Chen Jin, Henson Michael A
Department of Chemical Engineering, University of Massachusetts, Amherst, MA 01003, United States.
Department of Chemical Engineering, University of Massachusetts, Amherst, MA 01003, United States.
Metab Eng. 2016 Nov;38:389-400. doi: 10.1016/j.ymben.2016.10.002. Epub 2016 Oct 5.
Synthesis gas fermentation is one of the most promising routes to convert synthesis gas (syngas; mainly comprised of H and CO) to renewable liquid fuels and chemicals by specialized bacteria. The most commonly studied syngas fermenting bacterium is Clostridium ljungdahlii, which produces acetate and ethanol as its primary metabolic byproducts. Engineering of C. ljungdahlii metabolism to overproduce ethanol, enhance the synthesize of the native byproducts lactate and 2,3-butanediol, and introduce the synthesis of non-native products such as butanol and butyrate has substantial commercial value. We performed in silico metabolic engineering studies using a genome-scale reconstruction of C. ljungdahlii metabolism and the OptKnock computational framework to identify gene knockouts that were predicted to enhance the synthesis of these native products and non-native products, introduced through insertion of the necessary heterologous pathways. The OptKnock derived strategies were often difficult to assess because increase product synthesis was invariably accompanied by decreased growth. Therefore, the OptKnock strategies were further evaluated using a spatiotemporal metabolic model of a syngas bubble column reactor, a popular technology for large-scale gas fermentation. Unlike flux balance analysis, the bubble column model accounted for the complex tradeoffs between increased product synthesis and reduced growth rates of engineered mutants within the spatially varying column environment. The two-stage methodology for deriving and evaluating metabolic engineering strategies was shown to yield new C. ljungdahlii gene targets that offer the potential for increased product synthesis under realistic syngas fermentation conditions.
合成气发酵是利用特定细菌将合成气(主要由氢气和一氧化碳组成)转化为可再生液体燃料和化学品最具前景的途径之一。最常被研究的合成气发酵细菌是李氏梭菌,它产生乙酸盐和乙醇作为其主要代谢副产物。对李氏梭菌的代谢进行工程改造以过量生产乙醇、增强天然副产物乳酸和2,3-丁二醇的合成,并引入非天然产物如丁醇和丁酸盐的合成具有重大商业价值。我们使用李氏梭菌代谢的基因组规模重建和OptKnock计算框架进行了计算机代谢工程研究,以确定预测可增强这些天然产物和非天然产物合成的基因敲除,这些产物是通过插入必要的异源途径引入的。OptKnock衍生的策略往往难以评估,因为产物合成的增加总是伴随着生长的减少。因此,使用合成气泡柱反应器的时空代谢模型对OptKnock策略进行了进一步评估,合成气泡柱反应器是一种用于大规模气体发酵的常用技术。与通量平衡分析不同,气泡柱模型考虑了在空间变化的柱环境中工程突变体产物合成增加和生长速率降低之间的复杂权衡。推导和评估代谢工程策略的两阶段方法被证明产生了新的李氏梭菌基因靶点,这些靶点有可能在实际合成气发酵条件下提高产物合成。