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整合网络分析揭示帕金森病和糖尿病中的趋同分子通路。

Integrative network analysis unveils convergent molecular pathways in Parkinson's disease and diabetes.

作者信息

Santiago Jose A, Potashkin Judith A

机构信息

The Cellular and Molecular Pharmacology Department, The Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, Illinois, United States of America.

出版信息

PLoS One. 2013 Dec 20;8(12):e83940. doi: 10.1371/journal.pone.0083940. eCollection 2013.

Abstract

BACKGROUND

Shared dysregulated pathways may contribute to Parkinson's disease and type 2 diabetes, chronic diseases that afflict millions of people worldwide. Despite the evidence provided by epidemiological and gene profiling studies, the molecular and functional networks implicated in both diseases, have not been fully explored. In this study, we used an integrated network approach to investigate the extent to which Parkinson's disease and type 2 diabetes are linked at the molecular level.

METHODS AND FINDINGS

Using a random walk algorithm within the human functional linkage network we identified a molecular cluster of 478 neighboring genes closely associated with confirmed Parkinson's disease and type 2 diabetes genes. Biological and functional analysis identified the protein serine-threonine kinase activity, MAPK cascade, activation of the immune response, and insulin receptor and lipid signaling as convergent pathways. Integration of results from microarrays studies identified a blood signature comprising seven genes whose expression is dysregulated in Parkinson's disease and type 2 diabetes. Among this group of genes, is the amyloid precursor protein (APP), previously associated with neurodegeneration and insulin regulation. Quantification of RNA from whole blood of 192 samples from two independent clinical trials, the Harvard Biomarker Study (HBS) and the Prognostic Biomarker Study (PROBE), revealed that expression of APP is significantly upregulated in Parkinson's disease patients compared to healthy controls. Assessment of biomarker performance revealed that expression of APP could distinguish Parkinson's disease from healthy individuals with a diagnostic accuracy of 80% in both cohorts of patients.

CONCLUSIONS

These results provide the first evidence that Parkinson's disease and diabetes are strongly linked at the molecular level and that shared molecular networks provide an additional source for identifying highly sensitive biomarkers. Further, these results suggest for the first time that increased expression of APP in blood may modulate the neurodegenerative phenotype in type 2 diabetes patients.

摘要

背景

共同的失调通路可能导致帕金森病和2型糖尿病,这两种慢性病困扰着全球数百万人。尽管流行病学和基因谱研究提供了证据,但这两种疾病所涉及的分子和功能网络尚未得到充分探索。在本研究中,我们使用综合网络方法来研究帕金森病和2型糖尿病在分子水平上的关联程度。

方法与发现

利用人类功能连接网络中的随机游走算法,我们确定了一个由478个相邻基因组成的分子簇,这些基因与已确诊的帕金森病和2型糖尿病基因密切相关。生物学和功能分析确定蛋白丝氨酸 - 苏氨酸激酶活性、丝裂原活化蛋白激酶(MAPK)级联反应、免疫反应激活以及胰岛素受体和脂质信号传导为趋同通路。整合微阵列研究结果确定了一个由七个基因组成的血液特征,其表达在帕金森病和2型糖尿病中失调。在这组基因中,有淀粉样前体蛋白(APP),该蛋白先前与神经退行性变和胰岛素调节有关。对来自两项独立临床试验(哈佛生物标志物研究(HBS)和预后生物标志物研究(PROBE))的192个样本的全血RNA进行定量分析,结果显示与健康对照相比,帕金森病患者中APP的表达显著上调。生物标志物性能评估显示,APP的表达能够区分帕金森病患者和健康个体,在两个患者队列中的诊断准确率均为80%。

结论

这些结果首次证明帕金森病和糖尿病在分子水平上紧密相连,并且共享的分子网络为识别高度敏感的生物标志物提供了额外来源。此外,这些结果首次表明血液中APP表达的增加可能调节2型糖尿病患者的神经退行性表型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/abb7/3869818/d2f4aba33cc0/pone.0083940.g001.jpg

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