An in-silico study to find potential effective circRNAs in the progression of Huntington’s disease

Document Type : Original Article


1 Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

2 Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran


Objective(s): Huntington’s disease (HD) is identified as a progressive genetic disorder caused by a mutation in the Huntington gene. Although the pathogenesis of this disease has not been fully understood, investigations have demonstrated the role of various genes and non-coding RNAs in the disease progression. In this study, we aimed to discover the potential promising circRNAs which can bind to miRNAs of HD. 
Materials and Methods: We used several bioinformatics tools such as ENCORI, Cytoscape, circBase, Knime, and Enrichr to collect possible circRNAs and then evaluate their connections with target miRNAs to reach this goal. We also found the probable relationship between parental genes of these circRNAs and the disease progress. 
Results: According to the data collected, more than 370 thousand circRNA-miRNA interactions were found for 57 target miRNAs. Several of circRNAs were spliced out of parental genes involved in the etiology of HD. Some of them need to be further investigated to elucidate their role in this neurodegenerative disease.
Conclusion: This in silico investigation highlights the potential role of circRNAs in the progression of HD and opens up new horizons for drug discovery as well as diagnostic approaches for the disease.


Main Subjects

1. Nørremølle A, Budtz-Jørgensen E, Fenger K, Nielsen J, Sørensen S, Hasholt L. 4p16.3 haplotype modifying age at onset of Huntington disease. Clin Genet 2009;75:244-250. 
2. Gundry CN, Wittwer CT. SYBR Green I Analysis of the Trinucleotide Repeat Responsible for Huntington’s Disease. In: Rapid Cycle Real-Time PCR-Methods and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg; 2002. p. 57-63. 
3. Warby SC, Montpetit A, Hayden AR, Carroll JB, Butland SL, Visscher H, et al. CAG expansion in the huntington disease gene is associated with a specific and targetable predisposing haplogroup. Am J Hum Genet 2009;84:351–366. 
4. Myers RH. Huntington’s Disease Genetics. NeuroRx 2004;1:255-262. 
5. Lee ST, Kim M. Aging and neurodegeneration. Molecular mechanisms of neuronal loss in Huntington’s disease. Mech Ageing Dev 2006;127:432-435. 
6. Condrat CE, Thompson DC, Barbu MG, Bugnar OL, Boboc A, Cretoiu D, et al. miRNAs as biomarkers in disease: Latest findings regarding their role in diagnosis and prognosis. Cells 2020;9:276-307. 
7. Verduci L, Tarcitano E, Strano S, Yarden Y, Blandino G. CircRNAs: Role in human diseases and potential use as biomarkers. Cell Death Dis 2021;12:468-479. 
8. Xie R, Zhang Y, Zhang J, Li J, Zhou X. The role of circular RNAs in immune-related diseases. Front Immunol 2020;11:545-555. 
9. Viswambharan V, Thanseem I, Vasu MM, Poovathinal SA, Anitha A. miRNAs as biomarkers of neurodegenerative disorders. Biomark Med 2017;11:151-167. 
10. O’Brien J, Hayder H, Zayed Y, Peng C. Overview of MicroRNA biogenesis, mechanisms of actions, and circulation. Front Endocrinol (Lausanne) 2018;9:402-413. 
11. Hoss AG, Labadorf A, Latourelle JC, Kartha VK, Hadzi TC, Gusella JF, et al. miR-10b-5p expression in Huntington’s disease brain relates to age of onset and the extent of striatal involvement. BMC Med Genomics 2015;8:10-24. 
12. Reed ER, Latourelle JC, Bockholt JH, Bregu J, Smock J, Paulsen JS, et al. MicroRNAs in CSF as prodromal biomarkers for Huntington disease in the PREDICT-HD study. Neurology 2018;90:e264-272. 
13. Bonneau E, Neveu B, Kostantin E, Tsongalis GJ, De Guire V. How close are miRNAs from clinical practice? A perspective on the diagnostic and therapeutic market. EJIFCC 2019;30:114-127. 
14. Holdt LM, Kohlmaier A, Teupser D. Circular RNAs as therapeutic agents and targets. Front Physiol 2018;9:1262-1277. 
15. Chen L, Huang C, Wang X, Shan G. Circular RNAs in eukaryotic cells. Curr Genomics 2015;16:312-318. 
16. Panda AC. Circular RNAs Act as miRNA Sponges. In 2018. p. 67-79. 
17. Kumar L, Shamsuzzama, Haque R, Baghel T, Nazir A. Circular RNAs: The emerging class of non-coding rnas and their potential role in human neurodegenerative diseases. Mol Neurobiol 2017;54:7224-7234. 
18. Zhang Y, Zhao Y, Liu Y, Wang M, Yu W, Zhang L. Exploring the regulatory roles of circular RNAs in Alzheimer’s disease. Transl Neurodegener 2020;9:35-42. 
19. Feng Z, Zhang L, Wang S, Hong Q. Circular RNA circDLGAP4 exerts neuroprotective effects via modulating miR-134-5p/CREB pathway in Parkinson’s disease. Biochem Biophys Res Commun 2020;522:388-394. 
20. Moradi M, Golmohammadi R, Najafi A, Moosazadeh Moghaddam M, Fasihi-Ramandi M, Mirnejad R. A contemporary review on the important role of in silico approaches for managing different aspects of COVID-19 crisis. Informatics Med Unlocked 2022;28:100862-100871. 
21. Fang X, Lloyd CJ, Palsson BO. Reconstructing organisms in silico: genome-scale models and their emerging applications. Nat Rev Microbiol 2020;18:731-743. 
22. Hajighahramani N, Eslami M, Negahdaripour M, Ghoshoon MB, Dehshahri A, Erfani N, et al. Computational design of a chimeric epitope-based vaccine to protect against Staphylococcus aureus infections. Mol Cell Probes 2019;46:101414.
23. Patil K, Joseph S, Shah J, Mukherjee S. An integrated in silico analysis highlighted angiogenesis regulating miRNA-mRNA network in PCOS pathophysiology. J Assist Reprod Genet 2022;39:427-440. 
24. Hoss AG, Kartha VK, Dong X, Latourelle JC, Dumitriu A, Hadzi TC, et al. MicroRNAs located in the Hox gene clusters are implicated in huntington’s disease pathogenesis. PLoS Genet 2014;10:e1004188-1004201. 
25. Mastrokolias A, Ariyurek Y, Goeman JJ, van Duijn E, Roos RA, van der Mast RC, et al. Huntington’s disease biomarker progression profile identified by transcriptome sequencing in peripheral blood. Eur J Hum Genet 2015;23:1349-1356. 
26. Li JH, Liu S, Zhou H, Qu LH, Yang JH. StarBase v2.0: Decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 2014;42:92-97. 
27. Glažar P, Papavasileiou P, Rajewsky N. CircBase: A database for circular RNAs. RNA 2014;20:1666-1670. 
28. 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;13:2498-2504. 
29. Kuleshov M V., Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: A comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 2016;44:W90-97. 
30. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene Ontology: Tool for the unification of biology. Nat Genet 2000;25:25-29. 
31. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 2000;28:27-30. 
32. Wu YY, Kuo HC. Functional roles and networks of non-coding RNAs in the pathogenesis of neurodegenerative diseases. J Biomed Sci 2020;27:1-23. 
33. Tan X, Liu Y, Zhang T, Cong S. Integrated analysis of differentially expressed genes and construction of a competing endogenous RNA network in human Huntington neural progenitor cells. BMC Med Genomics 2021;14:48-61. 
34. Bhattacharyya NP, Banerjee M, Majumder P. Huntington’s disease: Roles of huntingtin-interacting protein 1 (HIP-1) and its molecular partner HIPPI in the regulation of apoptosis and transcription. FEBS J 2008;275:4271-4279. 
35. Dunah AW, Jeong H, Griffin A, Kim Y-M, Standaert DG, Hersch SM, et al. Sp1 and TAFII130 Transcriptional Activity Disrupted in Early Huntington’s Disease. Science 2002;296:2238-2243. 
36. Marchina E, Misasi S, Bozzato A, Ferraboli S, Agosti C, Rozzini L, et al. Gene expression profile in fibroblasts of Huntington’s disease patients and controls. J Neurol Sci 2014;337:42-46. 
37. Wilson H, De Micco R, Niccolini F, Politis M. Molecular imaging markers to track Huntington’s disease pathology. Front Neurol 2017;8:1-10. 
38. Valcárcel-Ocete L, Alkorta-Aranburu G, Iriondo M, Fullaondo A, García-Barcina M, Fernández-García JM, et al. Exploring genetic factors involved in huntington disease age of onset: E2F2 as a new potential modifier gene. PLoS One 2015;10:e0131573-131586. 
39. Vandeweyer G, Van Der Aa N, Reyniers E, Kooy RF. The contribution of CLIP2 haploinsufficiency to the clinical manifestations of the Williams-Beuren syndrome. Am J Hum Genet 2012;90:1071-1078. 
40. Chowdhury T, Lee Y, Kim S, Yu HJ, Ji SY, Bae JM, et al. A glioneuronal tumor with CLIP2-MET fusion. NPJ Genomic Med 2020;5:1-7. 
41. Li X, Meng Y. SUMOylation Regulator-Related Molecules Can Be Used as Prognostic Biomarkers for Glioblastoma. Front Cell Dev Biol 2021;9: 658856-658864.