微生物组在农业中的应用潜力与挑战

Charles Copeland, Paul Schulze-Lefert, Ka-Wai Ma
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摘要

植物微生物组可以通过多种机制促进植物健康和生产力。我们对植物微生物组相互作用的理解依赖于描述性的自然调查和在简化的实验室环境下进行的实验。虽然还原论方法对于理解植物-微生物相互作用的机制至关重要,但它们可能会错过自然界中出现的群落特性。为了弥合基础研究和实际应用之间的差距,我们必须考虑功能关联以及控制种间和界间相互作用的生态原则。本文以模式植物和重要农业作物为例,讨论了植物微生物组通过调节味觉和风味来促进植物生长、营养、抗逆性、病原体保护和商业价值的有益潜力。然后,我们讨论了微生物在常绿群落中的入侵和持久性,多种应激源下的权衡以及宿主和环境施加的调节下的群落不稳定性在合理设计微生物接种剂时应考虑到什么,然后是微生物递送方法的审查。我们综合了如何利用多组学数据(包括基因组学、转录组学和代谢组学)来识别菌株或功能研究感兴趣的靶基因,以及如何结合机器学习算法来预测植物微生物组相互作用的想法。基于微生物组的策略有望改善农业。尽管潜在的相互作用具有内在的复杂性,跨学科的方法正在不断地提供对微生物组功能和组装原理的见解,这是基于知识的微生物组工程提高和可持续作物性能的关键。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Potential and challenges for application of microbiomes in agriculture
The plant microbiome can promote plant health and productivity through a multitude of mechanisms. Our understanding of plant microbiome interaction relies on descriptive natural surveys and experiments performed under simplified laboratory environments. While reductionist approaches are essential to understand mechanisms of plant-microbiome interactions, they risk missing emergent community properties seen in nature. To bridge the gap between basic research and real-world deployment of the microbiome for translational application, one has to consider both functional association as well as ecological principles governing interspecies and interkingdom interactions. In this review, we discuss the beneficial potential of plant microbiomes to enhance plant growth, nutrition, stress tolerance, pathogen protection and commercial value through the modulation of taste and flavors, using examples from both model plants and agriculturally important crops. We then discuss how microbial invasion and persistence in standing communities, tradeoffs under multiple stressors and community instability under host- and environment-imposed modulation should be considered in the rational design of microbial inocula, followed by a scrutiny of the method of microbial delivery. We synthesize ideas on how multiomic data including genomics, transcriptomes and metabolomics can be leveraged to identify strains or target genes of interest for functional studies, and how machine learning algorithms can be incorporated to enable prediction of plant microbiome interactions. Microbiome-based strategies hold promise for improvements in agriculture. Despite the intrinsic complexity of the underlying interactions, interdisciplinary approaches are constantly providing insight into microbiome functioning and assembly principles, which is key towards knowledge-based engineering of the microbiome for increased and sustainable crop performance.
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