| Background:Heart failure is a serious manifestation and final stage of various heart diseases,The most frequent clinical presentations of which are dyspnea,decrease exercise tolerance and edema.Although new drugs of heart failure are emerging,the morbidity,mortality and readmission rate remain high,which brings a heavy burden to the patient’s family and health care system.In recent years,with the rapid development of gene chip and high-throughput sequencing technology,people began to explore the pathogenesis of heart failure at the genetic level,and found many key genes and signal pathways in the occurrence and development of heart failure,which promoted the rise of molecular target therapy of heart failure.As a key part of post-transcriptional regulation,alternative splicing is closely related to these key genes and signal pathways.Mining key genes and signal pathways of heart failure by using public database,and finding abnormal alternative splicing forms and regulation modes in the process of pathological changes of heart failure will help us find potential markers of heart failure,and provide a new target for functional research and drug development of heart failure.The purpose of this study is to find the hub genes in the protein-protein interaction network of differentially expressed genes in heart failure,to construct the regulatory network of alternative splicing in heart failure,and finally to explore the mechanism of the key RNA binding protein in heart failure.Part I: Data mining of differentially expressed genes in heart failureObjectives: To screen out differentially expressed genes of heart failure,and to find the hub genes in the protein-protein interaction network of differentially expressed genes in heart failure.Methods:(1)The high throughput sequencing data set of transcriptome in heart failure was screened and downloaded from GEO database.After data quality control,sequence alignment and fragment counting,the expression matrix of each data set was obtained.(2)Standardize the expression matrix,screen the differentially expressed genes between heart failure and non-heart failure samples,and use robust rank aggregation to integrate differentially expressed genes in all data sets.(3)The function and pathway enrichment analysis was performed to determine the significant enriched pathway of the differential genes.(4)The protein-protein interaction network of differentially expressed genes in heart failure is constructed,and the hub genes in the network are calculated according to certain topological algorithms.(5)Identification of hub genes in gene chip data of heart failure in GEO database using bioinformatics method.(6)Identification of hub genes in fourteen human heart tissues obtained from Chang Hai Hospital by q PCR.Results:(1)We downloaded the heart failure transcriptome sequencing data from GEO database.After data quality control and validation,we finally included the dataset of GSE46224,GSE116250,GSE133054 and GSE135055 for further analysis.There were100 samples of heart failure and 38 samples of non-heart failure donors in the four datasets.(2)The robust rank aggregation algorithm was used to integrate the differentially expressed genes in each dataset,and the software Cytoscape with plug-in cyto Hubba were used to determine that the hub genes were ASPN,COL1A1,COL9A2,COL10A1,COMP and FMOD.(3)Bioinformatics validation was carried out using the gene chip data of heart failure in GEO database.The histogram and ROC curve showed that ASPN,COL1A1,COMP and FMOD had significant differences in expression and good diagnostic efficiency in the four datasets.(4)Using human heart tissues from patients with heart failure and non-heart failure donors in Chang Hai Hospital,the histogram showed that there were significant differences in the expression of ASPN,COL1A1 and FMOD.Conclusions: ASPN,COL1A1 and FMOD are the hub genes in the protein-protein interaction network of differentially expressed genes in heart failure.Part II: Construction of m RNA alternative splicing regulatory network in heart failure based on bioinformaticsObjectives: To construct a network of RNA-binding proteins regulating alternative splicing in heart failure,and to screen the subnetworks which play an important role.Methods: Firstly,the events of differential splicing between heart failure and non-heart failure samples were analyzed,then the RNA-binding proteins related to these alternative splicing events were screened.Afterward,the genes with different splicing patterns after RNA-binding protein knockdown in ENCODE database were intersected to obtain the network of RNA-binding proteins regulating alternative splicing in heart failure.Finally,the RNA-binding proteins and alternative splicing events with high correlation with the hub genes in part I were screened in the network,so as to obtain the key subnetworks in the regulatory network.Results: The subnetwork related to ASPN is SRSF3,TRADBP and the alternative splicing events they regulate,the subnetwork related to COL1A1 is SRSF3,IGF2BP2 and the alternative splicing events they regulate,and the subnetwork related to FMOD is SRSF3,RBFOX2 and the alternative splicing events they regulate.SRSF3 exists in all three subnetworks.Conclusions: SRSF3 and the its regulation of alternative splicing events play a key role in heart failure.Part III: Exploring the function and mechanism of SRSF3 in heart failureObjectives: To preliminarily explore the molecular mechanism by which SRSF3 exerts its effect in heart failure.Methods:(1)Using Western Blot to detect the difference of SRSF3 expression between cardiomyocytes and non-cardiomyocytes.(2)qPCR and Western Blot were used to detect whether the expression of SRSF3 in rat cardiomyocyte hypertrophy model,the myocardial tissue of rat transverse aortic constriction model and patients with dilated heart disease were significantly different compared with the control group.(3)The siRNA of SRSF3 was constructed and transfected into rat cardiomyocytes,and Western Blot was used to detect the expression of related proteins in MAPK signal pathway and TGF-β signal pathway in SRSF3 knockdown rat cardiomyocytes.Results:(1)The results of Western Blot showed that the expression of SRSF3 in rat cardiomyocytes,human cardiomyocytes(AC16)and rat embryonic cardiomyocytes(H9c2)was significantly higher than that in human pulmonary artery smooth muscle cells,human aortic smooth muscle cells and human coronary artery endothelial cells.(2)Compared with the control group,the m RNA level of SRSF3 in the rat cardiomyocyte hypertrophy model had no statistical difference,but the protein level decreased significantly.(3)Compared with the sham group,the level of SRSF3 protein in the myocardial tissue of rats undergoing transverse aortic constriction was significantly lower than that in the sham group.(4)The m RNA and protein levels of SRSF3 in the myocardial tissue of patients with dilated cardiomyopathy were significantly lower than those of non-heart failure donors.(5)SRSF3 can increase the level of AKT phosphorylation in MAPK signal pathway of cardiomyocytes.(6)SRSF3 can increase the level of Smad2 phosphorylation in TGF-β signal pathway of cardiomyocytes.Conclusions:(1)SRSF3 has a high expression level in cardiomyocytes..(2)Compared with the control group,the expression of SRSF3 decreased significantly in the rat cardiomyocyte hypertrophy model,the myocardial tissue of rats undergoing transverse aortic constriction and the myocardial tissue of patients with dilated cardiomyopathy.(3)SRSF3 participates in the occurrence and development of heart failure by increasing the phosphorylation level of AKT in MAPK signal pathway and Smad2 in TGF-β signal pathway. |
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