Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks.
Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks.
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DOI：
10.1371/journal.pcbi.1004332
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发表时间：
2015-06
影响因子：
4.3
通讯作者：
Tan K
中科院分区：
文献类型：
--
作者：
Ma X;Gao L;Karamanlidis G;Gao P;Lee CF;Garcia-Menendez L;Tian R;Tan K
心脏病的发展是由基因通路的活性和连接性的动态变化驱动的。了解这些动态事件对于理解致病机制和开发有效治疗至关重要。目前，缺乏能够分析多个基因网络的计算方法，每个基因网络与基线/健康状况的网络相比表现出不同的活性。我们描述了iMDM算法，以确定跨多个差异共表达网络，称为M-DM（多个差异模块）的独特和共享的基因模块。我们将iMDM应用于使用在两种基因型上生成的鼠心力衰竭模型生成的时程RNA-Seq数据集。我们发现，iMDM在推断基因模块方面比使用单个或多个共表达网络实现了更高的准确性。我们发现，条件特异性M-DM表现出不同的活动，介导不同的生物学过程，并富含已知心血管表型的基因。通过分析存在于多种条件下的M-DM，我们揭示了心力衰竭条件下通路活性和连接性的动态变化。我们进一步表明，在心力衰竭的发展过程中，模块动力学与疾病表型的动力学相关。因此，通路动力学是理解发病机制的有力措施。iMDM提供了一种原则性的方法来剖析基因通路的动态及其与疾病表型动态的关系。随着组学数据的指数增长，我们的方法可以帮助生成对疾病进展的系统级见解。系统生物学的最新进展表明，基因网络的结构和活性变化在疾病的进展中起着至关重要的作用。心力衰竭是一种涉及多个分子通路的复杂疾病。然而，关于心力衰竭发展过程中心脏细胞基因网络的动态变化知之甚少。我们结合实验和计算方法来解决这个问题。我们开发了一种计算方法来分析多个基因网络，与健康状况的网络相比，每个基因网络都表现出不同的活性。通过这样做，我们能够在多个差异网络中识别独特和共享的基因通路。通过将我们的算法应用于心力衰竭的时程转录组数据，我们揭示了心力衰竭条件下通路活性和连接性的动态变化。我们进一步表明，在心力衰竭的发展过程中，通路动力学与疾病表型的动力学相关。我们的方法提供了一个原则性的方法来剖析基因通路的动态及其与疾病表型动态的关系。
Development of heart diseases is driven by dynamic changes in both the activity and connectivity of gene pathways. Understanding these dynamic events is critical for understanding pathogenic mechanisms and development of effective treatment. Currently, there is a lack of computational methods that enable analysis of multiple gene networks, each of which exhibits differential activity compared to the network of the baseline/healthy condition. We describe the iMDM algorithm to identify both unique and shared gene modules across multiple differential co-expression networks, termed M-DMs (multiple differential modules). We applied iMDM to a time-course RNA-Seq dataset generated using a murine heart failure model generated on two genotypes. We showed that iMDM achieves higher accuracy in inferring gene modules compared to using single or multiple co-expression networks. We found that condition-specific M-DMs exhibit differential activities, mediate different biological processes, and are enriched for genes with known cardiovascular phenotypes. By analyzing M-DMs that are present in multiple conditions, we revealed dynamic changes in pathway activity and connectivity across heart failure conditions. We further showed that module dynamics were correlated with the dynamics of disease phenotypes during the development of heart failure. Thus, pathway dynamics is a powerful measure for understanding pathogenesis. iMDM provides a principled way to dissect the dynamics of gene pathways and its relationship to the dynamics of disease phenotype. With the exponential growth of omics data, our method can aid in generating systems-level insights into disease progression. Recent advances in systems biology have revealed that changes in the structure and activity of gene network play a critical role in the disease progression. Heart failure is a complex disease involving multiple molecular pathways. Yet little is known regarding the dynamic changes in the gene network of heart cells during heart failure development. We have combined experimental and computational approaches to address this question. We developed a computational method to analyze multiple gene networks, each of which exhibits differential activity compared to the network of the healthy condition. In doing so, we are able to identify both unique and shared gene pathways across multiple differential networks. By applying our algorithm to our time-course transcriptome data of heart failure, we revealed dynamic changes in pathway activity and connectivity across heart failure conditions. We further showed that pathway dynamics were correlated with the dynamics of disease phenotypes during the development of heart failure. Our approach provides a principled way to dissect the dynamics of gene pathways and its relationship to the dynamics of disease phenotype.
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