BackgroundSevere Acute Respiratory Syndrome (SARS) corona virus (CoV) infections are a serious public health threat because of their pandemic-causing potential. This work is the first to analyze mRNA expression data from SARS infections through meta-analysis of gene signatures, possibly identifying therapeutic targets associated with major SARS infections.MethodsThis work defines 37 gene signatures representing SARS-CoV, Middle East Respiratory Syndrome (MERS)-CoV, and SARS-CoV2 infections in human lung cultures and/or mouse lung cultures or samples and compares them through Gene Set Enrichment Analysis (GSEA). To do this, positive and negative infectious clone SARS (icSARS) gene panels are defined from GSEA-identified leading-edge genes between two icSARS-CoV derived signatures, both from human cultures. GSEA then is used to assess enrichment and identify leading-edge icSARS panel genes between icSARS gene panels and 27 other SARS-CoV gene signatures. The meta-analysis is expanded to include five MERS-CoV and three SARS-CoV2 gene signatures. Genes associated with SARS infection are predicted by examining the intersecting membership of GSEA-identified leading-edges across gene signatures.ResultsSignificant enrichment (GSEA p<0.001) is observed between two icSARS-CoV derived signatures, and those leading-edge genes defined the positive (233 genes) and negative (114 genes) icSARS panels. Non-random significant enrichment (null distribution p<0.001) is observed between icSARS panels and all verification icSARSvsmock signatures derived from human cultures, from which 51 over- and 22 under-expressed genes are shared across leading-edges with 10 over-expressed genes already associated with icSARS infection. For the icSARSvsmock mouse signature, significant, non-random significant enrichment held for only the positive icSARS panel, from which nine genes are shared with icSARS infection in human cultures. Considering other SARS strains, significant, non-random enrichment (p<0.05) is observed across signatures derived from other SARS strains for the positive icSARS panel. Five positive icSARS panel genes, CXCL10, OAS3, OASL, IFIT3, and XAF1, are found across mice and human signatures regardless of SARS strains.ConclusionThe GSEA-based meta-analysis approach used here identifies genes with and without reported associations with SARS-CoV infections, highlighting this approach’s predictability and usefulness in identifying genes that have potential as therapeutic targets to preclude or overcome SARS infections.
BackgroundSevere Acute Respiratory Syndrome (SARS) corona virus (SARS-CoV) infections are a serious public health threat because of their pandemic-causing potential. This work uses mRNA expression data to predict genes associated with SARS-CoV infection through an innovative meta-analysis examining gene signatures (i.e., gene lists ranked by differential gene expression between SARS and mock infection).MethodsThis work defines 29 gene signatures representing SARS infection across seven strains with established mutations that vary virulence (infectious clone SARS (icSARS), Urbani, MA15, ΔORF6, BAT-SRBD, ΔNSP16, and ExoNI) and host (human lung cultures and/or mouse lung samples) and examines them through Gene Set Enrichment Analysis (GSEA). To do this, first positive and negative icSARS gene panels were defined from GSEA-identified leading-edge genes between 500 genes from positive or negative tails of the GSE47960-derived icSARSvsmock signature and the GSE47961-derived icSARSvsmock signature, both from human cultures. GSEA then was used to assess enrichment and identify leading-edge icSARS panel genes in the other 27 signatures. Genes associated with SARS-CoV infection are predicted by examining membership in GSEA-identified leading-edges across signatures.ResultsSignificant enrichment (GSEA p<0.001) was observed between GSE47960-derived and GSE47961-derived signatures, and those leading-edges defined the positive (233 genes) and negative (114 genes) icSARS panels. Non-random (null distribution p<0.001) significant enrichment (p<0.001) was observed between icSARS panels and all verification icSARSvsmock signatures derived from human cultures, from which 51 over- and 22 under-expressed genes were shared across leading-edges with 10 over-expressed genes already being associated with icSARS infection. For the icSARSvsmock mouse signature, significant, non-random enrichment (both p<0.001) held for only the positive icSARS panel, from which nine genes were shared with icSARS infection in human cultures. Considering other SARS strains, significant (p<0.01), non-random (p<0.002) enrichment was observed across signatures derived from other SARS strains for the positive icSARS panel. Five positive icSARS panel genes, CXCL10, OAS3, OASL, IFIT3, and XAF1, were found in mice and human signatures.ConclusionThe GSEA-based meta-analysis approach used here identified genes with and without reported associations with SARS-CoV infections, highlighting this approach’s predictability and usefulness in identifying genes that have potential as therapeutic targets to preclude or overcome SARS infections.
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