Systems metabolic engineering of <i>Streptomyces venezuelae</i> for the enhanced production of pikromycin

Cho, Min Kyung; Lee, Byung Tae; Kim, Hyun Uk; Oh, Min Kyu

doi:10.1002/bit.28114

Cited by 7 publications

(10 citation statements)

References 39 publications

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“…that has activity against Gram‐positive bacteria); rosamicin 33 (macrolide antibiotic produced by Streptomyces sp. that has activity against Gram‐positive bacteria); pikromycin 34 (macrolide antibiotic produced by Streptomyces venezuelae that has activity against Gram‐positive bacteria); paromomycin 35 (aminoglycoside antibiotic used to treat infections caused by Gram‐negative bacteria and parasites); niddamycin 36 (macrolide antibiotic produced by Streptomyces caelestis that has activity against Gram‐positive bacteria); neomycin 37 (aminoglycoside antibiotic used to treat infections caused by Gram‐negative bacteria); neomethymycin 38 (macrolide antibiotic produced by Streptomyces fradiae that has activity against Gram‐positive bacteria); narbomycin 38 (macrolide antibiotic produced by Streptomyces antibioticus that has activity against Gram‐positive bacteria); mycinamicin II 39 (macrolide antibiotic produced by Streptomyces lavendulae that has activity against Gram‐positive bacteria); methymycin 38 (macrolide antibiotic produced by Streptomyces erythreus that has activity against Gram‐positive bacteria); hydroxystreptomycin 40 (aminoglycoside antibiotic used to treat infections caused by Gram‐negative bacteria); gentamicin 41 (aminoglycoside antibiotic used to treat infections caused by Gram‐negative bacteria); erythromycin C 33,34,39,42 (macrolide antibiotic produced by Saccharopolyspora erythraea that has activity against Gram‐positive bacteria); and erythromycin B 33,34,39,42 (macrolide antibiotic produced by S. erythraea that has activity against Gram‐positive bacteria). The hypothesis that similar compounds might present similar properties is a fundamental assumption in chemistry and drug discovery.…”

Section: Resultsmentioning

confidence: 99%

“…Streptomyces erythreus that has activity against Gram-positive bacteria); hydroxystreptomycin 40 (aminoglycoside antibiotic used to treat infections caused by Gram-negative bacteria); gentamicin 41 (aminoglycoside antibiotic used to treat infections caused by Gram-negative bacteria); erythromycin C 33,34,39,42 (macrolide antibiotic produced by Saccharopolyspora erythraea that has activity against Gram-positive bacteria); and erythromycin B 33,34,39,42 (macrolide antibiotic produced by S. erythraea that has activity against Gram-positive bacteria). The hypothesis that similar compounds might present similar properties is a fundamental assumption in chemistry and drug discovery.…”

mentioning

confidence: 99%

“…that has activity against Gram-positive bacteria); rosamicin33 (macrolide antibiotic produced by Streptomyces sp. that has activity against Gram-positive bacteria); pikromycin34 (macrolide antibiotic produced by Streptomyces venezuelae that has activity against Gram-positive bacteria); paromomycin35 (aminoglycoside antibiotic used to treat infections caused by Gram-negative bacteria and parasites); niddamycin36 (macrolide F I G U R E 4 Similarity network of the NPAtlas database filtered by compounds that have a mibig_id (nodes in blue) together with the compounds filtered for "natural products" and "antibacterials for systemic use" (nodes in red) collected from ChEMBL. The network was created in Cytoscape using the files produced by BDsimilarity.…”

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confidence: 99%

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Data Base similarity (DBsimilarity) of natural products to aid compound identification on MS and NMR pipelines, similarity networking, and more

Borges,

de Assis Ferreira,

Campos

et al. 2023

Phytochemical Analysis

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IntroductionWe developed Data Base similarity (DBsimilarity), a user‐friendly tool designed to organize structure databases into similarity networks, with the goal of facilitating the visualization of information primarily for natural product chemists who may not have coding experience.MethodDBsimilarity, written in Jupyter Notebooks, converts Structure Data File (SDF) files into Comma‐Separated Values (CSV) files, adds chemoinformatics data, constructs an MZMine custom database file and an NMRfilter candidate list of compounds for rapid dereplication of MS and 2D NMR data, calculates similarities between compounds, and constructs CSV files formatted into similarity networks for Cytoscape.ResultsThe Lotus database was used as a source for Ginkgo biloba compounds, and DBsimilarity was used to create similarity networks including NPClassifier classification to indicate biosynthesis pathways. Subsequently, a database of validated antibiotics from natural products was combined with the G. biloba compounds to identify promising compounds. The presence of 11 compounds in both datasets points to possible antibiotic properties of G. biloba, and 122 compounds similar to these known antibiotics were highlighted. Next, DBsimilarity was used to filter the NPAtlas database (selecting only those with MIBiG reference) to identify potential antibacterial compounds using the ChEMBL database as a reference. It was possible to promptly identify five compounds found in both databases and 167 others worthy of further investigation.ConclusionChemical and biological properties are determined by molecular structures. DBsimilarity enables the creation of interactive similarity networks using Cytoscape. It is also in line with a recent review that highlights poor biological plausibility and unrealistic chromatographic behaviors as significant sources of errors in compound identification.

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Data Base similarity (DBsimilarity) of natural products to aid compound identification on MS and NMR pipelines, similarity networking, and more

Borges,

de Assis Ferreira,

Campos

et al. 2023

Phytochemical Analysis

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“…This approach suffers somewhat from the fact that genome annotations remain far from perfect and the fact that it excludes nonenzyme-catalyzed reactions but nevertheless can prove effective, especially as most enzyme reactions cause predictable mass differences between metabolites ( 99 ). Indeed, since more extensive genome-scale models of secondary metabolism in Streptomyces species were established ( 100 ), this method has been utilized to aid in systems metabolic engineering strategies ( 101 ). Moreover, the approach has been adopted to study predictive evolution of metabolic pathways in order to identify lineages with enhanced metabolite secretion ( 102 ).…”

Section: Directly Coupling the Metabolome To The Genomementioning

confidence: 99%

Plant and microbial sciences as key drivers in the development of metabolomics research

Aharoni

Goodacre

Fernie

2023

Proc. Natl. Acad. Sci. U.S.A.

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This year marks the 25th anniversary of the coinage of the term metabolome [S. G. Oliver et al ., Trends Biotech. 16 , 373–378 (1998)]. As the field rapidly advances, it is important to take stock of the progress which has been made to best inform the disciplines future. While a medical-centric perspective on metabolomics has recently been published [M. Giera et al ., Cell Metab. 34 , 21–34 (2022)], this largely ignores the pioneering contributions made by the plant and microbial science communities. In this perspective, we provide a contemporary overview of all fields in which metabolomics is employed with particular emphasis on both methodological and application breakthroughs made in plant and microbial sciences that have shaped this evolving research discipline from the very early days of its establishment. This will not cover all types of metabolomics assays currently employed but will focus mainly on those utilizing mass spectrometry–based measurements since they are currently by far the most prominent. Having established the historical context of metabolomics, we will address the key challenges currently facing metabolomics and offer potential approaches by which these can be faced. Most salient among these is the fact that the vast majority of mass features are as yet not annotated with high confidence; what we may refer to as definitive identification. We discuss the potential of both standard compound libraries and artificial intelligence technologies to address this challenge and the use of natural variance–based approaches such as genome-wide association studies in attempt to assign specific functions to the myriad of structurally similar and complex specialized metabolites. We conclude by stating our contention that as these challenges are epic and that they will need far greater cooperative efforts from biologists, chemists, and computer scientists with an interest in all kingdoms of life than have been made to date. Ultimately, a better linkage of metabolome and genome data will likely also be needed particularly considering the Earth BioGenome Project.

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“…Metabolic engineering of host strains using genome-scale metabolic model (GEM) has been suggested as a promising strategy to improve the secondary metabolite production [ 25 , 26 ]. The GEM is a computational model that can predict metabolic flux distributions associated with the precursors of secondary metabolites under certain conditions, rendering it a valuable tool in the systems metabolic engineering [ 27 – 29 ].…”

Section: Introductionmentioning

confidence: 99%

Heterologous overproduction of oviedomycin by refactoring biosynthetic gene cluster and metabolic engineering of host strain Streptomyces coelicolor

Gu,

Kim,

Kim

et al. 2023

Microb Cell Fact

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Background Oviedomycin is one among several polyketides known for their potential as anticancer agents. The biosynthetic gene cluster (BGC) for oviedomycin is primarily found in Streptomyces antibioticus. However, because this BGC is usually inactive under normal laboratory conditions, it is necessary to employ systematic metabolic engineering methods, such as heterologous expression, refactoring of BGCs, and optimization of precursor biosynthesis, to allow efficient production of these compounds. Results Oviedomycin BGC was captured from the genome of Streptomyces antibioticus by a newly constructed plasmid, pCBA, and conjugated into the heterologous strain, S. coelicolor M1152. To increase the production of oviedomycin, clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system was utilized in an in vitro setting to refactor the native promoters within the ovm BGC. The target promoters of refactoring were selected based on examination of factors such as transcription levels and metabolite profiling. Furthermore, genome-scale metabolic simulation was applied to find overexpression targets that could enhance the biosynthesis of precursors or cofactors related to oviedomycin production. The combined approach led to a significant increase in oviedomycin production, reaching up to 670 mg/L, which is the highest titer reported to date. This demonstrates the potential of the approach undertaken in this study. Conclusions The metabolic engineering approach used in this study led to the successful production of a valuable polyketide, oviedomycin, via BGC cloning, promoter refactoring, and gene manipulation of host metabolism aided by genome-scale metabolic simulation. This approach can be also useful for the efficient production of other secondary molecules encoded by ‘silent’ BGCs.

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Systems metabolic engineering of Streptomyces venezuelae for the enhanced production of pikromycin

Cited by 7 publications

References 39 publications

Data Base similarity (DBsimilarity) of natural products to aid compound identification on MS and NMR pipelines, similarity networking, and more

Data Base similarity (DBsimilarity) of natural products to aid compound identification on MS and NMR pipelines, similarity networking, and more

Plant and microbial sciences as key drivers in the development of metabolomics research

Heterologous overproduction of oviedomycin by refactoring biosynthetic gene cluster and metabolic engineering of host strain Streptomyces coelicolor

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