微生物学报

Model	Application	References
Lotka-Volterra model and consumer-resource model	The coupling law of resource allocation and metabolic network in microbial community was analyzed and the niche of microbial interaction was predicted	[50-51]
Coarse grained metabolic model	The response of the microbiota to interference was accurately predicted by simulating one-way, two-way, and multi-way cross-feeding	[52]
D-OptCom	It is used for multi-scale metabolic interaction simulation of microbial community and microbial community analysis	[53]
Genome-scale metabolic model	Systematically characterize the metabolic network of organisms, predict the exchange of metabolites between species (such as cross-feeding, electron transfer), and guide the optimization of strain combinations	[54-55]
Super community combinations	Simulating complex microbial community synergy for dynamic analysis of transmembrane metabolite exchange flux in microbial communities	[56]
Flux balance analysis	It is used to optimize the co-culture system, biological community metabolic network reconstruction and metabolic simulation	[57]

Model	Application	References
Lotka-Volterra model and consumer-resource model	The coupling law of resource allocation and metabolic network in microbial community was analyzed and the niche of microbial interaction was predicted	[50-51]
Coarse grained metabolic model	The response of the microbiota to interference was accurately predicted by simulating one-way, two-way, and multi-way cross-feeding	[52]
D-OptCom	It is used for multi-scale metabolic interaction simulation of microbial community and microbial community analysis	[53]
Genome-scale metabolic model	Systematically characterize the metabolic network of organisms, predict the exchange of metabolites between species (such as cross-feeding, electron transfer), and guide the optimization of strain combinations	[54-55]
Super community combinations	Simulating complex microbial community synergy for dynamic analysis of transmembrane metabolite exchange flux in microbial communities	[56]
Flux balance analysis	It is used to optimize the co-culture system, biological community metabolic network reconstruction and metabolic simulation	[57]

Plant	SynComs size ＆ origin	Mechanism	Results	References
Solanum lycopersicum L.	15 strains (isolated from Indigofera tinctoria Linn. root of Jizan)	Differential expression of salt stress-related genes and ion accumulation in aboveground parts	Under salt stress and non-sterile conditions, SynComs has a strong growth-promoting effect on Solanum lycopersicum L.	[85]
Arabidopsis thaliana (L.) Heynh.	22 strains (symbiotic bacteria isolated from Arabidopsis thaliana (L.) Heynh. roots)	Redox-mediated mechanism of SynComs	The molecular mechanism was established to elucidate the composition of microbial communities derived from plants, and the functional diversity of plant rhizosphere-dependent specialized metabolites was analyzed	[86]
Zea mays L.	6 strains (Bacillus strains isolated from roots and leaves of Zea mays L.)	Endophytic microorganisms fight pathogens by competing, producing disease-resistant substances, or activating plant immune systems	Promote plant growth and significantly reduce the incidence of band-shaped leaf blight and sheath blight	[87]
Oryza sativa L.	4 strains (isolated from intercropping Oryza sativa L. roots)	Synergistic soil phosphorus activation, root architecture remodeling, and transporter gene regulation to regulate phosphorus use efficiency	It effectively regulates the distribution of P in the aboveground and underground parts of Oryza sativa L., promotes root growth and increases the yield of rice plants	[88]
Triticum aestivum L.	4 strains (isolated from Triticum aestivum L. rhizosphere)	Indole acetic acid producing bacteria and some volatile-releasing bacteria interact with fungi	Protecting Triticum aestivum L. from Rhizoctonia solani AG8 infection, reducing the occurrence of Triticum aestivum L. root rot and affecting plant characteristics	[89]
Cucumis sativus L.	2 strains (Bacillus strains isolated from Cucumis sativus L. rhizosphere soil and native Pseudomonas strains beneficial to plants)	Synergistic metabolic promotion between strains	Synergistically promote the growth of Cucumis sativus L., significantly increased the plant branch height, dry weight, and chlorophyll content	[90]
Glycine max	12 strains (isolated from Glycine max rhizosphere)	To systematically regulate the N and P signal transduction network at the transcriptional level and enhance the growth pathway related to auxin response	It significantly increased the nodulation rate and nitrogenase activity, and increased the field yield of Glycine max by approximately 20%-30%	[91]

Plant	SynComs size ＆ origin	Mechanism	Results	References
Solanum lycopersicum L.	15 strains (isolated from Indigofera tinctoria Linn. root of Jizan)	Differential expression of salt stress-related genes and ion accumulation in aboveground parts	Under salt stress and non-sterile conditions, SynComs has a strong growth-promoting effect on Solanum lycopersicum L.	[85]
Arabidopsis thaliana (L.) Heynh.	22 strains (symbiotic bacteria isolated from Arabidopsis thaliana (L.) Heynh. roots)	Redox-mediated mechanism of SynComs	The molecular mechanism was established to elucidate the composition of microbial communities derived from plants, and the functional diversity of plant rhizosphere-dependent specialized metabolites was analyzed	[86]
Zea mays L.	6 strains (Bacillus strains isolated from roots and leaves of Zea mays L.)	Endophytic microorganisms fight pathogens by competing, producing disease-resistant substances, or activating plant immune systems	Promote plant growth and significantly reduce the incidence of band-shaped leaf blight and sheath blight	[87]
Oryza sativa L.	4 strains (isolated from intercropping Oryza sativa L. roots)	Synergistic soil phosphorus activation, root architecture remodeling, and transporter gene regulation to regulate phosphorus use efficiency	It effectively regulates the distribution of P in the aboveground and underground parts of Oryza sativa L., promotes root growth and increases the yield of rice plants	[88]
Triticum aestivum L.	4 strains (isolated from Triticum aestivum L. rhizosphere)	Indole acetic acid producing bacteria and some volatile-releasing bacteria interact with fungi	Protecting Triticum aestivum L. from Rhizoctonia solani AG8 infection, reducing the occurrence of Triticum aestivum L. root rot and affecting plant characteristics	[89]
Cucumis sativus L.	2 strains (Bacillus strains isolated from Cucumis sativus L. rhizosphere soil and native Pseudomonas strains beneficial to plants)	Synergistic metabolic promotion between strains	Synergistically promote the growth of Cucumis sativus L., significantly increased the plant branch height, dry weight, and chlorophyll content	[90]
Glycine max	12 strains (isolated from Glycine max rhizosphere)	To systematically regulate the N and P signal transduction network at the transcriptional level and enhance the growth pathway related to auxin response	It significantly increased the nodulation rate and nitrogenase activity, and increased the field yield of Glycine max by approximately 20%-30%	[91]

合成菌群在促进农业可持续发展中的研究进展

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罗汶婧 ²^,³^,⁴ , 王博瑞 ²^,³^,⁴ , 马红彬 ¹^,²^,³^,⁴ , 李慧萍 ¹^,²^,³^,⁴

微生物学报 | 综述 2025,65(10): 4308-4325

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微生物学报 | 综述 2025, 65(10): 4308-4325

合成菌群在促进农业可持续发展中的研究进展

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罗汶婧²^,³^,⁴, 王博瑞²^,³^,⁴, 马红彬¹^,²^,³^,⁴, 李慧萍¹^,²^,³^,⁴

作者信息

¹宁夏大学，西北土地退化与生态恢复省部共建国家重点实验室培育基地，宁夏银川

²宁夏大学，宁夏回族自治区草牧业工程技术研究中心，宁夏银川

³宁夏大学，农业农村部饲草高效生产模式创新重点实验室，宁夏银川

⁴宁夏大学林业与草业学院，宁夏银川

Research progress on synthetic microbial communities in promoting sustainable agriculture development

Wenjing LUO²^,³^,⁴, Borui WANG²^,³^,⁴, Hongbin MA¹^,²^,³^,⁴, Huiping LI¹^,²^,³^,⁴

Affiliations

¹Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwest China, Ningxia University, Yinchuan, Ningxia, China

²Grassland and Animal Husbandry Engineering Technology Research Center of Ningxia Hui Autonomous Region, Ningxia University, Yinchuan, Ningxia, China

³Key Laboratory for Model Innovation in Forage Production Efficiency, Ministry of Agriculture and Rural Affairs, Ningxia University, Yinchuan, Ningxia, China

⁴School of Forestry and Grassland Science, Ningxia University, Yinchuan, Ningxia, China

出版时间: 2025-09-04 doi: 10.13343/j.cnki.wsxb.20250213

文章导航

摘要

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目前，全球农业可持续发展面临土壤退化、资源限制和环境污染等多重压力。随着人口持续增长，以及对粮食品质需求的不断提高，提升土壤健康水平已成为保障粮食安全的重要基础。虽然化肥在保障植物高产优质方面发挥着重要作用，但其过量或不合理使用会造成土壤酸化、水体富营养化等环境问题。植物根际有益微生物在宿主养分吸收、胁迫耐受性以及适应环境变化过程中发挥着重要作用。其中，合成菌群(synthetic microbial communities, SynComs)是通过定向设计功能明确、遗传背景清晰的多个微生物组合，从而实现单一菌株无法完成的复杂功能。它是破解植物-土壤-微生物关键界面互作机制的有力工具，在植物养分高效利用、抗逆性提升和化肥减量增效等方面发挥着关键作用。本文分析了SynComs的概念演化以及当前的研究态势，系统阐述了其构建原则、方法及技术工具，并从促进植物生长、抵抗生物和非生物胁迫、改善和恢复土壤健康等方面，概述了SynComs在农业可持续发展中的作用。最后，展望了未来的研究方向，应重视靶向型菌剂的研发、人工智能(artificial intelligence, AI)技术在群落构建中的应用，以及提高SynComs在田间应用的效果，为利用近自然微生物手段解决粮食安全、资源高效利用和环境保护的多目标协同发展问题提供支撑，进而推动我国农业绿色发展。

关键词

合成菌群 / 菌株相互作用 / 植物-土壤系统健康 / 农业可持续发展

Abstract

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Currently, the sustainable development of global agriculture is facing multiple challenges, including soil degradation, resource constraints, and environmental pollution. With the continuous growth of the population and the increasing demand for food quality, improving soil health has become a crucial foundation for ensuring food security. Although chemical fertilizers play an important role in maintaining the high yields and high quality of plants, their excessive or unreasonable use can cause environmental problems, such as soil acidification and water eutrophication. Rhizosphere microbial communities play an essential role in plant nutrient acquisition, tolerance to environmental stress, and adaptation to environmental changes. Among them, synthetic microbial communities (SynComs) are designed via the targeted assembly of multiple microorganisms with well-defined functions and clear genetic backgrounds, enabling the achievement of complex functionalities that cannot be accomplished by single strains. They are powerful tools for deciphering the key interface interaction mechanisms among plants, soil, and microorganisms and play a vital role in promoting efficient utilization of plant nutrients, enhancing plant stress resistance, and increasing the efficiency and reducing the application of fertilizers. This study reviews the conceptual evolution, current research trends, and construction principles, methods, and tools of SynComs, and summarizes the role of SynComs in the sustainable development of agriculture from the aspects of promoting plant growth, inhibiting biotic and abiotic stresses, and improving and restoring soil health. Furthermore, this paper makes an outlook on the future research directions and emphasizes the research and development of targeted microbial agents, the application of artificial intelligence (AI) in community assembly, and the performance improvement of SynComs in field applications, aiming to support the coordinated and multi-objective development of food security, efficient resource utilization, and environmental protection through near-natural microbial means, thereby facilitating the green agricultural development of China.

Key words

synthetic microbial communities / microbial interactions / health of plant-soil systems / sustainable development of agriculture

引用本文

罗汶婧, 王博瑞, 马红彬, 李慧萍. 合成菌群在促进农业可持续发展中的研究进展. 微生物学报, 2025 , 65 (10) : 4308 -4325 . DOI: 10.13343/j.cnki.wsxb.20250213

Wenjing LUO, Borui WANG, Hongbin MA, Huiping LI. Research progress on synthetic microbial communities in promoting sustainable agriculture development[J]. Acta Microbiologica Sinica, 2025 , 65 (10) : 4308 -4325 . DOI: 10.13343/j.cnki.wsxb.20250213

正文

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随着人类社会对优质农产品需求的持续增长，我国耕地质量正面临急剧退化的严峻挑战。据农业农村部《2019年全国耕地质量等级情况公报》数据，我国存在突出障碍因子的农田土壤面积占耕地总面积的21.95%^[1]。其中，土壤盐渍化和酸化进程加速，致使耕作层理化性质发生改变；同时，传统耕作制度与重型农业机械的高频作业导致土壤机械压实程度不断加剧^[2]。土壤结构性障碍限制了土壤的透气性和保水保肥能力，使土壤有机质含量和养分有效性显著降低，引发土地退化、草地贫瘠化和林地沙化等一系列问题，最终造成因土壤功能性衰退而严重制约植物生长和产量的情况^[3-5]。预计到2050年，全球人口将接近百亿，人类对粮食的刚性需求将增加70%^[6]。因此在全球人口增长、资源与环境复合压力的背景下，确保全球粮食安全已成为亟待解决的难题。

虽然化肥被誉为现代农业的“营养针”，在保障粮食安全方面发挥着至关重要的作用，但过量和集约化施肥导致大量资源浪费、土壤质量下降、土壤酸化等问题，严重制约了农业可持续发展^[6-9]。因此在保障粮食安全的同时，如何实现资源高效利用与生态保护是当前农业可持续发展的核心目标。

根际是植物根系吸收养分和水分的门户，是根系-土壤-微生物相互作用的微区域，定殖于该微环境中的微生物被称为根际微生物^[10]。在自然环境中，它们会大量富集在植物根系内部和周围，这些微生物构成根际微生物组，根际微生物组通过活跃的物质转化和相互作用直接影响宿主植物的营养和健康，对于推动我国化肥减施、农药减量，促进农业绿色发展具有重要意义，因此根际微生物组也被视为植物的“第二基因组”^[11-15]。然而，由于自然环境的异质性和复杂性，将单一优良微生物应用到土壤后，其会与土著微生物发生复杂的相互作用(竞争拮抗或协同共存)，因此激发单个土壤微生物发挥功能仍存在一定限制^[13-16]。合成菌群(synthetic microbial communities, SynComs)是由多个功能菌种组成的复合体，能够实现单一菌株无法完成的复杂功能，从而弥补单一菌株功能受限的不足。大量研究表明，合成菌群在促进植物养分高效利用和提高植物抗逆性等方面展现出巨大潜力。例如，固氮菌与解磷菌的协同作用可显著提高氮磷利用率^[16]；添加SynComs能修复酸化土壤的微生物网络结构^[17]；由具有重金属抗性的细菌组成的SynComs能促进不同重金属污染土壤中植物的生长，降低重金属向植物地上部的转移，从而阻断重金属进入食物链的风险^[18]。此外，研究表明SynComs技术可实现化肥减施、提高作物养分效率的目的，并且到2030年，全球对生物肥料的需求将达50.2亿美元，表明以SynComs为核心的生物肥料是支撑农业绿色发展、保障国家粮食安全的重要途径^[19]。

目前，关于SynComs的研究虽进展迅速，但在其研究态势、构建工具及农业应用等方面仍存在诸多难题和挑战。本文采用CiteSpace等工具，基于Web of Science (WOS)核心合集数据库对其进行相关文献的可视化分析以总结其研究态势，阐述SynComs的构建原则、方法与工具，以及SynComs在农业可持续发展中的应用，以期为合成菌群在农业可持续发展中的应用提供理论参考。

1 合成菌群的概念及研究态势

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1.1 合成菌群的概念

合成菌群起源于医学研究中的合成生物学技术。Burlage等^[20]于1994年首次发现，将工程菌株引入微生物群落可形成共生微生物联盟，该联盟能够有效抑制病原菌的定殖，同时延长有益微生物的定殖时间，为微生物群落定向调控的相关研究开辟了新纪元^[20-21]。随后，Brenner等^[22]在整合合成生物学领域的研究中提出了“工程微生物菌落(engineering microbial consortia)”的概念，这被视为合成菌群的前身，为合成菌群的形成与发展奠定了基础。随着相关研究的不断深入，De Roy等^[23]对合成菌群进行了明确定义：在特定受控条件下，由2种或多种已知微生物所构成的共培养体系。韦中等^[24]基于合成生物学和生态学基本理论将合成菌群的概念扩充为：将分类地位明确、功能特性清晰的2个或多个不同微生物在特定可控条件下按预设比例进行混合培养，经定向进化最终获得一种高效、功能强、可控性好、易保存且便于应用的有机微生物群体^[24-27]。这些优良特性使其在微生物组功能和生态机制的解析、农业生物技术等方面展现出巨大的研究价值和应用潜力^[25]。

1.2 研究态势

SynComs属于合成生物学、微生物学和微生物组学的交叉领域，是新兴的研究方向^[22,25]。本文以“synthetic microbial consorti or synthetic microbial consortium or microbiota or microbiome or microbial community”和“soil”为关键词，根据Web of Science核心数据库检索结果和统计分析发现，近10年来SynComs相关研究呈快速增长趋势(图1A)，其跨学科研究特征显著。2015-2025年期间的学科分布数据显示，环境科学与生态学(38.86%)、农业(28.90%)、微生物学(18.11%)、植物科学(8.60%)是SynComs研究所涉及的主要领域(图1B)。此外，SynComs的相关研究也呈现地域分布特征(图1C)，中国学者以22 163篇的发文量位居全球首位；美国次之(8 301篇)；随后是德国(3 242篇)、澳大利亚(2 333篇)和西班牙(1 901篇)等国家。这表明SynComs在环境安全与农业生产应用中发挥关键作用，其调控植物-土壤-微生物关键互作界面的复杂机制是目前学术界关注的焦点^[28]，这为“破译”根际微生物菌群调控生态安全和植物健康的机制，科学指导根际菌群应用，保障农业可持续发展、粮食安全和生态健康提供科学依据和技术支持。

2 SynComs的构建

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构建SynComs是探究和利用复杂自然微生物群落的有效途径之一。其中，SynComs的构建原则是确保其稳定性、功能性和可重复性的关键。然而，关于合成菌群的构建原则学术界存在诸多见解与观点。基于生态位过程理论的研究表明，群落的结构和代谢功能主要由确定性因素驱动，这些因素包括物种的固有特征、物种间的相互作用以及环境条件的影响。具体而言，微生物群落的组装过程被认为是由生物因素(如物种间的竞争、捕食等相互作用)和非生物因素(如pH、温度等环境条件)共同塑造的；这种确定性过程的核心在于微生物对不同栖息地的偏好及其适应性策略存在差异，即不同物种在特定环境条件下的生存能力和功能表达决定了它们在群落中的分布和丰度，这种观点强调环境过滤和物种相互作用在群落构建中的主导作用^[29-30]。有学者则认为，遗传进化在SynComs的种间相互作用中扮演核心角色。群落内各成员通过基因水平转移、突变和自然选择等进化机制不断适应环境变化并优化其功能，导致群落内物种间形成协同或竞争关系，从而使整个生态系统逐渐趋近一种功能最优的稳定状态^[31-32]。Lawson等^[33]提出了一种以设计-构建-测试-学习(design-build-test-learn)循环为核心的研究范式，为SynComs的设计与优化提供了系统化框架。该方法要求首先根据目标设计并构建具有特定结构和功能的SynComs，随后通过高通量测序、代谢组学等手段测试其功能，并利用机器学习(machine learning, ML)算法和数据分析工具总结规律，为下一轮设计-构建-测试-学习循环提供理论依据和实践指导^[21,33]。SynComs的构建融合了生态学、合成生物学与系统生物学等基本原理，其核心在于通过设计实现微生物间的功能分工与协同作用。目前，对SynComs构建方法的研究多聚焦于基于功能的“自上而下(top-down)”或基于相互作用的“自下而上(bottom-up)”，而忽略了微生物的进化关系与功能特征、生物间潜在的代谢互作关系等。本文将对以上4种构建方法进行较为全面的分析。

2.1 SynComs的构建方法

2.1.1 基于分类学或系统发育的构建方法

基于分类学或系统发育的构建方法是指通过整合微生物的进化关系与功能特征来构建SynComs，从而为高效稳定的功能群落组装提供理论基础。在以分类学为导向的组装中，研究者通常基于自然微生物的组成差异来筛选核心功能类群，这类方法主要依赖于自然群落的生态位分化规律。例如，Zhuang等^[34]通过分析不同农业实践下的根际微生物丰度差异，筛选出与植物生长显著相关的菌株构建SynComs，并通过试验证实其可显著提高植物生物量。系统发育驱动的组装策略则更关注微生物的进化关联与功能协同性。通过宏基因组和代谢组数据关联分析发现糖用甜菜根际中壳多糖酶编码(chitinase-encoding)基因的富集特征，据此构建的小型SynComs可有效抑制土传病害^[35]。尽管分类学和系统发育组装方法各有侧重点，然而未来的研究趋势将更加强调对分类和系统发育数据的多维整合，从而进一步解决功能冗余和生态位竞争之间的平衡问题。

2.1.2 基于交叉喂养的代谢互作构建方法

基于表型互作的组装策略聚焦于菌株的代谢能力，通过筛选具有明确功能特征的菌株实现目标驱动的群落设计；交叉喂养(cross-feeding)正是以此为基础，通过设计微生物间的代谢互补关系以实现微生物群落稳定共存的基本方法；这种方法模拟了自然界中普遍存在的现象：大多数微生物无法合成自身所需的全部营养物质，而是通过与邻近微生物进行代谢物的交换，从而实现资源的互补与共享；此外，微生物群落通过代谢交叉喂养策略维持了从单一个体到复杂群落的功能完整性与生物多样性间的平衡^[36-37]。研究表明群体感应(quorum sensing, QS)是这些微生物间相互作用的核心调控机制，其通过协调营养物质的摄取与代谢过程使微生物群落能够精确响应并适应不断变化的环境^[38-39]。Ma等^[17]研究发现，红城红球菌(Rhodococcus erythropolis)和铜绿假单胞菌(Pseudomonas aeruginosa)组成的功能菌群对铝的耐受性优于单个菌株，并揭示了基于交叉喂养的微生物铝耐受性机制，为设计SynComs以支持酸性土壤地区的粮食安全和可持续农业提供新见解。然而，微生物交叉喂养相互作用的探索尚处于起步阶段，其分子识别机制、代谢物交换阈值及环境扰动响应等关键科学问题亟待系统解析^[40]。

2.1.3 基于功能的“自上而下”构建方法

基于功能的“自上而下”构建方法(图2)应用多组学分析技术，从宏观微生物群落入手，解析其系统运行规律，进而揭示维持系统稳定的分子机制^[41-43]，即通过简化天然土壤微生物群落，对天然存在的微生物群落进行定向改造，保留特定功能模块进行定向重建^[33,44]。该方法首先从自然环境中分离原始微生物群落，然后利用稀释技术或调整环境参数来模拟微生物群落的动态变化、自然演替或生态恢复过程，经过一系列继代培养后，获得一个简化的微生物群落体系，进而通过对该体系的精准操控以实现预期的特定功能^[45]。该策略可优先保留自然群落中已存在的微生物互作关系，规避人工组合菌株间的非自然竞争或拮抗效应。该策略在土壤修复应用中表现出良好的功能延续性，特别是在有机污染物降解方面展现出工程化群落难以比拟的优势^[46]。研究表明芽孢杆菌门(Bacillota)和拟杆菌门(Bacteroidota)的菌群在磺胺降解过程中发挥重要作用，其中芽孢杆菌属(Bacillus)和黄杆菌属(Flavobacterium)是关键菌群，这些微生物群落在1周内降解磺胺类抗生素近50%，4周后降解率可达78.3%^[47]。然而，“自上而下”策略需通过稀释培养或梯度富集获得低复杂度群落，但据估算，每克土壤中约含数万个物种、100亿左右微生物，其中仅有1%的物种可通过分离培养进行研究，因此该策略存在一定局限性。

2.1.4 基于相互作用的“自下而上”构建方法

随着高通量测序技术和生物信息学分析工具的飞速发展，研究者可通过整合测序数据与表型分析结果，精确模拟并预测微生物间的相互作用及其调控网络，并以此为依据设计具有特定功能的SynComs，这种方法被称为“自下而上”的构建方法，目前这类研究主要集中在模式微生物上^[44]。Chen等^[48]通过代谢组学与高通量分析揭示了氰化物会显著影响花生微生物群落的结构组成，而重组的花生根际微生物群落可促进其根部对养分的吸收，进而提高植物产量。也有学者通过宏基因组数据分析和网络分析精准识别关键微生物类群及其功能特征，进而构建特定功能的SynComs，实现对植物表型的定向调控^[35]。然而，在自然微生物群落中不同物种对整体群落功能的贡献程度各不相同，因此从复杂的自然微生物群落中鉴别并筛选核心微生物是构建SynComs的关键。基于相对丰度、功能表达和微生物网络分析是目前常见的分离筛选核心微生物的标准^[49]。然而，在实际研究中这3种筛选标准并非绝对孤立，而是相互补充、综合应用。通过结合相对丰度、功能表达和微生物网络分析等多方面信息可以更全面、深入地掌握微生物群落的结构和功能，从而更准确地筛选核心微生物。目前基于“自下而上”的SynComs的构建通常包括以下几个步骤：微生物的选择、培养条件的优化、群落的组装与验证(图3)。

2.2 构建SynComs的工具与技术

在生物信息学与群落生态学模型取得突破性进展的驱动下，学者基于计算模拟和实验验证相结合的策略开发出多种SynComs构建工具，为环境修复、生物制造等领域的工程化应用提供技术支撑(表1)。以广义Lotka-Volterra方程和消费者-资源动力学模型及代谢通量粗粒化模型为代表的群落生态模型，通过解析微生物群落内资源分配与代谢网络的耦合规律，建立了SynComs构建的定量调控框架^[58]。代谢网络建模技术则是通过通量平衡分析(flux balance analysis, FBA)预测菌群代谢物转运路径，指导构建具有协同代谢功能的人工群落^[59]。例如，通过多尺度代谢互作模拟动态代谢建模框架(dynamic OptCom, D-OptCom)预测微生物之间的代谢物交换，优化微生物群落^[60]；基因组规模代谢模型(genome-scale metabolic model, GEM)的约束重建和分析(如Cobra)方法，整合大量的生物学数据和数学方法，以研究混合微生物群落的代谢活动，为合理设计合成微生物群落提供指导，并为生态修复提供有效策略^[61]。

随着高通量基因组解析与多组学技术的快速发展，一些创新的计算模型和工具也应运而生，为处理大规模宏基因组数据集提供了便利，也为SynComs理性设计与功能优化提供了关键支撑^[62]。例如，Hale等^[63]采用合成“长读长”序列和亲和力测序技术，深入探究了大豆根际微生物群落的组成与多样性，并据此探讨了其与环境因素和植物表型特征之间的关系。在功能基因组解析层面，病原与宿主互作数据库(pathogen host interactions database, PHI-base) (用于致病性鉴定)通过整合病原与宿主相互作用(pathogen host interactions, PHI)与表型突变数据，构建了基因型-表型关联网络图谱，为宿主-病原互作机制解析提供了基准数据库^[64]；而MacSyFinder则基于蛋白结构域组成与基因簇排列规律，实现原核生物中大分子复合物的功能注释与进化解析^[65]；AntiSMASH则通过模块化算法精准注释次级代谢产物生物合成基因簇(biosynthetic gene cluster, BGC)，解析微生物化学多样性形成机制^[66]。在群落代谢建模领域，超级群落组合(super community combinations, SuperCC)作为整合动态通量平衡分析与多组学数据同化的计算框架，通过构建多物种代谢网络的约束优化模型，用于微生物群落中跨膜代谢物交换通量的动态解析^[56]。与其他模型相比，SuperCC能更精准模拟复杂微生物群落的协同代谢网络，为设计和构建天然微生物组提供指导^[56,67]。

随着人工智能(artificial intelligence, AI)技术的迅猛发展，机器学习算法已被成功应用于SynComs的理性设计与实验优化，通过高通量数据训练建立的预测模型，特别是在菌群组合预测、功能模块组装及生长参数优化等关键环节中，能够实现微生物群落组成-功能关系的精准预测(准确率>85%)，大幅提升菌群设计效率^[62]。其中，机器学习和传统机器学习算法如线性回归、随机森林(random forest, RF)和支持向量机(support vector machine, SVM)通过监督式特征筛选与非线性模式识别能力，在微生物组数据解析中展现出显著效能(P<0.05)，其预测精度与模型可解释性已通过多组学数据集验证^[68-69]。此外，研究表明与传统随机分类方法与经验驱动的非建模预测相比，机器学习算法可显著提升分类精度与预测性能^[70]。BacterAI (通过设计和使用实验平台生成成长数据，以便学者据此优化SynComs的模型设计)^[71]和自动化合成微生物群落设计器(automated synthetic microbial community designer, AutoCD, 设计稳定稳态共培养物)^[72]等均是机器学习算法的代表性技术。值得注意的是，深度学习(deep learning, DL)作为机器学习算法的重要分支，凭借其强大的特征提取能力已在多学科领域展现出卓越的适用性^[73]。例如，在生态学中进行表型分析^[74-75]，大规模监控的环境分析^[76-77]等。随着合成生物学和生物信息学技术的协同快速发展，SynComs构建的相关工具与技术在自动化程度和实用性方面不断优化，这将极大推动SynComs从实验室研究向产业应用的转化。

3 合成菌群在农业可持续发展中的应用

收起

近年来，SynComs在农业领域中的应用价值日益凸显，然而现代农业发展面临耕地资源锐减、气候变化加剧、土壤退化等多重挑战，这些挑战严重威胁农产品产量和安全，阻碍农业绿色发展。根际有益微生物作为植物微生态系统的关键成员，具有活化根区养分、促进植物生长、增强植物抗逆性、抑制土传病害等功能，已成为开发生物肥料和生物农药的重要资源，在推动化肥农药减量增效、实现农业绿色发展方面具有重要的价值^[78]。本课题组前期从多种生境中分离鉴定了诸多植物根际促生菌(plant growth promoting rhizobacteria, PGPR)菌株，并探究了这些菌株在促进植物生长、增强植物抗逆性(特别是抵御盐碱、干旱等非生物胁迫)方面的潜力，为新型微生物肥料的研发利用提供菌种资源^[79-83]。然而，单一微生物在实际应用中易受到自然环境的影响，当将其引入土壤环境后，这些外源微生物会与土著微生物建立复杂的生态关系(竞争抑制或协同共生等)，它们对植物-土壤系统的调控效果通常不稳定。因此，充分发挥单个土壤微生物的功能还存在一定限制。相比之下，基于生态位互补原理构建的SynComs通过多菌株的协同作用能够高效完成单一菌株难以实现的复杂生态功能，在促进植物养分高效利用和提高植物抗逆性方面具有巨大的应用前景^[84]。Gou等^[84]基于荒漠优势植物梭梭根际挖掘的有益微生物资源，构建了一种由4株功能互补的PGPR菌株[Bacillus sp. WM13-24、假单胞菌属(Pseudomonas sp.) M30-35、解淀粉芽孢杆菌(Bacillus amyloliquefaciens) GB03以及草木栖剑菌(Sinorhizobium meliloti)ACCC17578]组成的复合菌剂，其通过微生物互作网络显著改善了根际微生态环境，使土壤总氮含量提升21.89%，活菌生物量增加2.3倍，并显著提高了微生物群落多样性，从而显著提升了辣椒植株的生长、果实产量和品质。这进一步表明通过精心设计、构建特定的微生物组合来优化土壤微生物群落结构能显著提高植物产量和品质。同时，SynComs的应用对增强植物耐受性和抗病性、减少对化学肥料和农药的依赖等发挥重要作用，从而有力推动农业绿色发展(表2)。

3.1 SynComs在促进植物生长方面的应用

根际微生物作为植物的“第二基因组”，是植物生理调控的关键因子，在调节植物健康生长方面发挥重要作用，其群落结构与功能特征直接影响宿主植物的营养获取。研究表明SynComs的应用可显著提升长角豆(Ceratonia siliqua L.)中光合色素含量(如叶绿素a/b、类胡萝卜素)，并通过增加根系水力传导率以提高水分及养分吸收效率，从而增强植物的光合作用^[92]。将8种具有通过分泌有机酸来增溶土壤中难溶性无机磷能力的假单胞菌属的菌株构建成数量不同的SynComs，可发现随着SynComs中菌株多样性的增加，假单胞菌属菌株的性能、番茄磷含量以及生物量的积累都显著增加^[93]。此外，水稻氮转运和受体NRT1.1B基因在调控其根系微生物组中发挥关键作用。研究发现接种人工重组群落后籼稻根系比粳稻富集更多与氮循环相关的微生物，从而更好地改善有机氮条件下的水稻生长和氮素利用^[94]。也有学者基于功能菌株筛选的方法从甘蔗根际筛选并构建了包含伯克霍尔德菌(Burkholderia)和假单胞菌(Pseudomonas)的SynComs，该菌群能显著增加植物生物量(3.4倍)^[95]。在SynComs中引入固氮菌、解磷菌及有机质分解菌群，可显著提高养分利用效率，而且减少化肥和农药的使用^[96-97]。以上研究也表明解析植物基因型-微生物功能-环境因子间的互作网络是实现根系微生物组精准调控的关键。

3.2 SynComs在抵抗生物和非生物胁迫方面的应用

SynComs在增强植物抵御胁迫方面扮演至关重要的角色。在应对生物胁迫方面，SynComs通过调控微生物互作模式抑制病原菌入侵。例如，番茄根际微生物群落的竞争性互作(如营养争夺、生态位抢占)可显著降低病原菌定殖效率^[98]；由枯草芽孢杆菌(Bacillus subtilis)构建的具有广谱抑菌性的SynComs可通过分泌多种抗菌物质、拮抗细胞活性物质及抑菌蛋白，破坏病原菌细胞膜完整性，使番茄青枯病发病延迟，显著降低病情指数^[99]。在非生物胁迫响应方面，具有耐盐特性的细菌菌株所构成的SynComs可以有效提高植物耐盐性和产量^[100]。同时，耐盐植物(如野大豆、田菁等)可通过根际招募具有促生功能及调节Na⁺浓度能力的微生物，形成盐胁迫适应性微生物组^[101-102]。此外，功能微生物还可通过直接拮抗病原菌或诱导系统抗性这一双重机制来增强植物抵抗非生物胁迫的能力^[103-104]。例如，Castrillo等^[105]通过分析野生型植物与突变株的微生物群落揭示了植物磷胁迫响应与其共生微生物的互作关系，在磷胁迫下植物通常容易被微生物群落中潜在的机会主义竞争者定殖，从而加剧植物的磷饥饿(phosphate starvation response, PSR)。SynComs能够激活低磷胁迫下植物PSR应答核心调控因子——植物免疫系统基因PHR1的活性，这种协同效应不仅促进了正常植物根际微生物组的组装，还可构建由共生生物主导的生物防御屏障，从而抑制病原菌的入侵^[105-106]。这些研究可为利用微生物群落调控策略增强植物对生物和非生物胁迫的抵御能力提供支撑。

3.3 SynComs在改善与恢复土壤健康方面的应用

SynComs通过多维度调控机制在改善土壤生物组成部分、理化性质以及增强生态修复效能等方面展现出独特优势，有助于提升土壤肥力和健康水平。Sun等^[90]研究发现，将有益芽孢杆菌菌肥接种到土壤中可定向富集有益微生物类群，并通过代谢互作增强本土微生物群落的丰度和抑菌功能。Qiao等^[107]通过构建对苹果病原菌具有显著拮抗作用的SynComs，发现经SynComs处理后的植物根际有益菌丰度增加，病原真菌丰度降低，并且微生物网络复杂度(边数、平均度)显著提升，负相互作用减少(竞争比例从47%降至36%)。此外，Song等^[108]研究证实了在盐碱地改良中SynComs与有机改良剂协同施用可提升土壤碳储量9%-40%。其中，微生物分泌的类蛋白黏合剂可通过桥接作用促进土壤形成团粒结构，该过程可显著改善土壤三相结构比例，使水肥保持效率显著提升^[109]。在生物修复方面，20世纪80年代便有学者提出利用功能微生物解决重金属污染土壤的问题。通过微生物的生物化学过程可有效固定土壤中的重金属离子，并将其中有害的重金属离子转化为无害或毒性大幅降低的形态^[110]，为重金属污染土壤的生物修复开辟了新途径。SynComs可通过构建多级代谢网络实现污染物定向转化：(1) 在重金属修复方面，PGPR间的协同作用不仅能增强宿主植物应对各类环境胁迫的能力，还能调节植物对重金属的耐受性，并影响土壤中重金属的活化、稳定、迁移及转化过程^[111]；此外，土壤改良剂联合丛枝菌根真菌(arbuscular mycorrhizal fungi, AMF)、腐生丝状真菌Aspergillus terreus通过调控金属转运蛋白表达及氧化还原酶活性，将Cr(Ⅵ)还原为低毒态Cr(Ⅲ)，同时抑制重金属向植物地上部迁移^[112]；(2) 在有机污染物治理方面，高秀荣^[113]发现与传统单一菌株的修复技术相比，构建的多环芳烃(polycyclic aromatic hydrocarbons, PAHs)降解菌群可显著提高PAHs修复效果，土壤PAHs总去除率可达45.7%。上述结果表明，SynComs在土壤健康管理中发挥重要作用。

4 总结与展望

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SynComs作为一种新兴的交叉学科方向，其通过微生物成员间的功能互补和代谢互作在多变的环境中展现出比传统单一菌株更为独特的优势。这种多菌株协同体系不仅能够有效调节土壤微生态平衡，促进植物健康生长，提升植物抗逆性，还蕴含着巨大的生态修复潜力，为应对传统农业中病害频发、营养失衡及土壤退化等难题提供了近自然的微生物解决方案。然而，构建并应用稳定且功能强大的SynComs仍面临诸多难题。

如何突破“微生物暗物质”培养瓶颈，以及如何高效筛选和组合这些微生物菌株，是SynComs构建所面临的首要难题。因为自然环境具有时空异质性，研究者难以准确识别在特定环境或功能中发挥关键作用的核心微生物。加之目前分离方法的局限性，环境中绝大部分微生物仍不能被纯培养，这严重阻碍了对微生物生命活动规律的探究及其利用，可能会导致部分优良功能微生物尚未被发掘。因此，应结合各种先进的技术或手段，改进培养措施、开发新型培养技术或多维度筛选技术等，以充分挖掘潜在的功能微生物。

微生物在植物根际动态定殖过程中，SynComs成员与宿主发育阶段、组织微环境存在时空特异性互作，其表型调控的关键基因元件(如群体感应基因、抗生素合成基因)与代谢途径(如铁载体、植物激素合成)的精准解析仍需系统研究。然而，现有SynComs的构建通常局限于单一营养级的菌群组合，忽视了自然土壤微环境中微生物之间跨营养级的复杂互作。即在自然土壤微环境中细菌-真菌-原生生物-线虫形成的跨营养级互作网络，在实验室条件下常被简化为单一营养级菌群组合。这种策略难以模拟根际真实生态位，可能导致菌群田间应用存在“水土不服”现象。因此，今后在SynComs的研究中需构建跨营养级复合菌群系统，通过引入捕食者(如原生生物)与分解者(如真菌)形成生态闭环，从而提升SynComs的生态位适应能力，并全面解析复杂群落间的多层次互作关系。

需重视SynComs的中试、生产与示范，以精准评估其稳定性与效果，研发靶向型菌剂，最终实现SynComs在农业可持续发展中的精准应用。结合AI在农业生产、环境监测、微生物群落优化等实践中的应用，需通过多源数据融合与智能分析技术、AI驱动的微生物-植物互作研究平台构建、基于AI的微生物群落优化算法开发这三大核心路径，提高SynComs在植物健康中的应用效能，最终推动生态友好型农业体系的可持续发展。

基金

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国家自然科学基金(32402670)
宁夏回族自治区自然科学基金(2024AAC03141)

参考文献

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文献

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参考文献引证文献

排序方式：

[1]

农业农村部. 2019年全国耕地质量等级情况公报[J]. 中华人民共和国农业农村部公报, 2020(4): 113-121.

Ministry of Agriculture and Rural Affairs. Bulletin of national cultivated land quality grade in 2019[J]. Gazette of the Ministry of Agriculture and Rural Affairs of the People’s Republic of China, 2020(4): 113-121 (in Chinese).

[2]

刘学松, 王翼飞, 师嫄菲, 张方博, 伍梦起, 唐晓燕, 申建波, 金可默. 基于根际生命共同体理论的根区土壤结构构建与调控[J]. 植物营养与肥料学报, 2023, 29(5): 972-979.

LIU

, WANG

, SHI

, ZHANG

, WU

, TANG

, SHEN

, JIN

. Construction and regulation of soil structure in root zone based on the theory of rhizobiont[J]. Journal of Plant Nutrition and Fertilizers, 2023, 29(5): 972-979 (in Chinese).

[3]

徐明岗, 李建华. 土壤-植物互作中的利用与保护[J/OL]. 哈尔滨: 植物研究. [2025-04-15]. http://kns.cnki.net/kcms/detail/23.1480.S.20250110.1605.004.html

, LI

. The utilization and conservation of soil-plant interactions[J/OL]. Harbin: Bulletin of Botanical Research. [2025-04-15]. in Chinese). http://kns.cnki.net/kcms/detail/23.1480.S.20250110.1605.004.html

[4]

KHAN

, NAUSHAD

, LIMA

, ZHANG

, SHAHEEN

, RINKLEBE

. Global soil pollution by toxic elements: current status and future perspectives on the risk assessment and remediation strategies: a review[J]. Journal of Hazardous Materials, 2021, 417: 126039.

[5]

SHAHEB

, VENKATESH

, SHEARER

. A review on the effect of soil compaction and its management for sustainable crop production[J]. Journal of Biosystems Engineering, 2021, 46(4): 417-439.

[6]

HUNTER

, SMITH

, SCHIPANSKI

, ATWOOD

, MORTENSEN

. Agriculture in 2050: recalibrating targets for sustainable intensification[J]. BioScience, 2017, 67(4): 386-391.

[7]

LIANG

, GUO

, FAN

, DONG

, DING

, ZHANG

, TANG

, YANG

, KONG

, CAO

. Development of novel urease-responsive pendimethalin microcapsules using silica-IPTS-PEI as controlled release carrier materials[J]. ACS Sustainable Chemistry & Engineering, 2017, 5(6): 4802-4810.

[8]

章磊, 杨志洋. 加强农业环境污染治理实现绿色发展目标[J]. 河北农机, 2024(13): 121-123.

ZHANG

, YANG

. Strengthen agricultural environmental pollution control to achieve green development goals[J]. Hebei Agricultural Machinery, 2024(13): 121-123 (in Chinese).

[9]

刘丹, 路战远, 任永峰, 赵小庆, 张向前, 戴玉. 微生物肥料改土培肥研究进展[J]. 北方农业学报, 2024, 52(1): 87-93.

LIU

, LU

, REN

, ZHAO

, ZHANG

, DAI

. Research progress on the impact of microbial fertilizer improve soil fertilization[J]. Journal of Northern Agriculture, 2024, 52(1): 87-93 (in Chinese).

[10]

, LIU

, HE

, ZOU

, ZHOU

, ZHANG

, BAI

. Plant-root microbiota interactions in nutrient utilization[J]. Frontiers of Agricultural Science and Engineering, 2025, 12(1): 16-26.

[11]

FENG

, FU

, LUO

, HOU

, GAO

, SU

, XU

, MIAO

, LIU

, XU

, ZHANG

, SHEN

, XUN

, ZHANG

. Listening to plant’s Esperanto via root exudates: reprogramming the functional expression of plant growth-promoting rhizobacteria[J]. New Phytologist, 2023, 239(6): 2307-2319.

[12]

WANG

, GE

, MA

, WANG

, XIE

, WANG

, SONG

, JIANG

, YANG

, MURRAY

, WANG

, LIU

, CAO

, WANG

. Dynamic root microbiome sustains soybean productivity under unbalanced fertilization[J]. Nature Communications, 2024, 15: 1668.

[13]

, JOUSSET

, de BOER

, CARRIÓN

, ZHANG

, WANG

, KURAMAE

. Legacy of land use history determines reprogramming of plant physiology by soil microbiome[J]. The ISME Journal, 2018, 13(3): 738-751.

[14]

钱刘兵, 梁山峰, 魏占波, 张彬. 微生物多样性损失对农田土壤CO₂和N₂O排放功能稳定性的影响[J]. 应用生态学报, 2023, 34(5): 1313-1319.

QIAN

, LIANG

, WEI

, ZHANG

. Effects of microbial diversity loss on the stability of CO₂ production and N₂O emission in agricultural soils[J]. Chinese Journal of Applied Ecology, 2023, 34(5): 1313-1319 (in Chinese).

[15]

张瑞福. 根际微生物: 农业绿色发展中大有作为的植物第二基因组[J]. 生物技术通报, 2020, 36(9): 1-2.

ZHANG

. Rhizosphere microorganisms: a promising plant second genome in the green development of agriculture[J]. Biotechnology Bulletin, 2020, 36(9): 1-2 (in Chinese).

[16]

PARUNANDI

, MURUMKAR

, NAVALE

, SHELKE

. Unveiling the consortial role of heterotrophic nitrogen fixing, phosphate solubilizing and potash mobilizing bacteria: augmenting soil health, and growth and yield of wheat crop (T. aestivum) through integrated nutrient management strategy[J]. International Journal of Current Microbiology and Applied Sciences, 2023, 12(11): 138-167.

[17]

, JIANG

, LIU

, WANG

, BAI

, YUAN

, SHI

, ZHOU

, DING

, XIE

, ZHANG

, YANG

, SHEN

, CROWTHER

, ZHANG

, LIANG

. Quinolone-mediated metabolic cross-feeding develops aluminium tolerance in soil microbial consortia[J]. Nature Communications, 2024, 15: 10148.

[18]

ZHU

, JU

, BEIYUAN

, CHAO

, ZHANG

, CHEN

, CUI

, QIU

, ZHANG

, HUANG

, SHEN

, FANG

. Bacterial consortium amendment effectively reduces Pb/Cd bioavailability in soil and their accumulation in wheat[J]. Journal of Environmental Management, 2024, 370: 122789.

[19]

FADIJI

, XIONG

, EGIDI

, SINGH

. Formulation challenges associated with microbial biofertilizers in sustainable agriculture and paths forward[J]. Journal of Sustainable Agriculture and Environment, 2024, 3(3): e70006.

[20]

BURLAGE

, KUO

. Living biosensors for the management and manipulation of microbial consortia[J]. Annual Review of Microbiology, 1994, 48: 291-309.

[21]

翁凌胤, 栾冬冬, 周大朴, 郭庆港, 王光州, 张俊伶. 利用合成菌群促进作物健康: 进展与展望[J]. 应用生态学报, 2024, 35(3): 847-857.

WENG

, LUAN

, ZHOU

, GUO

, WANG

, ZHANG

. Improving crop health by synthetic microbial communities: progress and prospects[J]. Chinese Journal of Applied Ecology, 2024, 35(3): 847-857 (in Chinese).

[22]

BRENNER

, YOU

, ARNOLD

. Engineering microbial consortia: a new frontier in synthetic biology[J]. Trends in Biotechnology, 2008, 26(9): 483-489.

[23]

De ROY

, MARZORATI

, van den ABBEELE

, van de WIELE

, BOON

. Synthetic microbial ecosystems: an exciting tool to understand and apply microbial communities[J]. Environmental Microbiology, 2014, 16(6): 1472-1481.

[24]

韦中, 杨天杰, 任鹏, 胡洁, 李梅, 徐阳春, 沈其荣. 合成菌群在根际免疫研究中的现状与未来[J]. 南京农业大学学报, 2021, 44(4): 597-603.

WEI

, YANG

, REN

, HU

, LI

, XU

, SHEN

. Advances and perspectives on synthetic microbial community in the study of rhizosphere immunity[J]. Journal of Nanjing Agricultural University, 2021, 44(4): 597-603 (in Chinese).

[25]

曲泽鹏, 陈沫先, 曹朝辉, 左文龙, 陈业, 戴磊. 合成微生物群落研究进展[J]. 合成生物学, 2020, 1(6): 621-634.

, CHEN

, CAO

, ZUO

, CHEN

, DAI

. Research advances in synthetic microbial communities[J]. Synthetic Biology Journal, 2020, 1(6): 621-634 (in Chinese).

[26]

张鑫, 梁建东, 田维毅, 梁宗琦. 合成微生物群落共培养研究概况[J]. 天然产物研究与开发, 2019, 31(11): 2007-2014.

ZHANG

, LIANG

, TIAN

, LIANG

. Study profile on co-culture of synthetic microbial community[J]. Natural Product Research and Development, 2019, 31(11): 2007-2014 (in Chinese).

[27]

, HU

, WEI

, JOUSSET

, POMMIER

, YU

, XU

, SHEN

. Synthetic microbial communities: Sandbox and blueprint for soil health enhancement[J]. iMeta, 2024, 3(1): e172.

[28]

张瑞福, 刘云鹏, 崔晓双, 沈其荣. 根际微生物与植物互作研究进展与应用[C]//第十二届全国土壤微生物学术研讨会暨第五届全国微生物肥料生产技术研讨会论文集. 武汉: 中国微生物学会农业微生物学专业委员会, 中国土壤学会土壤生物和生物化学专业委员会, 2014: 21-22.

Zhang

, LIU

, CUI

, SHEN

. Advances and applications in research on rhizosphere microorganisms and plant Interactions[C]//Proceedings of the twelfth national symposium on soil microorganism and the fifth national symposium on microbial fertilizer production technology. Wuhan: Agricultural Microbiology Committee of China Microbiology Society, Professional Committee of Soil Biology and Biochemistry, Soil Society of China, 2014: 21-22 (in Chinese).

[29]

ZHANG

, ZENG

, ZHU

, CAI

, ZHOU

. The structure and assembly mechanisms of plastisphere microbial community in natural marine environment[J]. Journal of Hazardous Materials, 2022, 421: 126780.

[30]

YANG

, ZHANG

, LI

, YANG

, CHAO

, LIU

, BA

. Distribution patterns and community assembly processes of eukaryotic microorganisms along an altitudinal gradient in the middle reaches of the Yarlung Zangbo River[J]. Water Research, 2023, 239: 120047.

[31]

PANDHAL

, NOIREL

. Synthetic microbial ecosystems for biotechnology[J]. Biotechnology Letters, 2014, 36(6): 1141-1151.

[32]

WANG

, WEISS

, AQEEL

, WU

, LOPATKIN

, DAVID

, YOU

. Horizontal gene transfer enables programmable gene stability in synthetic microbiota[J]. Nature Chemical Biology, 2022, 18(11): 1245-1252.

[33]

LAWSON

, HARCOMBE

, HATZENPICHLER

, LINDEMANN

, LÖFFLER

, O’MALLEY

, GARCÍA MARTÍN

, PFLEGER

, RASKIN

, VENTURELLI

, WEISSBRODT

, NOGUERA

, McMAHON

. Common principles and best practices for engineering microbiomes[J]. Nature Reviews Microbiology, 2019, 17(12): 725-741.

[34]

ZHUANG

, LI

, WANG

, YU

, ZHANG

, YANG

, ZENG

, WANG

. Synthetic community with six Pseudomonas strains screened from garlic rhizosphere microbiome promotes plant growth[J]. Microbial Biotechnology, 2021, 14(2): 488-502.

[35]

CARRIÓN

, PEREZ-JARAMILLO

, CORDOVEZ

, TRACANNA

, de HOLLANDER

, RUIZ-BUCK

, MENDES

, van IJCKEN

WFJ

, GOMEZ-EXPOSITO

, ELSAYED

, MOHANRAJU

, ARIFAH

, van der OOST

, PAULSON

, MENDES

, van WEZEL

, MEDEMA

, RAAIJMAKERS

. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome[J]. Science, 2019, 366(6465): 606-612.

[36]

ZENGLER

, ZARAMELA

. The social network of microorganisms: how auxotrophies shape complex communities[J]. Nature Reviews Microbiology, 2018, 16(6): 383-390.

[37]

BERTNESS

, CALLAWAY

. Positive interactions in communities[J]. Trends in Ecology & Evolution, 1994, 9(5): 191-193.

[38]

ZENG

, ZOU

, ZHENG

, QIU

, LIU

, WEI

. Quorum sensing-mediated microbial interactions: mechanisms, applications, challenges and perspectives[J]. Microbiological Research, 2023, 273: 127414.

[39]

AMARNATH

, NARLA

, PONTRELLI

, DONG

, REDDAN

, TAYLOR

, CAGLAR

, SCHWARTZMAN

, SAUER

, CORDERO

, HWA T. Stress-induced metabolic exchanges between complementary bacterial types underly a dynamic mechanism of inter-species stress resistance[J]. Nature Communications, 2023, 14: 3165.

[40]

FRITTS

, McCULLY

, McKINLAY

. Extracellular metabolism sets the table for microbial cross-feeding[J]. Microbiology and Molecular Biology Reviews, 2021, 85(1): e00135-20.

[41]

WOYKE

, TEELING

, IVANOVA

, HUNTEMANN

, RICHTER

, GLOECKNER

, BOFFELLI

, ANDERSON

, BARRY

, SHAPIRO

, SZETO

, KYRPIDES

, MUSSMANN

, AMANN

, BERGIN

, RUEHLAND

, RUBIN

, DUBILIER

. Symbiosis insights through metagenomic analysis of a microbial consortium[J]. Nature, 2006, 443(7114): 950-955.

[42]

WANG

, JIN

, BALAN

, JONES

, LI

, DALE

, YUAN

. Comparative metabolic profiling revealed limitations in xylose-fermenting yeast during co-fermentation of glucose and xylose in the presence of inhibitors[J]. Biotechnology and Bioengineering, 2014, 111(1): 152-164.

[43]

WANG

, XIE

, ZHANG

. Marine metaproteomics: current status and future directions[J]. Journal of Proteomics, 2014, 97: 27-35.

[44]

GROßKOPF

, SOYER

. Synthetic microbial communities[J]. Current Opinion in Microbiology, 2014, 18: 72-77.

[45]

FUHRMAN

. Microbial community structure and its functional implications[J]. Nature, 2009, 459(7244): 193-199.

[46]

CAO

, YAN

, DING

, YUAN

. Construction of microbial consortia for microbial degradation of complex compounds[J]. Frontiers in Bioengineering and Biotechnology, 2022, 10: 1051233.

[47]

LIAO

, LI

, ZOU

, XIE

, YUAN

. Antibiotic sulfanilamide biodegradation by acclimated microbial populations[J]. Applied Microbiology and Biotechnology, 2016, 100(5): 2439-2447.

[48]

CHEN

, BONKOWSKI

, SHEN

, GRIFFITHS

, JIANG

, WANG

, SUN

. Root ethylene mediates rhizosphere microbial community reconstruction when chemically detecting cyanide produced by neighbouring plants[J]. Microbiome, 2020, 8(1): 4.

[49]

NIU

, PAULSON

, ZHENG

, KOLTER

. Simplified and representative bacterial community of maize roots[J]. Proceedings of the National Academy of Sciences of the United States of America, 2017, 114(12): E2450-E2459.

[50]

COYTE

, SCHLUTER

, FOSTER

. The ecology of the microbiome: networks, competition, and stability[J]. Science, 2015, 350(6261): 663-666.

[51]

NIEHAUS

, BOLAND

, LIU

, CHEN

, FU

, HENCKEL

, CHAUNG

, MIRANDA

, DYCKMAN

, CRUM

, DEDRICK

, SHOU

, MOMENI

. Microbial coexistence through chemical-mediated interactions[J]. Nature Communications, 2019, 10: 2052.

[52]

LIAO

, WANG

, MASLOV

, XAVIER

. Modeling microbial cross-feeding at intermediate scale portrays community dynamics and species coexistence[J]. PLoS Computational Biology, 2020, 16(8): e1008135.

[53]

ZOMORRODI

, ISLAM

, MARANAS

. D-OptCom: dynamic multi-level and multi-objective metabolic modeling of microbial communities[J]. ACS Synthetic Biology, 2014, 3(4): 247-257.

[54]

RAVIKRISHNAN

, BLANK

, SRIVASTAVA

, RAMAN

. Investigating metabolic interactions in a microbial co-culture through integrated modelling and experiments[J]. Computational and Structural Biotechnology Journal, 2020, 18: 1249-1258.

[55]

KIM

, THAPA

, ZHANG

, ALI H. A novel graph theoretical approach for modeling microbiomes and inferring microbial ecological relationships[J]. BMC Genomics, 2019, 20(): 945.

[56]

RUAN

, CHEN

, CAO

, MENG

, YANG

, XU

, XING

, LI

, FREILICH

, CHEN

, GAO

, JIANG

, XU

. Engineering natural microbiomes toward enhanced bioremediation by microbiome modeling[J]. Nature Communications, 2024, 15: 4694.

[57]

ORTH

, THIELE

, BØ

PALSSON

. What is flux balance analysis[J]. Nature Biotechnology, 2010, 28(3): 245-248.

[58]

BATSTONE

, PUYOL

, FLORES-ALSINA

, RODRÍGUEZ

. Mathematical modelling of anaerobic digestion processes: applications and future needs[J]. Reviews in Environmental Science and Bio/Technology, 2015, 14(4): 595-613.

[59]

GOLDFORD

, LU

, BAJIĆ

, ESTRELA

, TIKHONOV

, SANCHEZ-GOROSTIAGA

, SEGRÈ

, MEHTA

, SANCHEZ

. Emergent simplicity in microbial community assembly[J]. Science, 2018, 361(6401): 469-474.

[60]

ZOMORRODI

, MARANAS

. OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities[J]. PLoS Computational Biology, 2012, 8(2): e1002363.

[61]

TARZI

, ZAMPIERI

, SULLIVAN

, ANGIONE

. Emerging methods for genome-scale metabolic modeling of microbial communities[J]. Trends in Endocrinology & Metabolism, 2024, 35(6): 533-548.

[62]

JING

, GARBEVA

, RAAIJMAKERS

, MEDEMA

. Strategies for tailoring functional microbial synthetic communities[J]. The ISME Journal, 2024, 18(1): wrae049.

[63]

HALE

, WATTS

, CONATSER

, BROWN

, WIJERATNE

. Fine-scale characterization of the soybean rhizosphere microbiome via synthetic long reads and avidity sequencing[J]. Environmental Microbiome, 2024, 19(1): 46.

[64]

URBAN

, CUZICK

, SEAGER

, WOOD

, RUTHERFORD

, VENKATESH

, SAHU

, IYER

, KHAMARI

, de SILVA

, MARTINEZ

, PEDRO

, YATES

, HAMMOND-KOSACK

. PHI-base in 2022: a multi-species phenotype database for Pathogen-Host Interactions[J]. Nucleic Acids Research, 2022, 50(D1): D837-D847.

[65]

NÉRON

, DENISE

, COLUZZI

, TOUCHON

, ROCHA

EPC

, ABBY

. MacSyFinder v2: improved modelling and search engine to identify molecular systems in genomes[J]. Peer Community Journal, 2023, 3: e28.

[66]

BLIN

, SHAW

, AUGUSTIJN

, REITZ

, BIERMANN

, ALANJARY

, FETTER

, TERLOUW

, METCALF

, HELFRICH

EJN

, van WEZEL

, MEDEMA

, WEBER

. antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation[J]. Nucleic Acids Research, 2023, 51(W1): W46-W50.

[67]

方临川, 胡紫莹, 崔庆亮, 杨阳, 梁玉婷, 蔡鹏, 渠晨晨, 高春辉, 焦硕, 刘玉荣, 黄巧云, 谭文峰. 合成菌群构建与应用: 提升土壤健康新策略[J]. 土壤学报, 2025, 62(5): 10.11766/trxb202410120392.

FANG

, HU

, CUI

, YANG

, LIANG

, CAI

, QU

, GAO

, JIAO

, LIU

, HUANG

, TAN

. Construction and application of synthetic communities: a new strategy to improve soil health[J]. Acta Pedologica Sinica, 2025, 62(5): 10.11766/trxb202410120392 (in Chinese).

[68]

TARCA

, CAREY

, CHEN

, ROMERO

, DRĂGHICI

. Machine learning and its applications to biology[J]. PLoS Computational Biology, 2007, 3(6): e116.

[69]

RAAJARAAM

, RAMAN

. Modeling microbial communities: perspective and challenges[J]. ACS Synthetic Biology, 2024, 13(8): 2260-2270.

[70]

EMMENEGGER

, MASSONI

, PESTALOZZI

, BORTFELD-MILLER

, MAIER

, VORHOLT

. Identifying microbiota community patterns important for plant protection using synthetic communities and machine learning[J]. Nature Communications, 2023, 14: 7983.

[71]

DAMA

, KIM

, LEYVA

, LUNKES

, SCHMID

, JIJAKLI

, JENSEN

. BacterAI maps microbial metabolism without prior knowledge[J]. Nature Microbiology, 2023, 8(6): 1018-1025.

[72]

KARKARIA

, FEDOREC

AJH

, BARNES

. Automated design of synthetic microbial communities[J]. Nature Communications, 2021, 12: 672.

[73]

CHRISTIN

, HERVET

, LECOMTE

. Applications for deep learning in ecology[J]. Methods in Ecology and Evolution, 2019, 10(10): 1632-1644.

[74]

DOBRESCU

, GIUFFRIDA

, TSAFTARIS

. Leveraging multiple datasets for deep leaf counting[C]//2017 IEEE International Conference on Computer Vision Workshops (ICCVW). Venice, Italy: IEEE, 2017: 2072-2079.

[75]

DOUARRE

, SCHIELEIN

, FRINDEL

, GERTH

, ROUSSEAU

. Deep learning based root-soil segmentation from X-ray tomography images[J]. Journal of Imaging, 2018, 4(5): 65.

[76]

ASNER

, BRODRICK

, PHILIPSON

, VAUGHN

, MARTIN

, KNAPP

, HECKLER

, EVANS

, JUCKER

, GOOSSENS

, STARK

, REYNOLDS

, ONG R, RENNEBOOG

, KUGAN

, COOMES

. Mapped aboveground carbon stocks to advance forest conservation and recovery in Malaysian Borneo[J]. Biological Conservation, 2018, 217: 289-310.

[77]

, LI

, PATERSON

, JIANG

, SUN

, ROBERTSON

. Aerial images and convolutional neural network for cotton bloom detection[J]. Frontiers in Plant Science, 2018, 8: 2235.

[78]

SHAYANTHAN

, ORDOÑEZ

PAC

, ORESNIK

. The role of synthetic microbial communities (SynCom) in sustainable agriculture[J]. Frontiers in Agronomy, 2022, 4: 896307.

[79]

李慧萍, 甘雅楠, 韩庆庆, 姚丹, 陈佳, 张明旭, 何傲蕾, 张金林, 赵祺. 祁连山云杉林土壤溶磷细菌的分离及对白三叶的促生效应[J]. 草地学报, 2022, 30(4): 879-888.

, GAN

, HAN

, YAO

, CHEN

, ZHANG

, HE

, ZHANG

, ZHAO

. Isolation of phosphate solubilizing bacteria from soil of spruce forest in the Qilian Mountains and their growth promotion effects in white clover[J]. Acta Agrestia Sinica, 2022, 30(4): 879-888 (in Chinese).

[80]

李慧萍. 梭梭和云杉根际溶磷细菌的特性研究及溶磷机制解析[D]. 兰州: 兰州大学博士学位论文, 2023.

. Characteristics of phosphate-solubilizing bacteria from the rhizosphere of Haloxylon ammodendron and spruce and the phosphate solubilization mechanism[D]. Lanzhou: Doctoral Dissertation of Lanzhou University, 2023 (in Chinese).

[81]

, HAN

, LIU

, GAN

, RENSING

, RIVERA

, ZHAO

, ZHANG

. Roles of phosphate-solubilizing bacteria in mediating soil legacy phosphorus availability[J]. Microbiological Research, 2023, 272: 127375.

[82]

, GAN

, YUE

, HAN

, CHEN

, LIU

, ZHAO

, ZHANG

. Newly isolated Paenibacillus monticola sp. nov., a novel plant growth-promoting rhizobacteria strain from high-altitude spruce forests in the Qilian Mountains, China[J]. Frontiers in Microbiology, 2022, 13: 833313.

[83]

陈佳, 缑晶毅, 赵祺, 韩庆庆, 李慧萍, 姚丹, 张金林. 梭梭根际根瘤菌对紫花苜蓿生长及耐盐性的影响[J]. 南京林业大学学报(自然科学版), 2021, 45(6): 99-110.

CHEN

, GOU

, ZHAO

, HAN

, LI

, YAO

, ZHANG

. Induced growth and salt tolerance of alfalfa by Rhizobium strains from the rhizosphere of Haloxylon ammodendron [J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2021, 45(6): 99-110 (in Chinese).

[84]

GOU

, SUO

, SHAO

, ZHAO

, YAO

, LI

, ZHANG

, RENSING

. Biofertilizers with beneficial rhizobacteria improved plant growth and yield in chili (Capsicum annuum L.)[J]. World Journal of Microbiology and Biotechnology, 2020, 36(6): 86.

[85]

SCHMITZ

, YAN

, SCHNEIJDERBERG

, de ROIJ

, PIJNENBURG

, ZHENG

, FRANKEN

, DECHESNE

, TRINDADE

, van VELZEN

, BISSELING

, GEURTS

, CHENG

. Synthetic bacterial community derived from a desert rhizosphere confers salt stress resilience to tomato in the presence of a soil microbiome[J]. The ISME Journal, 2022, 16(8): 1907-1920.

[86]

VOGES

MJEEE

, BAI

, SCHULZE-LEFERT

, SATTELY

. Plant-derived coumarins shape the composition of an Arabidopsis synthetic root microbiome[J]. Proceedings of the National Academy of Sciences of the United States of America, 2019, 116(25): 12558-12565.

[87]

ALI M, AHMAD

, ASHRAF

, DONG

. Maize endophytic microbial-communities revealed by removing PCR and 16S rRNA sequencing and their synthetic applications to suppress maize banded leaf and sheath blight[J]. Microbiological Research, 2021, 242: 126639.

[88]

, ZHANG

, ZHENG

, LIU

, WANG

, TIAN

, WU

, ZHANG

. Synthetic microbial community isolated from intercropping system enhances P uptake in rice[J]. International Journal of Molecular Sciences, 2024, 25(23): 12819.

[89]

YIN

, HAGERTY

, PAULITZ

. Synthetic microbial consortia derived from rhizosphere soil protect wheat against a soilborne fungal pathogen[J]. Frontiers in Microbiology, 2022, 13: 908981.

[90]

SUN

, XU

, XIE

, HESSELBERG-THOMSEN

, TAN

, ZHENG

, STRUBE

, DRAGOŠ

, SHEN

, ZHANG

, ÁT

KOVÁCS

. Bacillus velezensis stimulates resident rhizosphere Pseudomonas stutzeri for plant health through metabolic interactions[J]. The ISME Journal, 2021, 16(3): 774-787.

[91]

WANG

, LI

, ZHANG

, MA

, ZHENG

, XU

, CUI

, LIU

, YANG

, ZHONG

, LIAO

. Functional assembly of root-associated microbial consortia improves nutrient efficiency and yield in soybean[J]. Journal of Integrative Plant Biology, 2021, 63(6): 1021-1035.

[92]

BOUTASKNIT

, BASLAM

, AIT-EL-MOKHTAR

, ANLI

, BEN-LAOUANE

, AIT-RAHOU

, MITSUI

, DOUIRA

, MODAFAR C

, WAHBI

, MEDDICH

. Assemblage of indigenous arbuscular mycorrhizal fungi and green waste compost enhance drought stress tolerance in carob (Ceratonia siliqua L.) trees[J]. Scientific Reports, 2021, 11: 22835.

[93]

, WEI

, WEIDNER

, FRIMAN

, XU

, SHEN

, JOUSSET

. Probiotic Pseudomonas communities enhance plant growth and nutrient assimilation via diversity-mediated ecosystem functioning[J]. Soil Biology and Biochemistry, 2017, 113: 122-129.

[94]

ZHANG

, LIU

, ZHANG

, HU

, JIN

, XU

, QIN

, YAN

, ZHANG

, GUO

, HUI

, CAO

, WANG

, QU

, FAN

, YUAN

, GARRIDO-OTER

, CHU

, et al. NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice[J]. Nature Biotechnology, 2019, 37(6): 676-684.

[95]

ARMANHI

JSL

, de SOUZA

RSC

, DAMASCENO

, de ARAÚJO

, IMPERIAL

, ARRUDA

. A community-based culture collection for targeting novel plant growth-promoting bacteria from the sugarcane microbiome[J]. Frontiers in Plant Science, 2018, 8: 2191.

[96]

NAHER

, OTHMAN

, PANHWAR Q

ALI

, ISMAIL

. Biofertilizer for sustainable rice production and reduction of environmental pollution[M]//Crop Production and Global Environmental Issues. Cham: Springer International Publishing, 2015: 283-291.

[97]

WANG

, LI

, ZHANG

, WEI

, LI

. Beneficial bacteria activate nutrients and promote wheat growth under conditions of reduced fertilizer application[J]. BMC Microbiology, 2020, 20(1): 38.

[98]

, WEI

, WANG

, JOUSSET

, FRIMAN

, XU

, SHEN

, POMMIER

. Facilitation promotes invasions in plant-associated microbial communities[J]. Ecology Letters, 2019, 22(1): 149-158.

[99]

岳童. 番茄青枯病拮抗微生物的构建及生防机理研究[D]. 兰州: 兰州理工大学硕士学位论文, 2023.

YUE

. Studies on the influence of different microbial inoculants on ensiling quality for sweet sorghum based on microbiome and metabolomics[D]. Lanzhou: Master’s Thesis of Lanzhou University of Technology, 2023 (in Chinese).

[100]

何泽平, 胡远艺, 陈莎, 李丁. 合成耐盐菌群对水稻品种五山丝苗耐盐性的影响[J]. 杂交水稻, 2024, 39(4): 16-26.

, HU

, CHEN

, LI

. Effects of synthetic salt-tolerant bacteria on salt tolerance of rice variety Wushansimiao[J]. Hybrid Rice, 2024, 39(4): 16-26 (in Chinese).

[101]

ZHENG

, XU

, LIU

, ZHOU

, MENG

, MA

, XIE

, LI

, ZHANG

. Patterns in the microbial community of salt-tolerant plants and the functional genes associated with salt stress alleviation[J]. Microbiology Spectrum, 2021, 9(2): e0076721.

[102]

ZHENG

, CAO

, ZHOU

, MA

, WANG

, LI

, ZHAO

, YANG

, ZHANG

, MENG

, XIE

, SUI

, XU

, LI

, ZHANG

. Purines enrich root-associated Pseudomonas and improve wild soybean growth under salt stress[J]. Nature Communications, 2024, 15: 3520.

[103]

MEENA

, SORTY

, BITLA

, CHOUDHARY

, GUPTA

, PAREEK

, SINGH

, PRABHA

, SAHU

, GUPTA

, SINGH

, KRISHANANI

, MINHAS

. Abiotic stress responses and microbe-mediated mitigation in plants: the omics strategies[J]. Frontiers in Plant Science, 2017, 8: 172.

[104]

ARIF

, BATOOL

, SCHENK

. Plant microbiome engineering: expected benefits for improved crop growth and resilience[J]. Trends in Biotechnology, 2020, 38(12): 1385-1396.

[105]

CASTRILLO

, TEIXEIRA

PJPL

, PAREDES

, LAW

, de LORENZO

, FELTCHER

, FINKEL

, BREAKFIELD

, MIECZKOWSKI

, JONES

, PAZ-ARES

, DANGL

. Root microbiota drive direct integration of phosphate stress and immunity[J]. Nature, 2017, 543(7646): 513-518.

[106]

FINKEL

, SALAS-GONZÁLEZ

, CASTRILLO

, SPAEPEN

, LAW

, TEIXEIRA

PJPL

, JONES

, DANGL

. The effects of soil phosphorus content on plant microbiota are driven by the plant phosphate starvation response[J]. PLoS Biology, 2019, 17(11): e3000534.

[107]

QIAO

, XU

, JIANG

, SONG

, WANG

, YANG

, GUO

, MAO

. Plant growth promotion and biocontrol properties of a synthetic community in the control of apple disease[J]. BMC Plant Biology, 2024, 24(1): 546.

[108]

SONG

, ZHANG

, CHANG

, YU

, ZHANG

, WANG

, LIU

, ZHOU

, LI

. Humic acid plus manure increases the soil carbon pool by inhibiting salinity and alleviating the microbial resource limitation in saline soils[J]. Catena, 2023, 233: 107527.

[109]

王伊琨, 高飞, 李昌伟, 郭阳, 张新刚, 赖勇, 李彬彬, 张春红. 微生物肥料对土壤和作物生长的影响[J]. 农业工程, 2024, 14(9): 68-72.

WANG

, GAO

, LI

, GUO

, ZHANG

, LAI

, LI

, ZHANG

. Effect of microbial fertilizer on soil and crop growth[J]. Agricultural Engineering, 2024, 14(9): 68-72 (in Chinese).

[110]

CHANMUGATHAS

, BOLLAG

. Microbial role in immobilization and subsequent mobilization of cadmium in soil suspensions[J]. Soil Science Society of America Journal, 1987, 51(5): 1184-1191.

[111]

马莹, 骆永明, 滕应, 李振高. 根际促生菌及其在污染土壤植物修复中的应用[J]. 土壤学报, 2013, 50(5): 1021-1031.

, LUO

, TENG

, LI

. Plant growth promoting rhizobacteria and their role in phytoremediation of heavy metal contaminated soils[J]. Acta Pedologica Sinica, 2013, 50(5): 1021-1031 (in Chinese).

[112]

AKHTAR

, KEHRI

, ZOOMI

. Arbuscular mycorrhiza and Aspergillus terreus inoculation along with compost amendment enhance the phytoremediation of Cr-rich technosol by Solanum lycopersicum under field conditions[J]. Ecotoxicology and Environmental Safety, 2020, 201: 110869.

[113]

高秀荣. 多环芳烃降解菌群构建及其强化修复污染土壤研究[D]. 北京: 中国科学院大学硕士学位论文, 2019.

GAO

. Study on the construction of polycyclic aromatic hydrocarbon degrading bacteria and its enhanced remediation of contaminated soil[D]. Beijing: Master’s Thesis of University of Chinese Academy of Sciences, 2019 (in Chinese).

2025年第65卷第10期

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doi: 10.13343/j.cnki.wsxb.20250213

接收时间：2025-03-18
首发时间：2025-11-03
出版时间：2025-09-04

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收稿日期：2025-03-18
录用日期：2025-05-07

基金

the National Natural Science Foundation of China(32402670)

国家自然科学基金(32402670)

the Ningxia Hui Autonomous Region Natural Science Foundation(2024AAC03141)

宁夏回族自治区自然科学基金(2024AAC03141)

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¹宁夏大学，西北土地退化与生态恢复省部共建国家重点实验室培育基地，宁夏银川

²宁夏大学，宁夏回族自治区草牧业工程技术研究中心，宁夏银川

³宁夏大学，农业农村部饲草高效生产模式创新重点实验室，宁夏银川

⁴宁夏大学林业与草业学院，宁夏银川

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 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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Model	Application	References
Lotka-Volterra model and consumer-resource model	The coupling law of resource allocation and metabolic network in microbial community was analyzed and the niche of microbial interaction was predicted	[50-51]
Coarse grained metabolic model	The response of the microbiota to interference was accurately predicted by simulating one-way, two-way, and multi-way cross-feeding	[52]
D-OptCom	It is used for multi-scale metabolic interaction simulation of microbial community and microbial community analysis	[53]
Genome-scale metabolic model	Systematically characterize the metabolic network of organisms, predict the exchange of metabolites between species (such as cross-feeding, electron transfer), and guide the optimization of strain combinations	[54-55]
Super community combinations	Simulating complex microbial community synergy for dynamic analysis of transmembrane metabolite exchange flux in microbial communities	[56]
Flux balance analysis	It is used to optimize the co-culture system, biological community metabolic network reconstruction and metabolic simulation	[57]

Model

Application

References

Lotka-Volterra model and consumer-resource model

The coupling law of resource allocation and metabolic network in microbial community was analyzed and the niche of microbial interaction was predicted

[50-51]

Coarse grained metabolic model

The response of the microbiota to interference was accurately predicted by simulating one-way, two-way, and multi-way cross-feeding

[52]

D-OptCom

It is used for multi-scale metabolic interaction simulation of microbial community and microbial community analysis

[53]

Genome-scale metabolic model

Systematically characterize the metabolic network of organisms, predict the exchange of metabolites between species (such as cross-feeding, electron transfer), and guide the optimization of strain combinations

[54-55]

Super community combinations

Simulating complex microbial community synergy for dynamic analysis of transmembrane metabolite exchange flux in microbial communities

[56]

Flux balance analysis

It is used to optimize the co-culture system, biological community metabolic network reconstruction and metabolic simulation

[57]

Plant	SynComs size ＆ origin	Mechanism	Results	References
Solanum lycopersicum L.	15 strains (isolated from Indigofera tinctoria Linn. root of Jizan)	Differential expression of salt stress-related genes and ion accumulation in aboveground parts	Under salt stress and non-sterile conditions, SynComs has a strong growth-promoting effect on Solanum lycopersicum L.	[85]
Arabidopsis thaliana (L.) Heynh.	22 strains (symbiotic bacteria isolated from Arabidopsis thaliana (L.) Heynh. roots)	Redox-mediated mechanism of SynComs	The molecular mechanism was established to elucidate the composition of microbial communities derived from plants, and the functional diversity of plant rhizosphere-dependent specialized metabolites was analyzed	[86]
Zea mays L.	6 strains (Bacillus strains isolated from roots and leaves of Zea mays L.)	Endophytic microorganisms fight pathogens by competing, producing disease-resistant substances, or activating plant immune systems	Promote plant growth and significantly reduce the incidence of band-shaped leaf blight and sheath blight	[87]
Oryza sativa L.	4 strains (isolated from intercropping Oryza sativa L. roots)	Synergistic soil phosphorus activation, root architecture remodeling, and transporter gene regulation to regulate phosphorus use efficiency	It effectively regulates the distribution of P in the aboveground and underground parts of Oryza sativa L., promotes root growth and increases the yield of rice plants	[88]
Triticum aestivum L.	4 strains (isolated from Triticum aestivum L. rhizosphere)	Indole acetic acid producing bacteria and some volatile-releasing bacteria interact with fungi	Protecting Triticum aestivum L. from Rhizoctonia solani AG8 infection, reducing the occurrence of Triticum aestivum L. root rot and affecting plant characteristics	[89]
Cucumis sativus L.	2 strains (Bacillus strains isolated from Cucumis sativus L. rhizosphere soil and native Pseudomonas strains beneficial to plants)	Synergistic metabolic promotion between strains	Synergistically promote the growth of Cucumis sativus L., significantly increased the plant branch height, dry weight, and chlorophyll content	[90]
Glycine max	12 strains (isolated from Glycine max rhizosphere)	To systematically regulate the N and P signal transduction network at the transcriptional level and enhance the growth pathway related to auxin response	It significantly increased the nodulation rate and nitrogenase activity, and increased the field yield of Glycine max by approximately 20%-30%	[91]

Plant

SynComs size ＆ origin

Mechanism

Results

References

Solanum lycopersicum L.

15 strains (isolated from Indigofera tinctoria Linn. root of Jizan)

Differential expression of salt stress-related genes and ion accumulation in aboveground parts

Under salt stress and non-sterile conditions, SynComs has a strong growth-promoting effect on Solanum lycopersicum L.

[85]

Arabidopsis thaliana (L.) Heynh.

22 strains (symbiotic bacteria isolated from Arabidopsis thaliana (L.) Heynh. roots)

Redox-mediated mechanism of SynComs

The molecular mechanism was established to elucidate the composition of microbial communities derived from plants, and the functional diversity of plant rhizosphere-dependent specialized metabolites was analyzed

[86]

Zea mays L.

6 strains (Bacillus strains isolated from roots and leaves of Zea mays L.)

Endophytic microorganisms fight pathogens by competing, producing disease-resistant substances, or activating plant immune systems

Promote plant growth and significantly reduce the incidence of band-shaped leaf blight and sheath blight

[87]

Oryza sativa L.

4 strains (isolated from intercropping Oryza sativa L. roots)

Synergistic soil phosphorus activation, root architecture remodeling, and transporter gene regulation to regulate phosphorus use efficiency

It effectively regulates the distribution of P in the aboveground and underground parts of Oryza sativa L., promotes root growth and increases the yield of rice plants

[88]

Triticum aestivum L.

4 strains (isolated from Triticum aestivum L. rhizosphere)

Indole acetic acid producing bacteria and some volatile-releasing bacteria interact with fungi

Protecting Triticum aestivum L. from Rhizoctonia solani AG8 infection, reducing the occurrence of Triticum aestivum L. root rot and affecting plant characteristics

[89]

Cucumis sativus L.

2 strains (Bacillus strains isolated from Cucumis sativus L. rhizosphere soil and native Pseudomonas strains beneficial to plants)

Synergistic metabolic promotion between strains

Synergistically promote the growth of Cucumis sativus L., significantly increased the plant branch height, dry weight, and chlorophyll content

[90]

Glycine max

12 strains (isolated from Glycine max rhizosphere)

To systematically regulate the N and P signal transduction network at the transcriptional level and enhance the growth pathway related to auxin response

It significantly increased the nodulation rate and nitrogenase activity, and increased the field yield of Glycine max by approximately 20%-30%

[91]