Abstract, ToC blurb 与 Introduction
摘要提出以 DBTL 循环组织微生物组工程,解决知识缺口并支持医学、农业、能源和环境应用。引言指出微生物群落能力巨大,但缺少可操作、机制化和预测性理解,仍受实验系统、未知基因/代谢物、未表征相互作用、测量与模拟工具不足以及精确操控方法有限等因素阻碍。
这篇综述提出,微生物组工程要从描述性研究走向可操作、可预测的技术,需要围绕 design-build-test-learn(DBTL)循环组织研究与开发。作者的问题是:如何把自上而下的生态系统过程操控、自下而上的定义菌群设计、高通量构建与功能测试、多组学和模型学习整合成可复用最佳实践。文章面向医学、农业、能源和环境应用,但更强调工程流程、原则和工具箱。
| 题名 | Common principles and best practices for engineering microbiomes |
| 作者 | Christopher E. Lawson, William R. Harcombe, Roland Hatzenpichler, Stephen R. Lindemann, Frank E. Löffler, Michelle A. O’Malley, Héctor García-Martin, Brian F. Pfleger, Lutgarde Raskin, Ophelia S. Venturelli, David G. Weissbrodt, Daniel R. Noguera, Katherine D. McMahon |
| 期刊/年份 | Nature Reviews Microbiology, 2019 December, 17(12):725–741; author manuscript available in PMC 2021 July 30. |
| DOI | 10.1038/s41579-019-0255-9 |
| 原文 PDF | 下载/查看 PDF |
这篇综述提出,微生物组工程要从描述性研究走向可操作、可预测的技术,需要围绕 design-build-test-learn(DBTL)循环组织研究与开发。作者的问题是:如何把自上而下的生态系统过程操控、自下而上的定义菌群设计、高通量构建与功能测试、多组学和模型学习整合成可复用最佳实践。文章面向医学、农业、能源和环境应用,但更强调工程流程、原则和工具箱。
摘要提出以 DBTL 循环组织微生物组工程,解决知识缺口并支持医学、农业、能源和环境应用。引言指出微生物群落能力巨大,但缺少可操作、机制化和预测性理解,仍受实验系统、未知基因/代谢物、未表征相互作用、测量与模拟工具不足以及精确操控方法有限等因素阻碍。
设计部分区分自上而下和自下而上。自上而下从生态系统过程出发,预测如何通过物理、化学和生物过程操控获得目标功能;自下而上从分离菌、基因组和代谢模型出发选择/设计成员和相互作用。作者主张复杂场景常需混合策略,并要求在一开始定义可测量设计目标。
作者把设计落到明确问题、收集先验知识、构建概念模型、建立定量模型、比较替代方案等步骤。定量模型包括过程模型、质量平衡、通量平衡分析、动态 FBA 和生态位模型等,可用于识别核心与可替换功能行会、环境变量和设计瓶颈。
构建阶段是物理组装设计好的微生物组。自组装路线通过反应器、环境选择、固定慢生长成员、改变底物或水力条件等操控自然群落;合成路线使用纯培养或富集培养直接组合成员。作者还讨论了定向适应/进化、原位微生物组工程和合成基因电路的潜力与挑战。
测试阶段需要测量微生物组相关表型和属性,判断设计结果是否达成以及结果是否由设计—构建方案导致。作者强调应测量系统层功能,也要测量成员丰度、代谢活动和网络表达;高通量筛选和自动化平台可加速多条件测试。
学习阶段从设计、构建和测试产生的数据中提炼可推广原则。作者认为必须给 learn 阶段足够资源,因为只有把数据转化为一般化、可复用知识,DBTL 才能超越单次经验优化。模型实验生态系统与真实复杂生态系统的交叉验证是学习稳健原则的重要途径。
Outlook 强调需要多轮 DBTL 捕捉稳态和瞬态功能,以及实验、计算、自动化和应用专家组成团队。术语表定义 microbiome engineering、metaphenotypes、functional guilds、syntrophy、self-assembled microbiome、synthetic microbiome、FBA、machine learning 等关键术语,为正式报告提供概念边界。
Box 1 展示创建具有目标功能的合成微生物组的 DBTL 例程,Box 2 汇总功能测量工具箱,Box 3 以生态位建模讨论工程原则。Figures 1–5 则分别对应 DBTL 循环、设计路线、构建路线、功能测试和学习原则。
图示信息:Figure 1. The design-build-test-learn cycle for microbiome engineering. The figure presents key aspects and approaches of each phase of the design-build-test-learn (DBTL) cycle. The cycle starts with a defined engineering objective that determines the design and produces an engineered microbiome that performs the desired function(s). Lawson et al. Page 34 Nat Rev Microbiol. Author manuscript; available in PMC 2021 July 30. Author Manuscript Author Manuscript Author Manuscript Author Manuscript
论文结果 / 观点:该图支撑作者把微生物组工程组织为 DBTL 闭环的观点:工程目标驱动设计,构建后的群落需要测试与学习反馈。
研究意义 / 边界:这张图用于支持作者的概念框架或案例归纳;实际迁移到其他系统时,仍需结合成员来源、环境条件、稳定性和功能验证。
来源:nihms-1724630.pdf,PDF 第 34 页;图像来自 PDF 内部可匹配 Figure caption 的图像块。
图示信息:Figure 2. Top-down and bottom-up approaches to design microbiomes. The left panel illustrates a bottom-up design workflow starting from pure isolates. Physiological characterization of individual organisms is performed, and metabolic modeling is used to design consortia for desired function (produce light blue compound from dark blue compound). Genetic engineering and synthetic biology strategies are used to optimize system function (identifying gene editing targets that re-route metabolic flux away from toxin (purple) and towards desired product; designing of toxin reporter strain). The right panel illustrates a top- down design starting with an inoculum containing uncultivated microorganisms from the environment. Community characterization of mixed microbiome is performed, and bioprocess modeling (mass balance analysis including kinetics and microbial growth) is used to develop selection strategies to achieve desired function (produce light blue compound from dark blue compound). Reactor engineering design is used to optimize system function. The middle panel shows an integrated top-down bottom-up design. Combinations of uncultivated consortia and defined cultures are selected to
论文结果 / 观点:该图支撑作者对 top-down 与 bottom-up 两类设计路径的比较,说明菌株来源、纯培养表征和自然群落筛选会影响可控性与生态相关性。
研究意义 / 边界:这张图用于支持作者的概念框架或案例归纳;实际迁移到其他系统时,仍需结合成员来源、环境条件、稳定性和功能验证。
来源:nihms-1724630.pdf,PDF 第 35 页;图像来自 PDF 内部可匹配 Figure caption 的图像块。
图示信息:Figure 3. Building self-assembled and synthetic microbiomes. (a) This example shows a protocol for assembling synthetic microbiomes from multiple microbiome sources. Complex microbiomes can be taken apart into key functional members using automated microfluidic cell sorting techniques. Isolated or enriched members can then be recombined into synthetic consortia using liquid handling robotics for downstream screening and/or cultivation. (b) Microbiome assembly can also be achieved through environmental selection via bioreactor manipulation or biostimulation (top) or using bioaugmentation with defined cultures (bottom). (c) Another option is microbiome assembly through directed adaptation and/or evolution of the microbiome to acquire or optimize a desired function. (d) In situ microbiome engineering can be used to add new functions to microbiomes residing in the environment. Lawson et al. Page 36 Nat Rev Microbiol. Author manuscript; available in PMC 2021 July 30. Author Manuscript Author Manuscript Author Manuscript Author Manuscript
论文结果 / 观点:该图支撑“群落构建后必须验证功能”的观点,强调组学、示踪、功能测定或模型不能脱离实验验证。
研究意义 / 边界:这张图用于支持作者的概念框架或案例归纳;实际迁移到其他系统时,仍需结合成员来源、环境条件、稳定性和功能验证。
来源:nihms-1724630.pdf,PDF 第 36 页;图像来自 PDF 内部可匹配 Figure caption 的图像块。
图示信息:Figure 4. Testing microbiome function. (a) Isotopic tracers combined with metaproteome can also be used to measure microbiome metabolic flux by analyzing isotopic labelling patterns of short peptides rather than amino acids (metabolome). (b) Biorthogonal non-canonical amino acid tagging (BONCAT) is a method for rapid profiling of the anabolic processes (growth) in situ using either fluorescent detection or metaproteomics. (c) Metagenomics, metatranscriptomics, metaproteomics, and metabolomics can be integrated to reconstruct and analysis metabolic network expression in microbiomes. (d) An automated microbioreactor platform enables high-throughput analysis of microbiome processes across diverse conditions (for example, with changing environmental or physiological variables). The platform can integrate tools for detailed functional analysis of individual microbiome members to complex communities. HPG: the amino acid homopropargylglycine. Lawson et al. Page 37 Nat Rev Microbiol. Author manuscript; available in PMC 2021 July 30. Author Manuscript Author Manuscript Author Manuscript Author Manuscript
论文结果 / 观点:该图把群落互作落实到代谢物、电子或营养物交换,是理解共培养功能涌现的关键证据。
研究意义 / 边界:这张图用于支持作者的概念框架或案例归纳;实际迁移到其他系统时,仍需结合成员来源、环境条件、稳定性和功能验证。
来源:nihms-1724630.pdf,PDF 第 37 页;图像来自 PDF 内部可匹配 Figure caption 的图像块。
图示信息:Figure 5. Learning fundamental principles for microbiome engineering. (a) Model laboratory ecosystems can be used for controlled experiments with simplified microbiomes and environmental properties, representing an in-between of pure lab conditions (such as test tubes or flasks) and complex natural environments (such as soil or the ocean). Continuous cross-examination between laboratory-scale models and natural complex ecosystems will be needed for developing engineering principles and practices that are robust in real systems, while also tractable in the lab. This will require close collaboration between multiple stakeholders, including researchers and end-users (such as hospitals or treatment plants) that have expertise and experience with issues specific to each scale. Key principles that need to be learned to enable systematic microbiome engineering are microbial interaction Lawson et al. Page 38 Nat Rev Microbiol. Author manuscript; available in PMC 2021 July 30. Author Manuscript Author Manuscript Author Manuscript Author Manuscript
论文结果 / 观点:该图说明作者把模型系统、数据学习和生态机制总结作为下一轮工程设计的依据。
研究意义 / 边界:这张图用于支持作者的概念框架或案例归纳;实际迁移到其他系统时,仍需结合成员来源、环境条件、稳定性和功能验证。
来源:nihms-1724630.pdf,PDF 第 38 页;图像来自 PDF 内部可匹配 Figure caption 的图像块。
经费 / 利益冲突:Competing interests 文本显示:The authors declare no competing interests. Acknowledgements 显示:University of Wisconsin-Madison College of Engineering 支持 Madison Microbiome Meeting workshop;CEL 获 NSERC PGS-D 和 Wisconsin Distinguished Graduate Fellowship;KDM/DRN 获 NSF CBET-1803055、MCB-1518130 和 UW-Madison WARF Microbiome Initiative 支持;DRN/BFP 获 DOE Great Lakes Bioenergy Research Center DE-SC0018409 支持;BFP 获 NSF CBET-1703504、MCB-1716594;MAO/HGM 获 DOE JBEI through DE-AC02-05CH11231;HGM 还获 DOE Agile BioFoundry、Basque Government BERC 2018-2021 和 MINECO BCAM Severo Ochoa SEV-2017-071;FEL 获 DOD SERDP 和 Governor’s Chair program 支持。
生成日期:2026-07-03