Adeno-associated virus 9-mediated RNA interference targeting SOCS3 alleviates diastolic heart failure in rats

Regulatory network analysis based on integrated miRNA-TF reveals key genes in heart failure

The etiology and pathophysiology of heart failure are still unknown. Increasing evidence suggests that abnormal microRNAs (miRNAs) and transcription factors (TFs) expression may be associated with the development of heart failure. Therefore, this study aims to explore key miRNAs, TFs, and related genes in heart failure to gain a greater understanding of the pathogenesis of heart failure. To search and download the dataset of mRNA chips related to heart failure from the GEO database (GSE59867, GSE9128, and GSE134766), we analyzed differential genes and screened the common differentially expressed genes on two chips using R language software. The binary interactions and circuits among miRNAs, TFs, and corresponding genes were determined by Pearson correlation coefficient. A regulatory network of miRNAs, TFs, and target genes was constructed based on bioinformatics. By comparing the sequences of patients with and without heart failure, five downregulated genes with hypermethylated mRNA and three upregulated genes with hypomethylated mRNA were identified. The miRNA-TF gene regulatory network consisted of 26 miRNAs, 22 TFs and six genes. GO and KEGG analysis results revealed that BP terms like cellular response to organic substance, cellular response to cytokine stimulus, and KEGG pathways like osteoclast differentiation, MAPK signaling pathway, and legionellosis were enriched of the DEGs. TMEM87A, PPP2R2A, DUSP1, and miR-92a have great potential as biomarkers for heart failure. The integrated analysis of the mRNA expression spectrum and microRNA-transcription factor-gene revealed the regulatory network of heart failure, which may provide clues to its alternative treatment.

CHI3L1 promotes myocardial fibrosis via regulating lncRNA TUG1/miR-495-3p/ETS1 axis

Abnormal levels of CHI3L1 and lnc TUG1 are often associated with myocardial fibrosis, and their specific expressions may be closely related to the process of myocardial fibrosis. In addition, CHI3L1 was found to significantly up-regulate the expression of lncTUG1. Therefore, this study further explored the major role of CHI3L1 in regulating the progression of myocardial fibrosis. Myocardial fibrosis in mice was established using an angiotensin (Ang II) model, and the degree of myocardial fibrosis was assessed by qPCR, western blot and pathological techniques. HL-1 cells with overexpression and silencing of CHI3L1 were constructed, and the cell migration ability was detected using the Transwell method. Biological information was used to predict the potential target miRNA of lnc TUG1, and the interaction between them was verified by dual luciferase reporter assay. Using functional rescue assay and the rAAV9 technique, CHI3L1 was verified to affect the fibrotic process of myocardial cells by regulating the lnc TUG1/miR-495-3p/ETS1 axis in vitro and in vivo. The myocardial fibrosis index in the model group was significantly upregulated, and expression of both CHI3L1 and lnc TUG1 was upregulated. Pathological results revealed fibrosis and collagen deposition in the myocardium. Overexpression of lnc TUG1 reversed the inhibitory effect of CHI3L1 silencing on myocardial fibrosis. Mechanistically, CH3L1 upregulates the expression of lnc TUG1, and lnc TUG1 weakens the inhibition of ETS1 through sponge absorption of miR-495-3p, promoting the process of myocardial fibrosis.

NRF2 Inhibits Cardiomyocyte Pyroptosis Via Regulating CTRP1 in Sepsis-Induced Myocardial Injury

ABSTRACT

C1q/tumor necrosis factor-related protein 1 (CTRP1) has been demonstrated as a crucial regulator in myocardial injury (MI). The present study aims to evaluate the mechanism of CTRP1 in sepsis-induced MI. The septic mouse model was established via cecal ligation and puncture and the in vitro cell model was established via lipopolysaccharide treatment. The mouse survival rate within 96 h was recorded. Morphologic changes of cardiomyocytes were observed and cell viability and cardiac functions were detected. CTRP1 and nuclear factor erythroid 2-related factor (Nrf2) expressions, creatine troponin-T, and creatine phosphokinase isoenzyme levels, and expressions of pyroptotic markers were determined. The binding relationship between Nrf2 and the CTRP1 promotor was predicted and verified. Rescue experiments were designed to confirm the role of CTRP1. CTRP1 was poorly expressed in septic mice. CTRP1 overexpression inhibited cardiomyocyte pyroptosis and improved cardiac functions, MI, and survival rate in septic mice. Nrf2was decreased in cecal ligation and puncture -treated mice. Nrf2 overexpression promoted CTRP1 expression via binding to the CTRP1 promotor and suppressed cardiomyocyte pyroptosis. CTRP1 downregulation abolished the inhibitory effect of Nrf2 overexpression on cardiomyocyte pyroptosis. Overall, Nrf2 promoted CTRP1 expression via binding to the CTRP1 promotor to inhibit cardiomyocyte pyroptosis, thereby alleviating MI in septic mice.

Activated Protein C Ameliorates Diabetic Cardiomyopathy via Modulating OTUB1/YB-1/MEF2B Axis

Aims: The pathogenesis of diabetic cardiomyopathy (DCM) is complex and the detailed mechanism remains unclear. Coagulation protease activated Protein C (aPC) has been reported to have a protective effect in diabetic microvascular disease. Here, we investigated whether aPC could play a protective role in the occurrence and development of major diabetic complication DCM, and its underlying molecular mechanism.

Methods and Results: In a mouse model of streptozotocin (STZ) induced DCM, endogenous aPC levels were reduced. Restoring aPC levels by exogenous administration of zymogen protein C (PC) improved cardiac function of diabetic mice measured by echocardiography and invasive hemodynamics. The cytoprotective effect of aPC in DCM is mediated by transcription factor Y-box binding protein-1 (YB-1). Mechanistically, MEF2B lies downstream of YB-1 and YB-1/MEF2B interaction restrains deleterious MEF2B promoter activity in DCM. The regulation of YB-1 on MEF2B transcription was analyzed by dual-luciferase and chromatin immunoprecipitation assays. In diabetic mice, aPC ameliorated YB-1 degradation via reducing its K48 ubiquitination through deubiquitinating enzyme otubain-1 (OTUB1) and improving the interaction between YB-1 and OTUB1. Using specific agonists and blocking antibodies, PAR1 and EPCR were identified as crucial receptors for aPC's dependent cytoprotective signaling.

Conclusion: These data identify that the cytoprotective aPC signaling via PAR1/EPCR maintains YB-1 levels by preventing the ubiquitination and subsequent proteasomal degradation of YB-1 via OTUB1. By suppressing MEF2B transcription, YB-1 can protect against DCM. Collectively, the current study uncovered the important role of OTUB1/YB-1/MEF2B axis in DCM and targeting this pathway might offer a new therapeutic strategy for DCM.

Translational Perspective: DCM is emerging at epidemic rate recently and the underlying mechanism remains unclear. This study explored the protective cell signaling mechanisms of aPC in mouse models of DCM. As a former FDA approved anti-sepsis drug, aPC along with its derivatives can be applied from bench to bed and can be explored as a new strategy for personalized treatment for DCM. Mechanistically, OTUB1/YB-1/MEF2B axis plays a critical role in the occurrence and development of DCM and offers a potential avenue for therapeutic targeting of DCM.

SOCS3 deficiency in cardiomyocytes elevates sensitivity of ischemic preconditioning that synergistically ameliorates myocardial ischemia reperfusion injury

Ischemic preconditioning (IPC) is the most powerful endogenous cardioprotective form of cellular adaptation. However, the inhibitory or augmenting mechanism underlying cardioprotection via IPC remains largely unknown. Suppressor of cytokine signaling-3 (SOCS3) is a cytokine-inducible potent negative feedback regulator of the signal transducer and activator of transcription-3 (STAT3) signaling pathway. Here, we aimed to determine whether cardiac SOCS3 deficiency and IPC would synergistically reduce infarct size after myocardial ischemia reperfusion injury. We evaluated STAT3 activation and SOCS3 induction after ischemic conditioning (IC) using western blot analysis and real-time PCR, and found that myocardial IC alone transiently activated myocardial STAT3 and correspondingly induced SOCS3 expression in wild-type mice. Compared with wild-type mice, cardiac-specific SOCS3 knockout (SOCS3-CKO) mice showed significantly greater and more sustained IC-induced STAT3 activation. Following ischemia reperfusion, IPC substantially reduced myocardial infarct size and significantly enhanced STAT3 phosphorylation in SOCS3-CKO mice compared to in wild-type mice. Real-time PCR array analysis revealed that SOCS3-CKO mice after IC exhibited significantly increased expressions of several anti-apoptotic genes and SAFE pathway-related genes. Moreover, real-time PCR analysis revealed that myocardial IC alone rapidly induced expression of the STAT3-activating cytokine erythropoietin in the kidney at 1 h post-IC. We also found that the circulating erythropoietin level was promptly increased at 1 h after myocardial IC. Myocardial SOCS3 deficiency and IPC exert synergistic effects in the prevention of myocardial injury after ischemia reperfusion. Our present results suggest that myocardial SOCS3 is a potent inhibitor of IPC-induced cardioprotection, and that myocardial SOCS3 inhibition augment IPC-mediated cardioprotection during ischemia reperfusion injury.

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

INTRODUCTION

Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules.

METHODS

The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein-protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene-miRNA regulatory network and target gene-TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed.

RESULTS

A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene-miRNA regulatory network and target gene-TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed.

CONCLUSION

The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

Identification of Genetic Biomarkers for Diagnosis of Myocardial Infarction Compared with Angina Patients

Background

Myocardial infarction (MI) is the most terrible appearance of cardiovascular disease. The incidence of heart failure, one of the complications of MI, has increased in the past few decades. Therefore, the identification of MI from angina patients and the determination of new diagnoses and therapies of MI are increasingly important. The present study was aimed at identifying differentially expressed genes and miRNAs as biomarkers for the clinical and prognosis factors of MI compared with angina using microarray data analysis.

Methods

Differentially expressed miRNAs and genes were manifested by GEO2R. The biological function of differentially expressed genes (DEGs) was examined by GO and KEGG. The construction of a protein-protein network was explored by STRING. cytoHubba was utilized to screen hub genes. Analysis of miRNA-gene pairs was executed by the miRWalk 3.0 database. The miRNA-target pairs overlapped with hub genes were seen as key genes. Logistic regressive analysis was performed by SPSS.

Results

A number of 779 DEGs were recorded. The biological function containing extracellular components, signaling pathways, and cell adhesion was enriched. Twenty-four hub genes and three differentially expressed miRNAs were noted. Eight key genes were demonstrated, and 6 out of these 8 key genes were significantly related to clinical and prognosis factors following MI.

Conclusions

CALCA, CDK6, MDM2, NRXN1, SOCS3, VEGFA, SMAD4, NCAM1, and hsa-miR-127-5p were thought to be potential diagnosis biomarkers for MI. Meanwhile, CALCA, CDK6, NRXN1, SMAD4, SOCS3, and NCAM1 were further identified to be potential diagnosis and therapy targets for MI.

Identification of key modules and hub genes associated with lung function in idiopathic pulmonary fibrosis

Background

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive interstitial lung disease, characterized by a decline in lung function. To date, the pathophysiologic mechanisms associated with lung dysfunction remain unclear, and no effective therapy has been identified to improve lung function.

Methods

In the present study, we used weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes associated with lung function in IPF. Three datasets, containing clinical information, were downloaded from Gene Expression Omnibus. WGCNA was performed on the GSE32537 dataset. Differentially expressed gene s (DEGs) between IPF patients and healthy controls were also identified to filter hub genes. The relationship between hub genes and lung function was then validated using the GSE47460 and GSE24206 datasets.

Results

The red module, containing 267 genes, was positively correlated with the St. George’s Respiratory Questionnaire score (r = 0.37, p < 0.001) and negatively correlated with the percent predicted forced vital capacity (FVC% predicted) (r =  − 0.46, p < 0.001) and the percent predicted diffusion capacity of the lung for carbon monoxide (Dlco% predicted) (r =  − 0.42, p < 0.001). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis suggested that the genes in the red module were primarily involved in inflammation and immune pathways. Based on Module Membership and Gene Significance, 32 candidate hub genes were selected in the red module to construct a protein-protein interaction network . Based on the identified DEGs and the degree of connectivity in the network, we identified three hub genes, including interleukin 6 (IL6), suppressor of cytokine signaling-3 (SOCS3), and serpin family E member 1 (SERPINE1). In the GSE47460 dataset, Spearman correlation coefficients between Dlco% predicted and expression levels of IL6, SERPINE1, SOCS3 were –0.32, –0.41, and –0.46, respectively. Spearman correlation coefficients between FVC% predicted and expression levels of IL6, SERPINE1, SOCS3 were –0.29, –0.33, and –0.27, respectively. In the GSE24206 dataset, all three hub genes were upregulated in patients with advanced IPF.

Conclusion

We identified three hub genes that negatively correlated with the lung function of IPF patients. Our results provide insights into the pathogenesis underlying the progressive disruption of lung function, and the identified hub genes may serve as biomarkers and potential therapeutictargets for the treatment of IPF patients.