Advances in yeast based adaptive laboratory evolution

doi:10.12211/2096-8280.2020-077

Abstract

Abstract:

Saccharomyces cerevisiae, as an eukaryotic model organism and microbial cell factory, has been widely used in metabolic engineering, system biology and synthetic biology. However, due to the limited knowledge of the complex cellular metabolism and inherent regulatory networks, it is difficult to obtain desired phenotypes through rational engineering. Among reported engineering strategies, evolutional engineering plays an important role in the construction of robust microbial cell factories, particularly when optimizing the whole metabolic or genome-scale network. Adaptive laboratory evolution mimics natural evolution in the laboratory via iterative cycles of culture and selection to isolate desirable phenotypes, such as tolerance of high salt concentration, low pH, high temperature condition as well as toxicities from substrate at excess and product accumulated to high titers. With recent advances in genome editing, synthetic biology and systems biology, development in evolution engineering has made great progress, including the computer-aided system and automatic continuous evolution technology. This review focuses on recent technological advances in evolution engineering tools for S. cerevisiae. Firstly, recently developed genome evolution strategies are discussed, including the recombinase-based genome-scale engineering system SCRaMbLE (synthetic chromosome rearrangement and modification by loxP-mediated evolution) and SCRaMbLE-in, oligo-mediated YOGE (yeast oligo-mediated genome engineering), oligo-based genome-scale engineering eMAGE (eukaryotic multiplex automated genome engineering), CRISPR mediated genome-scale engineering systems CHAnGE (CRISPR/Cas9-and homology-directed-repair-assisted genome-scale engineering), and MAGESTIC (multiplexed accurate genome editing with short, trackable, integrated cellular barcodes), Target-AID (target-activation induced cytidine deaminase). Then, yeast-based automated continuous evolution, such as OrthoRep (orthogonal error-prone replication), ICE (in vivo continuous evolution), eVOLVER (a scalable and automated continuous culture device), and ACE (automated continuous evolution, pairs OrthoRep with eVOLVER), are further addressed. These rapid editing strategies at the genome level can generate genetically diverse cell populations to identify key factors and synergies in a short period of time. Finally, we prospect future challenges and opportunities of evolution engineering approaches in advancing yeast-based microbial cell factories. Strategies learned from yeast will also guide the development of other microbial cell factories.

Key words: Saccharomyces cerevisiae, evolutionary engineering, multiplex engineering, automated continuous evolution, genome engineering

摘要：

酿酒酵母作为重要的模式生物和微生物细胞工厂已广泛用于代谢工程、系统生物学和合成生物学研究。由于微生物系统代谢和调控网络的复杂性，通过人工理性设计及基因扰动难以获得鲁棒性较好的表型，因此进化工程在构建稳定的微生物细胞工厂中发挥着重要作用。微生物适应性实验室进化工程通过模拟自然进化过程，构建大量遗传多样性的突变库，经过筛选可快速获得稳定性状的菌株。随着合成生物学和系统生物学的发展，研究人员开发了多种基因组规模的多位点快速进化工具，并结合计算机辅助进化系统实现了自动化连续进化技术。本综述着重介绍在酿酒酵母中基因组规模的多位点快速进化，包括合成型酿酒酵母基因组重排（SCRaMbLE）、寡核苷酸介导的多位点进化工具（YOGE和eMAGE）和基于CRISPR系统的多位点编辑（CHAnGE、MAGESTIC、Target-AID和yEvolvR）等基因组规模的进化策略的研究；以及自动化连续进化技术（eVOLVER、ICE和ACE等）。利用合成生物学方法通过在基因组水平进行多位点快速编辑或连续进化以产生具有遗传多样性的细胞群体，并在特定的筛选条件下对目标突变株进行富集，进而解析其基因型表型关系，可实现在短期内快速高效识别适应性进化的关键因素和协同作用。最后展望了实验室适应性进化工程与计算机辅助设计、自动化技术有机结合的机遇和在高通量筛选方向的挑战。

关键词: 酿酒酵母, 进化工程, 多位点编辑, 自动化连续进化, 基因组工程

CLC Number:

Yi LI, Zhenquan LIN, Zihe LIU. Advances in yeast based adaptive laboratory evolution[J]. Synthetic Biology Journal, 2021, 2(2): 287-301.

李祎, 林振泉, 刘子鹤. 酿酒酵母适应性实验室进化工具的最新进展[J]. 合成生物学, 2021, 2(2): 287-301.

Figures/Tables 7

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分类	进化策略	进化原理	优点	缺点	应用
重组酶介导的进化工程	SCRaMbLE	通过基因组重构，加入多个loxPsym位点，并利用Cre重组酶特异性识别loxPsym位点并介导位点之间重组	可同时实现缺失、倒置、重复和异位等重组事件	需提前构建具有多个loxPsym位点的基因组序列	提高紫堇素和青霉素的生物合成^[40]
重组酶介导的进化工程	SCRaMbLE-in	通过基因组重构，加入多个loxPsym位点，并利用Cre重组酶特异性识别loxPsym位点并介导位点之间重组	快速优化宿主以有效提高异源途径的表达；是SCRaMbLE技术的继承和发展	需提前构建具有多个loxPsym位点的基因组序列	提高β-胡萝卜素的产量^[41]
基于寡核苷酸的多位点进化工具	eMAGE	突变的ssODN与复制叉的后随链结合并整合到基因组上引入突变点	较高的等位基因替换效率（40%）	突变效率随单链DNA寡核苷酸突变序列的增加而降低	提高类胡萝卜素的产量^[42]
CRISPR 系统介导的多位点编辑	CHAnGE	通过gRNA介导的同源修复促进了基因组编辑的精确性和有效性	通过对gRNA的测序进行突变位点的靶向定位	依赖于gRNA和供体DNA的协同，突变效率随着片段长度的增加而降低	提高酿酒酵母对糠醛和乙酸的耐受性^[43]
	Targeted-AID	将胞嘧啶脱氨酶与CRISPR/Cas9系统融合，实现基因组DNA上胞嘧啶的靶向编辑	可在目标位点进行精确的碱基编辑	突变率较低	对ADE1和CAN1基因进行同时编辑突变率达到31%^[44]
	yEvolvR	由nCas9-gRNA引导的在目标基因座上形成缺口，PolⅠ-5M以低保真度合成新链并切割置换的链	在切割位点上游20 bp到下游40 bp的区域内进行编辑；靶标突变率提高12 434倍	酿酒酵母整体突变率较低	提高内源基因的多样性，即还原URA3中无义突变的同时使CAN1*失活^[45]

分类	进化策略	进化原理	优点	缺点	应用
重组酶介导的进化工程	SCRaMbLE	通过基因组重构，加入多个loxPsym位点，并利用Cre重组酶特异性识别loxPsym位点并介导位点之间重组	可同时实现缺失、倒置、重复和异位等重组事件	需提前构建具有多个loxPsym位点的基因组序列	提高紫堇素和青霉素的生物合成^[40]
重组酶介导的进化工程	SCRaMbLE-in	通过基因组重构，加入多个loxPsym位点，并利用Cre重组酶特异性识别loxPsym位点并介导位点之间重组	快速优化宿主以有效提高异源途径的表达；是SCRaMbLE技术的继承和发展	需提前构建具有多个loxPsym位点的基因组序列	提高β-胡萝卜素的产量^[41]
基于寡核苷酸的多位点进化工具	eMAGE	突变的ssODN与复制叉的后随链结合并整合到基因组上引入突变点	较高的等位基因替换效率（40%）	突变效率随单链DNA寡核苷酸突变序列的增加而降低	提高类胡萝卜素的产量^[42]
CRISPR 系统介导的多位点编辑	CHAnGE	通过gRNA介导的同源修复促进了基因组编辑的精确性和有效性	通过对gRNA的测序进行突变位点的靶向定位	依赖于gRNA和供体DNA的协同，突变效率随着片段长度的增加而降低	提高酿酒酵母对糠醛和乙酸的耐受性^[43]
	Targeted-AID	将胞嘧啶脱氨酶与CRISPR/Cas9系统融合，实现基因组DNA上胞嘧啶的靶向编辑	可在目标位点进行精确的碱基编辑	突变率较低	对ADE1和CAN1基因进行同时编辑突变率达到31%^[44]
	yEvolvR	由nCas9-gRNA引导的在目标基因座上形成缺口，PolⅠ-5M以低保真度合成新链并切割置换的链	在切割位点上游20 bp到下游40 bp的区域内进行编辑；靶标突变率提高12 434倍	酿酒酵母整体突变率较低	提高内源基因的多样性，即还原URA3中无义突变的同时使CAN1*失活^[45]

进化策略	进化原理	优点	缺点	应用
OrthoRep	利用游离质粒上的复制机制和宿主DNA复制的正交性，提高游离载体DNA聚合酶的突变率	可在不改变宿主基因组突变率的情况下，对正交质粒上的目标基因进行突变	需要特殊设计正交DNA复制系统并将其整合到酵母	提高酿酒酵母对乙胺嘧啶的耐受性^[76-77]
ICE	高突变率的Ty1逆转录酶将目标基因的mRNA逆转录为cDNA，Ty1整合酶再将其整合至Ty1识别位点以进行下一轮突变	ICE技术可以扩展到含有LTR逆转录转座子的真核生物中	突变效率受到同源重组的限制	提高酿酒酵母对1-丁醇的耐受性^[78]
eVOLVER	通过实时控制反应器中各种培养参数，从而获得种群的进化参数图	对培养程序的自主编程，培养参数的实时监控	复杂的系统组装和应用计算机语言设计培养程序	构建了在温度和盐度二维应力梯度下酿酒酵母遗传变异的适应性图^[79]
ACE	将OrthoRep和eVOLVER系统相结合	进化速度快、可伸缩性强	效率受体内OrthoRep系统的突变率和体外eVOLVER系统复杂组装的双重影响	提高酿酒酵母对3 mol/L乙胺嘧啶的耐受性^[80]

进化策略	进化原理	优点	缺点	应用
OrthoRep	利用游离质粒上的复制机制和宿主DNA复制的正交性，提高游离载体DNA聚合酶的突变率	可在不改变宿主基因组突变率的情况下，对正交质粒上的目标基因进行突变	需要特殊设计正交DNA复制系统并将其整合到酵母	提高酿酒酵母对乙胺嘧啶的耐受性^[76-77]
ICE	高突变率的Ty1逆转录酶将目标基因的mRNA逆转录为cDNA，Ty1整合酶再将其整合至Ty1识别位点以进行下一轮突变	ICE技术可以扩展到含有LTR逆转录转座子的真核生物中	突变效率受到同源重组的限制	提高酿酒酵母对1-丁醇的耐受性^[78]
eVOLVER	通过实时控制反应器中各种培养参数，从而获得种群的进化参数图	对培养程序的自主编程，培养参数的实时监控	复杂的系统组装和应用计算机语言设计培养程序	构建了在温度和盐度二维应力梯度下酿酒酵母遗传变异的适应性图^[79]
ACE	将OrthoRep和eVOLVER系统相结合	进化速度快、可伸缩性强	效率受体内OrthoRep系统的突变率和体外eVOLVER系统复杂组装的双重影响	提高酿酒酵母对3 mol/L乙胺嘧啶的耐受性^[80]

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