A network medicine-based approach to explore the relationship between depression and inflammation

Area of Science:

• Network Medicine
• Computational Biology
• Systems Biology

Background:

• Depression is a global health issue with unclear links to inflammation.
• Understanding the relationship between depression and inflammation is crucial for effective treatment.
• Network medicine offers a novel approach to explore complex disease associations.

Purpose of the Study:

• To investigate the correlation between depression and inflammation using a network medicine approach.
• To identify potential therapeutic targets and drugs for depression based on its association with inflammation.

Main Methods:

• Identified the depression disease module using a degree-preserving approach in the human interactome.
• Calculated disease-disease and disease-inflammation module overlaps using the Sab algorithm.
• Performed enrichment and pathway analyses, and utilized network proximity to predict drug efficacy for depression.

Main Results:

• Depression proteins form a large connected component, indicating network clustering.
• Significant overlaps were found between depression and vascular, gastrointestinal, and endocrine diseases.
• A strong correlation (S_di = -0.358) was observed between depression and inflammation modules.
• Key pathways involved in inflammation and depression include HIF-1 and PI3K-Akt signaling.
• Top drugs like Perphenazine, Clomipramine, and Amitriptyline showed network proximity to depression and possess anti-inflammatory properties.

Conclusions:

• Neuroimmune signaling pathways are critical in depression pathogenesis.
• The pathogenesis of depression is closely linked to inflammation.
• Anti-inflammatory antidepressants may offer effective treatment strategies for depression.