Computationally predicted SARS-COV-2 encoded microRNAs target NFKB, JAK/STAT and TGFB signaling pathways
© 2020 Elsevier Inc. All rights reserved..
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 NFKB1, NFKBIE, JAK1-2, STAT3-4, STAT5B, STAT6, SOCS1-6, IL2, IL8, IL10, IL17, TGFBR1-2, SMAD2-4, HDAC1-6 and JARID1A-C, JARID2 play important roles in NFKB, JAK/STAT and TGFB signaling pathways as well as cells' epigenetic regulation pathways. Our results may help to understand virus-host interaction and the role of viral miRNAs during SARS-CoV-2 infection. As there is no current drug and effective treatment available for COVID19, it may also help to develop new treatment strategies.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:22 |
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| Enthalten in: |
Gene reports - 22(2021) vom: 31. März, Seite 101012 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Aydemir, Merve Nur [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 10.11.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.genrep.2020.101012 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM319621618 |
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| 520 | |a © 2020 Elsevier Inc. All rights reserved. | ||
| 520 | |a 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 NFKB1, NFKBIE, JAK1-2, STAT3-4, STAT5B, STAT6, SOCS1-6, IL2, IL8, IL10, IL17, TGFBR1-2, SMAD2-4, HDAC1-6 and JARID1A-C, JARID2 play important roles in NFKB, JAK/STAT and TGFB signaling pathways as well as cells' epigenetic regulation pathways. Our results may help to understand virus-host interaction and the role of viral miRNAs during SARS-CoV-2 infection. As there is no current drug and effective treatment available for COVID19, it may also help to develop new treatment strategies | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a ACE-2, angiotensin-converting enzyme 2 | |
| 650 | 4 | |a AKT1, AKT serine/threonine kinase 1 | |
| 650 | 4 | |a BCL2, BCL2 apoptosis regulator | |
| 650 | 4 | |a CDK1, cyclin dependent kinase 1 | |
| 650 | 4 | |a CDKL2, cyclin dependent kinase like 2 | |
| 650 | 4 | |a COVID19, new type corona virus disease | |
| 650 | 4 | |a CTNNB1, catenin beta 1 | |
| 650 | 4 | |a CXCL1, C-X-C motif chemokine ligand 1 | |
| 650 | 4 | |a CXCL10, C-X-C motif chemokine ligand 10 | |
| 650 | 4 | |a CXCL11, C-X-C motif chemokine ligand 11 | |
| 650 | 4 | |a CXCL16, C-X-C motif chemokine ligand 16 | |
| 650 | 4 | |a CXCL9, C-X-C motif chemokine ligand 9 | |
| 650 | 4 | |a E2F1, E2F transcription factor 1 | |
| 650 | 4 | |a EIF4A1, eukaryotic translation initiation factor 4A1 | |
| 650 | 4 | |a GRB2, growth factor receptor bound protein 2 | |
| 650 | 4 | |a HDAC1, histone deacetylase 1 | |
| 650 | 4 | |a HDAC2, histone deacetylase 2 | |
| 650 | 4 | |a HDAC3, histone deacetylase 3 | |
| 650 | 4 | |a HIF1A, hypoxia inducible factor 1 subunit alpha | |
| 650 | 4 | |a ICTV, International Committee on Taxonomy of Viruses | |
| 650 | 4 | |a IFNGR2, interferon gamma receptor 2 | |
| 650 | 4 | |a IKBKE, inhibitor of nuclear factor kappa B kinase subunit epsilon | |
| 650 | 4 | |a IL10, interleukin 10 | |
| 650 | 4 | |a IL13, interleukin 13 | |
| 650 | 4 | |a IL15, interleukin 15 | |
| 650 | 4 | |a IL16, interleukin 16 | |
| 650 | 4 | |a IL17A, interleukin 17 A | |
| 650 | 4 | |a IL2, interleukin 2 | |
| 650 | 4 | |a IL21, interleukin 21 | |
| 650 | 4 | |a IL22, interleukin 22 | |
| 650 | 4 | |a IL24, interleukin 24 | |
| 650 | 4 | |a IL25, interleukin 25 | |
| 650 | 4 | |a IL33, interleukin 33 | |
| 650 | 4 | |a IL5, interleukin 5 | |
| 650 | 4 | |a IL7, interleukin 7 | |
| 650 | 4 | |a IL8, interleukin 8 | |
| 650 | 4 | |a JAK/STAT | |
| 650 | 4 | |a JAK1, Janus kinase 1 | |
| 650 | 4 | |a JAK2, Janus kinase 2 | |
| 650 | 4 | |a JARID1A, lysine demethylase 5A | |
| 650 | 4 | |a JARID1B, lysine demethylase 5B | |
| 650 | 4 | |a JARID1C, lysine demethylase 5C | |
| 650 | 4 | |a JARID2, Jumonji and AT-rich interaction domain containing 2 | |
| 650 | 4 | |a KEGG, Kyoto Encyclopedia of Genes and Genomes | |
| 650 | 4 | |a MAPK1, mitogen-activated protein kinase 1 | |
| 650 | 4 | |a MAPK3, mitogen-activated protein kinase 3 | |
| 650 | 4 | |a MAPK4, mitogen-activated protein kinase 4 | |
| 650 | 4 | |a MAPK6, mitogen-activated protein kinase 6 | |
| 650 | 4 | |a MAPK7, mitogen-activated protein kinase 7 | |
| 650 | 4 | |a NFKB | |
| 650 | 4 | |a NFKB1, nuclear factor kappa B subunit 1 | |
| 650 | 4 | |a NFKBIE, NFKB inhibitor epsilon | |
| 650 | 4 | |a NOS3, nitric oxide synthase 3 | |
| 650 | 4 | |a PANTHER, protein analysis through evolutionary relationships | |
| 650 | 4 | |a PIK3CA, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha | |
| 650 | 4 | |a PTEN, phosphatase and tensin homolog | |
| 650 | 4 | |a RB1, RB transcriptional corepressor 1 | |
| 650 | 4 | |a RHOA, ras homolog family member A | |
| 650 | 4 | |a SARS-CoV-2 | |
| 650 | 4 | |a SARS-CoV-2, severe acute respiratory disease coronavirus type 2 | |
| 650 | 4 | |a SMAD2, SMAD family member 2 | |
| 650 | 4 | |a SMAD3, SMAD family member 3 | |
| 650 | 4 | |a SMAD4, SMAD family member 4 | |
| 650 | 4 | |a SOCS1, suppressor of cytokine signaling 1 | |
| 650 | 4 | |a SOCS3, suppressor of cytokine signaling 3 | |
| 650 | 4 | |a SOCS4, suppressor of cytokine signaling 4 | |
| 650 | 4 | |a SOCS5, suppressor of cytokine signaling 5 | |
| 650 | 4 | |a SOCS6, suppressor of cytokine signaling 6 | |
| 650 | 4 | |a SOS1, SOS Ras/Rac guanine nucleotide exchange factor 1 | |
| 650 | 4 | |a SP1, Sp1 transcription factor | |
| 650 | 4 | |a STAT3, signal transducer and activator of transcription 3 | |
| 650 | 4 | |a STAT4, signal transducer and activator of transcription 4 | |
| 650 | 4 | |a STAT5B, signal transducer and activator of transcription 5B | |
| 650 | 4 | |a STAT6, signal transducer and activator of transcription 6 | |
| 650 | 4 | |a SUMO1, small ubiquitin like modifier 1 | |
| 650 | 4 | |a SUMO2, small ubiquitin like modifier 2 | |
| 650 | 4 | |a TBP, TATA-box binding protein | |
| 650 | 4 | |a TGFB | |
| 650 | 4 | |a TGFBR1, transforming growth factor beta receptor 1 | |
| 650 | 4 | |a TGFBR2, transforming growth factor beta receptor 2 | |
| 650 | 4 | |a TMPRSS11A, transmembrane serine protease 11A | |
| 650 | 4 | |a TMPRSS4, transmembrane serine protease 4 | |
| 650 | 4 | |a TNFRSF21, TNF receptor superfamily member 21 | |
| 650 | 4 | |a WHO, World Health Organization | |
| 650 | 4 | |a miRNA | |
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| 700 | 1 | |a Cekin, Nilgun |e verfasserin |4 aut | |
| 700 | 1 | |a Pinarbasi, Ergun |e verfasserin |4 aut | |
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