Plant microbiomes are not only diverse, but also appear to host a vast pool of secondary metabolites holding great promise for bioactive natural products and drug discovery. Yet, most microbes within plants appear to be uncultivable, and for those that can be cultivated, their metabolic potential lies largely hidden through regulatory silencing of biosynthetic genes. The recent explosion of powerful interdisciplinary approaches, including multi-omics methods to address multi-trophic interactions and artificial intelligence-based computational approaches to infer distribution of function, together present a paradigm shift in high-throughput approaches to natural product discovery from plant-associated microbes. Arguably, the key to characterizing and harnessing this biochemical capacity depends on a novel, systematic approach to characterize the triggers that turn on secondary metabolite biosynthesis through molecular or genetic signals from the host plant, members of the rich ‘in planta’ community, or from the environment. This review explores breakthrough approaches for natural product discovery from plant microbiomes, emphasizing the promise of deep learning as a tool for endophyte bioprospecting, endophyte biochemical novelty prediction, and endophyte regulatory control. It concludes with a proposed pipeline to harness global databases (genomic, metabolomic, regulomic, and chemical) to uncover and unsilence desirable natural products.

中文翻译：

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从植物内生微生物组中发现天然产物的深度学习方法

植物微生物组不仅多种多样，而且似乎还拥有大量的次生代谢产物，为生物活性天然产物和药物发现带来了巨大的前景。然而，植物内的大多数微生物似乎是不可培养的，而对于那些可以培养的微生物来说，它们的代谢潜力很大程度上隐藏在生物合成基因的调控沉默中。最近强大的跨学科方法的爆炸式增长，包括解决多营养相互作用的多组学方法和推断功能分布的基于人工智能的计算方法，共同呈现了从植物相关的天然产物发现的高通量方法的范式转变微生物。可以说，表征和利用这种生化能力的关键取决于一种新颖的、系统的方法来表征通过来自宿主植物、丰富的“植物内”群落成员或来自植物的分子或遗传信号开启次生代谢物生物合成的触发因素。环境。这篇综述探讨了从植物微生物组中发现天然产物的突破性方法，强调了深度学习作为内生菌生物勘探、内生菌生化新颖性预测和内生菌调控工具的前景。最后提出了一个利用全球数据库（基因组、代谢组、调节组和化学）来发现和消除所需天然产品的拟议管道。