Diémé Binta, Mavel Sylvie, Blasco Hélène, Tripi Gabriele, Bonnet-Brilhault Frédérique, Malvy Joëlle, Bocca Cinzia, Andres Christian R, Nadal-Desbarats Lydie, Emond Patrick
INSERM U930, Imagerie et Cerveau, Université François-Rabelais , 37000 Tours, France.
Service de Biochimie Et Biologie Moléculaire, Centre Hospitalier Régional Universitaire (CHRU) de Tours , 37044 Tours, France.
J Proteome Res. 2015 Dec 4;14(12):5273-82. doi: 10.1021/acs.jproteome.5b00699. Epub 2015 Nov 23.
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-(13)C 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.
自闭症谱系障碍（ASD）是一种没有临床生物标志物的神经发育障碍。本研究的目的是表征ASD的代谢特征，并评估多平台分析方法，以便开发用于诊断和疾病随访的预测工具。使用基于¹H和¹H-¹³C NMR的方法以及基于LC-HRMS的方法（在HILIC和C18色谱柱上进行ESI+和ESI-）对尿液样本进行分析。对46个尿液样本（22名自闭症儿童和24名对照）的训练集上从六种分析模式获得的数据表进行多变量分析（正交偏最小二乘判别分析，OPLS-DA）。然后使用16个样本（8名自闭症儿童和8名对照）的预测集和受试者工作特征曲线评估这些OPLS-DA模型中每个模型的预测。此后，从每种模式获得的6个最佳模型生成数据融合块缩放OPLS-DA模型。与每个分析模式模型相比，这种融合的OPLS-DA模型表现出增强的性能（R²Y（cum）= 0.88，Q²（cum）= 0.75），以及更好的预测能力（AUC = 0.91，p值 = 0.006）。自闭症儿童和对照儿童之间差异最显著的代谢物（p < 0.05）是硫酸吲哚酚、N-α-乙酰-L-精氨酸、甲基胍和苯乙酰谷氨酰胺。这种多模式方法有可能有助于找到可靠的生物标志物并表征ASD人群的代谢表型。
