Skip to main content
SpringerLink
Log in

Meta-analysis of Transcriptomic Data Reveals Pathophysiological Modules Involved with Atrial Fibrillation

  • Original Research Article
  • Published:
Molecular Diagnosis & Therapy Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Heeringa J, van der Kuip DAM, Hofman A, Kors JA, van Herpen G, Stricker BHC, et al. Prevalence, incidence and lifetime risk of atrial fibrillation: the Rotterdam study. Eur Heart J. 2006;27:949–53.

    PubMed  Google Scholar 

  2. Ball J, Carrington MJ, McMurray JJV, Stewart S. Atrial fibrillation: profile and burden of an evolving epidemic in the 21st century. Int J Cardiol. 2013;167:1807–24.

    PubMed  Google Scholar 

  3. Nattel S, Harada M. Atrial remodeling and atrial fibrillation: recent advances and translational perspectives. J Am Coll Cardiol. 2014;63:2335–45.

    PubMed  Google Scholar 

  4. Heijman J, Voigt N, Nattel S, Dobrev D. Cellular and molecular electrophysiology of atrial fibrillation initiation, maintenance, and progression. Circ Res. 2014;114:1483–99.

    CAS  PubMed  Google Scholar 

  5. Sühling M, Wolke C, Scharf C, Lendeckel U. Proteomics and transcriptomics in atrial fibrillation. Herzschrittmacherther Elektrophysiol. 2018;29:70–5.

    PubMed  Google Scholar 

  6. Rhodes DR, Chinnaiyan AM. Integrative analysis of the cancer transcriptome. Nat Genet. 2005. https://doi.org/10.1038/ng1570.

    Article  PubMed  Google Scholar 

  7. Nanni L, Romualdi C, Maseri A, Lanfranchi G. Differential gene expression profiling in genetic and multifactorial cardiovascular diseases. J Mol Cell Cardiol. 2006;41:934–48.

    CAS  PubMed  Google Scholar 

  8. Carpanini SM, Wishart TM, Gillingwater TH, Manson JC, Summers KM. Analysis of gene expression in the nervous system identifies key genes and novel candidates for health and disease. Neurogenetics. 2017;18:81–95.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Deshmukh A, Barnard J, Sun H, Newton D, Castel L, Pettersson G, et al. Left atrial transcriptional changes associated with atrial fibrillation susceptibility and persistence. Circ Arrhythm Electrophysiol. 2015;8:32–41.

    CAS  PubMed  Google Scholar 

  10. Kim YH, Lim DS, Lee JH, Shim WJ, Ro YM, Park GH, et al. Gene expression profiling of oxidative stress on atrial fibrillation in humans. Exp Mol Med. 2003;35:336–49.

    CAS  PubMed  Google Scholar 

  11. Walsh CJ, Hu P, Batt J, Santos CCD. Microarray meta-analysis and cross-platform normalization: integrative genomics for robust biomarker discovery. Microarrays (Basel). 2015;4:389–406.

    Google Scholar 

  12. Grills C, Jithesh PV, Blayney J, Zhang S-D, Fennell DA. Gene expression meta-analysis identifies VDAC1 as a predictor of poor outcome in early stage non-small cell lung cancer. PLoS ONE. 2011. https://doi.org/10.1371/journal.pone.0014635.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Ghosh D, Barette TR, Rhodes D, Chinnaiyan AM. Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer. Funct Integr Genom. 2003. https://doi.org/10.1007/s10142-003-0087-5.

    Article  Google Scholar 

  14. Ripley BD. The R project in statistical computing. MSOR Connect. 2001. https://doi.org/10.11120/msor.2001.01010023.

    Article  Google Scholar 

  15. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30:207–10.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Kauffmann A, Huber W. Microarray data quality control improves the detection of differentially expressed genes. Genomics. 2010;95:138–42.

    CAS  PubMed  Google Scholar 

  18. Ramasamy A, Mondry A, Holmes CC, Altman DG. Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Med. 2008;5:e184.

    PubMed  PubMed Central  Google Scholar 

  19. Kauffmann A, Gentleman R, Huber W. ArrayQualityMetrics—a bioconductor package for quality assessment of microarray data. Bioinformatics. 2009;25:415–6.

    CAS  PubMed  Google Scholar 

  20. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.

    PubMed  PubMed Central  Google Scholar 

  21. Thomas AM, Cabrera CP, Finlay M, Lall K, Nobles M, Schilling RJ, et al. Differentially expressed genes for atrial fibrillation identified by RNA sequencing from paired human left and right atrial appendages. Physiol Genom. 2019;51:323–32.

    CAS  Google Scholar 

  22. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003;4:249–64.

    PubMed  Google Scholar 

  23. Carvalho BS, Irizarry RA. A framework for oligonucleotide microarray preprocessing. Bioinformatics. 2010;26:2363–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Leek JT, Storey JD. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 2007;3:1724–35.

    CAS  PubMed  Google Scholar 

  25. Ritchie ME, Silver J, Oshlack A, Holmes M, Diyagama D, Holloway A, et al. A comparison of background correction methods for two-colour microarrays. Bioinformatics. 2007;23:2700–7.

    CAS  PubMed  Google Scholar 

  26. Silver JD, Ritchie ME, Smyth GK. Microarray background correction: maximum likelihood estimation for the normal-exponential convolution. Biostatistics. 2009;10:352–63.

    PubMed  Google Scholar 

  27. Yang YH, Thorne NP. Normalization for two-color cDNA microarray data. Inst Math Stat Lect Notes Monogr Ser. 2003. https://doi.org/10.1214/lnms/1215091155.

    Article  Google Scholar 

  28. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.

    CAS  PubMed  Google Scholar 

  29. McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 2012;40:4288–97.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:3.

    Google Scholar 

  31. Wang X, Kang DD, Shen K, Song C, Lu S, Chang L-C, et al. An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection. Bioinformatics. 2012. https://doi.org/10.1093/bioinformatics/bts485.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Marot G, Foulley J-L, Mayer C-D, Jaffrézic F. Moderated effect size and p value combinations for microarray meta-analyses. Bioinformatics. 2009;25:2692–9.

    CAS  PubMed  Google Scholar 

  33. Chang L-C, Lin H-M, Sibille E, Tseng GC. Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline. BMC Bioinform. 2013. https://doi.org/10.1186/1471-2105-14-368.

    Article  Google Scholar 

  34. Song C, Tseng GC. Hypothesis setting and order statistic for robust genomic meta-analysis. Ann Appl Stat. 2014;8:777–800.

    PubMed  PubMed Central  Google Scholar 

  35. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodological). 1995. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.

    Article  Google Scholar 

  36. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43:D447–D45252.

    CAS  PubMed  Google Scholar 

  37. Assenov Y, Ramírez F, Schelhorn S-E, Lengauer T, Albrecht M. Computing topological parameters of biological networks. Bioinformatics. 2008;24:282–4.

    CAS  PubMed  Google Scholar 

  38. Bader GD, Hogue CWV. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform. 2003;4:2.

    Google Scholar 

  39. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. The Gene Ontology Consortium. The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res. 2019;47:D330–D33838.

    Google Scholar 

  41. Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R Package for comparing biological themes among gene clusters. OMICS A J Integr Biol. 2012. https://doi.org/10.1089/omi.2011.0118.

    Article  Google Scholar 

  42. Barabási A-L, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004. https://doi.org/10.1038/nrg1272.

    Article  PubMed  Google Scholar 

  43. Park K, Kim D. Localized network centrality and essentiality in the yeast–protein interaction network. Proteomics. 2009;9:5143–54.

    CAS  PubMed  Google Scholar 

  44. Charitou T, Bryan K, Lynn DJ. Using biological networks to integrate, visualize and analyze genomics data. Genet Sel Evol. 2016. https://doi.org/10.1186/s12711-016-0205-1.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Roselli C, Chaffin MD, Weng L-C, Aeschbacher S, Ahlberg G, Albert CM, et al. Multi-ethnic genome-wide association study for atrial fibrillation. Nat Genet. 2018;50:1225–333.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. van Ouwerkerk AF, Hall AW, Kadow ZA, Lazarevic S, Reyat JS, Tucker NR, et al. Epigenetic and transcriptional networks underlying atrial fibrillation. Circ Res. 2020;127:34–50.

    PubMed  PubMed Central  Google Scholar 

  47. Zhang J, Huang X, Wang X, Gao Y, Liu L, Li Z, et al. Identification of potential crucial genes in atrial fibrillation: a bioinformatic analysis. BMC Med Genom. 2020;13:104.

    Google Scholar 

  48. Liu Y, Tang R, Zhao Y, Jiang X, Wang Y, Gu T. Identification of key genes in atrial fibrillation using bioinformatics analysis. BMC Cardiovasc Disord. 2020;20:363.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Nygaard V, Rødland EA, Hovig E. Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses. Biostatistics. 2016;17:29–39.

    PubMed  Google Scholar 

  50. Wiersma M, Meijering RAM, Qi X-Y, Zhang D, Liu T, Hoogstra-Berends F, et al. Endoplasmic reticulum stress is associated with autophagy and cardiomyocyte remodeling in experimental and human atrial fibrillation. J Am Heart Assoc. 2017. https://doi.org/10.1161/JAHA.117.006458.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Amen OM, Sarker SD, Ghildyal R, Arya A. Endoplasmic reticulum stress activates unfolded protein response signaling and mediates inflammation, obesity, and cardiac dysfunction: therapeutic and molecular approach. Front Pharmacol. 2019;10:977.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Allessie MA, de Groot NMS, Houben RPM, Schotten U, Boersma E, Smeets JL, et al. Electropathological substrate of long-standing persistent atrial fibrillation in patients with structural heart disease: longitudinal dissociation. Circ Arrhythm Electrophysiol. 2010;3:606–15.

    PubMed  Google Scholar 

  53. Hoogstra-Berends F, Meijering RAM, Zhang D, Heeres A, Loen L, Seerden J-P, et al. Heat shock protein-inducing compounds as therapeutics to restore proteostasis in atrial fibrillation. Trends Cardiovasc Med. 2012;22:62–8.

    CAS  PubMed  Google Scholar 

  54. Wang J, Lee J, Liem D, Ping P. HSPA5 Gene encoding Hsp70 chaperone BiP in the endoplasmic reticulum. Gene. 2017;618:14–23.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Fu HY, Minamino T, Tsukamoto O, Sawada T, Asai M, Kato H, et al. Overexpression of endoplasmic reticulum-resident chaperone attenuates cardiomyocyte death induced by proteasome inhibition. Cardiovasc Res. 2008;79:600–10.

    CAS  PubMed  Google Scholar 

  56. Schäuble N, Lang S, Jung M, Cappel S, Schorr S, Ulucan Ö, et al. BiP-mediated closing of the Sec61 channel limits Ca2+ leakage from the ER. EMBO J. 2012;31:3282–96.

    PubMed  PubMed Central  Google Scholar 

  57. Murley A, Nunnari J. The emerging network of mitochondria-organelle contacts. Mol Cell. 2016;61:648–53.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Pickles S, Vigié P, Youle RJ. Mitophagy and quality control mechanisms in mitochondrial maintenance. Curr Biol. 2018;28:R170–R185185.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Li J, Zhang D, Brundel BJJM, Wiersma M. Imbalance of ER and mitochondria interactions: prelude to cardiac ageing and disease? Cells. 2019. https://doi.org/10.3390/cells8121617.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Yang M, Tan H, Cheng L, He M, Wei Q, Tanguay RM, et al. Expression of heat shock proteins in myocardium of patients with atrial fibrillation. Cell Stress Chaperones. 2007;12:142–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Lin KM, Lin B, Lian IY, Mestril R, Scheffler IE, Dillmann WH. Combined and individual mitochondrial HSP60 and HSP10 expression in cardiac myocytes protects mitochondrial function and prevents apoptotic cell deaths induced by simulated ischemia-reoxygenation. Circulation. 2001;103:1787–92.

    CAS  PubMed  Google Scholar 

  62. Kirmanoglou K, Hannekum A, Schäfler AE. Expression of mortalin in patients with chronic atrial fibrillation. Basic Res Cardiol. 2004;99:404–8.

    CAS  PubMed  Google Scholar 

  63. Cunha SR, Mohler PJ. Ankyrin protein networks in membrane formation and stabilization. J Cell Mol Med. 2009;13:4364–76.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Skogestad J, Aronsen JM, Tovsrud N, Wanichawan P, Hougen K, Stokke MK, et al. Coupling of the Na+/K+-ATPase to Ankyrin B controls Na+/Ca2+ exchanger activity in cardiomyocytes. Cardiovasc Res. 2020;116:78–90.

    CAS  PubMed  Google Scholar 

  65. Shiferaw Y, Aistrup GL, Wasserstrom JA. Intracellular Ca2+ waves, afterdepolarizations, and triggered arrhythmias. Cardiovasc Res. 2012;95:265–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Popescu I, Galice S, Mohler PJ, Despa S. Elevated local [Ca2+] and CaMKII promote spontaneous Ca2+ release in ankyrin-B-deficient hearts. Cardiovasc Res. 2016;111:287–94.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Roberts JD, Murphy NP, Hamilton RM, Lubbers ER, James CA, Kline CF, et al. Ankyrin-B dysfunction predisposes to arrhythmogenic cardiomyopathy and is amenable to therapy. J Clin Investig. 2019;129:3171–84.

    PubMed  PubMed Central  Google Scholar 

  68. Hedhli N, Lizano P, Hong C, Fritzky LF, Dhar SK, Liu H, et al. Proteasome inhibition decreases cardiac remodeling after initiation of pressure overload. Am J Physiol Heart Circ Physiol. 2008;295:H1385–H13931393.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Hedhli N, Wang L, Wang Q, Rashed E, Tian Y, Sui X, et al. Proteasome activation during cardiac hypertrophy by the chaperone H11 Kinase/Hsp22. Cardiovasc Res. 2008;77:497–505.

    CAS  PubMed  Google Scholar 

  70. Tang M, Li J, Huang W, Su H, Liang Q, Tian Z, et al. Proteasome functional insufficiency activates the calcineurin-NFAT pathway in cardiomyocytes and promotes maladaptive remodelling of stressed mouse hearts. Cardiovasc Res. 2010;88:424–33.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Carrier L. Too much of a good thing is bad: proteasome inhibition induces stressed hearts to fail. Cardiovasc Res. 2010;88:389–90.

    CAS  PubMed  Google Scholar 

  72. Korantzopoulos P, Letsas K, Fragakis N, Tse G, Liu T. Oxidative stress and atrial fibrillation: an update. Free Radic Res. 2018;52:1199–209.

    CAS  PubMed  Google Scholar 

  73. Chiang C-E, Naditch-Brûlé L, Murin J, Goethals M, Inoue H, O’Neill J, et al. Distribution and risk profile of paroxysmal, persistent, and permanent atrial fibrillation in routine clinical practice: insight from the real-life global survey evaluating patients with atrial fibrillation international registry. Circ Arrhythm Electrophysiol. 2012;5:632–9.

    PubMed  Google Scholar 

  74. Bukowska A, Schild L, Keilhoff G, Hirte D, Neumann M, Gardemann A, et al. Mitochondrial dysfunction and redox signaling in atrial tachyarrhythmia. Exp Biol Med. 2008;233:558–74.

    CAS  Google Scholar 

  75. Xie W, Santulli G, Reiken SR, Yuan Q, Osborne BW, Chen B-X, et al. Mitochondrial oxidative stress promotes atrial fibrillation. Sci Rep. 2015;5:11427.

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Anzai K, Ogawa K, Kuniyasu A, Ozawa T, Yamamoto H, Nakayama H. Effects of hydroxyl radical and sulfhydryl reagents on the open probability of the purified cardiac ryanodine receptor channel incorporated into planar lipid bilayers. Biochem Biophys Res Commun. 1998;249:938–42.

    CAS  PubMed  Google Scholar 

  77. Youn J-Y, Zhang J, Zhang Y, Chen H, Liu D, Ping P, et al. Oxidative stress in atrial fibrillation: an emerging role of NADPH oxidase. J Mol Cell Cardiol. 2013;62:72–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Friedrichs K, Baldus S, Klinke A. Fibrosis in atrial fibrillation—role of reactive species and MPO. Front Physiol. 2012;3:214.

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Zhang J, Youn JY, Kim AY, Ramirez RJ, Gao L, Ngo D, et al. NOX4-dependent hydrogen peroxide overproduction in human atrial fibrillation and HL-1 atrial cells: relationship to hypertension. Front Physiol. 2012;3:140.

    PubMed  PubMed Central  Google Scholar 

  80. Zhu H, Tannous P, Johnstone JL, Kong Y, Shelton JM, Richardson JA, et al. Cardiac autophagy is a maladaptive response to hemodynamic stress. J Clin Investig. 2007;117:1782–93.

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Essick EE, Sam F. Oxidative stress and autophagy in cardiac disease, neurological disorders, aging and cancer. Oxid Med Cell Longev. 2010;3:168–77.

    PubMed  PubMed Central  Google Scholar 

  82. Yuan Y, Zhao J, Gong Y, Wang D, Wang X, Yun F, et al. Autophagy exacerbates electrical remodeling in atrial fibrillation by ubiquitin-dependent degradation of L-type calcium channel. Cell Death Dis. 2018;9:873.

    PubMed  PubMed Central  Google Scholar 

  83. Barker N. The canonical Wnt/beta-catenin signalling pathway. Methods Mol Biol. 2008;468:5–15.

    CAS  PubMed  Google Scholar 

  84. MacDonald BT, Tamai K, He X. Wnt/beta-catenin signaling: components, mechanisms, and diseases. Dev Cell. 2009;17:9–26.

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Frietze S, Wang R, Yao L, Tak YG, Ye Z, Gaddis M, et al. Cell type-specific binding patterns reveal that TCF7L2 can be tethered to the genome by association with GATA3. Genome Biol. 2012;13:R52.

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Nusse R, Clevers H. Wnt/β-catenin signaling, disease, and emerging therapeutic modalities. Cell. 2017;169:985–99.

    CAS  PubMed  Google Scholar 

  87. Malekar P, Hagenmueller M, Anyanwu A, Buss S, Streit MR, Weiss CS, et al. Wnt signaling is critical for maladaptive cardiac hypertrophy and accelerates myocardial remodeling. Hypertension. 2010;55:939–45.

    CAS  PubMed  Google Scholar 

  88. Dawson K, Aflaki M, Nattel S. Role of the Wnt-Frizzled system in cardiac pathophysiology: a rapidly developing, poorly understood area with enormous potential. J Physiol. 2013;591:1409–32.

    PubMed  Google Scholar 

  89. Liu P, Su J, Song X, Wang S. Activation of nuclear β-catenin/c-Myc axis promotes oxidative stress injury in streptozotocin-induced diabetic cardiomyopathy. Biochem Biophys Res Commun. 2017;493:1573–80.

    CAS  PubMed  Google Scholar 

  90. Huo R, Hu C, Zhao L, Sun L, Wang N, Lu Y, et al. Enhancement of β-catenin/T-cell factor 4 signaling causes susceptibility to cardiac arrhythmia by suppressing NaV1.5 expression in mice. Heart Rhythm. 2019;16:1720–8.

    PubMed  PubMed Central  Google Scholar 

  91. Lv X, Li J, Hu Y, Wang S, Yang C, Li C, et al. Overexpression of miR-27b-3p targeting Wnt3a regulates the signaling pathway of Wnt/β-catenin and attenuates atrial fibrosis in rats with atrial fibrillation. Oxid Med Cell Longev. 2019;2019:5703764.

    PubMed  PubMed Central  Google Scholar 

  92. Ponnusamy M, Li P-F, Wang K. Understanding cardiomyocyte proliferation: an insight into cell cycle activity. Cell Mol Life Sci. 2017;74:1019–34.

    CAS  PubMed  Google Scholar 

  93. Ahuja P, Sdek P, MacLellan WR. Cardiac myocyte cell cycle control in development, disease, and regeneration. Physiol Rev. 2007;87:521–44.

    CAS  PubMed  Google Scholar 

  94. Bicknell KA, Coxon CH, Brooks G. Forced expression of the cyclin B1-CDC2 complex induces proliferation in adult rat cardiomyocytes. Biochem J. 2004;382:411–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Andrade J, Khairy P, Dobrev D, Nattel S. The clinical profile and pathophysiology of atrial fibrillation: relationships among clinical features, epidemiology, and mechanisms. Circ Res. 2014;114:1453–68.

    CAS  PubMed  Google Scholar 

  96. da Silva RMFL. Influence of inflammation and atherosclerosis in atrial fibrillation. Curr Atheroscler Rep. 2017;19:2.

    PubMed  Google Scholar 

  97. Hu Y-F, Chen Y-J, Lin Y-J, Chen S-A. Inflammation and the pathogenesis of atrial fibrillation. Nat Rev Cardiol. 2015;12:230–43.

    CAS  PubMed  Google Scholar 

  98. Frustaci A, Chimenti C, Bellocci F, Morgante E, Russo MA, Maseri A. Histological substrate of atrial biopsies in patients with lone atrial fibrillation. Circulation. 1997;96:1180–4.

    CAS  PubMed  Google Scholar 

  99. Jalloul Y, Refaat MM. IL-6 rapidly induces reversible atrial electrical remodeling by downregulation of cardiac connexins. J Am Heart Assoc. 2019;8:e013638.

    PubMed  PubMed Central  Google Scholar 

  100. Kerr R, Stirling D, Ludlam CA. Interleukin 6 and haemostasis. Br J Haematol. 2001;115:3–12.

    CAS  PubMed  Google Scholar 

  101. Kaski JC, Arrebola-Moreno AL. Inflammation and thrombosis in atrial fibrillation. Rev Esp Cardiol. 2011;64:551–3.

    PubMed  Google Scholar 

  102. Gu J, Liu X, Wang Q-X, Tan H-W, Guo M, Jiang W-F, et al. Angiotensin II increases CTGF expression via MAPKs/TGF-β1/TRAF6 pathway in atrial fibroblasts. Exp Cell Res. 2012;318:2105–15.

    CAS  PubMed  Google Scholar 

  103. Zhang D, Liu X, Chen X, Gu J, Li F, Zhang W, et al. Role of the MAPKs/TGF-β1/TRAF6 signaling pathway in atrial fibrosis of patients with chronic atrial fibrillation and rheumatic mitral valve disease. Cardiology. 2014;129:216–23.

    CAS  PubMed  Google Scholar 

  104. Novo G, Guttilla D, Fazio G, Cooper D, Novo S. The role of the renin-angiotensin system in atrial fibrillation and the therapeutic effects of ACE-Is and ARBS. Br J Clin Pharmacol. 2008;66:345–51.

    PubMed  PubMed Central  Google Scholar 

  105. Shi J, Jiang Q, Ding X, Xu W, Wang DW, Chen M. The ER stress-mediated mitochondrial apoptotic pathway and MAPKs modulate tachypacing-induced apoptosis in HL-1 atrial myocytes. PLoS ONE. 2015;10:e0117567.

    PubMed  PubMed Central  Google Scholar 

  106. Barth AS, Merk S, Arnoldi E, Zwermann L, Kloos P, Gebauer M, et al. Reprogramming of the human atrial transcriptome in permanent atrial fibrillation: expression of a ventricular-like genomic signature. Circ Res. 2005;96:1022–9.

    CAS  PubMed  Google Scholar 

  107. Barth AS, Merk S, Arnoldi E, Zwermann L, Kloos P, Gebauer M, et al. Functional profiling of human atrial and ventricular gene expression. Pflugers Arch. 2005;450:201–8.

    CAS  PubMed  Google Scholar 

  108. Yeh Y-H, Kuo C-T, Lee Y-S, Lin Y-M, Nattel S, Tsai F-C, et al. Region-specific gene expression profiles in the left atria of patients with valvular atrial fibrillation. Heart Rhythm. 2013;10:383–91.

    PubMed  Google Scholar 

  109. Adam O, Lavall D, Theobald K, Hohl M, Grube M, Ameling S, et al. Rac1-induced connective tissue growth factor regulates connexin 43 and N-Cadherin expression in atrial fibrillation. J Am Coll Cardiol. 2010. https://doi.org/10.1016/j.jacc.2009.08.064.

    Article  PubMed  Google Scholar 

  110. Tsai F-C, Lin Y-C, Chang S-H, Chang G-J, Hsu Y-J, Lin Y-M, et al. Differential left-to-right atria gene expression ratio in human sinus rhythm and atrial fibrillation: implications for arrhythmogenesis and thrombogenesis. Int J Cardiol. 2016;222:104–12.

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Experimental and Molecular Cardiovascular Laboratory, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil

    Rodrigo Haas Bueno & Mariana Recamonde-Mendoza

  2. Bioinformatics Core, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil

    Rodrigo Haas Bueno & Mariana Recamonde-Mendoza

  3. Institute of Informatics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil

    Mariana Recamonde-Mendoza

Authors
  1. Rodrigo Haas Bueno
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mariana Recamonde-Mendoza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mariana Recamonde-Mendoza.

Ethics declarations

Funding

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).

Conflict of interest

The authors declare that they have no conflicts of interest.

Availability of data and material

All datasets analyzed in the current study are publicly available in the Gene Expression Omnibus (GEO) database.

Authors' contribution

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.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Code availability statements

Not applicable.

Electronic supplementary material

Below is the link to the electronic supplementary material.

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)

40291_2020_497_MOESM2_ESM.xlsx

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)

40291_2020_497_MOESM3_ESM.xlsx

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)

40291_2020_497_MOESM4_ESM.xlsx

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)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40291-020-00497-0

Search

Navigation