ASMPKS: an analysis system for modular polyketide synthases

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.

Natural products and their semi-synthetic derivatives against antimicrobial-resistant human pathogenic bacteria and fungi

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COVID-19 pandemic has traumatized the entire world. During this outbreak, an upsurge in MDR-associated pathogenic microbial organisms has been recorded.

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The increasing human microbial diseases pose a severe danger to global human safety.

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The infectious microbes have developed multiple tolerance strategies to overcome the negative drug impacts.

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Several naturally occurring chemicals produced from bacteria, plants, animals, marine species, and other sources with antimicrobial characteristics have been reviewed.

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These compounds show promise in minimizing the globally increasing microbial diseases.

Human infectious diseases caused by various microbial pathogens, in general, impact a large population of individuals every year. These microbial diseases that spread quickly remain to be a big issue in various health-related domains and to withstand the negative drug impacts, the antimicrobial-resistant pathogenic microbial organisms (pathogenic bacteria and pathogenic fungi) have developed a variety of resistance processes against many antimicrobial drug classes. During the COVID-19 outbreak, there seems to be an upsurge in drug and multidrug resistant-associated pathogenic microbial species. The preponderance of existing antimicrobials isn’t completely effective, which limits their application in clinical settings. Several naturally occurring chemicals produced from bacteria, plants, animals, marine species, and other sources are now being studied for antimicrobial characteristics. These natural antimicrobial compounds extracted from different sources have been demonstrated to be effective against a variety of diseases, although plants remain the most abundant source. These compounds have shown promise in reducing the microbial diseases linked to the development of drug tolerance and resistance. This paper offers a detailed review of some of the most vital and promising natural compounds and their derivatives against various human infectious microbial organisms. The inhibitory action of different natural antimicrobial compounds, and their possible mechanism of antimicrobial action against a range of pathogenic fungal and bacterial organisms, is provided. The review will be useful in refining current antimicrobial (antifungal and antibacterial) medicines as well as establishing new treatment strategies to tackle the rising number of human bacterial and fungal-associated infections.

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.

The bacterial phylum Planctomycetes as novel source for bioactive small molecules

Extensive knowledge and methodological expertise on the bacterial cell biology have been accumulated over the last decades and bacterial cells have now become an integral part of several (bio-)technological processes. While it appears reasonable to focus on a relatively small number of fast-growing and genetically easily manipulable model bacteria as biotechnological workhorses, the for the most part untapped diversity of bacteria needs to be explored when it comes to bioprospecting for natural product discovery. Members of the underexplored and evolutionarily deep-branching phylum Planctomycetes have only recently gained increased attention with respect to the production of small molecules with biomedical activities, e.g. as a natural source of novel antibiotics. Next-generation sequencing and metagenomics can provide access to the genomes of uncultivated bacteria from sparsely studied phyla, this, however, should be regarded as an addition rather than a substitute for classical strain isolation approaches. Ten years ago, a large sampling campaign was initiated to isolate planctomycetes from their varied natural habitats and protocols were developed to address complications during cultivation of representative species in the laboratory. The characterisation of approximately 90 novel strains by several research groups in the recent years opened a detailed in silico look into the coding potential of individual members of this phylum. Here, we review the current state of planctomycetal research, focusing on diversity, small molecule production and potential future applications. Although the field developed promising, the time frame of 10 years illustrates that the study of additional promising bacterial phyla as sources for novel small molecules needs to start rather today than tomorrow.

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.

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.

Soil protein as a potential antimicrobial agent against methicillin -resistant Staphylococcus aureus

Recently, the interest is increasing to find alternatives to replace the usage of antibiotics since their massive and improper usage enhance the antibiotic resistance in human pathogens. In this study, for the first time we showed that the soil proteins have very high antibacterial activity (98% of growth inhibition) against methicillin resistant Staphylococcus aureus (MRSA), one of the most threatening human pathogens. We found that the protein extract (C3) from the forest with past intensive management showed higher antibacterial activity than that of unmanaged forest. The MIC and IC₅₀ were found to be 30 and 15.0 μg protein g^-1 dry soil respectively. C3 was found to kill the bacteria by cell wall disruption and genotoxicity which was confirmed by optical and fluorescent microscopy and comet assay. According to qPCR study, the mecA (the antibiotic resistant gene) expression in MRSA was found to be down-regulated after C3 treatment. In contrast, C3 showed no hemolytic toxicity on human red blood cells which was confirmed by hemolytic assay. According to ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS), 144 proteins were identified in C3 among which the majority belonged to Gram negative bacteria (45.8%). Altogether, our results will help to develop novel, cost-effective, non-toxic and highly efficient antibacterial medicines from natural sources against antibiotic resistant infections.

The Genome of the Plant-Associated Lactic Acid Bacterium Lactococcus lactis KF147 Harbors a Hybrid NRPS-PKS System Conserved in Strains of the Dental Cariogenic Streptococcus mutans

Lactococcus lactis subsp. lactis KF147 as a non-dairy strain from lactic acid bacteria (LAB) can inhabit plant tissues. It can grow on complex carbohydrates derived from plant cell walls. Its genome size is one of the largest among the sequenced lactococcal strains, possessing many genes that do not have homologues in the published genome sequences of dairy-associated L. lactis strains. In silico analysis has identified a gene cluster encoding a hybrid NRPS-PKS system (composed of non-ribosomal peptide synthetases and polyketide synthases) in the L. lactis KF147 genome, as first example of a LAB possessing such hybrid mega-enzymes. Hybrid systems produce hybrid NRP-PK secondary metabolites (natural products) in a wide variety of bacteria, fungi, and plants. In the hybrid NRPS-PKS system of L. lactis KF147, a total of 21 NRPS and 8 PKS domains were identified that are arranged into 6 NRPS modules, 3 PKS modules, and two single functional domains (trans-acyl-transferase "transAT" and thioesterase). We found homologous hybrid systems having similar gene, module, and domain organization in six other L. lactis strains and 25 strains of the dental cariogenic Streptococcus mutans. This study mainly aimed to predict the structure and function of the hybrid NRP-PK product of L. lactis KF147 using comparative genomics techniques, and included a detailed analysis of the regulatory system. Various bioinformatical approaches were used to predict the substrate specificity of the six A domains and the iterative transAT domain. Functional conservation of the A domains within different-niche-associated strains supported the prediction of the primary core structure of the putative hybrid natural product to be Leu-DLeu-Asp-DAsn-Gly-MC-MC-MC-DAsp (MC = Malonyl-CoA). Oxidative stress resistance and biofilm formation are the most probable functions of this hybrid system. The need for such a system in two different niches is argued, as an adaptation of L. lactis and S. mutans to adhere to plant tissues and human teeth, respectively, in an oxidative environment.

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.

A Review of the Microbial Production of Bioactive Natural Products and Biologics

A variety of organisms, such as bacteria, fungi, and plants, produce secondary metabolites, also known as natural products. Natural products have been a prolific source and an inspiration for numerous medical agents with widely divergent chemical structures and biological activities, including antimicrobial, immunosuppressive, anticancer, and anti-inflammatory activities, many of which have been developed as treatments and have potential therapeutic applications for human diseases. Aside from natural products, the recent development of recombinant DNA technology has sparked the development of a wide array of biopharmaceutical products, such as recombinant proteins, offering significant advances in treating a broad spectrum of medical illnesses and conditions. Herein, we will introduce the structures and diverse biological activities of natural products and recombinant proteins that have been exploited as valuable molecules in medicine, agriculture and insect control. In addition, we will explore past and ongoing efforts along with achievements in the development of robust and promising microorganisms as cell factories to produce biologically active molecules. Furthermore, we will review multi-disciplinary and comprehensive engineering approaches directed at improving yields of microbial production of natural products and proteins and generating novel molecules. Throughout this article, we will suggest ways in which microbial-derived biologically active molecular entities and their analogs could continue to inspire the development of new therapeutic agents in academia and industry.

Salicylic acid derivatives and phenylspirodrimanes from the sponge-associated fungus Hansfordia sinuosae

Three new salicylic acid derivatives (1-3) and a known one, 6-(3'-hydroxypropyl)-2-hydroxybenzoic acid (4), together with seven known phenylspirodrimanes (5-11), were isolated from the sponge-associated fungus Hansfordia sinuosae, collected from the South China Sea. HRESIMS, FT-IR Spectroscopy, and NMR techniques including COSY, HSQC, and HMBC were used to elucidate the structures of these compounds. The inhibitory effects of the isolated compounds (1-11) against HIV-1 virus were evaluated, and most of the phenylspirodrimanes (5, 8-11) showed varying degrees of anti-HIV activity.

Complete genome sequence and expression profile of the commercial lytic enzyme producer Lysobacter enzymogenes M497-1

Lysobacter enzymogenes M497-1 is a producer of commercialized achromopeptidase and is expected to harbour genes encoding various other antimicrobial enzymes. Here, we present the complete sequence of the genome of M497-1 and the expression profiles of the genes for various antimicrobial enzymes. Of the 117 peptidase-encoding genes found in the 6.1-Mb genome of M497-1, 15 genes (aside from the gene encoding the achromopeptidase) were expressed at a level higher than that of the average ribosomal protein genes in the 24-h culture. Thus, the strain was found more valuable than hitherto considered. In addition, M497-1 harbours 98 genes involved in the biosynthesis of various natural products, 16 of which are M497-1-specific across 4 Lysobacter species. A gene cluster starting at LEN_2603 through LEN_2673 among the 98 genes closely resembled the lysobactin biosynthesis gene cluster of Lysobacter sp. ATCC 53042. It is likely that M497-1 may produce lysobactin or related antibacterial compounds. Furthermore, comparative genomic analysis of M497-1 and four other Lysobacter species revealed that their core genome structure comprises 3,737 orthologous groups. Our findings are expected to advance further biotechnological application of Lysobacter spp. as a promising source of natural bioactive compounds.

Using natural products for drug discovery: the impact of the genomics era

INTRODUCTION

Evolutionarily selected over billions of years for their interactions with biomolecules, natural products have been and continue to be a major source of pharmaceuticals. In the 1990s, pharmaceutical companies scaled down their natural product discovery programs in favor of synthetic chemical libraries due to major challenges such as high rediscovery rates, challenging isolation, and low production titers. Propelled by advances in DNA sequencing and synthetic biology technologies, insights into microbial secondary metabolism provided have inspired a number of strategies to address these challenges. Areas covered: This review highlights the importance of genomics and metagenomics in natural product discovery, and provides an overview of the technical and conceptual advances that offer unprecedented access to molecules encoded by biosynthetic gene clusters. Expert opinion: Genomics and metagenomics revealed nature's remarkable biosynthetic potential and her vast chemical inventory that we can now prioritize and systematically mine for novel chemical scaffolds with desirable bioactivities. Coupled with synthetic biology and genome engineering technologies, significant progress has been made in identifying and predicting the chemical output of biosynthetic gene clusters, as well as in optimizing cluster expression in native and heterologous host systems for the production of pharmaceutically relevant metabolites and their derivatives.

Identification by Genome Mining of a Type I Polyketide Gene Cluster from Streptomyces argillaceus Involved in the Biosynthesis of Pyridine and Piperidine Alkaloids Argimycins P

Genome mining of the mithramycin producer Streptomyces argillaceus ATCC 12956 revealed 31 gene clusters for the biosynthesis of secondary metabolites, and allowed to predict the encoded products for 11 of these clusters. Cluster 18 (renamed cluster arp) corresponded to a type I polyketide gene cluster related to the previously described coelimycin P1 and streptazone gene clusters. The arp cluster consists of fourteen genes, including genes coding for putative regulatory proteins (a SARP-like transcriptional activator and a TetR-like transcriptional repressor), genes coding for structural proteins (three PKSs, one aminotransferase, two dehydrogenases, two cyclases, one imine reductase, a type II thioesterase, and a flavin reductase), and one gene coding for a hypothetical protein. Identification of encoded compounds by this cluster was achieved by combining several strategies: (i) inactivation of the type I PKS gene arpPIII; (ii) inactivation of the putative TetR-transcriptional repressor arpRII; (iii) cultivation of strains in different production media; and (iv) using engineered strains with higher intracellular concentration of malonyl-CoA. This has allowed identifying six new alkaloid compounds named argimycins P, which were purified and structurally characterized by mass spectrometry and nuclear magnetic resonance spectroscopy. Some argimycins P showed a piperidine ring with a polyene side chain (argimycin PIX); others contain also a fused five-membered ring (argimycins PIV-PVI). Argimycins PI-PII showed a pyridine ring instead, and an additional N-acetylcysteinyl moiety. These compounds seem to play a negative role in growth and colony differentiation in S. argillaceus, and some of them show weak antibiotic activity. A pathway for the biosynthesis of argimycins P is proposed, based on the analysis of proposed enzyme functions and on the structure of compounds encoded by the arp cluster.

In silico methods for linking genes and secondary metabolites: The way forward

In silico methods for linking genomic space to chemical space have played a crucial role in genomics driven discovery of new natural products as well as biosynthesis of altered natural products by engineering of biosynthetic pathways. Here we give an overview of available computational tools and then briefly describe a novel computational framework, namely retro-biosynthetic enumeration of biosynthetic reactions, which can add to the repertoire of computational tools available for connecting natural products to their biosynthetic gene clusters. Most of the currently available bioinformatics tools for analysis of secondary metabolite biosynthetic gene clusters utilize the “Genes to Metabolites” approach. In contrast to the “Genes to Metabolites” approach, the “Metabolites to Genes” or retro-biosynthetic approach would involve enumerating the various biochemical transformations or enzymatic reactions which would generate the given chemical moiety starting from a set of precursor molecules and identifying enzymatic domains which can potentially catalyze the enumerated biochemical transformations. In this article, we first give a brief overview of the presently available in silico tools and approaches for analysis of secondary metabolite biosynthetic pathways. We also discuss our preliminary work on development of algorithms for retro-biosynthetic enumeration of biochemical transformations to formulate a novel computational method for identifying genes associated with biosynthesis of a given polyketide or nonribosomal peptide.

Dereplication, sequencing and identification of peptidic natural products: from genome mining to peptidogenomics to spectral networks

Covering: 2000 to 2015. While recent breakthroughs in the discovery of peptide antibiotics and other Peptidic Natural Products (PNPs) raise a challenge for developing new algorithms for their analyses, the computational technologies for high-throughput PNP discovery are still lacking. We discuss the computational bottlenecks in analyzing PNPs and review recent advances in genome mining, peptidogenomics, and spectral networks that are now enabling the discovery of new PNPs via mass spectrometry. We further describe the connections between these advances and the new generation of software tools for PNP dereplication, de novo sequencing, and identification.

Genome Mining of Streptomyces sp. Tü 6176: Characterization of the Nataxazole Biosynthesis Pathway

Streptomyces sp. Tü 6176 produces the cytotoxic benzoxazole nataxazole. Bioinformatic analysis of the genome of this organism predicts the presence of 38 putative secondary-metabolite biosynthesis gene clusters, including those involved in the biosynthesis of AJI9561 and its derivative nataxazole, the antibiotic hygromycin B, and ionophores enterobactin and coelibactin. The nataxazole biosynthesis gene cluster was identified and characterized: it lacks the O-methyltransferase gene required to convert AJI9561 into nataxazole. This O-methyltransferase activity might act as a resistance mechanism, as AJI9561 shows antibiotic activity whereas nataxazole is inactive. Moreover, heterologous expression of the nataxazole biosynthesis gene cluster in S. lividans JT46 resulted in the production of AJI9561. Nataxazole biosynthesis requires the shikimate pathway to generate 3-hydroxyanthranilate and an iterative type I PKS to generate 6-methylsalicylate. Production of nataxazole was improved up to fourfold by disrupting one regulatory gene in the cluster. An additional benzoxazole, 5-hydroxynataxazole is produced by Streptomyces sp. Tü 6176. 5-Hydroxynataxazole derives from nataxazole by the activity of an as yet unidentified oxygenase; this implies cross-talk between the nataxazole biosynthesis pathway and an unknown pathway.

Exploring cyanobacterial genomes for natural product biosynthesis pathways

Cyanobacteria produce a vast array of natural products, some of which are toxic to human health, while others possess potential pharmaceutical activities. Genome mining enables the identification and characterisation of natural product gene clusters; however, the current number of cyanobacterial genomes remains low compared to other phyla. There has been a recent effort to rectify this issue by increasing the number of sequenced cyanobacterial genomes. This has enabled the identification of biosynthetic gene clusters for structurally diverse metabolites, including non-ribosomal peptides, polyketides, ribosomal peptides, UV-absorbing compounds, alkaloids, terpenes and fatty acids. While some of the identified biosynthetic gene clusters correlate with known metabolites, genome mining also highlights the number and diversity of clusters for which the product is unknown (referred to as orphan gene clusters). A number of bioinformatic tools have recently been developed in order to predict the products of orphan gene clusters; however, in some cases the complexity of the cyanobacterial pathways makes the prediction problematic. This can be overcome by the use of mass spectrometry-guided natural product genome mining, or heterologous expression. Application of these techniques to cyanobacterial natural product gene clusters will be explored.

A roadmap for natural product discovery based on large-scale genomics and metabolomics

Actinobacteria encode a wealth of natural product biosynthetic gene clusters (NPGCs), whose systematic study is complicated by numerous repetitive motifs. By combining several metrics we developed a method for global classification of these gene clusters into families (GCFs) and analyzed the biosynthetic capacity of Actinobacteria in 830 genome sequences, including 344 obtained for this project. The GCF network, comprised of 11,422 gene clusters grouped into 4,122 GCFs, was validated in hundreds of strains by correlating confident mass spectrometric detection of known small molecules with the presence/absence of their established biosynthetic gene clusters. The method also linked previously unassigned GCFs to known natural products, an approach that will enable de novo, bioassay-free discovery of novel natural products using large data sets. Extrapolation from the 830-genome dataset reveals that Actinobacteria encode hundreds of thousands of future drug leads, while the strong correlation between phylogeny and GCFs frames a roadmap to efficiently access them.

Characterization and analysis of an industrial strain of Streptomyces bingchenggensis by genome sequencing and gene microarray

Streptomyces bingchenggensis is a soil bacterium that produces milbemycins, a family of macrolide antibiotics that are commercially important in crop protection and veterinary medicine. In addition, S. bingchenggensis produces many other natural products including the polyether nanchangmycin and novel cyclic pentapeptides. To identify the gene clusters involved in the biosynthesis of these compounds, and better clarify the biochemical pathways of these gene clusters, the whole genome of S. bingchenggensis was sequenced, and the transcriptome profile was subsequently investigated by microarray. In comparison with other sequenced genomes in Streptomyces, S. bingchenggensis has the largest linear chromosome consisting of 11 936 683 base pairs (bp), with an average GC content of 70.8%. The 10 023 predicted protein-coding sequences include at least 47 gene clusters correlated with the biosynthesis of known or predicted secondary metabolites. Transcriptional analysis demonstrated an extremely high expression level of the milbemycin gene cluster during the entire growth period and a moderately high expression level of the nanchangmycin gene cluster during the initial hours that subsequently decreased. However, other gene clusters appear to be silent. The genome-wide analysis of the secondary metabolite gene clusters in S. bingchenggensis, coupled with transcriptional analysis, will facilitate the rational development of high milbemycins-producing strains as well as the discovery of new natural products.

Activation and identification of five clusters for secondary metabolites in Streptomyces albus J1074

Streptomyces albus J1074 is a streptomycete strain widely used as a host for expression of secondary metabolite gene clusters. Bioinformatic analysis of the genome of this organism predicts the presence of 27 gene clusters for secondary metabolites. We have used three different strategies for the activation of some of these silent/cryptic gene clusters in S. albus J1074: two hybrid polyketide-non-ribosomal peptides (PK-NRP) (antimycin and 6-epi-alteramides), a type I PK (candicidin), a non-ribosomal peptides (NRP) (indigoidine) and glycosylated compounds (paulomycins). By insertion of a strong and constitutive promoter in front of selected genes of two clusters, production of the blue pigment indigoidine and of two novel members of the polycyclic tetramate macrolactam family (6-epi-alteramides A and B) was activated. Overexpression of positive regulatory genes from the same organism also activated the biosynthesis of 6-epi-alteramides and heterologous expression of the regulatory gene pimM of the pimaricin cluster activated the simultaneous production of candicidins and antimycins, suggesting some kind of cross-regulation between both clusters. A cluster for glycosylated compounds (paulomycins) was also identified by comparison of the high-performance liquid chromatography profiles of the wild-type strain with that of a mutant in which two key enzymes of the cluster were simultaneously deleted.

Genomic characteristics and comparative genomics analysis of Penicillium chrysogenum KF-25

Background

Penicillium chrysogenum has been used in producing penicillin and derived β-lactam antibiotics for many years. Although the genome of the mutant strain P. chrysogenum Wisconsin 54-1255 has already been sequenced, the versatility and genetic diversity of this species still needs to be intensively studied. In this study, the genome of the wild-type P. chrysogenum strain KF-25, which has high activity against Ustilaginoidea virens, was sequenced and characterized.

Results

The genome of KF-25 was about 29.9 Mb in size and contained 9,804 putative open reading frames (orfs). Thirteen genes were predicted to encode two-component system proteins, of which six were putatively involved in osmolarity adaption. There were 33 putative secondary metabolism pathways and numerous genes that were essential in metabolite biosynthesis. Several P. chrysogenum virus untranslated region sequences were found in the KF-25 genome, suggesting that there might be a relationship between the virus and P. chrysogenum in evolution. Comparative genome analysis showed that the genomes of KF-25 and Wisconsin 54-1255 were highly similar, except that KF-25 was 2.3 Mb smaller. Three hundred and fifty-five KF-25 specific genes were found and the biological functions of the proteins encoded by these genes were mainly unknown (232, representing 65%), except for some orfs encoding proteins with predicted functions in transport, metabolism, and signal transduction. Numerous KF-25-specific genes were found to be associated with the pathogenicity and virulence of the strains, which were identical to those of wild-type P. chrysogenum NRRL 1951.

Conclusion

Genome sequencing and comparative analysis are helpful in further understanding the biology, evolution, and environment adaption of P. chrysogenum, and provide a new tool for identifying further functional metabolites.

In silico tools for the analysis of antibiotic biosynthetic pathways

Natural products of bacteria and fungi are the most important source for antimicrobial drug leads. For decades, such compounds were exclusively found by chemical/bioactivity-guided screening approaches. The rapid progress in sequencing technologies only recently allowed the development of novel screening methods based on the genome sequences of potential producing organisms. The basic principle of such genome mining approaches is to identify genes, which are involved in the biosynthesis of such molecules, and to predict the products of the identified pathways. Thus, bioinformatics methods and tools are crucial for genome mining. In this review, a comprehensive overview is given on programs and databases for the identification and analysis of antibiotic biosynthesis gene clusters in genomic data.

Classification of the adenylation and acyl-transferase activity of NRPS and PKS systems using ensembles of substrate specific hidden Markov models

There is a growing interest in the Non-ribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs) of microbes, fungi and plants because they can produce bioactive peptides such as antibiotics. The ability to identify the substrate specificity of the enzyme's adenylation (A) and acyl-transferase (AT) domains is essential to rationally deduce or engineer new products. We here report on a Hidden Markov Model (HMM)-based ensemble method to predict the substrate specificity at high quality. We collected a new reference set of experimentally validated sequences. An initial classification based on alignment and Neighbor Joining was performed in line with most of the previously published prediction methods. We then created and tested single substrate specific HMMs and found that their use improved the correct identification significantly for A as well as for AT domains. A major advantage of the use of HMMs is that it abolishes the dependency on multiple sequence alignment and residue selection that is hampering the alignment-based clustering methods. Using our models we obtained a high prediction quality for the substrate specificity of the A domains similar to two recently published tools that make use of HMMs or Support Vector Machines (NRPSsp and NRPS predictor2, respectively). Moreover, replacement of the single substrate specific HMMs by ensembles of models caused a clear increase in prediction quality. We argue that the superiority of the ensemble over the single model is caused by the way substrate specificity evolves for the studied systems. It is likely that this also holds true for other protein domains. The ensemble predictor has been implemented in a simple web-based tool that is available at http://www.cmbi.ru.nl/NRPS-PKS-substrate-predictor/.

Computational Methods for Identification of Novel Secondary Metabolite Biosynthetic Pathways by Genome Analysis

Secondary metabolites belonging to polyketide and nonribosomal peptide families constitute a major class of natural products with diverse biological functions and a variety of pharmaceutically important properties. Experimental studies have shown that the biosynthetic machinery for polyketide and nonribosomal peptides involves multi-functional megasynthases like Polyketide Synthases (PKSs) and nonribosomal peptide synthetases (NRPSs) which utilize a thiotemplate mechanism similar to that for fatty acid biosynthesis. Availability of complete genome sequences for an increasing number of microbial organisms has provided opportunities for using in silico genome mining to decipher the secondary metabolite natural product repertoire encoded by these organisms. Therefore, in recent years there have been major advances in development of computational methods which can analyze genome sequences to identify genes involved in secondary metabolite biosynthesis and help in deciphering the putative chemical structures of their biosynthetic products based on analysis of the sequence and structural features of the proteins encoded by these genes. These computational methods for deciphering the secondary metabolite biosynthetic code essentially involve identification of various catalytic domains present in this PKS/NRPS family of enzymes; a prediction of various reactions in these enzymatic domains and their substrate specificities and also precise identification of the order in which these domains would catalyze various biosynthetic steps. Structural bioinformatics analysis of known secondary metabolite biosynthetic clusters has helped in formulation of predictive rules for deciphering domain organization, substrate specificity, and order of substrate channeling. In this chapter, the progress in development of various computational methods is discussed by different research groups, and specifically, the utility in identification of novel metabolites by genome mining and rational design of natural product analogs by biosynthetic engineering studies.

Databases of the thiotemplate modular systems (CSDB) and their in silico recombinants (r-CSDB)

Modular biosynthetic clusters are responsible for the synthesis of many important pharmaceutical products. They include polyketide synthases (PKS clusters), non-ribosomal synthetases (NRPS clusters), and mixed clusters (containing both PKS and NRPS modules). The ClustScan database (CSDB) contains highly annotated descriptions of 170 clusters. The database has a hierarchical organization, which allows easy extraction of DNA and protein sequences of polypeptides, modules, and domains as well as an organization of the annotation so as to be able to predict the product chemistry to view it or export it in a standard SMILES format. The recombinant ClustScan database contains information about predicted recombinants between PKS clusters. The recombinants are generated by modeling homologous recombination and are associated with annotation and prediction of product chemistry automatically generated by the model. The database contains over 20,000 recombinants and is a resource for in silico approaches to detecting promising new compounds. Methods are available to construct the corresponding recombinants in the laboratory.

Identification and bioanalysis of natural products from insect symbionts and pathogens

: With the development of several novel methods in genome sequencing, molecular biology, and analytical chemistry a new area of natural product chemistry is currently starting that allows the analysis of minute amounts of complex biological samples. The combination of these methods, as discussed in this review, also enables the analysis of bacteria living in symbiosis or being pathogenic to insects, which might be the largest reservoir for novel microbes associated with higher organisms due to the huge number of insect species.

A genomic approach to the cryptic secondary metabolome of the anaerobic world

A total of 211 complete and published genomes from anaerobic bacteria are analysed for the presence of secondary metabolite biosynthesis gene clusters, in particular those tentatively coding for polyketide synthases (PKS) and non-ribosomal peptide synthetases (NRPS). We investigate the distribution of these gene clusters according to bacterial phylogeny and, if known, correlate these to the type of metabolic pathways they encode. The potential of anaerobes as secondary metabolite producers is highlighted.

Navigating the fungal polyketide chemical space: from genes to molecules

The iterative type I polyketide synthases (IPKSs) are central to the biosynthesis of an enormously diverse array of natural products in fungi. These natural products, known as polyketides, exhibit a wide range of biological activities and include clinically important drugs as well as undesirable toxins. The PKSs synthesize these structurally diverse polyketides via a series of decarboxylative condensations of malonyl-CoA extender units and β-keto modifications in a highly programmed manner. Significant progress has been made over the past few years in understanding the biosynthetic mechanism and programming of fungal PKSs. The continuously expanding fungal genome sequence data have sparked genome-directed discoveries of new fungal PKSs and associated products. The increasing number of fungal PKSs that have been linked to their products along with in-depth biochemical and structural characterizations of these large enzymes have remarkably improved our knowledge on the molecular basis for polyketide structural diversity in fungi. This Perspective highlights the recent advances and examines how the newly expanded paradigm has contributed to our ability to link fungal PKS genes to chemical structures and vice versa. The knowledge will help us navigate through the logarithmically expanding seas of genomic information for polyketide compound discovery and provided opportunities to reprogram these megasynthases to generate new chemical entities.

PKMiner: a database for exploring type II polyketide synthases

Background

Bacterial aromatic polyketides are a pharmacologically important group of natural products synthesized by type II polyketide synthases (type II PKSs) in actinobacteria. Isolation of novel aromatic polyketides from microbial sources is currently impeded because of the lack of knowledge about prolific taxa for polyketide synthesis and the difficulties in finding and optimizing target microorganisms. Comprehensive analysis of type II PKSs and the prediction of possible polyketide chemotypes in various actinobacterial genomes will thus enable the discovery or synthesis of novel polyketides in the most plausible microorganisms.

Description

We performed a comprehensive computational analysis of type II PKSs and their gene clusters in actinobacterial genomes. By identifying type II PKS subclasses from the sequence analysis of 280 known type II PKSs, we developed highly accurate domain classifiers for these subclasses and derived prediction rules for aromatic polyketide chemotypes generated by different combinations of type II PKS domains. Using 319 available actinobacterial genomes, we predicted 231 type II PKSs from 40 PKS gene clusters in 25 actinobacterial genomes, and polyketide chemotypes corresponding to 22 novel PKS gene clusters in 16 genomes. These results showed that the microorganisms capable of producing aromatic polyketides are specifically distributed within a certain suborder of Actinomycetales such as Catenulisporineae, Frankineae, Micrococcineae, Micromonosporineae, Pseudonocardineae, Streptomycineae, and Streptosporangineae.

Conclusions

We could identify the novel candidates of type II PKS gene clusters and their polyketide chemotypes in actinobacterial genomes by comprehensive analysis of type II PKSs and prediction of aromatic polyketides. The genome analysis results indicated that the specific suborders in actinomycetes could be used as prolific taxa for polyketide synthesis. The chemotype-prediction rules with the suggested type II PKS modules derived using this resource can be used further for microbial engineering to produce various aromatic polyketides. All these resources, together with the results of the analysis, are organized into an easy-to-use database PKMiner, which is accessible at the following URL: http://pks.kaist.ac.kr/pkminer. We believe that this web-based tool would be useful for research in the discovery of novel bacterial aromatic polyketides.

Primer on agar-based microbial imaging mass spectrometry

Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) imaging mass spectrometry (IMS) applied directly to microbes on agar-based medium captures global information about microbial molecules, allowing for direct correlation of chemotypes to phenotypes. This tool was developed to investigate metabolic exchange factors of intraspecies, interspecies, and polymicrobial interactions. Based on our experience of the thousands of images we have generated in the laboratory, we present five steps of microbial IMS: culturing, matrix application, dehydration of the sample, data acquisition, and data analysis/interpretation. We also address the common challenges encountered during sample preparation, matrix selection and application, and sample adherence to the MALDI target plate. With the practical guidelines described herein, microbial IMS use can be extended to bio-based agricultural, biofuel, diagnostic, and therapeutic discovery applications.

Biosynthesis of a complex yersiniabactin-like natural product via the mic locus in phytopathogen Ralstonia solanacearum

A genome mining study in the plant pathogenic bacterium Ralstonia solanacearum GMI1000 unveiled a polyketide synthase/nonribosomal peptide synthetase gene cluster putatively involved in siderophore biosynthesis. Insertional mutagenesis confirmed the respective locus to be operational under iron-deficient conditions and spurred the isolation of the associated natural product. Bioinformatic analyses of the gene cluster facilitated the structural characterization of this compound, which was subsequently identified as the antimycoplasma agent micacocidin. The metal-chelating properties of micacocidin were evaluated in competition experiments, and the cellular uptake of gallium-micacocidin complexes was demonstrated in R. solanacearum GMI1000, indicating a possible siderophore role. Comparative genomics revealed a conservation of the micacocidin gene cluster in defined, but globally dispersed phylotypes of R. solanacearum.

SBSPKS: structure based sequence analysis of polyketide synthases

Polyketide synthases (PKSs) catalyze biosynthesis of a diverse family of pharmaceutically important secondary metabolites. Bioinformatics analysis of sequence and structural features of PKS proteins plays a crucial role in discovery of new natural products by genome mining, as well as in design of novel secondary metabolites by biosynthetic engineering. The availability of the crystal structures of various PKS catalytic and docking domains, and mammalian fatty acid synthase module prompted us to develop SBSPKS software which consists of three major components. Model_3D_PKS can be used for modeling, visualization and analysis of 3D structure of individual PKS catalytic domains, dimeric structures for complete PKS modules and prediction of substrate specificity. Dock_Dom_Anal identifies the key interacting residue pairs in inter-subunit interfaces based on alignment of inter-polypeptide linker sequences to the docking domain structure. In case of modular PKS with multiple open reading frames (ORFs), it can predict the cognate order of substrate channeling based on combinatorial evaluation of all possible interface contacts. NRPS–PKS provides user friendly tools for identifying various catalytic domains in the sequence of a Type I PKS protein and comparing them with experimentally characterized PKS/NRPS clusters cataloged in the backend databases of SBSPKS. SBSPKS is available at http://www.nii.ac.in/sbspks.html.

CLUSEAN: a computer-based framework for the automated analysis of bacterial secondary metabolite biosynthetic gene clusters

Bacterial secondary metabolites are an important source of antimicrobial and cytostatic drugs. These molecules are often synthesized in a stepwise fashion by multimodular megaenzymes that are encoded in clusters of genes encoding enzymes for precursor supply and modification. In this work,we present an open source software pipeline, CLUSEAN (CLUster SEquence ANalyzer) that helps to annotate and analyze such gene clusters. CLUSEAN integrates standard analysis tools, like BLAST and HMMer, with specific tools for the identification of the functional domains and motifs in nonribosomal peptide synthetases (NRPS)/type I polyketide synthases (PKS) and the prediction of specificities of NRPS.

Automated genome mining for natural products

BACKGROUND

Discovery of new medicinal agents from natural sources has largely been an adventitious process based on screening of plant and microbial extracts combined with bioassay-guided identification and natural product structure elucidation. Increasingly rapid and more cost-effective genome sequencing technologies coupled with advanced computational power have converged to transform this trend toward a more rational and predictive pursuit.

RESULTS

We have developed a rapid method of scanning genome sequences for multiple polyketide, nonribosomal peptide, and mixed combination natural products with output in a text format that can be readily converted to two and three dimensional structures using conventional software. Our open-source and web-based program can assemble various small molecules composed of twenty standard amino acids and twenty two other chain-elongation intermediates used in nonribosomal peptide systems, and four acyl-CoA extender units incorporated into polyketides by reading a hidden Markov model of DNA. This process evaluates and selects the substrate specificities along the assembly line of nonribosomal synthetases and modular polyketide synthases.

CONCLUSION

Using this approach we have predicted the structures of natural products from a diverse range of bacteria based on a limited number of signature sequences. In accelerating direct DNA to metabolomic analysis, this method bridges the interface between chemists and biologists and enables rapid scanning for compounds with potential therapeutic value.

Bioinformatic perspectives on NRPS/PKS megasynthases: advances and challenges

The increased understanding of both fundamental principles and mechanistic variations of NRPS/PKS megasynthases along with the unprecedented availability of microbial sequences has inspired a number of in silico studies of both enzyme families. The insights that can be extracted from these analyses go far beyond a rough classification of data and have turned bioinformatics into a frontier field of natural products research. As databases are flooded with NRPS/PKS gene sequence of microbial genomes and metagenomes, increasingly reliable structural prediction methods can help to uncover hidden treasures. Already, phylogenetic analyses have revealed that NRPS/PKS pathways should not simply be regarded as enzyme complexes, specifically evolved to product a selected natural product. Rather, they represent a collection of genetic opinions, allowing biosynthetic pathways to be shuffled in a process of perpetual chemical innovations and pathways diversification in nature can give impulses for specificities, protein interactions and genetic engineering of libraries of novel peptides and polyketides. The successful translation of the knowledge obtained from bioinformatic dissection of NRPS/PKS megasynthases into new techniques for drug discovery and design remain challenges for the future.

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

The program package ‘ClustScan’ (Cluster Scanner) is designed for rapid, semi-automatic, annotation of DNA sequences encoding modular biosynthetic enzymes including polyketide synthases (PKS), non-ribosomal peptide synthetases (NRPS) and hybrid (PKS/NRPS) enzymes. The program displays the predicted chemical structures of products as well as allowing export of the structures in a standard format for analyses with other programs. Recent advances in understanding of enzyme function are incorporated to make knowledge-based predictions about the stereochemistry of products. The program structure allows easy incorporation of additional knowledge about domain specificities and function. The results of analyses are presented to the user in a graphical interface, which also allows easy editing of the predictions to incorporate user experience. The versatility of this program package has been demonstrated by annotating biochemical pathways in microbial, invertebrate animal and metagenomic datasets. The speed and convenience of the package allows the annotation of all PKS and NRPS clusters in a complete Actinobacteria genome in 2–3 man hours. The open architecture of ClustScan allows easy integration with other programs, facilitating further analyses of results, which is useful for a broad range of researchers in the chemical and biological sciences.