Objective To investigate the role of period 2 (Per2) protein in the death of cardiomyocytes induced by β1-adrenergic receptor autoantibodies (β1-AA). Methods Sixteen male SD rats aged 6~8 weeks were randomly divided into active immunization (model) group and control group (n=8). The model group was immunized with the second extracellular loop of beta1-adrenoceptor (β1-AR-EC Ⅱ), and the control group was injected with Na2CO3 and other solutions. The rat serum was subsequently collected at 8 weeks, followed by the purification of β1-AA. H9c2 cardiomyocyte were selected and randomly divided into control group, β1-AA group, and β1-AR+β1-AA group. Cell viability of each group was detected by CCK-8 assay (n=8). Then H9c2 cells in the control group and β1-AA 1 μmol/L group were synchronized with dexamethasone for 4 h, the expression of Per2 in cardiomyocytes at different circadian time (CT) points was measured by Western blotting, and JTK_CYCLE was used to analyze the circadian rhythm parameters (n=11). Moreover, Per2 in H9c2 cells was knocked down or overexpressed by lentiviral shPer2 and lentiviral Per2, respectively; RT-PCR and Western blotting were performed to detect the changes of Per2 expression (n=6). On the basis of knockdown (n=8) or overexpression (n=10) of Per2, the H9c2 cells were further treated with β1-AA, and the cell survival rate was tested by CCK-8 assay. Results CCK-8 assay showed that the survival rate of H9c2 cells was significantly decreased after β1-AA treatment (P < 0.05). Western blotting demonstrated that β1-AA remarkably inhibited the rhythmic expression of Per2 protein in the cardiomyocytes (JTK_CYCLE, P>0.05), with the decrease at CT8 and CT16 most obviously (P < 0.01). Knockdown of Per2 expression reduced the survival rate of cardiomyocytes, which was further lowered after β1-AA treatment (P < 0.001). However, overexpression of Per2 notably reversed the decline in H9c2 survival rate induced by β1-AA (P < 0.001). Conclusion Per2 protein inhibits β1-AA induced H9c2 cardiomyocyte death.

Objective To explore the role of STX17 protein in Aβ-induced death of mouse hippocampal neuronal cells. Methods Based on Weighted correlation network analysis (WGCNA), the modules significantly related to Alzheimer's disease (AD) were selected, and key genes were screened for cross-fertilization with autophagy gene bank and SNARE family genes. Aβ31-35 at a concentration of 1 g/L was used to inject the C57BL/6 mouse hippocampal tissue, and HT22 hippocampal neuronal cells were treated with 5 μmol/L Aβ31-35. Then the protein expression of STX17, LC3 Ⅱ and P62 was detected by Western blotting. The survival rate of the HT22 hippocampal neuronal cells treated by using Aβ31-35 was detected by CCK-8 assay. Protein expression of LC3Ⅱ and P62 were determined by Western blotting after overexpression STX17 in HT22 hippocampal neuronal cells. Results The Salmon and Black modules significantly associated with AD crossed with autophagic gene pool and SNARE subfamily intersection STX17, respectively. Under the action of Aβ31-35 mouse hippocampal tissue and mouse HT22 hippocampal neuronal cells, and the survival rate of HT22 cells [(81.81±11.65)% vs (100.00±12.89)%, P < 0.05] were decreased significantly, and the expression of STX17 was significantly reduced in both tissue and cells (P < 0.05). The expression of tissue LC3Ⅱ and P62 proteins were significantly elevated in the tissue (P < 0.05), and in the cells (P < 0.05). After lentivirus-mediated overexpression of STX17 in HT22 hippocampal neuronal cells, the protein expression upregulation of LC3II and P62 induced by Aβ31-35 was significantly reversed, and the survival rate was improved in Aβ31-35-induced HT22 hippocampal neuronal cells [(101.91±13.81)% vs (79.21±8.75)%, P < 0.05]. Conclusion STX17 improves Aβ-induced death of mouse hippocampal neuronal cells by promoting autophagy.

目的:探讨β1-肾上腺素受体(β1-AR)自身抗体(β1-AA)对大鼠心肌细胞自噬标志物微管相关蛋白1轻链3(LC3)节律表达的影响及其在心肌细胞死亡中的作用.方法:实验材料为Sprague-Dawley(SD)大鼠和H9c2大鼠心肌细胞.将SD大鼠随机分为免疫组(β1-AR组)和对照(control)组,每组6只;将H9c2细胞随机分为control组、β1-AA组、慢病毒(LV)-NC组和LV-shPer2组(n=6);合成β1-AR细胞外第二环抗原肽段,用于主动免疫大鼠,并使用亲和层析法从大鼠血清中提纯β1-AA;β1-AA处理H9c2细胞24 h后使用CCK-8法检测细胞活力;用地塞米松同步化细胞后,再给予β1-AA处理,采用real-time PCR及Western blot法检测LC3的表达情况,采用Western blot法检测生物钟蛋白Per2的表达情况,使用JTK_CYCLE算法分析昼夜节律参数;用LV-shPer2感染H9c2细胞以破坏LC3的节律表达,进而采用CCK-8法检测细胞活力.结果:β1-AR组大鼠血清中β1-AA的A值与control组相比显著升高(P<0.05).β1-AA组H9c2细胞的活力显著低于control组(P<0.05).β1-AA可破坏H9c2细胞LC3和Per2的节律表达(JTK_CYCLE P<0.05).通过LV-shPer2干扰Per2基因而破坏H9c2细胞LC3节律表达(JTK_CYCLE P<0.05)后,细胞活力显著降低(P<0.05).结论:β1-AA破坏H9c2大鼠心肌细胞自噬标志物LC3的节律表达,从而促进细胞死亡.

Background: Kidney renal clear cell carcinoma is the malignant tumor with the highest incidence and poor prognosis in renal cell carcinoma. In view of its limited diagnostic strategies and poor prognosis, bioinformatics analysis has been used to explore the possible mechanisms of renal clear cell carcinoma and effective prognostic-related biomarkers. Method: The sequencing information of 3 types of RNA (mRNA, lncRNA and miRNA) in 539 cases of kidney renal clear cell carcinoma tumor tissues and 72 cases of normal tissues is obtained from the TCGA database. Heat map and volcano map of differentially expressed genes were drawn through R language; The CeRNA network was visualized by Cytoscape software (version 3.7.2). Methods such as univariate Cox regression analysis, lasso regression screening, and multivariate Cox regression analysis were used to construct a prognostic model based on the CeRNA network. The CIBERSORT algorithm was used to analyze the degree of infiltration of 22 kinds of immune cells from each sample of kidney renal clear cell carcinoma. Construction of a prognostic model based on tumor-infiltrating immune cells, The R "corrplot" software package was used for co-expression analysis based on the CeRNA network and tumor-infiltrating immune cells model. Results: There are 3074 differentially expressed mRNAs (1055 upregulated and 2019 downregulated), and 359 differentially expressed lncRNAs (71 upregulated and 280 downregulated) and 132 differentially expressed miRNAs (70 upregulated and 62 downregulated) that have been identified through differential analysis. A complete mRNA-miRNA-lncRNA (SIX1-hsa-miR-200b-3p-MALAT1) network was obtained based on the CeRNA network-based prognostic model construction. 2 immune cells (Mast cells resting, T cells follicular helper) were identified by constructing a prognostic model based on tumor-infiltrating immune cells. There was a negative correlation between lncRNA MALAT1 and Mast cells resting (R= -0.27, P<0.001); while there was a positive correlation between lncRNA MALAT1 and T cells follicular helper (R=0.23, P<0.001). Conclusion: Based on CeRNA network and tumor-infiltrating immune cells, we explored the possible mechanism of kidney renal clear cell carcinoma and obtained effective biomarkers for predicting prognosis by Bioinformatics analysis in this study.