系统与合成生物学工程微生物组综述：模型、线路与干预

图示信息：Figure 1. (a) Microbiome stability is a function of the community’s ability to recover its original functions following a disturbance. Functions could include the production of specific molecules over time. (b) Microbiome composition and function are shaped by multiple spatial and temporal factors. (i) At the broadest scale, availability of carbon and energy defines possible ecological niches within each environment. (ii) Within a community, interspecies interactions, including social behaviors, modulate how cells respond to their environment by modifying substrate availability (i.e., syntrophy or competition), releasing effectors, or occupying available space. Biofilms, in particular, provide resilience to specific environmental perturbations. (iii) Within each cell, function is constrained by individual metabolic capacity, which can be altered through genetic mutations or horizontal gene transfer. Membrane-bound proteins transduce signals and molecules from the environment to the cytosol as well as facilitate secretion of products from the cell to the environment. Figure adapted from images created with BioRender.com. Leggieri et al. Page 34 Annu Rev Biomed Eng. 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-1692056.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. Different meta-omics tools are suited to answer different questions about microbiome composition and function. Amplicon metagenomics can reveal which organisms are present in a microbiome but not necessarily what each microbe’s role in the community is. Shotgun metagenomics elucidates which microbes are present in the community and what functions they have the capacity to perform. Metatranscriptomics and metaproteomics are necessary to uncover which functions are actually being performed in the community; assigning these transcripts and proteins to the microbes that produced them typically requires high-quality reference genomes or concurrent metagenomics analyses. Metabolomics and fluxomics quantify the chemical composition of the microbiome environment; however, linking metabolites to the microbes that produce or consume them is challenging, even with reference genomes. Linking microbiome composition and function is facilitated by integrating multiple meta-omics techniques, for example, concurrent shotgun metagenomics, metaproteomics, and metabolomics studies to assess which enzymes are producing an observed small molecule of interest and which microbes could produce th

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-1692056.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. Microbiomes can be modeled on many scales, and the choice of modeling technique depends on the question at hand. At the most mechanistic level, molecular simulations may be used to model the thermodynamics and kinetics of individual enzymes identified through metaproteomics; however, these are not scalable to encompass the entire microbiome. GEMs enable prediction of the metabolic fluxes and end-product profiles within a microbiome and can offer mechanistic insight into metabolomic observations given high-quality genomic reconstructions and sufficient experimental model validation. Evolutionary game theory models and differential equation–based models are particularly useful when microbiome population dynamics are of the greatest interest, because detailed metabolic reconstructions are not needed for each organism to be modeled. AbMs offer flexibility in that the user may define which inputs and outputs to include in the model and are often the technique of choice when integrating both metabolic and physical interactions between microbes. Data- driven models, including emerging machine learning–based models, offer empirical predictions of microbiome behaviors under specif

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-1692056.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. Microbiome engineering tools can be used to modify existing functions within the community or to introduce novel functions. These tools vary substantially in scale, from modification of the genome of a single organism to introduction of an entirely new engineered community into the microbiome. Highlighted methods include environmental perturbations such as the addition of a molecular antibiotic or a wild-type or engineered strain or community to an existing microbiome. More recently, genetic engineering efforts and adaptive laboratory evolution, including bacteriophage-assisted gene transfer, increasingly sophisticated synthetic biology tools, and targeted horizontal gene transfer, have gained traction. Leggieri et al. Page 37 Annu Rev Biomed Eng. Author manuscript; available in PMC 2021 July 14. 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-1692056.pdf，PDF 第 37 页；图像来自 PDF 内部可匹配 Figure caption 的图像块。