Detecting tissue-specific early warning signals for complex diseases based on dynamical network biomarkers: study of type 2 diabetes by cross-tissue analysis
Detecting tissue-specific early warning signals for complex diseases based on dynamical network biomarkers: study of type 2 diabetes by cross-tissue analysis
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基于动态网络生物标志物检测复杂疾病的组织特异性早期预警信号：通过跨组织分析研究 2 型糖尿病
DOI：
10.1093/bib/bbt027
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发表时间：
2014-03-01
影响因子：
9.5
通讯作者：
Chen, Luonan
中科院分区：
文献类型：
--
作者：
Li, Meiyi;Zeng, Tao;Chen, Luonan
识别疾病进展过程中关键转变的早期预警信号是实现复杂疾病早期诊断的关键。通过利用高通量数据的丰富信息，开发了一种新的无模型方法来检测疾病的早期预警信号。其理论基础是基于动态网络生物标志物（DNB），DNB也被称为疾病的驱动（或主导）网络，因为DNB中的组分或分子实际上驱动整个系统从一个状态（例如正常状态）到另一个状态（例如疾病状态）。本文综述了DNB理论的概念和主要结果，并将其应用于2型糖尿病（T2DM）的分析。具体而言，基于T2DM的时空基因表达数据，我们鉴定了对应于T2DM发展和进展期间在肝脏、脂肪和肌肉中发生的关键转变的组织特异性DNB。事实上，我们发现在T2DM发展过程中存在两种不同的临界状态，其特征分别是对胰岛素抵抗和严重炎症的反应。有趣的是，发现了一种新的T2DM相关功能，即类固醇激素生物合成，并且这些相关基因在T2DM恶化期间的第一个关键转变中在肝脏和脂肪中显著失调。此外，在同一时期，肌肉中也检测到与反应激素相关的基因功能障碍。基于对T2DM致病分子机制的功能和网络分析，我们发现大多数DNB基因，尤其是核心基因，倾向于位于生物学通路的上游，这意味着DNB基因是作为致病因子而不是结果驱动下游分子改变其转录活性。这也验证了我们对DNB作为驱动网络的理论预测。本研究表明，DNB不仅可以为疾病的早期诊断提供关键转变的信号，而且还可以提供转变的因果网络，从而在网络水平上揭示疾病发生和发展的分子机制。
Identifying early warning signals of critical transitions during disease progression is a key to achieving early diagnosis of complex diseases. By exploiting rich information of high-throughput data, a novel model-free method has been developed to detect early warning signals of diseases. Its theoretical foundation is based on dynamical network biomarker (DNB), which is also called as the driver (or leading) network of the disease because components or molecules in DNB actually drive the whole system from one state (e.g. normal state) to another (e.g. disease state). In this article, we first reviewed the concept and main results of DNB theory, and then applied the new method to the analysis of type 2 diabetes mellitus (T2DM). Specifically, based on the temporal-spatial gene expression data of T2DM, we identified tissue-specific DNBs corresponding to the critical transitions occurring in liver, adipose and muscle during T2DM development and progression. Actually, we found that there are two different critical states during T2DM development characterized as responses to insulin resistance and serious inflammation, respectively. Interestingly, a new T2DM-associated function, i.e. steroid hormone biosynthesis, was discovered, and those related genes were significantly dysregulated in liver and adipose at the first critical transition during T2DM deterioration. Moreover, the dysfunction of genes related to responding hormone was also detected in muscle at the similar period. Based on the functional and network analysis on pathogenic molecular mechanism of T2DM, we showed that most of DNB genes, in particular the core ones, tended to be located at the upstream of biological pathways, which implied that DNB genes act as the causal factors rather than the consequence to drive the downstream molecules to change their transcriptional activities. This also validated our theoretical prediction of DNB as the driver network. As shown in this study, DNB can not only signal the emergence of the critical transitions for early diagnosis of diseases, but can also provide the causal network of the transitions for revealing molecular mechanisms of disease initiation and progression at a network level.