化工学报 ›› 2023, Vol. 74 ›› Issue (1): 1-13.DOI: 10.11949/0438-1157.20221322
毕浩然(), 张洋, 王凯, 徐晨晨, 霍奕影, 陈必强, 谭天伟()
收稿日期:
2022-10-08
修回日期:
2022-11-22
出版日期:
2023-01-05
发布日期:
2023-03-20
通讯作者:
谭天伟
作者简介:
毕浩然(1994—),男,博士研究生,bhr0720@163.com
基金资助:
Haoran BI(), Yang ZHANG, Kai WANG, Chenchen XU, Yiying HUO, Biqiang CHEN, Tianwei TAN()
Received:
2022-10-08
Revised:
2022-11-22
Online:
2023-01-05
Published:
2023-03-20
Contact:
Tianwei TAN
摘要:
微生物制造利用生物质和二氧化碳等可再生原料进行化学品的绿色生产,显示出了巨大的二氧化碳减排潜力,是促进实现“碳中和”目标的重要途径,其核心内容之一是高效微生物细胞工厂的设计与构建。综述了基于基因组规模代谢网络模型的代谢流分析和代谢途径预测研究进展;介绍了新型基因组编辑工具助力微生物细胞工厂的高效开发;总结了代谢调控策略用于提升细胞工厂生产能力。此外,还概述了微生物制造关键技术在第三代生物制造中的应用。最后,展望了未来微生物制造在化学品生产中的应用和发展方向。
中图分类号:
毕浩然, 张洋, 王凯, 徐晨晨, 霍奕影, 陈必强, 谭天伟. 微生物制造绿色化学品研究进展[J]. 化工学报, 2023, 74(1): 1-13.
Haoran BI, Yang ZHANG, Kai WANG, Chenchen XU, Yiying HUO, Biqiang CHEN, Tianwei TAN. Progress for green chemicals production by microbial manufacturing[J]. CIESC Journal, 2023, 74(1): 1-13.
产品 | 菌株 | 底物 | 改造策略 | 产量 | 文献 | |
---|---|---|---|---|---|---|
有机酸 | 柠檬酸 | 解脂耶氏酵母 | 甘油 | 发酵条件优化,无机营养素限制 | 112 g/L | [ |
柠檬酸 | 黑曲霉 | 葡萄糖 | 过表达柠檬酸转运蛋白 | 109 g/L | [ | |
苹果酸 | 大肠杆菌 | 葡萄糖 | 过表达苹果酸酶,敲除琥珀酸合成基因,增强辅因子供给 | 21.65 g/L | [ | |
丁二酸 | 大肠杆菌 | 葡萄糖 | 关键基因过表达,两阶段发酵策略 | 116.2 g/L | [ | |
丁二酸 | 解脂耶氏酵母 | 粗甘油 | 优化发酵曝气率和底物浓度 | 209.7 g/L | [ | |
戊二酸 | 谷氨酸棒杆菌 | 葡萄糖 | 优化表达外源基因,表达戊二酸外排蛋白 | 105.3 g/L | [ | |
己二酸 | 大肠杆菌 | 葡萄糖/甘油 | 构建反向己二酸降解途径,两阶段发酵策略 | 68 g/L | [ | |
阿魏酸 | 大肠杆菌 | 甘油/葡萄糖 | 优化辅因子循环系统,增强前体供给 | 5.09 g/L | [ | |
有机醇 | 1,3-丙二醇 | 肺炎克雷伯菌 | 甘油 | 阻断2,3-PDO途径,构建NADH再生系统 | 72.2 g/L | [ |
1,3-丙二醇 | 大肠杆菌 | 葡萄糖 | 过表达酵母的甘油合成途径和肺炎克雷伯菌的1,3-PDO 合成途径,多基因修饰 | 135 g/L | [ | |
1,4-丁二醇 | 大肠杆菌 | 葡萄糖 | 优化关键酶的表达,调节能量平衡供应,削弱副产物的合成 | 120 g/L | [ | |
萜类化合物 | β-法尼烯 | 酿酒酵母 | 甘蔗糖浆 | 使用非天然反应重连酵母中心碳代谢,多轮菌株突变 | 130 g/L | [ |
瓦伦烯 | 酿酒酵母 | 葡萄糖 | 基因筛选,蛋白质工程,耦联生长和生化途径诱导 | 16.6 g/L | [ | |
角鲨烯 | 酿酒酵母 | 葡萄糖、乙醇 | 过氧化物酶体工程,两阶段发酵策略 | 11 g/L | [ | |
虾青素 | 解脂耶氏酵母 | 葡萄糖 | 融合β-胡萝卜素酮酶和羟化酶,亚细胞区室化工程 | 858 mg/L | [ | |
脂类 | 游离脂肪酸 | 酿酒酵母 | 葡萄糖 | 系统工程,敲除脂肪酸降解途径 | 10.4 g/L | [ |
脂质 | 解脂耶氏酵母 | 乙酸 | 基于代谢模型和呼吸熵的发酵补料控制策略 | 115 g/L | [ | |
生物大分子 | 聚羟基烷酸酯(PHA) | 嗜盐单胞菌 | 葡萄糖、废弃葡萄糖酸盐 | 非无菌发酵工艺,建立发酵补料模型 | 60.4 g/L | [ |
多糖 | 肝素前体 | 大肠杆菌 | 葡萄糖 | 发酵溶氧控制 | 15 g/L | [ |
表1 微生物制造各类化学品
Table 1 Microbial manufacturing for various chemicals synthesis
产品 | 菌株 | 底物 | 改造策略 | 产量 | 文献 | |
---|---|---|---|---|---|---|
有机酸 | 柠檬酸 | 解脂耶氏酵母 | 甘油 | 发酵条件优化,无机营养素限制 | 112 g/L | [ |
柠檬酸 | 黑曲霉 | 葡萄糖 | 过表达柠檬酸转运蛋白 | 109 g/L | [ | |
苹果酸 | 大肠杆菌 | 葡萄糖 | 过表达苹果酸酶,敲除琥珀酸合成基因,增强辅因子供给 | 21.65 g/L | [ | |
丁二酸 | 大肠杆菌 | 葡萄糖 | 关键基因过表达,两阶段发酵策略 | 116.2 g/L | [ | |
丁二酸 | 解脂耶氏酵母 | 粗甘油 | 优化发酵曝气率和底物浓度 | 209.7 g/L | [ | |
戊二酸 | 谷氨酸棒杆菌 | 葡萄糖 | 优化表达外源基因,表达戊二酸外排蛋白 | 105.3 g/L | [ | |
己二酸 | 大肠杆菌 | 葡萄糖/甘油 | 构建反向己二酸降解途径,两阶段发酵策略 | 68 g/L | [ | |
阿魏酸 | 大肠杆菌 | 甘油/葡萄糖 | 优化辅因子循环系统,增强前体供给 | 5.09 g/L | [ | |
有机醇 | 1,3-丙二醇 | 肺炎克雷伯菌 | 甘油 | 阻断2,3-PDO途径,构建NADH再生系统 | 72.2 g/L | [ |
1,3-丙二醇 | 大肠杆菌 | 葡萄糖 | 过表达酵母的甘油合成途径和肺炎克雷伯菌的1,3-PDO 合成途径,多基因修饰 | 135 g/L | [ | |
1,4-丁二醇 | 大肠杆菌 | 葡萄糖 | 优化关键酶的表达,调节能量平衡供应,削弱副产物的合成 | 120 g/L | [ | |
萜类化合物 | β-法尼烯 | 酿酒酵母 | 甘蔗糖浆 | 使用非天然反应重连酵母中心碳代谢,多轮菌株突变 | 130 g/L | [ |
瓦伦烯 | 酿酒酵母 | 葡萄糖 | 基因筛选,蛋白质工程,耦联生长和生化途径诱导 | 16.6 g/L | [ | |
角鲨烯 | 酿酒酵母 | 葡萄糖、乙醇 | 过氧化物酶体工程,两阶段发酵策略 | 11 g/L | [ | |
虾青素 | 解脂耶氏酵母 | 葡萄糖 | 融合β-胡萝卜素酮酶和羟化酶,亚细胞区室化工程 | 858 mg/L | [ | |
脂类 | 游离脂肪酸 | 酿酒酵母 | 葡萄糖 | 系统工程,敲除脂肪酸降解途径 | 10.4 g/L | [ |
脂质 | 解脂耶氏酵母 | 乙酸 | 基于代谢模型和呼吸熵的发酵补料控制策略 | 115 g/L | [ | |
生物大分子 | 聚羟基烷酸酯(PHA) | 嗜盐单胞菌 | 葡萄糖、废弃葡萄糖酸盐 | 非无菌发酵工艺,建立发酵补料模型 | 60.4 g/L | [ |
多糖 | 肝素前体 | 大肠杆菌 | 葡萄糖 | 发酵溶氧控制 | 15 g/L | [ |
算法 | 发表年份 | 描述 | 文献 |
---|---|---|---|
rFBA | 2001 | 引入转录调控信息,在系统水平上解释、分析和预测转录调控对细胞代谢的影响 | [ |
dFBA | 2002 | 研究代谢网络的动力学,实现代谢网络的动态模拟 | [ |
OptKnock | 2003 | 对生物量方程和目标产物合成方程进行求解以预测基因敲除靶点 | [ |
OptStrain | 2004 | 通过反应添加和删除,指导微生物网络的路径修改 | [ |
iFBA | 2008 | 将FBA与调节布尔逻辑和常微分方程相结合 | [ |
E-FLUX | 2009 | 利用基因表达的全细胞测量来模拟代谢状态 | [ |
PROM | 2010 | 引入了代表基因状态和基因-转录因子相互作用的概率 | [ |
OptForce | 2010 | 比较实验菌株与野生型菌株之间的代谢通量差异来预测途径通量上调或下调的靶点 | [ |
SimOptStrain | 2011 | 同时考虑基因缺失和非天然反应添加 | [ |
GX-FBA | 2012 | 将基因表达数据与FBA(GX-FBA)相结合,使用mRNA表达数据来指导受代谢网络互连性影响的细胞代谢的 分级调节 | [ |
cFBA | 2013 | 考虑了来自反应化学计量、反应热力学和生态系统的约束 | [ |
ReacKnock | 2013 | 实现多基因靶点的预测 | [ |
OptSwap | 2013 | 用于微生物辅因子调控策略的预测与设计 | [ |
k-OptForce | 2014 | 整合动力学的多基因改造策略预测 | [ |
表2 优化算法总结
Table 2 Summary of optimization algorithm
算法 | 发表年份 | 描述 | 文献 |
---|---|---|---|
rFBA | 2001 | 引入转录调控信息,在系统水平上解释、分析和预测转录调控对细胞代谢的影响 | [ |
dFBA | 2002 | 研究代谢网络的动力学,实现代谢网络的动态模拟 | [ |
OptKnock | 2003 | 对生物量方程和目标产物合成方程进行求解以预测基因敲除靶点 | [ |
OptStrain | 2004 | 通过反应添加和删除,指导微生物网络的路径修改 | [ |
iFBA | 2008 | 将FBA与调节布尔逻辑和常微分方程相结合 | [ |
E-FLUX | 2009 | 利用基因表达的全细胞测量来模拟代谢状态 | [ |
PROM | 2010 | 引入了代表基因状态和基因-转录因子相互作用的概率 | [ |
OptForce | 2010 | 比较实验菌株与野生型菌株之间的代谢通量差异来预测途径通量上调或下调的靶点 | [ |
SimOptStrain | 2011 | 同时考虑基因缺失和非天然反应添加 | [ |
GX-FBA | 2012 | 将基因表达数据与FBA(GX-FBA)相结合,使用mRNA表达数据来指导受代谢网络互连性影响的细胞代谢的 分级调节 | [ |
cFBA | 2013 | 考虑了来自反应化学计量、反应热力学和生态系统的约束 | [ |
ReacKnock | 2013 | 实现多基因靶点的预测 | [ |
OptSwap | 2013 | 用于微生物辅因子调控策略的预测与设计 | [ |
k-OptForce | 2014 | 整合动力学的多基因改造策略预测 | [ |
宿主 | 原料 | 产品 | 生产能力 | 文献 |
---|---|---|---|---|
藻类 | CO2 | 脂质 | 7.4 g/(L·d) | [ |
藻类 | CO2 | 用于乙醇发酵的单糖 | 129 mg/g干微藻 | [ |
藻类 | CO2 | 氢气 | 40 ml/L | [ |
电活性细菌 | 光能、电能和CO2 | H2和CO混合气 | CO∶H2比例(0.1~6.8) | [ |
沼泽红假单胞菌 | 光能和CO2 | 类胡萝卜素和聚β-羟基丁酸酯 | 生物质、类胡萝卜素和聚β-羟基丁酸酯产量分别 增加到148%、122%和147% | [ |
梭菌 | 合成气 | 丙酮、异丙醇 | 3 g/(L·h) | [ |
产乙酸菌 | 合成气 | 乙酸 | 148 g/(L·d) | [ |
解脂耶氏酵母 | 从合成气生产的乙酸 | 脂质 | 18 g/L,0.19 g/(L·h) | [ |
米曲霉 | 从合成气生产的乙酸 | 苹果酸 | 0.37 g/g | [ |
产乙醇梭菌 | 从合成气生产的乙酸 | PHA | 占生物质的24% | [ |
酿酒酵母 | CO2电还原生产的乙酸 | 葡萄糖、脂肪酸 | 1.81 g/L葡萄糖,0.5 g/L脂肪酸 | [ |
C. necator | 电能和CO2 | α-葎草烯 | 17 mg/CDW | [ |
酿酒酵母 | 甲醇 | 丙酮酸 | 0.26 g/L | [ |
表3 利用CO2和C1原料生产化学品
Table 3 Production of chemicals from CO2 and C1 feedstock
宿主 | 原料 | 产品 | 生产能力 | 文献 |
---|---|---|---|---|
藻类 | CO2 | 脂质 | 7.4 g/(L·d) | [ |
藻类 | CO2 | 用于乙醇发酵的单糖 | 129 mg/g干微藻 | [ |
藻类 | CO2 | 氢气 | 40 ml/L | [ |
电活性细菌 | 光能、电能和CO2 | H2和CO混合气 | CO∶H2比例(0.1~6.8) | [ |
沼泽红假单胞菌 | 光能和CO2 | 类胡萝卜素和聚β-羟基丁酸酯 | 生物质、类胡萝卜素和聚β-羟基丁酸酯产量分别 增加到148%、122%和147% | [ |
梭菌 | 合成气 | 丙酮、异丙醇 | 3 g/(L·h) | [ |
产乙酸菌 | 合成气 | 乙酸 | 148 g/(L·d) | [ |
解脂耶氏酵母 | 从合成气生产的乙酸 | 脂质 | 18 g/L,0.19 g/(L·h) | [ |
米曲霉 | 从合成气生产的乙酸 | 苹果酸 | 0.37 g/g | [ |
产乙醇梭菌 | 从合成气生产的乙酸 | PHA | 占生物质的24% | [ |
酿酒酵母 | CO2电还原生产的乙酸 | 葡萄糖、脂肪酸 | 1.81 g/L葡萄糖,0.5 g/L脂肪酸 | [ |
C. necator | 电能和CO2 | α-葎草烯 | 17 mg/CDW | [ |
酿酒酵母 | 甲醇 | 丙酮酸 | 0.26 g/L | [ |
1 | 谭天伟, 陈必强, 张会丽, 等. 加快推进绿色生物制造助力实现“碳中和”[J]. 化工进展, 2021, 40(3): 1137-1141. |
Tan T W, Chen B Q, Zhang H L, et al. Accelerate promotion of green bio-manufacturing to help achieve “carbon neutrality”[J]. Chemical Industry and Engineering Progress, 2021, 40(3): 1137-1141. | |
2 | Morgunov I G, Kamzolova S V, Lunina J N. The citric acid production from raw glycerol by Yarrowia lipolytica yeast and its regulation[J]. Applied Microbiology and Biotechnology, 2013, 97(16): 7387-7397. |
3 | Steiger M G, Rassinger A, Mattanovich D, et al. Engineering of the citrate exporter protein enables high citric acid production in Aspergillus niger [J]. Metabolic Engineering, 2019, 52: 224-231. |
4 | Dong X X, Chen X L, Qian Y Y, et al. Metabolic engineering of Escherichia coli W3110 to produce L-malate[J]. Biotechnology and Bioengineering, 2017, 114(3): 656-664. |
5 | Wang D, Li Q, Song Z Y, et al. High cell density fermentation via a metabolically engineered Escherichia coli for the enhanced production of succinic acid[J]. Journal of Chemical Technology & Biotechnology, 2011, 86(4): 512-518. |
6 | Li C, Gao S, Yang X F, et al. Green and sustainable succinic acid production from crude glycerol by engineered Yarrowia lipolytica via agricultural residue based in situ fibrous bed bioreactor[J]. Bioresource Technology, 2018, 249: 612-619. |
7 | Han T, Kim G B, Lee S Y. Glutaric acid production by systems metabolic engineering of an L-lysine-overproducing Corynebacterium glutamicum [J]. Proceedings of the National Academy of Sciences of the United States of America, 2020, 117(48): 30328-30334. |
8 | Zhao M, Huang D X, Zhang X J, et al. Metabolic engineering of Escherichia coli for producing adipic acid through the reverse adipate-degradation pathway[J]. Metabolic Engineering, 2018, 47: 254-262. |
9 | Zhou Z, Zhang X Y, Wu J, et al. Targeting cofactors regeneration in methylation and hydroxylation for high level production of ferulic acid[J]. Metabolic Engineering, 2022, 73: 247-255. |
10 | Wu Z, Wang Z, Wang G Q, et al. Improved 1, 3-propanediol production by engineering the 2, 3-butanediol and formic acid pathways in integrative recombinant Klebsiella pneumoniae [J]. Journal of Biotechnology, 2013, 168(2): 194-200. |
11 | Nakamura C E, Whited G M. Metabolic engineering for the microbial production of 1, 3-propanediol[J]. Current Opinion in Biotechnology, 2003, 14(5): 454-459. |
12 | Guo H, Liu H, Jin Y H, et al. Advances in research on the bio-production of 1, 4-butanediol by the engineered microbes[J]. Biochemical Engineering Journal, 2022, 185: 108478. |
13 | Meadows A L, Hawkins K M, Tsegaye Y, et al. Rewriting yeast central carbon metabolism for industrial isoprenoid production[J]. Nature, 2016, 537(7622): 694-697. |
14 | Ye Z L, Huang Y L, Shi B, et al. Coupling cell growth and biochemical pathway induction in Saccharomyces cerevisiae for production of (+)-valencene and its chemical conversion to (+)-nootkatone[J]. Metabolic Engineering, 2022, 72: 107-115. |
15 | Liu G S, Li T, Zhou W, et al. The yeast peroxisome: a dynamic storage depot and subcellular factory for squalene overproduction[J]. Metabolic Engineering, 2020, 57: 151-161. |
16 | Ma Y S, Li J B, Huang S W, et al. Targeting pathway expression to subcellular organelles improves astaxanthin synthesis in Yarrowia lipolytica [J]. Metabolic Engineering, 2021, 68: 152-161. |
17 | Zhou Y J, Buijs N A, Zhu Z W, et al. Production of fatty acid-derived oleochemicals and biofuels by synthetic yeast cell factories[J]. Nature Communications, 2016, 7(1): 11709. |
18 | Xu J Y, Liu N, Qiao K J, et al. Application of metabolic controls for the maximization of lipid production in semicontinuous fermentation[J]. Proceedings of the National Academy of Sciences, 2017, 114(27): E5308-E5316. |
19 | Ye J W, Huang W Z, Wang D S, et al. Pilot scale-up of poly(3-hydroxybutyrate-co-4-hydroxybutyrate) production by Halomonas bluephagenesis via cell growth adapted optimization process[J]. Biotechnology Journal, 2018, 13(5): 1800074. |
20 | Wang Z Y, Ly M, Zhang F M, et al. E. coli K5 fermentation and the preparation of heparosan, a bioengineered heparin precursor[J]. Biotechnology and Bioengineering, 2010, 107(6): 964-973. |
21 | 袁海波, 李江华, 刘龙, 等. 基于系统生物学和合成生物学的重要平台化学品生物制造的研究进展[J]. 化工学报, 2016, 67(1): 129-139. |
Yuan H B, Li J H, Liu L, et al. Advances in production of important platform chemicals by bio-manufacturing based on systems biology and synthetic biology[J]. CIESC Journal, 2016, 67(1): 129-139. | |
22 | 刘立明, 陈坚. 基因组规模代谢网络模型构建及其应用[J]. 生物工程学报, 2010, 26(9): 1176-1186. |
Liu L M, Chen J. Reconstruction and application of genome-scale metabolic network model[J]. Chinese Journal of Biotechnology, 2010, 26(9): 1176-1186. | |
23 | 于勇, 朱欣娜, 张学礼. 大宗化学品细胞工厂的构建与应用[J]. 合成生物学, 2020, 1(6): 674-684. |
Yu Y, Zhu X N, Zhang X L. Construction and application of microbial cell factories for production of bulk chemicals[J]. Synthetic Biology Journal, 2020, 1(6): 674-684. | |
24 | Orth J D, Conrad T M, Na J, et al. A comprehensive genome-scale reconstruction of Escherichia coli metabolism: 2011[J]. Molecular Systems Biology, 2011, 7(1): 535. |
25 | Förster J, Famili I, Fu P, et al. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network[J]. Genome Research, 2003, 13(2): 244-253. |
26 | Oh Y K, Palsson B O, Park S M, et al. Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data[J]. The Journal of Biological Chemistry, 2007, 282(39): 28791-28799. |
27 | Feist A M, Palsson B Ø. The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli [J]. Nature Biotechnology, 2008, 26(6): 659-667. |
28 | Kim W J, Kim H U, Lee S Y. Current state and applications of microbial genome-scale metabolic models[J]. Current Opinion in Systems Biology, 2017, 2: 10-18. |
29 | Thiele I, Palsson B Ø. A protocol for generating a high-quality genome-scale metabolic reconstruction[J]. Nature Protocols, 2010, 5(1): 93-121. |
30 | Zhang C Y, Wu Y K, Xu X H, et al. Current status and future perspectives of metabolic network models of industrial microorganisms[J]. Chinese Journal of Biotechnology, 2021, 37(3): 860-873. |
31 | Duarte N C, Becker S A, Jamshidi N, et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data[J]. Proceedings of the National Academy of Sciences, 2007, 104(6): 1777-1782. |
32 | Oberhardt M A, Palsson B Ø, Papin J A. Applications of genome-scale metabolic reconstructions[J]. Molecular Systems Biology, 2009, 5(1): 320. |
33 | Orth J D, Thiele I, Palsson B Ø. What is flux balance analysis? [J]. Nature Biotechnology, 2010, 28(3): 245-248. |
34 | Price N D, Papin J A, Schilling C H, et al. Genome-scale microbial in silico models: the constraints-based approach[J]. Trends in Biotechnology, 2003, 21(4): 162-169. |
35 | Kauffman K J, Prakash P, Edwards J S. Advances in flux balance analysis[J]. Current Opinion in Biotechnology, 2003, 14(5): 491-496. |
36 | Covert M W, Schilling C H, Palsson B. Regulation of gene expression in flux balance models of metabolism[J]. Journal of Theoretical Biology, 2001, 213(1): 73-88. |
37 | Covert M W, Xiao N, Chen T J, et al. Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli [J]. Bioinformatics, 2008, 24(18): 2044-2050. |
38 | Mahadevan R, Edwards J S, Doyle F J III. Dynamic flux balance analysis of diauxic growth in Escherichia coli [J]. Biophysical Journal, 2002, 83(3): 1331-1340. |
39 | Khandelwal R A, Olivier B G, Röling W F M, et al. Community flux balance analysis for microbial consortia at balanced growth[J]. PLoS One, 2013, 8(5): e64567. |
40 | Colijn C, Brandes A, Zucker J, et al. Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production[J]. PLoS Computational Biology, 2009, 5(8): e1000489. |
41 | Navid A, Almaas E. Genome-level transcription data of Yersinia pestis analyzed with a new metabolic constraint-based approach[J]. BMC Systems Biology, 2012, 6: 150. |
42 | Chandrasekaran S, Price N D. Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis [J]. Proceedings of the National Academy of Sciences, 2010, 107(41): 17845-17850. |
43 | Agren R, Bordel S, Mardinoglu A, et al. Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT[J]. PLoS Computational Biology, 2012, 8(5): e1002518. |
44 | Shlomi T, Cabili M N, Herrgård M J, et al. Network-based prediction of human tissue-specific metabolism[J]. Nature Biotechnology, 2008, 26(9): 1003-1010. |
45 | Zur H, Ruppin E, Shlomi T. iMAT: an integrative metabolic analysis tool[J]. Bioinformatics, 2010, 26(24): 3140-3142. |
46 | Jerby L, Wolf L, Denkert C, et al. Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer[J]. Cancer Research, 2012, 72(22): 5712-5720. |
47 | Zhao Q Y, Kurata H. Genetic modification of flux for flux prediction of mutants[J]. Bioinformatics, 2009, 25(13): 1702-1708. |
48 | Burgard A P, Pharkya P, Maranas C D. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization[J]. Biotechnology and Bioengineering, 2003, 84(6): 647-657. |
49 | Xu Z X, Zheng P, Sun J B, et al. ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network[J]. PLoS One, 2013, 8(12): e72150. |
50 | King Z A, Feist A M. Optimizing cofactor specificity of oxidoreductase enzymes for the generation of microbial production strains—OptSwap[J]. Industrial Biotechnology, 2013, 9(4): 236-246. |
51 | Ranganathan S, Suthers P F, Maranas C D. OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions[J]. PLoS Computational Biology, 2010, 6(4): e1000744. |
52 | Chowdhury A, Zomorrodi A R, Maranas C D. k-OptForce: integrating kinetics with flux balance analysis for strain design[J]. PLoS Computational Biology, 2014, 10(2): e1003487. |
53 | Sánchez B J, Zhang C, Nilsson A, et al. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints[J]. Molecular Systems Biology, 2017, 13(8): 935. |
54 | Hädicke O, von Kamp A, Aydogan T, et al. OptMDFpathway: identification of metabolic pathways with maximal thermodynamic driving force and its application for analyzing the endogenous CO2 fixation potential of Escherichia coli [J]. PLoS Computational Biology, 2018, 14(9): e1006492. |
55 | Jiang S Y, Otero-Muras I, Banga J R, et al. OptDesign: identifying optimum design strategies in strain engineering for biochemical production[J]. ACS Synthetic Biology, 2022, 11(4): 1531-1541. |
56 | Pharkya P, Burgard A P, Maranas C D. OptStrain: a computational framework for redesign of microbial production systems[J]. Genome Research, 2004, 14(11): 2367-2376. |
57 | Kim J, Reed J L, Maravelias C T. Large-scale bi-level strain design approaches and mixed-integer programming solution techniques[J]. PLoS One, 2011, 6(9): e24162. |
58 | 李宁川. 解脂耶氏酵母代谢网络模型用于仿真及提升氧化还原辅因子在油脂合成中的作用[D]. 上海: 华东理工大学, 2018. |
Li N C. Improving lipid accumulation in Yarrowia lipolytica based on genome-scale metabolic model[D]. Shanghai: East China University of Science and Technology, 2018. | |
59 | Cheng F Y, Yu H M, Stephanopoulos G. Engineering Corynebacterium glutamicum for high-titer biosynthesis of hyaluronic acid[J]. Metabolic Engineering, 2019, 55: 276-289. |
60 | Murphy K C. Use of bacteriophage lambda recombination functions to promote gene replacement in Escherichia coli [J]. Journal of Bacteriology, 1998, 180(8): 2063-2071. |
61 | Zhang Y M, Buchholz F, Muyrers J P P, et al. A new logic for DNA engineering using recombination in Escherichia coli [J]. Nature Genetics, 1998, 20(2): 123-128. |
62 | Urnov F D, Rebar E J, Holmes M C, et al. Genome editing with engineered zinc finger nucleases[J]. Nature Reviews Genetics, 2010, 11(9): 636-646. |
63 | Miller J C, Tan S Y, Qiao G J, et al. A TALE nuclease architecture for efficient genome editing[J]. Nature Biotechnology, 2011, 29(2): 143-148. |
64 | Sternberg N, Hamilton D. Bacteriophage P1 site-specific recombination (Ⅰ): Recombination between loxP sites[J]. Journal of Molecular Biology, 1981, 150(4): 467-486. |
65 | Donohoue P D, Barrangou R, May A P. Advances in industrial biotechnology using CRISPR-Cas systems[J]. Trends in Biotechnology, 2018, 36(2): 134-146. |
66 | Choi K R, Lee S Y. CRISPR technologies for bacterial systems: current achievements and future directions[J]. Biotechnology Advances, 2016, 34(7): 1180-1209. |
67 | Wu Y K, Liu Y F, Lv X Q, et al. Applications of CRISPR in a microbial cell factory: from genome reconstruction to metabolic network reprogramming[J]. ACS Synthetic Biology, 2020, 9(9): 2228-2238. |
68 | Schwartz C, Shabbir-Hussain M, Frogue K, et al. Standardized markerless gene integration for pathway engineering in Yarrowia lipolytica [J]. ACS Synthetic Biology, 2017, 6(3): 402-409. |
69 | Holkenbrink C, Dam M I, Kildegaard K R, et al. EasyCloneYALI: CRISPR/Cas9-based synthetic toolbox for engineering of the yeast Yarrowia lipolytica [J]. Biotechnology Journal, 2018, 13(9): 1700543. |
70 | Zhu X N, Zhao D D, Qiu H N, et al. The CRISPR/Cas9-facilitated multiplex pathway optimization (CFPO) technique and its application to improve the Escherichia coli xylose utilization pathway[J]. Metabolic Engineering, 2017, 43: 37-45. |
71 | Zhang Y P, Wang J, Wang Z B, et al. A gRNA-tRNA array for CRISPR-Cas9 based rapid multiplexed genome editing in Saccharomyces cerevisiae [J]. Nature Communications, 2019, 10: 1053. |
72 | Yang S Q, Zhang Y W, Xu J Q, et al. Orthogonal CRISPR-associated transposases for parallel and multiplexed chromosomal integration[J]. Nucleic Acids Research, 2021, 49(17): 10192-10202. |
73 | Zhang Y W, Sun X M, Wang Q Z, et al. Multicopy chromosomal integration using CRISPR-associated transposases[J]. ACS Synthetic Biology, 2020, 9(8): 1998-2008. |
74 | Arazoe T, Kondo A, Nishida K. Targeted nucleotide editing technologies for microbial metabolic engineering[J]. Biotechnology Journal, 2018, 13(9): 1700596. |
75 | Rees H A, Liu D R. Base editing: precision chemistry on the genome and transcriptome of living cells[J]. Nature Reviews Genetics, 2018, 19(12): 770-788. |
76 | Molla K A, Yang Y N. CRISPR/Cas-mediated base editing: technical considerations and practical applications[J]. Trends in Biotechnology, 2019, 37(10): 1121-1142. |
77 | Komor A C, Kim Y B, Packer M S, et al. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage[J]. Nature, 2016, 533(7603): 420-424. |
78 | Nishida K, Arazoe T, Yachie N, et al. Targeted nucleotide editing using hybrid prokaryotic and vertebrate adaptive immune systems[J]. Science, 2016, 353(6305): aaf8729. |
79 | Gaudelli N M, Komor A C, Rees H A, et al. Programmable base editing of A·T to G·C in genomic DNA without DNA cleavage[J]. Nature, 2017, 551(7681): 464-471. |
80 | Banno S, Nishida K, Arazoe T, et al. Deaminase-mediated multiplex genome editing in Escherichia coli [J]. Nature Microbiology, 2018, 3(4): 423-429. |
81 | Garst A D, Bassalo M C, Pines G, et al. Genome-wide mapping of mutations at single-nucleotide resolution for protein, metabolic and genome engineering[J]. Nature Biotechnology, 2017, 35(1): 48-55. |
82 | Liang L Y, Liu R M, Garst A D, et al. CRISPR EnAbled Trackable Genome Engineering for isopropanol production in Escherichia coli [J]. Metabolic Engineering, 2017, 41: 1-10. |
83 | Liu R M, Liang L Y, Choudhury A, et al. Iterative genome editing of Escherichia coli for 3-hydroxypropionic acid production[J]. Metabolic Engineering, 2018, 47: 303-313. |
84 | Liu R M, Liang L Y, Choudhury A, et al. Multiplex navigation of global regulatory networks (MINR) in yeast for improved ethanol tolerance and production[J]. Metabolic Engineering, 2019, 51: 50-58. |
85 | Tyo K E J, Ajikumar P K, Stephanopoulos G. Stabilized gene duplication enables long-term selection-free heterologous pathway expression[J]. Nature Biotechnology, 2009, 27(8): 760-765. |
86 | Richardson S M, Mitchell L A, Stracquadanio G, et al. Design of a synthetic yeast genome[J]. Science, 2017, 355(6329): 1040-1044. |
87 | Liu W, Luo Z Q, Wang Y, et al. Rapid pathway prototyping and engineering using in vitro and in vivo synthetic genome SCRaMbLE-in methods[J]. Nature Communications, 2018, 9: 1936. |
88 | Ma L, Li Y X, Chen X Y, et al. SCRaMbLE generates evolved yeasts with increased alkali tolerance[J]. Microbial Cell Factories, 2019, 18(1): 52. |
89 | Ding Y, Wang K F, Wang W J, et al. Increasing the homologous recombination efficiency of eukaryotic microorganisms for enhanced genome engineering[J]. Applied Microbiology and Biotechnology, 2019, 103(11): 4313-4324. |
90 | Cui Z Y, Jiang X, Zheng H H, et al. Homology-independent genome integration enables rapid library construction for enzyme expression and pathway optimization in Yarrowia lipolytica [J]. Biotechnology and Bioengineering, 2019, 116(2): 354-363. |
91 | Liu M M, Zhang J, Liu X Q, et al. Rapid gene target tracking for enhancing β-carotene production using flow cytometry-based high-throughput screening in Yarrowia lipolytica [J]. Applied and Environmental Microbiology, 2022, 88(19): e0114922. |
92 | Chen Y, Siewers V, Nielsen J. Profiling of cytosolic and peroxisomal acetyl-CoA metabolism in Saccharomyces cerevisiae [J]. PLoS One, 2012, 7(8): e42475. |
93 | Wang J P, Ledesma-Amaro R, Wei Y J, et al. Metabolic engineering for increased lipid accumulation in Yarrowia lipolytica— a review[J]. Bioresource Technology, 2020, 313: 123707. |
94 | Zhang Q, Zeng W Z, Xu S, et al. Metabolism and strategies for enhanced supply of acetyl-CoA in Saccharomyces cerevisiae [J]. Bioresource Technology, 2021, 342: 125978. |
95 | Nielsen J. Synthetic biology for engineering acetyl coenzyme A metabolism in yeast[J]. mBio, 2014, 5(6): e02153. |
96 | Liu W S, Zhang B, Jiang R R. Improving acetyl-CoA biosynthesis in Saccharomyces cerevisiae via the overexpression of pantothenate kinase and PDH bypass[J]. Biotechnology for Biofuels, 2017, 10: 41. |
97 | Vadali R V, Bennett G N, San K Y. Applicability of CoA/acetyl-CoA manipulation system to enhance isoamyl acetate production in Escherichia coli [J]. Metabolic Engineering, 2004, 6(4): 294-299. |
98 | Yuzbasheva E Y, Agrimi G, Yuzbashev T V, et al. The mitochondrial citrate carrier in Yarrowia lipolytica: its identification, characterization and functional significance for the production of citric acid[J]. Metabolic Engineering, 2019, 54: 264-274. |
99 | Castegna A, Scarcia P, Agrimi G, et al. Identification and functional characterization of a novel mitochondrial carrier for citrate and oxoglutarate in Saccharomyces cerevisiae [J]. Journal of Biological Chemistry, 2010, 285(23): 17359-17370. |
100 | Chen Y, Daviet L, Schalk M, et al. Establishing a platform cell factory through engineering of yeast acetyl-CoA metabolism[J]. Metabolic Engineering, 2013, 15: 48-54. |
101 | Hara K Y, Kondo A. ATP regulation in bioproduction[J]. Microbial Cell Factories, 2015, 14: 198. |
102 | Chen H G, Zhang Y H P J. Enzymatic regeneration and conservation of ATP: challenges and opportunities[J]. Critical Reviews in Biotechnology, 2021, 41(1): 16-33. |
103 | Man Z W, Rao Z M, Xu M J, et al. Improvement of the intracellular environment for enhancing L-arginine production of Corynebacterium glutamicum by inactivation of H2O2-forming flavin reductases and optimization of ATP supply[J]. Metabolic Engineering, 2016, 38: 310-321. |
104 | Tong T, Tao Z Y, Chen X L, et al. A biosynthesis pathway for 3-hydroxypropionic acid production in genetically engineered Saccharomyces cerevisiae [J]. Green Chemistry, 2021, 23(12): 4502-4509. |
105 | Liew F, Henstra A M, Kӧpke M, et al. Metabolic engineering of Clostridium autoethanogenum for selective alcohol production[J]. Metabolic Engineering, 2017, 40: 104-114. |
106 | Singh A, Cher Soh K, Hatzimanikatis V, et al. Manipulating redox and ATP balancing for improved production of succinate in E. coli [J]. Metabolic Engineering, 2011, 13(1): 76-81. |
107 | Qi H S, Li S S, Zhao S M, et al. Model-driven redox pathway manipulation for improved isobutanol production in Bacillus subtilis complemented with experimental validation and metabolic profiling analysis[J]. PLoS One, 2014, 9(4): e93815. |
108 | Liang B, Sun G N, Wang Z B, et al. Production of 3-hydroxypropionate using a novel malonyl-CoA-mediated biosynthetic pathway in genetically engineered E. coli strain[J]. Green Chemistry, 2019, 21(22): 6103-6115. |
109 | Chen Y W, Liao Y, Kong W Z, et al. ATP dynamic regeneration strategy for enhancing co-production of glutathione and S-adenosylmethionine in Escherichia coli [J]. Biotechnology Letters, 2020, 42(12): 2581-2587. |
110 | Yu Y, Zhu X N, Xu H T, et al. Construction of an energy-conserving glycerol utilization pathways for improving anaerobic succinate production in Escherichia coli [J]. Metabolic Engineering, 2019, 56: 181-189. |
111 | Shen X L, Wang J, Li C Y, et al. Dynamic gene expression engineering as a tool in pathway engineering[J]. Current Opinion in Biotechnology, 2019, 59: 122-129. |
112 | David F, Nielsen J, Siewers V. Flux control at the malonyl-CoA node through hierarchical dynamic pathway regulation in Saccharomyces cerevisiae [J]. ACS Synthetic Biology, 2016, 5(3): 224-233. |
113 | Liu D, Xiao Y, Evans B S, et al. Negative feedback regulation of fatty acid production based on a malonyl-CoA sensor-actuator[J]. ACS Synthetic Biology, 2015, 4(2): 132-140. |
114 | Xu X H, Li X L, Liu Y F, et al. Pyruvate-responsive genetic circuits for dynamic control of central metabolism[J]. Nature Chemical Biology, 2020, 16(11): 1261-1268. |
115 | Sun J F, Tian K M, Wang J, et al. Improved ethanol productivity from lignocellulosic hydrolysates by Escherichia coli with regulated glucose utilization[J]. Microbial Cell Factories, 2018, 17(1): 66. |
116 | Hwang H J, Kim J W, Ju S Y, et al. Application of an oxygen-inducible nar promoter system in metabolic engineering for production of biochemicals in Escherichia coli [J]. Biotechnology and Bioengineering, 2017, 114(2): 468-473. |
117 | Kim E M, Woo H M, Tian T, et al. Autonomous control of metabolic state by a quorum sensing (QS)-mediated regulator for bisabolene production in engineered E. coli [J]. Metabolic Engineering, 2017, 44: 325-336. |
118 | Gupta A, Reizman I M B, Reisch C R, et al. Dynamic regulation of metabolic flux in engineered bacteria using a pathway-independent quorum-sensing circuit[J]. Nature Biotechnology, 2017, 35(3): 273-279. |
119 | Chen C Y, Yeh K L, Aisyah R, et al. Cultivation, photobioreactor design and harvesting of microalgae for biodiesel production: a critical review[J]. Bioresource Technology, 2011, 102(1): 71-81. |
120 | Hernández D, Riaño B, Coca M, et al. Saccharification of carbohydrates in microalgal biomass by physical, chemical and enzymatic pre-treatments as a previous step for bioethanol production[J]. Chemical Engineering Journal, 2015, 262: 939-945. |
121 | Batyrova K, Gavrisheva A, Ivanova E, et al. Sustainable hydrogen photoproduction by phosphorus-deprived marine green microalgae Chlorella sp[J]. International Journal of Molecular Sciences, 2015, 16(2): 2705-2716. |
122 | Lu L, Li Z D, Chen X, et al. Spontaneous solar syngas production from CO2 driven by energetically favorable wastewater microbial anodes[J]. Joule, 2020, 4(10): 2149-2161. |
123 | Wang B, Jiang Z F, Yu J C, et al. Enhanced CO2 reduction and valuable C2+ chemical production by a CdS-photosynthetic hybrid system[J]. Nanoscale, 2019, 11(19): 9296-9301. |
124 | Liew F E, Nogle R, Abdalla T, et al. Carbon-negative production of acetone and isopropanol by gas fermentation at industrial pilot scale[J]. Nature Biotechnology, 2022, 40(3): 335-344. |
125 | Kantzow C, Mayer A, Weuster-Botz D. Continuous gas fermentation by Acetobacterium woodii in a submerged membrane reactor with full cell retention[J]. Journal of Biotechnology, 2015, 212: 11-18. |
126 | Hu P, Chakraborty S, Kumar A, et al. Integrated bioprocess for conversion of gaseous substrates to liquids[J]. Proceedings of the National Academy of Sciences, 2016, 113(14): 3773-3778. |
127 | Oswald F, Dörsam S, Veith N, et al. Sequential mixed cultures: from syngas to malic acid[J]. Frontiers in Microbiology, 2016, 7: 891. |
128 | Lagoa-Costa B, Abubackar H N, Fernández-Romasanta M, et al. Integrated bioconversion of syngas into bioethanol and biopolymers[J]. Bioresource Technology, 2017, 239: 244-249. |
129 | Zheng T T, Zhang M L, Wu L H, et al. Upcycling CO2 into energy-rich long-chain compounds via electrochemical and metabolic engineering[J]. Nature Catalysis, 2022, 5(5): 388-396. |
130 | Krieg T, Sydow A, Faust S, et al. CO2 to terpenes: autotrophic and electroautotrophic α-humulene production with Cupriavidus necator [J]. Angewandte Chemie International Edition, 2018, 57(7): 1879-1882. |
131 | Dai Z X, Gu H L, Zhang S J, et al. Metabolic construction strategies for direct methanol utilization in Saccharomyces cerevisiae [J]. Bioresource Technology, 2017, 245: 1407-1412. |
132 | Liu Z H, Wang K, Chen Y, et al. Third-generation biorefineries as the means to produce fuels and chemicals from CO2 [J]. Nature Catalysis, 2020, 3(3): 274-288. |
133 | Tan X Y, Nielsen J. The integration of bio-catalysis and electrocatalysis to produce fuels and chemicals from carbon dioxide[J]. Chemical Society Reviews, 2022, 51(11): 4763-4785. |
134 | Gassler T, Sauer M, Gasser B, et al. The industrial yeast Pichia pastoris is converted from a heterotroph into an autotroph capable of growth on CO2 [J]. Nature Biotechnology, 2020, 38(2): 210-216. |
135 | Xia P F, Zhang G C, Walker B, et al. Recycling carbon dioxide during xylose fermentation by engineered Saccharomyces cerevisiae [J]. ACS Synthetic Biology, 2017, 6(2): 276-283. |
136 | Papapetridis I, Goudriaan M, Vázquez Vitali M, et al. Optimizing anaerobic growth rate and fermentation kinetics in Saccharomyces cerevisiae strains expressing Calvin-cycle enzymes for improved ethanol yield[J]. Biotechnology for Biofuels, 2018, 11: 17. |
137 | Zheng T T, Liu C X, Guo C X, et al. Copper-catalysed exclusive CO2 to pure formic acid conversion via single-atom alloying[J]. Nature Nanotechnology, 2021, 16(12): 1386-1393. |
138 | Gonzalez de la Cruz J, Machens F, Messerschmidt K, et al. Core catalysis of the reductive glycine pathway demonstrated in yeast[J]. ACS Synthetic Biology, 2019, 8(5): 911-917. |
[1] | 高子熹, 郭树奇, 费强. 生物转化温室气体生产单细胞蛋白的研究进展[J]. 化工学报, 2021, 72(6): 3202-3214. |
[2] | 高虎涛, 申晓林, 孙新晓, 王佳, 袁其朋. 代谢工程调控策略在生物合成氨基酸及其衍生物中的应用[J]. 化工学报, 2020, 71(9): 4058-4070. |
[3] | 陈宏文1,刘 薇1,杜 钰1,陈 国1,方柏山2. 工业微生物还原型辅酶Ⅱ的代谢调控研究进展[J]. 化工进展, 2012, 31(11): 2535-2541. |
阅读次数 | ||||||||||||||||||||||||||||||||||||||||||||||||||
全文 737
|
|
|||||||||||||||||||||||||||||||||||||||||||||||||
摘要 1078
|
|
|||||||||||||||||||||||||||||||||||||||||||||||||