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Meta-Analysis Identifies Type I Interferon Response as Top Pathway Associated with SARS Infection
Amber Park, Marius Nwobi, Laura K. Harris
Pages - 14 - 39     |    Revised - 25-07-2021     |    Published - 31-08-2021
Volume - 14   Issue - 2    |    Publication Date - August 2021  Table of Contents
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KEYWORDS
Pathway Activity, Meta-analysis, Gene Set Enrichment Analysis, SARS, Interferon.
ABSTRACT
Background: Severe Acute Respiratory Syndrome (SARS) corona virus (CoV) infections are a serious public health threat because of their pandemic-causing potential. This work examines pathway signatures derived from mRNA expression data as a measure of differential pathway activity between SARS and mock infection using a meta-analysis approach to predict pathways associated with SARS infection that may have potential as therapeutic targets to preclude or overcome SARS infections. This work applied a GSEA-based, meta-analysis approach for analyzing pathway signatures from gene expression data to determine if such an approach would overcome FET limitations and identify more pathways associated with SARS infections than observed in our previous work using gene signatures.

Methods: This work defines 37 pathway signatures, each a ranked list of pathway activity changes associated with a specific SARS infection. SARS infections include seven SARS-CoV1 strains with established mutations that vary virulence (infectious clone SARS (icSARS), Urbani, MA15, ORF6, Bat-SRBD, NSP16, and ExoNI), MERS-CoV, and SARS-CoV2 in human lung cultures and/or mouse lung samples. To compare across signatures, positive and negative icSARS pathway panels are defined from shared leading-edge pathways identified by Gene Set Enrichment Analysis (GSEA) between two icSARSvsmock signatures, both from human cultures. GSEA then assesses enrichment in all 37 signatures and identifies leading-edge icSARS panel pathways for each analysis. A meta-analysis across identified leading-edge pathways reveals commonalities which are ranked by Stouffer’s method for combining p-values.

Results: Significant enrichment (GSEA p<0.001) is observed between the two icSARSvsmock signatures used to define positive (195 pathways) and negative (173 pathways) icSARS panels. Consistent, non-random (null distribution p<0.01), significant enrichment of the positive icSARS pathway panel in all pathway signatures is observed but significant enrichment is inconsistent for the negative icSARS panel. After meta-analysis, 11 pathways are found in all GSEA-identified leading-edges from the positive icSARS panel. Identified pathways are involved in the immune system with response to type I interferon ranked highest.

Conclusion: This GSEA-based meta-analysis approach identifies pathways with and without reported associations with SARS infections, highlighting this approach’s predictability and usefulness in identifying pathway activity changes.
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Miss Amber Park
Science Department, Davenport University, Grand Rapids, 49512 - United States of America
Mr. Marius Nwobi
Biochemistry Department, Michigan State University, East Lansing, 48824 - United States of America
Dr. Laura K. Harris
Institute for Cyber Enabled Research, Michigan State University, East Lansing, 48824 - United States of America
oesterei@msu.edu