Data_Sheet_4_A network medicine-based approach to explore the relationship between depression and inflammation.PDF

BackgroundDepression is widespread global problem that not only severely impacts individuals’ physical and mental health but also imposes a heavy disease burden on nations and societies. The role of inflammation in the pathogenesis and pathophysiology of depression has received much attention, but the precise relationship between the two remains unclear. This study aims to investigate the correlation between depression and inflammation using a network medicine approach.MethodsWe utilized a degree-preserving approach to identify the large connected component (LCC) of all depression-related proteins in the human interactome. The LCC was deemed as the disease module for depression. To measure the association between depression and other diseases, we calculated the overlap between these disease protein modules using the Sab algorithm. A smaller Sab value indicates a stronger association between diseases. Building on the results of this analysis, we further explored the correlation between inflammation and depression by conducting enrichment and pathway analyses of critical targets. Finally, we used a network proximity approach to calculate drug-disease proximity to predict the efficacy of drugs for the treatment of depression. We calculated and ranked the distances between depression disease modules and 6,100 drugs. The top-ranked drugs were selected to explore their potential for treating depression based on the hypothesis that their antidepressant effects are related to reducing inflammation.ResultsIn the human interactome, all depression-related proteins are clustered into a large connected component (LCC) consisting of 202 proteins and multiple small subgraphs. This indicates that depression-related proteins tend to form clusters within the same network. We used the 202 LCC proteins as the key disease module for depression. Next, we investigated the potential relationships between depression and 299 other diseases. Our analysis identified over 18 diseases that exhibited significant overlap with the depression module. Where SAB = −0.075 for the vascular disease and depressive disorders module, SAB = −0.070 for the gastrointestinal disease and depressive disorders module, and SAB = −0.062 for the endocrine system disease and depressive disorders module. The distance between them SAB

背景：抑郁症是全球范围内普遍存在的难题，它不仅严重损害了个人身心健康，还给国家和社会带来了沉重的疾病负担。炎症在抑郁症的发病机制和病理生理学中的作用引起了广泛关注，但二者之间的确切关系尚不明确。本研究旨在利用网络药理学方法，探究抑郁症与炎症之间的相关性。方法：我们采用度保留方法，识别了人类相互作用网络中所有与抑郁症相关的蛋白质的大连通组件（LCC），将其视为抑郁症的疾病模块。为了衡量抑郁症与其他疾病之间的关联，我们使用Sab算法计算了这些疾病蛋白质模块之间的重叠。较小的Sab值表明疾病之间关联性更强。在此基础上，我们进一步通过关键靶点的富集和通路分析，探究了炎症与抑郁症之间的相关性。最后，我们采用网络邻近度方法计算药物与疾病之间的邻近度，以预测治疗抑郁症药物的疗效。我们计算并排序了抑郁症疾病模块与6,100种药物之间的距离，并选取排名靠前的药物，以探索其治疗抑郁症的潜力，基于假设其抗抑郁作用与减轻炎症有关。结果：在人类相互作用网络中，所有与抑郁症相关的蛋白质被聚集成一个包含202个蛋白质和多个小子图的大连通组件（LCC）。这表明，与抑郁症相关的蛋白质倾向于在同一网络中形成簇。我们使用这202个LCC蛋白质作为抑郁症的关键疾病模块。接下来，我们调查了抑郁症与299种其他潜在疾病之间的潜在关系。我们的分析发现，有超过18种疾病与抑郁症模块存在显著重叠。其中，血管疾病和抑郁障碍模块的SAB值为-0.075，胃肠道疾病和抑郁障碍模块的SAB值为-0.070，内分泌系统疾病和抑郁障碍模块的SAB值为-0.062。