Reconstruction and in silico analysis of metabolic network for an oleaginous yeast, Yarrowia lipolytica

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

Construction and application of the genome-scale metabolic model of Streptomyces radiopugnans

Geosmin is one of the most common earthy-musty odor compounds, which is mainly produced by Streptomyces. Streptomyces radiopugnans was screened in radiation-polluted soil, which has the potential to overproduce geosmin. However, due to the complex cellular metabolism and regulation mechanism, the phenotypes of S. radiopugnans were hard to investigate. A genome-scale metabolic model of S. radiopugnans named iZDZ767 was constructed. Model iZDZ767 involved 1,411 reactions, 1,399 metabolites, and 767 genes; its gene coverage was 14.1%. Model iZDZ767 could grow on 23 carbon sources and five nitrogen sources, which achieved 82.1% and 83.3% prediction accuracy, respectively. For the essential gene prediction, the accuracy was 97.6%. According to the simulation of model iZDZ767, D-glucose and urea were the best for geosmin fermentation. The culture condition optimization experiments proved that with D-glucose as the carbon source and urea as the nitrogen source (4 g/L), geosmin production could reach 581.6 ng/L. Using the OptForce algorithm, 29 genes were identified as the targets of metabolic engineering modification. With the help of model iZDZ767, the phenotypes of S. radiopugnans could be well resolved. The key targets for geosmin overproduction could also be identified efficiently.

Dissecting carbon metabolism of Yarrowia lipolytica type strain W29 using genome-scale metabolic modelling

Yarrowia lipolytica is a widely-used chassis cell in biotechnological applications. It has recently gained extensive research interest owing to its extraordinary ability of producing industrially valuable biochemicals from a variety of carbon sources. Genome-scale metabolic models (GSMMs) enable analyses of cellular metabolism for engineering various industrial hosts. In the present study, we developed a high-quality GSMM iYli21 for Y. lipolytica type strain W29 by extensive manual curation with Biolog experimental data. The model showed a high accuracy of 85.7% in predicting nutrient utilization. Transcriptomics data were integrated to delineate cellular metabolism of utilizing six individual metabolites as sole carbon sources. Comparisons showed that 302 reactions were commonly used, including those from TCA cycle, oxidative phosphorylation, and purine metabolism for energy and material supply. Whereas glycolytic reactions were employed only when glucose and glycerol used as sole carbon sources, gluconeogenesis and fatty acid oxidation reactions were specifically employed when fatty acid, alkane and glycerolipid were the sole carbon sources. Further test of 46 substrates for generating 5 products showed that hexanoate outcompeted other compounds in terms of maximum theoretical yield owing to the lowest carbon loss for energy supply. This newly generated model iYli21 will be a valuable tool in dissecting metabolic mechanism and guiding metabolic engineering of this important industrial cell factory.

Pareto Optimal Metabolic Engineering for the Growth-coupled Overproduction of Sustainable Chemicals

Our research aims to help industrial biotechnology develop a sustainable economy using green technology based on microorganisms and synthetic biology through two case studies that improve metabolic capacity in yeast models Yarrowia lipolytica (Y. lipolytica) and Saccharomyces cerevisiae (S. cerevisiae). We aim to increase the production capacity of beta‐carotene (β‐carotene) and succinic acid, which are among the highest market demands due to their versatile use in numerous consumer products. We performed simulations to identify in silico ranking of strains based on multiple objectives: the growth rate of yeast microorganisms, the number of used chromosomes, and the production capability of β‐carotene (for Y. lipolytica) and succinate (for S. cerevisiae). Our multiobjective optimization methodology identified notable gene deletions by searching a vast solution space to highlight near‐optimal strains on Pareto Fronts, balancing the above‐cited three objectives. Moreover, preserving the metabolic constraints and the essential genes, this study produced robust results: seven significant strains of Y. lipolytica and seven strains of S. cerevisiae. We examined gene knockout to study the function of genes and pathways. In fact, by studying the frequently silenced genes, we found that when the GPH1 gene is knocked out in S. cerevisiae, the isocitrate lyase enzyme is activated, which converts the isocitrate into succinate. Our goals are to simplify and facilitate the in vitro processes. Hence, we present strains with the least possible number of knockout genes and solutions in which the genes are turned off on the same chromosome. Therefore, we present results where the constraints mentioned above are met, like the strains where only two genes are switched off and other strains where half of the knockout genes are on the same chromosome. This study offers solutions for developing an efficient in vitro mutagenesis for microorganisms and demonstrates the efficiency of multiobjective optimization in automatizing metabolic engineering processes.

Metabolic engineering for the utilization of carbohydrate portions of lignocellulosic biomass

The petrochemical industry has grown to meet the need for massive production of energy and commodities along with an explosive population growth; however, serious side effects such as greenhouse gas emissions and global warming have negatively impacted the environment. Lignocellulosic biomass with myriad quantities on Earth is an attractive resource for the production of carbon-neutral fuels and chemicals through environmentally friendly processes of microbial fermentation. This review discusses metabolic engineering efforts to achieve economically feasible industrial production of fuels and chemicals from microbial cell factories using the carbohydrate portion of lignocellulosic biomass as substrates. The combined knowledge of systems biology and metabolic engineering has been applied to construct robust platform microorganisms with maximum conversion of monomeric sugars, such as glucose and xylose, derived from lignocellulosic biomass. By comprehensively revisiting carbon conversion pathways, we provide a rationale for engineering strategies, as well as their features, feasibility, and recent representative studies. In addition, we briefly discuss how tools in systems biology can be applied in the field of metabolic engineering to accelerate the development of microbial cell factories that convert lignocellulosic biomass into carbon-neutral fuels and chemicals with economic feasibility.

Yarrowia lipolytica Strains and Their Biotechnological Applications: How Natural Biodiversity and Metabolic Engineering Could Contribute to Cell Factories Improvement

Among non-conventional yeasts of industrial interest, the dimorphic oleaginous yeast Yarrowia lipolytica appears as one of the most attractive for a large range of white biotechnology applications, from heterologous proteins secretion to cell factories process development. The past, present and potential applications of wild-type, traditionally improved or genetically modified Yarrowia lipolytica strains will be resumed, together with the wide array of molecular tools now available to genetically engineer and metabolically remodel this yeast. The present review will also provide a detailed description of Yarrowia lipolytica strains and highlight the natural biodiversity of this yeast, a subject little touched upon in most previous reviews. This work intends to fill this gap by retracing the genealogy of the main Yarrowia lipolytica strains of industrial interest, by illustrating the search for new genetic backgrounds and by providing data about the main publicly available strains in yeast collections worldwide. At last, it will focus on exemplifying how advances in engineering tools can leverage a better biotechnological exploitation of the natural biodiversity of Yarrowia lipolytica and of other yeasts from the Yarrowia clade.

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.

Lipid production by oleaginous yeasts

Microbial lipid production has been studied extensively for years; however, lipid metabolic engineering in many of the extraordinarily high lipid-accumulating yeasts was impeded by inadequate understanding of the metabolic pathways including regulatory mechanisms defining their oleaginicity and the limited genetic tools available. The aim of this review is to highlight the prominent oleaginous yeast genera, emphasizing their oleaginous characteristics, in conjunction with diverse other features such as cheap carbon source utilization, withstanding the effect of inhibitory compounds, commercially favorable fatty acid composition-all supporting their future development as economically viable lipid feedstock. The unique aspects of metabolism attributing to their oleaginicity are accentuated in the pretext of outlining the various strategies successfully implemented to improve the production of lipid and lipid-derived metabolites. A large number of in silico data generated on the lipid accumulation in certain oleaginous yeasts have been carefully curated, as suggestive evidences in line with the exceptional oleaginicity of these organisms. The different genetic elements developed in these yeasts to execute such strategies have been scrupulously inspected, underlining the major types of newly-found and synthetically constructed promoters, transcription terminators, and selection markers. Additionally, there is a plethora of advanced genetic toolboxes and techniques described, which have been successfully used in oleaginous yeasts in the recent years, promoting homologous recombination, genome editing, DNA assembly, and transformation at remarkable efficiencies. They can accelerate and effectively guide the rational designing of system-wide metabolic engineering approaches pinpointing the key targets for developing industrially suitable yeast strains.

High-quality genome-scale metabolic model of Aurantiochytrium sp. T66

The long-chain, ω-3 polyunsaturated fatty acids (PUFAs) (e.g., eicosapentaenoic acid [EPA] and docosahexaenoic acid [DHA]), are essential for humans and animals, including marine fish species. Presently, the primary source of these PUFAs is fish oils. As the global production of fish oils appears to be reaching its limits, alternative sources of high-quality ω-3 PUFAs is paramount to support the growing aquaculture industry. Thraustochytrids are a group of heterotrophic protists with the capability to synthesize and accrue large amounts of DHA. Thus, the thraustochytrids are prime candidates to solve the increasing demand for ω-3 PUFAs using microbial cell factories. However, a systems-level understanding of their metabolic shift from cellular growth into lipid accumulation is, to a large extent, unclear. Here, we reconstructed a high-quality genome-scale metabolic model of the thraustochytrid Aurantiochytrium sp. T66 termed iVS1191. Through iterative rounds of model refinement and extensive manual curation, we significantly enhanced the metabolic scope and coverage of the reconstruction from that of previously published models, making considerable improvements with stoichiometric consistency, metabolic connectivity, and model annotations. We show that iVS1191 is highly consistent with experimental growth data, reproducing in vivo growth phenotypes as well as specific growth rates on minimal carbon media. The availability of iVS1191 provides a solid framework for further developing our understanding of T66's metabolic properties, as well as exploring metabolic engineering and process-optimization strategies in silico for increased ω-3 PUFA production.

Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species

Metabolic network reconstructions have become an important tool for probing cellular metabolism in the field of systems biology. They are used as tools for quantitative prediction but also as scaffolds for further knowledge contextualization. The yeast Saccharomyces cerevisiae was one of the first organisms for which a genome-scale metabolic model (GEM) was reconstructed, in 2003, and since then 45 metabolic models have been developed for a wide variety of relevant yeasts species. A systematic evaluation of these models revealed that-despite this long modeling history-the sequential process of tracing model files, setting them up for basic simulation purposes and comparing them across species and even different versions, is still not a generalizable task. These findings call the yeast modeling community to comply to standard practices on model development and sharing in order to make GEMs accessible and useful for a wider public.

Recent advances in lipid metabolic engineering of oleaginous yeasts

With the increasing demand to develop a renewable and sustainable biolipid feedstock, several species of non-conventional oleaginous yeasts are being explored. Apart from the platform oleaginous yeast Yarrowia lipolytica, the understanding of metabolic pathway and, therefore, exploiting the engineering prospects of most of the oleaginous species are still in infancy. However, in the past few years, enormous efforts have been invested in Rhodotorula, Rhodosporidium, Lipomyces, Trichosporon, and Candida genera of yeasts among others, with the rapid advancement of engineering strategies, significant improvement in genetic tools and techniques, generation of extensive bioinformatics and omics data. In this review, we have collated these recent progresses to make a detailed and insightful summary of the major developments in metabolic engineering of the prominent oleaginous yeast species. Such a comprehensive overview would be a useful resource for future strain improvement and metabolic engineering studies for enhanced production of lipid and lipid-derived chemicals in oleaginous yeasts.

Fine-tuning mitochondrial activity in Yarrowia lipolytica for citrate overproduction

Yarrowia lipolytica is a non-conventional yeast with promising industrial potentials for lipids and citrate production. It is also widely used for studying mitochondrial respiration due to a respiratory chain like those of mammalian cells. In this study we used a genome-scale model (GEM) of Y. lipolytica metabolism and performed a dynamic Flux Balance Analysis (dFBA) algorithm to analyze and identify metabolic levers associated with citrate optimization. Analysis of fluxes at stationary growth phase showed that carbon flux derived from glucose is rewired to citric acid production and lipid accumulation, whereas the oxidative phosphorylation (OxPhos) shifted to the alternative respiration mode through alternative oxidase (AOX) protein. Simulations of optimized citrate secretion flux resulted in a pronounced lipid oxidation along with reactive oxygen species (ROS) generation and AOX flux inhibition. Then, we experimentally challenged AOX inhibition by adding n-Propyl Gallate (nPG), a specific AOX inhibitor, on Y. lipolytica batch cultures at stationary phase. Our results showed a twofold overproduction of citrate (20.5 g/L) when nPG is added compared to 10.9 g/L under control condition (no nPG addition). These results suggest that ROS management, especially through AOX activity, has a pivotal role on citrate/lipid flux balance in Y. lipolytica. All taken together, we thus provide for the first time, a key for the understanding of a predominant metabolic mechanism favoring citrate overproduction in Y. lipolytica at the expense of lipids accumulation.

Genome-scale metabolic modeling underscores the potential of Cutaneotrichosporon oleaginosus ATCC 20509 as a cell factory for biofuel production

BACKGROUND

Cutaneotrichosporon oleaginosus ATCC 20509 is a fast-growing oleaginous basidiomycete yeast that is able to grow in a wide range of low-cost carbon sources including crude glycerol, a byproduct of biodiesel production. When glycerol is used as a carbon source, this yeast can accumulate more than 50% lipids (w/w) with high concentrations of mono-unsaturated fatty acids.

RESULTS

To increase our understanding of this yeast and to provide a knowledge base for further industrial use, a FAIR re-annotated genome was used to build a genome-scale, constraint-based metabolic model containing 1553 reactions involving 1373 metabolites in 11 compartments. A new description of the biomass synthesis reaction was introduced to account for massive lipid accumulation in conditions with high carbon-to-nitrogen (C/N) ratio in the media. This condition-specific biomass objective function is shown to better predict conditions with high lipid accumulation using glucose, fructose, sucrose, xylose, and glycerol as sole carbon source.

CONCLUSION

Contributing to the economic viability of biodiesel as renewable fuel, C. oleaginosus ATCC 20509 can effectively convert crude glycerol waste streams in lipids as a potential bioenergy source. Performance simulations are essential to identify optimal production conditions and to develop and fine tune a cost-effective production process. Our model suggests ATP-citrate lyase as a possible target to further improve lipid production.

Genome-scale metabolic reconstruction of the non-model yeast Issatchenkia orientalis SD108 and its application to organic acids production

Many platform chemicals can be produced from renewable biomass by microorganisms, with organic acids making up a large fraction. Intolerance to the resulting low pH growth conditions, however, remains a challenge for the industrial production of organic acids by microorganisms. Issatchenkia orientalis SD108 is a promising host for industrial production because it is tolerant to acidic conditions as low as pH 2.0. With the goal to systematically assess the metabolic capabilities of this non-model yeast, we developed a genome-scale metabolic model for I. orientalis SD108 spanning 850 genes, 1826 reactions, and 1702 metabolites. In order to improve the model’s quantitative predictions, organism-specific macromolecular composition and ATP maintenance requirements were determined experimentally and implemented. We examined its network topology, including essential genes and flux coupling analysis and drew comparisons with the Yeast 8.3 model for Saccharomyces cerevisiae. We explored the carbon substrate utilization and examined the organism’s production potential for the industrially-relevant succinic acid, making use of the OptKnock framework to identify gene knockouts which couple production of the targeted chemical to biomass production. The genome-scale metabolic model iIsor850 is a data-supported curated model which can inform genetic interventions for overproduction.

•

Genome-scale metabolic model iIsor850 describes metabolism of I. orientalis SD108.

•

Customized biomass reaction highlights differences with S. cerevisiae.

•

Chemostat data elucidate growth-associated ATP maintenance.

•

Substrate utilization and CRISPR/Cas9 gene knockout phenotypes validate model.

•

Model pinpoints candidate gene deletions coupling succinic acid production to growth.

Metabolic engineering for increased lipid accumulation in Yarrowia lipolytica - A Review

Current energy security and climate change policies encourage the development and utilization of bioenergy. Oleaginous yeasts provide a particularly attractive platform for the sustainable production of biofuels and industrial chemicals due to their ability to accumulate high amounts of lipids. In particular, microbial lipids in the form of triacylglycerides (TAGs) produced from renewable feedstocks have attracted considerable attention because they can be directly used in the production of biodiesel and oleochemicals analogous to petrochemicals. As an oleaginous yeast that is generally regarded as safe, Yarrowia lipolytica has been extensively studied, with large amounts of data on its lipid metabolism, genetic tools, and genome sequencing and annotation. In this review, we highlight the newest strategies for increasing lipid accumulation using metabolic engineering and summarize the research advances on the overaccumulation of lipids in Y. lipolytica. Finally, perspectives for future engineering approaches are proposed.

Synthetic biology, systems biology, and metabolic engineering of Yarrowia lipolytica toward a sustainable biorefinery platform

Yarrowia lipolytica is an oleaginous yeast that has been substantially engineered for production of oleochemicals and drop-in transportation fuels. The unique acetyl-CoA/malonyl-CoA supply mode along with the versatile carbon-utilization pathways makes this yeast a superior host to upgrade low-value carbons into high-value secondary metabolites and fatty acid-based chemicals. The expanded synthetic biology toolkits enabled us to explore a large portfolio of specialized metabolism beyond fatty acids and lipid-based chemicals. In this review, we will summarize the recent advances in genetic, omics, and computational tool development that enables us to streamline the genetic or genomic modification for Y. lipolytica. We will also summarize various metabolic engineering strategies to harness the endogenous acetyl-CoA/malonyl-CoA/HMG-CoA pathway for production of complex oleochemicals, polyols, terpenes, polyketides, and commodity chemicals. We envision that Y. lipolytica will be an excellent microbial chassis to expand nature's biosynthetic capacity to produce plant secondary metabolites, industrially relevant oleochemicals, agrochemicals, commodity, and specialty chemicals and empower us to build a sustainable biorefinery platform that contributes to the prosperity of a bio-based economy in the future.

Genome-wide sequencing and metabolic annotation of Pythium irregulare CBS 494.86: understanding Eicosapentaenoic acid production

BACKGROUND

Pythium irregulare is an oleaginous Oomycete able to accumulate large amounts of lipids, including Eicosapentaenoic acid (EPA). EPA is an important and expensive dietary supplement with a promising and very competitive market, which is dependent on fish-oil extraction. This has prompted several research groups to study biotechnological routes to obtain specific fatty acids rather than a mixture of various lipids. Moreover, microorganisms can use low cost carbon sources for lipid production, thus reducing production costs. Previous studies have highlighted the production of EPA by P. irregulare, exploiting diverse low cost carbon sources that are produced in large amounts, such as vinasse, glycerol, and food wastewater. However, there is still a lack of knowledge about its biosynthetic pathways, because no functional annotation of any Pythium sp. exists yet. The goal of this work was to identify key genes and pathways related to EPA biosynthesis, in P. irregulare CBS 494.86, by sequencing and performing an unprecedented annotation of its genome, considering the possibility of using wastewater as a carbon source.

RESULTS

Genome sequencing provided 17,727 candidate genes, with 3809 of them associated with enzyme code and 945 with membrane transporter proteins. The functional annotation was compared with curated information of oleaginous organisms, understanding amino acids and fatty acids production, and consumption of carbon and nitrogen sources, present in the wastewater. The main features include the presence of genes related to the consumption of several sugars and candidate genes of unsaturated fatty acids production.

CONCLUSIONS

The whole metabolic genome presented, which is an unprecedented reconstruction of P. irregulare CBS 494.86, shows its potential to produce value-added products, in special EPA, for food and pharmaceutical industries, moreover it infers metabolic capabilities of the microorganism by incorporating information obtained from literature and genomic data, supplying information of great importance to future work.

Metabolic traits specific for lipid-overproducing strain of Mucor circinelloides WJ11 identified by genome-scale modeling approach

The genome-scale metabolic model of a lipid-overproducing strain of Mucor circinelloides WJ11 was developed. The model (iNI1159) contained 1,159 genes, 648 EC numbers, 1,537 metabolites, and 1,355 metabolic reactions, which were localized in different compartments of the cell. Using flux balance analysis (FBA), the iNI1159 model was validated by predicting the specific growth rate. The metabolic traits investigated by phenotypic phase plane analysis (PhPP) showed a relationship between the nutrient uptake rate, cell growth, and the triacylglycerol production rate, demonstrating the strength of the model. A putative set of metabolic reactions affecting the lipid-accumulation process was identified when the metabolic flux distributions under nitrogen-limited conditions were altered by performing fast flux variability analysis (fastFVA) and relative flux change. Comparative analysis of the metabolic models of the lipid-overproducing strain WJ11 (iNI1159) and the reference strain CBS277.49 (iWV1213) using both fastFVA and coordinate hit-and-run with rounding (CHRR) showed that the flux distributions between these two models were significantly different. Notably, a higher flux distribution through lipid metabolisms such as lanosterol, zymosterol, glycerolipid and fatty acids biosynthesis in iNI1159 was observed, leading to an increased lipid production when compared to iWV1213. In contrast, iWV1213 exhibited a higher flux distribution across carbohydrate and amino acid metabolisms and thus generated a high flux for biomass production. This study demonstrated that iNI1159 is an effective predictive tool for the pathway engineering of oleaginous strains for the production of diversified oleochemicals with industrial relevance.

In silico identification of metabolic engineering strategies for improved lipid production in Yarrowia lipolytica by genome-scale metabolic modeling

BACKGROUND

Yarrowia lipolytica, an oleaginous yeast, is a promising platform strain for production of biofuels and oleochemicals as it can accumulate a high level of lipids in response to nitrogen limitation. Accordingly, many metabolic engineering efforts have been made to develop engineered strains of Y. lipolytica with higher lipid yields. Genome-scale model of metabolism (GEM) is a powerful tool for identifying novel genetic designs for metabolic engineering. Several GEMs for Y. lipolytica have recently been developed; however, not many applications of the GEMs have been reported for actual metabolic engineering of Y. lipolytica. The major obstacle impeding the application of Y. lipolytica GEMs is the lack of proper methods for predicting phenotypes of the cells in the nitrogen-limited condition, or more specifically in the stationary phase of a batch culture.

RESULTS

In this study, we showed that environmental version of minimization of metabolic adjustment (eMOMA) can be used for predicting metabolic flux distribution of Y. lipolytica under the nitrogen-limited condition and identifying metabolic engineering strategies to improve lipid production in Y. lipolytica. Several well-characterized overexpression targets, such as diglyceride acyltransferase, acetyl-CoA carboxylase, and stearoyl-CoA desaturase, were successfully rediscovered by our eMOMA-based design method, showing the relevance of prediction results. Interestingly, the eMOMA-based design method also suggested non-intuitive knockout targets, and we experimentally validated the prediction with a mutant lacking YALI0F30745g, one of the predicted targets involved in one-carbon/methionine metabolism. The mutant accumulated 45% more lipids compared to the wild-type.

CONCLUSION

This study demonstrated that eMOMA is a powerful computational method for understanding and engineering the metabolism of Y. lipolytica and potentially other oleaginous microorganisms.

Synthetic biology tools for engineering Yarrowia lipolytica

The non-conventional oleaginous yeast Yarrowia lipolytica shows great industrial promise. It naturally produces certain compounds of interest but can also artificially generate non-native metabolites, thanks to an engineering process made possible by the significant expansion of a dedicated genetic toolbox. In this review, we present recently developed synthetic biology tools that facilitate the manipulation of Y. lipolytica, including 1) DNA assembly techniques, 2) DNA parts for constructing expression cassettes, 3) genome-editing techniques, and 4) computational tools.

•

Due to its metabolic features, Y. lipolytica is a promising cell factory for producing compounds of biotechnological interest.

•

Fast and efficient synthetic biology tools have been developed, which has boosted engineering strategies for Y. lipolytica.

•

This review describes the latest advances in synthetic biology for Y. lipolytica.

•

DNA assembly tools, DNA parts for expression cassettes, genome-editing techniques, and computational tools are covered here.

Enhanced squalene biosynthesis in Yarrowia lipolytica based on metabolically engineered acetyl-CoA metabolism

As a bioactive triterpenoid, squalene is widely used in the food industry, cosmetics, and pharmacology. Squalene's major commercial sources are the liver oil of deep-sea sharks and plant oils. In this study, we focused on the enhancement of squalene biosynthesis in Yarrowia lipolytica, with particular attention to the engineering of acetyl-CoA metabolism based on genome-scale metabolic reaction network analysis. Although the overexpression of the rate-limiting endogenous ylHMG1 (3-hydroxy-3-methylglutaryl-CoA reductase gene) could improve squalene synthesis by 3.2-fold over that by the control strain, the availability of the key intracellular precursor, acetyl-CoA, was found to play a more significant role in elevating squalene production. Analysis of metabolic networks with the newly constructed genome-scale metabolic model of Y. lipolytica iYL_2.0 showed that the acetyl-CoA pool size could be increased by redirecting carbon flux of pyruvate dehydrogenation towards the ligation of acetate and CoA or the cleavage of citrate to form oxaloacetate and acetyl-CoA. The overexpression of either acetyl-CoA synthetase gene from Salmonella enterica (acs*) or the endogenous ATP citrate lyase gene (ylACL1) resulted in a more than 50% increase in the cytosolic acetyl-CoA level. Moreover, iterative chromosomal integration of the ylHMG1, asc*, and ylACL1 genes resulted in a significant improvement in squalene production (16.4-fold increase in squalene content over that in the control strain). We also found that supplementation with 10 mM citrate in a flask culture further enhanced squalene production to 10 mg/g DCW. The information obtained in this study demonstrates that rationally engineering acetyl-CoA metabolism to ensure the supply of this key metabolic precursor is an efficient strategy for the enhancement of squalene biosynthesis.

Prediction of Metabolite Concentrations, Rate Constants and Post-Translational Regulation Using Maximum Entropy-Based Simulations with Application to Central Metabolism of Neurospora crassa

We report the application of a recently proposed approach for modeling biological systems using a maximum entropy production rate principle in lieu of having in vivo rate constants. The method is applied in four steps: (1) a new ordinary differential equation (ODE) based optimization approach based on Marcelin’s 1910 mass action equation is used to obtain the maximum entropy distribution; (2) the predicted metabolite concentrations are compared to those generally expected from experiments using a loss function from which post-translational regulation of enzymes is inferred; (3) the system is re-optimized with the inferred regulation from which rate constants are determined from the metabolite concentrations and reaction fluxes; and finally (4) a full ODE-based, mass action simulation with rate parameters and allosteric regulation is obtained. From the last step, the power characteristics and resistance of each reaction can be determined. The method is applied to the central metabolism of Neurospora crassa and the flow of material through the three competing pathways of upper glycolysis, the non-oxidative pentose phosphate pathway, and the oxidative pentose phosphate pathway are evaluated as a function of the NADP/NADPH ratio. It is predicted that regulation of phosphofructokinase (PFK) and flow through the pentose phosphate pathway are essential for preventing an extreme level of fructose 1,6-bisphophate accumulation. Such an extreme level of fructose 1,6-bisphophate would otherwise result in a glassy cytoplasm with limited diffusion, dramatically decreasing the entropy and energy production rate and, consequently, biological competitiveness.

Comprehensive analysis of a metabolic model for lipid production in Rhodosporidium toruloides

The yeast Rhodosporidium toruloides has been extensively studied for its application in biolipid production. The knowledge of its metabolism capabilities and the application of constraint-based flux analysis methodology provide useful information for process prediction and optimization. The accuracy of the resulting predictions is highly dependent on metabolic models. A metabolic reconstruction for R. toruloides metabolism has been recently published. On the basis of this model, we developed a curated version that unblocks the central nitrogen metabolism and, in addition, completes charge and mass balances in some reactions neglected in the former model. Then, a comprehensive analysis of network capability was performed with the curated model and compared with the published metabolic reconstruction. The flux distribution obtained by lipid optimization with flux balance analysis was able to replicate the internal biochemical changes that lead to lipogenesis in oleaginous microorganisms. These results motivate the development of a genome-scale model for complete elucidation of R. toruloides metabolism.

Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica

Background

Recently, there have been several attempts to produce long-chain dicarboxylic acids (DCAs) in various microbial hosts. Of these, Yarrowia lipolytica has great potential due to its oleaginous characteristics and unique ability to utilize hydrophobic substrates. However, Y. lipolytica should be further engineered to make it more competitive: the current approaches are mostly intuitive and cumbersome, thus limiting its industrial application.

Results

In this study, we proposed model-guided metabolic engineering strategies for enhanced production of DCAs in Y. lipolytica. At the outset, we reconstructed genome-scale metabolic model (GSMM) of Y. lipolytica (iYLI647) by substantially expanding the previous models. Subsequently, the model was validated using three sets of published culture experiment data. It was finally exploited to identify genetic engineering targets for overexpression, knockout, and cofactor modification by applying several in silico strain design methods, which potentially give rise to high yield production of the industrially relevant long-chain DCAs, e.g., dodecanedioic acid (DDDA). The resultant targets include (1) malate dehydrogenase and malic enzyme genes and (2) glutamate dehydrogenase gene, in silico overexpression of which generated additional NADPH required for fatty acid synthesis, leading to the increased DDDA fluxes by 48% and 22% higher, respectively, compared to wild-type. We further investigated the effect of supplying branched-chain amino acids on the acetyl-CoA turn-over rate which is key metabolite for fatty acid synthesis, suggesting their significance for production of DDDA in Y. lipolytica.

Conclusion

In silico model-based strain design strategies allowed us to identify several metabolic engineering targets for overproducing DCAs in lipid accumulating yeast, Y. lipolytica. Thus, the current study can provide a methodological framework that is applicable to other oleaginous yeasts for value-added biochemical production.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0542-5) contains supplementary material, which is available to authorized users.

Metabolic Engineering of Oleaginous Yeasts for Production of Fuels and Chemicals

Oleaginous yeasts have been increasingly explored for production of chemicals and fuels via metabolic engineering. Particularly, there is a growing interest in using oleaginous yeasts for the synthesis of lipid-related products due to their high lipogenesis capability, robustness, and ability to utilize a variety of substrates. Most of the metabolic engineering studies in oleaginous yeasts focused on Yarrowia that already has plenty of genetic engineering tools. However, recent advances in systems biology and synthetic biology have provided new strategies and tools to engineer those oleaginous yeasts that have naturally high lipid accumulation but lack genetic tools, such as Rhodosporidium, Trichosporon, and Lipomyces. This review highlights recent accomplishments in metabolic engineering of oleaginous yeasts and recent advances in the development of genetic engineering tools in oleaginous yeasts within the last 3 years.

Biotechnological production of bio-based long-chain dicarboxylic acids with oleogenious yeasts

Long-chain α,ω-dicarboxylic acids (DCAs) are versatile chemical intermediates of industrial importance used as building blocks for the production of polymers, lubricants, or adhesives. The majority of industrial long-chain DCAs is produced from petro-chemical resources. An alternative is their biotechnological production from renewable materials like plant oil fatty acids by microbial fermentation using oleogenious yeasts. Oleogenious yeasts are natural long-chain DCA producers, which have to be genetically engineered for high-yield DCA production. Although, some commercialized fermentation processes using engineered yeasts are reported, bio-based long-chain DCAs are still far from being a mass product. Further progress in bioprocess engineering and rational strain design is necessary to advance their further commercialization. The present article reviews the basic strategies, as well as novel approaches in the strain design of oleogenious yeasts, such as the combination of traditional metabolic engineering with system biology strategies for high-yield long-chain DCA production. Therefore a detailed overview of the involved metabolic processes for the biochemical long-chain DCA synthesis is given.

Experimental design and metabolic flux analysis tools to optimize industrially relevant Haemophilus influenzae type b growth medium

Haemophilus influenzae type b (Hib), a Gram-negative capsulated bacterium, is a causative agent of meningitis worldwide. The capsular polysaccharide, a high molecular mass polymer consisting of the repeated units of the polyribosyl-ribitol-phosphate, is considered the main virulence factor and it is used as an antigen to vaccines, conjugated to a carrier protein. The industrial production of the polysaccharide requires the cultivation of Hib in rich medium, which impacts process costs and product recovery. In this study, a central composite rotational experimental design strategy was used to access the influence of key components of culture medium (soy peptone, yeast extract and glucose) on biomass formation and polysaccharide production in shake-flasks. The optimized medium formulation, containing half of the usual yeast extract and soytone concentrations, was further validated in batch bioreactor cultivations. High polysaccharide production (∼500 mg/L) was obtained in a cheaper and more competitive production process for use in Hib vaccine production. In addition, simulations of a metabolic model describing Hib central metabolism were used to assess the role of key amino acids on growth. A chemically defined medium supplemented only with amino acids from α-ketoglutarate and oxaloacetate families as well as phenylalanine was suggested as a promising alternative for reduced acetate accumulation and enhanced polysaccharide production in Hib cultures. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1508-1519, 2017.

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.

Comprehensive reconstruction and evaluation of Pichia pastoris genome-scale metabolic model that accounts for 1243 ORFs

Background

Pichia pastoris is one of the most important cell factories for production of industrial enzymes and heterogenous proteins. The genome-scale metabolic model of high quality is crucial for comprehensive understanding of the P. pastoris metabolism.

Methods

In this paper, we upgraded P. pastoris genome-scale metabolic model based on the combination of latest genome annotations and literatures. Then the performance of the new model was evaluated using the Cobra Toolbox v2.0.

Results

Compared with the recently published model iMT1026, the reaction number in the new model iRY1243 was increased from 2035 to 2407 and the metabolite number was increased from 1018 to 1094. Accordingly, the unique ORF number was increased from 1026 to 1243. To improve the metabolic functions of P. pastoris genome-scale metabolic model, the biosynthesis pathways of vitamins and cofactors were carefully added. iRY1243 showed good performances when predicting the growth capability on most of the reported carbon and nitrogen sources, the metabolic flux distribution with glucose as a sole carbon source, the essential and partially essential genes, and the effects of gene deletion or overexpression on cell growth and S-adenosyl-l-methionine production.

Conclusion

iRY1243 is an upgraded P. pastoris genome-scale metabolic model with significant improvements in the metabolic coverage and prediction ability, and thus it will be a potential platform for further systematic investigation of P. pastoris metabolism.

Electronic supplementary material

The online version of this article (doi:10.1186/s40643-017-0152-x) contains supplementary material, which is available to authorized users.

A molecular genetic toolbox for Yarrowia lipolytica

BACKGROUND

Yarrowia lipolytica is an ascomycete yeast used in biotechnological research for its abilities to secrete high concentrations of proteins and accumulate lipids. Genetic tools have been made in a variety of backgrounds with varying similarity to a comprehensively sequenced strain.

RESULTS

We have developed a set of genetic and molecular tools in order to expand capabilities of Y. lipolytica for both biological research and industrial bioengineering applications. In this work, we generated a set of isogenic auxotrophic strains with decreased non-homologous end joining for targeted DNA incorporation. Genome sequencing, assembly, and annotation of this genetic background uncovers previously unidentified genes in Y. lipolytica. To complement these strains, we constructed plasmids with Y. lipolytica-optimized superfolder GFP for targeted overexpression and fluorescent tagging. We used these tools to build the "Yarrowia lipolytica Cell Atlas," a collection of strains with endogenous fluorescently tagged organelles in the same genetic background, in order to define organelle morphology in live cells.

CONCLUSIONS

These molecular and isogenetic tools are useful for live assessment of organelle-specific protein expression, and for localization of lipid biosynthetic enzymes or other proteins in Y. lipolytica. This work provides the Yarrowia community with tools for cell biology and metabolism research in Y. lipolytica for further development of biofuels and natural products.

Comprehensive reconstruction and in silico analysis of Aspergillus niger genome-scale metabolic network model that accounts for 1210 ORFs

Aspergillus niger is one of the most important cell factories for industrial enzymes and organic acids production. A comprehensive genome-scale metabolic network model (GSMM) with high quality is crucial for efficient strain improvement and process optimization. The lack of accurate reaction equations and gene-protein-reaction associations (GPRs) in the current best model of A. niger named GSMM iMA871, however, limits its application scope. To overcome these limitations, we updated the A. niger GSMM by combining the latest genome annotation and literature mining technology. Compared with iMA871, the number of reactions in iHL1210 was increased from 1,380 to 1,764, and the number of unique ORFs from 871 to 1,210. With the aid of our transcriptomics analysis, the existence of 63% ORFs and 68% reactions in iHL1210 can be verified when glucose was used as the only carbon source. Physiological data from chemostat cultivations, ¹³ C-labeled and molecular experiments from the published literature were further used to check the performance of iHL1210. The average correlation coefficients between the predicted fluxes and estimated fluxes from ¹³ C-labeling data were sufficiently high (above 0.89) and the prediction of cell growth on most of the reported carbon and nitrogen sources was consistent. Using the updated genome-scale model, we evaluated gene essentiality on synthetic and yeast extract medium, as well as the effects of NADPH supply on glucoamylase production in A. niger. In summary, the new A. niger GSMM iHL1210 contains significant improvements with respect to the metabolic coverage and prediction performance, which paves the way for systematic metabolic engineering of A. niger. Biotechnol. Bioeng. 2017;114: 685-695. © 2016 Wiley Periodicals, Inc.

Biotechnological production of γ-decalactone, a peach like aroma, by Yarrowia lipolytica

The request for new flavourings increases every year. Consumer perception that everything natural is better is causing an increase demand for natural aroma additives. Biotechnology has become a way to get natural products. γ-Decalactone is a peach-like aroma widely used in dairy products, beverages and others food industries. In more recent years, more and more studies and industrial processes were endorsed to cost-effect this compound production. One of the best-known methods to produce γ-decalactone is from ricinoleic acid catalyzed by Yarrowia lipolytica, a generally regarded as safe status yeast. As yet, several factors affecting γ-decalactone production remain to be fully understood and optimized. In this review, we focus on the aromatic compound γ-decalactone and its production by Y. lipolytica. The metabolic pathway of lactone production and degradation are addressed. Critical analysis of novel strategies of bioprocess engineering, metabolic and genetic engineering and other strategies for the enhancement of the aroma productivity are presented.

Genome-scale metabolic modeling and in silico analysis of lipid accumulating yeast Candida tropicalis for dicarboxylic acid production

Recently, the bio-production of α,ω-dicarboxylic acids (DCAs) has gained significant attention, which potentially leads to the replacement of the conventional petroleum-based products. In this regard, the lipid accumulating yeast Candida tropicalis, has been recognized as a promising microbial host for DCA biosynthesis: it possess the unique ω-oxidation pathway where the terminal carbon of α-fatty acids is oxidized to form DCAs with varying chain lengths. However, despite such industrial importance, its cellular physiology and lipid accumulation capability remain largely uncharacterized. Thus, it is imperative to better understand the metabolic behavior of this lipogenic yeast, which could be achieved by a systems biological approach. To this end, herein, we reconstructed the genome-scale metabolic model of C. tropicalis, iCT646, accounting for 646 unique genes, 945 metabolic reactions, and 712 metabolites. Initially, the comparative network analysis of iCT646 with other yeasts revealed several distinctive metabolic reactions, mainly within the amino acid and lipid metabolism including the ω-oxidation pathway. Constraints-based flux analysis was, then, employed to predict the in silico growth rates of C. tropicalis which are highly consistent with the cellular phenotype observed in glucose and xylose minimal media chemostat cultures. Subsequently, the lipid accumulation capability of C. tropicalis was explored in comparison with Saccharomyces cerevisiae, indicating that the formation of "citrate pyruvate cycle" is essential to the lipid accumulation in oleaginous yeasts. The in silico flux analysis also highlighted the enhanced ability of pentose phosphate pathway as NADPH source rather than malic enzyme during lipogenesis. Finally, iCT646 was successfully utilized to highlight the key directions of C. tropicalis strain design for the whole cell biotransformation application to produce long-chain DCAs from alkanes. Biotechnol. Bioeng. 2016;113: 1993-2004. © 2016 Wiley Periodicals, Inc.

Regulation of amino-acid metabolism controls flux to lipid accumulation in Yarrowia lipolytica

Yarrowia lipolytica is a promising microbial cell factory for the production of lipids to be used as fuels and chemicals, but there are few studies on regulation of its metabolism. Here we performed the first integrated data analysis of Y. lipolytica grown in carbon and nitrogen limited chemostat cultures. We first reconstructed a genome-scale metabolic model and used this for integrative analysis of multilevel omics data. Metabolite profiling and lipidomics was used to quantify the cellular physiology, while regulatory changes were measured using RNAseq. Analysis of the data showed that lipid accumulation in Y. lipolytica does not involve transcriptional regulation of lipid metabolism but is associated with regulation of amino-acid biosynthesis, resulting in redirection of carbon flux during nitrogen limitation from amino acids to lipids. Lipid accumulation in Y. lipolytica at nitrogen limitation is similar to the overflow metabolism observed in many other microorganisms, e.g. ethanol production by Sacchromyces cerevisiae at nitrogen limitation.

Flux Balance Analysis Inspired Bioprocess Upgrading for Lycopene Production by a Metabolically Engineered Strain of Yarrowia lipolytica

Genome-scale metabolic models embody a significant advantage of systems biology since their applications as metabolic flux simulation models enable predictions for the production of industrially-interesting metabolites. The biotechnological production of lycopene from Yarrowia lipolytica is an emerging scope that has not been fully scrutinized, especially for what concerns cultivation conditions of newly generated engineered strains. In this study, by combining flux balance analysis (FBA) and Plackett-Burman design, we screened chemicals for lycopene production from a metabolically engineered strain of Y. lipolytica. Lycopene concentrations of 126 and 242 mg/L were achieved correspondingly from the FBA-independent and the FBA-assisted designed media in fed-batch cultivation mode. Transcriptional studies revealed upregulations of heterologous genes in media designed according to FBA, thus implying the efficiency of model predictions. Our study will potentially support upgraded lycopene and other terpenoids production from existing or prospect bioengineered strains of Y. lipolytica and/or closely related yeast species.

Optimization of lipid production with a genome-scale model of Yarrowia lipolytica

Background

Yarrowia lipolytica is a non-conventional yeast that is extensively investigated for its ability to excrete citrate or to accumulate large amounts of storage lipids, which is of great significance for single cell oil production. Both traits are thus of interest for basic research as well as for biotechnological applications but they typically occur simultaneously thus lowering the respective yields. Therefore, engineering of strains with high lipid content relies on novel concepts such as computational simulation to better understand the two competing processes and to eliminate citrate excretion.

Results

Using a genome-scale model (GSM) of baker's yeast as a scaffold, we reconstructed the metabolic network of Y. lipolytica and optimized it for use in flux balance analysis (FBA), with the aim to simulate growth and lipid production phases of this yeast. We validated our model and found the predictions of the growth behavior of Y. lipolytica in excellent agreement with experimental data. Based on these data, we successfully designed a fed-batch strategy to avoid citrate excretion during the lipid production phase. Further analysis of the network suggested that the oxygen demand of Y. lipolytica is reduced upon induction of lipid synthesis. According to this finding we hypothesized that a reduced aeration rate might induce lipid accumulation. This prediction was indeed confirmed experimentally. In a fermentation combining these two strategies lipid content of the biomass was increased by 80 %, and lipid yield was improved more than four-fold, compared to standard conditions.

Conclusions

Genome scale network reconstructions provide a powerful tool to predict the effects of genetic modifications and the metabolic response to environmental conditions. The high accuracy and the predictive value of a newly reconstructed GSM of Y. lipolytica to optimize growth conditions for lipid accumulation are demonstrated. Based on these findings, further strategies for engineering Y. lipolytica towards higher efficiency in single cell oil production are discussed.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0217-4) contains supplementary material, which is available to authorized users.