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GC-MS-based urine metabolic profiling of autism spectrum disorders

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Abstract

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders resulting from multiple factors. Diagnosis is based on behavioural and developmental signs detected before 3 years of age, and there is no reliable biological marker. The purpose of this study was to evaluate the value of gas chromatography combined with mass spectroscopy (GC-MS) associated with multivariate statistical modeling to capture the global biochemical signature of autistic individuals. GC-MS urinary metabolic profiles of 26 autistic and 24 healthy children were obtained by liq/liq extraction, and were or were not subjected to an oximation step, and then were subjected to a persilylation step. These metabolic profiles were then processed by multivariate analysis, in particular orthogonal partial least-squares discriminant analysis (OPLS-DA, R 2Y(cum) = 0.97, Q 2(cum) = 0.88). Discriminating metabolites were identified. The relative concentrations of the succinate and glycolate were higher for autistic than healthy children, whereas those of hippurate, 3-hydroxyphenylacetate, vanillylhydracrylate, 3-hydroxyhippurate, 4-hydroxyphenyl-2-hydroxyacetate, 1H-indole-3-acetate, phosphate, palmitate, stearate, and 3-methyladipate were lower. Eight other metabolites, which were not identified but characterized by a retention time plus a quantifier and its qualifier ion masses, were found to differ between the two groups. Comparison of statistical models leads to the conclusion that the combination of data obtained from both derivatization techniques leads to the model best discriminating between autistic and healthy groups of children.

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Abbreviations

ASD:

Autism spectrum disorders

BSTFA:

bis(trimethylsilyl)trifluoroacetamide

GC-MS:

Gas chromatography combined with mass spectroscopy

NMR:

Nuclear magnetic resonance

OPLS-DA:

Orthogonal partial least-squares discriminant analysis

Par:

Pareto

PCA:

Principal component analysis

PLS-DA:

Partial least-squares discriminant analysis

TMS:

Trimethylsilylated derivative

TMSO:

Trimethylsilylated and oximated derivative

UV:

Unit variance

VIP:

Variable importance on projection

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Acknowledgments

This work was supported by the “Institut National de la Santé et de la Recherche” INSERM and the University François-Rabelais. We thank the center “Sésame Autisme Loiret” for participation in this study. We thank the “Département d’Analyses Chimiques et S.R.M. Biologique et Médicale” (PPF, Tours, France) for GC-MS analyses.

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Authors and Affiliations

  1. INSERM U930, Equipe neurogénétique et neurométabolomique, CHRU de Tours, Hôpital Bretonneau, Université François-Rabelais, 10 Bv Tonnellé, 37044, Tours, France

    Patrick Emond, Sylvie Mavel, Nacima Aïdoud, Lydie Nadal-Desbarats, Frédéric Laumonnier, Patrick Vourc’h, Hélène Blasco & Christian R. Andres

  2. PPF “Analyses des Systèmes Biologiques”, UFR de Médecine, Université François-Rabelais, 10 Bv Tonnellé, 37044, Tours, France

    Patrick Emond, Lydie Nadal-Desbarats, Frédéric Montigny & Patrick Vourc’h

  3. INSERM U930, Equipe Autisme, CHRU de Tours, Université François-Rabelais, 10 Bv Tonnellé, 37044, Tours, France

    Frédérique Bonnet-Brilhault & Catherine Barthélémy

  4. Faculté de Médecine, Laboratoire de Biochimie, Université Henri Poincaré, 9 Av de la forêt de Haye, 54505, Vandoeuvre les Nancy, France

    Marc Merten

  5. CHRU de Montpellier, Hôpital Arnaud-de-Villeneuve, 34295, Montpellier cedex 5, France

    Pierre Sarda

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  1. Patrick Emond
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  2. Sylvie Mavel
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Correspondence to Patrick Emond.

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Emond, P., Mavel, S., Aïdoud, N. et al. GC-MS-based urine metabolic profiling of autism spectrum disorders. Anal Bioanal Chem 405, 5291–5300 (2013). https://doi.org/10.1007/s00216-013-6934-x

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  • DOI: https://doi.org/10.1007/s00216-013-6934-x

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