Data_Sheet_3_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 < 0 implies that the pathogenesis of depression is likely to be related to the pathogenesis of its co-morbidities of depression and that potential therapeutic approaches may be derived from the disease treatment libraries of these co-morbidities. Further, considering that the inflammation is ubiquitous in some disease, we calculate the overlap between the collected inflammation module (236 proteins) and the depression module (202 proteins), finding that they are closely related (Sdi = −0.358) in the human protein interaction network. After enrichment and pathway analysis of key genes, we identified the HIF-1 signaling pathway, PI3K-Akt signaling pathway, Th17 cell differentiation, hepatitis B, and inflammatory bowel disease as key to the inflammatory response in depression. Finally, we calculated the Z-score to determine the proximity of 6,100 drugs to the depression disease module. Among the top three drugs identified by drug-disease proximity analysis were Perphenazine, Clomipramine, and Amitriptyline, all of which had a greater number of targets in the network associated with the depression disease module. Notably, these drugs have been shown to exert both anti-inflammatory and antidepressant effects, suggesting that they may modulate depression through an anti-inflammatory mechanism. These findings demonstrate a correlation between depression and inflammation at the network medicine level, which has important implications for future elucidation of the etiology of depression and improved treatment outcomes.
ConclusionNeuroimmune signaling pathways play an important role in the pathogenesis of depression, and many classes of antidepressants exhibiting anti-inflammatory properties. The pathogenesis of depression is closely related to inflammation.

背景：抑郁症是全球性普遍问题，不仅严重损害个体的身心健康，更给国家与社会带来沉重的疾病负担。炎症在抑郁症的发病机制与病理生理过程中的作用已受到广泛关注，但二者的确切关联仍不明确。本研究旨在通过网络医学方法探究抑郁症与炎症的相关性。
方法：我们采用保度方法，在人类互作组（human interactome）中筛选出所有抑郁症相关蛋白质的最大连通分量（Large Connected Component，LCC），并将该分量视为抑郁症的疾病模块。为量化抑郁症与其他疾病的关联程度，我们借助Sab算法（Sab algorithm）计算不同疾病蛋白质模块间的重叠度，Sab值越小则代表两种疾病的关联越强。基于上述分析结果，我们通过对关键靶点进行富集分析与通路分析，进一步探究炎症与抑郁症的相关性。最后，我们采用网络邻近性方法计算药物-疾病邻近性，以预测抑郁症治疗药物的疗效：我们计算了抑郁症疾病模块与6100种药物间的距离并进行排序，基于"这些药物的抗抑郁作用与抗炎效应相关"的假设，选取排名靠前的药物探究其治疗抑郁症的潜力。
结果：在人类互作组中，所有抑郁症相关蛋白质聚集为一个包含202个蛋白质的最大连通分量（LCC）与多个小型子图，这表明抑郁症相关蛋白质倾向于在同一网络中形成集群。我们将这202个LCC蛋白质作为抑郁症的核心疾病模块。随后，我们探究了抑郁症与其余299种疾病的潜在关联，分析结果显示共有超过18种疾病与抑郁症模块存在显著重叠：其中血管疾病与抑郁症模块的SAB值为-0.075，胃肠道疾病与抑郁症模块的SAB值为-0.070，内分泌系统疾病与抑郁症模块的SAB值为-0.062。由于SAB<0，这提示抑郁症的发病机制可能与其共患病的发病机制存在关联，且潜在治疗策略可从这些共患病的治疗药物库中发掘。此外，鉴于炎症在部分疾病中普遍存在，我们计算了已收录的炎症模块（含236个蛋白质）与抑郁症模块（含202个蛋白质）间的重叠度，发现二者在人类蛋白质互作网络中存在紧密关联（Sdi=-0.358）。通过对关键基因进行富集分析与通路分析，我们确定HIF-1信号通路、PI3K-Akt信号通路、Th17细胞分化、乙型肝炎以及炎症性肠病为抑郁症炎症反应的关键通路与疾病。最后，我们通过计算Z值确定6100种药物与抑郁症疾病模块的邻近性：药物-疾病邻近性分析筛选出的排名前三的药物为奋乃静（Perphenazine）、氯米帕明（Clomipramine）与阿米替林（Amitriptyline），这些药物在抑郁症疾病模块相关的网络中均拥有更多靶点。值得注意的是，已有研究证实这些药物兼具抗炎与抗抑郁活性，这提示它们可能通过抗炎机制调节抑郁症进程。上述研究结果从网络医学层面证实了抑郁症与炎症的相关性，这为未来阐明抑郁症的病因学机制以及改善治疗效果提供了重要参考。
结论：神经免疫信号通路在抑郁症的发病机制中发挥重要作用，且多类抗抑郁药物均具有抗炎属性；抑郁症的发病机制与炎症密切相关。