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

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.

Revealing holistic metabolic responses associated with lipid and docosahexaenoic acid (DHA) production in Aurantiochytrium sp. SW1

Aurantiochytrium sp. SW1, a marine thraustochytrid, has been regarded as a potential candidate as a docosahexaenoic acid (DHA) producer. Even though the genomics of Aurantiochytrium sp. are available, the metabolic responses at a systems level are largely unknown. Therefore, this study aimed to investigate the global metabolic responses to DHA production in Aurantiochytrium sp. through transcriptome and genome-scale network-driven analysis. Of a total of 13,505 genes, 2527 differentially expressed genes (DEGs) were identified in Aurantiochytrium sp., unravelling the transcriptional regulations behinds lipid and DHA accumulation. The highest number of DEG were found for pairwise comparison between growth phase and lipid accumulating phase where a total of 1435 genes were down-regulated with 869 genes being up-regulated. These uncovered several metabolic pathways that contributing in DHA and lipid accumulation including amino acid and acetate metabolism which involve in the generation of crucial precursors. Upon applying network-driven analysis, hydrogen sulphide was found as potential reporter metabolite that could be associated with the genes related to acetyl-CoA synthesis for DHA production. Our findings suggest that the transcriptional regulation of these pathways is a ubiquitous feature in response to specific cultivation phases during DHA overproduction in Aurantiochytrium sp. SW1.

Model-driven approach for the production of butyrate from CO2/H2 by a novel co-culture of C. autoethanogenum and C. beijerinckii

One-carbon (C1) compounds are promising feedstocks for the sustainable production of commodity chemicals. CO₂ is a particularly advantageous C1-feedstock since it is an unwanted industrial off-gas that can be converted into valuable products while reducing its atmospheric levels. Acetogens are microorganisms that can grow on CO₂/H₂ gas mixtures and syngas converting these substrates into ethanol and acetate. Co-cultivation of acetogens with other microbial species that can further process such products, can expand the variety of products to, for example, medium chain fatty acids (MCFA) and longer chain alcohols. Solventogens are microorganisms known to produce MCFA and alcohols via the acetone-butanol-ethanol (ABE) fermentation in which acetate is a key metabolite. Thus, co-cultivation of an acetogen and a solventogen in a consortium provides a potential platform to produce valuable chemicals from CO₂. In this study, metabolic modeling was implemented to design a new co-culture of an acetogen and a solventogen to produce butyrate from CO₂/H₂ mixtures. The model-driven approach suggested the ability of the studied solventogenic species to grow on lactate/glycerol with acetate as co-substrate. This ability was confirmed experimentally by cultivation of Clostridium beijerinckii on these substrates in batch serum bottles and subsequently in pH-controlled bioreactors. Community modeling also suggested that a novel microbial consortium consisting of the acetogen Clostridium autoethanogenum, and the solventogen C. beijerinckii would be feasible and stable. On the basis of this prediction, a co-culture was experimentally established. C. autoethanogenum grew on CO₂/H₂ producing acetate and traces of ethanol. Acetate was in turn, consumed by C. beijerinckii together with lactate, producing butyrate. These results show that community modeling of metabolism is a valuable tool to guide the design of microbial consortia for the tailored production of chemicals from renewable resources.

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.

Rhythm of the Night (and Day): Predictive Metabolic Modeling of Diurnal Growth in Chlamydomonas

Economical production of photosynthetic organisms requires the use of natural day/night cycles. These induce strong circadian rhythms that lead to transient changes in the cells, requiring complex modeling to capture. In this study, we coupled times series transcriptomic data from the model green alga Chlamydomonas reinhardtii to a metabolic model of the same organism in order to develop the first transient metabolic model for diurnal growth of algae capable of predicting phenotype from genotype. We first transformed a set of discrete transcriptomic measurements (D. Strenkert, S. Schmollinger, S. D. Gallaher, P. A. Salomé, et al., Proc Natl Acad Sci U S A 116:2374–2383, 2019, https://doi.org/10.1073/pnas.1815238116) into continuous curves, producing a complete database of the cell’s transcriptome that can be interrogated at any time point. We also decoupled the standard biomass formation equation to allow different components of biomass to be synthesized at different times of the day. The resulting model was able to predict qualitative phenotypical outcomes of a starchless mutant. We also extended this approach to simulate all single-knockout mutants and identified potential targets for rational engineering efforts to increase productivity. This model enables us to evaluate the impact of genetic and environmental changes on the growth, biomass composition, and intracellular fluxes for diurnal growth.

IMPORTANCE We have developed the first transient metabolic model for diurnal growth of algae based on experimental data and capable of predicting phenotype from genotype. This model enables us to evaluate the impact of genetic and environmental changes on the growth, biomass composition and intracellular fluxes of the model green alga, Chlamydomonas reinhardtii. The availability of this model will enable faster and more efficient design of cells for production of fuels, chemicals, and pharmaceuticals.

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.

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.

Two-species community design of lactic acid bacteria for optimal production of lactate

Microbial communities that metabolise pentose and hexose sugars are useful in producing high-value chemicals, resulting in the effective conversion of raw materials to the product, a reduction in the production cost, and increased yield. Here, we present a computational analysis approach called CAMP (Co-culture/Community Analyses for Metabolite Production) that simulates and identifies appropriate communities to produce a metabolite of interest. To demonstrate this approach, we focus on the optimal production of lactate from various Lactic Acid Bacteria. We used genome-scale metabolic models (GSMMs) belonging to Lactobacillus, Leuconostoc, and Pediococcus species from the Virtual Metabolic Human (VMH; https://vmh.life/) resource and well-curated GSMMs of L. plantarum WCSF1 and L. reuteri JCM 1112. We analysed 1176 two-species communities using a constraint-based modelling method for steady-state flux-balance analysis of communities. Flux variability analysis was used to detect the maximum lactate flux in the communities. Using glucose or xylose as substrates separately or in combination resulted in either parasitism, amensalism, or mutualism being the dominant interaction behaviour in the communities. Interaction behaviour between members of the community was deduced based on variations in the predicted growth rates of monocultures and co-cultures. Acetaldehyde, ethanol, acetate, among other metabolites, were found to be cross-fed between community members. L. plantarum WCSF1 was found to be a member of communities with high lactate yields. In silico community optimisation strategies to predict reaction knock-outs for improving lactate flux were implemented. Reaction knock-outs of acetate kinase, phosphate acetyltransferase, and fumarate reductase in the communities were found to enhance lactate production.

Multiscale models quantifying yeast physiology: towards a whole-cell model

The yeast Saccharomyces cerevisiae is widely used as a cell factory and as an important eukaryal model organism for studying cellular physiology related to human health and disease. Yeast was also the first eukaryal organism for which a genome-scale metabolic model (GEM) was developed. In recent years there has been interest in expanding the modeling framework for yeast by incorporating enzymatic parameters and other heterogeneous cellular networks to obtain a more comprehensive description of cellular physiology. We review the latest developments in multiscale models of yeast, and illustrate how a new generation of multiscale models could significantly enhance the predictive performance and expand the applications of classical GEMs in cell factory design and basic studies of yeast physiology.

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

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

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

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

Recent advances in biotechnological production of polyunsaturated fatty acids by Yarrowia lipolytica

Owing to the important physiological functions, polyunsaturated fatty acids (PUFAs) play a vital role in protecting human health, such as preventing cancer, cardiovascular disease, and diabetes. Specifically, Yarrowia lipolytica has been identified as the most popular non-conventional oleaginous yeast, which can accumulate the abundant intracellular lipids, indicating that has great potential as an industrial host for production of PUFAs. Notably, some novel engineering strategies have been applied to endow and improve the abilities of Y. lipolytica to synthesize PUFAs, including construction and optimization of PUFAs biosynthetic pathways, improvement of preucrsors acetyl-coA and NADPH supply, inhibition of competing pathways, knockout of β-oxidation pathways, regulation of oxidative stress defense pathways, and regulation of genes involved in upstream lipid metabolism. Besides, some bypass approaches, such as strain mating, evolutionary engineering, and computational model based on omics, also have been proposed to improve the performance of engineering strains. Generally, in this review, we summarized the recent advances in engineering strategies and bypass approaches for improving PUFAs production by Y. lipolytica. In addition, we further summarized the latest efforts of CRISPR/Cas genome editing technology in Y. lipolytica, which is aimed to provide its potential applications in PUFAs production.

Towards a Systems Biology Approach to Understanding the Lichen Symbiosis: Opportunities and Challenges of Implementing Network Modelling

Lichen associations, a classic model for successful and sustainable interactions between micro-organisms, have been studied for many years. However, there are significant gaps in our understanding about how the lichen symbiosis operates at the molecular level. This review addresses opportunities for expanding current knowledge on signalling and metabolic interplays in the lichen symbiosis using the tools and approaches of systems biology, particularly network modelling. The largely unexplored nature of symbiont recognition and metabolic interdependency in lichens could benefit from applying a holistic approach to understand underlying molecular mechanisms and processes. Together with ‘omics’ approaches, the application of signalling and metabolic network modelling could provide predictive means to gain insights into lichen signalling and metabolic pathways. First, we review the major signalling and recognition modalities in the lichen symbioses studied to date, and then describe how modelling signalling networks could enhance our understanding of symbiont recognition, particularly leveraging omics techniques. Next, we highlight the current state of knowledge on lichen metabolism. We also discuss metabolic network modelling as a tool to simulate flux distribution in lichen metabolic pathways and to analyse the co-dependence between symbionts. This is especially important given the growing number of lichen genomes now available and improved computational tools for reconstructing such models. We highlight the benefits and possible bottlenecks for implementing different types of network models as applied to the study of lichens.

Lipid production by oleaginous yeasts

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

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

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

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.

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.

Yeast based biorefineries for oleochemical production

Biosynthesis of oleochemicals enables sustainable production of natural and unnatural alternatives from renewable feedstocks. Yeast cell factories have been extensively studied and engineered to produce a variety of oleochemicals, focusing on both central carbon metabolism and lipid metabolism. Here, we review recent progress towards oleochemical synthesis in yeast based biorefineries, as well as utilization of alternative renewable feedstocks, such as xylose and l-arabinose. We also review recent studies of C1 compound utilization or co-utilization and discuss how these studies can lead to third generation yeast based biorefineries for oleochemical production.

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

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

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

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

An Overview of Potential Oleaginous Microorganisms and Their Role in Biodiesel and Omega-3 Fatty Acid-Based Industries

Microorganisms are known to be natural oil producers in their cellular compartments. Microorganisms that accumulate more than 20% w/w of lipids on a cell dry weight basis are considered as oleaginous microorganisms. These are capable of synthesizing vast majority of fatty acids from short hydrocarbonated chain (C6) to long hydrocarbonated chain (C36), which may be saturated (SFA), monounsaturated (MUFA), or polyunsaturated fatty acids (PUFA), depending on the presence and number of double bonds in hydrocarbonated chains. Depending on the fatty acid profile, the oils obtained from oleaginous microorganisms are utilized as feedstock for either biodiesel production or as nutraceuticals. Mainly microalgae, bacteria, and yeasts are involved in the production of biodiesel, whereas thraustochytrids, fungi, and some of the microalgae are well known to be producers of very long-chain PUFA (omega-3 fatty acids). In this review article, the type of oleaginous microorganisms and their expertise in the field of biodiesel or omega-3 fatty acids, advances in metabolic engineering tools for enhanced lipid accumulation, upstream and downstream processing of lipids, including purification of biodiesel and concentration of omega-3 fatty acids are reviewed.

TAG pathway engineering via GPAT2 concurrently potentiates abiotic stress tolerance and oleaginicity in Phaeodactylum tricornutum

BACKGROUND

Despite the great potential of marine diatoms in biofuel sector, commercially viable biofuel production from native diatom strain is impractical. Targeted engineering of TAG pathway represents a promising approach; however, recruitment of potential candidate has been regarded as critical. Here, we identified a glycerol-3-phosphate acyltransferase 2 (GPAT2) isoform and overexpressed in Phaeodactylum tricornutum.

RESULTS

GPAT2 overexpression did not impair growth and photosynthesis. GPAT2 overexpression reduced carbohydrates and protein content, however, lipid content were significantly increased. Specifically, TAG content was notably increased by 2.9-fold than phospho- and glyco-lipids. GPAT2 overexpression elicited the push-and-pull strategy by increasing the abundance of substrates for the subsequent metabolic enzymes, thereby increased the expression of LPAAT and DGAT. Besides, GPAT2-mediated lipid overproduction coordinated the expression of NADPH biosynthetic genes. GPAT2 altered the fatty acid profile in TAGs with C16:0 as the predominant fatty acid moieties. We further investigated the impact of GPAT2 on conferring abiotic stress, which exhibited enhanced tolerance to hyposaline (70%) and chilling (10 ºC) conditions via altered fatty acid saturation level.

CONCLUSIONS

Collectively, our results exemplified the critical role of GPAT2 in hyperaccumulating TAGs with altered fatty acid profile, which in turn uphold resistance to abiotic stress conditions.