In Silico Evaluation of Missense SNPs in ERN1 and TRAF2 Genes Associated with Huntington's Disease

Abstract

Huntington's disease (HD) is a disease that results from the repetition of CAG trinucleotides in the HTT gene in the 4th arm of the chromosome, causing severe degeneration of brain neurons and may result in death. This study aimed to identify those with potentially harmful effects in the missense SNPs of ERN1 and TRAF2 genes associated with Huntington's disease, using bioinformatics software tools, and to evaluate their impact on the functions and stabilization of proteins. SNAP2, SIFT, PolyPhen-2 (HumDiv and HumVar), SNPs&GO, PhD-SNP, PANTHER and Meta-SNP to predict potentially deleterious effects from missense SNPs, I- Mutant 2.0 and MUpro for protein stabilization, Project HOPE for three-dimensional modeling, GeneMANIA for gene-gene interactions and STRING software tools for determination of protein-protein interactions were used. For ERN1 and TRAF2 genes associated with Huntington's disease, variants with 7 or more common deleterious effects were selected using 8 software tools in 7 different programs. As a result, a total of 4 variants were identified for the ERN1 and TRAF2 genes, which were thought to be associated with the disease. As a result of the study, it was determined that the rs138082110 (S224C), rs199512451 (G133R), rs370210153 (P623Q) variants for the ERN1 gene and the rs144405558 (C469R) variant for the TRAF2 gene may have potentially harmful effects. The data obtained as a result of these studies will be beneficial in further research and experimental studies on Huntington's disease.

Keywords

• Huntington
• ERN1
• TRAF2
• Bioinformatics Analysis

Huntington Hastalığı ile İlişkili ERN1 ve TRAF2 Genlerindeki Yanlış Anlamlı SNP'lerin In Silico Değerlendirilmesi

Abstract

Huntington hastalığı (HD), kromozomun 4. kolundaki HTT genindeki CAG trinükleotidlerinin tekrarı sonucu beyin nöronlarında ciddi dejenerasyona neden olan ve ölümle sonuçlanabilecek bir hastalıktır. Bu çalışma, Huntington hastalığı ile ilişkili ERN1 ve TRAF2 genlerinin yanlış anlamlı SNP'lerinde potansiyel olarak zararlı etkileri olanların biyoinformatik yazılım araçları kullanılarak belirlenmesini ve bunların proteinlerin fonksiyonları ve stabilizasyonu üzerindeki etkilerinin değerlendirilmesini amaçlamıştır. Yanlış anlamlı SNP'lerin potansiyel olarak zararlı etkilerini tahmin etmek için SNAP2, SIFT, PolyPhen-2 (HumDiv ve HumVar), SNPs&GO, PhD-SNP, PANTHER ve Meta-SNP, protein stabilizasyonu için I-Mutant 2.0 ve MUpro, üç boyutlu modelleme için Project HOPE, gen-gen etkileşimleri için GeneMANIA ve protein-protein etkileşimlerinin belirlenmesi için STRING yazılım araçları kullanıldı. Huntington hastalığı ile ilişkili ERN1 ve TRAF2 genleri için 7 farklı programda 8 yazılım aracı kullanılarak 7’si ve üzerinde ortak zararlı etkiye sahip olan varyantlar seçildi. Sonuç olarak hastalıkla ilişkili olduğu düşünülen ERN1 ve TRAF2 genleri için toplam 4 varyant belirlendi. ERN1 geni için rs138082110 (S224C), rs199512451 (G133R), rs370210153 (P623Q) varyantlarının, TRAF2 geni için ise rs144405558 (C469R) varyantının olası zararlı etkiye sahip olabileceği çalışma sonucunda belirlenmiştir. Bu çalışmalar sonucunda elde edilen veriler Huntington hastalığı ile ilgili yapılacak ileri araştırmalarda ve deneysel çalışmalarda fayda sağlayacaktır.

Keywords

• Huntington Hastalığı
• ERN1
• TRAF2
• Biyoinformatik Analiz

References

1. Pantiya, P., Thonusin, C., Chattipakorn, N., & Chattipakorn, S. C. (2020). Mitochondrial abnormalities in neurodegenerative models and possible interventions: Focus on Alzheimer’s disease, Parkinson’s disease, Huntington’s disease. Mitochondrion, 55, 14-47.
2. Lemoine, L., Lunven, M., Fraisse, N., Youssov, K., Bapst, B., Morgado, G., ... & Bachoud-Lévi, A. C. (2023). The striatum in time production: The model of Huntington's disease in longitudinal study. Neuropsychologia, 179, 108459.
3. Schapira, A. H., Olanow, C., Greenamyre, J., & Bezard, E. (2014). Slowing of neurodegeneration in Parkinson's disease and Huntington'sdisease: future therapeutic perspectives. The Lancet, 545-555.
4. Dong, X., & Cong, S. (2021). MicroRNAs in Huntington’s disease: Diagnostic biomarkers or therapeutic agents Frontiers in cellular neuroscience, 15, 705348.
5. Kim, S., Kim, D. K., Jeong, S., & Lee, J. (2022). The common cellular events in the neurodegenerative diseases and the associated role of endoplasmic reticulum stress. International journal of molecular sciences, 23(11), 5894.
6. Chen, L., Bi, M., Zhang, Z., Du, X., Chen, X., Jiao, Q., & Jiang, H. (2022). The functions of IRE1α in neurodegenerative diseases: beyond ER stress. Ageing Research Reviews, 101774.
7. da Silva, D. C., Valentão, P., Andrade, P. B., & Pereira, D. M. (2020). Endoplasmic reticulum stress signaling in cancer and neurodegenerative disorders: Tools and strategies to understand its complexity. Pharmacological Research, 155, 104702.
8. Krammes, L., Hart, M., Rheinheimer, S., Diener, C., Menegatti, J., Grässer, F., ... & Meese, E. (2020). Induction of the Endoplasmic-reticulum-stress response: MicroRNA-34a targeting of the IRE1α-branch. Cells, 9(6), 1442.

9. Wu, H., Ng, B. S., & Thibault, G. (2014). Endoplasmic reticulum stress response in yeast and humans. Bioscience reports, 34(4), e00118.
10. Shi, M., Chai, Y., Zhang, J., & Chen, X. (2022). Endoplasmic reticulum stress-associated neuronal death and innate immune response in neurological diseases. Frontiers in immunology, 12, 794580.
11. Maity, S., Komal, P., Kumar, V., Saxena, A., Tungekar, A., & Chandrasekar, V. (2022). Impact of ER stress and ER-mitochondrial crosstalk in Huntington’s disease. International Journal of Molecular Sciences, 23(2), 780.
12. Ajoolabady, A., Lindholm, D., Ren, J. & Pratico, D. (2022). Alzheimer hastalığında ER stresi ve UPR: Mekanizmalar, patogenez, tedaviler. Hücre ölümü ve hastalığı, 13 (8), 706.
13. Spencer, B. G., & Finnie, J. W. (2020). The role of endoplasmic reticulum stress in cell survival and death. Journal of Comparative Pathology, 181, 86-91.
14. Esmaeili, Y., Yarjanli, Z., Pakniya, F., Bidram, E., Łos, M. J., Eshraghi, M., ... & Zarrabi, A. (2022). Targeting autophagy, oxidative str
15. Asveda, T., Priti, T., & Ravanan, P. (2023). Exploring microglia and their phenomenal concatenation of stress responses in neurodegenerative disorders. Life Sciences, 121920.
16. Wang, C., Chang, Y., Zhu, J., Ma, R., & Li, G. (2022). Dual role of IRE1α-XBP1 signaling in neurodegenerative diseases. Neuroscience.ess, and ER stress for neurodegenerative disease treatment. Journal of Controlled Release, 345, 147-175.
17. Yarar, E. Z. (2021). Psikopatolojilerde gen-çevre etkileşimi: Stresle ilgili genetik ve epigenetik süreçler. Klinik Psikoloji Dergisi, 5(3), 275-288.
18. Ekşi, M. (2019). SNP Mikroarray Yöntemi ile Kalıtsal Metabolik Hastalıklardan Sorumlu Genlerin Tanımlanması. Yıldırım Beyazıt Üniversitesi/Sağlık Bilimleri Enstitüsü/Tıbbi Genetik Ana Bilim Dalı, Yüksek Lisans Tezi. 24,24s, Ankara.
19. Shin, J. W., Hong, E. P., Park, S. S., Choi, D. E., Zeng, S., Chen, R. Z., & Lee, J. M. (2022). PAM-altering SNP-based allele-specific CRISPR-Cas9 therapeutic strategies for Huntington’s disease. Molecular Therapy-Methods & Clinical Development, 26, 547-561.
20. Sattari, A., Nicknafs, F. ve Noroozi, R. (2020). Uzun kodlamayan RNA'lardaki tek nükleotid polimorfizmlerinin insan hastalıklarına duyarlılıktaki rolü. Ekolojik Genetik ve Genomik, 17, 100071.
21. Özlem, G. Ö. K., Aslan, A., & Erman, O. (2017). İnsan ENCODE, HapMap ve 1000 Genom Projeler. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 33(2), 35-42.
22. Tavacı, İ., Bülbül, Ö., Filoğlu, G., & Altunçul, H. (2020). X Kromozomunda Bulunan 15 SNP Lokusunun Türkiye Populasyonundaki Polimorfizmi. Turkiye Klinikleri Journal of Forensic Medicine & Forensic Sciences, 17(3).
23. Şenışık, M., Bülbül, Ö., & Filoğlu, G. (2023). Adli DNA Fenotipleme: Erkek Tipi Kellik: Geleneksel Derleme. Turkiye Klinikleri Journal of Forensic Medicine & Forensic Sciences, 20(1).
24. Kaman, T., Karasakal, Ö. F., Oktay, E. Ö., Ulucan, K., & Konuk, M. (2019). In silico approach to the analysis of SNPs in the human APAF1 gene. Turkish Journal of Biology, 43(6), 371-381.
25. Robert, F., & Pelletier, J. (2018). Exploring the impact of single-nucleotide polymorphisms on translation. Frontiers in genetics, 9, 507.
26. Sukhumsirichart, W. (2018). Polymorphisms. In (Ed.), Genetic Diversity and Disease Susceptibility. IntechOpen.
27. Fareed, M. M., Ullah, S., Aziz, S., Johnsen, T. A., & Shityakov, S. (2022). In-silico analysis of non-synonymous single nucleotide polymorphisms in human β-defensin type 1 gene reveals their impact on protein-ligand binding sites. Computational Biology and Chemistry, 98, 107669.
28. Fidanoğlu, P. (2013). Genom Ebadındaki Türk Popülasyonu Tnp Verilerinin Veri Tabanının Hazırlanması ve Sonuçların Hapmap Işığında Değerlendirilmesi. Ankara Üniversitesi. Biyoteknoloji Enstitüsü Temel Biyoteknoloji Doktora Tezi, 7s, Ankara
29. Ng, P. C., & Henikoff, S. (2001). Predicting deleterious amino acid substitutions. Genome research, 11(5), 863-874.
30. Adzhubei, I., Jordan, D. M., & Sunyaev, S. R. (2013). Predicting functional effect of human missense mutations using PolyPhen‐2. Current protocols in human genetics, 76(1), 7-20.
31. Capriotti, E., & Altman, R. B. (2011). Improving the prediction of disease-related variants using protein three-dimensional structure. BMC bioinformatics, 12(4), 1-11.
32. Capriotti, E., Calabrese, R., & Casadio, R. (2006). Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics, 22(22), 2729-2734.
33. Hecht, M., Bromberg, Y., & Rost, B. (2015). Better prediction of functional effects for sequence variants. BMC genomics, 16(8), 1-12.
34. Thomas, P. D., Ebert, D., Muruganujan, A., Mushayahama, T., Albou, L. P., & Mi, H. (2022). PANTHER: Making genome‐scale phylogenetics accessible to all. Protein Science, 31(1), 8-22.
35. Capriotti, E., Altman, R. B., & Bromberg, Y. (2013). Collective judgment predicts disease-associated single nucleotide variants. BMC genomics, 14, 1-9.
36. Bava, K. A., Gromiha, M. M., Uedaira, H., Kitajima, K., & Sarai, A. (2004). ProTherm, version 4.0: thermodynamic database for proteins and mutants. Nucleic acids research, 32(suppl_1), D120-D121.
37. Cheng, J., Randall, A., & Baldi, P. (2006). Prediction of protein stability changes for single‐site mutations using support vector machines. Proteins: Structure, Function, and Bioinformatics, 62(4), 1125-1132.
38. Venselaar, H., Te Beek, T. A., Kuipers, R. K., Hekkelman, M. L., & Vriend, G. (2010). Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces. BMC bioinformatics, 11(1), 1-10.
39. Warde-Farley, D., Donaldson, S. L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., ... & Morris, Q. (2010). The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic acids research, 38(suppl_2), W214-W220.
40. Szklarczyk, D., Kirsch, R., Koutrouli, M., Nastou, K., Mehryary, F., Hachilif, R., ... & von Mering, C. (2023). The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic acids research, 51(D1), D638-D646.
41. Mustafa, M. I., Murshed, N. S., Abdelmoneim, A. H., & Makhawi, A. M. (2020). In silico analysis of the functional and structural consequences of SNPs in human ARX gene associated with EIEE1. Informatics in Medicine Unlocked, 21, 100447
42. Szklarczyk, D., Gable, A. L., Nastou, K. C., Lyon, D., Kirsch, R., Pyysalo, S., ... & von Mering, C. (2021). The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic acids research, 49(D1), D605-D612.
43. Kermani, A. G., Kamandi, A., & Moeini, A. (2022). Integrating graph structure information and node attributes to predict protein-protein interactions. Journal of Computational Science, 64, 101837.
44. Yang, Z., Liu, M., Wang, B., & Wang, B. (2021). Classification of protein domains based on their three-dimensional shapes (CPD3DS). Synthetic and Systems Biotechnology, 6(3), 224-230.
45. Kohli, H., Kumar, P., & Ambasta, R. K. (2021). In silico designing of putative peptides for targeting pathological protein Htt in Huntington's disease. Heliyon, 7(2).
46. Saxena, S., Murthy, T. K., Chandramohan, V., Yadav, A. K., & Singh, T. R. (2021). Structural and functional analysis of disease-associated mutations in GOT1 gene: An in silico study. Computers in Biology and Medicine, 136, 104695.https://doi.org/10.1016/j.compbiomed.2021.104695
47. De Oliveira, C. C. S., Pereira, G. R. C., De Alcantara, J. Y. S., Antunes, D., Caffarena, E. R., & De Mesquita, J. F. (2019). In silico analysis of the V66M variant of human BDNF in psychiatric disorders: An approach to precision medicine. Plos one, 14(4), e0215508. https://doi.org/10.1371/journal.pone.0215508
48. Yu, K. E. (2022). Genetic Variation of Ern1 and Susceptibility To Type 2 Diabetes. Научные результаты биомедицинских исследований, 8(3), 268-277
49. Claassen, D. O., Corey-Bloom, J., Dorsey, E. R., Edmondson, M., Kostyk, S. K., LeDoux, M. S., ... & Panzara, M. A. (2020). Genotyping single nucleotide polymorphisms for allele-selective therapy in Huntington disease. Neurology Genetics, 6(3).
50. Berger, F., Vaslin, L., Belin, L., Asselain, B., Forlani, S., Humbert, S., ... & Hall, J. (2013). The impact of single-nucleotide polymorphisms (SNPs) in OGG1 and XPC on the age at onset of Huntington disease. Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 755(2), 115-119.
51. Coppedè, F., Migheli, F., Ceravolo, R., Bregant, E., Rocchi, A., Petrozzi, L., ... & Migliore, L. (2010). The hOGG1 Ser326Cys polymorphism and Huntington's disease. Toxicology, 278(2), 199-203.
52. Kumar, S., & Nussinov, R. (2002). Close‐range electrostatic interactions in proteins. ChemBioChem, 3(7), 604-617.https://doi.org/10.1002/1439-7633(20020703)