Cyr61 Mediates Angiotensin II-Induced Podocyte Apoptosis via the Upregulation of TXNIP.

Purpose: It is well documented that angiotensin II (Ang II) elevation promotes apoptosis of podocytes in vivo and vitro, but the potential mechanism is still oscular. The current study is aimed at probing into the assignment of cysteine-rich protein 61 (Cyr61) in Ang II-induced podocyte apoptosis. Methods: Podocytes were treated with Ang II (10-6 mol/L) for 48 hours to establish an injury model in vitro. Western blot assays were detected the expression of Cyr61, Cyt-c, Bax, and Bcl-2. Gene microarray was used to analyze the expression of mRNAs after treatment with Ang II. CRISPR/Cas9 technology was used to knock down Cyr61 and overexpress TXNIP gene, respectively. Results: The expression of Cyr61, TXNIP, Cyt-c, and Bax in podocytes treated with Ang II were upregulated, but the expression and apoptotic rates of Bcl-2 in podocytes were inhibited. The level of the above factors was not significantly different after the knockdown of Cyr61 with Ang II in podocytes. In Ang II group, when knocked down Cyr61, the expressed level of TXNIP, Cyt-c, and Bax was diminished after Ang II treatment; interestingly Bcl-2 expression and podocyte apoptotic rate were reduced. Under the stimulation of Ang II, the expression of Cyt-c and Bax were growing, whereas Bcl-2 was reduced, and the apoptotic rates were higher in the TXNIP overexpression group. Cyt-c and Bax were put on, whereas that of Bcl-2 was to be cut down when the Cyr61 was knockdown, and the apoptotic rates were gained in the TXNIP overexpression+Cyr61 knockdown group. Conclusions: The results of the study extrapolate that Cyr61 plays a dominant role in Ang II-induced podocyte apoptosis. Additionally, Cyr61 may mediate the Ang II-induced podocyte apoptosis by promoting the expression of TNXIP.

Bioinformatics analysis identifies diagnostic biomarkers and their correlation with immune infiltration in diabetic nephropathy.

Background: Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD). Currently, microalbuminuria is mainly used as a diagnostic indicator of DN, but there are still limitations and lack of immune-related diagnostic markers. In this study, we aimed to explore diagnostic biomarkers associated with immune infiltration of DN. Methods: Immune-related differentially expressed genes (DEGs) were derived from those at the intersection of the ImmPort database and DEGs identified from 3 datasets, which were based on the Gene Expression Omnibus (GEO). Functional enrichment analyses were performed; a protein-protein interaction (PPI) network was constructed; and hub genes were identified by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). After screening the key genes using least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE), a prediction model for DN was constructed. The predictive performance of the model was quantified by receiver-operating characteristic curve, decision curve analysis, and nomogram. Next, infiltration of 22 types of immune cells in DN kidney tissue was evaluated using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). Expression of diagnostic markers was analyzed in DN and control patient groups to determine the genes with the maximum diagnostic potential. Finally, we explored the correlation between diagnostic markers and immune cells. Results: Overall, 191 immune-related DEGs were identified, that primarily positively regulated with cell adhesion, T cell activation, leukocyte proliferation and migration, urogenital system development, lymphocyte differentiation and proliferation, and mononuclear cell proliferation. Gene sets were related to the PI3K-Akt, MAPK, Rap1, and WNT signaling pathways. Finally, CCL19, CD1C, and IL33 were identified as diagnostic markers of DN and recognized in the 3 datasets [area under the curve (AUC) =0.921]. Immune cell infiltration analysis demonstrated that CCL19 was positively correlated with macrophages M1 (R=0.47, P<0.001) and macrophages M2 (R=0.75, P<0.001). CD1C was positively correlated with macrophages M1 (R=0.47, P<0.05), macrophages M2 (R=0.75, P<0.01), and monocytes (R=0.42, P<0.01). IL33 was positively correlated with macrophages M1 (R=0.45, P<0.05), macrophages M2 (R=0.74, P<0.01), and monocytes (R=0.41, P<0.01). Conclusions: Our results provide evidence that CCL19, CD1C, and IL33, which are associated with immune infiltration, are the potential diagnostic biomarkers for DN candidates.