Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis
Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis
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基于动态网络构建和系统分析破译复杂疾病恶化机制
DOI：
10.1038/srep09283
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发表时间：
2015-03-19
影响因子：
4.6
通讯作者：
Zou, Xiufen
中科院分区：
文献类型：
--
作者：
Li, Yuanyuan;Jin, Suoqin;Zou, Xiufen
复杂疾病的早期诊断和致病机制的研究是生物学和医学领域最具挑战性的问题。基于网络的系统生物学是研究复杂疾病的重要技术。本研究构建了动态蛋白质相互作用（PPI）网络，从系统水平识别动态网络生物标志物（DNB），分析复杂疾病的潜在机制。我们开发了一个基于模型的框架，通过将高通量基因表达数据整合到PPI数据中来构建一系列时间序列网络。通过结合动态网络和分子模块，我们确定了四种复杂疾病的重要DNB，包括H3N2或H1N1引起的流感，急性肺损伤和2型糖尿病，这可以作为疾病恶化的警告信号。功能和途径分析表明，所确定的DNBs显着丰富的关键事件在早期疾病的发展。相关性和信息流分析表明，DNBs有效地区分不同的疾病过程和功能失调的调节和不成比例的信息流可能有助于增加疾病的严重程度。这项研究为揭示复杂疾病的恶化机制提供了一个通用的范式，并为早期诊断提供了新的见解。
The early diagnosis and investigation of the pathogenic mechanisms of complex diseases are the most challenging problems in the fields of biology and medicine. Network-based systems biology is an important technique for the study of complex diseases. The present study constructed dynamic protein-protein interaction (PPI) networks to identify dynamical network biomarkers (DNBs) and analyze the underlying mechanisms of complex diseases from a systems level. We developed a model-based framework for the construction of a series of time-sequenced networks by integrating high-throughput gene expression data into PPI data. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. Function and pathway analyses revealed that the identified DNBs were significantly enriched during key events in early disease development. Correlation and information flow analyses revealed that DNBs effectively discriminated between different disease processes and that dysfunctional regulation and disproportional information flow may contribute to the increased disease severity. This study provides a general paradigm for revealing the deterioration mechanisms of complex diseases and offers new insights into their early diagnoses.