Gene Expression Meta-Analysis Reveals Interferon-Induced Genes Associated With SARS Infection in Lungs

Abstract

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.

XAF1 Protects Host against Emerging RNA Viruses by Stabilizing IRF1-Dependent Antiviral Immunity

“…Given the rapid turnover of IRF1 mRNA and IRF1 proteins, XAF1 has more potential than IRF1 to be a sensitive and reliable diagnostic marker of viral infectious diseases. Our data and other clinical data sets have suggested that elevated XAF1 expression is positively correlated with the SARS-CoV, MERS-CoV, and SARS-CoV-2 infection in the lungs ( 49 , 50 ) as well as the symptoms of the COVID-19 and influenza patients ( 51 ).…”

XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers

“…Gao et al (23) found that XAF1 was abnormally strongly expressed in COVID-19 patients and positively correlated with the expression of ARS-CoV-2 invasion-related genes (ACE2, TMPRSS2, CTSB, and CTSL). In contrast, XAF1 was found to be associated with SARS infection by Park and Harris (24). A recent study found that OAS2 belongs to a subset of interferonstimulated genes, and OAS2 can be regarded as a potential candidate for a drug target in COVID-19 therapy (25).…”

Gene Expression Meta-Analysis of Colon Rectal Cancer Tumour Cells Reveals Genes in Association With Tumorogenesis

“…The resulting list of genes along with their T-test scores were used to define 13 gene signatures. This signatures are formed from the gene lists ranked from high to low differential gene expression between tumor versus normal tissue samples (Park & Harris, 2021). The signatures derived from the same dataset used the same control samples.…”

“…Previous work using a GSEA-based meta-analysis approach successfully identified known and novel genes associated with severe acute respiratory syndrome (SARS) infection through differential gene expression comparison between mRNA expression datasets (Park & Harris, 2021). Therefore, this paper applied the same GSEA-based approach to analyze mRNA expression data of tumor and normal CRC tissue derived from human colonic biopsy samples by defining and comparing gene expression signatures (i.e., gene lists ranked by differential expression) (15).…”