key: cord-0830515-blx0ybgg
authors: El-Hachem, Nehme; Eid, Edward; Nemer, Georges; Dbaibo, Ghassan; Abbas, Ossama; Rubeiz, Nelly; Zeineldine, Salah; Matar, Ghassan M.; Bikorimana, Jean-Pierre; Shammaa, Riam; Haibe-Kains, Benjamin; Kurban, Mazen; Rafei, Moutih
title: Integrative transcriptome analyses empower the anti-COVID-19 drug arsenal
date: 2020-10-19
journal: iScience
DOI: 10.1016/j.isci.2020.101697
sha: b7e6d92cfdd523cd7b3db51ba83b871eefb20628
doc_id: 830515
cord_uid: blx0ybgg

The beginning of the twenty-first century has been marked by three distinct waves of zoonotic coronavirus outbreaks into the human population. The COVID-19 (Coronavirus disease 2019) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and emerged as a global threat endangering the livelihoods of millions worldwide. Currently, and despite collaborative efforts, diverse therapeutic strategies from ongoing clinical trials are still debated. To address the need for such an immediate call of action, we leveraged the largest dataset of drug-induced transcriptomic perturbations, public SARS-CoV-2 transcriptomic datasets, and expression profiles from normal lung transcriptomes. Most importantly, our unbiased systems biology approach prioritized more than 50 repurposable drug candidates (e.g., Corticosteroids, Janus kinase and Bruton kinase inhibitors). Further clinical investigation of these FDA approved candidates as monotherapy or in combination with an antiviral regimen (e.g., Remdesivir) could lead to promising outcomes in COVID-19 patients.

m human (high and collapsed resolution he estimated cell populations from mouse timates) (Fig. 2f, Supplementary Fig. 1 ). We populations (1, 3, and 4); and was slightly less correlated with the collapsed hepatocyte population estimate (r = 0.64, p-value = 1.015 × 10 −21 ) (Fig. 2f, g) . This indicates that the low-resolution patients and on the other to the heavy-travel profile characteristic of most populations in the current era of globalization. COVID-19 can clinically manifest on a spectrum ranging from mild non-specific flu-like symptoms to near-fatal Acute Respiratory Distress Syndrome (ARDS) leading to inflammation, pneumonia, acute lung injury (ALI), and sepsis especially in the elderly and persons with underlying comorbidities (Chen et al., 2020) , (Wang et al., no date; Guan et al., 2020) .

The current understanding of COVID-19 pathogenesis, etiology, and clinical features has been first extrapolated from knowledge gathered from other similar zoonotic infections associated with upper respiratory illness, such as the severe acute respiratory syndrome coronavirus (SARS-CoV) and the Middle East respiratory syndrome coronavirus (MERS-CoV) (Menter et al., 2020) , (Rota, 2003) , (Gu and Korteweg, 2007; Ng et al., 2016) . It is now established that SARS-CoV-2 specifically recognizes the human angiotensin-converting enzyme 2 protein (ACE2) as its main target on the cell surface (Gheblawi et al., 2020) , (Hoffmann, Kleine-Weber, et al., 2020) . Following virus entry, the cell triggers the physiological response to the infection through several defense arms that are also thought to be implicated in the pathogenesis of the infection Thus, characterizing the downstream transcriptional hallmarks of COVID-19 is crucial to illuminating the mechanisms of this deadly respiratory infection and to guide potent therapeutic countermeasures. However, with the urgency of the current pandemic situation, developing new drugs capable of combating COVID-19 is deemed difficult. To that end, drug repurposing represents a rapid approach to expediting the process of finding a therapeutic candidate (Harrison, 2020) (Gordon et al., 2020; Harrison, 2020) .

Cases of drug repurposing have been serendipitous rather than data-driven discoveries and this extrapolates to COVID-19 research. The hydroxychloroquine-azithromycin combination has been proposed as a panacea for COVID-19 but has not been backed by a proven scientific rationale. Despite initial fanfare generated from favorable outcomes noted in an open-label, non-randomized clinical trial (Gautret et al., 2020) , the azithromycin-hydroxychloroquine combination fell short of expectations in later trials with therapeutic inefficacy overshadowed by cardiovascular side effects associated with its use (Bessière et al., 2020; Geleris et al., 2020; Rosenberg et al., 2020; Tang et al., J o u r n a l P r e -p r o o f 2020). This setback highlights the dire need for an unbiased methodology to further identify drug-target associations that can be efficiently translated to the clinic.

Motivated by both an unmatched global pandemic and a lack of plausible drug candidates to treat COVID-19, we have conducted a unique data-driven approach 

To fill the unmet need for effective COVID-10 treatments, we leveraged the Library of 

In order to address pharmacological gaps in SARS-CoV-2 research, it was of paramount importance to understand the molecular mechanisms underlying the SARS-CoV-2 infection. To this end, we have collected public SARS-CoV-2 transcriptome-wide datasets and normal lung transcriptomics from GTEx. Subsequently, we conducted a differential expression analysis to compare SARS-CoV-2 conditions with their respective control groups. Next, all genes from the different comparisons were merged by the shrunken log2 fold change values (no-cutoff) to yield a full matrix corresponding to 7433 genes x 7 contrasts. These are referred to as SARS2_BALF_WUHAN1-2, SARS2_LUNG, SARS2_NHBE, SARS2_A549_ACE2, SARS2_Calu3 and

For each of these seven contrasts, the fraction of significant up-and down-regulated genes (|Log2FoldChange| > 1 and p-adjusted-value < 0.05) and the corresponding overlap between contrasts is given in Supplementary Table S2 ) and genes were ranked accordingly.

The ranked lists of genes across all SARS2 contrasts as well as genes ranked by their correlation coefficient from ACE2_GTEX were investigated at the pathway level using gene set enrichment analysis (GSEA). We assessed enrichment against a collection of hallmark genesets and a curated set of genes associated with SARS_CoV infection from the literature (See methods section and Table S3 ). To assess the similarity between settings, we first computed all pairwise correlations of pathway enrichment scores from all GSEA analysis ( Figure  2A ). Interestingly, both SARS2_A549_ACE2_RUXO and ACE2_GTEX were strongly concordant (rho= 0.63, p J o u r n a l P r e -p r o o f < 0.01). This suggests that ruxolitinib treatment in ACE2-expressing A549 cells and ACE2 baseline expression in the lungs engage a similar set of molecular pathways.

Indeed, similarly to ACE2_GTEX, Ruxolitinib-induced pathways were highly anticorrelated with those from all the SARS2 settings (Fig 2A) . Although SARS2_LUNG was correlated with BALF settings, this correlation was not significant (p-value > 0.05, Fig 2A) suggesting a functional diversity in response to SARS-CoV-2.

We further investigated the enriched hallmarks/pathways in both the normal lung and SARS-CoV-2 infected in vivo and in vitro settings. By keeping all gene sets with a normalized enrichment score |NES| greater or equal to 1 and a p-adjusted-value threshold of 5%, our analysis identified a highly conserved hallmark process, the TNF Alpha (Tumor necrosis factor-alpha) signaling via NF-κB. This hallmark was significantly upregulated by the SARS-CoV-2 infection but downregulated in both ruxolitinib-treated and ACE2_GTEX groups ( Figure 2B ). Moreover, to investigate the pathogenic cellular response of SARS-CoV-2, we proceeded as follow: (1) (2) were negatively scoring genesets in ACE2_GTEX and positively enriched in all SARS2 settings but the WUHAN_BALF samples ( Figure 2B ). Across all settings, the resulting genes were assigned either the shrunken log2 fold change from their corresponding DEG analysis or the correlation value for ACE2_GTEX ( Figure 2C ).

From this collection of relevant genes of innate immunity, we identified a set of conserved upregulated genes in SARS2 settings (group1, Figure 2C ). These same upregulated genes were downregulated in ruxolitinib-treated cells and highly anticorrelated with reference to ACE2_GTEX. Most of these genes (e.g. 

In the previous section, we highlighted molecular vulnerabilities providing a rationale for targeting hyperinflammatory response in COVID-19. Although ruxolitinib provided a proof of concept that abrogating inflammation is an appealing therapeutic strategy, we conducted a full data-driven computational approach to identify potential drug J o u r n a l P r e -p r o o f candidates that, in addition to ruxolitinib, could perturb the pathological mechanism induced by the new SARS-CoV. To this end, we have interrogated the L1000, the largest repository of chemical-induced gene perturbation profiles. The curation and prefiltering of the L1000 dataset used in this study are described in the methods section. We first validated our approach on ruxolitinib signature in SARS-CoV2 infected cells (SARS2_A549_ACE2_RUXO). Our pipeline could accurately and unbiasedly identify Ruxolitinib from L1000 as the second-best matching hit out of 4,487 chemical compounds (cosine score= 0.18, p-value from 10,000 random permutations = 2e-04).

To further ascertain that our approach can identify not only ruxolitinib but its drug target class, we performed a drug set enrichment analysis. In this setting, a drug set is a target gene/protein that is associated with at least 3 drugs. We first ranked drugs by the cosine value and ran the enrichment analysis against 295 target genes (see method section for drug-target preparation and database). With respect to SARS2_A549_ACE2_RUXO, we could confirm that indeed JAK2 inhibitors were significantly overrepresented at the top of the ranked list of cosine scores (ES= 0.825, adjusted p-value=0.08) ( Figure 3C ).

Next, we investigated drug-target classes that display a negative enrichment score (their drug members likely reverse the phenotype) against a computed consensus cosine score which is the median value of the cosine spanning 4,487 drugs across all SARS2 settings. We postulate that the consensus score would emphasize drug classes J o u r n a l P r e -p r o o f that target generic processes induced by SARS-CoV-2 across diverse settings and infectivity phases/severity levels. Furthermore, drug-target enrichment analyses were conducted separately for each of the SARS2 settings (excluding RUXO group). ( Figure   3A ).

Importantly, when considering an enrichment drug score (ES) less or equal to -0.5 and adjusted-p-value < 0.25 ( Figure 3A ), our consensus signature exhibited the strongest enrichment for JAK2 inhibitors (filgotinib, baricitinib, fostamatinib), Bruton kinase-BTK inhibitors (GDC-0834, ibrutinib, dasatinib; Figure 3D ), CACNA1C blockers ( Given the important role of ACE2 in both SARS-CoV-2 infectivity and normal lung physiology, we next sought to identify drugs that could counteract the SARS-CoV-2 invasion by restoring the baseline co-expression network of ACE2 in the healthy lung.

In this particular analysis and similar to SARS2_A549_ACE2_RUXO, a positive enrichment score is the preferred outcome ( Figure 3B) . A drug-target class with an enrichment score equal or greater than 0.5 is considered a putative candidate to maintain a normal ACE2 expression whereas a drug-target with a negative enrichment score is likely to be beneficial for the virus. Figure 3E ). We are aware that only a limited fraction of the drugs is assigned to a target class (1030 out of 4487 drugs; 23%). As such, all results from our analyses will be made publicly available to accelerate the search for COVID-19 treatments.

To understand the mechanism by which the candidate drug candidates block the molecular events underlying COVID-19, we focused on significant drug-target lists associated with the previously described consensus score. This involved 25 drug-target families and an ensemble of 106 drugs (Supplementary Figure S2) . Given the small number of genes from the L1000 dataset (727 out of the 978 landmark genes are shared with our data), we opted out to choose gene set enrichment analysis but instead, we conducted a row-wise Welch's t-test to identify the landmark genes that show significant changes between the 106 drugs and the SARS2 groups. This analysis yielded a set of 124 genes that displayed extreme values from the two-tailed t-test (False discovery rate < 5%, 73 genes with positive t-stat values genes, 51 genes with negative t-stat values)( Figure 4A ).

When performing a hypergeometric test, separately for the negatively and positively modulated genes considering the set of significant genes, we showed that several down-regulated processes were enriched for Cytokine Signaling, Jak-STAT signaling, 

In this study, we focus on (i) defining aspects of molecular pathogenesis with regards to SARS-CoV-2 and (ii) validating potential therapeutic targets using integrative datadriven computational analyses. This was carried on large transcriptomic datasets encompassing both in vitro and in vivo SARS-CoV-2 infected samples as well as normal human lung biopsy samples in order to provide a global solid basis to validate our results.

GSEA was initially carried out on all of the above datasets to ascertain the molecular pathways involved in SARS-CoV-2 pathogenesis. TNF alpha signaling via NFkB was found to be a highly conserved hallmark process across all SARS-CoV-2 models. This pathogenesis. It has been reported that serum levels of TNFα along with many other pro-inflammatory cytokines and chemokines were higher in SARS-CoV-2 ICU patients relative to their non-ICU counterparts (Siu et al., 2019; Huang et al., 2020) . TNFα plays an instrumental role in orchestrating the clinical outcomes seen in severe SARS-CoV and SARS-CoV-2 infections (reviewed in (Tay et al., 2020) ). Besides its role in the ominous cytokine storm, TNF-α strongly induces hyaluronan-synthase-2 (HAS2) in pulmonary epithelial and fibroblasts potentially leading to ARDS (Xu et al., 2020) .

the acute lung injury characteristic of severe SARS-CoV and possibly SARS-CoV-2 (Kuba et al., 2005) , (Hoffmann, Kleine-Weber, et al., 2020) . In one study, it was shown that SARS-CoV S protein downregulates ACE2 by upregulating TNFα production which in turn acts in an autocrine fashion to induce the TNF-α-converting enzyme (TACE)dependent shedding of the ACE2 ectodomain (Haga et al., 2008) . Very recently, Feldman and colleagues (Robinson et al., 2020) severe SARS-CoV-2 infection (Channappanavar et al., 2017; Bi et al., 2020; Stelzig et al., 2020) . Furthermore, estrogen (E2) was found to decrease ACE2 expression in vitro J o u r n a l P r e -p r o o f cell models and thus potentially regulate SARS-CoV-2 infection (Bi et al., 2020; Stelzig et al., 2020) . Interferon-related genes such as STAT1 and CXCL10 as well as inflammatory response and IL-6-JAK-STAT pathway genes were significantly upregulated in the lung biopsy samples and minimally expressed in BALF tissue samples. The latter result possibly stems from the impressive arsenal of anti-IFN molecular tricks that beta coronaviruses have evolved to evade the impending immune response. The SARS-CoV nsp1 protein has demonstrated the ability to interfere with IFN-signaling pathways by decreasing the levels of phosphorylated STAT1 (Wathelet et al., 2007) while MERS-CoV membrane protein (M protein) and SARS-CoV ORF3b and -6 were found to prevent nuclear IRF3 translocation and subsequent interferonstimulated genes expression (Kopecky-Bromberg et al., 2007; Yang et al., 2013) . A testament to its pathogenic prowess, SARS-CoV-2 was found to trigger significantly lower levels of IFNs than SARS-CoV while boasting a superior infection and replication rate in the lungs (Chu et al., 2020) . In case of progression into severe SARS-CoV-2, the immune milieu adopts the transcriptomic profile of the lung sample dataset. Unabated viral replication eventually induces the delayed release of IFN-α/β and the subsequent influx of inflammatory macrophages. Consequently, proinflammatory cytokines (TNFalpha, IL-6, and IL1-β) are released to devastating effect. Inevitably, the aforementioned "cytokine storm" hinders viral clearance via T cell apoptosis and damages pulmonary microvascular and alveolar epithelial cell barriers via endothelial and epithelial cell apoptosis. The resultant vascular leakage and alveolar edema induce ARDS and lung damage (Ye, Wang and Mao, 2020) . Interestingly and in stark contrast to the other in vitro SARS-CoV-2 datasets, the NHBE SARS-CoV-2 set failed to register any tangible hallmark-associated gene expression. This could be explained by the cell tropism of SARS-CoV-2 in the human lungs that favors type I pneumocytes, type II pneumocytes, and alveolar macrophages over bronchial epithelia (Chu et al., 2020) .

Alternatively, NHBE might be innately capable of clearing the virus. Elucidation of the exact cause might be addressed in future cellular studies.

To identify potential repurposing candidates, we mined the L1000 connectivity map for Other enriched targets interfere with viral entry. During SARS-CoV and SARS-CoV-2 entry, cell surface-associated transmembrane protease serine 2 (TMPRSS2) cleaves the S protein to permit viral and cellular membrane fusion (Glowacka et al., 2011) .

Nafamostat (RACONA trial; NCT04352400) and to a lesser extent, camostat (NCT04353284), both enriched in our analysis, have been shown to prevent SARS-CoV-2 entry by inhibiting TMPRSS2 (Glowacka et al., 2011; Hoffmann, Kleine-Weber, et al., 2020; Hoffmann, Schroeder, et al., 2020) . Clathrin-mediated endocytosis then ensues with regulation afforded by numb-associated kinases (NAK), such as AP2associated protein kinase 1 (AAK1) and cyclin G-associated kinase (GAK) (Sorrell et al., J o u r n a l P r e -p r o o f 2016). In addition to the JAK-STAT blockade discussed above, the JAK inhibitor baricitinib might also disrupt viral entry via AAK1 and GAK inhibition (Richardson et al., 2020) . Interestingly, chloroquine and hydroxychloroquine were not significantly enriched in our drug analysis. This pertinent negative finding is in line with the failure of hydroxychloroquine to demonstrate clinical efficacy. In effect, this finding lends credence to our data-driven analysis as it outperformed extrapolated biological validation of chloroquine and hydroxychloroquine, albeit on non-SARS-CoV-2 cell models.

Finally, and given the limited set of genes profiled in LINCS L1000, we investigated whether the observed drug mechanism of action is associated with molecular events et al., 2006 , 2009 Lokugamage et al., 2012) . This results in the shutoff mechanism whereby viral RNA is preferentially transcribed and translated over host mRNA. Moreover, the massive metabolic suppression observed concur with recent findings from proteomic and metabolomic characterization from a severe case of COVID-19 (Shen et al., no date) .

In conclusion, through a rigorous data-driven computational analysis, this study validates aspects of a complex pathogenetic framework for SARS-CoV-2 infection; however, targeted biological validation is still a requisite for a comprehensive With that in mind and as time is of the essence, the notion of combining drug candidates with different mechanisms of actions might provide a synergistic effect while simultaneously decreasing the risk of associated adverse drug reactions and expediting the process of testing each therapeutic candidate based on the severity of COVID-19

symptoms.

The study does not provide experimental validation were significantly downregulated/upregulated in response to top candidate drugs (A) Welch's t-test was performed between gene levels induced by 106 selected drugs from L1000 and SARS-CoV-2 expression settings. Heatmap illustrating the expression level of 124 genes that significantly separate the two groups (p-adjusted value from the t-test of less than 0.05). (B) Heatplot for core pathways in which gene members were significantly downregulated in response to L1000 drugs. (C) Heatplot for core pathways in which gene members were significantly upregulated in response to L1000 drugs, in contrast to SARS2 settings (p-value < 0.05).

Summary of public datasets used in the analytical pipeline J o u r n a l P r e -p r o o f IDO1  BCL2A1  CXCL10  IL1A  RSAD2  IFIT2  MX2  IFIT1  IFIT3  OAS2  MX1  ISG15  IFITM1  TNFSF10  IFIH1  DDX58  IFITM3  IFITM2  CEACAM1  OAS1  OAS3  EIF2AK2  IFI27   EGFR  CCL2  CEBPD  BIRC3  ISG20  EDN1  NFAT5  ABCA1  NFE2L2  SMAD3  BCL3  IRF2  NAMPT  RIPK2  PELI1  CFLAR  TLR2  CD55  B2M  ELF1  CASP4  STAT4  TLR1  LYN  TLR4  IFNAR2  CDK6  CDKN1A  AHR  TRAF3  CREB1  EIF4E  TNIP1  PTPN2  PANX1  XBP1  IL18  F2RL1  IL1R2  SLAMF7  CFH  HIF1A  IL1R1   PTAFR  IRS2  PTGER4  CEBPB  BIRC2  CASP8  TRAFD1  RBCK1  IFNGR1  MOV10  IFNGR2  SDC4  AXL  IRF5  C5AR1  PTPN6  HLA−B  TYK2  HLA−C  TGFB1  CBL  HMOX1  CASP7  RELA  PLA2G4A  IL13RA1  CTSB  TNFRSF1A  IRF3  CD40  STAT6  CASP9  STAT5A  MAP2K3  IL4R  SMAD7  HBEGF  CSF1   IL2RG  IL1B  ZC3H12A  STAT1  UBE2L6  TRIM21  DDX60  STAT2  DHX58  PLSCR1  SAMHD1  TRIM5  CASP1  PML  JAK2  PLAUR  TRIM25  MYD88  GBP2  TRIM14  C1R  ADAR  TXNIP  IRF7  CD274  NLRC5  BST2  SERPING1  UBA7   CXCL2  EGR1  TNFAIP3  NFKBIA  ATF3  NR4A3  TRAF1  CCL5  PTGS2  EREG  SOCS3  NFIL3  NFKBIE  REL  IL23A  NFKB1  NFKB2  TANK  DDIT3  VEGFA  RCAN1  TBK1  ATF4  TRAF6  MYC  ICAM1  MAP3K8  RELB  RASGRP1  IRF1  NOD2  DUSP1  JUN  SPHK1  FOS   SARS2_A549_ACE2  SARS2_Calu3  SARS2_LUNG  SARS2_NHBE  SARS2_BALF_WUHAN_1  SARS2_BALF_WUHAN_2  SARS2_A549_ACE2_RUXO   group1 ZNF586  CDK7  RALA  PMAIP1  TLK2  DNAJB6  FRS2  TRIB1  CCL2  TNFRSF21  ARL4C  UBE2L6  IKBKB  CSNK2A2  SPRED2  WDR7  SHB  TFAP2A  SOCS2  ADAT1  KIAA0753  DUSP11  PAFAH1B1  TRIM13  PTPRF  TES  ATP11B  DNMT3A  EDN1  GNA15  CASP10  IFNAR1  NFE2L2  SUPV3L1  CAMSAP2  STX4  CRKL  EXT1  NVL  NIT1  SNX13  LAP3  FBXL12  UBE2J1  HSPA4  PIK3CA  CD40  RAB21  CAST   PIP4K2B  ELAVL1  YKT6  HDAC2  GNAI2  DCTD  VAPB  PGM1  NNT  SYPL1  MLEC  LBR  ST7  CLTC  TM9SF2  LIG1  NUP62  MSH6  NUP133  ETFB  PFKL  MRPS16  CDCA4  PSMD10  ADH5  WDR61  DAG1  HK1  HSD17B10  SMARCD2  SLC5A6  PSMG1  ANXA7  GLOD4  CRTAP  TRAPPC3  NUDCD3  AKR7A2  TSPAN4  PPIE  COPS7A  BLMH  IARS2  PRPF4  STMN1  NUP88  C2CD5  USP22  NUP85  XPO7  ZW10  COASY  XPNPEP1  SORBS3  HERC6  TMEM109  RRP1B  MCM3  RPA1  ICMT  SACM1L  UBE3C  MPZL1  RPN1  SCARB1  LRPAP1  ITGB5  VAT1  INTS3  ARHGAP1  CAT  HMG20B  CLSTN1 

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The authors would like to thank all researchers who shared their data publicly and made this project possible. This work is partially supported by a Discovery Grant from the National Sciences and Engineering Research Council of Canada (RGPIN/06101-2014) and an operating grant from the Cancer Research Society (OG24054). M.R holds a Fonds de la Recherche en Santé du Québec Junior II Award.All authors declare no conflict of interest 

R.S. is founder of IntelliStem Technologies Inc., Toronto, ON, Canada. The other authors have no competing interests.

Processed data will be available upon request. A shiny app has been made available for public use