Dataset Information

Time Dependency of Chemodiversity and Biosynthetic Pathways: An LC-MS Metabolomic Study of Marine-Sourced Penicillium.

ABSTRACT: This work aimed at studying metabolome variations of marine fungal strains along their growth to highlight the importance of the parameter "time" for new natural products discovery. An untargeted time-scale metabolomic study has been performed on two different marine-derived Penicillium strains. They were cultivated for 18 days and their crude extracts were analyzed by HPLC-DAD-HRMS (High Performance Liquid Chromatography-Diode Array Detector-High Resolution Mass Spectrometry) each day. With the example of griseofulvin biosynthesis, a pathway shared by both strains, this work provides a new approach to study biosynthetic pathway regulations, which could be applied to other metabolites and more particularly new ones. Moreover, the results of this study emphasize the interest of such an approach for the discovery of new chemical entities. In particular, at every harvesting time, previously undetected features were observed in the LC-MS (Liquid Chromatography-Mass Spectrometry) data. Therefore, harvesting times for metabolite extraction should be performed at different time points to access the hidden metabolome.

SUBMITTER: Roullier C

PROVIDER: S-EPMC4882577 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Publications

Time Dependency of Chemodiversity and Biosynthetic Pathways: An LC-MS Metabolomic Study of Marine-Sourced Penicillium.

Roullier Catherine C Bertrand Samuel S Blanchet Elodie E Peigné Mathilde M Robiou du Pont Thibaut T Guitton Yann Y Pouchus Yves François YF Grovel Olivier O

Marine drugs 20160521 5

This work aimed at studying metabolome variations of marine fungal strains along their growth to highlight the importance of the parameter "time" for new natural products discovery. An untargeted time-scale metabolomic study has been performed on two different marine-derived Penicillium strains. They were cultivated for 18 days and their crude extracts were analyzed by HPLC-DAD-HRMS (High Performance Liquid Chromatography-Diode Array Detector-High Resolution Mass Spectrometry) each day. With the ...[more]

PMID: 27213411

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Project description:Very little is known about chemical interactions between fungi and their mollusc host within marine environments. Here, we investigated the metabolome of a Penicillium restrictum MMS417 strain isolated from the blue mussel Mytilus edulis collected on the Loire estuary, France. Following the OSMAC approach with the use of 14 culture media, the effect of salinity and of a mussel-derived medium on the metabolic expression were analysed using HPLC-UV/DAD-HRMS/MS. An untargeted metabolomics study was performed using principal component analysis (PCA), orthogonal projection to latent structure discriminant analysis (O-PLSDA) and molecular networking (MN). It highlighted some compounds belonging to sterols, macrolides and pyran-2-ones, which were specifically induced in marine conditions. In particular, a high chemical diversity of pyran-2-ones was found to be related to the presence of mussel extract in the culture medium. Mass spectrometry (MS)- and UV-guided purification resulted in the isolation of five new natural fungal pyran-2-one derivatives-5,6-dihydro-6S-hydroxymethyl-4-methoxy-2H-pyran-2-one (1), (6S, 1'R, 2'S)-LL-P880β (3), 5,6-dihydro-4-methoxy-6S-(1'S, 2'S-dihydroxy pent-3'(E)-enyl)-2H-pyran-2-one (4), 4-methoxy-6-(1'R, 2'S-dihydroxy pent-3'(E)-enyl)-2H-pyran-2-one (6) and 4-methoxy-2H-pyran-2-one (7)-together with the known (6S, 1'S, 2'S)-LL-P880β (2), (1'R, 2'S)-LL-P880γ (5), 5,6-dihydro-4-methoxy-2H-pyran-2-one (8), (6S, 1'S, 2'R)-LL-P880β (9), (6S, 1'S)-pestalotin (10), 1'R-dehydropestalotin (11) and 6-pentyl-4-methoxy-2H-pyran-2-one (12) from the mussel-derived culture medium extract. The structures of 1-12 were determined by 1D- and 2D-MMR experiments as well as high-resolution tandem MS, ECD and DP4 calculations. Some of these compounds were evaluated for their cytotoxic, antibacterial, antileishmanial and in-silico PTP1B inhibitory activities. These results illustrate the utility in using host-derived media for the discovery of new natural products.

Project description:BackgroundThe analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis. However, most of these strategies involve chromatographic alignment and peak shaping and often associate each "feature" (i.e., chromatographic peak) with a unique m/z measurement. Thus, the development of an alternative data analysis strategy that is applicable to most types of MS datasets and properly addresses these issues is still a challenge in the metabolomics field.ResultsHere, we present an alternative approach called ROIMCR to: i) filter and compress massive LC-MS datasets while transforming their original structure into a data matrix of features without losing relevant information through the search of regions of interest (ROIs) in the m/z domain and ii) resolve compressed data to identify their contributing pure components without previous alignment or peak shaping by applying a Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis. In this study, the basics of the ROIMCR method are presented in detail and a detailed description of its implementation is also provided. Data were analyzed using the MATLAB (The MathWorks, Inc., www.mathworks.com ) programming and computing environment. The application of the ROIMCR methodology is described in detail, with an example of LC-MS data generated in a lipidomic study and with other examples of recent applications.ConclusionsThe methodology presented here combines the benefits of data filtering and compression based on the searching of ROI features, without the loss of spectral accuracy. The method has the benefits of the application of the powerful MCR-ALS data resolution method without the necessity of performing chromatographic peak alignment or modelling. The presented method is a powerful alternative to other existing data analysis approaches that do not use the MCR-ALS method to resolve LC-MS data. The ROIMCR method also represents an improved strategy compared to the direct applications of the MCR-ALS method that use less-powerful data compression strategies such as binning and windowing. Overall, the strategy presented here confirms the usefulness of the ROIMCR chemometrics method for analyzing LC-MS untargeted metabolomics data.

Dataset Information

Time Dependency of Chemodiversity and Biosynthetic Pathways: An LC-MS Metabolomic Study of Marine-Sourced Penicillium.

Publications

Time Dependency of Chemodiversity and Biosynthetic Pathways: An LC-MS Metabolomic Study of Marine-Sourced Penicillium.

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