Article(id=1259888471729160572, tenantId=1146029695717560320, journalId=1192105938417971205, issueId=1259888457367806489, articleNumber=null, orderNo=null, doi=10.13343/j.cnki.wsxb.20250849, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1762876800000, receivedDateStr=2025-11-12, revisedDate=null, revisedDateStr=null, acceptedDate=1766937600000, acceptedDateStr=2025-12-29, onlineDate=1778310419255, onlineDateStr=2026-05-09, pubDate=1777824000000, pubDateStr=2026-05-04, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1778310419255, onlineIssueDateStr=2026-05-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1778310419255, creator=13701087609, updateTime=1778310419255, updator=13701087609, issue=Issue{id=1259888457367806489, tenantId=1146029695717560320, journalId=1192105938417971205, year='2026', volume='66', issue='5', pageStart='2031', pageEnd='2556', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=0, articleOrder=1, issueType=-1, specialIssue=null, createTime=1778310415832, creator=13701087609, updateTime=1778320153326, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1259929299465921482, tenantId=1146029695717560320, journalId=1192105938417971205, issueId=1259888457367806489, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1259929299465921483, tenantId=1146029695717560320, journalId=1192105938417971205, issueId=1259888457367806489, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2103, endPage=2116, ext={EN=ArticleExt(id=1259888475629863334, articleId=1259888471729160572, tenantId=1146029695717560320, journalId=1192105938417971205, language=EN, title=Research progress in the biosynthesis and metabolic regulation of microbial-derived γ-aminobutyric acid, columnId=1192149543727808575, journalTitle=Acta Microbiologica Sinica, columnName=Review, runingTitle=null, highlight=null, articleAbstract=

γ-Aminobutyric acid (GABA), a key inhibitory neurotransmitter in the central nervous system, plays a vital role in physiological functions such as promoting sleep, relieving tremors, and regulating blood pressure. Currently, a variety of microorganisms capable of synthesizing GABA have been identified, offering diverse strategic options for the biosynthesis of GABA through different metabolic pathways. This review provides a detailed summary of the major pathways—the GABA shunt pathway and the putrescine pathway—for GABA synthesis in various microorganisms. It systematically outlines the key synthases and metabolites involved in the two pathways, while comparing their synthesis efficiency and respective advantages. Furthermore, this study delves into the regulatory mechanisms underlying GABA biosynthesis in different microorganisms, along with key regulatory targets for enhancing synthesis efficiency. The work aims to establish a theoretical framework for the regulatory mechanisms of microbial-derived GABA synthesis and to provide a scientific basis for improving the efficiency of GABA biosynthesis.

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E-mail:
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These authors contributed equally to this work.

, authorsList=Chenyue ZOU, Ying LI, Shuxiang LIU, Xin FAN, Lanyan HUANG, Moutong CHEN), CN=ArticleExt(id=1259888484454679036, articleId=1259888471729160572, tenantId=1146029695717560320, journalId=1192105938417971205, language=CN, title=微生物源γ-氨基丁酸生物合成及其代谢调控研究进展, columnId=1192149543882997826, journalTitle=微生物学报, columnName=综述, runingTitle=null, highlight=null, articleAbstract=

γ-氨基丁酸(γ-aminobutyric acid, GABA)是中枢神经系统的关键抑制性递质,在促进睡眠、缓解震颤、调节血压等生理功能方面发挥着至关重要的作用。目前已鉴定出多种能够合成GABA的微生物,这些微生物通过不同的代谢途径合成GABA,为GABA的生物合成提供了多样化的策略。本文详细总结了各类微生物合成GABA的主要途径,即GABA支路和腐胺途径,系统归纳了这2条途径中的关键酶及代谢产物,同时对比了不同途径的合成效率及优势。在此基础上,本文深入分析了各类微生物代谢合成GABA的调控机制以及提升合成效率的关键调控靶点,为微生物源GABA的合成调控机制构建了理论框架,也为提高GABA生物合成效率提供了科学依据。

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作者贡献声明

邹陈悦:负责综述初稿撰写、表格制作和图形绘制;李滢:对综述草稿进行修改和补充;刘书香:参与文献的深入分析和讨论;凡鑫:参与文献整理与筛选;黄兰艳:参与全文综述校对;陈谋通:主题选择,提供了该领域内的专业见解和建议。

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Solid black boxes denote key enzymes in the γ-aminobutyric acid (GABA) biosynthetic pathway, whereas dashed gray boxes represent intermediate metabolites. Arrows indicate the direction of the biosynthetic flow., figureFileSmall=5D6K3BNNpQHL9Gvxdt7xBw==, figureFileBig=SsxHbTQdSXH2qHZdloIgOA==, tableContent=null), ArticleFig(id=1259928483329917307, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1259888471729160572, language=CN, label=图2, caption=微生物GABA合成途径:GABA支路(黄色路径)和腐胺途径(蓝色路径), figureFileSmall=5D6K3BNNpQHL9Gvxdt7xBw==, figureFileBig=SsxHbTQdSXH2qHZdloIgOA==, tableContent=null), ArticleFig(id=1259928484596597124, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1259888471729160572, language=EN, label=Table 1, caption=

Comparative analysis of advantages and disadvantages of different GABA-producing microorganisms

, figureFileSmall=null, figureFileBig=null, tableContent=
SpeciesSynthesis pathwayProduction modeHighest yield/(g/L)AdvantagesDisadvantagesReferences
Escherichia coli

GABA shunt

putrescine pathway

Whole-cell biocatalysis720.00Well-characterized genetic background, substantial potential for metabolic engineeringSafety risks[16]
Lactic acid bacteriaGABA shuntWhole-cell biocatalysis44.40High safety, wide range of applicationsRelatively low yield, relatively single pathway[17]
Corynebacterium glutamicumGABA shuntDe novo synthesis38.60High safety, glutamate synthesis capabilityMismatch of key enzymes[18]
BacillusGABA shuntDe novo synthesis327.00Good safety profile, feasible genetic manipulationLow yield in wild-type strains, inefficient key enzyme systems[19]
MonascusGABA shuntDe novo synthesis12.47Traditional food-grade microbe, potential for co-cultureInefficient synthesis, time-consuming processes[20]
), ArticleFig(id=1259928485481595277, tenantId=1146029695717560320, journalId=1192105938417971205, articleId=1259888471729160572, language=CN, label=表1, caption=

不同种类产GABA微生物优缺点分析

, figureFileSmall=null, figureFileBig=null, tableContent=
SpeciesSynthesis pathwayProduction modeHighest yield/(g/L)AdvantagesDisadvantagesReferences
Escherichia coli

GABA shunt

putrescine pathway

Whole-cell biocatalysis720.00Well-characterized genetic background, substantial potential for metabolic engineeringSafety risks[16]
Lactic acid bacteriaGABA shuntWhole-cell biocatalysis44.40High safety, wide range of applicationsRelatively low yield, relatively single pathway[17]
Corynebacterium glutamicumGABA shuntDe novo synthesis38.60High safety, glutamate synthesis capabilityMismatch of key enzymes[18]
BacillusGABA shuntDe novo synthesis327.00Good safety profile, feasible genetic manipulationLow yield in wild-type strains, inefficient key enzyme systems[19]
MonascusGABA shuntDe novo synthesis12.47Traditional food-grade microbe, potential for co-cultureInefficient synthesis, time-consuming processes[20]
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微生物源γ-氨基丁酸生物合成及其代谢调控研究进展
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邹陈悦 1, 2 , 李滢 1 , 刘书香 2 , 凡鑫 1 , 黄兰艳 1 , 陈谋通 1
微生物学报 | 综述 2026,66(5): 2103-2116
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微生物学报 | 综述 2026, 66(5): 2103-2116
微生物源γ-氨基丁酸生物合成及其代谢调控研究进展
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邹陈悦1, 2, 李滢1, 刘书香2, 凡鑫1, 黄兰艳1, 陈谋通1
作者信息
  • 1.广东省科学院微生物研究所,华南应用微生物国家重点实验室,广东省微生物安全与健康重点实验室,国家卫健委微生物食品营养与安全科技创新平台,国家市场监督管理总局重点实验室(食品微生物安全大数据技术),广东 广州
  • 2.四川农业大学 食品学院,四川 雅安
Research progress in the biosynthesis and metabolic regulation of microbial-derived γ-aminobutyric acid
Chenyue ZOU1, 2, Ying LI1, Shuxiang LIU2, Xin FAN1, Lanyan HUANG1, Moutong CHEN1
Affiliations
  • 1.State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Food Microbial Safety Big Data Technology (State Administration for Market Regulation), Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, Guangdong, China
  • 2.College of Food Science, Sichuan Agricultural University, Ya’an, Sichuan, China
出版时间: 2026-05-04 doi: 10.13343/j.cnki.wsxb.20250849
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γ-氨基丁酸(γ-aminobutyric acid, GABA)是中枢神经系统的关键抑制性递质,在促进睡眠、缓解震颤、调节血压等生理功能方面发挥着至关重要的作用。目前已鉴定出多种能够合成GABA的微生物,这些微生物通过不同的代谢途径合成GABA,为GABA的生物合成提供了多样化的策略。本文详细总结了各类微生物合成GABA的主要途径,即GABA支路和腐胺途径,系统归纳了这2条途径中的关键酶及代谢产物,同时对比了不同途径的合成效率及优势。在此基础上,本文深入分析了各类微生物代谢合成GABA的调控机制以及提升合成效率的关键调控靶点,为微生物源GABA的合成调控机制构建了理论框架,也为提高GABA生物合成效率提供了科学依据。

γ-氨基丁酸  /  微生物  /  合成基因  /  合成通路  /  代谢调控

γ-Aminobutyric acid (GABA), a key inhibitory neurotransmitter in the central nervous system, plays a vital role in physiological functions such as promoting sleep, relieving tremors, and regulating blood pressure. Currently, a variety of microorganisms capable of synthesizing GABA have been identified, offering diverse strategic options for the biosynthesis of GABA through different metabolic pathways. This review provides a detailed summary of the major pathways—the GABA shunt pathway and the putrescine pathway—for GABA synthesis in various microorganisms. It systematically outlines the key synthases and metabolites involved in the two pathways, while comparing their synthesis efficiency and respective advantages. Furthermore, this study delves into the regulatory mechanisms underlying GABA biosynthesis in different microorganisms, along with key regulatory targets for enhancing synthesis efficiency. The work aims to establish a theoretical framework for the regulatory mechanisms of microbial-derived GABA synthesis and to provide a scientific basis for improving the efficiency of GABA biosynthesis.

γ-aminobutyric acid  /  microorganisms  /  biosynthetic genes  /  biosynthetic pathway  /  metabolic regulation
邹陈悦, 李滢, 刘书香, 凡鑫, 黄兰艳, 陈谋通. 微生物源γ-氨基丁酸生物合成及其代谢调控研究进展. 微生物学报, 2026 , 66 (5) : 2103 -2116 . DOI: 10.13343/j.cnki.wsxb.20250849
Chenyue ZOU, Ying LI, Shuxiang LIU, Xin FAN, Lanyan HUANG, Moutong CHEN. Research progress in the biosynthesis and metabolic regulation of microbial-derived γ-aminobutyric acid[J]. Acta Microbiologica Sinica, 2026 , 66 (5) : 2103 -2116 . DOI: 10.13343/j.cnki.wsxb.20250849
γ-氨基丁酸(γ-aminobutyric acid, GABA),又称4-氨基丁酸,是一种广泛分布于动物、植物及微生物中的四碳非蛋白质氨基酸。GABA主要由谷氨酸脱羧酶(glutamate decarboxylase, GAD)催化谷氨酸脱羧生成,并作为抑制性神经递质在人体中枢和外周神经系统中发挥作用[1]。自20世纪中期GABA在哺乳动物中枢神经系统被发现后,其在多种生理过程中的调节功能逐渐被揭示,如促进睡眠[2]、抗炎[3]、缓解震颤[4]、降血压[5]、降血糖[6]以及改善肝肾功能[7]等(图1)。随着对GABA生理功能研究的不断深入,其在医药和食品领域中的应用日益广泛[8]
由于天然产物中GABA含量有限,难以通过直接提取满足工业生产需求,因此研究者开发了多种人工合成GABA的策略以应对日益增长的市场需求。目前,工业中GABA常用的生产方法可分为化学合成、植物富集和微生物发酵3种大类[9]。化学合成法通常以γ-氯丁氰与邻苯二甲酰亚胺钾等为原料,在高温条件下反应,最终通过结晶分离技术提纯获得GABA[10]。尽管该方法反应快、产量高,但使用有毒溶剂,存在安全与环境隐患,限制了该方法合成的GABA在食品和医药行业的广泛应用[11]。与化学合成法相比,植物富集法主要利用低温或高盐胁迫处理从植物中富集GABA[12]。该方法操作简便、安全性高且环境友好,但存在分离提取效率低、成本较高的问题,通过该方法获取的GABA难以满足庞大的市场需求[13]。微生物发酵法以葡萄糖等简单碳源为底物,通过工程化微生物的代谢网络实现GABA的绿色生物制造[14],具有过程清洁、底物转化率高、生产周期短、易于规模化等突出优势[9]。然而,其产业化仍受限于代谢反馈抑制、关键酶活性不足等瓶颈[8]。因此,本文深入分析了微生物源GABA的生物合成途径与代谢调控机制,系统梳理了GABA合成生物学领域的最新研究进展,为提升微生物发酵法合成GABA的效率提供理论依据。
利用微生物生产GABA时通常选择具有GABA合成能力的微生物作为底盘生物。目前,研究者已鉴定出多种具有GABA合成能力的微生物,包括大肠杆菌、乳酸菌、谷氨酸棒状杆菌、枯草芽孢杆菌和丝状真菌等[13,15]。这些微生物广泛分布于多种发酵食品中,如泡菜、奶酪、发酵豆制品[21]。它们主要通过全细胞催化(以外源谷氨酸为底物)或从头合成(以葡萄糖等碳源为底物) 2种模式进行生产。
大肠杆菌(Escherichia coli)是首个被发现具有GABA合成能力的微生物,目前研究者主要以大肠杆菌为模式生物解析微生物合成GABA的通路。在大肠杆菌中GABA的生物合成主要通过GABA支路(GABA shunt)以及腐胺途径(putrescine utilization, Puu)[22]实现(图2)。由于大肠杆菌遗传背景清晰,且可用于代谢工程改造的工具成熟,因此被作为早期GABA工程化研究的主要底盘生物(表1)。然而,其潜在的安全风险促使目前研究转向更安全的益生菌工程菌。
乳酸菌(lactic acid bacteria, LAB)被公认为安全的可食用有益菌,在食品发酵和健康领域具有重要应用价值。目前研究发现,部分乳酸菌也具备合成GABA的能力,包括乳杆菌属(Lactobacillus)、乳球菌属(Lactococcus)、链球菌属(Streptococcus)、片球菌属(Pediococcus)和肠球菌属(Enterococcus)。由于乳酸菌缺乏Puu通路中的起始酶(putrescine utilization protein A, PuuA),因此其GABA的合成主要通过GABA支路完成[23](图2)。由于乳酸菌具备改善胃肠道功能和提高免疫力等益生特性,且不存在类似大肠杆菌的安全隐患,因此在食品和医药领域中得到了广泛的应用与研究[8](表1)。Zhong等[4]在前期研究中筛选到一株具有促睡眠作用的植物乳植杆菌L5,该菌具有较高的GABA生产能力,在MRS培养液中GABA产量为262 mg/L。Li等[24]通过响应面法对短促生乳杆菌(Lactobacillus brevis)NCL912的发酵培养基组分进行优化,优化后培养60 h,其GABA产量达35.57 g/L,比初始培养基产量提升130%。Binh等[17]从泡菜中分离出多种产GABA的乳酸菌,并对其中一株L. brevis K203的培养条件进行优化。研究发现,在添加6% L-谷氨酸、初始pH值为5.25、37 ℃培养条件下,L. brevis K203发酵72 h后可合成高达44.40 g/L的GABA (表1)。
谷氨酸棒杆菌(Corynebacterium glutamicum)作为工业氨基酸发酵中的关键微生物,因其出色的谷氨酸合成能力在工业生产中备受重视,目前也被认为是生产GABA理想的底盘生物(表1)。与大肠杆菌在能量受限或低氨浓度条件下谷氨酸合成受限的生理特征不同,谷氨酸棒状杆菌在利用葡萄糖生长过程中能够持续高效地合成谷氨酸(glutamic acid, Glu),因此其GABA的合成效率明显提高[25]。与大肠杆菌类似,谷氨酸棒状杆菌也通过GABA支路合成GABA (图2)。不同于大肠杆菌和乳酸菌,谷氨酸棒状杆菌具备高效的葡萄糖至谷氨酸的从头合成能力,使其无需额外添谷氨酸即可作为GABA合成的理想底盘。然而,谷氨酸棒状杆菌的胞内pH环境为中性,与GAD发挥活性所需的最适pH (4.0-5.0)不匹配,且其存在谷氨酸外排与GABA胞内积累的转运失衡问题,限制了其GABA合成效率[26-28] (表1)。由于谷氨酸棒状杆菌是一般认为安全(generally recognized as safe, GRAS)的食品级微生物,这为其在生产功能性食品和医药级GABA提供了潜在的应用优势[29]。另外,谷氨酸棒状杆菌通过中心碳代谢产生的α-酮戊二酸,在谷氨酸脱氢酶(glutamate dehydrogenase, GDH)催化下生成谷氨酸,从而为GABA合成提供充沛的前体。
芽孢杆菌属(Bacillus) 也是天然合成GABA的重要微生物类群。其中,巨大芽孢杆菌(Bacillus megaterium)、地衣芽孢杆菌(Bacillus licheniformis)等在正常生理代谢过程中能够自然产生GABA[30]。研究指出,芽孢杆菌主要通过GABA支路合成GABA (图2)。然而,在大部分情况下,野生型芽孢杆菌不具备全套GABA合成元件,通常需要特定条件或改造才能高效合成GABA。即使部分芽孢杆菌含有GAD,但其酶活性较低,从而限制了其GABA的合成产量[31]。另外,GAD发挥作用需要磷酸吡哆醛(pyridoxal phosphate, PLP)作为辅酶,而部分芽孢杆菌缺乏有效的PLP再生途径,导致辅酶供应不足,进而影响了GABA的持续合成[32]。除此之外,细胞内合成的GABA需要转运到细胞外才能被有效收集和利用,但芽孢杆菌自身的GABA转运系统效率低下,导致无法及时将胞内合成的大量GABA转运至胞外,从而造成产物反馈抑制,限制了GABA的进一步合成[33]。总体而言,芽孢杆菌在自然状态下合成GABA的水平较低,难以满足工业化生产的需求,因此需要改造来提高其合成GABA的能力(表1)。
红曲霉属(Monascus)作为中国传统发酵工业中的重要微生物资源,拥有悠久的应用历史。近年来,红曲霉通过从头合成途径协同合成GABA和天然色素等多种高价值产物,因此吸引了众多研究者探索其发酵生产中的潜力[34]。红曲霉主要是通过GAD催化合成GABA (图2),其GAD活性与孢子的萌发有关,参与了菌株能量代谢并发挥调控作用。虽然红曲霉具有完整的GABA合成系统,但在自然条件下,其合成GABA的效率相对较低且耗时较长,导致其工业应用受限(表1)。目前研究发现,采用红曲霉与乳酸菌共培养或优化红曲霉的发酵条件可以明显提高其GABA产量。刘志强等[35]将红曲霉SM048和植物乳植杆菌Lac.1共同接种于TYG培养基中,发现反应体系中的GABA产量为520 mg/L,比SM048单独发酵时提高了147.62%。Jiang等[36]从豆腐乳中分离得到了一株红曲霉M6,其初始GABA产量仅为3.657 g/L。Su等[37]在发酵培养基中添加磷酸二氢钾,将该菌株的GABA产量从1.27 g/L提升到1.50 g/L。类似地,后家衡等[20]通过响应面设计优化了菌株的发酵条件(培养温度35 ℃、初始pH 5.0、摇床速度120 r/min),将该株的GABA产量从5.527 g/L提高至7.826 g/L,在摇瓶发酵优化的基础上进行分批发酵初探,其GABA产量可以达到12.47 g/L (表1)。上述研究表明,通过发酵工程优化可显著提升红曲霉的GABA生产效率,为工业化应用提供了新的研究路径。
微生物源GABA合成与调控是一个涉及微生物代谢工程的复杂过程,主要通过微生物体内的代谢途径实现GABA的合成和精细调控。微生物发酵生产GABA主要依赖2条核心代谢途径:GABA支路和腐胺途径(图2)。GABA支路是三羧酸(tricarboxylic acid, TCA)循环的一条重要分支,广泛存在于原核与真核微生物中,是目前微生物合成GABA最主要和最普遍的途径[1]。为了提高GABA的产量,研究者可通过调控GAD酶活、优化发酵工艺、调整微生物的代谢网络以及利用基因工程技术改造微生物菌株等方法,实现GABA的高效生物合成。
GABA支路是连接三羧酸循环、负责GABA合成与再利用的核心代谢途径,该途径始于TCA循环中的α-酮戊二酸,在GDH催化下经还原氨基化生成L-谷氨酸,继而由GAD催化发生不可逆的脱羧反应,生成GABA与CO2。此步骤为限速步骤,GAD的活性与稳定性直接决定GABA的合成通量[38]。生成的GABA可进一步经GABA转氨酶(γ-aminobutyric acid transaminase, GABA-T)和琥珀酸脱氢酶转化为琥珀酸,重新进入TCA循环,构成一个代谢闭环[39]。李龙岩[18]研究证实,筛选出的天然携带GAD的益生菌GABA产量明显增高。
腐胺途径主要包括腐胺的合成及其后续的降解转化,其中降解过程主要通过腐胺利用(Puu)途径等多胺降解途径实现。在腐胺途径中GABA可由腐胺、其他多胺或鸟氨酸经过一系列降解反应生成[39]。常见的多胺类物质主要包括腐胺、亚精胺与精胺[40],它们分别在二胺氧化酶和多胺氧化酶催化下生成γ-氨基丁醛,然后经γ-氨基丁醛脱氢酶作用生成GABA,最后通过GABA支路进一步转化为琥珀酸进入TCA循环[41]。在腐胺降解转化过程中,Puu途径首先将腐胺γ-谷氨酰化,形成γ-谷氨酰腐胺,然后通过一系列的酶促反应最终转化为GABA和琥珀酸[42]。大肠杆菌可以通过腐胺途径合成GABA,而乳酸菌则缺乏该途径。由于腐胺及其代谢产物对宿主细胞具有很强的毒性,因此目前采用此方法合成GABA的报道较少[43]
微生物源GABA的合成受到精细的多层次调控,这不仅包括对合成途径关键酶的直接调控,更涉及细胞响应内外环境的全局性调控网络。研究表明,GABA合成相关基因常以操纵子结构发挥作用,包括gadBgadCgadR[44-45]。这些操纵子在不同细菌中的基因组成与结构具有多样性[46-47]。例如,在L. brevis中存在2个独立的谷氨酸脱羧酶编码基因gadAgadB[48-49]。相比之下,植物乳植杆菌中包含gadB基因[18],嗜热链球菌和青春双歧杆菌中含有1个gadB和1个gadC基因[49-50],而罗伊氏乳杆菌则携带有1个gadB基因和2个gadC基因[51]。在GABA合成过程中,gadBgadC基因的表达调控对GABA的合成及其产量有着直接影响;在转录调控层面,GadR蛋白作为特异性转录激活因子,能够激活gadBgadC基因的表达,但对gadA的调控作用相对较弱[52]。GABA支路本身是微生物一个重要的酸耐受机制。在酸性环境下,GAD消耗胞内质子合成GABA,直接缓解酸胁迫。因此,gad操纵子的表达受到严格的pH控制。在大肠杆菌和乳酸菌中,这一过程由双组分系统和/或特异性转录激活因子GadR调控[51];在全局调控水平上,氮代谢中心调控因子GlnR通过直接结合gadR操纵子抑制其转录,从而将GABA合成与细胞内氮源状态相偶联[53]。此外,碳代谢调控因子、应激响应系统及群体感应系统参与不同微生物中GABA合成的多层次调控,形成一个响应环境信号与代谢状态的协同网络[53]。环境胁迫响应系统以及群体感应信号也被证实能间接调制GABA合成基因的表达,形成一种多信号集成、适应动态环境的全局调控网络[54]。这些调控机制为微生物高效合成GABA提供了分子基础。目前,研究者结合全基因组测序与人工智能技术,可实现产GABA菌株的理性筛选与设计。另一方面,机器学习算法可通过学习已知高产菌株的基因组特征,构建表型预测模型,用于从宏基因组或菌种库中快速挖掘潜在高产菌株[55]。此外,深度学习模型可对启动子-操纵子序列进行功能预测,辅助设计表达优化元件,为构建高效合成底盘提供序列层面的指导[56]。总体而言,对多层次调控网络的深入理解,为通过合成生物学手段理性重构代谢流、突破天然调控限制奠定了基础。
基于对GABA合成途径及其内在调控网络的理解,研究者运用代谢工程与合成生物学策略对微生物细胞进行理性设计,以突破天然调控的限制。未来的代谢工程不应仅聚焦于gad基因的过表达,更应考虑重塑这些全局调控网络。目前GABA合成生物学的研究主要集中在全细胞催化、GAD酶改造、基因线路以及生物传感器等方面。
全细胞催化法利用微生物细胞作为生物催化剂,通过其内部代谢途径将前体物质转化为GABA[57]。Yu Plokhov等[58]通过基因工程手段提高GAD活性,并利用改造后的大肠杆菌催化合成GABA,成功实现280-300 g/L的GABA产量。李文强等[16]使用重组大肠杆菌BL21以谷氨酸为底物进行全细胞催化,使GABA产量提升至720.00 g/L,这也是目前文献报道的最高GABA产量(表1)。张六六等[19]通过共表达谷氨酸脱羧酶基因gadA和辅酶PLP再生关键基因pdxH,构建了重组枯草芽孢杆菌。通过高密度发酵,并在外源添加高浓度谷氨酸底物的条件下,优化催化体系的pH与离子强度(pH 5.0,40 °C,添加Ca2+/Mg2+),该工程菌在24 h内GABA产量高达327.00 g/L,且底物转化率接近完全(表1),展示了其在高效生物催化中的应用前景。为提高安全性,Lan等[59]利用CRISPR/Cas技术改造益生菌E. coli Nissle1917,实现了在无抗生素系统中高效生产GABA (17.9 g/L)。杨帆[60]采用大肠杆菌全细胞转化策略,成功将GABA产量由18.1 g/L提高至60.0 g/L。Yang等[61]从改善GAD的pH适应性、消除底物转运限制、增强酶的可溶性表达以及阻断GABA降解通路等方面入手,最终在全细胞催化条件下达到44.0 g/(L·h)的GABA产率。总体而言,全细胞催化技术已成为微生物源GABA生产直接发酵法的重要互补路线,凭借其催化高效的独特优势,成功用于GABA的规模化生产。
在分子机制层面,GAD催化效率的限制主要源于其酶学特性与微生物胞内环境不匹配。GAD活性高度依赖酸性pH环境,这与活性中心结构密切相关。在酸性条件下,GAD活性口袋中保守的质子化残基能够稳定底物谷氨酸的羧基并促进磷酸吡哆醛辅酶的电子转移,从而高效催化脱羧反应。当环境pH升至中性时,这些关键残基的去质子化会导致活性中心微环境改变、底物亲和力急剧下降,致使酶活下降90%以上[62]。GadC是一种依赖于质子动力势的逆向转运蛋白,在酸胁迫响应中,其同时介导胞外谷氨酸摄取和胞内GABA外排。这一过程不仅为GAD提供了底物,也协助维持了胞内pH稳态,但其转运效率与表达调控同样受到环境pH和转录因子GadR的严格调控[50]。因此,GAD的pH敏感性与GadC的转运耦合机制共同决定了微生物合成GABA的基础效率,也成为代谢工程与酶改造的核心靶点。定点突变是一种利用重叠延伸PCR进行的酶定向改造技术,该技术可通过改变特定基因的碱基序列来获得目标突变体。与随机诱变的不确定性相比,定点突变技术具有精确性和可预测性等特点。近年来,以生物信息学为基础的定点突变技术在酶改造中广泛应用,对酶法转化生产GABA起到了很大的促进作用。Thu等[63]对来自大肠杆菌的GAD进行删除C末端15个氨基酸残基(A452-466)操作,所获得的突变体GAD的最适pH作用范围向碱性偏移了1.0个pH单位,该突变体在pH 7.0时活性显著下降;若进一步在第89位氨基酸位点进行Glu→Gln突变,其最适pH作用范围则继续向碱性偏移了0.5个pH单位,在pH 7.5时基本完全丧失活力。研究指出,由于突变体的GAD在细菌胞内仍有较高活性,因此突变后的谷氨酸棒状杆菌GABA产量显著提升,最终GABA产量提升至38.60 g/L[18]。类似地,林玲等[64]通过序列比对,将突变目标锁定在短促生乳杆菌GAD第307位(位于β-片层)的氨基酸残基;实验表明,突变体在pH 6.0时的酶活力显著提升至原始酶的2倍,这归因于β-片层区域的残基突变可能引发的结构或取向变化,从而直接影响了酶的催化能力。这些研究共同表明,通过定点突变技术精准改造GAD的关键氨基酸残基,能够有效调控其最适pH范围并提升催化效率,从而显著提高GABA的产量。
代谢工程是利用基因工程和蛋白质工程技术对生物体的代谢途径进行改造,从而提高GABA合成效率和产量的方法。近年来,研究者采用代谢工程策略提高GABA的合成能力,具体方法包括4个方面。(1) 基因表达调控:通过敲除与GABA分解途径相关的基因来减少GABA的消耗,如敲除编码GABA通透酶的gabP基因,减少胞外GABA的吸收;或敲除编码GABA转氨酶的gabTpuuE基因,抑制GABA和琥珀酸半醛之间的可逆互变。Yu等[65]研究通过在E. coli BL21(DE3)中共表达谷氨酸脱羧酶基因gadAgadB以及谷氨酸转运蛋白基因gadC,使工程菌的GABA产量达到3.98 g/L。进一步在gabT转氨酶基因敲除的背景下,利用蛋白质支架对GAD与GadC进行空间共定位表达,从而将GABA产量提升至5.65 g/L[66]。Shi等[67]通过在解淀粉芽孢杆菌中过表达ppc基因来增加草酰乙酸的供应,从而提高L-Glu的积累和GABA的产量。(2) 代谢途径改造:Pham等[68]采用了酶空间组装策略,借助蛋白质支架将异柠檬酸脱氢酶、谷氨酸合成酶与谷氨酸脱羧酶三者偶联,在大肠杆菌中构建了高效的酶催化通道,使GABA产量提高了2.2倍。为进一步优化GABA生产,研究者对谷氨酸棒状杆菌实施了代谢工程改造:一方面过表达大肠杆菌的thrABC基因簇(该基因编码双功能的天冬氨酸激酶Ⅰ/高丝氨酸脱氢酶Ⅰ、高丝氨酸激酶及苏氨酸合成酶),以此强化中心碳代谢流向天冬氨酸族氨基酸的通量;另一方面敲除alaT基因(编码丙氨酸氨基转移酶)以减少苏氨酸合成途径的碳损失。该组合策略有效扩大了胞内谷氨酸的库容,最终成功提升了GABA的合成效率[69]。(3) 解除反馈抑制:为解除代谢产物对关键酶的调控,Wada等[70]在谷氨酸棒杆菌ATCC13032中敲除(pyruvate kinase, PYK)基因。该操作成功消除了天冬氨酸对磷酸烯醇式丙酮酸羧化酶的反馈抑制,从而有效提升了谷氨酸的合成产量。(4) 动态调控技术:Wei等[71]开发了一种可调的生长期依赖性自主双功能遗传开关。利用生长阶段特异性的启动子驱动阻遏蛋白(如TetR)表达。在指数生长期,阻遏蛋白累积并抑制GABA合成基因;进入稳定期后,阻遏蛋白被特异性降解,GABA合成模块自动解除抑制并高效表达,成功缓解了菌体生长与产物合成的资源竞争矛盾。未来的演进将聚焦于开发响应更多元内源信号的复杂逻辑门控网络以实现对细胞代谢的全方位精准调控。
基因线路是依据工程设计原理对天然存在的各种酶、调控分子等进行简单化、模块化处理,设计出具有各种基本功能的元件[72]。利用基因线路元件,可以实现天然基因线路的重编程,用于探索传统生物学难以研究的一些基本科学问题。2009年,美国加州理工大学Elowitz等[73]在枯草芽孢杆菌中构建了一个“先正后负”的人工负反馈基因线路,替换了天然的“先正后负”的负反馈网络。研究发现人工负反馈网络产生的感受态响应持续时间短、噪声小,而天然负反馈网络的感受态持续时间长短不一,分布特别宽,即利用放大基因表达噪声来适应环境的多变和不确定性[74]。基因线路作为各种合成生物学应用的可编程控制组件,通常能够实现一些传统技术难以实现的、“智能化”的控制方式。在代谢工程领域,基因线路也展现出了“智能化”控制的潜力,提供了更好的解决方案。基于细菌群体感应功能设计的基因线路能够根据细菌数量对目的基因表达进行动态调节,使对细菌生长有负面影响的基因在细菌达到一定数量之后再表达,规避了传统发酵过程中微生物生长和发酵产物生产的矛盾,实现了对发酵过程“先生长,再生产”的动态调控,同时也避免了使用昂贵的诱导剂[75]
生物传感器是以生物学组件作为主要功能元件,能够感应特定的待测物质,并按照一定规律将其转换成可识别信号的器件或装置[76]。最早的生物传感器于1962年被开发,利用葡萄糖氧化酶来测量葡萄糖浓度。此后,生物传感器被设计用于多种应用,包括医疗诊断、环境监测和食品安全等领域。一种有效的GABA生物传感器可以促进GABA的快速简便测量和高通量筛选。双组分系统(two-component system, TCS)广泛存在于微生物信号传导通路中,能够感知和响应各类细胞内外刺激,是开发具有合成生物学应用的生物传感器的宝贵资源。Zhao等[76]设计将化学趋化受体和双组分系统的组氨酸激酶形成嵌合受体PctC/PhoQ,通过嵌合体文库筛选构建正交绝缘的双组分突变体对PhoQ/PhoP,构建得到了GABA的生物传感器。通过响应调节子PhoP的DNA结合结构域工程、传感器启动子和核糖体结合位点强度的精调以及在传感器中引入烟草蚀纹病毒蛋白酶降解系统,进一步降低了GABA传感器的本底泄漏。优化后的生物传感器具有增大的动态范围(15.8倍)和较高的检测阈值(22.7 g/L)。最终研究者将该传感器与液滴微流控技术结合,高通量筛选获得高产GABA菌株,证明了该生物传感器胞外检测GABA的有效性,为GABA的进一步代谢工程提供了高效可靠的工具。未来的研究重点将集中于开发更高性能、更特异性的GABA生物传感器,并设计与之一体化的复杂基因线路,最终实现GABA生产的全自动化、智能化控制。
目前,多国企业已实现GABA功能性饮料、压片糖果等产品的商业化,主要采用乳酸菌发酵工艺进行生产。从研究基础来看,国外学者对微生物发酵合成GABA的探索起步较早,已建立较为完善的研发和应用体系,特别是在日本,相关技术已基本实现工业化。已有研究利用植物乳植杆菌在葡萄汁中发酵,可获得0.4 g/L的GABA;也有研究从蓝莓汁、酒糟中筛选出短促生乳杆菌菌株,其GABA产量可达6.3-26.9 g/L[77-79]。然而,从实验室研究向大规模低成本生产转化仍面临若干核心瓶颈。(1) 菌株性能缺陷:野生型菌株的GABA产量普遍较低,GAD的活性高度依赖酸性pH,而大多数微生物胞内环境接近中性,这种不兼容性导致在正常生理条件下GAD的催化效率低下[79]。(2) 代谢产物复杂与副产物竞争:GABA合成与TCA循环、氮代谢等中心代谢途径紧密相连,代谢流容易分散至菌体生长或其他副产物(如琥珀酸)的合成过程,造成碳源和氮源的利用效率降低,进而导致底物转化率下降[80]。(3) 代谢区室化与转运障碍:在谷氨酸棒状杆菌等菌株中存在谷氨酸外排和GABA胞内积累的矛盾现象。这种空间分布失衡不仅可能引发GABA的降解,还会通过反馈抑制严重限制其合成效率[13]。(4) 发酵工艺经济挑战:虽然以谷氨酸为底物的全细胞催化能够获得较高产量,但底物成本高昂;而以葡萄糖为底物的从头合成通常面临发酵周期长且下游分离纯化工艺复杂等问题,整体生产成本仍有进一步降低的空间[13]
随着制药、食品和保健品行业对GABA需求的持续增长,提高其生产效率与降低制造成本已成为推动产业化的关键。为此,未来的研究和技术开发应聚焦于以下方向。(1) 高效底盘构建:突破传统模式菌株的限制,利用合成生物学工具开发从头设计的“底盘菌株”。对具有天然高谷氨酸合成能力的谷氨酸棒状杆菌进行系统性重塑,敲除冗余途径、引入外源高效GAD,并优化辅酶再生系统[26]。(2) 动态调整策略:开发动态、自适应调控的基因线路,实现对菌体生长与产物合成阶段的时序性精准调控[71]。构建响应群体感应信号、特定代谢物或生长阶段变化的智能基因开关,在菌体生物量达到较高水平后自动启动GABA合成模块,并抑制竞争性途径。(3) 人工智能与酶工程的深度融合:利用机器学习算法分析GAD蛋白的序列与结构数据,精准预测其热稳定性、最适pH和活性的关键氨基酸残基位点,指导定点突变以改造出适于中性胞内环境的GAD变体[56]。(4) 低成本培养基开发:利用糖蜜、玉米浆等工业副产物替代精制碳源及氮源。(5) 连续发酵与产物分离过程耦合:开发固定化细胞反应器与膜分离集成工艺[81]
  • 国家重点研发计划(2022YFD2100703)
  • 国家自然科学基金(32222068)
  • 广东省科学院青年人才专项(2023GDASQNRC-0102)
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2026年第66卷第5期
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doi: 10.13343/j.cnki.wsxb.20250849
  • 接收时间:2025-11-12
  • 首发时间:2026-05-09
  • 出版时间:2026-05-04
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  • 收稿日期:2025-11-12
  • 录用日期:2025-12-29
基金
The National Key Research and Development Program of China(2022YFD2100703)
国家重点研发计划(2022YFD2100703)
The National Natural Science Foundation of China(32222068)
国家自然科学基金(32222068)
The Young Talents Special Project of Guangdong Academy of Sciences(2023GDASQNRC-0102)
广东省科学院青年人才专项(2023GDASQNRC-0102)
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    1.广东省科学院微生物研究所,华南应用微生物国家重点实验室,广东省微生物安全与健康重点实验室,国家卫健委微生物食品营养与安全科技创新平台,国家市场监督管理总局重点实验室(食品微生物安全大数据技术),广东 广州
    2.四川农业大学 食品学院,四川 雅安
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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