中国科学院微生物研究所、中国微生物学会主办
文章信息
- 刘志凤, 王勇
- Liu Zhifeng, Wang Yong
- 历久弥新:进化中的代谢工程
- An evolving and flourishing metabolic engineering
- 生物工程学报, 2021, 37(5): 1494-1509
- Chinese Journal of Biotechnology, 2021, 37(5): 1494-1509
- 10.13345/j.cjb.200729
-
文章历史
- Received: November 15, 2020
- Accepted: February 14, 2021
- Published: February 23, 2021
2. 中国科学院大学,北京 100039
2. University of Chinese Academy of Sciences, Beijing 100039, China
1991年6月,Science期刊发表了“生物技术前沿”专辑,其中James E. Bailey的“Toward a science of metabolic engineering”以及Stephanopoulos & Vallino的“Network rigidity and metabolic engineering in metabolite overproduction”系统总结了20世纪80年代以来科学工作者对生物反应系统的设计与操作,奠定了代谢工程的科学基础,是代谢工程正式诞生的标志。经典代谢工程,旨在利用基因工程技术调控特定的基因与反应、改善细胞功能、提高目标化合物的产量[1-2]。随着技术的发展以及工程理念的创新,代谢工程在过去的30年里经历了4个不同的发展阶段(图 1)。2000年,人类基因组计划的完成使得生命科学研究全面进入“组学”时代,组学技术与代谢工程相结合,数学模型、计算机算法在生物系统中的应用,极大地推动了代谢工程对生物系统的模拟、分析、设计与改造能力[3]。2004年10月,Nature Biotechnology期刊出版了系统生物学专辑,Stephanopoulos等[4]首次系统阐述了利用系统生物学方法研究生物系统,并利用生物的复杂性进行菌株改造,为修饰细胞机器、实现特定的代谢工程目标提供无限的可能性。代谢工程诞生10年后,合成生物学开始起步。合成基因回路的出现[5-6]与DNA组装技术、染色体工程、基因表达调控等合成生物学技术极大地丰富了经典代谢工程途径构建、合成测试、流量优化三步循环的内涵,使得复杂细胞工厂的建立成为可能。2007年,Stephanopoulos实验室在Trends in Biotechnology期刊撰文提出将合成生物学作为4种主要工具之一,在代谢工程的系统设计及表型控制方面发挥作用[7]。2012年5月,Metabolic Engineering期刊出版了“合成生物学:在代谢工程过程中的新方法和应用”专辑,充分肯定了利用代谢工程的原理和方法,结合合成生物学新工具的重要性。2019年,Sang Yup Lee实验室提出了系统代谢工程的内涵是将系统生物学、合成生物学、进化工程与传统的代谢工程相结合,促进高性能菌株的发展[8]。可以看出,过去30年,代谢工程不断整合新学科、新技术,其研究手段和学科内涵不断更新发展,组学工具的发展、代谢工程策略的进步、in silico代谢模拟、遗传和基因组工程、高通量筛选正在加速优化代谢通量,以提高目标生物产品的生产。本文梳理了过去30年来从经典代谢工程到系统代谢工程的发展过程和趋势。
代谢工程的本质是对宿主的代谢网络进行改造,从而实现目标化合物的高效合成。因此,选择合适的宿主体系是代谢工程的基础。大肠杆菌Escherichia coli与酿酒酵母Saccharomyces cerevisiae等代谢相对清晰、遗传操作技术成熟的模式生物被广泛地用作代谢工程宿主[9-13]。在全基因组测序、基因组编辑技术、DNA大片段合成与组装技术的促进下,代谢工程宿主不再局限于E. coli与S. cerevisiae,而可以根据目标产物的合成特点,选择相应的宿主(如前体供应充足、还原力丰富、耐受性强等) (图 2)。比如,谷氨酸棒杆菌Corynebacterium glutamicum适合生产氨基酸;而梭状芽孢杆菌Clostridium sp. 用于生产丁醇;红球菌Rhodococcus opacus与解脂耶氏酵母Yarrowia lipolytica油脂合成能力突出;而琥珀酸曼氏杆菌Mannheimia succiniciproducens高产琥珀酸等。极端微生物如恶臭假单胞杆菌Pseudomonas putida由于其极强的极端环境耐受能力,还原型化合物如NAD(P)H合成能力突出,是合成毒性化合物的理想宿主。以儿茶酚(Catechol) 为底物发酵生产黏康酸(Cis, cis-muconic acid,MA) 时,产物MA诱导型启动子Pcat诱导儿茶酚1, 2-双加氧酶(基因catA与catA2) 表达,儿茶酚耐受性、双加氧酶的表达水平以及儿茶酚的转化率均得到提高,P. putida KT2440改造的工程菌株MA-6可生产高达64.2 g/L的黏康酸[14]。除微生物宿主之外,其他各具特色的体系如重组蛋白表达良好的小立碗藓Physcomitrella patens[15]、脂类代谢丰富的拟球藻Nannochloropsis sp.[16]以及模式植物底盘本氏烟草Nicotiana benthamiana[17-18]等也正广泛应用于代谢工程。
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图 2 代谢设计从简单到复杂
Fig. 2 Metabolic engineering design: from simple to complex.
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此外,新型代谢工程反应体系也在不断发展。无细胞体系不受细胞代谢调控的影响,可实现精确的在线控制,也广泛应用于遗传回路的体外分析、生物装置的体外组装、非天然化合物与生物聚合物的反应等方面[19]。Zhang等[20]利用化学酶反应平台,在10步反应内合成了9个高度氧化与骨架多样的二萜化合物。2015年,Stephanopoulos实验室将紫杉醇前体的合成途径分成两个模块,分别导入工程改造的E. coli与S. cerevisiae中,在混合培养过程中互惠共生,最终发酵生产氧化紫杉醇33 g/L[21]。Wang等[22]将环烷烃生成脂肪族α, ω二羧酸(Aliphatic α, ω-dicarboxylic acids,DCAs) 的合成途径分成3个模块导入不同的细胞进行混合培养,实现环烷烃到DCAs的高效合成。
1.2 合成途径30年来,越来越多的复杂代谢物的生物合成机制得以解析。S. cerevisiae、N. benthamiana等工程改造的底盘体系也为复杂代谢物的合成提供了许多关键中间体。因而,代谢工程合成的复杂代谢物也越来越多(图 2)。抗疟疾药物前体青蒿酸(Artemisinic acid) 在S. cerevisiae中异源合成是其中的典型。S. cerevisiae可以合成法尼基焦磷酸(Farnesyl pyrophosphate,FPP),但无法合成紫穗槐二烯(Amorphadiene),因此需引入黄花蒿Artemisia annua来源的紫穗槐二烯合酶(Amorphadiene synthase,ADS)。紫穗槐二烯生成青蒿酸需要3步反应,分别由紫穗槐二烯氧化酶(Amorphadiene oxidase,CYP71AV1、CPR1、CYB5)、青蒿醛脱氢酶(Artemisinic aldehyde dehydrogenase,ALDH1)、醇脱氢酶(Alcohol dehydrogenase,ADH1) 催化完成。因此,需要在S. cerevisiae中表达4个关键基因才能实现青蒿酸的合成[13]。此后,Luo等[23]在S. cerevisiae中表达9个基因成功合成大麻素(Cannabinoids)。Brown等[24]在S. cerevisiae中表达14个基因合成单萜吲哚生物碱异胡豆苷(Strictosidine)。在此基础上,15个基因参与合成的莨菪烷类生物碱(Tropane alkaloids)[25],需13个异源基因的鸦片(Opioids) 与25个异源基因的那可汀(Noscapine) 均在S. cerevisiae中成功合成[26-27]。最近,秋水仙碱(Colchicine) 在N. benthamiana中也已成功合成[17]。复杂化合物的合成过程涉及区间化修饰。东莨菪碱(Scopolamine) 在S. cerevisiae中的合成需经过线粒体、过氧化物酶体、核膜、液泡与高尔基体等多个不同区间内部或者膜修饰过程[25]。
1.3 工程设计早期代谢工程主要是提升细胞原有的代谢能力,合成生物技术引入代谢工程领域,大大提升了细胞“从无到有”的代谢能力。一方面,通过工程改造或者定向进化获得新途径或者新化合物(图 2)。如理性设计的乙醇醛合酶(GALS) 与磷酸转酮酶(ACPS) 组成的全新乙酰辅酶A合成途径[28]。而工程改造的2-酮酸脱羧酶(KIVD) 与醇脱氢酶(ADH6),在E. coli中实现支链氨基酸到非天然长链醇的合成[29]。另一方面,代谢途径重新设计也取得了成功。Schwander等[30]将9个不同物种来源的17个酶组装成体外CO2固定途径,经过多次酶工程改造与代谢验证,每分钟每毫克蛋白可固定5 nmol的CO2。South等[31]将苹果酸合成酶以及绿藻来源的乙醇酸脱氢酶转到烟草叶绿体中,使乙醇酸在叶绿体中不断生成苹果酸进入卡尔文循环,从而提高光合作用效率。计算机辅助的途径预测工具也用于代谢途径设计。Yim等[9]利用SimPheny BioPathway Predictor预测获得琥珀酰辅酶A与α-酮戊二酸为前体的1, 4-丁二醇的合成途径,两条途径在E. coli中同时表达可发酵生产18 g/L的1, 4-丁二醇。
工程设计增加菌株的代谢性能,比如利用C1资源的能力,随着环境保护、资源供应等问题的突出而越来越受到关注。Kim等[32]在E. coli中建立的甘氨酸可逆剪切途径以及卡尔文循环(Calvin-cycle) 与四氢叶酸(Tetrahydrofolate) 循环途径均可利用CO2与甲酸。染色体进一步整合ftl、fch、mtd基因,过表达甘氨酸剪切反应,增加丙酮酸合成等,E. coli最终可直接利用CO2或者甲酸进行生长。Gleizer等[33]在敲除中心代谢途径的E. coli中,共表达卡尔文循环途径(Rubisco)与甲酸脱氢酶(Formate dehydrogenase,FDH)、磷酸核糖激酶(Phosphoribulo-kinase,Prk)、碳酸酐酶(Carbonic anhydrase),通过实验室适应性进化也成功获得了直接利用CO2的菌株。
2 流量优化:从静态到动态系统生物学的研究,使得代谢流量与代谢控制分析(Metabolic fluxes and metabolic control)、代谢工程的计算方法(Computational methods of metabolic engineering) 等理论与技术得以发展[34-35]。随着合成生物学技术的发展,代谢工程改造从传统的过表达与途径敲除,发展成了基于代谢流量分布的理性控制。通过增加前体供应、辅因子循环[36]、启动子工程[11]、核糖体工程[37]、基因间区调控(Tunable intergenic regions,TIGRs)[38]等策略平衡各基因的表达;利用底物通道[39],途径模块化等代谢流量优化方法,减少中间产物积累,提高代谢工程的产量。随着基因组尺度代谢模型(Genome-scale metabolic model,GSM) 在多个生物体系中的建立[40-46],基于基因组代谢模型的菌株优化方法也广泛应用于代谢工程改造[47]。为解决工程改造引起的菌株生长缺陷与代谢负担,合成小调控RNA (Synthetic small regulatory RNA)[48],CRISPR干扰(Clustered regularly interspaced short palindromic repeats interference)[49]等转录水平调控方法也不断发展(图 3)。利用细胞的生存压力驱动目标产物合成也是平衡细胞生长与产物合成的重要方法。比如敲除E. coli BW25113的丙酮酸合成途径后,邻氨基苯甲酸(Anthranilate) 合成途径成为丙酮酸的唯一来源,在生长压力下,邻氨基苯甲酸实现高效合成[50]。
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图 3 静态代谢调控策略
Fig. 3 Strategies for static metabolic engineering.
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随着调控元件(如转录调控因子、核糖开关)的不断丰富,动态调控机制研究的逐渐深入,动态调控系统已经成功应用于代谢工程的精细调控[51]。根据调控信号的不同,动态调控可以分为温度、光、pH、溶氧等环境因素诱导;异丙基-β-D-硫代吡喃半乳糖苷(Isopropyl β-D-thiogalactoside,IPTG) 等化学诱导物诱导,群体感应诱导以及细胞代谢产物诱导几大类(图 4)。温敏抑制子cI857调控的PL与PR启动子是目前应用最广泛的温度诱导系统[52]。海滨赤杆菌Erythrobacter litoralis来源的光遗传转录系统,可控制S. cerevisiae在光照条件下生长,而在黑暗条件下进行产物合成[53]。在黑曲霉Aspergillus niger中生产有机酸时,Pgas启动子在pH 2.0启动基因表达;而在pH高于5.0时关闭基因表达[54]。在E. coli中合成2, 3-丁二醇和1, 3-丙二醇时,Nar启动子响应厌氧条件[55]。化学诱导物常用于双稳态切换系统。当细胞生长到一定程度时,添加化学诱导物启动目标产物合成[56]。群体感应是依赖于细胞密度的调控系统,具有广泛适用性。通过调控感应系统的表达强度,信号响应可以在不同细胞密度发生,从而提高肌醇、葡糖二酸的产量[57]。多个群体感应系统的多层代谢调控也成功应用于代谢工程改造[58]。途径代谢物动态调控可通过转录因子与核糖开关应答实现。其中转录因子响应代谢物浓度变化的调控系统包括响应acyl-CoA的FadR[59]、响应malonyl-CoA的FapR[60]、响应柚皮素的FdeR[61]、响应香兰素的HucR[62]、响应6-磷酸葡糖胺的NagR与GamR[63]等。核糖开关通过控制转录起始调控基因的表达。目前已经有响应茶碱[64]、硫胺素焦磷酸[65]、赖氨酸[66]、甘氨酸[67]以及唾液酸[68]等核糖开关成功应用于代谢工程控制。
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图 4 动态代谢调控策略
Fig. 4 Strategies for dynamic metabolic engineering.
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由于细胞代谢、调控以及信号网络尚不完全清楚,理性改造提高宿主的代谢性能面临诸多挑战。进化工程通过模拟自然进化,迅速获得优良细胞特性,而无需深入地理解细胞代谢,是理性代谢工程的互补方法。随着进化工程与自动化细胞培养、在线监测、高通量测序、多组学分析等技术的结合,其在系统代谢工程中发挥着不可替代的作用。我们从定向进化(Directed evolution)与实验室适应性进化(Adaptive laboratory evolution,ALE) 两个方面对进化工程在代谢工程中的应用进行总结(图 5)。
1993年美国科学家Frances H. Arnold首次提出“酶的定向进化”,旨在通过快速随机突变与高通量筛选在短时间内实现酶的功能优化或者改造。由此,Arnold实验室获得了不依赖于TrpA的色氨酸合酶TrpB[69]、高效氧化烷烃生成醇类化合物的P450[70]以及氧化烯烃生成醛的P450氧化酶[71]。因其在酶的定向进化方面的开创性贡献,2018年Arnold被授予诺贝尔化学奖。此外,定向进化获得的异戊二烯合酶(Isoprene synthase) 在工程改造的S. cerevisiae中可发酵生产3.7 g/L的异戊二烯[72]。定向进化的突变体文库可通过由随机突变或者定点突变产生。易错PCR突变方法。全局转录机制工程(Global transcription machinery engineering,gTME)[74]、多重自动基因组工程(Multiplex automated genome engineering,MAGE)[75]、转录因子激活样效应核酸酶(Transcription activator-like effector nucleases,TALENs)[76]、CRISPR/Cas9[77]等基因组水平的进化方法,也用于增加突变体库的遗传多样性。最近开发的M13噬菌体辅助连续进化系统(PACE),进化速度比传统的定向进化快100倍,可实现微生物“自发”的连续定向进化[78]。
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图 5 进化工程:定向进化与实验室适应性进化
Fig. 5 Evolutionary engineering: directed evolution and adaptive laboratory evolution.
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与酶的定向进化不同,ALE直接对菌株进行连续培养和筛选,以获得耐受性能改善、生长速率提高、碳源利用增加的菌株。例如,利用ALE重塑S. cerevisiae的代谢途径,获得高产脂肪酸的菌株[79]。缺乏丝氨酸降解途径的E. coli经过45 d的适应性进化,最终可发酵生产37.3 g/L的丝氨酸[80]。酪氨酸缺陷型菌株以苯丙氨酸羟化酶为遗传选择压力,在限制性培养基中进化后,成功将苯丙氨酸转化成酪氨酸维持细胞生长,并带动辅因子循环途径[81]。
4 机器学习:从代谢设计到流量优化代谢工程改造需要长期的试验与纠错过程,才能最终获得成功。比如,Amyris公司需要花费150每人每年的时间生产青蒿酸;Dupont公司则需要575每人每年的时间生产丙二醇[82]。这种低效的模式显然是不可持续的,亟需成熟的生物设计来减少试错的过程。而成熟的生物设计面临的最大挑战是准确预测代谢工程的结果。组学数据的爆发式增长,为基因发现、生物功能理解、生物改造提供了强大的支撑。然而,缺乏深度解析的数据却不能为代谢工程改造提供可行的策略。
利用多功能组学数据系统改善菌株性能,为生物设计提供预测的机器学习,是解决以上问题的关键。目前,机器学习在自动驾驶[83]、自动翻译[84]、面部识别[85]、自然语言解析[86]、癌症检测[87]、歌词显示[88]等领域已获得巨大成功。作为人工智能(Artificial intelligence,AI) 的子学科,机器学习是通过训练自动提高计算机算法预测能力的过程。目前应用于代谢工程的机器学习算法包括深度学习(Deep learning)、人工神经元网络(Artificial neural network,ANN)、聚类(Clustering)、决策树(Decision tree)、线性回归(Linear regression)、偏最小二乘法回归(Partial least squares regression)、高斯过程(Gaussian process) 以及支持向量机(Support vector machine,SVM) 等[89]。
机器学习在代谢设计的基因注释、途径设计与构建、代谢流量优化等方面均有应用(图 6)。预测翻译起始位点以及开放读码框的DeepRibo[90]、预测酶学委员会编号的DeepEC[91]均是深度神经元网络训练的。3个人工神经元网络与蒙特卡洛树搜索算法(Monte carlo tree search algorithm,3N-MCTS) 组成的逆合成法可用于代谢途径发现,为代谢设计提供更多选择[92]。当合成途径的酶未知时,支持向量机[93]和高斯过程[94]可用于预测酶催化反应。机器学习辅助的定向进化,可帮助获得催化效率提高[95]、颜色改变[96]、热力学稳定性更好[97]的新酶。在深度学习算法的帮助下,理性蛋白设计也已经成功实现[98-100]。在代谢流量优化方面,神经元网络预测基因表达[101],偏最小二乘法回归优化启动子强度与诱导物浓度和时间[102],随机预测与神经元网络预测核糖开关的动态范围[103]等,均是对基因表达剂量的优化。机器学习还直接对多基因代谢途径进行优化,包括支持向量回归指导柠檬烯(Limonene) 在E. coli中合成[104]、高斯过程指导番茄红素(Lycopene) 在E. coli中合成[105]、模型集成指导色氨酸在S. cerevisiae中合成[106]。此外,机器学习也用于改善代谢工程工具如CRISPR的基因编辑效率[107]、DNA组装与转化效率[108]。最后,机器学习算法比如决策树、遗传算法等也应用于代谢放大过程中的发酵参数分析[109]。为工业发酵,例如德巴利氏酵母Debaryomyces nepalensis发酵生产木糖醇(Xylitol),提供了重要的参考意见[110]。
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图 6 机器学习在代谢工程中的应用
Fig. 6 Application of machine learning in metabolic engineering.
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科学认知的不断深刻,遗传操作技术的迅猛发展,生物元件的开发,进化工程与机器学习的持续创新,工程方法与策略在应用中大放异彩,代谢工程在30年进化过程中,取得了巨大的成就(表 1)。
Host | Product/goal | Strategies/tools | References | |
Metabolic design | E. coli | Reticuline | Selected enzymes from different host | [12] |
S. cerevisiae | Artemisinic acid | Precursor improvement/new P450 | [13] | |
P. putida | Cis, cis-muconic acid | Synthetic promoter | [14] | |
N. benthamiana | Colchicine alkaloid | Co-expression/truncation | [17] | |
Flux optimization | E. coli | Taxadine | Pathway modularization | [11] |
E. coli | Resveratrol | CRISPRi | [49] | |
E. coli | Anthranilate | Metabolite addiction | [50] | |
A. niger | Itaconic acid | Low-pH-inducible promoter, Pgas | [54] | |
E. coli | 5-aminolevulinic acid | Glycine Riboswitch | [67] | |
Evolutionary engineering | S. cerevisiae | Isoprene | Directed evolution | [72] |
S. cerevisiae | Free fatty acids | Adaptive laboratory evolution | [79] | |
E. coli | L-serine | Adaptive laboratory evolution | [80] | |
E. coli | Grow | Adaptive laboratory evolution | [81] | |
Machine learning | E. coli | Expression of dxs | Neural network | [101] |
E. coli | Limonene | Support vector machine | [104] | |
E. coli | Lycopene | Gaussian process | [105] | |
S. cerevisiae | Tryptophan | Ensemble models | [106] | |
D. nepalensis | Xylitol | Artificial neural network | [110] |
以5G技术为标志,人类社会正在进入一个全新的智能时代。生物技术与人工智能、自动化、云计算以及物联网等技术的联系越来越紧密。合成生物学与电子信息、材料等其他学科的交叉融合产生了许多新的方向。智能手机控制小鼠体内的血糖浓度已经成功实现[111],全自动化合物合成机器人也已问世[112],现代生物工厂已进入标准化、自动化、智能化模式[113-114]。2019年,美国科学基金会提出了半导体生物学发展指南,拟解决合成生物学与半导体的集成问题,制造新型材料,研发新的信息存储技术等,从而突破生物与电子元器件的界限,开拓新的生物技术领域。这预示着代谢工程未来发展的方向:通过自动化、智能化的设计,全面提升细胞和细胞、细胞和传感器、细胞和反应器、细胞和计算机之间的交互能力。人工智能强大的数据采集与过程处理能力,有望实现代谢过程的自动化与智能化过程控制。可以预见,代谢工程与其他新兴学科和技术相结合,仍将焕发持续的生机和活力。
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