Publications by authors named "Florence Vinson"

11 Publications

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Multiplatform metabolomics for an integrative exploration of metabolic syndrome in older men.

EBioMedicine 2021 Jun 19;69:103440. Epub 2021 Jun 19.

Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France. Electronic address:

Background: Metabolic syndrome (MetS), a cluster of factors associated with risks of developing cardiovascular diseases, is a public health concern because of its growing prevalence. Considering the combination of concomitant components, their development and severity, MetS phenotypes are largely heterogeneous, inducing disparity in diagnosis.

Methods: A case/control study was designed within the NuAge longitudinal cohort on aging. From a 3-year follow-up of 123 stable individuals, we present a deep phenotyping approach based on a multiplatform metabolomics and lipidomics untargeted strategy to better characterize metabolic perturbations in MetS and define a comprehensive MetS signature stable over time in older men.

Findings: We characterize significant changes associated with MetS, involving modulations of 476 metabolites and lipids, and representing 16% of the detected serum metabolome/lipidome. These results revealed a systemic alteration of metabolism, involving various metabolic pathways (urea cycle, amino-acid, sphingo- and glycerophospholipid, and sugar metabolisms…) not only intrinsically interrelated, but also reflecting environmental factors (nutrition, microbiota, physical activity…).

Interpretation: These findings allowed identifying a comprehensive MetS signature, reduced to 26 metabolites for future translation into clinical applications for better diagnosing MetS.
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http://dx.doi.org/10.1016/j.ebiom.2021.103440DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8237302PMC
June 2021

Improving lipid mapping in Genome Scale Metabolic Networks using ontologies.

Metabolomics 2020 03 25;16(4):44. Epub 2020 Mar 25.

UMR1331, Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, 31300, Toulouse, France.

Introduction: To interpret metabolomic and lipidomic profiles, it is necessary to identify the metabolic reactions that connect the measured molecules. This can be achieved by putting them in the context of genome-scale metabolic network reconstructions. However, mapping experimentally measured molecules onto metabolic networks is challenging due to differences in identifiers and level of annotation between data and metabolic networks, especially for lipids.

Objectives: To help linking lipids from lipidomics datasets with lipids in metabolic networks, we developed a new matching method based on the ChEBI ontology. The implementation is freely available as a python library and in MetExplore webserver.

Methods: Our matching method is more flexible than an exact identifier-based correspondence since it allows establishing a link between molecules even if a different level of precision is provided in the dataset and in the metabolic network. For instance, it can associate a generic class of lipids present in the network with the molecular species detailed in the lipidomics dataset. This mapping is based on the computation of a distance between molecules in ChEBI ontology.

Results: We applied our method to a chemical library (968 lipids) and an experimental dataset (32 modulated lipids) and showed that using ontology-based mapping improves and facilitates the link with genome scale metabolic networks. Beyond network mapping, the results provide ways for improvements in terms of network curation and lipidomics data annotation.

Conclusion: This new method being generic, it can be applied to any metabolomics data and therefore improve our comprehension of metabolic modulations.
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http://dx.doi.org/10.1007/s11306-020-01663-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7096385PMC
March 2020

Large-Scale Modeling Approach Reveals Functional Metabolic Shifts during Hepatic Differentiation.

J Proteome Res 2019 01 19;18(1):204-216. Epub 2018 Nov 19.

UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France.

Being able to explore the metabolism of broad metabolizing cells is of critical importance in many research fields. This article presents an original modeling solution combining metabolic network and omics data to identify modulated metabolic pathways and changes in metabolic functions occurring during differentiation of a human hepatic cell line (HepaRG). Our results confirm the activation of hepato-specific functionalities and newly evidence modulation of other metabolic pathways, which could not be evidenced from transcriptomic data alone. Our method takes advantage of the network structure to detect changes in metabolic pathways that do not have gene annotations and exploits flux analyses techniques to identify activated metabolic functions. Compared to the usual cell-specific metabolic network reconstruction approaches, it limits false predictions by considering several possible network configurations to represent one phenotype rather than one arbitrarily selected network. Our approach significantly enhances the comprehensive and functional assessment of cell metabolism, opening further perspectives to investigate metabolic shifts occurring within various biological contexts.
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http://dx.doi.org/10.1021/acs.jproteome.8b00524DOI Listing
January 2019

MetaboRank: network-based recommendation system to interpret and enrich metabolomics results.

Bioinformatics 2019 01;35(2):274-283

Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France.

Motivation: Metabolomics has shown great potential to improve the understanding of complex diseases, potentially leading to therapeutic target identification. However, no single analytical method allows monitoring all metabolites in a sample, resulting in incomplete metabolic fingerprints. This incompleteness constitutes a stumbling block to interpretation, raising the need for methods that can enrich those fingerprints. We propose MetaboRank, a new solution inspired by social network recommendation systems for the identification of metabolites potentially related to a metabolic fingerprint.

Results: MetaboRank method had been used to enrich metabolomics data obtained on cerebrospinal fluid samples from patients suffering from hepatic encephalopathy (HE). MetaboRank successfully recommended metabolites not present in the original fingerprint. The quality of recommendations was evaluated by using literature automatic search, in order to check that recommended metabolites could be related to the disease. Complementary mass spectrometry experiments and raw data analysis were performed to confirm these suggestions. In particular, MetaboRank recommended the overlooked α-ketoglutaramate as a metabolite which should be added to the metabolic fingerprint of HE, thus suggesting that metabolic fingerprints enhancement can provide new insight on complex diseases.

Availability And Implementation: Method is implemented in the MetExplore server and is available at www.metexplore.fr. A tutorial is available at https://metexplore.toulouse.inra.fr/com/tutorials/MetaboRank/2017-MetaboRank.pdf.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/bty577DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6330003PMC
January 2019

MetExplore: collaborative edition and exploration of metabolic networks.

Nucleic Acids Res 2018 07;46(W1):W495-W502

INRA, UMR1331, Toxalim, F-31000 Toulouse, France.

Metabolism of an organism is composed of hundreds to thousands of interconnected biochemical reactions responding to environmental or genetic constraints. This metabolic network provides a rich knowledge to contextualize omics data and to elaborate hypotheses on metabolic modulations. Nevertheless, performing this kind of integrative analysis is challenging for end users with not sufficiently advanced computer skills since it requires the use of various tools and web servers. MetExplore offers an all-in-one online solution composed of interactive tools for metabolic network curation, network exploration and omics data analysis. In particular, it is possible to curate and annotate metabolic networks in a collaborative environment. The network exploration is also facilitated in MetExplore by a system of interactive tables connected to a powerful network visualization module. Finally, the contextualization of metabolic elements in the network and the calculation of over-representation statistics make it possible to interpret any kind of omics data. MetExplore is a sustainable project maintained since 2010 freely available at https://metexplore.toulouse.inra.fr/metexplore2/.
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http://dx.doi.org/10.1093/nar/gky301DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030842PMC
July 2018

MetExploreViz: web component for interactive metabolic network visualization.

Bioinformatics 2018 Jan;34(2):312-313

Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France.

Summary: MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyse omics data in a biochemical context.

Availability And Implementation: Documentation and link to GIT code repository (GPL 3.0 license) are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc/.
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http://dx.doi.org/10.1093/bioinformatics/btx588DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860210PMC
January 2018

A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale Metabolic Networks.

Front Mol Biosci 2016 16;3. Epub 2016 Feb 16.

TOXALIM (Research Centre in Food Toxicology), Institut National de la Recherche Agronomique, UMR1331, Université de Toulouse Toulouse, France.

This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities.
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http://dx.doi.org/10.3389/fmolb.2016.00002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4754433PMC
February 2016

Dynamic Metabolic Disruption in Rats Perinatally Exposed to Low Doses of Bisphenol-A.

PLoS One 2015 30;10(10):e0141698. Epub 2015 Oct 30.

UMR1331, TOXALIM, Research Centre in Food Toxicology, Institut National de la Recherche Agronomique, INRA, Université de Toulouse, Toulouse, France.

Along with the well-established effects on fertility and fecundity, perinatal exposure to endocrine disrupting chemicals, and notably to xeno-estrogens, is strongly suspected of modulating general metabolism. The metabolism of a perinatally exposed individual may be durably altered leading to a higher susceptibility of developing metabolic disorders such as obesity and diabetes; however, experimental designs involving the long term study of these dynamic changes in the metabolome raise novel challenges. 1H-NMR-based metabolomics was applied to study the effects of bisphenol-A (BPA, 0; 0.25; 2.5, 25 and 250 μg/kg BW/day) in rats exposed perinatally. Serum and liver samples of exposed animals were analyzed on days 21, 50, 90, 140 and 200 in order to explore whether maternal exposure to BPA alters metabolism. Partial Least Squares-Discriminant Analysis (PLS-DA) was independently applied to each time point, demonstrating a significant pair-wise discrimination for liver as well as serum samples at all time-points, and highlighting unequivocal metabolic shifts in rats perinatally exposed to BPA, including those exposed to lower doses. In BPA exposed animals, metabolism of glucose, lactate and fatty acids was modified over time. To further explore dynamic variation, ANOVA-Simultaneous Component Analysis (A-SCA) was used to separate data into blocks corresponding to the different sources of variation (Time, Dose and Time*Dose interaction). A-SCA enabled the demonstration of a dynamic, time/age dependent shift of serum metabolome throughout the rats' lifetimes. Variables responsible for the discrimination between groups clearly indicate that BPA modulates energy metabolism, and suggest alterations of neurotransmitter signaling, the latter finding being compatible with the neurodevelopmental effect of this xenoestrogen. In conclusion, long lasting metabolic effects of BPA could be characterized over 200 days, despite physiological (and thus metabolic) changes connected with sexual maturation and aging.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141698PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4627775PMC
June 2016

TrypanoCyc: a community-led biochemical pathways database for Trypanosoma brucei.

Nucleic Acids Res 2015 Jan 9;43(Database issue):D637-44. Epub 2014 Oct 9.

University of Liverpool, Liverpool, Merseyside L69 3BX, UK.

The metabolic network of a cell represents the catabolic and anabolic reactions that interconvert small molecules (metabolites) through the activity of enzymes, transporters and non-catalyzed chemical reactions. Our understanding of individual metabolic networks is increasing as we learn more about the enzymes that are active in particular cells under particular conditions and as technologies advance to allow detailed measurements of the cellular metabolome. Metabolic network databases are of increasing importance in allowing us to contextualise data sets emerging from transcriptomic, proteomic and metabolomic experiments. Here we present a dynamic database, TrypanoCyc (http://www.metexplore.fr/trypanocyc/), which describes the generic and condition-specific metabolic network of Trypanosoma brucei, a parasitic protozoan responsible for human and animal African trypanosomiasis. In addition to enabling navigation through the BioCyc-based TrypanoCyc interface, we have also implemented a network-based representation of the information through MetExplore, yielding a novel environment in which to visualise the metabolism of this important parasite.
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http://dx.doi.org/10.1093/nar/gku944DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4384016PMC
January 2015

Exposure to pesticides and risk of childhood cancer: a meta-analysis of recent epidemiological studies.

Occup Environ Med 2011 Sep 23;68(9):694-702. Epub 2011 May 23.

INRA, UMR1089 Xénobiotiques, 180 Chemin de Tournefeuille, BP93173, F-31027 Toulouse Cedex 3, France.

Objectives: The authors performed a meta-analysis of case-control and cohort studies to clarify the possible relationship between exposure to pesticides and childhood cancers.

Methods: Two cohort and 38 case-control studies were selected for the first meta-analysis. After evaluating homogeneity among studies using the Cochran Q test, the authors calculated a pooled meta-OR stratified on each cancer site. The authors then constructed a list of variables believed to play an important role in explaining the relation between parental exposure to pesticide and childhood cancer, and performed a series of meta-analyses. The authors also performed a distinct meta-analysis for three cohort studies with RR data.

Results: Meta-analysis of the three cohort studies did not show any positive links between parental pesticide exposure and childhood cancer incidence. However, the meta-analysis of the 40 studies with OR values showed that the risk of lymphoma and leukaemia increased significantly in exposed children when their mother was exposed during the prenatal period (OR=1.53; 95% CI 1.22 to 1.91 and OR=1.48; 95% CI 1.26 to 1.75). The risk of brain cancer was correlated with paternal exposure either before or after birth (OR=1.49; 95% CI 1.23 to 1.79 and OR=1.66; 95% CI 1.11 to 2.49). The OR of leukaemia and lymphoma was higher when the mother was exposed to pesticides (through household use or professional exposure). Conversely, the incidence of brain cancer was influenced by the father's exposure (occupational activity or use of household or garden pesticides).

Conclusion: Despite some limitations in this study, the incidence of childhood cancer does appear to be associated with parental exposure during the prenatal period.
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http://dx.doi.org/10.1136/oemed-2011-100082DOI Listing
September 2011

MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks.

Nucleic Acids Res 2010 Jul 5;38(Web Server issue):W132-7. Epub 2010 May 5.

INRA, UMR1089, Xénobiotiques, F-31000 Toulouse, France.

High-throughput metabolomic experiments aim at identifying and ultimately quantifying all metabolites present in biological systems. The metabolites are interconnected through metabolic reactions, generally grouped into metabolic pathways. Classical metabolic maps provide a relational context to help interpret metabolomics experiments and a wide range of tools have been developed to help place metabolites within metabolic pathways. However, the representation of metabolites within separate disconnected pathways overlooks most of the connectivity of the metabolome. By definition, reference pathways cannot integrate novel pathways nor show relationships between metabolites that may be linked by common neighbours without being considered as joint members of a classical biochemical pathway. MetExplore is a web server that offers the possibility to link metabolites identified in untargeted metabolomics experiments within the context of genome-scale reconstructed metabolic networks. The analysis pipeline comprises mapping metabolomics data onto the specific metabolic network of an organism, then applying graph-based methods and advanced visualization tools to enhance data analysis. The MetExplore web server is freely accessible at http://metexplore.toulouse.inra.fr.
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http://dx.doi.org/10.1093/nar/gkq312DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2896158PMC
July 2010