Tuebingen Natural Product discover Class
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q Exactive HF
ORGANISM(S): Streptomyces Sp. (ncbitaxon:1931)
SUBMITTER: 
Daniel Petras
PROVIDER: MSV000089828 | MassIVE | Thu Jul 07 01:21:00 BST 2022
REPOSITORIES: MassIVE
Similar Datasets
Project description:Non-targeteed metabolomics of Streptomyces extracts
2022-07-07 | MSV000089828 | GNPS
Project description:Streptomyces has the largest repertoire of natural product biosynthetic gene clusters (BGCs), yet developing a universal engineering strategy for each Streptomyces species is challenging. Given that some Streptomyces species have larger BGC repertoires than others, we hypothesized that a set of genes co-evolved with BGCs to support biosynthetic proficiency must exist in those strains, and that their identification may provide universal strategies to improve the productivity of other strains. We show here that genes co-evolved with natural product BGCs in Streptomyces can be identified by phylogenomics analysis. Among the 597 genes that co-evolved with polyketide BGCs, 11 genes in the “coenzyme” category have been examined, including a gene cluster encoding for the co-factor pyrroloquinoline quinone (PQQ). When the pqq gene cluster was engineered into 11 Streptomyces strains, it enhanced production of 16,385 metabolites, including 36 known natural products with up to 40-fold improvement and several activated silent gene clusters. This study provides a new engineering strategy for improving polyketide production and discovering new biosynthetic gene clusters.
2024-03-01 | GSE256209 | GEO
Project description:Mass spectrometry metabolomics has become increasingly popular as an integral aspect of studies to identify active compounds from natural product mixtures. Classical metabolomics data analysis approaches do not consider the possibility that interactions (such as synergy) could occur between mixture components. With this study, we developed "interaction metabolomics" to overcome this limitation. The innovation of interaction metabolomics is the inclusion of compound interaction terms (CITs), which are calculated as the product of the intensities of each pair of features (detected ions) in the data matrix. Herein, we tested the utility of interaction metabolomics by spiking known concentrations of an antimicrobial compound (berberine) and a synergist (piperine) into a set of inactive matrices. We measured the antimicrobial activity for each of the resulting mixtures against Staphylococcus aureus and analyzed the mixtures with liquid chromatography coupled to high-resolution mass spectrometry. When the data set was processed without CITs (classical metabolomics), statistical analysis yielded a pattern of false positives. However, interaction metabolomics correctly identified berberine and piperine as the compounds responsible for the synergistic activity. To further validate the interaction metabolomics approach, we prepared mixtures from extracts of goldenseal (Hydrastis canadensis) and habañero pepper (Capsicum chinense) and correctly correlated synergistic activity of these mixtures to the combined action of berberine and several capsaicinoids. Our results demonstrate the utility of a conceptually new approach for identifying synergists in mixtures that may be useful for applications in natural products research and other research areas that require comprehensive mixture analysis.
| S-EPMC10152448 | biostudies-literature
Project description:Streptomyces has the largest repertoire of natural product biosynthetic gene clusters (BGCs), yet developing a universal engineering strategy for each Streptomyces species is challenging. Given that some Streptomyces species have larger BGC repertoires than others, we hypothesized that a set of genes co-evolved with BGCs to support biosynthetic proficiency must exist in those strains, and that their identification may provide universal strategies to improve the productivity of other strains. We show here that genes co-evolved with natural product BGCs in Streptomyces can be identified by phylogenomics analysis. Among the 597 genes that co-evolved with polyketide BGCs, 11 genes in the “coenzyme” category have been examined, including a gene cluster encoding for the co-factor pyrroloquinoline quinone (PQQ). When the pqq gene cluster was engineered into 11 Streptomyces strains, it enhanced production of 16,385 metabolites, including 36 known natural products with up to 40-fold improvement and several activated silent gene clusters. This study provides a new engineering strategy for improving polyketide production and discovering new biosynthetic gene clusters.
2024-02-12 | PXD049454 |
Project description:The risk of methicillin-resistant Staphylococcus aureus (MRSA) infection is increasing in both the developed and developing countries. New approaches to overcome this problem are in need. A ligand-based strategy to discover new inhibiting agents against MRSA infection was built through exploration of machine learning techniques. This strategy is based in two quantitative structure⁻activity relationship (QSAR) studies, one using molecular descriptors (approach A) and the other using descriptors (approach B). In the approach A, regression models were developed using a total of 6645 molecules that were extracted from the ChEMBL, PubChem and ZINC databases, and recent literature. The performance of the regression models was successfully evaluated by internal and external validation, the best model achieved R² of 0.68 and RMSE of 0.59 for the test set. In general natural product (NP) drug discovery is a time-consuming process and several strategies for dereplication have been developed to overcome this inherent limitation. In the approach B, we developed a new NP drug discovery methodology that consists in frontloading samples with 1D NMR descriptors to predict compounds with antibacterial activity prior to bioactivity screening for NPs discovery. The NMR QSAR classification models were built using 1D NMR data (¹H and 13C) as descriptors, from crude extracts, fractions and pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 77% for both training and test sets.
| S-EPMC6356832 | biostudies-literature
Project description:Argyrins represent a family of cyclic octapeptides exhibiting promising immunomodulatory activity via inhibiting mitochondrial protein synthesis, which leads to reduced IL-17 production by the T-helper 17 cells. Argyrins are formed by a non-ribosomal peptide synthetase (NRPS), originating from the myxobacterial producer strains Archangium gephyra Ar8082 and Cystobacter sp. SBCb004. In this work, a previously established heterologous production platform was employed to provide evidence of direct D-configured amino acid incorporation by the argyrin assembly line. An adenylation domain of the argyrin NRPS was characterized and shown to have a high preference for D-configured amino acids. Eight novel argyrin derivatives were generated via biosynthetic engineering of the heterologous production system. The system was also optimized to enable formation of methylated argyrin C and D derivatives with improved immunosuppressive activity compared with their unmethylated counterparts. Furthermore, the optimization of cultivation conditions allowed exclusive production of one major derivative at a time, drastically improving the purification process. Importantly, engineering of transcription and translation initiation resulted in a substantially improved production titre reaching 350-400 mg l-1 . The optimized system presented herein thus provides a versatile platform for production of this promising class of immunosuppressants at a scale that should provide sufficient supply for upcoming pre-clinical development.
| S-EPMC8719831 | biostudies-literature
Project description:Lanthipeptides are a class of cyclic peptides characterized by the presence of one or more lanthionine (Lan) or methyllanthionine (MeLan) thioether rings. These cross-links are produced by α,β-unsaturation of Ser or Thr residues in peptide substrates by dehydration, followed by a Michael-type conjugate addition of Cys residues onto the dehydroamino acids. Lanthipeptides may be broadly classified into at least five different classes, and the biosynthesis of classes I-IV lanthipeptides requires catalysis by LanC cyclases that control both the site-specificity and the stereochemistry of the conjugate addition. In contrast, there are no current examples of LanCs that occur in class V biosynthetic clusters, despite the presence of lanthionine rings in these compounds. In this work, bioinformatics-guided co-occurrence analysis identifies more than 240 putative class V lanthipeptide clusters that contain a LanC cyclase. Reconstitution studies demonstrate that the cyclase-catalyzed product is notably distinct from the product formed spontaneously. Stereochemical analysis shows that the cyclase diverts the final product to a configuration that is distinct from one that is energetically favored. Structural characterization of the final product by multi-dimensional NMR spectroscopy reveals that it forms a helical stapled peptide. Mutational analysis identified a plausible order for cyclization and suggests that enzymatic rerouting to the final structure is largely directed by the construction of the first lanthionine ring. These studies show that lanthipeptide cyclases are needed for the biosynthesis of some constrained peptides, the formations of which would otherwise be energetically unfavored.
| S-EPMC9621591 | biostudies-literature
Project description:Rapid advances in mass spectrometry (MS) data analysis have accelerated the identification of natural products from complex mixtures such as natural product extracts. However, limitations in MS data in metabolite libraries and dereplication strategies are still lacking for assigning structures to known compounds and searching for unidentified compounds. To overcome these limitations, we present an approach that combines molecular networking with MS database-derived mass defect analysis to preferentially discover new compounds with high structural novelty in the initial stage of a discovery workflow. Specifically, unknown metabolites or clusters generated from molecular networking are assigned to a compound class based on their relative mass defects (RMDs) calculated using open-source databases. If ancillary data such as ultraviolet and MS/MS spectra of the unknown clusters are incongruent with the RMD-assigned compound class, metabolites are considered to have a new skeleton that exhibits a large difference in RMD value due to structural changes. Here, we applied this RMD-assisted method to a desert-derived bacterial strain library and validated it through the discovery of brasiliencin A (1), a new 18-membered macrolide from Nocardia brasiliensis. A putative biosynthetic pathway of brasiliencin A was proposed through whole-genome sequence analysis, and an additional 29 analogs were detected using absolute mass defect filtering (AMDF) based on plausible biosynthetic products. This led to the isolation of three additional macrolides, brasiliencins B-D (2-4). The structures of the brasiliencins (1-4) were fully elucidated through spectroscopic data analysis and quantum chemical calculations including ROE distance and 13C NMR chemical shift calculations, and experimental and theoretical electronic circular dichroism (ECD). Brasiliencin A showed strong activity against Mycobacterium smegmatis and Streptococcus australis (MIC = 31.3 nM and 7.81 μM, respectively) compared to brasiliencin B (MIC = 1000 nM and 62.5 μM, respectively) that differs at a single stereocenter.
| S-EPMC11862952 | biostudies-literature
Project description:Marine ecosystems are highly dependent on macroalgea in providing food and shelter for aquatic organisms, interacting with many bacteria and mostly producing secondary metabolites of potent therapeutic antibacterial property. Screening of marine microbial secondary metabolites of valuable biotechnological and therapeutical applications are now extensively studied. In this study, Bacillus spp. identified by DNA sequencing and found associated with Turbinaria ornata, was screened and characterized for its cell free supernatant (CFS) possible antimicrobial and antibiofilm applications. Among the 7 microbial isolates tested, CFS greatly affected Bacillus subitilis (12 mm) and inhibited equally the yeast isolates Candida albicans, Candida tropicalis and Candida glabrata (10 mm) and had no or negligible effect on S.aureus, E.coli, P. aeruginosa. As for the CFS antibiofilm activity, no difference was revealed from the positive control. Algal crude extracts (methanol, acetone and aqueous), on the other hand, were similarly tested for their antimicrobial activity against the seven microbial isolates, where highest activity was observed with the aqueous crude extract against Staphylococcus aureus(10 mm) and Pseudomonas aeruginosa (9 mm) compared to the negligible effects of methanol and acetone crude extracts. Chemical analysis was performed to reveal the major constituents of both crude algal extracts and Bacillus spp. CFS. FTIR spectrum of the bacterial CFS indicated the presence of bacteriocin as the major lipopeptide responsible for its biological activity. Whereas, methanol and water crude algal extract GC-MS spectra revealed different chemical groups of various combined therapeutical activity mainly Naphthalene, amino ethane-sulfonic acid, pyrlene, Biotin and mercury chloromethyl correspondingly. Thus, the present study, demonstrated the moderate activity of both crude algal extract and the bacterial CFS, however, further investigations are needed for a better biological activity.
| S-EPMC9072896 | biostudies-literature
Project description:Covering: 2016 to 2021With genetic information available for hundreds of thousands of organisms in publicly accessible databases, scientists have an unprecedented opportunity to meticulously survey the diversity and inner workings of life. The natural product research community has harnessed this breadth of sequence information to mine microbes, plants, and animals for biosynthetic enzymes capable of producing bioactive compounds. Several orthogonal genome mining strategies have been developed in recent years to target specific chemical features or biological properties of bioactive molecules using biosynthetic, resistance, or transporter proteins. These "biosynthetic hooks" allow researchers to query for biosynthetic gene clusters with a high probability of encoding previously undiscovered, bioactive compounds. This review highlights recent case studies that feature orthogonal approaches that exploit genomic information to specifically discover bioactive natural products and their gene clusters.
| S-EPMC8597713 | biostudies-literature