Optimization of lipid production with a genome-scale model of 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.

Genome-scale model development and genomic sequencing of the oleaginous clade Lipomyces

The Lipomyces clade contains oleaginous yeast species with advantageous metabolic features for biochemical and biofuel production. Limited knowledge about the metabolic networks of the species and limited tools for genetic engineering have led to a relatively small amount of research on the microbes. Here, a genome-scale metabolic model (GSM) of Lipomyces starkeyi NRRL Y-11557 was built using orthologous protein mappings to model yeast species. Phenotypic growth assays were used to validate the GSM (66% accuracy) and indicated that NRRL Y-11557 utilized diverse carbohydrates but had more limited catabolism of organic acids. The final GSM contained 2,193 reactions, 1,909 metabolites, and 996 genes and was thus named iLst996. The model contained 96 of the annotated carbohydrate-active enzymes. iLst996 predicted a flux distribution in line with oleaginous yeast measurements and was utilized to predict theoretical lipid yields. Twenty-five other yeasts in the Lipomyces clade were then genome sequenced and annotated. Sixteen of the Lipomyces species had orthologs for more than 97% of the iLst996 genes, demonstrating the usefulness of iLst996 as a broad GSM for Lipomyces metabolism. Pathways that diverged from iLst996 mainly revolved around alternate carbon metabolism, with ortholog groups excluding NRRL Y-11557 annotated to be involved in transport, glycerolipid, and starch metabolism, among others. Overall, this study provides a useful modeling tool and data for analyzing and understanding Lipomyces species metabolism and will assist further engineering efforts in Lipomyces.

Compound K Production: Achievements and Perspectives

Compound K (CK) is one of the major metabolites found in mammalian blood and organs following oral administration of Panax plants. CK, also known as minor ginsenoside, can be absorbed in the systemic circulation. It has garnered significant attention in healthcare and medical products due to its pharmacological activities, such as antioxidation, anticancer, antiproliferation, antidiabetics, neuroprotection, and anti-atherogenic activities. However, CK is not found in natural ginseng plants but in traditional chemical synthesis, which uses toxic solvents and leads to environmental pollution during the harvest process. Moreover, enzymatic reactions are impractical for industrial CK production due to low yield and high costs. Although CK could be generated from major ginsenosides, most ginsenosides, including protopanaxatriol-oleanane and ocotillol-type, are not converted into CK by catalyzing β-glucosidase. Therefore, microbial cell systems have been used as a promising solution, providing a safe and efficient approach to CK production. This review provides a summary of various approaches for the production of CK, including chemical and enzymatic reactions, biotransformation by the human intestinal bacteria and endophytes as well as engineered microbes. Moreover, the approaches for CK production have been discussed to improve the productivity of target compounds.

Genetic inactivation of the Carnitine/Acetyl-Carnitine mitochondrial carrier of Yarrowia lipolytica leads to enhanced odd-chain fatty acid production

BACKGROUND

Mitochondrial carriers (MCs) can deeply affect the intracellular flux distribution of metabolic pathways. The manipulation of their expression level, to redirect the flux toward the production of a molecule of interest, is an attractive target for the metabolic engineering of eukaryotic microorganisms. The non-conventional yeast Yarrowia lipolytica is able to use a wide range of substrates. As oleaginous yeast, it directs most of the acetyl-CoA therefrom generated towards the synthesis of lipids, which occurs in the cytoplasm. Among them, the odd-chain fatty acids (OCFAs) are promising microbial-based compounds with several applications in the medical, cosmetic, chemical and agricultural industries.

RESULTS

In this study, we have identified the MC involved in the Carnitine/Acetyl-Carnitine shuttle in Y. lipolytica, YlCrc1. The Y. lipolytica Ylcrc1 knock-out strain failed to grow on ethanol, acetate and oleic acid, demonstrating the fundamental role of this MC in the transport of acetyl-CoA from peroxisomes and cytoplasm into mitochondria. A metabolic engineering strategy involving the deletion of YlCRC1, and the recombinant expression of propionyl-CoA transferase from Ralstonia eutropha (RePCT), improved propionate utilization and its conversion into OCFAs. These genetic modifications and a lipogenic medium supplemented with glucose and propionate as the sole carbon sources, led to enhanced accumulation of OCFAs in Y. lipolytica.

CONCLUSIONS

The Carnitine/Acetyl-Carnitine shuttle of Y. lipolytica involving YlCrc1, is the sole pathway for transporting peroxisomal or cytosolic acetyl-CoA to mitochondria. Manipulation of this carrier can be a promising target for metabolic engineering approaches involving cytosolic acetyl-CoA, as demonstrated by the effect of YlCRC1 deletion on OCFAs synthesis.

A nutrition algorithm to optimize feed and medium composition using genome-scale metabolic models

The optimization of animal feeds and cell culture media are problems of interest to a wide range of industries and scientific disciplines. Both problems are dictated by the properties of an organism's metabolism. However, due to the tremendous complexity of metabolic systems, it can be difficult to predict how metabolism will respond to changes in nutrient availability. A common tool used to capture the complexity of metabolism in a computational framework is a genome-scale metabolic model (GEM). GEMs are useful for predicting the fluxes of reactions within an organism's metabolism. To optimize feed or media, in silico experiments can be performed with GEMs by systematically varying nutritional constraints and predicting metabolic activity. In this way, the influence of various nutritional changes on metabolic outcomes can be evaluated. However, this methodology does not guarantee an optimal solution. Here, we develop a nutrition algorithm that utilizes linear programming to search the entire flux solution space of possible dietary intervention strategies to identify the most efficient changes to nutrition for a desirable metabolic outcome. We illustrate the utility of the nutrition algorithm on GEMs of Atlantic salmon (Salmo salar) and Chinese hamster ovary (CHO) cell metabolism and find that the nutrition algorithm makes predictions that not only align with experimental findings but reveal new insights into promising feeding strategies. We show that the nutrition algorithm is highly versatile and customizable to meet the user's needs. For instance, we demonstrate that the nutrition algorithm can be used to predict feed/media compositions that maximize profit margins. While the nutrition algorithm can be used to define an optimal feed/medium ab initio, it can also identify minimal changes to be made to an existing feed/medium to drive the largest metabolic shift. Moreover, the nutrition algorithm can target multiple metabolic pathways simultaneously with only a marginal increase in computational expense. While the nutrition algorithm has its limitations, we believe that this tool can be leveraged in a broad range of biotechnological applications to enhance the feed/medium optimization process.

What makes Yarrowia lipolytica well suited for industry?

Yarrowia lipolytica possesses natural and engineered traits that make it a good host for the industrial bioproduction of chemicals, fuels, foods, and pharmaceuticals. In recent years, academic and industrial researchers have assessed its potential, developed synthetic biology techniques, improved its features, scaled its processes, and identified its limitations. Both publications and patents related to Y. lipolytica have shown a drastic increase during the past decade. Here, we discuss the characteristics of this yeast that make it suitable for industry and the remaining challenges for its wider use at large scale. We present evidence herein that shows the importance and potential of Y. lipolytica in bioproduction such that it may soon be one of the preferred choices of industry.

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.

Metabolomics-driven strain improvement: A mini review

In recent years, mass spectrometry-based metabolomics has been established as a powerful and versatile technique for studying cellular metabolism by comprehensive analysis of metabolites in the cell. Although there are many scientific reports on the use of metabolomics for the elucidation of mechanism and physiological changes occurring in the cell, there are surprisingly very few reports on its use for the identification of rate-limiting steps in a synthetic biological system that can lead to the actual improvement of the host organism. In this mini review, we discuss different strategies for improving strain performance using metabolomics data and compare the application of metabolomics-driven strain improvement techniques in different host microorganisms. Finally, we highlight several success stories on the use of metabolomics-driven strain improvement strategies, which led to significant bioproductivity improvements.

Papiliotrema laurentii: general features and biotechnological applications

Papiliotrema laurentii, previously classified as Cryptococcus laurentii, is an oleaginous yeast that has been isolated from soil, plants, and agricultural and industrial residues. This variety of habitats reflects the diversity of carbon sources that it can metabolize, including monosaccharides, oligosaccharides, glycerol, organic acids, and oils. Compared to other oleaginous yeasts, such as Yarrowia lipolytica and Rhodotorula toruloides, there is little information regarding its genetic and physiological characteristics. From a biotechnological point of view, P. laurentii can produce surfactants, enzymes, and high concentrations of lipids, which can be used as feedstock for fatty acid-derived products. Moreover, it can be applied for the biocontrol of phytopathogenic fungi, contributing to quality maintenance in post- and pre-harvest fruits. It can also improve mycorrhizal colonization, nitrogen nutrition, and plant growth. P. laurentii is also capable of degrading polyester and diesel derivatives and acting in the bioremediation of heavy metals. In this review, we present the current knowledge about the basic and applied aspects of P. laurentii, underscoring its biotechnological potential and future perspectives. KEY POINTS: • The physiological characteristics of P. laurentii confer a wide range of biotechnological applications. • The regulation of the acetyl-CoA carboxylase in P. laurentii is different from most other oleaginous yeasts. • The GEM is a valuable tool to guide the construction of engineered P. laurentii strains with improved features for bio-based products.

Enhancing very long chain fatty acids production in Yarrowia lipolytica

Background

Very long chain fatty acids (VLCFA) and their derivatives are industrially attractive compounds. The most important are behenic acid (C22:0) and erucic acid (C22:1Δ¹³), which are used as lubricants, and moisturizers. C22:0 and C22:1Δ¹³ have also potential for biofuel production. These fatty acids are conventionally obtained from plant oils. Yarrowia lipolytica is an oleaginous yeast with a long history of gene manipulations resulting in the production of industrially interesting compounds, such as organic acids, proteins, and various lipophilic molecules. It has been shown previously that it has potential for the production of VLCFA enriched single cell oils.

Results

The metabolism of Y. lipolytica was redesigned to achieve increased production of VLCFA. The effect of native diacylglycerol acyltransferases of this yeast YlLro1p, YlDga1p, and YlDga2p on the accumulation of VLCFA was examined. It was found that YlDga1p is the only enzyme with a beneficial effect. Further improvement of accumulation was achieved by overexpression of 3-ketoacyl-CoA synthase (TaFAE1) under 8UAS-pTEF promoter and blockage fatty acid degradation pathway by deletion of YlMFE1. The best-producing strain YL53 (Δmfe, pTEF-YlDGA1, 8UAS-pTEF-TaFAE1) produced 120 µg of very long chain fatty acids per g of produced biomass, which accounted for 34% of total fatty acids in biomass.

Conclusions

Recombinant strains of Y. lipolytica have proved to be good producers of VLCFA. Redesign of lipid metabolism pathways had a positive effect on the accumulation of C22:1Δ¹³ and C22:0, which are technologically attractive compounds.

Linear programming based gene expression model (LPM-GEM) predicts the carbon source for Bacillus subtilis

BACKGROUND

Elucidating cellular metabolism led to many breakthroughs in biotechnology, synthetic biology, and health sciences. To date, deriving metabolic fluxes by ¹³C tracer experiments is the most prominent approach for studying metabolic fluxes quantitatively, often with high accuracy and precision. However, the technique has a high demand for experimental resources. Alternatively, flux balance analysis (FBA) has been employed to estimate metabolic fluxes without labeling experiments. It is less informative but can benefit from the low costs and low experimental efforts and gain flux estimates in experimentally difficult conditions. Methods to integrate relevant experimental data have been emerged to improve FBA flux estimations. Data from transcription profiling is often selected since it is easy to generate at the genome scale, typically embedded by a discretization of differential and non-differential expressed genes coding for the respective enzymes.

RESULT

We established the novel method Linear Programming based Gene Expression Model (LPM-GEM). LPM-GEM linearly embeds gene expression into FBA constraints. We implemented three strategies to reduce thermodynamically infeasible loops, which is a necessary prerequisite for such an omics-based model building. As a case study, we built a model of B. subtilis grown in eight different carbon sources. We obtained good flux predictions based on the respective transcription profiles when validating with ¹³C tracer based metabolic flux data of the same conditions. We could well predict the specific carbon sources. When testing the model on another, unseen dataset that was not used during training, good prediction performance was also observed. Furthermore, LPM-GEM outperformed a well-established model building methods.

CONCLUSION

Employing LPM-GEM integrates gene expression data efficiently. The method supports gene expression-based FBA models and can be applied as an alternative to estimate metabolic fluxes when tracer experiments are inappropriate.

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.

Evaluation of Lignocellulosic Wastewater Valorization with the Oleaginous Yeasts R. kratochvilovae EXF7516 and C. oleaginosum ATCC 20509

During the conversion of lignocellulose, phenolic wastewaters are generated. Therefore, researchers have investigated wastewater valorization processes in which these pollutants are converted to chemicals, i.e., lipids. However, wastewaters are lean feedstocks, so these valorization processes in research typically require the addition of large quantities of sugars and sterilization, which increase costs. This paper investigates a repeated batch fermentation strategy with Rhodotorula kratochvilovae EXF7516 and Cutaneotrichosporon oleaginosum ATCC 20509, without these requirements. The pollutant removal and its conversion to microbial oil were evaluated. Because of the presence of non-monomeric substrates, the ligninolytic enzyme activity was also investigated. The repeated batch fermentation strategy was successful, as more lipids accumulated every cycle, up to a total of 5.4 g/L (23% cell dry weight). In addition, the yeasts consumed up to 87% of monomeric substrates, i.e., sugars, aromatics, and organics acids, and up to 23% of non-monomeric substrates, i.e., partially degraded xylan, lignin, cellulose. Interestingly, lipid production was only observed during the harvest phase of each cycle, as the cells experienced stress, possibly due to oxygen limitation. This work presents the first results on the feasibility of valorizing non-sterilized lignocellulosic wastewater with R. kratochvilovae and C. oleaginosum using a cost-effective repeated batch strategy.

Engineering the Lipid and Fatty Acid Metabolism in Yarrowia lipolytica for Sustainable Production of High Oleic Oils

Oleic acid is widely applied in the chemical, material, nutritional, and pharmaceutical industries. However, the current production of oleic acid via high oleic plant oils is limited by the long growth cycle and climatic constraints. Moreover, the global demand for high oleic plant oils, especially the palm oil, has emerged as the driver of tropical deforestation causing tropical rainforest destruction, climate change, and biodiversity loss. In the present study, an alternative and sustainable strategy for high oleic oil production was established by reprogramming the metabolism of the oleaginous yeast Yarrowia lipolytica using a two-layer "push-pull-block" strategy. Specifically, the fatty acid synthesis pathway was first engineered to increase oleic acid proportion by altering the fatty acid profiles. Then, the content of storage oils containing oleic acid was boosted by engineering the synthesis and degradation pathways of triacylglycerides. The strain resulting from this two-layer engineering strategy produced the highest titer of high oleic microbial oil reaching 56 g/L with 84% oleic acid in fed-batch fermentation, representing a remarkable improvement of a 110-fold oil titer and 2.24-fold oleic acid proportion compared with the starting strain. This alternative and sustainable method for high oleic oil production shows the potential of substitute planting.

Genome-scale modeling of yeast metabolism: retrospectives and perspectives

Yeasts have been widely used for production of bread, beer and wine, as well as for production of bioethanol, but they have also been designed as cell factories to produce various chemicals, advanced biofuels and recombinant proteins. To systematically understand and rationally engineer yeast metabolism, genome-scale metabolic models (GEMs) have been reconstructed for the model yeast Saccharomyces cerevisiae and nonconventional yeasts. Here, we review the historical development of yeast GEMs together with their recent applications, including metabolic flux prediction, cell factory design, culture condition optimization and multi-yeast comparative analysis. Furthermore, we present an emerging effort, namely the integration of proteome constraints into yeast GEMs, resulting in models with improved performance. At last, we discuss challenges and perspectives on the development of yeast GEMs and the integration of proteome constraints.

The history, state of the art and future prospects for oleaginous yeast research

Lipid-based biofuels, such as biodiesel and hydroprocessed esters, are a central part of the global initiative to reduce the environmental impact of the transport sector. The vast majority of production is currently from first-generation feedstocks, such as rapeseed oil, and waste cooking oils. However, the increased exploitation of soybean oil and palm oil has led to vast deforestation, smog emissions and heavily impacted on biodiversity in tropical regions. One promising alternative, potentially capable of meeting future demand sustainably, are oleaginous yeasts. Despite being known about for 143 years, there has been an increasing effort in the last decade to develop a viable industrial system, with currently around 100 research papers published annually. In the academic literature, approximately 160 native yeasts have been reported to produce over 20% of their dry weight in a glyceride-rich oil. The most intensively studied oleaginous yeast have been Cutaneotrichosporon oleaginosus (20% of publications), Rhodotorula toruloides (19%) and Yarrowia lipolytica (19%). Oleaginous yeasts have been primarily grown on single saccharides (60%), hydrolysates (26%) or glycerol (19%), and mainly on the mL scale (66%). Process development and genetic modification (7%) have been applied to alter yeast performance and the lipids, towards the production of biofuels (77%), food/supplements (24%), oleochemicals (19%) or animal feed (3%). Despite over a century of research and the recent application of advanced genetic engineering techniques, the industrial production of an economically viable commodity oil substitute remains elusive. This is mainly due to the estimated high production cost, however, over the course of the twenty-first century where climate change will drastically change global food supply networks and direct governmental action will likely be levied at more destructive crops, yeast lipids offer a flexible platform for localised, sustainable lipid production. Based on data from the large majority of oleaginous yeast academic publications, this review is a guide through the history of oleaginous yeast research, an assessment of the best growth and lipid production achieved to date, the various strategies employed towards industrial production and importantly, a critical discussion about what needs to be built on this huge body of work to make producing a yeast-derived, more sustainable, glyceride oil a commercial reality.

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.

Engineering of Saccharomyces cerevisiae for the accumulation of high amounts of triacylglycerol

BACKGROUND

Fatty acid-based substances play an important role in many products, from food supplements to pharmaceutical products and biofuels. The production of fatty acids, mainly in their esterified form as triacylglycerol (TAG), has been intensively studied in oleaginous yeasts, whereas much less effort has been invested into non-oleaginous species. In the present work, we engineered the model yeast Saccharomyces cerevisiae, which is commonly regarded as non-oleaginous, for the storage of high amounts of TAG, comparable to the contents achieved in oleaginous yeasts.

RESULTS

We investigated the effects of several mutations with regard to increased TAG accumulation and identified six of them as important for this phenotype: a point mutation in the acetyl-CoA carboxylase Acc1p, overexpression of the diacylglycerol acyltransferase Dga1p, deletions of genes coding for enzymes involved in the competing pathways glycogen and steryl ester synthesis and TAG hydrolysis, and a deletion of CKB1, the gene coding for one of the regulatory subunits of casein kinase 2. With the combination of these mutations in a S. cerevisiae strain with a relatively high neutral lipid level already in the non-engineered state, we achieved a TAG content of 65% in the dry biomass. High TAG levels were not only obtained under conditions that favor lipid accumulation, but also in defined standard carbon-limited media.

CONCLUSIONS

Baker's yeast, which is usually regarded as inefficient in the storage of TAG, can be converted into a highly oleaginous strain that could be useful in processes aiming at the synthesis of fatty acid-based products. This work emphasizes the importance of strain selection in combination with metabolic engineering to obtain high product levels.

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.

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.

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.

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.

Analysis of the Yarrowia lipolytica proteome reveals subtle variations in expression levels between lipogenic and non-lipogenic conditions

Oleaginous yeasts have the ability to store greater than 20% of their mass as neutral lipids, in the form of triacylglycerides. The ATP citrate lyase is thought to play a key role in triacylglyceride synthesis, but the relationship between expression levels of this and other related enzymes is not well understood in the role of total lipid accumulation conferring the oleaginous phenotype. We conducted comparative proteomic analyses with the oleaginous yeast, Yarrowia lipolytica, grown in either nitrogen-sufficient rich media or nitrogen-limited minimal media. Total proteins extracted from cells collected during logarithmic and late stationary growth phases were analyzed by 1D liquid chromatography, followed by mass spectroscopy. The ATP citrate lyase enzyme was expressed at similar concentrations in both conditions, in both logarithmic and stationary phase, but many upstream and downstream enzymes showed drastically different expression levels. In non-lipogenic conditions, several pyruvate enzymes were expressed at higher concentration. These enzymes, especially the pyruvate decarboxylase and pyruvate dehydrogenase, may be regulating carbon flux away from central metabolism and reducing the amount of citrate being produced in the mitochondria. While crucial for the oleaginous phenotype, the constitutively expressed ATP citrate lyase appears to cleave citrate in response to carbon flux upstream from other enzymes creating the oleaginous phenotype.

Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts

Microbial bioproduction of chemicals, proteins, and primary metabolites from cheap carbon sources is currently an advancing area in industrial research. The model yeast, Saccharomyces cerevisiae, is a well-established biorefinery host that has been used extensively for commercial manufacturing of bioethanol from myriad carbon sources. However, its Crabtree-positive nature often limits the use of this organism for the biosynthesis of commercial molecules that do not belong in the fermentative pathway. To avoid extensive strain engineering of S. cerevisiae for the production of metabolites other than ethanol, non-conventional yeasts can be selected as hosts based on their natural capacity to produce desired commodity chemicals. Non-conventional yeasts like Kluyveromyces marxianus, K. lactis, Yarrowia lipolytica, Pichia pastoris, Scheffersomyces stipitis, Hansenula polymorpha, and Rhodotorula toruloides have been considered as potential industrial eukaryotic hosts owing to their desirable phenotypes such as thermotolerance, assimilation of a wide range of carbon sources, as well as ability to secrete high titers of protein and lipid. However, the advanced metabolic engineering efforts in these organisms are still lacking due to the limited availability of systems and synthetic biology methods like in silico models, well-characterised genetic parts, and optimized genome engineering tools. This review provides an insight into the recent advances and challenges of systems and synthetic biology as well as metabolic engineering endeavours towards the commercial usage of non-conventional yeasts. Particularly, the approaches in emerging non-conventional yeasts for the production of enzymes, therapeutic proteins, lipids, and metabolites for commercial applications are extensively discussed here. Various attempts to address current limitations in designing novel cell factories have been highlighted that include the advances in the fields of genome-scale metabolic model reconstruction, flux balance analysis, 'omics'-data integration into models, genome-editing toolkit development, and rewiring of cellular metabolisms for desired chemical production. Additionally, the understanding of metabolic networks using ¹³C-labelling experiments as well as the utilization of metabolomics in deciphering intracellular fluxes and reactions have also been discussed here. Application of cutting-edge nuclease-based genome editing platforms like CRISPR/Cas9, and its optimization towards efficient strain engineering in non-conventional yeasts have also been described. Additionally, the impact of the advances in promising non-conventional yeasts for efficient commercial molecule synthesis has been meticulously reviewed. In the future, a cohesive approach involving systems and synthetic biology will help in widening the horizon of the use of unexplored non-conventional yeast species towards industrial biotechnology.

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.

Genetic and bioprocess engineering to improve squalene production in Yarrowia lipolytica

Squalene is the precursor for triterpene-based natural products and steroids-based drugs. It has been widely used as pharmaceutical intermediates and personal care products. The aim of this work is to test the feasibility of engineering Yarrowia lipolytica as a potential host for squalene production. The bottleneck of the pathway was removed by overexpressing native HMG-CoA (3-hydroxy-3-methylglutaryl-CoA) reductase. With the recycling of NADPH from the mannitol cycle, the engineered strain produced about 180.3 mg/L and 188.2 mg/L squalene from glucose or acetate minimal media. By optimizing the C/N ratio, controlling the media pH and mitigating acetyl-CoA flux competition from lipogenesis, the engineered strain produced 502.7 mg/L squalene, a 28-fold increase over the parental strain (17.2 mg/L). This work may serve as a baseline to harness Y. lipolytica as an oleaginous cell factory for sustainable production of squalene or terpenoids-based chemicals and natural products.

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.

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

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Customized biomass reaction highlights differences with S. cerevisiae.

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Chemostat data elucidate growth-associated ATP maintenance.

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

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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.

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.

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.

Biosynthesis of terpene compounds using the non-model yeast Yarrowia lipolytica: grand challenges and a few perspectives

Yarrowia lipolytica has emerged as an important non-model host for terpene production. However, three main challenges remain in industrial production using this yeast. First, considerable knowledge gaps exist in metabolic flux across multiple compartments, cofactor generation, and catabolism of non-sugar carbon sources. Second, many enzymatic steps in the complex-terpene synthesis pathway can pose rate-limitations, causing accumulation of toxic intermediates and increased metabolic burdens. Third, metabolic shifts, morphological changes, and genetic mutations are poorly characterized under industrial fermentation conditions. To overcome these challenges, systems metabolic analysis, protein engineering, novel pathway engineering, model-guided strain design, and fermentation optimization have been attempted with some successes. Further developments that address these challenges are needed to advance the Yarrowia lipolytica platform for industrial-scale production of high-value terpenes, including those with highly complex structures such as anticancer molecules withanolides and insecticidal limonoids.

Recombinant β-Carotene Production by Yarrowia lipolytica - Assessing the Potential of Micro-Scale Fermentation Analysis in Cell Factory Design and Bioreaction Optimization

The production of β-carotene has become increasingly interesting within the biotechnological industry due to a rising demand for safer and more natural colorants, nutritional supplements, and antioxidants. A recent study has described the potential of Yarrowia lipolytica as a β-carotene-producing cell factory, reporting the highest titer of recombinant β-carotene produced to date. Finding the best conditions to maximize production and scaling up the process to full scale, a costly and time-consuming process, it is often a bottleneck in biotechnology. In this work, we explored the benefits of using micro-fermentation equipment to significantly reduce the time spent on design and optimization of bioreaction conditions, especially in the early stages of process development. In this proof-of-concept study, a β-carotene producing Y. lipolytica strain was tested in micro-fermentations partly to assess the robustness of the cell factory design and partly to perform media optimization. The medium optimization led us to an improvement of up to 50% in the yield of β-carotene production in the best of the conditions. Overall, the micro-fermentation system had a high degree of reliability in all tests.

Application of Stable Isotope Tracing to Elucidate Metabolic Dynamics During Yarrowia lipolytica α-Ionone Fermentation

Targeted metabolite analysis in combination with ¹³C-tracing is a convenient strategy to determine pathway activity in biological systems; however, metabolite analysis is limited by challenges in separating and detecting pathway intermediates with current chromatographic methods. Here, a hydrophilic interaction chromatography tandem mass spectrometry approach was developed for improved metabolite separation, isotopologue analysis, and quantification. The physiological responses of a Yarrowia lipolytica strain engineered to produce ∼400 mg/L α-ionone and temporal changes in metabolism were quantified (e.g., mevalonate secretion, then uptake) indicating bottleneck shifts in the engineered pathway over the course of fermentation. Dynamic labeling results indicated limited tricarboxylic acid cycle label incorporation and, combined with a measurable ATP shortage during the high ionone production phase, suggested that electron transport and oxidative phosphorylation may limit energy supply and strain performance. The results provide insights into terpenoid pathway metabolic dynamics of non-model yeasts and offer guidelines for sensor development and modular engineering.

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A HILIC method is demonstrated for efficient separation of 57 cellular metabolites

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Production of α-ionone was ∼400 mg/L in bench-top bioreactors

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Engineered Y. lipolytica secreted then consumed mevalonate during fermentation

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Oxidative phosphorylation may limit performance in high-cell-density fermentations

A comprehensive genome-scale model for Rhodosporidium toruloides IFO0880 accounting for functional genomics and phenotypic data

Rhodosporidium toruloides is a red, basidiomycetes yeast that can accumulate a large amount of lipids and produce carotenoids. To better assess this non-model yeast's metabolic capabilities, we reconstructed a genome-scale model of R. toruloides IFO0880's metabolic network (iRhto1108) accounting for 2204 reactions, 1985 metabolites and 1108 genes. In this work, we integrated and supplemented the current knowledge with in-house generated biomass composition and experimental measurements pertaining to the organism's metabolic capabilities. Predictions of genotype-phenotype relations were improved through manual curation of gene-protein-reaction rules for 543 reactions leading to correct recapitulations of 84.5% of gene essentiality data (sensitivity of 94.3% and specificity of 53.8%). Organism-specific macromolecular composition and ATP maintenance requirements were experimentally measured for two separate growth conditions: (i) carbon and (ii) nitrogen limitations. Overall, iRhto1108 reproduced R. toruloides's utilization capabilities for 18 alternate substrates, matched measured wild-type growth yield, and recapitulated the viability of 772 out of 819 deletion mutants. As a demonstration to the model's fidelity in guiding engineering interventions, the OptForce procedure was applied on iRhto1108 for triacylglycerol overproduction. Suggested interventions recapitulated many of the previous successful implementations of genetic modifications and put forth a few new ones.

Volatile fatty acids as novel building blocks for oil-based chemistry via oleaginous yeast fermentation

Microbial oils are proposed as a suitable alternative to petroleum-based chemistry in terms of environmental preservation. These oils have traditionally been studied using sugar-based feedstock, which implies high costs, substrate limitation, and high contamination risks. In this sense, low-cost carbon sources such as volatile fatty acids (VFAs) are envisaged as promising building blocks for lipid biosynthesis to produce oil-based bioproducts. VFAs can be generated from a wide variety of organic wastes through anaerobic digestion and further converted into lipids by oleaginous yeasts (OYs) in a fermentation process. These microorganisms can accumulate in the form of lipid bodies, lipids of up to 60% wt/wt of their biomass. In this context, OY is a promising biotechnological tool for biofuel and bioproduct generation using low-cost VFA media as substrates. This review covers recent advances in microbial oil production from VFAs. Production of VFAs via anaerobic digestion processes and the involved metabolic pathways are reviewed. The main challenges as well as recent approaches for lipid overproduction are also discussed.

Comparison of metabolic profiles of yeasts based on the difference of the Crabtree positive and negative

The Crabtree effect involves energy management in which yeasts utilize glycolysis as the terminal electron acceptor instead of oxygen, despite the presence of sufficient dissolved oxygen, when oxygen concentrations exceed a certain limit. The Crabtree effect is detrimental to bakery yeast production, because it results in lower cellular glucose yields. Batch culture of Saccharomyces cerevisiae, a Crabtree positive yeast, decreased the cell yield of glucose and produced large amounts of ethanol despite a high specific glucose consumption rate compared to Candida utilis, a Crabtree negative yeast. This study investigated the effect of these characteristics on metabolite levels. We performed metabolome analysis of both yeasts during each growth phase of batch culture using liquid chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry. Principle component analysis of metabolome data indicated that the Crabtree effect affected metabolites related to NADH synthesis in central metabolism. The amount of these metabolites in S. cerevisiae was lower than that in C. utilis. However, to maintain the specific glucose consumption rate at high levels, yeasts must avoid depletion of NAD⁺, which is essential for glucose utilization. Our results indicated that NADH was oxidized by converting acetaldehyde to ethanol in S. cerevisiae, which is in accordance with previous reports. Therefore, the specific NADH production rates of S. cerevisiae and C. utilis did not show a difference. This study suggested that NAD⁺/NADH ratio is disrupted by the Crabtree effect, which in turn influenced central metabolism and that S. cerevisiae maintained the NAD⁺/NADH ratio by producing ethanol.

Common aspects in the engineering of yeasts for fatty acid- and isoprene-based products

The biosynthetic pathways for most lipophilic metabolites share several common principles. These substances are built almost exclusively from acetyl-CoA as the donor for the carbon scaffold and NADPH is required for the reductive steps during biosynthesis. Due to their hydrophobicity, the end products are sequestered into the same cellular compartment, the lipid droplet. In this review, we will summarize the efforts in the metabolic engineering of yeasts for the production of two major hydrophobic substance classes, fatty acid-based lipids and isoprenoids, with regard to these common aspects. We will compare and discuss the results of genetic engineering strategies to construct strains with enhanced synthesis of the precursor acetyl-CoA and with modified redox metabolism for improved NADPH supply. We will also discuss the role of the lipid droplet in the storage of the hydrophobic product and review the strategies to either optimize this organelle for higher capacity or to achieve excretion of the product into the medium.

Oleaginous yeast Meyerozyma guilliermondii shows fermentative metabolism of sugars in the biosynthesis of ethanol and converts raw glycerol and cheese whey permeate into polyunsaturated fatty acids

We studied the biotechnological potential of the recently isolated yeast Meyerozyma guilliermondii BI281A to produce polyunsaturated fatty acids and ethanol, comparing products yields using glucose, raw glycerol from biodiesel synthesis, or whey permeate as substrates. The yeast metabolism was evaluated for different C/N ratios (100:1 and 50:1). Results found that M. guilliermondii BI281A was able to assimilate all tested substrates, and the most efficient conversion obtained was observed using raw glycerol as carbon source (C/N ratio 50:1), concerning biomass formation (5.67 g·L^-1 ) and lipid production (1.04 g·L^-1 ), representing 18% of dry cell weight. Bioreactors experiments under pH and aeration-controlled conditions were conducted. Obtained fatty acids were composed of ~67% of unsaturated fatty acids, distributed as palmitoleic acid (C_16:1 , 9.4%), oleic acid (C_18:1 , 47.2%), linoleic acid (C_{18:2 n-6} , 9.6%), and linolenic acid (C_{18:3 n-3} , 1.3%). Showing fermentative metabolism, which is unusual for oleaginous yeasts, M. guilliermondii produced 13.7 g·L^-1 of ethanol (yields of 0.27) when growing on glucose medium. These results suggest the promising use of this uncommonly studied yeast to produce unsaturated fatty acids and ethanol using cheap agro-industrial residues as substrates in bioprocess.

Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis

MOTIVATION

Genome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organism's metabolism, yet their integration to achieve biological insight remains challenging.

RESULTS

We investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data. The sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to detect the difference between experimental conditions. The method, named metaboGSE, is validated on public data for Yarrowia lipolytica and mouse. It is shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO.

AVAILABILITY AND IMPLEMENTATION

The metaboGSE R package is available at https://CRAN.R-project.org/package=metaboGSE.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

Understanding lipogenesis by dynamically profiling transcriptional activity of lipogenic promoters in Yarrowia lipolytica

Lipogenesis is a complicated process involving global transcriptional reprogramming of lipogenic pathways. It is commonly believed that nitrogen starvation triggers a metabolic shift that reroutes carbon flux from Krebs cycles to lipogenesis. In this study, we systematically surveyed and dynamically profiled the transcriptional activity of 22 lipogenic promoters aiming to delineate a picture how nitrogen starvation regulates lipogenesis in Y. lipolytica. These lipogenic promoters drive the expression of critical pathways that are responsible for the generation of reducing equivalents (NADPH), carbon backbones (acetyl-CoA, malonyl-CoA, DHAP, etc.), synthesis and degradation of fatty acids. Specifically, our investigated promoters span across an array of metabolic pathways, including glycolysis, Krebs cycle, pentose phosphate pathway, mannitol cycle, glutamine-GABA cycle, fatty acid and lipid synthesis, glyoxylate, β-oxidation, and POM (pyruvate-oxaloacetate-malate) cycle. Our work provides evidences that mannitol cycle, glutamine-GABA cycle and amino acid degradation, pyruvate oxidation, and acetate assimilation pathways are lipogenesis-related steps involved in generating cytosolic NADPH and acetyl-CoA precursors. This systematic investigation and dynamic profiling of lipogenic promoters may help us better understand lipogenesis, facilitate the formulation of structure-based kinetic models, as well as develop efficient cell factories for fuels and chemical production in oleaginous species.

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.

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

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

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

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