Human microRNA‐30 inhibits influenza virus infection by suppressing the expression of SOCS1, SOCS3, and NEDD4

Lin, Xian; Yu, Shiman; Ren, Peilei; Sun, Xiaomei; Jin, Meilin

doi:10.1111/cmi.13150

Cited by 28 publications

(24 citation statements)

References 48 publications

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“…Enriched genes such as KMO (kynurenine 3-monooxygenase) [ Swainson et al, 2019 ], KYNU (kynureninase) [ Finney et al, 2019 ], BIRC3 [ Rouka, 2018 ], GBP2 [ Yu et al, 2020 ], DDX58 [ Zhu et al, 2019 ], IRF8 [ Sun et al, 2016 ], IFIT2 [ Butchi et al, 2014 ], TRIM5 [ van Manen et al, 2008 ], RSAD2 [ Kurokawa et al, 2019 ], IFI6 [ Richardson et al, 2018 ], SP100 [ Kim et al, 2011 ], TRIM21 [ Fan et al, 2016 ], CXCL9 [ Huang et al, 2012 ], CCL8 [ Rom et al, 2010 ], CXCL11 [ Chalin et al, 2019 ], ELMO1 [ Janardhan et al, 2004 ], ITK (IL2 inducible T cell kinase) [ He et al, 2014 ], CYP27A1 [ Yang et al, 2019 ], RPS13 [ Robichaux et al, 2016 ], RPS17 [ Kenney and Meng, 2015 ], RPS19 [ Ganaie et al, 2014 ], RPL4 [ Chen et al, 2016 ], RPL13 [ Han et al, 2020 ], RPL18 [ R. Wang et al, 2018 ], RUVBL2 [ Morwitzer et al, 2019 ], JUN (Jun proto-oncogene, AP-1 transcription factor subunit) [ Benn et al, 1996 ] and GPX4 [ Brault et al, 2016 ] were involved in development of different viral infections, but these genes may be responsible for progression of SARS-CoV-2 infection. Enriched genes such as IDO1 [ Fox et al, 2015 ], CCL2 [ Lai et al, 2017 ], AIM2 [ Zhang et al, 2017 ], STAT2 [ Warnking et al, 2015 ], GBP5 [ Feng et al, 2017 ], CASP1 [ Ren et al, 2017 ], OAS2 [ Zhao et al, 2019 ], STAT4 [ Bot et al, 2003 ], TRIM22 [ Di Pietro et al, 2013 ], PML (promyelocyticleukemia) [ Li et al, 2009 ], IFITM1 [ Yu et al, 2015 ], ISG15 [ Sanyal et al, 2013 ], MX1 [ Verhelst et al, 2012 ], MX2 [ Jin et al, 2001 ], IRF4 [ Ainsua-Enrich et al, 2019 ], NEDD4 [ Lin et al, 2020 ], HERC5 [ Tang et al, 2010 ], CXCR2 [ Washburn et al, 2019 ]...…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analyses of significant genes, related pathways, and candidate diagnostic biomarkers and molecular targets in SARS-CoV-2/COVID-19

Vastrad¹,

Vastrad²,

Tengli

2020

Gene Reports

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Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection is a leading cause of pneumonia and death. The aim of this investigation is to identify the key genes in SARS-CoV-2 infection and uncover their potential functions. We downloaded the expression profiling by high throughput sequencing of GSE152075 from the Gene Expression Omnibus database. Normalization of the data from primary SARS-CoV-2 infected samples and negative control samples in the database was conducted using R software. Then, joint analysis of the data was performed. Pathway and Gene ontology (GO) enrichment analyses were performed, and the protein-protein interaction (PPI) network, target gene - miRNA regulatory network, target gene - TF regulatory network of the differentially expressed genes (DEGs) were constructed using Cytoscape software. Identification of diagnostic biomarkers was conducted using receiver operating characteristic (ROC) curve analysis. 994 DEGs (496 up regulated and 498 down regulated genes) were identified. Pathway and GO enrichment analysis showed up and down regulated genes mainly enriched in the NOD-like receptor signaling pathway, Ribosome, response to external biotic stimulus and viral transcription in SARS-CoV-2 infection. Down and up regulated genes were selected to establish the PPI network, modules, target gene - miRNA regulatory network, target gene - TF regulatory network revealed that these genes were involved in adaptive immune system, fluid shear stress and atherosclerosis, influenza A and protein processing in endoplasmic reticulum. In total, ten genes (CBL, ISG15, NEDD4, PML, REL, CTNNB1, ERBB2, JUN, RPS8 and STUB1) were identified as good diagnostic biomarkers. In conclusion, the identified DEGs, hub genes and target genes contribute to the understanding of the molecular mechanisms underlying the advancement of SARS-CoV-2 infection and they may be used as diagnostic and molecular targets for the treatment of patients with SARS-CoV-2 infection in the future.

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Section: Discussionmentioning

confidence: 99%

Bioinformatics analyses of significant genes, related pathways, and candidate diagnostic biomarkers and molecular targets in SARS-CoV-2/COVID-19

Vastrad¹,

Vastrad²,

Tengli

2020

Gene Reports

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show abstract

“…A total of 928 DEGs were identi ed, including 461 up-regulated genes and 467 down-regulated genes. Genes such as SOCS3 [48] and IL1A [49] were liable for progression of in uenza virus infection, but these genes may be linked with the progress of SARS-CoV-2 infection as well. Genes such as KLF6 [50], DUSP1 [51] and OLFM4 [52] were associated with the development of respiratory syncytial virus infection, but these genes may be responsible for the advancement of SARS-CoV-2 infection.…”

mentioning

confidence: 99%

Identification of Differentially Expressed Genes and Enriched Pathways in SARS-CoV-2/ COVID-19 using Bioinformatics Analysis

Alshabi

Shaikh

Vastrad

et al. 2020

Preprint

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BackgroundThe exact molecular mechanisms of the progression of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection remain unclear. The current investigation strived to understand and functionally analyze the differentially expressed genes (DEGs) between SARS-CoV-2 infection and mock samples applying extensive bioinformatics analyses.MethodsGSE148729 dataset was downloaded from the Gene Expression Omnibus (GEO) and investigated utilising the limma package in R software to identify DEGs. Pathway and gene ontology (GO) enrichment analysis of the up and down-regulated genes were performed in ToppGene. The HIPPIE database was applied to estimate the interactions of up and down-regulated genes and to construct a protein-protein interaction (PPI) network using Cytoscape software. Receiver operating characteristic (ROC) was utilized for validation.ResultsA total of 928 DEGs (461 up-regulated genes and 467 down-regulated genes) were identified between SARS-CoV-2 infection and mock samples. The up and down-regulated genes were significantly enriched in cytokine-cytokine receptor interaction, and ascorbate and aldarate metabolism. Several significant GO terms, including the response to biotic stimulus and oxoacid metabolic process, were identified. The top hub genes and target genes included JUN, FBXO6, PCLAF, CFTR, TXNIP, PMAIP1, BRI3BP, FAHD1, PROX1, CXCL11, SERHL2 and CFI. ROC curve analysis showed that messenger RNA levels of these ten genes (DDX58, IFITM2, IRF1, PML, SAMHD1, ACSS1, CYP2U1, DDC, PNMT and UGT2A3) exhibited better diagnostic efficiency between SARS-CoV-2 infection and mock.ConclusionsThe current investigation distinguished vital genes and pathways that may be implicated in the progression of SARS-CoV-2 infection, providing a new understanding of the underlying molecular mechanisms of SARS-CoV-2 infection.

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“…Previous studies report that miR-30c facilitated porcine reproductive and respiratory syndrome virus infection by targeting JAK1 and IFN-alpha/beta receptor beta chain ( 34 , 35 ). However, influenza A virus restrained host type I IFN-mediated antiviral immune responses by decreasing the expression of miR-30 family members via targeting SOCS1 and SOCS3 expression ( 36 ).…”

Section: Discussionmentioning

confidence: 99%

MicroRNA-30a Modulates Type I Interferon Responses to Facilitate Coxsackievirus B3 Replication Via Targeting Tripartite Motif Protein 25

Xie

et al. 2021

Front. Immunol.

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Viral myocarditis is caused by a viral infection and characterized by the inflammation of the myocardium. Coxsackievirus B3 (CVB3) infection is one of the most common among the infections caused by this virus. The host’s early innate immune response to CVB3 infection particularly depends on the functions of type I interferons (IFNs). In this study, we report that a host microRNA, miR-30a, was upregulated by CVB3 to facilitate its replication. We demonstrated that miR-30a was a potent negative regulator of IFN-I signaling by targeting tripartite motif protein 25 (TRIM25). In addition, we found that TRIM25 overexpression significantly suppressed CVB3 replication, whereas TRIM25 knockdown increased viral titer and VP1 protein expression. MiR-30a inhibits the expression of TRIM25 and TRIM25-mediated retinoic acid-inducible gene (RIG)-I ubiquitination to suppress IFN-β activation and production, thereby resulting in the enhancement of CVB3 replication. These results indicate the proviral role of miR-30a in modulating CVB3 infection for the first time. This not only provides a new strategy followed by CVB3 in order to modulate IFN-I–mediated antiviral immune responses by engaging host miR-30a but also improves our understanding of its pathogenesis.

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Human microRNA‐30 inhibits influenza virus infection by suppressing the expression of SOCS1, SOCS3, and NEDD4

Cited by 28 publications

References 48 publications

Bioinformatics analyses of significant genes, related pathways, and candidate diagnostic biomarkers and molecular targets in SARS-CoV-2/COVID-19

Bioinformatics analyses of significant genes, related pathways, and candidate diagnostic biomarkers and molecular targets in SARS-CoV-2/COVID-19

Identification of Differentially Expressed Genes and Enriched Pathways in SARS-CoV-2/ COVID-19 using Bioinformatics Analysis

MicroRNA-30a Modulates Type I Interferon Responses to Facilitate Coxsackievirus B3 Replication Via Targeting Tripartite Motif Protein 25

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