Reaping the Chemical Diversity of <i>Morinagamyces vermicularis</i> Using Feature-Based Molecular Networking

Moringadepsin (<b>6</b>) and chaetone B (<b>7</b>) were isolated by us in the course of a conventional chemical screening of <i>Morinagamyces vermicularis</i> CBS 303.81, a fungus belonging to the relatively underexplored family Schizotheciaceae of the phylum Ascomycota. Since these metabolites did not account for the antifungal activity observed in a crude extract of this fungus, we utilized an MS/MS-based molecular networking approach to get a thorough insight into the secondary metabolites produced by this strain. Manual annotation of high-resolution fragmentation mass spectra by CANOPUS classified a major molecular family as putatively new thiodiketopiperazines. However, these results were opposite to the results of ChemWalker analysis based solely on MS/MS data, assigning these metabolites as various polyketides. Thus, targeted preparative HPLC isolation focusing on the most abundant features within this major molecular family resulted in the isolation of five secondary metabolites. Their structures were elucidated based on HRMS and NMR data as four new thiodiketopiperazine derivatives, botryosulfuranols D-G (<b>1</b>-<b>4</b>), alongside the known botryosulfuranol A (<b>5</b>). Compounds <b>1</b>-<b>3</b> and <b>5</b> exhibited moderate to weak antifungal activity against different test strains, accounting for the initial antifungal activity observed for its crude extract. Our study stressed the importance of full NMR-based structure elucidation for metabolomics research.

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Publication Details

Journal
Journal of Natural Products
Published
2024-09-16
DOI
https://doi.org/10.1021/acs.jnatprod.4c00654
Citations
9
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
3.25

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article

Reaping the Chemical Diversity of Morinagamyces vermicularis Using Feature-Based Molecular Networking

Karen Harms, Yasmina Marín-Felix, Frank Surup, Esteban Charria‐Girón et al.
9 citations
Journal of Natural Products
Computational Drug Discovery Methods
3.25
article

Reaping the Chemical Diversity of Morinagamyces vermicularis Using Feature-Based Molecular Networking

Karen Harms, Yasmina Marín-Felix, Frank Surup, Esteban Charria‐Girón, Alberto M. Stchigel
article en
9 citations

Abstract

Moringadepsin (<b>6</b>) and chaetone B (<b>7</b>) were isolated by us in the course of a conventional chemical screening of <i>Morinagamyces vermicularis</i> CBS 303.81, a fungus belonging to the relatively underexplored family Schizotheciaceae of the phylum Ascomycota. Since these metabolites did not account for the antifungal activity observed in a crude extract of this fungus, we utilized an MS/MS-based molecular networking approach to get a thorough insight into the secondary metabolites produced by this strain. Manual annotation of high-resolution fragmentation mass spectra by CANOPUS classified a major molecular family as putatively new thiodiketopiperazines. However, these results were opposite to the results of ChemWalker analysis based solely on MS/MS data, assigning these metabolites as various polyketides. Thus, targeted preparative HPLC isolation focusing on the most abundant features within this major molecular family resulted in the isolation of five secondary metabolites. Their structures were elucidated based on HRMS and NMR data as four new thiodiketopiperazine derivatives, botryosulfuranols D-G (<b>1</b>-<b>4</b>), alongside the known botryosulfuranol A (<b>5</b>). Compounds <b>1</b>-<b>3</b> and <b>5</b> exhibited moderate to weak antifungal activity against different test strains, accounting for the initial antifungal activity observed for its crude extract. Our study stressed the importance of full NMR-based structure elucidation for metabolomics research.

Journal of Natural ProductsVol. 87(9)
Helmholtz Centre for Infection Research (DE), Universitat Rovira i Virgili (ES), Technische Universität Braunschweig (DE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 7%
Computational Drug Discovery Methods
3.25
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