A network medicine-based approach to explore the relationship between depression and inflammation - Hu, Xiaonan; Pang, Huaxin; Liu, Jia; Wang, Yu; Lou, Yifang; Zhao, Yufeng (Frontiers in Psychiatry, 2023)
Background: Depression is widespread worldwide, and it not only severely damages 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 relationship between the two is still undiscovered. This paper aims to investigate the correlation between depression and inflammation using a network medicine approach. Methods: We employed the degree-preserving approach to calculate the large connected component (LCC) of all depression proteins in the human interactome.To measure the association between depression and other diseases, we used the Sab algorithm to calculate the overlap of these disease proteins modules.Based on the Sab algorithm, we further explored emphatically the correlation between inflammation and depression by enrichment and pathway analysis of critical targets. Finally, we used the network proximity approach to calculate drug-disease proximity to predict the efficacy of drugs. Results: In the human interactome, all depression-targeted proteins form a large connected component (LCC) consisting of 202 proteins and multiple small subgraphs, which indicates that depression-targeted proteins cluster near the same network.Next, we assessed the relationship between depression and 299 other diseases, finding that more than 18 diseases overlap with the depression module. 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 differentiationas key to the inflammatory response in depression. Finally, Z-score was calculated to show the proximity of 6100 drugs to the disease module. According to the drug-disease proximity results, Perphenazine, Clomipramine, and Amitriptyline, the top three drugs, all have more targets in the network with the depression disease module. Conclusion: Neuroimmune signaling pathways play an important role in the pathogenesis of depression, and antidepressants generally have anti-inflammatory effects. The pathogenesis of depression is closely related to inflammation.