The tomato cytochrome P450 CYP712G1 catalyses the double oxidation of orobanchol en route to the rhizosphere signalling strigolactone, solanacol.

Strigolactones (SLs) are rhizosphere signalling molecules and phytohormones. The biosynthetic pathway of SLs in tomato has been partially elucidated, but the structural diversity in tomato SLs predicts that additional biosynthetic steps are required. Here, root RNA-seq data and co-expression analysis were used for SL biosynthetic gene discovery. This strategy resulted in a candidate gene list containing several cytochrome P450s. Heterologous expression in Nicotiana benthamiana and yeast showed that one of these, CYP712G1, can catalyse the double oxidation of orobanchol, resulting in the formation of three didehydro-orobanchol (DDH) isomers. Virus-induced gene silencing and heterologous expression in yeast showed that one of these DDH isomers is converted to solanacol, one of the most abundant SLs in tomato root exudate. Protein modelling and substrate docking analysis suggest that hydroxy-orbanchol is the likely intermediate in the conversion from orobanchol to the DDH isomers. Phylogenetic analysis demonstrated the occurrence of CYP712G1 homologues in the Eudicots only, which fits with the reports on DDH isomers in that clade. Protein modelling and orobanchol docking of the putative tobacco CYP712G1 homologue suggest that it can convert orobanchol to similar DDH isomers as tomato.

MIBiG 2.0: a repository for biosynthetic gene clusters of known function.

Fueled by the explosion of (meta)genomic data, genome mining of specialized metabolites has become a major technology for drug discovery and studying microbiome ecology. In these efforts, computational tools like antiSMASH have played a central role through the analysis of Biosynthetic Gene Clusters (BGCs). Thousands of candidate BGCs from microbial genomes have been identified and stored in public databases. Interpreting the function and novelty of these predicted BGCs requires comparison with a well-documented set of BGCs of known function. The MIBiG (Minimum Information about a Biosynthetic Gene Cluster) Data Standard and Repository was established in 2015 to enable curation and storage of known BGCs. Here, we present MIBiG 2.0, which encompasses major updates to the schema, the data, and the online repository itself. Over the past five years, 851 new BGCs have been added. Additionally, we performed extensive manual data curation of all entries to improve the annotation quality of our repository. We also redesigned the data schema to ensure the compliance of future annotations. Finally, we improved the user experience by adding new features such as query searches and a statistics page, and enabled direct link-outs to chemical structure databases. The repository is accessible online at https://mibig.secondarymetabolites.org/.

Drivers of metabolic diversification: how dynamic genomic neighbourhoods generate new biosynthetic pathways in the Brassicaceae.

Plants produce an array of specialized metabolites with important ecological functions. The mechanisms underpinning the evolution of new biosynthetic pathways are not well-understood. Here, we exploit available genome sequence resources to investigate triterpene biosynthesis across the Brassicaceae. Oxidosqualene cyclases (OSCs) catalyze the first committed step in triterpene biosynthesis. Systematic analysis of 13 sequenced Brassicaceae genomes was performed to identify all OSC genes. The genome neighbourhoods (GNs) around a total of 163 OSC genes were investigated to identify Pfam domains significantly enriched in these regions. All-vs-all comparisons of OSC neighbourhoods and phylogenomic analysis were used to investigate the sequence similarity and evolutionary relationships of the numerous candidate triterpene biosynthetic gene clusters (BGCs) observed. Functional analysis of three representative BGCs was carried out and their triterpene pathway products were elucidated. Our results indicate that plant genomes are remarkably plastic, and that dynamic GNs generate new biosynthetic pathways in different Brassicaceae lineages by shuffling the genes encoding a core palette of triterpene-diversifying enzymes, presumably in response to strong environmental selection pressure. These results illuminate a genomic basis for diversification of plant-specialized metabolism through natural combinatorics of enzyme families, which can be mimicked using synthetic biology to engineer diverse bioactive molecules.

Genomic Identification and Analysis of Specialized Metabolite Biosynthetic Gene Clusters in Plants Using PlantiSMASH.

Plants produce a vast diversity of specialized metabolites, which play important roles in the interactions with their microbiome, as well as with animals and other plants. Many such molecules have valuable biological activities that render them (potentially) useful as medicines, flavors and fragrances, nutritional ingredients, or cosmetics. Recently, plant scientists have discovered that the genes for many biosynthetic pathways for the production of such specialized metabolites are physically clustered on the chromosome within biosynthetic gene clusters (BGCs). The Plant Secondary Metabolite Analysis Shell (plantiSMASH) allows for the automated identification of such plant BGCs, facilitates comparison of BGCs across genomes, and helps users to predict the functional interactions of pairs of genes within and between BGCs based on coexpression analysis. In this chapter, we provide a detailed protocol on how to install and run plantiSMASH, and how to interpret its results to draw biological conclusions that are supported by the data.

antiSMASH 4.0-improvements in chemistry prediction and gene cluster boundary identification.

Many antibiotics, chemotherapeutics, crop protection agents and food preservatives originate from molecules produced by bacteria, fungi or plants. In recent years, genome mining methodologies have been widely adopted to identify and characterize the biosynthetic gene clusters encoding the production of such compounds. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' has assisted researchers in efficiently performing this, both as a web server and a standalone tool. Here, we present the thoroughly updated antiSMASH version 4, which adds several novel features, including prediction of gene cluster boundaries using the ClusterFinder method or the newly integrated CASSIS algorithm, improved substrate specificity prediction for non-ribosomal peptide synthetase adenylation domains based on the new SANDPUMA algorithm, improved predictions for terpene and ribosomally synthesized and post-translationally modified peptides cluster products, reporting of sequence similarity to proteins encoded in experimentally characterized gene clusters on a per-protein basis and a domain-level alignment tool for comparative analysis of trans-AT polyketide synthase assembly line architectures. Additionally, several usability features have been updated and improved. Together, these improvements make antiSMASH up-to-date with the latest developments in natural product research and will further facilitate computational genome mining for the discovery of novel bioactive molecules.

plantiSMASH: automated identification, annotation and expression analysis of plant biosynthetic gene clusters.

Plant specialized metabolites are chemically highly diverse, play key roles in host-microbe interactions, have important nutritional value in crops and are frequently applied as medicines. It has recently become clear that plant biosynthetic pathway-encoding genes are sometimes densely clustered in specific genomic loci: biosynthetic gene clusters (BGCs). Here, we introduce plantiSMASH, a versatile online analysis platform that automates the identification of candidate plant BGCs. Moreover, it allows integration of transcriptomic data to prioritize candidate BGCs based on the coexpression patterns of predicted biosynthetic enzyme-coding genes, and facilitates comparative genomic analysis to study the evolutionary conservation of each cluster. Applied on 48 high-quality plant genomes, plantiSMASH identifies a rich diversity of candidate plant BGCs. These results will guide further experimental exploration of the nature and dynamics of gene clustering in plant metabolism. Moreover, spurred by the continuing decrease in costs of plant genome sequencing, they will allow genome mining technologies to be applied to plant natural product discovery. The plantiSMASH web server, precalculated results and source code are freely available from http://plantismash.secondarymetabolites.org.