Metabolic Engineering of Yarrowia lipolytica for Conversion of Waste Cooking Oil into Omega-3 Eicosapentaenoic Acid

IF 4.3 Q2 ENGINEERING, CHEMICAL
Jiansong Qin, Na Liu, Umer Abid, Sarah M. Coleman, Yongdan Wang, Qiang Fu, Seongkyu Yoon, Hal S. Alper* and Dongming Xie*, 
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引用次数: 0

Abstract

Omega-3 polyunsaturated fatty acids (PUFAs), especially eicosapentaenoic acid (EPA, C20:5), are crucial dietary fats known for their numerous health benefits. However, traditional sources of EPA, like fish oil, raise sustainability and environmental concerns, underscoring the need for alternative production methods. The engineered oleaginous yeast Yarrowia lipolytica has emerged as a promising candidate for sustainable production of EPA. This study explores the efficient production of EPA with an earlier engineeredY. lipolytica strain Y8412, utilizing waste cooking oil (WCO) as an alternative carbon source. While cofeeding WCO resulted in increased total lipid content, it also caused an increase in intracellular free fatty acid (FFA) levels, which can be toxic to cells and reduce EPA synthesis. To solve this issue, we first overexpressed FAA1 and GPD1 genes converting excess FFAs into triglycerides (TAGs). Additionally, we knocked out TGL3/4 genes, which encode lipases linked to lipid bodies, to minimize the degradation of TAGs back into FFAs. The modified strains significantly reduced intracellular FFA levels and improved EPA production. Notably, the TGL4 knockout strain Y8412T4 showed 57% increase in EPA production titer and nearly 50% increase in carbon conversion yield compared to the parental strain Y8412 fed with glucose only. These findings suggest that preventing TAG degradation by knocking out TGL4 is an effective approach for enhanced EPA production when WCO is used to partially replace glucose as the carbon source. This study offers an effective engineering strategy for low-cost, high-yield, and sustainable production of omega-3 fatty acids from waste feedstocks.

废食用油转化为Omega-3二十碳五烯酸的解脂耶氏菌代谢工程研究
Omega-3多不饱和脂肪酸(PUFAs),尤其是二十碳五烯酸(EPA, C20:5),是一种重要的膳食脂肪,因其对健康有许多益处而闻名。然而,传统的EPA来源,如鱼油,引起了可持续性和环境问题,强调需要替代生产方法。工程产油酵母解脂耶氏酵母已成为可持续生产EPA的一个有前途的候选人。本研究探讨了用较早的工程原料高效生产EPA的方法。利用废食用油(WCO)作为替代碳源。在共喂WCO导致总脂含量增加的同时,也导致细胞内游离脂肪酸(FFA)水平增加,这可能对细胞有毒并减少EPA的合成。为了解决这个问题,我们首先过度表达FAA1和GPD1基因,将多余的FFAs转化为甘油三酯(TAGs)。此外,我们敲除了编码与脂质体相关的脂肪酶的TGL3/4基因,以最大限度地减少标签降解回FFAs。改良菌株显著降低细胞内FFA水平,提高EPA产量。值得注意的是,与仅饲喂葡萄糖的亲本菌株Y8412相比,TGL4敲除菌株Y8412T4 -的EPA生产滴度提高了57%,碳转化率提高了近50%。这些发现表明,当WCO用于部分替代葡萄糖作为碳源时,通过敲除TGL4来防止TAG降解是提高EPA产量的有效方法。本研究为从废原料中低成本、高产、可持续地生产omega-3脂肪酸提供了有效的工程策略。
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来源期刊
ACS Engineering Au
ACS Engineering Au 化学工程技术-
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期刊介绍: )ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)
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