微生物细胞工厂的设计构建：从诱变育种到全基因组定制化创制

doi:10.12211/2096-8208.2020-050

合成生物学 ›› 2020, Vol. 1 ›› Issue (6): 656-673.DOI: 10.12211/2096-8208.2020-050

微生物细胞工厂的设计构建：从诱变育种到全基因组定制化创制

袁姚梦¹, 邢新会¹^,², 张翀¹

^1.工业生物催化教育部重点实验室，清华大学化工系生物化工研究所，清华大学合成与系统生物学研究中心，北京　100084
^2.清华大学深圳国际研究生院生物医药与健康工程研究院，广东深圳 518055

收稿日期:2020-04-16 修回日期:2020-09-26 出版日期:2020-12-31 发布日期:2021-01-15
通讯作者: 张翀
作者简介:袁姚梦（1997—），女，博士研究生。主要研究方向为合成生物学、代谢工程。E-mail：2631825401@qq.com
张翀（1979—），男，博士，副教授，博士生导师。主要研究方向为合成生物学、代谢工程、生物化工、系统生物学。E-mail：chongzhang@mail.tsinghua.edu.cn
基金资助:
国家自然科学基金重点项目(2193000018);国家重点研发计划(2019YFA0904802)

Progress and prospective of engineering microbial cell factories： from random mutagenesis to customized design in genome scale

YUAN Yaomeng¹, XING Xinhui¹^,², ZHANG Chong¹

^1.MOE Key Laboratory for Industrial Biocatalysis，Institute of Biochemical Engineering，Department of Chemical Engineering，Center for Synthetic & Systems Biology，Tsinghua University，Beijing 100084，China
^2.Institute of Biopharmaceutical and Health Engineering，Tsinghua Shenzhen International Graduate School，Shenzhen 518055，Guangdong，China

Received:2020-04-16 Revised:2020-09-26 Online:2020-12-31 Published:2021-01-15
Contact: ZHANG Chong

摘要/Abstract

摘要：

微生物细胞工厂（microbial cell factories，MCFs）被广泛用于生产丰富多样的化学品、食品、药品和能源，是绿色生物制造的核心环节。早期主要通过天然微生物的筛选和诱变育种的方式获得高产菌种，然而作为一种“以时间（人力）换水平”的非理性策略，其创制效率极低。随着分子生物学和基因工程研究方法的不断发展，对微生物系统认知和改造能力的进步促使代谢工程学科诞生。基于生物学知识的理性/半理性代谢工程设计和构建策略，目前已发展了从分子、途径到基因组层次不同的MCFs设计和工程化构建策略。本文结合实际案例对MCFs的设计及构建策略进行综述，首先回顾传统诱变育种和代谢工程指导的理性/半理性设计策略，探讨如何突破代谢工程经典框架的限制，实现全基因组水平定制化MCFs的快速构建，最后对这一新的构建范式的未来进行展望。

关键词: 生物制造, 微生物细胞工厂, 代谢工程, 基因型-表型关联, 数据驱动

Abstract:

As the core of green bio-manufacturing and bioeconomy, microbial cell factories (MCFs) are widely used to produce a variety of chemicals, foods, medicines and fuels. In the early years, isolation and random mutagenesis of natural microbes were time-consuming but widely used for developing well-performed MCFs. With the development of molecular biology and genetic engineering, the advancements in understanding of microbial systems prompted the establishment of metabolic engineering. Nowadays, different MCF-construction strategies in terms of protein, pathway and genome-wide engineering have been well developed based on rational or semi-rational metabolic engineering strategies. However, due to limited biological knowledge, these strategies mainly rely on the iterative cycle of ‘Design-Build-Test-Learning (DBTL)’, usually taking 50—300 person-years and hundreds of millions of dollars to develop a MCF that can meet industrial demands. Combining high-throughput genome editing and phenotype screening and selection technologies, the genome-wide customized engineering allows one to obtain large-scale genotype-phenotype association (GPA) data sets quickly. Based on these results, data science technologies can be further applied to mine a large number of unknown genes or loci associated with the specific phenotypes. This strategy has a wider search scope for genotype (genome-wide) and does not rely on existing biological knowledge (data-driven), thus making it possible to explore phenotypes that could not be achieved by the above mentioned rational/semi-rational strategies and develop MCFs with superior performance more efficiently. This paper reviews general strategies and application cases for the design and construction of MCFs. We will firstly summarize the overview of random mutagenesis strategies, and the history and latest progress in metabolic engineering for the construction of MCFs. Then we discuss the potential of the newly emerging MCF-design and construction paradigm, and the customized design strategies at the whole genome scales. Finally, we conclude with our perspectives on the development of novel strategies for the engineering of MCFs.

Key words: Bio-manufacturing, microbial cell factories, metabolic engineering, genotype-phenotype associations, data-driven

中图分类号:

袁姚梦, 邢新会, 张翀. 微生物细胞工厂的设计构建：从诱变育种到全基因组定制化创制[J]. 合成生物学, 2020, 1(6): 656-673.

YUAN Yaomeng, XING Xinhui, ZHANG Chong. Progress and prospective of engineering microbial cell factories： from random mutagenesis to customized design in genome scale[J]. Synthetic Biology Journal, 2020, 1(6): 656-673.

图/表 9

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分类	诱变技术/诱变剂	描述	参考文献
物理诱变	电离辐射（X射线、γ射线等）	引起DNA双链或单链断裂，实现DNA的删除或结构改变	[30]
	非电离辐射（紫外线）	使嘧啶形成二聚体，实现GC的删除、移码突变以及GC→AT的转换	[32]
化学诱变	烷化剂（烷基磺酸盐、芥子气等）	使DNA碱基发生烷化，导致DNA复制时发生配对错误	[32]
	碱基类似物（嘧啶类似物、嘌呤类似物）	与DNA碱基结构类似，在DNA复制时掺入并引发配对错误	[30]
	移码诱变剂（原黄素、吖啶橙等）	与DNA结合导致碱基增添或缺失	[30]
	脱氨剂（亚硝酸）	引起A、C、G碱基的脱氨，实现GC与AT的相互转换；引起DNA交联作用，引发突变	[33]
	羟化剂（羟胺）	引起胞嘧啶脱氨，实现GC→AT的转换	[35]
生物诱变	噬菌体、质粒和DNA转座子	通过碱基取代和DNA链断裂实现碱基的删除、重复和插入	[30]
	原生质体融合	将两个亲株的原生质体进行融合，形成杂合二倍体，使两个亲株发生基因组重组	[31]
	DNA改组	对多个同源序列组成的文库进行随机片段化，再利用PCR使其发生随机的重组，实现多亲本的基因重组	[34]
	基因组重排	先利用传统诱变手段获得多个表型改进的菌株，然后模拟DNA改组的反应条件，对原生质体进行多次递推式融合，实现正向突变的富集	[26]
复合诱变		结合多种诱变方式提高诱变效率	[30]
新型诱变技术	离子注入诱变	离子注入细胞导致DNA损伤，细胞修复损伤的过程中出现突变	[30]
新型诱变技术	等离子体诱变	等离子体作用于细胞造成DNA损伤，细胞修复损伤的过程中出现突变	[27，30]

分类	诱变技术/诱变剂	描述	参考文献
物理诱变	电离辐射（X射线、γ射线等）	引起DNA双链或单链断裂，实现DNA的删除或结构改变	[30]
	非电离辐射（紫外线）	使嘧啶形成二聚体，实现GC的删除、移码突变以及GC→AT的转换	[32]
化学诱变	烷化剂（烷基磺酸盐、芥子气等）	使DNA碱基发生烷化，导致DNA复制时发生配对错误	[32]
	碱基类似物（嘧啶类似物、嘌呤类似物）	与DNA碱基结构类似，在DNA复制时掺入并引发配对错误	[30]
	移码诱变剂（原黄素、吖啶橙等）	与DNA结合导致碱基增添或缺失	[30]
	脱氨剂（亚硝酸）	引起A、C、G碱基的脱氨，实现GC与AT的相互转换；引起DNA交联作用，引发突变	[33]
	羟化剂（羟胺）	引起胞嘧啶脱氨，实现GC→AT的转换	[35]
生物诱变	噬菌体、质粒和DNA转座子	通过碱基取代和DNA链断裂实现碱基的删除、重复和插入	[30]
	原生质体融合	将两个亲株的原生质体进行融合，形成杂合二倍体，使两个亲株发生基因组重组	[31]
	DNA改组	对多个同源序列组成的文库进行随机片段化，再利用PCR使其发生随机的重组，实现多亲本的基因重组	[34]
	基因组重排	先利用传统诱变手段获得多个表型改进的菌株，然后模拟DNA改组的反应条件，对原生质体进行多次递推式融合，实现正向突变的富集	[26]
复合诱变		结合多种诱变方式提高诱变效率	[30]
新型诱变技术	离子注入诱变	离子注入细胞导致DNA损伤，细胞修复损伤的过程中出现突变	[30]
新型诱变技术	等离子体诱变	等离子体作用于细胞造成DNA损伤，细胞修复损伤的过程中出现突变	[27，30]

类别	分析方法	描述	参考文献
动力学分析	ODE&米氏方程	利用常微分方程（ODE）和米氏方程，能够描述胞内代谢物浓度随时间的变化趋势，从而建立代谢网络的动力学模型	[36]
动力学分析	代谢控制分析（MCA）	MCA可用于估算动力学模型中各个通量的控制系数，从而确定代谢途径中需要过表达的目标基因，增加通过途径的通量	[37]
代谢网络分析	代谢通量分析（MFA）	MFA根据胞内代谢物的质量平衡确定线性方程组并求解，能够计算特定培养条件下细胞内的实际代谢通量分布。MFA需要依赖大量实验数据来增加可测量通量的数量，从而计算出不可测量通量向量	[38]
	通量平衡分析（FBA）	FBA可用于确定细胞代谢网络中每个反应的最佳通量，该方法基于凸分析，通过对系统施加最大化（最小化）的目标函数来确定代谢通量矢量	[39-40]
	代谢途径分析（MPA）	MPA可用于识别代谢网络中存在的所有代谢通量向量，该方法仅以化学计量学和反应热力学为约束条件，不需要动力学参数进行计算（例如基元模式分析）	[41-42]
整合了热力学的代谢网络分析	网络嵌入的热力学分析（NET）	以热力学第二定律为依据，可用于检测代谢组学数据和假定的通量方向是否符合热力学一致性	[43]
整合了热力学的代谢网络分析	基于热力学的代谢通量分析（TMFA）	TMFA同时使用热力学方向性约束和质量守恒约束计算代谢通量分布	[44]
计算机应变优化算法	基于约束的重构与分析（COBRA）	COBRA采用基因组规模的计算机模拟，可用于代谢途径预测和优化，从而改善生产速率和产量	[45]
	最小化代谢调节（MOMA）	MOMA用于使野生型菌株和缺失突变体之间代谢通量分布的差别最小化，能够预测基因操作对代谢网络的影响	[46]
	开/关最小化调节（ROOM）	与MOMA的优化目标相同	[47]
	OptKnock	以产品产率为优化目标的基因敲除分析工具	[48]
	生化网络集成计算浏览器（BNICE）	使用广义酶反应规则发现新代谢途径的计算工具	[49]

类别	分析方法	描述	参考文献
动力学分析	ODE&米氏方程	利用常微分方程（ODE）和米氏方程，能够描述胞内代谢物浓度随时间的变化趋势，从而建立代谢网络的动力学模型	[36]
动力学分析	代谢控制分析（MCA）	MCA可用于估算动力学模型中各个通量的控制系数，从而确定代谢途径中需要过表达的目标基因，增加通过途径的通量	[37]
代谢网络分析	代谢通量分析（MFA）	MFA根据胞内代谢物的质量平衡确定线性方程组并求解，能够计算特定培养条件下细胞内的实际代谢通量分布。MFA需要依赖大量实验数据来增加可测量通量的数量，从而计算出不可测量通量向量	[38]
	通量平衡分析（FBA）	FBA可用于确定细胞代谢网络中每个反应的最佳通量，该方法基于凸分析，通过对系统施加最大化（最小化）的目标函数来确定代谢通量矢量	[39-40]
	代谢途径分析（MPA）	MPA可用于识别代谢网络中存在的所有代谢通量向量，该方法仅以化学计量学和反应热力学为约束条件，不需要动力学参数进行计算（例如基元模式分析）	[41-42]
整合了热力学的代谢网络分析	网络嵌入的热力学分析（NET）	以热力学第二定律为依据，可用于检测代谢组学数据和假定的通量方向是否符合热力学一致性	[43]
整合了热力学的代谢网络分析	基于热力学的代谢通量分析（TMFA）	TMFA同时使用热力学方向性约束和质量守恒约束计算代谢通量分布	[44]
计算机应变优化算法	基于约束的重构与分析（COBRA）	COBRA采用基因组规模的计算机模拟，可用于代谢途径预测和优化，从而改善生产速率和产量	[45]
	最小化代谢调节（MOMA）	MOMA用于使野生型菌株和缺失突变体之间代谢通量分布的差别最小化，能够预测基因操作对代谢网络的影响	[46]
	开/关最小化调节（ROOM）	与MOMA的优化目标相同	[47]
	OptKnock	以产品产率为优化目标的基因敲除分析工具	[48]
	生化网络集成计算浏览器（BNICE）	使用广义酶反应规则发现新代谢途径的计算工具	[49]

模型范围	模型名称	菌株	反应/代谢物	参考文献
核心代谢模型	未报道	大肠杆菌	95/94	[54]
	未报道	谷氨酸棒杆菌	未报道	[55]
	未报道	酿酒酵母	70/83	[56]
	未报道	酿酒酵母	78/98	[57]
	未报道	酿酒酵母	37/27	[58]
全基因组代谢模型（GSMM）	iFF708	酿酒酵母	1145/825 +708基因	[53]
	iND750	酿酒酵母	1149/646 +750基因	[59]
	Yeast 4.0	酿酒酵母	1865/1319 +932基因	[60]
	iJO1366	大肠杆菌	2583/1805 +1366基因	[61]
	未报道	谷氨酸棒杆菌	495/408 +411基因	[62]
	未报道	乳酸菌	621/509 +358基因	[63]
	未报道	枯草芽孢杆菌	754/637 +614基因	[64]
全基因组代谢模型 +基因表达水平	T. maritima	海栖热袍菌	17535/18209	[65]
全基因组代谢模型 +基因表达水平	iOL1650-ME	大肠杆菌	76414/56902	[66]
全基因组代谢模型 +蛋白质结构特性	T. maritima	海栖热袍菌	645/503 +478酶结构	[67]
全基因组代谢模型 +蛋白质结构特性	E. coli GEM-PRO	大肠杆菌	2583/1805 +1268酶结构	[68-69]
全基因组代谢模型 +转录调控	iAF1260 PROM	大肠杆菌	2583/1805 +1773转录调控作用	[70]
全基因组代谢模型 +转录调控	iMH805/837	酿酒酵母	1489/972 +805基因+837转录调控作用	[71]
全细胞模型	‘Whole-cell’ M. genitalium	生殖支原体	28个细胞过程子模块 +525基因	[72]

微生物细胞工厂的设计构建：从诱变育种到全基因组定制化创制

Progress and prospective of engineering microbial cell factories： from random mutagenesis to customized design in genome scale

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产物	宿主	原料	公司	参考文献
琥珀酸	大肠杆菌	玉米糖	BioAmber	[75]
	大肠杆菌克鲁斯假丝酵母	蔗糖	Myriant（现名GC Innovation America）
	酿酒酵母	淀粉、糖类	Reverdia
	巴斯夫产琥珀酸菌	甘油、糖类	Succinity
1,4-丁二醇	大肠杆菌	糖类	Genomatica和DuPont Tate & Lyle	[76-77]
1,3-丙二醇	大肠杆菌	糖类	DuPont Tate & Lyle	[77-78]
聚羟基链烷酸酯（PHA）	大肠杆菌	糖类	Metabolix（现名 Yeild10 science）	[79]
3-羟基丙酸	大肠杆菌	未报道	OPXbio & Dow Chemical	[80]
3-羟基丙酸	未报道	未报道	Perstorp	[80]
乙醇	酿酒酵母运动发酵单胞菌马克斯克鲁维酵母	蔗糖、玉米糖、木质纤维素		[81]
异丁醇	酿酒酵母	糖类	Gevo Butalco Butamax	[82]
法尼烯	酿酒酵母	未报道	Amyris	[74，83]
青蒿素（半合成）	酿酒酵母	未报道	Amyris	[84-85]

分类	名称	描述	参考文献
不可追踪技术	MAGE	基于重组的基因组编辑技术，可使用多个ssDNA同时对多个目标位点进行修饰。与其他基因编辑工具（如CRISPR/Cas9）联用可进一步提高基因编辑效率。主要用于原核基因组编辑	[89]
不可追踪技术	YOGE	原理与MAGE类似，主要用于真核基因组编辑	[105]
可追踪技术	TRMR	基于同源重组的基因组编辑技术，能够同时对基因组上千个基因位点进行修饰	[90]
	CREATE	该技术基于同源重组和CRISPR/Cas9基因编辑技术，能够在全基因组范围内实现可追踪编辑	[106]
	Prime Editor	使用融合了工程逆转录酶的催化活性受损的Cas9和pegRNA，以更高的效率、更低的脱靶率在全基因组范围内实现所有12种单碱基的自由转换以及多碱基的精准插入和删除	[107]
	Target AID	激活诱导的胞嘧啶脱氨酶（AID）可实现C到T的突变，Target AID技术以核酸酶活性缺失的CRISPR/Cas9系统作为AID的DNA靶向模块，实现定点诱变	[108]
	TAM	该系统将AID-P182X与dCas9蛋白融合，可将G或C突变为另外三个碱基，从而在目标位点处产生大量突变	[109]
	CRISPR-X	使用dCas9募集更高活性的AIDΔ和MS2修饰的sgRNA变体，能以较低的脱靶率同时实现多个靶基因的特异性突变	[110]
	EVOLVR	该系统由一个CRISPR引导的切口酶和一个易错DNA聚合酶组成，可在靶位点处可调窗口长度内实现所有核苷酸的突变	[95]
	CRISPRi	使用dCas9蛋白及其sgRNA阻断靶基因转录，实现基因表达水平的下调	[111-112]
	CRISPRa	使用与转录激活因子融合的dCas9蛋白实现靶基因表达水平的上调	[93，113]

技术	通量	功能	案例	参考文献
FACS	约10⁵细胞/s	分选	利用色氨酸传感器，实现紫色杆菌素高产大肠杆菌筛选	[119]
			利用甲羟戊酸细胞传感器，实现甲羟戊酸高产甲基杆菌筛选	[120]
			利用赖氨酸传感器，实现赖氨酸高产谷氨酸棒杆菌筛选	[121]
FADS	约10⁴液滴/s	培养+分选	将胞外重组酶活性与液滴荧光强度耦合，实现酶活测定及胞外重组酶高产菌株筛选	[122]
			将传感器菌与MCF菌株共培养，实现对香豆酸高产酵母的筛选	[123]
			将β-半乳糖苷酶产量与液滴荧光强度耦合，实现β-半乳糖苷酶高产大肠杆菌筛选	[124]
Droplet-FACS		培养+分选	通过检测核黄素的荧光，实现核黄素高产解脂耶氏酵母筛选	[125]
Gel FACS	约3000液滴/s	培养+分选	将胞外木聚糖酶的产量与凝胶液滴的荧光强度耦合，实现胞外木聚糖酶高产毕赤酵母筛选	[126]
RADS	260细胞/min	培养+分选	利用虾青素的拉曼光谱，实现虾青素高产雨生红球藻的筛选	[127]

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