Phylogenomic Analysis of Natural Products Biosynthetic Gene Clusters Allows Discovery of Arseno-Organic Metabolites in Model Streptomycetes

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.

Synteruptor: mining genomic islands for non-classical specialized metabolite gene clusters

Microbial specialized metabolite biosynthetic gene clusters (SMBGCs) are a formidable source of natural products of pharmaceutical interest. With the multiplication of genomic data available, very efficient bioinformatic tools for automatic SMBGC detection have been developed. Nevertheless, most of these tools identify SMBGCs based on sequence similarity with enzymes typically involved in specialised metabolism and thus may miss SMBGCs coding for undercharacterised enzymes. Here we present Synteruptor (https://bioi2.i2bc.paris-saclay.fr/synteruptor), a program that identifies genomic islands, known to be enriched in SMBGCs, in the genomes of closely related species. With this tool, we identified a SMBGC in the genome of Streptomyces ambofaciens ATCC23877, undetected by antiSMASH versions prior to antiSMASH 5, and experimentally demonstrated that it directs the biosynthesis of two metabolites, one of which was identified as sphydrofuran. Synteruptor is also a valuable resource for the delineation of individual SMBGCs within antiSMASH regions that may encompass multiple clusters, and for refining the boundaries of these SMBGCs.

Lipophilic arsenic compounds in the cultured green alga Chlamydomonas reinhardtii

In this study, arsenic (As) speciation was investigated in the freshwater alga Chlamydomonas reinhardtii treated with 20 μg/L arsenate using fractionation as well as ICP-MS/ESI-MS analyses and was compared with the known As metabolite profile of wild-grown Saccharina latissima. While the total As accumulation in C. reinhardtii was about 85% lower than in S. latissima, the relative percentage of arsenolipids was significantly higher in C. reinhardtii (57.0% vs. 5.01%). As-containing hydrocarbons and phospholipids dominated the hydrophobic As profile in S. latissima, but no As-containing hydrocarbons were detectable in C. reinhardtii. Instead for the first time, an arsenoriboside-containing phytol (AsSugPhytol) was found to dominate the hydrophobic arsenicals of C. reinhardtii. Interestingly, this compound and its relatives had so far been only found in green marine microalgae, open sea plankton (mixed assemblage), and sediments but not in brown or red macroalgae. This compound family might therefore relate to differences in the arsenic metabolism between the algae phyla.

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.

Insights into Arsenic Secondary Metabolism in Actinomycetes from the Structure and Biosynthesis of Bisenarsan

The unique bioactivities of arsenic-containing secondary metabolites have been revealed recently, but studies on arsenic secondary metabolism in microorganisms have been extremely limited. Here, we focused on the organoarsenic metabolite with an unknown chemical structure, named bisenarsan, produced by well-studied model actinomycetes and elucidated its structure by combining feeding of the putative biosynthetic precursor (2-hydroxyethyl)arsonic acid to Streptomyces lividans 1326 and detailed NMR analyses. Bisenarsan is the first characterized actinomycete-derived arsenic secondary metabolite and may function as a prototoxin form of an antibacterial agent or be a detoxification product of inorganic arsenic species. We also verified the previously proposed genes responsible for bisenarsan biosynthesis, especially the (2-hydroxyethyl)arsonic acid moiety. Notably, we suggest that a C-As bond in bisenarsan is formed by the intramolecular rearrangement of a pentavalent arsenic species (arsenoenolpyruvate) by the cofactor-independent phosphoglycerate mutase homologue BsnN, that is entirely distinct from the conventional biological C-As bond formation through As-alkylation of trivalent arsenic species by S-adenosylmethionine-dependent enzymes. Our findings will speed up the development of arsenic natural product biosynthesis.

The Cytotoxic Properties of Extreme Fungi's Bioactive Components-An Updated Metabolic and Omics Overview

Fungi are the most diverse living organisms on planet Earth, where their ubiquitous presence in various ecosystems offers vast potential for the research and discovery of new, naturally occurring medicinal products. Concerning human health, cancer remains one of the leading causes of mortality. While extensive research is being conducted on treatments and their efficacy in various stages of cancer, finding cytotoxic drugs that target tumor cells with no/less toxicity toward normal tissue is a significant challenge. In addition, traditional cancer treatments continue to suffer from chemical resistance. Fortunately, the cytotoxic properties of several natural products derived from various microorganisms, including fungi, are now well-established. The current review aims to extract and consolidate the findings of various scientific studies that identified fungi-derived bioactive metabolites with antitumor (anticancer) properties. The antitumor secondary metabolites identified from extremophilic and extremotolerant fungi are grouped according to their biological activity and type. It became evident that the significance of these compounds, with their medicinal properties and their potential application in cancer treatment, is tremendous. Furthermore, the utilization of omics tools, analysis, and genome mining technology to identify the novel metabolites for targeted treatments is discussed. Through this review, we tried to accentuate the invaluable importance of fungi grown in extreme environments and the necessity of innovative research in discovering naturally occurring bioactive compounds for the development of novel cancer treatments.

Crosstalk between primary and secondary metabolism: Interconnected fatty acid and polyketide biosynthesis in prokaryotes

In primary metabolism, fatty acid synthases (FASs) biosynthesize fatty acids via sequential Claisen-like condensations of malonyl-CoA followed by reductive processing. Likewise, polyketide synthases (PKSs) share biosynthetic logic with FAS which includes utilizing the same precursors and cofactors. However, PKS biosynthesize structurally diverse, complex secondary metabolites, many of which are pharmaceutically relevant. This digest covers examples of interconnected biosynthesis between primary and secondary metabolism in fatty acid and polyketide metabolism. Taken together, further understanding the biosynthetic linkage between polyketide biosynthesis and fatty acid biosynthesis may lead to improved discovery and production of novel drug leads from polyketide metabolites.

Genome Analysis of a Variant of Streptomyces coelicolor M145 with High Lipid Content and Poor Ability to Synthetize Antibiotics

Streptomyces coelicolor M145 is a model strain extensively studied to elucidate the regulation of antibiotic biosynthesis in Streptomyces species. This strain abundantly produces the blue polyketide antibiotic, actinorhodin (ACT), and has a low lipid content. In a process designed to delete the gene encoding the isocitrate lyase (sco0982) of the glyoxylate cycle, an unexpected variant of S. coelicolor was obtained besides bona fide sco0982 deletion mutants. This variant produces 7- to 15-fold less ACT and has a 3-fold higher triacylglycerol and phosphatidylethanolamine content than the original strain. The genome of this variant was sequenced and revealed that 704 genes were deleted (9% of total number of genes) through deletions of various sizes accompanied by the massive loss of mobile genetic elements. Some deletions include genes whose absence could be related to the high total lipid content of this variant such as those encoding enzymes of the TCA and glyoxylate cycles, enzymes involved in nitrogen assimilation as well as enzymes belonging to some polyketide and possibly trehalose biosynthetic pathways. The characteristics of this deleted variant of S. coelicolor are consistent with the existence of the previously reported negative correlation existing between lipid content and antibiotic production in Streptomyces species.

On the evolution of natural product biosynthesis

Natural products are the raw material for drug discovery programmes. Bioactive natural products are used extensively in medicine and agriculture and have found utility as antibiotics, immunosuppressives, anti-cancer drugs and anthelminthics. Remarkably, the natural role and what mechanisms drive evolution of these molecules is relatively poorly understood. The exponential increase in genome and chemical data in recent years, coupled with technical advances in bioinformatics and genetics have enabled progress to be made in understanding the evolution of biosynthetic gene clusters and the products of their enzymatic machinery. Here we discuss the diversity of natural products, incorporating the mechanisms that govern evolution of metabolic pathways and how this can be applied to biosynthetic gene clusters. We build on the nomenclature of natural products in terms of primary, integrated, secondary and specialised metabolism and place this within an ecology-evolutionary-developmental biology framework. This eco-evo-devo framework we believe will help to clarify the nature and use of the term specialised metabolites in the future.

Streptomyces lividans 66 produces a protease inhibitor via a tRNA-utilizing enzyme interacting with a C-minus NRPS

Small peptide aldehydes (SPAs) with protease inhibitory activity are naturally occurring compounds shown to be synthesized by non-ribosomal peptide synthetases (NRPS). SPAs are widely used in biotechnology and have been utilized as therapeutic agents. They are also physiologically relevant and have been postulated to regulate the development of their producing microorganisms. Previously, we identified an NRPS-like biosynthetic gene cluster (BGC) in Streptomyces lividans 66 that lacked a condensation (C) domain but included a tRNA-utilizing enzyme (tRUE) belonging to the leucyl/phenylalanyl (L/F) transferase family. This system was predicted to direct the synthesis of a novel SPA, which we named livipeptin. Using evolutionary genome mining approaches, here, we confirm the presence of L/F transferase tRUEs within the genomes of diverse Streptomyces and related organisms, including fusions with the anticipated C-minus NRPS-like protein. We then demonstrate genetic functional cooperation between the identified L/F-transferase divergent tRUE homolog with the C-minus NRPS, leading to the synthesis of a metabolic fraction with protease inhibitory activity. Semisynthetic assays in the presence of RNAse revealed that the productive interaction between the tRUE and the C-minus NRPS enzymes is indeed tRNA dependent. We expect our findings to boost the discovery of SPAs, as well as the development of protease-mediated biotechnologies, by exploiting the uncovered genetic basis for synthesizing putative acetyl-leu/phe-arginine protease inhibitors. Furthermore, these results will facilitate the purification and structural elucidation of livipeptin, which has proven difficult to chemically characterize.

SIGNIFICANCE

The discovery of natural products biosynthetic genes marks a significant advancement in our understanding of these metabolites, for example of their evolution, activity, and biosynthesis, but also opens biotechnological opportunities and knowledge to advance genome mining approaches. We made this possible by uncovering a new biosynthetic pathway in Streptomyces lividans 66 shown to direct the synthesis of a strong protease inhibitor, termed livipeptin, following unprecedented biosynthetic rules and genes. Thus, by shedding light on the genetic mechanisms predicted to govern the production of acetyl-leu/phe-arginine protease inhibitors, including the elusive livipeptin, this study enables novel protease-mediated biotechnologies as well as approaches for discovering protease inhibitors from genome data.

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.

Review of Escherichia coli O157:H7 Prevalence, Pathogenicity, Heavy Metal and Antimicrobial Resistance, African Perspective

Escherichia coli O157:H7 is an important food-borne and water-borne pathogen that causes hemorrhagic colitis and the hemolytic-uremic syndrome in humans and may cause serious morbidity and large outbreaks worldwide. People with bloody diarrhea have an increased risk of developing serious complications such as acute renal failure and neurological damage. The hemolytic-uremic syndrome (HUS) is a serious condition, and up to 50% of HUS patients can develop long-term renal dysfunction or blood pressure-related complications. Children aged two to six years have an increased risk of developing HUS. Clinical enteropathogenic Escherichia coli (EPEC) infections show fever, vomiting, and diarrhea. The EPEC reservoir is unknown but is suggested to be an asymptomatic or symptomatic child or an asymptomatic adult carrier. Spreading is often through the fecal-oral route. The prevalence of EPEC in infants is low, and EPEC is highly contagious in children. EPEC disease in children tends to be clinically more severe than other diarrheal infections. Some children experience persistent diarrhea that lasts for more than 14 days. Enterotoxigenic Escherichia coli (ETEC) strains are a compelling cause of the problem of diarrheal disease. ETEC strains are a global concern as the bacteria are the leading cause of acute watery diarrhea in children and the leading cause of traveler’s diarrhea. It is contagious to children and can cause chronic diarrhea that can affect the development and well-being of children. Infections with diarrheagenic E. coli are more common in African countries. Antimicrobial agents should be avoided in the acute phase of the disease since studies showed that antimicrobial agents may increase the risk of HUS in children. The South African National Veterinary Surveillance and Monitoring Programme for Resistance to Antimicrobial Drugs has reported increased antimicrobial resistance in E. coli. Pathogenic bacterial strains have developed resistance to a variety of antimicrobial agents due to antimicrobial misuse. The induced heavy metal tolerance may also enhance antimicrobial resistance. The prevalence of antimicrobial resistance depends on the type of the antimicrobial agent, bacterial strain, dose, time, and mode of administration. Developing countries are severely affected by increased resistance to antimicrobial agents due to poverty, lack of proper hygiene, and clean water, which can lead to bacterial infections with limited treatment options due to resistance.

How to Completely Squeeze a Fungus-Advanced Genome Mining Tools for Novel Bioactive Substances

Fungal species have the capability of producing an overwhelming diversity of bioactive substances that can have beneficial but also detrimental effects on human health. These so-called secondary metabolites naturally serve as antimicrobial "weapon systems", signaling molecules or developmental effectors for fungi and hence are produced only under very specific environmental conditions or stages in their life cycle. However, as these complex conditions are difficult or even impossible to mimic in laboratory settings, only a small fraction of the true chemical diversity of fungi is known so far. This also implies that a large space for potentially new pharmaceuticals remains unexplored. We here present an overview on current developments in advanced methods that can be used to explore this chemical space. We focus on genetic and genomic methods, how to detect genes that harbor the blueprints for the production of these compounds (i.e., biosynthetic gene clusters, BGCs), and ways to activate these silent chromosomal regions. We provide an in-depth view of the chromatin-level regulation of BGCs and of the potential to use the CRISPR/Cas technology as an activation tool.

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.

Chemometrics and genome mining reveal an unprecedented family of sugar acid-containing fungal nonribosomal cyclodepsipeptides

Xylomyrocins, a unique group of nonribosomal peptide secondary metabolites, were discovered in Paramyrothecium and Colletotrichum spp. fungi by employing a combination of high-resolution tandem mass spectrometry (HRMS/MS)-based chemometrics, comparative genome mining, gene disruption, stable isotope feeding, and chemical complementation techniques. These polyol cyclodepsipeptides all feature an unprecedented d-xylonic acid moiety as part of their macrocyclic scaffold. This biosynthon is derived from d-xylose supplied by xylooligosaccharide catabolic enzymes encoded in the xylomyrocin biosynthetic gene cluster, revealing a novel link between carbohydrate catabolism and nonribosomal peptide biosynthesis. Xylomyrocins from different fungal isolates differ in the number and nature of their amino acid building blocks that are nevertheless incorporated by orthologous nonribosomal peptide synthetase (NRPS) enzymes. Another source of structural diversity is the variable choice of the nucleophile for intramolecular macrocyclic ester formation during xylomyrocin chain termination. This nucleophile is selected from the multiple available alcohol functionalities of the polyol moiety, revealing a surprising polyspecificity for the NRPS terminal condensation domain. Some xylomyrocin congeners also feature N-methylated amino acid residues in positions where the corresponding NRPS modules lack N-methyltransferase (M) domains, providing a rare example of promiscuous methylation in the context of an NRPS with an otherwise canonical, collinear biosynthetic program.

Targeted Large-Scale Genome Mining and Candidate Prioritization for Natural Product Discovery

Large-scale genome-mining analyses have identified an enormous number of cryptic biosynthetic gene clusters (BGCs) as a great source of novel bioactive natural products. Given the sheer number of natural product (NP) candidates, effective strategies and computational methods are keys to choosing appropriate BGCs for further NP characterization and production. This review discusses genomics-based approaches for prioritizing candidate BGCs extracted from large-scale genomic data, by highlighting studies that have successfully produced compounds with high chemical novelty, novel biosynthesis pathway, and potent bioactivities. We group these studies based on their BGC-prioritization logics: detecting presence of resistance genes, use of phylogenomics analysis as a guide, and targeting for specific chemical structures. We also briefly comment on the different bioinformatics tools used in the field and examine practical considerations when employing a large-scale genome mining study.

Exploring Newer Biosynthetic Gene Clusters in Marine Microbial Prospecting

Marine microbes genetically evolved to survive varying salinity, temperature, pH, and other stress factors by producing different bioactive metabolites. These microbial secondary metabolites (SMs) are novel, have high potential, and could be used as lead molecule. Genome sequencing of microbes revealed that they have the capability to produce numerous novel bioactive metabolites than observed under standard in vitro culture conditions. Microbial genome has specific regions responsible for SM assembly, termed biosynthetic gene clusters (BGCs), possessing all the necessary genes to encode different enzymes required to generate SM. In order to augment the microbial chemo diversity and to activate these gene clusters, various tools and techniques are developed. Metagenomics with functional gene expression studies aids in classifying novel peptides and enzymes and also in understanding the biosynthetic pathways. Genome shuffling is a high-throughput screening approach to improve the development of SMs by incorporating genomic recombination. Transcriptionally silent or lower level BGCs can be triggered by artificially knocking promoter of target BGC. Additionally, bioinformatic tools like antiSMASH, ClustScan, NAPDOS, and ClusterFinder are effective in identifying BGCs of existing class for annotation in genomes. This review summarizes the significance of BGCs and the different approaches for detecting and elucidating BGCs from marine microbes.

Fungal secondary metabolites in food and pharmaceuticals in the era of multi-omics

Fungi produce several bioactive metabolites, pigments, dyes, antioxidants, polysaccharides, and industrial enzymes. Fungal products are also the primary sources of functional food and nutrition, and their pharmacological products are used for healthy aging. Their molecular properties are validated through the use of recent high-throughput genomic, transcriptomic, and metabolomic tools and techniques. Together, these updated multi-omic tools have been used to study fungal metabolites structure and their mode of action on biological and cellular processes. Diverse groups of fungi produce different proteins and secondary metabolites, which possess tremendous biotechnological and pharmaceutical applications. Furthermore, its use and acceptability can be accelerated by adopting multi-omics, bioinformatics, and machine learning tools that generate a huge amount of molecular data. The integration of artificial intelligence and machine learning tools in the era of omics and big data has opened up a new outlook in both basic and applied researches in the area of nutraceuticals and functional food and nutrition. KEY POINTS: • Multi-omic tool helps in the identification of novel fungal metabolites • Intra-omic data from genomics to bioinformatics • Novel metabolites and application in human health.

Strategies to access biosynthetic novelty in bacterial genomes for drug discovery

Bacteria provide a rich source of natural products with potential therapeutic applications, such as novel antibiotic classes or anticancer drugs. Bioactivity-guided screening of bacterial extracts and characterization of biosynthetic pathways for drug discovery is now complemented by the availability of large (meta)genomic collections, placing researchers into the postgenomic, big-data era. The progress in next-generation sequencing and the rise of powerful computational tools provide unprecedented insights into unexplored taxa, ecological niches and 'biosynthetic dark matter', revealing diverse and chemically distinct natural products in previously unstudied bacteria. In this Review, we discuss such sources of new chemical entities and the implications for drug discovery with a particular focus on the strategies that have emerged in recent years to identify and access novelty.

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.

Isolation and characterization of Streptomyces bacteriophages and Streptomyces strains encoding biosynthetic arsenals

The threat to public health posed by drug-resistant bacteria is rapidly increasing, as some of healthcare's most potent antibiotics are becoming obsolete. Approximately two-thirds of the world's antibiotics are derived from natural products produced by Streptomyces encoded biosynthetic gene clusters. Thus, to identify novel gene clusters, we sequenced the genomes of four bioactive Streptomyces strains isolated from the soil in San Diego County and used Bacterial Cytological Profiling adapted for agar plate culturing in order to examine the mechanisms of bacterial inhibition exhibited by these strains. In the four strains, we identified 104 biosynthetic gene clusters. Some of these clusters were predicted to produce previously studied antibiotics; however, the known mechanisms of these molecules could not fully account for the antibacterial activity exhibited by the strains, suggesting that novel clusters might encode antibiotics. When assessed for their ability to inhibit the growth of clinically isolated pathogens, three Streptomyces strains demonstrated activity against methicillin-resistant Staphylococcus aureus. Additionally, due to the utility of bacteriophages for genetically manipulating bacterial strains via transduction, we also isolated four new phages (BartholomewSD, IceWarrior, Shawty, and TrvxScott) against S. platensis. A genomic analysis of our phages revealed nearly 200 uncharacterized proteins, including a new site-specific serine integrase that could prove to be a useful genetic tool. Sequence analysis of the Streptomyces strains identified CRISPR-Cas systems and specific spacer sequences that allowed us to predict phage host ranges. Ultimately, this study identified Streptomyces strains with the potential to produce novel chemical matter as well as integrase-encoding phages that could potentially be used to manipulate these strains.

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 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.

Metabolomics and genomics in natural products research: complementary tools for targeting new chemical entities

Covering: 2010 to 2021Organisms in nature have evolved into proficient synthetic chemists, utilizing specialized enzymatic machinery to biosynthesize an inspiring diversity of secondary metabolites. Often serving to boost competitive advantage for their producers, these secondary metabolites have widespread human impacts as antibiotics, anti-inflammatories, and antifungal drugs. The natural products discovery field has begun a shift away from traditional activity-guided approaches and is beginning to take advantage of increasingly available metabolomics and genomics datasets to explore undiscovered chemical space. Major strides have been made and now enable -omics-informed prioritization of chemical structures for discovery, including the prospect of confidently linking metabolites to their biosynthetic pathways. Over the last decade, more integrated strategies now provide researchers with pipelines for simultaneous identification of expressed secondary metabolites and their biosynthetic machinery. However, continuous collaboration by the natural products community will be required to optimize strategies for effective evaluation of natural product biosynthetic gene clusters to accelerate discovery efforts. Here, we provide an evaluative guide to scientific literature as it relates to studying natural product biosynthesis using genomics, metabolomics, and their integrated datasets. Particular emphasis is placed on the unique insights that can be gained from large-scale integrated strategies, and we provide source organism-specific considerations to evaluate the gaps in our current knowledge.

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.

Complete Genome Sequence of Two Deep-Sea Streptomyces Isolates from Madeira Archipelago and Evaluation of Their Biosynthetic Potential

The deep-sea constitutes a true unexplored frontier and a potential source of innovative drug scaffolds. Here, we present the genome sequence of two novel marine actinobacterial strains, MA3_2.13 and S07_1.15, isolated from deep-sea samples (sediments and sponge) and collected at Madeira archipelago (NE Atlantic Ocean; Portugal). The de novo assembly of both genomes was achieved using a hybrid strategy that combines short-reads (Illumina) and long-reads (PacBio) sequencing data. Phylogenetic analyses showed that strain MA3_2.13 is a new species of the Streptomyces genus, whereas strain S07_1.15 is closely related to the type strain of Streptomyces xinghaiensis. In silico analysis revealed that the total length of predicted biosynthetic gene clusters (BGCs) accounted for a high percentage of the MA3_2.13 genome, with several potential new metabolites identified. Strain S07_1.15 had, with a few exceptions, a predicted metabolic profile similar to S. xinghaiensis. In this work, we implemented a straightforward approach for generating high-quality genomes of new bacterial isolates and analyse in silico their potential to produce novel NPs. The inclusion of these in silico dereplication steps allows to minimize the rediscovery rates of traditional natural products screening methodologies and expedite the drug discovery process.

Mining genomes to illuminate the specialized chemistry of life

All organisms produce specialized organic molecules, ranging from small volatile chemicals to large gene-encoded peptides, that have evolved to provide them with diverse cellular and ecological functions. As natural products, they are broadly applied in medicine, agriculture and nutrition. The rapid accumulation of genomic information has revealed that the metabolic capacity of virtually all organisms is vastly underappreciated. Pioneered mainly in bacteria and fungi, genome mining technologies are accelerating metabolite discovery. Recent efforts are now being expanded to all life forms, including protists, plants and animals, and new integrative omics technologies are enabling the increasingly effective mining of this molecular diversity.

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.

Streptomyces: host for refactoring of diverse bioactive secondary metabolites

Microbial secondary metabolites are intensively explored due to their demands in pharmaceutical, agricultural and food industries. Streptomyces are one of the largest sources of secondary metabolites having diverse applications. In particular, the abundance of secondary metabolites encoding biosynthetic gene clusters and presence of wobble position in Streptomyces strains make it potential candidate as a native or heterologous host for secondary metabolite production including several cryptic gene clusters expression. Here, we have discussed the developments in Streptomyces strains genome mining, its exploration as a suitable host and application of synthetic biology for refactoring genetic systems for developing chassis for enhanced as well as novel secondary metabolites with reduced genome and cleaned background.

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.

The ARC2 response in Streptomcyes coelicolor requires the global regulatory genes afsR and afsS

ARC2 is a synthetic compound, related in structure and mechanism to the antibiotic triclosan, that activates the production of many specialized metabolites in the Streptomyces genus of bacteria. In this work, we demonstrate that the addition of ARC2 to Streptomyces coelicolor cultures results in considerable alterations in overall gene expression including most notably the specialized metabolic genes. Using actinorhodin production as a model system, we show that the effect of ARC2 depends on the pleiotropic regulators afsR and afsS but not afsK. We find that the constitutive expression of afsS can bypass the need for afsR but not the reverse, while the constitutive expression of afsK had no effect on actinorhodin production. These data are consistent with a model in which ARC2 activates a cell stress response that depends on AfsR activating the expression of the afsS gene such that AfsS then triggers the production of actinorhodin.

Synthetic biology-inspired strategies and tools for engineering of microbial natural product biosynthetic pathways

Microbial-derived natural products (NPs) and their derivative products are of great importance and used widely in many fields, especially in pharmaceutical industries. However, there is an immediate need to establish innovative approaches, strategies, and techniques to discover new NPs with novel or enhanced biological properties, due to the less productivity and higher cost on traditional drug discovery pipelines from natural bioresources. Revealing of untapped microbial cryptic biosynthetic gene clusters (BGCs) using DNA sequencing technology and bioinformatics tools makes genome mining possible for NP discovery from microorganisms. Meanwhile, new approaches and strategies in the area of synthetic biology offer great potentials for generation of new NPs by engineering or creating synthetic systems with improved and desired functions. Development of approaches, strategies and tools in synthetic biology can facilitate not only exploration and enhancement in supply, and also in the structural diversification of NPs. Here, we discussed recent advances in synthetic biology-inspired strategies, including bioinformatics and genetic engineering tools and approaches for identification, cloning, editing/refactoring of candidate biosynthetic pathways, construction of heterologous expression hosts, fitness optimization between target pathways and hosts and detection of NP production.

Coelimycin Synthesis Activatory Proteins Are Key Regulators of Specialized Metabolism and Precursor Flux in Streptomyces coelicolor A3(2)

Many microbial specialized metabolites are industrially relevant agents but also serve as signaling molecules in intra-species and even inter-kingdom interactions. In the antibiotic-producing Streptomyces, members of the SARP (Streptomyces antibiotic regulatory proteins) family of regulators are often encoded within biosynthetic gene clusters and serve as their direct activators. Coelimycin is the earliest, colored specialized metabolite synthesized in the life cycle of the model organism Streptomyces coelicolor A3(2). Deletion of its two SARP activators cpkO and cpkN abolished coelimycin synthesis and resulted in dramatic changes in the production of the later, stationary-phase antibiotics. The underlying mechanisms of these phenotypes were deregulation of precursor flux and quorum sensing, as shown by label-free, bottom-up shotgun proteomics. Detailed profiling of promoter activities demonstrated that CpkO is the upper-level cluster activator that induces CpkN, while CpkN activates type II thioesterase ScoT, necessary for coelimycin synthesis. What is more, we show that cpkN is regulated by quorum sensing gamma-butyrolactone receptor ScbR.

Recent Advances in the Heterologous Biosynthesis of Natural Products from Streptomyces

Streptomyces is a significant source of natural products that are used as therapeutic antibiotics, anticancer and antitumor agents, pesticides, and dyes. Recently, with the advances in metabolite analysis, many new secondary metabolites have been characterized. Moreover, genome mining approaches demonstrate that many silent and cryptic biosynthetic gene clusters (BGCs) and many secondary metabolites are produced in very low amounts under laboratory conditions. One strain many compounds (OSMAC), overexpression/deletion of regulatory genes, ribosome engineering, and promoter replacement have been utilized to activate or enhance the production titer of target compounds. Hence, the heterologous expression of BGCs by transferring to a suitable production platform has been successfully employed for the detection, characterization, and yield quantity production of many secondary metabolites. In this review, we introduce the systematic approach for the heterologous production of secondary metabolites from Streptomyces in Streptomyces and other hosts, the genome analysis tools, the host selection, and the development of genetic control elements for heterologous expression and the production of secondary metabolites.

BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters

BACKGROUND

Genome mining for biosynthetic gene clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools are hindered by a bottleneck caused by the expensive network-based approach used to group these BGCs into gene cluster families (GCFs).

RESULTS

Here, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes within 10 days on a typical 36-core CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a "query mode" that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration.

CONCLUSIONS

BiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs. As more genomes are sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-group/bigslice.

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.

Expansion of RiPP biosynthetic space through integration of pan-genomics and machine learning uncovers a novel class of lanthipeptides

Microbial natural products constitute a wide variety of chemical compounds, many which can have antibiotic, antiviral, or anticancer properties that make them interesting for clinical purposes. Natural product classes include polyketides (PKs), nonribosomal peptides (NRPs), and ribosomally synthesized and post-translationally modified peptides (RiPPs). While variants of biosynthetic gene clusters (BGCs) for known classes of natural products are easy to identify in genome sequences, BGCs for new compound classes escape attention. In particular, evidence is accumulating that for RiPPs, subclasses known thus far may only represent the tip of an iceberg. Here, we present decRiPPter (Data-driven Exploratory Class-independent RiPP TrackER), a RiPP genome mining algorithm aimed at the discovery of novel RiPP classes. DecRiPPter combines a Support Vector Machine (SVM) that identifies candidate RiPP precursors with pan-genomic analyses to identify which of these are encoded within operon-like structures that are part of the accessory genome of a genus. Subsequently, it prioritizes such regions based on the presence of new enzymology and based on patterns of gene cluster and precursor peptide conservation across species. We then applied decRiPPter to mine 1,295 Streptomyces genomes, which led to the identification of 42 new candidate RiPP families that could not be found by existing programs. One of these was studied further and elucidated as a representative of a novel subfamily of lanthipeptides, which we designate class V. The 2D structure of the new RiPP, which we name pristinin A3 (1), was solved using nuclear magnetic resonance (NMR), tandem mass spectrometry (MS/MS) data, and chemical labeling. Two previously unidentified modifying enzymes are proposed to create the hallmark lanthionine bridges. Taken together, our work highlights how novel natural product families can be discovered by methods going beyond sequence similarity searches to integrate multiple pathway discovery criteria.

This study shows that decRiPPter, an innovative algorithmic approach using pan-genomics and machine learning, can discover novel types of ribosomally synthesized peptide (RIPP) natural products, including a new class of lanthipeptides.

Cryptic or Silent? The Known Unknowns, Unknown Knowns, and Unknown Unknowns of Secondary Metabolism

Microbial natural products, particularly those produced by filamentous Actinobacteria, underpin the majority of clinically used antibiotics. Unfortunately, only a few new antibiotic classes have been discovered since the 1970s, which has exacerbated fears of a postapocalyptic world in which antibiotics have lost their utility. Excitingly, the genome sequencing revolution painted an entirely new picture, one in which an average strain of filamentous Actinobacteria harbors 20 to 50 natural product biosynthetic pathways but expresses very few of these under laboratory conditions.

Microbial natural products, particularly those produced by filamentous Actinobacteria, underpin the majority of clinically used antibiotics. Unfortunately, only a few new antibiotic classes have been discovered since the 1970s, which has exacerbated fears of a postapocalyptic world in which antibiotics have lost their utility. Excitingly, the genome sequencing revolution painted an entirely new picture, one in which an average strain of filamentous Actinobacteria harbors 20 to 50 natural product biosynthetic pathways but expresses very few of these under laboratory conditions. Development of methodology to access this “hidden” biochemical diversity has the potential to usher in a second Golden Era of antibiotic discovery. The proliferation of genomic data has led to inconsistent use of “cryptic” and “silent” when referring to biosynthetic gene clusters identified by bioinformatic analysis. In this Perspective, we discuss this issue and propose to formalize the use of this terminology.

Making and Breaking Leupeptin Protease Inhibitors in Pathogenic Gammaproteobacteria

Leupeptin is a bacterial small molecule that is used worldwide as a protease inhibitor. However, its biosynthesis and genetic distribution remain unknown. We identified a family of leupeptins in gammaproteobacterial pathogens, including Photorhabdus, Xenorhabdus, and Klebsiella species, amongst others. Through genetic, metabolomic, and heterologous expression analyses, we established their construction by discretely expressed ligases and accessory enzymes. In Photorhabdus species, a hypothetical protein required for colonizing nematode hosts was established as a new class of proteases. This enzyme cleaved the tripeptide aldehyde protease inhibitors, leading to the formation of "pro-pyrazinones" featuring a hetero-tricyclic architecture. In Klebsiella oxytoca, the pathway was enriched in clinical isolates associated with respiratory tract infections. Thus, the bacterial production and proteolytic degradation of leupeptins can be associated with animal colonization phenotypes.

Data-driven rational biosynthesis design: from molecules to cell factories

A proliferation of chemical, reaction and enzyme databases, new computational methods and software tools for data-driven rational biosynthesis design have emerged in recent years. With the coming of the era of big data, particularly in the bio-medical field, data-driven rational biosynthesis design could potentially be useful to construct target-oriented chassis organisms. Engineering the complicated metabolic systems of chassis organisms to biosynthesize target molecules from inexpensive biomass is the main goal of cell factory design. The process of data-driven cell factory design could be divided into several parts: (1) target molecule selection; (2) metabolic reaction and pathway design; (3) prediction of novel enzymes based on protein domain and structure transformation of biosynthetic reactions; (4) construction of large-scale DNA for metabolic pathways; and (5) DNA assembly methods and visualization tools. The construction of a one-stop cell factory system could achieve automated design from the molecule level to the chassis level. In this article, we outline data-driven rational biosynthesis design steps and provide an overview of related tools in individual steps.

Recent development of antiSMASH and other computational approaches to mine secondary metabolite biosynthetic gene clusters

Many drugs are derived from small molecules produced by microorganisms and plants, so-called natural products. Natural products have diverse chemical structures, but the biosynthetic pathways producing those compounds are often organized as biosynthetic gene clusters (BGCs) and follow a highly conserved biosynthetic logic. This allows for the identification of core biosynthetic enzymes using genome mining strategies that are based on the sequence similarity of the involved enzymes/genes. However, mining for a variety of BGCs quickly approaches a complexity level where manual analyses are no longer possible and require the use of automated genome mining pipelines, such as the antiSMASH software. In this review, we discuss the principles underlying the predictions of antiSMASH and other tools and provide practical advice for their application. Furthermore, we discuss important caveats such as rule-based BGC detection, sequence and annotation quality and cluster boundary prediction, which all have to be considered while planning for, performing and analyzing the results of genome mining studies.

Differential transcription of expanded gene families in central carbon metabolism of Streptomyces coelicolor A3(2)

Background

Streptomycete bacteria are prolific producers of specialized metabolites, many of which have clinically relevant bioactivity. A striking feature of their genomes is the expansion of gene families that encode the same enzymatic function. Genes that undergo expansion events, either by horizontal gene transfer or duplication, can have a range of fates: genes can be lost, or they can undergo neo-functionalization or sub-functionalization. To test whether expanded gene families in Streptomyces exhibit differential expression, an RNA-Seq approach was used to examine cultures of wild-type Streptomyces coelicolor grown with either glucose or tween as the sole carbon source.

Results

RNA-Seq analysis showed that two-thirds of genes within expanded gene families show transcriptional differences when strains were grown on tween compared to glucose. In addition, expression of specialized metabolite gene clusters (actinorhodin, isorenieratane, coelichelin and a cryptic NRPS) was also influenced by carbon source.

Conclusions

Expression of genes encoding the same enzymatic function had transcriptional differences when grown on different carbon sources. This transcriptional divergence enables partitioning to function under different physiological conditions. These approaches can inform metabolic engineering of industrial Streptomyces strains and may help develop cultivation conditions to activate the so-called silent biosynthetic gene clusters.

Unraveling the iterative type I polyketide synthases hidden in Streptomyces

Type I polyketide synthases (T1PKSs) are one of the most extensively studied PKSs, which can act either iteratively or via an assembly-line mechanism. Domains in the T1PKSs can readily be predicted by computational tools based on their highly conserved sequences. However, to distinguish between iterative and noniterative at the module level remains an overwhelming challenge, which may account for the seemingly biased distribution of T1PKSs in fungi and bacteria: small iterative monomodular T1PKSs that are responsible for the enormously diverse fungal natural products exist almost exclusively in fungi. Here we report the discovery of iterative T1PKSs that are unexpectedly both abundant and widespread in Streptomyces Seven of 11 systematically selected T1PKS monomodules from monomodular T1PKS biosynthetic gene clusters (BGCs) were experimentally confirmed to be iteratively acting, synthesizing diverse branched/nonbranched linear intermediates, and two of them produced bioactive allenic polyketides and citreodiols as end products, respectively. This study indicates the huge potential of iterative T1PKS BGCs from streptomycetes in the discovery of novel polyketides.

Computational Tools for Discovering and Engineering Natural Product Biosynthetic Pathways

Natural products (NPs), also known as secondary metabolites, are produced in bacteria, fungi, and plants. NPs represent a rich source of antibacterial, antifungal, and anticancer agents. Recent advances in DNA sequencing technologies and bioinformatics unveiled nature's great potential for synthesizing numerous NPs that may confer unprecedented structural and biological features. However, discovering novel bioactive NPs by genome mining remains a challenge. Moreover, even with interesting bioactivity, the low productivity of many NPs significantly limits their practical applications. Here we discuss the progress in developing bioinformatics tools for efficient discovery of bioactive NPs. In addition, we highlight computational methods for optimizing the productivity of NPs of pharmaceutical importance.

A computational framework to explore large-scale biosynthetic diversity

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

Evolution and Diversity of Assembly-Line Polyketide Synthases

Assembly-line polyketide synthases (PKSs) are among the most complex protein machineries known in nature, responsible for the biosynthesis of numerous compounds used in the clinic. Their present-day diversity is the result of an evolutionary path that has involved the emergence of a multimodular architecture and further diversification of assembly-line PKSs. In this review, we provide an overview of previous studies that investigated PKS evolution and propose a model that challenges the currently prevailing view that gene duplication has played a major role in the emergence of multimodularity. We also analyze the ensemble of orphan PKS clusters sequenced so far to evaluate how large the entire diversity of assembly-line PKS clusters and their chemical products could be. Finally, we examine the existing techniques to access the natural PKS diversity in natural and heterologous hosts and describe approaches to further expand this diversity through engineering.

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.

ClusterScan: simple and generalistic identification of genomic clusters

Summary

Studies on gene clusters proved to be an excellent source of information to understand genomes evolution and identifying specific metabolic pathways or gene families. Improvements in sequencing methods have resulted in a large increase of sequenced genomes for which cluster annotation could be performed and standardized. Currently available programs are developed to search for specific cluster types and none of them is suitable for a broad range of user-based choices. We have developed ClusterScan which allows identifying clusters of any kind of feature simply based on their genomic coordinates and user-defined categorical annotations.

Availability and implementation

The tool is written in Python, distributed under the GNU General Public License (GPL) and available on Github at http://bit.ly/ClusterScan or as Docker image at sangeslab/clusterscan: latest. It is supported through a mailing-list on http://bit.ly/ClusterScanSupport.

Supplementary information

Supplementary data are available at Bioinformatics online.