Deep learning approaches for natural product discovery from plant endophytic microbiomes

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 li...

Full description

Saved in:

Bibliographic Details
Published in:	Environmental microbiome 2021-03, Vol.16 (1), p.6-20, Article 6
Main Authors:	Aghdam, Shiva Abdollahi, Brown, Amanda May Vivian
Format:	Article
Language:	eng
Subjects:	Artificial intelligence Bacteria Biomass Bioprospecting Cancer Computer applications Deep learning Drugs Endohyphal bacteria Endophytes Endophytic fungi Fungi Gene silencing Genes Genomes Host plants Metabolism Metabolites Metabolomics Microbiomes Microbiota Mycovirus Natural product Natural products Plant communities Review Secondary metabolites Taxonomy
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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. The online version contains supplementary material available at 10.1186/s40793-021-00375-0.
ISSN:	2524-6372 2524-6372