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Raphaël Lugan

Bio: Raphaël Lugan is an academic researcher from University of Avignon. The author has contributed to research in topics: Botrytis cinerea & Lycium. The author has an hindex of 4, co-authored 5 publications receiving 41 citations.

Papers
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Journal ArticleDOI
TL;DR: A machine learning approach, MSHub, is engineered to enable auto-deconvolution of gas chromatography–mass spectrometry data and workflows are designed to enable the community to store, process, share, annotate, compare and perform molecular networking of GC–MS data within theGNPS Molecular Networking analysis platform.
Abstract: We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

65 citations

Journal ArticleDOI
TL;DR: Human exposure to genipap reveals the production of derivative forms of bioactive compounds such as genipic and genipinic acid, and the findings suggest thatgenipap consumption triggers effects on metabolic signatures.
Abstract: Genipap (Genipa americana L.) is a native fruit from Amazonia that contains bioactive compounds with a wide range of bioactivities. However, the response to genipap juice ingestion in the human exposome has never been studied. To identify biomarkers of genipap exposure, the untargeted metabolomics approach in human urine was applied. Urine samples from 16 healthy male volunteers, before and after drinking genipap juice, were analyzed by liquid chromatography–high-resolution mass spectrometry. XCMS package was used for data processing in the R environment and t-tests were applied on log-transformed and Pareto-scaled data to select the significant metabolites. The principal component analysis (PCA) score plots showed a clear distinction between experimental groups. Thirty-three metabolites were putatively annotated and the most discriminant were mainly related to the metabolic pathways of iridoids and phenolic derivatives. For the first time, the bioavailability of genipap iridoids after human consumption is reported. Dihydroxyhydrocinnamic acid, (1R,6R)-6-hydroxy-2-succinylcyclohexa-2,4-diene-1-carboxylate, hydroxyhydrocinnamic acid, genipic acid, 12-demethylated-8-hydroxygenipinic acid, 3(7)-dehydrogenipinic acid, genipic acid glucuronide, nonate, and 3,4-dihydroxyphenylacetate may be considered biomarkers of genipap consumption. Human exposure to genipap reveals the production of derivative forms of bioactive compounds such as genipic and genipinic acid. These findings suggest that genipap consumption triggers effects on metabolic signatures.

14 citations

Journal ArticleDOI
TL;DR: Optimal analytical methods for metabolic profiling in the fruits of three Solanaceae species, reported here for the first time to the authors' knowledge, revealed compounds discriminating the Lycium species were more abundant in Lycium chinense, whereas Lycium barbarum accumulated more lycibarbarphenylpropanoids A-B, coumaric acid, fructose and glucose.
Abstract: Metabolic profile is a key component of fruit quality, which is a challenge to study due to great compound diversity, especially in species with high nutritional value. This study presents optimized analytical methods for metabolic profiling in the fruits of three Solanaceae species: Lycium barbarum, Lycium chinense and Solanumlycopersicum. It includes the most important chemical classes involved in nutrition and taste, i.e., carotenoids, phenolic compounds and primary compounds. Emphasis has been placed on the systematic achievement of good extraction yields, sample stability, and high response linearity using common LC-ESI-TQ-MS and GC-EI-MS apparatuses. A set of 13 carotenoids, 46 phenolic compounds and 67 primary compounds were profiled in fruit samples. Chemometrics revealed metabolic markers discriminating Lycium and Solanum fruits but also Lycium barbarum and Lycium chinense fruits and the effect of the crop environment. Typical tomato markers were found to be lycopene, carotene, glutamate and GABA, while lycibarbarphenylpropanoids and zeaxanthin esters characterized goji (Lycium spp.) fruits. Among the compounds discriminating the Lycium species, reported here for the first time to our knowledge, chlorogenic acids, asparagine and quinic acid were more abundant in Lycium chinense, whereas Lycium barbarum accumulated more lycibarbarphenylpropanoids A-B, coumaric acid, fructose and glucose.

7 citations

Posted ContentDOI
14 Jan 2020-bioRxiv
TL;DR: A scalable machine learning workflow is engineered to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data, and introduces a “balance score” that quantifies the reproducibility of fragmentation patterns across all samples.
Abstract: Fil: Aksenov, Alexander. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos

6 citations

Journal ArticleDOI
TL;DR: It can be hypothesized that supplementary sucrose cleavage by sucrose synthases is dedicated to the production of cell wall components from UDP-glucose, or to the additional implication of fructose in the synthesis of antimicrobial compounds, or both.

6 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, the authors present guidelines covering sample preparation, replication and randomization, quantification, recovery and recombination, ion suppression and peak misidentification, as a means to enable high-quality reporting of liquid chromatography and gas chromatography-mass spectrometry-derived data.
Abstract: Mass spectrometry-based metabolomics approaches can enable detection and quantification of many thousands of metabolite features simultaneously. However, compound identification and reliable quantification are greatly complicated owing to the chemical complexity and dynamic range of the metabolome. Simultaneous quantification of many metabolites within complex mixtures can additionally be complicated by ion suppression, fragmentation and the presence of isomers. Here we present guidelines covering sample preparation, replication and randomization, quantification, recovery and recombination, ion suppression and peak misidentification, as a means to enable high-quality reporting of liquid chromatography- and gas chromatography-mass spectrometry-based metabolomics-derived data.

257 citations

Journal ArticleDOI
TL;DR: In this paper, a review of tools, packages, tools, databases, and other utilities for metabolomics is presented, focusing on tools, resources, databases and solutions that help in harnessing the concealed information in the generated data for eventual translational success.
Abstract: Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success. In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community. In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.

85 citations

Journal ArticleDOI
TL;DR: In this article, the authors provide a foundational introduction to common forms of untargeted mass spectrometry and the types of data that can be obtained in the context of microbiome analysis.
Abstract: Microbiotas are a malleable part of ecosystems, including the human ecosystem. Microorganisms affect not only the chemistry of their specific niche, such as the human gut, but also the chemistry of distant environments, such as other parts of the body. Mass spectrometry-based metabolomics is one of the key technologies to detect and identify the small molecules produced by the human microbiota, and to understand the functional role of these microbial metabolites. This Review provides a foundational introduction to common forms of untargeted mass spectrometry and the types of data that can be obtained in the context of microbiome analysis. Data analysis remains an obstacle; therefore, the emphasis is placed on data analysis approaches and integrative analysis, including the integration of microbiome sequencing data.

80 citations

Journal ArticleDOI
TL;DR: This review highlights multiple network strategies that can be applied for metabolomics data analysis from different perspectives including: association networks based on quantitative information, mass spectra similarity networks to assist metabolite annotation and biochemical networks for systematic data interpretation.
Abstract: Metabolomics has become a crucial part of systems biology; however, data analysis is still often undertaken in a reductionist way focusing on changes in individual metabolites. Whilst such approach...

57 citations

Journal ArticleDOI
TL;DR: In this article, the authors highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020, and discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data.

49 citations