Consensus transcriptional regulatory networks of coronavirus-infected human cells

Establishing consensus around the transcriptional interface between coronavirus (CoV) infection and human cellular signaling pathways can catalyze the development of novel anti-CoV therapeutics. Here, we used publicly archived transcriptomic datasets to compute consensus regulatory signatures, or consensomes, that rank human genes based on their rates of differential expression in MERS-CoV (MERS), SARS-CoV-1 (SARS1) and SARS-CoV-2 (SARS2)-infected cells. Validating the CoV consensomes, we show that high confidence transcriptional targets (HCTs) of CoV infection intersect with HCTs of signaling pathway nodes with known roles in CoV infection. Among a series of novel use cases, we gather evidence for hypotheses that SARS2 infection efficiently represses E2F family target genes encoding key drivers of DNA replication and the cell cycle; that progesterone receptor signaling antagonizes SARS2-induced inflammatory signaling in the airway epithelium; and that SARS2 HCTs are enriched for genes involved in epithelial to mesenchymal transition. The CoV infection consensomes and HCT intersection analyses are freely accessible through the Signaling Pathways Project knowledgebase, and as Cytoscape-style networks in the Network Data Exchange repository.

(SARS1) and SARS-CoV-2 (SARS2)-infected cells. Validating the CoV consensomes, Infection of humans by coronaviruses (CoV) represents a major current global public 37 health concern. Signaling within and between airway epithelial and immune cells in ) and 2 (SARS-CoV-2, or SARS2) 4-9 . To date however the field has lacked a 49 resource that fully capitalizes on these datasets by, firstly, using them to identify human 50 genes that are most consistently transcriptionally responsive to CoV infection and 51 secondly, contextualizing these transcriptional responses by integrating them with 52 'omics data points relevant to host cellular signaling pathways. 53 We recently described the Signaling Pathways Project (SPP) 10 , an integrated 'omics 54 knowledgebase designed to assist bench researchers in leveraging publically archived 55 transcriptomic and ChIP-Seq datasets to generate research hypotheses. A unique 56 aspect of SPP is its collection of consensus regulatory signatures, or consensomes, 57 which rank genes based on the frequency of their significant differential expression  To illuminate human signaling pathways orchestrating the transcriptional response to 133 CoV infection, we next compared transcripts with elevated rankings in the CoV 134 consensomes with those that have predicted high confidence regulatory relationships 135 with cellular signaling pathway nodes. We generated four lists of genes corresponding 136 to the MERS, SARS1, SARS2 and IAV transcriptomic consensome 95 th percentiles. We for nodes whose role in the transcriptional biology of CoV infection is previously 157 uncharacterized, but consistent with their roles in the response to other viral infections. 158 In the following sections all q-values refer to those obtained using the GeneOverlap 159 analysis package in R 17 . 1e-7-1e-9; SARS2, 9e-3-2e-3; MERS, 1e-3-1e-4) 33-35 , IRF (q-value ranges: SARS1, 2e-189 2-1e-31; SARS2, 2e-4-1e-17; MERS, 9e-4-7e-5) 36 and STAT (q-value ranges: SARS1, 190 1e-7-1e-55; SARS2, 2e-3-3e-29; MERS, 5e-2-3e-5) 37-39 transcription factor families 191 (Fig. 3). Consistent with the similarity between SARS1 and IAV consensomes with 192 respect to elevated rankings of ISGs ( Fig. 2a & (Fig. 4). Again, the subsequent proteomic analysis we alluded to earlier 32 226 independently corroborated our prediction of a role for CDK6 in the response to SARS2 227 infection.

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CCNT2 is another member of the cyclin family that, along with CDK9, is a component of    The data points underlying the CoV consensomes indicate evidence for tissue-specific 258 differences in the nature of the regulatory relationship between ACE2 and viral infection.

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In response to SARS1 infection, for example, ACE2 is induced in pulmonary cells but 260 repressed in kidney cells (Fig. 5). On the other hand, in response to SARS2 infection,     Given that EMT has been linked to ARDs 72 , we speculated that the evidence connecting 348 EMT and SARS2 acquired through our analysis might be reflected in the relatively  the initial rendering of the full SARS2 (Fig. 10a) and other consensome networks shows 442 a sample (Fig. 10a, red arrow 1 consensome data, such as rank, GMFC and family, to be examined in detail using the 452 information panel (Fig. 10b, right panel). Highlighted to exemplify this feature is IL6, an 453 inflammatory ligand that has been previously linked to SARS2 pathology 8,113 . HCT intersection networks, node size is proportional to the q-value, such that the larger 465 the circle, the lower the q-value, and the higher the confidence that a particular node or 466 node family is involved in the transcriptional response to viral infection.

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The visual organization of the NDEx interface offers insights into the impact of CoV  suitable archived datasets become available, we will be better positioned to generate 543 more specific consensomes of this nature.

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The human CoV and IAV consensomes and their underlying datasets are intended as 545 "living" resources in SPP and NDEx that will be updated and versioned with appropriate 546 datasets as resources permit. This will be particularly important in the case of SARS2, 547 given the expanded budget that worldwide funding agencies are likely to allocate to 548 research into the impact of this virus on human health. Incorporation of future datasets 549 26 will allow for clarification of observations that are intriguing, but whose significance is 550 currently unclear, such as the intersection between the CoV HCTs and those of the 551 telomerase catalytic subunit (figshare File F2), as well as the enrichment of EMT genes 552 among those with elevated rankings in the SARS2 consensome (Fig. 7). Although they 553 are currently available on the SPP website, distribution of the CoV consensome data 554 points via the SPP RESTful API 10 will be essential for the research community to fully     Table 1.                IAV   IFIT2  IFI27  MX1  IFITM1  IFITM3  IRF9   IFIT1  IFIT3  ISG15  OAS1  DDX58  IRF7  IFIH1  IFI35  IFIT5  ISG20  IFITM2   IFI6