An Integrated Bioinformatics Analysis of the Potential Regulatory Effects of miR-21 on  T-cell Related Target Genes in Multiple Sclerosis

Mostafa  Manian; Ehsan Sohrabi; Nahid Eskandari; Mohammad-Ali Assarehzadegan; Gordon A. Ferns; Mitra Nourbakhsh; Mir Hadi Jazayeri; Reza Nedaeinia

doi:10.18502/ajmb.v13i3.6364

Mostafa Manian Department of Immunology, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
Ehsan Sohrabi Department of Medical Genetics and Molecular Biology, Faculty of Medicine, Iran University of Medical Sciences,Tehran, Iran
Nahid Eskandari Department of Immunology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
Mohammad-Ali Assarehzadegan Department of Immunology, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
Gordon A. Ferns Brighton and Sussex Medical School, Division of Medical Education, Falmer, Brighton BN1 9PH, Sussex, UK
Mitra Nourbakhsh Department of Biochemistry and Nutrition, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
Mir Hadi Jazayeri Department of Immunology, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
Reza Nedaeinia Pediatric Inherited Diseases Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

DOI: https://doi.org/10.18502/ajmb.v13i3.6364

Keywords: Bioinformatics, MicroRNAs, Multiple sclerosis, T-cell

Abstract

Background: Overexpression of miR-21 is a characteristic feature of patients with Multiple Sclerosis (MS) and is involved in gene regulation and the expression enhancement of pro-inflammatory factors including IFNγ and TNF-α following stimulation of T-cells via the T Cell Receptor (TCR). In this study, a novel integrated bioinformatics analysis was used to obtain a better understanding of the involvement of miR-21 in the development of MS, its protein biomarker signatures, RNA levels, and drug interactions through existing microarray and RNA-seq datasets of MS.

Methods: In order to obtain data on the Differentially Expressed Genes (DEGs) in patients with MS and normal controls, the GEO2R web tool was used to analyze the Gene Expression Omnibus (GEO) datasets, and then Protein-Protein Interaction (PPI) networks of co-expressed DEGs were designed using STRING. A molecular network of miRNA-genes and drugs based on differentially expressed genes was created for T-cells of MS patients to identify the targets of miR-21, that may act as important regulators and potential biomarkers for early diagnosis, prognosis and, potential therapeutic targets for MS.

Results: It found that seven genes (NRIP1, ARNT, KDM7A, S100A10, AK2, TGFβR2, and IL-6R) are regulated by drugs used in MS and miR-21. Finally, three overlapping genes (S100A10, NRIP1, KDM7A) were identified between miRNA-gene-drug network and nineteen genes as hub genes which can reflect the pathophysiology of MS.

Conclusion: Our findings suggest that miR-21 and MS-related drugs can act synergistically to regulate several genes in the existing datasets, and miR-21 inhibitors have the potential to be used in MS treatment.