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Fungal infections pose a great threat to public health and there are only four main types of antifungal drugs, which are often limited with toxicity, drug-drug interactions and antibiotic resistance. Streptomyces is an important source of antibiotics, represented by the clinical drug amphotericin B. Here we report the discovery of alligamycin A (1) as an antifungal compound from the rapamycin-producer Streptomyces iranensis through genome-mining, genetics and natural product chemistry approaches. Alligamycin A harbors a unique chemical scaffold with 13 chiral centers, featuring a β-lactone moiety, a [6,6]-spiroketal ring, and an unreported 7-oxo-octylmalonyl-CoA extender unit incorporated by a potential crotonyl-CoA carboxylase/reductase. It is biosynthesized by a type I polyketide synthase which is confirmed through CRISPR-based gene editing. Alligamycin A displayed potent antifungal effects against numerous clinically relevant filamentous fungi, including resistant Aspergillus and Talaromyces species. β-Lactone ring is essential for the antifungal activity since alligamycin B (2) with disruption in the ring abolished the antifungal effect. Proteomics analysis revealed alligamycin A potentially disrupts the integrity of fungal cell walls and induces the expression of stress-response proteins in Aspergillus niger. Discovery of the potent antifungal candidate alligamycin A expands the limited antifungal chemical space. From the rapamycin-producer Streptomyces iranensis, authors report on the discovery of the antifungal candidate alligamycin A, which exhibited strong activities against several drug-resistant Aspergillus and Talaromyces. [ABSTRACT FROM AUTHOR]

The use of compounds produced by hosts or symbionts for defence against antagonists has been identified in many organisms, including in fungus-farming termites (Macrotermitinae). The obligate mutualistic fungus Termitomyces plays a pivotal role in plant biomass decomposition and as the primary food source for these termites. Despite the isolation of various specialized metabolites from different Termitomyces species, our grasp of their natural product repertoire remains incomplete. To address this knowledge gap, we conducted a comprehensive analysis of 39 Termitomyces genomes, representing 21 species associated with members of five termite host genera. We identified 754 biosynthetic gene clusters (BGCs) coding for specialized metabolites and categorized 660 BGCs into 61 biosynthetic gene cluster families (GCFs) spanning five compound classes. Seven GCFs were shared by all 21 Termitomyces species and 21 GCFs were present in all genomes of subsets of species. Evolutionary constraint analyses on the 25 most abundant GCFs revealed distinctive evolutionary histories, signifying that millions of years of termite-fungus symbiosis have influenced diverse biosynthetic pathways. This study unveils a wealth of non-random and largely undiscovered chemical potential within Termitomyces and contributes to our understanding of the intricate evolutionary trajectories of biosynthetic gene clusters in the context of long-standing symbiosis. Systematic identification and comparison of biosynthetic gene clusters among Termitomyces species provides insight into their evolutionary history and potential functions of BGCs and the chemicals synthesized by these pathways. [ABSTRACT FROM AUTHOR]

Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of metabolic pathway designs for production of a key precursor to medicinal alkaloids in yeast. The teemi platform is publicly available at PyPi and GitHub. Author summary: Bioengineering holds fantastic perspectives and is poised to change how we produce foods, materials, and medicines. However, rapid progress is limited by a lack of mechanistic knowledge in even the simplest model organisms, such as bacteria and yeast. Thus, to compensate, we often have to construct and study a large number of engineered cells and select the cells with the greatest potential for the given objective function. The targeted construction of engineered cells is often described as an iterative process of design, build, test, and learn (the DBTL cycle). Literate programming is a paradigm that encourages the combination of text and computer code with the potential to describe all workflows covered by the DBTL cycle. The purpose of the present work is to give a first estimate of the extent to which we can accelerate the individual steps in the DBTL cycle by using end-to-end literate programming workflows. To achieve this, we established an open-source platform called teemi, and used it to optimize production of a key precursor to medicinal alkaloids in yeast. We expect that teemi will enable higher DBTL throughput with fewer errors, better integration of IT tools with laboratory resources, and more effective knowledge capture. [ABSTRACT FROM AUTHOR]

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]

Globomycin is a cyclic lipodepsipeptide originally isolated from several Streptomyces species which displays strong and selective antibacterial activity against Gram-negative pathogens. Its mode of action is based on the competitive inhibition of the lipoprotein signal peptidase II (LspA), which is absent in eukaryotes and considered an attractive target for the development of new antibiotics. Despite its interesting biological properties, the gene cluster encoding its biosynthesis has not yet been identified. In this study we employed a genome-mining approach in the globomycin-producing Streptomyces sp. CA-278952 to identify a candidate gene cluster responsible for its biosynthesis. A null mutant was constructed using CRISPR base editing where production was abolished, strongly suggesting its involvement in the biosynthesis. The putative gene cluster was then cloned and heterologously expressed in Streptomyces albus J1074 and Streptomyces coelicolor M1146, therefore unambiguously linking globomycin and its biosynthetic gene cluster. Our work paves the way for the biosynthesis of new globomycin derivatives with improved pharmacological properties. [ABSTRACT FROM AUTHOR]

Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC–MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets. [ABSTRACT FROM AUTHOR]

Streptomyces albidoflavus J1074 is a popular platform to discover novel natural products via the expression of heterologous biosynthetic gene clusters (BGCs). There is keen interest in improving the ability of this platform to overexpress BGCs and, consequently, enable the purification of specialized metabolites. Mutations within gene rpoB for the β-subunit of RNA polymerase are known to increase rifampicin resistance and augment the metabolic capabilities of streptomycetes. Yet, the effects of rpoB mutations on J1074 remained unstudied, and we decided to address this issue. A target collection of strains that we studied carried spontaneous rpoB mutations introduced in the background of the other drug resistance mutations. The antibiotic resistance spectra, growth, and specialized metabolism of the resulting mutants were interrogated using a set of microbiological and analytical approaches. We isolated 14 different rpoB mutants showing various degrees of rifampicin resistance; one of them (S433W) was isolated for the first time in actinomycetes. The rpoB mutations had a major effect on antibiotic production by J1074, as evident from bioassays and LC-MS data. Our data support the idea that rpoB mutations are useful tools to enhance the ability of J1074 to produce specialized metabolites. [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]

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Recently described rhizolutin and collinolactone isolated from Streptomyces Gö 40/10 share the same novel carbon scaffold. Analyses by NMR and X‐Ray crystallography verify the structure of collinolactone and propose a revision of rhizolutin's stereochemistry. Isotope‐labeled precursor feeding shows that collinolactone is biosynthesized via type I polyketide synthase with Baeyer–Villiger oxidation. CRISPR‐based genetic strategies led to the identification of the biosynthetic gene cluster and a high‐production strain. Chemical semisyntheses yielded collinolactone analogues with inhibitory effects on L929 cell line. Fluorescence microscopy revealed that only particular analogues induce monopolar spindles impairing cell division in mitosis. Inspired by the Alzheimer‐protective activity of rhizolutin, we investigated the neuroprotective effects of collinolactone and its analogues on glutamate‐sensitive cells (HT22) and indeed, natural collinolactone displays distinct neuroprotection from intracellular oxidative stress. [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]

CRISPR base editing is a powerful method to engineer bacterial genomes. However, it restricts editing to single-nucleotide substitutions. Here, to address this challenge, we adapt a CRISPR-Prime Editing-based, DSB-free, versatile, and single-nucleotide resolution genetic manipulation toolkit for prokaryotes. It can introduce substitutions, deletions, insertions, and the combination thereof, both in plasmids and the chromosome of E. coli with high fidelity. Notably, under optimal conditions, the efficiency of 1-bp deletions reach up to 40%. Moreover, deletions of up to 97 bp and insertions up to 33 bp were successful with the toolkit in E. coli, however, efficiencies dropped sharply with increased fragment sizes. With a second guide RNA, our toolkit can achieve multiplexed editing albeit with low efficiency. Here we report not only a useful addition to the genome engineering arsenal for E. coli, but also a potential basis for the development of similar toolkits for other bacteria. CRISPR prime editing enables double-strand break free engineering of the genome. Here the authors present a toolkit for prime editing in E. coli. [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]

Medicinal plants are a prolific source of natural products with remarkable chemical and biological properties, many of which have considerable remedial benefits. Numerous medicinal plants are suffering from wildcrafting, and thus biotechnological production processes of their natural products are urgently needed. The plant Aster tataricus is widely used in traditional Chinese medicine and contains unique active ingredients named astins. These are macrocyclic peptides showing promising antitumor activities and usually containing the highly unusual moiety 3,4-dichloroproline. The biosynthetic origins of astins are unknown despite being studied for decades. Here we show that astins are produced by the recently discovered fungal endophyte Cyanodermella asteris. We were able to produce astins in reasonable and reproducible amounts using axenic cultures of the endophyte. We identified the biosynthetic gene cluster responsible for astin biosynthesis in the genome of C. asteris and propose a production pathway that is based on a nonribosomal peptide synthetase. Striking differences in the production profiles of endophyte and host plant imply a symbiotic cross-species biosynthesis pathway for astin C derivatives, in which plant enzymes or plant signals are required to trigger the synthesis of plant-exclusive variants such as astin A. Our findings lay the foundation for the sustainable biotechnological production of astins independent from aster plants. [ABSTRACT FROM AUTHOR]

Streptomycetes serve as major producers of various pharmacologically and industrially important natural products. Although CRISPR-Cas9 systems have been developed for more robust genetic manipulations, concerns of genome instability caused by the DNA double-strand breaks (DSBs) and the toxicity of Cas9 remain. To overcome these limitations, here we report development of the DSB-free, single-nucleotide-resolution genome editing system CRISPR-BEST (CRISPR-Base Editing SysTem), which comprises a cytidine (CRISPR-cBEST) and an adenosine (CRISPRaBEST) deaminase-based base editor. Specifically targeted by an sgRNA, CRISPR-cBEST can efficiently convert a C:G base pair to a T:A base pair and CRISPR-aBEST can convert an A:T base pair to a G:C base pair within a window of approximately 7 and 6 nucleotides, respectively. CRISPR-BEST was validated and successfully used in different Streptomyces species. Particularly in nonmodel actinomycete Streptomyces collinus Tü365, CRISPR-cBEST efficiently inactivated the 2 copies of kirN gene that are in the duplicated kirromycin biosynthetic pathways simultaneously by STOP codon introduction. Generating such a knockout mutant repeatedly failed using the conventional DSB-based CRISPR-Cas9. An unbiased, genome-wide off-target evaluation indicates the high fidelity and applicability of CRISPR-BEST. Furthermore, the system supports multiplexed editing with a single plasmid by providing a Csy4-based sgRNA processing machinery. To simplify the protospacer identification process, we also updated the CRISPy-web (https://crispy.secondarymetabolites.org), and now it allows designing sgRNAs specifically for CRISPR-BEST applications. [ABSTRACT FROM AUTHOR]

Covering: up to February, 2018 This review briefly introduces and summarizes current knowledge about the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated (Cas) – CRISPR/Cas system and how it was engineered to become one of the most important and versatile genome editing techniques that are currently revolutionizing the whole field of molecular biology. It aims to highlight and discuss the applications and remaining challenges of CRISPR/Cas (mainly focusing on CRISPR/SpCas9)-based genome editing in natural product discovery. The organisms covered include bacteria such as Streptomyces, Corynebacteria, and Myxobacteria; filamentous fungi such as Aspergillus, Beauveria, and Ganoderma; microalgae; and some plants. As closing remarks, the prospects of using CRISPR/Cas in natural product discovery will be discussed. [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]

The software antiSMASH examines microbial genome data to identify and analyze biosynthetic gene clusters for a wide range of natural products. So far, type II polyketide synthase (PKS) gene clusters could only be identified, but no detailed predictions for type II PKS gene clusters could be provided. In this study, an antiSMASH module for analyzing type II PKS gene clusters has been developed. The module detects genes/proteins in the type II PKS gene cluster involved with polyketide biosynthesis and is able to make predictions about the aromatic polyketide product. Predictions include the putative starter unit, the number of malonyl elongations during polyketide biosynthesis, the putative class and the molecular weight of the product. Furthermore, putative cyclization patterns are predicted. The accuracy of the predictions generated with the new PKSII antiSMASH module was evaluated using a leave-one-out cross validation. The prediction module is available in antiSMASH version 5 at https://antismash.secondarymetabolites.org. [ABSTRACT FROM AUTHOR]

It has been hypothesized that some antibiotic resistance genes (ARGs) found in pathogenic bacteria derive from antibiotic-producing actinobacteria. Here we provide bioinformatic and experimental evidence supporting this hypothesis. We identify genes in proteobacteria, including some pathogens, that appear to be closely related to actinobacterial ARGs known to confer resistance against clinically important antibiotics. Furthermore, we identify two potential examples of recent horizontal transfer of actinobacterial ARGs to proteobacterial pathogens. Based on this bioinformatic evidence, we propose and experimentally test a ‘carry-back’ mechanism for the transfer, involving conjugative transfer of a carrier sequence from proteobacteria to actinobacteria, recombination of the carrier sequence with the actinobacterial ARG, followed by natural transformation of proteobacteria with the carrier-sandwiched ARG. Our results support the existence of ancient and, possibly, recent transfers of ARGs from antibiotic-producing actinobacteria to proteobacteria, and provide evidence for a defined mechanism. [ABSTRACT FROM AUTHOR]

Talaromyces islandicus (‘Penicillium islandicum’) is a widespread foodborne mold that produces numerous secondary metabolites, among them potent mycotoxins belonging to different chemical classes. A notable metabolite is the hepatotoxic and carcinogenic pentapeptide cyclochlorotine that contains the unusual amino acids β-phenylalanine, 2-aminobutyrate and 3,4-dichloroproline. Although the chemical structure has been known for over five decades, nothing is known about the biosynthetic pathway of cyclochlorotine. Bioinformatic analysis of the recently sequenced genome of T. islandicus identified a wealth of gene clusters potentially coding for the synthesis of secondary metabolites. Here, we show by RNA interference-mediated gene silencing that a nonribosomal peptide synthetase, CctN, is responsible for the synthesis of cyclochlorotine. Moreover, we identified novel cyclochlorotine chemical variants, whose production also depended on cctN expression. Surprisingly, the halogenase required for cyclochlorotine biosynthesis is not encoded in the cct cluster. Nonetheless, our findings enabled us to propose a detailed model for cyclochlorotine biosynthesis. In addition, comparative genomics revealed that cct-like clusters are present in all of the sequenced Talaromyces strains indicating a high prevalence of cyclochlorotine production ability. [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]

Background: The filamentous actinomycete Microbispora ATCC-PTA-5024 produces the lantibiotic NAI-107, which is an antibiotic peptide effective against multidrug-resistant Gram-positive bacteria. In actinomycetes, antibiotic production is often associated with a physiological differentiation program controlled by a complex regulatory and metabolic network that may be elucidated by the integration of genomic, proteomic and bioinformatic tools. Accordingly, an extensive evaluation of the proteomic changes associated with NAI-107 production was performed on Microbispora ATCC-PTA-5024 by combining two-dimensional difference in gel electrophoresis, mass spectrometry and gene ontology approaches. Results: Microbispora ATCC-PTA-5024 cultivations in a complex medium were characterized by stages of biomass accumulation (A) followed by biomass yield decline (D). NAI-107 production started at 90 h (A stage), reached a maximum at 140 h (D stage) and decreased thereafter. To reveal patterns of differentially represented proteins associated with NAI-107 production onset and maintenance, differential proteomic analyses were carried-out on biomass samples collected: i) before (66 h) and during (90 h) NAI-107 production at A stage; ii) during three time-points (117, 140, and 162 h) at D stage characterized by different profiles of NAI-107 yield accumulation (117 and 140 h) and decrement (162 h). Regulatory, metabolic and unknown-function proteins, were identified and functionally clustered, revealing that nutritional signals, regulatory cascades and primary metabolism shift-down trigger the accumulation of protein components involved in nitrogen and phosphate metabolism, cell wall biosynthesis/maturation, lipid metabolism, osmotic stress response, multi-drug resistance, and NAI-107 transport. The stimulating role on physiological differentiation of a TetR-like regulator, originally identified in this study, was confirmed by the construction of an over-expressing strain. Finally, the possible role of cellular response to membrane stability alterations and of multi-drug resistance ABC transporters as additional self-resistance mechanisms toward the lantibiotic was confirmed by proteomic and confocal microscopy experiments on a Microbispora ATCC-PTA-5024 lantibiotic-null producer strain which was exposed to an externally-added amount of NAI-107 during growth. Conclusion: This study provides a net contribution to the elucidation of the regulatory, metabolic and molecular patterns controlling physiological differentiation in Microbispora ATCC-PTA-5024, supporting the relevance of proteomics in revealing protein players of antibiotic biosynthesis in actinomycetes. [ABSTRACT FROM AUTHOR]