M.Sc. Program in Clinical Biochemistry and Molecular Medicine, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand.
PhD Program in Clinical Biochemistry and Molecular Medicine, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand.
PLoS One. 2019 Mar 28;14(3):e0214198. doi: 10.1371/journal.pone.0214198. eCollection 2019.
The mechanisms underlying autism spectrum disorder (ASD) remain unclear, and clinical biomarkers are not yet available for ASD. Differences in dysregulated proteins in ASD have shown little reproducibility, which is partly due to ASD heterogeneity. Recent studies have demonstrated that subgrouping ASD cases based on clinical phenotypes is useful for identifying candidate genes that are dysregulated in ASD subgroups. However, this strategy has not been employed in proteome profiling analyses to identify ASD biomarker proteins for specific subgroups.
We therefore conducted a cluster analysis of the Autism Diagnostic Interview-Revised (ADI-R) scores from 85 individuals with ASD to predict subgroups and subsequently identified dysregulated genes by reanalyzing the transcriptome profiles of individuals with ASD and unaffected individuals. Proteome profiling of lymphoblastoid cell lines from these individuals was performed via 2D-gel electrophoresis, and then mass spectrometry. Disrupted proteins were identified and compared to the dysregulated transcripts and reported dysregulated proteins from previous proteome studies. Biological functions were predicted using the Ingenuity Pathway Analysis (IPA) program. Selected proteins were also analyzed by Western blotting.
The cluster analysis of ADI-R data revealed four ASD subgroups, including ASD with severe language impairment, and transcriptome profiling identified dysregulated genes in each subgroup. Screening via proteome analysis revealed 82 altered proteins in the ASD subgroup with severe language impairment. Eighteen of these proteins were further identified by nano-LC-MS/MS. Among these proteins, fourteen were predicted by IPA to be associated with neurological functions and inflammation. Among these proteins, diazepam-binding inhibitor (DBI) protein was confirmed by Western blot analysis to be expressed at significantly decreased levels in the ASD subgroup with severe language impairment, and the DBI expression levels were correlated with the scores of several ADI-R items.
By subgrouping individuals with ASD based on clinical phenotypes, and then performing an integrated transcriptome-proteome analysis, we identified DBI as a novel candidate protein for ASD with severe language impairment. The mechanisms of this protein and its potential use as an ASD biomarker warrant further study.
自闭症谱系障碍(ASD)的发病机制尚不清楚,也没有用于 ASD 的临床生物标志物。ASD 中失调蛋白的差异显示出很少的重现性,部分原因是 ASD 的异质性。最近的研究表明,根据临床表型对 ASD 病例进行亚组分类有助于识别在 ASD 亚组中失调的候选基因。然而,这种策略尚未应用于蛋白质组谱分析,以确定特定亚组的 ASD 生物标志物蛋白。
我们对 85 名 ASD 患者的自闭症诊断访谈修订版(ADI-R)评分进行聚类分析,以预测亚组,然后通过重新分析 ASD 患者和未受影响个体的转录组谱来鉴定失调基因。对这些个体的淋巴母细胞系进行二维凝胶电泳和质谱分析进行蛋白质组谱分析。鉴定出失调蛋白,并与失调转录本和以前蛋白质组研究中报道的失调蛋白进行比较。使用Ingenuity Pathway Analysis(IPA)程序预测生物学功能。还通过 Western blot 分析对选定的蛋白质进行分析。
ADI-R 数据的聚类分析显示了四个 ASD 亚组,包括有严重语言障碍的 ASD,转录组谱分析确定了每个亚组中的失调基因。通过蛋白质组分析筛选发现,严重语言障碍的 ASD 亚组中有 82 种改变的蛋白质。通过纳升 LC-MS/MS 进一步鉴定了其中的 18 种蛋白质。在这些蛋白质中,IPA 预测有 14 种与神经功能和炎症有关。在这些蛋白质中,地西泮结合抑制剂(DBI)蛋白通过 Western blot 分析证实,在严重语言障碍的 ASD 亚组中表达水平显著降低,DBI 表达水平与 ADI-R 多项评分呈正相关。
通过根据临床表型对 ASD 患者进行亚组分类,然后进行综合转录组-蛋白质组分析,我们确定 DBI 是严重语言障碍 ASD 的一种新候选蛋白。该蛋白的机制及其作为 ASD 生物标志物的潜在用途值得进一步研究。