Use of Transcriptome Sequencing to Analyze the Effects of Different Doses of an Astragalus-Rhubarb-Saffron Mixture in Mice with Diabetic Kidney Disease.

Purpose: To investigate the therapeutic effect and underlying mechanism of a traditional Chinese medicine (TCM) mixture consisting of Astragalus, rhubarb, and saffron in a mouse model of diabetic kidney disease (DKD). Methods: Forty-eight db/db mice received no TCM (DKD model), low-dose TCM, medium-dose TCM, or high-dose TCM, and an additional 12 db/m mice received no TCM (normal control). Intragastric TCM or saline (controls) was administered daily for 24 weeks. Blood glucose, body weight, serum creatinine (SCr), blood urea nitrogen (BUN), blood lipids, and urinary microalbumin were measured every four weeks, and the urinary albumin excretion rate (UAER) was calculated. After 24 weeks, kidney tissues were collected for transcriptome sequencing, and the main functions of these genes were determined via functional enrichment analysis. Results: Compared with the DKD model group, the medium-dose and high-dose TCM groups had significantly decreased levels of SCr, BUN, total cholesterol, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and UAER (all p<0.05). We identified 42 genes that potentially functioned in this therapeutic response, and the greatest effect on gene expression was in the high-dose TCM group. We also performed functional enrichment analysis to explore the potential mechanisms of action of these different genes. Conclusion: A high-dose of the Astragalus-rhubarb-saffron TCM provided the best prevention of DKD. Analysis of the kidney transcriptome suggested that this TCM mixture may prevent DKD by altering immune responses and oxygen delivery by hemoglobin.

Co-differential genes between DKD and aging: implications for a diagnostic model of DKD.

Objective: Diabetic kidney disease (DKD) is a serious complication of diabetes mellitus (DM) that is closely related to aging. In this study, we found co-differential genes between DKD and aging and established a diagnostic model of DKD based on these genes. Methods: Differentially expressed genes (DEGs) in DKD were screened using GEO datasets. The intersection of the DEGs of DKD and aging-related genes revealed DKD and aging co-differential genes. Based on this, a genetic diagnostic model for DKD was constructed using LASSO regression. The characteristics of these genes were investigated using consensus clustering, WGCNA, functional enrichment, and immune cell infiltration. Finally, the expression of diagnostic model genes was analyzed using single-cell RNA sequencing (scRNA-seq) in DKD mice (model constructed by streptozotocin (STZ) injection and confirmed by tissue section staining). Results: First, there were 159 common differential genes between DKD and aging, 15 of which were significant. These co-differential genes were involved in stress, glucolipid metabolism, and immunological functions. Second, a genetic diagnostic model (including IGF1, CETP, PCK1, FOS, and HSPA1A) was developed based on these genes. Validation of these model genes in scRNA-seq data revealed statistically significant variations in FOS, HSPA1A, and PCK1 gene expression between the early DKD and control groups. Validation of these model genes in the kidneys of DKD mice revealed that Igf1, Fos, Pck1, and Hspa1a had lower expression in DKD mice, with Igf1 expression being statistically significant. Conclusion: Our findings suggest that DKD and aging co-differential genes are significant in DKD diagnosis, providing a theoretical basis for novel research directions on DKD.