酿酒酵母基因组进化的研究进展

doi:10.12211/2096-8280.2020-044

合成生物学 ›› 2020, Vol. 1 ›› Issue (5): 556-569.DOI: 10.12211/2096-8280.2020-044

酿酒酵母基因组进化的研究进展

夏思杨¹^,²(), 江丽红¹^,², 蔡谨¹, 黄磊¹, 徐志南¹, 连佳长¹^,²

^1.浙江大学化学工程与生物工程学院，生物质化工教育部重点实验室，浙江杭州 310027
^2.浙江大学化学工程与生物工程学院，合成生物学研究中心，浙江杭州 310027

收稿日期:2020-04-08 修回日期:2020-09-28 出版日期:2020-10-31 发布日期:2020-12-03
通讯作者: 蔡谨,连佳长
作者简介:作者简介:夏思杨（1996—），女，硕士研究生。研究方向为基因组进化研究。E-mail：21828174@zju.edu.cn
蔡谨（1960—），男，博士，副教授。研究方向为工业微生物学。E-mail：caij@zju.edu.cn
连佳长（1984—），男，博士，研究员。研究方向为合成生物学。E-mail：jzlian@zju.edu.cn
基金资助:
国家重点研发计划(2018YFA0901800);国家自然科学基金(21808199);浙江省自然科学基金(R20B060006)

Advances in genome evolution of Saccharomyces cerevisiae

XIA Siyang¹^,²(), JIANG Lihong¹^,², CAI Jin¹, HUANG Lei¹, XU Zhinan¹, LIAN Jiazhang¹^,²

^1.Key Laboratory of Biomass Chemical Engineering of Ministry of Education，College of Chemical and Biological Engineering，Zhejiang University，Hangzhou 310027，Zhejiang，China
^2.Center for Synthetic Biology，College of Chemical and Biological Engineering，Zhejiang University，Hangzhou 310027，Zhejiang，China

Received:2020-04-08 Revised:2020-09-28 Online:2020-10-31 Published:2020-12-03
Contact: CAI Jin, LIAN Jiazhang

摘要/Abstract

摘要：

由于细胞代谢和调控网络的复杂性，尤其是对于多基因调控的复杂性状和遗传工具有限的生物系统而言，基因组进化在微生物细胞工厂的构建中起着至关重要的作用。基因组进化通过人为创造多样化性状以及功能筛选的迭代循环，在实验室中模拟且加速自然进化的过程，从而快速获得满足目标需求的进化突变体。酿酒酵母是代谢工程中重要的底盘细胞，全基因组进化是对其进行系统性改造的最有效合成生物学手段之一。本文总结了基因组进化在构建高效的酿酒酵母细胞工厂中的技术进展和应用，包括基因组改组、转座子插入诱变和全局转录机制工程（gTME）等基于随机突变的非理性基因组进化以及诸如酵母寡核苷酸介导的基因组工程（YOGE），真核基因组多重位点自动改造技术（eMAGE）、RNAi辅助的基因组进化方法（RAGE）以及基于CRISPR体系的基因组规模改造技术（CHAnGE、MAGIC和MAGESTIC）等可示踪的半理性基因组进化，并简要介绍了基因组进化面临的挑战和高通量筛选方法的发展前景。

关键词: 微生物细胞工厂, 基因组进化, 随机突变, 可示踪基因组进化, 酿酒酵母

Abstract:

Due to our limited knowledge of the complicated cellular networks, genome evolution has played critical roles in the construction and optimization of microbial cell factories, especially for those complex traits regulated by multi-genes and for organisms with few genetic engineering tools. Directed genome evolution mimics natural evolution in the laboratory via iterative rounds of genetic diversification and functional screening or selection to isolate evolved mutants with the desirable phenotypes. Genome evolution has been found to be one of the most effective synthetic biology tools for systematic modification and optimization of Saccharomyces cerevisiae, one of the most important chassises in metabolic engineering. This review summarized the advances and applications of genome evolution techniques in the construction and optimization of efficient S. cerevisiae cell factories. Firstly, random mutagenesis based genome evolution strategies, including chemical/physical mutagenesis, genome shuffling, transposon mediated mutagenesis, global transcriptional machinery engineering, recombinase mediated mutagenesis, as well as adaptive laboratory evolution, are introduced. Then, the recently developed trackable genome-scale engineering techniques, including YOGE (yeast oligo-mediated genome engineering), eMAGE (eukaryotic multiplex automated genome engineering), RAGE (RNAi-assisted genome evolution), CHAnGE (CRISPR/Cas9- and homology-directed-repair-assisted genome-scale engineering), MAGIC (multi-functional genome-wide CRISPR system), and MAGESTIC (multiplexed accurate genome editing with short, trackable, integrated cellular barcodes), are discussed in details. In addition, the applications of these irrational and semi-rational genome evolution techniques in engineering yeast cell factories to expand substrate utilization, enhance product formation, and improve cellular properties, are also presented. Finally, the challenges and future directions of genome evolution, particularly when in combination with the high-throughput screening methodologies, are prospected.

Key words: microbial cell factory, genome evolution, random mutagenesis, trackable genome-scale engineering, Saccharomyces cerevisiae

中图分类号:

Q 815

夏思杨, 江丽红, 蔡谨, 黄磊, 徐志南, 连佳长. 酿酒酵母基因组进化的研究进展[J]. 合成生物学, 2020, 1(5): 556-569.

XIA Siyang, JIANG Lihong, CAI Jin, HUANG Lei, XU Zhinan, LIAN Jiazhang. Advances in genome evolution of Saccharomyces cerevisiae[J]. Synthetic Biology Journal, 2020, 1(5): 556-569.

图/表 6

图1 酿酒酵母基因组进化的主要技术策略（基因组进化主要包括基于随机突变的基因组进化技术和基于高效基因组编辑技术的可示踪基因组进化技术）gTME—全局转录机制工程；SCRaMbLE—LoxP介导的合成染色体重组和修饰进化系统；YOGE—酵母寡核苷酸介导的基因组工程；eMAGE—真核多重自动化基因组工程；RAGE—RNAi辅助基因组进化；CHAnGE—CRISPR/Cas9和同源定向修复辅助的全基因组进化；MAGIC—多功能全基因组CRISPR系统; MAGESTIC—基于短、可示踪、整合的细胞条形码的多重精准基因编辑技术； NGS—二代测序

Fig. 1 Major technologies for genome evolution in Saccharomyces cerevisiae, including random genome evolution and trackable genome evolutiongTME—global transcription machinery engineering; SCRaMbLE—synthetic chromosome recombination and modification by LoxP-mediated evolution; YOGE—yeast oligo-mediated genome engineering; eMAGE—eukaryotic multiplex automated genome engineering; RAGE—RNAi-assisted genome evolution; CHAnGE—CRISPR/Cas9- and homology-directed-repair-assisted genome-scale engineering; MAGIC—multi-functional genome-wide CRISPR system; MAGESTIC—multiplexed accurate genome editing with short, trackable, integrated cellular barcodes; NGS—next-generation sequencing

表1 酿酒酵母基因组进化策略

Tab. 1 Genome evolution strategies for S. cerevisiae

技术名称	进化策略	技术特征	参考文献
gTME	对转录复合体中的关键转录元件定向进化	靶向反式作用因子，无需修饰靶基因座，在转录水平上产生全基因组规模的多样性；不易阐述其分子机制	[14]
实验室适应性进化	环境压力	不需要考虑错综复杂的代谢网络，只需根据目标设计相应的选择压力，适用广泛；多重突变的存在使得分析进化表型的分子机制难度较大	[19,20,21,22,23,24,25,26]
逆向代谢工程	基于全基因组突变分析构建酵母细胞工厂	全面、系统地确定优良突变菌株的基因型-表型关系，可去除不良突变来最大程度地减少进化弊端；瓶颈在于如何在大量随机突变中确定有益突变	[26,27,28]
YOGE	合成单链DNA库	酿酒酵母中首次由单链寡核苷酸介导重组的基因组工程；编辑效率低	[29]
eMAGE	合成单链DNA库	比YOGE更加精确和高效的单链寡核苷酸整合技术；靶序列需要紧密接近复制起点以及URA3标记的共同选择	[30]
RAGE	合成全基因组基因的反向全长cDNA库	全长cDNA的表达可实现基因过表达，全长反义RNA的转录（RNAi）可实现基因下调；cDNA文库突变率有待进一步提高	[31,32]
CHAnGE	基于全基因组构建 gRNA和同源模板库	基于CRISPR/Cas9的同源修复实现全基因组进化，可利用独特的条形码实现可追踪的编辑；只有基因敲除单一功能调控	[33]
MAGIC	基于全基因组构建抑制、激活和敲除的gRNA库	基于CRISPR/Cas9构建了最全面最多样化的酵母基因组文库，gRNA作为独特的基因条形码，可通过二代测序进行追踪；基因激活效率有待进一步提升	[34]
MAGESTIC	基于目标序列构建 gRNA和同源模板库	通过基因组条形码整合进行追踪，可对上百万个细胞进行高通量基因编辑；其在基因组进化的应用有待进一步验证	[35]

表1 酿酒酵母基因组进化策略

Tab. 1 Genome evolution strategies for S. cerevisiae

技术名称	进化策略	技术特征	参考文献
gTME	对转录复合体中的关键转录元件定向进化	靶向反式作用因子，无需修饰靶基因座，在转录水平上产生全基因组规模的多样性；不易阐述其分子机制	[14]
实验室适应性进化	环境压力	不需要考虑错综复杂的代谢网络，只需根据目标设计相应的选择压力，适用广泛；多重突变的存在使得分析进化表型的分子机制难度较大	[19,20,21,22,23,24,25,26]
逆向代谢工程	基于全基因组突变分析构建酵母细胞工厂	全面、系统地确定优良突变菌株的基因型-表型关系，可去除不良突变来最大程度地减少进化弊端；瓶颈在于如何在大量随机突变中确定有益突变	[26,27,28]
YOGE	合成单链DNA库	酿酒酵母中首次由单链寡核苷酸介导重组的基因组工程；编辑效率低	[29]
eMAGE	合成单链DNA库	比YOGE更加精确和高效的单链寡核苷酸整合技术；靶序列需要紧密接近复制起点以及URA3标记的共同选择	[30]
RAGE	合成全基因组基因的反向全长cDNA库	全长cDNA的表达可实现基因过表达，全长反义RNA的转录（RNAi）可实现基因下调；cDNA文库突变率有待进一步提高	[31,32]
CHAnGE	基于全基因组构建 gRNA和同源模板库	基于CRISPR/Cas9的同源修复实现全基因组进化，可利用独特的条形码实现可追踪的编辑；只有基因敲除单一功能调控	[33]
MAGIC	基于全基因组构建抑制、激活和敲除的gRNA库	基于CRISPR/Cas9构建了最全面最多样化的酵母基因组文库，gRNA作为独特的基因条形码，可通过二代测序进行追踪；基因激活效率有待进一步提升	[34]
MAGESTIC	基于目标序列构建 gRNA和同源模板库	通过基因组条形码整合进行追踪，可对上百万个细胞进行高通量基因编辑；其在基因组进化的应用有待进一步验证	[35]

图2 基于RNAi的酿酒酵母自动化多重基因组进化技术[31-32](a)酵母RNAi工作原理,核酸内切酶Dicer切割双链RNA(dsRNA)生成短干扰RNAs (siRNA),siRNA与效应蛋白Argonaute形成RNA诱导的沉默复合体RISC，siRNA反义链与靶标mRNA结合，指导Dicer干扰靶标基因的转录；(b)基于RNAi机制构建全基因组规模调控文库,在组成型启动子下定向克隆全长的cDNA文库，有义和反义构型分别引起目标基因的过表达或敲降；(c)基于RNAi的多重基因组突变方法,编码各种遗传修饰的基因调节部分的侧翼是同源δ序列，用于迭代和多重整合到重复的基因组序列。为了使CRISPR/Cas能进行高效且无需选择的δ整合，将Cas9表达盒整合到含有RNAi机制的酵母菌株中

Fig. 2 Scheme of automated RNAi-assisted genome evolution in yeast(a) RNAi mechanism in yeast. The double-stranded RNA (dsRNA) is digested by the endonuclease Dicer into short interference RNA (siRNA), which binds to the effector protein Argonaute to form the RNA-induced silencing complex (RISC). The non-sense strand of siRNA binds to the target mRNA, leading to the degradation and interference of the transcription of the target gene. (b) Construction of a genome-wide modulation part library in the yeast strain with the reconstituted RNAi machinery. Full-length cDNA library was directionally cloned under the control of a constitutive promoter. The sense and anti-sense configurations resulted in genetic overexpression and knockdown, respectively. (c) RNAi-assisted multiplex genomic mutations in yeast. Gene modulation parts were flanked by homologous arms for iterative and multiplex δ integration into the repetitive genomic sequences. To enable efficient and selection-free δ integration, a Cas9 expression cassette was integrated into the RNAi harboring yeast strain

图3 MAGIC用于全基因组基因型-表型关系研究[34, 42](a)使用三种正交的CRISPR蛋白开发CRISPR-AID体系，其中CRISPRa由核酸酶缺陷的CRISPR蛋白与激活域融合（dLbCpf1-VP）实现转录激活，CRISPRi由核酸酶缺陷的突变体与抑制域融合（dSpCas9-RD1152）实现转录抑制，CRISPRd由具有催化活性的CRISPR蛋白（SaCas9）实现基因敲除；(b)通过DNA芯片寡核苷酸阵列的形式合成用于基因组规模激活（橙色）、干扰（浅蓝色）和缺失（品红色）的gRNA文库，并克隆到相应的gRNA表达质粒中。通过将质粒文库转入CRISPR-AID整合的酵母菌株中构建MAGIC文库，通过高通量筛选或者生长富集，并结合二代测序分析富集的gRNA序列。MAGIC可用于更好地理解、设计和改造复杂表型

Fig. 3 MAGIC for genome-wide mapping genotype-phenotype relationships[34,42](a) Development of CRISPR-AID using three orthogonal CRISPR proteins, a nuclease-deficient CRISPR protein fused with an activation domain (dLbCpf1-VP) for CRISPRa, a nuclease-deficient mutant fused with a repression domain (dSpCas9-RD1152) for CRISPRi, and a catalytically active CRISPR protein (SaCas9) for CRISPRd. (b) Guide sequences for genome-wide activation (orange), interference (light blue), and deletion (magenta) were synthesized as arrayed oligos on DNA chip and cloned into the corresponding gRNA expression plasmids. The transformation of the pooled plasmid libraries into the CRISPR-AID integrated yeast strain resulted in the construction of the MAGIC library. The MAGIC library was subject to growth enrichment or high throughput screening, and the corresponding enrichment or depletion of guide sequences were profiled using next-generation sequencing. MAGIC can be employed to better understand and engineer complex phenotypes

图4 同时利用木质纤维素碳源，如纤维二糖、木糖和乙酸的酵母菌株的构建XR—木糖还原酶；XDH—木糖醇脱氢酶；ACS—乙酰辅酶A合成酶；AADH—乙酰化乙醛脱氢酶；ADH—醇脱氢酶

Fig. 4 Construction of a recombinant yeast strain for simultaneous utilization of lignocellulosic carbons, such as cellobiose, xylose, and acetateXR—xylose reductase; XDH—xylitol dehydrogenase; ACS—acetyl-CoA synthetase; AADH—acetylating acetaldehyde dehydrogenase; ADH—alcohol dehydrogenase

图5 高效生产FFA酵母菌株的构建[48](野生型酿酒酵母中，中心碳代谢是“葡萄糖转化为乙醇”；高产脂肪酸酿酒酵母中，为了实现“葡萄糖转化为油”的代谢模型，建立了FFA生产的有效途径；产油酿酒酵母中，通过适应性进化，酒精发酵成功地重新编程为纯脂肪酸合成)PPP—磷酸戊糖途径；TCA—三羧酸循环；FFA—游离脂肪酸

Fig. 5 Construction of yeast cell factories for an efficient production of FFA[48](In wild-type yeast, the major carbon metabolism is to drive ethanol fermentation from glucose. In the fatty acid-overproducing yeast, metabolic engineering strategies were employed to establish efficient biosynthetic pathway from glucose to fatty acid. In the lipogenesis yeast, genome evolution was performed to completely reprogram the cellular metabolism from alcohol fermentation to fatty acid biosynthesis)PPP—pentose phosphate pathway; TCA—tricarboxylic acid; FFA—free fatty acid

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酿酒酵母基因组进化的研究进展

Advances in genome evolution of Saccharomyces cerevisiae

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