Evidence map›Paper›PMID 33398248›Full record

ArticleGene reports2021

Computationally predicted SARS-COV-2 encoded microRNAs target NFKB, JAK/STAT and TGFB signaling pathways.

Merve Nur Aydemir, Habes Bilal Aydemir, Ertan Mahir Korkmaz, Mahir Budak, Nilgun Cekin, Ergun Pinarbasi

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Gene reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed, 1 pooled it
5.7field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

57 citing papers in PubMed, 1 synthesis or guideline pooled it, 90 citations in OpenAlex.

  1. Pooled it
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  11. Exploring the underlying molecular mechanisms of acute myocardial infarction after SARS-CoV-2 infection.American heart journal plus : cardiology research and practice · 2024
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  20. SARS-CoV-2 viral protein ORF3A injures renal tubules by interacting with TRIM59 to induce STAT3 activation.Molecular therapy : the journal of the American Society of Gene Therapy · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Merve Nur AydemirDepartment of Molecular Biology and Genetics, Faculty of Science, Sivas Cumhuriyet University, Sivas, Turkey.
Habes Bilal AydemirDepartment of Molecular Biology and Genetics, Faculty of Arts and Sciences, Gaziosmanpaşa University, Tokat, Turkey.
Ertan Mahir KorkmazDepartment of Molecular Biology and Genetics, Faculty of Science, Sivas Cumhuriyet University, Sivas, Turkey.
Mahir BudakDepartment of Molecular Biology and Genetics, Faculty of Science, Sivas Cumhuriyet University, Sivas, Turkey.
Nilgun CekinSivas Cumhuriyet University, Faculty of Medicine, Department of Medical Biology, 58140 Sivas, Turkey.
Ergun PinarbasiSivas Cumhuriyet University, Faculty of Medicine, Department of Medical Biology, 58140 Sivas, Turkey.
Sivas Cumhuriyet Üniversitesi · TRTokat Gaziosmanpaşa Üniversitesi · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently an outbreak that emerged in Wuhan, China in December 2019, spread to the whole world in a short time and killed >1,410,000 people. It was determined that a new type of beta coronavirus called severe acute respiratory disease coronavirus type 2 (SARS-CoV-2) was causative agent of this outbreak and the disease caused by the virus was named as coronavirus disease 19 (COVID19). Despite the information obtained from the viral genome structure, many aspects of the virus-host interactions during infection is still unknown. In this study we aimed to identify SARS-CoV-2 encoded microRNAs and their cellular targets. We applied a computational method to predict miRNAs encoded by SARS-CoV-2 along with their putative targets in humans. Targets of predicted miRNAs were clustered into groups based on their biological processes, molecular function, and cellular compartments using GO and PANTHER. By using KEGG pathway enrichment analysis top pathways were identified. Finally, we have constructed an integrative pathway network analysis with target genes. We identified 40 SARS-CoV-2 miRNAs and their regulated targets. Our analysis showed that targeted genes including

Indexed as

ACE-2, angiotensin-converting enzyme 2AKT1, AKT serine/threonine kinase 1BCL2, BCL2 apoptosis regulatorCDK1, cyclin dependent kinase 1CDKL2, cyclin dependent kinase like 2COVID19, new type corona virus diseaseCTNNB1, catenin beta 1CXCL10, C-X-C motif chemokine ligand 10CXCL11, C-X-C motif chemokine ligand 11CXCL16, C-X-C motif chemokine ligand 16CXCL1, C-X-C motif chemokine ligand 1CXCL9, C-X-C motif chemokine ligand 9E2F1, E2F transcription factor 1EIF4A1, eukaryotic translation initiation factor 4A1GRB2, growth factor receptor bound protein 2HDAC1, histone deacetylase 1HDAC2, histone deacetylase 2HDAC3, histone deacetylase 3HIF1A, hypoxia inducible factor 1 subunit alphaICTV, International Committee on Taxonomy of VirusesIFNGR2, interferon gamma receptor 2IKBKE, inhibitor of nuclear factor kappa B kinase subunit epsilonIL10, interleukin 10IL13, interleukin 13IL15, interleukin 15IL16, interleukin 16IL17A, interleukin 17 AIL21, interleukin 21IL22, interleukin 22IL24, interleukin 24IL25, interleukin 25IL2, interleukin 2IL33, interleukin 33IL5, interleukin 5IL7, interleukin 7IL8, interleukin 8JAK1, Janus kinase 1JAK2, Janus kinase 2JAK/STATJARID1A, lysine demethylase 5AJARID1B, lysine demethylase 5BJARID1C, lysine demethylase 5CJARID2, Jumonji and AT-rich interaction domain containing 2KEGG, Kyoto Encyclopedia of Genes and GenomesMAPK1, mitogen-activated protein kinase 1MAPK3, mitogen-activated protein kinase 3MAPK4, mitogen-activated protein kinase 4MAPK6, mitogen-activated protein kinase 6MAPK7, mitogen-activated protein kinase 7miRNANFKBNFKB1, nuclear factor kappa B subunit 1NFKBIE, NFKB inhibitor epsilonNOS3, nitric oxide synthase 3PANTHER, protein analysis through evolutionary relationshipsPIK3CA, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alphaPTEN, phosphatase and tensin homologRB1, RB transcriptional corepressor 1RHOA, ras homolog family member ASARS-CoV-2SARS-CoV-2, severe acute respiratory disease coronavirus type 2SMAD2, SMAD family member 2SMAD3, SMAD family member 3SMAD4, SMAD family member 4SOCS1, suppressor of cytokine signaling 1SOCS3, suppressor of cytokine signaling 3SOCS4, suppressor of cytokine signaling 4SOCS5, suppressor of cytokine signaling 5SOCS6, suppressor of cytokine signaling 6SOS1, SOS Ras/Rac guanine nucleotide exchange factor 1SP1, Sp1 transcription factorSTAT3, signal transducer and activator of transcription 3STAT4, signal transducer and activator of transcription 4STAT5B, signal transducer and activator of transcription 5BSTAT6, signal transducer and activator of transcription 6SUMO1, small ubiquitin like modifier 1SUMO2, small ubiquitin like modifier 2TBP, TATA-box binding proteinTGFBTGFBR1, transforming growth factor beta receptor 1TGFBR2, transforming growth factor beta receptor 2TMPRSS11A, transmembrane serine protease 11ATMPRSS4, transmembrane serine protease 4TNFRSF21, TNF receptor superfamily member 21WHO, World Health Organization

Identifiers

PMID33398248
PMCPMC7773562
OpenAlexW3117154968

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.