Construction of LncRNA-miRNA-mRNA regulatory network in IgA nephropathy mice model by RNA-sequencing and bioinformatics analysis.

BACKGROUND

Immunoglobulin A nephropathy (IgAN) represents the most prevalent form of primary glomerulonephritis. Long noncoding RNAs (lncRNAs) play critical roles in the initiation and progression of various diseases, including kidney disorders. However, the involvement of lncRNAs in IgAN remains underexplored. This study aims to investigate the potential role of lncRNAs in IgAN by constructing a comprehensive lncRNA-miRNA-mRNA regulatory network.

METHODS

An IgAN mouse model was established for the study. The expression profiles of lncRNAs and mRNAs were obtained via RNA sequencing (RNA-seq) to identify differentially expressed mRNAs and lncRNAs. A lncRNA-mediated competing endogenous RNA (ceRNA) regulatory network was subsequently constructed through bioinformatics analysis, and pathway enrichment was explored through functional analyses. Validation of lncRNAs, miRNAs, and mRNAs was performed via Real-time quantitative polymerase chain reaction (RT-qPCR).

RESULTS

Histological analysis using hematoxylin and eosin (HE) staining and immunofluorescence confirmed the successful development of the IgAN mouse model. Enzyme-linked immunosorbent assay (ELISA) and biochemical index testing indicated significant increases in renal function markers, including Immunoglobulin A (IgA), Blood Urea Nitrogen (BUN), and Creatinine (CRE). RNA-seq analysis revealed 388 differentially expressed lncRNAs and 256 differentially expressed mRNAs. A bioinformatics-based ceRNA network, focused on inflammation and immune response, was successfully constructed, comprising 19 lncRNAs, 7 mRNAs, and 5 miRNAs. Functional enrichment analysis identified significant involvement of these lncRNAs in the MAPK, Ras, and PI3K-Akt signaling pathways. Finally, the RT-qPCR validation data agreed with the RNA-seq.

CONCLUSION

This study identified a novel lncRNA/miRNA/mRNA regulatory network involved in the pathogenesis of IgAN.