Miao Hu, Yi Deng, Yujie Bai, Jiayan Zhang, Xiahong Shen, Lei Shen, Ling Zhou

Department of Nephrology, The First Affiliated Hospital of Soochow University, Suzhou, People’s Republic of China

Correspondence: Ling Zhou, Email zl66060@163.com

Background: Renal failure related death caused by diabetic kidney disease (DKD) is an inevitable outcome for most patients. This study aimed to identify the critical genes involved in the onset and progression of DKD and to explore potential therapeutic targets of DKD.
Methods: We conducted a batch of protein quantitative trait loci (pQTL) Mendelian randomization analysis to obtain a group of proteins with causal relationships with DKD and then identified key proteins through colocalization analysis to determine correlations between variant proteins and disease outcomes. Subsequently, the specific mechanisms of key regulatory genes involved in disease progression were analyzed through transcriptome and single-cell analysis. Finally, we validated the mRNA expression of five key genes in the DKD mice model using reverse transcription quantitative PCR (RT-qPCR).
Results: Five characteristic genes, known as protein kinase B beta (AKT2), interleukin-2 receptor beta (IL2RB), neurexin 3(NRXN3), slit homolog 3(SLIT3), and TATA box binding protein like protein 1 (TBPL1), demonstrated causal relationships with DKD. These key genes are associated with the infiltration of immune cells, and they are related to the regulatory genes associated with immunity. In addition, we also conducted gene enrichment analysis to explore the complex network of potential signaling pathways that may regulate these key genes. Finally, we identified the effectiveness and reliability of these selected key genes through RT-qPCR in the DKD mice model.
Conclusion: Our results indicated that the AKT2, IL2RB, NRXN3, SLIT3, and TBPL1 genes are closely related to DKD, which may be useful in the diagnosis and therapy of DKD.

Keywords: Mendelian randomization analysis, diabetic kidney disease, clinical correlated genes, biomarker, immune cell infiltration