A transcriptional regulatory atlas of coronavirus infection of human cells

Abstract

Abstract Identifying transcriptional responses that are most consistently associated with experimental coronavirus (CoV) infection can help illuminate human cellular signaling pathways impacted by CoV infection. Here, we distilled over 3,000,000 data points from publically archived CoV infection transcriptomic datasets into consensus regulatory signatures, or consensomes, that rank genes based on their transcriptional responsiveness to infection of human cells by MERS, SARS-CoV-1 and SARS-CoV-2 subtypes. We computed overlap between genes with elevated rankings in the CoV consensomes against those from transcriptomic and ChIP-Seq consensomes for nearly 880 cellular signaling pathway nodes. Validating the CoV infection consensomes, we identified robust overlap between their highly ranked genes and high confidence targets of signaling pathway nodes with known roles in CoV infection. We then developed a series of use cases that illustrate the utility of the CoV consensomes for hypothesis generation around mechanistic aspects of the cellular response to CoV infection. We make the CoV infection consensomes and their universe of underlying data points freely accessible through the Signaling Pathways Project web knowledgebase.