Meta-analysis of host responses induced by SARS-CoV-2 infection reveals a conserved immune network across multiple species


Identification: Cremin-Conor


Description

Meta-analysis of host responses induced by SARS-CoV-2 infection reveals a conserved immune network across multiple species

Authors
CONOR CREMIN(1), Bobo Wing-Yee Mok(1), Sara Ballouz(2), Megan Crow(3), Michi Miura(1), Wenjun Song(1), Pin Chen(1), Pui Wang(1), Honglian Liu(1), Siwen Liu(1), Jesse Gillis(3) & Honglin Chen(1)
Affiliations 
1.      Department of Microbiology and State Key Laboratory for Emerging Infectious Diseases, The University of Hong Kong, Hong Kong, China
2.      Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghust, NSW 2010, Australia 
3.      The Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
Abstract 
The current strife experienced by the 2020 COVID-19 pandemic has facilitated a significant push to understand the underlining pathogenicity of the SARS-CoV-2 virus. However, the comprehensive picture of how the virus interacts with its infected hosts remains elusive. Here, we utilized a twopronged approach to robustly characterize gene expression induced by SARS-CoV-2 infection. First, a meta-analysis of SARS-CoV-2 infection studies using human, mouse, ferret, and hamster was performed and identified a recurrent differentially expressed gene signature across all species. Then, using aggregated co-expression networks that were constructed from many gene expression datasets, we identified a gene module that characterizes this recurrent response which was conserved across each species. Gene enrichment analysis suggests that this module is mainly associated with immune processes. The identification of this module reveals SARS-CoV-2 infection can induce the expression of a universal core gene network thus highlighting upstream processes potentially modulated by SARS-CoV-2 interference.
This methodology has been applied in the expression analysis of various infection diseases and demonstrated to be highly adaptable with improved precision at identifying transcriptomic signatures from various experimental conditions.

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