Selection of MicroRNAs Associated between Neural Stem Cells and Multiple Sclerosis
DOI:
https://doi.org/10.31661/gmj.v11i.2497Keywords:
MicroRNAs, Neural Stem Cells, Multiple Sclerosis, BioinformaticsAbstract
Background: Diagnosis and treatment of multiple sclerosis (MS) in its advanced state have been one of the medical community's concerns so far. Cell therapy has been a modern and successful treatment. However, it has not yet been effective enough to treat MS. This study aimed to find the relationship between neural stem cells (NSCs) and MS, and by considering important signaling pathways of pathogenesis, the most important microRNAs (miRNAs) for its diagnosis and treatment were investigated. Materials and Methods: Using the bioinformatics approaches and appropriate databases, the relationship between NSCs and MS were recognized, and after obtaining common genes between them, the protein products by them were evaluated. Finally, after nominating essential genes, we isolated and analyzed the microarrays involved in these pathways. Results: In the first step, 76 upregulated and 1600 down-regulated common genes between NSCs and MS were recognized. Upregulated genes obtained axon guidance, NCAM, and RHO signaling pathways, and the cell cycle, RNA metabolism, and DNA repair signaling pathways by down-regulated genes. Then, high-expression PAK3, ROBO2, and LIMK2, and low-expression AURKA, BIRC5, BLM, and BRCA1 proteins were identified. Accordingly, high-expression miRNAs included hsa-miR-4790-5p, hsa-miR-4281, and hsa-miR-4327, but low-expression miRNAs included hsa-miR-103b, hsa-miR-638, and hsa-miR-4537 were recognized. Conclusion: Our study indicated that the abovementioned important miRNAs have a major role in diagnosing and treating MS.
References
Kobelt G, Thompson A, Berg J, Gannedahl M, Eriksson J, MSCOI Study Group, et al. New insights into the burden and costs of multiple sclerosis in Europe. Mult Scler J. 2017;23(8):1123-36. https://doi.org/10.1177/1352458517694432PMid:28273775 PMCid:PMC5476197 Kaisey M, Solomon AJ, Mph ML, Giesser BS, Sicotte NL. Incidence of multiple sclerosis misdiagnosis in referrals to two academic centers. Mult Scler Relat Disord. 2019;30:51-6. https://doi.org/10.1016/j.msard.2019.01.048PMid:30738280 Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2017;17(2):162-73. https://doi.org/10.1016/S1474-4422(17)30470-2PMid:29275977 Scolding NJ, Pasquini M, Reingold SC, Cohen JA, Atkins H, Banwell B, et al. Cell-based therapeutic strategies for multiple sclerosis Immunoablation followed by haematopoietic stem cell. Brain. 2017;140(11):2776-96. https://doi.org/10.1093/brain/awx154PMid:29053779 PMCid:PMC5841198 Zhang C, Cao J, Li X, Xu H, Wang W, Wang L, et al. Treatment of multiple sclerosis by transplantation of neural stem cells derived from induced pluripotent stem cells. Sci China Life. 2016;59(9):950-7. https://doi.org/10.1007/s11427-016-0114-9PMid:27233903 Pluchino S, Martino G. Pluchino S, Martino G. The therapeutic plasticity of neural stem/precursor cells in multiple sclerosis. J Neurol Sci. 2008;265:105-10. https://doi.org/10.1016/j.jns.2007.07.020PMid:17706971 Thompson AJ. Challenge of progressive multiple sclerosis therapy. Curr Opin Neurol. 2017;30(3):237-40. https://doi.org/10.1097/WCO.0000000000000453PMid:28338498 Tutunchi S, Akhavan S, Bereimipour A, Hossein Ghaderian SM. Evaluation of Important Molecular Pathways and Candidate Diagnostic Biomarkers of Noninvasive to Invasive Stages in Gastric Cancer by In Silico Analysis. J Oncol. 2021;2021:5571413. https://doi.org/10.1155/2021/5571413PMid:34054953 PMCid:PMC8131151 Mirzaei M, Sheikholeslami SA, Jalili A, Bereimipour A, Sharbati S, Kaveh V, Salari S. Investigating the molecular mechanisms of Tamoxifen on the EMT pathway among patients with breast cancer. J Med Life. 2022;15(6):835-44. https://doi.org/10.25122/jml-2022-0085PMid:35928368 PMCid:PMC9321501 Liu M, Hou X, Zhang P, Hao Y, Yang Y, Wu X, et al. Microarray gene expression profiling analysis combined with bioinformatics in multiple sclerosis Microarray gene expression profiling analysis combined with bioinformatics in multiple sclerosis. Mol Biol. 2013;40(5):3731-7. https://doi.org/10.1007/s11033-012-2449-3PMid:23456643 Islam T, Rahman MR, Karim MR, Huq F, Quinn JM, Moni MA. Unlocked Detection of multiple sclerosis using blood and brain cells transcript pro fi les: Insights from comprehensive bioinformatics approach. Informatics Med Unlocked. 2019;16:100201. https://doi.org/10.1016/j.imu.2019.100201 Grochowski C, Radzikowska E, Maciejewski R. Neural stem cell therapy-Brief review. Clin Neurol Neurosurg. 2018;1(173):8-14. https://doi.org/10.1016/j.clineuro.2018.07.013PMid:30053745 Fri D, Korchak JA, Zubair AC. Challenges and translational considerations of mesenchymal stem / stromal cell therapy for Parkinson ' s disease. NPJ Regen Med. 2020;3(5):1-10. https://doi.org/10.1038/s41536-020-00106-yPMid:33298940 PMCid:PMC7641157 Greenfield AL, Hauser SL, Francisco S. Bâ€cell Therapy for Multiple Sclerosis: Entering an era. Ann Neurol. 2019;83(1):13-26. https://doi.org/10.1002/ana.25119PMid:29244240 PMCid:PMC5876115 Bonab MM, Sahraian MA, Aghsaie A, Karvigh SA, Hosseinian SM, Nikbin B, et al. Autologous Mesenchymal Stem Cell Therapy in Progressive Multiple Sclerosis: An Open Label Study. Curr Stem Cell Res Ther. 2012;7(6):407-14. https://doi.org/10.2174/157488812804484648PMid:23061813 Kular L, Needhamsen M, Adzemovic MZ, Kramarova T, Gomez-cabrero D, Ewing E, et al. Neuronal methylome reveals CREB- associated neuro-axonal impairment in multiple sclerosis. Clin Epigenetics. 2019;11(1):1-20. https://doi.org/10.1186/s13148-019-0678-1PMid:31146783 PMCid:PMC6543588 Mulero P, Córdova C, Hernández M, MartÃn R, Gutiérrez B, Muñoz JC, et al. Netrin-1 and multiple sclerosis: a new biomarker for neuroinflammation? Eur J Neurol. 2017;24(9):1-8. https://doi.org/10.1111/ene.13340PMid:28677863 Chu C, Gao Y, Lan X, Thomas A, Li S. NCAM Mimetic Peptides: Potential Therapeutic Target for Neurological Disorders. Neurochem Res.2018;43(9):1714-22. https://doi.org/10.1007/s11064-018-2594-8PMid:30003388 Ziliotto N, Zivadinov R, Jakimovski D, Baroni M, Tisato V, Secchiero P, et al. Plasma levels of soluble NCAM in multiple sclerosis. J Neurol Sci. 2019;396:36-41. https://doi.org/10.1016/j.jns.2018.10.023PMid:30412901 Huang GH, Sun ZL, Li HJ, Feng DF. Molecular and Cellular Neuroscience Rho GTPase-activating proteins: Regulators of Rho GTPase activity in neuronal development and CNS diseases. Mol Cell Neurosci. 2017;80:18-31. https://doi.org/10.1016/j.mcn.2017.01.007PMid:28163190 Groot M, Lee H. Sorting Mechanisms for MicroRNAs into Extracellular Vesicles and Their Associated Diseases. Cells. 2020;9(4):1-16. https://doi.org/10.3390/cells9041044PMid:32331346 PMCid:PMC7226101 Maillart E. Multiple Sclerosis on behalf SFSEP Treatment of progressive multiple sclerosis: Challenges and promising perspectives. Rev Neurol. 2018:1-8. Bame M, McInnis MG, O'Shea KS. MicroRNA alterations in induced pluripotent stem cell-derived neurons from bipolar disorder patients: pathways involved in neuronal differentiation, axon guidance, and plasticity. Stem Cells Dev. 2020;29(17):1145-59. https://doi.org/10.1089/scd.2020.0046PMid:32438891 PMCid:PMC7469698 Tűzesi Ã, Kling T, Wenger A, Lunavat TR, Jang SC, Rydenhag B, et al. Pediatric brain tumor cells release exosomes with a miRNA repertoire that differs from exosomes secreted by normal cells. Oncotarget. 2017;8(52):90164. https://doi.org/10.18632/oncotarget.21621PMid:29163818 PMCid:PMC5685739 Pichler S, Gu W, Hartl D, Gasparoni G, Leidinger P, Keller A, et al. The miRNome of Alzheimer's disease: consistent downregulation of the miR-132/212 cluster. Neurobiol Aging. 2017;50:167-e1. https://doi.org/10.1016/j.neurobiolaging.2016.09.019PMid:27816213 Kim SH, Yun SW, Kim HR, Chae SA. Eosomal microRNA expression pro fi les of cerebrospinal fl uid in febrile seizure patients. Seizure. 2020;81:47-52. https://doi.org/10.1016/j.seizure.2020.07.015PMid:32745947 Kiltschewskij D, Cairns MJ. Post-Transcriptional Mechanisms of Neuronal Translational Control in Synaptic Plasticity. In Synaptic Plast. London: IntechOpen; 2017. https://doi.org/10.5772/67603 Ravnik-GlavaÄ M, GlavaÄ D. Circulating RNAs as potential biomarkers in amyotrophic lateral sclerosis. Mol Sci. 2020;21(4):1714-24. https://doi.org/10.3390/ijms21051714PMid:32138249 PMCid:PMC7084402 Gao Z, Zhang J, Wu Y. TFAP2A inhibits microRNA-126 expression at a transcriptional level to aggravate ischemic neuronal injury. Biochem Cell Biol. 2021;99(4):403-13. https://doi.org/10.1139/bcb-2020-0361PMid:33264079 De Felice B, Guida M, Guida M, Coppola C, De Mieri G, Cotrufo R. A miRNA signature in leukocytes from sporadic amyotrophic lateral sclerosis. Gene. 2012;508(1):35-40. https://doi.org/10.1016/j.gene.2012.07.058PMid:22903028 T Tanito M, Sugihara K, Hara K, Takai Y. Different glaucoma types and glaucoma surgeries among different age groups. Graefes Arch Clin Exp. 2018;256(10):2013-4. https://doi.org/10.1007/s00417-018-4058-xPMid:29946902 Li X, Diao H. Circular RNA circ _ 0001946 acts as a competing endogenous RNA to inhibit glioblastoma progression by modulating miR †671 †5p and CDR1. J Cell Physiol. 2019;34:1-13. https://doi.org/10.1002/jcp.28061PMid:30663767 Uwatoko H, Hama Y, Iwata IT, Shirai S, Matsushima M, Yabe I, Utsumi J, Sasaki H. Identification of plasma microRNA expression changes in multiple system atrophy and Parkinson ' s disease. Mol Brain. 2019;22(4):1-10. https://doi.org/10.1186/s13041-019-0471-2PMid:31088501 PMCid:PMC6518614 Ma X, Zhou J, Zhong Y, Jiang L, Mu P, Li Y, et al. Expression, Regulation and Function of MicroRNAs in Multiple Sclerosis. Int J Med Sci. 2014;11(3):23-33. https://doi.org/10.7150/ijms.8647PMid:24936144 PMCid:PMC4057480 Watanabe K, Yamaji R, Ohtsuki T. Genes to Cells 5p promotes neuronal differentiation of SH-  SY5Y cells Genes to Cells. Genes to Cells. 2018;27:225-33. https://doi.org/10.1111/gtc.12559PMid:29341475 Casalino G, Castellano G, Consiglio A, Liguori M, Nuzziello N, Primiceri D. A predictive model for microrna expressions in pediatric multiple sclerosis detection. Int Conf Model Decis Artif Intell. 2019:177-88. https://doi.org/10.1007/978-3-030-26773-5_16
