Bio-Synergy Research Center, Daejeon, 34141, South Korea.
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea.
BMC Bioinformatics. 2018 Jun 13;19(Suppl 8):213. doi: 10.1186/s12859-018-2193-3.
Finding common molecular interactions from different samples is essential work to understanding diseases and other biological processes. Coexpression networks and their modules directly reflect sample-specific interactions among genes. Therefore, identification of common coexpression network or modules may reveal the molecular mechanism of complex disease or the relationship between biological processes. However, there has been no quantitative network comparison method for coexpression networks and we examined previous methods for other networks that cannot be applied to coexpression network. Therefore, we aimed to propose quantitative comparison methods for coexpression networks and to find common biological mechanisms between Huntington's disease and brain aging by the new method.
We proposed two similarity measures for quantitative comparison of coexpression networks. Then, we performed experiments using known coexpression networks. We showed the validity of two measures and evaluated threshold values for similar coexpression network pairs from experiments. Using these similarity measures and thresholds, we quantitatively measured the similarity between disease-specific and aging-related coexpression modules and found similar Huntington's disease-aging coexpression module pairs.
We identified similar Huntington's disease-aging coexpression module pairs and found that these modules are related to brain development, cell death, and immune response. It suggests that up-regulated cell signalling related cell death and immune/ inflammation response may be the common molecular mechanisms in the pathophysiology of HD and normal brain aging in the frontal cortex.
从不同样本中寻找共同的分子相互作用对于理解疾病和其他生物过程至关重要。共表达网络及其模块直接反映了基因之间特定于样本的相互作用。因此，识别共同的共表达网络或模块可能揭示复杂疾病的分子机制或生物过程之间的关系。然而，目前还没有用于共表达网络的定量网络比较方法，我们检查了其他网络的先前方法，但这些方法不能应用于共表达网络。因此，我们旨在提出用于共表达网络的定量比较方法，并通过新方法在亨廷顿病和大脑老化之间找到共同的生物学机制。
我们提出了两种用于共表达网络定量比较的相似性度量方法。然后，我们使用已知的共表达网络进行了实验。我们展示了两种度量的有效性，并从实验中评估了相似共表达网络对的阈值。使用这些相似性度量和阈值，我们对疾病特异性和衰老相关的共表达模块之间的相似性进行了定量测量，并发现了相似的亨廷顿病-衰老共表达模块对。
我们确定了相似的亨廷顿病-衰老共表达模块对，并发现这些模块与大脑发育、细胞死亡和免疫反应有关。这表明，上调的细胞信号相关的细胞死亡和免疫/炎症反应可能是亨廷顿病和正常大脑衰老的共同分子机制在额叶皮质中。
