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 meta-analysis 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.
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This work was supported by the Brazilian funding agency Conselho Nacional de Desenvolvimento Científico e Tecnológico-CNPq. RHB was a recipient of the CNPq/UFRGS Institutional Program of Initiation Scholarships in Technological Development and Innovation (PIBITI).
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All datasets analyzed in the current study are publicly available in the Gene Expression Omnibus (GEO) database.
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RHB and MRM designed the experiment. RHB performed the experiment. RHB and MRM analyzed the data. RHB wrote the paper. MRM revised the paper. Both authors read and approved the manuscript.
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40291_2020_497_MOESM1_ESM.xlsx
Table S1. Demographic and clinical characteristics of samples for datasets included in our study, as reported by the original publication or in the GEO database. (XLSX 304 kb)
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Table S2. Results for differential expression analysis carried out or each dataset included in our meta-analysis. Statistical significance and fold change is provided for each gene originally analyzed by the corresponding transcriptomic technology, i.e., before selection of common genes across all datasets. (XLSX 16439 kb)
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Table S3. List of all differentially expressed genes identified in the meta-analysis with their corresponding differential expression pattern (up- or down-regulation) and FDR. (XLSX 40 kb)
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Table S4. Biological pathways enriched in each network cluster, their differentially expressed genes, and respective differential expression patterns. (XLSX 61 kb)
40291_2020_497_MOESM5_ESM.pdf
Figure S1. Intersection between meta-analysis and region-specific analysis from GSE79768 and GSE128188 datasets: A) Overlap between DE genes from left atria (LA) x right atria (RA) from both datasets and the meta-analysis. As expected, the meta-analysis was not able to capture these differences, since the observed overlap is small. This is due to the focus of a meta-analysis in detecting overall differences across all datasets. B) Comparison of genes identified by the meta-analysis with genes differentially expressed between LA - AF x SR - and RA-AF x SR. The subsets of DE genes from left and right atria were obtained from the intersection between the same comparisons from both GSE79768 and GSE128188. The low overlap among our list and the DE genes from left and right atria AF samples against control corroborates the fact that our list is related to overall changes in the heart (common to left and right atria), rather than to region-specific changes. (PDF 207 kb)
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Haas Bueno, R., Recamonde-Mendoza, M. Meta-analysis of Transcriptomic Data Reveals Pathophysiological Modules Involved with Atrial Fibrillation. Mol Diagn Ther 24, 737–751 (2020). https://doi.org/10.1007/s40291-020-00497-0
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DOI: https://doi.org/10.1007/s40291-020-00497-0