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10.3389/fimmu.2021.733171

http://scihub22266oqcxt.onion/10.3389/fimmu.2021.733171
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suck abstract from ncbi


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pmid34880855      Front+Immunol 2021 ; 12 (ä): 733171
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  • Transcriptional Profiling and Machine Learning Unveil a Concordant Biosignature of Type I Interferon-Inducible Host Response Across Nasal Swab and Pulmonary Tissue for COVID-19 Diagnosis #MMPMID34880855
  • Zhang C; Feng YG; Tam C; Wang N; Feng Y
  • Front Immunol 2021[]; 12 (ä): 733171 PMID34880855show ga
  • BACKGROUND: COVID-19, caused by SARS-CoV-2 virus, is a global pandemic with high mortality and morbidity. Limited diagnostic methods hampered the infection control. Since the direct detection of virus mainly by RT-PCR may cause false-negative outcome, host response-dependent testing may serve as a complementary approach for improving COVID-19 diagnosis. OBJECTIVE: Our study discovered a highly-preserved transcriptional profile of Type I interferon (IFN-I)-dependent genes for COVID-19 complementary diagnosis. METHODS: Computational language R-dependent machine learning was adopted for mining highly-conserved transcriptional profile (RNA-sequencing) across heterogeneous samples infected by SARS-CoV-2 and other respiratory infections. The transcriptomics/high-throughput sequencing data were retrieved from NCBI-GEO datasets (GSE32155, GSE147507, GSE150316, GSE162835, GSE163151, GSE171668, GSE182569). Mathematical approaches for homological analysis were as follows: adjusted rand index-related similarity analysis, geometric and multi-dimensional data interpretation, UpsetR, t-distributed Stochastic Neighbor Embedding (t-SNE), and Weighted Gene Co-expression Network Analysis (WGCNA). Besides, Interferome Database was used for predicting the transcriptional factors possessing IFN-I promoter-binding sites to the key IFN-I genes for COVID-19 diagnosis. RESULTS: In this study, we identified a highly-preserved gene module between SARS-CoV-2 infected nasal swab and postmortem lung tissue regulating IFN-I signaling for COVID-19 complementary diagnosis, in which the following 14 IFN-I-stimulated genes are highly-conserved, including BST2, IFIT1, IFIT2, IFIT3, IFITM1, ISG15, MX1, MX2, OAS1, OAS2, OAS3, OASL, RSAD2, and STAT1. The stratified severity of COVID-19 may also be identified by the transcriptional level of these 14 IFN-I genes. CONCLUSION: Using transcriptional and computational analysis on RNA-seq data retrieved from NCBI-GEO, we identified a highly-preserved 14-gene transcriptional profile regulating IFN-I signaling in nasal swab and postmortem lung tissue infected by SARS-CoV-2. Such a conserved biosignature involved in IFN-I-related host response may be leveraged for COVID-19 diagnosis.
  • |COVID-19 Testing/*methods[MESH]
  • |COVID-19/*diagnosis/genetics/metabolism[MESH]
  • |Diagnosis, Differential[MESH]
  • |Gene Expression Profiling[MESH]
  • |Gene Regulatory Networks[MESH]
  • |Humans[MESH]
  • |Interferon Type I/*metabolism[MESH]
  • |Lung/metabolism[MESH]
  • |Machine Learning[MESH]
  • |Nasopharynx/metabolism[MESH]
  • |Respiratory Tract Infections[MESH]
  • |SARS-CoV-2/*isolation & purification[MESH]
  • |Severity of Illness Index[MESH]


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