A computational framework for systematic exploration of biosynthetic diversity from large-scale genomic data

Abstract

Genome mining has become a key technology to explore and exploit natural product diversity through the identification and analysis of biosynthetic gene clusters (BGCs). Initially, this was performed on a single-genome basis; currently, the process is being scaled up to large-scale mining of pan-genomes of entire genera, complete strain collections and metagenomic datasets from which thousands of bacterial genomes can be extracted at once. However, no bioinformatic framework is currently available for the effective analysis of datasets of this size and complexity. Here, we provide a streamlined computational workflow, tightly integrated with antiSMASH and MIBiG, that consists of two new software tools, BiG-SCAPE and CORASON. BiG-SCAPE facilitates rapid calculation and interactive visual exploration of BGC sequence similarity networks, grouping gene clusters at multiple hierarchical levels, and includes a 'glocal' alignment mode that accurately groups both complete and fragmented BGCs. CORASON employs a phylogenomic approach to elucidate the detailed evolutionary relationships between gene clusters by computing high-resolution multi-locus phylogenies of all BGCs within and across gene cluster families (GCFs), and allows researchers to comprehensively identify all genomic contexts in which particular biosynthetic gene cassettes are found. We validate BiG-SCAPE by correlating its GCF output to metabolomic data across 403 actinobacterial strains. Furthermore, we demonstrate the discovery potential of the platform by using CORASON to comprehensively map the phylogenetic diversity of the large detoxin/rimosamide gene cluster clan, prioritizing three new detoxin families for subsequent characterization of six new analogs using isotopic labeling and analysis of tandem mass spectrometric data.

Comparative genomics reveals complex natural product biosynthesis capacities and carbon metabolism across host‐associated and free‐living Aquimarina (Bacteroidetes, Flavobacteriaceae) species

“…Host-associated microorganisms, particularly obligate symbionts, usually have reduced genome sizes due to the loss of non-essential genes in the host-associated habitat (McCutcheon and Moran, 2012;Nayfach et al, 2019). Consistent with generalist, 'bi-modal' host/FL lifestrategies (Van Elsas et al, 2011;Karimi et al, 2019) and a complex nutrient and secondary metabolism, Aquimarina genomes were prevalently large usually exceeding 5.0 Mb (Table 1).…”

“…The tool also delivers chemical structure predictions, when possible, and amino acid sequence files for each predicted BGC. All structure predictions obtained with antiSMASH were inventoried, while the retrieved amino acid sequences, in GenBank format, were used as input data for downstream analyses with the 'Biosynthetic Genes Similarity Clustering and Prospecting Engine' (BiG-SCAPE) (Navarro-Muñoz et al, 2018). This tool uses the Pfam database and the hmmscan algorithm, from the HMMER suite (Eddy, 2011), to predict Pfam entries in each sequence, thus using hidden Markov models to summarize each BGC as a linear string of Pfams (Cimermancic et al, 2014).…”

“…This tool uses the Pfam database and the hmmscan algorithm, from the HMMER suite (Eddy, 2011), to predict Pfam entries in each sequence, thus using hidden Markov models to summarize each BGC as a linear string of Pfams (Cimermancic et al, 2014). For every pair of BGCs in the set, the overall distance between them is then calculated as the weighted combination of their dissimilarity in protein domain content, synteny, copy number and sequence identity (Navarro-muñoz et al, 2018). An upper distance cut-off value of 0.3 was used to define 'Gene Cluster Families' (GCFs) for BGCs identified by antiSMASH based on their distances as explained above.…”

“…Moreover, structure elucidation of cuniculene, the first trans ‐AT polyketide ever reported from Aquimarina , has just been achieved (Helfrich et al ., ). In this context, this study benefits from recent advances in computational biology and in silico genome mining (Medema et al ., ; Blin et al ., ; Medema, ; Navarro‐muñoz et al ., ) to examine the abundance, diversity, phylogenetic relationships and co‐occurrence networks of secondary metabolite biosynthetic gene clusters (BGCs) across all publicly available Aquimarina genomes as of February 25, 2019, substantially enlarging the known genetic space underlying natural product biosynthesis within the Bacteroidetes phylum. Moreover, we use a suite of phylogenomics measures to deliver the first comprehensive genome‐wide phylogeny for the genus, determine core‐ and pan‐genomes, and link evolutionary trajectories with functional genomics and the secondary metabolite machinery within the group.…”

EvoMining reveals the origin and fate of natural products biosynthetic enzymes

“…As a related but independent follow-up of EvoMining, we have very recently released a phlyogenomic approach, termed CORe Analysis of Syntenic Orthologs to prioritize Natural products BGC, or CORASON. This algorithm addresses the evolutionary relationships between BGC, allowing to comprehensively identifying all genomic vicinities in which particular biosynthetic gene cassettes are found (21). Based in similar evolutionary ideas to those embraced by EvoMining, the Antibiotic Resistance Target Seeker, or ARTS (22) exploits the fact that some antibiotics function by interfering central metabolic enzymes, and therefore antibiotic-producing bacteria have mechanisms of self-protection encoded in extra gene copies.…”