Liu Yu, Koyutürk Mehmet, Barnholtz-Sloan Jill S, Chance Mark R
Center for Proteomics & Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA.
BMC Syst Biol. 2012 Jun 13;6:65. doi: 10.1186/1752-0509-6-65.
The molecular behavior of biological systems can be described in terms of three fundamental components: (i) the physical entities, (ii) the interactions among these entities, and (iii) the dynamics of these entities and interactions. The mechanisms that drive complex disease can be productively viewed in the context of the perturbations of these components. One challenge in this regard is to identify the pathways altered in specific diseases. To address this challenge, Gene Set Enrichment Analysis (GSEA) and others have been developed, which focus on alterations of individual properties of the entities (such as gene expression). However, the dynamics of the interactions with respect to disease have been less well studied (i.e., properties of components ii and iii).
Here, we present a novel method called Gene Interaction Enrichment and Network Analysis (GIENA) to identify dysregulated gene interactions, i.e., pairs of genes whose relationships differ between disease and control. Four functions are defined to model the biologically relevant gene interactions of cooperation (sum of mRNA expression), competition (difference between mRNA expression), redundancy (maximum of expression), or dependency (minimum of expression) among the expression levels. The proposed framework identifies dysregulated interactions and pathways enriched in dysregulated interactions; points out interactions that are perturbed across pathways; and moreover, based on the biological annotation of each type of dysregulated interaction gives clues about the regulatory logic governing the systems level perturbation. We demonstrated the potential of GIENA using published datasets related to cancer.
We showed that GIENA identifies dysregulated pathways that are missed by traditional enrichment methods based on the individual gene properties and that use of traditional methods combined with GIENA provides coverage of the largest number of relevant pathways. In addition, using the interactions detected by GIENA, specific gene networks both within and across pathways associated with the relevant phenotypes are constructed and analyzed.
生物系统的分子行为可以用三个基本组成部分来描述:(i)物理实体,(ii)这些实体之间的相互作用,以及(iii)这些实体和相互作用的动态变化。驱动复杂疾病的机制可以在这些组成部分受到扰动的背景下有效地加以考察。在这方面的一个挑战是识别特定疾病中发生改变的通路。为应对这一挑战,已经开发了基因集富集分析(GSEA)及其他方法,这些方法聚焦于实体个体属性的改变(如基因表达)。然而,关于疾病方面相互作用的动态变化研究较少(即组成部分ii和iii的属性)。
在此,我们提出一种名为基因相互作用富集与网络分析(GIENA)的新方法,以识别失调的基因相互作用,即疾病组与对照组之间关系存在差异的基因对。定义了四个函数来模拟表达水平之间具有生物学相关性的合作(mRNA表达之和)、竞争(mRNA表达之差)、冗余(最大表达量)或依赖(最小表达量)的基因相互作用。所提出的框架识别失调的相互作用以及富集在失调相互作用中的通路;指出跨通路受到扰动的相互作用;此外,基于每种失调相互作用的生物学注释给出关于控制系统水平扰动的调控逻辑的线索。我们使用已发表的与癌症相关的数据集证明了GIENA的潜力。
我们表明,GIENA能够识别基于单个基因属性的传统富集方法所遗漏的失调通路,并且将传统方法与GIENA结合使用能够覆盖最多数量的相关通路。此外,利用GIENA检测到的相互作用,构建并分析了与相关表型相关的通路内和跨通路的特定基因网络。