Hepatitis C virus down-regulates SERPINE1/PAI-1 expression to facilitate its replication

Yang, Chee-Hing; Li, Huichun; Ku, Tzu-Shan; Wu, Pi-Ching; Yeh, Yung-Ju; Cheng, Ju‐Chien; Lin, Teng-Yi; Lo, Shih-Yen

doi:10.1099/jgv.0.000901

Cited by 12 publications

(22 citation statements)

References 41 publications

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“…Down-regulated SERPINE1 could accelerate the progression of inflammation. 56 NFKBIA belongs to the NF-κB inhibitor family and could interact with REL dimers to inhibit NF-κB/REL complexes that reported to be involved in inflammatory response. 57 Therefore, SERPINE1 and NFKBIA could negatively regulate inflammatory progress, which is consisted with our result.…”

Section: Discussionmentioning

confidence: 99%

“…We therefore speculated that chemokine CCL5 In OA, we discovered that all genes in DEGsOA (coexpressed genes and OA-specific genes) were all playing the role of promoting inflammation progress by activating TNF signalling pathway. 56 NFKBIA belongs to the NF-κB inhibitor family and could interact with REL dimers to inhibit NF-κB/REL complexes that reported to be involved in inflammatory response. We next investigated which part might play the greater role in RA development between coexpressed and RA-specific genes.…”

Section: Identify Biomarkers and Pathway In Ramentioning

confidence: 99%

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Comparison of rheumatoid arthritis (RA) and osteoarthritis (OA) based on microarray profiles of human joint fibroblast‐like synoviocytes

Cai

Jiang

et al. 2018

Cell Biochemistry & Function

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The purpose of the present study was to investigate the underlying molecular mechanism of osteoarthritis (OA) and rheumatoid arthritis (RA) based on microarray profiles. Three human joint fibroblast-like synoviocytes (FLSs) microarray profiles including 26 OA samples, 33 RA samples, and 20 healthy control (HC) samples were downloaded from the GEO database. Differentially expressed genes (DEGs) between OA and HC (DEGsOA) and RA and HC (DEGsRA) were identified. Co-expressed and specific genes were analysed between DEGsOA and DEGsRA. Gene ontology, KEGG pathway enrichment, PPI network, and GSEA were performed to predict the function of DEGs. Two hundred seventy-six and 410 differential genes in DEGsOA and DEGsRA were observed. One hundred fifty coexpressed genes and 126 OA-specific genes (SELE, SERPINE1, and NFKBIA were the key genes) between DEGsOA and DEGsRA were enriched in the tumour necrosis factor (TNF) signalling pathway. However, 260 RA-specific genes of which the key genes were CCR5, CCR7, CXCR4, CCL5, and CCR4 were enriched in chemokine signalling pathway. Therefore, FLSs might exert an inflammatory effect by regulating TNF signalling pathway, targeting SELE, SERPINE1, and NFKBIA during the process of OA. Although TNF signalling pathway was also involved in the synovitis of RA, chemokine signalling pathway played the key role in RA FLSs mediating cell migration, invasion, and release of chemotaxis. In addition, CCR5, CCR7, CXCR4, CCL5, and CCR4 might be hub genes in RA. The different biomarkers and pathways identified in OA and RA may provide references for further study.List of abbreviations: CCL5, C-C motif chemokine ligand 5; CCRs, C-C motif chemokine receptors; CD95, FAS cell surface death receptor; Co-expressed genes, the common genes both in DEGsOA and DEGsRA groups; CXCR4, C-X-C motif chemokine receptor 4; DAVID, Database for Annotation, Visualization, and Integrated Discovery; DEGs, differently expressed genes; DEGsOA, differently expressed genes between osteoarthritis and healthy fibroblast-like synoviocytes; DEGsRA, differently expressed genes between rheumatoid arthritis and healthy fibroblast-like synoviocytes; DLL1, delta-like protein 1

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Section: Discussionmentioning

confidence: 99%

Section: Identify Biomarkers and Pathway In Ramentioning

confidence: 99%

Comparison of rheumatoid arthritis (RA) and osteoarthritis (OA) based on microarray profiles of human joint fibroblast‐like synoviocytes

Cai

Jiang

et al. 2018

Cell Biochemistry & Function

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“…Similarly, GO enrichment analysis, many up and down-regulated genes may play a very key role in the progression of SARS-CoV-2 infection. Enriched genes such as IFNL4 [117], ZFP36 [118], CD274 [119], MIR21 [120], CYP1A1 [121], CMPK2 [122], C4A [123], SERPINE1 [124], CCRL2 [125], CD69 [126], FAH (fumarylacetoacetate hydrolase) [127], ERFE (erythroferrone) [128], SERINC5 [129], ADI1 [130], AP2M1 [131], PRIMPOL (primase and DNA directed polymerase) [132], BIRC5 [133] and TF (transferrin) [134] were responsible for the advancement of various viral infections, but these genes may be involved in the progression of SARS-CoV-2 infection. Enriched genes such as APOBEC3G [135], NLRC5 [136], PTX3 [137], FAP ( broblast activation protein alpha) [138] and CAT (catalase) [139] were linked with the development of in uenza viral infection, but these genes may be associated with progression of SARS-CoV-2 infection as well.…”

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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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“…Similarly, GO enrichment analysis, many up and down regulated genes may play a very key role in progression of SARS-CoV-2 infection. Enriched genes such as IFNL4 [116], ZFP36 [117], CD274 [118], MIR21 [119], CYP1A1 [120], CMPK2 [121], C4A [122], SERPINE1 [123], CCRL2 [124], CD69 [125], FAH (fumarylacetoacetate hydrolase) [126], ERFE (erythroferrone) [127], SERINC5 [128], ADI1 [129], AP2M1 [130], PRIMPOL (primase and DNA directed polymerase) [131], BIRC5 [132] and TF (transferrin) [133] were responsible for advancement of various viral infections, but these genes may be involved in progression of SARS-CoV-2 infection. Enriched genes such as APOBEC3G [134], NLRC5 [135], PTX3 [136], FAP ( broblast activation protein alpha) [137] and CAT (catalase) [138] were linked with development of in uenza viral infection, but these genes may be associated with progression of SARS-CoV-2 infection.…”

Section: Roc Analysismentioning

confidence: 99%

Bioinformatics analysis of expression profiling by high throughput sequencing for identification of potential key genes among SARS-CoV-2/COVID 19

Vastrad

Vastrad²

2021

Preprint

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Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) is pandemic recently emerged and is rapidly spreading in humans. However, the precise molecular mechanisms of the advancement and progression of SARS-CoV-2 infection remain unclear. The current investigation attempted to identify and functionally analyze the differentially expressed genes (DEGs) between SARS-CoV-2 infection and mock by using comprehensive bioinformatics analyses. The GSE148729 expression profiling by high throughput sequencing was downloaded from the Gene Expression Omnibus (GEO) and analyzed using 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 used to evaluate the interactions of up and down regulated genes and to construct a protein-protein interaction (PPI) network using Cytoscape software. Hub genes were selected using the Network Analyzer plugin. Subsequently, extensive target prediction and network analyses methods were used to assess, target gene - miRNA regulatory network and target gene - TF regulatory network. Receiver operating characteristic (ROC) analysis was utilized for validation. A total of 928 DEGs (461 up regulated genes and 467 down regulated genes) were identified between SARS-CoV-2 infection and mock samples. The Pathway enrichment analysis results showed that these 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 as being closely associated with these up and down regulated genes. The top hub genes and target genes were screened and 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 for SARS-CoV-2 infection and mock. The current investigation identified a series of key genes and pathways that may be involved 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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Hepatitis C virus down-regulates SERPINE1/PAI-1 expression to facilitate its replication

Cited by 12 publications

References 41 publications

Comparison of rheumatoid arthritis (RA) and osteoarthritis (OA) based on microarray profiles of human joint fibroblast‐like synoviocytes

Comparison of rheumatoid arthritis (RA) and osteoarthritis (OA) based on microarray profiles of human joint fibroblast‐like synoviocytes

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

Bioinformatics analysis of expression profiling by high throughput sequencing for identification of potential key genes among SARS-CoV-2/COVID 19

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