| Background:Atrial fibrillation(AF)is the most common rapid arrhythmia in the clinic,with increasing incidence year by year.AF can increase mortality,stroke risk,and peripheral thromboembolism risk,leading to depression and cognitive impairment,which has great harmfulness.Although there have been a large number of basic and clinical studies on AF,its pathogenesis has not yet been fully clarified,and the therapeutic effects are unsatisfactory.At present,the medical treatment of AF includes drug therapy and catheter ablation,but they all have some limitations.The pathogenesis of AF mainly include atrial electrical remodeling,structural remodeling,and autonomic nerve remodeling.Among them,atrial structural remodeling is the matrix factor for the occurrence and maintenance of AF,mainly manifested in significant myocardial fibrosis.Therefore,further study on the molecular mechanism of AF atrial fibrosis is of great significance for the effective prevention and treatment of AF.The susceptibility of AF has been showed to be related to genetics.Over the past decade it has been found that AF,lone AF in particular,is strongly genetically influenced.It also lays a foundation for AF risk assessment and mechanism research based on genetics and epigenomics.Based on this,the occurrence and maintenance of AF,from single nucleotide polymorphism at the DNA level,to transcription and regulation at the RNA level,and then to translation and modification at the protein level,is bound to have a very complex molecular biological mechanism,which also provides the possibility to find the hub genes and signal pathways of AF.Omics research at all levels of AF is increasing,and with the accumulation of a large number of research data,the application of gene expression profiling analysis of multiple queues and large samples to identify hub genes,signal pathways and biomarkers will be more reliable,and is expected to provide new ideas for the study of the mechanism of AF and provide a new theoretical basis for its prevention and treatment.Aims:In this study,the high-throughput gene expression profile data of multiple groups of AF patients were integrated and analyzed to identify differentially expressed genes and hub genes.Then the external data set validation,cell model validation and clinical serum sample validation were carried out.The cluster analysis of hub genes was conducted to propose the molecular mechanism by which IGF-1R regulates myocardial fibrosis in AF through the PI3K/Akt/FoxO3a pathway.This mechanism was then verified in human cardiac fibroblasts and rats transfected with IGF-1 overexpression type 9 adeno-associated viruses.Methods:1.Screening hub genes based on gene expression databaseData on AF patients were retrieved from the Gene Expression Omnibus(GEO)database(http://www.ncbi.nlm.nih.gov/geo/).The original data of the chip was downloaded and standardized RMA-background correction method was used.Genes with expression multiples of 1.3 and FDR<0.05 were selected.The Limma R package was used to screen differentially expressed genes(DEGs)between the samples.The R software package edgR was used to screen the DEGs between the samples in the RNA-seq dataset.Weighted gene co-expression network analysis(WGCNA)was used for constructing a scale-free co-expression network of DEGs,and the adjacency was converted to a topological overlap matrix(TOM)that measures the network connectivity of a DEG defined as the sum of its adjacency with all other DEGs.The DEGs with similar expression profiles were classified into DEGs modules using average-linkage hierarchical clustering according to the TOM-based dissimilarity.To further analyze the modules,we calculated the dissimilarity of module eigen DEGs,determined a cut line for module dendrogram and merged some modules.Pathway enrichment analysis based on Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)was performed using the clusterProfiler for DEGs,overrepresented GO terms and KEGG pathway,which were then visualized using the R package GOplot.For both analyses,a FDR of<0.05 was considered to indicate a statistical significance.Protein-protein interaction(PPI)network was established by retrieving the interaction data of AF specific genes in HIPPIE,BIOGRID,BioPlex,CCSB and HINT databases.Cytoscape software was used for visualization,and cytoHubba was used to identify hub genes from PPI networks.The top nodes with the highest degree,closeness and EPC was chosed as the hub genes.2.Verification of hub genesValidation of hub genes in external datasets:Hub genes were used to construct a diagnostic model based on support vector machines(SVM)classification to predict AF and SR samples.The model was constructed in the training data set,and the classification performance of the model was verified by using the ten-fold cross validation method.The established model was then used to predict the samples in the validated data set,and the predictive performance of the model was examined by area under the ROC curve(AUC).Validation of hub genes in AF cell model:The heart of newborn Wistar rats was taken,and the primary cardiomyocytes were isolated and cultured to construct the experimental AF cell model by stimulating with high-frequency electric field(10 Hz,1.5 V/cm).After 72 h,the total RNA was extracted and RNA concentration was measured using a spectrophotometer.RNA was converted to cDNA and qRT-PCR was performed to obtain the relative expression levels of the target genes,which were calculated using the 2-ΔΔCT method.Validation of hub genes in clinical serum samples:Patients with AF or without AF were recruited and signed informed consent.Three milliliter of venous blood was extracted from patients and the serum was centrifuged.The protein expression level of hub genes in serum samples were detected by enzyme-linked immunosorbent assay(Elisa)kit.The absorbance was measured with an enzyme labeling instrument with a wavelength of 450 nm,and the serum concentration was calculated.3.Prediction of key signal pathwaysHub genes were input into the DAVID online analysis platform,then GO function and KEGG pathway enrichment analyses were performed.The annotation information of function and pathway with statistical significance was obtained.Taking P<0.05 as the threshold.Then PPI network analysis was conducted and a comprehensive score>0.40 was set as the threshold.The interactions between the proteins encoded by the hub genes were evaluated in the STRING database,and the genes with a high degree of interaction(Degree)were screened for further research.4.Validation of key signaling pathways in vitro and in vivoValidation in vitro:Taking human cardiac fibroblasts(HCF)as the research object,exogenous IGF-1R agonists(IGF-1),IGF-1R inhibitors(AG1024),PI3K inhibitors(LY294002)and FoxO3a SiRNA were used for intervention and transfection.The activity of HCF was detected by cell counting kit(CCK)-8 method,and the protein was extracted for Western Blotting to verify the expression difference of target genes at protein level.Validation in vivo:The type 9 adeno-associated viruses(AAV),including overexpressed IGF-1,were constructed to infect Wistar rats in vivo.The IGF-1 overexpression model was constructed,and LY294002 was injected intraperitoneally.After 4 weeks of intervention,the rats underwent electrophysiological examination to detect the atrial effective refractory period(AERP)and AF inducibility.Then,the left atrial samples were taken.The tissue RNA was extracted for qRT-PCR,and the tissue protein was extracted for Western blotting to verify the expression difference of target genes at the level of RNA and protein.As well,Masson staining was used to evaluate myocardial fibrosis.5.Statistical analysisThe baseline characteristics of statistical analysis were descriptive statistics.All numerical variables are presented as the mean± standard deviation.T-test was used to determine differences between the two groups,while ANOVA was used to determine differences among multiple groups.Categorical variables are presented as counts and percentages using chi-square test.Statistical analysis was conducted with IBM SPSS Statistics 23.0 and GraphPad Prism 8 software;The P<0.05 was considered as statistically significant and P<0.01 as highly significant.Results:1.Screening of AF hub genesFour data sets(GSE128188,GSE2240,GSE79768 and GSE41177)were determined based on the GEOs screened,with consistent data distribution after standardization.GSE128188 and GSE79768 datasets were selected for DEGs filtering.The analysis on the DEGs identified a total of 3208 DEGs in GSE128188 dataset and 3072 DEGs in GSE79768 dataset.WGCNA construction on datasets GSE128188 and GSE79768 were performed to better identify AF-related genes.The "WGCNA" package in R was used to group DEGs with similar expression patterns into modules through average cascading of hierarchical clustering.In the GSE128188 dataset,a total of 20 modules were identified.In the GSE79768 dataset,a total of 20 modules were identified.Select the gene module with the highest AF correlation in the two datasets,and take the intersection as the AF specific expression gene set,which contains 360 genes.GO and KEGG functional enrichment analysis was performed on the 360 genes associated with the AF-specific expressed genes to better understand their functional involvement in AF.These genes were enriched in 5 KEGG pathways and 12 GO gene clusters.A total of 71 AF-specific expressed genes were included in these significantly enriched pathways.PPI network of 360 genes was constructed and visualized using Cytoscape.The 360 DEGs were mapped to 217 nodes in the network.The top 10 nodes with the highest degree,closeness and EPC were choose as the hub genes of the channel network,including PLEKHA7,YWHAQ,PPP1CB,WDR1,AKT1,IGF1R,CANX,MAPK1,SRPK2,and SRSF10.2.Verification of hub genesSVM algorithm was applied to verify the external data set.The expression differences of the 10 candidate genes in AF and SR were compared.Gene expressions were relatively consistent expression in datasets GSE128188 and GSE79768,in which YWHAQ(P<0.01&P<0.05),PPP1CB(P<0.05&P<0.05),AKT1(P<0.05&P<0.01)and SRPK2(P<0.01&P<0.001)were found significantly high-expressed in AF.The four genes were selected as the classification features and GSE79768(n=26)was used as the training data set to construct the classification and prediction model of AF.Then take GSE128188(n=20),GSE41177(n=12)and GSE2240(n=19)as the validation data set,use the established model to predict the samples in the validation data set,and evaluation of AUC suggests a better classification performance(AUC>70%).Primary rat cardiomyocytes were stimulated by high-frequency electric field(10 Hz,1.5 V/cm).After 72 h,RNA was extracted and verified by qRT-PCR.The results showed that the transcription levels of YWHAQ(P<0.001),SRPK2(P<0.001),AKT1(P<0.01),and PPP1CB(P<0.001)in the high-frequency electric field stimulation group were significantly increased.Thirty-one patients with AF were recruited,including 18 patients with paroxysmal AF and 13 patients with persistent AF.Thirty patients with sinus rhythm(SR)were recruited as also.The left atrial diameter(LAD)in the AF group was significantly larger than that in the SR group.There was no statistical difference in other indicators,including sex,age,left ventricular diameter,left ventricular ejection fraction(LVEF),smoking,unstable angina,old myocardial infarction,coronary stent implantation,diabetes,and hypertension.The serum levels of IGF-1R and AKT1 were determined by Elisa kit.The results showed no significant difference in the IGF-1R concentration between the two groups,while the concentration of AKT1 was significantly increased in patients with AF(P<0.05).3.Prediction of key signaling pathways and genes of AF through bioinformatics analysisGO and KEGG enrichment analyses of hub genes were conducted using the DAVID online analysis tool.The PPI network was constructed through the STRING database.After enrichment analysis and PPI network analysis of hub genes,we speculated that IGF-1R affects AF through the PI3K/Akt/FoxO3a pathway.The signal axis involves the hsa04151:PI3K-Akt signaling pathway and the hsa04068:FoxO signaling pathway,and the related genes include IGF1R(Degree=2),AKT1(Degree=6)and YWHAQ(Degree=5).4.IGF-1R promote myocardial fibrosis by activating PI3K/Akt/FoxO3a pathway in vitroThe agonist(IGF-1)and inhibitor(AG1024)of IGF-1R were used to intervene HCF to investigate the effect of IGF-1R on atrial fibrosis.The CCK-8 results showed that the proliferation and viability of HCF increased after 48 h of intervention with different concentrations of IGF-1,but there was no significant difference.After combining with 20 μM AG1024 intervention,the proliferation and activity of HCF were significantly reduced in each group compared to IGF-1 intervention alone.The expression of fibrosis associated protein collagen I were significantly upregulated by IGF-1,and AG1024 reversed this phenomenon.The expression level of pAkt and the expression ratio of pAkt/Akt in the IGF-1 intervention group were significantly upregulated.The pAkt expression level and pAkt/Akt expression ratio decreased after combined with LY294002 intervention,simultaneously,LY294002 reversed the up-regulation effect of IGF-1 on collagen I.The expression of FoxO3a was significantly upregulated by IGF-1.FoxO3a expression was significantly down-regulated after transfection with FoxO3a siRNA,while the anti-fibrosis effect of LY294002 was reversed.The results revealed that IGF-1 R promotes HCF fibrosis through the PI3K/Akt/FoxO3a pathway.5.Activation of IGF-1R shorten the atrial AERP,increase the incidence of AF and promotes atrial fibrosis in ratsElectrophysiological examination and Masson staining revealed that the AERP was significantly shortened,incidence of AF and paroxysmal atrial tachycardia(PAT)was increased,and the degree of atrial fibrosis was worse in the IGF-1 overexpression group.When combined with LY294002 intervention,the AERP was prolonged,the incidence of AF and PAT was reduced,and the degree of fibrosis was reversed.qRT-PCR showed that the transcription level of collagen I in the IGF-1 overexpression group was significantly increased,while LY294002 intervention reversed this phenomenon.Western Blotting showed that the expression level of pAkt,the expression ratio of pAkt/Akt,and the level of collagen I in the IGF-1 overexpression group were significantly upregulated.When combined with LY294002 intervention,the pAkt expression level and pAkt/Akt expression ratio were decreased,and the up-regulation effect of IGF-1 on collagen I was reversed.Conclusions:1.There are obvious gene expression profile changes in atrial tissue of patients with AF.This study integrated and analyzed the change characteristics based on multiple data sets,and identified PLEKHA7,YWHAQ,PPP1CB,WDR1,AKT1,IGF1R,CANX,MAPK1,SRPK2 and SRSF10 as the hub genes of AF,which may play an important role in the occurrence and maintenance of AF.2.External data set validation,AF cell model validation and clinical serum samples validation confirmed that YWHAQ,PPP1CB,AKT1 and SRPK2 are not only hub genes of AF,but also show obvious expression differences.3.IGF-1R is closely related to atrial structural remodeling of AF.Through in vitro and in vivo experiments,it is confirmed that IGF-1R promotes myocardial fibrosis and facilitates the occurrence and maintenance of AF by activating PI3K/Akt/FoxO3a signal pathway. |
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