Oxygen dependence of metabolic fluxes and energy generation of Saccharomyces cerevisiae CEN.PK113-1A

The effect of DMSO on Saccharomyces cerevisiae yeast with different energy metabolism and antioxidant status

We studied the effect of dimethyl sulfoxide (DMSO) on the biochemical and physiological parameters of S. cerevisiae yeast cells with varied energy metabolism and antioxidant status. The wild-type cells of varied genetic backgrounds and their isogenic mutants with impaired antioxidant defences (Δsod mutants) or response to environmental stress (ESR) (Δmsn2, Δmsn4 and double Δmsn2msn4 mutants) were used. Short-term exposure to DMSO even at a wide range of concentrations (2-20%) had little effect on the metabolic activity of the yeast cells and the stability of their cell membranes, but induced free radicals production and clearly altered their proliferative activity. Cells of the Δsod1 mutant showed greater sensitivity to DMSO in these conditions. DMSO at concentrations from 4 to 10-14% (depending on the strain and genetic background) activated the ESR programme. The effects of long-term exposure to DMSO were mainly depended on the type of energy metabolism and antioxidant system efficiency. Yeast cells with reduced antioxidant system efficiency and/or aerobic respiration were more susceptible to the toxic effects of DMSO than cells with a wild-type phenotype and respiro-fermentative or fully fermentative metabolism. These studies suggest a key role of stress response programs in both the processes of cell adaptation to small doses of this xenobiotic and the processes related to its toxicity resulting from large doses or chronic exposure to DMSO.

Comparative responses of flocculating and nonflocculating yeasts to cell density and chemical stress in lactic acid fermentation

While flocculation has demonstrated its efficacy in enhancing yeast robustness and ethanol production, its potential application for lactic acid fermentation remains largely unexplored. Our study examined the differences between flocculating and nonflocculating Saccharomyces cerevisiae strains in terms of their metabolic dynamics when incorporating an exogenous lactic acid pathway, across varying cell densities and in the presence of lignocellulose-derived byproducts. Comparative gene expression profiles revealed that cultivating a nonflocculant strain at higher cell density yielded a substantial upregulation of genes associated with glycolysis, energy metabolism, and other key pathways, resulting in elevated levels of fermentation products. Meanwhile, the flocculating strain displayed an inherent ability to sustain high glycolytic activity regardless of the cell density. Moreover, our investigation revealed a significant reduction in glycolytic activity under chemical stress, potentially attributable to diminished ATP supply during the energy investment phase. Conversely, the formation of flocs in the flocculating strain conferred protection against toxic chemicals present in the medium, fostering more stable lactic acid production levels. Additionally, the distinct flocculation traits observed between the two examined strains may be attributed to variations in the nucleotide sequences of the flocculin genes and their regulators. This study uncovers the potential of flocculation for enhanced lactic acid production in yeast, offering insights into metabolic mechanisms and potential gene targets for strain improvement.

Compositional and temporal division of labor modulates mixed sugar fermentation by an engineered yeast consortium

Synthetic microbial communities have emerged as an attractive route for chemical bioprocessing. They are argued to be superior to single strains through microbial division of labor (DOL), but the exact mechanism by which DOL confers advantages remains unclear. Here, we utilize a synthetic Saccharomyces cerevisiae consortium along with mathematical modeling to achieve tunable mixed sugar fermentation to overcome the limitations of single-strain fermentation. The consortium involves two strains with each specializing in glucose or xylose utilization for ethanol production. By controlling initial community composition, DOL allows fine tuning of fermentation dynamics and product generation. By altering inoculation delay, DOL provides additional programmability to parallelly regulate fermentation characteristics and product yield. Mathematical models capture observed experimental findings and further offer guidance for subsequent fermentation optimization. This study demonstrates the functional potential of DOL in bioprocessing and provides insight into the rational design of engineered ecosystems for various applications.

Beneficial effect of optimizing the expression balance of the mevalonate pathway introduced into the mitochondria on terpenoid production in Saccharomyces cerevisiae

Terpenoids are used in various industries, and Saccharomyces cerevisiae is a promising microorganism for terpenoid production. Introducing the mevalonate (MVA) pathway into the mitochondria of a strain with an augmented inherent cytosolic MVA pathway increased terpenoid production but also led to the accumulation of toxic pyrophosphate intermediates that negatively affected terpenoid production. We first engineered the inherent MVA pathway in the cytosol and then introduced the MVA pathway into the mitochondria using several promoter combinations, considering the toxicity of pyrophosphate intermediates. However, the highest titer, 183 mg/L, tends to be only 5% higher than that of the strain that only augmented the inherent MVA pathway (SYCM1; 174 mg/L). Next, we hypothesized that, in addition to the toxicity of pyrophosphate, other compounds in the MVA pathway could affect the squalene titer. Thus, we constructed a combinatorial strain library expressing MVA pathway enzymes in the mitochondria with various promoter combinations. The highest squalene titer (230 mg/L) was 32% higher than that of SYCM1. The promoter set revealed that mitigation of mono- and pyrophosphate compound accumulation was important for mitochondrial usage. This study demonstrated that a combinatorial strain library is useful for discovering the optimal gene expression balance in engineering yeast.

PHB production from cellobiose with Saccharomyces cerevisiae

Replacement of petrochemical-based materials with microbially produced biodegradable alternatives calls for industrially attractive fermentation processes. Lignocellulosic materials offer non-edible alternatives for cultivated sugars, but require often use of expensive sugar releasing enzymes, such as β-glucosidases. These cellulose treatment costs could be reduced if microbial production hosts could use short cellodextrins such as cellobiose directly as their substrates. In this study, we demonstrate production of poly(hydroxybutyrate) (PHB) in yeast Saccharomyces cerevisiae using cellobiose as a sole carbon source. Yeast strains expressing PHB pathway genes from Cupriavidus necator and cellodextrin transporter gene CDT-1 from Neurospora crassa were complemented either with β-glucosidase gene GH1-1 from N. crassa or with cellobiose phosphorylase gene cbp from Ruminococcus flavefaciens. These cellobiose utilization routes either with Gh1-1 or Cbp enzymes differ in energetics and dynamics. However, both routes enabled higher PHB production per consumed sugar and higher PHB accumulation % of cell dry weight (CDW) than use of glucose as a carbon source. As expected, the strains with Gh1-1 consumed cellobiose faster than the strains with Cbp, both in flask and bioreactor batch cultures. In shake flasks, higher final PHB accumulation % of CDW was reached with Cbp route (10.0 ± 0.3%) than with Gh1-1 route (8.1 ± 0.2%). However, a higher PHB accumulation was achieved in better aerated and pH-controlled bioreactors, in comparison to shake flasks, and the relative performance of strains switched. In bioreactors, notable PHB accumulation levels per CDW of 13.4 ± 0.9% and 18.5 ± 3.9% were achieved with Cbp and Gh1-1 routes, respectively. The average molecular weights of accumulated PHB were similar using both routes; approximately 500 kDa and 450 kDa for strains expressing either cbp or GH1-1 genes, respectively. The formation of PHB with high molecular weights, combined with efficient cellobiose conversion, demonstrates a highly potential solution for improving attractiveness of sustainable polymer production using microbial cells.

Enhancing Saccharomyces cerevisiae Taxane Biosynthesis and Overcoming Nutritional Stress-Induced Pseudohyphal Growth

The recent technological advancements in synthetic biology have demonstrated the extensive potential socio-economic benefits at laboratory scale. However, translations of such technologies to industrial scale fermentations remains a major bottleneck. The existence and lack of understanding of the major discrepancies in cultivation conditions between scales often leads to the selection of suboptimal bioprocessing conditions, crippling industrial scale productivity. In this study, strategic design of experiments approaches were coupled with state-of-the-art bioreactor tools to characterize and overcome nutritional stress for the enhanced production of precursors to the blockbuster chemotherapy drug, Taxol, in S. cerevisiae cell factories. The batch-to-batch variation in yeast extract composition was found to trigger nutritional stress at a mini-bioreactor scale, resulting in profound changes in cellular morphology and the inhibition of taxane production. The cells shifted from the typical budding morphology into striking pseudohyphal cells. Doubling initial yeast extract and peptone concentrations (2×YP) delayed filamentous growth, and taxane accumulation improved to 108 mg/L. Through coupling a statistical definitive screening design approach with the state-of-the-art high-throughput micro-bioreactors, the total taxane titers were improved a further two-fold, compared to the 2×YP culture, to 229 mg/L. Filamentous growth was absent in nutrient-limited microscale cultures, underlining the complex and multifactorial nature of yeast stress responses. Validation of the optimal microscale conditions in 1L bioreactors successfully alleviated nutritional stress and improved the titers to 387 mg/L. Production of the key Taxol precursor, T5αAc, was improved two-fold to 22 mg/L compared to previous maxima. The present study highlights the importance of following an interdisciplinary approach combining synthetic biology and bioprocessing technologies for effective process optimization and scale-up.

Sod1 integrates oxygen availability to redox regulate NADPH production and the thiol redoxome

Cu/Zn superoxide dismutase (Sod1) is a key antioxidant enzyme, and its importance is underscored by the fact that its ablation in cell and animal models results in oxidative stress; metabolic defects; and reductions in cell proliferation, viability, and lifespan. Curiously, Sod1 detoxifies superoxide radicals (O₂^•−) in a manner that produces an oxidant as byproduct, hydrogen peroxide (H₂O₂). While much is known about the necessity of scavenging O₂^•−, it is less clear what the physiological roles of Sod1-derived H₂O₂ are. We discovered that Sod1-derived H₂O₂ plays an important role in antioxidant defense by stimulating the production of NADPH, a vital cellular reductant required for reactive oxygen species scavenging enzymes, as well as redox regulating a large network of enzymes.

Cu/Zn superoxide dismutase (Sod1) is a highly conserved and abundant antioxidant enzyme that detoxifies superoxide (O₂^•−) by catalyzing its conversion to dioxygen (O₂) and hydrogen peroxide (H₂O₂). Using Saccharomyces cerevisiae and mammalian cells, we discovered that a major aspect of the antioxidant function of Sod1 is to integrate O₂ availability to promote NADPH production. The mechanism involves Sod1-derived H₂O₂ oxidatively inactivating the glycolytic enzyme, GAPDH, which in turn reroutes carbohydrate flux to the oxidative phase of the pentose phosphate pathway (oxPPP) to generate NADPH. The aerobic oxidation of GAPDH is dependent on and rate-limited by Sod1. Thus, Sod1 senses O₂ via O₂^•− to balance glycolytic and oxPPP flux, through control of GAPDH activity, for adaptation to life in air. Importantly, this mechanism for Sod1 antioxidant activity requires the bulk of cellular Sod1, unlike for its role in protection against O₂^•− toxicity, which only requires <1% of total Sod1. Using mass spectrometry, we identified proteome-wide targets of Sod1-dependent redox signaling, including numerous metabolic enzymes. Altogether, Sod1-derived H₂O₂ is important for antioxidant defense and a master regulator of metabolism and the thiol redoxome.

Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism

BACKGROUND

Several computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accuracy of inferences of intracellular metabolic flux distributions. Even though existing methods use transcript abundances as a proxy for enzyme activity, each method uses a different hypothesis and assumptions. Most methods implicitly assume a proportionality between transcript levels and flux through the corresponding function, although these proportionality constant(s) are often not explicitly mentioned nor discussed in any of the published methods. E-Flux is one such method and, in this algorithm, flux bounds are related to expression data, so that reactions associated with highly expressed genes are allowed to carry higher flux values.

RESULTS

Here, we extended E-Flux and systematically evaluated the impact of an assumed proportionality constant on model predictions. We used data from published experiments with Escherichia coli and Saccharomyces cerevisiae and we compared the predictions of the algorithm to measured extracellular and intracellular fluxes.

CONCLUSION

We showed that detailed modelling using a proportionality constant can greatly impact the outcome of the analysis. This increases accuracy and allows for extraction of better physiological information.

Methods for Oxygenation of Continuous Cultures of Brewer’s Yeast, Saccharomyces cerevisiae

Maintaining steady-state, aerobic cultures of yeast in a bioreactor depends on the configuration of the bioreactor system as well as the growth medium used. In this paper, we compare several conventional aeration methods with newer filter methods using a novel optical sensor array to monitor dissolved oxygen, pH, and biomass. With conventional methods, only a continuously stirred tank reactor configuration gave high aeration rates for cultures in yeast extract peptone dextrose (YPD) medium. For filters technologies, only a polydimethylsiloxan filter provided sufficient aeration of yeast cultures. Further, using the polydimethylsiloxan filter, the YPD medium gave inferior oxygenation rates of yeast compared to superior results with Synthetic Complete medium. It was found that the YPD medium itself, not the yeast cells, interfered with the filter giving the low oxygen transfer rates based on the volumetric transfer coefficient (KLa). The results are discussed for implications of miniaturized bioreactors in low-gravity environments.

Protein Abundance Prediction Through Machine Learning Methods

Proteins are responsible for most physiological processes, and their abundance provides crucial information for systems biology research. However, absolute protein quantification, as determined by mass spectrometry, still has limitations in capturing the protein pool. Protein abundance is impacted by translation kinetics, which rely on features of codons. In this study, we evaluated the effect of codon usage bias of genes on protein abundance. Notably, we observed differences regarding codon usage patterns between genes coding for highly abundant proteins and genes coding for less abundant proteins. Analysis of synonymous codon usage and evolutionary selection showed a clear split between the two groups. Our machine learning models predicted protein abundances from codon usage metrics with remarkable accuracy, achieving strong correlation with experimental data. Upon integration of the predicted protein abundance in enzyme-constrained genome-scale metabolic models, the simulated phenotypes closely matched experimental data, which demonstrates that our predictive models are valuable tools for systems metabolic engineering approaches.

Yeast grown in continuous culture systems can detect mutagens with improved sensitivity relative to the Ames test

Continuous culture systems allow for the controlled growth of microorganisms over a long period of time. Here, we develop a novel test for mutagenicity that involves growing yeast in continuous culture systems exposed to low levels of mutagen for a period of approximately 20 days. In contrast, most microorganism-based tests for mutagenicity expose the potential mutagen to the biological reporter at a high concentration of mutagen for a short period of time. Our test improves upon the sensitivity of the well-established Ames test by at least 20-fold for each of two mutagens that act by different mechanisms (the intercalator ethidium bromide and alkylating agent methyl methanesulfonate). To conduct the tests, cultures were grown in small, inexpensive continuous culture systems in media containing (potential) mutagen, and the resulting mutagenicity of the added compound was assessed via two methods: a canavanine-based plate assay and whole genome sequencing. In the canavanine-based plate assay, we were able to detect a clear relationship between the amount of mutagen and the number of canavanine-resistant mutant colonies over a period of one to three weeks of exposure. Whole genome sequencing of yeast grown in continuous culture systems exposed to methyl methanesulfonate demonstrated that quantification of mutations is possible by identifying the number of unique variants across each strain. However, this method had lower sensitivity than the plate-based assay and failed to distinguish the different concentrations of mutagen. In conclusion, we propose that yeast grown in continuous culture systems can provide an improved and more sensitive test for mutagenicity.

Very High Gravity Bioethanol Revisited: Main Challenges and Advances

Over the last decades, the constant growth of the world-wide industry has been leading to more and more concerns with its direct impact on greenhouse gas (GHG) emissions. Resulting from that, rising efforts have been dedicated to a global transition from an oil-based industry to cleaner biotechnological processes. A specific example refers to the production of bioethanol to substitute the traditional transportation fuels. Bioethanol has been produced for decades now, mainly from energy crops, but more recently, also from lignocellulosic materials. Aiming to improve process economics, the fermentation of very high gravity (VHG) mediums has for long received considerable attention. Nowadays, with the growth of multi-waste valorization frameworks, VHG fermentation could be crucial for bioeconomy development. However, numerous obstacles remain. This work initially presents the main aspects of a VHG process, giving then special emphasis to some of the most important factors that traditionally affect the fermentation organism, such as nutrients depletion, osmotic stress, and ethanol toxicity. Afterwards, some factors that could possibly enable critical improvements in the future on VHG technologies are discussed. Special attention was given to the potential of the development of new fermentation organisms, nutritionally complete culture media, but also on alternative process conditions and configurations.

Comparative proteomic analyses reveal the metabolic aspects and biotechnological potential of nitrate assimilation in the yeast Dekkera bruxellensis

The yeast Dekkera bruxellensis is well-known for its adaptation to industrial ethanol fermentation processes, which can be further improved if nitrate is present in the substrate. To date, the assimilation of nitrate has been considered inefficient because of the apparent energy cost imposed on cell metabolism. Recent research, however, has shown that nitrate promotes growth rate and ethanol yield when oxygen is absent from the environment. Given this, the present work aimed to identify the biological mechanisms behind this physiological behaviour. Proteomic analyses comparing four contrasting growth conditions gave some clues on how nitrate could be used as primary nitrogen source by D. bruxellensis GDB 248 (URM 8346) cells in anaerobiosis. The superior anaerobic growth in nitrate seems to be a consequence of increased cell metabolism (glycolytic pathway, production of ATP and NADPH and anaplerotic reactions providing metabolic intermediates) regulated by balanced activation of TORC1 and NCR de-repression mechanisms. On the other hand, the poor growth observed in aerobiosis is likely due to an oxidative stress triggered by nitrate when oxygen is present. These results represent a milestone regarding the knowledge about nitrate metabolism and might be explored for future use of D. bruxellensis as an industrial yeast. KEY POINTS: • Nitrate can be regarded as preferential nitrogen source for D. bruxellensis. • Oxidative stress limits the growth of D. bruxellensis in nitrate in aerobiosis. • Nitrate is a nutrient for novel industrial bioprocesses using D. bruxellensis.

Rewiring yeast metabolism to synthesize products beyond ethanol

Saccharomyces cerevisiae, Baker's yeast, is the industrial workhorse for producing ethanol and the subject of substantial metabolic engineering research in both industry and academia. S. cerevisiae has been used to demonstrate production of a wide range of chemical products from glucose. However, in many cases, the demonstrations report titers and yields that fall below thresholds for industrial feasibility. Ethanol synthesis is a central part of S. cerevisiae metabolism, and redirecting flux to other products remains a barrier to industrialize strains for producing other molecules. Removing ethanol producing pathways leads to poor fitness, such as impaired growth on glucose. Here, we review metabolic engineering efforts aimed at restoring growth in non-ethanol producing strains with emphasis on relieving glucose repression associated with the Crabtree effect and rewiring metabolism to provide access to critical cellular building blocks. Substantial progress has been made in the past decade, but many opportunities for improvement remain.

Genome-Scale Metabolic Model of the Human Pathogen Candida albicans: A Promising Platform for Drug Target Prediction

Candida albicans is one of the most impactful fungal pathogens and the most common cause of invasive candidiasis, which is associated with very high mortality rates. With the rise in the frequency of multidrug-resistant clinical isolates, the identification of new drug targets and new drugs is crucial in overcoming the increase in therapeutic failure. In this study, the first validated genome-scale metabolic model for Candida albicans, iRV781, is presented. The model consists of 1221 reactions, 926 metabolites, 781 genes, and four compartments. This model was reconstructed using the open-source software tool merlin 4.0.2. It is provided in the well-established systems biology markup language (SBML) format, thus, being usable in most metabolic engineering platforms, such as OptFlux or COBRA. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. Finally, this genome-scale metabolic reconstruction was tested as a platform for the identification of drug targets, through the comparison between known drug targets and the prediction of gene essentiality in conditions mimicking the human host. Altogether, this model provides a promising platform for global elucidation of the metabolic potential of C. albicans, possibly guiding the identification of new drug targets to tackle human candidiasis.

Assessment of transcriptomic constraint-based methods for central carbon flux inference

MOTIVATION

Determining intracellular metabolic flux through isotope labeling techniques such as 13C metabolic flux analysis (13C-MFA) incurs significant cost and effort. Previous studies have shown transcriptomic data coupled with constraint-based metabolic modeling can determine intracellular fluxes that correlate highly with 13C-MFA measured fluxes and can achieve higher accuracy than constraint-based metabolic modeling alone. These studies, however, used validation data limited to E. coli and S. cerevisiae grown on glucose, with significantly similar flux distribution for central metabolism. It is unclear whether those results apply to more diverse metabolisms, and therefore further, extensive validation is needed.

RESULTS

In this paper, we formed a dataset of transcriptomic data coupled with corresponding 13C-MFA flux data for 21 experimental conditions in different unicellular organisms grown on varying carbon substrates and conditions. Three computational flux-balance analysis (FBA) methods were comparatively assessed. The results show when uptake rates of carbon sources and key metabolites are known, transcriptomic data provides no significant advantage over constraint-based metabolic modeling (average correlation coefficients, transcriptomic E-Flux2 0.725 and SPOT 0.650 vs non-transcriptomic pFBA 0.768). When uptake rates are unknown, however, predictions obtained utilizing transcriptomic data are generally good and significantly better than those obtained using constraint-based metabolic modeling alone (E-Flux2 0.385 and SPOT 0.583 vs pFBA 0.237). Thus, transcriptomic data coupled with constraint-based metabolic modeling is a promising method to obtain intracellular flux estimates in microorganisms, particularly in cases where uptake rates of key metabolites cannot be easily determined, such as for growth in complex media or in vivo conditions.

Differential Activation of Ferulic Acid Catabolic Pathways of Amycolatopsis sp. ATCC 39116 in Submerged and Surface Cultures

Amycolatopsis sp. ATCC 39116 catabolizes ferulic acid by the non-oxidative deacetylation and β-oxidation pathways to produce vanillin and vanillic acid, respectively. In submerged culture, vanillin productivity decreased more than 8-fold, when ferulic, p-coumaric, and caffeic acids were employed in pre-cultures of the microorganism in order to activate the ferulic acid catabolic pathways, resulting in a carbon redistribution since vanillic acid and guaiacol productivities increased more than 5-fold compared with control. In contrast, in surface culture, the effects of ferulic and sinapic acids in pre-cultures were totally opposite to those of the submerged culture, directing the carbon distribution into vanillin formation. In surface culture, more than 30% of ferulic acid can be used as carbon source for other metabolic processes, such as ATP regeneration. In this way, the intracellular ATP concentration remained constant during the biotransformation process by surface culture (100 μg ATP/mg protein), demonstrating a high energetic state, which can maintain active the non-oxidative deacetylation pathway. In contrast, in submerged culture, it decreased 3.15-fold at the end of the biotransformation compared with the initial content, showing a low energetic state, while the NAD⁺/NADH ratio (23.15) increased 1.81-fold. It seems that in submerged culture, low energetic and high oxidative states are the physiological conditions that can redirect the ferulic catabolism into β-oxidative pathway and/or vanillin oxidation to produce vanillic acid.

Improving cytosolic aspartate biosynthesis increases glucoamylase production in Aspergillus niger under oxygen limitation

Background

Glucoamylase is one of the most industrially applied enzymes, produced by Aspergillus species, like Aspergillus niger. Compared to the traditional ways of process optimization, the metabolic engineering strategies to improve glucoamylase production are relatively scarce.

Results

In the previous study combined multi-omics integrative analysis and amino acid supplementation experiment, we predicted four amino acids (alanine, glutamate, glycine and aspartate) as the limited precursors for glucoamylase production in A. niger. To further verify this, five mutants namely OE-ala, OE-glu, OE-gly, OE-asp1 and OE-asp2, derived from the parental strain A. niger CBS 513.88, were constructed respectively for the overexpression of five genes responsible for the biosynthesis of the four kinds of amino acids (An11g02620, An04g00990, An05g00410, An04g06380 and An16g05570). Real-time quantitative PCR revealed that all these genes were successfully overexpressed at the mRNA level while the five mutants exhibited different performance in glucoamylase production in shake flask cultivation. Notably, the results demonstrated that mutant OE-asp2 which was constructed for reinforcing cytosolic aspartate synthetic pathway, exhibited significantly increased glucoamylase activity by 23.5% and 60.3% compared to CBS 513.88 in the cultivation of shake flask and the 5 L fermentor, respectively. Compared to A. niger CBS 513.88, mutant OE-asp2 has a higher intracellular amino acid pool, in particular, alanine, leucine, glycine and glutamine, while the pool of glutamate was decreased.

Conclusion

Our study combines the target prediction from multi-omics analysis with the experimental validation and proves the possibility of increasing glucoamylase production by enhancing limited amino acid biosynthesis. In short, this systematically conducted study will surely deepen the understanding of resources allocation in cell factory and provide new strategies for the rational design of enzyme production strains.

Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis

Motivation

Transcriptomics and proteomics data have been integrated into constraint-based models to influence flux predictions. However, it has been reported recently for Escherichia coli and Saccharomyces cerevisiae, that model predictions from parsimonious flux balance analysis (pFBA), which does not use expression data, are as good or better than predictions from various algorithms that integrate transcriptomics or proteomics data into constraint-based models.

Results

In this paper, we describe a novel constraint-based method called Linear Bound Flux Balance Analysis (LBFBA), which uses expression data (either transcriptomic or proteomic) to predict metabolic fluxes. The method uses expression data to place soft constraints on individual fluxes, which can be violated. Parameters in the soft constraints are first estimated from a training expression and flux dataset before being used to predict fluxes from expression data in other conditions. We applied LBFBA to E.coli and S.cerevisiae datasets and found that LBFBA predictions were more accurate than pFBA predictions, with average normalized errors roughly half of those from pFBA. For the first time, we demonstrate a computational method that integrates expression data into constraint-based models and improves quantitative flux predictions over pFBA.

Availability and implementation

Code is available in the Supplementary data available at Bioinformatics online.

Supplementary information

Supplementary data are available at Bioinformatics online.

Osmolyte homeostasis controls single-cell growth rate and maximum cell size of Saccharomyces cerevisiae

Cell growth is well described at the population level, but precisely how nutrient and water uptake and cell wall expansion drive the growth of single cells is poorly understood. Supported by measurements of single-cell growth trajectories and cell wall elasticity, we present a single-cell growth model for yeast. The model links the thermodynamic quantities, such as turgor pressure, osmolarity, cell wall elasto-plasticity, and cell size, applying concepts from rheology and thin shell theory. It reproduces cell size dynamics during single-cell growth, budding, and hyper-osmotic or hypo-osmotic stress. We find that single-cell growth rate and final size are primarily governed by osmolyte uptake and consumption, while bud expansion requires additionally different cell wall extensibilities between mother and bud. Based on first principles the model provides a more accurate description of size dynamics than previous attempts and its analytical simplification allows for easy combination with models for other cell processes.

The molecular biology of fruity and floral aromas in beer and other alcoholic beverages

Aroma compounds provide attractiveness and variety to alcoholic beverages. We discuss the molecular biology of a major subset of beer aroma volatiles, fruity and floral compounds, originating from raw materials (malt and hops), or formed by yeast during fermentation. We introduce aroma perception, describe the most aroma-active, fruity and floral compounds in fruits and their presence and origin in beer. They are classified into categories based on their functional groups and biosynthesis pathways: (1) higher alcohols and esters, (2) polyfunctional thiols, (3) lactones and furanones, and (4) terpenoids. Yeast and hops are the main sources of fruity and flowery aroma compounds in beer. For yeast, the focus is on higher alcohols and esters, and particularly the complex regulation of the alcohol acetyl transferase ATF1 gene. We discuss the release of polyfunctional thiols and monoterpenoids from cysteine- and glutathione-S-conjugated compounds and glucosides, respectively, the primary biological functions of the yeast enzymes involved, their mode of action and mechanisms of regulation that control aroma compound production. Furthermore, we discuss biochemistry and genetics of terpenoid production and formation of non-volatile precursors in Humulus lupulus (hops). Insight in these pathways provides a toolbox for creating innovative products with a diversity of pleasant aromas.

Next-Generation Genome-Scale Models Incorporating Multilevel 'Omics Data: From Yeast to Human

Genome-scale modelling in eukaryotes has been pioneered by the yeast Saccharomyces cerevisiae. Early metabolic networks have been reconstructed based on genome sequence and information accumulated in the literature on biochemical reactions. Protein-protein interaction networks have been constructed based on experimental observations such as yeast-2-hybrid method. Gene regulatory networks were based on a variety of data types, including information on TF-promoter binding and gene coexpression. The aforementioned networks have been improved gradually, and methods for their integration were developed. Incorporation of omics data including genomics, metabolomics, transcriptomics, fluxome, and phosphoproteome led to next-generation genome-scale models. The methods tested on yeast have later been implemented in human, further, cellular components found to be important in yeast physiology under (ab)normal conditions, and (dis)regulation mechanisms in yeast shed light to the healthy and disease states in human. This chapter provides a historical perspective on next-generation genome-scale models incorporating multilevel 'omics data, from yeast to human.

Production efficiency of the bacterial non-ribosomal peptide indigoidine relies on the respiratory metabolic state in S. cerevisiae

BACKGROUND

Beyond pathway engineering, the metabolic state of the production host is critical in maintaining the efficiency of cellular production. The biotechnologically important yeast Saccharomyces cerevisiae adjusts its energy metabolism based on the availability of oxygen and carbon sources. This transition between respiratory and non-respiratory metabolic state is accompanied by substantial modifications of central carbon metabolism, which impact the efficiency of metabolic pathways and the corresponding final product titers. Non-ribosomal peptide synthetases (NRPS) are an important class of biocatalysts that provide access to a wide array of secondary metabolites. Indigoidine, a blue pigment, is a representative NRP that is valuable by itself as a renewably produced pigment.

RESULTS

Saccharomyces cerevisiae was engineered to express a bacterial NRPS that converts glutamine to indigoidine. We characterize carbon source use and production dynamics, and demonstrate that indigoidine is solely produced during respiratory cell growth. Production of indigoidine is abolished during non-respiratory growth even under aerobic conditions. By promoting respiratory conditions via controlled feeding, we scaled the production to a 2 L bioreactor scale, reaching a maximum titer of 980 mg/L.

CONCLUSIONS

This study represents the first use of the Streptomyces lavendulae NRPS (BpsA) in a fungal host and its scale-up. The final product indigoidine is linked to the activity of the TCA cycle and serves as a reporter for the respiratory state of S. cerevisiae. Our approach can be broadly applied to investigate diversion of flux from central carbon metabolism for NRPS and other heterologous pathway engineering, or to follow a population switch between respiratory and non-respiratory modes.

Multi-omics integrative analysis with genome-scale metabolic model simulation reveals global cellular adaptation of Aspergillus niger under industrial enzyme production condition

Oxygen limitation is regarded as a useful strategy to improve enzyme production by mycelial fungus like Aspergillus niger. However, the intracellular metabolic response of A. niger to oxygen limitation is still obscure. To address this, the metabolism of A. niger was studied using multi-omics integrated analysis based on the latest GEMs (genome-scale metabolic model), including metabolomics, fluxomics and transcriptomics. Upon sharp reduction of the oxygen supply, A. niger metabolism shifted to higher redox level status, as well as lower energy supply, down-regulation of genes for fatty acid synthesis and a rapid decrease of the specific growth rate. The gene expression of the glyoxylate bypass was activated, which was consistent with flux analysis using the A. niger GEMs iHL1210. The increasing flux of the glyoxylate bypass was assumed to reduce the NADH formation from TCA cycle and benefit maintenance of the cellular redox balance under hypoxic conditions. In addition, the relative fluxes of the EMP pathway were increased, which possibly relieved the energy demand for cell metabolism. The above multi-omics integrative analysis provided new insights on metabolic regulatory mechanisms of A. niger associated with enzyme production under oxygen-limited condition, which will benefit systematic design and optimization of the A. niger microbial cell factory.

l-Arabinose triggers its own uptake via induction of the arabinose-specific Gal2p transporter in an industrial Saccharomyces cerevisiae strain

Bioethanol production processes with Saccharomyces cerevisiae using lignocellulosic biomass as feedstock are challenged by the simultaneous utilization of pentose and hexose sugars from biomass hydrolysates. The pentose uptake into the cell represents a crucial role for the efficiency of the process. The focus of the here presented study was to understand the uptake and conversion of the pentose l-arabinose in S. cerevisiae and reveal its regulation by d-glucose and d-galactose. Gal2p-the most prominent transporter enabling l-arabinose uptake in S. cerevisiae wild-type strains-has an affinity for the transport of l-arabinose, d-glucose, and d-galactose. d-Galactose was reported for being mandatory for inducing GAL2 expression. GAL2 expression is also known to be regulated by d-glucose-mediated carbon catabolite repression, as well as catabolite inactivation. The results of the present study demonstrate that l-arabinose can be used as sole carbon and energy source by the recombinant industrial strain S. cerevisiae DS61180. RT-qPCR and RNA-Seq experiments confirmed that l-arabinose can trigger its own uptake via the induction of GAL2 expression. Expression levels of GAL2 during growth on l-arabinose reached up to 21% of those obtained with d-galactose as sole carbon and energy source. l-Arabinose-induced GAL2 expression was also subject to catabolite repression by d-glucose. Kinetic investigations of substrate uptake, biomass, and product formation during growth on a mixture of d-glucose/l-arabinose revealed impairment of growth and ethanol production from l-arabinose upon d-glucose depletion. The presence of d-glucose is thus preventing the fermentation of l-arabinose in S. cerevisiae DS61180. Comparative transcriptome studies including the wild-type and a precursor strain delivered hints for an increased demand in ATP production and cofactor regeneration during growth of S. cerevisiae DS61180 on l-arabinose. Our results thus emphasize that cofactor and energy metabolism demand attention if the combined conversion of hexose and pentose sugars is intended, for example in biorefineries using lignocellulosics.

L-Lactic acid production from glucose and xylose with engineered strains of Saccharomyces cerevisiae: aeration and carbon source influence yields and productivities

BACKGROUND

Saccharomyces cerevisiae, engineered for L-lactic acid production from glucose and xylose, is a promising production host for lignocellulose-to-lactic acid processes. However, the two principal engineering strategies-pyruvate-to-lactic acid conversion with and without disruption of the competing pyruvate-to-ethanol pathway-have not yet resulted in strains that combine high lactic acid yields (Y_LA) and productivities (Q_LA) on both sugar substrates. Limitations seemingly arise from a dependency on the carbon source and the aeration conditions, but the underlying effects are poorly understood. We have recently presented two xylose-to-lactic acid converting strains, IBB14LA1 and IBB14LA1_5, which have the L-lactic acid dehydrogenase from Plasmodium falciparum (pfLDH) integrated at the pdc1 (pyruvate decarboxylase) locus. IBB14LA1_5 additionally has its pdc5 gene knocked out. In this study, the influence of carbon source and oxygen on Y_LA and Q_LA in IBB14LA1 and IBB14LA1_5 was investigated.

RESULTS

In anaerobic fermentation IBB14LA1 showed a higher Y_LA on xylose (0.27 g g _Xyl^-1 ) than on glucose (0.18 g g _Glc^-1 ). The ethanol yields (Y_EtOH, 0.15 g g _Xyl^-1 and 0.32 g g _Glc^-1 ) followed an opposite trend. In IBB14LA1_5, the effect of the carbon source on Y_LA was less pronounced (~ 0.80 g g _Xyl^-1 , and 0.67 g g _Glc^-1 ). Supply of oxygen accelerated glucose conversions significantly in IBB14LA1 (Q_LA from 0.38 to 0.81 g L^-1 h^-1) and IBB14LA1_5 (Q_LA from 0.05 to 1.77 g L^-1 h^-1) at constant Y_LA (IBB14LA1 ~ 0.18 g g _Glc^-1 ; IBB14LA1_5 ~ 0.68 g g _Glc^-1 ). In aerobic xylose conversions, however, lactic acid production ceased completely in IBB14LA1 and decreased drastically in IBB14LA1_5 (Y_LA aerobic ≤ 0.25 g g _Xyl^-1 and anaerobic ~ 0.80 g g _Xyl^-1 ) at similar Q_LA (~ 0.04 g L^-1 h^-1). Switching from aerobic to microaerophilic conditions (pO₂ ~ 2%) prevented lactic acid metabolization, observed for fully aerobic conditions, and increased Q_LA and Y_LA up to 0.11 g L^-1 h^-1 and 0.38 g g _Xyl^-1 , respectively. The pfLDH and PDC activities in IBB14LA1 were measured and shown to change drastically dependent on carbon source and oxygen.

CONCLUSION

Evidence from conversion time courses together with results of activity measurements for pfLDH and PDC show that in IBB14LA1 the distribution of fluxes at the pyruvate branching point is carbon source and oxygen dependent. Comparison of the performance of strain IBB14LA1 and IBB14LA1_5 in conversions under different aeration conditions (aerobic, anaerobic, and microaerophilic) further suggest that xylose, unlike glucose, does not repress the respiratory response in both strains. This study proposes new genetic engineering targets for rendering genetically engineering S. cerevisiae better suited for lactic acid biorefineries.

13 C metabolic flux profiling of Pichia pastoris grown in aerobic batch cultures on glucose revealed high relative anabolic use of TCA cycle and limited incorporation of provided precursors of branched-chain amino acids

Carbon metabolism of Crabtree-negative yeast Pichia pastoris was profiled using ¹³ C nuclear magnetic resonance (NMR) to delineate regulation during exponential growth and to study the import of two precursors for branched-chain amino acid biosynthesis, α-ketoisovalerate and α-ketobutyrate. Cells were grown in aerobic batch cultures containing (a) only glucose, (b) glucose along with the precursors, or (c) glucose and Val. The study provided the following new insights. First, ¹³ C flux ratio analyses of central metabolism reveal an unexpectedly high anaplerotic supply of the tricarboxylic acid cycle for a Crabtree-negative yeast, and show that a substantial fraction of glucose catabolism proceeds through the pentose phosphate pathway. A comparison with previous flux ratio analyses for batch cultures of Crabtree-negative Pichia stipitis and Crabtree-positive Saccharomyces cerevisiae indicate that the overall regulation of central carbon metabolism in P. pastoris is intermediate in between P. stipitis and S. cerevisiae. Second, excess α-ketoisovalerate in the medium is not transported into the cytoplasm indicating that P. pastoris lacks a suitable transporter. In contrast, excess Val is efficiently taken up and largely fulfills demands for both Val and Leu for protein synthesis. Third, excess α-ketobutyrate is transported into the mitochondria for Ile biosynthesis. However, the import does not efficiently inhibit the synthesis of α-ketobutyrate from pyruvate indicating that P. pastoris has not been optimized evolutionarily to take full advantage of this carbon source. These findings have direct implications for preparing uniformly ² H,¹³ C,¹⁵ N-labeled proteins containing protonated Ile, Val, and Leu methyl groups in P. pastoris for NMR-based structural biology.

ENZYMES

Acetohydroxy acid isomeroreductase (EC 1.1.1.86), branched-chain amino acid aminotransferase (BCAT, EC 2.6.1.42), fumarase (EC 4.2.1.2), malic enzyme (EC 1.1.1.39/1.1.1.40), phosphoenolpyruvate carboxykinase (EC 4.1.1.49), pyruvate carboxylase (EC 6.4.1.1), pyruvate kinase (EC 2.7.1.40), l-serine hydroxymethyltransferase (EC 2.1.2.1), threonine aldolase (EC 4.1.2.5), threonine dehydratase (EC 4.3.1.19); transketolase (EC 2.2.1.1), transaldolase (EC 2.2.1.2).

Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints

Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering.

Carbon 13-Metabolic Flux Analysis derived constraint-based metabolic modelling of Clostridium acetobutylicum in stressed chemostat conditions

The metabolism of butanol producing bacteria Clostridium acetobutylicum was studied in chemostat with glucose limited conditions, butanol stimulus, and as a reference cultivation. COnstraint-Based Reconstruction and Analysis (COBRA) was applied using additional constraints from (13)C Metabolic Flux Analysis ((13)C-MFA) and experimental measurement results. A model consisting of 451 metabolites and 604 reactions was utilized in flux balance analysis (FBA). The stringency of the flux spaces considering different optimization objectives, i.e. growth rate maximization, ATP maintenance, and NADH/NADPH formation, for flux variance analysis (FVA) was studied in the different modelled conditions. Also a previously uncharacterized exopolysaccharide (EPS) produced by C. acetobutylicum was characterized on monosaccharide level. The major monosaccharide components of the EPS were 40n-% rhamnose, 34n-% glucose, 13n-% mannose, 10n-% galactose, and 2n-% arabinose. The EPS was studied to have butanol adsorbing property, 70(butanol)mg(EPS)g(-1) at 37°C.

Systems-level analysis of mechanisms regulating yeast metabolic flux

Cellular metabolic fluxes are determined by enzyme activities and metabolite abundances. Biochemical approaches reveal the impact of specific substrates or regulators on enzyme kinetics but do not capture the extent to which metabolite and enzyme concentrations vary across physiological states and, therefore, how cellular reactions are regulated. We measured enzyme and metabolite concentrations and metabolic fluxes across 25 steady-state yeast cultures. We then assessed the extent to which flux can be explained by a Michaelis-Menten relationship between enzyme, substrate, product, and potential regulator concentrations. This revealed three previously unrecognized instances of cross-pathway regulation, which we biochemically verified. One of these involved inhibition of pyruvate kinase by citrate, which accumulated and thereby curtailed glycolytic outflow in nitrogen-limited yeast. Overall, substrate concentrations were the strongest driver of the net rates of cellular metabolic reactions, with metabolite concentrations collectively having more than double the physiological impact of enzymes.

Can we predict the intracellular metabolic state of a cell based on extracellular metabolite data?

The analysis of extracellular metabolites presents many technical advantages over the analysis of intracellular compounds, which made this approach very popular in recent years as a high-throughput tool to assess the metabolic state of microbial cells. However, very little effort has been made to determine the actual relationship between intracellular and extracellular metabolite levels. The secretion of intracellular metabolites has been traditionally interpreted as a consequence of an intracellular metabolic overflow, which is based on the premise that for a metabolite to be secreted, it must be over-produced inside the cell. Therefore, we expect to find a secreted metabolite at increased levels inside the cells. Here we present a time-series metabolomics study of Saccharomyces cerevisiae growing on a glucose-limited chemostat with parallel measurements of intra- and extracellular metabolites. Although most of the extracellular metabolites were also detected in the intracellular samples and showed a typical metabolic overflow behaviour, we demonstrate that the secretion of many metabolites could not be explained by the metabolic overflow theory.

E-Flux2 and SPOT: Validated Methods for Inferring Intracellular Metabolic Flux Distributions from Transcriptomic Data

Background

Several methods have been developed to predict system-wide and condition-specific intracellular metabolic fluxes by integrating transcriptomic data with genome-scale metabolic models. While powerful in many settings, existing methods have several shortcomings, and it is unclear which method has the best accuracy in general because of limited validation against experimentally measured intracellular fluxes.

Results

We present a general optimization strategy for inferring intracellular metabolic flux distributions from transcriptomic data coupled with genome-scale metabolic reconstructions. It consists of two different template models called DC (determined carbon source model) and AC (all possible carbon sources model) and two different new methods called E-Flux2 (E-Flux method combined with minimization of l² norm) and SPOT (Simplified Pearson cOrrelation with Transcriptomic data), which can be chosen and combined depending on the availability of knowledge on carbon source or objective function. This enables us to simulate a broad range of experimental conditions. We examined E. coli and S. cerevisiae as representative prokaryotic and eukaryotic microorganisms respectively. The predictive accuracy of our algorithm was validated by calculating the uncentered Pearson correlation between predicted fluxes and measured fluxes. To this end, we compiled 20 experimental conditions (11 in E. coli and 9 in S. cerevisiae), of transcriptome measurements coupled with corresponding central carbon metabolism intracellular flux measurements determined by ¹³C metabolic flux analysis (¹³C-MFA), which is the largest dataset assembled to date for the purpose of validating inference methods for predicting intracellular fluxes. In both organisms, our method achieves an average correlation coefficient ranging from 0.59 to 0.87, outperforming a representative sample of competing methods. Easy-to-use implementations of E-Flux2 and SPOT are available as part of the open-source package MOST (http://most.ccib.rutgers.edu/).

Conclusion

Our method represents a significant advance over existing methods for inferring intracellular metabolic flux from transcriptomic data. It not only achieves higher accuracy, but it also combines into a single method a number of other desirable characteristics including applicability to a wide range of experimental conditions, production of a unique solution, fast running time, and the availability of a user-friendly implementation.

Improving the flux distributions simulated with genome-scale metabolic models of Saccharomyces cerevisiae

Genome-scale metabolic models (GEMs) can be used to evaluate genotype-phenotype relationships and their application to microbial strain engineering is increasing in popularity. Some of the algorithms used to simulate the phenotypes of mutant strains require the determination of a wild-type flux distribution. However, the accuracy of this reference, when calculated with flux balance analysis, has not been studied in detail before.

Here, the wild-type simulations of selected GEMs for Saccharomyces cerevisiae have been analysed and most of the models tested predicted erroneous fluxes in central pathways, especially in the pentose phosphate pathway. Since the problematic fluxes were mostly related to areas of the metabolism consuming or producing NADPH/NADH, we have manually curated all reactions including these cofactors by forcing the use of NADPH/NADP⁺ in anabolic reactions and NADH/NAD⁺ for catabolic reactions. The curated models predicted more accurate flux distributions and performed better in the simulation of mutant phenotypes.

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The flux distributions of the genome-scale models of Saccharomyces cerevisiae were evaluated

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Most of the tested models showed fluxes inconsistent with experimental data

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A manual curation process was performed on all reactions including NADH or NADPH

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The curated models showed flux distributions more consistent with experimental data

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Phenotype simulations improved when the curated flux distributions were used

Dynamic metabolic models in context: biomass backtracking

Mathematical modeling has proven to be a powerful tool to understand and predict functional and regulatory properties of metabolic processes. High accuracy dynamic modeling of individual pathways is thereby opposed by simplified but genome scale constraint based approaches. A method that links these two powerful techniques would greatly enhance predictive power but is so far lacking. We present biomass backtracking, a workflow that integrates the cellular context in existing dynamic metabolic models via stoichiometrically exact drain reactions based on a genome scale metabolic model. With comprehensive examples, for different species and environmental contexts, we show the importance and scope of applications and highlight the improvement compared to common boundary formulations in existing metabolic models. Our method allows for the contextualization of dynamic metabolic models based on all available information. We anticipate this to greatly increase their accuracy and predictive power for basic research and also for drug development and industrial applications.

Production of succinic acid by metabolically engineered microorganisms

Succinic acid (SA) has been recognized as one of the most important bio-based building block chemicals due to its numerous potential applications. For the economical bio-based production of SA, extensive research works have been performed on developing microbial strains by metabolic engineering as well as fermentation and downstream processes. Here we review metabolic engineering strategies applied for bio-based production of SA using representative microorganisms, including Saccharomyces cerevisiae, Pichia kudriavzevii, Escherichia coli, Mannheimia succiniciproducens, Basfia succiniciproducens, Actinobacillus succinogenes, and Corynebacterium glutamicum. In particular, strategies employed for developing engineered strains of these microorganisms leading to the best performance indices (titer, yield, and productivity) are showcased based on the published papers as well as patents. Those processes currently under commercialization are also analyzed and future perspectives are provided.

Achieving Metabolic Flux Analysis for S. cerevisiae at a Genome-Scale: Challenges, Requirements, and Considerations

Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted.

Effect of aerobic and microaerophilic culture in the growth dynamics of Saccharomyces cerevisiae and in training of quiescent and non-quiescent subpopulations

Saccharomyces cerevisiae is industrially the most important yeast, and its growth in different concentrations of oxygen can be used to improve various application processes. The aims of this work were to study in aerobic and microaerophilic growth conditions the cell size and tendency of morphological changes in S. cerevisiae in different stages of growth and to assess the effect of the two growth conditions in the differentiation of quiescent and non-quiescent subpopulations in the stationary phase. Dissolved oxygen levels in the culture medium for aerobic and microaerophilic conditions were 6.6 and 5.2 mg L(-1), respectively. In both growth conditions, similar viable cell populations were obtained, although in aerobic conditions the stationary phase was reached and the quiescent and non-quiescent subpopulations were also differentiated. The microaerophilic growth produced a significant reduction in the specific growth rate and consequently also in glucose and oxygen consumption. The most notable changes in cellular size and morphology occurred with the depletion of glucose and oxygen. The concentration of dissolved oxygen in the culture medium significantly modulated the growth kinetics of S. cerevisiae and their development and differentiation to quiescent cells. This could justify the need to readjust small variations in oxygen levels during yeast cultures in biotechnological processes.

Enhanced hexose fermentation by Saccharomyces cerevisiae through integration of stoichiometric modeling and genetic screening

In order to determine beneficial gene deletions for ethanol production by the yeast Saccharomyces cerevisiae, we performed an in silico gene deletion experiment based on a genome-scale metabolic model. Genes coding for two oxidative phosphorylation reactions (cytochrome c oxidase and ubiquinol cytochrome c reductase) were identified by the model-based simulation as potential deletion targets for enhancing ethanol production and maintaining acceptable overall growth rate in oxygen-limited conditions. Since the two target enzymes are composed of multiple subunits, we conducted a genetic screening study to evaluate the in silico results and compare the effect of deleting various portions of the respiratory enzyme complexes. Over two-thirds of the knockout mutants identified by the in silico study did exhibit experimental behavior in qualitative agreement with model predictions, but the exceptions illustrate the limitation of using a purely stoichiometric model-based approach. Furthermore, there was a substantial quantitative variation in phenotype among the various respiration-deficient mutants that were screened in this study, and three genes encoding respiratory enzyme subunits were identified as the best knockout targets for improving hexose fermentation in microaerobic conditions. Specifically, deletion of either COX9 or QCR9 resulted in higher ethanol production rates than the parental strain by 37% and 27%, respectively, with slight growth disadvantages. Also, deletion of QCR6 led to improved ethanol production rate by 24% with no growth disadvantage. The beneficial effects of these gene deletions were consistently demonstrated in different strain backgrounds and with four common hexoses. The combination of stoichiometric modeling and genetic screening using a systematic knockout collection was useful for narrowing a large set of gene targets and identifying targets of interest.

Multi-capillary column-ion mobility spectrometry of volatile metabolites emitted by Saccharomyces cerevisiae

Volatile organic compounds (VOCs) produced during microbial fermentations determine the flavor of fermented food and are of interest for the production of fragrances or food additives. However, the microbial synthesis of these compounds from simple carbon sources has not been well investigated so far. Here, we analyzed the headspace over glucose minimal salt medium cultures of Saccharomyces cerevisiae using multi-capillary column-ion mobility spectrometry (MCC-IMS). The high sensitivity and fast data acquisition of the MCC-IMS enabled online analysis of the fermentation off-gas and 19 specific signals were determined. To four of these volatile compounds, we could assign the metabolites ethanol, 2-pentanone, isobutyric acid, and 2,3-hexanedione by MCC-IMS measurements of pure standards and cross validation with thermal desorption-gas chromatography-mass spectrometry measurements. Despite the huge biochemical knowledge of the biochemistry of the model organism S. cerevisiae, only the biosynthetic pathways for ethanol and isobutyric acid are fully understood, demonstrating the considerable lack of research of volatile metabolites. As monitoring of VOCs produced during microbial fermentations can give valuable insight into the metabolic state of the organism, fast and non-invasive MCC-IMS analyses provide valuable data for process control.

Use of chemostat cultures mimicking different phases of wine fermentations as a tool for quantitative physiological analysis

Background

Saccharomyces cerevisiae is the most relevant yeast species conducting the alcoholic fermentation that takes place during winemaking. Although the physiology of this model organism has been extensively studied, systematic quantitative physiology studies of this yeast under winemaking conditions are still scarce, thus limiting the understanding of fermentative metabolism of wine yeast strains and the systematic description, modelling and prediction of fermentation processes. In this study, we implemented and validated the use of chemostat cultures as a tool to simulate different stages of a standard wine fermentation, thereby allowing to implement metabolic flux analyses describing the sequence of metabolic states of S. cerevisae along the wine fermentation.

Results

Chemostat cultures mimicking the different stages of standard wine fermentations of S. cerevisiae EC1118 were performed using a synthetic must and strict anaerobic conditions. The simulated stages corresponded to the onset of the exponential growth phase, late exponential growth phase and cells just entering stationary phase, at dilution rates of 0.27, 0.04, 0.007 h⁻¹, respectively. Notably, measured substrate uptake and product formation rates at each steady state condition were generally within the range of corresponding conversion rates estimated during the different batch fermentation stages.

Moreover, chemostat data were further used for metabolic flux analysis, where biomass composition data for each condition was considered in the stoichiometric model. Metabolic flux distributions were coherent with previous analyses based on batch cultivations data and the pseudo-steady state assumption.

Conclusions

Steady state conditions obtained in chemostat cultures reflect the environmental conditions and physiological states of S. cerevisiae corresponding to the different growth stages of a typical batch wine fermentation, thereby showing the potential of this experimental approach to systematically study the effect of environmental relevant factors such as temperature, sugar concentration, C/N ratio or (micro) oxygenation on the fermentative metabolism of wine yeast strains.

Constant growth rate can be supported by decreasing energy flux and increasing aerobic glycolysis

Fermenting glucose in the presence of enough oxygen to support respiration, known as aerobic glycolysis, is believed to maximize growth rate. We observed increasing aerobic glycolysis during exponential growth, suggesting additional physiological roles for aerobic glycolysis. We investigated such roles in yeast batch cultures by quantifying O2 consumption, CO2 production, amino acids, mRNAs, proteins, posttranslational modifications, and stress sensitivity in the course of nine doublings at constant rate. During this course, the cells support a constant biomass-production rate with decreasing rates of respiration and ATP production but also decrease their stress resistance. As the respiration rate decreases, so do the levels of enzymes catalyzing rate-determining reactions of the tricarboxylic-acid cycle (providing NADH for respiration) and of mitochondrial folate-mediated NADPH production (required for oxidative defense). The findings demonstrate that exponential growth can represent not a single metabolic/physiological state but a continuum of changing states and that aerobic glycolysis can reduce the energy demands associated with respiratory metabolism and stress survival.

Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism

Constraint-based models of metabolism are a widely used framework for predicting flux distributions in genome-scale biochemical networks. The number of published methods for integration of transcriptomic data into constraint-based models has been rapidly increasing. So far the predictive capability of these methods has not been critically evaluated and compared. This work presents a survey of recently published methods that use transcript levels to try to improve metabolic flux predictions either by generating flux distributions or by creating context-specific models. A subset of these methods is then systematically evaluated using published data from three different case studies in E. coli and S. cerevisiae. The flux predictions made by different methods using transcriptomic data are compared against experimentally determined extracellular and intracellular fluxes (from 13C-labeling data). The sensitivity of the results to method-specific parameters is also evaluated, as well as their robustness to noise in the data. The results show that none of the methods outperforms the others for all cases. Also, it is observed that for many conditions, the predictions obtained by simple flux balance analysis using growth maximization and parsimony criteria are as good or better than those obtained using methods that incorporate transcriptomic data. We further discuss the differences in the mathematical formulation of the methods, and their relation to the results we have obtained, as well as the connection to the underlying biological principles of metabolic regulation.

Metabolic distance estimation based on principle component analysis of metabolic turnover

Visualization of metabolic dynamism is important for various types of metabolic studies including studies on optimization of bio-production processes and studies of metabolism-related diseases. Many methodologies have been developed for metabolic studies. Among these, metabolic turnover analysis (MTA) is often used to analyze metabolic dynamics. MTA involves observation of changes in the isotopomer ratio of metabolites over time following introduction of isotope-labeled substrates. MTA has several advantages compared with (13)C-metabolic flux analysis, including the diversity of applicable samples, the variety of isotope tracers, and the wide range of target pathways. However, MTA produces highly complex data from which mining useful information becomes difficult. For easy understanding of MTA data, a new approach was developed using principal component analysis (PCA). The resulting PCA score plot visualizes the metabolic distance, which is defined as distance between metabolites on the real metabolic map. And the score plot gives us some hints of interesting metabolism for further study. We used this method to analyze the central metabolism of Saccharomyces cerevisiae under moderated aerobic conditions, and time course data for 77 isotopomers of 14 metabolites were obtained. The PCA score plot for this dataset represented a metabolic map and indicated interesting phenomena such as activity of fumarate reductase under aerated condition. These findings show the importance of a multivariate analysis to MTA. In addition, because the approach is not biased, this method has potential application for analysis of less-studied pathways and organisms.