Computational Analysis of MicroRNA-Mediated Interactions in SARS-CoV Infection

dc.contributor.author Demirci, Muserref Duygu Sacar
dc.contributor.author Adan, Aysun
dc.date.accessioned 2025-09-25T10:43:02Z
dc.date.available 2025-09-25T10:43:02Z
dc.date.issued 2020
dc.description Adan, Aysun/0000-0002-3747-8580; Sacar Demirci, Muserref Duygu/0000-0003-2012-0598 en_US
dc.description.abstract MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression found in more than 200 diverse organisms. Although it is still not fully established if RNA viruses could generate miRNAs, there are examples of miRNA like sequences from RNA viruses with regulatory functions. In the case of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there are several mechanisms that would make miRNAs impact the virus, like interfering with viral replication, translation and even modulating the host expression. In this study, we performed a machine learning based miRNA prediction analysis for the SARS-CoV-2 genome to identify miRNA-like hairpins and searched for potential miRNA-based interactions between the viral miRNAs and human genes and human miRNAs and viral genes. Overall, 950 hairpin structured sequences were extracted from the virus genome and based on the prediction results, 29 of them could be precursor miRNAs. Targeting analysis showed that 30 viral mature miRNA-like sequences could target 1,367 different human genes. PANTHER gene function analysis results indicated that viral derived miRNA candidates could target various human genes involved in crucial cellular processes including transcription, metabolism, defense system and several signaling pathways such as Wnt and EGFR signalings. Protein class-based grouping of targeted human genes showed that host transcription might be one of the main targets of the virus since 96 genes involved in transcriptional processes were potential targets of predicted viral miRNAs. For instance, basal transcription machinery elements including several components of human mediator complex (MED1, MED9, MED 12L, MED 19), basal transcription factors such as TAF4, TAF5, TAF7L and site-specific transcription factors such as STATI were found to be targeted. In addition, many known human miRNAs appeared to be able to target viral genes involved in viral life cycle such as S, M, N, E proteins and ORF lab, ORF3a, ORF8, ORF7a and ORF10. Considering the fact that miRNA-based therapies have been paid attention, based on the findings of this study, comprehending mode of actions of miRNAs and their possible roles during SARS-CoV-2 infections could create new opportunities for the development and improvement of new therapeutics. en_US
dc.identifier.doi 10.7717/peerj.9369
dc.identifier.issn 2167-8359
dc.identifier.scopus 2-s2.0-85089205004
dc.identifier.uri https://doi.org/10.7717/peerj.9369
dc.identifier.uri https://hdl.handle.net/20.500.12573/3513
dc.language.iso en en_US
dc.publisher PeerJ Inc en_US
dc.relation.ispartof PEERJ en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject SARS-CoV-2 en_US
dc.subject MicroRNA en_US
dc.subject COVID19 en_US
dc.subject Host-Virus Interaction en_US
dc.title Computational Analysis of MicroRNA-Mediated Interactions in SARS-CoV Infection en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.id Adan, Aysun/0000-0002-3747-8580
gdc.author.id Sacar Demirci, Muserref Duygu/0000-0003-2012-0598
gdc.author.scopusid 55735789200
gdc.author.scopusid 56684634500
gdc.author.wosid Sacar Demirci, Muserref Duygu/N-7458-2017
gdc.bip.impulseclass C2
gdc.bip.influenceclass C4
gdc.bip.popularityclass C3
gdc.coar.access open access
gdc.coar.type text::journal::journal article
gdc.collaboration.industrial false
gdc.description.department Abdullah Gül University en_US
gdc.description.departmenttemp [Demirci, Muserref Duygu Sacar] Abdullah Gul Univ, Bioinformat, Kayseri, Turkey; [Adan, Aysun] Abdullah Gul Univ, Mol Biol & Genet, Kayseri, Turkey en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q3
gdc.description.startpage e9369
gdc.description.volume 8 en_US
gdc.description.woscitationindex Science Citation Index Expanded
gdc.description.wosquality Q2
gdc.identifier.openalex W3033005856
gdc.identifier.pmid 32547891
gdc.identifier.wos WOS:000538335100008
gdc.index.type WoS
gdc.index.type Scopus
gdc.index.type PubMed
gdc.oaire.accesstype GOLD
gdc.oaire.diamondjournal false
gdc.oaire.downloads 74
gdc.oaire.impulse 154.0
gdc.oaire.influence 6.5554278E-9
gdc.oaire.isgreen true
gdc.oaire.keywords SARS-CoV-2
gdc.oaire.keywords COVID19
gdc.oaire.keywords QH301-705.5
gdc.oaire.keywords Bioinformatics
gdc.oaire.keywords R
gdc.oaire.keywords MicroRNA
gdc.oaire.keywords Host-virus interaction
gdc.oaire.keywords Medicine
gdc.oaire.keywords COVID-19 ; Host–virus interaction ; MicroRNA ; SARS-CoV-2
gdc.oaire.keywords Biology (General)
gdc.oaire.keywords Host–virus interaction
gdc.oaire.popularity 1.192867E-7
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 0301 basic medicine
gdc.oaire.sciencefields 03 medical and health sciences
gdc.oaire.views 200
gdc.openalex.collaboration National
gdc.openalex.fwci 13.4221
gdc.openalex.normalizedpercentile 0.99
gdc.openalex.toppercent TOP 1%
gdc.opencitations.count 168
gdc.plumx.mendeley 258
gdc.plumx.patentfamcites 1
gdc.plumx.pubmedcites 130
gdc.plumx.scopuscites 155
gdc.scopus.citedcount 155
gdc.virtual.author Saçar Demirci, Müşerref Duygu
gdc.virtual.author Adan, Aysun
gdc.wos.citedcount 151
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