low percentage in the analysed micro-organisms. A correlation was observed between VR activity and the RAPD patterns.
Significance and Impact of the Study:  This is the first study that quantifies the CD and VR enzyme activities in D. bruxellensis, demonstrating that these activities are not present in all isolates of this yeast.     

Use of genome-scale metabolic models for understanding microbial physiology

The exploitation of microorganisms in industrial, medical, food and environmental biotechnology requires a comprehensive understanding of their physiology. The availability of genome sequences and accumulation of high-throughput data allows gaining understanding of microbial physiology at the systems level, and genome-scale metabolic models represent a valuable framework for integrative analysis of metabolism of microorganisms. Genome-scale metabolic models are reconstructed based on a combination of genome sequence information and detailed biochemical information, and these reconstructed models can be used for analyzing and simulating the operation of metabolism in response to different stimuli. Here we discuss the requirement for having detailed physiological insight in order to exploit microorganisms for production of fuels, chemicals and pharmaceuticals. We further describe the reconstruction process of genome-scale metabolic models and different algorithms that can be used to apply these models to gain improved insight into microbial physiology.