Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM)

Endophytic Streptomyces: an underexplored source with potential for novel natural drug discovery and development

Streptomyces has long been considered as key sources for natural compounds discovery in medicine and agriculture. These compounds have been demonstrated to possess different biological activities, including antibiotic, antifungal, anticancer, and antiviral effects. As a result, new pharmaceuticals and antibiotics have been developed. Nevertheless, there have been only a few novel discoveries of bioactive compounds in the past decades from Streptomyces in natural habitats. There is, therefore, now a renewed search for new Streptomyces species having the potential to produce many compounds from one strain in lesser explored natural habitats that may be helpful in fighting diseases. Consequently, modern genome mining approaches are imperative for discovering structurally novel natural compounds with therapeutic applications from untapped sources. In light of these facts, endophytic Streptomyces from plants may offer new avenues for the discovery of bioactive compounds with distinctive chemical properties and activities. In the present review, we present the progress made in isolating natural compounds from endophytic Streptomyces originating from plants which have remarkable antimicrobial, cytotoxic, and antifungal properties. A different of distinct structural classes of compounds were reported from endophytic Streptomyces, such as indolosequiterpene, macrolides, flavones, peptides, naphthoquinones, and terpenoids. Further, we discussed modern genomics progress in finding biosynthetic gene clusters (BGCs) encoding compounds. Overall, this review might provide valuable insights into the potential for novel drug discovery from untapped endophytic Streptomyces in the future.

A Review on Bacteriocin Extraction Techniques from Lactic Acid Bacteria

Lactic acid bacteria (LAB) are widely known for the production of secondary metabolites such as organic acids and other bioactive compounds such as bacteriocins. Finding a broad application in food and healthcare, bacteriocins have received increased attention due to their inherent antimicrobial properties. However, the extraction of bacteriocins is often plagued with low yields due to the complexity of the extraction processes and the diversity of bacteriocins themselves. Here, we review the current knowledge related to bacteriocin extraction on the different extraction techniques for isolating bacteriocins from LAB. The advantages and disadvantages of each technique will also be critically appraised, taking into account factors such as extraction efficiency, scalability and cost-effectiveness. This review aims to guide researchers and professionals in selecting the most suitable approach for bacteriocin extraction from LAB by illuminating the respective advantages and limitations of various extraction techniques.

Mining metagenomic data to gain a new insight into the gut microbial biosynthetic potential in placental mammals

Mammals host a remarkable diversity and abundance of gut microbes. Biosynthetic gene clusters (BGCs) in microbial genomes encode biologically active chemical products and play an important role in microbe-host interactions. Traditionally, the exploration of gut microbial metabolic functions has relied on the pure culture method. However, given the limited amounts of microbes being cultivated, insights into the metabolism of gut microbes in mammals continued to be very limited. In this study, we adopted a computational pipeline for mining the metagenomic data (named taxonomy-guided identification of biosynthetic gene clusters, TaxiBGC) to identify experimentally verified BGCs in 373 metagenomes across 53 mammalian species in an unbiased manner. We demonstrated that polyketides (PKs) and nonribosomal peptides (NRPs) are representative of mammals, and the products derived from them were associated with cell-cell communication and resistance to inflammation. Large carnivores had the highest number of BGCs, followed by large herbivores and small mammals. We also observed that the large mammals had more common BGCs that aid in the biosynthesis of a variety of natural products. However, small mammals not only had fewer BGCs but were also unique to each species. Our results provide novel insights into the mining of metagenomic data sets to identify active BGCs and their products across mammals.IMPORTANCEThe gut microbes host numerous biosynthetic gene clusters (BGCs) that biosynthesize natural products and impact the host's physiology. Historically, our understanding of BGCs in mammalian gut microbes was largely based on studies on cultured isolates; however, only a small fraction of mammal-associated microbes have been investigated. The biochemical diversity of the mammalian gut microbiota is poorly understood. Metagenomic sequencing contains data from a vast number of organisms and provides information on the total gene content of communities. Unfortunately, the existing BGC prediction tools are designed for individual microbial genomes. Recently, a BGC prediction tool called the taxonomy-guided identification of biosynthetic gene clusters (TaxiBGC) that directly mine the metagenome was developed. To gain new insights into the microbial metabolism, we used TaxiBGC to predict BGCs from 373 metagenomes across 53 mammalian species representing seven orders. Our findings elucidate the functional activities of complex microbial communities in the gut.

CSEL-BGC: A Bioinformatics Framework Integrating Machine Learning for Defining the Biosynthetic Evolutionary Landscape of Uncharacterized Antibacterial Natural Products

The sluggish pace of new antibacterial drug development reflects a vulnerability in the face of the current severe threat posed by bacterial resistance. Microbial natural products (NPs), as a reservoir of immense chemical potential, have emerged as the most promising avenue for the discovery of next generation antibacterial agent. Directly accessing the antibacterial activity of potential products derived from biosynthetic gene clusters (BGCs) would significantly expedite the process. To tackle this issue, we propose a CSEL-BGC framework that integrates machine learning (ML) techniques. This framework involves the development of a novel cascade-stacking ensemble learning (CSEL) model and the establishment of a groundbreaking model evaluation system. Based on this framework, we predict 6,666 BGCs with antibacterial activity from 3,468 complete bacterial genomes and elucidate a biosynthetic evolutionary landscape to reveal their antibacterial potential. This provides crucial insights for interpretating the synthesis and secretion mechanisms of unknown NPs.

Genomic Insights into Pseudomonas protegens E1BL2 from Giant Jala Maize: A Novel Bioresource for Sustainable Agriculture and Efficient Management of Fungal Phytopathogens

The relationships between plants and bacteria are essential in agroecosystems and bioinoculant development. The leaf endophytic Pseudomonas protegens E1BL2 was previously isolated from giant Jala maize, which is a native Zea mays landrace of Nayarit, Mexico. Using different Mexican maize landraces, this work evaluated the strain's plant growth promotion and biocontrol against eight phytopathogenic fungi in vitro and greenhouse conditions. Also, a plant field trial was conducted on irrigated fields using the hybrid maize Supremo. The grain productivity in this assay increased compared with the control treatment. The genome analysis of P. protegens E1BL2 showed putative genes involved in metabolite synthesis that facilitated its beneficial roles in plant health and environmental adaptation (bdhA, acoR, trpE, speE, potA); siderophores (ptaA, pchC); and extracellular enzymes relevant for PGPB mechanisms (cel3, chi14), protection against oxidative stress (hscA, htpG), nitrogen metabolism (nirD, nit1, hmpA), inductors of plant-induced systemic resistance (ISR) (flaA, flaG, rffA, rfaP), fungal biocontrol (phlD, prtD, prnD, hcnA-1), pest control (vgrG-1, higB-2, aprE, pslA, ppkA), and the establishment of plant-bacteria symbiosis (pgaA, pgaB, pgaC, exbD). Our findings suggest that P. protegens E1BL2 significantly promotes maize growth and offers biocontrol benefits, which highlights its potential as a bioinoculant.

Creating and leveraging bespoke large-scale knowledge graphs for comparative genomics and multi-omics drug discovery with SocialGene

The rapid expansion of multi-omics data has transformed biological research, offering unprecedented opportunities to explore complex genomic relationships across diverse organisms. However, the vast volume and heterogeneity of these datasets presents significant challenges for analyses. Here we introduce SocialGene, a comprehensive software suite designed to collect, analyze, and organize multi-omics data into structured knowledge graphs, with the ability to handle small projects to repository-scale analyses. Originally developed to enhance genome mining for natural product drug discovery, SocialGene has been effective across various applications, including functional genomics, evolutionary studies, and systems biology. SocialGene's concerted Python and Nextflow libraries streamline data ingestion, manipulation, aggregation, and analysis, culminating in a custom Neo4j database. The software not only facilitates the exploration of genomic synteny but also provides a foundational knowledge graph supporting the integration of additional diverse datasets and the development of advanced search engines and analyses. This manuscript introduces some of SocialGene's capabilities through brief case studies including targeted genome mining for drug discovery, accelerated searches for similar and distantly related biosynthetic gene clusters in biobank-available organisms, integration of chemical and analytical data, and more. SocialGene is free, open-source, MIT-licensed, designed for adaptability and extension, and available from github.com/socialgene.

Investigating Polyextremophilic Bacteria in Al Wahbah Crater, Saudi Arabia: A Terrestrial Model for Life on Saturn's Moon Enceladus

The study of extremophilic microorganisms has sparked interest in understanding extraterrestrial microbial life. Such organisms are fundamental for investigating life forms on Saturn's icy moons, such as Enceladus, which is characterized by potentially habitable saline and alkaline niches. Our study focused on the salt-alkaline soil of the Al Wahbah crater in Saudi Arabia, where we identified microorganisms that could be used as biological models to understand potential life on Enceladus. The search involved isolating 48 bacterial strains, sequencing the genomes of two thermo-haloalkaliphilic strains, and characterizing them for astrobiological application. A deeper understanding of the genetic composition and functional capabilities of the two novel strains of Halalkalibacterium halodurans provided valuable insights into their survival strategies and the presence of coding genes and pathways related to adaptations to environmental stressors. We also used mass spectrometry with a molecular network approach, highlighting various classes of molecules, such as phospholipids and nonproteinogenic amino acids, as potential biosignatures. These are essential features for understanding life's adaptability under extreme conditions and could be used as targets for biosignatures in upcoming missions exploring Enceladus' orbit. Furthermore, our study reinforces the need to look at new extreme environments on Earth that might contribute to the astrobiology field.

Metabolomics approach to understand molecular mechanisms involved in fungal pathogen-citrus pathosystems

Citrus is a crucial crop with a significant economic impact globally. However, postharvest decay caused by fungal pathogens poses a considerable threat, leading to substantial financial losses. Penicillium digitatum, Penicillium italicum, Geotrichum citri-aurantii and Phyllosticta citricarpa are the main fungal pathogens, causing green mold, blue mold, sour rot and citrus black spot diseases, respectively. The use of chemical fungicides as a control strategy in citrus raises concerns about food and environmental safety. Therefore, understanding the molecular basis of host-pathogen interactions is essential to find safer alternatives. This review highlights the potential of the metabolomics approach in the search for bioactive compounds involved in the pathogen-citrus interaction, and how the integration of metabolomics and genomics contributes to the understanding of secondary metabolites associated with fungal virulence and the fungal infection mechanisms. Our goal is to provide a pipeline combining metabolomics and genomics that can effectively guide researchers to perform studies aiming to contribute to the understanding of the fundamental chemical and biochemical aspects of pathogen-host interactions, in order to effectively develop new alternatives for fungal diseases in citrus cultivation. We intend to inspire the scientific community to question unexplored biological systems, and to employ diverse analytical approaches and metabolomics techniques to address outstanding questions about the non-studied pathosystems from a chemical biology perspective.

Genome analysis of a coral-associated bacterial consortium highlights complementary hydrocarbon degradation ability and other beneficial mechanisms for the host

Here we report the oil degradation genetic potential of six oil-degrading bacteria (ODB), previously used as a bioremediation consortium, isolated from the hydrocoral Millepora alcicornis and seawater. The strains were identified as Halomonas sp. (LC_1), Cobetia sp. (LC_6), Pseudoalteromonas shioyasakiensis (LC_2), Halopseudomonas aestusnigri (LC_3), Shewanella algae (LC_4), and Brucella intermedia (LC_5). The taxonomic identification differed from that of the original paper when we used whole genome gene markers instead of just 16S rRNA gene. Genes responsible for the degradation of aromatic hydrocarbons and n-alkanes were found in all genomes, although different (and complementary) steps of the metabolic pathways were unique to each strain. Genes for naphthalene and toluene degradation were found in various strains. We annotated quinate degradation genes in LC_6, while LC_3 and LC_5 presented genes for biosurfactant and rhamnolipid biosynthesis. We also annotated genes related to beneficial mechanisms for corals, such as genes involved in nitrogen and DMSP metabolism, cobalamin biosynthesis and antimicrobial compounds production. Our findings reinforce the importance of using bacterial consortia for bioremediation approaches instead of single strains, due to their complementary genomic arsenals. We also propose a genome-based framework to select complementary ODB that can provide additional benefits to coral health.

Recent Approaches for Downplaying Antibiotic Resistance: Molecular Mechanisms

Antimicrobial resistance (AMR) is a ubiquitous public health menace. AMR emergence causes complications in treating infections contributing to an upsurge in the mortality rate. The epidemic of AMR in sync with a high utilization rate of antimicrobial drugs signifies an alarming situation for the fleet recovery of both animals and humans. The emergence of resistant species calls for new treatments and therapeutics. Current records propose that health drug dependency, veterinary medicine, agricultural application, and vaccination reluctance are the primary etymology of AMR gene emergence and spread. Recently, several encouraging avenues have been presented to contest resistance, such as antivirulent therapy, passive immunization, antimicrobial peptides, vaccines, phage therapy, and botanical and liposomal nanoparticles. Most of these therapies are used as cutting-edge methodologies to downplay antibacterial drugs to subdue the resistance pressure, which is a featured motive of discussion in this review article. AMR can fade away through the potential use of current cutting-edge therapeutics, advancement in antimicrobial susceptibility testing, new diagnostic testing, prompt clinical response, and probing of new pharmacodynamic properties of antimicrobials. It also needs to promote future research on contemporary methods to maintain host homeostasis after infections caused by AMR. Referable to the microbial ability to break resistance, there is a great ultimatum for using not only appropriate and advanced antimicrobial drugs but also other neoteric diverse cutting-edge therapeutics.

Unveiling Microbial Chemical Interactions Based on Metabolomics Approaches

Microorganisms are ubiquitous in diverse habitats and studying their chemical interactions with the environment and comprehend its complex relations with both hosts and environment, are crucial for the development of strategies to control microbial diseases. This chapter discusses the importance of studying microorganisms with agricultural benefits, using specialized metabolites as examples. Herein we highlight the challenges and opportunities in utilizing microorganisms as alternatives to synthetic pesticides and fertilizers in agriculture. Genome-guided investigations and improved analytical methodologies are necessary to characterize diverse and complex biomolecules produced by microorganisms. Predicting and isolating bioproducts based on genetic information have become a focus for researchers, aided by tools like antiSMASH, BiG-SCAPE, PRISM, and others. However, translating genomic data into practical applications can be complex. Therefore, integrating genomics, transcriptomics, and metabolomics enhances chemical characterization, aiding in discovering new metabolic pathways and specialized metabolites. Additionally, elicitation is one promising strategy to enhance beneficial metabolite production. Finally, identify and characterize microbial secondary metabolites remain challenging due to their low production, complex chemical structure characterization and different environmental factors necessary for metabolite in vitro production.

TaxiBGC: a Taxonomy-Guided Approach for Profiling Experimentally Characterized Microbial Biosynthetic Gene Clusters and Secondary Metabolite Production Potential in Metagenomes

Biosynthetic gene clusters (BGCs) in microbial genomes encode bioactive secondary metabolites (SMs), which can play important roles in microbe-microbe and host-microbe interactions. Given the biological significance of SMs and the current profound interest in the metabolic functions of microbiomes, the unbiased identification of BGCs from high-throughput metagenomic data could offer novel insights into the complex chemical ecology of microbial communities. Currently available tools for predicting BGCs from shotgun metagenomes have several limitations, including the need for computationally demanding read assembly, predicting a narrow breadth of BGC classes, and not providing the SM product. To overcome these limitations, we developed taxonomy-guided identification of biosynthetic gene clusters (TaxiBGC), a command-line tool for predicting experimentally characterized BGCs (and inferring their known SMs) in metagenomes by first pinpointing the microbial species likely to harbor them. We benchmarked TaxiBGC on various simulated metagenomes, showing that our taxonomy-guided approach could predict BGCs with much-improved performance (mean F1 score, 0.56; mean PPV score, 0.80) compared with directly identifying BGCs by mapping sequencing reads onto the BGC genes (mean F1 score, 0.49; mean PPV score, 0.41). Next, by applying TaxiBGC on 2,650 metagenomes from the Human Microbiome Project and various case-control gut microbiome studies, we were able to associate BGCs (and their SMs) with different human body sites and with multiple diseases, including Crohn's disease and liver cirrhosis. In all, TaxiBGC provides an in silico platform to predict experimentally characterized BGCs and their SM production potential in metagenomic data while demonstrating important advantages over existing techniques. IMPORTANCE Currently available bioinformatics tools to identify BGCs from metagenomic sequencing data are limited in their predictive capability or ease of use to even computationally oriented researchers. We present an automated computational pipeline called TaxiBGC, which predicts experimentally characterized BGCs (and infers their known SMs) in shotgun metagenomes by first considering the microbial species source. Through rigorous benchmarking techniques on simulated metagenomes, we show that TaxiBGC provides a significant advantage over existing methods. When demonstrating TaxiBGC on thousands of human microbiome samples, we associate BGCs encoding bacteriocins with different human body sites and diseases, thereby elucidating a possible novel role of this antibiotic class in maintaining the stability of microbial ecosystems throughout the human body. Furthermore, we report for the first time gut microbial BGC associations shared among multiple pathologies. Ultimately, we expect our tool to facilitate future investigations into the chemical ecology of microbial communities across diverse niches and pathologies.

Navigating and expanding the roadmap of natural product genome mining tools

Natural products are structurally highly diverse and exhibit a wide array of biological activities. As a result, they serve as an important source of new drug leads. Traditionally, natural products have been discovered by bioactivity-guided fractionation. The advent of genome sequencing technology has resulted in the introduction of an alternative approach towards novel natural product scaffolds: Genome mining. Genome mining is an in-silico natural product discovery strategy in which sequenced genomes are analyzed for the potential of the associated organism to produce natural products. Seemingly universal biosynthetic principles have been deciphered for most natural product classes that are used to detect natural product biosynthetic gene clusters using pathway-encoded conserved key enzymes, domains, or motifs as bait. Several generations of highly sophisticated tools have been developed for the biosynthetic rule-based identification of natural product gene clusters. Apart from these hard-coded algorithms, multiple tools that use machine learning-based approaches have been designed to complement the existing genome mining tool set and focus on natural product gene clusters that lack genes with conserved signature sequences. In this perspective, we take a closer look at state-of-the-art genome mining tools that are based on either hard-coded rules or machine learning algorithms, with an emphasis on the confidence of their predictions and potential to identify non-canonical natural product biosynthetic gene clusters. We highlight the genome mining pipelines' current strengths and limitations by contrasting their advantages and disadvantages. Moreover, we introduce two indirect biosynthetic gene cluster identification strategies that complement current workflows. The combination of all genome mining approaches will pave the way towards a more comprehensive understanding of the full biosynthetic repertoire encoded in microbial genome sequences.

Genome Mining as an Alternative Way for Screening the Marine Organisms for Their Potential to Produce UV-Absorbing Mycosporine-like Amino Acid

Mycosporine-like amino acids (MAAs) are small molecules with robust ultraviolet (UV)-absorbing capacities and a huge potential to be used as an environmentally friendly natural sunscreen. MAAs, temperature, and light-stable compounds demonstrate powerful photoprotective capacities and the ability to capture light in the UV-A and UV-B ranges without the production of damaging free radicals. The biotechnological uses of these secondary metabolites have been often limited by the small quantities restored from natural resources, variation in MAA expression profiles, and limited success in heterologous expression systems. Overcoming these obstacles requires a better understanding of MAA biosynthesis and its regulatory processes. MAAs are produced to a certain extent via a four-enzyme pathway, including genes encoding enzymes dehydroquinate synthase, enzyme O-methyltransferase, adenosine triphosphate grasp, and a nonribosomal peptide synthetase. However, there are substantial genetic discrepancies in the MAA genetic pathway in different species, suggesting further complexity of this pathway that is yet to be fully explored. In recent years, the application of genome-mining approaches allowed the identification of biosynthetic gene clusters (BGCs) that resulted in the discovery of many new compounds from unconventional sources. This review explores the use of novel genomics tools for linking BGCs and secondary metabolites based on the available omics data, including MAAs, and evaluates the potential of using novel genome-mining tools to reveal a cryptic potential for new bioproduct screening approaches and unrevealing new MAA producers.

Proteomining-Based Elucidation of Natural Product Biosynthetic Pathways in Streptomyces

The genus Streptomyces is known to harbor numerous biosynthetic gene clusters (BGCs) of potential utility in synthetic biology applications. However, it is often difficult to link uncharacterized BGCs with the secondary metabolites they produce. Proteomining refers to the strategy of identifying active BGCs by correlating changes in protein expression with the production of secondary metabolites of interest. In this study, we devised a shotgun proteomics-based workflow to identify active BGCs during fermentation when a variety of compounds are being produced. Mycelia harvested during the non-producing growth phase served as the background. Proteins that were differentially expressed were clustered based on the proximity of the genes in the genome to highlight active BGCs systematically from label-free quantitative proteomics data. Our software tool is easy-to-use and requires only 1 point of comparison where natural product biosynthesis was significantly different. We tested our proteomining clustering method on three Streptomyces species producing different compounds. In Streptomyces coelicolor A3(2), we detected the BGCs of calcium-dependent antibiotic, actinorhodin, undecylprodigiosin, and coelimycin P1. In Streptomyces chrestomyceticus BCC24770, 7 BGCs were identified. Among them, we independently re-discovered the type II PKS for albofungin production previously identified by genome mining and tedious heterologous expression experiments. In Streptomyces tenebrarius, 5 BGCs were detected, including the known apramycin and tobramycin BGC as well as a newly discovered caerulomycin A BGC in this species. The production of caerulomycin A was confirmed by LC-MS and the inactivation of the caerulomycin A BGC surprisingly had a significant impact on the secondary metabolite regulation of S. tenebrarius. In conclusion, we developed an unbiased, high throughput proteomics-based method to complement genome mining methods for the identification of biosynthetic pathways in Streptomyces sp.

Bioprospecting the microbiome of Red Sea Atlantis II brine pool for peptidases and biosynthetic genes with promising antibacterial activity

Background

The search for novel antimicrobial agents is crucial as antibiotic-resistant pathogens continue to emerge, rendering the available antibiotics no longer effective. Likewise, new anti-cancer drugs are needed to combat the emergence of multi-drug resistant tumors. Marine environments are wealthy sources for natural products. Additionally, extreme marine environments are interesting niches to search for bioactive natural compounds. In the current study, a fosmid library of metagenomic DNA isolated from Atlantis II Deep Lower Convective Layer (ATII LCL), was functionally screened for antibacterial activity as well as anticancer effects.

Results

Two clones exhibited antibacterial effects against the marine Bacillus Cc6 strain, namely clones 102-5A and 88-1G and they were further tested against eleven other challenging strains, including six safe relatives of ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter  spp.), a safe relative to Mycobacterium tuberculosis and four resistant clinical isolates. Clone 88-1G resulted in clear zones of inhibition against eight bacterial strains, while clone 102-5A resulted in zones of inhibition against five bacterial strains. The whole cell lysates of clone 88-1G showed 15% inhibition of Mtb ClpP protease -Mycobacterium tuberculosis drug target-, while whole cell lysates of clone 102-5A showed 19% inhibition of Mtb ClpP protease. Whole cell lysates from the selected clones exhibited anticancer effects against MCF-7 breast cancer cells (cell viability at 50% v/v was 46.2% ± 9.9 for 88-1G clone and 38% ± 7 for 102-5A clone), U2OS osteosarcoma cells (cell viability at 50% v/v was 64.6% ± 12.3 for 88-1G clone and 28.3% ± 1.7 for 102-5A clone) and 1BR hTERT human fibroblast cells (cell viability at 50% v/v was 74.4% ± 5.6 for 88-1G clone and 57.6% ± 8.9 for 102-5A clone). Sequencing of 102-5A and 88-1G clones, and further annotation detected putative proteases and putative biosynthetic genes in clones 102-5A and 88-1G, respectively.

Conclusions

The ATII LCL metagenome hosts putative peptidases and biosynthetic genes that confer antibiotic and anti-cancer effects. The tested clones exhibited promising antibacterial activities against safe relative strains to ESKAPE pathogens and Mycobacterium tuberculosis. Thus, searching the microbial dark matter of extreme environments is a promising approach to identify new molecules with pharmaceutical potential use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01835-z.

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.

Resistance-Guided Mining of Bacterial Genotoxins Defines a Family of DNA Glycosylases

Unique DNA repair enzymes that provide self-resistance against therapeutically important, genotoxic natural products have been discovered in bacterial biosynthetic gene clusters (BGCs). Among these, the DNA glycosylase AlkZ is essential for azinomycin B production and belongs to the HTH_42 superfamily of uncharacterized proteins. Despite their widespread existence in antibiotic producers and pathogens, the roles of these proteins in production of other natural products are unknown. Here, we determine the evolutionary relationship and genomic distribution of all HTH_42 proteins from Streptomyces and use a resistance-based genome mining approach to identify homologs associated with known and uncharacterized BGCs. We find that AlkZ-like (AZL) proteins constitute one distinct HTH_42 subfamily and are highly enriched in BGCs and variable in sequence, suggesting each has evolved to protect against a specific secondary metabolite. As a validation of the approach, we show that the AZL protein, HedH4, associated with biosynthesis of the alkylating agent hedamycin, excises hedamycin-DNA adducts with exquisite specificity and provides resistance to the natural product in cells. We also identify a second, phylogenetically and functionally distinct subfamily whose proteins are never associated with BGCs, are highly conserved with respect to sequence and genomic neighborhood, and repair DNA lesions not associated with a particular natural product. This work delineates two related families of DNA repair enzymes-one specific for complex alkyl-DNA lesions and involved in self-resistance to antimicrobials and the other likely involved in protection against an array of genotoxins-and provides a framework for targeted discovery of new genotoxic compounds with therapeutic potential. IMPORTANCE Bacteria are rich sources of secondary metabolites that include DNA-damaging genotoxins with antitumor/antibiotic properties. Although Streptomyces produce a diverse number of therapeutic genotoxins, efforts toward targeted discovery of biosynthetic gene clusters (BGCs) producing DNA-damaging agents is lacking. Moreover, work on toxin-resistance genes has lagged behind our understanding of those involved in natural product synthesis. Here, we identified over 70 uncharacterized BGCs producing potentially novel genotoxins through resistance-based genome mining using the azinomycin B-resistance DNA glycosylase AlkZ. We validate our analysis by characterizing the enzymatic activity and cellular resistance of one AlkZ ortholog in the BGC of hedamycin, a potent DNA alkylating agent. Moreover, we uncover a second, phylogenetically distinct family of proteins related to Escherichia coli YcaQ, a DNA glycosylase capable of unhooking interstrand DNA cross-links, which differs from the AlkZ-like family in sequence, genomic location, proximity to BGCs, and substrate specificity. This work defines two families of DNA glycosylase for specialized repair of complex genotoxic natural products and generalized repair of a broad range of alkyl-DNA adducts and provides a framework for targeted discovery of new compounds with therapeutic potential.

An inventory of early branch points in microbial phosphonate biosynthesis

Microbial phosphonate biosynthetic machinery has been identified in ~5 % of bacterial genomes and encodes natural products like fosfomycin as well as cell surface decorations. Almost all biological phosphonates originate from the rearrangement of phosphoenolpyruvate (PEP) to phosphonopyruvate (PnPy) catalysed by PEP mutase (Ppm), and PnPy is often converted to phosphonoacetaldehyde (PnAA) by PnPy decarboxylase (Ppd). Seven enzymes are known or likely to act on either PnPy or PnAA as early branch points en route to diverse biosynthetic outcomes, and these enzymes may be broadly classified into three reaction types: hydride transfer, aminotransfer, and carbon-carbon bond formation. However, the relative abundance of these branch points in microbial phosphonate biosynthesis is unknown. Also unknown is the proportion of ppm-containing gene neighbourhoods encoding new branch point enzymes and potentially novel phosphonates. In this study we computationally sorted 434 ppm-containing gene neighbourhoods based on these seven branch point enzymes. Unsurprisingly, the majority (56 %) of these pathways encode for production of the common naturally occurring compound 2-aminoethylphosphonate (AEP) or a hydroxylated derivative. The next most abundant genetically encoded intermediates were phosphonoalanine (PnAla, 9.2 %), 2-hydroxyethylphosphonate (HEP, 8.5 %), and phosphonoacetate (PnAc, 6 %). Significantly, about 13 % of the gene neighbourhoods could not be assigned to any of the seven branch points and may encode novel phosphonates. Sequence similarity network analysis revealed families of unusual gene neighbourhoods including possible production of phosphonoacrylate and phosphonofructose, the apparent biosynthetic use of the C-P lyase operon, and a virus-encoded phosphonate. Overall, these results highlight the utility of branch point inventories to identify novel gene neighbourhoods and guide future phosphonate discovery efforts.

Natural-Product-Based Solutions for Tropical Infectious Diseases

About half of the world's population and 80% of the world's biodiversity can be found in the tropics. Many diseases are specific to the tropics, with at least 41 diseases caused by endemic bacteria, viruses, parasites, and fungi. Such diseases are of increasing concern, as the geographic range of tropical diseases is expanding due to climate change, urbanization, change in agricultural practices, deforestation, and loss of biodiversity. While traditional medicines have been used for centuries in the treatment of tropical diseases, the active natural compounds within these medicines remain largely unknown. In this review, we describe infectious diseases specific to the tropics, including their causative pathogens, modes of transmission, recent major outbreaks, and geographic locations. We further review current treatments for these tropical diseases, carefully consider the biodiscovery potential of the tropical biome, and discuss a range of technologies being used for drug development from natural resources. We provide a list of natural products with antimicrobial activity, detailing the source organisms and their effectiveness as treatment. We discuss how technological advancements, such as next-generation sequencing, are driving high-throughput natural product screening pipelines to identify compounds with therapeutic properties. This review demonstrates the impact natural products from the vast tropical biome have in the treatment of tropical infectious diseases and how high-throughput technical capacity will accelerate this discovery process.

Bacillus velezensis: A Treasure House of Bioactive Compounds of Medicinal, Biocontrol and Environmental Importance

Bacillus velezensis gram-positive bacterium, is frequently isolated from diverse niches mainly soil, water, plant roots, and fermented foods. B. velezensis is ubiquitous, non-pathogenic and endospore forming. Being frequently isolated from diverse plant holobionts it is considered host adapted microorganism and recognized of high economic importance given its ability to promote plant growth under diverse biotic and abiotic stress conditions. Additionally, the species suppress many plant diseases, including bacterial, oomycete, and fungal diseases. It is also able after plant host root colonization to induce unique physiological situation of host plant called primed state. Primed host plants are able to respond more rapidly and/or effectively to biotic or abiotic stress. Moreover, B. velezenis have the ability to resist diverse environmental stresses and help host plants to cope with, including metal and xenobiotic stresses. Within species B. velezensis strains have unique abilities allowing them to adopt different life styles. Strain level abilities knowledge is warranted and could be inferred using the ever-expanding new genomes list available in genomes databases. Pangenome analysis and subsequent identification of core, accessory and unique genomes is actually of paramount importance to decipher species full metabolic capacities and fitness across diverse environmental conditions shaping its life style. Despite the crucial importance of the pan genome, its assessment among large number of strains remains sparse and systematic studies still needed. Extensive knowledge of the pan genome is needed to translate genome sequencing efforts into developing more efficient biocontrol agents and bio-fertilizers. In this study, a genome survey of B. velezensis allowed us to (a) highlight B. velezensis species boundaries and show that Bacillus suffers taxonomic imprecision that blurs the debate over species pangenome; (b) identify drivers of their successful acquisition of specific life styles and colonization of new niches; (c) describe strategies they use to promote plant growth and development; (d) reveal the unlocked strain specific orphan secondary metabolite gene clusters (biosynthetic clusters with corresponding metabolites unknown) that product identification is still awaiting to amend our knowledge of their putative role in suppression of pathogens and plant growth promotion, and (e) to describe a dynamic pangenome with a secondary metabolite rich accessory genome.

Synthetic Natural Product Inspired Cyclic Peptides

Natural products are a bountiful source of bioactive molecules. Unfortunately, discovery of novel bioactive natural products is challenging due to cryptic biosynthetic gene clusters, low titers, and arduous purifications. Herein, we describe SNaPP (Synthetic Natural Product Inspired Cyclic Peptides), a method for identifying NP-inspired bioactive peptides. SNaPP expedites bioactive molecule discovery by combining bioinformatics predictions of nonribosomal peptide synthetases with chemical synthesis of the predicted natural products (pNPs). SNaPP utilizes a recently discovered cyclase, the penicillin binding protein-like cyclase, as the lynchpin for the development of a library of head-to-tail cyclic peptide pNPs. Analysis of 500 biosynthetic gene clusters allowed for identification of 131 novel pNPs. Fifty-one diverse pNPs were synthesized using solid phase peptide synthesis and solution-phase cyclization. Antibacterial testing revealed 14 pNPs with antibiotic activity, including activity against multidrug-resistant Gram-negative bacteria. Overall, SNaPP demonstrates the power of combining bioinformatics predictions with chemical synthesis to accelerate the discovery of bioactive molecules.

Small Molecule Metabolites at the Host-Microbiota Interface

The trillions of bacteria that constitutively colonize the human gut collectively generate thousands of unique small molecules. These microbial metabolites can accumulate both locally and systemically and potentially influence nearly all aspects of mammalian biology, including immunity, metabolism, and even mood and behavior. In this review, we briefly summarize recent work identifying bioactive microbiota metabolites, the means through which they are synthesized, and their effects on host physiology. Rather than offering an exhaustive list of all known bioactive microbial small molecules, we select a few examples from each key class of metabolites to illustrate the diverse impacts of microbiota-derived compounds on the host. In addition, we attempt to address the microbial logic behind specific biotransformations. Finally, we outline current and emerging strategies for identifying previously undiscovered bioactive microbiota metabolites that may shape human health and disease.

A Leap Forward Towards Unraveling Newer Anti-infective Agents from an Unconventional Source: a Draft Genome Sequence Illuminating the Future Promise of Marine Heterotrophic Bacillus sp. Against Drug-Resistant Pathogens

During the previous decade, genome-built researches on marine heterotrophic microorganisms displayed the chemical heterogeneity of natural product resources coupled with the efficacies of harnessing the genetic divergence in various strains. Herein, we describe the whole genome data of heterotrophic Bacillus amyloliquefaciens MB6 (MTCC 12,716), isolated from a marine macroalga Hypnea valentiae, a 4,107,511-bp circular chromosome comprising 186 contigs, with 4154 protein-coding DNA sequences and a coding ratio of 86%. Simultaneously, bioactivity-guided purification of the bacterial extract resulted in six polyketide classes of compounds with promising antibacterial activity. Draft genome sequence of B. amyloliquefaciens MB6 unveiled biosynthetic gene clusters (BGCs) engaged in the biosynthesis of polyketide-originated macrolactones with prospective antagonistic activity (MIC ≤ 5 µg/mL) against nosocomial pathogens. Genome analysis manifested 34 putative BGCs necessitated to synthesize biologically active polyketide-originated frameworks or their derivatives. These results provide insights into the genetic basis of heterotrophic B. amyloliquefaciens MTCC 12,716 as a prospective lead for biotechnological and pharmaceutical applications.

Identification of Novel Rotihibin Analogues in Streptomyces scabies, Including Discovery of Its Biosynthetic Gene Cluster

Streptomyces scabies is a phytopathogen associated with common scab disease. This is mainly attributed to its ability to produce the phytotoxin thaxtomin A, the biosynthesis of which is triggered by cellobiose. During a survey of other metabolites released in the presence of cellobiose, we discovered additional compounds in the thaxtomin-containing extract from Streptomyces scabies. Structural analysis by mass spectrometry (MS) and nuclear magnetic resonance (NMR) revealed that these compounds are amino acid sequence variants of the TOR (target of rapamycin) kinase (TORK) pathway-inhibitory lipopeptide rotihibin A, and the main compounds were named rotihibins C and D. In contrast to thaxtomin, the production of rotihibins C and D was also elicited in the presence of glucose, indicating different regulation of their biosynthesis. Through a combination of shotgun and targeted proteomics, the putative rotihibin biosynthetic gene cluster rth was identified in the publicly available genome of S. scabies 87-22. This cluster spans 33 kbp and encodes 2 different nonribosomal peptide synthetases (NRPSs) and 12 additional enzymes. Homologous rth biosynthetic gene clusters were found in other publicly available and complete actinomycete genomes. Rotihibins C and D display herbicidal activity against Lemna minor and Arabidopsis thaliana at low concentrations, shown by monitoring the effects on growth and the maximal photochemistry efficiency of photosystem II. IMPORTANCE Rotihibins A and B are plant growth inhibitors acting on the TORK pathway. We report the isolation and characterization of new sequence analogues of rotihibin from Streptomyces scabies, a major cause of common scab in potato and other tuber and root vegetables. By combining proteomics data with genomic analysis, we found a cryptic biosynthetic gene cluster coding for enzyme machinery capable of rotihibin production. This work may lead to the biotechnological production of variants of this lipopeptide to investigate the exact mechanism by which it can target the plant TORK pathway in Arabidopsis thaliana. In addition, bioinformatics revealed the existence of other variants in plant-associated Streptomyces strains, both pathogenic and nonpathogenic species, raising new questions about the actual function of this lipopeptide. The discovery of a module in the nonribosomal peptide synthetase (NRPS) that incorporates the unusual citrulline residue may improve the prediction of peptides encoded by cryptic NRPS gene clusters.

Integrating perspectives in actinomycete research: an ActinoBase review of 2020-21

Last year ActinoBase, a Wiki-style initiative supported by the UK Microbiology Society, published a review highlighting the research of particular interest to the actinomycete community. Here, we present the second ActinoBase review showcasing selected reports published in 2020 and early 2021, integrating perspectives in the actinomycete field. Actinomycetes are well-known for their unsurpassed ability to produce specialised metabolites, of which many are used as therapeutic agents with antibacterial, antifungal, or immunosuppressive activities. Much research is carried out to understand the purpose of these metabolites in the environment, either within communities or in host interactions. Moreover, many efforts have been placed in developing computational tools to handle big data, simplify experimental design, and find new biosynthetic gene cluster prioritisation strategies. Alongside, synthetic biology has provided advances in tools to elucidate the biosynthesis of these metabolites. Additionally, there are still mysteries to be uncovered in understanding the fundamentals of filamentous actinomycetes' developmental cycle and regulation of their metabolism. This review focuses on research using integrative methodologies and approaches to understand the bigger picture of actinomycete biology, covering four research areas: i) technology and methodology; ii) specialised metabolites; iii) development and regulation; and iv) ecology and host interactions.

Heterologous production of cyanobacterial compounds

Cyanobacteria produce a plethora of compounds with unique chemical structures and diverse biological activities. Importantly, the increasing availability of cyanobacterial genome sequences and the rapid development of bioinformatics tools have unraveled the tremendous potential of cyanobacteria in producing new natural products. However, the discovery of these compounds based on cyanobacterial genomes has progressed slowly as the majority of their corresponding biosynthetic gene clusters (BGCs) are silent. In addition, cyanobacterial strains are often slow-growing, difficult for genetic engineering, or cannot be cultivated yet, limiting the use of host genetic engineering approaches for discovery. On the other hand, genetically tractable hosts such as Escherichia coli, Actinobacteria, and yeast have been developed for the heterologous expression of cyanobacterial BGCs. More recently, there have been increased interests in developing model cyanobacterial strains as heterologous production platforms. Herein, we present recent advances in the heterologous production of cyanobacterial compounds in both cyanobacterial and noncyanobacterial hosts. Emerging strategies for BGC assembly, host engineering, and optimization of BGC expression are included for fostering the broader applications of synthetic biology tools in the discovery of new cyanobacterial natural products.

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.

Brevibacillin 2V, a Novel Antimicrobial Lipopeptide With an Exceptionally Low Hemolytic Activity

Bacterial non-ribosomally produced peptides (NRPs) form a rich source of antibiotics, including more than 20 of these antibiotics that are used in the clinic, such as penicillin G, colistin, vancomycin, and chloramphenicol. Here we report the identification, purification, and characterization of a novel NRP, i.e., brevibacillin 2V (lipo-tridecapeptide), from Brevibacillus laterosporus DSM 25. Brevibacillin 2V has a strong antimicrobial activity against Gram-positive bacterial pathogens (minimum inhibitory concentration = 2 mg/L), including difficult-to-treat antibiotic-resistant Enterococcus faecium, Enterococcus faecalis, and Staphylococcus aureus. Notably, brevibacillin 2V has a much lower hemolytic activity (HC₅₀ > 128 mg/L) and cytotoxicity (CC₅₀ = 45.49 ± 0.24 mg/L) to eukaryotic cells than previously reported NRPs of the lipo-tridecapeptide family, including other brevibacillins, which makes it a promising candidate for antibiotic development. In addition, our results demonstrate that brevibacillins display a synergistic action with established antibiotics against Gram-negative bacterial pathogens. Probably due to the presence of non-canonical amino acids and D-amino acids, brevibacillin 2V showed good stability in human plasma. Thus, we identified and characterized a novel and promising antimicrobial candidate (brevibacillin 2V) with low hemolytic activity and cytotoxicity, which can be used either on its own or as a template for further total synthesis and modification.

An interpreted atlas of biosynthetic gene clusters from 1,000 fungal genomes

Fungi are prolific producers of natural products, compounds which have had a large societal impact as pharmaceuticals, mycotoxins, and agrochemicals. Despite the availability of over 1,000 fungal genomes and several decades of compound discovery efforts from fungi, the biosynthetic gene clusters (BGCs) encoded by these genomes and the associated chemical space have yet to be analyzed systematically. Here, we provide detailed annotation and analyses of fungal biosynthetic and chemical space to enable genome mining and discovery of fungal natural products. Using 1,037 genomes from species across the fungal kingdom (e.g., Ascomycota, Basidiomycota, and non-Dikarya taxa), 36,399 predicted BGCs were organized into a network of 12,067 gene cluster families (GCFs). Anchoring these GCFs with reference BGCs enabled automated annotation of 2,026 BGCs with predicted metabolite scaffolds. We performed parallel analyses of the chemical repertoire of fungi, organizing 15,213 fungal compounds into 2,945 molecular families (MFs). The taxonomic landscape of fungal GCFs is largely species specific, though select families such as the equisetin GCF are present across vast phylogenetic distances with parallel diversifications in the GCF and MF. We compare these fungal datasets with a set of 5,453 bacterial genomes and their BGCs and 9,382 bacterial compounds, revealing dramatic differences between bacterial and fungal biosynthetic logic and chemical space. These genomics and cheminformatics analyses reveal the large extent to which fungal and bacterial sources represent distinct compound reservoirs. With a >10-fold increase in the number of interpreted strains and annotated BGCs, this work better regularizes the biosynthetic potential of fungi for rational compound discovery.

Future trends in synthetic biology in Asia

Synthetic biology research and technology translation has garnered increasing interest from the governments and private investors in Asia, where the technology has great potential in driving a sustainable bio-based economy. This Perspective reviews the latest developments in the key enabling technologies of synthetic biology and its application in bio-manufacturing, medicine, food and agriculture in Asia. Asia-centric strengths in synthetic biology to grow the bio-based economy, such as advances in genome editing and the presence of biofoundries combined with the availability of natural resources and vast markets, are also highlighted. The potential barriers to the sustainable development of the field, including inadequate infrastructure and policies, with suggestions to overcome these by building public-private partnerships, more effective multi-lateral collaborations and well-developed governance framework, are presented. Finally, the roles of technology, education and regulation in mitigating potential biosecurity risks are examined. Through these discussions, stakeholders from different groups, including academia, industry and government, are expectantly better positioned to contribute towards the establishment of innovation and bio-economy hubs in Asia.

Medicinal Properties and Bioactive Compounds from Wild Mushrooms Native to North America

Mushrooms, the fruiting bodies of fungi, are known for a long time in different cultures around the world to possess medicinal properties and are used to treat various human diseases. Mushrooms that are parts of traditional medicine in Asia had been extensively studied and this has led to identification of their bioactive ingredients. North America, while home to one of the world's largest and diverse ecological systems, has not subjected its natural resources especially its diverse array of mushroom species for bioprospecting purposes: Are mushrooms native to North America a good source for drug discovery? In this review, we compile all the published studies up to September 2020 on the bioprospecting of North American mushrooms. Out of the 79 species that have been investigated for medicinal properties, 48 species (60%) have bioactivities that have not been previously reported. For a mere 16 selected species, 17 new bioactive compounds (10 small molecules, six polysaccharides and one protein) have already been isolated. The results from our literature search suggest that mushrooms native to North America are indeed a good source for drug discovery.

Discovery of new enzymatic functions and metabolic pathways using genomic enzymology web tools

The continuing expansion of protein and genome sequence databases is an opportunity to identify novel enzymes with biotechnological applications. Whether applied to enzymology, chemical biology, systems biology, and microbiology, database mining must be 'user-friendly' so that experimentalists can devise focused strategies to discover the in vitro activities and in vivo functions of uncharacterized enzymes. We developed a suite of genomic enzymology tools (https://efi.igb.illinois.edu/) to (1) generate sequence similarity networks (SSNs) for exploration of sequence-function space in protein families (EFI-EST) and (2) provide genome context for members of protein families (EFI-GNT). Integrated analysis of this complementary information allows to generate testable hypotheses about new functions. After a brief overview of EFI-EST and EFI-GNT, we describe applications that illustrate their use.

The Integration of Genome Mining, Comparative Genomics, and Functional Genetics for Biosynthetic Gene Cluster Identification

Antimicrobial resistance is a worldwide health crisis for which new antibiotics are needed. One strategy for antibiotic discovery is identifying unique antibiotic biosynthetic gene clusters that may produce novel compounds. The aim of this study was to demonstrate how an integrated approach that combines genome mining, comparative genomics, and functional genetics can be used to successfully identify novel biosynthetic gene clusters that produce antimicrobial natural products. Secondary metabolite clusters of an antibiotic producer are first predicted using genome mining tools, generating a list of candidates. Comparative genomic approaches are then used to identify gene suites present in the antibiotic producer that are absent in closely related non-producers. Gene sets that are common to the two lists represent leading candidates, which can then be confirmed using functional genetics approaches. To validate this strategy, we identified the genes responsible for antibiotic production in Pantoea agglomerans B025670, a strain identified in a large-scale bioactivity survey. The genome of B025670 was first mined with antiSMASH, which identified 24 candidate regions. We then used the comparative genomics platform, EDGAR, to identify genes unique to B025670 that were not present in closely related strains with contrasting antibiotic production profiles. The candidate lists generated by antiSMASH and EDGAR were compared with standalone BLAST. Among the common regions was a 14 kb cluster consisting of 14 genes with predicted enzymatic, transport, and unknown functions. Site-directed mutagenesis of the gene cluster resulted in a reduction in antimicrobial activity, suggesting involvement in antibiotic production. An integrated approach that combines genome mining, comparative genomics, and functional genetics yields a powerful, yet simple strategy for identifying potentially novel antibiotics.

TOUCAN: a framework for fungal biosynthetic gene cluster discovery

Fungal secondary metabolites (SMs) are an important source of numerous bioactive compounds largely applied in the pharmaceutical industry, as in the production of antibiotics and anticancer medications. The discovery of novel fungal SMs can potentially benefit human health. Identifying biosynthetic gene clusters (BGCs) involved in the biosynthesis of SMs can be a costly and complex task, especially due to the genomic diversity of fungal BGCs. Previous studies on fungal BGC discovery present limited scope and can restrict the discovery of new BGCs. In this work, we introduce TOUCAN, a supervised learning framework for fungal BGC discovery. Unlike previous methods, TOUCAN is capable of predicting BGCs on amino acid sequences, facilitating its use on newly sequenced and not yet curated data. It relies on three main pillars: rigorous selection of datasets by BGC experts; combination of functional, evolutionary and compositional features coupled with outperforming classifiers; and robust post-processing methods. TOUCAN best-performing model yields 0.982 F-measure on BGC regions in the Aspergillus niger genome. Overall results show that TOUCAN outperforms previous approaches. TOUCAN focuses on fungal BGCs but can be easily adapted to expand its scope to process other species or include new features.

Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences

Novel antibiotics are urgently needed to address the looming global crisis of antibiotic resistance. Historically, the primary source of clinically used antibiotics has been microbial secondary metabolism. Microbial genome sequencing has revealed a plethora of uncharacterized natural antibiotics that remain to be discovered. However, the isolation of these molecules is hindered by the challenge of linking sequence information to the chemical structures of the encoded molecules. Here, we present PRISM 4, a comprehensive platform for prediction of the chemical structures of genomically encoded antibiotics, including all classes of bacterial antibiotics currently in clinical use. The accuracy of chemical structure prediction enables the development of machine-learning methods to predict the likely biological activity of encoded molecules. We apply PRISM 4 to chart secondary metabolite biosynthesis in a collection of over 10,000 bacterial genomes from both cultured isolates and metagenomic datasets, revealing thousands of encoded antibiotics. PRISM 4 is freely available as an interactive web application at http://prism.adapsyn.com.

Large-scale sequencing efforts have uncovered a large number of secondary metabolic pathways, but the chemicals they synthesise remain unknown. Here the authors present PRISM 4, which predicts the chemical structures encoded by microbial genome sequences, including all classes of bacterial antibiotics in clinical use.

Genome mining and UHPLC-QTOF-MS/MS to identify the potential antimicrobial compounds and determine the specificity of biosynthetic gene clusters in Bacillus subtilis NCD-2

Background

Bacillus subtilis strain NCD-2 is an excellent biocontrol agent against plant soil-borne diseases and shows broad-spectrum antifungal activities. This study aimed to explore some secondary metabolite biosynthetic gene clusters and related antimicrobial compounds in strain NCD-2. An integrative approach combining genome mining and structural identification technologies using ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight tandem mass spectrometry (UHPLC-MS/MS), was adopted to interpret the chemical origins of metabolites with significant biological activities.

Results

Genome mining revealed nine gene clusters encoding secondary metabolites with predicted functions, including fengycin, surfactin, bacillaene, subtilosin, bacillibactin, bacilysin and three unknown products. Fengycin, surfactin, bacillaene and bacillibactin were successfully detected from the fermentation broth of strain NCD-2 by UHPLC-QTOF-MS/MS. The biosynthetic gene clusters of bacillaene, subtilosin, bacillibactin, and bacilysin showed 100% amino acid sequence identities with those in B. velezensis strain FZB42, whereas the identities of the surfactin and fengycin gene clusters were only 83 and 92%, respectively. Further comparison revealed that strain NCD-2 had lost the fenC and fenD genes in the fengycin biosynthetic operon. The biosynthetic enzyme-related gene srfAB for surfactin was divided into two parts. Bioinformatics analysis suggested that FenE in strain NCD-2 had a similar function to FenE and FenC in strain FZB42, and that FenA in strain NCD-2 had a similar function to FenA and FenD in strain FZB42. Five different kinds of fengycins, with 26 homologs, and surfactin, with 4 homologs, were detected from strain NCD-2. To the best of our knowledge, this is the first report of a non-typical gene cluster related to fengycin synthesis.

Conclusions

Our study revealed a number of gene clusters encoding antimicrobial compounds in the genome of strain NCD-2, including a fengycin synthetic gene cluster that might be unique by using genome mining and UHPLC–QTOF–MS/MS. The production of fengycin, surfactin, bacillaene and bacillibactin might explain the biological activities of strain NCD-2.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-020-07160-2.

Biosynthesis of depsipeptides, or Depsi: The peptides with varied generations

Depsipeptides are compounds that contain both ester bonds and amide bonds. Important natural product depsipeptides include the piscicide antimycin, the K⁺ ionophores cereulide and valinomycin, the anticancer agent cryptophycin, and the antimicrobial kutzneride. Furthermore, database searches return hundreds of uncharacterized systems likely to produce novel depsipeptides. These compounds are made by specialized nonribosomal peptide synthetases (NRPSs). NRPSs are biosynthetic megaenzymes that use a module architecture and multi-step catalytic cycle to assemble monomer substrates into peptides, or in the case of specialized depsipeptide synthetases, depsipeptides. Two NRPS domains, the condensation domain and the thioesterase domain, catalyze ester bond formation, and ester bonds are introduced into depsipeptides in several different ways. The two most common occur during cyclization, in a reaction between a hydroxy-containing side chain and the C-terminal amino acid residue in a peptide intermediate, and during incorporation into the growing peptide chain of an α-hydroxy acyl moiety, recruited either by direct selection of an α-hydroxy acid substrate or by selection of an α-keto acid substrate that is reduced in situ. In this article, we discuss how and when these esters are introduced during depsipeptide synthesis, survey notable depsipeptide synthetases, and review insight into bacterial depsipeptide synthetases recently gained from structural studies.

Assessment of the Contribution of Antagonistic Secondary Metabolites to the Antifungal and Biocontrol Activities of Pseudomonas fluorescens NBC275

An understanding of the contribution of secondary metabolites (SMs) to the antagonistic and biocontrol activities of bacterial biocontrol agents serves to improve biocontrol potential of the strain. In this study, to evaluate the contribution of each SM produced by Pseudomonas fluorescens NBC275 (Pf275) to its antifungal and biocontrol activity, we combined in silico analysis of the genome with our previous study of transposon (Tn) mutants. Thirteen Tn mutants, which belonged to 6 biosynthetic gene clusters (BGCs) of a total 14 BGCs predicted by the antiSMASH tool were identified by the reduction of antifungal activity. The biocontrol performance of Pf275 was significantly dependent on 2,4-diacetylphloroglucinol and pyoverdine. The clusters that encode for arylpolyene and an unidentified small linear lipopeptide influenced antifungal and biocontrol activities. To our knowledge, our study identified the contribution of SMs, such as a small linear lipopeptide and arylpolyene, to biocontrol efficacy for the first time.

Recent advances in the biosynthesis of RiPPs from multicore-containing precursor peptides

Ribosomally synthesized and post-translationally modified peptides (RiPPs) compose a large structurally and functionally diverse family of natural products. The biosynthesis system of RiPPs typically involves a precursor peptide comprising of a leader and core motif and nearby processing enzymes that recognize the leader and act on the core for producing modified peptides. Interest in RiPPs has increased substantially in recent years as improvements in genome mining techniques have dramatically improved access to these peptides and biochemical and engineering studies have supported their applications. A less understood, intriguing feature in the RiPPs biosynthesis is the precursor peptides of multiple RiPPs families produced by bacteria, fungi and plants carrying multiple core motifs, which we term "multicore". Herein, we present the prevalence of the multicore systems, their biosynthesis and engineering for applications.

Completion of draft bacterial genomes by long-read sequencing of synthetic genomic pools

BACKGROUND

Illumina technology currently dominates bacterial genomics due to its high read accuracy and low sequencing cost. However, the incompleteness of draft genomes generated by Illumina reads limits their application in comprehensive genomics analyses. Alternatively, hybrid assembly using both Illumina short reads and long reads generated by single molecule sequencing technologies can enable assembly of complete bacterial genomes, yet the high per-genome cost of long-read sequencing limits the widespread use of this approach in bacterial genomics. Here we developed a protocol for hybrid assembly of complete bacterial genomes using miniaturized multiplexed Illumina sequencing and non-barcoded PacBio sequencing of a synthetic genomic pool (SGP), thus significantly decreasing the overall per-genome cost of sequencing.

RESULTS

We evaluated the performance of SGP hybrid assembly on the genomes of 20 bacterial isolates with different genome sizes, a wide range of GC contents, and varying levels of phylogenetic relatedness. By improving the contiguity of Illumina assemblies, SGP hybrid assembly generated 17 complete and 3 nearly complete bacterial genomes. Increased contiguity of SGP hybrid assemblies resulted in considerable improvement in gene prediction and annotation. In addition, SGP hybrid assembly was able to resolve repeat elements and identify intragenomic heterogeneities, e.g. different copies of 16S rRNA genes, that would otherwise go undetected by short-read-only assembly. Comprehensive comparison of SGP hybrid assemblies with those generated using multiplexed PacBio long reads (long-read-only assembly) also revealed the relative advantage of SGP hybrid assembly in terms of assembly quality. In particular, we observed that SGP hybrid assemblies were completely devoid of both small (i.e. single base substitutions) and large assembly errors. Finally, we show the ability of SGP hybrid assembly to differentiate genomes of closely related bacterial isolates, suggesting its potential application in comparative genomics and pangenome analysis.

CONCLUSION

Our results indicate the superiority of SGP hybrid assembly over both short-read and long-read assemblies with respect to completeness, contiguity, accuracy, and recovery of small replicons. By lowering the per-genome cost of sequencing, our parallel sequencing and hybrid assembly pipeline could serve as a cost effective and high throughput approach for completing high-quality bacterial genomes.

Genome mining strategies for ribosomally synthesised and post-translationally modified peptides

Genome mining is a computational method for the automatic detection and annotation of biosynthetic gene clusters (BGCs) from genomic data. This approach has been increasingly utilised in natural product (NP) discovery due to the large amount of sequencing data that is now available. Ribosomally synthesised and post-translationally modified peptides (RiPPs) are a class of structurally complex NP with diverse bioactivities. RiPPs have recently been shown to occupy a much larger expanse of genomic and chemical space than previously appreciated, indicating that annotation of RiPP BGCs in genomes may have been overlooked in the past. This review provides an overview of the genome mining tools that have been specifically developed to aid in the discovery of RiPP BGCs, which have been built from an increasing knowledgebase of RiPP structures and biosynthesis. Given these recent advances, the application of targeted genome mining has great potential to accelerate the discovery of important molecules such as antimicrobial and anticancer agents whilst increasing our understanding about how these compounds are biosynthesised in nature.

Mini review: Genome mining approaches for the identification of secondary metabolite biosynthetic gene clusters in Streptomyces

Streptomyces are a large and valuable resource of bioactive and complex secondary metabolites, many of which have important clinical applications. With the advances in high throughput genome sequencing methods, various in silico genome mining strategies have been developed and applied to the mapping of the Streptomyces genome. These studies have revealed that Streptomyces possess an even more significant number of uncharacterized silent secondary metabolite biosynthetic gene clusters (smBGCs) than previously estimated. Linking smBGCs to their encoded products has played a critical role in the discovery of novel secondary metabolites, as well as, knowledge-based engineering of smBGCs to produce altered products. In this mini review, we discuss recent progress in Streptomyces genome sequencing and the application of genome mining approaches to identify and characterize smBGCs. Furthermore, we discuss several challenges that need to be overcome to accelerate the genome mining process and ultimately support the discovery of novel bioactive compounds.

Linking biosynthetic and chemical space to accelerate microbial secondary metabolite discovery

Secondary metabolites can be viewed as a chemical language, facilitating communication between microorganisms. From an ecological point of view, this metabolite exchange is in constant flux due to evolutionary and environmental pressures. From a biomedical perspective, the chemistry is unsurpassed for its antibiotic properties. Genome sequencing of microorganisms has revealed a large reservoir of Biosynthetic Gene Clusters (BGCs); however, linking these to the secondary metabolites they encode is currently a major bottleneck to chemical discovery. This linking of genes to metabolites with experimental validation will aid the elicitation of silent or cryptic (not expressed under normal laboratory conditions) BGCs. As a result, this will accelerate chemical dereplication, our understanding of gene transcription and provide a comprehensive resource for synthetic biology. This will ultimately provide an improved understanding of both the biosynthetic and chemical space. In recent years, integrating these complex metabolomic and genomic data sets has been achieved using a spectrum of manual and automated approaches. In this review, we cover examples of these approaches, while addressing current challenges and future directions in linking these data sets.

IMG-ABC v.5.0: an update to the IMG/Atlas of Biosynthetic Gene Clusters Knowledgebase

Microbial secondary metabolism is a reservoir of bioactive compounds of immense biotechnological and biomedical potential. The biosynthetic machinery responsible for the production of these secondary metabolites (SMs) (also called natural products) is often encoded by collocated groups of genes called biosynthetic gene clusters (BGCs). High-throughput genome sequencing of both isolates and metagenomic samples combined with the development of specialized computational workflows is enabling systematic identification of BGCs and the discovery of novel SMs. In order to advance exploration of microbial secondary metabolism and its diversity, we developed the largest publicly available database of predicted BGCs combined with experimentally verified BGCs, the Integrated Microbial Genomes Atlas of Biosynthetic gene Clusters (IMG-ABC) (https://img.jgi.doe.gov/abc-public). Here we describe the first major content update of the IMG-ABC knowledgebase, since its initial release in 2015, refreshing the BGC prediction pipeline with the latest version of antiSMASH (v5) as well as presenting the data in the context of underlying environmental metadata sourced from GOLD (https://gold.jgi.doe.gov/). This update has greatly improved the quality and expanded the types of predicted BGCs compared to the previous version.

Discovery of an Isothiazolinone-Containing Antitubercular Natural Product Levesquamide

Antitubercular agent levesquamide is a new polyketide-nonribosomal peptide (PK-NRP) hybrid marine natural product isolated from Streptomyces sp. RKND-216. The structure contains a rare isothiazolinone moiety which has only been reported in collismycin SN. Structure elucidation by NMR spectroscopy was a significant challenge due to a deficiency of protons in this aromatic moiety. Therefore, the genome of Streptomyces sp. RKND-216 was sequenced to identify the levesquamide biosynthetic gene cluster (BGC). Analysis of the BGC provided structural insights and guided stable-isotope labeling experiments, which led to the assignment of the fused pyridine-isothiazolinone moiety. The BGC and the labeling experiments provide further insights into the biosynthetic origin of isothiazolinones. Levesquamide exhibited antimicrobial activity in the microplate alamarBlue assay (MABA) and low oxygen recovery assay (LORA) against Mycobacterium tuberculosis H37Rv with minimum inhibitory concentration (MIC) values of 9.65 and 22.28 μM, respectively. Similar activity was exhibited against rifampicin- and isoniazid-resistant M. tuberculosis strains with MIC values of 9.46 and 9.90 μM, respectively. This result suggests levesquamide has a different mode of action against M. tuberculosis compared to the two first-line antitubercular drugs rifampicin and isoniazid. Furthermore, levesquamide shows no cytotoxicity against the Vero cell line, suggesting it may have a useful therapeutic window.

Recent Advances in Strategies for Activation and Discovery/Characterization of Cryptic Biosynthetic Gene Clusters in Streptomyces

Streptomyces spp. are prolific sources of valuable natural products (NPs) that are of great interest in pharmaceutical industries such as antibiotics, anticancer chemotherapeutics, immunosuppressants, etc. Approximately two-thirds of all known antibiotics are produced by actinomycetes, most predominantly by Streptomyces. Nevertheless, in recent years, the chances of the discovery of novel and bioactive compounds from Streptomyces have significantly declined. The major hindrance for obtaining such bioactive compounds from Streptomyces is that most of the compounds are not produced in significant titers, or the biosynthetic gene clusters (BGCs) are cryptic. The rapid development of genome sequencing has provided access to a tremendous number of NP-BGCs embedded in the microbial genomes. In addition, the studies of metabolomics provide a portfolio of entire metabolites produced from the strain of interest. Therefore, through the integrated approaches of different-omics techniques, the connection between gene expression and metabolism can be established. Hence, in this review we summarized recent advancements in strategies for activating cryptic BGCs in Streptomyces by utilizing diverse state-of-the-art techniques.

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.

Genome Mining as New Challenge in Natural Products Discovery

Drug discovery is based on bioactivity screening of natural sources, traditionally represented by bacteria fungi and plants. Bioactive natural products and their secondary metabolites have represented the main source for new therapeutic agents, used as drug leads for new antibiotics and anticancer agents. After the discovery of the first biosynthetic genes in the last decades, the researchers had in their hands the tool to understand the biosynthetic logic and genetic basis leading to the production of these compounds. Furthermore, in the genomic era, in which the number of available genomes is increasing, genome mining joined to synthetic biology are offering a significant help in drug discovery. In the present review we discuss the importance of genome mining and synthetic biology approaches to identify new natural products, also underlining considering the possible advantages and disadvantages of this technique. Moreover, we debate the associated techniques that can be applied following to genome mining for validation of data. Finally, we review on the literature describing all novel natural drugs isolated from bacteria, fungi, and other living organisms, not only from the marine environment, by a genome-mining approach, focusing on the literature available in the last ten years.

Review on natural products databases: where to find data in 2020

Natural products (NPs) have been the centre of attention of the scientific community in the last decencies and the interest around them continues to grow incessantly. As a consequence, in the last 20 years, there was a rapid multiplication of various databases and collections as generalistic or thematic resources for NP information. In this review, we establish a complete overview of these resources, and the numbers are overwhelming: over 120 different NP databases and collections were published and re-used since 2000. 98 of them are still somehow accessible and only 50 are open access. The latter include not only databases but also big collections of NPs published as supplementary material in scientific publications and collections that were backed up in the ZINC database for commercially-available compounds. Some databases, even published relatively recently are already not accessible anymore, which leads to a dramatic loss of data on NPs. The data sources are presented in this manuscript, together with the comparison of the content of open ones. With this review, we also compiled the open-access natural compounds in one single dataset a COlleCtion of Open NatUral producTs (COCONUT), which is available on Zenodo and contains structures and sparse annotations for over 400,000 non-redundant NPs, which makes it the biggest open collection of NPs available to this date.