Yong Zhang

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 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.

The high prevalence and substantial burden of kidney diseases necessitate advanced approaches to elucidate molecular mechanisms and promote precision medicine. Mass spectrometry (MS)‐based proteomics has evolved into a widely used analytical platform, delivering high‐sensitivity, high‐throughput protein profiling capabilities that have contributed substantially to biomarker discovery, mechanistic dissection, and therapeutic target identification across a broad range of kidney diseases. This review provides a comprehensive overview of MS‐based proteomics applications in kidney disease research, covering progress in biomarker identification, pathogenic mechanism interrogation, and clinical translation. It highlights methodological advances, emerging trends, and persistent challenges that shape the field. Existing literature has uncovered abundant disease‐specific biomarkers and revealed key pathogenic pathways, including podocyte injury‐associated protein interaction networks, dysregulated complement activation, and metabolic reprogramming, and have critically assessed their translational potential. Additionally, investigations into posttranslational modifications such as phosphorylation and glycosylation have provided valuable insights for targeted therapies. Collectively, these findings illustrate the contributions of MS‐based proteomics to the characterization of disease molecular heterogeneity, the identification of key pathogenic drivers, and the development of precision medicine approaches. This review further addresses current challenges in clinical applications, including sample heterogeneity, data complexity, and standardization issues. We additionally emphasize the critical need for minimum reporting standards and multicenter harmonization to accelerate the clinical translation of renal proteomics. Future research should focus on integrating multiomics and artificial intelligence‐driven data mining to enhance precise disease subtyping, dynamic monitoring, and personalized treatment strategies.

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy that is often diagnosed after curative treatment is no longer feasible. Existing biomarkers, particularly CA19‐9, have limited sensitivity and specificity. Plasma proteins that capture tumor‐associated biological alterations may therefore provide useful signals for earlier detection. Methods: Plasma samples from 99 patients with PDAC and 30 healthy controls were analyzed using data‐independent acquisition (DIA) proteomics. Differentially expressed proteins were identified using predefined statistical thresholds and further examined by functional enrichment analysis. Selected candidate biomarkers were validated by ELISA in an independent subset. Results: Among 565 quantified plasma proteins, 52 were differentially expressed between PDAC and controls. These proteins were enriched in extracellular processes, cholesterol metabolism, complement and coagulation cascades, and pancreatic secretion pathways. ELISA validation confirmed higher plasma levels of Cathepsin S, CTRB2, MARCO, PIGR, PRDX6, REG1A, Trypsin‐2, and PEP‐FAP in patients with PDAC compared with healthy controls. ROC analyses showed moderate‐to‐good discriminatory performance for several candidates, and the MARCO + PEP‐FAP model improved classification compared with either marker alone. Conclusion: These findings reveal circulating proteins linked to key PDAC‐related biological processes and identify eight candidates for further evaluation in multi‐protein diagnostic panels. Larger validation studies incorporating clinically relevant disease control groups are warranted to determine their diagnostic specificity and clinical utility.

The aim of the study was to evaluate the diagnostic accuracy of mucins in pancreatic ductal adenocarcinoma (PDAC) and biliary tract cancer (BTC) based on available evidence and to assess their role in differential diagnosis. A comprehensive search was conducted across PubMed/MEDLINE, Web of Science, Embase and Cochrane Library from database inception to September 2024. All immunoassay-based cases or clinical studies that investigated mucin (MUC) in relation to PDAC or BTC were included. The pooled sensitivity and specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), negative likelihood ratio (NLR), the area under the summary receiver operating characteristic (SROC) curve, and the area under the curve (AUC) for several mucins in diagnosing PDAC and BTC were assessed. A total of 56 studies involving 6823 patients were included in the analysis. Current mucin-based diagnostic approaches for PDAC and BTC primarily focus on mucin 1 (MUC1), mucin 2 (MUC2), mucin 4 (MUC4), mucin 5AC (MUC5AC), and mucin 6 (MUC6). Among them, MUC1 demonstrated exceptional diagnostic performance in PDAC with a sensitivity of 0.96 and a specificity of 0.76 and in BTC with a sensitivity of 0.80 and a specificity of 0.93. Additionally, MUC4 also exhibited good diagnostic efficacy in PDAC, with a sensitivity of 0.77 and a specificity of 0.76. MUC5AC demonstrated significant diagnostic efficacy in PDAC with a sensitivity of 0.79 and a specificity of 0.61 and in BTC with a sensitivity of 0.69 and a specificity of 0.77. However, the other two proteins, MUC2 and MUC6, exhibited poor diagnostic performance for PDAC and BTC, with a sensitivities of less than 0.47 and a specificities of less than 0.68. In conclusion, this meta-analysis of patients with PDAC and BTC indicates that MUC markers may have high diagnostic accuracy, particularly some specific key mucins. The differential expression of the same mucin in both cancers as well as in their corresponding benign diseases offers valuable insights into the differential diagnosis. The online version contains supplementary material available at https://doi.org/10.1007/s43657-025-00288-9.

Kidney diseases present substantial clinical challenges, with aberrant glycoproteins emerging as key pathogenic drivers. Minor glomerular abnormalities (MGAs), a category of unclassified glomerular lesions defined by subtle structural changes, are commonly detected in patients with persistent, asymptomatic, isolated proteinuria or microhematuria. Still their site-specific N-glycosylation patterns remain unexplored. To address this gap, a laboratory-developed pressure cycling technology-based quantitative glycoproteomics workflow was applied to compare intact N-glycopeptides (IGPs) among distant non-neoplastic tissues (DNTs; n = 24) and trace renal biopsy samples from MGA patients (n = 27). Integrated with high-resolution mass spectrometry, 672 upregulated IGPs (FC > 1.5, p < 0.05) and 573 downregulated IGPs (FC < 0.67, p < 0.05) in MGA tissues were quantified. Compared with DNTs, 24 glycoproteins associated with the PI3K-Akt signaling pathway exhibited broadly elevated IGP abundances in MGA samples. Site-specific N-glycosylation analysis further revealed distinct patterns among IgG subclasses and complement-related markers that distinguish MGA from DNT, offering new mechanistic insights into MGA pathogenesis. These novel glyco-signatures clarify the role of N-glycosylation in renal disease and validate this workflow as a powerful tool for trace-tissue analysis. This study lays the groundwork for translating N-glycosylation findings into clinical applications to improve MGA diagnosis and management.

Background: Minor glomerular abnormalities (MGAs) are histopathologically heterogeneous renal lesions with subtle structural changes and latent clinical manifestations, yet their molecular mechanisms remain poorly characterized and underexplored. Methods: In this study, we employed pressure cycling technology-assisted sample preparation combined with data-independent acquisition mass spectrometry to systematically compare the proteomic profiles of distant non-neoplastic tissues (n = 24) and MGA tissues (n = 27). Results: A total of 9 529 protein groups were quantified with a false discovery rate < 1%, and 1 338 differentially expressed protein groups were identified (fold-change > 2 or < 0.5, P < 0.05), including 190 downregulated and 1 148 upregulated protein groups in MGA tissues. Gene ontology analysis revealed that the downregulated proteins were enriched in cell adhesion, ion binding, and molecular transport, whereas the upregulated proteins were enriched in transcriptional regulation, DNA replication/repair, and nucleic acid binding. Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated inhibition of metabolic pathways and the peroxisome proliferator-activated receptor signaling pathway, as well as the activation of basal transcription factors and nucleotide excision repair in MGAs. Further screening revealed 13 core upregulated nuclear proteins (e.g. YY1, TAF9, RFC1, and POLR1D) with a >90% detection rate in MGA tissues; these proteins are functionally associated with renal inflammation, cell proliferation, and the DNA damage response. Conclusion: Our study establishes a high-resolution proteomic landscape of MGAs, provides novel insights into their molecular pathogenesis, and identifies potential tissue biomarkers and therapeutic targets. The pressure cycling technology-assisted data-independent acquisition workflow also offers a robust technical framework for proteomic analysis of microscale renal biopsy samples.