Evidence map›Paper›PMID 34843526›Full record

ArticlePloS one2021

An optimized LC-HRMS untargeted metabolomics workflow for multi-matrices investigations in the three-spined stickleback.

Emmanuelle Lebeau-Roche, Gaëlle Daniele, Aurélie Fildier, Cyril Turies, Odile Dedourge-Geffard, Jean-Marc Porcher, Alain Geffard, Emmanuelle Vulliet

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Emmanuelle Lebeau-RocheUMR-I 02 SEBIO (Stress Environnementaux et BIOsurveillance des milieux aquatiques), Université de Reims Champagne Ardenne, UFR Sciences Exactes et Naturelles, Campus Moulin de Housse, Reims cedex 2, France.
Gaëlle DanieleUniv Lyon, CNRS, Université Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR 5280, Villeurbanne, France.
Aurélie FildierUniv Lyon, CNRS, Université Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR 5280, Villeurbanne, France.
Cyril TuriesInstitut National de l'Environnement Industriel et des Risques (INERIS), UMR-I 02 SEBIO, Parc Technologique Alata, Verneuil-en-Halatte, France.ORCID 0000-0002-6238-4885
Odile Dedourge-GeffardUMR-I 02 SEBIO (Stress Environnementaux et BIOsurveillance des milieux aquatiques), Université de Reims Champagne Ardenne, UFR Sciences Exactes et Naturelles, Campus Moulin de Housse, Reims cedex 2, France.
Jean-Marc PorcherInstitut National de l'Environnement Industriel et des Risques (INERIS), UMR-I 02 SEBIO, Parc Technologique Alata, Verneuil-en-Halatte, France.
Alain GeffardUMR-I 02 SEBIO (Stress Environnementaux et BIOsurveillance des milieux aquatiques), Université de Reims Champagne Ardenne, UFR Sciences Exactes et Naturelles, Campus Moulin de Housse, Reims cedex 2, France.ORCID 0000-0001-9236-2890
Emmanuelle VullietUniv Lyon, CNRS, Université Claude Bernard Lyon 1, Institut des Sciences Analytiques, UMR 5280, Villeurbanne, France.ORCID 0000-0002-6325-1531

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Environmental metabolomics has become a growing research field to understand biological and biochemical perturbations of organisms in response to various abiotic or biotic stresses. It focuses on the comprehensive and systematic analysis of a biologic system's metabolome. This allows the recognition of biochemical pathways impacted by a stressor, and the identification of some metabolites as biomarkers of potential perturbations occurring in a body. In this work, we describe the development and optimization of a complete reliable methodology based on liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) for untargeted metabolomics studies within a fish model species, the three-spined stickleback (Gasterosteus aculeatus). We evaluated the differences and also the complementarities between four different matrices (brain, gills, liver and whole fish) to obtain metabolome information. To this end, we optimized and compared sample preparation and the analytical method, since the type and number of metabolites detected in any matrix are closely related to these latter. For the sample preparation, a solid-liquid extraction was performed on a low quantity of whole fish, liver, brain, or gills tissues using combinations of methanol/water/heptane. Based on the numbers of features observed in LC-HRMS and on the responses of analytical standards representative of different metabolites groups (amino acids, sugars…), we discuss the influence of the nature, volume, and ratio of extraction solvents, the sample weight, and the reconstitution solvent. Moreover, the analytical conditions (LC columns, pH and additive of mobile phases and ionization modes) were also optimized so as to ensure the maximum metabolome coverages. Thus, two complementary chromatographic procedures were combined in order to cover a broader range of metabolites: a reversed phase separation (RPLC) on a C18 column followed by detection with positive ionization mode (ESI+) and a hydrophilic interaction chromatography (HILIC) on a zwitterionic column followed by detection with negative ionization mode (ESI-). This work provides information on brain, gills, liver, vs the whole body contribution to the stickleback metabolome. These information would help to guide ecotoxicological and biomonitoring studies.

Indexed as

MetabolomeAnimalsBrainChromatography, LiquidFemaleGillsLiverMaleMass SpectrometryMetabolomicsSmegmamorphaWorkflow

Identifiers

PMID34843526
PMCPMC8629232

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.