Comparative LC–MS-based metabolite profiling of the ancient tropical rainforest tree Symphonia globulifera

Cottet, Kevin; Genta‐Jouve, Grégory; Fromentin, Yann; Odonne, Guillaume; Duplais, Christophe; Laprévôte, Olivier; Michel, Sylvie; Lallemand, Marie‐Christine

doi:10.1016/j.phytochem.2014.09.009

Cited by 13 publications

(10 citation statements)

References 32 publications

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“…By this we were able to consider all truly biology-derived analytical features for the characterization of the plant extracts and the comparison of the different solvent mixtures. In contrast to other studies, which are mainly restricted to the comparison of pre-known substances and/or significantly differing features, e.g., [ 15 , 16 ], it was possible to accurately estimate the total number of detectable metabolites. Since very little noise is contained in the data matrix, our approach also allows the direct comparison of the abundance of all metabolites commonly detected across the investigated extraction mixtures.…”

Section: Discussionmentioning

confidence: 90%

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Stable Isotope-Assisted Evaluation of Different Extraction Solvents for Untargeted Metabolomics of Plants

Doppler

Kluger

Bueschl

et al. 2016

IJMS

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The evaluation of extraction protocols for untargeted metabolomics approaches is still difficult. We have applied a novel stable isotope-assisted workflow for untargeted LC-HRMS-based plant metabolomics , which allows for the first time every detected feature to be considered for method evaluation. The efficiency and complementarity of commonly used extraction solvents, namely 1 + 3 (v/v) mixtures of water and selected organic solvents (methanol, acetonitrile or methanol/acetonitrile 1 + 1 (v/v)), with and without the addition of 0.1% (v/v) formic acid were compared. Four different wheat organs were sampled, extracted and analysed by LC-HRMS. Data evaluation was performed with the in-house-developed MetExtract II software and R. With all tested solvents a total of 871 metabolites were extracted in ear, 785 in stem, 733 in leaf and 517 in root samples, respectively. Between 48% (stem) and 57% (ear) of the metabolites detected in a particular organ were found with all extraction mixtures, and 127 of 996 metabolites were consistently shared between all extraction agent/organ combinations. In aqueous methanol, acidification with formic acid led to pronounced pH dependency regarding the precision of metabolite abundance and the number of detectable metabolites, whereas extracts of acetonitrile-containing mixtures were less affected. Moreover, methanol and acetonitrile have been found to be complementary with respect to extraction efficiency. Interestingly, the beneficial properties of both solvents can be combined by the use of a water-methanol-acetonitrile mixture for global metabolite extraction instead of aqueous methanol or aqueous acetonitrile alone.

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Section: Discussionmentioning

confidence: 90%

“…Sample extraction is mostly followed by centrifugation and/or filtration and subsequent LC-HRMS measurement. Some approaches include additional sample preparation steps such as solid phase extraction [ 8 , 18 ] or evaporation of the raw extract and re-dissolution in different solvents [ 11 , 15 , 17 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Stable Isotope-Assisted Evaluation of Different Extraction Solvents for Untargeted Metabolomics of Plants

Doppler

Kluger

Bueschl

et al. 2016

IJMS

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“…However, some of these approaches (sum and mean) can introduce artificial correlations in the data in the case of large differences between the groups of samples in one or a few metabolites (Dieterle et al 2006 ; Li et al 2015 ). Probabilistic quotient normalisation (PQN) (Dieterle et al 2006 ) was developed to reduce this effect and has successfully been applied for normalization in many metabolomics studies (Hrydziuszko and Viant 2012 ; Davies et al 2014 ; Cottet et al 2014 ). Different methods applying internal standards have been developed (De Livera et al 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling

et al. 2016

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Introduction The generic metabolomics data processing workflow is constructed with a serial set of processes including peak picking, quality assurance, normalisation, missing value imputation, transformation and scaling. The combination of these processes should present the experimental data in an appropriate structure so to identify the biological changes in a valid and robust manner. Objectives Currently, different researchers apply different data processing methods and no assessment of the permutations applied to UHPLC-MS datasets has been published. Here we wish to define the most appropriate data processing workflow. Methods We assess the influence of normalisation, missing value imputation, transformation and scaling methods on univariate and multivariate analysis of UHPLC-MS datasets acquired for different mammalian samples.Results Our studies have shown that once data are filtered, missing values are not correlated with m/z, retention time or response. Following an exhaustive evaluation, we recommend PQN normalisation with no missing value imputation and no transformation or scaling for univariate analysis. For PCA we recommend applying PQN normalisation with Random Forest missing value imputation, glog transformation and no scaling method. For PLS-DA we recommend PQN normalisation, KNN as the missing value imputation method, generalised logarithm transformation and no scaling. These recommendations are based on searching for the biologically important metabolite features independent of their measured abundance. Conclusion The appropriate choice of normalisation, missing value imputation, transformation and scaling methods differs depending on the data analysis method and the choice of method is essential to maximise the biological derivations from UHPLC-MS datasets.

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“…Previous studies have identified some secondary metabolites isolated from Symphonia globulifera [26]. The metabolites include polycyclic poly-prenylated-acylphloroglucinols (PPAPs), xanthones and biflavonoids, which have been shown to be related with antiparasitic activities [27].…”

Section: Secondary Metabolites Changes and The Response Of Populus Tomentioning

confidence: 99%

Metabolite profiles of Populus in response to pathogen stress

Chen

Zhou³

et al. 2015

Biochemical and Biophysical Research Communications

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Comparative LC–MS-based metabolite profiling of the ancient tropical rainforest tree Symphonia globulifera

Cited by 13 publications

References 32 publications

Stable Isotope-Assisted Evaluation of Different Extraction Solvents for Untargeted Metabolomics of Plants

Stable Isotope-Assisted Evaluation of Different Extraction Solvents for Untargeted Metabolomics of Plants

Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling

Metabolite profiles of Populus in response to pathogen stress

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