Mixing and Matching of Hybrid Megasynthases is a Hub for the Evolution of Metabolic Diversity in Cyanobacteria

Modular megasynthases, such as polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs), are molecular assembly lines that biosynthesize many pharmaceutically and ecologically important natural products. Understanding how these compounds evolve could inspire the artificial evolution of compound diversity by metabolic engineering. Over the past two decades, a number of seminal studies have significantly contributed to our understanding of natural product evolution. However, the evolution of NRPS and PKS assembly lines remains poorly understood, especially for NRPS/PKS hybrids. Here, we provide substantial evidence for a remarkable cluster-mixing event involving three cyanobacterial biosynthetic gene clusters (BGCs), resulting in the emergence of novel peptide-polyketide hybrids that were named minutumamides. By combining retro-evolutionary analysis with structure-guided genome mining, we could discover a potential evolutionary ancestor that links nostopeptolide and minutumamide biosynthesis. In addition, we were able to trace nostopeptolide-related module and domain blocks in various other biosynthetic pathways, indicating a surprisingly vivid mixing and matching of biosynthesis genes in the evolution of NRPS and cis-acyltransferase PKS/NRPS pathways, which was previously regarded as a unique feature of trans-acyltransferase (trans-AT) PKS. These remarkable insights into the evolutionary plasticity of NRPS-PKS assembly lines provide valuable guidance for pathway engineers looking for productive combinations that yield "nonnatural" hybrid natural products.

Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products

Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products.

Triumphs and Challenges of Natural Product Discovery in the Postgenomic Era

Natural products have played significant roles as medicine and food throughout human history. Here, we first provide a brief historical overview of natural products, their classification and biosynthetic origins, and the microbiological and genetic methods used for their discovery. We also describe and discuss the technologies that revolutionized the field, which transitioned from classic genetics to genome-centric discovery approximately two decades ago. We then highlight the most recent advancements and approaches in the current postgenomic era, in which genome mining is a standard operation and high-throughput analytical methods allow parallel discovery of genes and molecules at an unprecedented pace. Finally, we discuss the new challenges faced by the field of natural products and the future of systematic heterologous expression and strain-independent discovery, which promises to deliver more molecules in vials than ever before.

Predicting metallophore structure and function through genome mining

Metallophores are small molecule chelators that many microbes use to obtain trace metals from their environment. Through genome mining, where genomes are scanned for metallophore biosynthesis genes, one can not only identify which organisms are likely to produce a metallophore, but also predict the metallophore structure, thus preventing undesired reisolation of known compounds and accelerating characterization. Furthermore, the presence of accessory genes for the transport, utilization, and regulation can suggest the biological function and fate of a metallophore. Modern, user-friendly tools have made powerful genomic analyses accessible to scientists with no bioinformatics experience, but these tools are often not utilized to their full potential. This chapter provides an introduction to metallophore genomics and demonstrates how to use the free, publicly available antiSMASH platform to infer metallophore function and structure.

Mining natural products for advanced biofuels and sustainable bioproducts

Recently, there has been growing interest in the sustainable production of biofuels and bioproducts derived from renewable sources. Natural products, the largest and more structurally diverse group of metabolites, hold significant promise as sources for such bio-based products. However, there are two primary challenges in harnessing natural products' potential: precise mining of biosynthetic gene clusters (BGCs) that can be used as scaffolds or bioparts and their functional expression for biofuel and bioproduct manufacture. In this review, we explore recent advances in the development of bioinformatic tools for BGC mining and the manipulation of various hosts for natural product-based biofuels and bioproducts manufacture. Moreover, we discuss potential strategies for expanding the chemical diversity of biofuels and bioproducts and enhancing their overall yield.

Biomimetic engineering of nonribosomal peptide synthesis

Nonribosomal peptides (NRPs) have gained attention due to their diverse biological activities and potential applications in medicine and agriculture. The natural diversity of NRPs is a result of evolutionary processes that have occurred over millions of years. Recent studies have shed light on the mechanisms by which nonribosomal peptide synthetases (NRPSs) evolve, including gene duplication, recombination, and horizontal transfer. Mimicking natural evolution could be a useful strategy for engineering NRPSs to produce novel compounds with desired properties. Furthermore, the emergence of antibiotic-resistant bacteria has highlighted the urgent need for new drugs, and NRPs represent a promising avenue for drug discovery. This review discusses the engineering potential of NRPSs in light of their evolutionary history.

Structural diversity, biosynthesis, and biological functions of lipopeptides from Streptomyces

Covering: up to 2022Streptomyces are ubiquitous in terrestrial and marine environments, where they display a fascinating metabolic diversity. As a result, these bacteria are a prolific source of active natural products. One important class of these natural products is the nonribosomal lipopeptides, which have diverse biological activities and play important roles in the lifestyle of Streptomyces. The importance of this class is highlighted by the use of related antibiotics in the clinic, such as daptomycin (tradename Cubicin). By virtue of recent advances spanning chemistry and biology, significant progress has been made in biosynthetic studies on the lipopeptide antibiotics produced by Streptomyces. This review will serve as a comprehensive guide for researchers working in this multidisciplinary field, providing a summary of recent progress regarding the investigation of lipopeptides from Streptomyces. In particular, we highlight the structures, properties, biosynthetic mechanisms, chemical and chemoenzymatic synthesis, and biological functions of lipopeptides. In addition, the application of genome mining techniques to Streptomyces that have led to the discovery of many novel lipopeptides is discussed, further demonstrating the potential of lipopeptides from Streptomyces for future development in modern medicine.

Phylogeny-guided genome mining of roseocin family lantibiotics to generate improved variants of roseocin

Roseocin, the two-peptide lantibiotic from Streptomyces roseosporus, carries extensive intramolecular (methyl)lanthionine bridging in the peptides and exhibits synergistic antibacterial activity against clinically relevant Gram-positive pathogens. Both peptides have a conserved leader but a diverse core region. The biosynthesis of roseocin involves post-translational modification of the two precursor peptides by a single promiscuous lanthipeptide synthetase, RosM, to install an indispensable disulfide bond in the Rosα core along with four and six thioether rings in Rosα and Rosβ cores, respectively. RosM homologs in the phylum actinobacteria were identified here to reveal twelve other members of the roseocin family which diverged into three types of biosynthetic gene clusters (BGCs). Further, the evolutionary rate among the BGC variants and analysis of variability within the core peptide versus leader peptide revealed a phylum-dependent lanthipeptide evolution. Analysis of horizontal gene transfer revealed its role in the generation of core peptide diversity. The naturally occurring diverse congeners of roseocin peptides identified from the mined novel BGCs were carefully aligned to identify the conserved sites and the substitutions in the core peptide region. These selected sites in the Rosα peptide were mutated for permitted substitutions, expressed heterologously in E. coli, and post-translationally modified by RosM in vivo. Despite a limited number of generated variants, two variants, RosαL8F and RosαL8W exhibited significantly improved inhibitory activity in a species-dependent manner compared to the wild-type roseocin. Our study proves that a natural repository of evolved variants of roseocin is present in nature and the key variations can be used to generate improved variants.

Genomics-Guided Efficient Identification of 2,5-Diketopiperazine Derivatives from Actinobacteria

Secondary metabolites derived from microorganism constitute an important part of natural products. Mining of the microbial genomes revealed a large number of uncharacterized biosynthetic gene clusters, indicating their greater potential to synthetize specialized or secondary metabolites (SMs) than identified by classic fermentation and isolation approaches. Various bioinformatics tools have been developed to analyze and identify such gene clusters, thus accelerating significantly the mining process. Heterologous expression of an individual biosynthetic gene cluster has been proven as an efficient way to activate the genes and identify the encoded metabolites that cannot be detected under normal laboratory cultivation conditions. Herein, we describe a concept of genomics-guided approach by performing genome mining and heterologous expression to uncover novel CDPS-derived DKPs and functionally characterize novel tailoring enzymes embedded in the biosynthetic pathways. Recent works focused on the identification of the nucleobase-related and dimeric DKPs are also presented.

Recent advances in research for potential utilization of unexplored lichen metabolites

Several research studies have shown that lichens are productive organisms for the synthesis of a broad range of secondary metabolites. Lichens are a self-sustainable stable microbial ecosystem comprising an exhabitant fungal partner (mycobiont) and at least one or more photosynthetic partners (photobiont). The successful symbiosis is responsible for their persistence throughout time and allows all the partners (holobionts) to thrive in many extreme habitats, where without the synergistic relationship they would be rare or non-existent. The ability to survive in harsh conditions can be directly correlated with the production of some unique metabolites. Despite the potential applications, these unique metabolites have been underutilised by pharmaceutical and agrochemical industries due to their slow growth, low biomass availability and technical challenges involved in their artificial cultivation. However, recent development of biotechnological tools such as molecular phylogenetics, modern tissue culture techniques, metabolomics and molecular engineering are opening up a new opportunity to exploit these compounds within the lichen holobiome for industrial applications. This review also highlights the recent advances in culturing the symbionts and the computational and molecular genetics approaches of lichen gene regulation recognized for the enhanced production of target metabolites. The recent development of multi-omics novel biodiscovery strategies aided by synthetic biology in order to study the heterologous expressed lichen-derived biosynthetic gene clusters in a cultivatable host offers a promising means for a sustainable supply of specialized metabolites.

Resources of Iranian agarics (Basidiomycota) with an outlook on their antioxidant potential

Agaric fungi are an important group of macromycetes with diverse ecological and functional properties, yet are poorly studied in many parts of the world. Here, we comprehensively analyzed 558 agaric species in Iran to reveal their resources of edible and poisonous species as well as their ecological guilds and luminescence potential. We also made a thorough survey of the antioxidant activity of the species. Phylogenetic relationships were reconstructed based on nuclear ribosomal LSU and ITS sequences. Our results reveal that agarics of Iran comprise about 189 edible, 128 poisonous, 254 soil saprotrophic, 172 ectomycorrhizal, 146 wood-inhabiting, 18 leaf/litter-inhabiting, 9 parasitic, and 19 luminescent species. Twenty percent of the Iranian agaric species possess antioxidant activity, phylogenetically distributed in four orders and 21 agaric families. About 5% of the antioxidant species can be considered strong antioxidants, many of which are also edible and could be utilized to develop functional foods. This is the first study combining phylogeny and antioxidant potential of agaric mushrooms in a large scale, and the obtained results would guide the selection of agaric taxa to be examined in the future for taxonomic revisions, biotechnological applications, and applied phylogeny studies.

Integrated Metabolomic-Genomic Workflows Accelerate Microbial Natural Product Discovery

The pairing of analytical chemistry with genomic techniques represents a new wave in natural product chemistry. With an increase in the availability of sequencing and assembly of microbial genomes, interrogation into the biosynthetic capability of producers with valuable secondary metabolites is possible. However, without the development of robust, accessible, and medium to high throughput tools, the bottleneck in pairing metabolic potential and compound isolation will continue. Several innovative approaches have proven useful in the nascent stages of microbial genome-informed drug discovery. Here, we consider a number of these approaches which have led to prioritization of strain targets and have mitigated rediscovery rates. Likewise, we discuss integration of principles of comparative evolutionary studies and retrobiosynthetic predictions to better understand biosynthetic mechanistic details and link genome sequence to structure. Lastly, we discuss advances in engineering, chemistry, and molecular networking and other computational approaches that are accelerating progress in the field of omic-informed natural product drug discovery. Together, these strategies enhance the synergy between cutting edge omics, chemical characterization, and computational technologies that pitch the discovery of natural products with pharmaceutical and other potential applications to the crest of the wave where progress is ripe for rapid advances.

Biological Dark Matter Exploration using Data Mining for the Discovery of Antimicrobial Natural Products

The discovery of novel antimicrobials has significantly slowed down over the last three decades. At the same time, humans rely increasingly on antimicrobials because of the progressive antimicrobial resistance in medical practices, human communities, and the environment. Data mining is currently considered a promising option in the discovery of new antibiotics. Some of the advantages of data mining are the ability to predict chemical structures from sequence data, anticipation of the presence of novel metabolites, the understanding of gene evolution, and the corroboration of data from multiple omics technologies. This review analyzes the state-of-the-art for data mining in the fields of bacteria, fungi, and plant genomic data, as well as metabologenomics. It also summarizes some of the most recent research accomplishments in the field, all pinpointing to innovation through uncovering and implementing the next generation of antimicrobials.

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.

Compendium of specialized metabolite biosynthetic diversity encoded in bacterial genomes

Bacterial specialized metabolites are a proven source of antibiotics and cancer therapies, but whether we have sampled all the secondary metabolite chemical diversity of cultivated bacteria is not known. We analysed ~170,000 bacterial genomes and ~47,000 metagenome assembled genomes (MAGs) using a modified BiG-SLiCE and the new clust-o-matic algorithm. We estimate that only 3% of the natural products potentially encoded in bacterial genomes have been experimentally characterized. We show that the variation in secondary metabolite biosynthetic diversity drops significantly at the genus level, identifying it as an appropriate taxonomic rank for comparison. Equal comparison of genera based on relative evolutionary distance revealed that Streptomyces bacteria encode the largest biosynthetic diversity by far, with Amycolatopsis, Kutzneria and Micromonospora also encoding substantial diversity. Finally, we find that several less-well-studied taxa, such as Weeksellaceae (Bacteroidota), Myxococcaceae (Myxococcota), Pleurocapsa and Nostocaceae (Cyanobacteria), have potential to produce highly diverse sets of secondary metabolites that warrant further investigation.

Combination of high-throughput microfluidics and FACS technologies to leverage the numbers game in natural product discovery

High-throughput platforms facilitating screening campaigns of environmental samples are needed to discover new products of natural origin counteracting the spreading of antimicrobial resistances constantly threatening human and agricultural health. We applied a combination of droplet microfluidics and fluorescence-activated cell sorting (FACS)-based technologies to access and assess a microbial environmental sample. The cultivation performance of our microfluidics workflow was evaluated in respect to the utilized cultivation media by Illumina amplicon sequencing of a pool of millions of droplets, respectively. This enabled the rational selection of a growth medium supporting the isolation of microbial diversity from soil (five phyla affiliated to 57 genera) including a member of the acidobacterial subgroup 1 (genus Edaphobacter). In a second phase, the entire diversity covered by 1071 cultures was used for an arrayed bioprospecting campaign, resulting in > 6000 extracts tested against human pathogens and agricultural pests. After redundancy curation by using a combinatorial chemical and genomic fingerprinting approach, we assigned the causative agents present in the extracts. Utilizing UHPLC-QTOF-MS/MS-guided fractionation and microplate-based screening assays in combination with molecular networking the production of bioactive ionophorous macrotetrolides, phospholipids, the cyclic lipopetides massetolides E, F, H and serratamolide A and many derivatives thereof was shown.

Unveiling the genomic potential of Pseudomonas type strains for discovering new natural products

Microbes host a huge variety of biosynthetic gene clusters that produce an immeasurable array of secondary metabolites with many different biological activities such as antimicrobial, anticarcinogenic and antiviral. Despite the complex task of isolating and characterizing novel natural products, microbial genomic strategies can be useful for carrying out these types of studies. However, although genomic-based research on secondary metabolism is on the increase, there is still a lack of reports focusing specifically on the genus Pseudomonas. In this work, we aimed (i) to unveil the main biosynthetic systems related to secondary metabolism in Pseudomonas type strains, (ii) to study the evolutionary processes that drive the diversification of their coding regions and (iii) to select Pseudomonas strains showing promising results in the search for useful natural products. We performed a comparative genomic study on 194 Pseudomonas species, paying special attention to the evolution and distribution of different classes of biosynthetic gene clusters and the coding features of antimicrobial peptides. Using EvoMining, a bioinformatic approach for studying evolutionary processes related to secondary metabolism, we sought to decipher the protein expansion of enzymes related to the lipid metabolism, which may have evolved toward the biosynthesis of novel secondary metabolites in Pseudomonas. The types of metabolites encoded in Pseudomonas type strains were predominantly non-ribosomal peptide synthetases, bacteriocins, N-acetylglutaminylglutamine amides and ß-lactones. Also, the evolution of genes related to secondary metabolites was found to coincide with Pseudomonas species diversification. Interestingly, only a few Pseudomonas species encode polyketide synthases, which are related to the lipid metabolism broadly distributed among bacteria. Thus, our EvoMining-based search may help to discover new types of secondary metabolite gene clusters in which lipid-related enzymes are involved. This work provides information about uncharacterized metabolites produced by Pseudomonas type strains, whose gene clusters have evolved in a species-specific way. Our results provide novel insight into the secondary metabolism of Pseudomonas and will serve as a basis for the prioritization of the isolated strains. This article contains data hosted by Microreact.

Evolutionary Genome Mining for the Discovery and Engineering of Natural Product Biosynthesis

Genome mining has become an invaluable tool in natural products research to quickly identify and characterize the biosynthetic pathways that assemble secondary or specialized metabolites. Recently, evolutionary principles have been incorporated into genome mining strategies in an effort to better assess and prioritize novelty and understand their chemical diversification for engineering purposes. Here, we provide an introduction to the principles underlying evolutionary genome mining, including bioinformatic strategies and natural product biosynthetic databases. We introduce workflows for traditional genome mining, focusing on the popular pipeline antiSMASH, and methods to predict enzyme substrate specificity from genomic information. We then provide an in-depth discussion of evolutionary genome mining workflows, including EvoMining, CORASON, ARTS, and others, as adopted by our group for the discovery and prioritization of natural products biosynthetic gene clusters and their products.

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.

Evaluating the Distribution of Bacterial Natural Product Biosynthetic Genes across Lake Huron Sediment

Environmental microorganisms continue to serve as a major source of bioactive natural products (NPs) and as an inspiration for many other scaffolds in the toolbox of modern medicine. Nearly all microbial NP-inspired therapies can be traced to field expeditions to collect samples from the environment. Despite the importance of these expeditions in the search for new drugs, few studies have attempted to document the extent to which NPs or their corresponding production genes are distributed within a given environment. To gain insights into this, the geographic occurrence of NP ketosynthase (KS) and adenylation (A) domains was documented across 53 and 58 surface sediment samples, respectively, covering 59,590 square kilometers of Lake Huron. Overall, no discernible NP geographic distribution patterns were observed for 90,528 NP classes of nonribosomal peptides and polyketides detected in the survey. While each sampling location harbored a similar number of A domain operational biosynthetic units (OBUs), a limited overlap of OBU type was observed, suggesting that at the sequencing depth used in this study, no single location served as a NP "hotspot". These data support the hypothesis that there is ample variation in NP occurrence between sampling sites and suggest that extensive sample collection efforts are required to fully capture the functional chemical diversity of sediment microbial communities on a regional scale.

A roadmap for metagenomic enzyme discovery

Covering: up to 2021Metagenomics has yielded massive amounts of sequencing data offering a glimpse into the biosynthetic potential of the uncultivated microbial majority. While genome-resolved information about microbial communities from nearly every environment on earth is now available, the ability to accurately predict biocatalytic functions directly from sequencing data remains challenging. Compared to primary metabolic pathways, enzymes involved in secondary metabolism often catalyze specialized reactions with diverse substrates, making these pathways rich resources for the discovery of new enzymology. To date, functional insights gained from studies on environmental DNA (eDNA) have largely relied on PCR- or activity-based screening of eDNA fragments cloned in fosmid or cosmid libraries. As an alternative, shotgun metagenomics holds underexplored potential for the discovery of new enzymes directly from eDNA by avoiding common biases introduced through PCR- or activity-guided functional metagenomics workflows. However, inferring new enzyme functions directly from eDNA is similar to searching for a 'needle in a haystack' without direct links between genotype and phenotype. The goal of this review is to provide a roadmap to navigate shotgun metagenomic sequencing data and identify new candidate biosynthetic enzymes. We cover both computational and experimental strategies to mine metagenomes and explore protein sequence space with a spotlight on natural product biosynthesis. Specifically, we compare in silico methods for enzyme discovery including phylogenetics, sequence similarity networks, genomic context, 3D structure-based approaches, and machine learning techniques. We also discuss various experimental strategies to test computational predictions including heterologous expression and screening. Finally, we provide an outlook for future directions in the field with an emphasis on meta-omics, single-cell genomics, cell-free expression systems, and sequence-independent methods.

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.

In Silico/In Vitro Strategies Leading to the Discovery of New Nonribosomal Peptide and Polyketide Antibiotics Active against Human Pathogens

Antibiotics are majorly important molecules for human health. Following the golden age of antibiotic discovery, a period of decline ensued, characterised by the rediscovery of the same molecules. At the same time, new culture techniques and high-throughput sequencing enabled the discovery of new microorganisms that represent a potential source of interesting new antimicrobial substances to explore. The aim of this review is to present recently discovered nonribosomal peptide (NRP) and polyketide (PK) molecules with antimicrobial activity against human pathogens. We highlight the different in silico/in vitro strategies and approaches that led to their discovery. As a result of technological progress and a better understanding of the NRP and PK synthesis mechanisms, these new antibiotic compounds provide an additional option in human medical treatment and a potential way out of the impasse of antibiotic resistance.

Rhodococcus comparative genomics reveals a phylogenomic-dependent non-ribosomal peptide synthetase distribution: insights into biosynthetic gene cluster connection to an orphan metabolite

Natural products (NPs) are synthesized by biosynthetic gene clusters (BGCs), whose genes are involved in producing one or a family of chemically related metabolites. Advances in comparative genomics have been favourable for exploiting huge amounts of data and discovering previously unknown BGCs. Nonetheless, studying distribution patterns of novel BGCs and elucidating the biosynthesis of orphan metabolites remains a challenge. To fill this knowledge gap, our study developed a pipeline for high-quality comparative genomics for the actinomycete genus Rhodococcus , which is metabolically versatile, yet understudied in terms of NPs, leading to a total of 110 genomes, 1891 BGCs and 717 non-ribosomal peptide synthetases (NRPSs). Phylogenomic inferences showed four major clades retrieved from strains of several ecological habitats. BiG-SCAPE sequence similarity BGC networking revealed 44 unidentified gene cluster families (GCFs) for NRPS, which presented a phylogenomic-dependent evolution pattern, supporting the hypothesis of vertical gene transfer. As a proof of concept, we analysed in-depth one of our marine strains, Rhodococcus sp. H-CA8f, which revealed a unique BGC distribution within its phylogenomic clade, involved in producing a chloramphenicol-related compound. While this BGC is part of the most abundant and widely distributed NRPS GCF, corason analysis unveiled major differences regarding its genetic context, co-occurrence patterns and modularity. This BGC is composed of three sections, two well-conserved right/left arms flanking a very variable middle section, composed of nrps genes. The presence of two non-canonical domains in H-CA8f’s BGC may contribute to adding chemical diversity to this family of NPs. Liquid chromatography-high resolution MS and dereplication efforts retrieved a set of related orphan metabolites, the corynecins, which to our knowledge are reported here for the first time in Rhodococcus . Overall, our data provide insights to connect BGC uniqueness with orphan metabolites, by revealing key comparative genomic features supported by models of BGC distribution along phylogeny.

Mining and unearthing hidden biosynthetic potential

Genetically encoded small molecules (secondary metabolites) play eminent roles in ecological interactions, as pathogenicity factors and as drug leads. Yet, these chemical mediators often evade detection, and the discovery of novel entities is hampered by low production and high rediscovery rates. These limitations may be addressed by genome mining for biosynthetic gene clusters, thereby unveiling cryptic metabolic potential. The development of sophisticated data mining methods and genetic and analytical tools has enabled the discovery of an impressive array of previously overlooked natural products. This review shows the newest developments in the field, highlighting compound discovery from unconventional sources and microbiomes.

Natural products are an important source of bioactive compounds and have versatile applications in different fields, but their discovery is challenging. Here, the authors review the recent developments in genome mining for discovery of natural products, focusing on compounds from unconventional microorganisms and microbiomes.

Analysis of Unusual Sulfated Constituents and Anti-infective Properties of Two Indonesian Mangroves, Lumnitzera littorea and Lumnitzera racemosa (Combretaceae)

Lumnitzera littorea and Lumnitzera racemosa are mangrove species distributed widely along the Indonesian coasts. Besides their ecological importance, both are of interest owing to their wealth of natural products, some of which constitute potential sources for medicinal applications. We aimed to discover and characterize new anti-infective compounds, based on population-level sampling of both species from across the Indonesian Archipelago. Root metabolites were investigated by TLC, hyphenated LC-MS/MS and isolation, the internal transcribed spacer (ITS) region of rDNA was used for genetic characterization. Phytochemical characterization of both species revealed an unusual diversity in sulfated constituents with 3,3’,4’-tri-O-methyl-ellagic acid 4-sulfate representing the major compound in most samples. None of these compounds was previously reported for mangroves. Chemophenetic comparison of L. racemosa populations from different localities provided evolutionary information, as supported by molecular phylogenetic evidence. Samples of both species from particular locations exhibited anti-bacterial potential (Southern Nias Island and East Java against Gram-negative bacteria, Halmahera and Ternate Island against Gram-positive bacteria). In conclusion, Lumnitzera roots from natural mangrove stands represent a promising source for sulfated ellagic acid derivatives and further sulfur containing plant metabolites with potential human health benefits.

Targeted Isolation of Saalfelduracin B-D from Amycolatopsis saalfeldensis Using LC-MS/MS-Based Molecular Networking

High-resolution tandem mass spectrometry (HR-MS²)-based metabolomic studies of Amycolatopsis saalfeldensis, isolated from the "Saalfelder Feengrotten" caves in Germany, led to the isolation of three ribosomally synthesized and post-translationally modified type II thiopeptides, saalfelduracin B-D (1-3) and the known saalfelduracin A (4). The structures of all four compounds were determined by comparative two-dimensional NMR analysis and high-resolution tandem mass spectrometry.

Deep learning approaches for natural product discovery from plant endophytic microbiomes

Plant microbiomes are not only diverse, but also appear to host a vast pool of secondary metabolites holding great promise for bioactive natural products and drug discovery. Yet, most microbes within plants appear to be uncultivable, and for those that can be cultivated, their metabolic potential lies largely hidden through regulatory silencing of biosynthetic genes. The recent explosion of powerful interdisciplinary approaches, including multi-omics methods to address multi-trophic interactions and artificial intelligence-based computational approaches to infer distribution of function, together present a paradigm shift in high-throughput approaches to natural product discovery from plant-associated microbes. Arguably, the key to characterizing and harnessing this biochemical capacity depends on a novel, systematic approach to characterize the triggers that turn on secondary metabolite biosynthesis through molecular or genetic signals from the host plant, members of the rich 'in planta' community, or from the environment. This review explores breakthrough approaches for natural product discovery from plant microbiomes, emphasizing the promise of deep learning as a tool for endophyte bioprospecting, endophyte biochemical novelty prediction, and endophyte regulatory control. It concludes with a proposed pipeline to harness global databases (genomic, metabolomic, regulomic, and chemical) to uncover and unsilence desirable natural products.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s40793-021-00375-0.

Thioesterase-mediated side chain transesterification generates potent Gq signaling inhibitor FR900359

The potent and selective Gq protein inhibitor depsipeptide FR900359 (FR), originally discovered as the product of an uncultivable plant endosymbiont, is synthesized by a complex biosynthetic system comprising two nonribosomal peptide synthetase (NRPS) assembly lines. Here we characterize a cultivable bacterial FR producer, enabling detailed investigations into biosynthesis and attachment of the functionally important FR side chain. We reconstitute side chain assembly by the monomodular NRPS FrsA and the non-heme monooxygenase FrsH, and characterize intermolecular side chain transesterification to the final macrocyclic intermediate FR-Core, mediated by the FrsA thioesterase domain. We harness FrsA substrate promiscuity to generate FR analogs with altered side chains and demonstrate indispensability of the FR side chain for efficient Gq inhibition by comparative bioactivity, toxicity and docking studies. Finally, evolution of FR and side chain biosynthesis is discussed based on bioinformatics analyses. Side chain transesterification boosts potency and target affinity of selective Gq inhibitor natural products.

FR900359 (FR) is a Gq protein inhibitor depsipeptide isolated from an uncultivable plant endosymbiont and synthesized by non-ribosomal peptide synthetases. Here, the authors discover a cultivable bacterial FR producer and show that FrsA thioesterase domain catalyses intermolecular transesterification of the FR side chain to the depsipeptide core during biosynthesis, improving Gq inhibition properties.

ActDES - a curated Actinobacterial Database for Evolutionary Studies

Actinobacteria is a large and diverse phylum of bacteria that contains medically and ecologically relevant organisms. Many members are valuable sources of bioactive natural products and chemical precursors that are exploited in the clinic and made using the enzyme pathways encoded in their complex genomes. Whilst the number of sequenced genomes has increased rapidly in the last 20 years, the large size, complexity and high G+C content of many actinobacterial genomes means that the sequences remain incomplete and consist of large numbers of contigs with poor annotation, which hinders large-scale comparative genomic and evolutionary studies. To enable greater understanding and exploitation of actinobacterial genomes, specialized genomic databases must be linked to high-quality genome sequences. Here, we provide a curated database of 612 high-quality actinobacterial genomes from 80 genera, chosen to represent a broad phylogenetic group with equivalent genome re-annotation. Utilizing this database will provide researchers with a framework for evolutionary and metabolic studies, to enable a foundation for genome and metabolic engineering, to facilitate discovery of novel bioactive therapeutics and studies on gene family evolution. This article contains data hosted by Microreact.

Gene Cluster Activation in a Bacterial Symbiont Leads to Halogenated Angucyclic Maduralactomycins and Spirocyclic Actinospirols

Growth from spores activated a biosynthetic gene cluster in Actinomadura sp. RB29, resulting in the identification of two novel groups of halogenated polyketide natural products, named maduralactomycins and actinospirols. The unique tetracyclic and spirocyclic structures were assigned based on a combination of NMR analysis, chemoinformatic calculations, X-ray crystallography, and ¹³C labeling studies. On the basis of HRMS² data, genome mining, and gene expression studies, we propose an underlying noncanonical angucycline biosynthesis and extensive post-polyketide synthase (PKS) oxidative modifications.

Structure elucidation of bacterial nonribosomal lipopeptides

We provide a summary of the tools, which allow elucidate the structures of nonribosomal lipopetides.

Evolutionary dynamics of natural product biosynthesis in bacteria

Covering: 2008 up to 2019The forces of biochemical adaptive evolution operate at the level of genes, manifesting in complex phenotypes and the global biodiversity of proteins and metabolites. While evolutionary histories have been deciphered for some other complex traits, the origins of natural product biosynthesis largely remain a mystery. This fundamental knowledge gap is surprising given the many decades of research probing the genetic, chemical, and biophysical mechanisms of bacterial natural product biosynthesis. Recently, evolutionary thinking has begun to permeate this otherwise mechanistically dominated field. Natural products are now sometimes referred to as 'specialized' rather than 'secondary' metabolites, reinforcing the importance of their biological and ecological functions. Here, we review known evolutionary mechanisms underlying the overwhelming chemical diversity of bacterial secondary metabolism, focusing on enzyme promiscuity and the evolution of enzymatic domains that enable metabolic traits. We discuss the mechanisms that drive the assembly of natural product biosynthetic gene clusters and propose formal definitions for 'specialized' and 'secondary' metabolism. We further explore how biosynthetic gene clusters evolve to synthesize related molecular species, and in turn how the biological and ecological roles that emerge from metabolic diversity are acted on by selection. Finally, we reconcile chemical, functional, and genetic data into an evolutionary model, the dynamic chemical matrix evolutionary hypothesis, in which the relationships between chemical distance, biomolecular activity, and relative fitness shape adaptive landscapes.