Reverse engineering the soil microbiome: detecting, modeling, and optimizing signal impacts on microbiome metabolic functions

对土壤微生物组进行逆向工程：检测、建模和优化信号对微生物组代谢功能的影响

• 批准号: NE/T010959/1
• 负责人: Eriko Takano
• 金额: $ 109.5万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FT010959%2F1
• 关键词: Reverse; engineering; soil; microbiome; detecting

Overview: Our project will provide fundamental insights into the roles of signals in mediating the ecology of soil microbes and suppression of plant diseases. This work establishes a foundation for engineering functional soil microbiomes for precision agriculture. Our team consists of experts in soil ecology, genetic engineering, metabolomics, and community modeling from the UK and USA.Objectives: 1) Develop and test genetic recorder (GR) strains to 'listen and report' on signals in the soil that regulate primary and secondary metabolic pathways in Streptomyces spp. isolated from disease suppressive soils; 2) Model and test how species-species interactions that rely on primary and secondary metabolic induction impact multi-species communities; and 3) Discover effects of potential signals on Streptomyces metabolism and harness signals to optimize microbial functional capacities in soil.Methods: 1) We will create GRs to detect the activation of genes/pathways of interest in soil microbes using serine integrase-mediated recombination. The GRs will be triggered by yet unknown chemical, physical, and biological signals to produce a state change that can be easily quantified using Next-Generation Sequencing technology. Using these GRs, we will be able to simultaneously record the activation of hundreds of metabolic activities, across diverse microbial species, in a single high-throughput experiment. 2) We will utilize genome-scale metabolic models, transcriptomics, and metabolomics to connect signals to functions. We will extend existing metabolic modeling platforms to incorporate novel functionality to understand how signals will influence the physiology of individual bacteria and alter emergent ecosystem dynamics. Transcriptomic and metabolomic data will be used to validate and extend current knowledge of exo-metabolite roles in system behavior and advance systems-level understanding of soil microbiomes. 3) We will screen potential signals for their direct effects on Streptomyces antibiotic inhibitory and nutrient use phenotypes in vitro. We will use the GRs to screen presumptive signals for their role in mediating Streptomyces primary and secondary metabolic activities, providing both a signal discovery platform and a direct comparison with phenotypic data. We will create signal-optimized isolate combinations or isolate/signal combinations and test their capacities to reduce plant diseases on wheat and radish seedlings in soil. Finally, we will test the effectiveness of the GRs in detecting signals in complex soil communities.Intellectual Merit: The proposed research will advance fundamental understanding of the roles of signals in mediating the assembly, dynamics, and functional behaviors of complex soil microbiomes; provide novel tools for studying signaling dynamics in vivo, with potential to serve as sensors of microbial activities in soil; enhance systems-level understanding and modeling of primary and secondary metabolic activities within microbiomes; and test the capacity of signal-optimized inoculants to enhance plant health and productivity in soil systems. sustainable cropping systems worldwide.

概述：我们的项目将为信号在介导土壤微生物生态和抑制植物病害中的作用提供基本见解。这项工作为精准农业功能土壤微生物组工程奠定了基础。我们的团队由来自英国和美国的土壤生态学、基因工程、代谢组学和群落建模专家组成。目标：1）开发和测试遗传记录（GR）菌株，以“倾听和报告”土壤中调节链霉菌初级和次级代谢途径的信号。从病害抑制土壤中分离; 2）模拟和测试依赖于初级和次级代谢诱导的种-种相互作用如何影响多物种群落;以及3）发现潜在信号对链霉菌代谢的影响并利用信号来优化土壤中的微生物功能能力。1）我们将创建GR，以使用丝氨酸整合酶介导的重组来检测土壤微生物中感兴趣的基因/途径的激活。GR将由未知的化学，物理和生物信号触发，以产生可以使用下一代测序技术轻松量化的状态变化。使用这些GR，我们将能够在单个高通量实验中同时记录不同微生物物种中数百种代谢活动的激活。2)我们将利用基因组规模的代谢模型，转录组学和代谢组学连接信号功能。我们将扩展现有的代谢建模平台，以纳入新的功能，以了解信号将如何影响单个细菌的生理学并改变新兴的生态系统动态。转录组学和代谢组学数据将用于验证和扩展目前的知识外代谢物在系统行为中的作用，并推进系统水平的土壤微生物组的理解。3)我们将筛选潜在的信号，其直接影响链霉菌抗生素抑制和营养素使用表型在体外。我们将使用GR来筛选它们在介导链霉菌初级和次级代谢活动中的作用的假定信号，提供信号发现平台和与表型数据的直接比较。我们将创建信号优化的分离物组合或分离物/信号组合，并测试它们减少土壤中小麦和萝卜幼苗的植物病害的能力。知识价值：该研究将促进对信号在复杂土壤微生物群落组装、动力学和功能行为中的作用的基本理解，为研究体内信号动力学提供新的工具，具有作为土壤微生物活动传感器的潜力，并将为土壤微生物的研究提供新的思路。提高系统水平的理解和建模的初级和次级代谢活动的微生物组内;和测试信号优化接种剂的能力，以提高植物健康和土壤系统的生产力。全球可持续种植系统。

项目成果

Harnessing intercellular signals to engineer the soil microbiome.

• DOI: 10.1039/d1np00034a
• 发表时间: 2021-12
• 期刊: Natural product reports
• 影响因子: 11.9
• 作者: Jack A Connolly;W. Harcombe;M. Smanski;L. Kinkel;E. Takano;R. Breitling

ipaPy2: Integrated Probabilistic Annotation (IPA) 2.0-an improved Bayesian-based method for the annotation of LC-MS/MS untargeted metabolomics data.

• DOI: 10.1093/bioinformatics/btad455
• 发表时间: 2023-07-01
• 期刊: Bioinformatics (Oxford, England)

Microbial production of the plant flavanone hesperetin from caffeic acid.

• DOI: 10.1186/s13104-023-06620-8
• 发表时间: 2023-11-18
• 期刊: BMC RESEARCH NOTES
• 影响因子: 1.8
• 作者: Hanko, Erik K. R.;Correia, Joao;Souza, Caio S.;Green, Alison;Chromy, Jakub;Stoney, Ruth;Yan, Cunyu;Takano, Eriko;Lousa, Diana;Soares, Claudio M.;Breitling, Rainer
• 通讯作者: Breitling, Rainer

Multi-Omics Analysis of the Effect of cAMP on Actinorhodin Production in Streptomyces coelicolor.

• DOI: 10.3389/fbioe.2020.595552
• 发表时间: 2020
• 期刊: Frontiers in bioengineering and biotechnology
• 影响因子: 5.7
• 作者: Nitta K;Carratore FD;Breitling R;Takano E;Putri SP;Fukusaki E
• 通讯作者: Fukusaki E

Synthetic biology approaches to actinomycete strain improvement.

• DOI: 10.1093/femsle/fnab060
• 发表时间: 2021-06-11
• 期刊: FEMS microbiology letters
• 影响因子: 2.1
• 作者: Breitling R;Avbelj M;Bilyk O;Carratore FD;Filisetti A;Hanko EKR;Iorio M;Redondo RP;Reyes F;Rudden M;Severi E;Slemc L;Schmidt K;Whittall DR;Donadio S;García AR;Genilloud O;Kosec G;De Lucrezia D;Petković H;Thomas G;Takano E
• 通讯作者: Takano E