Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States of America.
Sheffield Institute for Translational Neuroscience, Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom.
PLoS Comput Biol. 2018 Mar 19;14(3):e1006042. doi: 10.1371/journal.pcbi.1006042. eCollection 2018 Mar.
A goal of genomics is to understand the relationships between biological processes. Pathways contribute to functional interplay within biological processes through complex but poorly understood interactions. However, limited functional references for global pathway relationships exist. Pathways from databases such as KEGG and Reactome provide discrete annotations of biological processes. Their relationships are currently either inferred from gene set enrichment within specific experiments, or by simple overlap, linking pathway annotations that have genes in common. Here, we provide a unifying interpretation of functional interaction between pathways by systematically quantifying coexpression between 1,330 canonical pathways from the Molecular Signatures Database (MSigDB) to establish the Pathway Coexpression Network (PCxN). We estimated the correlation between canonical pathways valid in a broad context using a curated collection of 3,207 microarrays from 72 normal human tissues. PCxN accounts for shared genes between annotations to estimate significant correlations between pathways with related functions rather than with similar annotations. We demonstrate that PCxN provides novel insight into mechanisms of complex diseases using an Alzheimer's Disease (AD) case study. PCxN retrieved pathways significantly correlated with an expert curated AD gene list. These pathways have known associations with AD and were significantly enriched for genes independently associated with AD. As a further step, we show how PCxN complements the results of gene set enrichment methods by revealing relationships between enriched pathways, and by identifying additional highly correlated pathways. PCxN revealed that correlated pathways from an AD expression profiling study include functional clusters involved in cell adhesion and oxidative stress. PCxN provides expanded connections to pathways from the extracellular matrix. PCxN provides a powerful new framework for interrogation of global pathway relationships. Comprehensive exploration of PCxN can be performed at http://pcxn.org/.
基因组学的目标是理解生物过程之间的关系。途径通过复杂但理解不深的相互作用，为生物过程中的功能相互作用做出贡献。然而，全局途径关系的功能参考有限。KEGG 和 Reactome 等数据库中的途径提供了生物过程的离散注释。它们的关系目前要么是从特定实验中的基因集富集推断出来的，要么是通过简单的重叠，将具有共同基因的途径注释联系起来。在这里，我们通过系统地量化来自分子特征数据库（MSigDB）的 1330 个经典途径之间的共表达，为途径之间的功能相互作用提供了一个统一的解释，以建立途径共表达网络（PCxN）。我们使用来自 72 个人体正常组织的 3207 个微阵列的精心收集来估计在广泛背景下有效的经典途径之间的相关性。PCxN 考虑了注释之间的共享基因，以估计具有相关功能的途径之间的显著相关性，而不是具有相似注释的途径。我们使用阿尔茨海默病（AD）病例研究证明了 PCxN 如何为复杂疾病的机制提供新的见解。PCxN 检索到与专家编辑的 AD 基因列表显著相关的途径。这些途径与 AD 有已知的关联，并且与 AD 独立相关的基因显著富集。作为进一步的步骤，我们展示了 PCxN 如何通过揭示富集途径之间的关系以及识别其他高度相关的途径来补充基因集富集方法的结果。PCxN 表明，来自 AD 表达谱研究的相关途径包括涉及细胞粘附和氧化应激的功能簇。PCxN 提供了与细胞外基质途径的扩展连接。PCxN 为全局途径关系的研究提供了一个强大的新框架。可以在 http://pcxn.org/ 上全面探索 PCxN。
