Molecular Network Analysis of the Urinary Proteome of Alzheimer’s Disease Patients

Abstract

Background/Aims: The identification of predictive biomarkers for Alzheimer’s disease (AD) from urine would aid in screening for the disease, but information about biological and pathophysiological changes in the urine of AD patients is limited. This study aimed to explore the comprehensive profile and molecular network relations of urinary proteins in AD patients. Methods: Urine samples collected from 18 AD patients and 18 age- and sex-matched cognitively normal controls were analyzed by mass spectrometry and semiquantified with the normalized spectral index method. Bioinformatics analyses were performed on proteins which significantly increased by more than 2-fold or decreased by less than 0.5-fold compared to the control (p < 0.05) using DAVID bioinformatics resources and KeyMolnet software. Results: The levels of 109 proteins significantly differed between AD patients and controls. Among these, annotation clusters related to lysosomes, complement activation, and gluconeogenesis were significantly enriched. The molecular relation networks derived from these proteins were mainly associated with pathways of lipoprotein metabolism, heat shock protein 90 signaling, matrix metalloproteinase signaling, and redox regulation by thioredoxin. Conclusion: Our findings suggest that changes in the urinary proteome of AD patients reflect systemic changes related to AD pathophysiology.