Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Network Science Institute, Center for Complex Network Research, Department of Physics, Northeastern University, Boston, MA, USA.
Nat Commun. 2020 Feb 10;11(1):811. doi: 10.1038/s41467-020-14600-w.
The molecular and clinical features of a complex disease can be influenced by other diseases affecting the same individual. Understanding disease-disease interactions is therefore crucial for revealing shared molecular mechanisms among diseases and designing effective treatments. Here we introduce Flow Centrality (FC), a network-based approach to identify the genes mediating the interaction between two diseases in a protein-protein interaction network. We focus on asthma and COPD, two chronic respiratory diseases that have been long hypothesized to share common genetic determinants and mechanisms. We show that FC highlights potential mediator genes between the two diseases, and observe similar outcomes when applying FC to 66 additional pairs of related diseases. Further, we perform in vitro perturbation experiments on a widely replicated asthma gene, GSDMB, showing that FC identifies candidate mediators of the interactions between GSDMB and COPD-associated genes. Our results indicate that FC predicts promising gene candidates for further study of disease-disease interactions.
一种复杂疾病的分子和临床特征可能受到影响同一个体的其他疾病的影响。因此，了解疾病-疾病相互作用对于揭示疾病之间的共同分子机制和设计有效的治疗方法至关重要。在这里，我们介绍了基于网络的流度（FC）方法，该方法可用于在蛋白质-蛋白质相互作用网络中识别两种疾病之间相互作用的基因。我们专注于哮喘和 COPD，这两种慢性呼吸道疾病长期以来一直被假设具有共同的遗传决定因素和机制。我们表明，FC 突出了两种疾病之间的潜在中介基因，并在将 FC 应用于 66 对其他相关疾病时观察到类似的结果。此外，我们对广泛复制的哮喘基因 GSDMB 进行了体外扰动实验，表明 FC 鉴定了 GSDMB 与 COPD 相关基因之间相互作用的候选中介基因。我们的结果表明，FC 预测了有希望的候选基因，可进一步研究疾病-疾病相互作用。
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