Prediction of single-nucleotide polymorphisms within microRNAs binding sites of neuronal genes related to multiple sclerosis: A preliminary study
Reyhaneh Dehghanzad1, Somayeh Panahi Moghadam2, Zeinab Shirvani Farsani2
1 Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
2 Department of Cellular and Molecular Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran
|Date of Submission||19-Jun-2020|
|Date of Acceptance||27-Sep-2020|
|Date of Web Publication||26-Feb-2021|
Dr. Zeinab Shirvani Farsani
Department of Cellular and Molecular Biology, Faculty of life Sciences and Biotechnology, Shahid Beheshti University, Tehran
Source of Support: None, Conflict of Interest: None
Background: Different genetic variants, including the single-nucleotide polymorphisms (SNPs) present in microRNA recognition elements (MREs) within 3'UTR of genes, can affect miRNA-mediated gene regulation and susceptibility to a variety of human diseases such as multiple sclerosis (MS), a disease of the central nervous system. Since the expression of many genes associated with MS is controlled by microRNAs (miRNAs), the aim of this study was to analyze SNPs within miRNA binding sites of some neuronal genes associated with MS. Materials and Methods: Fifty-seven neuronal genes related to MS were achieved using dbGaP, DAVID, DisGeNET, and Oviddatabases. 3'UTR of candidate genes were assessed for SNPs, and miRNAs' target prediction databases were used for predicting miRNA binding sites. Results: Three hundred and eight SNPs (minor allele frequency >0.05) were identified in miRNA binding sites of 3'UTR of 44 genes. Among them, 42 SNPs in 22 genes had miRNA binding sites and miRNA prediction tools suggested 71 putative miRNAs binding sites on these genes. Moreover, in silico analysis predicted 22 MRE-modulating SNPs and 22 MRE-creating SNPs in the 3'UTR of these candidate genes. Conclusions: These candidate MRE-SNPs can alter miRNAs binding sites and mRNA gene regulation. Therefore, these genetic variants and miRNAs might be involved in MS susceptibility and pathogenesis and hence would be valuable for further functional verification investigation.
Keywords: microRNAs, multiple sclerosis, Polymorphism, Single Nucleotide
|How to cite this article:|
Dehghanzad R, Panahi Moghadam S, Shirvani Farsani Z. Prediction of single-nucleotide polymorphisms within microRNAs binding sites of neuronal genes related to multiple sclerosis: A preliminary study. Adv Biomed Res 2021;10:8
|How to cite this URL:|
Dehghanzad R, Panahi Moghadam S, Shirvani Farsani Z. Prediction of single-nucleotide polymorphisms within microRNAs binding sites of neuronal genes related to multiple sclerosis: A preliminary study. Adv Biomed Res [serial online] 2021 [cited 2021 Oct 16];10:8. Available from: https://www.advbiores.net/text.asp?2021/10/1/8/310331
| Introduction|| |
Single-nucleotide polymorphisms (SNPs) may play potential roles in the pathogenesis of multifactorial diseases such as multiple sclerosis (MS). MS is the most common autoimmune disease of the central nervous system (CNS)., In the past years, different studies have demonstrated that microRNAs (miRNAs) can play key roles in the pathogenesis of neurodegenerative diseases such as MS. SNPs in miRNA recognition elements (MREs) located in 3'UTR of miRNA target genes may increase or decrease MRE affinity, create new elements or remove existing elements, which influence the expression of target genes. Therefore, the discovery of SNPs in miRNA binding sites, as well as miRNAs, is vital for understanding the pathophysiology of MS and other complex diseases, and could play functional roles in the diagnosis and treatment of MS. At this point, bioinformatic approaches allow identifying functional genetic variants within miRNA binding sites. In this study, SNPs in MREs located in the 3'UTR of MS-related neuronal Genes were investigated using In silico methods.
| Materials and Methods|| |
In silico analysis of neuronal genes related to multiple sclerosis
Neuronal genes related to MS were obtained from DisGeNET, dbGaP (https://www.ncbi.nlm.nih.gov/gap/phegeni), Ovid (http://www.ovid.com), and DAVID Bioinformatics Resources 6.8 (https://david.ncifcrf.gov/).
Investigation of the single-nucleotide polymorphisms within 3'UTR of candidate genes
To identify the SNPs within 3'UTR of candidate genes, we used the “1000 Genome database” (https://www.internationalgenome.org/category/dbsnp/). Moreover, the allele's frequencies were checked, and the SNPs with the minor allele frequency (MAF) higher than 0.05 were chosen.
In silico predictions of microRNAs target binding sites
miRNA target prediction databases including miRdSNP (http://mirdsnp.ccr.buffalo.edu/search.php/), MirSNP. (http://22.214.171.124/hmdd/mirsnp/search/), TargetScan Human 6.2 (http://www. targetscan. org), miRNASNP 2.0 (http://www.bioguo.org/miRNASNP/search.php), and PolymiRTS 3.0. (http://compbio.uthsc.edu/miRSNP/) were used to identify putative miRNA binding sites on 3'UTR of each selected gene. miRNA sequences were got from miRBase 21 (http://mirbase. org). mirRNAs' function can be affected by SNPs within MREs so the function was obtained from MirSNP.
Calculation of the binding free energy
The Gibbs binding free energy (ΔG, kCal/mol) was assessed for the wild and variant alleles using RNAcofold (http://rna.tbi.univie.ac.at/cgi-bin/RNAcofold.cgi). Then, the variation of ΔG between two alleles (i.e., ΔΔG) was calculated to assess the stability of the mRNA: miRNA duplex.
| Results|| |
The neuronal genes and single nucleotide polymorphisms associated with multiple sclerosis
By researching in the different databases (DisGeNET, dbGaP, Ovid and DAVID), 57 MS-related neuronal genes were found. Next, 308 SNPs (MAF >0.05) on 3'UTR of 44 candidate genes were identified.
Single nucleotide polymorphisms predicted within the microRNAs binding site
Three hundred and eight SNPs locating on 3'UTR of these 44 genes were investigated in detail. The results indicated that 42 SNPs of 22 genes had the miRNA binding sites and 71 putative miRNAs were predicted to target them. Thirty-seven miRNAs were identified by SNPMIR, 44 miRNAs were identified by PolymiRTS 3.0, and 18 miRNAs were identified by miRNASNP v2. These SNPs were MRE-modulating (enhancing, decreasing, and disrupting) or MRE-creating. Sixteen SNPs of them could disrupt miRNA target sites, 5 SNPs could enhance, 1 SNP could decrease and 22 SNPs could create a novel miRNA target site [Table 1].
|Table 1: Predicted single nucleotide polymorphisms and microRNAs analyzed using microRNA target prediction databases|
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| Discussion|| |
Genetic variations have been reported to play important roles on susceptibility to common autoimmune diseases. Despite recent advances in genetics, the pathogenesis of MS remains largely unknown suggesting that further studies are needed to evaluate the genetic basis of susceptibility to this disease., SNPs are the most prevalent types of genetic variations among individuals and associated with clinical progression of human disease, and response to treatment. The SNPs within 3'UTR, which can be the binding sites for miRNA, change the ability of miRNAs binding to target genes, influence gene regulation and the susceptibility risk to MS.
In this paper, the main approach was finding SNP variants that are likely to disrupt miRNAs binding target sites and consequently cause dysregulation of gene expression or creates new target sites in 3´UTR of genes and cause down-regulated gene expression. Since, Functional studies are time-consuming and expensive, bioinformatics tools have been designed to facilitate investigations into biological interactions and pathogenomics pathways. Here, our emphasis was on suggesting a list of miRNAs and their target sequences in the candidate neuronal genes which have not yet been reported.
Since demyelination and degeneration of the nervous system are main parameters in the development of MS, in this way, 22 genes were chosen that at least one SNP was at their MREs. 3´UTR contains highly conserved MREs that even a single-nucleotide change in these regions can disrupt the thermodynamic stability of miRNAs binding to the mRNA. Therefore, in the next step, the SNPs were investigated to define different types of change such as removing the previous element, creating a new element, increasing or decreasing MRE affinity, and then cause the difference of the free energies between the two alleles was assessed [variation of ΔG or ΔΔG, Table 1]. The higher ΔΔG indicates the bigger impact of SNP on the miRNA binding site. Hence, further experiments of miRNAs with significant ΔΔG can be valuable. For example, ΔΔG between wild type allele and rs13515 variant allele in MAPK1 gene is 48 KJ/mol, which is the highest ΔΔG in our list. After further assessments in miRDB and TarBase V.8 databases, 15 miRNAs of the total 71 identified miRNAs were reported that could downregulate the expression of target genes [these miRNAs have been marked with one asterisk (*) in Table 1].
Up and downregulation of hsa-mir-196b-5p,, hsa-mir-26a,, hsa-mir-4479, has-mir-660,, has-mir-484,, has-4665-5p,, has-mir-199a-3p, in MS have been reported by previous studies. For instance, Hsa-miR-484 showed significantly different expression levels between MS and healthy controls. Hsa-miR-484 has an important function in the regulation of neural progenitor cells and its downregulation is associated with dysregulation of synaptogenesis, which leads to neurodevelopmental diseases such as epilepsy, autism, and hyperactivity. In contrast, increased expression of hsa-miR-484 has been detected in MS patients' sera and may indicate the activation of neurogenesis pathways as part of an ongoing repair process. C/T substitution in 3´UTR of hsa-miR-484 target, MBP, creates new MRE and perhaps increases the affinity of hsa-miR-484 to that gene [Table 1]. Quintana et al. showed that the expression of miR-199a-3p along with 5 other miRNAs in cerebrospinal fluid of MS patients has down-regulated in comparison with the control group. G/A substitution in MRE of the LINGO-1 gene as a target of miR-199a-3p can remove miRNA binding site. Interestingly, LINGO-1 is an important negative regulator of myelination by oligodendrocytes in the CNS and its overexpression can result in increased demyelination of axons. In another experiment, Selmaj et al. studied the expression of miRNAs in the serum of 19 patients with relapsing remitting MS. They observed that there was a significant difference between expression levels of 4 miRNA, including has-mir-196b-5p in patients compared with the control group and the expression of these miRNAs was significantly reduced during the relapse. dysregulation of 2 miRNAs of the 71 miRNAs represented in [Table 1] was reported in other neurological diseases. One of them, hsa-mir-34b, is a stable microRNA in plasma of Huntington's patients that is used as a biomarker for the diagnosis of the presymptom condition in HD gene carriers. Moreover, it may regulate the entry of dendritic cells into the CNS in an animal model of MS. The other miRNA, has-mir-3613-5p, has shown a significantly increased expression in serum exosomes from mTLE-HS) mesial temporal lobe epilepsy with hippocampal sclerosis.
| Conclusions|| |
Due to the existence of common pathways in the nervous diseases, further investigations on these two miRNAs can be done in MS. Other remaining mirRNAs and SNPs, which have not been investigated yet in MS, are proposed as potentially attractive targets in association studies or functional investigations in MS.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Hecker M, Boxberger N, Illner N, Fitzner B, Schröder I, Winkelmann A, et al
. A genetic variant associated with multiple sclerosis inversely affects the expression of CD58 and microRNA-548ac from the same gene. PLoS Genet 2019;15:e1007961.
Nakahara J, Maeda M, Aiso S, Suzuki N. Current concepts in multiple sclerosis: Autoimmunity versus oligodendrogliopathy. Clin Rev Allergy Immunol 2012;42:26-34.
Zindler E, Zipp F. Neuronal injury in chronic CNS inflammation. Best Pract Res Clin Anaesthesiol 2010;24:551-62.
Regev K, Paul A, Healy B, von Glenn F, Diaz-Cruz C, Gholipour T, et al
. Comprehensive evaluation of serum microRNAs as biomarkers in multiple sclerosis. Neurol Neuroimmunol Neuroinflamm 2016;3:e267.
Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E. The role of site accessibility in microRNA target recognition. Nat Genet 2007;39:1278-84.
Lheureux S, Lambert B, Krieger S, Legros A, Vaur D, Denoyelle C, et al
. Two novel variants in the 3'UTR of the BRCA1 gene in familial breast and/or ovarian cancer. Breast Cancer Res Treat 2011;125:885-91.
Piñero J, Bravo À, Queralt-Rosinach N, Gutiérrez-Sacristán A, Deu-Pons J, Centeno E, et al
. DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res 2017;45:D833-9.
Bruno AE, Li L, Kalabus JL, Pan Y, Yu A, Hu Z. miRdSNP: A database of disease-associated SNPs and microRNA target sites on 3'UTRs of human genes. BMC Genomics 2012;13:44.
Liu C, Zhang F, Li T, Lu M, Wang L, Yue W, et al
. MirSNP, a database of polymorphisms altering miRNA target sites, identifies miRNA-related SNPs in GWAS SNPs and eQTLs. BMC Genomics 2012;13:661.
Kumar A, Wong AK, Tizard ML, Moore RJ, Lefèvre C. miRNA_Targets: A database for miRNA target predictions in coding and non-coding regions of mRNAs. Genomics 2012;100:352-6.
Lipchina I, Elkabetz Y, Hafner M, Sheridan R, Mihailovic A, Tuschl T, et al
. Genome-wide identification of microRNA targets in human ES cells reveals a role for miR-302 in modulating BMP response. Genes Dev 2011;25:2173-86.
Ziebarth JD, Bhattacharya A, Chen A, Cui Y. PolymiRTS Database 2.0: Linking polymorphisms in microRNA target sites with human diseases and complex traits. Nucleic Acids Res 2012;40:D216-21.
Landi D, Barale R, Gemignani F, Landi S. Prediction of the Biological Effect of Polymorphisms Within microRNA Binding Sites. Methods Mol. Biol.,; 2011.676, p. 197-210.
Hauser SL, Oksenberg JR. The neurobiology of multiple sclerosis: Genes, inflammation, and neurodegeneration. Neuron 2006;52:61-76.
Dehghanzad R, Pahlevan Kakhki M, Alikhah A, Sahraian MA, Behmanesh M. The putative association of TOB1-AS1 long non-coding RNA with immune tolerance: A study on multiple sclerosis patients. Neuromolecular Med 2020;22:100-10.
International HapMap 3 Consortium, Altshuler DM, Gibbs RA, Peltonen L, Altshuler DM, Gibbs RA, et al
. Integrating common and rare genetic variation in diverse human populations. Nature 2010;467:52-8.
Lill CM, Schilling M, Ansaloni S, Schröder J, Jaedicke M, Luessi F, et al
. Assessment of microRNA-related SNP effects in the 3′ untranslated region of the IL22RA2 risk locus in multiple sclerosis. Neurogenetics 2014;15:129-34.
Douglas CM. The role of bioinformatics in facilitating translational science and medicine. TECNOSCIENZA Ital J Sci Technol Stud 2014;5:141-64.
Selmaj I, Cichalewska M, Namiecinska M, Galazka G, Horzelski W, Selmaj KW, et al
. Global exosome transcriptome profiling reveals biomarkers for multiple sclerosis. Ann Neurol 2017;81:703-17.
Ghafouri-Fard S, Taheri M. A comprehensive review of non-coding RNAs functions in multiple sclerosis. Eur J Pharmacol 2020;879:173127.
Mameli G, Arru G, Caggiu E, Niegowska M, Leoni S, Madeddu G, et al
. Natalizumab therapy modulates miR-155, miR-26a and proinflammatory cytokine expression in MS patients. PLoS One 2016;11:e0157153.
Søndergaard HB, Hesse D, Krakauer M, Sørensen PS, Sellebjerg F. Differential microRNA expression in blood in multiple sclerosis. Mult Scler 2013;19:1849-57.
Martinez B, Peplow PV. MicroRNAs in blood and cerebrospinal fluid as diagnostic biomarkers of multiple sclerosis and to monitor disease progression. Neural Regen Res 2020;15:606-19.
] [Full text]
Regev K, Healy BC, Paul A, Diaz-Cruz C, Mazzola MA, Raheja R, et al.
Identification of MS-specific serum miRNAs in an international multicenter study. Neurol Neuroimmunol Neuroinflamm 2018;5:e491.
Zhou Z, Xiong H, Xie F, Wu Z, Feng Y. A Meta-Analytic Review of the Value of miRNA for Multiple Sclerosis Diagnosis. Front Neurol 2020;11:132.
Kheiri S, Aliarab A, Haghighatfard H, Sadeghi H. Prioritization of rs187728237 and rs80320514 as miRNA-related variants of human AEG-1 gene. Med Lab J 2018;12:6-11.
Casalino G, Castellano G, Consiglio A, Liguori M, Nuzziello N, Primiceri D. A predictive model for microRNA expressions in pediatric multiple sclerosis detection. In Proceedings of the International Conference on Modeling Decisions for Artificial Intelligence, Berlin, Germany, 26–30 August 2019; pp.
Quintana E, Ortega FJ, Robles-Cedeño R, Villar ML, Buxó M, Mercader JM, et al
. miRNAs in cerebrospinal fluid identify patients with MS and specifically those with lipid-specific oligoclonal IgM bands. Mult Scler 2017;23:1716-26.
Mi S, Miller RH, Lee X, Scott ML, Shulag-Morskaya S, Shao Z, et al
. LINGO-1 negatively regulates myelination by oligodendrocytes. Nat Neurosci 2005;8:745-51.
Gaughwin PM, Ciesla M, Lahiri N, Tabrizi SJ, Brundin P, Björkqvist M. Hsa-miR-34b is a plasma-stable microRNA that is elevated in pre-manifest Huntington's disease. Hum Mol Genet 2011;20:2225-37.
Hoye ML, Archambault AS, Gordon TM, Oetjen LK, Cain MD, Klein RS, et al
. MicroRNA signature of central nervous system-infiltrating dendritic cells in an animal model of multiple sclerosis. Immunology 2018;155:112-22.
Yan S, Zhang H, Xie W, Meng F, Zhang K, Jiang Y, et al
. Altered microRNA profiles in plasma exosomes from mesial temporal lobe epilepsy with hippocampal sclerosis. Oncotarget 2017;8:4136-46.