Molecular networking as a natural products discovery strategy

Zhang, Mi; Otsuki, Kouharu; Li, Wei

doi:10.15212/amm-2023-0007

Cited by 11 publications

(8 citation statements)

References 98 publications

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“…However, 34 MFs had at least one metabolite automatically matched to the GNPS libraries, a similar trend that was also observed in the MFs found in flower MN. Considering that MN is rationally constructed based on spectral similarities (Aron et al, 2020; Vincenti et al, 2020; Yu et al, 2022; Zhang et al, 2023), automated annotation one node in a cluster can aid in decoding and annotating other structurally similar metabolites or features in the same molecular family. As such, MN improves on metabolite annotation, decoding ‘dark matter’ in spectral data, which subsequently provides an improved coverage on the annotated metabolome.…”

Section: Resultsmentioning

confidence: 99%

Charting the Cannabis plant chemical space with computational metabolomics

Myoli,

Choene,

Kappo

et al. 2024

Preprint

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IntroductionThe chemical classification of Cannabis is typically confined to the cannabinoid content, whilst Cannabis encompasses diverse chemical classes that vary in abundance among all its varieties. Hence, neglecting other chemical classes within Cannabis strains results in a restricted and biased comprehension of elements that may contribute to chemical intricacy and the resultant medicinal qualities of the plant.ObjectivesThus, herein, we report a computational metabolomics study to elucidate the Cannabis metabolic map beyond the cannabinoids.MethodsMass spectrometry-based computational tools were used to mine and evaluate the methanolic leaf and flower extracts of two Cannabis cultivars: Amnesia haze (AMNH) and Royal dutch cheese (RDC).ResultsThe results revealed the presence of different chemical compound classes including cannabinoids, but extending it to flavonoids, polyketides, and phospholipids at varying distributions across the cultivar plant tissues. Therefore, the two cultivars were differentiated based on the overall chemical content of their plant tissues where AMNH was observed to be more dominant in the flavonoid content while RDC was more dominant in the lipid-like molecules. Additionally,in silicomolecular docking studies in combination with biological assay studies indicated the potentially differing anti-cancer properties of the two cultivars resulting from the elucidated chemical profiles.ConclusionThese findings highlight distinctive chemical profiles beyond cannabinoids in Cannabis strains. This novel mapping of the metabolomic landscape of Cannabis provides actionable insights into plant biochemistry and justifies selecting certain varieties for medicinal use.

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Section: Resultsmentioning

confidence: 99%

Charting the Cannabis plant chemical space with computational metabolomics

Myoli,

Choene,

Kappo

et al. 2024

Preprint

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“…Firstly, the antiSMASH analysis revealed the biosynthetic potential of strain A133 to produce diverse secondary metabolites including rifamycins. The visualization, clustering, and annotation of the secondary metabolites were achieved by the UPLC-QTOF-MS/MS-based molecular networking ( Le Loarer et al, 2023 ; Zhang et al, 2023 ). This method enabled us to annotate two families of compounds, namely rifamycins and zampanolides.…”

Section: Discussionmentioning

confidence: 99%

Bioprospecting for the soil-derived actinobacteria and bioactive secondary metabolites on the Western Qinghai-Tibet Plateau

Liu,

Liu

et al. 2023

Front. Microbiol.

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IntroductionThe increase in incidence of multidrug-resistant bacteria and the inadequacy of new antimicrobial drugs have led to a widespread outbreak of bacterial antimicrobial resistance. To discover new antibiotics, biodiversity, and novelty of culturable actinobacteria dwelled in soil of the Western Qinghai-Tibet Plateau were investigated. By integrating antibacterial assay with omics tools, Amycolatopsis sp. A133, a rare actinobacterial strain and its secondary metabolites were further studied.MethodCulture-dependent method was used to obtain actinobacterial strains from two soil samples collected from Ali region in Qinghai-Tibet Plateau. The cultural extractions of representative strains were assayed against “ESKAPE” pathogens by paper-disk diffusion method and the double fluorescent protein reporter “pDualrep2” system. An Amycolatopsis strain coded as A133 was prioritized and its secondary metabolites were further analyzed and annotated by omics tools including antiSMASH and GNPS (Global Natural Social Molecular Networking). The predicted rifamycin analogs produced by Amycolatopsis sp. A133 were isolated and identified by chromatographic separation, such as Sephadex LH-20 and HPLC, and spectral analysis, such as NMR and UPLC-HRESI-MS/MS, respectively.ResultsA total of 406 actinobacteria strains affiliated to 36 genera in 17 families of 9 orders were isolated. Out of 152 representative strains, 63 isolates exhibited antagonistic activity against at least one of the tested pathogens. Among them, 7 positive strains were identified by the “pDualrep2” system as either an inhibitor of protein translation or DNA biosynthesis. The cultural broth of Amycolatopsis sp. A133 exhibited a broader antimicrobial activity and can induce expression of TurboRFP. The secondary metabolites produced by strain A133 was annotated as rifamycins and zampanolides by antiSMASH and GNPS analysis. Five members of rifamycins, including rifamycin W, protorifamycin I, rifamycin W-M1, proansamycin B, and rifamycin S, were purified and identified. Rifamycin W-M1, was found as a new member of the naturally occurring rifamycin group of antibiotics.DiscussionAssisted by omics tools, the successful and highly efficient discovery of rifamycins, a group of clinically used antibiotics from actinobacteria in Ali area encouraged us to devote more energy to explore new antibiotics from the soils on the Western Tibetan Plateau.

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“…The current molecular biology toolkit for GPCR has powered drug discovery towards the generation of several potent molecules for treatment of metabolic diseases [119,120]. One of the major medical needs is the prevention or therapeutics for metabolic diseases, like obesity.…”

Section: Future Perspectivesmentioning

confidence: 99%

G protein-coupled receptor-biased signaling: potential drug discovery to facilitate treatment of metabolic diseases

Shen,

Liao,

et al. 2024

Acta Materia Medica

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G protein-coupled receptors (GPCRs) are important, potential drug targets for the treatment of metabolic disorders, such as obesity. GPCRs crosstalk with several transducers, including heterotrimeric G proteins, GPCR kinases (GRKs), and β-arrestins. GPCR-biased agonism has raised the potential of novel drug development to preferentially activate therapeutic signaling pathways over pathways that lead to unwanted side effects. The obesity epidemic and its metabolic complications continue to be a major global public health threat but effective treatments are limited. The accelerated development of structural techniques, like X-ray crystallography and cryo-electron microscopy, has paved the way to understanding how biased agonism measured at GPCRs results in specific downstream physiologic responses. Herein some well-validated GPCR targets are briefly summarized and several new and promising receptors for obesity treatment are outlined. This review highlights the significance of deciphering the role of GPCRs in obesity pathology and biased signaling for drug development. We anticipate the review will facilitate the development of novel GPCR-targeted anti-obesity drugs that lead to heightened therapeutic efficacy with decreased side effect profiles.

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Molecular networking as a natural products discovery strategy

Cited by 11 publications

References 98 publications

Charting the Cannabis plant chemical space with computational metabolomics

Charting the Cannabis plant chemical space with computational metabolomics

Bioprospecting for the soil-derived actinobacteria and bioactive secondary metabolites on the Western Qinghai-Tibet Plateau

G protein-coupled receptor-biased signaling: potential drug discovery to facilitate treatment of metabolic diseases

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