Processing of NMR and MS metabolomics data using chemometrics methods: a global tool for fungi biotransformation reactions monitoring

Palaric, Cécile; Pilard, Serge; Fontaine, Jean-Xavier; Bernard, Julien; Mathiron, David; Rigaud, Sébastien; Cailleu, Dominique; Mesnard, François; Gut, Yoann; Renaud, Tristan; Petit, Alain; Beaumal, Jean-Yves; Molinié, Roland

doi:10.1007/s11306-019-1567-5

Cited by 8 publications

(10 citation statements)

References 30 publications

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“…3,131 But even more promising results arise from the recent development of advanced statistical approaches dedicated to the integration of analytical data from multiple platforms. 132,133 While NMR has a lot to bring to metabolomics and fluxomics, these recent works certainly predict a bright future for multi-technique analytical workflows in the field.…”

Section: Resultsmentioning

confidence: 99%

NMR-based metabolomics and fluxomics: developments and future prospects

Giraudeau

2020

Analyst

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Section: Resultsmentioning

confidence: 99%

NMR-based metabolomics and fluxomics: developments and future prospects

Giraudeau

2020

Analyst

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“…To overcome the block size differences effect, the unsupervised multivariate ComDim strategy (also called Common Components and Specific Weights Analysis – CCSWA) [39] was used to analyse the data. This multiblock strategy is still very rarely used to explore metabolomic data, particularly in the natural products field [41]. However, in addition to perform a simultaneous analysis of all data, it provides also informations about the importance of each data block.…”

Section: Resultsmentioning

confidence: 99%

Global metabolome changes induced by environmentally relevant conditions in a marine-sourcedPenicillium restrictum

Le,

Bertrand,

Brandolini-Bunlon

et al. 2023

Preprint

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Marine fungi have been found in all habitats and are able to adapt to their environmental niche con-ditions. In this study, a combination of LC-HRMS and GC-MS analytical approaches was used to analyse the whole metabolic changes of a marine sourced Penicillium restrictum strain isolated from a marine shellfish area. The P. restrictum MMS417 strain was grown on seven different media in-cluding an ecological one with two different water sources (synthetic sea water and distilled water) conditions following the OSMAC approach. Extracts of all media were analysed by LC-HRMS (lipids and specialised metabolites profiling) and GC-MS (fatty acids profiling). Aquired data were analysed using a multiblock strategy to highlight metabolic modification in regards to water condi-tions and to environmentally relevant conditions (mussel-based culture medium). This revealed that fatty acid composition of lipids was the most altered part of the explored metabolisms either looking to water effect and to environmentally relevant conditions. In particular, data showed that P. re-strictum MMS417 is able to produce lipids that include fatty acids usually produced by the mussel itself. This study also provides insight into the P. restrictum adaptation to marine salinity through fatty acids alteration. and shows that lipid metabolisms if far more altered in an OSMAC approach than the specialized metabolism. This study finally highlights the need for using environnement-mimicking culture conditions to reveal the metabolic potentialities of marine microbes.

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“…For instance, C-PLS-DA and C-O-PLS-DA have been applied to integrate MS with two NMR data sets, one 1 H data set and one 2D J-resolved (J-res) NMR. 93 Also, data fusion can be based on multiple kernel learning (MKL). This approach was applied to explore plasma metabolic alterations in three different chronic diseases for example, namely, acute coronary syndrome, breast and colon cancers.…”

Section: Multiblock Fusionmentioning

confidence: 99%

“…Multiblock data fusion is thus starting to be anchored in the metabolomics landscape, and some attempts are made to refine and bring some originality into those methods and to increase their application scope. For instance, C-PLS-DA and C-O-PLS-DA have been applied to integrate MS with two NMR data sets, one 1 H data set and one 2D J-resolved (J-res) NMR . Also, data fusion can be based on multiple kernel learning (MKL).…”

Section: Nmr and Ms Data Set Combination For Metabolomics Analysismentioning

confidence: 99%

Combined Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry Approaches for Metabolomics

2020

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Processing of NMR and MS metabolomics data using chemometrics methods: a global tool for fungi biotransformation reactions monitoring

Cited by 8 publications

References 30 publications

NMR-based metabolomics and fluxomics: developments and future prospects

NMR-based metabolomics and fluxomics: developments and future prospects

Global metabolome changes induced by environmentally relevant conditions in a marine-sourcedPenicillium restrictum

Combined Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry Approaches for Metabolomics

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