Genome-scale model-driven strain design for dicarboxylic acid production in <Emphasis Type="Italic">Yarrowia lipolytica</Emphasis>

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.

     

解脂耶氏酵母表达调控工具的开发及天然产物合成的研究进展

解脂耶氏酵母是一种重要的产油酵母,由于其能利用多种疏水性底物,具有良好的耐酸、耐盐等胁迫耐受性,具有高通量的三羧酸循环,可提供充足的乙酰辅酶A前体等特点,被认为是生产萜类、聚酮类和黄酮类等天然产物的理想宿主,在代谢工程领域有着广泛的应用。近年来,越来越多的基因编辑、表达和调控工具被逐渐开发,这促进了解脂耶氏酵母合成各种天然产物的研究。文中综述了近年来解脂耶氏酵母中基因表达和天然产物合成方面的研究进展,并探讨了在该酵母中异源合成天然产物所面临的挑战和可能的解决方案。     

High‐yield lipid production from lignocellulosic biomass using engineered xylose‐utilizing Yarrowia lipolytica

Lignocellulosic biomass shows high potential as a renewable feedstock for use in biodiesel production via microbial fermentation. Yarrowia lipolytica, an emerging oleaginous yeast, has been engineered to efficiently convert xylose, the second most abundant sugar in lignocellulosic biomass, into lipids for lignocellulosic biodiesel production. Yet, the lipid yield from xylose or lignocellulosic biomass remains far lower than that from glucose. Here we developed an efficient xylose‐utilizing Y. lipolytica strain, expressing an isomerase‐based pathway, to achieve high‐yield lipid production from lignocellulosic biomass. The newly developed xylose‐utilizing Y. lipolytica, YSXID, produced 12.01 g/L lipids with a maximum yield of 0.16 g/g, the highest ever reported, from lignocellulosic hydrolysates. Consequently, this study shows the potential of isomerase‐based xylose‐utilizing Y. lipolytica for economical and sustainable production of biodiesel and oleochemicals from lignocellulosic biomass.     

Yarrowia lipolytica: A model and a tool to understand the mechanisms implicated in lipid accumulation

The oleaginous yeast Yarrowia lipolytica is known to inhabit various lipid-containing environments. One of the most striking features in this yeast is the presence of several multigene families involved in the metabolic pathways of hydrophobic substrate utilization. The complexity and the multiplicity of these genes give Y. lipolytica a wide capability range towards hydrophobic substrate (HS) utilization and storage. The combination of the increasing knowledge of this yeast's metabolism and the development of more efficient genetic tools is offering new perspectives in using Y. lipolytica as a model organism to study the mechanisms involved in lipid metabolism associated to fat uptake, storage, deposition, mobilization and regulation. Nutrient status and culture conditions seem to play a major role in obesity.     

Construction of a Genome-Scale Metabolic Model of Arthrospira platensis NIES-39 and Metabolic Design for Cyanobacterial Bioproduction

Arthrospira (Spirulina) platensis is a promising feedstock and host strain for bioproduction because of its high accumulation of glycogen and superior characteristics for industrial production. Metabolic simulation using a genome-scale metabolic model and flux balance analysis is a powerful method that can be used to design metabolic engineering strategies for the improvement of target molecule production. In this study, we constructed a genome-scale metabolic model of A. platensis NIES-39 including 746 metabolic reactions and 673 metabolites, and developed novel strategies to improve the production of valuable metabolites, such as glycogen and ethanol. The simulation results obtained using the metabolic model showed high consistency with experimental results for growth rates under several trophic conditions and growth capabilities on various organic substrates. The metabolic model was further applied to design a metabolic network to improve the autotrophic production of glycogen and ethanol. Decreased flux of reactions related to the TCA cycle and phosphoenolpyruvate reaction were found to improve glycogen production. Furthermore, in silico knockout simulation indicated that deletion of genes related to the respiratory chain, such as NAD(P)H dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source.     

Engineering <Emphasis Type="Italic">Yarrowia lipolytica</Emphasis> for the production of cyclopropanated fatty acids

Traditional synthesis of biodiesel competes with food sources and has limitations with storage, particularly due to limited oxidative stability. Microbial synthesis of lipids provides a platform to produce renewable fuel with improved properties from various renewable carbon sources. Specifically, biodiesel properties can be improved through the introduction of a cyclopropane ring in place of a double bond. In this study, we demonstrate the production of C19 cyclopropanated fatty acids in the oleaginous yeast Yarrowia lipolytica through the heterologous expression of the Escherichia coli cyclopropane fatty acid synthase. Ultimately, we establish a strain capable of 3.03?±?0.26 g/L C19 cyclopropanated fatty acid production in bioreactor fermentation where this functionalized lipid comprises over 32% of the total lipid pool. This study provides a demonstration of the flexibility of lipid metabolism in Y. lipolytica to produce specialized fatty acids.     

High-efficiency production of bisabolene from waste cooking oil by metabolically engineered Yarrowia lipolytica

The natural plant product bisabolene serves as a precursor for the production of a wide range of industrially relevant chemicals. However, the low abundance of bisabolene in plants renders its isolation from plant sources non-economically viable. Therefore, creation of microbial cell factories for bisabolene production supported by synthetic biology and metabolic engineering strategies presents a more competitive and environmentally sustainable method for industrial production of bisabolene. In this proof-of-principle study, for the first time, we engineered the oleaginous yeast Yarrowia lipolytica to produce α-bisabolene, β-bisabolene and γ-bisabolene through heterologous expression of the α-bisabolene synthase from Abies grandis, the β-bisabolene synthase gene from Zingiber officinale and the γ-bisabolene synthase gene from Helianthus annuus respectively. Subsequently, two metabolic engineering approaches, including overexpression of the endogenous mevalonate pathway genes and introduction of heterologous multidrug efflux transporters, were employed in order to improve bisabolene production. Furthermore, the fermentation conditions were optimized to maximize bisabolene production by the engineered Y. lipolytica strains from glucose. Finally, we explored the potential of the engineered Y. lipolytica strains for bisabolene production from the waste cooking oil. To our knowledge, this is the first report of bisabolene production in Y. lipolytica using metabolic engineering strategies. These findings provide valuable insights into the engineering of Y. lipolytica for a higher-level production of bisabolene and its utilization in converting waste cooking oil into various industrially valuable products.     

Genome-scale reconstruction and <Emphasis Type="Italic">in silico</Emphasis> analysis of the <Emphasis Type="Italic">Clostridium acetobutylicum</Emphasis> ATCC 824 metabolic network

To understand the metabolic characteristics of Clostridium acetobutylicum and to examine the potential for enhanced butanol production, we reconstructed the genome-scale metabolic network from its annotated genomic sequence and analyzed strategies to improve its butanol production. The generated reconstructed network consists of 502 reactions and 479 metabolites and was used as the basis for an in silico model that could compute metabolic and growth performance for comparison with fermentation data. The in silico model successfully predicted metabolic fluxes during the acidogenic phase using classical flux balance analysis. Nonlinear programming was used to predict metabolic fluxes during the solventogenic phase. In addition, essential genes were predicted via single gene deletion studies. This genome-scale in silico metabolic model of C. acetobutylicum should be useful for genome-wide metabolic analysis as well as strain development for improving production of biochemicals, including butanol. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. J. L. and H. Y. equally contributed to this work.     

Physiologically Shrinking the Solution Space of a Saccharomyces cerevisiae Genome-Scale Model Suggests the Role of the Metabolic Network in Shaping Gene Expression Noise

Sampling the solution space of genome-scale models is generally conducted to determine the feasible region for metabolic flux distribution. Because the region for actual metabolic states resides only in a small fraction of the entire space, it is necessary to shrink the solution space to improve the predictive power of a model. A common strategy is to constrain models by integrating extra datasets such as high-throughput datasets and C¹³-labeled flux datasets. However, studies refining these approaches by performing a meta-analysis of massive experimental metabolic flux measurements, which are closely linked to cellular phenotypes, are limited. In the present study, experimentally identified metabolic flux data from 96 published reports were systematically reviewed. Several strong associations among metabolic flux phenotypes were observed. These phenotype-phenotype associations at the flux level were quantified and integrated into a Saccharomyces cerevisiae genome-scale model as extra physiological constraints. By sampling the shrunken solution space of the model, the metabolic flux fluctuation level, which is an intrinsic trait of metabolic reactions determined by the network, was estimated and utilized to explore its relationship to gene expression noise. Although no correlation was observed in all enzyme-coding genes, a relationship between metabolic flux fluctuation and expression noise of genes associated with enzyme-dosage sensitive reactions was detected, suggesting that the metabolic network plays a role in shaping gene expression noise. Such correlation was mainly attributed to the genes corresponding to non-essential reactions, rather than essential ones. This was at least partially, due to regulations underlying the flux phenotype-phenotype associations. Altogether, this study proposes a new approach in shrinking the solution space of a genome-scale model, of which sampling provides new insights into gene expression noise.     

13C Metabolic Flux Analysis of acetate conversion to lipids by Yarrowia lipolytica

Volatile fatty acids (VFAs) are an inexpensive and renewable carbon source that can be generated from gas fermentation and anaerobic digestion of fermentable wastes. The oleaginous yeast Yarrowia lipolytica is a promising biocatalyst that can utilize VFAs and convert them into triacylglycerides (TAGs). However, currently there is limited knowledge on the metabolism of Y. lipolytica when cultured on VFAs. To develop a better understanding, we used acetate as the sole carbon source to culture two strains, a control strain and a previously engineered strain for lipid overaccumulation. For both strains, metabolism during the growth phase and lipid production phase were investigated by metabolic flux analysis using two parallel sodium acetate tracers. The resolved flux distributions demonstrate that the glyoxylate shunt pathway is constantly active and the flux through gluconeogenesis varies depending on strain and phase. In particular, by regulating the activities of malate transport and pyruvate kinase, the cells divert only a portion of the glyoxylate shunt flux required to satisfy the needs for anaplerotic reactions and NADPH production through gluconeogenesis and the oxidative pentose phosphate pathway (PPP). Excess flux flows back to the tricarboxylic acid (TCA) cycle for energy production. As with the case of glucose as the substrate, the primary source for lipogenic NADPH is derived from the oxidative PPP.     

Identification of genome integration sites for developing a CRISPR-based gene expression toolkit in Yarrowia lipolytica

With the rapid development of synthetic biology, the oleaginous yeast Yarrowia lipolytica has become an attractive microorganism for chemical production. To better optimize and reroute metabolic pathways, we have expanded the CRISPR-based gene expression toolkit of Y. lipolytica. By sorting the integration sites associated with high expression, new neutral integration sites associated with high expression and high integration efficiency were identified. Diverse genetic components, including promoters and terminators, were also characterized to expand the expression range. We found that in addition to promoters, the newly characterized terminators exhibited large variations in gene expression. These genetic components and integration sites were then used to regulate genes involved in the lycopene biosynthesis pathway, and different levels of lycopene production were achieved. The CRISPR-based gene expression toolkit developed in this study will facilitate the genetic engineering of Y. lipolytica.     

The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production.     

Integrated knowledge mining,genome-scale modeling,and machine learning for predicting Yarrowia lipolytica bioproduction

Predicting bioproduction titers from microbial hosts has been challenging due to complex interactions between microbial regulatory networks, stress responses, and suboptimal cultivation conditions. This study integrated knowledge mining, feature extraction, genome-scale modeling (GSM), and machine learning (ML) to develop a model for predicting Yarrowia lipolytica chemical titers (i.e., organic acids, terpenoids, etc.). First, Y. lipolytica production data, including cultivation conditions, genetic engineering strategies, and product information, was manually collected from literature (~100 papers) and stored as either numerical (e.g., substrate concentrations) or categorical (e.g., bioreactor modes) variables. For each case recorded, central pathway fluxes were estimated using GSMs and flux balance analysis (FBA) to provide metabolic features. Second, a ML ensemble learner was trained to predict strain production titers. Accurate predictions on the test data were obtained for instances with production titers >1 g/L (R² = 0.87). However, the model had reduced predictability for low performance strains (0.01–1 g/L, R² = 0.29) potentially due to biosynthesis bottlenecks not captured in the features. Feature ranking indicated that the FBA fluxes, the number of enzyme steps, the substrate inputs, and thermodynamic barriers (i.e., Gibbs free energy of reaction) were the most influential factors. Third, the model was evaluated on other oleaginous yeasts and indicated there were conserved features for some hosts that can be potentially exploited by transfer learning. The platform was also designed to assist computational strain design tools (such as OptKnock) to screen genetic targets for improved microbial production in light of experimental conditions.     

Homology-independent genome integration enables rapid library construction for enzyme expression and pathway optimization in Yarrowia lipolytica

Yarrowia lipolytica is an important oleaginous industrial microorganism used to produce biofuels and other value-added compounds. Although several genetic engineering tools have been developed for Y. lipolytica, there is no efficient method for genomic integration of large DNA fragments. In addition, methods for constructing multigene expression libraries for biosynthetic pathway optimization are still lacking in Y. lipolytica. In this study, we demonstrate that multiple and large DNA fragments can be randomly and efficiently integrated into the genome of Y. lipolytica in a homology-independent manner. This homology-independent integration generates variation in the chromosomal locations of the inserted fragments and in gene copy numbers, resulting in the expression differences in the integrated genes or pathways. Because of these variations, gene expression libraries can be easily created through one-step integration. As a proof of concept, a LIP2 (producing lipase) expression library and a library of multiple genes in the β-carotene biosynthetic pathway were constructed, and high-production strains were obtained through library screening. Our work demonstrates the potential of homology-independent genome integration for library construction, especially for multivariate modular libraries for metabolic pathways in Y. lipolytica, and will facilitate pathway optimization in metabolic engineering applications.     

Regulatory properties of malic enzyme in the oleaginous yeast, Yarrowia lipolytica, and its non-involvement in lipid accumulation

Malic enzyme (EC 1.1.1.40) converts l-malate to pyruvate and CO₂ providing NADPH for metabolism especially for lipid biosynthesis in oleaginous microorganisms. However, its role in the oleaginous yeast, Yarrowia lipolytica, is unclear. We have cloned the malic enzyme gene (YALI0E18634g) from Y. lipolytica into pET28a, expressed it in Escherichia coli and purified the recombinant protein (YlME). YlME used NAD⁺ as the primary cofactor. K_m values for NAD⁺ and NADP⁺ were 0.63 and 3.9 mM, respectively. Citrate, isocitrate and α-ketoglutaric acid (>5 mM) were inhibitory while succinate (5–15 mM) increased NADP⁺- but not NAD⁺-dependent activity. To determine if fatty acid biosynthesis could be increased in Y. lipolytica by providing additional NADPH from an NADP⁺-dependent malic enzyme, the malic enzyme gene (mce2) from an oleaginous fungus, Mortierella alpina, was expressed in Y. lipolytica. No significant changes occurred in lipid content or fatty acid profiles suggesting that malic enzyme is not the main source of NADPH for lipid accumulation in Y. lipolytica.     

Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

Accurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within an in silico model using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model of Geobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.