FunARTS, the Fungal bioActive compound Resistant Target Seeker, an exploration engine for target-directed genome mining in fungi.

There is an urgent need to diversify the pipeline for discovering novel natural products due to the increase in multi-drug resistant infections. Like bacteria, fungi also produce secondary metabolites that have potent bioactivity and rich chemical diversity. To avoid self-toxicity, fungi encode resistance genes which are often present within the biosynthetic gene clusters (BGCs) of the corresponding bioactive compounds. Recent advances in genome mining tools have enabled the detection and prediction of BGCs responsible for the biosynthesis of secondary metabolites. The main challenge now is to prioritize the most promising BGCs that produce bioactive compounds with novel modes of action. With target-directed genome mining methods, it is possible to predict the mode of action of a compound encoded in an uncharacterized BGC based on the presence of resistant target genes. Here, we introduce the 'fungal bioactive compound resistant target seeker' (FunARTS) available at https://funarts.ziemertlab.com. This is a specific and efficient mining tool for the identification of fungal bioactive compounds with interesting and novel targets. FunARTS rapidly links housekeeping and known resistance genes to BGC proximity and duplication events, allowing for automated, target-directed mining of fungal genomes. Additionally, FunARTS generates gene cluster networking by comparing the similarity of BGCs from multi-genomes.

ARTS-DB: a database for antibiotic resistant targets.

As a result of the continuous evolution of drug resistant bacteria, new antibiotics are urgently needed. Encoded by biosynthetic gene clusters (BGCs), antibiotic compounds are mostly produced by bacteria. With the exponential increase in the number of publicly available, sequenced genomes and the advancements of BGC prediction tools, genome mining algorithms have uncovered millions of uncharacterized BGCs for further evaluation. Since compound identification and characterization remain bottlenecks, a major challenge is prioritizing promising BGCs. Recently, researchers adopted self-resistance based strategies allowing them to predict the biological activities of natural products encoded by uncharacterized BGCs. Since 2017, the Antibiotic Resistant Target Seeker (ARTS) facilitated this so-called target-directed genome mining (TDGM) approach for the prioritization of BGCs encoding potentially novel antibiotics. Here, we present the ARTS database, available at https://arts-db.ziemertlab.com/. The ARTS database provides pre-computed ARTS results for >70,000 genomes and metagenome assembled genomes in total. Advanced search queries allow users to rapidly explore the fundamental criteria of TDGM such as BGC proximity, duplication and horizontal gene transfers of essential housekeeping genes. Furthermore, the ARTS database provides results interconnected throughout the bacterial kingdom as well as links to known databases in natural product research.

Secondary Metabolite Transcriptomic Pipeline (SeMa-Trap), an expression-based exploration tool for increased secondary metabolite production in bacteria.

For decades, natural products have been used as a primary resource in drug discovery pipelines to find new antibiotics, which are mainly produced as secondary metabolites by bacteria. The biosynthesis of these compounds is encoded in co-localized genes termed biosynthetic gene clusters (BGCs). However, BGCs are often not expressed under laboratory conditions. Several genetic manipulation strategies have been developed in order to activate or overexpress silent BGCs. Significant increases in production levels of secondary metabolites were indeed achieved by modifying the expression of genes encoding regulators and transporters, as well as genes involved in resistance or precursor biosynthesis. However, the abundance of genes encoding such functions within bacterial genomes requires prioritization of the most promising ones for genetic manipulation strategies. Here, we introduce the 'Secondary Metabolite Transcriptomic Pipeline' (SeMa-Trap), a user-friendly web-server, available at https://sema-trap.ziemertlab.com. SeMa-Trap facilitates RNA-Seq based transcriptome analyses, finds co-expression patterns between certain genes and BGCs of interest, and helps optimize the design of comparative transcriptomic analyses. Finally, SeMa-Trap provides interactive result pages for each BGC, allowing the easy exploration and comparison of expression patterns. In summary, SeMa-Trap allows a straightforward prioritization of genes that could be targeted via genetic engineering approaches to (over)express BGCs of interest.

The Natural Product Domain Seeker version 2 (NaPDoS2) webtool relates ketosynthase phylogeny to biosynthetic function.

The Natural Product Domain Seeker (NaPDoS) webtool detects and classifies ketosynthase (KS) and condensation domains from genomic, metagenomic, and amplicon sequence data. Unlike other tools, a phylogeny-based classification scheme is used to make broader predictions about the polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) genes in which these domains are found. NaPDoS is particularly useful for the analysis of incomplete biosynthetic genes or gene clusters, as are often observed in poorly assembled genomes and metagenomes, or when loci are not clustered, as in eukaryotic genomes. To help support the growing interest in sequence-based analyses of natural product biosynthetic diversity, here we introduce version 2 of the webtool, NaPDoS2, available at http://napdos.ucsd.edu/napdos2. This update includes the addition of 1417 KS sequences, representing a major expansion of the taxonomic and functional diversity represented in the webtool database. The phylogeny-based KS classification scheme now recognizes 41 class and subclass assignments, including new type II PKS subclasses. Workflow modifications accelerate run times, allowing larger datasets to be analyzed. In addition, default parameters were established using statistical validation tests to maximize KS detection and classification accuracy while minimizing false positives. We further demonstrate the applications of NaPDoS2 to assess PKS biosynthetic potential using genomic, metagenomic, and PCR amplicon datasets. These examples illustrate how NaPDoS2 can be used to predict biosynthetic potential and detect genes involved in the biosynthesis of specific structure classes or new biosynthetic mechanisms.

Origin of the 3-methylglutaryl moiety in caprazamycin biosynthesis.

BACKGROUND: Caprazamycins are liponucleoside antibiotics showing bioactivity against Gram-positive bacteria including clinically relevant Mycobacterium tuberculosis by targeting the bacterial MraY-translocase. Their chemical structure contains a unique 3-methylglutaryl moiety which they only share with the closely related liposidomycins. Although the biosynthesis of caprazamycin is understood to some extent, the origin of 3-methylglutaryl-CoA for caprazamycin biosynthesis remains elusive. RESULTS: In this work, we demonstrate two pathways of the heterologous producer Streptomyces coelicolor M1154 capable of supplying 3-methylglutaryl-CoA: One is encoded by the caprazamycin gene cluster itself including the 3-hydroxy-3-methylglutaryl-CoA synthase Cpz5. The second pathway is part of primary metabolism of the host cell and encodes for the leucine/isovalerate utilization pathway (Liu-pathway). We could identify the liu cluster in S. coelicolor M1154 and gene deletions showed that the intermediate 3-methylglutaconyl-CoA is used for 3-methylglutaryl-CoA biosynthesis. This is the first report of this intermediate being hijacked for secondary metabolite biosynthesis. Furthermore, Cpz20 and Cpz25 from the caprazamycin gene cluster were found to be part of a common route after both individual pathways are merged together. CONCLUSIONS: The unique 3-methylglutaryl moiety in caprazamycin originates both from the caprazamycin gene cluster and the leucine/isovalerate utilization pathway of the heterologous host. Our study enhanced the knowledge on the caprazamycin biosynthesis and points out the importance of primary metabolism of the host cell for biosynthesis of natural products.

A rapid and efficient strategy to identify and recover biosynthetic gene clusters from soil metagenomes.

Culture-independent metagenomic approaches offer a promising solution to the discovery of therapeutically relevant compounds such as antibiotics by enabling access to the hidden biosynthetic potential of microorganisms. These strategies, however, often entail laborious, multi-step, and time-consuming procedures to recover the biosynthetic gene clusters (BGCs) from soil metagenomes for subsequent heterologous expression. Here, we developed an efficient method we called single Nanopore read cluster mining (SNRCM), which enables the fast recovery of complete BGCs from a soil metagenome using long- and short-read sequencing. A metagenomic fosmid library of 83,700 clones was generated and sequenced using Nanopore as well as Illumina technologies. Hybrid assembled contigs of the sequenced fosmid library were subsequently analyzed to identify BGCs encoding secondary metabolites. Using SNRCM, we aligned the identified BGCs directly to Nanopore long-reads and were able to detect complete BGCs on single fosmids. This enabled us to select for and recover BGCs of interest for subsequent heterologous expression attempts. Additionally, the sequencing data of the fosmid library and its corresponding metagenomic DNA enabled us to assemble and recover a large nonribosomal peptide synthetase (NRPS) BGC from three different fosmids of our library and to directly amplify and recover a complete lasso peptide BGC from the high-quality metagenomic DNA. Overall, the strategies presented here provide a useful tool for accelerating and facilitating the identification and production of potentially interesting bioactive compounds from soil metagenomes. KEY POINTS: • An efficient approach for the recovery of BGCs from soil metagenomes was developed to facilitate natural product discovery. • A fosmid library was constructed from soil metagenomic HMW DNA and sequenced via Illumina and Nanopore. • Nanopore long-reads enabled the direct identification and recovery of complete BGCs on single fosmids.

ARTS 2.0: feature updates and expansion of the Antibiotic Resistant Target Seeker for comparative genome mining.

Multi-drug resistant pathogens have become a major threat to human health and new antibiotics are urgently needed. Most antibiotics are derived from secondary metabolites produced by bacteria. In order to avoid suicide, these bacteria usually encode resistance genes, in some cases within the biosynthetic gene cluster (BGC) of the respective antibiotic compound. Modern genome mining tools enable researchers to computationally detect and predict BGCs that encode the biosynthesis of secondary metabolites. The major challenge now is the prioritization of the most promising BGCs encoding antibiotics with novel modes of action. A recently developed target-directed genome mining approach allows researchers to predict the mode of action of the encoded compound of an uncharacterized BGC based on the presence of resistant target genes. In 2017, we introduced the 'Antibiotic Resistant Target Seeker' (ARTS). ARTS allows for specific and efficient genome mining for antibiotics with interesting and novel targets by rapidly linking housekeeping and known resistance genes to BGC proximity, duplication and horizontal gene transfer (HGT) events. Here, we present ARTS 2.0 available at http://arts.ziemertlab.com. ARTS 2.0 now includes options for automated target directed genome mining in all bacterial taxa as well as metagenomic data. Furthermore, it enables comparison of similar BGCs from different genomes and their putative resistance genes.

The confluence of big data and evolutionary genome mining for the discovery of natural products.

This review covers literature between 2003-2021The development and application of genome mining tools has given rise to ever-growing genetic and chemical databases and propelled natural products research into the modern age of Big Data. Likewise, an explosion of evolutionary studies has unveiled genetic patterns of natural products biosynthesis and function that support Darwin's theory of natural selection and other theories of adaptation and diversification. In this review, we aim to highlight how Big Data and evolutionary thinking converge in the study of natural products, and how this has led to an emerging sub-discipline of evolutionary genome mining of natural products. First, we outline general principles to best utilize Big Data in natural products research, addressing key considerations needed to provide evolutionary context. We then highlight successful examples where Big Data and evolutionary analyses have been combined to provide bioinformatic resources and tools for the discovery of novel natural products and their biosynthetic enzymes. Rather than an exhaustive list of evolution-driven discoveries, we highlight examples where Big Data and evolutionary thinking have been embraced for the evolutionary genome mining of natural products. After reviewing the nascent history of this sub-discipline, we discuss the challenges and opportunities of genomic and metabolomic tools with evolutionary foundations and/or implications and provide a future outlook for this emerging and exciting field of natural product research.

AutoMLST: an automated web server for generating multi-locus species trees highlighting natural product potential.

Understanding the evolutionary background of a bacterial isolate has applications for a wide range of research. However generating an accurate species phylogeny remains challenging. Reliance on 16S rDNA for species identification currently remains popular. Unfortunately, this widespread method suffers from low resolution at the species level due to high sequence conservation. Currently, there is now a wealth of genomic data that can be used to yield more accurate species designations via modern phylogenetic methods and multiple genetic loci. However, these often require extensive expertise and time. The Automated Multi-Locus Species Tree (autoMLST) was thus developed to provide a rapid 'one-click' pipeline to simplify this workflow at: https://automlst.ziemertlab.com. This server utilizes Multi-Locus Sequence Analysis (MLSA) to produce high-resolution species trees; this does not preform multi-locus sequence typing (MLST), a related classification method. The resulting phylogenetic tree also includes helpful annotations, such as species clade designations and secondary metabolite counts to aid natural product prospecting. Distinct from currently available web-interfaces, autoMLST can automate selection of reference genomes and out-group organisms based on one or more query genomes. This enables a wide range of researchers to perform rigorous phylogenetic analyses more rapidly compared to manual MLSA workflows.

antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline.

Secondary metabolites produced by bacteria and fungi are an important source of antimicrobials and other bioactive compounds. In recent years, genome mining has seen broad applications in identifying and characterizing new compounds as well as in metabolic engineering. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' (https://antismash.secondarymetabolites.org) has assisted researchers in this, both as a web server and a standalone tool. It has established itself as the most widely used tool for identifying and analysing biosynthetic gene clusters (BGCs) in bacterial and fungal genome sequences. Here, we present an entirely redesigned and extended version 5 of antiSMASH. antiSMASH 5 adds detection rules for clusters encoding the biosynthesis of acyl-amino acids, ß-lactones, fungal RiPPs, RaS-RiPPs, polybrominated diphenyl ethers, C-nucleosides, PPY-like ketones and lipolanthines. For type II polyketide synthase-encoding gene clusters, antiSMASH 5 now offers more detailed predictions. The HTML output visualization has been redesigned to improve the navigation and visual representation of annotations. We have again improved the runtime of analysis steps, making it possible to deliver comprehensive annotations for bacterial genomes within a few minutes. A new output file in the standard JavaScript object notation (JSON) format is aimed at downstream tools that process antiSMASH results programmatically.

Mining Indonesian Microbial Biodiversity for Novel Natural Compounds by a Combined Genome Mining and Molecular Networking Approach.

Indonesia is one of the most biodiverse countries in the world and a promising resource for novel natural compound producers. Actinomycetes produce about two thirds of all clinically used antibiotics. Thus, exploiting Indonesia's microbial diversity for actinomycetes may lead to the discovery of novel antibiotics. A total of 422 actinomycete strains were isolated from three different unique areas in Indonesia and tested for their antimicrobial activity. Nine potent bioactive strains were prioritized for further drug screening approaches. The nine strains were cultivated in different solid and liquid media, and a combination of genome mining analysis and mass spectrometry (MS)-based molecular networking was employed to identify potential novel compounds. By correlating secondary metabolite gene cluster data with MS-based molecular networking results, we identified several gene cluster-encoded biosynthetic products from the nine strains, including naphthyridinomycin, amicetin, echinomycin, tirandamycin, antimycin, and desferrioxamine B. Moreover, 16 putative ion clusters and numerous gene clusters were detected that could not be associated with any known compound, indicating that the strains can produce novel secondary metabolites. Our results demonstrate that sampling of actinomycetes from unique and biodiversity-rich habitats, such as Indonesia, along with a combination of gene cluster networking and molecular networking approaches, accelerates natural product identification.

New Nocobactin Derivatives with Antimuscarinic Activity, Terpenibactins A-C, Revealed by Genome Mining of Nocardia terpenica IFM 0406.

We report a genomics-guided exploration of the metabolic potential of the brasilicardin producer strain Nocardia terpenica IFM 0406. Bioinformatics analysis of the whole genome sequence revealed the presence of a biosynthetic gene cluster presumably responsible for the generation of formerly unknown nocobactin derivatives. Mass spectrometry-assisted isolation led to the identification of three new siderophores, terpenibactins A (1), B (2) and C (3), which belong to the class of nocobactins. Their structures were elucidated by employing spectroscopic techniques. Compounds 1-3 demonstrated inhibitory activity towards the muscarinic M3 receptor, while exhibiting only a low cytotoxicity.

SYN-View: A Phylogeny-Based Synteny Exploration Tool for the Identification of Gene Clusters Linked to Antibiotic Resistance.

The development of new antibacterial drugs has become one of the most important tasks of the century in order to overcome the posing threat of drug resistance in pathogenic bacteria. Many antibiotics originate from natural products produced by various microorganisms. Over the last decades, bioinformatical approaches have facilitated the discovery and characterization of these small compounds using genome mining methodologies. A key part of this process is the identification of the most promising biosynthetic gene clusters (BGCs), which encode novel natural products. In 2017, the Antibiotic Resistant Target Seeker (ARTS) was developed in order to enable an automated target-directed genome mining approach. ARTS identifies possible resistant target genes within antibiotic gene clusters, in order to detect promising BGCs encoding antibiotics with novel modes of action. Although ARTS can predict promising targets based on multiple criteria, it provides little information about the cluster structures of possible resistant genes. Here, we present SYN-view. Based on a phylogenetic approach, SYN-view allows for easy comparison of gene clusters of interest and distinguishing genes with regular housekeeping functions from genes functioning as antibiotic resistant targets. Our aim is to implement our proposed method into the ARTS web-server, further improving the target-directed genome mining strategy of the ARTS pipeline.

Applied evolution: phylogeny-based approaches in natural products research.

Covering: up to 2019Phylogenetic 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.

The ADEP Biosynthetic Gene Cluster in Streptomyces hawaiiensis NRRL 15010 Reveals an Accessory clpP Gene as a Novel Antibiotic Resistance Factor.

The increasing threat posed by multiresistant bacterial pathogens necessitates the discovery of novel antibacterials with unprecedented modes of action. ADEP1, a natural compound produced by Streptomyces hawaiiensis NRRL 15010, is the prototype for a new class of acyldepsipeptide (ADEP) antibiotics. ADEP antibiotics deregulate the proteolytic core ClpP of the bacterial caseinolytic protease, thereby exhibiting potent antibacterial activity against Gram-positive bacteria, including multiresistant pathogens. ADEP1 and derivatives, here collectively called ADEP, have been previously investigated for their antibiotic potency against different species, structure-activity relationship, and mechanism of action; however, knowledge on the biosynthesis of the natural compound and producer self-resistance have remained elusive. In this study, we identified and analyzed the ADEP biosynthetic gene cluster in S. hawaiiensis NRRL 15010, which comprises two NRPSs, genes necessary for the biosynthesis of (4S,2R)-4-methylproline, and a type II polyketide synthase (PKS) for the assembly of highly reduced polyenes. While no resistance factor could be identified within the gene cluster itself, we discovered an additional clpP homologous gene (named clpPADEP) located further downstream of the biosynthetic genes, separated from the biosynthetic gene cluster by several transposable elements. Heterologous expression of ClpPADEP in three ADEP-sensitive Streptomyces species proved its role in conferring ADEP resistance, thereby revealing a novel type of antibiotic resistance determinant.IMPORTANCE Antibiotic acyldepsipeptides (ADEPs) represent a promising new class of potent antibiotics and, at the same time, are valuable tools to study the molecular functioning of their target, ClpP, the proteolytic core of the bacterial caseinolytic protease. Here, we present a straightforward purification procedure for ADEP1 that yields substantial amounts of the pure compound in a time- and cost-efficient manner, which is a prerequisite to conveniently study the antimicrobial effects of ADEP and the operating mode of bacterial ClpP machineries in diverse bacteria. Identification and characterization of the ADEP biosynthetic gene cluster in Streptomyces hawaiiensis NRRL 15010 enables future bioinformatics screenings for similar gene clusters and/or subclusters to find novel natural compounds with specific substructures. Most strikingly, we identified a cluster-associated clpP homolog (named clpPADEP) as an ADEP resistance gene. ClpPADEP constitutes a novel bacterial resistance factor that alone is necessary and sufficient to confer high-level ADEP resistance to Streptomyces across species.

The Antibiotic Resistant Target Seeker (ARTS), an exploration engine for antibiotic cluster prioritization and novel drug target discovery.

With the rise of multi-drug resistant pathogens and the decline in number of potential new antibiotics in development there is a fervent need to reinvigorate the natural products discovery pipeline. Most antibiotics are derived from secondary metabolites produced by microorganisms and plants. To avoid suicide, an antibiotic producer harbors resistance genes often found within the same biosynthetic gene cluster (BGC) responsible for manufacturing the antibiotic. Existing mining tools are excellent at detecting BGCs or resistant genes in general, but provide little help in prioritizing and identifying gene clusters for compounds active against specific and novel targets. Here we introduce the 'Antibiotic Resistant Target Seeker' (ARTS) available at https://arts.ziemertlab.com. ARTS allows for specific and efficient genome mining for antibiotics with interesting and novel targets. The aim of this web server is to automate the screening of large amounts of sequence data and to focus on the most promising strains that produce antibiotics with new modes of action. ARTS integrates target directed genome mining methods, antibiotic gene cluster predictions and 'essential gene screening' to provide an interactive page for rapid identification of known and putative targets in BGCs.

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.

Genomic insights into specialized metabolism in the marine actinomycete Salinispora.

Comparative genomics is providing new opportunities to address the diversity and distributions of genes encoding the biosynthesis of specialized metabolites. An analysis of 119 genome sequences representing three closely related species of the marine actinomycete genus Salinispora reveals extraordinary biosynthetic diversity in the form of 176 distinct biosynthetic gene clusters (BGCs) of which only 24 have been linked to their products. Remarkably, more than half of the BGCs were observed in only one or two strains, suggesting they were acquired relatively recently in the evolutionary history of the genus. These acquired gene clusters are concentrated in specific genomic islands, which represent hot spots for BGC acquisition. While most BGCs are stable in terms of their chromosomal position, others migrated to different locations or were exchanged with unrelated gene clusters suggesting a plug and play type model of evolution that provides a mechanism to test the relative fitness effects of specialized metabolites. Transcriptome analyses were used to address the relationships between BGC abundance, chromosomal position and product discovery. The results indicate that recently acquired BGCs can be functional and that complex evolutionary processes shape the micro-diversity of specialized metabolism observed in closely related environmental bacteria.

Diversity and evolution of secondary metabolism in the marine actinomycete genus Salinispora.

Access to genome sequence data has challenged traditional natural product discovery paradigms by revealing that the products of most bacterial biosynthetic pathways have yet to be discovered. Despite the insight afforded by this technology, little is known about the diversity and distributions of natural product biosynthetic pathways among bacteria and how they evolve to generate structural diversity. Here we analyze genome sequence data derived from 75 strains of the marine actinomycete genus Salinispora for pathways associated with polyketide and nonribosomal peptide biosynthesis, the products of which account for some of today's most important medicines. The results reveal high levels of diversity, with a total of 124 pathways identified and 229 predicted with continued sequencing. Recent horizontal gene transfer accounts for the majority of pathways, which occur in only one or two strains. Acquired pathways are incorporated into genomic islands and are commonly exchanged within and between species. Acquisition and transfer events largely involve complete pathways, which subsequently evolve by gene gain, loss, and duplication followed by divergence. The exchange of similar pathway types at the precise chromosomal locations in different strains suggests that the mechanisms of integration include pathway-level homologous recombination. Despite extensive horizontal gene transfer there is clear evidence of species-level vertical inheritance, supporting the concept that secondary metabolites represent functional traits that help define Salinispora species. The plasticity of the Salinispora secondary metabolome provides an effective mechanism to maximize population-level secondary metabolite diversity while limiting the number of pathways maintained within any individual genome.

The evolution of genome mining in microbes - a review.

Covering: 2006 to 2016The computational mining of genomes has become an important part in the discovery of novel natural products as drug leads. Thousands of bacterial genome sequences are publically available these days containing an even larger number and diversity of secondary metabolite gene clusters that await linkage to their encoded natural products. With the development of high-throughput sequencing methods and the wealth of DNA data available, a variety of genome mining methods and tools have been developed to guide discovery and characterisation of these compounds. This article reviews the development of these computational approaches during the last decade and shows how the revolution of next generation sequencing methods has led to an evolution of various genome mining approaches, techniques and tools. After a short introduction and brief overview of important milestones, this article will focus on the different approaches of mining genomes for secondary metabolites, from detecting biosynthetic genes to resistance based methods and "evo-mining" strategies including a short evaluation of the impact of the development of genome mining methods and tools on the field of natural products and microbial ecology.