Meta-analysis of Transcriptomic Data Reveals Pathophysiological Modules Involved with Atrial Fibrillation
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ORIGINAL RESEARCH ARTICLE
Meta‑analysis of Transcriptomic Data Reveals Pathophysiological Modules Involved with Atrial Fibrillation Rodrigo Haas Bueno1,2 · Mariana Recamonde‑Mendoza1,2,3 Accepted: 30 September 2020 © Springer Nature Switzerland AG 2020
Abstract Background Atrial fibrillation (AF) is a complex disease and affects millions of people around the world. The biological mechanisms that are involved with AF are complex and still need to be fully elucidated. Therefore, we performed a metaanalysis of transcriptome data related to AF to explore these mechanisms aiming at more sensitive and reliable results. Methods Ten public transcriptomic datasets were downloaded, analyzed for quality control, and individually pre-processed. Differential expression analysis was carried out for each dataset, and the results were meta-analytically aggregated using the rth ordered p value method. We analyzed the final list of differentially expressed genes through network analysis, namely topological and modularity analysis, and functional enrichment analysis. Results The meta-analysis of transcriptomes resulted in 1197 differentially expressed genes, whose protein–protein interaction network presented 39 hubs-bottlenecks and four main identified functional modules. These modules were enriched for 39, 20, 64, and 10 biological pathways involved with the pathophysiology of AF, especially with the disease’s structural and electrical remodeling processes. The stress of the endoplasmic reticulum, protein catabolism, oxidative stress, and inflammation are some of the enriched processes. Among hub-bottlenecks genes, which are highly connected and probably have a key role in regulating these processes, HSPA5, ANK2, CTNNB1, and MAPK1 were identified. Conclusion Our approach based on transcriptome meta-analysis revealed a set of key genes that demonstrated consistent overall changes in expression patterns associated with AF despite data heterogeneity related, among others, to type of tissue. Further experimental investigation of our findings may shed light on the pathophysiology of the disease and contribute to the identification of new therapeutic targets.
Key Points Transcriptome meta-analysis shows great potential to uncover mechanisms involved in atrial fibrillation.
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s40291-020-00497-0) contains supplementary material, which is available to authorized users. * Mariana Recamonde‑Mendoza [email protected] 1
Experimental and Molecular Cardiovascular Laboratory, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil
2
Bioinformatics Core, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil
3
Institute of Informatics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
1197 differentially expressed genes were identified in a consistent manner across multiple studies and types of tissue. Protein–protein interaction analysis revealed four main disease-relat
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