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10.1038/s41597-020-00628-6 http://scihub22266oqcxt.onion/10.1038/s41597-020-00628-6 32963239!7509801!32963239     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32963239&cmd=llinks): Failed to open stream: HTTP request failed! 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Consensus transcriptional regulatory networks of coronavirus-infected human cells |pdf=|usr=}}{{32963239}} gab.com Text Consensus transcriptional regulatory networks of coronavirus-infected human cells JO: Sci Data http://www.kidney.de/pget.php?&tw=1&pmid=32963239 #FOAvip Establishing consensus around the transcriptional interface between coronavirus (CoV) infection and human cellular signaling pathways can catalyze the development of novel anti-CoV therapeutics. Here, we used publicly archived transcriptomic datasets to compute consensus regulatory signatures, or consensomes, that rank human genes based on their rates of differential expression in MERS-CoV (MERS), SARS-CoV-1 (SARS1) and SARS-CoV-2 (SARS2)-infected cells. Validating the CoV consensomes, we show that high confidence transcriptional targets (HCTs) of MERS, SARS1 and SARS2 infection intersect with HCTs of signaling pathway nodes with known roles in CoV infection. Among a series of novel use cases, we gather evidence for hypotheses that SARS2 infection efficiently represses E2F family HCTs encoding key drivers of DNA replication and the cell cycle; that progesterone receptor signaling antagonizes SARS2-induced inflammatory signaling in the airway epithelium; and that SARS2 HCTs are enriched for genes involved in epithelial to mesenchymal transition. The CoV infection consensomes and HCT intersection analyses are freely accessible through the Signaling Pathways Project knowledgebase, and as Cytoscape-style networks in the Network Data Exchange repository. Twit Text FOAVip Consensus transcriptional regulatory networks of coronavirus-infected human cells ????Sci Data http://www.kidney.de/pget.php?&tw=1&pmid=32963239 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32963239 #FOAvip English Wikipedia <ref>{{Cite pmid|32963239}}</ref> Consensus transcriptional regulatory networks of coronavirus-infected human cells #MMPMID32963239 Ochsner SA; Pillich RT; McKenna NJ Sci Data 2020[Sep]; 7 (1): 314 PMID32963239show ga Establishing consensus around the transcriptional interface between coronavirus (CoV) infection and human cellular signaling pathways can catalyze the development of novel anti-CoV therapeutics. Here, we used publicly archived transcriptomic datasets to compute consensus regulatory signatures, or consensomes, that rank human genes based on their rates of differential expression in MERS-CoV (MERS), SARS-CoV-1 (SARS1) and SARS-CoV-2 (SARS2)-infected cells. Validating the CoV consensomes, we show that high confidence transcriptional targets (HCTs) of MERS, SARS1 and SARS2 infection intersect with HCTs of signaling pathway nodes with known roles in CoV infection. Among a series of novel use cases, we gather evidence for hypotheses that SARS2 infection efficiently represses E2F family HCTs encoding key drivers of DNA replication and the cell cycle; that progesterone receptor signaling antagonizes SARS2-induced inflammatory signaling in the airway epithelium; and that SARS2 HCTs are enriched for genes involved in epithelial to mesenchymal transition. The CoV infection consensomes and HCT intersection analyses are freely accessible through the Signaling Pathways Project knowledgebase, and as Cytoscape-style networks in the Network Data Exchange repository. |*Transcriptome[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Cell Cycle[MESH] |Consensus[MESH] |Coronavirus Infections/*genetics[MESH] |DNA Replication[MESH] |Datasets as Topic[MESH] |Epithelial-Mesenchymal Transition/*genetics[MESH] |Gene Expression[MESH] |Humans[MESH] |Interferon Regulatory Factors/genetics[MESH] |Middle East Respiratory Syndrome Coronavirus[MESH] |Pandemics[MESH] |Pneumonia, Viral/*genetics[MESH] |Receptors, Progesterone[MESH] |SARS-CoV-2[MESH] |Severe acute respiratory syndrome-related coronavirus[MESH]Consensus transcriptional regulatory networks of coronavirus-infected (epmc).pdf DeepDyve Pubget Overpricing