Disease Modeling and Disease Gene Discovery in Cardiomyopathies: A Molecular Study of Induced Pluripotent Stem Cell Generated Cardiomyocytes

Kumar, Satish; Curran, Joanne E.; Kumar, K. N. Vijaya; DeLeon, Erica; Leandro, Ana C; Peralta, Juan M.; Williams‐Blangero, Sarah; Blangero, John

doi:10.3390/ijms22073311

Cited by 6 publications

(5 citation statements)

References 86 publications

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“…IRF1 could bind to the putative promoter of the lncRNA EPB41L4A-AS1 and PUM2 as observed in the ChIPBase (Figure 10A). Expression of EPB41L4A-AS1 was increased in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) [15,97], cultured cells infected with SARS-CoV-2 [12] and PBMC of COVID-19 patients [14], and expression of PUM2 was also increased [12,15,97],). Thus, IRF1 regulated EPB41L4A-AS1 could bind to DNA/promoter of PUM2 and facilitate the expression of PUM2 by IRF1 (Figure 10A).…”

Section: Coregulation Of Pcgs By Tf and Lncrna That Interacts With Dn...mentioning

confidence: 99%

“…PVT1 interacts with MYC protein as cataloged in the NPInter database, thus the interaction of PVT1 with MYC protein is likely to stabilize the MYC protein, which in turn might increase the expression of PVT1. Both MYC and PVT1 were increased SARS-CoV-2 infected cells in culture ( [12], GSE147507), human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) infected with SARS-CoV-2 [15,97] GSE150392). Thus, in SARS-CoV-2 infected cells, MYC might regulate the expression of PVT1 and there might be a positive feedback loop between PVT1 and MYC.…”

Section: Modification Of Gene Expression Of Pcg By Knocking Down or O...mentioning

confidence: 99%

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Co-Regulation of Protein Coding Genes by Transcription Factor and Long Non-Coding RNA in SARS-CoV-2 Infected Cells: An In Silico Analysis

Saha

Laha

Chatterjee

et al. 2021

ncRNA

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Altered expression of protein coding gene (PCG) and long non-coding RNA (lncRNA) have been identified in SARS-CoV-2 infected cells and tissues from COVID-19 patients. The functional role and mechanism (s) of transcriptional regulation of deregulated genes in COVID-19 remain largely unknown. In the present communication, reanalyzing publicly available gene expression data, we observed that 66 lncRNA and 5491 PCG were deregulated in more than one experimental condition. Combining our earlier published results and using different publicly available resources, it was observed that 72 deregulated lncRNA interacted with 3228 genes/proteins. Many targets of deregulated lncRNA could also interact with SARS-CoV-2 coded proteins, modulated by IFN treatment and identified in CRISPR screening to modulate SARS-CoV-2 infection. The majority of the deregulated lncRNA and PCG were targets of at least one of the transcription factors (TFs), interferon responsive factors (IRFs), signal transducer, and activator of transcription (STATs), NFκB, MYC, and RELA/p65. Deregulated 1069 PCG was joint targets of lncRNA and TF. These joint targets are significantly enriched with pathways relevant for SARS-CoV-2 infection indicating that joint regulation of PCG could be one of the mechanisms for deregulation. Over all this manuscript showed possible involvement of lncRNA and mechanisms of deregulation of PCG in the pathogenesis of COVID-19.

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Section: Coregulation Of Pcgs By Tf and Lncrna That Interacts With Dn...mentioning

confidence: 99%

Section: Modification Of Gene Expression Of Pcg By Knocking Down or O...mentioning

confidence: 99%

Co-Regulation of Protein Coding Genes by Transcription Factor and Long Non-Coding RNA in SARS-CoV-2 Infected Cells: An In Silico Analysis

Saha

Laha

Chatterjee

et al. 2021

ncRNA

View full text Add to dashboard Cite

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“…Three gene expression datasets with GEO accession ids were processed as follows: GSE150392 – RNA seq of Human iPSC-cardiomyocytes infected with SARS-CoV-2. The DEGs were extracted from the published supplementary dataset of the original study [ 101 ]. GSE122903 - RNA-Seq data for global analysis of circRNA-associated ceRNA network for investigating underlying pathogenesis of constrictive pericarditis.…”

Section: Methodsmentioning

confidence: 99%

“…GSE150392 – RNA seq of Human iPSC-cardiomyocytes infected with SARS-CoV-2. The DEGs were extracted from the published supplementary dataset of the original study [ 101 ].…”

Section: Methodsmentioning

confidence: 99%

Network analysis of host-pathogen protein interactions in microbe induced cardiovascular diseases

Singh

Rai

Bhatnagar

et al. 2022

ISB

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Large-scale visualization and analysis of HPIs involved in microbial CVDs can provide crucial insights into the mechanisms of pathogenicity. The comparison of CVD associated HPIs with the entire set of HPIs can identify the pathways specific to CVDs. Therefore, topological properties of HPI networks in CVDs and all pathogens was studied using Cytoscape3.5.1. Ontology and pathway analysis were done using KOBAS 3.0. HPIs of Papilloma, Herpes, Influenza A virus as well as Yersinia pestis and Bacillus anthracis among bacteria were predominant in the whole (wHPI) and the CVD specific (cHPI) network. The central viral and secretory bacterial proteins were predicted virulent. The central viral proteins had higher number of interactions with host proteins in comparison with bacteria. Major fraction of central and essential host proteins interacts with central viral proteins. Alpha-synuclein, Ubiquitin ribosomal proteins, TATA-box-binding protein, and Polyubiquitin-C &B proteins were the top interacting proteins specific to CVDs. Signaling by NGF, Fc epsilon receptor, EGFR and ubiquitin mediated proteolysis were among the top enriched CVD specific pathways. DEXDc and HELICc were enriched host mimicry domains that may help in hijacking of cellular machinery by pathogens. This study provides a system level understanding of cardiac damage in microbe induced CVDs.

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“…Dataset related to SARS-CoV-2 infected cardiomyocytes was obtained from the Gene Expression Omnibus (GEO) datasets (https://www.ncbi.nlm.nih.gov/gds/) with accession number GSE150392(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150392) [21,22] which from GPL18573 Illumina NextSeq 500 (Homo sapiens). There were 6 groups of the GSE150392 dataset, including SARS-CoV-2 infected human induced pluripotent stem cellderived cardiomyocytes (hiPSC-CMs) groups (n = 3) and Mock hiPSC-CMs (n = 3) groups.…”

Section: Rna-sequencing Data Collectionmentioning

confidence: 99%

Identification of critical genes and molecular pathways in COVID-19 myocarditis and constructing gene regulatory networks by bioinformatic analysis

Zhang

et al. 2022

PLoS ONE

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Background There is growing evidence of a strong relationship between COVID-19 and myocarditis. However, there are few bioinformatics-based analyses of critical genes and the mechanisms related to COVID-19 Myocarditis. This study aimed to identify critical genes related to COVID-19 Myocarditis by bioinformatic methods, explore the biological mechanisms and gene regulatory networks, and probe related drugs. Methods The gene expression data of GSE150392 and GSE167028 were obtained from the Gene Expression Omnibus (GEO), including cardiomyocytes derived from human induced pluripotent stem cells infected with SARS-CoV-2 in vitro and GSE150392 from patients with myocarditis infected with SARS-CoV-2 and the GSE167028 gene expression dataset. Differentially expressed genes (DEGs) (adjusted P-Value <0.01 and |Log2 Fold Change| ≥2) in GSE150392 were assessed by NetworkAnalyst 3.0. Meanwhile, significant modular genes in GSE167028 were identified by weighted gene correlation network analysis (WGCNA) and overlapped with DEGs to obtain common genes. Functional enrichment analyses were performed by using the "clusterProfiler" package in the R software, and protein-protein interaction (PPI) networks were constructed on the STRING website (https://cn.string-db.org/). Critical genes were identified by the CytoHubba plugin of Cytoscape by 5 algorithms. Transcription factor-gene (TF-gene) and Transcription factor-microRibonucleic acid (TF-miRNA) coregulatory networks construction were performed by NetworkAnalyst 3.0 and displayed in Cytoscape. Finally, Drug Signatures Database (DSigDB) was used to probe drugs associated with COVID-19 Myocarditis. Results Totally 850 DEGs (including 449 up-regulated and 401 down-regulated genes) and 159 significant genes in turquoise modules were identified from GSE150392 and GSE167028, respectively. Functional enrichment analysis indicated that common genes were mainly enriched in biological processes such as cell cycle and ubiquitin-protein hydrolysis. 6 genes (CDK1, KIF20A, PBK, KIF2C, CDC20, UBE2C) were identified as critical genes. TF-gene interactions and TF-miRNA coregulatory network were constructed successfully. A total of 10 drugs, (such as Etoposide, Methotrexate, Troglitazone, etc) were considered as target drugs for COVID-19 Myocarditis. Conclusions Through bioinformatics method analysis, this study provides a new perspective to explore the pathogenesis, gene regulatory networks and provide drug compounds as a reference for COVID-19 Myocarditis. It is worth highlighting that critical genes (CDK1, KIF20A, PBK, KIF2C, CDC20, UBE2C) may be potential biomarkers and treatment targets of COVID-19 Myocarditis for future study.

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Disease Modeling and Disease Gene Discovery in Cardiomyopathies: A Molecular Study of Induced Pluripotent Stem Cell Generated Cardiomyocytes

Cited by 6 publications

References 86 publications

Co-Regulation of Protein Coding Genes by Transcription Factor and Long Non-Coding RNA in SARS-CoV-2 Infected Cells: An In Silico Analysis

Co-Regulation of Protein Coding Genes by Transcription Factor and Long Non-Coding RNA in SARS-CoV-2 Infected Cells: An In Silico Analysis

Network analysis of host-pathogen protein interactions in microbe induced cardiovascular diseases

Identification of critical genes and molecular pathways in COVID-19 myocarditis and constructing gene regulatory networks by bioinformatic analysis

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