DOI: 10.4103/indianjpsychiatry_975_25 ISSN: 0019-5545

Unmasking the invisible: Proteomic traces of depression in human serum - A pilot study

Jiya Singh, Sabyasachi Bandyopadhyay, Anindya Das, Anissa A. Mirza, Sarama Saha
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ABSTRACT

Background:

Depression is a major cause of reduced productivity and increased morbidity.

Aim:

Given its complex and multifactorial pathophysiology, this study investigated serum proteomic profiles in individuals with depression and healthy controls to identify differentially expressed proteins and their functional significance.

Methods:

This is a cross-sectional observational study. Individuals with depression ( n = 6) were enlisted from the outpatient department of psychiatry at AIIMS Rishikesh, while age-matched healthy controls ( n = 6) were included for comparison. Peripheral whole blood samples were obtained from all participants under fasting conditions. Baseline characteristics were analyzed using Statistical Package for the Social Sciences, (IBM) 21. Continuous and categorical variables were expressed as mean ± SD and number (%), respectively. Proteomic data were analyzed and visualized using MetaboAnalyst 6.0, Cytoscape v3.10.3, Reactome Pathway, and SR plots.

Result:

In our main finding of the liquid chromatography–tandem mass spectrometry (LC-MS/MS) study of serum samples, a total of 242 proteins were identified, out of that 9 were upregulated and 11 were downregulated in the serum from the depression group and in the healthy control group. Receiver operating characteristic (ROC) analysis of significantly altered proteins showed strong discriminatory ability for fibrinogen, PSG9 , and AarF , with area under the curves (AUCs) of 0.944, 0.889, and 0.861, respectively.

Conclusion:

This study identifies a distinct molecular signature of depression, with PSG9, FGB, ADCK5, C8B , and SORCS1 emerging as key proteins. These markers reflect dysregulation of immune, vascular, mitochondrial, and signaling pathways, highlighting depression as a multisystem disorder, and offering potential targets for biomarker-based diagnosis and personalized therapeutic strategies.

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