Mining metagenomes for natural product biosynthetic gene clusters: unlocking new potential with ultrafast techniques

Abstract

Microorganisms produce an immense variety of natural products through the expression of Biosynthetic Gene Clusters (BGCs): physically clustered genes that encode the enzymes of a specialized metabolic pathway. These natural products cover a wide range of chemical classes (e.g., aminoglycosides, lantibiotics, nonribosomal peptides, oligosaccharides, polyketides, terpenes) that are highly valuable for industrial and medical applications1. Metagenomics, as a culture-independent approach, has greatly enhanced our ability to survey the functional potential of microorganisms and is growing in popularity for the mining of BGCs. However, to effectively exploit metagenomic data to this end, it will be crucial to more efficiently identify these genomic elements in highly complex and ever-increasing volumes of data2. Here, we address this challenge by developing the ultrafast Biosynthetic Gene cluster MEtagenomic eXploration toolbox (BiG-MEx). BiG-MEx rapidly identifies a broad range of BGC protein domains, assess their diversity and novelty, and predicts the abundance profile of natural product BGC classes in metagenomic data. We show the advantages of BiG-MEx compared to standard BGC-mining approaches, and use it to explore the BGC domain and class composition of samples in the TARA Oceans3 and Human Microbiome Project datasets4. In these analyses, we demonstrate BiG-MEx’s applicability to study the distribution, diversity, and ecological roles of BGCs in metagenomic data, and guide the exploration of natural products with clinical applications.

Compendium of secondary metabolite biosynthetic diversity encoded in bacterial genomes

“…Furthermore, apart from the few metagenomic projects whose MAGs we incorporated in the first part of our analysis, there are multiple such projects publicly available, some of which have been the focus of NP studies 76,77 . Although the reconstruction of genomes from metagenomes remains a challenge [78][79][80] and the assembly will often miss BGCs [81][82][83][84] , which has indirectly prevented their comparison to the cultured bacteria in the current project, metagenomics is proving a promising source of information on NPs and their producers 14,47,61,74,76,85,86 , as made apparent in the present investigation. We expect the effect of this field on NP research to become more evident in the following years.…”

NPOmix: a machine learning classifier to connect mass spectrometry fragmentation data to biosynthetic gene clusters

“…The GNPS molecular family information could be used to select a consensus prediction among different MS/MS spectra from the same family. The BGCs assembled from the metagenomic samples could be improved using tools like metaBGC (45) and BiG-Mex (46). Enrichment of the current Paired Omics Data Platform dataset (we could now use 1,040 PoDP samples) with higher quality samples as well as more validated BGC-MS/MS links will further drive the development of tools such as NPOmix, and this will spark the discovery of more novel NPs.…”

Evolutionary investigations of the biosynthetic diversity in the skin microbiome using lsaBGC

“…Due to difficulties in the cultivation of many bacterial taxa, several recent studies have begun to explore metagenomic datasets to unearth novel secondary metabolites [15][16][17]. While previous endeavours relied on performing metagenomic assembly, two recent read-based approaches were described to reliably and sensitively search for key BGC domains in metagenomes [16,18]. This highlights a larger trend toward seeking highly novel secondary metabolites and, in turn, prioritized investigation of poorly studied or newly discovered taxa [19].…”