Abstract

OBJECTIVES

Diabetic kidney disease (DKD) is driven by mitochondrial dysfunction and immune dysregulation, yet the mechanistic interplay remains poorly defined. This study aimed to identify key molecular networks linking mitochondrial and immune pathways to DKD progression, with a focus on uncovering biomarkers and therapeutic targets.

METHODS

We conducted an integrative analysis of human DKD cohorts (GSE30122, GSE96804) using weighted gene co-expression network analysis (WGCNA) to identify gene modules enriched for immune response genes and mitochondrial pathways (from MitoCarta3.0). Machine learning algorithms were employed to prioritize key biomarkers for further investigation. Experimental validation was performed using a DKD rat model.

RESULTS

WGCNA revealed significant gene modules associated with immune responses and mitochondrial functions. Machine learning analysis highlighted two central biomarkers: aminoadipate-semialdehyde synthase (AASS) and caspase-3 (CASP3). In the DKD rat model, elevated levels of AASS and CASP3 were found to correlate with increased oxidative stress. Mechanistically, AASS was shown to drive mitochondrial damage via lysine metabolism, while CASP3 amplified inflammatory apoptosis pathways.

CONCLUSIONS

Our findings establish AASS and CASP3 as dual biomarkers and therapeutic targets, bridging mitochondrial-immune crosstalk to DKD pathogenesis. This multi-omics framework provides actionable insights for targeting kidney damage in diabetes.

MeSH

Kidney Diseases, Genes, Diabetic Nephropathies, Caspase 3, Kidney, Biomarkers, Uremia, Immunity, Therapeutics, Learning, Acute Kidney Injury