Metabolomics Study of Urine in Autism Spectrum Disorders Using a Multiplatform Analytical Methodology

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with no clinical biomarker. The aims of this study were to characterize a metabolic signature of ASD and to evaluate multiplatform analytical methodologies in order to develop predictive tools for diagnosis and disease follow-up. Urine samples were analyzed using 1 H and 1 H− 13C NMR-based approaches and LC−HRMS-based approaches (ESI+ and ESI− on HILIC and C18 chromatography columns). Data tables obtained from the six analytical modalities on a training set of 46 urine samples (22 autistic children and 24 controls) were processed by multivariate analysis (orthogonal partial least-squares discriminant analysis, OPLS-DA). The predictions from each of these OPLS-DA models were then evaluated using a prediction set of 16 samples (8 autistic children and 8 controls) and receiver operating characteristic curves. Thereafter, a data fusion block-scaling OPLS-DA model was generated from the 6 best models obtained for each modality. This fused OPLS-DA model showed an enhanced performance (R 2 Y(cum) = 0.88, Q 2 (cum) = 0.75) compared to each analytical modality model, as well as a better predictive capacity (AUC = 0.91, p-value = 0.006). Metabolites that are most significantly different between autistic and control children (p < 0.05) are indoxyl sulfate, N-α-acetyl-L-arginine, methyl guanidine, and phenylacetylglutamine. This multimodality approach has the potential to contribute to find robust biomarkers and characterize a metabolic phenotype of the ASD population.

Urinary metabolomics of young Italian autistic children supports abnormal tryptophan and purine metabolism

“…Similar age-related changes in ASD have been previously described for other parameters, such as brain serotonin synthesis capacity [22, 23] and excessive head growth rates [24]. Finally, some studies have contrasted ASD patients with unrelated population controls [11, 14, 16, 17], while others have enrolled unaffected siblings as controls [15] and one study has used both [10]. These strategies are not equivalent, as first-degree relatives often fall within the broad autism spectrum (i.e., they display behavioral phenotypes intermediate between patients and population controls) [25].…”

“…A few studies have recently begun exploring the potential of urinary metabolomics in identifying ASD-specific metabolic patterns or in stratifying ASD patients into pathophysiologically meaningful subgroups [10–17]. Most studies have been performed on urines [10–16]; one study has explored blood plasma [17]. The analytical platforms most commonly used to identify and quantify metabolites are gas or liquid chromatography combined with mass spectroscopy (gas chromatography (GC)-mass spectroscopy (MS) and liquid chromatography (LC)-MS, respectively) [12, 16] and nuclear magnetic resonance spectroscopy (NMR) [10, 13, 14, 16, 18, 19].…”

“…Other studies using GC-MS, either alone [12, 15] or in combination with liquid chromatography [11], also identified perturbations in amino acid metabolism and gut microbial co-metabolites, as well as metabolic signatures of oxidative stress. Only one very recent study used both NMR and LC-MS, providing support for abnormalities in tryptophan metabolism, gut bacterial-derived compounds, purine and pyrimidine metabolism [16]. The only study exploring blood plasma reported metabolomic patterns compatible with (a) mitochondrial dysfunction, yielding reduced energy production and unbalanced redox status, (b) excess gut microbial co-metabolites, and (c) unbalances in various metabolic pathways, such as the Krebs cycle [17].…”

Assessment of metabolic phenotypic variability in children’s urine using 1H NMR spectroscopy

“…Sex and pubertal development (Tanner stage) were characterised clearly in 12–15 year old children based on metabolic profiles26. Metabonomics in paediatric populations has mainly found applications in respiratory diseases, neuro-developmental and obesity outcomes343536. However, studies assessing the long term stability of the metabolic milieu which represents dietary intake, lifestyle and genetic factors, and disease risk factors would be of interest.…”

Depletion of stercobilin in fecal matter from a mouse model of autism spectrum disorders

“…While a GUT for ASD etiology may be unrealistic due to the heterogeneity in causation and severity, the search for potential biomarkers with a focus on metabolites has gained considerable momentum in the past decade (Adamo et al 2014; Altieri et al 2011; Dager et al 2015; Dieme et al 2015; Durieux et al 2016; Faber et al 2009; Gabriele et al 2014; Goldenthal et al 2015; Gorrindo et al 2013; Herbert et al 2006; Jyonouchi et al 2008; Masi et al 2017; Ming et al 2012; Ming et al 2005; Pastural et al 2009; Ramsey et al 2013; Rudolph et al 2013; Woods et al 2015; James et al 2004; Frye et al 2013; Ngounou Wetie et al 2015). Expanding the search to identify potential subgroups within ASD has been of interest.…”