Oakland University-William Beaumont School of Medicine, Rochester, MI, 48309, USA.
Research Institute, Metabolomics Division, Beaumont Health, Royal Oak, MI, 48073, USA.
Metabolomics. 2020 Apr 24;16(5):59. doi: 10.1007/s11306-020-01685-z.
Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders characterized by deficiencies in social interactions and communication, combined with restricted and repetitive behavioral issues.
As little is known about the etiopathophysiology of ASD and early diagnosis is relatively subjective, we aim to employ a targeted, fully quantitative metabolomics approach to biochemically profile post-mortem human brain with the overall goal of identifying metabolic pathways that may have been perturbed as a result of the disease while uncovering potential central diagnostic biomarkers.
Using a combination of H NMR and DI/LC-MS/MS we quantitatively profiled the metabolome of the posterolateral cerebellum from post-mortem human brain harvested from people who suffered with ASD (n = 11) and compared them with age-matched controls (n = 10).
We accurately identified and quantified 203 metabolites in post-mortem brain extracts and performed a metabolite set enrichment analyses identifying 3 metabolic pathways as significantly perturbed (p < 0.05). These include Pyrimidine, Ubiquinone and Vitamin K metabolism. Further, using a variety of machine-based learning algorithms, we identified a panel of central biomarkers (9-hexadecenoylcarnitine (C16:1) and the phosphatidylcholine PC ae C36:1) capable of discriminating between ASD and controls with an AUC = 0.855 with a sensitivity and specificity equal to 0.80 and 0.818, respectively.
For the first time, we report the use of a multi-platform metabolomics approach to biochemically profile brain from people with ASD and report several metabolic pathways which are perturbed in the diseased brain of ASD sufferers. Further, we identified a panel of biomarkers capable of distinguishing ASD from control brains. We believe that these central biomarkers may be useful for diagnosing ASD in more accessible biomatrices.
自闭症谱系障碍(ASD)是一组神经发育障碍,其特征是社交互动和沟通能力缺陷,以及受限和重复的行为问题。
由于对 ASD 的病因病理生理学知之甚少,且早期诊断相对主观,我们旨在采用靶向、完全定量的代谢组学方法对人脑进行生化分析,总体目标是确定可能因疾病而受到干扰的代谢途径,同时揭示潜在的中枢诊断生物标志物。
我们使用 H NMR 和 DI/LC-MS/MS 相结合的方法,对从患有 ASD(n=11)的人的死后大脑的后外侧小脑进行代谢组学定量分析,并将其与年龄匹配的对照组(n=10)进行比较。
我们准确地鉴定和定量了死后大脑提取物中的 203 种代谢物,并进行了代谢物组富集分析,确定了 3 个代谢途径显著受到干扰(p<0.05)。这些途径包括嘧啶、泛醌和维生素 K 代谢。此外,我们使用各种基于机器的学习算法,确定了一组中枢生物标志物(9-十六烯酰肉碱(C16:1)和磷脂酰胆碱 PC ae C36:1),能够以 AUC=0.855 的准确度区分 ASD 和对照组,其灵敏度和特异性分别为 0.80 和 0.818。
我们首次报告了使用多平台代谢组学方法对 ASD 患者的大脑进行生化分析,并报告了几个在 ASD 患者大脑中受到干扰的代谢途径。此外,我们确定了一组能够区分 ASD 和对照大脑的生物标志物。我们相信这些中枢生物标志物可能有助于在更易获取的生物基质中诊断 ASD。