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Soil microbiota can confer fitness advantages to plants and increase crop resilience to drought and other abiotic stressors. However, there is little evidence on the mechanisms correlating a microbial trait with plant abiotic stress tolerance. Here, we report that Streptomyces effectively alleviate drought and salinity stress by producing spiroketal polyketide pteridic acid H (1) and its isomer F (2), both of which promote root growth in Arabidopsis at a concentration of 1.3 nM under abiotic stress. Transcriptomics profiles show increased expression of multiple stress responsive genes in Arabidopsis seedlings after pteridic acids treatment. We confirm in vivo a bifunctional biosynthetic gene cluster for pteridic acids and antimicrobial elaiophylin production. We propose it is mainly disseminated by vertical transmission and is geographically distributed in various environments. This discovery reveals a perspective for understanding plant-Streptomyces interactions and provides a promising approach for utilising beneficial Streptomyces and their secondary metabolites in agriculture to mitigate the detrimental effects of climate change. Soil microbiota can increase crop resilience to abiotic stressors. Here the authors show that Streptomyces produce bioactive spiroketal polyketides to enhance plant growth under drought and salt stress. [ABSTRACT FROM AUTHOR]

Background: Escherichia coli Nissle 1917 (EcN) is a probiotic bacterium used to treat various gastrointestinal diseases. EcN is increasingly being used as a chassis for the engineering of advanced microbiome therapeutics. To aid in future engineering efforts, our aim was to construct an updated metabolic model of EcN with extended secondary metabolite representation. Results: An updated high-quality genome-scale metabolic model of EcN, iHM1533, was developed based on comparison with 55 E. coli/Shigella reference GEMs and manual curation, including expanded secondary metabolite pathways (enterobactin, salmochelins, aerobactin, yersiniabactin, and colibactin). The model was validated and improved using phenotype microarray data, resulting in an 82.3% accuracy in predicting growth phenotypes on various nutrition sources. Flux variability analysis with previously published 13C fluxomics data validated prediction of the internal central carbon fluxes. A standardised test suite called Memote assessed the quality of iHM1533 to have an overall score of 89%. The model was applied by using constraint-based flux analysis to predict targets for optimisation of secondary metabolite production. Modelling predicted design targets from across amino acid metabolism, carbon metabolism, and other subsystems that are common or unique for influencing the production of various secondary metabolites. Conclusion: iHM1533 represents a well-annotated metabolic model of EcN with extended secondary metabolite representation. Phenotype characterisation and the iHM1533 model provide a better understanding of the metabolic capabilities of EcN and will help future metabolic engineering efforts. [ABSTRACT FROM AUTHOR]

In silico genome mining provides easy access to secondary metabolite biosynthetic gene clusters (BGCs) encoding the biosynthesis of many bioactive compounds, which are the basis for many important drugs used in human medicine. However, the association between BGCs and other functions encoded in the genomes of producers have remained elusive. Here, we present a systems biology workflow that integrates genome mining with a detailed pangenome analysis for detecting genes associated with a particular BGC. We analyzed 3,889 enterobacterial genomes and found 13,266 BGCs, represented by 252 distinct BGC families and 347 additional singletons. A pangenome analysis revealed 88 genes putatively associated with a specific BGC coding for the colon cancer-related colibactin that code for diverse metabolic and regulatory functions. The presented workflow opens up the possibility to discover novel secondary metabolites, better understand their physiological roles, and provides a guide to identify and analyze BGC associated gene sets. [ABSTRACT FROM AUTHOR]

Streptomyces griseofuscus DSM 40191 is a fast growing Streptomyces strain that remains largely underexplored as a heterologous host. Here, we report the genome mining of S. griseofuscus, followed by the detailed exploration of its phenotype, including the production of native secondary metabolites and ability to utilise carbon, nitrogen, sulphur and phosphorus sources. Furthermore, several routes for genetic engineering of S. griseofuscus were explored, including use of GusA-based vectors, CRISPR-Cas9 and CRISPR-cBEST-mediated knockouts. Two out of the three native plasmids were cured using CRISPR-Cas9 technology, leading to the generation of strain S. griseofuscus DEL1. DEL1 was further modified by the full deletion of a pentamycin BGC and an unknown NRPS BGC, leading to the generation of strain DEL2, lacking approx. 500 kbp of the genome, which corresponds to a 5.19% genome reduction. DEL2 can be characterized by faster growth and inability to produce three main native metabolites: lankacidin, lankamycin, pentamycin and their derivatives. To test the ability of DEL2 to heterologously produce secondary metabolites, the actinorhodin BGC was used. We were able to observe a formation of a blue halo, indicating a potential production of actinorhodin by both DEL2 and a wild type. [ABSTRACT FROM AUTHOR]

Polyketides such as the antibiotic erythromycin or the immunosuppressant rapamycin, and non-ribosomal peptides, such as the antibiotics penicillin or vancomycin, are important classes of natural products. The core of these molecules are biosynthesized by large polyketide synthases (PKS) and non-ribosomal peptide synthetases (NRPS), respectively. The modular architecture of these enzymatic assembly lines makes them interesting candidates for synthetic biology approaches. The re-engineering efforts aim to understand the molecular structure, produce new compounds, produce analogs of known compounds, tag the products or improve activity and/or yield. Here, we first consider the definition of PKS and NRPS modules, then give an overview of different strategies for re-engineering and finally review recent examples of PKS and NRPS reengineering. [ABSTRACT FROM AUTHOR]

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

Covering: 2006 to 2016 The computational mining of genomes has become an important part in the discovery of novel natural products as drug leads. Thousands of bacterial genome sequences are publically available these days containing an even larger number and diversity of secondary metabolite gene clusters that await linkage to their encoded natural products. With the development of high-throughput sequencing methods and the wealth of DNA data available, a variety of genome mining methods and tools have been developed to guide discovery and characterisation of these compounds. This article reviews the development of these computational approaches during the last decade and shows how the revolution of next generation sequencing methods has led to an evolution of various genome mining approaches, techniques and tools. After a short introduction and brief overview of important milestones, this article will focus on the different approaches of mining genomes for secondary metabolites, from detecting biosynthetic genes to resistance based methods and “evo-mining” strategies including a short evaluation of the impact of the development of genome mining methods and tools on the field of natural products and microbial ecology. [ABSTRACT FROM AUTHOR]

Covering: 2012 to 2016 Metabolic engineering using systems biology tools is increasingly applied to overproduce secondary metabolites for their potential industrial production. In this Highlight, recent relevant metabolic engineering studies are analyzed with emphasis on host selection and engineering approaches for the optimal production of various prokaryotic secondary metabolites: native versus heterologous hosts (e.g., Escherichia coli) and rational versus random approaches. This comparative analysis is followed by discussions on systems biology tools deployed in optimizing the production of secondary metabolites. The potential contributions of additional systems biology tools are also discussed in the context of current challenges encountered during optimization of secondary metabolite production. [ABSTRACT FROM AUTHOR]

Streptomycetes are prolific sources of novel biologically active secondary metabolites with pharmaceutical potential . S. collinus Tü 365 is a Streptomyces strain, isolated 1972 from Kouroussa (Guinea). It is best known as producer of the antibiotic kirromycin, an inhibitor of the protein biosynthesis interacting with elongation factor EF-Tu. Genome Mining revealed 32 gene clusters encoding the biosynthesis of diverse secondary metabolites in the genome of Streptomyces collinus Tü 365, indicating an enormous biosynthetic potential of this strain. The structural diversity of secondary metabolisms predicted for S. collinus Tü 365 includes PKS, NRPS, PKS-NRPS hybrids, a lanthipeptide, terpenes and siderophores. While some of these gene clusters were found to contain genes related to known secondary metabolites, which also could be detected in HPLC-MS analyses, most of the uncharacterized gene clusters are not expressed under standard laboratory conditions. With this study we aimed to characterize the genome information of S. collinus Tü 365 to make use of gene clusters, which previously have not been described for this strain. We were able to connect the gene clusters of a lanthipeptide, a carotenoid, five terpenoid compounds, an ectoine, a siderophore and a spore pigment-associated gene cluster to their respective biosynthesis products. [ABSTRACT FROM AUTHOR]

Summary Pristinamycin, produced by Streptomyces pristinaespiralis Pr11, is a streptogramin antibiotic consisting of two chemically unrelated compounds, pristinamycin I and pristinamycin II. The semi-synthetic derivatives of these compounds are used in human medicine as therapeutic agents against methicillin-resistant Staphylococcus aureus strains. Only the partial sequence of the pristinamycin biosynthetic gene cluster has been previously reported. To complete the sequence, overlapping cosmids were isolated from a S. pristinaespiralis Pr11 gene library and sequenced. The boundaries of the cluster were deduced, limiting the cluster size to approximately 210 kb. In the central region of the cluster, previously unknown pristinamycin biosynthetic genes were identified. Combining the current and previously identified sequence information, we propose that all essential pristinamycin biosynthetic genes are included in the 210 kb region. A pristinamycin biosynthetic pathway was established. Furthermore, the pristinamycin gene cluster was found to be interspersed by a cryptic secondary metabolite cluster, which probably codes for a glycosylated aromatic polyketide. Gene inactivation experiments revealed that this cluster has no influence on pristinamycin production. Overall, this work provides new insights into pristinamycin biosynthesis and the unique genetic organization of the pristinamycin gene region, which is the largest antibiotic 'supercluster' known so far. [ABSTRACT FROM AUTHOR]

Streptomyces are well-known producers of a range of different secondary metabolites, including antibiotics and other bioactive compounds. Recently, it has been demonstrated that "silent" biosynthetic gene clusters (BGCs) can be activated by heterologously expressing transcriptional regulators from other BGCs. Here, we have activated a silent BGC in Streptomyces sp. CA-256286 by overexpression of a set of SARP family transcriptional regulators. The structure of the produced compound was elucidated by NMR and found to be an N-acetyl cysteine adduct of the pyranonaphtoquinone polyketide 3′-O-α-d-forosaminyl-(+)-griseusin A. Employing a combination of multi-omics and metabolic engineering techniques, we identified the responsible BGC. These methods include genome mining, proteomics and transcriptomics analyses, in combination with CRISPR induced gene inactivations and expression of the BGC in a heterologous host strain. This work demonstrates an easy-to-implement workflow of how silent BGCs can be activated, followed by the identification and characterization of the produced compound, the responsible BGC, and hints of its biosynthetic pathway. [ABSTRACT FROM AUTHOR]

By culturing microorganisms under standard laboratory conditions, most biosynthetic gene clusters (BGCs) are not expressed, and thus, the products are not produced. To explore this biosynthetic potential, we developed a novel "semi-targeted" approach focusing on activating "silent" BGCs by concurrently introducing a group of regulator genes into streptomycetes of the Tübingen strain collection. We constructed integrative plasmids containing two classes of regulatory genes under the control of the constitutive promoter ermE*p (cluster situated regulators (CSR) and Streptomyces antibiotic regulatory proteins (SARPs)). These plasmids were introduced into Streptomyces sp. TÜ17, Streptomyces sp. TÜ10 and Streptomyces sp. TÜ102. Introduction of the CSRs-plasmid into strain S. sp. TÜ17 activated the production of mayamycin A. By using the individual regulator genes, we proved that Aur1P, was responsible for the activation. In strain S. sp. TÜ102, the introduction of the SARP-plasmid triggered the production of a chartreusin-like compound. Insertion of the CSRs-plasmid into strain S. sp. TÜ10 resulted in activating the warkmycin-BGC. In both recombinants, activation of the BGCs was only possible through the simultaneous expression of aur1PR3 and griR in S. sp. TÜ102 and aur1P and pntR in of S. sp. TÜ10. [ABSTRACT FROM AUTHOR]

Understanding a complex working mechanism of biosynthetic gene clusters (BGCs) encoding secondary metabolites is a key to discovery of new antibiotics. Computational resources continue to be developed in order to better process increasing volumes of genome and chemistry data, and thereby better understand BGCs. In this context, this review highlights recent advances in computational resources for secondary metabolites with emphasis on genome mining, compound identification and dereplication as well as databases. We also introduce an updated version of Secondary Metabolite Bioinformatics Portal (SMBP; http://www.secondarymetabolites.org ), which we previously released as a curated gateway to all the computational tools and databases useful for discovery and engineering of secondary metabolites. [ABSTRACT FROM AUTHOR]

Fungal aromatic polyketides display a very diverse and widespread group of natural products. Due to their excellent light absorption properties and widely studied biological activities, they offer numerous application for food, textile and pharmaceutical industry. The biosynthetic pathways of fungal aromatic polyketides usually involve a set of successive enzymes, in which a non-reductive polyketide synthase iteratively catalyzes the essential assembly of simple building blocks into (often polycyclic) aromatic compounds. However, only a limited number of such pathways have been described so far and further elucidation of the individual biosynthetic steps is needed to fully exploit the biotechnological and medicinal potential of these compounds. Here, we identified the bisanthraquinone skyrin as the main pigment of the fungus Cyanodermella asteris , an endophyte that has recently been isolated from the traditional Chinese medicinal plant Aster tataricus . The genome of C. asteris was sequenced, assembled and annotated, which enables first insights into a genome from a non-lichenized member of the class Lecanoromycetes. Genetic and in silico analyses led to the identification of a gene cluster of five genes suggested to encode the enzymatic pathway for skyrin. Our study is a starting point for rational pathway engineering in order to drive the production towards higher yields or more active derivatives. Moreover, our investigations revealed a large potential of secondary metabolite production in C. asteris as well as in all Lecanoromycetes of which genomes were available. These findings convincingly emphasize that Lecanoromycetes are prolific producers of secondary metabolites. [ABSTRACT FROM AUTHOR]