MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters.

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.

Evolutionary relationships of adenylation domains in fungi.

Non-ribosomal peptide synthetases (NRPSs) and NRPS-like enzymes are abundant in microbes as they are involved in the production of primary and secondary metabolites. In contrast to the well-studied NRPSs, known to produce non-ribosomal peptides, NRPS-like enzymes exhibit more diverse activities and their evolutionary relationships are unclear. Here, we present the first in-depth phylogenetic analysis of fungal NRPS-like A domains from functionally characterized pathways, and their relationships to characterized A domains found in fungal NRPSs. This study clearly differentiated amino acid reductases, including NRPSs, from CoA/AMP ligases, which could be divided into 10 distinct phylogenetic clades that reflect their conserved domain organization, substrate specificity and enzymatic activity. In particular, evolutionary relationships of adenylate forming reductases could be refined and explained the substrate specificity difference. Consistent with their phylogeny, the deduced amino acid code of A domains differentiated amino acid reductases from other enzymes. However, a diagnostic code was found for α-keto acid reductases and clade 7 CoA/AMP ligases only. Comparative genomics of loci containing these enzymes revealed that they can be independently recruited as tailoring genes in diverse secondary metabolite pathways. Based on these results, we propose a refined and clear phylogeny-based classification of A domain-containing enzymes, which will provide a robust framework for future functional analyses and engineering of these enzymes to produce new bioactive molecules.

A Bioinformatics Workflow for Investigating Fungal Biosynthetic Gene Clusters.

Predicting secondary metabolite biosynthetic gene clusters is a routine analysis performed for each newly sequenced fungal genome. Yet, the usefulness of such predictions remains restricted as they provide total numbers of biosynthetic pathways with only very limited biological significance. In this chapter, we describe a workflow to predict and analyze biosynthetic gene clusters in fungal genomes. It relies on similarity networking and phylogeny to perform genetic dereplication and to prioritize candidate gene clusters that potentially produce new compounds. This basic workflow includes the generation of high-quality figures for publication.

Evolution-Informed Discovery of the Naphthalenone Biosynthetic Pathway in Fungi.

Fungi produce a wide diversity of secondary metabolites with interesting biological activities for the health, industrial, and agricultural sectors. While fungal genomes have revealed an unexpectedly high number of biosynthetic pathways that far exceeds the number of known molecules, accessing and characterizing this hidden diversity remain highly challenging. Here, we applied a combined phylogenetic dereplication and comparative genomics strategy to explore eight lichenizing fungi. The determination of the evolutionary relationships of aromatic polyketide pathways resulted in the identification of an uncharacterized biosynthetic pathway that is conserved in distant fungal lineages. The heterologous expression of the homologue from Aspergillus parvulus linked this pathway to naphthalenone compounds, which were detected in cultures when the pathway was expressed. Our unbiased and rational strategy generated evolutionary knowledge that ultimately linked biosynthetic genes to naphthalenone polyketides. Applied to many more genomes, this approach can unlock the full exploitation of the fungal kingdom for molecule discovery. IMPORTANCE Fungi have provided us with life-changing small bioactive molecules, with the best-known examples being the first broad-spectrum antibiotic penicillin, immunosuppressive cyclosporine, and cholesterol-lowering statins. Since the 1980s, exploration of chemical diversity in nature has been highly reduced. However, the genomic era has revealed that fungal genomes are concealing an unexpected and largely unexplored chemical diversity. So far, fungal genomes have been exploited to predict the production potential of bioactive compounds or to find genes that control the production of known molecules of interest. But accessing and characterizing the full fungal chemical diversity require rational and, thus, efficient strategies. Our approach is to first determine the evolutionary relationships of fungal biosynthetic pathways in order to identify those that are already characterized and those that show a different evolutionary origin. This knowledge allows prioritizing the choice of the pathway to functionally characterize in a second stage using synthetic-biology tools like heterologous expression. A particular strength of this strategy is that it is always successful: it generates knowledge about the evolution of bioactive-molecule biosynthesis in fungi, it either yields novel molecules or links the studied pathway to already known molecules, and it reveals the chemical diversity within a given pathway, all at once. The strategy is very powerful to avoid studying the same pathway again and can be used with any fungal genome. Functional characterization using heterologous expression is particularly suitable for fungi that are difficult to grow or not genetically tractable. Thanks to the decreasing cost of gene synthesis, ultimately, only the genome sequence is needed to identify novel pathways and characterize the molecules that they produce. Such an evolution-informed strategy allows the efficient exploitation of the chemical diversity hidden in fungal genomes and is very promising for molecule discovery.

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/.

A computational framework to explore large-scale biosynthetic diversity.

Genome mining has become a key technology to exploit natural product diversity. Although initially performed on a single-genome basis, the process is now being scaled up to mine entire genera, strain collections and microbiomes. However, no bioinformatic framework is currently available for effectively analyzing datasets of this size and complexity. In the present study, a streamlined computational workflow is provided, consisting of two new software tools: the 'biosynthetic gene similarity clustering and prospecting engine' (BiG-SCAPE), which facilitates fast and interactive sequence similarity network analysis of biosynthetic gene clusters and gene cluster families; and the 'core analysis of syntenic orthologues to prioritize natural product gene clusters' (CORASON), which elucidates phylogenetic relationships within and across these families. BiG-SCAPE is validated by correlating its output to metabolomic data across 363 actinobacterial strains and the discovery potential of CORASON is demonstrated by comprehensively mapping biosynthetic diversity across a range of detoxin/rimosamide-related gene cluster families, culminating in the characterization of seven detoxin analogues.

The High-Quality Complete Genome Sequence of the Opportunistic Fungal Pathogen Candida vulturna CBS 14366T.

Candida vulturna is a new member of the Candida haemulonii species complex that recently received much attention as it includes the emerging multidrug-resistant pathogen Candida auris. Here, we describe the high-quality genome sequence of C. vulturna type strain CBS 14366T to cover all genomes of pathogenic C. haemulonii species complex members.