key: cord-0778126-8jbg9dwu authors: Su, Chen; Rousseau, Simon; Emad, Amin title: Identification of COVID-19-relevant transcriptional regulatory networks and associated kinases as potential therapeutic targets date: 2020-12-23 journal: bioRxiv DOI: 10.1101/2020.12.23.424177 sha: 66b3e527adf4d0ce85766c24d75d968a41798a65 doc_id: 778126 cord_uid: 8jbg9dwu Identification of transcriptional regulatory mechanisms and signaling networks involved in the response of host to infection by SARS-CoV-2 is a powerful approach that provides a systems biology view of gene expression programs involved in COVID-19 and may enable identification of novel therapeutic targets and strategies to mitigate the impact of this disease. In this study, we combined a series of recently developed computational tools to identify transcriptional regulatory networks involved in the response of epithelial cells to infection by SARS-CoV-2, and particularly regulatory mechanisms that are specific to this virus. In addition, using network-guided analyses, we identified signaling pathways that are associated with these networks and kinases that may regulate them. The results identified classical antiviral response pathways including Interferon response factors (IRFs), interferons (IFNs), and JAK-STAT signaling as key elements upregulated by SARS-CoV-2 in comparison to mock-treated cells. In addition, comparing SARS-Cov-2 infection of airway epithelial cells to other respiratory viruses identified pathways associated with regulation of inflammation (MAPK14) and immunity (BTK, MBX) that may contribute to exacerbate organ damage linked with complications of COVID-19. The regulatory networks identified herein reflect a combination of experimentally validated hits and novel pathways supporting the computational pipeline to quickly narrow down promising avenue of investigations when facing an emerging and novel disease such as COVID-19. Host responses to various insults is regulated by distinct sets of regulatory networks coordinating 20 responses matched to the insult. Viral infections of human cells lead to the production of interferons 21 (IFNs) as an antiviral mechanism [1] . TRIF, RIG-I and MDA-5-mediated activation of Interferon response 22 factors (IRFs) responsible for the expression of antiviral genes, such as type I, II and III IFNs, are amongst 23 critical regulators of antiviral immunity. In turn, Type I, II and III interferons will activate JAK-STAT signaling 24 to further promote antiviral host responses [2] . This response must be kept in balance as viral clearance 25 mechanisms can lead to tissue damage if not kept in check [3] . This balance can be especially hard to 26 maintain in the presence of new emerging infections such as the novel coronavirus severe acute 27 respiratory syndrome coronavirus -2 (SARS-CoV-2) responsible for coronavirus disease 2019 (COVID- 19) , 28 for which the host is naïve. The loss of a measured response can lead to severe complications of viral 29 illnesses such as severe acute respiratory distress syndrome (ARDS), which has been observed in COVID-30 19 patients [1] . TFs in the SvOV network (STAT1, RCOR1, EGR1, ZNF512B) were present in this dataset. Out of the 45 123 targets found for these TFs by InPheRNo, 37 were confirmed using GTRD (p = 2.36E-15, hypergeometric 124 test). 125 126 Table S4 ). Pathways related to cytokine signaling and interferon signaling 161 (interferon gamma signaling and interferon alpha/beta signaling) were implicated for both TRNs and 162 using standard and network-guided analysis. Next, we repeated the network-guided analysis above by 163 considering each TF and the set of its target genes in the SvM and SvOV networks as a separate query 164 gene set ( Fig. 1 and Supplementary Tables S5-S6 ). Fig. 1 To identify kinases most associated with COVID-19 we used foRWaRD, a computational tool that we 179 recently developed to rank nodes and sets of nodes in a heterogenous network based on their relevance 180 to a set of query set using random walk with restarts (RWR) [20] . As input to foRWaRD, we provided the another run with all nodes as the restart set (to be used as control). Each run of the RWR provides a 187 probability score for each node (including those corresponding to kinases), representing the relevance 188 of the node to the restart set. Finally, a normalized score for each kinase is obtained by comparing the 189 scores of the two runs of the RWR, and kinases are ranked based on how much their query set score is 190 higher than their background (i.e. control) score. Table 3 As can be seen in Table 3 the ratio of their query set probability score to their background probability score. Any ratio score >1 implies that the kinase is 206 scored higher using the top TF query set compared to its control. SvM network) susceptibility to viral infections is expected, with high titers of virus likely increasing infectivity. This is 253 the case for the loss of function of TLR7 [28] or type I and type III IFN-related genes [29, 30] leading to 254 more severe COVID-19 disease in younger individuals. As expected, pathway enrichment analysis 255 showed that immune (cytokine) signaling related to interferon were the top hits, as is expected for viral 256 infection of host cells [31] . Amongst the lists of TFs identified is IRF9, a TF shown to be activated by 257 SARS-CoV-2 [10] that forms a trimeric complex with STAT1 and STAT2 termed IFN-stimulated gene factor 258 3 (ISG3) that binds to the IFN stimulated response element (ISRE) and controls the expression of type I 259 and III IFN essential for the control of Influenza A virus replication [32] . Interferons alpha and beta (IFN-a 260 and IFN-b) are among the type I IFNs that regulate the activity of immune system and act as antiviral 261 cytokines. A recent study has shown an association between impaired interferon type I response 262 (represented by low activity of IFN-a and IFN-b) and severity of COVID-19 and has suggested blood 263 deficiency in type I IFN as a hallmark of severe manifestation of the disease [28] . In addition, several 264 studies have suggested type I IFNs as potential antiviral treatments for COVID-19 [29, 30] . IFN-g is a 265 cytokine involved in innate and adaptive immunity and is the only member of type II IFNs. A recent study 266 has shown a correlation between COVID-19 severity and a decrease in the production of IFN-g by CD4+ T 267 cells [31] . [19] . After removing duplicate edges, we formed a KSI To construct the TRNs using InPheRNo, we first performed differential expression analysis using EdgeR 378 [54] with the cell type as a confounding factor. In the case of SvOV, since for different viruses the 379 measurements were obtained at different time-points, we also included the time of measurement post 380 infection as a confounding factor. Next, we quantile normalized the gene expression profiles using voom 381 [55] and then z-score normalized the results. We constructed the SvM and SvOV TRNs using 500 most 382 differentially expressed genes. We ran InPheRNo (downloaded from For the knowledge-guided mode of the GSC pipeline, we used 'STRING Experimental PPI' option for the 395 knowledge network (which corresponds to experimentally verified protein-protein interaction edges 396 from the STRING database [14] ) and the amount of network smoothing was set to the default 50%. Only 397 pathways with 'difference score' larger than 0.5, which correspond to those that have a query score 398 higher than the background score, were considered associated with the input query gene set. For 399 enrichment analysis of the set of TFs, the input file to KnowEnG's GSC pipeline was designed such that 400 the universe (i.e. population) would be equal to the set of all TFs present in our study. For enrichment 401 analysis of a TF and its target genes, the universe was set to be the set of all genes and TFs present in 402 our study. 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