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; 邢新会; 张翀

doi:10.12211/2096-8208.2020-050

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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

特约评述 | 更新时间：2025-12-24

- 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
- 合成生物学 2020年1卷第6期页码：656-673
- 作者机构：
  
  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
- 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" ]
- 基金信息：
  
  国家自然科学基金重点项目(2193000018);国家重点研发计划(2019YFA0904802)
- DOI：10.12211/2096-8208.2020-050
  中图分类号： Q81
- 收稿：2020-04-16，
  
  修回：2020-09-26，
  
  纸质出版：2020-12-31
- 稿件说明：
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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
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 DOI： 10.12211/2096-8208.2020-050.

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 DOI： 10.12211/2096-8208.2020-050.

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。

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.

关键词

Keywords

references

NIELSEN J . Cell factory engineering for improved production of natural products [J ] . Natural Product Reports , 2019 , 36 ( 9 ): 1233 - 1236 .

SANO C . History of glutamate production [J ] . The American Journal of Clinical Nutrition , 2009 , 90 ( 3 ), 728S - 732S .

OTERO J M , NIELSEN J . Industrial systems biology [J ] . Biotechnology and Bioengineering , 2010 , 105 ( 3 ), 439 - 460 .

Presidential green chemistry challenge awards [E B/OL ] . https://www.epa.gov/greenchemistry/green-chemistry-challenge-winners https://www.epa.gov/greenchemistry/green-chemistry-challenge-winners .

ATSUMI S , HIGASHIDE W , LIAO J C . Direct photosynthetic recycling of carbon dioxide to isobutyraldehyde [J ] . Nature Biotechnology , 2009 , 27 ( 12 ), 1177 - 1180 .

GALANIE S , THODEY K , TRENCHARD I J , et al . Complete biosynthesis of opioids in yeast [J ] . Science , 2015 , 349 ( 6252 ): 1095 - 1100 .

PARK J H , LEE K H , KIM T Y , et al . Metabolic engineering of Escherichia coli for the production of L-valine based on transcriptome analysis and in silico gene knockout simulation [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2007 , 104 ( 19 ): 7797 - 7802 .

XIA X X , QIAN Z G , KI C S , et al . Native-sized recombinant spider silk protein produced in metabolically engineered Escherichia coli results in a strong fiber [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2010 , 107 ( 32 ): 14059 - 14063 .

STEEN E J , KANG Y , BOKINSKY G , et al . Microbial production of fatty-acid-derived fuels and chemicals from plant biomass [J ] . Nature , 2010 , 463 ( 7280 ): 559 - 562 .

LEE G , NA J . Future of microbial polyesters [J ] . Microbial Cell Factories , 2013 , 12 ( 1 ): 54 .

BROWN S W , OLIVER S G . Isolation of ethanol-tolerant mutants of yeast by continuous selection [J ] . European Journal of Applied Microbiology and Biotechnology , 1982 , 16 ( 2/3 ): 119 - 122 .

MEADOWS L , HAWKINS K M , TSEGAYE Y , et al . Rewriting yeast central carbon metabolism for industrial isoprenoid production [J ] . Nature , 2016 , 537 ( 7622 ): 694 - 697 .

WEIKERT C , SAUER U , BAILEY J . Use of a glycerol-limited, long-term chemostat for isolation of Escherichia coli mutants with improved physiological properties [J ] . Microbiology , 1997 , 143 ( 5 ): 1567 - 1574 .

SILMAN N J , CARVER M A , JONES C W . Physiology of amidase production by methylophilus methylotrophus: isolation of hyperactive strains using continuous culture [J ] . Microbiology , 1989 , 135 ( 11 ): 3153 - 3164 .

CORNISH A , GREENWOOD J A , JONES C W . Binding-protein-dependent sugar transport by Agrobacterium radiobacter and A. tumefaciens grown in continuous culture [J ] . Microbiology , 1989 , 135 ( 11 ): 3001 - 3013 .

BAILEY J . Toward a science of metabolic engineering [J ] . Science , 1991 , 252 ( 5013 ): 1668 - 1675 .

STEPHANOPOULOS G , VALLINO J . Network rigidity and metabolic engineering in metabolite overproduction [J ] . Science , 1991 , 252 ( 5013 ): 1675 - 1681 .

STEPHANOPOULOS G , SINSKEY A J . Metabolic engineering-methodologies and future prospects [J ] . Trends in Biotechnology , 1993 , 11 ( 9 ): 392 - 396 .

FURUSAWA C , HORINOUCHI T , HIRSAWA T , et al . Systems metabolic engineering: the creation of microbial cell factories by rational metabolic design and evolution [J ] . Advances in Biochemical Engineering/Biotechnology , 2012 , 131 ( 3 ): 1 - 23 .

NIELSEN J . Production of bio-pharmaceutical proteins by yeast: advances through metabolic engineering [J ] . Bioengineered , 2012 , 4 ( 4 ): 207 - 211 .

HONG K K , NIELSEN J . Metabolic engineering of Saccharomyces cerevisiae : a key cell factory platform for future biorefineries [J ] . Cellular and Molecular Life Sciences , 2012 , 69 ( 16 ), 2671 - 2690 .

CHEN Z , RAPPERT S , SUN J , et al . Integrating molecular dynamics and co-evolutionary analysis for reliable target prediction and deregulation of the allosteric inhibition of aspartokinase for amino acid production [J ] . Journal of Biotechnology , 2011 , 154 ( 4 ): 248 - 254 .

CHO B K , FEDEROWICZ S , PARK Y S , et al . Deciphering the transcriptional regulatory logic of amino acid metabolism [J ] . Nature Chemical Biology , 2011 , 8 ( 1 ): 65 - 71 .

JIN Y S , STEPHANOPOULOS G . Multi-dimensional gene target search for improving lycopene biosynthesis in Escherichia coli [J ] . Metabolic Engineering , 2007 , 9 ( 4 ): 337 - 347 .

ÖZAYDIN B , BURD H , LEE T S , et al . Carotenoid-based phenotypic screen of the yeast deletion collection reveals new genes with roles in isoprenoid production [J ] . Metabolic Engineering , 2013 , 15 : 174 - 183 .

ZHANG Y X , PERRY K , VINCI V A , et al . Genome shuffling leads to rapid phenotypic improvement in bacteria [J ] . Nature , 2002 , 415 ( 6872 ), 644 - 646 .

ZHANG X , ZHANG X F , LI H P , et al . Atmospheric and room temperature plasma (ARTP) as a new powerful mutagenesis tool [J ] . Applied Microbiology and Biotechnology , 2014 , 98 ( 12 ): 5387 - 5396 .

NICOLAOU S A , GAIDA S M , PAPOUTSAKIS E T . Coexisting/Coexpressing genomic libraries (CoGeL) identify interactions among distantly located genetic loci for developing complex microbial phenotypes [J ] . Nucleic Acids Research , 2011 , 39 ( 22 ): e152 .

WANG T , CHANG G G , JIA H G , et al . Pooled CRISPR interference screening enables genome-scale functional genomics study in bacteria with superior performance [J ] . Nature Communications , 2018 , 9 ( 1 ): 2475 .

李翔 . 微生物诱变育种技术 [J ] . 现代商贸工业 , 2017 ( 34 ): 185 - 187 .

LI X . Microbial mutation breeding technology [J ] . Modern Business Trade Industry , 2017 ( 34 ): 185 - 187 .

DEMAIN A L . From natural products discovery to commercialization: a success story [J ] . Journal of Industrial Microbiology & Biotechnology , 2006 , 33 ( 7 ), 486 - 495 .

季宇彬 , 朱兴杰 , 徐昶儒 , 等 . 微生物诱变育种方法的研究进展 [C ] // 中国毒理学会环境与生态毒理学专业委员会成立大会. 2008 .

JI Y B , ZHU X J , XU C R , et al . Research progress of microbial mutation breeding methods [C ] // The Founding Conference of the Professional Committee of Environmental and Ecotoxicology of the Chinese Society of Toxicology . 2008 .

微生物诱变育种编写组 . 微生物诱变育种 [M ] . 北京 : 科学出版社 , 1973 : 25 - 32 .

Microbiology Mutation Breeding Compilation Group . Microbial mutation breeding [M ] . Beijing : Science Press , 1973 : 25 - 32 .

NESS J E , WELCH M , GIVER L , et al . DNA shuffling of subgenomic sequences of subtilisin [J ] . Nature Biotechnology , 1999 , 17 ( 9 ): 893 - 896 .

施巧琴 , 吴松钢 . 工业微生物育种 [M ] . 福州 : 福建科学技术出版社 , 1991 : 54 - 55 .

SHI Q Q , WU S G . Breeding of industrial microbes [M ] . Fuzhou : Fujian Science and Technology Publishing House , 1991 : 54 - 55 .

CVIJOVIC M , BORDEL S , NIELSEN J . Mathematical models of cell factories: moving towards the core of industrial biotechnology [J ] . Microbial Biotechnology , 2010 , 4 ( 5 ): 572 - 584 .

FELL D A . Metabolic control analysis: a survey of its theoretical and experimental development [J ] . Biochemical Journal , 1992 , 286 ( 2 ): 313 - 330 .

WIECHERT W . 13 C metabolic flux analysis [J ] . Metabolic Engineering , 2001 , 3 ( 3 ): 195 - 206 .

STEPHANOPOULOS G . Metabolic fluxes and metabolic engineering [J ] . Metabolic Engineering , 1999 , 1 ( 1 ): 1 - 11 .

VARMA A , PALSSON B O . Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110 [J ] . Applied and Environmental Microbiology , 1994 , 60 ( 10 ): 3724 - 3731 .

PFEIFFER T , SANCHEZ-VALDENEBRO I , NUNO J , et al . METATOOL: for studying metabolic networks [J ] . Bioinformatics , 1999 , 15 ( 3 ): 251 - 257 .

TRINH C T , WLASCHIN A , SRIENC F . Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism [J ] . Applied Microbiology and Biotechnology , 2009 , 81 ( 5 ): 813 - 826 .

ZAMBONI N , KÜMMEL A , HEINEMANN M . anNET: a tool for network-embedded thermodynamic analysis of quantitative metabolome data [J ] . BMC Bioinformatics , 2008 , 9 ( 1 ): 199 .

HENRY C S , BROADBELT L J , HATZIMANIKATIS V . Thermodynamics-based metabolic flux analysis [J ] . Biophysical Journal , 2007 , 92 ( 5 ), 1792 - 1805 .

BECKER S A , FEIST A M , MO M L , et al . Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox [J ] . Nature Protocols , 2007 , 2 ( 3 ): 727 - 738 .

SEGRE D , VITKUP D , CHURCH G M . Analysis of optimality in natural and perturbed metabolic networks [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2002 , 99 ( 23 ): 15112 - 15117 .

SHLOMI T , BERKMAN O , RUPPIN E . Regulatory on/off minimization of metabolic flux changes after genetic perturbations [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2005 , 102 ( 21 ): 7695 - 7700 .

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 .

HATZIMANIKATIS V , LI C IONITA J A , et al . Exploring the diversity of complex metabolic networks [J ] . Bioinformatics , 2004 , 21 ( 8 ): 1603 - 1609 .

EDWARDS J S , PALSSON B O . The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2000 , 97 ( 10 ): 5528 - 5533 .

COVERT M W , SCHILLING C H , FAMILI I , et al . Metabolic modeling of microbial strains in silico [J ] . Trends in Biochemical Sciences , 2001 , 26 ( 3 ): 179 - 186 .

ÖSTERLUND T , NOOKAEW I , NIELSEN J . Fifteen years of large scale metabolic modeling of yeast: developments and impacts [J ] . Biotechnology Advances , 2012 , 30 ( 5 ): 979 - 988 .

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 .

VARMA A , BOESCH B W , PALSSON B O . Biochemical production capabilities of Escherichia coli [J ] . Biotechnology and Bioengineering , 1993 , 42 ( 1 ): 59 - 73 .

VALLINO J J , STEPHANOPOULOS G . Carbon flux distributions at the glucose 6-phosphate branch point in Corynebacterium glutamicum during lysine overproduction [J ] . Biotechnology Progress , 1994 , 10 ( 3 ): 327 - 334 .

VAN GULIK W M , HEIJNEN J J . A metabolic network stoichiometry analysis of microbial growth and product formation [J ] . Biotechnology and Bioengineering , 1995 , 48 ( 6 ): 681 - 698 .

VANROLLEGHEM P A , DE JONG-GUBBELS P , VAN GULIK W M , et al . Validation of a metabolic network for Saccharomyces cerevisiae using mixed substrate studies [J ] . Biotechnology Progress , 1996 , 12 ( 4 ): 434 - 448 .

NISSEN T L , SCHULZE U , NIELSEN J , et al . Flux distributions in anaerobic, glucose-limited continuous cultures of Saccharomyces cerevisiae [J ] . Microbiology , 1997 , 143 ( 1 ): 203 - 218 .

DUARTE N C . Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model [J ] . Genome Research , 2004 , 14 ( 7 ): 1298 - 1309 .

DOBSON P D , SMALLBONE K , JAMESON D , et al . Further developments towards a genome-scale metabolic model of yeast [J ] . BMC Systems Biology , 2010 , 4 ( 1 ): 145 .

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 .

KJELDSEN K R , NIELSEN J . In silico genome-scale reconstruction and validation of the Corynebacterium glutamicum metabolic network [J ] . Biotechnology and Bioengineering , 2009 , 102 ( 2 ): 583 - 597 .

OLIVEIRA A , NIELSEN J , FORSTER J . Modeling Lactococcus lactis using a genome-scale flux model [J ] . BMC Microbiology , 2005 , 5 ( 1 ): 39 .

PARK S , SCHILLING C , PALSSON B O : Compositions and methods for modeling Bacillus subtilis metabolism : US 20030224363 [P ] . 2003-12-04 .

LERMAN J A , HYDUKE D R , LATIF H , et al . In silico method for modelling metabolism and gene product expression at genome scale [J ] . Nature Communications , 2012 , 3 ( 1 ): 929 .

LIU J K , O'BRIEN E J , LERMAN J A , et al . Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scale [J ] . BMC Systems Biology , 2014 , 8 : 110 .

ZHANG Y , THIELE I , WEEKES D , et al . Thre e-dimensional structural view of the central metabolic network of Thermotoga maritima [J ] . Science , 2009 , 325 ( 5947 ): 1544 - 1549 .

CHANG R L , ANDREWS K , KIM D , et al . Structural systems biology evaluation of metabolic thermotolerance in Escherichia coli [J ] . Science , 2013 , 340 ( 6137 ): 1220 - 1223 .

CHANG R L , XIE L , BOURNE P E , et al . Antibacterial mechanisms identified through structural systems pharmacology [J ] . BMC Systems Biology , 2013 , 7 ( 1 ): 102 .

CHANDRASEKARAN S , PRICE N D . Probabilistic integrative mod eling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis [J ] . Proceedings of the National Academy Sciences of the United States of America , 2010 , 107 ( 41 ): 17845 - 17850 .

HERRGARD M J . Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae [J ] . Genome Research , 2006 , 16 ( 5 ): 627 - 635 .

KARR J R , SANGHVI J C , MACKLIN D N , et al . A whole-cell computational model predicts phenotype from genotype [J ] . Cell , 2012 , 150 ( 2 ): 389 - 401 .

BECKER J , ZELDER O , STEFAN HäFNER , et al . From zero to hero-design-based systems metabolic engineering of Corynebacterium glutamicum for L-lysine production [J ] . Metabolic Engineering , 2011 , 13 ( 2 ): 159 - 168 .

CHOI K R , JANG W D , YANG D , et al . Systems metabolic engineering strategies: integrating systems and synthetic biology with metabolic engineering [J ] . Trends in Biotechnology , 2019 , 37 ( 8 ): 817 - 837 .

JANSET M L , VAN GULIK W M . Towards large scale fermentative production of succinic acid [J ] . Current Opinion in Biotechnology , 2014 , 30 : 190 - 197 .

BURK M J . Sustainable production of industrial chemicals from sugars [J ] . International Sugar Journal , 2010 , 112 ( 1333 ): 30 - 35 .

DATTA J , KASPRZYK P , BŁAŻEK K , et al . Synthesis, structure and properties of poly(ester-urethane)s obtained using bio-based and petrochemical 1,3-propanediol and 1,4-butanediol [J ] . Journal of Thermal Analysis and Calorimetry , 2017 , 130 : 261 - 276 .

NAKAMURA C E , WHITE G M . Metabolic engineering for the microbial production of 1,3-propanediol [J ] . Current Opinion in Biotechnology , 2003 , 14 ( 5 ): 454 - 459 .

Yield 10 bioscience agricultural company-Corporate Overview [OL ] . https://www.yield10bio.com/Corporate-Overview https://www.yield10bio.com/Corporate-Overview .

BECKER J , LANGE A , FABARIUS J . Top value platform chemicals: bio-based production of organic acids [J ] . Current Opinion in Biotechnology , 2015 , 36 : 168 - 175 .

GUSTAVSSON M , LEE S Y . Prospects of microbial cell factories developed through systems metabolic engineering [J ] . Microbial Biotechnology , 2016 , 9 ( 5 ): 610 - 617 .

BUIJS N A , SIEWERS V , NIELSEN J . Advanced biofuel production by the yeast Saccharomyces cerevisiae [J ] . Current Opinion in Chemical Biology , 2013 , 17 ( 3 ): 480 - 488 .

GEORGE K W , ALONSO-GUTIERREZ J , KEASLING J D , et al . Isoprenoid drugs, biofuels, and chemicals-artemisinin, farnesene, and beyond [J ] . Advances in Biochemical Engineering/Biotechnology , 2015 , 355 - 389 .

PADDON C J , KEASLING J D . Semi-synthetic artemisinin: a model for the use of synthetic biology inpharmaceutical development [J ] . Nature Reviews Microbiology , 2014 , 12 ( 5 ): 355 - 367 .

PADDON C J , WESTFALL P J , PITERA D J , et al . High level semi-synthetic production of the potent antimalarial artemisinin [J ] . Nature , 2013 , 496 ( 7446 ): 528 - 532 .

FREED E F , WINKLER J D , WEISS S J , et al . Genome-wide tuning of protein expression levels to rapidly engineer microbial traits [J ] . ACS Synthetic Biology , 2015 , 4 ( 11 ): 1244 - 1253 .

BABA T , ARA T , HASEGAWA M , et al . Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection [J ] . Molecular Systems Biology , 2006 . DOI: 10.1038/msb4100050 http://dx.doi.org/10.1038/msb4100050 .

KOO B M , KRITIKOS G , FARELLI J D , et al . Construction and analysis of two genome-scale deletion libraries for Bacillus subtilis [J ] . Cell systems , 2017 , 4 ( 3 ): 291 - 305 .

WANG H H , ISAACS F J , CARR P A , et al . Programming cells by multiplex genome engineering and accelerated evolution [J ] . Nature , 2009 , 460 ( 7257 ): 894 - 898 .

WARNER J R , REEDER P J , KARIMPOUR-FARD A , et al . Rapid profiling of a microbial genome using mixtures of barcoded oligonucleotides [J ] . Nature Biotechnology , 2010 , 28 ( 8 ): 856 - 862 .

DOUDNA J A , CHARPENTIER E . The new frontier of genome engineering with CRISPR-Cas9 [J ] . Science , 2014 , 346 ( 6213 ): 1077 .

QI L S , LARSON M H , GILBERT L A , et al . Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression [J ] . Cell , 2013 , 152 ( 5 ): 1173 - 1183 .

DONG C , FONTANA J , PATEL A , et al . Synthetic CRISPR-Cas gene activators for transcriptional reprogramming in bacteria [J ] . Nature Communications , 2018 , 9 ( 1 ): 2489 .

MANS R , VAN ROSSUM H M , WIJSMAN M , et al . CRISPR/Cas9: a molecular Swiss army knife for simultaneous introduction of multiple genetic modifications in Saccharomyces cerevisiae [J ] . FEMS Yeast Research , 2015 , 15 ( 2 ). DOI: 1093/femsyr/fov004 http://dx.doi.org/1093/femsyr/fov004 .

HALPERIN S O , TOU C J , WONG E B , et al . CRISPR-guided DNA polymerases enable diversification of all nucleotides in a tunable window [J ] . Nature , 2018 , 560 ( 7717 ): 248 - 252 .

JIANG W , BIKARD D , COX D , et al . RNA-guided editing of bacterial genomes using CRISPR-Cas systems [J ] . Nature Biotechnology , 2013 , 31 : 233 - 239 .

OH J H , van PIJKEREN J P . CRISPR-Cas9-assisted recombineering in Lactobacillus reuteri [J ] . Nucleic Acids Research 2014 , 42 : e131 .

CHOUDHARY E , THAKUR P , PAREEK M , et al . Gene silencing by CRISPR interference in mycobacteria [J ] . Nature Communications , 2015 , 6 ( 1 ): 6267 .

YAO L , CENGIC I , ANFELT J , et al . Multiple gene repression in cyanobacteria using CRISPRi [J ] . ACS Synthetic Biology 2016 , 5 : 207 - 212 .

XU T , LI Y , SHI Z , et al . Efficient genome editing in Clostridium cellulolyticum via CRISPR-Cas9 nickase .[J ] . Applied and Environmental Microbiology , 2015 , 81 : 4423 - 4431 .

PETERS J M , COLAVIN A , SHI H , et al . A comprehensive, CRISPR-based functional analysis of essential genes in bacteria [J ] . Cell , 2016 , 165 : 1493 - 1506 .

JIANG Y , QIAN F , YANG J , et al . CRISPR-Cpf1 assisted genome editing of Corynebacterium glutamicum [J ] . Nature Communications , 2017 , 8 ( 1 ): 15179 .

NAYAK D D , METCALF W W . Cas9-mediated genome editing in the methanogenic archaeon Methanosarcina acetivorans [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2017 , 114 : 2976 - 2981 .

王天民 . 基于 CRISPR 干扰技术的微生物高通量功能基因组学方法 [D ] . 北京 : 清华大学 , 2018 .

WANG T M . CRISPR interference based high-throughput functional genomics platform method in bacteria [D ] . Beijing : Tsinghua University , 2018 .

DICARLO J E , CONLEY A J , PENTTILA M , et al . Yeast oligo-mediated genome engineering (YOGE) [J ] . ACS Synthetic Biology , 2013 , 2 ( 12 ): 741 - 749 .

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 , 2016 , 35 ( 1 ): 48 - 55 .

ANZALONE A V , RANDOLPH P B , DAVIS J R , et al . Search-and-replace genome editing without double-strand breaks or donor DNA [J ] . Nature , 2019 , 576 ( 7785 ): 149 - 157 .

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 .

MA Y , ZHANG J , YIN W , et al . Targeted AID-mediated mutagenesis (TAM) enables efficient genomic diversification in mammalian cells [J ] . Nature Methods , 2016 , 13 ( 12 ): 1029 - 1035 .

HESS G T , FRESARD L , HAN K , et al . Directed evolution using dCas9-targeted somatic hypermutation in mammalian cells [J ] . Nature Methods , 2016 , 13 ( 12 ): 1036 - 1042 .

LI Q , SEO J H , STRANGER B , et al . Integrative eQTL-Based analyses reveal the biology of breast cancer risk loci [J ] . Cell , 2013 , 152 ( 3 ): 633 - 641 .

WU J , ZHOU P , ZHANG X , et al . Efficient de novo synthesis of resveratrol by metabolically engineered Escherichia coli [J ] . Journal of Industrial Microbiology & Biotechnology , 2017 , 44 ( 7 ): 1083 - 1095 .

GILBERT L A , LARSON M H , MORSUT L , et al . CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes [J ] . Cell , 2013 , 154 ( 2 ): 442 - 451 .

HALPERIN S O , TOU C J , WANG E B , et al . CRISPR-guided DNA polymerases enable diversification of all nucleotides in a tunable window [J ] . Nature , 2018 , 560 : 248 - 252 .

WONG B G , MANCUSO C P , KIRIAKOV S , et al . Precise, automated control of conditions for high-throughput growth of yeast and bacteria with eVOLVER [J ] . Nature Biotechnology , 2018 , 36 ( 7 ): 614 - 623 .

JIAN X , GUO X , WANG J , et al . Microbial microdroplet culture system (MMC): an integrated platform for automated, high-throughput microbial cultivation and adaptive evolution [J ] . Biotechnology and Bioengineering , 2019 . DOI: 10.1101/2019.12. 19.883561 http://dx.doi.org/10.1101/2019.12.19.883561 .

MICHENER J K , THODEY K , LIANG J C , et al . Applications of genetically-encoded biosensors for the construction and control of biosynthetic pathways [J ] . Metabolic Engineering , 2012 , 14 ( 3 ): 212 - 222 .

LIU D , EVANS T , ZHANG F Z , et al . Applications and advances of metabolite biosensors for metabolic engineering [J ] . Metabolic Engineering , 2015 , 31 : 15 - 22 .

FANG M , WANG T , ZHANG C , et al . Intermediate-sensor assisted push-pull strategy and its application in heterologous deoxyviolacein production in Escherichia coli [J ] . Metabolic Engineering , 2016 , 33 : 41 - 51 .

LIANG W , CUI L , CUI J , et al . Biosensor-assisted transcriptional regulator engineering for Methylobacterium extorquen s AM1 to improve mevalonate synthesis by increasing the acetyl-CoA supply [J ] . Metabolic Engineering , 2017 , 39 : 159 - 168 .

BINDER S , SCHENDZIELORZ G , STABLER N , et al . A high-throughput approach to identify genomic variants of bacterial metabolite producers at the single-cell level [J ] . Genome Biology , 2012 , 13 : R40 .

BENEYTON T , THOMAS S , GRIFFITHS A D , et al . Droplet-based microfluidic high-throughput screening of heterologous enzymes secreted by the yeast Yarrowia lipolytica [J ] . Microbial Cell Factories , 2017 , 16 ( 1 ): 18 .

SIEDLER S , KHATRI N K , ZSOHAR A , et al . Development of a bacterial biosensor for rapid screening of yeast p -coumaric acid production [J ] . ACS Synthetic Biology , 2017 , 6 ( 10 ): 1860 - 1869 .

BARET J C , MILLER O J , TALY V , et al . Fluorescence-activated droplet sorting (FADS): efficient microfluidic cell sorting based on enzymatic activity [J ] . Lab on a Chip , 2009 , 9 ( 13 ): 1850 .

WAGNER J M , LIU L , YUAN S et . al. A comparative analysis of single cell and droplet-based FACS for improving production phenotypes : riboflavin overproduction in Yarrowia lipolytica [J ] . Metabolic Engineering , 2018 , 47 : 346 - 356 .

MA C , TAN Z L , LIN Y , et al . Gel microdroplet-based high-throughput screening for directed evolution of xylanase-producing Pichia pastoris [J ] . Journal of Bioscience and Bioengineering , 2019 , 128 ( 6 ): 662 - 668 .

WANG X , REN L , SU Y , et al . Raman-activated droplet sorting (RADS) for label-free high-throughput screening of microalgal single-cells [J ] . Analytical Chemistry , 2017 , 89 ( 22 ): 12569 - 12577 .

DIXIT A , PARNAS O , LI B , et al . Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens [J ] . Cell , 2016 , 167 : 1853 - 1866 .

TROPINI C , EARLE K A , HUANG K C , et al . The gut microbiome: connecting spatial organization to function [J ] . Cell Host Microbe , 2017 , 21 : 433 - 442 .

SHI H , COLAVIN A , LEE T K , et al . Strain library imaging protocol for high-throughput, automated single-cell microscopy of large bacterial collections arrayed on multiwell plates [J ] . Nature Protocols Erecipes for Researchers , 2017 , 12 : 429 - 438 .

LAWSON M J , CAMSUND D , LARSSON J , et al . In situ genotyping of a pooled strain library after characterizing complex phenotypes [J ] . Molecular Systems Biology , 2017 , 13 : 947 .

ZONG Y , ZHANG H M , LYU C , et al . Insulated transcriptional elements enable precise design of genetic circuits [J ] . Nature Communications , 2017 , 8 : 52 .

BASSALO M C , GARST A D , CHOUDHURY A , et al . Deep scanning lysine metabolism in Escherichia coli [J ] . Molecular Systems Biology , 2018 , 14 ( 11 ): e8371 .

LIANG L , LIU R , GARST A D , et al . CRISPR EnAbled trackable genome engineering for isopropanol production in Escherichia coli [J ] . Metabolic Engineering , 2017 , 41 : 1 - 10 .

LIU R , LIANG L , GARST A D , et al . Directed combinator ial mutagenesis of Escherichia coli for complex phenotype engineering [J ] . Metabolic Engineering , 2018 , 47 : 10 - 20 .

LIANG L , LIU R , FOSTER K E , et al . Genome engineering of E. coli for improved styrene production [J ] . Metabolic Engineering , 2020 , 57 : 74 - 84 .

ANGERMUELLER C , PÄRNAMAA T , PARTS L , et al . Deep learning for computational biology [J ] . Molecular Systems Biology , 2016 , 12 ( 7 ): 878 .

COSTELLO Z , MARTIN H G . A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data [J ] . NPJ Systems Biology and Applications , 2018 , 4 ( 1 ): 1 - 14 .

CUPERUS J T , GROVES B , KUCHINA A , et al . Deep learning of the regulatory grammar of yeast 5' untranslated regions from 500,000 random sequences [J ] . Genome Research , 2017 , 27 ( 12 ): 2015 - 2024 .

ZHOU Y , LI G , DONG J , et al . MiYA, an efficient machine-learning workflow in conjunction with the YeastFab assembly strategy for combinatorial optimization of heterologous metabolic pathways in Saccharomyces cerevisiae [J ] . Metabolic Engineering , 2018 , 47 : 294 - 302 .

DENBY C M , LI R A , VU V T , et al . Industrial brewing yeast engineered for the production of primary flavor determinants in hopped beer [J ] . Nature Communications , 2018 , 9 ( 1 ): 965 .

ZHANG J , PSTERSEN S D , RADIVOJEVIC T , et al . Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism [J ] . Nature Communications , 2020 , 11 ( 1 ): 4880 .

GUO J , WANG T , GUAN C , et al . Improved sgRNA design in bacteria via genome-wide activity profiling [J ] . Nucleic Acids Research , 2018 , 46 ( 14 ): 7052 - 7069 .

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