Genome-scale modeling enables metabolic engineering of Saccharomyces cerevisiae for succinic acid production

Recent advancements in the evolution, production, and degradation of biodegradable mulch films: A review

Biomass-based plastic production systems play a crucial role in fostering a sustainable society. Biodegradable mulch films (BDMs) have emerged as a practical solution to environmental pollution in agriculture. Various types of BDMs, including polybutylene adipate-co-terephthalate, polybutylene succinate, and polybutylene succinate-co-adipate, have been developed, though many are still derived from fossil-fuel-based plastics. Furthermore, the adoption of biodegradable materials in agricultural practices remains limited. This review critically assesses the evolution and significance of mulch films, highlighting the transition from traditional polyethylene (PE) to BDMs in response to environmental challenges. We provide an overview of the biorefinery approach to producing biomass-derived BDMs, discussing biomass pretreatment, saccharification, production of plastic monomers using microbial cell factories, purification, and polymerization. The review also explores techniques to enhance the biodegradation capabilities of mulch films during polymerization. Additionally, we emphasize the necessity for advancements in controlling the degradation rates of BDMs. By addressing the environmental concerns associated with the disposal of these materials, this review underscores the importance of developing effective strategies for a more sustainable and environmentally friendly agricultural landscape.

Advancing Succinic Acid Biomanufacturing Using the Nonconventional Yeast Yarrowia lipolytica

Succinic acid is an essential bulk chemical with wide-ranging applications in materials, food, and pharmaceuticals. With the advancement of biotechnology, there has been a surge in focus on low-carbon sustainable microbial synthesis methods for producing biobased succinic acid. Due to its high intrinsic acid tolerance, Yarrowia lipolytica has gained recognition as a competitive chassis for the industrial manufacture of succinic acid. This review summarizes the research progress on succinic acid biomanufacturing using Y. lipolytica. First, it introduces the major metabolic routes for succinic acid biosynthesis and the pertinent engineering approaches for building efficient cell factories. Subsequently, we offer a review of methods employed for succinic acid synthesis by Y. lipolytica utilizing alternative substrates as well as the relevant optimization strategies for the fermentation process. Finally, future research directions for improving succinic acid biomanufacturing in Y. lipolytica are delineated in light of the recent progress, obstacles, and trends in this area.

Flux balance analysis of metabolic networks for efficient engineering of microbial cell factories

Metabolic engineering principles have long been applied to explore the metabolic networks of complex microbial cell factories under a variety of environmental constraints for effective deployment of the microorganisms in the optimal production of biochemicals like biofuels, polymers, amino acids, recombinant proteins. One of the methodologies used for analyzing microbial metabolic networks is the Flux Balance Analysis (FBA), which employs applications of optimization techniques for forecasting biomass growth and metabolic flux distribution of industrially important products under specified environmental conditions. The in silico flux simulations are instrumental for designing the production-specific microbial cell factories. However, FBA has some inherent limitations. The present review emphasizes how the incorporation of additional kinetic, thermodynamic, expression and regulatory constraints and integration of omics data into the classical FBA platform improve the prediction accuracy of FBA. A programmed comparison of the simulated data with the experimental observations is presented for supporting the claim. The review further accounts for the successful implementation of classical FBA in biotechnological applications and identifies areas in which classical FBA fails to make correct predictions. The analysis of the predictive capabilities of the different FBA strategies presented here is expected to help researchers in finding new avenues in engineering highly efficient microbial metabolic pathways and identify the key metabolic bottlenecks during the process. Based on the appropriate metabolic network design, fermentation engineers will be able to effectively design the bioreactors and optimize large-scale biochemical production through suitable pathway modifications.

High-level succinic acid production by overexpressing a magnesium transporter in Mannheimia succiniciproducens

Succinic acid (SA), a dicarboxylic acid of industrial importance, can be efficiently produced by metabolically engineered Mannheimia succiniciproducens. Although the importance of magnesium (Mg2+) ion on SA production has been evident from our previous studies, the role of Mg2+ ion remains largely unexplored. In this study, we investigated the impact of Mg2+ ion on SA production and developed a hyper-SA producing strain of M. succiniciproducens by reconstructing the Mg2+ ion transport system. To achieve this, optimal alkaline neutralizer comprising Mg2+ ion was developed and the physiological effect of Mg2+ ion was analyzed. Subsequently, the Mg2+ ion transport system was reconstructed by introducing an efficient Mg2+ ion transporter from Salmonella enterica. A high-inoculum fed-batch fermentation of the final engineered strain produced 152.23 ± 0.99 g/L of SA, with a maximum productivity of 39.64 ± 0.69 g/L/h. These findings highlight the importance of Mg2+ ions and transportation system optimization in succinic acid production by M. succiniciproducens.

Response mechanisms of different Saccharomyces cerevisiae strains to succinic acid

BACKGROUND

The production of succinic acid (SA) from biomass has attracted worldwide interest. Saccharomyces cerevisiae is preferred for SA production due to its strong tolerance to low pH conditions, ease of genetic manipulation, and extensive application in industrial processes. However, when compared with bacterial producers, the SA titers and productivities achieved by engineered S. cerevisiae strains were relatively low. To develop efficient SA-producing strains, it's necessary to clearly understand how S. cerevisiae cells respond to SA.

RESULTS

In this study, we cultivated five S. cerevisiae strains with different genetic backgrounds under different concentrations of SA. Among them, KF7 and NBRC1958 demonstrated high tolerance to SA, whereas NBRC2018 displayed the least tolerance. Therefore, these three strains were chosen to study how S. cerevisiae responds to SA. Under a concentration of 20 g/L SA, only a few differentially expressed genes were observed in three strains. At the higher concentration of 60 g/L SA, the response mechanisms of the three strains diverged notably. For KF7, genes involved in the glyoxylate cycle were significantly downregulated, whereas genes involved in gluconeogenesis, the pentose phosphate pathway, protein folding, and meiosis were significantly upregulated. For NBRC1958, genes related to the biosynthesis of vitamin B6, thiamin, and purine were significantly downregulated, whereas genes related to protein folding, toxin efflux, and cell wall remodeling were significantly upregulated. For NBRC2018, there was a significant upregulation of genes connected to the pentose phosphate pathway, gluconeogenesis, fatty acid utilization, and protein folding, except for the small heat shock protein gene HSP26. Overexpression of HSP26 and HSP42 notably enhanced the cell growth of NBRC1958 both in the presence and absence of SA.

CONCLUSIONS

The inherent activities of small heat shock proteins, the levels of acetyl-CoA and the strains' potential capacity to consume SA all seem to affect the responses and tolerances of S. cerevisiae strains to SA. These factors should be taken into consideration when choosing host strains for SA production. This study provides a theoretical basis and identifies potential host strains for the development of robust and efficient SA-producing strains.

Metabolic modeling of Halomonas campaniensis improves polyhydroxybutyrate production under nitrogen limitation

Poly-hydroxybutyrate (PHB) is an environmentally friendly alternative for conventional fossil fuel-based plastics that is produced by various microorganisms. Large-scale PHB production is challenging due to the comparatively higher biomanufacturing costs. A PHB overproducer is the haloalkaliphilic bacterium Halomonas campaniensis, which has low nutritional requirements and can grow in cultures with high salt concentrations, rendering it resistant to contamination. Despite its virtues, the metabolic capabilities of H. campaniensis as well as the limitations hindering higher PHB production remain poorly studied. To address this limitation, we present HaloGEM, the first high-quality genome-scale metabolic network reconstruction, which encompasses 888 genes, 1528 reactions (1257 gene-associated), and 1274 metabolites. HaloGEM not only displays excellent agreement with previous growth data and experiments from this study, but it also revealed nitrogen as a limiting nutrient when growing aerobically under high salt concentrations using glucose as carbon source. Among different nitrogen source mixtures for optimal growth, HaloGEM predicted glutamate and arginine as a promising mixture producing increases of 54.2% and 153.4% in the biomass yield and PHB titer, respectively. Furthermore, the model was used to predict genetic interventions for increasing PHB yield, which were consistent with the rationale of previously reported strategies. Overall, the presented reconstruction advances our understanding of the metabolic capabilities of H. campaniensis for rationally engineering this next-generation industrial biotechnology platform. KEY POINTS: A comprehensive genome-scale metabolic reconstruction of H. campaniensis was developed. Experiments and simulations predict N limitation in minimal media under aerobiosis. In silico media design increased experimental biomass yield and PHB titer.

Co-Cultivated Enzyme Constraint Metabolic Network Model for Rational Guidance in Constructing Synthetic Consortia to Achieve Optimal Pathway Allocation Prediction

Synthetic consortia have emerged as a promising biosynthetic platform that offers new opportunities for biosynthesis. Genome-scale metabolic network models (GEMs) with complex constraints are extensively utilized to guide the synthesis in monocultures. However, few methods are currently available to guide the rational construction of synthetic consortia for predicting the optimal allocation strategy of synthetic pathways aimed at enhancing product synthesis. A standardized method to construct the co-cultivated Enzyme Constraint metabolic network model (CulECpy) is proposed, which integrates enzyme constraints and modular interaction scale constraints based on the research concept of "independent + global". This method is applied to construct several synthetic consortia models, which encompassed different target products, strains, synthetic pathways, and compositional structures. Analyzing the model, the optimal pathway allocation and initial inoculum ratio that enhance the synthesis of target products by synthetic consortia are predicted and verified. When comparing with the constructed co-culture synthesis system, the normalized root mean square error of all optimal theoretical yield simulations is found to be less than or equal to 0.25. The analyses and verifications demonstrate that the method CulECpy can guide the rational construction of synthetic consortia systems to facilitate biochemical synthesis.

A genome-scale metabolic model of the effect of dissolved oxygen on 1,3-propanediol fermentation by Klebsiella pneumoniae

Although 1,3-propanediol (1,3-PD) is usually considered an anaerobic fermentation product from glycerol by Klebsiella pneumoniae, microaerobic conditions proved to be more conducive to 1,3-PD production. In this study, a genome-scale metabolic model (GSMM) specific to K. pneumoniae KG2, a high 1.3-PD producer, was constructed. The iZY1242 model contains 2090 reactions, 1242 genes and 1433 metabolites. The model was not only able to accurately characterise cell growth, but also accurately simulate the fed-batch 1,3-PD fermentation process. Flux balance analyses by iZY1242 was performed to dissect the mechanism of stimulated 1,3-PD production under microaerobic conditions, and the maximum yield of 1,3-PD on glycerol was 0.83 mol/mol under optimal microaerobic conditions. Combined with experimental data, the iZY1242 model is a useful tool for establishing the best conditions for microaeration fermentation to produce 1,3-PD from glycerol in K. pneumoniae.

A Kinetic Framework for Modeling Oleochemical Biosynthesis in E. coli

Microorganisms build fatty acids with biocatalytic assembly lines, or fatty acid synthases (FASs), that can be repurposed to produce a broad set of fuels and chemicals. Despite their versatility, the product profiles of FAS-based pathways are challenging to adjust without experimental iteration, and off-target products are common. This study uses a detailed kinetic model of the E. coli FAS as a foundation to model nine oleochemical pathways. These models provide good fits to experimental data and help explain unexpected results from in vivo studies. An analysis of pathways for alkanes and fatty acid ethyl esters, for example, suggests that reductions in titer caused by enzyme overexpression-an experimentally consistent phenomenon-can result from shifts in metabolite pools that are incompatible with the substrate specificities of downstream enzymes, and a focused examination of multiple alcohol pathways indicates that coordinated shifts in enzyme concentrations provide a general means of tuning the product profiles of pathways with promiscuous components. The study concludes by integrating all models into a graphical user interface. The models supplied by this work provide a versatile kinetic framework for studying oleochemical pathways in different biochemical contexts. This article is protected by copyright. All rights reserved.

Metabolic flux sampling predicts strain-dependent differences related to aroma production among commercial wine yeasts

BACKGROUND

Metabolomics coupled with genome-scale metabolic modeling approaches have been employed recently to quantitatively analyze the physiological states of various organisms, including Saccharomyces cerevisiae. Although yeast physiology in laboratory strains is well-studied, the metabolic states under industrially relevant scenarios such as winemaking are still not sufficiently understood, especially as there is considerable variation in metabolism between commercial strains. To study the potential causes of strain-dependent variation in the production of volatile compounds during enological conditions, random flux sampling and statistical methods were used, along with experimental extracellular metabolite flux data to characterize the differences in predicted intracellular metabolic states between strains.

RESULTS

It was observed that four selected commercial wine yeast strains (Elixir, Opale, R2, and Uvaferm) produced variable amounts of key volatile organic compounds (VOCs). Principal component analysis was performed on extracellular metabolite data from the strains at three time points of cell cultivation (24, 58, and 144 h). Separation of the strains was observed at all three time points. Furthermore, Uvaferm at 24 h, for instance, was most associated with propanol and ethyl hexanoate. R2 was found to be associated with ethyl acetate and Opale could be associated with isobutanol while Elixir was most associated with phenylethanol and phenylethyl acetate. Constraint-based modeling (CBM) was employed using the latest genome-scale metabolic model of yeast (Yeast8) and random flux sampling was performed with experimentally derived fluxes at various stages of growth as constraints for the model. The flux sampling simulations allowed us to characterize intracellular metabolic flux states and illustrate the key parts of metabolism that likely determine the observed strain differences. Flux sampling determined that Uvaferm and Elixir are similar while R2 and Opale exhibited the highest degree of differences in the Ehrlich pathway and carbon metabolism, thereby causing strain-specific variation in VOC production. The model predictions also established the top 20 fluxes that relate to phenotypic strain variation (e.g. at 24 h). These fluxes indicated that Opale had a higher median flux for pyruvate decarboxylase reactions compared with the other strains. Conversely, R2 which was lower in all VOCs, had higher median fluxes going toward central metabolism. For Elixir and Uvaferm, the differences in metabolism were most evident in fluxes pertaining to transaminase and hexokinase associated reactions. The applied analysis of metabolic divergence unveiled strain-specific differences in yeast metabolism linked to fusel alcohol and ester production.

CONCLUSIONS

Overall, this approach proved useful in elucidating key reactions in amino acid, carbon, and glycerophospholipid metabolism which suggest genetic divergence in activity in metabolic subsystems among these wine strains related to the observed differences in VOC formation. The findings in this study could steer more focused research endeavors in developing or selecting optimal aroma-producing yeast stains for winemaking and other types of alcoholic fermentations.

Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes

Succinic acid (SA) is one of the top candidate value-added chemicals that can be produced from biomass via microbial fermentation. A considerable number of cell factories have been proposed in the past two decades as native as well as non-native SA producers. Actinobacillus succinogenes is among the best and earliest known natural SA producers. However, its industrial application has not yet been realized due to various underlying challenges. Previous studies revealed that the optimization of environmental conditions alone could not entirely resolve these critical problems. On the other hand, microbial in silico metabolic modeling approaches have lately been the center of attention and have been applied for the efficient production of valuable commodities including SA. Then again, literature survey results indicated the absence of up-to-date reviews assessing this issue, specifically concerning SA production. Hence, this review was designed to discuss accomplishments and future perspectives of in silico studies on the metabolic capabilities of SA producers. Herein, research progress on SA and A. succinogenes, pathways involved in SA production, metabolic models of SA-producing microorganisms, and status, limitations and prospects on in silico studies of A. succinogenes were elaborated. All in all, this review is believed to provide insights to understand the current scenario and to develop efficient mathematical models for designing robust SA-producing microbial strains.

Model-guided development of an evolutionarily stable yeast chassis

First-principle metabolic modelling holds potential for designing microbial chassis that are resilient against phenotype reversal due to adaptive mutations. Yet, the theory of model-based chassis design has rarely been put to rigorous experimental test. Here, we report the development of Saccharomyces cerevisiae chassis strains for dicarboxylic acid production using genome-scale metabolic modelling. The chassis strains, albeit geared for higher flux towards succinate, fumarate and malate, do not appreciably secrete these metabolites. As predicted by the model, introducing product-specific TCA cycle disruptions resulted in the secretion of the corresponding acid. Adaptive laboratory evolution further improved production of succinate and fumarate, demonstrating the evolutionary robustness of the engineered cells. In the case of malate, multi-omics analysis revealed a flux bypass at peroxisomal malate dehydrogenase that was missing in the yeast metabolic model. In all three cases, flux balance analysis integrating transcriptomics, proteomics and metabolomics data confirmed the flux re-routing predicted by the model. Taken together, our modelling and experimental results have implications for the computer-aided design of microbial cell factories.

Systems-level approaches for understanding and engineering of the oleaginous cell factory Yarrowia lipolytica

Concerns about climate change and the search for renewable energy sources together with the goal of attaining sustainable product manufacturing have boosted the use of microbial platforms to produce fuels and high-value chemicals. In this regard, Yarrowia lipolytica has been known as a promising yeast with potentials in diverse array of biotechnological applications such as being a host for different oleochemicals, organic acid, and recombinant protein production. Having a rapidly increasing number of molecular and genetic tools available, Y. lipolytica has been well studied amongst oleaginous yeasts and metabolic engineering has been used to explore its potentials. More recently, with the advancement in systems biotechnology and the implementation of mathematical modeling and high throughput omics data-driven approaches, in-depth understanding of cellular mechanisms of cell factories have been made possible resulting in enhanced rational strain design. In case of Y. lipolytica, these systems-level studies and the related cutting-edge technologies have recently been initiated which is expected to result in enabling the biotechnology sector to rationally engineer Y. lipolytica-based cell factories with favorable production metrics. In this regard, here, we highlight the current status of systems metabolic engineering research and assess the potential of this yeast for future cell factory design development.

Machine learning applied for metabolic flux-based control of micro-aerated fermentations in bioreactors

Various bio-based processes depend on controlled micro-aerobic conditions to achieve a satisfactory product yield. However, the limiting oxygen concentration varies according to the micro-organism employed, while for industrial applications, there is no cost-effective way of measuring it at low levels. This study proposes a machine learning procedure within a metabolic flux-based control strategy (SUPERSYS_MCU) to address this issue. The control strategy used simulations of a genome-scale metabolic model to generate a surrogate model in the form of an artificial neural network, to be used in a micro-aerobic fermentation strategy (MF-ANN). The meta-model provided setpoints to the controller, allowing adjustment of the inlet air flow to control the oxygen uptake rate. The strategy was evaluated in micro-aerobic batch cultures employing industrial Saccharomyces cerevisiae yeast, with defined medium and glucose as the carbon source, as a case study. The performance of the proposed control scheme was compared with a conventional fermentation and with three previously reported micro-aeration strategies, including respiratory quotient-based control and constant air flow rate. Due to maintenance of the oxidative balance at the anaerobiosis threshold, the MF-ANN provided volumetric ethanol productivity of 4.16 g·L^-1 ·h^-1 and a yield of 0.48 g_ethanol .g_substrate ^-1 , which were higher than the values achieved for the other conditions studied (maximum of 3.4 g·L^-1 ·h^-1 and 0.35-0.40 g_ethanol ·g_substrate ^-1 , respectively). Due to its modular character, the MF-ANN strategy could be adapted to other micro-aerated bioprocesses.

Promising advancement in fermentative succinic acid production by yeast hosts

As a platform chemical with various applications, succinic acid (SA) is currently produced by petrochemical processing from oil-derived substrates such as maleic acid. In order to replace the environmental unsustainable hydrocarbon economy with a renewable environmentally sound carbohydrate economy, bio-based SA production process has been developed during the past two decades. In this review, recent advances in the valorization of solid organic wastes including mixed food waste, agricultural waste and textile waste for efficient, green and sustainable SA production have been reviewed. Firstly, the application, market and key global players of bio-SA are summarized. Then achievements in SA production by several promising yeasts including Saccharomyces cerevisiae and Yarrowia lipolytica are detailed, followed by calculation and comparison of SA production costs between oil-based substrates and raw materials. Lastly, challenges in engineered microorganisms and fermentation processes are presented together with perspectives on the development of robust yeast SA producers via genome-scale metabolic optimization and application of low-cost raw materials as fermentation substrates. This review provides valuable insights for identifying useful directions for future bio-SA production improvement.

The Role of Yeasts and Lactic Acid Bacteria on the Metabolism of Organic Acids during Winemaking

The main role of acidity and pH is to confer microbial stability to wines. No less relevant, they also preserve the color and sensory properties of wines. Tartaric and malic acids are generally the most prominent acids in wines, while others such as succinic, citric, lactic, and pyruvic can exist in minor concentrations. Multiple reactions occur during winemaking and processing, resulting in changes in the concentration of these acids in wines. Two major groups of microorganisms are involved in such modifications: the wine yeasts, particularly strains of Saccharomyces cerevisiae, which carry out alcoholic fermentation; and lactic acid bacteria, which commonly conduct malolactic fermentation. This review examines various such modifications that occur in the pre-existing acids of grape berries and in others that result from this microbial activity as a means to elucidate the link between microbial diversity and wine composition.

Enhanced succinic acid production by Mannheimia employing optimal malate dehydrogenase

Succinic acid (SA), a dicarboxylic acid of industrial importance, can be efficiently produced by metabolically engineered Mannheimia succiniciproducens. Malate dehydrogenase (MDH) is one of the key enzymes for SA production, but has not been well characterized. Here we report biochemical and structural analyses of various MDHs and development of hyper-SA producing M. succiniciproducens by introducing the best MDH. Corynebacterium glutamicum MDH (CgMDH) shows the highest specific activity and least substrate inhibition, whereas M. succiniciproducens MDH (MsMDH) shows low specific activity at physiological pH and strong uncompetitive inhibition toward oxaloacetate (ki of 67.4 and 588.9 μM for MsMDH and CgMDH, respectively). Structural comparison of the two MDHs reveals a key residue influencing the specific activity and susceptibility to substrate inhibition. A high-inoculum fed-batch fermentation of the final strain expressing cgmdh produces 134.25 g L-1 of SA with the maximum productivity of 21.3 g L-1 h-1, demonstrating the importance of enzyme optimization in strain development.

QTL mapping of modelled metabolic fluxes reveals gene variants impacting yeast central carbon metabolism

The yeast Saccharomyces cerevisiae is an attractive industrial microorganism for the production of foods and beverages as well as for various bulk and fine chemicals, such as biofuels or fragrances. Building blocks for these biosyntheses are intermediates of yeast central carbon metabolism (CCM), whose intracellular availability depends on balanced single reactions that form metabolic fluxes. Therefore, efficient product biosynthesis is influenced by the distribution of these fluxes. We recently demonstrated great variations in CCM fluxes between yeast strains of different origins. However, we have limited understanding of flux modulation and the genetic basis of flux variations. In this study, we investigated the potential of quantitative trait locus (QTL) mapping to elucidate genetic variations responsible for differences in metabolic flux distributions (fQTL). Intracellular metabolic fluxes were estimated by constraint-based modelling and used as quantitative phenotypes, and differences in fluxes were linked to genomic variations. Using this approach, we detected four fQTLs that influence metabolic pathways. The molecular dissection of these QTLs revealed two allelic gene variants, PDB1 and VID30, contributing to flux distribution. The elucidation of genetic determinants influencing metabolic fluxes, as reported here for the first time, creates new opportunities for the development of strains with optimized metabolite profiles for various applications.

Advances in microbial production of medium-chain dicarboxylic acids for nylon materials

Medium-chain dicarboxylic acids (MDCAs) are widely used in the production of nylon materials, and among which, succinic, glutaric, adipic, pimelic, suberic, azelaic and sebacic acids are particularly important for that purpose.

Metabolic engineering and transcriptomic analysis of Saccharomyces cerevisiae producing p-coumaric acid from xylose

BACKGROUND

Aromatic amino acids and their derivatives are valuable chemicals and are precursors for different industrially compounds. p-Coumaric acid is the main building block for complex secondary metabolites in commercial demand, such as flavonoids and polyphenols. Industrial scale production of this compound from yeast however remains challenging.

RESULTS

Using metabolic engineering and a systems biology approach, we developed a Saccharomyces cerevisiae platform strain able to produce 242 mg/L of p-coumaric acid from xylose. The same strain produced only 5.35 mg/L when cultivated with glucose as carbon source. To characterise this platform strain further, transcriptomic analysis was performed, comparing this strain's growth on xylose and glucose, revealing a strong up-regulation of the glyoxylate pathway alongside increased cell wall biosynthesis and unexpectedly a decrease in aromatic amino acid gene expression when xylose was used as carbon source.

CONCLUSIONS

The resulting S. cerevisiae strain represents a promising platform host for future production of p-coumaric using xylose as a carbon source.

Metabolic fluxes-oriented control of bioreactors: a novel approach to tune micro-aeration and substrate feeding in fermentations

Background

Fine-tuning the aeration for cultivations when oxygen-limited conditions are demanded (such as the production of vaccines, isobutanol, 2–3 butanediol, acetone, and bioethanol) is still a challenge in the area of bioreactor automation and advanced control. In this work, an innovative control strategy based on metabolic fluxes was implemented and evaluated in a case study: micro-aerated ethanol fermentation.

Results

The experiments were carried out in fed-batch mode, using commercial Saccharomyces cerevisiae, defined medium, and glucose as carbon source. Simulations of a genome-scale metabolic model for Saccharomyces cerevisiae were used to identify the range of oxygen and substrate fluxes that would maximize ethanol fluxes. Oxygen supply and feed flow rate were manipulated to control oxygen and substrate fluxes, as well as the respiratory quotient (RQ). The performance of the controlled cultivation was compared to two other fermentation strategies: a conventional “Brazilian fuel-ethanol plant” fermentation and a strictly anaerobic fermentation (with ultra-pure nitrogen used as the inlet gas). The cultivation carried out under the proposed control strategy showed the best average volumetric ethanol productivity (7.0 g L⁻¹ h⁻¹), with a final ethanol concentration of 87 g L⁻¹ and yield of 0.46 g_ethanol g_substrate⁻¹. The other fermentation strategies showed lower yields (close to 0.40 g_ethanol g_substrate⁻¹) and ethanol productivity around 4.0 g L⁻¹ h⁻¹.

Conclusion

The control system based on fluxes was successfully implemented. The proposed approach could also be adapted to control several bioprocesses that require restrict aeration.

Evaluation of pyruvate decarboxylase-negative Saccharomyces cerevisiae strains for the production of succinic acid

Dicarboxylic acids are important bio‐based building blocks, and Saccharomyces cerevisiae is postulated to be an advantageous host for their fermentative production. Here, we engineered a pyruvate decarboxylase‐negative S. cerevisiae strain for succinic acid production to exploit its promising properties, that is, lack of ethanol production and accumulation of the precursor pyruvate. The metabolic engineering steps included genomic integration of a biosynthesis pathway based on the reductive branch of the tricarboxylic acid cycle and a dicarboxylic acid transporter. Further modifications were the combined deletion of GPD1 and FUM1 and multi‐copy integration of the native PYC2 gene, encoding a pyruvate carboxylase required to drain pyruvate into the synthesis pathway. The effect of increased redox cofactor supply was tested by modulating oxygen limitation and supplementing formate. The physiologic analysis of the differently engineered strains focused on elucidating metabolic bottlenecks. The data not only highlight the importance of a balanced activity of pathway enzymes and selective export systems but also shows the importance to find an optimal trade‐off between redox cofactor supply and energy availability in the form of ATP.

Biotechnological Production of Organic Acids from Renewable Resources

Biotechnological processes are promising alternatives to petrochemical routes for overcoming the challenges of resource depletion in the future in a sustainable way. The strategies of white biotechnology allow the utilization of inexpensive and renewable resources for the production of a broad range of bio-based compounds. Renewable resources, such as agricultural residues or residues from food production, are produced in large amounts have been shown to be promising carbon and/or nitrogen sources. This chapter focuses on the biotechnological production of lactic acid, acrylic acid, succinic acid, muconic acid, and lactobionic acid from renewable residues, these products being used as monomers for bio-based material and/or as food supplements. These five acids have high economic values and the potential to overcome the "valley of death" between laboratory/pilot scale and commercial/industrial scale. This chapter also provides an overview of the production strategies, including microbial strain development, used to convert renewable resources into value-added products.

OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling

The ultimate goal of metabolic engineering is to produce desired compounds on an industrial scale in a cost effective manner. To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. However, most of these strain optimization algorithms utilize a metabolic network alone, with few approaches providing strategies that also include transcriptional regulation. Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. OptRAM is based on our previous IDREAM integrated network framework, which makes it able to deduce a regulatory network from data. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chemical and cell growth. The other advance we propose is a systematic evaluation metric of multiple solutions, by considering the essential genes, flux variation, and engineering manipulation cost. We applied OptRAM to generate strain designs for succinate, 2,3-butanediol, and ethanol overproduction in yeast, which predicted high minimum predicted target production rate compared with other methods and previous literature values. Moreover, most of the genes and TFs proposed to be altered by OptRAM in these scenarios have been validated by modification of the exact genes or the target genes regulated by the TFs, for overproduction of these desired compounds by in vivo experiments cataloged in the LASER database. Particularly, we successfully validated the predicted strain optimization strategy for ethanol production by fermentation experiment. In conclusion, OptRAM can provide a useful approach that leverages an integrated transcriptional regulatory network and metabolic network to guide metabolic engineering applications.

Genome-Scale Metabolic Modeling from Yeast to Human Cell Models of Complex Diseases: Latest Advances and Challenges

Genome-scale metabolic models (GEMs) are mathematical models that enable systematic analysis of metabolism. This modeling concept has been applied to study the metabolism of many organisms including the eukaryal model organism, the yeast Saccharomyces cerevisiae, that also serves as an important cell factory for production of fuels and chemicals. With the application of yeast GEMs, our knowledge of metabolism is increasing. Therefore, GEMs have also been used for modeling human cells to study metabolic diseases. Here we introduce the concept of GEMs and provide a protocol for reconstructing GEMs. Besides, we show the historic development of yeast GEMs and their applications. Also, we review human GEMs as well as their uses in the studies of complex diseases.

Genomic and transcriptomic analysis of Saccharomyces cerevisiae isolates with focus in succinic acid production

Succinic acid is a platform chemical that plays an important role as precursor for the synthesis of many valuable bio-based chemicals. Its microbial production from renewable resources has seen great developments, specially exploring the use of yeasts to overcome the limitations of using bacteria. The objective of the present work was to screen for succinate-producing isolates, using a yeast collection with different origins and characteristics. Four strains were chosen, two as promising succinic acid producers, in comparison with two low producers. Genome of these isolates was analysed, and differences were found mainly in genes SDH1, SDH3, MDH1 and the transcription factor HAP4, regarding the number of single nucleotide polymorphisms and the gene copy-number profile. Real-time PCR was used to study gene expression of 10 selected genes involved in the metabolic pathway of succinic acid production. Results show that for the non-producing strain, higher expression of genes SDH1, SDH2, ADH1, ADH3, IDH1 and HAP4 was detected, together with lower expression of ADR1 transcription factor, in comparison with the best producer strain. This is the first study showing the capacity of natural yeast isolates to produce high amounts of succinic acid, together with the understanding of the key factors associated, giving clues for strain improvement.

Engineering and Evolution of Saccharomyces cerevisiae to Produce Biofuels and Chemicals

To mitigate global climate change caused partly by the use of fossil fuels, the production of fuels and chemicals from renewable biomass has been attempted. The conversion of various sugars from renewable biomass into biofuels by engineered baker's yeast (Saccharomyces cerevisiae) is one major direction which has grown dramatically in recent years. As well as shifting away from fossil fuels, the production of commodity chemicals by engineered S. cerevisiae has also increased significantly. The traditional approaches of biochemical and metabolic engineering to develop economic bioconversion processes in laboratory and industrial settings have been accelerated by rapid advancements in the areas of yeast genomics, synthetic biology, and systems biology. Together, these innovations have resulted in rapid and efficient manipulation of S. cerevisiae to expand fermentable substrates and diversify value-added products. Here, we discuss recent and major advances in rational (relying on prior experimentally-derived knowledge) and combinatorial (relying on high-throughput screening and genomics) approaches to engineer S. cerevisiae for producing ethanol, butanol, 2,3-butanediol, fatty acid ethyl esters, isoprenoids, organic acids, rare sugars, antioxidants, and sugar alcohols from glucose, xylose, cellobiose, galactose, acetate, alginate, mannitol, arabinose, and lactose.

Genome-scale modeling of yeast: chronology, applications and critical perspectives

Over the last 15 years, several genome-scale metabolic models (GSMMs) were developed for different yeast species, aiding both the elucidation of new biological processes and the shift toward a bio-based economy, through the design of in silico inspired cell factories. Here, an historical perspective of the GSMMs built over time for several yeast species is presented and the main inheritance patterns among the metabolic reconstructions are highlighted. We additionally provide a critical perspective on the overall genome-scale modeling procedure, underlining incomplete model validation and evaluation approaches and the quest for the integration of regulatory and kinetic information into yeast GSMMs. A summary of experimentally validated model-based metabolic engineering applications of yeast species is further emphasized, while the main challenges and future perspectives for the field are finally addressed.

Restoring of Glucose Metabolism of Engineered Yarrowia lipolytica for Succinic Acid Production via a Simple and Efficient Adaptive Evolution Strategy

Succinate dehydrogenase inactivation in Yarrowia lipolytica has been demonstrated for robust succinic acid production, whereas the inefficient glucose metabolism has hindered its practical application. In this study, a simple and efficient adaptive evolution strategy via cell immobilization was conducted in shake flasks, with an aim to restore the glucose metabolism of Y. lipolytica mutant PGC01003. After 21 days with 14 generations evolution, glucose consumption rate increased to 0.30 g/L/h in YPD medium consisting of 150 g/L initial glucose concentration, while poor yeast growth was observed in the same medium using the initial strain without adaptive evolution. Succinic acid productivity of the evolved strain also increased by 2.3-fold, with stable cell growth in YPD medium with high initial glucose concentration. Batch fermentations resulted in final succinic acid concentrations of 65.7 g/L and 87.9 g/L succinic acid using YPD medium and food waste hydrolysate, respectively. The experimental results in this study show that a simple and efficient strategy could facilitate the glucose uptake rate in succinic acid fermentation using glucose-rich substrates.

Positive-feedback, ratiometric biosensor expression improves high-throughput metabolite-producer screening efficiency in yeast

Biosensors are valuable and versatile tools in synthetic biology that are used to modulate gene expression in response to a wide range of stimuli. Ligand responsive transcription factors are a class of biosensor that can be used to couple intracellular metabolite concentration with gene expression to enable dynamic regulation and high-throughput metabolite producer screening. We have established the Saccharomyces cerevisiae WAR1 transcriptional regulator and PDR12 promoter as an organic acid biosensor that can be used to detect varying levels of para-hydroxybenzoic acid (PHBA) production from the shikimate pathway and output green fluorescent protein (GFP) expression in response. The dynamic range of GFP expression in response to PHBA was dramatically increased by engineering positive-feedback expression of the WAR1 transcriptional regulator from its target PDR12 promoter. In addition, the noise in GFP expression at the population-level was controlled by normalising GFP fluorescence to constitutively expressed mCherry fluorescence within each cell. These biosensor modifications increased the high-throughput screening efficiency of yeast cells engineered to produce PHBA by 5,000-fold, enabling accurate fluorescence activated cell sorting isolation of producer cells that were mixed at a ratio of 1 in 10,000 with non-producers. Positive-feedback, ratiometric transcriptional regulator expression is likely applicable to many other transcription-factor/promoter pairs used in synthetic biology and metabolic engineering for both dynamic regulation and high-throughput screening applications.

Application of theoretical methods to increase succinate production in engineered strains

Computational methods have enabled the discovery of non-intuitive strategies to enhance the production of a variety of target molecules. In the case of succinate production, reviews covering the topic have not yet analyzed the impact and future potential that such methods may have. In this work, we review the application of computational methods to the production of succinic acid. We found that while a total of 26 theoretical studies were published between 2002 and 2016, only 10 studies reported the successful experimental implementation of any kind of theoretical knowledge. None of the experimental studies reported an exact application of the computational predictions. However, the combination of computational analysis with complementary strategies, such as directed evolution and comparative genome analysis, serves as a proof of concept and demonstrates that successful metabolic engineering can be guided by rational computational methods.

Reconstruction of a genome-scale metabolic model and in silico analysis of the polymalic acid producer Aureobasidium pullulans CCTCC M2012223

Aureobasidium pullulans is a yeast-like fungus used for producing biopolymers e.g. polymalic acid (PMA) and pullulan. A high PMA producing strain, A. pullulans CCTCC M2012223, was isolated and sequenced in our previous study. To understand its metabolic performance, a genome-scale metabolic model, iZX637, consisting of 637 genes, 1347 reactions and 1133 metabolites, was reconstructed based on genome annotation and literature mining studies. The iZX637 model was validated by simulating cell growth, utilization of carbon and nitrogen sources, and gene essentiality analysis in A. pullulans. We further validated our model, designed a simulation program for the prediction of PMA production using experimental data, and further analyzed the carbon flux distribution and change with increasing PMA synthesis rates. Through the calculated flux distribution, NADPH- and NADH-dependent methylenetetrahydrofolate dehydrogenase (MTHFD) were found to be associated with the transfer of reducing equivalents from NADPH to NADH for supplementing NADH in the metabolic network. Furthermore, under the high PMA synthesis rate, a large amount of carbon flux was through pyruvate into malic acid via the reductive TCA cycle. Thus, pyruvate carboxylase, which can convert pyruvate to oxaloacetate with CO₂ fixation, may also be an important target for PMA synthesis. These results illustrated that the model iZX637 was a powerful tool for optimization of A. pullulans metabolism and identification of targets for guiding metabolic engineering.

N-Acetyl-L-cysteine Protects the Enterocyte against Oxidative Damage by Modulation of Mitochondrial Function

The neonatal small intestine is susceptible to damage caused by oxidative stress. This study aimed to evaluate the protective role of antioxidant N-acetylcysteine (NAC) in intestinal epithelial cells against oxidative damage induced by H₂O₂. IPEC-J2 cells were cultured in DMEM-H with NAC and H₂O₂. After 2-day incubation, IPEC-J2 cells were collected for analysis of DNA synthesis, antioxidation capacity, mitochondrial respiration, and cell apoptosis. The results showed that H₂O₂ significantly decreased (P < 0.05) proliferation rate, mitochondrial respiration, and antioxidation capacity and increased cell apoptosis and the abundance of associated proteins, including cytochrome C, Bcl-XL, cleaved caspase-3, and total caspase-3. NAC supplementation remarkably increased (P < 0.05) proliferation rate, antioxidation capacity, and mitochondrial bioenergetics but decreased cell apoptosis. These findings indicate that NAC might rescue the intestinal injury induced by H₂O₂.

UII/GPR14 is involved in NF-κB-mediated colonic inflammation in vivo and in vitro

The present study was conducted to investigate the molecular mechanism of urotensin II (UII) and its receptor, G protein‑coupled receptor 14 (GPR14), in colonic inflammation. Urantide, a special antagonist of GPR14, and GPR14-siRNA were used to inhibit GPR14 signaling in dextran sulfate sodium (DSS)‑induced inflammation in mice and Caco-2 cells. The results showed that urantide alleviated rectal bleeding, histological injury and production of interleukin (IL)-17 and tumor necrosis factor‑α (TNF‑α) caused by DSS in mice. GPR14-siRNA transfection subsequent with GPR14 inhibition reduced DSS-induced interferon-γ (IFN)-γ production in Caco-2 cells. Meanwhile, both in vivo and in vitro data demonstrated that inhibition of UII/GPR14 alleviated nuclear factor-κB (NF-κB) activation caused by DSS. In conclusion, UII/GPR14 signaling was involved in the DSS-induced colonic inflammation and its inhibition may serve as a potential therapeutic target, which may be associated with the NF-κB signaling pathway.

Genome scale models of yeast: towards standardized evaluation and consistent omic integration

Genome scale models (GEMs) have enabled remarkable advances in systems biology, acting as functional databases of metabolism, and as scaffolds for the contextualization of high-throughput data. In the case of Saccharomyces cerevisiae (budding yeast), several GEMs have been published and are currently used for metabolic engineering and elucidating biological interactions. Here we review the history of yeast's GEMs, focusing on recent developments. We study how these models are typically evaluated, using both descriptive and predictive metrics. Additionally, we analyze the different ways in which all levels of omics data (from gene expression to flux) have been integrated in yeast GEMs. Relevant conclusions and current challenges for both GEM evaluation and omic integration are highlighted.

Diversity of flux distribution in central carbon metabolism of S. cerevisiae strains from diverse environments

Background

S. cerevisiae has attracted considerable interest in recent years as a model for ecology and evolutionary biology, revealing a substantial genetic and phenotypic diversity. However, there is a lack of knowledge on the diversity of metabolic networks within this species.

Results

To identify the metabolic and evolutionary constraints that shape metabolic fluxes in S. cerevisiae, we used a dedicated constraint-based model to predict the central carbon metabolism flux distribution of 43 strains from different ecological origins, grown in wine fermentation conditions. In analyzing these distributions, we observed a highly contrasted situation in flux variability, with quasi-constancy of the glycolysis and ethanol synthesis yield yet high flexibility of other fluxes, such as the pentose phosphate pathway and acetaldehyde production. Furthermore, these fluxes with large variability showed multimodal distributions that could be linked to strain origin, indicating a convergence between genetic origin and flux phenotype.

Conclusions

Flux variability is pathway-dependent and, for some flux, a strain origin effect can be found. These data highlight the constraints shaping the yeast operative central carbon network and provide clues for the design of strategies for strain improvement.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-016-0456-0) contains supplementary material, which is available to authorized users.

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.

Systematic development of biomass overproducing Scheffersomyces stipitis for high-cell-density fermentations

The development of economically feasible bio-based process requires efficient cell factories capable of producing the desired product at high titer under high-cell-density fermentation. Herein we present a combinatorial approach based on systems metabolic engineering and metabolic evolution for the development of efficient biomass-producing strain. Systems metabolic engineering guided by flux balance analysis (FBA) was first employed to rationally design mutant strains of Scheffersomyces stipitis with high biomass yield. By experimentally implementing these mutations, the biomass yield was improved by 30% in GPD1, 25% in TKL1, 30% in CIT1, and 44% in ZWF1 overexpressed mutants compared to wild-type. These designed mutants were further fine-tuned through metabolic evolution resulting in the maximal biomass yield of 0.49 g-cdw/g-glucose, which matches well with predicted yield phenotype. The constructed mutants are beneficial for biotechnology applications dealing with high cell titer cultivations. This work demonstrates a solid confirmation of systems metabolic engineering in combination with metabolic evolution approach for efficient strain development, which could assist in rapid optimization of cell factory for an economically viable and sustainable bio-based process.

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.

Applications of computational modeling in metabolic engineering of yeast

Generally, a microorganism's phenotype can be described by its pattern of metabolic fluxes. Although fluxes cannot be measured directly, inference of fluxes is well established. In biotechnology the aim is often to increase the capacity of specific fluxes. For this, metabolic engineering methods have been developed and applied extensively. Many of these rely on balancing of intracellular metabolites, redox, and energy fluxes, using genome-scale models (GEMs) that in combination with appropriate objective functions and constraints can be used to predict potential gene targets for obtaining a preferred flux distribution. These methods point to strategies for altering gene expression; however, fluxes are often controlled by post-transcriptional events. Moreover, GEMs are usually not taking into account metabolic regulation, thermodynamics and enzyme kinetics. To facilitate metabolic engineering, tools from synthetic biology have emerged, enabling integration and assembly of naturally nonexistent, but well-characterized components into a living organism. To describe these systems kinetic models are often used and to integrate these systems with the standard metabolic engineering approach, it is necessary to expand the modeling of metabolism to consider kinetics of individual processes. This review will give an overview about models available for metabolic engineering of yeast and discusses their applications.

Transcriptional program for nitrogen starvation-induced lipid accumulation in Chlamydomonas reinhardtii

BACKGROUND

Algae accumulate lipids to endure different kinds of environmental stresses including macronutrient starvation. Although this response has been extensively studied, an in depth understanding of the transcriptional regulatory network (TRN) that controls the transition into lipid accumulation remains elusive. In this study, we used a systems biology approach to elucidate the transcriptional program that coordinates the nitrogen starvation-induced metabolic readjustments that drive lipid accumulation in Chlamydomonas reinhardtii.

RESULTS

We demonstrate that nitrogen starvation triggered differential regulation of 2147 transcripts, which were co-regulated in 215 distinct modules and temporally ordered as 31 transcriptional waves. An early-stage response was triggered within 12 min that initiated growth arrest through activation of key signaling pathways, while simultaneously preparing the intracellular environment for later stages by modulating transport processes and ubiquitin-mediated protein degradation. Subsequently, central metabolism and carbon fixation were remodeled to trigger the accumulation of triacylglycerols. Further analysis revealed that these waves of genome-wide transcriptional events were coordinated by a regulatory program orchestrated by at least 17 transcriptional regulators, many of which had not been previously implicated in this process. We demonstrate that the TRN coordinates transcriptional downregulation of 57 metabolic enzymes across a period of nearly 4 h to drive an increase in lipid content per unit biomass. Notably, this TRN appears to also drive lipid accumulation during sulfur starvation, while phosphorus starvation induces a different regulatory program. The TRN model described here is available as a community-wide web-resource at http://networks.systemsbiology.net/chlamy-portal.

CONCLUSIONS

In this work, we have uncovered a comprehensive mechanistic model of the TRN controlling the transition from N starvation to lipid accumulation. The program coordinates sequentially ordered transcriptional waves that simultaneously arrest growth and lead to lipid accumulation. This study has generated predictive tools that will aid in devising strategies for the rational manipulation of regulatory and metabolic networks for better biofuel and biomass production.