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Abstract: SA-PO941

Exploring Differentially Expressed Proteins in Plasma Extracellular Vesicles for Early Detection of IgA Nephropathy

Session Information

Category: Glomerular Diseases

  • 1402 Glomerular Diseases: Clinical, Outcomes, and Trials

Author

  • Kao, Chih-chin, Taipei Medical University College of Medicine, Taipei, Taiwan
Background

IgA nephropathy (IgAN) is the most common primary glomerulonephritis and may develop end stage renal disease. The overproduction of galactose-deficient IgA1 (Gd-IgA1) leads to the production of autoantibodies, resulting in the formation of nephritogenic immune complexes. Extracellular vesicles (EVs) are cell-derived membranous vesicles encapsulating various proteins, lipids, and mRNAs. We aimed to develop EV-biomarkers for the early diagnosis of IgAN.

Methods

Size exclusion chromatography was used for isolating plasma EVs. A total of 60 plasma samples from individuals with IgAN, chronic kidney injury (CKD), and control group were utilized in this study. Each plasma had been reduced by dithiothreitol (DTT), alkylated by Iodoacetamide (IAA), and digested by trypsin. Tryptic peptides were analyzed using nano-liquid chromatography-mass spectrometry. PEAKS Proteomics, Reactome Pathway Database, FunRich, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to identify differentially expressed proteins in IgAN patients.

Results

There were 86 differentially expressed proteins between the three groups. Of these, 92.9% of the cellular component was associated with the extracellular protein. After analyzing by Reactome Pathway Database, our results showed that the main functional pathways of the EVs from clinical plasma samples were the immune system pathway and the vesicle-mediated transport. We also compared the protein components of plasma-EVs from clinical samples. 12 out of 19 proteins showed significant differences, which may be served as EV markers for IgAN.

Conclusion

We have developed and validated a workflow to isolate plasma-EVs for proteomics analysis, and proteins that showed statistical differences among the three groups were identified. Potential EV biomarkers were discovered in this study and are under validation as well.