Quantitative Glycoproteomic Profiling Identifies Distinct N-Glycosylation Signatures in Renal Biopsies from Healthy Kidney Donors and Patients with Diabetic Kidney Disease.

Authors

Yong Zhang, Min Liu, Yingying Ling, Fei Cai, Tao Su, Rajeev K Singla, Yu Fan, Ling Li

Published in

Journal of proteome research. Volume 25. Issue 11. Pages 6430-6445. Nov 06, 2026.

Abstract

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD) worldwide, but site-specific N-glycosylation signatures associated with renal injury in DKD remain poorly characterized. Unlike previous studies relying on suboptimal control tissues, we utilized intact healthy kidney donor (HKD) cortical biopsies as physiologically relevant reference tissues. We applied an optimized quantitative glycoproteomic pipeline combining homogenization and noncontact ultrasound-assisted protein extraction, ZIC-HILIC enrichment, and EThcD-sceHCD-MS/MS to profile intact N-glycopeptides (IGPs) from 20 DKD patients and 20 HKD controls. We identified 1186 significantly differentially expressed IGPs and 111 altered N-glycans derived from 294 glycoproteins in DKD renal tissues (adjusted p value of <0.05, FC of >1.5 or <0.67). GO enrichment and KEGG pathway analyses revealed the enrichment of differentially glycosylated proteins in the complement and coagulation cascades and immune-related glycoproteins. Hybrid, complex, fucosylated, and sialylated N-glycans were more frequently represented among upregulated in DKD, whereas high-mannose structures were more frequently represented among downregulated IGP. Subclass- and site-specific N-glycosylation profiling of IgGs, IgA2, and complement proteins uncovered unique DKD-associated glycosomal alterations. This study identifies novel DKD glyco-signatures from immunoglobulins and complement components, demonstrates the feasibility of our workflow for limited clinical biopsy material, and offers mechanistic insights alongside candidate molecular signatures for future validation.

PMID:
42992925
Bibliographic data and abstract were imported from PubMed on 06 Nov 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.