Back

Tandem Mass Tag-Based Serum Proteomics Reveals Distinct Apolipoprotein Signatures in Diabetic and Coronary Artery Disease Patients

Rajkumar, A.; Challa, A. K.; Zang, N.; Parsawar, k.; Panchalingam, G.; M, S.; Soorappan, R. N.; Periandavan, K.; Sunny, S.

2025-12-02 cardiovascular medicine
10.64898/2025.12.01.25341339 medRxiv
Show abstract

Coronary artery disease (CAD) and diabetes mellitus (DM) frequently coexist, accelerating atherosclerotic progression through oxidative and metabolic dysregulation. However, the distinct serum proteomic signatures that differentiate CAD alone from CAD with DM comorbidity remain poorly characterized. We employed tandem mass tag (TMT)-based quantitative proteomics to delineate the molecular and oxidative alterations that distinguish CAD alone from CAD with diabetic comorbidity, with an emphasis on apolipoprotein remodeling and redox-lipid interactions. Serum samples from healthy controls, CAD, and diabetic cohorts (n = 6/group) were subjected to high-resolution LC-MS/MS analysis and redox biomarker profiling (LPO, GSH, PON1). Differentially expressed proteins and oxidative post-translational modifications (PTMs) were analyzed using gene ontology and protein-protein interaction (PPI) network approaches. TMT proteomics identified distinct redox-metabolic signatures segregating the three cohorts. CAD serum exhibited enrichment of inflammatory, proteolytic, and extracellular matrix remodeling pathways, whereas diabetic samples showed dominant metabolic and ER-stress networks. Notably, cysteine oxidation in ApoE was unique to diabetic patients, while lysine oxidation in ApoB and cysteine oxidation in ApoD characterized CAD, revealing disease-specific oxidative remodeling of apolipoproteins. PPI network mapping positioned ApoB and ApoE as central hubs linking lipid transport, coagulation, and complement cascades. This study provides the first redox-informed serum proteomic atlas distinguishing diabetic and CAD patients, uncovering oxidative PTMs in apolipoproteins as key determinants of lipoprotein dysfunction and atherogenic risk.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.