Inherited Cardiomyopathies and the Role of Mutations in Non-coding Regions of the Genome

Salman, Oday; El-Rayess, Hebah M.; Khalil, Charbel Abi; Nemer, Georges; Refaat, Marwan M.

doi:10.3389/fcvm.2018.00077

Cited by 19 publications

(15 citation statements)

References 105 publications

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“…In recent years, several studies (41,42) have investigated the functions and clinical implications of non-coding RNAs in HCM. However, the impact of RNA crosstalk on HCM has not been previously addressed.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis of the regulatory lncRNA‑miRNA‑mRNA network and drug prediction in patients with hypertrophic cardiomyopathy

Zheng

et al. 2019

Mol Med Report

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Hypertrophic cardiomyopathy (HCM) is a complex inherited cardiovascular disease. The present study investigated the long noncoding (lnc)RNA/microRNA (mi)RNA/mRNA expression pattern of patients with HCM and aimed to identify key molecules involved in the development of this condition. An integrated strategy was conducted to identify differentially expressed miRNAs (DEmiRs), differentially expressed lncRNAs (DElncs) and differentially expressed genes (DEGs) based on the GSE36961 (mRNA), GSE36946 (miRNA), GSE68316 (lncRNA/mRNA) and GSE32453 (mRNA) expression profiles downloaded from the Gene Expression Omnibus datasets. Bioinformatics tools were employed to perform function and pathway enrichment analysis, protein-protein interaction, lncRNA-miRNA-mRNA and hub gene networks. Subsequently, DEGs were used as targets to predict drugs. The results indicated that a total of 2,234 DElncs (1,120 upregulated and 1,114 downregulated), 5 DEmiRs (2 upregulated and 3 downregulated) and 42 DEGs (35 upregulated and 7 downregulated) were identified in 4 microarray profiles. Gene ontology analysis revealed that DEGs were mainly involved in actin filament and stress fiber formation and in calcium ion binding, whereas Kyoto Encyclopedia of Genes and Genomes pathway analysis identified the hypoxia inducible factor-1, transforming growth factor-β and tumor necrosis factor signaling pathways as the main pathways involved in these processes. The hub genes were screened using cytoHubba. A total of 1,086 lncRNA-miRNA-mRNA interactions including 67 lncRNAs, 5 miRNAs and 25 mRNAs were mined in the present study based on prediction websites. Drug prediction indicated that the targeted drugs mainly included angiotensin converting enzyme inhibitors or β-blockers. A comprehensive bioinformatics analysis of the molecular regulatory lncRNA-miRNA-mRNA network was performed and potential therapeutic applications of drugs were predicted in HCM patients. The data may unravel the future molecular mechanism of HCM.

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Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis of the regulatory lncRNA‑miRNA‑mRNA network and drug prediction in patients with hypertrophic cardiomyopathy

Zheng

et al. 2019

Mol Med Report

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“…The miRNAs, lncRNAs and TFs that interact with the co-expressed key genes were then screened to obtain a multi-factor regulatory network. To date, several studies have reported the features of ncRNAs in the HOCM (Ntelios et al, 2017;Salman et al, 2018). Nevertheless, the details of RNA crosstalk in HOCM have not been elucidated.…”

Section: Discussionmentioning

confidence: 99%

Analysis of multi-factor regulated network and different clusters in hypertrophic obstructive cardiomyopathy

Huang

Chen

Wen

et al. 2020

Preprint

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Background Hypertrophic obstructive cardiomyopathy (HOCM) is a kind of hypertrophic cardiomyopathy (HCM) with more severe symptoms. We herein explored the regulatory network between lncRNA, miRNAs, mRNAs, and TFs in the HOCM and divided HOCM samples into different clusters to identify key genes and regulatory factors that are involved in the process of HOCM. Methods Differentially expressed genes and online associated genes were integrated as a candidate gene set. WGCNA method was used to obtain key modules and genes related to HOCM. Subsequently, core genes were used to construct a multi-factor regulatory network and divided HOCM into different clusters. Lastly, the key regulatory factors and significant genes were identified in different clusters. Results The four gene sets from GEO, GENE and OMIM databases were integrated and WGCNA network was used to obtain two modules and 32 core genes. Through online database, miRNA-lncRNA, miRNA-mRNA, and TF-mRNA interaction pairs were obtained to screen the regulatory factors that interact with the co-expressed key genes (N = 32), and finally a multi-factor regulatory network with 175 interaction pairs was obtained. The first 7 regulatory factors were obtained as the core regulatory factors, which included the lncRNAs (XIST, MALAT1, H19), TFs (SPI1 and SP1) and miRNAs (hsa-miR-29b-39 and has-miR-29a-3p). Finally, the unsupervised clustering method was used to divide HOCM samples into 4 clusters. As a result, four genes including COMP, FMOD, AEBP1 and SULF1 showed significant expression in different clusters. Conclusion Bioinformatics method was used to obtain the molecular regulatory lncRNA-miRNA-mRNA and TF network and classified HOCM samples into different clusters. Finally, 32 co-expressed key genes that are of great significance for HOCM typing and 4 genes are considered as important biomarkers for different progressive stages or prognosis of HOCM. These might assist in discovering molecular mechanisms of HOCM in the future.

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“…According to Salman et al, 2018, there is a substantial amount of pathogenic intronic mutations, namely about 10%, which may alter splicing mechanisms [ 125 ]. Thereby, insertions or deletions occur which may alter the reading frame and induce a premature stop codon leading to a truncated protein.…”

Section: Molecular Mechanisms In Rcmmentioning

confidence: 99%

“…The resulting contractile dysfunction might impair the structural integrity of sarcomeres and, in concert with altered protein expression and altered interactions with associated proteins as well as altered post-translational modifications, might pivotally contribute to the specific phenotype. In addition, it is thought that modifiers as sex hormones, polymorphisms in other genes (exons and introns) or even other mutations also contribute to the disease phenotype [ 125 , 144 , 145 ]. A specific feature of RCM is protein aggregation.…”

Section: Problems and Prospects In Rcmmentioning

confidence: 99%

Genetic Restrictive Cardiomyopathy: Causes and Consequences—An Integrative Approach

Cimiotti

Budde

Hassoun

et al. 2021

IJMS

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The sarcomere as the smallest contractile unit is prone to alterations in its functional, structural and associated proteins. Sarcomeric dysfunction leads to heart failure or cardiomyopathies like hypertrophic (HCM) or restrictive cardiomyopathy (RCM) etc. Genetic based RCM, a very rare but severe disease with a high mortality rate, might be induced by mutations in genes of non-sarcomeric, sarcomeric and sarcomere associated proteins. In this review, we discuss the functional effects in correlation to the phenotype and present an integrated model for the development of genetic RCM.

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Inherited Cardiomyopathies and the Role of Mutations in Non-coding Regions of the Genome

Cited by 19 publications

References 105 publications

Bioinformatics analysis of the regulatory lncRNA‑miRNA‑mRNA network and drug prediction in patients with hypertrophic cardiomyopathy

Bioinformatics analysis of the regulatory lncRNA‑miRNA‑mRNA network and drug prediction in patients with hypertrophic cardiomyopathy

Analysis of multi-factor regulated network and different clusters in hypertrophic obstructive cardiomyopathy

Genetic Restrictive Cardiomyopathy: Causes and Consequences—An Integrative Approach

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