Meta-Analysis of Dilated Cardiomyopathy Using Cardiac RNA-Seq Transcriptomic Datasets

Alimadadi, Ahmad; Munroe, Patricia B.; Joe, Bina; Cheng, Xi

doi:10.3390/genes11010060

Cited by 34 publications

(30 citation statements)

References 42 publications

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“…A calculated combined p-value for each gene was considered significant if lower than 0.05 (Fisher, 1992;Xia et al, 2015; 1 https://www.networkanalyst.ca/ Alimadadi et al, 2020). The differential significant gene list obtained was then called the meta-gene dataset in this paper.…”

Section: Discussionmentioning

confidence: 99%

Integrated Microarray and RNAseq Transcriptomic Analysis of Retinal Pigment Epithelium/Choroid in Age-Related Macular Degeneration

Dhirachaikulpanich

Porter

et al. 2020

Front. Cell Dev. Biol.

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We report for the first time an integrated transcriptomic analysis of RPE/choroid dysfunction in AMD (mixed stages) based on combining data from publicly available microarray (GSE29801) and RNAseq (GSE135092) datasets aimed at increasing the ability and power of detection of differentially expressed genes and AMD-associated pathways. The analysis approach employed an integrating quantitative method designed to eliminate bias among different transcriptomic studies. The analysis highlighted 764 meta-genes (366 downregulated and 398 upregulated) in macular AMD RPE/choroid and 445 meta-genes (244 downregulated and 201 upregulated) in non-macular AMD RPE/choroid. Of these, 731 genes were newly detected as differentially expressed (DE) genes in macular AMD RPE/choroid and 434 genes in non-macular AMD RPE/choroid compared with controls. Over-representation analysis of KEGG pathways associated with these DE genes mapped revealed two most significantly associated biological processes in macular RPE/choroid in AMD, namely the neuroactive ligand-receptor interaction pathway (represented by 30 DE genes) and the extracellular matrix-receptor interaction signaling pathway (represented by 12 DE genes). Furthermore, protein-protein interaction (PPI) network identified two central hub genes involved in the control of cell proliferation/differentiation processes, HDAC1 and CDK1. Overall, the analysis provided novel insights for broadening the exploration of AMD pathogenesis by extending the number of molecular determinants and functional pathways that underpin AMD-associated RPE/choroid dysfunction.

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Section: Discussionmentioning

confidence: 99%

Integrated Microarray and RNAseq Transcriptomic Analysis of Retinal Pigment Epithelium/Choroid in Age-Related Macular Degeneration

Dhirachaikulpanich

Porter

et al. 2020

Front. Cell Dev. Biol.

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“…ACE2, SP100, CITED2, CEBPD, BCL3, CREB, SMARCA4, NCAM1 and SFRP4 have been previously reported as transcriptional regulators in heart failure [29][30][31][32][33][34]. Although CITED2, CREB5 (belongs to CREB family), CREB3L1 (belongs to CREB family), SMARCA4, NCAM1 and SFRP4 were significant DEGs in our dataset ( Supplementary Table S1), only SFRP4 was identified as the significantly activated transcriptional regulator based on significant activation z-score (absolute z-score > 2.0) from our IPA analysis (Figure 4).…”

Section: Discussionmentioning

confidence: 97%

“…RNA-seq analyses of these four individual studies were previously published [49,50,52,53], thus all four individual datasets have been validated for our current meta-analysis. Detailed information of the data processing and bioinformatics analysis has been described in our recently published paper [32] and is shown in Figure 6. Briefly, FASTQ files were downloaded from the European Nucleotide Archive website (https://www.ebi.ac.uk/ena).…”

Section: Discussionmentioning

confidence: 99%

Identification of Upstream Transcriptional Regulators of Ischemic Cardiomyopathy Using Cardiac RNA-Seq Meta-Analysis

Alimadadi

Aryal

Manandhar

et al. 2020

IJMS

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Ischemic cardiomyopathy (ICM), characterized by pre-existing myocardial infarction or severe coronary artery disease, is the major cause of heart failure (HF). Identification of novel transcriptional regulators in ischemic HF can provide important biomarkers for developing new diagnostic and therapeutic strategies. In this study, we used four RNA-seq datasets from four different studies, including 41 ICM and 42 non-failing control (NF) samples of human left ventricle tissues, to perform the first RNA-seq meta-analysis in the field of clinical ICM, in order to identify important transcriptional regulators and their targeted genes involved in ICM. Our meta-analysis identified 911 differentially expressed genes (DEGs) with 582 downregulated and 329 upregulated. Interestingly, 54 new DEGs were detected only by meta-analysis but not in individual datasets. Upstream regulator analysis through Ingenuity Pathway Analysis (IPA) identified three key transcriptional regulators. TBX5 was identified as the only inhibited regulator (z-score = −2.89). F2R and SFRP4 were identified as the activated regulators (z-scores = 2.56 and 2.00, respectively). Multiple downstream genes regulated by TBX5, F2R, and SFRP4 were involved in ICM-related diseases such as HF and arrhythmia. Overall, our study is the first to perform an RNA-seq meta-analysis for clinical ICM and provides robust candidate genes, including three key transcriptional regulators, for future diagnostic and therapeutic applications in ischemic heart failure.

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“…Several studies made their data sets and protocols publicly available on platforms like NCBI's gene expression omnibus. However, few attempts have been made to compare transcriptomic HF studies [59][60][61]. The continuing development of sophisticated data analysis methods invites the retrospective re-analysis and integration of published HF studies, although data integration from different platforms, centers, and technologies presents many challenges [62].…”

Section: Transcriptomicsmentioning

confidence: 99%

Big Data Approaches in Heart Failure Research

Lanzer

Leuschner

Kramann

et al. 2020

Curr Heart Fail Rep

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Purpose of Review The goal of this review is to summarize the state of big data analyses in the study of heart failure (HF). We discuss the use of big data in the HF space, focusing on “omics” and clinical data. We address some limitations of this data, as well as their future potential. Recent Findings Omics are providing insight into plasmal and myocardial molecular profiles in HF patients. The introduction of single cell and spatial technologies is a major advance that will reshape our understanding of cell heterogeneity and function as well as tissue architecture. Clinical data analysis focuses on HF phenotyping and prognostic modeling. Summary Big data approaches are increasingly common in HF research. The use of methods designed for big data, such as machine learning, may help elucidate the biology underlying HF. However, important challenges remain in the translation of this knowledge into improvements in clinical care.

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Meta-Analysis of Dilated Cardiomyopathy Using Cardiac RNA-Seq Transcriptomic Datasets

Cited by 34 publications

References 42 publications

Integrated Microarray and RNAseq Transcriptomic Analysis of Retinal Pigment Epithelium/Choroid in Age-Related Macular Degeneration

Integrated Microarray and RNAseq Transcriptomic Analysis of Retinal Pigment Epithelium/Choroid in Age-Related Macular Degeneration

Identification of Upstream Transcriptional Regulators of Ischemic Cardiomyopathy Using Cardiac RNA-Seq Meta-Analysis

Big Data Approaches in Heart Failure Research

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