ClustScan: an integrated program package for the semi-automatic annotation of modular biosynthetic gene clusters and in silico prediction of novel chemical structures

Bioinformatics assisted construction of the link between biosynthetic gene clusters and secondary metabolites in fungi

Fungal secondary metabolites are considered as important resources for drug discovery. Despite various methods being employed to facilitate the discovery of new fungal secondary metabolites, the trend of identifying novel secondary metabolites from fungi is inevitably slowing down. Under laboratory conditions, the majority of biosynthetic gene clusters, which store information for secondary metabolites, remain inactive. Therefore, establishing the link between biosynthetic gene clusters and secondary metabolites would contribute to understanding the genetic logic underlying secondary metabolite biosynthesis and alleviating the current challenges in discovering novel natural products. Bioinformatics methods have garnered significant attention due to their powerful capabilities in data mining and analysis, playing a crucial role in various aspects. Thus, we have summarized successful cases since 2016 in which bioinformatics methods were utilized to establish the link between fungal biosynthetic gene clusters and secondary metabolites, focusing on their biosynthetic gene clusters and associated secondary metabolites, with the goal of aiding the field of natural product discovery.

A guide to genome mining and genetic manipulation of biosynthetic gene clusters in Streptomyces

Streptomyces are a crucial source of bioactive secondary metabolites with significant clinical applications. Recent studies of bacterial and metagenome-assembled genomes have revealed that Streptomyces harbors a substantial number of uncharacterized silent secondary metabolite biosynthetic gene clusters (BGCs). These BGCs represent a vast diversity of biosynthetic pathways for natural product synthesis, indicating significant untapped potential for discovering new metabolites. To exploit this potential, genome mining using comprehensive strategies that leverage extensive genomic databases can be conducted. By linking BGCs to their encoded products and integrating genetic manipulation techniques, researchers can greatly enhance the identification of new secondary metabolites with therapeutic relevance. In this context, we present a step-by-step guide for using the antiSMASH pipeline to identify secondary metabolite-coding BGCs within the complete genome of a novel Streptomyces strain. This protocol also outlines gene manipulation methods that can be applied to Streptomyces to activate cryptic clusters of interest and validate the functions of biosynthetic genes. By following these guidelines, researchers can pave the way for discovering and characterizing valuable natural products.

BGC heteroexpression strategy for production of novel microbial secondary metabolites

Biosynthetic gene clusters (BGCs) in microbial genomes play a crucial role in the biosynthesis of diverse secondary metabolites (SMs) with pharmaceutical potential. However, most BGCs remain silent under conventional conditions, resulting in the frequently repeated discovery of known SMs. Fortunately, in the past two decades, the heterologous expression of BGCs in genetically tractable hosts has emerged as a powerful strategy to awaken microbial metabolic pathways for making novel microbial SMs. In this review, we comprehensively delineated the development and application of this strategy, highlighting various BGC cloning and assembly techniques and their technical characteristics. We also summarized 519 novel SMs from BGC hetero-expression-derived strains and described their occurrence, bioactivity, mode of action, and biosynthetic logic. Lastly, current challenges and future perspectives for developing more efficient BGC hetero-expression strategies were discussed in this review.

Computational advances in biosynthetic gene cluster discovery and prediction

Biosynthetic gene clusters (BGCs) are groups of clustered genes found in bacteria, fungi, and some plants and animals that are crucial for synthesizing secondary metabolites. In recent years, genome mining of BGCs has emerged as a prominent research focus, particularly in natural product discovery and drug development. Compared to traditional experimental methods, applying computational techniques has significantly enhanced the efficiency of BGC identification and annotation, thereby facilitating the discovery of novel metabolites. The advent of artificial intelligence, particularly machine learning models and more advanced deep learning algorithms, has significantly enhanced both the speed and precision of BGC mining. This review offers a comprehensive introduction to currently developed BGC databases and prediction tools, highlighting the potential of machine learning technologies in BGC mining. Additionally, it summarizes the challenges computational methods face in this area and discusses future research directions.

Current Approaches for Genetic Manipulation of Streptomyces spp.-Key Bacteria for Biotechnology and Environment

Organisms from the genus Streptomyces feature actinobacteria with complex developmental cycles and a great ability to produce a variety of natural products. These soil bacteria produce more than 2/3 of antibiotics used in medicine, and a large variety of bioactive compounds for industrial, medical and agricultural use. Although Streptomyces spp. have been studied for decades, the engineering of these bacteria remains challenging, and the available genetic tools are rather limited. Furthermore, most biosynthetic gene clusters in these bacteria are silent and require strategies to activate them and exploit their production potential. In order to explore, understand and manipulate the capabilities of Streptomyces spp. as a key bacterial for biotechnology, synthetic biology strategies emerged as a valuable component of Streptomyces research. Recent advancements in strategies for genetic manipulation of Streptomyces involving proposals of a large variety of synthetic components for the genetic toolbox, as well as new approaches for genome mining, assembly of genetic constructs and their delivery into the cell, allowed facilitation of the turnaround time of strain engineering and efficient production of new natural products at an industrial scale, but still have strain- and design-dependent limitations. A new perspective offered recently by technical advances in DNA sequencing, analysis and editing proposed strategies to overcome strain- and construct-specific difficulties in the engineering of Streptomyces. In this review, challenges and recent developments of approaches for Streptomyces engineering are discussed, an overview of novel synthetic biology strategies is provided and examples of successful application of new technologies in molecular genetic engineering of Streptomyces are highlighted.

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.

Endophytic fungi: A future prospect for breast cancer therapeutics and drug development

Globally, breast cancer is a primary contributor to cancer-related fatalities and illnesses among women. Consequently, there is a pressing need for safe and effective treatments for breast cancer. Bioactive compounds from endophytic fungi that live in symbiosis with medicinal plants have garnered significant interest in pharmaceutical research due to their extensive chemical composition and prospective medicinal attributes. This review underscores the potentiality of fungal endophytes as a promising resource for the development of innovative anticancer agents specifically tailored for breast cancer therapy. The diversity of endophytic fungi residing in medicinal plants, success stories of key endophytic bioactive metabolites tested against breast cancer and the current progress with regards to in vivo studies and clinical trials on endophytic fungal metabolites in breast cancer research forms the underlying theme of this article. A thorough compilation of putative anticancer compounds sourced from endophytic fungi that have demonstrated therapeutic potential against breast cancer, spanning the period from 1990 to 2022, has been presented. This review article also outlines the latest trends in endophyte-based drug discovery, including the use of artificial intelligence, machine learning, multi-omics approaches, and high-throughput strategies. The challenges and future prospects associated with fungal endophytes as substitutive sources for developing anticancer drugs targeting breast cancer are also being highlighted.

High-Throughput Mining of Novel Compounds from Known Microbes: A Boost to Natural Product Screening

Advanced techniques can accelerate the pace of natural product discovery from microbes, which has been lagging behind the drug discovery era. Therefore, the present review article discusses the various interdisciplinary and cutting-edge techniques to present a concrete strategy that enables the high-throughput screening of novel natural compounds (NCs) from known microbes. Recent bioinformatics methods revealed that the microbial genome contains a huge untapped reservoir of silent biosynthetic gene clusters (BGC). This article describes several methods to identify the microbial strains with hidden mines of silent BGCs. Moreover, antiSMASH 5.0 is a free, accurate, and highly reliable bioinformatics tool discussed in detail to identify silent BGCs in the microbial genome. Further, the latest microbial culture technique, HiTES (high-throughput elicitor screening), has been detailed for the expression of silent BGCs using 500-1000 different growth conditions at a time. Following the expression of silent BGCs, the latest mass spectrometry methods are highlighted to identify the NCs. The recently emerged LAESI-IMS (laser ablation electrospray ionization-imaging mass spectrometry) technique, which enables the rapid identification of novel NCs directly from microtiter plates, is presented in detail. Finally, various trending 'dereplication' strategies are emphasized to increase the effectiveness of NC screening.

MyxoPortal: a database of myxobacterial genomic features

Myxobacteria are predatory bacteria with antimicrobial activity, utilizing complex mechanisms to kill their prey and assimilate their macromolecules. Having large genomes encoding hundreds of secondary metabolites, hydrolytic enzymes and antimicrobial peptides, these organisms are widely studied for their antibiotic potential. MyxoPortal is a comprehensive genomic database hosting 262 genomes of myxobacterial strains. Datasets included provide genome annotations with gene locations, functions, amino acids and nucleotide sequences, allowing analysis of evolutionary and taxonomical relationships between strains and genes. Biosynthetic gene clusters are identified by AntiSMASH, and dbAMP-generated antimicrobial peptide sequences are included as a resource for novel antimicrobial discoveries, while curated datasets of CRISPR/Cas genes, regulatory protein sequences, and phage associated genes give useful insights into each strain's biological properties. MyxoPortal is an intuitive open-source database that brings together application-oriented genomic features that can be used in taxonomy, evolution, predation and antimicrobial research. MyxoPortal can be accessed at http://dicsoft1.physics.iisc.ac.in/MyxoPortal/. Database URL:  http://dicsoft1.physics.iisc.ac.in/MyxoPortal/. Graphical Abstract.

A Multi-Label Learning Framework for Predicting Chemical Classes and Biological Activities of Natural Products from Biosynthetic Gene Clusters

Natural products (NP) or secondary metabolites, as a class of small chemical molecules that are naturally synthesized by chromosomally clustered biosynthesis genes (also called biosynthetic gene clusters, BGCs) encoded enzymes or enzyme complexes, mediates the bioecological interactions between host and microbiota and provides a natural reservoir for screening drug-like therapeutic pharmaceuticals. In this work, we propose a multi-label learning framework to functionally annotate natural products or secondary metabolites solely from their catalytical biosynthetic gene clusters without experimentally conducting NP structural resolutions. All chemical classes and bioactivities constitute the label space, and the sequence domains of biosynthetic gene clusters that catalyse the biosynthesis of natural products constitute the feature space. In this multi-label learning framework, a joint representation of features (BGCs domains) and labels (natural products annotations) is efficiently learnt in an integral and low-dimensional space to accurately define the inter-class boundaries and scale to the learning problem of many imbalanced labels. Computational results on experimental data show that the proposed framework achieves satisfactory multi-label learning performance, and the learnt patterns of BGCs domains are transferrable across bacteria, or even across kingdom, for instance, from bacteria to Arabidopsis thaliana. Lastly, take Arabidopsis thaliana and its rhizosphere microbiome for example, we propose a pipeline combining existing BGCs identification tools and this proposed framework to find and functionally annotate novel natural products for downstream bioecological studies in terms of plant-microbiota-soil interactions and plant environmental adaption.

Bioactive Metabolites from Terrestrial and Marine Actinomycetes

Actinomycetes inhabit both terrestrial and marine ecosystems and are highly proficient in producing a wide range of natural products with diverse biological functions, including antitumor, immunosuppressive, antimicrobial, and antiviral activities. In this review, we delve into the life cycle, ecology, taxonomy, and classification of actinomycetes, as well as their varied bioactive metabolites recently discovered between 2015 and 2023. Additionally, we explore promising strategies to unveil and investigate new bioactive metabolites, encompassing genome mining, activation of silent genes through signal molecules, and co-cultivation approaches. By presenting this comprehensive and up-to-date review, we hope to offer a potential solution to uncover novel bioactive compounds with essential activities.

Applications of machine learning in microbial natural product drug discovery

INTRODUCTION

Natural products (NPs) are a desirable source of new therapeutics due to their structural diversity and evolutionarily optimized bioactivities. NPs and their derivatives account for roughly 70% of approved pharmaceuticals. However, the rate at which novel NPs are discovered has decreased. To accelerate the microbial NP discovery process, machine learning (ML) is being applied to numerous areas of NP discovery and development.

AREAS COVERED

This review explores the utility of ML at various phases of the microbial NP drug discovery pipeline, discussing concrete examples throughout each major phase: genome mining, dereplication, and biological target prediction. Moreover, the authors discuss how ML approaches can be applied to semi-synthetic approaches to drug discovery.

EXPERT OPINION

Despite the important role that microbial NPs play in the development of novel drugs, their discovery has declined due to challenges associated with the conventional discovery process. ML is positioned to overcome these limitations given its ability to model complex datasets and generalize to novel chemical and sequence space. Unsurprisingly, ML comes with its own limitations that must be considered for its successful implementation. The authors stress the importance of continuing to build high quality and open access NP datasets to further increase the utility of ML in NP discovery.

Comparative genome features and secondary metabolite biosynthetic potential of Kutzneria chonburiensis and other species of the genus Kutzneria

Actinobacteria are well known as a rich source of diversity of bioactive secondary metabolites. Kutzneria, a rare actinobacteria belonging to the family Pseudonocardiaceae has abundance of secondary metabolite biosynthetic gene clusters (BGCs) and is one of important source of natural products and worthy of priority investigation. Currently, Kutzneria chonburiensis SMC256^T has been the latest type-strain of the genus and its genome sequence has not been reported yet. Therefore, we present the first report of new complete genome sequence of SMC256^T (genome size of 10.4 Mbp) with genome annotation and feature comparison between SMC256^T and other publicly available Kutzneria species. The results from comparative and functional genomic analyses regarding the phylogenomic and the clusters of orthologous groups of proteins (COGs) analyses indicated that SMC256^T is most closely related to Kutzneria sp. 744, Kutzneria kofuensis, Kutzneria sp. CA-103260 and Kutzneria buriramensis. Furthermore, a total of 322 BGCs were also detected and showed diversity among the Kutzneria genomes. Out of which, 38 clusters showing the best hit to the most known BGCs were predicted in the SMC256^Tgenome. We observed that six clusters responsible for biosynthesis of antimicrobials/antitumor metabolites were strain-specific in Kutzneria chonburiensis. These putative metabolites include virginiamycin S1, lysolipin I, esmeraldin, rakicidin, aclacinomycin and streptoseomycin. Based on these findings, the genome of Kutzneria chonburiensis contains distinct and unidentified BGCs different from other members of the genus, and the use of integrative genomic-based approach would be a useful alternative effort to target, isolate and identify putative and undiscovered secondary metabolites suspected to have new and/or specific bioactivity in the Kutzneria.

Natural and engineered cyclodipeptides: Biosynthesis, chemical diversity, and engineering strategies for diversification and high-yield bioproduction

Cyclodipeptides are diverse chemical scaffolds that show a broad range of bioactivities relevant for medicine, agriculture, chemical catalysis, and material sciences. Cyclodipeptides can be synthesized enzymatically through two unrelated enzyme families, non-ribosomal peptide synthetases (NRPS) and cyclodipeptide synthases (CDPSs). The chemical diversity of cyclodipeptides is derived from the two amino acid side chains and the modification of those side-chains by cyclodipeptide tailoring enzymes. While a large spectrum of chemical diversity is already known today, additional chemical space - and as such potential new bioactivities - could be accessed by exploring yet undiscovered NRPS and CDPS gene clusters as well as via engineering. Further, to exploit cyclodipeptides for applications, the low yield of natural biosynthesis needs to be overcome. In this review we summarize current knowledge on NRPS and CDPS-based cyclodipeptide biosynthesis, engineering approaches to further diversity the natural chemical diversity as well as strategies for high-yield production of cyclodipeptides, including a discussion of how advancements in synthetic biology and metabolic engineering can accelerate the translational potential of cyclodipeptides.

Advances in mining and expressing microbial biosynthetic gene clusters

Natural products (NPs) especially the secondary metabolites originated from microbes exhibit great importance in biomedical, industrial and agricultural applications. However, mining biosynthetic gene clusters (BGCs) to produce novel NPs has been hindered owing that a large population of environmental microbes are unculturable. In the past decade, strategies to explore BGCs directly from (meta)genomes have been established along with the fast development of high-throughput sequencing technologies and the powerful bioinformatics data-processing tools, which greatly expedited the exploitations of novel BGCs from unculturable microbes including the extremophilic microbes. In this review, we firstly summarized the popular bioinformatics tools and databases available to mine novel BGCs from (meta)genomes based on either pure cultures or pristine environmental samples. Noticeably, approaches rooted from machine learning and deep learning with focuses on the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs) were dramatically increased in recent years. Moreover, synthetic biology techniques to express the novel BGCs in culturable native microbes or heterologous hosts were introduced. This working pipeline including the discovery and biosynthesis of novel NPs will greatly advance the exploitations of the abundant but unexplored microbial BGCs.

Combatting insects mediated biotic stress through plant associated endophytic entomopathogenic fungi in horticultural crops

Horticultural production is a vital catalyst for economic growth, yet insect infestations reduce horticultural crop yield and quality. Pesticides and other pest control methods are used during planting to eliminate pests that cause direct and indirect losses. In such situations, endophytic entomo-pathogenic fungi (EEPF) can act as a potential tools for biological control. They protect plants by boosting growth, nutrition, morpho-physiology and salt or iron tolerance. Antixenosis, antibiosis and plant tolerance change insect performance and preferences. EEPF- plant colonisation slows herbivore development, food consumption, oviposition and larval survival. EEPF changes plant physio-chemical properties like volatile emission profile and secondary metabolite production to regulate insect pest defences. EEPF produces chitinases, laccases, amylases, and cellulases for plant defence. Recent studies focused on EEPF species' significance, isolation, identification and field application. Realizing their full potential is difficult due to insufficient mass production, storage stability and formulation. Genetic-molecular and bioinformatics can help to build EEPF-based biological control systems. Metagenomics helps study microbial EEPF taxonomy and function. Multi-omics and system biology can decode EEPF interactions with host plants and microorganisms. NGS (Next Generation Sequencing), comparative genomics, proteomics, transcriptomics, metabolomics, metatranscriptomics and microarrays are used to evaluate plant-EEPF relationships. IPM requires understanding the abiotic and biotic elements that influence plant-EEPF interaction and the physiological mechanisms of EEPF colonisation. Due to restricted research, there are hundreds of unexplored EEPFs, providing an urgent need to uncover and analyse them.

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.

Engineering site-selective incorporation of fluorine into polyketides

Although natural products and synthetic small molecules both serve important medicinal functions, their structures and chemical properties are relatively distinct. To expand the molecular diversity available for drug discovery, one strategy is to blend the effective attributes of synthetic and natural molecules. A key feature found in synthetic compounds that is rare in nature is the use of fluorine to tune drug behavior. We now report a method to site-selectively incorporate fluorine into complex structures to produce regioselectively fluorinated full-length polyketides. We engineered a fluorine-selective trans-acyltransferase to produce site-selectively fluorinated erythromycin precursors in vitro. We further demonstrated that these analogs could be produced in vivo in Escherichia coli on engineering of the fluorinated extender unit pool. By using engineered microbes, elaborate fluorinated compounds can be produced by fermentation, offering the potential for expanding the identification and development of bioactive fluorinated small molecules.

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.

Endophytic Fungi: Key Insights, Emerging Prospects, and Challenges in Natural Product Drug Discovery

Plant-associated endophytes define an important symbiotic association in nature and are established bio-reservoirs of plant-derived natural products. Endophytes colonize the internal tissues of a plant without causing any disease symptoms or apparent changes. Recently, there has been a growing interest in endophytes because of their beneficial effects on the production of novel metabolites of pharmacological significance. Studies have highlighted the socio-economic implications of endophytic fungi in agriculture, medicine, and the environment, with considerable success. Endophytic fungi-mediated biosynthesis of well-known metabolites includes taxol from Taxomyces andreanae, azadirachtin A and B from Eupenicillium parvum, vincristine from Fusarium oxysporum, and quinine from Phomopsis sp. The discovery of the billion-dollar anticancer drug taxol was a landmark in endophyte biology/research and established new paradigms for the metabolic potential of plant-associated endophytes. In addition, endophytic fungi have emerged as potential prolific producers of antimicrobials, antiseptics, and antibiotics of plant origin. Although extensively studied as a “production platform” of novel pharmacological metabolites, the molecular mechanisms of plant–endophyte dynamics remain less understood/explored for their efficient utilization in drug discovery. The emerging trends in endophytic fungi-mediated biosynthesis of novel bioactive metabolites, success stories of key pharmacological metabolites, strategies to overcome the existing challenges in endophyte biology, and future direction in endophytic fungi-based drug discovery forms the underlying theme of this article.

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.

Genome mining of novel rubiginones from Streptomyces sp. CB02414 and characterization of the post-PKS modification steps in rubiginone biosynthesis

Background

Rubiginones belong to the angucycline family of aromatic polyketides, and they have been shown to potentiate the vincristine (VCR)-induced cytotoxicity against VCR-resistant cancer cell lines. However, the biosynthetic gene clusters (BGCs) and biosynthetic pathways for rubiginones have not been reported yet.

Results

In this study, based on bioinformatics analysis of the genome of Streptomyces sp. CB02414, we predicted the functions of the two type II polyketide synthases (PKSs) BGCs. The rub gene cluster was predicted to encode metabolites of the angucycline family. Scale-up fermentation of the CB02414 wild-type strain led to the discovery of eight rubiginones, including five new ones (rubiginones J, K, L, M, and N). Rubiginone J was proposed to be the final product of the rub gene cluster, which features extensive oxidation on the A-ring of the angucycline skeleton. Based on the production profiles of the CB02414 wild-type and the mutant strains, we proposed a biosynthetic pathway for the rubiginones in CB02414.

Conclusions

A genome mining strategy enabled the efficient discovery of new rubiginones from Streptomyces sp. CB02414. Based on the isolated biosynthetic intermediates, a plausible biosynthetic pathway for the rubiginones was proposed. Our research lays the foundation for further studies on the mechanism of the cytochrome P450-catalyzed oxidation of angucyclines and for the generation of novel angucyclines using combinatorial biosynthesis strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01681-5.

Identification of non-ribosomal peptide synthetase in Ganoderma boninense Pat. that was expressed during the interaction with oil palm

Basal stem rot (BSR) of oil palm is a disastrous disease caused by a white-rot fungus Ganoderma boninense Pat. Non-ribosomal peptides (NRPs) synthesized by non-ribosomal peptide synthetases (NRPSs) are a group of secondary metabolites that act as fungal virulent factors during pathogenesis in the host. In this study, we aimed to isolate NRPS gene of G. boninense strain UPMGB001 and investigate the role of this gene during G. boninense-oil palm interaction. The isolated NRPS DNA fragment of 8322 bp was used to predict the putative peptide sequence of different domains and showed similarity with G. sinense (85%) at conserved motifs of three main NRPS domains. Phylogenetic analysis of NRPS peptide sequences demonstrated that NRPS of G. boninense belongs to the type VI siderophore family. The roots of 6-month-old oil palm seedlings were artificially inoculated for studying NRPS gene expression and disease severity in the greenhouse. The correlation between high disease severity (50%) and high expression (67-fold) of G. boninense NRPS gene at 4 months after inoculation and above indicated that this gene played a significant role in the advancement of BSR disease. Overall, these findings increase our knowledge on the gene structure of NRPS in G. boninense and its involvement in BSR pathogenesis as an effector gene.

Production and Activity of Cristazarin in the Lichen-Forming Fungus Cladonia metacorallifera

Lichens are a natural source of bioactive compounds. Cladonia metacorallifera var. reagens KoLRI002260 is a rare lichen known to produce phenolic compounds, such as rhodocladonic, thamnolic, and didymic acids. However, these metabolites have not been detected in isolated mycobionts. We investigated the effects of six carbon sources on metabolite biosynthesis in the C. metacorallifera mycobiont. Red pigments appeared only in Lilly and Barnett's media with fructose at 15 °C after 3 weeks of culture and decreased after 6 weeks. We purified these red pigments using preparative-scale high performance liquid chromatography and analyzed them via nuclear magnetic resonance. Results indicated that 1% fructose-induced cristazarin and 6-methylcristazarin production under light conditions. In total, 27 out of 30 putative polyketide synthase genes were differentially expressed after 3 weeks of culture, implying that these genes may be required for cristazarin production in C. metacorallifera. Moreover, the white collar genes Cmwc-1 and Cmwc-2 were highly upregulated at all times under light conditions, indicating a possible correlation between cristazarin production and gene expression. The cancer cell lines AGS, CT26, and B16F1 were sensitive to cristazarin, with IC50 values of 18.2, 26.1, and 30.9 μg/mL, respectively, which highlights the value of cristazarin. Overall, our results suggest that 1% fructose under light conditions is required for cristazarin production by C. metacorallifera mycobionts, and cristazarin could be a good bioactive compound.

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.

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.

Deep learning approaches for natural product discovery from plant endophytic microbiomes

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

SUPPLEMENTARY INFORMATION

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

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.

Systems and synthetic biology to elucidate secondary metabolite biosynthetic gene clusters encoded in Streptomyces genomes

Covering: 2010 to 2020 Over the last few decades, Streptomyces have been extensively investigated for their ability to produce diverse bioactive secondary metabolites. Recent advances in Streptomyces research have been largely supported by improvements in high-throughput technology 'omics'. From genomics, numerous secondary metabolite biosynthetic gene clusters were predicted, increasing their genomic potential for novel bioactive compound discovery. Additional omics, including transcriptomics, translatomics, interactomics, proteomics and metabolomics, have been applied to obtain a system-level understanding spanning entire bioprocesses of Streptomyces, revealing highly interconnected and multi-layered regulatory networks for secondary metabolism. The comprehensive understanding derived from this systematic information accelerates the rational engineering of Streptomyces to enhance secondary metabolite production, integrated with the exploitation of the highly efficient 'Design-Build-Test-Learn' cycle in synthetic biology. In this review, we describe the current status of omics applications in Streptomyces research to better understand the organism and exploit its genetic potential for higher production of valuable secondary metabolites and novel secondary metabolite discovery.

Engineering the Erythromycin-Producing Strain Saccharopolyspora erythraea HOE107 for the Heterologous Production of Polyketide Antibiotics

Bacteria of the genus Saccharopolyspora produce important polyketide antibiotics, including erythromycin A (Sac. erythraea) and spinosad (Sac. spinosa). We herein report the development of an industrial erythromycin-producing strain, Sac. erythraea HOE107, into a host for the heterologous expression of polyketide biosynthetic gene clusters (BGCs) from other Saccharopolyspora species and related actinomycetes. To facilitate the integration of natural product BGCs and auxiliary genes beneficial for the production of natural products, the erythromycin polyketide synthase (ery) genes were replaced with two bacterial attB genomic integration sites associated with bacteriophages ϕC31 and ϕBT1. We also established a highly efficient conjugation protocol for the introduction of large bacterial artificial chromosome (BAC) clones into Sac. erythraea strains. Based on this optimized protocol, an arrayed BAC library was effectively transferred into Sac. erythraea. The large spinosad gene cluster from Sac. spinosa and the actinorhodin gene cluster from Streptomyces coelicolor were successfully expressed in the ery deletion mutant. Deletion of the endogenous giant polyketide synthase genes pkeA1-pkeA4, the product of which is not known, and the flaviolin gene cluster (rpp) from the bacterium increased the heterologous production of spinosad and actinorhodin. Furthermore, integration of pJTU6728 carrying additional beneficial genes dramatically improved the yield of actinorhodin in the engineered Sac. erythraea strains. Our study demonstrated that the engineered Sac. erythraea strains SLQ185, LJ161, and LJ162 are good hosts for the expression of heterologous antibiotics and should aid in expression-based genome-mining approaches for the discovery of new and cryptic antibiotics from Streptomyces and rare actinomycetes.

Strategies for Discovering New Antibiotics from Bacteria in the Post-Genomic Era

New antibiotics are urgently required in clinical treatment and agriculture with the development of antimicrobial resistance. However, products discovered by repeating previous strategies are either not antibiotics or already known antibiotics. There is a growing demand for efficient strategies to discover new antibiotics. With the continuous improvement of gene sequencing technology and genomic data, some mining strategies have emerged. These strategies are expected to alleviate the current dilemma of antibiotics. In this review, we discuss the recent advances in discovery of bacterial antibiotics from the following aspects: activation of silent gene clusters, genome mining and metagenome mining. In the future, we envision the discovery of natural antibiotic will be accelerated by the combination of these strategies.

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.

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.

Antimicrobial production by strictly anaerobic Clostridium spp

Antimicrobial resistance continues to rise on a global scale, affecting the environment, humans, animals and food systems. Use of natural antimicrobials has been favoured over synthetic molecules in food preservation owing to concerns over the adverse health effects of synthetic chemicals. The continuing need for novel natural antimicrobial compounds has spurred research to investigate natural sources, such as bacteria, for antimicrobials. The antimicrobial-producing potential of bacteria has been investigated in numerous studies. However, the discovery of antimicrobials has been biased towards aerobes and facultative anaerobes, and strict anaerobes such as Clostridium spp. have been largely neglected. In recent years, genomic studies have indicated the genetic potential of strict anaerobes to produce putative bioactive molecules and this has encouraged the exploration of Clostridium spp. for their antimicrobial production. So far, only a limited number of antimicrobial compounds have been isolated, identified and characterised from the genus Clostridium. This review discusses our current knowledge and understanding of clostridial antimicrobial compounds as well as recent genome mining studies of Clostridium spp. focused at identification of putative gene clusters encoding bacterial secondary metabolite groups and peptides reported to possess antimicrobial properties. Furthermore, opportunities and challenges in the identification of antimicrobials from Clostridium spp. using genomic-guided approaches are discussed. The limited studies conducted so far have identified the genus Clostridium as a viable source of antimicrobial compounds for future investigations.

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.

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.

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.

Amphibian skin-associated Pigmentiphaga: Genome sequence and occurrence across geography and hosts

The bacterial communities colonizing amphibian skin have been intensively studied due to their interactions with pathogenic chytrid fungi that are causing drastic amphibian population declines. Bacteria of the family Alcaligenaceae, and more specifically of the genus Pigmentiphaga, have been found to be associated specifically to arboreal frogs. Here we analyze their occurrence in a previously assembled global skin microbiome dataset from 205 amphibian species. Pigmentiphaga made up about 5% of the total number of reads in this global dataset. They were mostly found in unrelated arboreal frogs from Madagascar (Mantellidae and Hyperoliidae), but also occurred at low abundances on Neotropical frogs. Based on their 16S sequences, most of the sequences belong to a clade within Pigmentiphaga not assignable to any type strains of the five described species of the genus. One isolate from Madagascar clustered with Pigmentiphaga aceris (>99% sequence similarity on 16S rRNA gene level). Here, we report the full genome sequence of this bacterium which, based on 16S sequences of >97% similarity, has previously been found on human skin, floral nectar, tree sap, stream sediment and soil. Its genome consists of a single circular chromosome with 6,165,255 bp, 5,300 predicted coding sequences, 57 tRNA genes, and three rRNA operons. In comparison with other known Pigmentiphaga genomes it encodes a higher number of genes associated with environmental information processing and cellular processes. Furthermore, it has a biosynthetic gene cluster for a nonribosomal peptide syntethase, and bacteriocin biosynthetic genes can be found, but clusters for β-lactones present in other comparative Pigmentiphaga genomes are lacking.

Nonribosomal peptides in fungal cell factories: from genome mining to optimized heterologous production

Fungi are notoriously prolific producers of secondary metabolites including nonribosomal peptides (NRPs). The structural complexity of NRPs grants them interesting activities such as antibiotic, anti-cancer, and anti-inflammatory properties. The discovery of these compounds with attractive activities can be achieved by using two approaches: either by screening samples originating from various environments for their biological activities, or by identifying the related clusters in genomic sequences thanks to bioinformatics tools. This genome mining approach has grown tremendously due to recent advances in genome sequencing, which have provided an incredible amount of genomic data from hundreds of microbial species. Regarding fungal organisms, the genomic data have revealed the presence of an unexpected number of putative NRP-related gene clusters. This highlights fungi as a goldmine for the discovery of putative novel bioactive compounds. Recent development of NRP dedicated bioinformatics tools have increased the capacity to identify these gene clusters and to deduce NRPs structures, speeding-up the screening process for novel metabolites discovery. Unfortunately, the newly identified compound is frequently not or poorly produced by native producers due to a lack of expression of the related genes cluster. A frequently employed strategy to increase production rates consists in transferring the related biosynthetic pathway in heterologous hosts. This review aims to provide a comprehensive overview about the topic of NRPs discovery, from gene cluster identification by genome mining to the heterologous production in fungal hosts. The main computational tools and methods for genome mining are herein presented with an emphasis on the particularities of the fungal systems. The different steps of the reconstitution of NRP biosynthetic pathway in heterologous fungal cell factories will be discussed, as well as the key factors to consider for maximizing productivity. Several examples will be developed to illustrate the potential of heterologous production to both discover uncharacterized novel compounds predicted in silico by genome mining, and to enhance the productivity of interesting bio-active natural products.

Insights into the Diversity of Secondary Metabolites of Planktothrix Using a Biphasic Approach Combining Global Genomics and Metabolomics

Cyanobacteria are an ancient lineage of slow-growing photosynthetic bacteria and a prolific source of natural products with diverse chemical structures and potent biological activities and toxicities. The chemical identification of these compounds remains a major bottleneck. Strategies that can prioritize the most prolific strains and novel compounds are of great interest. Here, we combine chemical analysis and genomics to investigate the chemodiversity of secondary metabolites based on their pattern of distribution within some cyanobacteria. Planktothrix being a cyanobacterial genus known to form blooms worldwide and to produce a broad spectrum of toxins and other bioactive compounds, we applied this combined approach on four closely related strains of Planktothrix. The chemical diversity of the metabolites produced by the four strains was evaluated using an untargeted metabolomics strategy with high-resolution LC-MS. Metabolite profiles were correlated with the potential of metabolite production identified by genomics for the different strains. Although, the Planktothrix strains present a global similarity in terms of a biosynthetic cluster gene for microcystin, aeruginosin, and prenylagaramide for example, we found remarkable strain-specific chemodiversity. Only few of the chemical features were common to the four studied strains. Additionally, the MS/MS data were analyzed using Global Natural Products Social Molecular Networking (GNPS) to identify molecular families of the same biosynthetic origin. In conclusion, we depict an efficient, integrative strategy for elucidating the chemical diversity of a given genus and link the data obtained from analytical chemistry to biosynthetic genes of cyanobacteria.

From genomics to metabolomics, moving toward an integrated strategy for the discovery of fungal secondary metabolites

Fungal secondary metabolites are defined by bioactive properties that ensure adaptation of the fungus to its environment. Although some of these natural products are promising sources of new lead compounds especially for the pharmaceutical industry, others pose risks to human and animal health. The identification of secondary metabolites is critical to assessing both the utility and risks of these compounds. Since fungi present biological specificities different from other microorganisms, this review covers the different strategies specifically used in fungal studies to perform this critical identification. Strategies focused on the direct detection of the secondary metabolites are firstly reported. Particularly, advances in high-throughput untargeted metabolomics have led to the generation of large datasets whose exploitation and interpretation generally require bioinformatics tools. Then, the genome-based methods used to study the entire fungal metabolic potential are reported. Transcriptomic and proteomic tools used in the discovery of fungal secondary metabolites are presented as links between genomic methods and metabolomic experiments. Finally, the influence of the culture environment on the synthesis of secondary metabolites by fungi is highlighted as a major factor to consider in research on fungal secondary metabolites. Through this review, we seek to emphasize that the discovery of natural products should integrate all of these valuable tools. Attention is also drawn to emerging technologies that will certainly revolutionize fungal research and to the use of computational tools that are necessary but whose results should be interpreted carefully.

PRISM 3: expanded prediction of natural product chemical structures from microbial genomes

Microbial natural products represent a rich resource of pharmaceutically and industrially important compounds. Genome sequencing has revealed that the majority of natural products remain undiscovered, and computational methods to connect biosynthetic gene clusters to their corresponding natural products therefore have the potential to revitalize natural product discovery. Previously, we described PRediction Informatics for Secondary Metabolomes (PRISM), a combinatorial approach to chemical structure prediction for genetically encoded nonribosomal peptides and type I and II polyketides. Here, we present a ground-up rewrite of the PRISM structure prediction algorithm to derive prediction of natural products arising from non-modular biosynthetic paradigms. Within this new version, PRISM 3, natural product scaffolds are modeled as chemical graphs, permitting structure prediction for aminocoumarins, antimetabolites, bisindoles and phosphonate natural products, and building upon the addition of ribosomally synthesized and post-translationally modified peptides. Further, with the addition of cluster detection for 11 new cluster types, PRISM 3 expands to detect 22 distinct natural product cluster types. Other major modifications to PRISM include improved sequence input and ORF detection, user-friendliness and output. Distribution of PRISM 3 over a 300-core server grid improves the speed and capacity of the web application. PRISM 3 is available at http://magarveylab.ca/prism/.

Detecting and prioritizing biosynthetic gene clusters for bioactive compounds in bacteria and fungi

Secondary metabolites (SM) produced by fungi and bacteria have long been of exceptional interest owing to their unique biomedical ramifications. The traditional discovery of new natural products that was mainly driven by bioactivity screening has now experienced a fresh new approach in the form of genome mining. Several bioinformatics tools have been continuously developed to detect potential biosynthetic gene clusters (BGCs) that are responsible for the production of SM. Although the principles underlying the computation of these tools have been discussed, the biological background is left underrated and ambiguous. In this review, we emphasize the biological hypotheses in BGC formation driven from the observations across genomes in bacteria and fungi, and provide a comprehensive list of updated algorithms/tools exclusively for BGC detection. Our review points to a direction that the biological hypotheses should be systematically incorporated into the BGC prediction and assist the prioritization of candidate BGC.

New Approaches to Detect Biosynthetic Gene Clusters in the Environment

Microorganisms in the environment can produce a diverse range of secondary metabolites (SM), which are also known as natural products. Bioactive SMs have been crucial in the development of antibiotics and can also act as useful compounds in the biotechnology industry. These natural products are encoded by an extensive range of biosynthetic gene clusters (BGCs). The developments in omics technologies and bioinformatic tools are contributing to a paradigm shift from traditional culturing and screening methods to bioinformatic tools and genomics to uncover BGCs that were previously unknown or transcriptionally silent. Natural product discovery using bioinformatics and omics workflow in the environment has demonstrated an extensive distribution of BGCs in various environments, such as soil, aquatic ecosystems and host microbiome environments. Computational tools provide a feasible and culture-independent route to find new secondary metabolites where traditional approaches cannot. This review will highlight some of the advances in the approaches, primarily bioinformatic, in identifying new BGCs, especially in environments where microorganisms are rarely cultured. This has allowed us to tap into the huge potential of microbial dark matter.

Accessing Bioactive Natural Products from the Human Microbiome

Natural products have long played a pivotal role in the development of therapeutics for a variety of diseases. Traditionally, soil and marine environments have provided a rich reservoir from which diverse chemical scaffolds could be discovered. Recently, the human microbiome has been recognized as a promising niche from which secondary metabolites with therapeutic potential have begun to be isolated. In this Review, we address how the expansive history of identifying bacterial natural products in other environments is informing the approaches being brought to bear on the study of the human microbiota. We also touch on how these tools can lead to insights about microbe-microbe and host-microbe interactions and help generate biological hypotheses that may lead to developments of new therapeutic modalities.