在基于OptKnock框架的Saccharomyces Cerevisiae中改善烯酸生产
1School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, People's Republic of China.
Studies in health technology and informatics
|November 26, 2023
概括
使用基因组规模模型和OptKnock的代谢工程改善了Saccharomyces cerevisiae中的酸 (OA) 生产. 在LK2和LK5菌株中,基因淘汰显著增加了OA产量,证明了微生物细胞工厂的有效方法.
科学领域:
- 合成生物学 合成生物学
- 代谢工程是代谢工程.
- 生物技术是生物技术.
背景情况:
- 在Saccharomyces cerevisiae中,植物性天然产品的异质生产往往由于基因不相容,因此效率低.
- 传统的代谢工程依赖于广泛的试验,消耗大量资源.
- 烯酸 (OA) 是一种具有药理潜力的植物三烯酸,需要改进生产方法.
研究的目的:
- 为了增强基因工程的Saccharomyces cerevisiae菌株中oleanolic acid (OA) 的生物合成.
- 开发和应用基因组规模的代谢模型和计算策略,以优化异质生产.
- 通过in silico基因淘汰策略指导分子操作.
主要方法:
- 开发一个基因组规模的代谢模型 (酵母-OA07) 工程化S. cerevisiae OA07菌株.
- 使用OptKnock,一个流量平衡分析算法,以确定基因淘汰策略.
- 基于预测的淘汰策略,构建和描述突变菌株.
主要成果:
- 基因组规模模型酵母-OA07包括1133个基因,2702个代谢物和3997个反应.
- 突变菌株LK2 (基因删除FOL1,FDH1) 的OA产量达到125.04 mg/L,比原始菌株 (89.50 mg/L) 显著增加.
- 突变菌株LK5 (基因删除ABZ1,SHM1) 产生了207.37 mg/L的OA,进一步改善了产量. 然而,在LK6中组合策略降低了产量,这表明直接在体内应用的局限性.
结论:
- 建立了一种新有效的方法,用于改善微生物细胞工厂中异质植物代谢物生产.
- 基因组规模的代谢建模与OptKnock相结合,提供了一种强大的in silico方法来指导代谢工程的努力.
- 虽然有希望,但多种in silico策略的直接应用需要进一步研究以获得最佳的体内结果.
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