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Updated: Jul 13, 2025
Comparative Proteomic Analysis of Whole Kidney, Medulla, and Cortical Tubules in Diabetic Pathogenesis of Kidney Injury in Mice
Published on: May 2, 2025
Identifying key genes for diabetic kidney disease by bioinformatics analysis
Yushan Xu1, Lan Li2, Ping Tang3
1Department of Endocrinology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650031, China.
Background:
There are no reliable molecular targets for early diagnosis and effective treatment in the clinical management of diabetic kidney disease (DKD). To identify novel gene factors underlying the progression of DKD.
Methods:
The public transcriptomic datasets of the alloxan-induced DKD model and the streptozotocin-induced DKD model were retrieved to perform an integrative bioinformatic analysis of differentially expressed genes (DEGs) shared by two experimental animal models. The dominant biological processes and pathways associated with DEGs were identified through enrichment analysis. The expression changes of the key DEGs were validated in the classic db/db DKD mouse model.
Results:
The downregulated and upregulated genes in DKD models were uncovered from GSE139317 and GSE131221 microarray datasets. Enrichment analysis revealed that metabolic process, extracellular exosomes, and hydrolase activity are shared biological processes and molecular activity is altered in the DEGs. Importantly, Hmgcs2, angptl4, and Slco1a1 displayed a consistent expression pattern across the two DKD models. In the classic db/db DKD mice, Hmgcs2 and angptl4 were also found to be upregulated while Slco1a1 was downregulated in comparison to the control animals.
Conclusions:
In summary, we identified the common biological processes and molecular activity being altered in two DKD experimental models, as well as the novel gene factors (Hmgcs2, Angptl4, and Slco1a1) which may be implicated in DKD. Future works are warranted to decipher the biological role of these genes in the pathogenesis of DKD.
Insights
Researchers identified novel gene factors, Hmgcs2, Angptl4, and Slco1a1, involved in diabetic kidney disease (DKD) progression. This study highlights key molecular targets for potential DKD diagnosis and treatment.
Area of Science:
- Nephrology
- Genomics
- Biochemistry
Background:
- Diabetic kidney disease (DKD) lacks reliable molecular targets for early diagnosis and effective treatment.
- Identifying novel genetic factors is crucial for understanding DKD pathogenesis and progression.
Purpose of the Study:
- To identify novel gene factors underlying the progression of diabetic kidney disease (DKD).
- To uncover shared biological processes and molecular activities altered in DKD models.
Main Methods:
- Integrative bioinformatic analysis of public transcriptomic datasets from alloxan- and streptozotocin-induced DKD models.
- Enrichment analysis to identify differentially expressed genes (DEGs), biological processes, and pathways.
- Validation of key DEG expression in a classic db/db DKD mouse model.
Main Results:
- Common DEGs were identified across two DKD models, revealing altered metabolic processes, extracellular exosomes, and hydrolase activity.
- Hmgcs2, angptl4, and Slco1a1 showed consistent expression patterns across different DKD models.
- Hmgcs2 and angptl4 were upregulated, while Slco1a1 was downregulated in db/db mice compared to controls.
Conclusions:
- Novel gene factors (Hmgcs2, Angptl4, Slco1a1) implicated in DKD pathogenesis were identified.
- Common biological processes and molecular activities altered in DKD were uncovered.
- Further research is warranted to elucidate the specific roles of these genes in DKD development.
