Consensus transcriptional regulatory networks of coronavirus-infected human cells

Abstract

15Identifying transcriptional responses that are most consistently associated with 16 experimental coronavirus (CoV) infection can help illuminate human cellular signaling 17 pathways impacted by CoV infection. Here, we distilled over 3,000,000 data points from 18 publically archived CoV infection transcriptomic datasets into consensus regulatory 19 signatures, or consensomes, that rank genes based on their transcriptional 20 responsiveness to infection of human cells by MERS, SARS-CoV-1 and SARS-CoV-2 21 subtypes. We computed overlap between genes with elevated rankings in the CoV 22 consensomes against those from transcriptomic and ChIP-Seq consensomes for nearly 23 880 cellular signaling pathway nodes. Validating the CoV infection consensomes, we 24 identified robust overlap between their highly ranked genes and high confidence targets 25 of signaling pathway nodes with known roles in CoV infection. We then developed a 26 series of use cases that illustrate the utility of the CoV consensomes for hypothesis 27 generation around mechanistic aspects of the cellular response to CoV infection. We 28 make the CoV infection consensomes and their universe of underlying data points freely 29 accessible through the Signaling Pathways Project web knowledgebase. 30 31 65 points in a series of use cases illuminates previously uncharacterized intersections 66 between CoV infection and human cellular signaling pathways. The CoV infection 67 consensome and its underlying datasets provide researchers with a unique and freely 68 accessible framework within which to generate and pressure test hypotheses around 69 human cellular signaling pathways impacted by CoV infection.70 71 72 73 5 Results 74Generation of the CoV consensomes 75 We first set out to generate a set of consensomes ranking human genes based on the 76 frequency of their significant differential expression in response to infection by MERS, 77 SARS1 and SARS2 CoVs. To do this we searched the Gene Expression Omnibus 78 (GEO) and ArrayExpress databases to identify datasets involving infection of human 79 cells by these species. From this initial collection of datasets, we next carried out a 80 three step quality control check as previously described (Ochsner et al., 2019), yielding 81 a total of 3,041,047 million data points in 111 experiments from 25 independent CoV 82 infection transcriptomic datasets (Supplementary information, Section 1). Using these 83 curated datasets, we next generated consensomes for each CoV species, as well as 84 one ranking genes across all CoV infection experiments (ALL CoV). As a reference 85 consensome for a virus whose transcriptional impact on human cells has been studied 86 in depth, we also generated a consensome for human influenza A virus (IAV) infection. 87The Supplementary information files contain the full human ALL CoV (Section 2), MERS 88 (Section 3), SARS1 (Section 4), SARS2 (Section 5) and IAV (Section 6) infection 89 transcriptomic consensomes. To assist researchers in inferring transcriptional regulation 90 of sig...

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