综述调控植物微生物组的策略

摘要

（1）Plant-associated microbiomes are central to crop productivity, nutrient efficiency, and stress resilience, yet conventional microbiome manipulation strategies, largely based on microbial inoculation and agronomic management, often suffer from inconsistent field performance and limited persistence. 与植物相关的微生物组对作物生产力、养分效率和胁迫恢复力至关重要，然而传统的微生物组调控策略（主要基于微生物接种和农艺管理）往往存在田间表现不一致、持久性有限的问题。

（2）Although several recent reviews have discussed CRISPR-mediated plant-microbe engineering and synthetic microbial community (SynCom) design separately, few reviews integrate genome editing, ecological stability of microbiomes, and climate-resilient agricultural applications within a unified conceptual framework. 尽管近期的一些综述已分别讨论了CRISPR介导的植物-微生物工程和合成微生物群落设计，但很少有综述将基因组编辑、微生物组的生态稳定性以及气候适应性农业应用整合在一个统一的概念框架内。

（3）Recent advances in molecular biotechnology are transforming this landscape by enabling precision engineering of plant-microbe interactions at genetic, metabolic, and community levels.分子生物技术的最新进展正在改变这一局面，它们使得在遗传、代谢和群落水平上对植物-微生物相互作用进行精准工程化改造成为可能。

（4）In particular, synthetic biology tools including CRISPR/Cas genome editing, RNA interference, and synthetic microbial communities (SynComs), now allow targeted modification of plant traits governing microbial recruitment, microbial pathways underpinning nutrient cycling and stress tolerance, and community-level functional complementarity.特别是，包括CRISPR/Cas基因组编辑、RNA干扰和合成微生物群落在内的合成生物学工具，如今允许对控制微生物招募的植物性状、支撑养分循环和胁迫耐受性的微生物途径，以及群落水平的功能互补性进行靶向修饰。

（5）This review integrates molecular genetics, microbial ecology, and systems-level microbiome design to frame the plant and its microbiome as an engineerable holobiont.本综述整合了分子遗传学、微生物生态学和系统水平的微生物组设计，将植物及其微生物组构建为一个可工程化的全生物体。

（6）We integrate insights from genome editing in plants and microbes, omics-guided SynCom design, climate-resilience mechanisms, and emerging AI-assisted decision frameworks, including machine learning and ecological modeling approaches used to analyze multi-omics datasets, and predict plant-microbiome interactions across experimental and field-based studies. 我们整合了来自植物和微生物基因组编辑、组学引导的合成微生物群落设计、气候适应性机制以及新兴的人工智能辅助决策框架的见解，这些框架包括用于分析多组学数据集以及预测实验和田间研究中植物-微生物组相互作用的机器学习和生态建模方法。

（7）Importantly, we critically assess limitations related to ecological stability, trait trade-offs, biosafety, and regulatory challenges that constrain large-scale deployment.重要的是，我们批判性地评估了与生态稳定性、性状权衡、生物安全以及制约大规模部署的监管挑战相关的局限性。

（8）By bridging genome-enabled microbiome manipulation with ecological design principles, this review proposes an integrative framework for climate-smart microbiome engineering and identifies key research priorities required to transition from empirical inoculation toward predictive, sustainable, and socially responsible agricultural biotechnology.通过将基因组驱动的微生物组调控与生态设计原则相连接，本综述提出了一个用于气候智能型微生物组工程的整合框架，并确定了从经验性接种向可预测、可持续以及对社会负责的农业生物技术转型所需的关键研究优先事项。