Prediction of Single-Nucleotide Polymorphisms within microRNAs Binding Sites of Neuronal Genes Related to Multiple Sclerosis

Dehghanzad, Reyhaneh; Moghadam, Somayeh Panahi; Shirvani-Farsani, Zeinab

doi:10.4103/abr.abr_143_20

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…hsa-mir-4484 [552], hsa-mir-4511 [553], STAT3 [554], KLF5 [555], SRF (Serum response factor) [556], GATA3 [557] and USF1 [558] have been implicated as a principal mediator of kidney diseases. Studies have reported that hsa-mir-190b [559], hsamir-4693-5p [560], hsa-mir-1470 [560], hsa-mir-1825 [561], STAT3 [562], SREBF2 [563] and SRF (Serum response factor) [564] are necessary for neurological diseases. hsa-mir-4693-5p [565] and STAT3 [432] expression have been identified in COVID-19 infection.…”

Section: Discussionmentioning

confidence: 99%

Screening key genes and signaling pathways in COVID-19 infection and its associated complications by integrated bioinformatics analysis

Vastrad¹,

Vastrad²

2021

Preprint

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Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2)/ coronavirus disease 2019 (COVID-19) infection is the leading cause of respiratory tract infection associated mortality worldwide. The aim of the current investigation was to identify the differentially expressed genes (DEGs) and enriched pathways in COVID-19 infection and its associated complications by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid next-generation sequencing (NGS) data of 93 COVID 19 samples and 100 non COVID 19 samples (GSE156063) were obtained from the Gene Expression Omnibus database. Gene ontology (GO) and REACTOME pathway enrichment analysis was conducted to identify the biological role of DEGs. In addition, a protein-protein interaction network, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network and receiver operating characteristic curve (ROC) analysis were used to identify the key genes. A total of 738 DEGs were identified, including 415 up regulated genes and 323 down regulated genes. Most of the DEGs were significantly enriched in immune system process, cell communication, immune system and signaling by NTRK1 (TRKA). Through PPI, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network analysis, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were selected as hub genes, which were expressed in COVID-19 samples relative to those in non COVID-19 samples, respectively. Among them, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were suggested to be diagonstic factors for COVID-19. The findings from this bioinformatics analysis study identified molecular mechanisms and the key hub genes that might contribute to COVID-19 infection and its associated complications.

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Section: Discussionmentioning

confidence: 99%

Screening key genes and signaling pathways in COVID-19 infection and its associated complications by integrated bioinformatics analysis

Vastrad¹,

Vastrad²

2021

Preprint

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“…Despite strong evidence of microRNA dysregulation in MS patient samples and model organisms, there is limited literature on the role of microRNA variation in MS [ 12 – 15 ]. In contrast, variants in microRNAs and their processing machinery have been implicated in complex conditions such as cardio-metabolic conditions, colorectal cancer, glaucoma, Alzheimer’s disease and Parkinson’s disease [ 16 – 22 ].…”

Section: Introductionmentioning

confidence: 99%

In silico prioritisation of microRNA-associated common variants in multiple sclerosis

2023

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Background Genome-wide association studies (GWAS) have highlighted over 200 autosomal variants associated with multiple sclerosis (MS). However, variants in non-coding regions such as those encoding microRNAs have not been explored thoroughly, despite strong evidence of microRNA dysregulation in MS patients and model organisms. This study explores the effect of microRNA-associated variants in MS, through the largest publicly available GWAS, which involved 47,429 MS cases and 68,374 controls. Methods We identified SNPs within the coordinates of microRNAs, ± 5-kb microRNA flanking regions and predicted 3′UTR target-binding sites using miRBase v22, TargetScan 7.0 RNA22 v2.0 and dbSNP v151. We established the subset of microRNA-associated SNPs which were tested in the summary statistics of the largest MS GWAS by intersecting these datasets. Next, we prioritised those microRNA-associated SNPs which are among known MS susceptibility SNPs, are in strong linkage disequilibrium with the former or meet a microRNA-specific Bonferroni-corrected threshold. Finally, we predicted the effects of those prioritised SNPs on their microRNAs and 3′UTR target-binding sites using TargetScan v7.0, miRVaS and ADmiRE. Results We have identified 30 candidate microRNA-associated variants which meet at least one of our prioritisation criteria. Among these, we highlighted one microRNA variant rs1414273 (MIR548AC) and four 3′UTR microRNA-binding site variants within SLC2A4RG (rs6742), CD27 (rs1059501), MMEL1 (rs881640) and BCL2L13 (rs2587100). We determined changes to the predicted microRNA stability and binding site recognition of these microRNA and target sites. Conclusions We have systematically examined the functional, structural and regulatory effects of candidate MS variants among microRNAs and 3′UTR targets. This analysis allowed us to identify candidate microRNA-associated MS SNPs and highlights the value of prioritising non-coding RNA variation in GWAS. These candidate SNPs could influence microRNA regulation in MS patients. Our study is the first thorough investigation of both microRNA and 3′UTR target-binding site variation in multiple sclerosis using GWAS summary statistics.

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“…Rs1713418 A > G SNP is located in the 3′UTR region of the TEP1 gene [ 26 ]. SNP in the 3′UTR alters the ability of miRNA to bind to the target gene, which affects gene regulation and increases the risk of MS [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

The Influence of TEP1 and TERC Genetic Variants on the Susceptibility to Multiple Sclerosis

Rumšaitė,

Gedvilaitė,

Balnytė

et al. 2023

JCM

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Multiple sclerosis (MS) is a chronic inflammatory autoimmune disease of the central nervous system. According to recent studies, cellular senescence caused by telomere shortening may contribute to the development of MS. Aim of the study: Our aim was to determine the associations of TEP1 rs1760904, rs1713418, TERC rs12696304, rs35073794 gene polymorphisms with the occurrence of MS. Methods: The study included 200 patients with MS and 230 healthy controls. Genotyping of TEP1 rs1760904, rs1713418 and TERC rs12696304, rs35073794 was performed using RT-PCR. The obtained data were analysed using the program “IBM SPSS Statistics 29.0”. Haplotype analysis was performed using the online program “SNPStats”. Results: The TERC rs12696304 G allele of this SNP is associated with 1.4-fold lower odds of developing MS (p = 0.035). TERC rs35073794 is associated with approximately 2.4-fold reduced odds of MS occurrence in the codominant, dominant, overdominant, and additive models (p < 0.001; p < 0.001; p < 0.001; p < 0.001, respectively). Haplotype analysis shows that the rs1760904-G—rs1713418-A haplotype is statistically significantly associated with 1.75-fold increased odds of developing MS (p = 0.006). The rs12696304-C–rs35073794-A haplotype is statistically significantly associated with twofold decreased odds of developing MS (p = 0.008). In addition, the rs12696304-G—rs35073794-A haplotype was found to be statistically significantly associated with 5.3-fold decreased odds of developing MS (p < 0.001). Conclusion: The current evidence may suggest a protective role of TERC SNP in the occurrence of MS, while TEP1 has the opposite effect.

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Prediction of Single-Nucleotide Polymorphisms within microRNAs Binding Sites of Neuronal Genes Related to Multiple Sclerosis

Cited by 6 publications

References 32 publications

Screening key genes and signaling pathways in COVID-19 infection and its associated complications by integrated bioinformatics analysis

Screening key genes and signaling pathways in COVID-19 infection and its associated complications by integrated bioinformatics analysis

In silico prioritisation of microRNA-associated common variants in multiple sclerosis

The Influence of TEP1 and TERC Genetic Variants on the Susceptibility to Multiple Sclerosis

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