Pressure cycling technology-assisted data-independent acquisition proteomics reveals molecular alterations and potential therapeutic targets in minor glomerular abnormalities

Abstract

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.

Keywords

kidney tissue / minor glomerular abnormality / distant non-neoplastic tissue / proteomics / pressure cycling technology / LC-MS/MS

Ethics statement

This study was conducted in strict accordance with the ethical principles of the Declaration of Helsinki and relevant national and institutional regulations for medical research involving human subjects. All procedures related to the collection, handling, and analysis of human kidney tissue samples were reviewed and ap-proved by the Ethics Committee of West China Hospital, Sichuan University [Approval No. 2024(1221)].

Acknowledgements

We are grateful for the financial support from the National Natural Science Foundation of China (grant No. 92478101), National Key R&D Program of China (grant No. 2022YFF0608401), Natural Science Foundation of Sichuan Province (grant No. 2024NSFSC0587), and Sichuan Science and Technology Program (grant No. 2025YFHZ0213).

Author contributions

Ling Li (Conceptualization, Funding acquisition, Resources, Validation, Writing—review & editing), Yingying Ling (Formal analysis, Visualization), Fei Cai (Formal analysis, Visualization), Yi Zhong (Data curation, Formal analysis, Visualization), Hao Yang (Formal analysis, Visualization), Fang Liu (Formal analysis, Visualization), Guisen Li (Formal analysis, Visualization), Xinfang Xie (Formal analysis, Visualization), Rajeev K Singla (Writing—review & editing), Dengyan Ma (Conceptualization, Resources, Writing—original draft), and Yong Zhang (Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing—original draft, Writing—review & editing).

Supplementary material

Supplementary material is available at PCMEDI Journal online.

Declaration of competing interest

None declared.

Data availability

The mass spectrometry data have been deposited to the Proteome Xchange Consortium via the iProX partner repository with the dataset identifier PXD070517.

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