慢性肾病模型验证与转录组分析

2025-05-13 MedSci xAi 发表于广东省
本文通过UUO模型验证慢性肾病的转录组分析,揭示炎症相关通路与核心CKD基因的表达变化,并展示KEGG通路分析与基因集验证的最新研究成果。

To validate the stability of aforementioned bioinformatics screening and model construction, we established a common CKD model, the UUO (Unilateral Ureteral Obstruction) model, in mice and obtained transcriptome sequencing data from the kidneys. As shown in Figure 5A, a schematic diagram illustrates the ligation of the left ureter during UUO model construction. On day 7, we harvested kidney tissues and serum from the mice. As shown in Figure 5B, the levels of blood creatinine (P=0.0002) and urea nitrogen (P=0.0002) were significantly higher in the UUO group compared to the sham surgery group. As shown in Figure 5C, after species gene conversion, the 29 key human CKD genes identified previously, including Col1a1 and Fn1, were marked in the differential gene analysis. Figure 5D shows that qPCR confirmed significant upregulation of Col1a1 (P<0.0001), Fn1 (P<0.0001), and Ccl2 (P<0.0001) in the UUO group. KEGG pathway enrichment analysis of differentially expressed genes (DEGs) between UUO and sham groups, as shown in Figures 5E and 5F, revealed that seven major pathways were significantly altered after UUO modeling in mice, including immune-related pathways such as cytokine-cytokine receptor interaction and TNF signaling, and inflammation-related pathways such as inflammatory bowel disease and asthma. We found that the transcriptional profile of the affected kidney in the UUO model closely resembled the transcriptomic changes observed in human CKD. Further, we identified five significantly expressed core human CKD genes (Ccl2, Slc34a3, Alpl, Acadm, and Suclg1) from the DEGs in the mouse model and designated them as gene set 1 (gs1). The 29 initially screened genes were named gene set 2 (gs2). Using the z-score integration algorithm, we found that the diagnostic performance for CKD in mice was excellent (AUC > 0.9) regardless of the size of the custom gene sets. In summary, through validation using the mouse CKD model, we have demonstrated that the bioinformatics analysis of human CKD datasets is highly reliable and valuable for experimental research.

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