Differential Gene Expression Analysis of Human Atrial Fibroblasts Reveals Dysregulation of RNA Metabolism and Translational Machinery in Atrial Fibrillation

Open Access

Year : 2026 | Volume : 04 | Issue : 02 | Page : 15 25
    By

    Padidam Lakshmi Sravya,

  1. Student, Department of Biotechnology & Microbiology, M.S. Ramaiah College of Arts, Science and Commerce (MSRCASC),, Bengaluru, Karnataka, India

Abstract

Atrial fibrillation (AF) is a complex cardiac arrhythmia characterized by extensive structural remodeling and the activation of atrial fibroblasts, which drive the progression of fibrosis. To identify the underlying transcriptomic alterations, we analyzed six human atrial fibroblast RNA-Seq datasets (three control and three AF) retrieved from the Sequence Read Archive. After performing rigorous quality control and adapter trimming, we aligned the reads to the GRCh38 human reference genome using a splice-aware mapping approach. Differential expression analysis was conducted within a negative binomial framework, ultimately identifying 78 genes that were significantly dysregulated in AF samples (adjusted p-value 1). Principal component analysis (PCA) demonstrated a clear transcriptional divergence between the conditions, highlighting a distinct molecular signature associated with the disease state. Functional enrichment analysis revealed that these dysregulated genes are heavily involved in cytoplasmic translation, RNA splicing, and ribosome biogenesis. These findings suggest that AF-associated remodeling in fibroblasts is mediated by specific perturbations in RNA metabolism and translational control. Consequently, this study provides a new framework for exploring post-transcriptional regulatory mechanisms as potential therapeutic targets for managing AF.

Keywords: Atrial fibrillation, atrial fibroblasts, cardiac fibrosis, differential gene expression, RNA sequencing, transcriptomic analysis

[This article belongs to International Journal of Bioinformatics and Computational Biology ]

How to cite this article:
Padidam Lakshmi Sravya. Differential Gene Expression Analysis of Human Atrial Fibroblasts Reveals Dysregulation of RNA Metabolism and Translational Machinery in Atrial Fibrillation. International Journal of Bioinformatics and Computational Biology. 2026; 04(02):15-25.
How to cite this URL:
Padidam Lakshmi Sravya. Differential Gene Expression Analysis of Human Atrial Fibroblasts Reveals Dysregulation of RNA Metabolism and Translational Machinery in Atrial Fibrillation. International Journal of Bioinformatics and Computational Biology. 2026; 04(02):15-25. Available from: https://journals.stmjournals.com/ijbcb/article=2026/view=245003


References

1. Miyazawa K, Ito K, Ito M, Zou Z, Kubota M, Nomura S, et al. Cross-ancestry genome-wide analysis of atrial fibrillation unveils disease biology and enables cardioembolic risk prediction. Nat Genet. 2023 Feb;55(2):187–97.

2. An N, Yang F, Zhang G, Jiang Y, Liu H, Gao Y, et al. Single-cell RNA sequencing reveals the contribution of smooth muscle cells and endothelial cells to fibrosis in human atrial tissue with atrial fibrillation. Mol Med. 2024 Dec 19;30(1):247.

3. Belfiori M, Lazzari L, Hezzell M, Angelini GD, Dong T. Transcriptomics, proteomics and bioinformatics in atrial fibrillation: A descriptive review. Bioengineering. 2025 Feb 4;12(2):149.

4. Park DS, Santucci J, Hall S. Advances in transcriptional regulation of the heart rhythm. Heart Rhythm. 2025 Jan [cited 2026 May 9];22(1):287–8. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC11706356/

5. Leinonen R, Sugawara H, Shumway M, International Nucleotide Sequence Database Collaboration. The sequence read archive. Nucleic Acids Res. 2010 Nov 8;39(suppl_1):D19–21.

6. Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019 Aug;37(8):907–15.

7. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014 Dec 5;15(12):550.

8. Chen W, Zhao Y, Chen X, Yang Z, Xu X, Bi Y, et al. A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples. Nat Biotechnol. 2021 Sep;39(9):1103–14.

9. Liao Y, Smyth GK, Shi W. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Res. 2019 May 7;47(8):e47.

10. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: Archive for functional genomics data sets—Update. Nucleic Acids Res. 2013 Jan 1;41(D1):D991– D995. doi: 10.1093/nar/gks1193.

11. Babraham Bioinformatics. FastQC: A quality control tool for high throughput sequence data. 2026 [cited 2026 May 9]. Available from: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

12. Chen S. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta. 2023 May;2(2):e107.

13. Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021 Feb;10(2):giab008.

14. Team RC. R: A language and environment for statistical computing. R Foundation for Statistical Computing. 2020.

15. Aleksander SA, Balhoff J, Carbon S, Cherry JM, Drabkin HJ, Ebert D, et al. The gene ontology knowledgebase in 2023. Genetics. 2023 May 2;224(1):iyad031.

16. Ge SX, Jung D, Yao R. ShinyGO: A graphical gene-set enrichment tool for animals and plants. Bioinformatics. 2020 Apr 15;36(8):2628–9.

17. Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy- based analysis of pathways and genomes. Nucleic Acids Res. 2023 Jan 6;51(D1):D587–92.

18. de Lena PG, Morales D, Escandón M, Meijón M, Valledor L, Cañal MJ, et al J. RNA sequencing platforms and bioinformatics tools. In: Plant Transcriptomics and Epitranscriptomics: Decoding the RNA Landscape. Singapore: Springer Nature Singapore; 2026 Jan 3. p. 31–65.

19. Ma Y, Sun S, Shang X, Keller ET, Chen M, Zhou X. Integrative differential expression and gene set enrichment analysis using summary statistics for scRNA-seq studies. Nat Commun. 2020;11:1585. doi: 10.1038/s41467-020-15298-6.

20. Chen W, Zhao Y, Chen X, Yang Z, Xu X, Bi Y, et al. A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples. Nat Biotechnol. 2021 Sep;39(9):1103–14.

21. Belfiori M, Lazzari L, Hezzell M, Angelini GD, Dong T. Transcriptomics, proteomics and bioinformatics in atrial fibrillation: A descriptive review. Bioengineering. 2025 Feb 4;12(2):149.

22. Miyazawa K, Ito K, Ito M, Zou Z, Kubota M, Nomura S, et al. Cross-ancestry genome-wide analysis of atrial fibrillation unveils disease biology and enables cardioembolic risk prediction. Nat Genet. 2023 Feb;55(2):187–97.

23. Jameson HS, Hanley A, Hill MC, Xiao L, Ye J, Bapat A, et al. Loss of the atrial fibrillation-related gene, Zfhx3, results in atrial dilation and arrhythmias. Circ Res. 2023 Aug 4;133(4):313–29.


Regular Issue Open Access Original Research
Volume 04
Issue 02
Received 05/05/2026
Accepted 08/05/2026
Published 18/05/2026
Publication Time 13 Days


Login


My IP

PlumX Metrics