Trim65 attenuates isoproterenol-induced cardiac hypertrophy by promoting autophagy and ameliorating mitochondrial dysfunction via the Jak1/Stat1 signaling pathway

Liu, Hui Ting; Zhou, Zhixiang; Deng, HuaNian; Zhang, Tian; Wu, ZeFan; Liu, XiYan; Ren, Zhongzhou; Jiang, Zhisheng

doi:10.1016/j.ejphar.2023.175735

Cited by 12 publications

(4 citation statements)

References 31 publications

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“…For a more in-depth understanding of these DEGs, we analyzed the selected genes for GO and REACTOME pathway enrichment analyses and found that GO terms and signaling pathways were significant. [106], FCN1 [107], CARNS1 [108], AMH (anti-Mullerian hormone) [109], E2F1 [110], PF4 [111], RPL3L [112], TRIM72 [113], HOXB4 [114], TP73 [115], KCNH2 [116], (advanced glycosylation end-product specific receptor) [117], SMPD3 [118], TYMP (thymidine phosphorylase) [119], RIPPLY3 [120], GREM1 [121], CYP11B2 [122], MYLK2 [123], WNT3A [124], MSX1 [125], COMP (cartilage oligomeric matrix protein) [126], FLI1 [127], ACTA1 [128], TCAP (titincap) [129], TUBB1 [130], TNNI3 [131], HSPB7 [132], DES (desmin) [133], RAP1B [134], TNNT1 [135], BHMT (betaine--homocysteine S-methyltransferase) [136], ANGPTL3 [137], CYP3A5 [138], KMO (kynurenine 3-monooxygenase) [139], HMGCS2 [140], AGXT2 [141], FABP1 [142], SLC22A12 [143], CUBN (cubilin) [144], MIOX (myo-inositol oxygenase) [145], FBP1 [146], ARG2 [147], FGF1 [148], CRY1 [149], PPARGC1A [150], UGT1A6 [151], ECHDC3 [152], CYP2C8 [153], ACOX2…”

Section: Discussionmentioning

confidence: 99%

“…Signaling pathways include neuronal system [90], GPCR ligand binding [91], metabolism [92] and metabolism of lipids [93] were responsible for progression of AKI. MYH7 [94], BMI1 [95], IGF2 [96], ADORA2A [97], KLK10 [98], MEIS2 [99], IRF7 [100], PRKCB (protein kinase C beta) [101], CCL5 [102], ADAM33 [103], EEF1A2 [104], ACTN3 [105], TRIM65 [106], FCN1 [107], CARNS1 [108], AMH (anti-Mullerian hormone) [109], E2F1 [110], PF4 [111], RPL3L [112], TRIM72 [113], HOXB4 [114], TP73 [115], KCNH2 [116], (advanced glycosylation end-product specific receptor) [117], SMPD3 [118], TYMP (thymidine phosphorylase) [119], RIPPLY3 [120], GREM1 [121], CYP11B2 [122], MYLK2 [123], WNT3A [124], MSX1 [125], COMP (cartilage oligomeric matrix protein) [126], FLI1 [127], ACTA1 [128], TCAP (titin-cap) [129], TUBB1 [130], TNNI3 [131], HSPB7 [132], DES (desmin) [133], RAP1B [134], TNNT1 [135], BHMT (betaine--homocysteine S-methyltransferase) [136], ANGPTL3 [137], CYP3A5 [138], KMO (kynurenine 3-monooxygenase) [139], HMGCS2 [140], AGXT2 [141], FABP1 [142], SLC22A12 [143], CUBN (cubilin) [144], MIOX (myo-inositol oxygenase) [145], FBP1 [146], ARG2 [147], FGF1 [148], CRY1 [149], PPARGC1A [150], UGT1A6 [151], ECHDC3 [152], CYP2C8 [153], ACOX2 [154], SLC2A9 [155], MSRA (methionine sulfoxidereductase A) [156], GC (GC vitamin D binding protein) [157], VNN1 [158], NOX4 [159], GOT2 [160], EPHX2 […”

Section: Discussionmentioning

confidence: 99%

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Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Acute kidney injury (AKI) is a type of renal disease occurs frequently in hospitalized patients, which may cause abnormal renal function and structure with increase in serum creatinine level with or without reduced urine output. With the incidence of AKI is increasing. However, the molecular mechanisms of AKI have not been elucidated. It is significant to further explore the molecular mechanisms of AKI. We downloaded the GSE139061 next generation sequencing (NGS) dataset from the Gene Expression Omnibus (GEO) database. Limma R bioconductor package was used to screen the differentially expressed genes (DEGs). Then, the enrichment analysis of DEGs in Gene Ontology (GO) function and REACTOME pathways was analyzed by g:Profiler. Next, the protein-protein interaction (PPI) network and modules was constructed and analyzed, and the hub genes were identified. Next, the miRNA-hub gene regulatory network and TF-hub gene regulatory network were built. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. Overall, 956 DEGs were identified, including 478 up regulated and 478 down regulated genes. The enrichment functions and pathways of DEGs involve primary metabolic process, small molecule metabolic process, striated muscle contraction and metabolism. Topological analysis of the PPI network and module revealed that hub genes, including PPP1CC, RPS2, MDFI, BMI1, RPL23A, VCAM1, ALB, SNCA, DPP4 and RPL26L1, might be involved in the development of AKI. miRNA-hub gene and TF-hub gene regulatory networks revealed that miRNAs and TFs including hsa-mir-510-3p, hsa-mir-6086 and mir-146a-5p, MAX and PAX2, might be involved in the development of AKI. Various known and newtherapeutic targets were obtained via network analysis. The results of the current investigation might be beneficial for the diagnosis and treatment of AKI.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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“…In this study, we found that stimulation of β1-AR by β1-AA increased the expression of S100a9 and decreased myocardial autophagy. Other studies have shown that the β-AR agonist ISO also increased S100a9 expression in myocardial injury [ 64 ], and autophagy activity was also significantly reduced in myocardial hypertrophy induced by ISO [ 65 , 66 ]. In addition, a previous study showed that atenolol, a selective β 1 -adrenergic receptor antagonist, partially inhibited the expression of S100a9 [ 67 ].…”

Section: Discussionmentioning

confidence: 99%

S100a9 inhibits Atg9a transcription and participates in suppression of autophagy in cardiomyocytes induced by β1-adrenoceptor autoantibodies

Zhi,

Shi,

et al. 2023

Cell Mol Biol Lett

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Background Cardiomyocyte death induced by autophagy inhibition is an important cause of cardiac dysfunction. In-depth exploration of its mechanism may help to improve cardiac dysfunction. In our previous study, we found that β1-adrenergic receptor autoantibodies (β1-AAs) induced a decrease in myocardial autophagy and caused cardiomyocyte death, thus resulting in cardiac dysfunction. Through tandem mass tag (TMT)-based quantitative proteomics, autophagy-related S100a9 protein was found to be significantly upregulated in the myocardial tissue of actively immunized mice. However, whether S100a9 affects the cardiac function in the presence of β1-AAs through autophagy and the specific mechanism are currently unclear. Methods In this study, the active immunity method was used to establish a β1-AA-induced mouse cardiac dysfunction model, and RT-PCR and western blot were used to detect changes in gene and protein expression in cardiomyocytes. We used siRNA to knockdown S100a9 in cardiomyocytes. An autophagy PCR array was performed to screen differentially expressed autophagy-related genes in cells transfected with S100a9 siRNA and negative control siRNA. Cytoplasmic nuclear separation, co-immunoprecipitation (Co-IP), and immunofluorescence were used to detect the binding of S100a9 and hypoxia inducible factor-1α (HIF-1α). Finally, AAV9-S100a9-RNAi was injected into mice via the tail vein to knockdown S100a9 in cardiomyocytes. Cardiac function was detected via ultrasonography. Results The results showed that β1-AAs induced S100a9 expression. The PCR array indicated that Atg9a changed significantly in S100a9siRNA cells and that β1-AAs increased the binding of S100a9 and HIF-1α in cytoplasm. Knockdown of S100a9 significantly improved autophagy levels and cardiac dysfunction. Conclusion Our research showed that β1-AAs increased S100a9 expression in cardiomyocytes and that S100a9 interacted with HIF-1α, which prevented HIF-1α from entering the nucleus normally, thus inhibiting the transcription of Atg9a. This resulted in autophagy inhibition and cardiac dysfunction.

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“…The treatment of mice with high doses of isoproterenol induces, however, myocardial necrosis, hypertrophy, fibrosis, and left ventricular dysfunction. These changes are associated with increased apoptosis and autophagic flux in the cardiac tissue [ 145 , 146 , 147 , 148 ]. In the isoproterenol model, a study reporting the cardioprotective effect of the CD47 antibody pointed to reduced apoptosis in the case of increased expression of autophagic markers [ 147 ].…”

Section: Autophagy In Cardiac Injurymentioning

confidence: 99%

Autophagy in Heart Failure: Insights into Mechanisms and Therapeutic Implications

Bielawska,

Warszyńska,

Stefańska

et al. 2023

JCDD

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Autophagy, a dynamic and complex process responsible for the clearance of damaged cellular components, plays a crucial role in maintaining myocardial homeostasis. In the context of heart failure, autophagy has been recognized as a response mechanism aimed at counteracting pathogenic processes and promoting cellular health. Its relevance has been underscored not only in various animal models, but also in the human heart. Extensive research efforts have been dedicated to understanding the significance of autophagy and unravelling its complex molecular mechanisms. This review aims to consolidate the current knowledge of the involvement of autophagy during the progression of heart failure. Specifically, we provide a comprehensive overview of published data on the impact of autophagy deregulation achieved by genetic modifications or by pharmacological interventions in ischemic and non-ischemic models of heart failure. Furthermore, we delve into the intricate molecular mechanisms through which autophagy regulates crucial cellular processes within the three predominant cell populations of the heart: cardiomyocytes, cardiac fibroblasts, and endothelial cells. Finally, we emphasize the need for future research to unravel the therapeutic potential associated with targeting autophagy in the management of heart failure.

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Trim65 attenuates isoproterenol-induced cardiac hypertrophy by promoting autophagy and ameliorating mitochondrial dysfunction via the Jak1/Stat1 signaling pathway

Cited by 12 publications

References 31 publications

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

S100a9 inhibits Atg9a transcription and participates in suppression of autophagy in cardiomyocytes induced by β1-adrenoceptor autoantibodies

Autophagy in Heart Failure: Insights into Mechanisms and Therapeutic Implications

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