Proteomic biomarkers of Kleine-Levin syndrome.
Proteomic biomarkers of Kleine-Levin syndrome.
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克莱恩-莱文综合征的蛋白质组生物标志物。
作者：
Hédou,Julien;Cederberg,KatieL;Ambati,Aditya;Lin,Ling;Farber,Neal;Dauvilliers,Yves;Quadri,Mohammed;Bourgin,Patrice;Plazzi,Giuseppe;Andlauer,Olivier;Hong,Seung-Chul;Huang,Yu-Shu;Leu-Semenescu,Smaranda;Arnulf,Isabelle;Taheri,Sha
研究目的Kleine-Levin综合征(KLS)以反复发作、反复发作的过度睡眠、认知障碍和行为障碍为特征。方法用SomaScan对30例KLS患者和134例正常对照的1133份脑脊液蛋白进行分析，并对26例KLS患者和65名正常对照的1109份血清蛋白进行分析。7例患者同时进行脑脊液和血清蛋白检测。用单变量和多变量分析寻找差异表达蛋白(DEP)。结果单因素分析发现在脑脊液和血清中分别有28和141个差异表达蛋白(假发现率为0.1%)。脑脊液中IL-34、IL-27、转化生长因子-b、胰岛素样生长因子-1和骨连接蛋白表达上调，而DKK4和vWF表达下调。通路分析显示小胶质细胞改变并破坏血脑屏障通透性。血清图谱显示，与细胞生长、运动和激活有关的蛋白质Src家族蛋白(SFK)表达上调。对上调和下调的蛋白质的TEA分析显示，大脑蛋白质(p<；6×10−5)发生了变化，特别是在脑桥、延髓和中脑。多变量机器学习分类器表现良好，在验证队列中，脑脊液和血清的接收器操作曲线面积分别为0.9(95%可信区间[CI]=0.78-1.0，p=0.0006)和1.0(95%可信区间=1.0-1.0，p=0.0002)，具有一定的跨组织共性，因为在血清样本上训练的模型也区分了对照组和KLS病例的脑脊液样本。结论我们的研究发现了具有诊断潜力的蛋白质组KLS生物标志物，并为深入了解生物学机制提供了指导，将指导KLS的未来研究。
Study ObjectivesKleine–Levin syndrome (KLS) is characterized by relapsing–remitting episodes of hypersomnia, cognitive impairment, and behavioral disturbances. We quantified cerebrospinal fluid (CSF) and serum proteins in KLS cases and controls.MethodsSomaScan was used to profile 1133 CSF proteins in 30 KLS cases and 134 controls, while 1109 serum proteins were profiled in serum from 26 cases and 65 controls. CSF and serum proteins were both measured in seven cases. Univariate and multivariate analyses were used to find differentially expressed proteins (DEPs). Pathway and tissue enrichment analyses (TEAs) were performed on DEPs.ResultsUnivariate analyses found 28 and 141 proteins differentially expressed in CSF and serum, respectively (false discovery rate <0.1%). Upregulated CSF proteins included IL-34, IL-27, TGF-b, IGF-1, and osteonectin, while DKK4 and vWF were downregulated. Pathway analyses revealed microglial alterations and disrupted blood–brain barrier permeability. Serum profiles show upregulation of Src-family kinases (SFKs), proteins implicated in cellular growth, motility, and activation. TEA analysis of up- and downregulated proteins revealed changes in brain proteins (p < 6 × 10−5), notably from the pons, medulla, and midbrain. A multivariate machine-learning classifier performed robustly, achieving a receiver operating curve area under the curve of 0.90 (95% confidence interval [CI] = 0.78–1.0,p= 0.0006) in CSF and 1.0 (95% CI = 1.0–1.0,p= 0.0002) in serum in validation cohorts, with some commonality across tissues, as the model trained on serum sample also discriminated CSF samples of controls versus KLS cases.ConclusionsOur study identifies proteomic KLS biomarkers with diagnostic potential and provides insight into biological mechanisms that will guide future research in KLS.