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Deep learning approaches for natural product discovery from plant endophytic microbiomes.

ABSTRACT: 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.

Supplementary information

The online version contains supplementary material available at 10.1186/s40793-021-00375-0.

SUBMITTER: Aghdam SA

PROVIDER: S-EPMC7972023 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAntibiotic resistance is a growing problem that can be ameliorated by the discovery of novel drug candidates. Bacterial associates are often the source of pharmaceutically active natural products isolated from marine invertebrates, and thus, important targets for drug discovery. While the microbiomes of many marine organisms have been extensively studied, microbial communities from chemically-rich nudibranchs, marine invertebrates that often possess chemical defences, are relatively unknown.MethodsWe applied both culture-dependent and independent approaches to better understand the biochemical potential of microbial communities associated with nudibranchs. Gram-positive microorganisms isolated from nudibranchs collected in the Red Sea were screened for antibacterial and antitumor activity. To assess their biochemical potential, the isolates were screened for the presence of natural product biosynthetic gene clusters, including polyketide synthase (PKS) and non-ribosomal peptide synthetase (NRPS) genes, using PCR. The microbiomes of the nudibranchs were investigated by high-throughput sequencing of 16S rRNA amplicons.ResultsIn screens against five model microorganisms, 51% of extracts displayed antimicrobial activity against more than one organism, and 19% exhibited antitumor activity against Ehrlich's ascites carcinoma. Sixty-four percent of isolates contained PKS and NRPS genes, suggesting their genomes contain gene clusters for natural product biosynthesis. Thirty-five percent were positive for more than one class of biosynthetic gene. These strains were identified as belonging to the Firmicutes and Actinobacteria phyla via 16S rRNA gene sequencing. In addition, 16S rRNA community amplicon sequencing revealed all bacterial isolates were present in the uncultured host-associated microbiome, although they were a very small percentage of the total community. Taken together, these results indicate that bacteria associated with marine nudibranchs are potentially a rich source of bioactive compounds and natural product biosynthetic genes.

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Deep learning approaches for natural product discovery from plant endophytic microbiomes.

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