Department of Medical Biology, School of Medicine, SANKO University, 27090, Gaziantep,Turkey.
Turkish Medicines and Medical Devices Agency, Ankara,Turkey.
Curr Drug Deliv. 2021;18(10):1595-1610. doi: 10.2174/1567201818666210301102320.
The outbreak of COVID-19 caused by SARS-CoV-2 has promptly spread worldwide. This study aimed to predict mature miRNA sequences in the SARS-CoV-2 genome, their effects on protein-protein interactions in the affected cells, and gene-drug relationships to detect possible drug candidates.
Viral hairpin structure prediction, classification of hairpins, mutational examination of precursor miRNA candidate sequences, Minimum Free Energy (MFE) and regional entropy analysis, mature miRNA sequences, target gene prediction, gene ontology enrichment, and Protein-Protein Interaction (PPI) analysis, and gene-drug interactions were performed.
A total of 62 candidate hairpins were detected by VMir analysis. Three hairpin structures were classified as true precursor miRNAs by miRBoost. Five different mutations were detected in precursor miRNA sequences in 100 SARS-CoV-2 viral genomes. Mutations slightly elevated MFE values and entropy in precursor miRNAs. Gene ontology terms associated with fibrotic pathways and immune system were found to be enriched in PANTHER, KEGG and Wiki pathway analysis. PPI analysis showed a network between 60 genes. CytoHubba analysis showed SMAD1 as a hub gene in the network. The targets of the predicted miRNAs, FAM214A, PPM1E, NUFIP2 and FAT4, were downregulated in SARS-CoV-2 infected A549 cells.
miRNAs in the SARS-CoV-2 virus genome may contribute to the emergence of the Covid-19 infection by activating pathways associated with fibrosis in the cells infected by the virus and modulating the innate immune system. The hub protein between these pathways may be the SMAD1, which has an effective role in TGF signal transduction.
由 SARS-CoV-2 引起的 COVID-19 疫情迅速在全球范围内蔓延。本研究旨在预测 SARS-CoV-2 基因组中的成熟 miRNA 序列，它们对受感染细胞中蛋白质-蛋白质相互作用的影响，以及基因-药物关系，以检测可能的候选药物。
进行病毒发夹结构预测、发夹分类、前体 miRNA 候选序列的突变检查、最小自由能（MFE）和区域熵分析、成熟 miRNA 序列、靶基因预测、基因本体论富集、蛋白-蛋白相互作用（PPI）分析和基因-药物相互作用。
通过 Vmir 分析共检测到 62 个候选发夹。miRBoost 将 3 个发夹结构分类为真正的前体 miRNA。在 100 个 SARS-CoV-2 病毒基因组中检测到前体 miRNA 序列中的 5 种不同突变。突变略微提高了前体 miRNA 中的 MFE 值和熵。PANTHER、KEGG 和 Wiki 通路分析发现与纤维化途径和免疫系统相关的基因本体论术语富集。PPI 分析显示了 60 个基因之间的网络。CytoHubba 分析显示 SMAD1 是网络中的一个枢纽基因。预测的 miRNAs 的靶标，FAM214A、PPM1E、NUFIP2 和 FAT4，在 SARS-CoV-2 感染的 A549 细胞中下调。
SARS-CoV-2 病毒基因组中的 miRNAs 可能通过激活与病毒感染细胞中的纤维化途径相关的途径，并调节先天免疫系统，导致新冠病毒感染的出现。这些途径之间的枢纽蛋白可能是 SMAD1，它在 TGF 信号转导中具有有效的作用。
