Bioinformatics insights into transcriptomic biomarkers for atopic dermatitis

Abstract

Atopic dermatitis (AD) is a long-term inflammatory skin condition characterized by a complex interplay of genetic and molecular factors. Understanding the underlying transcriptomic changes can aid in identifying biomarkers for diagnosis and therapeutic targets. This study aimed to discover and characterize transcriptomic biomarkers in AD using bioinformatics tools and techniques. Two pre-existing datasets, GSE6012 and GSE16161, were analyzed using the R limma package to identify differentially expressed genes (DEGs). Gene Ontology (GO) and REACTOME pathway enrichment analyses were conducted using WebGestalt 2019 to explore the biological properties and pathways associated with the identified genes. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape, with MCODE and CytoHubba plugins used to identify significant gene clusters and hub genes. The analysis identified 352 DEGs (158 upregulated, 194 downregulated) in GSE6012 and 5451 DEGs (2962 upregulated, 2489 downregulated) in GSE16161, with 226 overlapping genes. GO enrichment analysis revealed significant roles in cell proliferation, epidermis development, and immune response. REACTOME pathway analysis highlighted significant modifications in pathways related to skin structure and immune defense, including cornified envelope formation and antimicrobial peptides. The PPI network analysis identified three primary subclusters and pinpointed APOE and STAT1 as key hub genes. This research offers an understanding of the transcriptomic biomarkers of AD. The identified DEGs, enriched biological functions, pathways, and key hub genes offer valuable information for understanding AD's molecular mechanisms and potential therapeutic targets.

Keywords

• Atopic dermatitis
• bioinformatics
• biomarkers
• transcriptomics

References

1. [1] Nutten S. Atopic dermatitis: global epidemiology and risk factors. Ann Nutr Metab. 2015;66 Suppl 1:8-16. https://doi.org/10.1159/000370220.
2. [2] Rozalski M, Rudnicka L, Samochocki Z. Atopic and non-atopic eczema. Acta Dermatovenerol Croat. 2016;24(2):110-115.
3. [3] Margolis JS, Abuabara K, Bilker W, Hoffstad O, Margolis DJ. Persistence of mild to moderate atopic dermatitis. JAMA Dermatol. 2014;150(6):593-600. https://doi.org/10.1001/jamadermatol.2013.10271.
4. [4] Goh MS, Yun JS, Su JC. Management of atopic dermatitis: a narrative review. Med J Aust. 2022;216(11):587-593. https://doi.org/10.5694/mja2.51560.
5. [5] Tamari M, Hirota T. Genome-wide association studies of atopic dermatitis. J Dermatol. 2014;41(3):213-220. https://doi.org/10.1111/1346-8138.12321.
6. [6] Chong AC, Visitsunthorn K, Ong PY. Genetic/environmental contributions and immune dysregulation in children with atopic dermatitis. J Asthma Allergy. 2022;15:1681-1700. https://doi.org/10.2147/JAA.S293900.
7. [7] Paternoster L, Standl M, Waage J, Baurecht H, Hotze M, Strachan DP, Curtin JA, Bonnelykke K, Tian C, Takahashi A, Esparza-Gordillo J, Alves AC, Thyssen JP, den Dekker HT, Ferreira MA, Altmaier E, Sleiman PM, Xiao FL, Gonzalez JR, Marenholz I, Kalb B, Yanes MP, Xu CJ, Carstensen L, Groen- of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis. Nat Genet. 2015;47(12):1449-1456. https://doi.org/10.1038/ng.3424.
8. [8] Dyjack N, Goleva E, Rios C, Kim BE, Bin L, Taylor P, Bronchick C, Hall CF, Richers BN, Seibold MA, Leung DYM. Minimally invasive skin tape strip RNA sequencing identifies novel characteristics of the type 2-high atopic dermatitis disease endotype. J Allergy Clin Immunol. 2018;141(4):1298-1309. https://doi.org/10.1016/j.jaci.2017.10.046.

9. [9] Al-Shobaili HA, Ahmed AA, Alnomair N, Alobead ZA, Rasheed Z. Molecular genetic of atopic dermatitis: An update. Int J Health Sci (Qassim). 2016;10(1):96-120.
10. [10] Atta L, Fan J. Computational challenges and opportunities in spatially resolved transcriptomic data analysis. Nat Commun. 2021;12(1):5283. https://doi.org/10.1038/s41467-021-25557-9.
11. [11] Le BL, Andreoletti G, Oskotsky T, Vallejo-Gracia A, Rosales R, Yu K, Kosti I, Leon KE, Bunis DG, Li C, Kumar GR, White KM, Garcia-Sastre A, Ott M, Sirota M. Transcriptomics-based drug repositioning pipeline identifies therapeutic candidates for COVID-19. Sci Rep. 2021;11(1):12310. https://doi.org/10.1038/s41598-021-91625-1.
12. [12] Ghosh D, Ding L, Sivaprasad U, Geh E, Biagini Myers J, Bernstein JA, Khurana Hershey GK, Mersha TB. Multiple transcriptome data analysis reveals biologically relevant atopic dermatitis signature genes and pathways. PLoS One. 2015;10(12):e0144316. https://doi.org/10.1371/journal.pone.0144316.
13. [13] Chen L, Qi X, Wang J, Yin J, Sun P, Sun Y, Wu Y, Zhang L, Gao X. Identification of novel candidate genes and predicted miRNAs in atopic dermatitis patients by bioinformatic methods. Sci Rep. 2022;12(1):22067. https://doi.org/10.1038/s41598-022-26689-8.
14. [14] Weidinger S, Novak N. Atopic dermatitis. Lancet. 2016;387(10023):1109-1122. https://doi.org/10.1016/S0140-6736(15)00149-X.
15. [15] Panel AAJADG, Chu DK, Schneider L, Asiniwasis RN, Boguniewicz M, De Benedetto A, Ellison K, Frazier WT, Greenhawt M, Huynh J, Kim E, LeBovidge J, ford D, Chu DK. Atopic dermatitis (eczema) guidelines: 2023 American Academy of Allergy, Asthma and Immunology/American College of Allergy, Asthma and Immunology Joint Task Force on Practice Parameters GRADE- and Institute of Medicine-based recommendations. Ann Allergy Asthma Immunol. 2024;132(3):274-312. https://doi.org/10.1016/j.anai.2023.11.009.
16. [16] Frazier W, Bhardwaj N. Atopic dermatitis: Diagnosis and treatment. Am Fam Physician. 2020;101(10):590-598.
17. [17] Santri IN, Irham LM, Djalilah GN, Perwitasari DA, Wardani Y, Phiri YVA, Adikusuma W. Identification of hub genes and potential biomarkers for childhood asthma by utilizing an established bioinformatic analysis approach. Biomedicines. 2022;10(9):2311. https://doi.org/10.3390/biomedicines10092311.
18. [18] Czarnowicki T, Krueger JG, Guttman-Yassky E. Novel concepts of prevention and treatment of atopic dermatitis through barrier and immune manipulations with implications for the atopic march. J Allergy Clin Immunol. 2017;139(6):1723-1734. https://doi.org/10.1016/j.jaci.2017.04.004.
19. [19] Eyerich K, Eyerich S, Biedermann T. The multi-modal immune pathogenesis of atopic eczema. Trends Immunol. 2015;36(12):788-801. https://doi.org/10.1016/j.it.2015.10.006.
20. [20] Nakatsuji T, Gallo RL. Antimicrobial peptides: Old molecules with new ideas. J Invest Dermatol. 2012;132(3 Pt 2):887-895. https://doi.org/10.1038/jid.2011.387.
21. [21] Elias PM, Hatano Y, Williams ML. Basis for the barrier abnormality in atopic dermatitis: outside-inside-outside pathogenic mechanisms. J Allergy Clin Immunol. 2008;121(6):1337-1343. https://doi.org/10.1016/j.jaci.2008.01.022.
22. [22] Leung DY. New insights into atopic dermatitis: role of skin barrier and immune dysregulation. Allergol Int. 2013;62(2):151-161. https://doi.org/10.2332/allergolint.13-RAI-0564.
23. [23] Ali SM, Yosipovitch G. Skin pH: from basic science to basic skin care. Acta Derm Venereol. 2013;93(3):261-267. https://doi.org/10.2340/00015555-1531.
24. [24] Harvey-Seutcheu C, Hopkins G, Fairclough LC. The role of extracellular vesicles in atopic dermatitis. Int J Mol Sci. 2024;25(6):3255. https://doi.org/10.3390/ijms25063255.[25] Afshari M, Kolackova M, Rosecka M, Èelakovská J, Krejsek J. Unraveling the skin; a comprehensive review of atopic dermatitis, current understanding, and approaches. Front Immunol. 2024;15:1361005. https://doi.org/10.3389/fimmu.2024.1361005.
25. [26] Torrealba MP, Yoshikawa FSY, Aoki V, Sato MN, Orfali RL. State of the art on the role of Staphylococcus aureus extracellular vesicles in the pathogenesis of atopic dermatitis. Microorganisms. 2024;12(3):531. https://doi.org/10.3390/microorganisms12030531.
26. [27] Nomura H, Suganuma M, Takeichi T, Kono M, Isokane Y, Sunagawa K, Kobashi M, Sugihara S, Kajita A, Miyake T, Hirai Y, Yamasaki O, Akiyama M, Morizane S. Multifaceted analyses of epidermal serine protease activity in patients with atopic dermatitis. Int J Mol Sci. 2020;21(3):913. https://doi.org/10.3390/ijms21030913.
27. [28] Sroka-Tomaszewska J, Trzeciak M. Molecular mechanisms of atopic dermatitis pathogenesis. Int J Mol Sci. 2021;22(8):4130. https://doi.org/10.3390/ijms22084130.
28. [29] Huebbe P, Dose J, Schloesser A, Campbell G, Glüer CC, Gupta Y, Ibrahim S, Minihane AM, Baines JF, Nebel A, Rimbach G. Apolipoprotein E (APOE) genotype regulates body weight and fatty acid utilization-Studies in gene-targeted replacement mice. Mol Nutr Food Res. 2015;59(2):334-343. https://doi.org/10.1002/mnfr.201400636.
29. [30] Hubacek JA, Peasey A, Pikhart H, Stavek P, Kubinova R, Marmot M, Bobak M. APOE polymorphism and its effect on plasma C-reactive protein levels in a large general population sample. Hum Immunol. 2010;71(3):304-308. https://doi.org/10.1016/j.humimm.2010.01.008.
30. [31] Park TS, Panek RL, Mueller SB, Hanselman JC, Rosebury WS, Robertson AW, Kindt EK, Homan R, Karathanasis SK, Rekhter MD. Inhibition of sphingomyelin synthesis reduces atherogenesis in apolipoprotein E-knockout mice. Circulation. 2004;110(22):3465-3471. https://doi.org/10.1161/01.Cir.0000148370.60535.22.
31. [32] Stachowitz S, Alessandrini F, Abeck D, Ring J, Behrendt H. Permeability barrier disruption increases the level of serine palmitoyltransferase in human epidermis. J Invest Dermatol. 2002;119(5):1048-1052. https://doi.org/10.1046/j.1523-1747.2002.19524.x.
32. [33] Huang IH, Chung WH, Wu PC, Chen CB. JAK-STAT signaling pathway in the pathogenesis of atopic dermatitis: An updated review. Front Immunol. 2022;13:1068260. https://doi.org/10.3389/fimmu.2022.1068260.
33. [34] Bao L, Zhang H, Chan LS. The involvement of the JAK-STAT signaling pathway in chronic inflammatory skin disease atopic dermatitis. JAKSTAT. 2013;2(3):e24137. https://doi.org/10.4161/jkst.24137.
34. [35] Adikusuma W, Irham LM, Chou WH, Wong HS, Mugiyanto E, Ting J, Perwitasari DA, Chang WP, Chang WC. Drug repurposing for atopic dermatitis by integration of gene networking and genomic information. Front Immunol. 2021;12:724277. https://doi.org/10.3389/fimmu.2021.724277.
35. [36] Olsson M, Broberg A, Jernas M, Carlsson L, Rudemo M, Suurkula M, Svensson PA, Benson M. Increased expression of aquaporin 3 in atopic eczema. Allergy. 2006;61(9):1132-1137. https://doi.org/10.1111/j.1398-9995.2006.01151.x.
36. [37] Guttman-Yassky E, Suarez-Farinas M, Chiricozzi A, Nograles KE, Shemer A, Fuentes-Duculan J, Cardinale I, Lin P, Bergman R, Bowcock AM, Krueger JG. Broad defects in epidermal cornification in atopic dermatitis identified through genomic analysis. J Allergy Clin Immunol. 2009;124(6):1235-44 e58. https://doi.org/10.1016/j.jaci.2009.09.031.
37. [38] Gene Ontology C, Aleksander SA, Balhoff J, Carbon S, CherPourcel L, Poux S, Rivoire C, Sundaram S, Bateman A, Bowler-Barnett E, Bye AJH, Denny P, Ignatchenko A, Ishtiaq R, Lock A, Lussi Y, Magrane M, Martin MJ, Orchard S, Raposo P, Speretta E, Tyagi N, Warner K, Zaru R, Diehl AD, Lee R, Chan J, Diamantakis S, Raciti D, Zarowiecki M, Fisher M, James-Zorn C, Ponferrada V, Zorn A, Ramachandran S, Ruzicka L, Westerfield M. The Gene Ontology knowledgebase in 2023. Genetics. 2023;224(1):iyad031. https://doi.org/10.1093/genetics/iyad031.
38. [39] Milacic M, Beavers D, Conley P, Gong C, Gillespie M, Griss J, Haw R, Jassal B, Matthews L, May B, Petryszak R, Ragueneau E, Rothfels K, Sevilla C, Shamovsky V, Stephan R, Tiwari K, Varusai T, Weiser J, Wright A, Wu G, Stein L, Hermjakob H, D'Eustachio P. The Reactome Pathway Knowledgebase 2024. Nucleic Acids Res. 2024;52(D1):D672-D8. https://doi.org/10.1093/nar/gkad1025.
39. [40] Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 2019;47(W1):W199-w205. https://doi.org/10.1093/nar/gkz401.
40. [41] Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering CV. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607-D13. https://doi.org/10.1093/nar/gky1131.
41. [42] Franz M, Lopes CT, Huck G, Dong Y, Sumer O, Bader GD. Cytoscape.js: a graph theory library for visualisation and analysis. Bioinformatics. 2016;32(2):309-311. https://doi.org/10.1093/bioinformatics/btv557.
42. [43] Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003;4:2. https://doi.org/10.1186/1471-2105-4-2.
43. [44] Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8 Suppl 4(Suppl 4):S11. https://doi.org/10.1186/1752-0509-8-S4-S11.
44. [45] Wickham H. Ggplot2: Elegant Graphics for Data Analysis. 2nd Edition ed. New York: Springer; 2009.
45. [46] Chen H, Boutros PC. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics. 2011;12:35. https://doi.org/10.1186/1471-2105-12-35.
46. [47] Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498-2504. https://doi.org/10.1101/gr.1239303.