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从基因组学到帕金森病的组学景观:揭示分子机制。

From Genomics to Omics Landscapes of Parkinson's Disease: Revealing the Molecular Mechanisms.

机构信息

1 Pharmacogenetics Laboratory, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana , Ljubljana, Slovenia .

2 Department of Animal Science, Biotechnical Faculty, University of Ljubljana , Ljubljana, Slovenia .

出版信息

OMICS. 2018 Jan;22(1):1-16. doi: 10.1089/omi.2017.0181.

Abstract

Molecular mechanisms of Parkinson's disease (PD) have already been investigated in various different omics landscapes. We reviewed the literature about different omics approaches between November 2005 and November 2017 to depict the main pathological pathways for PD development. In total, 107 articles exploring different layers of omics data associated with PD were retrieved. The studies were grouped into 13 omics layers: genomics-DNA level, transcriptomics, epigenomics, proteomics, ncRNomics, interactomics, metabolomics, glycomics, lipidomics, phenomics, environmental omics, pharmacogenomics, and integromics. We discussed characteristics of studies from different landscapes, such as main findings, number of participants, sample type, methodology, and outcome. We also performed curation and preliminary synthesis of multiple omics data, and identified overlapping results, which could lead toward selection of biomarkers for further validation of PD risk loci. Biomarkers could support the development of targeted prognostic/diagnostic panels as a tool for early diagnosis and prediction of progression rate and prognosis. This review presents an example of a comprehensive approach to revealing the underlying processes and risk factors of a complex disease. It urges scientists to structure the already known data and integrate it into a meaningful context.

摘要

帕金森病(PD)的分子机制已经在各种不同的组学领域进行了研究。我们回顾了 2005 年 11 月至 2017 年 11 月期间有关不同组学方法的文献,以描述 PD 发展的主要病理途径。总共检索到 107 篇探索与 PD 相关的不同组学数据层的文章。这些研究分为 13 个组学层:基因组学-DNA 水平、转录组学、表观基因组学、蛋白质组学、非编码 RNA 组学、相互作用组学、代谢组学、糖组学、脂质组学、表型组学、环境组学、药物基因组学和整合组学。我们讨论了来自不同领域的研究的特点,如主要发现、参与者数量、样本类型、方法和结果。我们还对多个组学数据进行了策展和初步综合,并确定了重叠的结果,这可能有助于选择生物标志物以进一步验证 PD 风险基因座。生物标志物可以支持靶向预后/诊断面板的开发,作为早期诊断和预测进展速度和预后的工具。本综述提供了一个综合方法揭示复杂疾病潜在过程和风险因素的示例。它敦促科学家对已知数据进行结构化,并将其整合到有意义的上下文中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a35b/5784788/7a69cd864e1a/fig-1.jpg

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