摘要
背景:阿尔茨海默氏病((AD)是世界上最常见的神经退行性疾病。大多数的临床症状阿尔茨海默氏病出现在一个非常晚的阶段,因此,疾病标志物的识别是必不可少的,它可以帮助正确检测的阿尔茨海默氏病在早期阶段,并减缓其进展。研究表明,表观遗传的生物标志物,如DNA甲基化,组蛋白修饰和非编码RNA介导的调节在几个疾病的进展,包括阿尔茨海默氏病的关键作用。 目的:本研究的目的是通过实验验证了人类蛋白质相互作用数据库拓扑显著阿尔茨海默病相关蛋白、次黄嘌呤鸟嘌呤磷酸核糖转移酶(作用)和寻找新的分子标志物。 方法:在这个计算工作,我们从阿尔茨海默氏病基因列表和蛋白质相互作用构建了阿尔茨海默氏病的具体疾病模型。使用这种疾病模型筛选蛋白质相互作用的新参数,即度带和相似性指数和确定阿尔茨海默氏病相关蛋白的帮助。监管网络涉及阿尔茨海默氏病相关的蛋白质,而不是以前已知的是与阿尔茨海默氏病构建。几个网络图案和这些图案的调节器的表观遗传修饰模式进行了研究。 结果:我们的研究发现22表观遗传基因和11计算预测基因,从网络结构来看,没有已知的与阿尔茨海默氏病相关联。这些基因大多数和大鹏显示脑组织特异性表达。在表观遗传修饰模式,这些监管机构关于组蛋白修饰研究的进一步深入,CpG岛和lncRNAs加强协会在阿尔茨海默氏病. 结论:计算预测的基因和大鹏在我们的研究中发现,可能提供洞察新的表观遗传阿尔茨海默氏病的治疗目标。
关键词: 阿尔茨海默病,表观遗传修饰, lncRNAs
Current Alzheimer Research
Title:Insight into the Epigenetics of Alzheimer's Disease: A Computational Study from Human Interactome
Volume: 13 Issue: 12
Author(s): Paulami Chatterjee, Debjani Roy
Affiliation:
关键词: 阿尔茨海默病,表观遗传修饰, lncRNAs
摘要: Background: Alzheimer's disease (AD) is the most prevalent neurodegenerative disease throughout the world. Most of the clinical symptoms of AD appear at a very later stage, therefore, the identification of disease markers is essential which can help proper detection of AD at an earlier stage and slow down its progression. Studies have implicated that epigenetic biomarkers, such as DNA methylation, histone modification and non coding RNA mediated regulation serve crucial roles in several disease progression including AD.
Objective: The aim of our study was to identify the topologically significant AD-related proteins from experimentally validated human protein-protein interaction database, HPRD (interactome) and find out novel epigenetic biomarkers.
Method: In this computational work, we constructed AD specific diseasome from AD genelist and interactome. Using this diseasome we screened the interactome with the help of novel parameters namely degree band and similarity index and identified AD related proteins. Regulatory network involving AD related proteins, not previously known to be associated with AD was constructed. Several network motifs and epigenetic modification patterns of regulators of these motifs were studied.
Result: Our study identified computationally predicted 22 epigenetic genes and 11 epigenetic miRs, not previously known to be associated with AD, from the network motifs. Most of these genes and miRs show brain specific expression. Further study on the epigenetic modification patterns of these regulators regarding histone modification, CpG island and lncRNAs strengthened their association in AD.
Conclusion: Computationally predicted genes and miRs identified in our study might provide insight into new epigenetic AD therapeutic targets.
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Cite this article as:
Paulami Chatterjee, Debjani Roy , Insight into the Epigenetics of Alzheimer's Disease: A Computational Study from Human Interactome, Current Alzheimer Research 2016; 13 (12) . https://dx.doi.org/10.2174/1567205013666160803151101
DOI https://dx.doi.org/10.2174/1567205013666160803151101 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
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