Dermatomyositis merupakan penyakit autoimun yang termasuk jenis idiopatik inflamasi miopati (IIM), penyakit ini dapat mempengaruhi kulit dan otot manusia. Gejala klinis Dermatomyositis pada sebagian besar pasien adalah kelemahan otot tubuh, ruam kulit dan kulit bersisik. Salah satu faktor penyebab Dermatomyositis yang sering dilaporkan adalah faktor genetik. Hingga kini,  penelitian terkait Dermatomyositis masih terbatas pada identifikasi jenis variasi gen yang mempengaruhi, namun tidak melaporkan variasi gen mana yang paling berkontribusi pada Dermatomyositis khususnya yang bersifat missense/nonsense. Sehingga pada penelitian ini kami memanfaatkan database genomik dan analisis bioinformatik  untuk mengidentifikasi variasi gen yang paling berhubungan dengan penyakit Dermatomyositis. Penelitian ini menggunakan beberapa database, termasuk GWAS catalog, PheWAS catalog, HaploReg (v41.), dan GTEx portal. Hasil dari penelitian ini ditemukan bahwa gen ZBP1 berkaitan erat dengan penyakit Dermatomyositis dan menunjukkan ekpresi yang tinggi pada beberapa jaringan seperti paru-paru, lambung, esophagus, kulit, jantung dan otot. Variasi gen berdasarkan frekuensi varian alel (rs59626664, rs60542959, rs2066807, rs1048661, rs745400, rs2305480, rs2305479) terkait Dermatomyositis menunjukkan ekspresi jaringan tertinggi di kulit suprapubic, kulit dibawah lengan, otot rangka, dan esofagus. Penelitian ini menekankan bahwa integrasi database genomik dan analisis bioinformatik menunjukkan variasi gen yang berperan dalam patogenesis Dermatomyositis khususnya yang bersifat missense/nonsense. Kami menyarankan untuk peneliti selanjutnya untuk fokus pada variasi gen tersebut untuk divalidasi di fase klinis khusunya di populasi Indonesia.

References

  1. A.R. Afief, L.M. Irham, W. Adikusuma, D.A. Perwitasari, A. Brahmadhi, R. Cheung. Integration of genomic variants and bioinformatic-based approach to drive drug repurposing for multiple sclerosis. Biochem.Biophys.Rep., 32 (2022), Article 101337, 10.1016/j.bbrep.2022.101337
  2. Bendewald, M. J., Wetter, D. A., Li, X., & Davis, M. D. P. (2010). Incidence of dermatomyositis and clinically amyopathic dermatomyositis: A population-based study in Olmsted County, Minnesota. Archives of Dermatology, 146(1), 26–30. https://doi.org/10.1001/archdermatol.2009.328
  3. Burbelo, P. D., Ambatipudi, K., & Alevizos, I. (2014). Genome-wide association studies in Sjögren’s syndrome: What do the genes tell us about disease pathogenesis? Autoimmunity Reviews, 13(7), 756–761. https://doi.org/10.1016/j.autrev.2014.02.002
  4. Bush, W. S., & Moore, J. H. (2012). Chapter 11: Genome-Wide Association Studies. PLoS Computational Biology, 8(12). https://doi.org/10.1371/journal.pcbi.1002822
  5. Ciccacci, C., Latini, A., Perricone, C., Conigliaro, P., Colafrancesco, S., Ceccarelli, F., Priori, R., Conti, F., Perricone, R., Novelli, G., & Borgiani, P. (2019). TNFAIP3 gene polymorphisms in three common autoimmune diseases: Systemic lupus erythematosus, rheumatoid arthritis, and primary sjogren syndrome - association with disease susceptibility and clinical phenotypes in Italian patients. Journal of Immunology Research, 2019. https://doi.org/10.1155/2019/6728694
  6. Chao, K. L., Kulakova, L., & Herzberg, O. (2017). Gene polymorphism linked to increased asthma and IBD risk alters gasdermin-B structure, a sulfatide and phosphoinositide binding protein. Proceedings of the National Academy of Sciences of the United States of America, 114(7), E1128–E1137. https://doi.org/10.1073/pnas.1616783114
  7. Dourmishev, A. L., & Dourmishev, L. A. (1999). Dermatomyositis and drugs. Advances in Experimental Medicine and Biology, 455, 187–191. https://doi.org/10.1007/978-1-4615-4857-7_27
  8. Deakin, C. T., Bowes, J., Rider, L. G., Miller, F. W., Pachman, L. M., Sanner, H., . . . the Myositis Genetics, C. (2022). Association with HLA-DRβ1 position 37 distinguishes juvenile dermatomyositis from adult-onset myositis. Human Molecular Genetics, 31(14), 2471-2481. doi:10.1093/hmg/ddac019
  9. Fadista, J., Manning, A. K., Florez, J. C., & Groop, L. (2016). The (in)famous GWAS P-value threshold revisited and updated for low-frequency variants. European Journal of Human Genetics, 24(8), 1202–1205. https://doi.org/10.1038/ejhg.2015.269
  10. Huang, C. M., Huang, P. H., Chen, C. L., Lin, Y. J., Tsai, C. H., Huang, W. L., & Tsai, F. J. (2012). Association of toll-like receptor 9 gene polymorphism in Chinese patients with systemic lupus erythematosus in Taiwan. Rheumatology International, 32(7), 2105–2109. https://doi.org/10.1007/s00296-011-1925-8
  11. Lener, M. S. (2016). Triggers of Inflammatory Myopathy: Insights into Pathogenesis. Physiology & Behavior, 176(1), 139–148.
  12. Lin, F. R., Niparko, J. K., & Ferrucci, and L. (2014). Dermatomyosititis. Bone, 23(1), 1–7. https://doi.org/10.1159/000131751.Dermatomyositis
  13. L.M. Irham, W. Adikusuma, D.A. Perwitasari, H. Dania, R. Maliza, I.N. Faridah, I.N. Santri, Y.V.A. Phiri, R. Cheung. The use of genomic variants to drive drug repurposing for chronic hepatitis B. Biochem.Biophys.Rep., 31 (2022), Article 101307, 10.1016/j.bbrep.2022.101307
  14. L.M. Irham, H.S.-C. Wong, W.-H. Chou, W. Adikusuma, E. Mugiyanto, W.-C. Huang, W.-C. Chang. Integration of genetic variants and gene network for drug repurposing in colorectal cancer. Pharmacol. Res., 161 (2020), Article 105203, 10.1016/j.phrs.2020.105203
  15. L.M. Irham, W. Adikusuma, D.A. Perwitasari. Genomic variants-driven drug repurposing for tuberculosis by utilizing the established bioinformatic-based approach. Biochem.Biophys.Rep., 32 (2022), Article 101334, 10.1016/j.bbrep.2022.101334
  16. Moffatt, M. F., Gut, I. G., Demenais, F., Strachan, D. P., Bouzigon, E., Heath, S., … Cookson, W. O. C. M. (2010). A Large-Scale, Consortium-Based Genomewide Association Study of Asthma. New England Journal of Medicine, 363(13), 1211–1221. https://doi.org/10.1056/nejmoa0906312
  17. O’Hanlon, T. P., Carrick, D. M., Arnett, F. C., Reveille, J. D., Carrington, M., Gao, X., Oddis, C. V., Morel, P. A., Malley, J. D., Malley, K., Dreyfuss, J., Shamim, E. A., Rider, L. G., Chanock, S. J., Foster, C. B., Bunch, T., Plotz, P. H., Love, L. A., & Miller, F. W. (2005). Immunogenetic risk and protective factors for the idiopathic inflammatory myopathies: Distinct HLA-A, -B, -Cw, -DRB1 and -DQA1 allelic profiles and motifs define clinicopathologic groups in Caucasians. Medicine, 84(6), 338–349. https://doi.org/10.1097/01.md.0000189818.63141.8c
  18. Okogbaa, J., & Batiste, L. (2019). Dermatomyositis: An Acute Flare and Current Treatments. Clinical Medicine Insights: Case Reports, 12. https://doi.org/10.1177/1179547619855370
  19. Pendergrass, S. A., Dudek, S. M., Crawford, D. C., & Ritchie, M. D. (2012). Visually integrating and exploring high throughput Phenome-Wide Association Study (PheWAS) results using PheWAS-View. BioData Mining, 5(1), 1. https://doi.org/10.1186/1756-0381-5-5
  20. Sontheimer, R. D. (2002). Dermatomyositis: An overview of recent progress with emphasis on dermatologic aspects. Dermatologic Clinics, 20(3), 387–408. https://doi.org/10.1016/S0733-8635(02)00021-9
  21. Takaoka, A., Wang, Z., Choi, M. K., Yanai, H., Negishi, H., Ban, T., Lu, Y., Miyagishi, M., Kodama, T., Honda, K., Ohba, Y., & Taniguchi, T. (2007). DAI (DLM-1/ZBP1) is a cytosolic DNA sensor and an activator of innate immune response. Nature, 448(7152), 501–505. https://doi.org/10.1038/nature06013
  22. Takaoka, A., Wang, Z., Choi, M. K., Yanai, H., Negishi, H., Ban, T., Lu, Y., Miyagishi, M., Kodama, T., Honda, K., Ohba, Y., & Taniguchi, T. (2016). ZBP1/DAI is an innate sensor of influenza virus triggering the NLRP3 inflammasome and programmed cell death pathways. Physiology & Behavior, 176(3), 139–148. https://doi.org/10.1126/sciimmunol.aag2045.ZBP1/DAI
  23. The GTEx Consortium. (2013). The Genotype-Tissue Expression (GTEx) project The GTEx Consortium* Abstract. Database: National Center for Biomedical Information, 45(6), 580–585. https://doi.org/10.1038/ng.2653.The
  24. Ward, L. D., & Kellis, M. (2016). HaploReg v4: Systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Research, 44(D1), D877–D881. https://doi.org/10.1093/nar/gkv1340
  25. Zhang, Z., Miteva, M. A., Wang, L., & Alexov, E. (2012). Analyzing effects of naturally occurring missense mutations. Computational and Mathematical Methods in Medicine, 2012. https://doi.org/10.1155/2012/805827
Irham, L. M., Puspitaningrum, A. N., Adikusuma, W., Mugiyanto, E., Brahmadhi, A., Djalilah, G. N. ., Satria, R. D. ., Firdayani, & Abdi Wira Septama. (2023). Identifikasi Variasi Gen yang Bersifat Missense/Nonsense Pada Dermatomyositis Dengan Memanfaatkan Database Genomik Dan Bioinformatik. Majalah Farmasi Dan Farmakologi, 27(1), 5-9. https://doi.org/10.20956/mff.v27i01.22185

Downloads

Download data is not yet available.
Fulltext