Developing genome mining tools for the discovery of bioactive secondary metabolites

With the rise of Multi-resistant strains of previously treatable pathogenic microorganisms, some of which immune to all known antibiotics, we face a public health crisis that threatens the lives of anyone prone to infection. This challenge needs to be faced on many fronts and an important step to finding a solution is to replenish our antibiotic arsenals with new drugs that evade current antibiotic resistance strategies. The majority of these compounds have traditionally been sourced from, or inspired by, natural products – compounds produced by living things. This continues to be a valuable resource as the millennia of development through natural selection has made for precisely adapted molecules with desired antibiotic properties. Unfortunately natural products research has experienced stagnation due to high rates of rediscovery and low returns on research investment. Fortunately the widespread use of cheap sequencing technologies, influx of complete whole genomes, and tools used to process them have simultaneously been on the rise. These “genome mining” tools have only begun to highlight chemical potential that has been hidden from traditional approaches from a diverse set of genera. As the detection of various classes of Biosynthetic Gene Clusters (BGCs), areas of the genome responsible for production of these compounds, has matured there are now more leads generated than can be experimentally verified. The problem now is to prioritize these leads for those that have the highest potential for downstream experiments. Common prioritization schemes include: using comparative genomics to highlight unique or shared BGCs, focusing on novel genera besides the traditional prolific producing organisms, and highlighting BGCs that imply antibiotic activity via antibiotic resistance determinates. This research is focused on providing automated and accessible tools to preform these analyses in high-throughput. In addition to the prioritization and de-replication of potential BGCs, applications to enrich for novel leads via resistance determinant and target screening are also presented. As the number of genomes from different taxa begins to rise, shifting from a single genome analysis to a comparative pan-genome approach also shows promise to reinvigorate natural products research. The tools in this research that leverage these approaches will be continually maintained on free public servers for the furthered research and discovery of new antibiotic and anti-infective compounds to ensure the threat of antibiotic resistance is controlled

The evolution of genome mining in microbes – a review

This article reviews the development of genome mining strategies in bacteria during the last decade.</p

Draft Genome Sequences of the Obligatory Marine Myxobacterial Strains Enhygromyxa salina SWB005 and SWB007

The two marine myxobacterial strains Enhygromyxa salina SWB005 and SWB007 were isolated from coastal soil samples using Escherichia coli as bait for these predatory strains. These strains produce unique specialized metabolites. Genomes were assembled into 312 contigs for E. salina SWB005 (9.0 Mbp) and 192 contigs for E. salina SWB007 (10.6 Mbp)

Function-related replacement of bacterial siderophore pathways

© The Author(s) 2018. Bacterial genomes are rife with orphan biosynthetic gene clusters (BGCs) associated with secondary metabolism of unrealized natural product molecules. Often up to a tenth of the genome is predicted to code for the biosynthesis of diverse metabolites with mostly unknown structures and functions. This phenomenal diversity of BGCs coupled with their high rates of horizontal transfer raise questions about whether they are really active and beneficial, whether they are neutral and confer no advantage, or whether they are carried in genomes because they are parasitic or addictive. We previously reported that Salinispora bacteria broadly use the desferrioxamine family of siderophores for iron acquisition. Herein we describe a new and unrelated group of peptidic siderophores called salinichelins from a restricted number of Salinispora strains in which the desferrioxamine biosynthesis genes have been lost. We have reconstructed the evolutionary history of these two different siderophore families and show that the acquisition and retention of the new salinichelin siderophores co-occurs with the loss of the more ancient desferrioxamine pathway. This identical event occurred at least three times independently during the evolution of the genus. We surmise that certain BGCs may be extraneous because of their functional redundancy and demonstrate that the relative evolutionary pace of natural pathway replacement shows high selective pressure against retention of functionally superfluous gene clusters

The surfactin-like lipopeptides from Bacillus spp.: natural biodiversity and synthetic biology for a broader application range

International audienceSurfactin is a lipoheptapeptide produced by several Bacillus species and identified for the first time in 1969. At first, the biosynthesis of this remarkable biosurfactant was described in this review. The peptide moiety of the surfactin is synthesized using huge multienzymatic proteins called NonRibosomal Peptide Synthetases. This mechanism is responsible for the peptide biodiversity of the members of the surfactin family. In addition, on the fatty acid side, fifteen different isoforms (from C12 to C17) can be incorporated so increasing the number of the surfactin-like biomolecules. The review also highlights the last development in metabolic modelling and engineering and in synthetic biology to direct surfactin biosynthesis but also to generate novel derivatives. This large set of different biomolecules leads to a broad spectrum of physico-chemical properties and biological activities. The last parts of the review summarized the numerous studies related to the production processes optimization as well as the approaches developed to increase the surfactin productivity of Bacillus cells taking into account the different steps of its biosynthesis from gene transcription to surfactin degradation in the culture medium

Comparative genomics reveals phylogenetic distribution patterns of secondary metabolites in Amycolatopsis species

Background Genome mining tools have enabled us to predict biosynthetic gene clusters that might encode compounds with valuable functions for industrial and medical applications. With the continuously increasing number of genomes sequenced, we are confronted with an overwhelming number of predicted clusters. In order to guide the effective prioritization of biosynthetic gene clusters towards finding the most promising compounds, knowledge about diversity, phylogenetic relationships and distribution patterns of biosynthetic gene clusters is necessary. Results Here, we provide a comprehensive analysis of the model actinobacterial genus Amycolatopsis and its potential for the production of secondary metabolites. A phylogenetic characterization, together with a pan-genome analysis showed that within this highly diverse genus, four major lineages could be distinguished which differed in their potential to produce secondary metabolites. Furthermore, we were able to distinguish gene cluster families whose distribution correlated with phylogeny, indicating that vertical gene transfer plays a major role in the evolution of secondary metabolite gene clusters. Still, the vast majority of the diverse biosynthetic gene clusters were derived from clusters unique to the genus, and also unique in comparison to a database of known compounds. Our study on the locations of biosynthetic gene clusters in the genomes of Amycolatopsis’ strains showed that clusters acquired by horizontal gene transfer tend to be incorporated into non-conserved regions of the genome thereby allowing us to distinguish core and hypervariable regions in Amycolatopsis genomes. Conclusions Using a comparative genomics approach, it was possible to determine the potential of the genus Amycolatopsis to produce a huge diversity of secondary metabolites. Furthermore, the analysis demonstrates that horizontal and vertical gene transfer play an important role in the acquisition and maintenance of valuable secondary metabolites. Our results cast light on the interconnections between secondary metabolite gene clusters and provide a way to prioritize biosynthetic pathways in the search and discovery of novel compounds

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/

Applied evolution: Phylogeny-based approaches in natural products research

Covering: Up to 2019 Phylogenetic methods become increasingly important in natural product research. The growing amount of genetic data available today is enabling us to infer the evolutionary history of secondary metabolite gene clusters and their encoded compounds. We are starting to understand patterns and mechanisms of how the enormous diversity of chemical compounds produced by nature has evolved and are able to use phylogenetic inference to facilitate functional predictions of involved enzymes. In this review, we highlight how phylogenetic methods can aid natural product discovery and predictions and demonstrate several examples how these have been used in the past. We are featuring a number of easy to use tools that aid tree building and analysis and are providing a short overview how to create and interpret a phylogenetic tree.</p