Comparison and Analysis of Published Genome-scale Metabolic Models of Yarrowia lipolytica

Metabolic Pathway Coupled with Fermentation Process Optimization for High-Level Production of Retinol in Yarrowia lipolytica

Retinol is a lipid-soluble form of vitamin A that is crucial for human visual and immune functions. The production of retinol through microbial fermentation has been the focus of recent exploration. However, the obtained titer remains limited and the product is often a mixture of retinal, retinol, and retinoic acid, necessitating purification. To achieve efficient biosynthesis of retinol in Yarrowia lipolytica, we improved the metabolic flux of β-carotene to provide sufficient precursors for retinol in this study. Coupled with the optimization of the expression level of β-carotene 15,15'-dioxygenase, de novo production of retinol was achieved. Furthermore, Tween 80 was used as an extractant and butylated hydroxytoluene as an antioxidant to extract intracellular retinol and prevent retinol oxidation, respectively. This strategy significantly increased the level of retinol production. By optimizing the enzymes converting retinal to retinol, the proportion of extracellular retinol in the produced retinoids reached 100%, totaling 1042.3 mg/L. Finally, total retinol production reached 5.4 g/L through fed-batch fermentation in a 5 L bioreactor, comprising 4.2 g/L extracellular retinol and 1.2 g/L intracellular retinol. This achievement represents the highest reported titer so far and advances the industrial production of retinol.

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.

Using oils and fats to replace sugars as feedstocks for biomanufacturing: Challenges and opportunities for the yeast Yarrowia lipolytica

More than 200 million tons of plant oils and animal fats are produced annually worldwide from oil, crops, and the rendered animal fat industry. Triacylglycerol, an abundant energy-dense compound, is the major form of lipid in oils and fats. While oils or fats are very important raw materials and functional ingredients for food or related products, a significant portion is currently diverted to or recovered as waste. To significantly increase the value of waste oils or fats and expand their applications with a minimal environmental footprint, microbial biomanufacturing is presented as an effective strategy for adding value. Though both bacteria and yeast can be engineered to use oils or fats as the biomanufacturing feedstocks, the yeast Yarrowia lipolytica is presented as one of the most attractive platforms. Y. lipolytica is oleaginous, generally regarded as safe, demonstrated as a promising industrial producer, and has unique capabilities for efficient catabolism and bioconversion of lipid substrates. This review summarizes the major challenges and opportunities for Y. lipolytica as a new biomanufacturing platform for the production of value-added products from oils and fats. This review also discusses relevant cellular and metabolic engineering strategies such as fatty acid transport, fatty acid catabolism and bioconversion, redox balances and energy yield, cell morphology and stress response, and bioreaction engineering. Finally, this review highlights specific product classes including long-chain diacids, wax esters, terpenes, and carotenoids with unique synthesis opportunities from oils and fats in Y. lipolytica.

Efficient production of retinol in Yarrowia lipolytica by increasing stability using antioxidant and detergent extraction

The demand for bio-based retinol (vitamin A) is currently increasing, however its instability represents a major bottleneck in microbial production. Here, we developed an efficient method to selectively produce retinol in Yarrowia lipolytica. The β-carotene 15,15'-dioxygenase (BCO) cleaves β-carotene into retinal, which is reduced to retinol by retinol dehydrogenase (RDH). Therefore, to produce retinol, we first generated β-carotene-producing strain based on a high-lipid-producer via overexpressing genes including heterologous β-carotene biosynthetic genes, GGS1^F43I mutant of endogenous geranylgeranyl pyrophosphate synthase isolated by directed evolution, and FAD1 encoding flavin adenine dinucleotide synthetase, while deleting several genes previously known to be beneficial for carotenoid production. To produce retinol, 11 copies of BCO gene from marine bacterium 66A03 (Mb.Blh) were integrated into the rDNA sites of the β-carotene overproducer. The resulting strain produced more retinol than retinal, suggesting strong endogenous promiscuous RDH activity in Y. lipolytica. The introduction of Mb.BCO led to a considerable reduction in β-carotene level, but less than 5% of the consumed β-carotene could be detected in the form of retinal or retinol, implying severe degradation of the produced retinoids. However, addition of the antioxidant butylated hydroxytoluene (BHT) led to a >20-fold increase in retinol production, suggesting oxidative damage is the main cause of intracellular retinol degradation. Overexpression of GSH2 encoding glutathione synthetase further improved retinol production. Raman imaging revealed co-localization of retinol with lipid droplets, and extraction of retinol using Tween 80 was effective in improving retinol production. By combining BHT treatment and extraction using Tween 80, the final strain CJ2104 produced 4.86 g/L retinol and 0.26 g/L retinal in fed-batch fermentation in a 5-L bioreactor, which is the highest retinol production titer ever reported. This study demonstrates that Y. lipolytica is a suitable host for the industrial production of bio-based retinol.

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.

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.

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.