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Proteomic analysis of lymphoblastoid cell lines from schizophrenic patients

机译:精神分裂症患者淋巴母细胞系的蛋白质组学分析

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Although a number of studies have identified several convincing candidate genes or molecules, the pathophysiology of schizophrenia (SCZ) has not been completely elucidated. Therapeutic optimization based on pathophysiology should be performed as early as possible to improve functional outcomes and prognosis; to detect useful biomarkers for SCZ, which reflect pathophysiology and can be utilized for timely diagnosis and effective therapy. To explore biomarkers for SCZ, we employed fluorescence two-dimensional differential gel electrophoresis (2D-DIGE) of lymphoblastoid cell lines (LCLs) (1st sample set: 30 SCZ and 30 CON). Differentially expressed proteins were sequenced by liquid chromatography tandem-mass spectrometry (LC-MS/MS) and identified proteins were confirmed by western blotting (WB) (1st and 2nd sample set: 60 SCZ and 60 CON). Multivariate logistic regression analysis was performed to identify an optimal combination of biomarkers to create a prediction model for SCZ. Twenty protein spots were differentially expressed between SCZ and CON in 2D-DIGE analysis and 22 unique proteins were identified by LC-MS/MS. Differential expression of eight of 22 proteins was confirmed by WB. Among the eight candidate proteins (HSPA4L, MX1, GLRX3, UROD, MAPRE1, TBCB, IGHM, and GART), we successfully constructed logistic regression models comprised of 4- and 6-markers with good discriminative ability between SCZ and CON. In both WB and gene expression analysis of LCL, MX1 showed reproducibly significant associations. Moreover, Mx1 and its related proinflamatory genes (Mx2, Il1b, and Tnf) were also up-regulated in poly I:C-treated mice. Differentially expressed proteins might be associated with molecular pathophysiology of SCZ, including dysregulation of immunological reactions and potentially provide diagnostic and prognostic biomarkers.
机译:尽管许多研究已经确定了几个令人信服的候选基因或分子,但尚未完全阐明精神分裂症(SCZ)的病理生理学。应尽早进行基于病理生理学的治疗优化,以改善功能预后和预后。检测SCZ的有用生物标志物,这些标志物反映了病理生理学,可用于及时诊断和有效治疗。为了探索SCZ的生物标记,我们采用了淋巴母细胞样细胞系(LCL)的荧光二维差分凝胶电泳(2D-DIGE)(第一个样品组:30 SCZ和30 CON)。通过液相色谱串联质谱(LC-MS / MS)对差异表达的蛋白质进行测序,并通过蛋白质印迹(WB)确认已鉴定的蛋白质(第一个和第二个样品组:60 SCZ和60 CON)。进行多元逻辑回归分析以鉴定生物标志物的最佳组合,以创建SCZ的预测模型。在2D-DIGE分析中,在SCZ和CON之间差异表达了20个蛋白质斑点,并且通过LC-MS / MS鉴定出22个独特的蛋白质。 WB证实了22种蛋白质中的8种的差异表达。在八个候选蛋白(HSPA4L,MX1,GLRX3,UROD,MAPRE1,TBCB,IGHM和GART)中,我们成功构建了由4和6标记组成的逻辑回归模型,在SCZ和CON之间具有良好的判别能力。在WB和LCL的基因表达分析中,MX1显示出可重复的重要关联。此外,Mx1及其相关的促炎基因(Mx2,Il1b和Tnf)在经poly I:C处理的小鼠中也被上调。差异表达的蛋白可能与SCZ的分子病理生理有关,包括免疫反应异常,并可能提供诊断和预后生物标志物。

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