Single-nucleus lung transcriptomics and inflammatory responses in lethal COVID-19 reveal potential drugs in advanced-stage clinical trials

Single-nucleus lung transcriptomics and inflammatory responses in lethal COVID-19 reveal potential drugs in advanced-stage clinical trials | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Single-nucleus lung transcriptomics and inflammatory responses in lethal COVID-19 reveal potential drugs in advanced-stage clinical trials Andrés López-Cortés, Santiago Guerrero, Esteban Ortiz-Prado, Verónica Yumiceba, and 24 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-808746/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract There is pressing urgency to identify drugs that allow treating COVID-19 patients effectively. Respiratory failure is the leading cause of death in patients with severe COVID-19, and the host inflammatory response at the lungs remains poorly understood. Therefore, we retrieved data from postmortem lungs from COVID-19 patients and performed in-depth in silico analyses of single-nucleus RNA sequencing data, inflammatory protein interactome network, functional enrichment, and shortest pathways to cancer hallmark phenotypes to reveal potential therapeutic targets and drugs in advanced-stage COVID-19 clinical trials. Herein, we analyzed transcriptomics data of 719 inflammatory response genes across 19 cell types (116,313 nuclei) from lung autopsies. The functional enrichment analysis of the 233 significantly expressed genes showed that the most relevant biological annotations were: inflammatory response, innate immune response, cytokine production, interferon production, macrophage activation, thymic stromal lymphopoietin, blood coagulation, IL-1 and megakaryocytes in obesity, NLRP3 inflammasome complex, and the TLR, JAK-STAT, NF- κ B, TNF, oncostatin M, AGE-RAGE signaling pathways. Subsequently, we identified 34 essential inflammatory proteins with both high-confidence protein interactions and shortest pathways to inflammation, cell death, glycolysis, and angiogenesis. Lastly, we propose five small molecules involved in advanced-stage COVID-19 clinical trials: baricitinib, pacritinib, and ruxolitinib are tyrosine-protein kinase JAK2 inhibitors, losmapimod is a MAP kinase p38 alpha inhibitor, and eritoran is a TLR4/MD-2 antagonist. After being thoroughly analyzed in COVID-19 clinical trials, these drugs can be considered for treating severe COVID-19 patients. Immunology Bioinformatics lethal COVID-19 inflammatory proteins drugs clinical trials Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The first zoonotic transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occurred in China in late December 2019 1 , and it is the etiological agent of the coronavirus disease 2019 (COVID-19) 2 . Since the World Health Organization (WHO) declared the outbreak of COVID-19 as a pandemic on March 11, 2020, the SARS-CoV-2 infection has led to more than 200 million cases and more than 4 million deaths globally 3 . SARS-CoV-2 is the seventh CoV known to infect humans, along with HKU1, OC43, NL63, 229E, SARS-CoV, and Middle East respiratory syndrome (MERS) 4 . The novel coronavirus is a single-stranded positive-sense RNA virus of about 30 kb in length 5 , 6 , which encompasses a 5’ terminal cap, 14 open reading frames (ORFs) encoding for 29 proteins, and a 3’ poly A tail 7 . ORF1a and ORF1ab encode 16 non-structural proteins (nsps) involved in antiviral response (nsp1), viral replication (the nsp3-nsp4-nsp6 complex), the protease 3C pro (nsp5), the RNA polymerase (the nsp7-nsp8 complex), the single strand RNA binding protein (nsp9), the methyltransferase activity (nsp10 and nsp16), the RNA-dependent RNA polymerase (nsp12), the helicase/triphosphatase (nsp13), the 3’-5’ exonuclease (nsp14), the uridine-specific endoribonuclease (nsp15), and the RNA-cap (nsp16) 8 – 10 . The remaining genes encode structural proteins: the spike (S) glycoprotein, the nucleocapsid (N) protein, the membrane (M) glycoprotein, the envelope (E) protein, and several accessory proteins 7 , 11 . At the molecular level, amino-acid changes that result in reduced fitness are generally removed by negative selection, whereas changes that increase virus fitness are maintained by positive selection 12 . The most significant mutation observed in SARS-CoV-2 is probably the D614G substitution in the S1 subunit of the S protein. This mutation confers a 20% increase in infectivity and is associated with a higher ACE2-binding affinity 13 . Additionally, SARS-CoV-2 fitness is also enhanced in presence of the E484K mutation, which increases resistance to antibodies 14 . This transmissibility advantage was further increased to approximately 50% by the emergence of the B.1.1.7 variant 15 . Subsequently more variants have emerged, some of them capable of escaping monoclonal antibodies, partially eluding the polyclonal immune responses induced by previous infection or even allowing re-infections. It should be noted that recent improvements in immune escape are linked to mutations that alter the N-terminal domain (NTD) rather than the receptor-binding domain (RBD) of the S protein, where early and functionally important alterations prevailed 16 . However, improved transmissibility, rather than immunoevasion or increased lethality, are considered as the main route for the virus to become fitter and more viable 17 . The variants that are being carefully monitored include: A) Variants of concern (VOCs): characterized by increased transmissibility, cause a more severe manifestation of the disease or significant reduction in neutralizing antibodies generated during a previous infection or after vaccination, reduced effectiveness of treatments, or diagnostic detection failures. This group includes the B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), and P.1 (Gamma) variants. B) Variants of interest (VOIs): present reduced neutralization by antibodies induced by previous infection or vaccines, reduced efficacy of treatments, potential diagnostic escape and expected increase in transmissibility or severity of COVID-19. Among them, we find the B.1.427 / 429 (Epsilon), B.1.525 (Eta), B.1.526 (Iota), B.1.617.1 (Kappa), C.37 (Lambda), B.1.617.3, P.2 (Zeta), and B.1.621 variants. C) Variants under monitoring: could have properties similar to those of VOCs, however precise information is still lacking. D) High consequence variants: those that have significantly reduced the efficacy of vaccines in relation to previously circulating variants, and additionally cause failures in their diagnosis; however, there are not SARS-CoV-2 variants that rise to the level of high consequence yet 18 , 19 . It is expected that more variants will emerge over time that will need to be closely monitored, since they are a potential threat to public health. Nevertheless, this will not happen indefinitely because -over time- the virus will reach its maximum transmission point, therefore, new variants will not acquire more advantages in terms of infectivity. Thereafter, virus infectivity will stabilize and experience occasional and minimal variations 20 , 21 . SARS-CoV-2, enriched by the previously mentioned genomic variants, has the ability to infect human body cells –especially in the lung microenvironment– through the angiotensin-converting enzyme 2 (ACE2) protein receptor 4 . Lung homeostasis necessitates a fine balance between tolerance mechanisms against non-pathogenic agents, pro-inflammatory immune system activation to fight off respiratory tract infections and anti-inflammatory and pro-fibrotic processes that will minimize the immune-mediated tissular lesion and promote tissue remodeling and repair. These complex mechanisms are mediated by a variety of tissue-resident and recruited cell types. The pulmonary alveolar epithelium is mainly composed of alveolar type I cells (AT1), which are essential for the gas-exchange function of the lungs, and alveolar type II cells (AT2), which are best known for their functions in synthesizing and secreting pulmonary surfactant factors 22 . Airway epithelial cells are central players in mucociliary clearance of the lungs. They produce a variety of antimicrobial substances, cytokines, and growth factors that mediate leukocyte recruitment, modulation of innate and adaptive immunity, and tissue repair 23 , 24 . They also constitute the first cells that contact invading pathogens and are responsible for early pathogen recognition and induction of the antiviral state through pro-inflammatory cytokines and type I interferon secretion 25 . Pulmonary endothelial cells are localized in the interface between the pulmonary tissue and the bloodstream. Their strategic location is also reflected in their pleiotropic functions that range from gas interchange to regulating vascular tone and facilitating immune cell recruitment and diapedesis upon receiving pro-inflammatory stimuli 26 . Mast cells are innate immune cells, involved in immune defense and surveillance. They are filled with secretory granules, which upon activation, release bioactive mediators to fight pathogens or induce allergic reactions 27 . Macrophages are key sentinel cells residing in peripheral tissues that detect pathogen invasion or tissue damage and initiate acute inflammatory processes triggering recruitment and activation of innate and –in second step- adaptive immune responses. Macrophages are also key inducers of the respiratory burst, professional antigen-presenting cells and tissue repair and remodeling mediators 28 . Dendritic cells form a heterogeneous population of the immune system that have a wide array of immune functions. Conventional dendritic cells bridge the innate and adaptive immune responses as they are constantly sampling antigens from the airways and/or the infected lung tissue, migrate to T-cell areas of secondary lymphoid organs and present it to T lymphocytes thereby activating them 29 . Monocytes, subsets of leukocytes mostly originated from myeloid progenitors in the bone marrow, are able to differentiate into macrophages or dendritic cells in peripheral tissues. They seed tissues with enough macrophages to replace their loss through infection and tissue damage and can adopt specific macrophage or dendritic cell phenotypes depending on the cytokine milieu they encounter upon arrival to the inflamed tissue 30 . Natural killer (NK) cells are lymphocytes of the innate immune system, that play a main role in anti-viral and anti-tumor responses 31 – 33 . They can identify and kill infected or stressed cells by releasing perforin and granzymes or by death receptor signaling (FasL/Fas interactions and the subsequent induction of apoptosis). NK cells can also release IFNγ upon activation, thereby contributing to naive T helper cell activation and differentiation and classical activation of macrophages 34 . The CD4 + T helper cell population orchestrates the innate and adaptive immune responses in acute and chronic viral infections by secreting a panel of immunomodulatory cytokines. These cells also play a key role for the establishment of long-term cellular and humoral antigen-specific immunity, which is the basis of long-term protection induced by a plethora of viral infections and vaccines 35 . The cytotoxic CD8 + T cells play a pivotal role in controlling infections caused by intracellular pathogens. These cells can be considered the adaptive immunity counterparts of NK cells, but unlike their innate immunity counterparts CD8 + T cells are activated by specific pathogen or tumor-derived antigen presented on class I major histocompatibility complex molecules (MHC I). The three major mechanisms of action of these cells are quite similar to NK cell functions: a) direct killing of infected or tumor cells by release of perforin and granzymes, b) indirect destruction of cells via death receptor signaling (Fas/FasL interactions), and c) secretion of cytokines that can direct and potentiate immune responses of nearby cells 36 . Treg cells are potent immunosuppressive cells that play a vital role in maintaining immune homeostasis and in the prevention of autoimmune responses by suppressing the activation of conventional T-cells 37 , 38 . B cells have a key role in the humoral adaptive immune response and –once activated- are responsible for the production of antigen-specific immunoglobulins 39 . Plasma blasts are terminally differentiated populations of effector B cells cells, which produce antibodies, providing immunity during initial exposure to a pathogen and mediating the protective effects of vaccination 40 . Fibroblasts are key cells in the wound repair process and tissue scarring. They participate in the immune response by producing cytokines and chemokines that initiate the recruitment and retention of bone marrow-derived immune effector cells 41 . Smooth muscle cells provide the main support for the vessel wall structure and regulate vascular tone to maintain intravascular pressure and tissue perfusion 42 . Lastly, neuronal cells release neurotransmitters and neuropeptides that allow fast communication with immune cells, maintaining homeostasis and fighting infections. Neuroimmune interactions are also implicated in several chronic inflammatory conditions 43 . Previous studies have reported profound SARS-CoV-2-induced transcriptional and immunological changes in animal models 44 , as well as in bronchoalveolar lavage fluid (BALV) 45 , nasopharyngeal 46 , and human blood samples 47 . However, the respiratory failure is the leading cause of death in patients with severe COVID-19 disease and the host inflammatory response at the lung tissue level remains poorly understood 48 , 49 . To shed light on this physiological response, we retrieved data from COVID-19 autopsies and performed in-depth in silico analyses of single-nucleus RNA sequencing (snRNA-seq) data, inflammatory protein-protein interactome (iPPI) network, miRNome enrichment, gene ontology (GO), and the shortest paths to cancer hallmark phenotypes to reveal potential therapeutic targets and drugs in advanced stage COVID-19 clinical trials. Methods Protein sets. We have retrieved a total of 719 inflammatory response proteins from the David Bioinformatics Resource ( https://david.ncifcrf.gov/ ) 50 using the gene ontology (GO) term: 0006954 inflammatory response. We have also retrieved the 332 human proteins physically interacting with 26 of the 29 SARS-CoV-2 proteins proposed by Gordon et al 10 . Single-nucleus RNA sequencing data. Melms et al have previously published the molecular single-cell lung atlas of lethal COVID-19 through the snRNA-seq technology needed to profile hard-to-dissociate tissues 51 , 52 . Motivated by this study, we performed an in-depth in silico analysis comparing the transcriptional data of 719 genes involved in the inflammatory response between 9608 alveolar type I cells, 11341 alveolar type II cells, 7332 airway epithelial cells, 1845 B cells, 7586 CD4 + T cells, 3561 CD8 + T cells, 2814 cycling NK / T cells, 1083 dendritic cells, 5386 endothelial cells, 21472 fibroblast cells, 25960 macrophages, 1438 mast cells, 3464 monocytes, 2141 NK cells, 2017 neuronal cells, 5391 plasma cells, 1437 smooth muscle cells, 649 Treg cells, and 1788 other epithelial cells. The snRNA-seq database was taken from the ‘COVID-19 Studies’ section of the Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell/covid19 ), and the transcriptomics data of 116,313 nuclei was taken from ‘Columbia University / NYP COVID-19 Lung Atlas’ study ( https://singlecell.broadinstitute.org/single_cell/study/SCP1219/columbia-university-nyp-covid-19-lung-atlas?cluster=UMAP&spatialGroups=--&annotation=cell_type_intermediate--group--study&subsample=all#study-summary ) 51 . The criteria of the analysis of the lung transcriptomics data was the following: ‘uniform manifold approximation and projection (UMAP)’ as load cluster, ‘cell type intermediate’ as selected annotation, and ‘all cells’ as subsampling threshold. Additionally, we adjusted the mRNA expression taking into account the Z-scores, that is, overexpressed mRNAs with Z-scores ≥ 2 and underexpressed mRNAs with Z-scores ≤ -2. Regarding visualization of transcriptomics data, we designed dot plots to visualize the percentage of cells expressing a certain gene, box plots to compare the mean Z-score across cell types, and scatter plots of 2D UMAPs to visualize significantly expressed multiple genes per subpopulation cell, and biological annotations across cell types. Functional enrichment analysis. The functional enrichment analysis gives curated signatures of gene sets generated from omics-scale experiments 11 , 53 , 54 . We performed the enrichment analysis to validate the correlation between significantly expressed genes and biological annotations related to lethal COVID-19. The enrichment was calculated using g:Profiler version e101_eg48_p14_baf17f0 ( https://biit.cs.ut.ee/gprofiler/gost ) to obtain significant annotations (Benjamini-Hochberg FDR q-value < 0.001) related to gene ontology: biological processes, the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways, Reactome signaling pathways, and Wikipathways 53 – 57 . Lastly, the expression of genes involved in significant annotations was visualized in scatter plots, and the significant terms related to lethal COVID-19 pathology were manually curated. miRNome enrichment analysis. The Gene Set Enrichment Analysis (GSEA) ( https://www.gsea-msigdb.org/gsea/index.jsp ) is a powerful analytical method for interpreting gene expression data that share common biological functions or regulations 58 . Therefore, we performed a miRNome enrichment analysis using the ‘microRNA targets’ option to compute overlaps between miRNAs and significantly expressed mRNAs in > 50% of lung cells from lethal COVID-19 patients. Lastly, we proposed the most significant miRNAs with a false discovery rate (FDR) q-value < 0.01. Inflammatory protein-protein interactome network. The iPPI network with zero node addition and a highest confidence cutoff of 0.9 was created between the human proteins physically associated with SARS-CoV-2 and human proteins involved in the pulmonary inflammatory response. This network was generated using the human proteome from the Cytoscape StringAPP 59 , 60 , which imports protein interactions from the STRING database 60 . The degree centrality represents the number of edges the node has in a network 11 , 61 , 62 , and it was calculated using the CytoNCA app 63 . The network elements were organized through the organic layout producing a clear representation of complex networks, and the iPPI network was visualized through the Cytoscape software v.3.7.1 64 . Finally, we ranked the inflammatory response proteins with the highest protein-protein interactions to the human-SARS-CoV-2 proteins. Shortest paths from inflammatory response proteins to cancer hallmark phenotypes. CancerGenNet ( https://signor.uniroma2.it/CancerGeneNet/ ) is a resource that links frequently altered proteins to cancer hallmark phenotypes 65 . This bioinformatic tool, curated by SIGNOR 66 , is based on experimental information that allows to infer likely paths of causal interactions linking proteins to oncogenic phenotypes. The shortest distance scores or paths from proteins to cancer phenotypes were programmatically implemented using the shortest path function of igraph R package 65 . Hence, we calculated the shortest distance scores of positive regulation from the inflammatory response proteins with the highest confidence interactions to the human-SARS-CoV-2 proteins to the inflammation, cell death, angiogenesis, and glycolysis hallmark phenotypes. Drugs involved in current COVID-19 clinical trials. The Open Targets Platform version 21.06 ( https://www.targetvalidation.org ) is comprehensive and robust data integration for access to and visualization of potential drug targets associated with several diseases including COVID-19 67 . This platform has developed the COVID-19 Target Prioritization Tool ( https://covid19.opentargets.org/ ) that integrates molecular data from the ChEMBL database 68 to provide an evidence-based framework to support decision-making on potential drug targets for COVID-19. Lastly, this platform shows all drugs in clinical trials associated with target proteins, detailing its modality, mechanism of action, phase, status, type of drug, target class, and clinical trial number 67 . Results Single-nucleus RNA sequencing data. Single-nucleus biology is a powerful approach of omics medicine, needed to profile hard-to-dissociate tissues, that provides unprecented resolution to the cellular underpinnings of biological processes in order to find druggable targets for complex diseases 52 , 69 , 70 . Here, we identified 233 inflammatory response genes with significant expression in 116,313 nuclei belonging to 19 different lung cell types. Genes with the highest mean Z-score (3.26) and the most significant p-value (0.001) were identified in neural cells, followed by B cells (3.24; 0.001), mast cells (3.14; 0.002), fibroblast cells (3.0; 0.003), alveolar type II cells (2.96; 0.003), cycling NK / T cells (2.94; 0.003), endothelial cells (2.89; 0.004), macrophages (2.88; 0.004), airway epithelial cells (2.76; 0.006), alveolar type I cells (2.74; 0.006), NK cells (2.73; 0.006), dendritic cells (2.70; 0.007), smooth cells (2.68; 0.007), Treg cells (2.67; 0.008), plasma cells (2.62; 0.009), monocytes (2.47; 0.014), other epithelial cells (2.41; 0.016), CD4 + T cells (2.39; 0.017), and CD8 + T cells (2.24; 0.025) (Fig. 1 ). Figure 2 shows scatter plots of significant mean log normalized gene expression and dot plots of genes with the highest percentage of cells expressing per lung cell type. MECOM has the highest percentage of cells expressing in alveolar type I cells, LRRK2 in alveolar type II cells, ELF3 in airway epithelial cells, PXK in B cells, CAMK4 in CD4 + T cells, AOAH in CD8 + T cells, HMGB1 in cycling NK / T cells, CIITA in dendritic cells, RBPJ in macrophages, KIT in mast cells, SLC11A1 in monocytes cells, APP in neuronal cells, AOAH in NK cells, CALCRL in endothelial cells, RORA in fibroblasts, ASH1L in plasma cells, FN1 in smooth muscle cells, and SGMS1 in Treg cells. Lastly, the 26 inflammatory response genes significantly expressed in more that 50% of lung cells were ABR , ACER3 , AOAH , APP , ASH1L , ATM , CALCRL , CAMK1D , CAMK4 , CD163 , CIITA , EGFR , FN1 , HDAC9 , IL18R1 , IL1R1 , KIT , LRRK2 , LYN , MECOM , PRKCA , PRKCZ , RBPJ , RORA , SLC11A1 , and SLIT2 (Supplementary Table 1). Functional enrichment analysis. The functional enrichment analysis was performed using g:Profiler to obtain significant GO: biological processes, KEGG signaling pathways, Reactome signaling pathways, and Wikipathways related to lethal COVID-19 (Benjamini-Hochberg FDR q < 0.001) 11 , 53 , 54 . Figure 3 shows scatter plots of significantly expressed genes (n = 233) in lung cells of lethal COVID-19 autopsies. After a manual curation of biological annotations, the most significant GO terms were inflammatory response (5.9 x 10 − 241 ), cytokine production (9.5 x 10 − 62 ), innate immune response (1.0 x 10 − 30 ), macrophage activation (1.1 x 10 − 29 ), toll-like receptor signaling pathway (3.8 x 10 − 15 ), type I and II interferon production (1.8 x 10 − 13 ), the Janus Kinase (JAK) / Signal Transducers and Activators of Transcription (STAT) signaling pathway (9.0 x 10 − 8 ), NF- κ B signaling pathway (2.0 x 10 − 6 ), thymic stromal lymphopoietin (TSLP) (4.5 x 10 − 6 ), TNF signaling pathway (4.9 x 10 − 6 ), blood coagulation (5.6 x 10 − 6 ), oncostatin M signaling pathway (5.9 x 10 − 6 ), AGE-RAGE signaling pathway (5.9 x 10 − 6 ), IL-1 and megakaryocytes in obesity (6.8 x 10 − 6 ), and NLRP3 inflammasome complex (2.5 x 10 − 4 ) (Supplementary Table 2). miRNome enrichment analysis. After identifying the significantly expressed genes in lung cells of lethal COVID-19 autopsies, we performed the GSEA analysis to compute overlaps between miRNAs and mRNAs 58 . Figure 4 shows a circos plot of the 18 most significant miRNAs (FDR q-value 50% of lung cells. The most significant miRNAs were MIR6867_5P (q = 0.002), MIR2662 (q = 0.002), MIR32_3P (q = 0.002), MIR548A0_5P_MIR548AX (q = 0.003), MIR570_3P (q = 0.004), MIR338_5P (q = 0.005), MIR144_3P (q = 0.005), MIR4711_3P (q = 0.005), MIR628_5P (q = 0.005), MIR548AJ_3P_MIR548X_3P (q = 0.005), MIR1290 (q = 0.006), MIR4496 (q = 0.007), MIR12136 (q = 0.007), MIR9718 (q = 0.007), MIR875_3P (q = 0.008), MIR4698 (q = 0.008), MIR3941 (q = 0.009), and MIR4789_3P (q = 0.009). Inflammatory protein-protein interactome network. We generated the iPPI network encompassing 265 nodes and 2052 edges (Fig. 5 ). Of them, 159 pulmonary inflammatory response proteins had a mean of degree centrality of 8 and 108 human-SARS-CoV-2 proteins had a mean of degree centrality of 7.2. The top ten inflammatory response proteins with the highest degree centrality were APP (38), NFKB1 (36), STAT3 (34), C3 (31), ITGAM (29), FN1 (26), PTAFR (24), JAK2 (22), EGFR (20), and LYN (20). The top ten human-SARS-CoV-2 proteins with the highest degree centrality were GNB1 (29), GNG5 (25), RHOA (23), ITGB1 (22), STOM (20), RAB14 (20), PRKAR2B (17), RAB8A (17), PRKACA (17), and ANO6 (16). Additionally, 111 pulmonary inflammatory response proteins had the highest confidence interactions (cutoff = 0.9) with human-SARS-CoV-2 proteins, being the top ten: C3 (11 interactions), FN1 (11), NFKB1 (10), RPS19 (10), CTSC (9), HSPD1 (9), APP (8), ITGAM (8), SNAP23 (8), and MAPK14 (7) (Supplementary Table 3). Shortest pathways from inflammatory response proteins to cancer hallmark phenotypes. We analyzed the 111 pulmonary inflammatory response proteins with the highest condifence interactions (cutoff = 0.9) to human-SARS-CoV-2 proteins in order to find the shortest pathways toward inflammation, cell death, angiogenesis, and glycolysis according to Iannuccelli et al 65 . Figure 6 A shows box plots encompassing proteins with the shortest distance scores to cancer hallmark phenotypes related to COVID-19 pathology. Cell death was the phenotype with the shortest mean of distance score (2.82), followed by inflammation (3.06), glycolysis (3.12), and angiogenesis (3.79). Figure 6 B shows a Venn diagram integrating inflammatory proteins with shortest pathways to biological phenotypes related to COVID-19. We found 34 essential inflammatory response proteins with shortest pathways simultaneously to inflammation, glycolysis, cell death, and angiogenesis (Supplementary Table 4). Figure 6 C details the ranking of inflammatory response proteins with positive response and shortest distance to cell death, inflammation, glycolysis, and angiogenesis. The top ten essential proteins with shortest pathways of positive regulation to cell death were ATM (1.20), NFKBIA (1.42), TNFRSF1B (1.64), APP (1.73), MAPK14 (1.73), PRKCZ (1.93), TLR4 (1.97), JAK2 (2.26), TGFB1 (2.35), and MECOM (2.36). The top ten essential proteins with shortest pathways of positive regulation to inflammation were PTGS2 (0.53), PRKCZ (1.39), NFKB1A (1.71), MAPK14 (1.92), TNFRSF1B (2.40), TLR4 (2.44), ATM (2.45), MECOM (2.57), PIK3CG (2.63), and EGFR (2.64). The top ten essential proteins with shortest pathways of positive regulation to glycolysis were ATM (1.67), CD28 (1.84), EGFR (1.98), TNFAIP3 (2.00), HGF (2.03), CYLD (2.09), PRKCZ (2.21), EPHA2 (2.25), JAK2 (2.27), and PIK3CG (2.34). The top ten essential proteins with shortest pathways of positive regulation to angiogenesis were TGFB1 (0.86), STAT3 (1.97), MAPK14 (1.98), EGFR (2.48), JAK2 (2.58), ATM (2.91), NFKBIA (2.96), PRKCZ (3.13), TLR4 (3.35), and PTAFR (3.46) (Supplementary Table 5). Lastly, Fig. 7 details all shortest pathways and distance scores of positive regulation from the 34 essential proteins to the inflammation phenotype. Drugs involved in current COVID-19 clinical trials. Figure 8 details the current status of COVID-19 clinical trials regarding to essential inflammatory proteins, according to the Open Targets Platform 67 . There are 5 drugs (small molecules) that are being analyzed in 8 clinical trials in advanced stages (phases III and IV) for 3 essential inflammatory proteins. Baricitinib is a tyrosine-protein kinase JAK1/2 inhibitor that acts on the JAK proteins and it is being studied in 4 clinical trials in phase III (NCT04640168, NCT04693026, NCT04421027, and NCT04401579). Similarly, pacritinib and ruxolitinib are tyrosine-protein kinase JAK1/2 inhibitors. They are currently been evaluated in a phase III clinical trial NCT04404361 and NCT04362137, respectively. Losmapimod is a MAP kinase p38 alpha inhibitor that acts on the MAPK14 protein and it is being studied in a phase III clinical trial (NCT04511819). Lastly, eritoran is a toll-like receptor 4/MD-2 antagonist that acts on the TLR4 protein and it is being studied in one clinical trial in phase IV (NCT02735707). Discussion Since the identification of patient zero in China, a wide spectrum of clinical features have been discovered in severe COVID-19. For instance, dyspnea, acute respiratory distress syndrome (ARDS) 71 , respiratory failure, lung edema, severe hypoxemia, cardiac arrhythmias, lymphopenia 72 , hyperferritinemia, rhabdomyolysis, intravascular coagulopathy 73 , 74 , and pulmonary thromboembolism 75 . Nowadays, it is known that SARS-CoV-2 not only causes respiratory tract infection, but also skin, kidneys, blood, and central neural system pathologies 76 . Therefore, it is imperative to continuously review the clinical manifestations and physiopathological mechanisms of the SARS-CoV-2 infection, especially with the appearance of new genomic variants. Single-cell biology provides unprecedented resolution to the cellular underpinnings of biological processes in order to find therapeutically actionable targets for complex diseases 69 , 70 . Melms et al have previously published the molecular single-cell lung atlas of lethal COVID-19 51 . Motivated by this study, we performed an in-depth in silico analysis comparing the transcriptional data of 719 inflammatory response genes across 19 lung cell types belonging to COVID-19 autopsies. The functional enrichment analysis of the 233 significantly expressed inflammatory genes showed that the most significant biological annotations were inflammatory response (5.9 x 10 − 241 ), cytokine production (9.5 x 10 − 62 ), innate immune response (1.0 x 10 − 30 ), macrophage activation (1.1 x 10 − 29 ), TLR signaling pathway (3.8 x 10 − 15 ), type I and II interferon production (1.8 x 10 − 13 ), JAK-STAT signaling pathway (9.0 x 10 − 8 ), NF- κ B signaling pathway (2.0 x 10 − 6 ), thymic stromal lymphopoietin (4.5 x 10 − 6 ), TNF signaling pathway (4.9 x 10 − 6 ), blood coagulation (5.6 x 10 − 6 ), oncostatin M signaling pathway (5.9 x 10 − 6 ), AGE-RAGE signaling pathway (5.9 x 10 − 6 ), IL-1 and megakaryocytes in obesity (6.8 x 10 − 6 ), and the NLRP3 inflammasome complex (2.5 x 10 − 4 ). The innate immune response is the first line of defense against new invading pathogens 77 . Pattern recognition receptors (PRRs) are capable of recognizing molecules with conserved motifs commonly shared by pathogen groups 78 . Recognition by these receptors triggers innate immune responses and induces multiple IFN and pro-inflammatory cytokine secretion in COVID-19 patients 79 . Upon ligand recognition PRRs initiate a signaling pathway that activate key transcription factors, such as NF- κ B, AP-1, and interferon regulatory factors (IRF3 and IRF7) that induce pro-inflammatory cytokines and type I interferon. Type I IFNs are responsible for inducing the JAK-STAT signaling pathway to activate IFN-stimulated genes and develop the “anti-viral state” in the infected organism 80 , 81 . Interferon is a cytoplasmic glycoprotein with antiviral activity. This cytokine is another contributing factor in the humoral immunological response against respiratory viruses 82 , 83 . Through a bronchoalveolar lavage in severely ill patients, evidence of local induction of interferon and the stimulation of interferon genes was found. In contrast, minimal levels of interferon were found in peripheral blood of severely ill patients 84 . This increased cytokine production with limited interferon levels might be due to an antagonist mechanism of the nsp1 protein against interferon signaling 85 . Regarding the genetics underlying severe COVID-19, Zhang et al concluded that genetics may determine the clinical course of SARS-CoV-2 infection identifying mutations in genes involved in the regulation of type I and III IFN immunity 86 , and Bastard et al identified high titers of neutralizing autoantibodies against type I IFN-α2 and IFN-ω in 10% of patients with severe COVID-19 87 . Macrophages are cells that perform crucial functions in the immune system, from the phagocytosis of the viruses and bacteria to maintaining homeostasis 88 . Precisely, macrophages produce high amounts of pro-inflammatory cytokines in patients with ARDS, who present an activated state known as cytokine storm or macrophage activation syndrome 89 . The overexpression of cytokines (i.e., TNF-α, IL-2, IL-10, IL-1, and IL-6) leads to a hyperinflammatory response, which has been reported as a remarkable feature of SARS-CoV-2 infection 90 – 92 . IL-6 plays a main role in the severity of COVID-19, while TNF-α and IL-1β trigger the NF- κ B signaling pathway 93 , 94 . The excessive production of cytokines leads to development of pathological symptoms, such as lung damage, cell death, severe pneumonia, ARDS, lung fibrosis, and multiple organ failure 93 , 95 . Hence, this cytokine storm plays a crucial role in the progression of SARS-CoV-2 infection and is considered as one of the main causes of lethal COVID-19 92,93 . TNF is considered as one of the most important pro-inflammatory cytokines, affecting different parts of the immune system and regulating various pathological and physiological processes 96 . Therefore, the TNFα-NF- κ B axis is considered as a potential therapeutic target in COVID-19 97 . Initially, NF- κ B is present within the cytoplasm, after activation of I κ B through phosphorylation of I κ B kinase, NF- κ B is activated and translocated to the nucleus where it regulates the transcription of various target genes 98 , 99 . To date, SARS-CoV-2-mediated NF- κ B activation has been observed in several cells such as macrophages of liver, kidney, lung, central nervous system, cardiovascular system, and gastrointestinal system. This causes a chronic production of IL-1, IL-2, IL-6, IL-12, TNF-α, LT-α, LT-β, GM-CSF, and several chemokines, leading to the aforementioned pathological symptoms 100 . Catanzaro et al have recently published a report analyzing the role of the TNFα-NF- κ B pathway in COVID-19. In their report, it was suggested that inhibiting this axis may prevent pulmonary complications in COVID-19 patients 97 . This was also observed in SARS-CoV infection. NF- κ B expression was elevated in the lungs of recombinant SARS-CoV-1-infected mice, while NF- κ B inhibitors reduced SARS-CoV-related expanding survival of these mice 101 . The cytokine signaling depends on the JAK and STAT which are phosphorylated and activated upon cytokines binding to their receptors. The STAT homodimers translocate into the nucleus, where they upregulate the transcription of several genes that participate not only in cytokine production but also in apoptosis, immune regulation, and cell cycle differentiation 102 . In the context of SARS-CoV-2 infection, inhibition of the JAK-STAT pathway seems as promising approach to prevent cytokines storm in fatal cases or in patients with comorbidities that express high levels of inflammatory markers such as of IL-6, TNFα, IL-17a, GM-CSF, and G-CSF 103 . In fact, the GenOMICC GWAS study suggests that individuals with a variant on chromosome 19: 10,466,123 that affects expression of tyrosine kinase 2 (TYK2), member of the JAK family, could be associated with a host-driven inflammatory response that leads to severe lung injury 104 . Thus, several clinical trials have shown that baricitinib, a JAK inhibitors possesses a good safety and efficacy profiles in reducing cytokine levels of severe COVD-19 patients without side effects 105 . Nevertheless, the JAK/SAT pathway is also necessary to mediate the immune response to clear viral infections and prolonged inhibition of the pathway could lead to immunosuppression and prolonged infections 106 . For instance, SARS-CoV-2 is able to hijack the JAK/STAT pathway in order to increase its proliferation by evading the immune response. Li et al showed that SARS-CoV-2 infected cell had a decreased expression of JAK1, JAK2, TYK2, and STAT2 proteins. This is explained by action of viral nsp1, ORF6, and ORF8 that prevent the phosphorylation of STAT1 and STAT3 to inhibit IFN production 107 , 108 . Therefore, the timeline for administration of JAK/STAT inhibitors should be carefully analyzed since reducing the hyperinflammation could affect viral clearance. Due to the narrow therapeutic window of JAK/STAT inhibitors, dosage should aim to restore the immune response homeostasis. The incidence of thrombotic events in COVID-19 patients responsible for strokes and heart attacks raises the concern about the abnormal coagulation patterns and poor prognosis in the actual pandemic. Tang et al reported that 71.4% of non-surviving COVID-19 patients met the criteria for disseminated intravascular coagulation and presented high levels of coagulation-related biomarkers such as D-dimer and fibrin degradation products 109 . The mechanisms of the coagulopathy are not clear; however, some reports indicate that dysregulated immune responses are involved in such processes. Exacerbation of inflammatory cytokines promoting proliferation of megakaryocytes, lymphocyte cell-death, hypoxia, endothelial damage contributing to ischemia and organ dysfunction, and the association between autoantibodies and neutrophil extracellular traps seem to be involved in the abnormal thrombotic events in COVID-19 patients 110 – 112 . Oncostatin M is a cytokine involved in homeostasis and chronic inflammation that has pleiotropic functions such as cell differentiation and proliferation, and it is present in hematopoietic, immunological, and inflammatory networks 113 . One of the most important functions of oncostatin M is the stimulation of the chemokines CCL1, CCL7 and CCL8 in primary human dermal fibroblasts at a faster kinetics than IL-1β or TNF-α 114 . In 2020, it was proposed as a new mortality biomarker in patients with acute respiratory failure supported by venous-venous extracorporeal membrane oxygenation 115 . In the case of COVID-19, an increase of OSM plasma levels and other inflammatory mediators was detected; this finding was correlated with the severity of disease and the increase of bacterial products in plasma 116 . Finally, OSM is curiously elevated in obese patients and upon recognition by its specific receptor (OSMRβ) induces obesity and insulin resistance conditions 117 . Obesity is one of the main risk factors associated with lethal COVID-19, and levels of pro-inflammatory cytokines increase under this pathology 118 . Low NAD + levels in obese individuals decrease the activity of SIRT1, a molecule that modulates cytokine production 119 . However, the excess of amino acid availability hyperactivates the mTOR signaling pathway increasing viral replication and inflammatory response 120 . Additionally, because adipose tissue has a considerable level of ACE2 expression, viral shedding increases, as well as the production of pro-inflammatory factors 121 . This inflammatory process contributes to thrombotic problems, a probable cause of multiorgan failure, which has been evidenced by the presence of elevated levels of megakaryocytes in COVID-19 autopsies 122 , 123 . Thymic stromal lymphopoietin is an epithelial cytokine normally produced by airway epithelial cells. It has been associated with T-helper type 2 (Th2) responses in allergic diseases, highlighting its role in inflammatory disease pathogenesis. It has been discovered that TSLP can be triggered by respiratory viral infections, bacteria, allergens and injuries 124 . TSLP acts upon cells with TSLP receptor such as hematopoietic progenitor cells, eosinophils, basophils, mast cells, airway smooth muscle cells, group 2 innate lymphoid cells, lymphocytes, dendritic cells and monocytes/macrophages. When several immune mediators were measured in patient’s plasma suffering from influenza A (H1N1) and COVID-19, TSLP levels were significantly upregulated in COVID-19 patients. This fact suggests a possible contribution of TSLP in COVID-19 pathogenesis and perhaps aids differential diagnosis 125 . Besides, since TSLP concentration was reported to be higher in severely affected than in mild and moderated COVID-19 cases, it may be potentially used as a biomarker for disease severity 126 . Optimal NLRP3 inflammasome activation is crucial for host immune defense against several pathogenic infections 127 . SARS-CoV-2 activates inflammasomes, which are large multiprotein assemblies that are broadly responsive to pathogen-associated cellular insults, leading to secretion of cytokines and an inflammatory form of cell death 128 . However, excessive activation can lead to systemic inflammation and tissue damage which are detrimental to the host 129 . Patients with severe COVID-19 have been found to have higher serum concentrations of pro-inflammatory cytokines and chemokines such as granulocyte-colony stimulating factor (GCSF), monocyte chemoattractant protein 1 (MCP1), TNF, IL-6, and IL-1β compared with healthy individuals. A unified mechanism for NLRP3 inflammasome activation has not been proposed yet; however, some researchers have found that SARS-CoV-2 ORF-8b interacts with the LRR domain of NLRP3 inflammasome activating IL-1β secretion in THP-1 macrophages 130 . Findings suggest that SARS-Cov-2 infection leads to NLRP3 inflammasome activation, caspase-1 cleavage, and the release of IL-1β stimulating pyroptosis in peripheral blood mononuclear cells from severe COVID-19 patients 131 . In a biological system approach, SARS-CoV-2 employs a suite of virulent proteins that interacts with host targets to extensively rewire the flow of information and cause COVID-19 11,132−134 . The human proteins physically associated with SARS-CoV-2 are the first line of host proteins 10 , which also interact with proteins involved in a wide spectrum of signaling pathways and biological processes within lung cells. In this study, we identified 111 pulmonary inflammatory response proteins with the highest confidence interactions to human-SARS-CoV-2 proteins, being the top ten: C3, FN1, NFKB1, RPS19, CTSC, HSPD1, APP, ITGAM, SNAP23, and MAPK14. Subsequently, we analyzed these 111 inflammatory response proteins to identify those with the shortest pathways to four cancer hallmark phenotypes. Inflammation is a hallmark of cancer observed in patients with SARS-CoV-2 infection 135 . The chronic inflammatory process causes cell death 136 , 137 , angiogenesis 138 , and during the peak of inflammation, immune cells preferentially use glycolysis as a source of energy 139 . These facts provide a biological rationale to analyze and prioritize the inflammatory response proteins with the shortest distance scores to these biological phenotypes. Consequently, we identified 34 essential inflammatory response proteins highly associated with cell death, glycolysis, and angiogenesis. These proteins were: PTGS2, PRKCZ, NFKBIA, MAPK14, TNFRSF1B, TLR4, ATM, MECOM, PIK3CG, EGFR, JAK2, LYN, CYLD, PRKCQ, STAT3, TGFB1, RBPJ, TNFAIP3, NOTCH1, IGF1, CD28, CCL5, PTAFR, FPR1, EDNRA, EDNRB, CYSLTR1, CNR2, HGF, EPHA2, FN1, CSF1, PTGFR, and APP. The SARS-CoV-2 infection of lung epithelial cells activates caspase-8 to trigger the three major cell death pathways, including apoptosis, pyroptosis, and necroptosis. Cell death and inflammatory responses are intimately linked during SARS-CoV-2 infection 140 . Lastly, analysis of postmortem lung sections of lethal COVID-19 patients has revealed that inflammatory responses from lung epithelial cells may induce infiltration of inflammatory cells that trigger strong immune pathogenesis 137 . Recent studies showed that SARS-CoV-2 rewires human monocytes in a high glucose culture medium. This induces viral replication and cytokine production, and might be the reason why people suffering from diabetes, obesity and other related metabolic diseases are more susceptible to developing severe COVID-19 139 . For instance, people with type 2 diabetes show an increased glucose metabolism due to hyperglycemia, which may boost SARS-CoV-2 pathogenesis 139 . Codo et al proved that glycolytic flux is essential for SARS-CoV-2 impact 141 . Through several assays, they inhibited glycolysis by blocking 2-deoxy-D-glucose (2-DG) and glycolytic enzymes 6-phospho-fructo-2-kinase/fructose-2,6-biphosphatase-3 (PFKFB3) and lactate dehydrogenase A (LDH-A), as a consequence, they observed that both viral replication and cytokine response stopped 141 . The metabolic transcription factor HIF-1α activity and related genes are strongly stimulated in SARS-CoV-2 infected blood monocytes isolated from severe COVID-19 patients 141 . HIF-1α is also a major glycolysis regulator, when inhibited, viral replication and cytokine expression were also blocked. Overall, these experiments showed that high glucose concentration and glycolysis are essential for SARS-CoV-2 replication, inflammatory response, and upregulation of ACE2 141 . Angiogenesis occurs in response to the activation of acute inflammation or chronic systemic hypoxia pathways that increase the expression of proteins and factors (HIF-1α, VEGF, NO) associated with its development 142 . During the SARS-CoV-2 infection, local endothelial damage, known as endotheliitis, is associated with acute inflammation of the outermost endovascular layers, triggering a cascade of reactions that result in endothelial inflammation, platelet aggregation, and impaired laminar flow 138 , 143 . In the context of COVID-19 disease, the reported vasoconstriction and subsequent hypoxia, stimulate the formation of new blood vessels by promoting branching of pre-existing blood vessels (intussusception) and de novo angiogenesis that contributes to the already established systemic hypoxia 144 . This process together with the systemic hypoxia observed in severe COVID-19 patients cause a structural and functional reorganization of the pullmonary tissue, which ultimate function is to allow an adequate gas exchange between the tissue and the cells 142 . Regarding drugs against COVID-19 disease, in this study we propose five small molecules (ruxolitinib, baricitinib, pacritinib, losmapimod, and eritoran) that after being thoroughly analyzed in COVID-19 clinical trials, these drugs can be considered for treating severe COVID-19 patients. A systematic review and meta-analysis published by Walz et al concluded that Janus kinase-inhibitor treatment is significantly associated with positive clinical outcomes in terms of mortality, intensive care unit admission, and discharge 145 . Ruxolitinib is a tyrosine-protein kinase JAK1/2 inhibitor 146 that is currently used for myelofibrosis and polycythemia vera, both hematologic malignancies. The use of ruxolitinib in these diseases is based on its ability of being a kinase inhibitor, which mediates the signaling of a number of cytokines and growth factors that are important in hematopoiesis and immune function. Based on this principle, it is reasonable then, from a clinical point of view, to use this drug to specifically manage cytokine storm in COVID-19 147 . According to Yan et al , ruxolitinib normalized interferon signature genes and all complement gene transcripts induced by SARS-CoV-2 in lung epithelial cell lines. They proposed that combination therapy with JAK inhibitors and drugs that normalize NF- κ B-signaling could potentially have clinical application for severe COVID-19 146 . Baricitinib is a tyrosine-protein kinase JAK1/2 inhibitor 148 mainly used for rheumatoid arthritis, and among its pharmacological properties it has an antiviral effect on the entry of a virus 149 . At the moment, baricitinib is approved by the WHO, the Food and Drug Administration (FDA) of the United States, and the National Institutes of Health (NIH) for emergent use in severe pneumonia due to COVID-19 150 . The use of baricitinib is indicated in COVID-19 critically ill patients with high oxygen needs despite the use of dexamethasone (the only approved corticosteroid), however it should not be used when IL-6 inhibitors such as tocilizumab have been started, given that its combined use has not yet been tested as well as its safety. The known efficacy of Baricitinib is from emerging data from an unpublished article where the 27.8% of participants receiving baricitinib vs 30.5% receiving placebo progressed (primary endpoint, odds ratio 0.85, 95% CI 0.67–1.08; p = 0.18), and the all-cause mortality was 8.1% for baricitinib and 13.1% for placebo, corresponding to a 38.2% reduction in mortality (hazard ratio [HR] 0.57, 95% CI 0.41–0.78; nominal p = 0.002) 151 . Pacritinib is also a protein-kinase inhibitor mainly focused on JAK2 and FLT3 protein targets. This small molecule has been developed for the treatment of myelofibrosis 152 . On the other hand, losmapimod is a MAP kinase p38 alpha inhibitor that has been investigated for the prevention of chronic obstructive pulmonary disease and cardiovascular disease 153 . The therapeutic hypothesis for the use of losmapimod in COVID-19 is that increased mortality is caused by p38 MAPK-mediated exaggerated acute inflammatory response resulting in SARS-CoV-2 infection. Lastly, eritoran is a Toll-like receptor 4 / MD-2 antagonist that downregulates the intracellular generation of pro-inflammatory cytokines IL-6 and TNF-alpha in human monocytes, and has been developed for the treatment of severe sepsis. Shirey et al examined how antagonizing TLR4 signaling has been effective experimentally in ameliorating acute lung injury and lethal infection in challenge models triggered by acute lung injury-inducing viruses 154 . Considering the enormous pressure that health systems are facing due to the COVID-19 pandemic and the continuous need to present and implement comprehensive health strategies that can address the global situation; mainly after the emergence of different variants, it is imperative to recognize the urgent need to diminish the gaps between research and the implementation of public health measures. In fact, it may be unprecedented in the history of science to know how many research articles related to COVID-19 have been submitted and published. However, according to Park et al , the research community has emphasized on “the new norm of publishing: quantity over quality” and this is also related to the well known problems that clinical trials faced even before the pandemic 155 . This is of particular interest to our research given that we acknowledge that clinical trials are essential in evidence-based medicine, and consequently, in the decision making process of public health policies and strategies. Relevantly, the need to smartly invest not only in randomized clinical trials but also in large-scale clinical trials with master protocols and conducted by coordinated and collaborative structures, as also supported by Park et al 155 . These clinical trials networks are essential to coordinate actions between clinical researchers and health practitioners, also promoting knowledge sharing, leadership, and cost-time reductions. In addition, it is critical to decentralize, improve and increase clinical trials in low and middle-income countries, as current evidence shows large inequalities and concentrations of funds and information in high-income countries 156 . This holds true especially for Latin America, one of the most affected regions in the world by the pandemic 157 . The role of health research is fundamental in the response to COVID-19, considering the importance of data sharing and assuring efficiency, equity, and effectiveness in the diverse processes. Contradictorily, a large number of clinical trials might never be completed and others are done with doubtful methodologies 155 , 158 . Thus, analyzing potential drugs targets for COVID-19, especially the ones which can serve for severe cases, need an urgent and efficient development of well designed and managed clinical trials, which can provide potential interventions that help people to live longer, diminish long-term effects, manage pain and/or possible disabilities; not to mention the possible positive effects on the reduction of hospitalization costs, both at the individual level and in terms of possible savings for the national health system. As another study also mentioned, the potential and benefits of repositioning clinical trials are directed to use the already available information of safe and affordable generic drugs and propose “potential, prompt, cost- effective, and safe solutions for the public and global health problems, with a human-centered approach” 11 . This is also conveyed by the Pan American Health Organization (PAHO), which adds to the benefits, the idea of having already pharmaceutical formed supply chains 159 . Finally, as other authors have contributed, the current global research situation must be guided towards a collaborative and synergetic approach instead of being conceived as a competitive and isolated process. The COVID-19 pandemic assures the need to eliminate structural barriers that increase health inequalities, and in this perspective, benefits, knowledge, and of course potential treatments must be available for all, in order to achieve universal health coverage and equity. Declarations Data Availability Statement The datasets generated for this study are included in this published article (and its Supplementary Information files). Author Contributions AL-C conceived the subject and the conceptualization of the study. NCK gave conceptual advice and valuable scientific input. AL-C, PG-R, AA-S, AL-S, EA, AAP-M, KN-J, AM, and FA-E did data curation and supplementary data. AL-C did funding acquisition. All authors wrote and edited the manuscript. Lastly, all authors reviewed and approved the manuscript. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding No information Acknowledgments This work was supported by the Latin American Society of Pharmacogenomics and Personalized Medicine (SOLFAGEM). References Tay, M. Z., Poh, C. M., Rénia, L., MacAry, P. A. & Ng, L. F. P. The trinity of COVID-19: immunity, inflammation and intervention. Nat. Rev. Immunol. (2020) doi: 10.1038/s41577-020-0311-8 . Sanders, J. M., Monogue, M. L., Jodlowski, T. Z. & Cutrell, J. B. Pharmacologic Treatments for Coronavirus Disease 2019 (COVID-19): A Review. JAMA - Journal of the American Medical Association (2020) doi: 10.1001/jama.2020.6019 . WHO. COVID-19 Weekly Epidemiological Update 35. World Heal. Organ. 1–3 (2020). Ortiz-Prado, E. et al. Clinical, molecular, and epidemiological characterization of the SARS-CoV-2 virus and the Coronavirus Disease 2019 (COVID-19), a comprehensive literature review. Diagnostic Microbiology and Infectious Disease (2020) doi: 10.1016/j.diagmicrobio.2020.115094 . Ziegler, C. G. K. et al. SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues. Cell (2020) doi: 10.1016/j.cell.2020.04.035 . Zhou, P. et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature (2020) doi: 10.1038/s41586-020-2012-7 . Wu, A. et al. Genome Composition and Divergence of the Novel Coronavirus (2019-nCoV) Originating in China. Cell Host Microbe 27 , 325–328 (2020). Zhang, L. et al. Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors. Science (2020) doi: 10.1126/science.abb3405 . Gao, Y. et al. Structure of the RNA-dependent RNA polymerase from COVID-19 virus. Science (80-.). eabb7498 (2020) doi: 10.1126/science.abb7498 . Gordon, D. E. et al. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature (2020) doi: 10.1038/s41586-020-2286-9 . López-Cortés, A. et al. In silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19. Front. Pharmacol. 12 , 1–24 (2021). Lo Presti, A., Rezza, G. & Stefanelli, P. Selective pressure on SARS-CoV-2 protein coding genes and glycosylation site prediction. Heliyon 6 , (2020). Zhou, B. et al. SARS-CoV-2 spike D614G change enhances replication and transmission. Nature 592 , (2021). Mccormick, B. K. D., Jacobs, J. L. & Mellors, J. W. The emerging plasticity of SARS-CoV-2. Science (80-.). 371 , 1306–1308 (2021). States, U. et al. Article Emergence and rapid transmission of SARS-CoV-2 ll Article Emergence and rapid transmission of SARS-CoV-2 B. 1. 1. 7 in the United States. Cell 184 , 2587–2594.e7 (2021). Burioni, R. & Topol, E.. Has SARS-CoV-2 reached peak fitness? Nat Med (2021) doi: https://doi.org/10.1038/s41591-021-01421-7 . Boehm, E. et al. Novel SARS-CoV-2 variants: the pandemics within the pandemic. Clin. Microbiol. Infect. (2021) doi: 10.1016/j.cmi.2021.05.022 . CDC. SARS-CoV-2 Variant Classifications and Definitions. Variant Surveillance (2021). ECDC. SARS-CoV-2 variants of concern as of 8 July 2021. Situation updates on COVID-19 (2021). Harvey, W. T. et al. SARS-CoV-2 variants, spike mutations and immune escape. Nat. Rev. Microbiol. 19 , (2021). Burton, D. & Topol, E. Toward superhuman SARS-CoV-2 immunity? Nat Med 27 , 5–6 (2021). Wang, Y. et al. Pulmonary alveolar type I cell population consists of two distinct subtypes that differ in cell fate. Proc. Natl. Acad. Sci. U. S. A. 115 , (2018). Hiemstra, P. S., McCray, P. B. & Bals, R. The innate immune function of airway epithelial cells in inflammatory lung disease. Eur. Respir. J. 45 , (2015). Weitnauer, M., Mijošek, V. & Dalpke, A. H. Control of local immunity by airway epithelial cells. Mucosal Immunology vol. 9 (2016). Yoo, J. K., Kim, T. S., Hufford, M. M. & Braciale, T. J. Viral infection of the lung: Host response and sequelae. Journal of Allergy and Clinical Immunology vol. 132 (2013). Niethamer, T. K. et al. Defining the role of pulmonary endothelial cell heterogeneity in the response to acute lung injury. Elife 9 , (2020). Espinosa, E. & Valitutti, S. New roles and controls of mast cells. Current Opinion in Immunology vol. 50 (2018). Biswas, S. K. & Mantovani, A. Macrophages: Biology and role in the pathology of diseases . Macrophages: Biology and Role in the Pathology of Diseases (2014). doi: 10.1007/978-1-4939-1311-4 . Schraml, B. U. & Reis e Sousa, C. Defining dendritic cells. Current Opinion in Immunology vol. 32 (2015). Murray, P. J. Immune regulation by monocytes. Seminars in Immunology vol. 35 (2018). Bi, J. & Tian, Z. NK cell exhaustion. Frontiers in Immunology vol. 8 (2017). van Eeden, C., Khan, L., Osman, M. S. & Tervaert, J. W. C. Natural killer cell dysfunction and its role in covid-19. International Journal of Molecular Sciences vol. 21 (2020). Maucourant, C. et al. Natural killer cell immunotypes related to COVID-19 disease severity. Sci. Immunol. 5 , (2020). Culley, F. J. Natural killer cells in infection and inflammation of the lung. Immunology vol. 128 (2009). Peng, X. et al. Sharing CD4 + T Cell Loss: When COVID-19 and HIV Collide on Immune System. Frontiers in Immunology vol. 11 (2020). Zhang, N. & Bevan, M. J. CD8 + T Cells: Foot Soldiers of the Immune System. Immunity vol. 35 (2011). Savage, P. A., Klawon, D. E. J. & Miller, C. H. Regulatory T Cell Development. Annu. Rev. Immunol. 38 , (2020). Gladstone, D. E., Kim, B. S., Mooney, K., Karaba, A. H. & D’Alessio, F. R. Regulatory T Cells for Treating Patients With COVID-19 and Acute Respiratory Distress Syndrome: Two Case Reports. Ann. Intern. Med. 173 , (2020). Shuwa, H. A. et al. Alterations in T and B cell function persist in convalescent COVID-19 patients. Med 2 , (2021). Nutt, S. L., Hodgkin, P. D., Tarlinton, D. M. & Corcoran, L. M. The generation of antibody-secreting plasma cells. Nat. Rev. Immunol. 15 , (2015). Smith, R. S., Smith, T. J., Blieden, T. M. & Phipps, R. P. Fibroblasts as sentinel cells. Synthesis of chemokines and regulation of inflammation. The American journal of pathology vol. 151 (1997). Wang, G., Jacquet, L., Karamariti, E. & Xu, Q. Origin and differentiation of vascular smooth muscle cells. J. Physiol. 593 , (2015). Blake, K. J., Jiang, X. R. & Chiu, I. M. Neuronal Regulation of Immunity in the Skin and Lungs. Trends in Neurosciences vol. 42 (2019). Blanco-Melo, D. et al. Imbalanced host response to SARS-CoV-2 drives development of COVID-19. Cell (2020) doi: 10.1016/j.cell.2020.04.026 . Liao, M. et al. The landscape of lung bronchoalveolar immune cells in COVID-19 revealed by single-cell RNA sequencing. medRxiv (2020) doi: 10.1101/2020.02.23.20026690 . Butler, D. et al. Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions. Nat. Commun. 12 , 1–17 (2021). Wilk, A. J. et al. A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat. Med. 26 , 1070–1076 (2020). Dong, Y. et al. Epidemiology of COVID-19 among children in China. Pediatrics 145 , (2020). Zhou, F. et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395 , 1054–1062 (2020). Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4 , 44–57 (2009). Melms, J. C. et al. A molecular single-cell lung atlas of lethal COVID-19. Nature (2021) doi: 10.1038/s41586-021-03569-1 . Slyper, M. et al. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat. Med. 26 , (2020). Raudvere, U. et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. (2019) doi: 10.1093/nar/gkz369 . Reimand, J. et al. g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res . (2016) doi: 10.1093/nar/gkw199 . Slenter, D. N. et al. WikiPathways: A multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Res. (2018) doi: 10.1093/nar/gkx1064 . Jassal, B. et al. The reactome pathway knowledgebase. Nucleic Acids Res. (2020) doi: 10.1093/nar/gkz1031 . Ogata, H. et al. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research vol. 27 29–34 (1999). Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U. S. A. 102 , 15545–15550 (2005). Doncheva, N. T., Morris, J. H., Gorodkin, J. & Jensen, L. J. Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data. J. Proteome Res. (2019) doi: 10.1021/acs.jproteome.8b00702 . Szklarczyk, D. et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43 , D447-52 (2015). López-Cortés, A. et al. Gene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis. Sci. Rep. 8 , 16679 (2018). López-Cortés, A. et al. OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine. Sci. Rep. 10 , 5285 (2020). Tang, Y., Li, M., Wang, J., Pan, Y. & Wu, F. X. CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks. BioSystems (2015) doi: 10.1016/j.biosystems.2014.11.005 . Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13 , 2498–504 (2003). Iannuccelli, M. et al. CancerGeneNet: Linking driver genes to cancer hallmarks. Nucleic Acids Res. (2020) doi: 10.1093/nar/gkz871 . Perfetto, L. et al. SIGNOR: A database of causal relationships between biological entities. Nucleic Acids Res. (2016) doi: 10.1093/nar/gkv1048 . Carvalho-Silva, D. et al. Open Targets Platform: New developments and updates two years on. Nucleic Acids Res. (2019) doi: 10.1093/nar/gky1133 . Gaulton, A. et al. The ChEMBL database in 2017. Nucleic Acids Res. (2017) doi: 10.1093/nar/gkw1074 . Gawel, D. R. et al. A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases. Genome Med. (2019) doi: 10.1186/s13073-019-0657-3 . Stephenson, E. et al. Single-cell multi-omics analysis of the immune response in COVID-19. Nat. Med. 27 , (2021). Montenegro, F. et al. Acute respiratory distress syndrome (ARDS) caused by the novel coronavirus disease (COVID-19): a practical comprehensive literature review. Expert Rev. Respir. Med. (2020) doi: 10.1080/17476348.2020.1820329 . Terpos, E. et al. Hematological findings and complications of COVID-19. American journal of hematology (2020) doi: 10.1002/ajh.25829 . Zhang, Y. et al. Coagulopathy and Antiphospholipid Antibodies in Patients with Covid-19. N. Engl. J. Med. 382 , e38 (2020). Fogarty, H. et al. COVID-19 Coagulopathy in Caucasian patients. Br. J. Haematol. (2020) doi: 10.1111/bjh.16749 . Rotzinger, D. C., Beigelman-Aubry, C., von Garnier, C. & Qanadli, S. D. Pulmonary embolism in patients with COVID-19: Time to change the paradigm of computed tomography. Thrombosis Research (2020) doi: 10.1016/j.thromres.2020.04.011 . Delorey, T. M. et al. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature 595 , (2021). Alberts, B. et al. Innate Immunity. in Molecular Biology of the Cell vol. 4 (Garland Science, 2002). Amarante-Mendes, G. P. et al. Pattern recognition receptors and the host cell death molecular machinery. Frontiers in Immunology vol. 9 (2018). Boechat, J. L., Chora, I., Morais, A. & Delgado, L. The immune response to SARS-CoV-2 and COVID-19. Pulmonology 1–15 (2021) doi: https://doi.org/10.1016/j.pulmoe.2021.03.008 . Channappanavar, R. et al. IFN-I response timing relative to virus replication determines MERS coronavirus infection outcomes. J. Clin. Invest. (2019) doi: 10.1172/JCI126363 . Perlman, S. & Dandekar, A. A. Immunopathogenesis of coronavirus infections: Implications for SARS. Nat. Rev. Immunol. 5 , 917–927 (2005). Lopez, L., Sang, P. C., Tian, Y. & Sang, Y. Dysregulated interferon response underlying severe covid-19. Viruses vol. 12 (2020). Billiau, A. Interferon: The pathways of discovery. I. Molecular and cellular aspects. Cytokine and Growth Factor Reviews vol. 17 381–409 (2006). Acharya, D., Liu, G. Q. & Gack, M. U. Dysregulation of type I interferon responses in COVID-19. Nature Reviews Immunology vol. 20 397–398 (2020). Samudrala, P. K. et al. Virology, pathogenesis, diagnosis and in-line treatment of COVID-19. Eur. J. Pharmacol. 883 , (2020). Zhang, Q. et al. Inborn errors of type I IFN immunity in patients with life-threatening COVID-19. Science (80-.). 370 , (2020). Bastard, P. et al. Autoantibodies against type I IFNs in patients with life-threatening COVID-19. Science (80-.) . 370 , (2020). Gracia-Hernandez, M., Sotomayor, E. M. & Villagra, A. Targeting Macrophages as a Therapeutic Option in Coronavirus Disease 2019. Front. Pharmacol. 11 , (2020). Otsuka, R. & Seino, K. I. Macrophage activation syndrome and COVID-19. Inflamm. Regen. 40 , (2020). Zhang, X., Zhang, Y., Qiao, W., Zhang, J. & Qi, Z. Baricitinib, a drug with potential effect to prevent SARS-COV-2 from entering target cells and control cytokine storm induced by COVID-19. Int. Immunopharmacol. 86 , 106749 (2020). Kahn, R. et al. Mismatch between circulating cytokines and spontaneous cytokine production by leukocytes in hyperinflammatory COVID-19. J. Leukoc. Biol. 109 , 115–120 (2021). Rabaan, A. A. et al. Role of inflammatory cytokines in covid-19 patients: A review on molecular mechanisms, immune functions, immunopathology and immunomodulatory drugs to counter cytokine storm. Vaccines 9 , (2021). Rowaiye, A. B. et al. Attenuating the effects of novel COVID-19 (SARS-CoV-2) infection-induced cytokine storm and the implications. J. Inflamm. Res. 14 , 1487–1510 (2021). Tjan, L. H. et al. Early Differences in Cytokine Production by Severity of Coronavirus Disease 2019. J. Infect. Dis. 223 , 1145–1149 (2021). Mustafa, M. I., Abdelmoneim, A. H., Mahmoud, E. M. & Makhawi, A. M. Cytokine Storm in COVID-19 Patients, Its Impact on Organs and Potential Treatment by QTY Code-Designed Detergent-Free Chemokine Receptors. Mediators Inflamm. 2020, (2020). Choudhary, S., Sharma, K. & Silakari, O. The interplay between inflammatory pathways and COVID-19: A critical review on pathogenesis and therapeutic options. Microb. Pathog. 150 , 104673 (2021). Catanzaro, M. et al. Immune response in COVID-19: addressing a pharmacological challenge by targeting pathways triggered by SARS-CoV-2. Signal Transduct. Target. Ther. 5 , (2020). Zhou, Q., Mrowietz, U. & Rostami-Yazdi, M. Oxidative stress in the pathogenesis of psoriasis. Free Radical Biology and Medicine vol. 47 891–905 (2009). Recent Pat. Inflamm. Allergy Drug Discov. 3 , 40–48 (2009). Hariharan, A., Hakeem, A. R., Radhakrishnan, S., Reddy, M. S. & Rela, M. The Role and Therapeutic Potential of NF-kappa-B Pathway in Severe COVID-19 Patients. Inflammopharmacology vol. 29 (2021). DeDiego, M. L. et al. Inhibition of NF- B-Mediated Inflammation in Severe Acute Respiratory Syndrome Coronavirus-Infected Mice Increases Survival. J. Virol. 88 , (2014). Luo, W. et al. Targeting JAK-STAT Signaling to Control Cytokine Release Syndrome in COVID-19. Trends Pharmacol. Sci. 41 , 531–543 (2020). Rojas, P. & Sarmiento, M. JAK/STAT Pathway Inhibition May Be a Promising Therapy for COVID-19-Related Hyperinflammation in Hematologic Patients. Acta Haematol. 144 , 1 (2021). Pairo-Castineira, E. et al. Genetic mechanisms of critical illness in COVID-19. Nat. 2020 5917848 591 , 92–98 (2020). F, C. et al. Baricitinib therapy in COVID-19: A pilot study on safety and clinical impact. J. Infect. 81 , 318–356 (2020). Satarker, S. et al. JAK-STAT Pathway Inhibition and their Implications in COVID-19 Therapy. Postgrad. Med. 133 , 1 (2020). MG, W., M, O., MB, F. & RS, B. Severe acute respiratory syndrome coronavirus evades antiviral signaling: role of nsp1 and rational design of an attenuated strain. J. Virol. 81 , 11620–11633 (2007). JY, L. et al. The ORF6, ORF8 and nucleocapsid proteins of SARS-CoV-2 inhibit type I interferon signaling pathway. Virus Res. 286 , (2020). Tang, N., Li, D., Wang, X. & Sun, Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J. Thromb. Haemost. (2020) doi: 10.1111/jth.14768 . Vinayagam, S. & Sattu, K. SARS-CoV-2 and coagulation disorders in different organs. Life Sciences (2020) doi: 10.1016/j.lfs.2020.118431 . Biswas, S. et al. Blood clots in COVID-19 patients: Simplifying the curious mystery. Med. Hypotheses 146 , (2021). Blasco, A. et al. Assessment of Neutrophil Extracellular Traps in Coronary Thrombus of a Case Series of Patients with COVID-19 and Myocardial Infarction. JAMA Cardiol . 6 , (2021). Richards, C. D. The Enigmatic Cytokine Oncostatin M and Roles in Disease. ISRN Inflamm. 2013, (2013). Hintzen, C., Haan, C., Tuckermann, J. P., Heinrich, P. C. & Hermanns, H. M. Oncostatin M-Induced and Constitutive Activation of the JAK2/STAT5/CIS Pathway Suppresses CCL1, but Not CCL7 and CCL8, Chemokine Expression. J. Immunol. 181 , (2008). Setiadi, H. et al. Oncostatin M as a Biomarker to Predict the Outcome of V-V ECMO Supported Patients with Acute Pulmonary Failure. J. Hear. Lung Transplant. 39 , (2020). Arunachalam, P. S. et al. Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans. Science (80-.). 369 , 1210–1220 (2020). Sanchez-Infantes, D. & Stephens, J. M. Adipocyte Oncostatin Receptor Regulates Adipose Tissue Homeostasis and Inflammation. Frontiers in Immunology vol. 11 (2021). Michalakis, K. & Ilias, I. SARS-CoV-2 infection and obesity: Common inflammatory and metabolic aspects. Diabetes Metab. Syndr. Clin. Res. Rev. 14 , (2020). Miller, R., Wentzel, A. R. & Richards, G. A. COVID-19: NAD + deficiency may predispose the aged, obese and type2 diabetics to mortality through its effect on SIRT1 activity. Med. Hypotheses 144 , (2020). Philips, A. M. & Khan, N. Amino acid sensing pathway: A major check point in the pathogenesis of obesity and COVID-19. Obes. Rev. 22 , (2021). Belančić, A., Kresović, A. & Rački, V. Potential pathophysiological mechanisms leading to increased COVID-19 susceptibility and severity in obesity. Obesity Medicine vol. 19 (2020). Campbell, R. A., Boilard, E. & Rondina, M. T. Is there a role for the ACE2 receptor in SARS-CoV-2 interactions with platelets? J. Thromb. Haemost. 19 , (2021). Rapkiewicz, A. V. et al. Megakaryocytes and platelet-fibrin thrombi characterize multi-organ thrombosis at autopsy in COVID-19: A case series. EClinicalMedicine 24 , (2020). Kato, A., Favoreto, S., Avila, P. C. & Schleimer, R. P. TLR3- and Th2 Cytokine-Dependent Production of Thymic Stromal Lymphopoietin in Human Airway Epithelial Cells. J. Immunol. 179 , (2007). Choreño-Parra, J. A. et al. Clinical and Immunological Factors That Distinguish COVID-19 From Pandemic Influenza A(H1N1). Front. Immunol. 12 , (2021). Caterino, M. et al. Dysregulation of lipid metabolism and pathological inflammation in patients with COVID-19. Sci. Rep. 11 , (2021). Kelley, N., Jeltema, D., Duan, Y. & He, Y. The NLRP3 Inflammasome: An Overview of Mechanisms of Activation and Regulation. Int. J. Mol. Sci. 20 , (2019). Vora, S. M., Lieberman, J. & Wu, H. Inflammasome activation at the crux. Nat. Rev. Immunol. doi: 10.1038/s41577-021-00588-x . Lee, S., Channappanavar, R. & Kanneganti, T.-D. Coronaviruses: Innate Immunity, Inflammasome Activation, Inflammatory Cell Death, and Cytokines. Trends Immunol. 41 , 1083–1099 (2020). Shi, C.-S., Nabar, N. R., Huang, N.-N. & Kehrl, J. H. SARS-Coronavirus Open Reading Frame-8b triggers intracellular stress pathways and activates NLRP3 inflammasomes. Cell Death Discov. 2019 51 5 , 1–12 (2019). Rodrigues, T. S. et al. Inflammasome activation in COVID-19 patients. medRxiv 2020.08.05.20168872 (2020) doi: 10.1101/2020.08.05.20168872 . Vidal, M., Cusick, M. E. & Barabási, A. L. Interactome networks and human disease. Cell (2011) doi: 10.1016/j.cell.2011.02.016 . Pan, A., Lahiri, C., Rajendiran, A. & Shanmugham, B. Computational analysis of protein interaction networks for infectious diseases. Brief. Bioinform. (2016) doi: 10.1093/bib/bbv059 . Kumar, N., Mishra, B., Mehmood, A., Mohammad Athar & M Shahid Mukhtar. Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis. iScience (2020) doi: 10.1016/j.isci.2020.101526 . Saini, K. S. et al. Repurposing anticancer drugs for COVID-19-induced inflammation, immune dysfunction, and coagulopathy. British Journal of Cancer (2020) doi: 10.1038/s41416-020-0948-x . Lee, S. J., Channappanavar, R. & Kanneganti, T. D. Coronaviruses: Innate Immunity, Inflammasome Activation, Inflammatory Cell Death, and Cytokines. Trends Immunol. 41 , 1083–1099 (2020). Li, S. et al. SARS-CoV-2 triggers inflammatory responses and cell death through caspase-8 activation. Signal Transduct. Target. Ther. 5 , (2020). Ackermann, M., Mentzer, S. J., Kolb, M. & Jonigk, D. Inflammation and intussusceptive angiogenesis in COVID-19: Everything in and out of flow. Eur. Respir. J. 56 , (2020). Ardestani, A. & Azizi, Z. Targeting glucose metabolism for treatment of COVID-19. Signal Transduct. Target. Ther. 6 , 1–2 (2021). Amaral, M. P. & Bortoluci, K. R. Caspase-8 and FADD: Where Cell Death and Inflammation Collide. Immunity 52 , (2020). Codo, A. C. et al. Elevated Glucose Levels Favor SARS-CoV-2 Infection and Monocyte Response through a HIF-1α/Glycolysis-Dependent Axis. Cell Metab. 32 , (2020). Ortiz-Prado, E., Dunn, J. F., Vasconez, J., Castillo, D. & Viscor, G. Partial pressure of oxygen in the human body: a general review. Am. J. Blood Res. (2019). Price, L. C., McCabe, C., Garfield, B. & Wort, S. J. Thrombosis and COVID-19 pneumonia: The clot thickens! European Respiratory Journal vol. 56 (2020). Huertas, A. et al. Endothelial cell dysfunction: A major player in SARS-CoV-2 infection (COVID-19)? European Respiratory Journal vol. 56 (2020). Walz, L. et al. JAK-inhibitor and type I interferon ability to produce favorable clinical outcomes in COVID-19 patients: a systematic review and meta-analysis. BMC Infect. Dis. 21 , (2021). Yan, B. et al. SARS-CoV-2 drives JAK1/2-dependent local complement hyperactivation. Sci. Immunol. 6 , 1–20 (2021). Nct. Phase 3 Randomized, Double-blind, Placebo-controlled Multi-center Study to Assess the Efficacy and Safety of Ruxolitinib in Patients With COVID-19 Associated Cytokine Storm (RUXCOVID). https://clinicaltrials.gov/show/NCT04362137 (2020). Fridman, J. S. et al. Selective Inhibition of JAK1 and JAK2 Is Efficacious in Rodent Models of Arthritis: Preclinical Characterization of INCB028050. J. Immunol. 184 , (2010). Cantini, F. et al. Immune Therapy, or Antiviral Therapy, or Both for COVID-19: A Systematic Review. Drugs vol. 80 (2020). COVID-19 Treatment Guidelines Panel. Coronavirus Disease 2019 (COVID-19) Treatment Guidelines. Disponible en: https://covid19treatmentguidelines.nih.gov/ . Natl. Inst. Heal. 2019, (2020). Marconi, V. C. et al. Baricitinib plus Standard of Care for Hospitalized Adults with COVID-19. medRxiv (2021). William, A. D. et al. Discovery of the macrocycle 11-(2-pyrrolidin-1-yl-ethoxy)-14,19-dioxa-5,7, 26-triaza-tetracyclo[19.3.1.1(2,6).1(8,12)]heptacosa-1(25),2(26),3,5,8,10,12(27),16,21,23-decaene (SB1518), a potent Janus Kinase 2/Fms-like tyrosine kinase-3 (JAK2/FLT3) inhibitor for the treatment of myelofibrosis and lymphoma. J. Med. Chem. 54 , (2011). Willette, R. N. et al. Differential effects of p38 mitogen-activated protein kinase and cyclooxygenase 2 inhibitors in a model of cardiovascular disease. J. Pharmacol. Exp. Ther. 330 , (2009). Shirey, K. A., Blanco, J. C. G. & Vogel, S. N. Targeting TLR4 Signaling to Blunt Viral-Mediated Acute Lung Injury. Frontiers in Immunology vol. 12 (2021). Park, J. J. H. et al. How COVID-19 has fundamentally changed clinical research in global health. The Lancet Global Health vol. 9 (2021). Global coalition to accelerate COVID-19 clinical research in resource-limited settings. The Lancet vol. 395 (2020). WHO. WHO Coronavirus (COVID-19) Dashboard . World Health Organisation (2021). Scoggins, J. F. & Ramsey, S. D. A National cancer clinical trials system for the 21st century: Reinvigorating the NCI cooperative group program. Journal of the National Cancer Institute vol. 102 (2010). Altay, O. et al. Current Status of COVID-19 Therapies and Drug Repositioning Applications. iScience 23 , 101303 (2020). Supplementary Files SupplementaryDataset.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-808746","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":46322899,"identity":"d6456b64-06f9-43ae-95f6-bf1b7a40a33e","order_by":0,"name":"Andrés López-Cortés","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYFAC5gaGDzw2QAZj4wEitTA2MM6QSQMziNfCzGNzGMwkTgt/+8HGjzNyztutbT8MtKXGJpqgFokzic0SH87cTt52JhGo5VhabgNBPQcS2xhn9txONjsA1MLYcJiwFvnzD9uYef+dSzY7/5BILQY3EtuYeXgO2JndINYWwxsPmyVn8CQnmN0A2pJAjF/kzicf/PCBx87e7Hz6wwcfamyI8D4UJIJVJhCrHATsSVE8CkbBKBgFIwwAAAQlS1so49llAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1503-1929","institution":"Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Andrés","middleName":"","lastName":"López-Cortés","suffix":""},{"id":46322928,"identity":"e29acdf5-0cf5-4128-a60d-e46795a34a02","order_by":1,"name":"Santiago Guerrero","email":"","orcid":"https://orcid.org/0000-0003-3473-7214","institution":"Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Madrid, Spain","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Santiago","middleName":"","lastName":"Guerrero","suffix":""},{"id":46322976,"identity":"b1bc6b40-6ed0-4efd-b701-d2e8b29a0523","order_by":2,"name":"Esteban Ortiz-Prado","email":"","orcid":"https://orcid.org/0000-0002-1895-7498","institution":"One Health Research Group, Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Esteban","middleName":"","lastName":"Ortiz-Prado","suffix":""},{"id":46322978,"identity":"78e3989f-6085-4803-8ca4-7d9a909eab5f","order_by":3,"name":"Verónica Yumiceba","email":"","orcid":"https://orcid.org/0000-0001-6998-7913","institution":"Institut für Humangenetik Lübeck, Universität zu Lübeck, Lübeck, Germany","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Verónica","middleName":"","lastName":"Yumiceba","suffix":""},{"id":46323002,"identity":"5f9b6376-67d7-450a-b78a-40875e51253b","order_by":4,"name":"Antonella Vera-Gupi","email":"","orcid":"","institution":"Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Antonella","middleName":"","lastName":"Vera-Gupi","suffix":""},{"id":46323003,"identity":"5b28879d-0e93-4aaf-adf6-5678ebad50ec","order_by":5,"name":"Ángela León Cáceres","email":"","orcid":"https://orcid.org/0000-0002-4517-9409","institution":"Heidelberg Institute of Global Health, Faculty of Medicine, University of Heidelberg, Germany","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ángela","middleName":"León","lastName":"Cáceres","suffix":""},{"id":46323004,"identity":"1fe53560-b469-448f-a7ea-53e5f78bdaa0","order_by":6,"name":"Katherine Simbaña-Rivera","email":"","orcid":"https://orcid.org/0000-0002-8130-5361","institution":"One Health Research Group, Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Katherine","middleName":"","lastName":"Simbaña-Rivera","suffix":""},{"id":46323644,"identity":"2a469093-38a3-4f2a-929b-9376b9fd46c9","order_by":7,"name":"Ana María Gómez-Jaramillo","email":"","orcid":"https://orcid.org/0000-0003-2180-7316","institution":"Faculty of Medicine, Pontifical Catholic University of Ecuador, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ana","middleName":"María","lastName":"Gómez-Jaramillo","suffix":""},{"id":46323770,"identity":"d60b7f0c-5e2d-43a9-bd3d-0c860dd5fa50","order_by":8,"name":"Patricia Guevara-Ramírez","email":"","orcid":"https://orcid.org/0000-0002-4829-3653","institution":"Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Madrid, Spain","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Guevara-Ramírez","suffix":""},{"id":46323771,"identity":"497aa169-0cd3-4ae7-b67c-35efdedb550b","order_by":9,"name":"Jennyfer M. García-Cárdenas","email":"","orcid":"https://orcid.org/0000-0001-9035-7668","institution":"Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jennyfer","middleName":"M.","lastName":"García-Cárdenas","suffix":""},{"id":46323943,"identity":"cbd04ef2-d6cb-47b2-aea9-b7fbbaf87ec2","order_by":10,"name":"Alejandro Cabrera-Andrade","email":"","orcid":"https://orcid.org/0000-0001-9702-6618","institution":"Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alejandro","middleName":"","lastName":"Cabrera-Andrade","suffix":""},{"id":46324117,"identity":"4cb0bde9-27fc-4382-af84-55c81a3cd159","order_by":11,"name":"Gabriela Echeverría-Garcés","email":"","orcid":"https://orcid.org/0000-0002-1941-7864","institution":"Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Madrid, Spain","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gabriela","middleName":"","lastName":"Echeverría-Garcés","suffix":""},{"id":46324178,"identity":"8e0f3cb5-21ab-4c95-b84a-17d804671eeb","order_by":12,"name":"Lourdes Puig San Andrés","email":"","orcid":"https://orcid.org/0000-0002-1388-3151","institution":"BIOscience Research Group, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lourdes","middleName":"Puig San","lastName":"Andrés","suffix":""},{"id":46324179,"identity":"c8eaa69b-564f-41b7-b6c7-27fd32f34a6d","order_by":13,"name":"Doménica Cevallos-Robalino","email":"","orcid":"https://orcid.org/0000-0002-7820-4359","institution":"BIOscience Research Group, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Doménica","middleName":"","lastName":"Cevallos-Robalino","suffix":""},{"id":46324180,"identity":"1301db6d-adfc-4dfa-b08f-75718247f562","order_by":14,"name":"Jhommara Bautista","email":"","orcid":"https://orcid.org/0000-0002-9717-8096","institution":"BIOscience Research Group, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jhommara","middleName":"","lastName":"Bautista","suffix":""},{"id":46324277,"identity":"3eb9bc50-afd9-42d5-843a-9122c6b95ad7","order_by":15,"name":"Isaac Armendáriz-Castillo","email":"","orcid":"https://orcid.org/0000-0003-2636-2707","institution":"Facultade de Ciencias, Universidade da Coruña, A Coruña, Spain","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Isaac","middleName":"","lastName":"Armendáriz-Castillo","suffix":""},{"id":46324278,"identity":"0567a331-b3b7-4cfe-8008-d4ee5e451aaa","order_by":16,"name":"Andy Pérez-Villa","email":"","orcid":"https://orcid.org/0000-0001-6758-4299","institution":"Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Madrid, Spain","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andy","middleName":"","lastName":"Pérez-Villa","suffix":""},{"id":46324279,"identity":"58a3fb8f-da7c-4a56-9e69-5fb3aee1b793","order_by":17,"name":"Andrea Abad-Sojos","email":"","orcid":"https://orcid.org/0000-0003-2919-7487","institution":"BIOscience Research Group, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Abad-Sojos","suffix":""},{"id":46324961,"identity":"2e85951d-2959-460c-bf85-7503f5f26e2f","order_by":18,"name":"María José Ramos-Medina","email":"","orcid":"","institution":"BIOscience Research Group, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"María","middleName":"José","lastName":"Ramos-Medina","suffix":""},{"id":46325057,"identity":"499ae01f-c909-4a5c-af68-e208758ec4af","order_by":19,"name":"Ariana León-Sosa","email":"","orcid":"","institution":"BIOscience Research Group, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ariana","middleName":"","lastName":"León-Sosa","suffix":""},{"id":46325058,"identity":"20897b4a-222a-4348-b7f6-134871f4ff5a","order_by":20,"name":"Estefanía Abarca","email":"","orcid":"","institution":"BIOscience Research Group, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Estefanía","middleName":"","lastName":"Abarca","suffix":""},{"id":46325592,"identity":"b8346a16-bc05-4770-b5d8-ac617a2f1b10","order_by":21,"name":"Álvaro A. Pérez-Meza","email":"","orcid":"","institution":"Biotechnology Engineering Career, Faculty of Life Sciences, Universidad Regional Amazónica Ikiam, Tena, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Álvaro","middleName":"A.","lastName":"Pérez-Meza","suffix":""},{"id":46325593,"identity":"59d7047c-56af-4de1-821f-d79936f01f10","order_by":22,"name":"Karol Nieto-Jaramillo","email":"","orcid":"","institution":"BIOscience Research Group, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Karol","middleName":"","lastName":"Nieto-Jaramillo","suffix":""},{"id":46325594,"identity":"bc859736-c5cc-489a-8644-c4f077d8be93","order_by":23,"name":"Andrea V. Jácome","email":"","orcid":"","institution":"Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"V.","lastName":"Jácome","suffix":""},{"id":46325618,"identity":"cb109cfd-386b-4934-b6a0-192c5a4ded4e","order_by":24,"name":"Andrea Morillo","email":"","orcid":"","institution":"BIOscience Research Group, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Morillo","suffix":""},{"id":46325672,"identity":"c56a4290-2091-4741-bb30-2c68b5f5dfea","order_by":25,"name":"Fernanda Arias-Erazo","email":"","orcid":"","institution":"BIOscience Research Group, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fernanda","middleName":"","lastName":"Arias-Erazo","suffix":""},{"id":46325765,"identity":"73e788d4-983a-4b21-ba2d-e314c435cf1b","order_by":26,"name":"Luis Fuenmayor-González","email":"","orcid":"https://orcid.org/0000-0001-6141-7692","institution":"BIOscience Research Group, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Luis","middleName":"","lastName":"Fuenmayor-González","suffix":""},{"id":46326288,"identity":"536be621-c5fa-446e-814a-6d27a1b02731","order_by":27,"name":"Nikolaos C Kyriakidis","email":"","orcid":"https://orcid.org/0000-0003-1382-5661","institution":"One Health Research Group, Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nikolaos","middleName":"C","lastName":"Kyriakidis","suffix":""}],"badges":[],"createdAt":"2021-08-13 06:36:09","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-808746/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-808746/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":12504774,"identity":"7dd0c756-d664-4d8b-aefd-0ee5447217db","added_by":"auto","created_at":"2021-08-17 15:30:38","extension":"tif","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":363638,"visible":true,"origin":"","legend":"Significantly expressed genes across lung cell types. Box plots show the number of significantly expressed genes, their Z-scores, and p-values per each lung cell type. Neural cells were the cell type with the highest mean Z-score, followed by B cells, mast cells, fibroblast cells, alveolar type II cells, cycling natural killer / T cells, endothelial cells, macrophages, airway epithelial cells, alveolar type I cells, natural killer cells, dendritic cells, smooth cells, Treg cells, plasma cells, monocytes, other epithelial cells, CD4+ T cells, and CD8+ T cells. ","description":"","filename":"1.tif","url":"https://assets-eu.researchsquare.com/files/rs-808746/v1/7391c8938c9f97e357398d4b.tif"},{"id":12505031,"identity":"d0c01c3d-f06a-41e7-ba19-69e623bb52f8","added_by":"auto","created_at":"2021-08-17 15:33:38","extension":"tif","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3260371,"visible":true,"origin":"","legend":"Transcriptomics data of 116,313 lung nuclei from lethal COVID-19 patients. UMAPs show the mean log normalized expression of significantly expressed genes per lung cell type. Dot plots show the ranking of genes with the highest percentage of cells expressing. UMAP: uniform manifold approximation and projection for dimension reduction; NK: natural killer; OG: overexpressed genes; and UG: underexpressed genes. ","description":"","filename":"2.tif","url":"https://assets-eu.researchsquare.com/files/rs-808746/v1/0311976313ba2e2247cfa2a2.tif"},{"id":12504782,"identity":"1484efb7-ab6e-4f37-ba52-2d7cfd9b7dbb","added_by":"auto","created_at":"2021-08-17 15:30:39","extension":"tif","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4185135,"visible":true,"origin":"","legend":"Functional enrichment analysis. UMAPs show the most significant genes per lung cell type involved in gene ontology biological processes and signaling pathways. The most significant biological term was inflammatory response, followed by cytokine production, innate immune response, macrophage activation, Toll-like receptor signaling pathway, interferon production, JAK-STAT signaling pathway, NF-κB signaling pathway, thymic stromal lymphopoietin, TNF signaling pathway, blood coagulation, oncostatin M signaling pathway, AGE-RAGE signaling pathway, IL-1 and megakaryocytes in obesity, and NLRP3 inflammasome complex. UMAP: uniform manifold approximation and projection for dimension reduction. ","description":"","filename":"3.tif","url":"https://assets-eu.researchsquare.com/files/rs-808746/v1/dde95da2b3d9e1969d630453.tif"},{"id":12504777,"identity":"413fcb32-f5cb-4baf-b1b6-3e7270665922","added_by":"auto","created_at":"2021-08-17 15:30:38","extension":"tif","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2111405,"visible":true,"origin":"","legend":"miRNome enrichment analysis. Circos plot show a GSEA to compute overlaps between the 18 most significant miRNAs (FDR q-value \u003c 0.01) and the 19 significantly expressed genes in \u003e 50% of lung cells. The most significant miRNA was MIR6867_5P, followed by MIR2662, MIR32_3P, MIR548A0_5P_MIR548AX, MIR570_3P, MIR338_5P, MIR144_3P, MIR4711_3P, MIR628_5P, MIR548AJ_3P_MIR548X_3P, MIR1290, MIR4496, MIR12136, MIR9718, MIR875_3P, MIR4698, MIR3941, and MIR4789_3P. GSEA: gene set enrichment analysis. ","description":"","filename":"4.tif","url":"https://assets-eu.researchsquare.com/files/rs-808746/v1/fcf84fa19d86b81fb3df5fde.tif"},{"id":12504775,"identity":"1691a220-d2eb-48f9-ad1e-58726b45b74c","added_by":"auto","created_at":"2021-08-17 15:30:38","extension":"tif","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2208746,"visible":true,"origin":"","legend":"Inflammatory protein-protein interactome network. iPPI network made up of 265 nodes and 2052 edges. Of them, 159 pulmonary inflammatory response proteins had a mean of degree centrality of 8 and 108 human-SARS-CoV-2 proteins had a mean of degree centrality of 7.2. The top ten inflammatory response proteins with the highest degree centrality were APP, NFKB1, STAT3, C3, ITGAM, FN1, PTAFR, JAK2, EGFR, and LYN. The top ten human-SARS-CoV-2 proteins with the highest degree centrality were GNB1, GNG5, RHOA, ITGB1, STOM, RAB14, PRKAR2B, RAB8A, PRKACA, and ANO6. Additionally, 111 pulmonary inflammatory response proteins had the highest confidence interactions (cutoff = 0.9) with human-SARS-CoV-2 proteins, being the top ten: C3, FN1, NFKB1, RPS19, CTSC, HSPD1, APP, ITGAM, SNAP23, and MAPK14. ","description":"","filename":"5.tif","url":"https://assets-eu.researchsquare.com/files/rs-808746/v1/bbb688b597677e33cc03a962.tif"},{"id":12505032,"identity":"2c930563-092e-4a47-88c8-26a68bbea7a7","added_by":"auto","created_at":"2021-08-17 15:33:39","extension":"tif","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":967658,"visible":true,"origin":"","legend":"Shortest paths to cancer hallmark phenotypes. A) Box plots encompassing inflammatory response proteins with the shortest mean of distance score per phenotype. Cell death was the phenotype with the shortest paths, followed by inflammation, glycolysis, and angiogenesis. B) Venn diagram of inflammatory response proteins with shortest paths to hallmarks of cancer related to COVID-19. C) Ranking of the most essential proteins with shortest paths to cell death, inflammation, glycolysis, and angiogenesis. ","description":"","filename":"6.tif","url":"https://assets-eu.researchsquare.com/files/rs-808746/v1/125669812a3175047ea082eb.tif"},{"id":12504781,"identity":"fedf4f61-2ce9-4805-bd8b-cd74788b8c06","added_by":"auto","created_at":"2021-08-17 15:30:39","extension":"tif","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1160987,"visible":true,"origin":"","legend":"Essential proteins with the shortest distance score to the inflammation phenotype. The essential proteins with positive regulation to inflammation were PTGS2, PRKCZ, NFKBIA, MAPK14, TNFRSF1B, TLR4, ATM, MECOM, PIK3CG, EGFR, JAK2, LYN, CYLD, PRKCQ, STAT3, TGFB1, RBPJ, TNFAIP3, NOTCH1, IGF1, CD28, CCL5, PTAFR, FPR1, EDNRA, EDNRB, CYSLTR1, CNR2, HGF, EPHA2, FN1, CSF1, PTGFR, and APP. ","description":"","filename":"7.tif","url":"https://assets-eu.researchsquare.com/files/rs-808746/v1/18b27852da09d279808c3f28.tif"},{"id":12504780,"identity":"a0f23a80-63f2-4f68-9ce8-051a3254daa3","added_by":"auto","created_at":"2021-08-17 15:30:39","extension":"tif","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":682684,"visible":true,"origin":"","legend":"Drugs involved in advanced-stage COVID-19 clinical trials. Drug name, class type, druggable target, structure, pharmacological indication, and clinical trial number related to small molecules involved in advanced-stage COVID-19 clinical trials. ","description":"","filename":"8.tif","url":"https://assets-eu.researchsquare.com/files/rs-808746/v1/03e029bb7b581368f7cd77f3.tif"},{"id":13709658,"identity":"d7c79176-b5ae-42bf-bc26-e0234ab3c3e2","added_by":"auto","created_at":"2021-09-17 14:14:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6902238,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-808746/v1/ccddbc3f-350c-44f1-b8e4-d5e3d324c7e8.pdf"},{"id":12504779,"identity":"b29bbab6-8ae4-4f16-ad3d-9c0391ded2bf","added_by":"auto","created_at":"2021-08-17 15:30:38","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5887102,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDataset.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-808746/v1/f406df3faee689ea58d37129.xlsx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eSingle-nucleus lung transcriptomics and inflammatory responses in lethal COVID-19 reveal potential drugs in advanced-stage clinical trials\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe first zoonotic transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occurred in China in late December 2019 \u003csup\u003e1\u003c/sup\u003e, and it is the etiological agent of the coronavirus disease 2019 (COVID-19) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Since the World Health Organization (WHO) declared the outbreak of COVID-19 as a pandemic on March 11, 2020, the SARS-CoV-2 infection has led to more than 200\u0026nbsp;million cases and more than 4\u0026nbsp;million deaths globally \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSARS-CoV-2 is the seventh CoV known to infect humans, along with HKU1, OC43, NL63, 229E, SARS-CoV, and Middle East respiratory syndrome (MERS) \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The novel coronavirus is a single-stranded positive-sense RNA virus of about 30 kb in length \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, which encompasses a 5\u0026rsquo; terminal cap, 14 open reading frames (ORFs) encoding for 29 proteins, and a 3\u0026rsquo; poly A tail \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. ORF1a and ORF1ab encode 16 non-structural proteins (nsps) involved in antiviral response (nsp1), viral replication (the nsp3-nsp4-nsp6 complex), the protease 3C\u003csup\u003epro\u003c/sup\u003e (nsp5), the RNA polymerase (the nsp7-nsp8 complex), the single strand RNA binding protein (nsp9), the methyltransferase activity (nsp10 and nsp16), the RNA-dependent RNA polymerase (nsp12), the helicase/triphosphatase (nsp13), the 3\u0026rsquo;-5\u0026rsquo; exonuclease (nsp14), the uridine-specific endoribonuclease (nsp15), and the RNA-cap (nsp16) \u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The remaining genes encode structural proteins: the spike (S) glycoprotein, the nucleocapsid (N) protein, the membrane (M) glycoprotein, the envelope (E) protein, and several accessory proteins \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt the molecular level, amino-acid changes that result in reduced fitness are generally removed by negative selection, whereas changes that increase virus fitness are maintained by positive selection \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The most significant mutation observed in SARS-CoV-2 is probably the D614G substitution in the S1 subunit of the S protein. This mutation confers a 20% increase in infectivity and is associated with a higher ACE2-binding affinity \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Additionally, SARS-CoV-2 fitness is also enhanced in presence of the E484K mutation, which increases resistance to antibodies \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis transmissibility advantage was further increased to approximately 50% by the emergence of the B.1.1.7 variant \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Subsequently more variants have emerged, some of them capable of escaping monoclonal antibodies, partially eluding the polyclonal immune responses induced by previous infection or even allowing re-infections. It should be noted that recent improvements in immune escape are linked to mutations that alter the N-terminal domain (NTD) rather than the receptor-binding domain (RBD) of the S protein, where early and functionally important alterations prevailed \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, improved transmissibility, rather than immunoevasion or increased lethality, are considered as the main route for the virus to become fitter and more viable \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe variants that are being carefully monitored include: A) Variants of concern (VOCs): characterized by increased transmissibility, cause a more severe manifestation of the disease or significant reduction in neutralizing antibodies generated during a previous infection or after vaccination, reduced effectiveness of treatments, or diagnostic detection failures. This group includes the B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), and P.1 (Gamma) variants. B) Variants of interest (VOIs): present reduced neutralization by antibodies induced by previous infection or vaccines, reduced efficacy of treatments, potential diagnostic escape and expected increase in transmissibility or severity of COVID-19. Among them, we find the B.1.427 / 429 (Epsilon), B.1.525 (Eta), B.1.526 (Iota), B.1.617.1 (Kappa), C.37 (Lambda), B.1.617.3, P.2 (Zeta), and B.1.621 variants. C) Variants under monitoring: could have properties similar to those of VOCs, however precise information is still lacking. D) High consequence variants: those that have significantly reduced the efficacy of vaccines in relation to previously circulating variants, and additionally cause failures in their diagnosis; however, there are not SARS-CoV-2 variants that rise to the level of high consequence yet \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. It is expected that more variants will emerge over time that will need to be closely monitored, since they are a potential threat to public health. Nevertheless, this will not happen indefinitely because -over time- the virus will reach its maximum transmission point, therefore, new variants will not acquire more advantages in terms of infectivity. Thereafter, virus infectivity will stabilize and experience occasional and minimal variations \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSARS-CoV-2, enriched by the previously mentioned genomic variants, has the ability to infect human body cells \u0026ndash;especially in the lung microenvironment\u0026ndash; through the angiotensin-converting enzyme 2 (ACE2) protein receptor \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Lung homeostasis necessitates a fine balance between tolerance mechanisms against non-pathogenic agents, pro-inflammatory immune system activation to fight off respiratory tract infections and anti-inflammatory and pro-fibrotic processes that will minimize the immune-mediated tissular lesion and promote tissue remodeling and repair. These complex mechanisms are mediated by a variety of tissue-resident and recruited cell types. The pulmonary alveolar epithelium is mainly composed of alveolar type I cells (AT1), which are essential for the gas-exchange function of the lungs, and alveolar type II cells (AT2), which are best known for their functions in synthesizing and secreting pulmonary surfactant factors \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Airway epithelial cells are central players in mucociliary clearance of the lungs. They produce a variety of antimicrobial substances, cytokines, and growth factors that mediate leukocyte recruitment, modulation of innate and adaptive immunity, and tissue repair \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. They also constitute the first cells that contact invading pathogens and are responsible for early pathogen recognition and induction of the antiviral state through pro-inflammatory cytokines and type I interferon secretion \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Pulmonary endothelial cells are localized in the interface between the pulmonary tissue and the bloodstream. Their strategic location is also reflected in their pleiotropic functions that range from gas interchange to regulating vascular tone and facilitating immune cell recruitment and diapedesis upon receiving pro-inflammatory stimuli \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Mast cells are innate immune cells, involved in immune defense and surveillance. They are filled with secretory granules, which upon activation, release bioactive mediators to fight pathogens or induce allergic reactions \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Macrophages are key sentinel cells residing in peripheral tissues that detect pathogen invasion or tissue damage and initiate acute inflammatory processes triggering recruitment and activation of innate and \u0026ndash;in second step- adaptive immune responses. Macrophages are also key inducers of the respiratory burst, professional antigen-presenting cells and tissue repair and remodeling mediators \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Dendritic cells form a heterogeneous population of the immune system that have a wide array of immune functions. Conventional dendritic cells bridge the innate and adaptive immune responses as they are constantly sampling antigens from the airways and/or the infected lung tissue, migrate to T-cell areas of secondary lymphoid organs and present it to T lymphocytes thereby activating them \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Monocytes, subsets of leukocytes mostly originated from myeloid progenitors in the bone marrow, are able to differentiate into macrophages or dendritic cells in peripheral tissues. They seed tissues with enough macrophages to replace their loss through infection and tissue damage and can adopt specific macrophage or dendritic cell phenotypes depending on the cytokine milieu they encounter upon arrival to the inflamed tissue \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Natural killer (NK) cells are lymphocytes of the innate immune system, that play a main role in anti-viral and anti-tumor responses \u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. They can identify and kill infected or stressed cells by releasing perforin and granzymes or by death receptor signaling (FasL/Fas interactions and the subsequent induction of apoptosis). NK cells can also release IFNγ upon activation, thereby contributing to naive T helper cell activation and differentiation and classical activation of macrophages \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The CD4\u0026thinsp;+\u0026thinsp;T helper cell population orchestrates the innate and adaptive immune responses in acute and chronic viral infections by secreting a panel of immunomodulatory cytokines. These cells also play a key role for the establishment of long-term cellular and humoral antigen-specific immunity, which is the basis of long-term protection induced by a plethora of viral infections and vaccines \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells play a pivotal role in controlling infections caused by intracellular pathogens. These cells can be considered the adaptive immunity counterparts of NK cells, but unlike their innate immunity counterparts CD8\u0026thinsp;+\u0026thinsp;T cells are activated by specific pathogen or tumor-derived antigen presented on class I major histocompatibility complex molecules (MHC I). The three major mechanisms of action of these cells are quite similar to NK cell functions: a) direct killing of infected or tumor cells by release of perforin and granzymes, b) indirect destruction of cells via death receptor signaling (Fas/FasL interactions), and c) secretion of cytokines that can direct and potentiate immune responses of nearby cells \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Treg cells are potent immunosuppressive cells that play a vital role in maintaining immune homeostasis and in the prevention of autoimmune responses by suppressing the activation of conventional T-cells \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. B cells have a key role in the humoral adaptive immune response and \u0026ndash;once activated- are responsible for the production of antigen-specific immunoglobulins \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Plasma blasts are terminally differentiated populations of effector B cells cells, which produce antibodies, providing immunity during initial exposure to a pathogen and mediating the protective effects of vaccination \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Fibroblasts are key cells in the wound repair process and tissue scarring. They participate in the immune response by producing cytokines and chemokines that initiate the recruitment and retention of bone marrow-derived immune effector cells \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Smooth muscle cells provide the main support for the vessel wall structure and regulate vascular tone to maintain intravascular pressure and tissue perfusion \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Lastly, neuronal cells release neurotransmitters and neuropeptides that allow fast communication with immune cells, maintaining homeostasis and fighting infections. Neuroimmune interactions are also implicated in several chronic inflammatory conditions \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies have reported profound SARS-CoV-2-induced transcriptional and immunological changes in animal models \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, as well as in bronchoalveolar lavage fluid (BALV) \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, nasopharyngeal \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, and human blood samples \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. However, the respiratory failure is the leading cause of death in patients with severe COVID-19 disease and the host inflammatory response at the lung tissue level remains poorly understood \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. To shed light on this physiological response, we retrieved data from COVID-19 autopsies and performed in-depth \u003cem\u003ein silico\u003c/em\u003e analyses of single-nucleus RNA sequencing (snRNA-seq) data, inflammatory protein-protein interactome (iPPI) network, miRNome enrichment, gene ontology (GO), and the shortest paths to cancer hallmark phenotypes to reveal potential therapeutic targets and drugs in advanced stage COVID-19 clinical trials.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eProtein sets.\u003c/b\u003e We have retrieved a total of 719 inflammatory response proteins from the David Bioinformatics Resource (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e using the gene ontology (GO) term: 0006954 inflammatory response. We have also retrieved the 332 human proteins physically interacting with 26 of the 29 SARS-CoV-2 proteins proposed by Gordon \u003cem\u003eet al\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSingle-nucleus RNA sequencing data.\u003c/b\u003e Melms \u003cem\u003eet al\u003c/em\u003e have previously published the molecular single-cell lung atlas of lethal COVID-19 through the snRNA-seq technology needed to profile hard-to-dissociate tissues \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Motivated by this study, we performed an in-depth \u003cem\u003ein silico\u003c/em\u003e analysis comparing the transcriptional data of 719 genes involved in the inflammatory response between 9608 alveolar type I cells, 11341 alveolar type II cells, 7332 airway epithelial cells, 1845 B cells, 7586 CD4\u0026thinsp;+\u0026thinsp;T cells, 3561 CD8\u0026thinsp;+\u0026thinsp;T cells, 2814 cycling NK / T cells, 1083 dendritic cells, 5386 endothelial cells, 21472 fibroblast cells, 25960 macrophages, 1438 mast cells, 3464 monocytes, 2141 NK cells, 2017 neuronal cells, 5391 plasma cells, 1437 smooth muscle cells, 649 Treg cells, and 1788 other epithelial cells. The snRNA-seq database was taken from the \u0026lsquo;COVID-19 Studies\u0026rsquo; section of the Single Cell Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://singlecell.broadinstitute.org/single_cell/covid19\u003c/span\u003e\u003c/span\u003e), and the transcriptomics data of 116,313 nuclei was taken from \u0026lsquo;Columbia University / NYP COVID-19 Lung Atlas\u0026rsquo; study (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://singlecell.broadinstitute.org/single_cell/study/SCP1219/columbia-university-nyp-covid-19-lung-atlas?cluster=UMAP\u0026amp;spatialGroups=--\u0026amp;annotation=cell_type_intermediate--group--study\u0026amp;subsample=all#study-summary\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe criteria of the analysis of the lung transcriptomics data was the following: \u0026lsquo;uniform manifold approximation and projection (UMAP)\u0026rsquo; as load cluster, \u0026lsquo;cell type intermediate\u0026rsquo; as selected annotation, and \u0026lsquo;all cells\u0026rsquo; as subsampling threshold. Additionally, we adjusted the mRNA expression taking into account the Z-scores, that is, overexpressed mRNAs with Z-scores\u0026thinsp;\u0026ge;\u0026thinsp;2 and underexpressed mRNAs with Z-scores \u0026le; -2. Regarding visualization of transcriptomics data, we designed dot plots to visualize the percentage of cells expressing a certain gene, box plots to compare the mean Z-score across cell types, and scatter plots of 2D UMAPs to visualize significantly expressed multiple genes per subpopulation cell, and biological annotations across cell types.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional enrichment analysis.\u003c/b\u003e The functional enrichment analysis gives curated signatures of gene sets generated from omics-scale experiments \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. We performed the enrichment analysis to validate the correlation between significantly expressed genes and biological annotations related to lethal COVID-19. The enrichment was calculated using g:Profiler version e101_eg48_p14_baf17f0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biit.cs.ut.ee/gprofiler/gost\u003c/span\u003e\u003c/span\u003e) to obtain significant annotations (Benjamini-Hochberg FDR q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) related to gene ontology: biological processes, the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways, Reactome signaling pathways, and Wikipathways \u003csup\u003e\u003cspan additionalcitationids=\"CR54 CR55 CR56\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Lastly, the expression of genes involved in significant annotations was visualized in scatter plots, and the significant terms related to lethal COVID-19 pathology were manually curated.\u003c/p\u003e \u003cp\u003e \u003cb\u003emiRNome enrichment analysis.\u003c/b\u003e The Gene Set Enrichment Analysis (GSEA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/index.jsp\u003c/span\u003e\u003c/span\u003e) is a powerful analytical method for interpreting gene expression data that share common biological functions or regulations \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Therefore, we performed a miRNome enrichment analysis using the \u0026lsquo;microRNA targets\u0026rsquo; option to compute overlaps between miRNAs and significantly expressed mRNAs in \u0026gt;\u0026thinsp;50% of lung cells from lethal COVID-19 patients. Lastly, we proposed the most significant miRNAs with a false discovery rate (FDR) q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInflammatory protein-protein interactome network.\u003c/b\u003e The iPPI network with zero node addition and a highest confidence cutoff of 0.9 was created between the human proteins physically associated with SARS-CoV-2 and human proteins involved in the pulmonary inflammatory response. This network was generated using the human proteome from the Cytoscape StringAPP \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, which imports protein interactions from the STRING database \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. The degree centrality represents the number of edges the node has in a network \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, and it was calculated using the CytoNCA app \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. The network elements were organized through the organic layout producing a clear representation of complex networks, and the iPPI network was visualized through the Cytoscape software v.3.7.1 \u003csup\u003e64\u003c/sup\u003e. Finally, we ranked the inflammatory response proteins with the highest protein-protein interactions to the human-SARS-CoV-2 proteins.\u003c/p\u003e \u003cp\u003e \u003cb\u003eShortest paths from inflammatory response proteins to cancer hallmark phenotypes.\u003c/b\u003e CancerGenNet (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://signor.uniroma2.it/CancerGeneNet/\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e is a resource that links frequently altered proteins to cancer hallmark phenotypes \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. This bioinformatic tool, curated by SIGNOR \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, is based on experimental information that allows to infer likely paths of causal interactions linking proteins to oncogenic phenotypes. The shortest distance scores or paths from proteins to cancer phenotypes were programmatically implemented using the shortest path function of \u003cem\u003eigraph\u003c/em\u003e R package \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Hence, we calculated the shortest distance scores of positive regulation from the inflammatory response proteins with the highest confidence interactions to the human-SARS-CoV-2 proteins to the inflammation, cell death, angiogenesis, and glycolysis hallmark phenotypes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDrugs involved in current COVID-19 clinical trials.\u003c/b\u003e The Open Targets Platform version 21.06 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.targetvalidation.org\u003c/span\u003e\u003c/span\u003e) is comprehensive and robust data integration for access to and visualization of potential drug targets associated with several diseases including COVID-19 \u003csup\u003e67\u003c/sup\u003e. This platform has developed the COVID-19 Target Prioritization Tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://covid19.opentargets.org/\u003c/span\u003e\u003c/span\u003e) that integrates molecular data from the ChEMBL database \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e to provide an evidence-based framework to support decision-making on potential drug targets for COVID-19. Lastly, this platform shows all drugs in clinical trials associated with target proteins, detailing its modality, mechanism of action, phase, status, type of drug, target class, and clinical trial number \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eSingle-nucleus RNA sequencing data.\u003c/b\u003e Single-nucleus biology is a powerful approach of omics medicine, needed to profile hard-to-dissociate tissues, that provides unprecented resolution to the cellular underpinnings of biological processes in order to find druggable targets for complex diseases \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Here, we identified 233 inflammatory response genes with significant expression in 116,313 nuclei belonging to 19 different lung cell types. Genes with the highest mean Z-score (3.26) and the most significant p-value (0.001) were identified in neural cells, followed by B cells (3.24; 0.001), mast cells (3.14; 0.002), fibroblast cells (3.0; 0.003), alveolar type II cells (2.96; 0.003), cycling NK / T cells (2.94; 0.003), endothelial cells (2.89; 0.004), macrophages (2.88; 0.004), airway epithelial cells (2.76; 0.006), alveolar type I cells (2.74; 0.006), NK cells (2.73; 0.006), dendritic cells (2.70; 0.007), smooth cells (2.68; 0.007), Treg cells (2.67; 0.008), plasma cells (2.62; 0.009), monocytes (2.47; 0.014), other epithelial cells (2.41; 0.016), CD4\u0026thinsp;+\u0026thinsp;T cells (2.39; 0.017), and CD8\u0026thinsp;+\u0026thinsp;T cells (2.24; 0.025) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows scatter plots of significant mean log normalized gene expression and dot plots of genes with the highest percentage of cells expressing per lung cell type. \u003cem\u003eMECOM\u003c/em\u003e has the highest percentage of cells expressing in alveolar type I cells, \u003cem\u003eLRRK2\u003c/em\u003e in alveolar type II cells, \u003cem\u003eELF3\u003c/em\u003e in airway epithelial cells, \u003cem\u003ePXK\u003c/em\u003e in B cells, \u003cem\u003eCAMK4\u003c/em\u003e in CD4\u0026thinsp;+\u0026thinsp;T cells, \u003cem\u003eAOAH\u003c/em\u003e in CD8\u0026thinsp;+\u0026thinsp;T cells, \u003cem\u003eHMGB1\u003c/em\u003e in cycling NK / T cells, \u003cem\u003eCIITA\u003c/em\u003e in dendritic cells, \u003cem\u003eRBPJ\u003c/em\u003e in macrophages, \u003cem\u003eKIT\u003c/em\u003e in mast cells, \u003cem\u003eSLC11A1\u003c/em\u003e in monocytes cells, \u003cem\u003eAPP\u003c/em\u003e in neuronal cells, \u003cem\u003eAOAH\u003c/em\u003e in NK cells, \u003cem\u003eCALCRL\u003c/em\u003e in endothelial cells, \u003cem\u003eRORA\u003c/em\u003e in fibroblasts, \u003cem\u003eASH1L\u003c/em\u003e in plasma cells, \u003cem\u003eFN1\u003c/em\u003e in smooth muscle cells, and \u003cem\u003eSGMS1\u003c/em\u003e in Treg cells. Lastly, the 26 inflammatory response genes significantly expressed in more that 50% of lung cells were \u003cem\u003eABR\u003c/em\u003e, \u003cem\u003eACER3\u003c/em\u003e, \u003cem\u003eAOAH\u003c/em\u003e, \u003cem\u003eAPP\u003c/em\u003e, \u003cem\u003eASH1L\u003c/em\u003e, \u003cem\u003eATM\u003c/em\u003e, \u003cem\u003eCALCRL\u003c/em\u003e, \u003cem\u003eCAMK1D\u003c/em\u003e, \u003cem\u003eCAMK4\u003c/em\u003e, \u003cem\u003eCD163\u003c/em\u003e, \u003cem\u003eCIITA\u003c/em\u003e, \u003cem\u003eEGFR\u003c/em\u003e, \u003cem\u003eFN1\u003c/em\u003e, \u003cem\u003eHDAC9\u003c/em\u003e, \u003cem\u003eIL18R1\u003c/em\u003e, \u003cem\u003eIL1R1\u003c/em\u003e, \u003cem\u003eKIT\u003c/em\u003e, \u003cem\u003eLRRK2\u003c/em\u003e, \u003cem\u003eLYN\u003c/em\u003e, \u003cem\u003eMECOM\u003c/em\u003e, \u003cem\u003ePRKCA\u003c/em\u003e, \u003cem\u003ePRKCZ\u003c/em\u003e, \u003cem\u003eRBPJ\u003c/em\u003e, \u003cem\u003eRORA\u003c/em\u003e, \u003cem\u003eSLC11A1\u003c/em\u003e, and \u003cem\u003eSLIT2\u003c/em\u003e (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional enrichment analysis.\u003c/b\u003e The functional enrichment analysis was performed using g:Profiler to obtain significant GO: biological processes, KEGG signaling pathways, Reactome signaling pathways, and Wikipathways related to lethal COVID-19 (Benjamini-Hochberg FDR q\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows scatter plots of significantly expressed genes (n\u0026thinsp;=\u0026thinsp;233) in lung cells of lethal COVID-19 autopsies. After a manual curation of biological annotations, the most significant GO terms were inflammatory response (5.9 x 10\u003csup\u003e\u0026minus;\u0026thinsp;241\u003c/sup\u003e), cytokine production (9.5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;62\u003c/sup\u003e), innate immune response (1.0 x 10\u003csup\u003e\u0026minus;\u0026thinsp;30\u003c/sup\u003e), macrophage activation (1.1 x 10\u003csup\u003e\u0026minus;\u0026thinsp;29\u003c/sup\u003e), toll-like receptor signaling pathway (3.8 x 10\u003csup\u003e\u0026minus;\u0026thinsp;15\u003c/sup\u003e), type I and II interferon production (1.8 x 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e), the Janus Kinase (JAK) / Signal Transducers and Activators of Transcription (STAT) signaling pathway (9.0 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), NF-\u003cem\u003eκ\u003c/em\u003eB signaling pathway (2.0 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), thymic stromal lymphopoietin (TSLP) (4.5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), TNF signaling pathway (4.9 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), blood coagulation (5.6 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), oncostatin M signaling pathway (5.9 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), AGE-RAGE signaling pathway (5.9 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), IL-1 and megakaryocytes in obesity (6.8 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), and NLRP3 inflammasome complex (2.5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003emiRNome enrichment analysis.\u003c/b\u003e After identifying the significantly expressed genes in lung cells of lethal COVID-19 autopsies, we performed the GSEA analysis to compute overlaps between miRNAs and mRNAs \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows a circos plot of the 18 most significant miRNAs (FDR q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) overlapped with the 19 significantly expressed genes in \u0026gt;\u0026thinsp;50% of lung cells. The most significant miRNAs were MIR6867_5P (q\u0026thinsp;=\u0026thinsp;0.002), MIR2662 (q\u0026thinsp;=\u0026thinsp;0.002), MIR32_3P (q\u0026thinsp;=\u0026thinsp;0.002), MIR548A0_5P_MIR548AX (q\u0026thinsp;=\u0026thinsp;0.003), MIR570_3P (q\u0026thinsp;=\u0026thinsp;0.004), MIR338_5P (q\u0026thinsp;=\u0026thinsp;0.005), MIR144_3P (q\u0026thinsp;=\u0026thinsp;0.005), MIR4711_3P (q\u0026thinsp;=\u0026thinsp;0.005), MIR628_5P (q\u0026thinsp;=\u0026thinsp;0.005), MIR548AJ_3P_MIR548X_3P (q\u0026thinsp;=\u0026thinsp;0.005), MIR1290 (q\u0026thinsp;=\u0026thinsp;0.006), MIR4496 (q\u0026thinsp;=\u0026thinsp;0.007), MIR12136 (q\u0026thinsp;=\u0026thinsp;0.007), MIR9718 (q\u0026thinsp;=\u0026thinsp;0.007), MIR875_3P (q\u0026thinsp;=\u0026thinsp;0.008), MIR4698 (q\u0026thinsp;=\u0026thinsp;0.008), MIR3941 (q\u0026thinsp;=\u0026thinsp;0.009), and MIR4789_3P (q\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInflammatory protein-protein interactome network.\u003c/b\u003e We generated the iPPI network encompassing 265 nodes and 2052 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Of them, 159 pulmonary inflammatory response proteins had a mean of degree centrality of 8 and 108 human-SARS-CoV-2 proteins had a mean of degree centrality of 7.2. The top ten inflammatory response proteins with the highest degree centrality were APP (38), NFKB1 (36), STAT3 (34), C3 (31), ITGAM (29), FN1 (26), PTAFR (24), JAK2 (22), EGFR (20), and LYN (20). The top ten human-SARS-CoV-2 proteins with the highest degree centrality were GNB1 (29), GNG5 (25), RHOA (23), ITGB1 (22), STOM (20), RAB14 (20), PRKAR2B (17), RAB8A (17), PRKACA (17), and ANO6 (16). Additionally, 111 pulmonary inflammatory response proteins had the highest confidence interactions (cutoff\u0026thinsp;=\u0026thinsp;0.9) with human-SARS-CoV-2 proteins, being the top ten: C3 (11 interactions), FN1 (11), NFKB1 (10), RPS19 (10), CTSC (9), HSPD1 (9), APP (8), ITGAM (8), SNAP23 (8), and MAPK14 (7) (Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eShortest pathways from inflammatory response proteins to cancer hallmark phenotypes.\u003c/b\u003e We analyzed the 111 pulmonary inflammatory response proteins with the highest condifence interactions (cutoff\u0026thinsp;=\u0026thinsp;0.9) to human-SARS-CoV-2 proteins in order to find the shortest pathways toward inflammation, cell death, angiogenesis, and glycolysis according to Iannuccelli \u003cem\u003eet al\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA shows box plots encompassing proteins with the shortest distance scores to cancer hallmark phenotypes related to COVID-19 pathology. Cell death was the phenotype with the shortest mean of distance score (2.82), followed by inflammation (3.06), glycolysis (3.12), and angiogenesis (3.79). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB shows a Venn diagram integrating inflammatory proteins with shortest pathways to biological phenotypes related to COVID-19. We found 34 essential inflammatory response proteins with shortest pathways simultaneously to inflammation, glycolysis, cell death, and angiogenesis (Supplementary Table\u0026nbsp;4). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC details the ranking of inflammatory response proteins with positive response and shortest distance to cell death, inflammation, glycolysis, and angiogenesis. The top ten essential proteins with shortest pathways of positive regulation to cell death were ATM (1.20), NFKBIA (1.42), TNFRSF1B (1.64), APP (1.73), MAPK14 (1.73), PRKCZ (1.93), TLR4 (1.97), JAK2 (2.26), TGFB1 (2.35), and MECOM (2.36). The top ten essential proteins with shortest pathways of positive regulation to inflammation were PTGS2 (0.53), PRKCZ (1.39), NFKB1A (1.71), MAPK14 (1.92), TNFRSF1B (2.40), TLR4 (2.44), ATM (2.45), MECOM (2.57), PIK3CG (2.63), and EGFR (2.64). The top ten essential proteins with shortest pathways of positive regulation to glycolysis were ATM (1.67), CD28 (1.84), EGFR (1.98), TNFAIP3 (2.00), HGF (2.03), CYLD (2.09), PRKCZ (2.21), EPHA2 (2.25), JAK2 (2.27), and PIK3CG (2.34). The top ten essential proteins with shortest pathways of positive regulation to angiogenesis were TGFB1 (0.86), STAT3 (1.97), MAPK14 (1.98), EGFR (2.48), JAK2 (2.58), ATM (2.91), NFKBIA (2.96), PRKCZ (3.13), TLR4 (3.35), and PTAFR (3.46) (Supplementary Table\u0026nbsp;5). Lastly, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e details all shortest pathways and distance scores of positive regulation from the 34 essential proteins to the inflammation phenotype.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDrugs involved in current COVID-19 clinical trials.\u003c/b\u003e Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e details the current status of COVID-19 clinical trials regarding to essential inflammatory proteins, according to the Open Targets Platform \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. There are 5 drugs (small molecules) that are being analyzed in 8 clinical trials in advanced stages (phases III and IV) for 3 essential inflammatory proteins. Baricitinib is a tyrosine-protein kinase JAK1/2 inhibitor that acts on the JAK proteins and it is being studied in 4 clinical trials in phase III (NCT04640168, NCT04693026, NCT04421027, and NCT04401579). Similarly, pacritinib and ruxolitinib are tyrosine-protein kinase JAK1/2 inhibitors. They are currently been evaluated in a phase III clinical trial NCT04404361 and NCT04362137, respectively. Losmapimod is a MAP kinase p38 alpha inhibitor that acts on the MAPK14 protein and it is being studied in a phase III clinical trial (NCT04511819). Lastly, eritoran is a toll-like receptor 4/MD-2 antagonist that acts on the TLR4 protein and it is being studied in one clinical trial in phase IV (NCT02735707).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSince the identification of patient zero in China, a wide spectrum of clinical features have been discovered in severe COVID-19. For instance, dyspnea, acute respiratory distress syndrome (ARDS) \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e, respiratory failure, lung edema, severe hypoxemia, cardiac arrhythmias, lymphopenia \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, hyperferritinemia, rhabdomyolysis, intravascular coagulopathy \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e, and pulmonary thromboembolism \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Nowadays, it is known that SARS-CoV-2 not only causes respiratory tract infection, but also skin, kidneys, blood, and central neural system pathologies \u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Therefore, it is imperative to continuously review the clinical manifestations and physiopathological mechanisms of the SARS-CoV-2 infection, especially with the appearance of new genomic variants.\u003c/p\u003e \u003cp\u003eSingle-cell biology provides unprecedented resolution to the cellular underpinnings of biological processes in order to find therapeutically actionable targets for complex diseases \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Melms \u003cem\u003eet al\u003c/em\u003e have previously published the molecular single-cell lung atlas of lethal COVID-19 \u003csup\u003e51\u003c/sup\u003e. Motivated by this study, we performed an in-depth \u003cem\u003ein silico\u003c/em\u003e analysis comparing the transcriptional data of 719 inflammatory response genes across 19 lung cell types belonging to COVID-19 autopsies. The functional enrichment analysis of the 233 significantly expressed inflammatory genes showed that the most significant biological annotations were inflammatory response (5.9 x 10\u003csup\u003e\u0026minus;\u0026thinsp;241\u003c/sup\u003e), cytokine production (9.5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;62\u003c/sup\u003e), innate immune response (1.0 x 10\u003csup\u003e\u0026minus;\u0026thinsp;30\u003c/sup\u003e), macrophage activation (1.1 x 10\u003csup\u003e\u0026minus;\u0026thinsp;29\u003c/sup\u003e), TLR signaling pathway (3.8 x 10\u003csup\u003e\u0026minus;\u0026thinsp;15\u003c/sup\u003e), type I and II interferon production (1.8 x 10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e), JAK-STAT signaling pathway (9.0 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), NF-\u003cem\u003eκ\u003c/em\u003eB signaling pathway (2.0 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), thymic stromal lymphopoietin (4.5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), TNF signaling pathway (4.9 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), blood coagulation (5.6 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), oncostatin M signaling pathway (5.9 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), AGE-RAGE signaling pathway (5.9 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), IL-1 and megakaryocytes in obesity (6.8 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), and the NLRP3 inflammasome complex (2.5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eThe innate immune response is the first line of defense against new invading pathogens \u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Pattern recognition receptors (PRRs) are capable of recognizing molecules with conserved motifs commonly shared by pathogen groups \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Recognition by these receptors triggers innate immune responses and induces multiple IFN and pro-inflammatory cytokine secretion in COVID-19 patients \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. Upon ligand recognition PRRs initiate a signaling pathway that activate key transcription factors, such as NF-\u003cem\u003eκ\u003c/em\u003eB, AP-1, and interferon regulatory factors (IRF3 and IRF7) that induce pro-inflammatory cytokines and type I interferon. Type I IFNs are responsible for inducing the JAK-STAT signaling pathway to activate IFN-stimulated genes and develop the \u0026ldquo;anti-viral state\u0026rdquo; in the infected organism \u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e,\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInterferon is a cytoplasmic glycoprotein with antiviral activity. This cytokine is another contributing factor in the humoral immunological response against respiratory viruses \u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. Through a bronchoalveolar lavage in severely ill patients, evidence of local induction of interferon and the stimulation of interferon genes was found. In contrast, minimal levels of interferon were found in peripheral blood of severely ill patients \u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. This increased cytokine production with limited interferon levels might be due to an antagonist mechanism of the nsp1 protein against interferon signaling \u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. Regarding the genetics underlying severe COVID-19, Zhang \u003cem\u003eet al\u003c/em\u003e concluded that genetics may determine the clinical course of SARS-CoV-2 infection identifying mutations in genes involved in the regulation of type I and III IFN immunity \u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e, and Bastard et al identified high titers of neutralizing autoantibodies against type I IFN-α2 and IFN-ω in 10% of patients with severe COVID-19 \u003csup\u003e87\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMacrophages are cells that perform crucial functions in the immune system, from the phagocytosis of the viruses and bacteria to maintaining homeostasis \u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. Precisely, macrophages produce high amounts of pro-inflammatory cytokines in patients with ARDS, who present an activated state known as cytokine storm or macrophage activation syndrome \u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. The overexpression of cytokines (i.e., TNF-α, IL-2, IL-10, IL-1, and IL-6) leads to a hyperinflammatory response, which has been reported as a remarkable feature of SARS-CoV-2 infection \u003csup\u003e\u003cspan additionalcitationids=\"CR91\" citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. IL-6 plays a main role in the severity of COVID-19, while TNF-α and IL-1β trigger the NF-\u003cem\u003eκ\u003c/em\u003eB signaling pathway \u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e,\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e. The excessive production of cytokines leads to development of pathological symptoms, such as lung damage, cell death, severe pneumonia, ARDS, lung fibrosis, and multiple organ failure \u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e,\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. Hence, this cytokine storm plays a crucial role in the progression of SARS-CoV-2 infection and is considered as one of the main causes of lethal COVID-19 \u003csup\u003e92,93\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTNF is considered as one of the most important pro-inflammatory cytokines, affecting different parts of the immune system and regulating various pathological and physiological processes \u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. Therefore, the TNFα-NF-\u003cem\u003eκ\u003c/em\u003eB axis is considered as a potential therapeutic target in COVID-19 \u003csup\u003e97\u003c/sup\u003e. Initially, NF-\u003cem\u003eκ\u003c/em\u003eB is present within the cytoplasm, after activation of I\u003cem\u003eκ\u003c/em\u003eB through phosphorylation of I\u003cem\u003eκ\u003c/em\u003eB kinase, NF-\u003cem\u003eκ\u003c/em\u003eB is activated and translocated to the nucleus where it regulates the transcription of various target genes \u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e,\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e. To date, SARS-CoV-2-mediated NF-\u003cem\u003eκ\u003c/em\u003eB activation has been observed in several cells such as macrophages of liver, kidney, lung, central nervous system, cardiovascular system, and gastrointestinal system. This causes a chronic production of IL-1, IL-2, IL-6, IL-12, TNF-α, LT-α, LT-β, GM-CSF, and several chemokines, leading to the aforementioned pathological symptoms \u003csup\u003e\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e. Catanzaro \u003cem\u003eet al\u003c/em\u003e have recently published a report analyzing the role of the TNFα-NF-\u003cem\u003eκ\u003c/em\u003eB pathway in COVID-19. In their report, it was suggested that inhibiting this axis may prevent pulmonary complications in COVID-19 patients \u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e. This was also observed in SARS-CoV infection. NF-\u003cem\u003eκ\u003c/em\u003eB expression was elevated in the lungs of recombinant SARS-CoV-1-infected mice, while NF-\u003cem\u003eκ\u003c/em\u003eB inhibitors reduced SARS-CoV-related expanding survival of these mice \u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe cytokine signaling depends on the JAK and STAT which are phosphorylated and activated upon cytokines binding to their receptors. The STAT homodimers translocate into the nucleus, where they upregulate the transcription of several genes that participate not only in cytokine production but also in apoptosis, immune regulation, and cell cycle differentiation \u003csup\u003e\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e. In the context of SARS-CoV-2 infection, inhibition of the JAK-STAT pathway seems as promising approach to prevent cytokines storm in fatal cases or in patients with comorbidities that express high levels of inflammatory markers such as of IL-6, TNFα, IL-17a, GM-CSF, and G-CSF \u003csup\u003e\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e. In fact, the GenOMICC GWAS study suggests that individuals with a variant on chromosome 19: 10,466,123 that affects expression of tyrosine kinase 2 (TYK2), member of the JAK family, could be associated with a host-driven inflammatory response that leads to severe lung injury \u003csup\u003e\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e. Thus, several clinical trials have shown that baricitinib, a JAK inhibitors possesses a good safety and efficacy profiles in reducing cytokine levels of severe COVD-19 patients without side effects \u003csup\u003e\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNevertheless, the JAK/SAT pathway is also necessary to mediate the immune response to clear viral infections and prolonged inhibition of the pathway could lead to immunosuppression and prolonged infections \u003csup\u003e\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e. For instance, SARS-CoV-2 is able to hijack the JAK/STAT pathway in order to increase its proliferation by evading the immune response. Li \u003cem\u003eet al\u003c/em\u003e showed that SARS-CoV-2 infected cell had a decreased expression of JAK1, JAK2, TYK2, and STAT2 proteins. This is explained by action of viral nsp1, ORF6, and ORF8 that prevent the phosphorylation of STAT1 and STAT3 to inhibit IFN production \u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e,\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e. Therefore, the timeline for administration of JAK/STAT inhibitors should be carefully analyzed since reducing the hyperinflammation could affect viral clearance. Due to the narrow therapeutic window of JAK/STAT inhibitors, dosage should aim to restore the immune response homeostasis.\u003c/p\u003e \u003cp\u003eThe incidence of thrombotic events in COVID-19 patients responsible for strokes and heart attacks raises the concern about the abnormal coagulation patterns and poor prognosis in the actual pandemic. Tang \u003cem\u003eet al\u003c/em\u003e reported that 71.4% of non-surviving COVID-19 patients met the criteria for disseminated intravascular coagulation and presented high levels of coagulation-related biomarkers such as D-dimer and fibrin degradation products \u003csup\u003e\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/sup\u003e. The mechanisms of the coagulopathy are not clear; however, some reports indicate that dysregulated immune responses are involved in such processes. Exacerbation of inflammatory cytokines promoting proliferation of megakaryocytes, lymphocyte cell-death, hypoxia, endothelial damage contributing to ischemia and organ dysfunction, and the association between autoantibodies and neutrophil extracellular traps seem to be involved in the abnormal thrombotic events in COVID-19 patients \u003csup\u003e\u003cspan additionalcitationids=\"CR111\" citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOncostatin M is a cytokine involved in homeostasis and chronic inflammation that has pleiotropic functions such as cell differentiation and proliferation, and it is present in hematopoietic, immunological, and inflammatory networks \u003csup\u003e\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e. One of the most important functions of oncostatin M is the stimulation of the chemokines CCL1, CCL7 and CCL8 in primary human dermal fibroblasts at a faster kinetics than IL-1β or TNF-α \u003csup\u003e\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e. In 2020, it was proposed as a new mortality biomarker in patients with acute respiratory failure supported by venous-venous extracorporeal membrane oxygenation \u003csup\u003e\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u003c/sup\u003e. In the case of COVID-19, an increase of OSM plasma levels and other inflammatory mediators was detected; this finding was correlated with the severity of disease and the increase of bacterial products in plasma \u003csup\u003e\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u003c/sup\u003e. Finally, OSM is curiously elevated in obese patients and upon recognition by its specific receptor (OSMRβ) induces obesity and insulin resistance conditions \u003csup\u003e\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eObesity is one of the main risk factors associated with lethal COVID-19, and levels of pro-inflammatory cytokines increase under this pathology \u003csup\u003e\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e\u003c/sup\u003e. Low NAD\u0026thinsp;+\u0026thinsp;levels in obese individuals decrease the activity of SIRT1, a molecule that modulates cytokine production \u003csup\u003e\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e\u003c/sup\u003e. However, the excess of amino acid availability hyperactivates the mTOR signaling pathway increasing viral replication and inflammatory response \u003csup\u003e\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e\u003c/sup\u003e. Additionally, because adipose tissue has a considerable level of ACE2 expression, viral shedding increases, as well as the production of pro-inflammatory factors \u003csup\u003e\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e\u003c/sup\u003e. This inflammatory process contributes to thrombotic problems, a probable cause of multiorgan failure, which has been evidenced by the presence of elevated levels of megakaryocytes in COVID-19 autopsies \u003csup\u003e\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e,\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThymic stromal lymphopoietin is an epithelial cytokine normally produced by airway epithelial cells. It has been associated with T-helper type 2 (Th2) responses in allergic diseases, highlighting its role in inflammatory disease pathogenesis. It has been discovered that TSLP can be triggered by respiratory viral infections, bacteria, allergens and injuries \u003csup\u003e\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e\u003c/sup\u003e. TSLP acts upon cells with TSLP receptor such as hematopoietic progenitor cells, eosinophils, basophils, mast cells, airway smooth muscle cells, group 2 innate lymphoid cells, lymphocytes, dendritic cells and monocytes/macrophages. When several immune mediators were measured in patient\u0026rsquo;s plasma suffering from influenza A (H1N1) and COVID-19, TSLP levels were significantly upregulated in COVID-19 patients. This fact suggests a possible contribution of TSLP in COVID-19 pathogenesis and perhaps aids differential diagnosis \u003csup\u003e\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e\u003c/sup\u003e. Besides, since TSLP concentration was reported to be higher in severely affected than in mild and moderated COVID-19 cases, it may be potentially used as a biomarker for disease severity \u003csup\u003e\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOptimal NLRP3 inflammasome activation is crucial for host immune defense against several pathogenic infections \u003csup\u003e\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e\u003c/sup\u003e. SARS-CoV-2 activates inflammasomes, which are large multiprotein assemblies that are broadly responsive to pathogen-associated cellular insults, leading to secretion of cytokines and an inflammatory form of cell death \u003csup\u003e\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e\u003c/sup\u003e. However, excessive activation can lead to systemic inflammation and tissue damage which are detrimental to the host \u003csup\u003e\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e\u003c/sup\u003e. Patients with severe COVID-19 have been found to have higher serum concentrations of pro-inflammatory cytokines and chemokines such as granulocyte-colony stimulating factor (GCSF), monocyte chemoattractant protein 1 (MCP1), TNF, IL-6, and IL-1β compared with healthy individuals. A unified mechanism for NLRP3 inflammasome activation has not been proposed yet; however, some researchers have found that SARS-CoV-2 ORF-8b interacts with the LRR domain of NLRP3 inflammasome activating IL-1β secretion in THP-1 macrophages \u003csup\u003e\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e\u003c/sup\u003e. Findings suggest that SARS-Cov-2 infection leads to NLRP3 inflammasome activation, caspase-1 cleavage, and the release of IL-1β stimulating pyroptosis in peripheral blood mononuclear cells from severe COVID-19 patients \u003csup\u003e\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn a biological system approach, SARS-CoV-2 employs a suite of virulent proteins that interacts with host targets to extensively rewire the flow of information and cause COVID-19 \u003csup\u003e11,132\u0026minus;134\u003c/sup\u003e. The human proteins physically associated with SARS-CoV-2 are the first line of host proteins \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, which also interact with proteins involved in a wide spectrum of signaling pathways and biological processes within lung cells. In this study, we identified 111 pulmonary inflammatory response proteins with the highest confidence interactions to human-SARS-CoV-2 proteins, being the top ten: C3, FN1, NFKB1, RPS19, CTSC, HSPD1, APP, ITGAM, SNAP23, and MAPK14.\u003c/p\u003e \u003cp\u003eSubsequently, we analyzed these 111 inflammatory response proteins to identify those with the shortest pathways to four cancer hallmark phenotypes. Inflammation is a hallmark of cancer observed in patients with SARS-CoV-2 infection \u003csup\u003e\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e\u003c/sup\u003e. The chronic inflammatory process causes cell death \u003csup\u003e\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e,\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e\u003c/sup\u003e, angiogenesis \u003csup\u003e\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e\u003c/sup\u003e, and during the peak of inflammation, immune cells preferentially use glycolysis as a source of energy \u003csup\u003e\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e\u003c/sup\u003e. These facts provide a biological rationale to analyze and prioritize the inflammatory response proteins with the shortest distance scores to these biological phenotypes. Consequently, we identified 34 essential inflammatory response proteins highly associated with cell death, glycolysis, and angiogenesis. These proteins were: PTGS2, PRKCZ, NFKBIA, MAPK14, TNFRSF1B, TLR4, ATM, MECOM, PIK3CG, EGFR, JAK2, LYN, CYLD, PRKCQ, STAT3, TGFB1, RBPJ, TNFAIP3, NOTCH1, IGF1, CD28, CCL5, PTAFR, FPR1, EDNRA, EDNRB, CYSLTR1, CNR2, HGF, EPHA2, FN1, CSF1, PTGFR, and APP.\u003c/p\u003e \u003cp\u003eThe SARS-CoV-2 infection of lung epithelial cells activates caspase-8 to trigger the three major cell death pathways, including apoptosis, pyroptosis, and necroptosis. Cell death and inflammatory responses are intimately linked during SARS-CoV-2 infection \u003csup\u003e\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e\u003c/sup\u003e. Lastly, analysis of postmortem lung sections of lethal COVID-19 patients has revealed that inflammatory responses from lung epithelial cells may induce infiltration of inflammatory cells that trigger strong immune pathogenesis \u003csup\u003e\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent studies showed that SARS-CoV-2 rewires human monocytes in a high glucose culture medium. This induces viral replication and cytokine production, and might be the reason why people suffering from diabetes, obesity and other related metabolic diseases are more susceptible to developing severe COVID-19 \u003csup\u003e139\u003c/sup\u003e. For instance, people with type 2 diabetes show an increased glucose metabolism due to hyperglycemia, which may boost SARS-CoV-2 pathogenesis \u003csup\u003e\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e\u003c/sup\u003e. Codo \u003cem\u003eet al\u003c/em\u003e proved that glycolytic flux is essential for SARS-CoV-2 impact \u003csup\u003e\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e\u003c/sup\u003e. Through several assays, they inhibited glycolysis by blocking 2-deoxy-D-glucose (2-DG) and glycolytic enzymes 6-phospho-fructo-2-kinase/fructose-2,6-biphosphatase-3 (PFKFB3) and lactate dehydrogenase A (LDH-A), as a consequence, they observed that both viral replication and cytokine response stopped \u003csup\u003e\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e\u003c/sup\u003e. The metabolic transcription factor HIF-1α activity and related genes are strongly stimulated in SARS-CoV-2 infected blood monocytes isolated from severe COVID-19 patients \u003csup\u003e\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e\u003c/sup\u003e. HIF-1α is also a major glycolysis regulator, when inhibited, viral replication and cytokine expression were also blocked. Overall, these experiments showed that high glucose concentration and glycolysis are essential for SARS-CoV-2 replication, inflammatory response, and upregulation of ACE2 \u003csup\u003e141\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAngiogenesis occurs in response to the activation of acute inflammation or chronic systemic hypoxia pathways that increase the expression of proteins and factors (HIF-1α, VEGF, NO) associated with its development \u003csup\u003e\u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e\u003c/sup\u003e. During the SARS-CoV-2 infection, local endothelial damage, known as endotheliitis, is associated with acute inflammation of the outermost endovascular layers, triggering a cascade of reactions that result in endothelial inflammation, platelet aggregation, and impaired laminar flow \u003csup\u003e\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e,\u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e143\u003c/span\u003e\u003c/sup\u003e. In the context of COVID-19 disease, the reported vasoconstriction and subsequent hypoxia, stimulate the formation of new blood vessels by promoting branching of pre-existing blood vessels (intussusception) and \u003cem\u003ede novo\u003c/em\u003e angiogenesis that contributes to the already established systemic hypoxia \u003csup\u003e\u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e144\u003c/span\u003e\u003c/sup\u003e. This process together with the systemic hypoxia observed in severe COVID-19 patients cause a structural and functional reorganization of the pullmonary tissue, which ultimate function is to allow an adequate gas exchange between the tissue and the cells \u003csup\u003e\u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRegarding drugs against COVID-19 disease, in this study we propose five small molecules (ruxolitinib, baricitinib, pacritinib, losmapimod, and eritoran) that after being thoroughly analyzed in COVID-19 clinical trials, these drugs can be considered for treating severe COVID-19 patients.\u003c/p\u003e \u003cp\u003eA systematic review and meta-analysis published by Walz \u003cem\u003eet al\u003c/em\u003e concluded that Janus kinase-inhibitor treatment is significantly associated with positive clinical outcomes in terms of mortality, intensive care unit admission, and discharge \u003csup\u003e\u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e145\u003c/span\u003e\u003c/sup\u003e. Ruxolitinib is a tyrosine-protein kinase JAK1/2 inhibitor \u003csup\u003e\u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e146\u003c/span\u003e\u003c/sup\u003e that is currently used for myelofibrosis and polycythemia vera, both hematologic malignancies. The use of ruxolitinib in these diseases is based on its ability of being a kinase inhibitor, which mediates the signaling of a number of cytokines and growth factors that are important in hematopoiesis and immune function. Based on this principle, it is reasonable then, from a clinical point of view, to use this drug to specifically manage cytokine storm in COVID-19 \u003csup\u003e147\u003c/sup\u003e. According to Yan \u003cem\u003eet al\u003c/em\u003e, ruxolitinib normalized interferon signature genes and all complement gene transcripts induced by SARS-CoV-2 in lung epithelial cell lines. They proposed that combination therapy with JAK inhibitors and drugs that normalize NF-\u003cem\u003eκ\u003c/em\u003eB-signaling could potentially have clinical application for severe COVID-19 \u003csup\u003e146\u003c/sup\u003e. Baricitinib is a tyrosine-protein kinase JAK1/2 inhibitor \u003csup\u003e\u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e148\u003c/span\u003e\u003c/sup\u003e mainly used for rheumatoid arthritis, and among its pharmacological properties it has an antiviral effect on the entry of a virus \u003csup\u003e\u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e149\u003c/span\u003e\u003c/sup\u003e. At the moment, baricitinib is approved by the WHO, the Food and Drug Administration (FDA) of the United States, and the National Institutes of Health (NIH) for emergent use in severe pneumonia due to COVID-19 \u003csup\u003e150\u003c/sup\u003e. The use of baricitinib is indicated in COVID-19 critically ill patients with high oxygen needs despite the use of dexamethasone (the only approved corticosteroid), however it should not be used when IL-6 inhibitors such as tocilizumab have been started, given that its combined use has not yet been tested as well as its safety. The known efficacy of Baricitinib is from emerging data from an unpublished article where the 27.8% of participants receiving baricitinib vs 30.5% receiving placebo progressed (primary endpoint, odds ratio 0.85, 95% CI 0.67\u0026ndash;1.08; p\u0026thinsp;=\u0026thinsp;0.18), and the all-cause mortality was 8.1% for baricitinib and 13.1% for placebo, corresponding to a 38.2% reduction in mortality (hazard ratio [HR] 0.57, 95% CI 0.41\u0026ndash;0.78; nominal p\u0026thinsp;=\u0026thinsp;0.002) \u003csup\u003e\u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e151\u003c/span\u003e\u003c/sup\u003e. Pacritinib is also a protein-kinase inhibitor mainly focused on JAK2 and FLT3 protein targets. This small molecule has been developed for the treatment of myelofibrosis \u003csup\u003e\u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e152\u003c/span\u003e\u003c/sup\u003e. On the other hand, losmapimod is a MAP kinase p38 alpha inhibitor that has been investigated for the prevention of chronic obstructive pulmonary disease and cardiovascular disease \u003csup\u003e\u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e153\u003c/span\u003e\u003c/sup\u003e. The therapeutic hypothesis for the use of losmapimod in COVID-19 is that increased mortality is caused by p38 MAPK-mediated exaggerated acute inflammatory response resulting in SARS-CoV-2 infection. Lastly, eritoran is a Toll-like receptor 4 / MD-2 antagonist that downregulates the intracellular generation of pro-inflammatory cytokines IL-6 and TNF-alpha in human monocytes, and has been developed for the treatment of severe sepsis. Shirey et al examined how antagonizing TLR4 signaling has been effective experimentally in ameliorating acute lung injury and lethal infection in challenge models triggered by acute lung injury-inducing viruses \u003csup\u003e\u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e154\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConsidering the enormous pressure that health systems are facing due to the COVID-19 pandemic and the continuous need to present and implement comprehensive health strategies that can address the global situation; mainly after the emergence of different variants, it is imperative to recognize the urgent need to diminish the gaps between research and the implementation of public health measures. In fact, it may be unprecedented in the history of science to know how many research articles related to COVID-19 have been submitted and published. However, according to Park \u003cem\u003eet al\u003c/em\u003e, the research community has emphasized on \u0026ldquo;the new norm of publishing: quantity over quality\u0026rdquo; and this is also related to the well known problems that clinical trials faced even before the pandemic \u003csup\u003e\u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e155\u003c/span\u003e\u003c/sup\u003e. This is of particular interest to our research given that we acknowledge that clinical trials are essential in evidence-based medicine, and consequently, in the decision making process of public health policies and strategies. Relevantly, the need to smartly invest not only in randomized clinical trials but also in large-scale clinical trials with master protocols and conducted by coordinated and collaborative structures, as also supported by Park \u003cem\u003eet al\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e155\u003c/span\u003e\u003c/sup\u003e. These clinical trials networks are essential to coordinate actions between clinical researchers and health practitioners, also promoting knowledge sharing, leadership, and cost-time reductions. In addition, it is critical to decentralize, improve and increase clinical trials in low and middle-income countries, as current evidence shows large inequalities and concentrations of funds and information in high-income countries \u003csup\u003e\u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e156\u003c/span\u003e\u003c/sup\u003e. This holds true especially for Latin America, one of the most affected regions in the world by the pandemic \u003csup\u003e\u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e157\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe role of health research is fundamental in the response to COVID-19, considering the importance of data sharing and assuring efficiency, equity, and effectiveness in the diverse processes. Contradictorily, a large number of clinical trials might never be completed and others are done with doubtful methodologies \u003csup\u003e\u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e155\u003c/span\u003e,\u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e158\u003c/span\u003e\u003c/sup\u003e. Thus, analyzing potential drugs targets for COVID-19, especially the ones which can serve for severe cases, need an urgent and efficient development of well designed and managed clinical trials, which can provide potential interventions that help people to live longer, diminish long-term effects, manage pain and/or possible disabilities; not to mention the possible positive effects on the reduction of hospitalization costs, both at the individual level and in terms of possible savings for the national health system. As another study also mentioned, the potential and benefits of repositioning clinical trials are directed to use the already available information of safe and affordable generic drugs and propose \u0026ldquo;potential, prompt, cost- effective, and safe solutions for the public and global health problems, with a human-centered approach\u0026rdquo; \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This is also conveyed by the Pan American Health Organization (PAHO), which adds to the benefits, the idea of having already pharmaceutical formed supply chains \u003csup\u003e\u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e159\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFinally, as other authors have contributed, the current global research situation must be guided towards a collaborative and synergetic approach instead of being conceived as a competitive and isolated process. The COVID-19 pandemic assures the need to eliminate structural barriers that increase health inequalities, and in this perspective, benefits, knowledge, and of course potential treatments must be available for all, in order to achieve universal health coverage and equity.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets generated for this study are included in this published article (and its Supplementary Information files).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAL-C conceived the subject and the conceptualization of the study. NCK gave conceptual advice and valuable scientific input. AL-C, PG-R, AA-S, AL-S, EA, AAP-M, KN-J, AM, and FA-E did data curation and supplementary data. AL-C did funding acquisition. All authors wrote and edited the manuscript. Lastly, all authors reviewed and approved the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo information\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Latin American Society of Pharmacogenomics and Personalized Medicine (SOLFAGEM).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTay, M. Z., Poh, C. M., R\u0026eacute;nia, L., MacAry, P. A. \u0026amp; Ng, L. F. P. The trinity of COVID-19: immunity, inflammation and intervention. \u003cem\u003eNat. Rev. Immunol.\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41577-020-0311-8\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanders, J. M., Monogue, M. L., Jodlowski, T. Z. \u0026amp; Cutrell, J. B. Pharmacologic Treatments for Coronavirus Disease 2019 (COVID-19): A Review. \u003cem\u003eJAMA - Journal of the American Medical Association\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2020.6019\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. COVID-19 Weekly Epidemiological Update 35. \u003cem\u003eWorld Heal. Organ.\u003c/em\u003e 1\u0026ndash;3 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtiz-Prado, E. \u003cem\u003eet al.\u003c/em\u003e Clinical, molecular, and epidemiological characterization of the SARS-CoV-2 virus and the Coronavirus Disease 2019 (COVID-19), a comprehensive literature review. \u003cem\u003eDiagnostic Microbiology and Infectious Disease\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.diagmicrobio.2020.115094\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZiegler, C. G. K. \u003cem\u003eet al.\u003c/em\u003e SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues. \u003cem\u003eCell\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2020.04.035\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, P. \u003cem\u003eet al.\u003c/em\u003e A pneumonia outbreak associated with a new coronavirus of probable bat origin. \u003cem\u003eNature\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-020-2012-7\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, A. \u003cem\u003eet al.\u003c/em\u003e Genome Composition and Divergence of the Novel Coronavirus (2019-nCoV) Originating in China. \u003cem\u003eCell Host Microbe\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 325\u0026ndash;328 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, L. \u003cem\u003eet al.\u003c/em\u003e Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors. \u003cem\u003eScience\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.abb3405\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao, Y. \u003cem\u003eet al.\u003c/em\u003e Structure of the RNA-dependent RNA polymerase from COVID-19 virus. \u003cem\u003eScience (80-.).\u003c/em\u003e eabb7498 (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.abb7498\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGordon, D. E. \u003cem\u003eet al.\u003c/em\u003e A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. \u003cem\u003eNature\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-020-2286-9\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026oacute;pez-Cort\u0026eacute;s, A. \u003cem\u003eet al.\u003c/em\u003e In silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19. \u003cem\u003eFront. Pharmacol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 1\u0026ndash;24 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLo Presti, A., Rezza, G. \u0026amp; Stefanelli, P. Selective pressure on SARS-CoV-2 protein coding genes and glycosylation site prediction. \u003cem\u003eHeliyon\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, B. \u003cem\u003eet al.\u003c/em\u003e SARS-CoV-2 spike D614G change enhances replication and transmission. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e592\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMccormick, B. K. D., Jacobs, J. L. \u0026amp; Mellors, J. W. The emerging plasticity of SARS-CoV-2. \u003cem\u003eScience (80-.).\u003c/em\u003e \u003cb\u003e371\u003c/b\u003e, 1306\u0026ndash;1308 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStates, U. \u003cem\u003eet al.\u003c/em\u003e Article Emergence and rapid transmission of SARS-CoV-2 ll Article Emergence and rapid transmission of SARS-CoV-2 B. 1. 1. 7 in the United States. \u003cem\u003eCell\u003c/em\u003e \u003cb\u003e184\u003c/b\u003e, 2587\u0026ndash;2594.e7 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurioni, R. \u0026amp; Topol, E.. Has SARS-CoV-2 reached peak fitness? \u003cem\u003eNat Med\u003c/em\u003e (2021) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41591-021-01421-7\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoehm, E. \u003cem\u003eet al.\u003c/em\u003e Novel SARS-CoV-2 variants: the pandemics within the pandemic. \u003cem\u003eClin. Microbiol. Infect.\u003c/em\u003e (2021) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cmi.2021.05.022\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCDC. SARS-CoV-2 Variant Classifications and Definitions. \u003cem\u003eVariant Surveillance\u003c/em\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eECDC. SARS-CoV-2 variants of concern as of 8 July 2021. \u003cem\u003eSituation updates on COVID-19\u003c/em\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarvey, W. T. \u003cem\u003eet al.\u003c/em\u003e SARS-CoV-2 variants, spike mutations and immune escape. \u003cem\u003eNat. Rev. Microbiol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurton, D. \u0026amp; Topol, E. Toward superhuman SARS-CoV-2 immunity? \u003cem\u003eNat Med\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 5\u0026ndash;6 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y. \u003cem\u003eet al.\u003c/em\u003e Pulmonary alveolar type I cell population consists of two distinct subtypes that differ in cell fate. \u003cem\u003eProc. Natl. Acad. Sci. U. S. A.\u003c/em\u003e \u003cb\u003e115\u003c/b\u003e, (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiemstra, P. S., McCray, P. B. \u0026amp; Bals, R. The innate immune function of airway epithelial cells in inflammatory lung disease. \u003cem\u003eEur. Respir. J.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e, (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeitnauer, M., Mijošek, V. \u0026amp; Dalpke, A. H. Control of local immunity by airway epithelial cells. \u003cem\u003eMucosal Immunology\u003c/em\u003e vol. 9 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoo, J. K., Kim, T. S., Hufford, M. M. \u0026amp; Braciale, T. J. Viral infection of the lung: Host response and sequelae. \u003cem\u003eJournal of Allergy and Clinical Immunology\u003c/em\u003e vol.\u0026nbsp;132 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiethamer, T. K. \u003cem\u003eet al.\u003c/em\u003e Defining the role of pulmonary endothelial cell heterogeneity in the response to acute lung injury. \u003cem\u003eElife\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEspinosa, E. \u0026amp; Valitutti, S. New roles and controls of mast cells. \u003cem\u003eCurrent Opinion in Immunology\u003c/em\u003e vol.\u0026nbsp;50 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiswas, S. K. \u0026amp; Mantovani, A. \u003cem\u003eMacrophages: Biology and role in the pathology of diseases\u003c/em\u003e. \u003cem\u003eMacrophages: Biology and Role in the Pathology of Diseases\u003c/em\u003e (2014). doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-1-4939-1311-4\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchraml, B. U. \u0026amp; Reis e Sousa, C. Defining dendritic cells. \u003cem\u003eCurrent Opinion in Immunology\u003c/em\u003e vol.\u0026nbsp;32 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurray, P. J. Immune regulation by monocytes. \u003cem\u003eSeminars in Immunology\u003c/em\u003e vol.\u0026nbsp;35 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBi, J. \u0026amp; Tian, Z. NK cell exhaustion. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e vol.\u0026nbsp;8 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Eeden, C., Khan, L., Osman, M. S. \u0026amp; Tervaert, J. W. C. Natural killer cell dysfunction and its role in covid-19. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e vol.\u0026nbsp;21 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaucourant, C. \u003cem\u003eet al.\u003c/em\u003e Natural killer cell immunotypes related to COVID-19 disease severity. \u003cem\u003eSci. Immunol.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCulley, F. J. Natural killer cells in infection and inflammation of the lung. \u003cem\u003eImmunology\u003c/em\u003e vol.\u0026nbsp;128 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng, X. \u003cem\u003eet al.\u003c/em\u003e Sharing CD4 + T Cell Loss: When COVID-19 and HIV Collide on Immune System. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e vol.\u0026nbsp;11 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, N. \u0026amp; Bevan, M. J. CD8 + T Cells: Foot Soldiers of the Immune System. \u003cem\u003eImmunity\u003c/em\u003e vol.\u0026nbsp;35 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSavage, P. A., Klawon, D. E. J. \u0026amp; Miller, C. H. Regulatory T Cell Development. \u003cem\u003eAnnu. Rev. Immunol.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGladstone, D. E., Kim, B. S., Mooney, K., Karaba, A. H. \u0026amp; D\u0026rsquo;Alessio, F. R. Regulatory T Cells for Treating Patients With COVID-19 and Acute Respiratory Distress Syndrome: Two Case Reports. \u003cem\u003eAnn. Intern. Med.\u003c/em\u003e \u003cb\u003e173\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShuwa, H. A. \u003cem\u003eet al.\u003c/em\u003e Alterations in T and B cell function persist in convalescent COVID-19 patients. \u003cem\u003eMed\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNutt, S. L., Hodgkin, P. D., Tarlinton, D. M. \u0026amp; Corcoran, L. M. The generation of antibody-secreting plasma cells. \u003cem\u003eNat. Rev. Immunol.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith, R. S., Smith, T. J., Blieden, T. M. \u0026amp; Phipps, R. P. Fibroblasts as sentinel cells. Synthesis of chemokines and regulation of inflammation. \u003cem\u003eThe American journal of pathology\u003c/em\u003e vol.\u0026nbsp;151 (1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, G., Jacquet, L., Karamariti, E. \u0026amp; Xu, Q. Origin and differentiation of vascular smooth muscle cells. \u003cem\u003eJ. Physiol.\u003c/em\u003e \u003cb\u003e593\u003c/b\u003e, (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlake, K. J., Jiang, X. R. \u0026amp; Chiu, I. M. Neuronal Regulation of Immunity in the Skin and Lungs. \u003cem\u003eTrends in Neurosciences\u003c/em\u003e vol.\u0026nbsp;42 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlanco-Melo, D. \u003cem\u003eet al.\u003c/em\u003e Imbalanced host response to SARS-CoV-2 drives development of COVID-19. \u003cem\u003eCell\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2020.04.026\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiao, M. \u003cem\u003eet al.\u003c/em\u003e The landscape of lung bronchoalveolar immune cells in COVID-19 revealed by single-cell RNA sequencing. \u003cem\u003emedRxiv\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2020.02.23.20026690\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eButler, D. \u003cem\u003eet al.\u003c/em\u003e Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 1\u0026ndash;17 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilk, A. J. \u003cem\u003eet al.\u003c/em\u003e A single-cell atlas of the peripheral immune response in patients with severe COVID-19. \u003cem\u003eNat. Med.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e, 1070\u0026ndash;1076 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong, Y. \u003cem\u003eet al.\u003c/em\u003e Epidemiology of COVID-19 among children in China. \u003cem\u003ePediatrics\u003c/em\u003e \u003cb\u003e145\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, F. \u003cem\u003eet al.\u003c/em\u003e Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e395\u003c/b\u003e, 1054\u0026ndash;1062 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, D. W., Sherman, B. T. \u0026amp; Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. \u003cem\u003eNat. Protoc.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, 44\u0026ndash;57 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelms, J. C. \u003cem\u003eet al.\u003c/em\u003e A molecular single-cell lung atlas of lethal COVID-19. \u003cem\u003eNature\u003c/em\u003e (2021) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-021-03569-1\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlyper, M. \u003cem\u003eet al.\u003c/em\u003e A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. \u003cem\u003eNat. Med.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaudvere, U. \u003cem\u003eet al.\u003c/em\u003e g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). \u003cem\u003eNucleic Acids Res.\u003c/em\u003e (2019) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkz369\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReimand, J. \u003cem\u003eet al.\u003c/em\u003e g:Profiler-a web server for functional interpretation of gene lists (2016 update). \u003cem\u003eNucleic Acids Res\u003c/em\u003e. (2016) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkw199\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlenter, D. N. \u003cem\u003eet al.\u003c/em\u003e WikiPathways: A multifaceted pathway database bridging metabolomics to other omics research. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e (2018) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkx1064\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJassal, B. \u003cem\u003eet al.\u003c/em\u003e The reactome pathway knowledgebase. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkz1031\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgata, H. \u003cem\u003eet al.\u003c/em\u003e KEGG: Kyoto encyclopedia of genes and genomes. \u003cem\u003eNucleic Acids Research\u003c/em\u003e vol.\u0026nbsp;27 29\u0026ndash;34 (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian, A. \u003cem\u003eet al.\u003c/em\u003e Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. \u003cem\u003eProc. Natl. Acad. Sci. U. S. A.\u003c/em\u003e \u003cb\u003e102\u003c/b\u003e, 15545\u0026ndash;15550 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoncheva, N. T., Morris, J. H., Gorodkin, J. \u0026amp; Jensen, L. J. Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data. \u003cem\u003eJ. Proteome Res.\u003c/em\u003e (2019) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.jproteome.8b00702\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzklarczyk, D. \u003cem\u003eet al.\u003c/em\u003e STRING v10: protein-protein interaction networks, integrated over the tree of life. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e, D447-52 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026oacute;pez-Cort\u0026eacute;s, A. \u003cem\u003eet al.\u003c/em\u003e Gene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 16679 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026oacute;pez-Cort\u0026eacute;s, A. \u003cem\u003eet al.\u003c/em\u003e OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 5285 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang, Y., Li, M., Wang, J., Pan, Y. \u0026amp; Wu, F. X. CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks. \u003cem\u003eBioSystems\u003c/em\u003e (2015) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.biosystems.2014.11.005\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShannon, P. \u003cem\u003eet al.\u003c/em\u003e Cytoscape: a software environment for integrated models of biomolecular interaction networks. \u003cem\u003eGenome Res.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 2498\u0026ndash;504 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIannuccelli, M. \u003cem\u003eet al.\u003c/em\u003e CancerGeneNet: Linking driver genes to cancer hallmarks. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkz871\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerfetto, L. \u003cem\u003eet al.\u003c/em\u003e SIGNOR: A database of causal relationships between biological entities. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e (2016) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkv1048\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarvalho-Silva, D. \u003cem\u003eet al.\u003c/em\u003e Open Targets Platform: New developments and updates two years on. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e (2019) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gky1133\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaulton, A. \u003cem\u003eet al.\u003c/em\u003e The ChEMBL database in 2017. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e (2017) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkw1074\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGawel, D. R. \u003cem\u003eet al.\u003c/em\u003e A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases. \u003cem\u003eGenome Med.\u003c/em\u003e (2019) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13073-019-0657-3\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStephenson, E. \u003cem\u003eet al.\u003c/em\u003e Single-cell multi-omics analysis of the immune response in COVID-19. \u003cem\u003eNat. Med.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontenegro, F. \u003cem\u003eet al.\u003c/em\u003e Acute respiratory distress syndrome (ARDS) caused by the novel coronavirus disease (COVID-19): a practical comprehensive literature review. \u003cem\u003eExpert Rev. Respir. Med.\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/17476348.2020.1820329\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerpos, E. \u003cem\u003eet al.\u003c/em\u003e Hematological findings and complications of COVID-19. \u003cem\u003eAmerican journal of hematology\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ajh.25829\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y. \u003cem\u003eet al.\u003c/em\u003e Coagulopathy and Antiphospholipid Antibodies in Patients with Covid-19. \u003cem\u003eN. Engl. J. Med.\u003c/em\u003e \u003cb\u003e382\u003c/b\u003e, e38 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFogarty, H. \u003cem\u003eet al.\u003c/em\u003e COVID-19 Coagulopathy in Caucasian patients. \u003cem\u003eBr. J. Haematol.\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/bjh.16749\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRotzinger, D. C., Beigelman-Aubry, C., von Garnier, C. \u0026amp; Qanadli, S. D. Pulmonary embolism in patients with COVID-19: Time to change the paradigm of computed tomography. \u003cem\u003eThrombosis Research\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.thromres.2020.04.011\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelorey, T. M. \u003cem\u003eet al.\u003c/em\u003e COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e595\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlberts, B. \u003cem\u003eet al.\u003c/em\u003e Innate Immunity. in \u003cem\u003eMolecular Biology of the Cell\u003c/em\u003e vol.\u0026nbsp;4 (Garland Science, 2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmarante-Mendes, G. P. \u003cem\u003eet al.\u003c/em\u003e Pattern recognition receptors and the host cell death molecular machinery. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e vol.\u0026nbsp;9 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoechat, J. L., Chora, I., Morais, A. \u0026amp; Delgado, L. The immune response to SARS-CoV-2 and COVID-19. \u003cem\u003ePulmonology\u003c/em\u003e 1\u0026ndash;15 (2021) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pulmoe.2021.03.008\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChannappanavar, R. \u003cem\u003eet al.\u003c/em\u003e IFN-I response timing relative to virus replication determines MERS coronavirus infection outcomes. \u003cem\u003eJ. Clin. Invest.\u003c/em\u003e (2019) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1172/JCI126363\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerlman, S. \u0026amp; Dandekar, A. A. Immunopathogenesis of coronavirus infections: Implications for SARS. \u003cem\u003eNat. Rev. Immunol.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 917\u0026ndash;927 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopez, L., Sang, P. C., Tian, Y. \u0026amp; Sang, Y. Dysregulated interferon response underlying severe covid-19. \u003cem\u003eViruses\u003c/em\u003e vol.\u0026nbsp;12 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBilliau, A. Interferon: The pathways of discovery. I. Molecular and cellular aspects. \u003cem\u003eCytokine and Growth Factor Reviews\u003c/em\u003e vol.\u0026nbsp;17 381\u0026ndash;409 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcharya, D., Liu, G. Q. \u0026amp; Gack, M. U. Dysregulation of type I interferon responses in COVID-19. \u003cem\u003eNature Reviews Immunology\u003c/em\u003e vol.\u0026nbsp;20 397\u0026ndash;398 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamudrala, P. K. \u003cem\u003eet al.\u003c/em\u003e Virology, pathogenesis, diagnosis and in-line treatment of COVID-19. \u003cem\u003eEur. J. Pharmacol.\u003c/em\u003e \u003cb\u003e883\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Q. \u003cem\u003eet al.\u003c/em\u003e Inborn errors of type I IFN immunity in patients with life-threatening COVID-19. \u003cem\u003eScience (80-.).\u003c/em\u003e \u003cb\u003e370\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBastard, P. \u003cem\u003eet al.\u003c/em\u003e Autoantibodies against type I IFNs in patients with life-threatening COVID-19. \u003cem\u003eScience (80-.)\u003c/em\u003e. \u003cb\u003e370\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGracia-Hernandez, M., Sotomayor, E. M. \u0026amp; Villagra, A. Targeting Macrophages as a Therapeutic Option in Coronavirus Disease 2019. \u003cem\u003eFront. Pharmacol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOtsuka, R. \u0026amp; Seino, K. I. Macrophage activation syndrome and COVID-19. \u003cem\u003eInflamm. Regen.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, X., Zhang, Y., Qiao, W., Zhang, J. \u0026amp; Qi, Z. Baricitinib, a drug with potential effect to prevent SARS-COV-2 from entering target cells and control cytokine storm induced by COVID-19. \u003cem\u003eInt. Immunopharmacol.\u003c/em\u003e \u003cb\u003e86\u003c/b\u003e, 106749 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKahn, R. \u003cem\u003eet al.\u003c/em\u003e Mismatch between circulating cytokines and spontaneous cytokine production by leukocytes in hyperinflammatory COVID-19. \u003cem\u003eJ. Leukoc. Biol.\u003c/em\u003e \u003cb\u003e109\u003c/b\u003e, 115\u0026ndash;120 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabaan, A. A. \u003cem\u003eet al.\u003c/em\u003e Role of inflammatory cytokines in covid-19 patients: A review on molecular mechanisms, immune functions, immunopathology and immunomodulatory drugs to counter cytokine storm. \u003cem\u003eVaccines\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRowaiye, A. B. \u003cem\u003eet al.\u003c/em\u003e Attenuating the effects of novel COVID-19 (SARS-CoV-2) infection-induced cytokine storm and the implications. \u003cem\u003eJ. Inflamm. Res.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1487\u0026ndash;1510 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTjan, L. H. \u003cem\u003eet al.\u003c/em\u003e Early Differences in Cytokine Production by Severity of Coronavirus Disease 2019. \u003cem\u003eJ. Infect. Dis.\u003c/em\u003e \u003cb\u003e223\u003c/b\u003e, 1145\u0026ndash;1149 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMustafa, M. I., Abdelmoneim, A. H., Mahmoud, E. M. \u0026amp; Makhawi, A. M. Cytokine Storm in COVID-19 Patients, Its Impact on Organs and Potential Treatment by QTY Code-Designed Detergent-Free Chemokine Receptors. \u003cem\u003eMediators Inflamm.\u003c/em\u003e 2020, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoudhary, S., Sharma, K. \u0026amp; Silakari, O. The interplay between inflammatory pathways and COVID-19: A critical review on pathogenesis and therapeutic options. \u003cem\u003eMicrob. Pathog.\u003c/em\u003e \u003cb\u003e150\u003c/b\u003e, 104673 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCatanzaro, M. \u003cem\u003eet al.\u003c/em\u003e Immune response in COVID-19: addressing a pharmacological challenge by targeting pathways triggered by SARS-CoV-2. \u003cem\u003eSignal Transduct. Target. Ther.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, Q., Mrowietz, U. \u0026amp; Rostami-Yazdi, M. Oxidative stress in the pathogenesis of psoriasis. \u003cem\u003eFree Radical Biology and Medicine\u003c/em\u003e vol.\u0026nbsp;47 891\u0026ndash;905 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eRecent Pat. Inflamm. Allergy Drug Discov.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e, 40\u0026ndash;48 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHariharan, A., Hakeem, A. R., Radhakrishnan, S., Reddy, M. S. \u0026amp; Rela, M. The Role and Therapeutic Potential of NF-kappa-B Pathway in Severe COVID-19 Patients. \u003cem\u003eInflammopharmacology\u003c/em\u003e vol.\u0026nbsp;29 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeDiego, M. L. \u003cem\u003eet al.\u003c/em\u003e Inhibition of NF- B-Mediated Inflammation in Severe Acute Respiratory Syndrome Coronavirus-Infected Mice Increases Survival. \u003cem\u003eJ. Virol.\u003c/em\u003e \u003cb\u003e88\u003c/b\u003e, (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, W. \u003cem\u003eet al.\u003c/em\u003e Targeting JAK-STAT Signaling to Control Cytokine Release Syndrome in COVID-19. \u003cem\u003eTrends Pharmacol. Sci.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, 531\u0026ndash;543 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRojas, P. \u0026amp; Sarmiento, M. JAK/STAT Pathway Inhibition May Be a Promising Therapy for COVID-19-Related Hyperinflammation in Hematologic Patients. \u003cem\u003eActa Haematol.\u003c/em\u003e \u003cb\u003e144\u003c/b\u003e, 1 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePairo-Castineira, E. \u003cem\u003eet al.\u003c/em\u003e Genetic mechanisms of critical illness in COVID-19. \u003cem\u003eNat. 2020 5917848\u003c/em\u003e \u003cb\u003e591\u003c/b\u003e, 92\u0026ndash;98 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eF, C. \u003cem\u003eet al.\u003c/em\u003e Baricitinib therapy in COVID-19: A pilot study on safety and clinical impact. \u003cem\u003eJ. Infect.\u003c/em\u003e \u003cb\u003e81\u003c/b\u003e, 318\u0026ndash;356 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSatarker, S. \u003cem\u003eet al.\u003c/em\u003e JAK-STAT Pathway Inhibition and their Implications in COVID-19 Therapy. \u003cem\u003ePostgrad. Med.\u003c/em\u003e \u003cb\u003e133\u003c/b\u003e, 1 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMG, W., M, O., MB, F. \u0026amp; RS, B. Severe acute respiratory syndrome coronavirus evades antiviral signaling: role of nsp1 and rational design of an attenuated strain. \u003cem\u003eJ. Virol.\u003c/em\u003e \u003cb\u003e81\u003c/b\u003e, 11620\u0026ndash;11633 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJY, L. \u003cem\u003eet al.\u003c/em\u003e The ORF6, ORF8 and nucleocapsid proteins of SARS-CoV-2 inhibit type I interferon signaling pathway. \u003cem\u003eVirus Res.\u003c/em\u003e \u003cb\u003e286\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang, N., Li, D., Wang, X. \u0026amp; Sun, Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. \u003cem\u003eJ. Thromb. Haemost.\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jth.14768\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVinayagam, S. \u0026amp; Sattu, K. SARS-CoV-2 and coagulation disorders in different organs. \u003cem\u003eLife Sciences\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.lfs.2020.118431\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiswas, S. \u003cem\u003eet al.\u003c/em\u003e Blood clots in COVID-19 patients: Simplifying the curious mystery. \u003cem\u003eMed. Hypotheses\u003c/em\u003e \u003cb\u003e146\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlasco, A. \u003cem\u003eet al.\u003c/em\u003e Assessment of Neutrophil Extracellular Traps in Coronary Thrombus of a Case Series of Patients with COVID-19 and Myocardial Infarction. \u003cem\u003eJAMA Cardiol\u003c/em\u003e. \u003cb\u003e6\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichards, C. D. The Enigmatic Cytokine Oncostatin M and Roles in Disease. \u003cem\u003eISRN Inflamm.\u003c/em\u003e 2013, (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHintzen, C., Haan, C., Tuckermann, J. P., Heinrich, P. C. \u0026amp; Hermanns, H. M. Oncostatin M-Induced and Constitutive Activation of the JAK2/STAT5/CIS Pathway Suppresses CCL1, but Not CCL7 and CCL8, Chemokine Expression. \u003cem\u003eJ. Immunol.\u003c/em\u003e \u003cb\u003e181\u003c/b\u003e, (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSetiadi, H. \u003cem\u003eet al.\u003c/em\u003e Oncostatin M as a Biomarker to Predict the Outcome of V-V ECMO Supported Patients with Acute Pulmonary Failure. \u003cem\u003eJ. Hear. Lung Transplant.\u003c/em\u003e \u003cb\u003e39\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArunachalam, P. S. \u003cem\u003eet al.\u003c/em\u003e Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans. \u003cem\u003eScience (80-.).\u003c/em\u003e \u003cb\u003e369\u003c/b\u003e, 1210\u0026ndash;1220 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanchez-Infantes, D. \u0026amp; Stephens, J. M. Adipocyte Oncostatin Receptor Regulates Adipose Tissue Homeostasis and Inflammation. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e vol.\u0026nbsp;11 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichalakis, K. \u0026amp; Ilias, I. SARS-CoV-2 infection and obesity: Common inflammatory and metabolic aspects. \u003cem\u003eDiabetes Metab. Syndr. Clin. Res. Rev.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller, R., Wentzel, A. R. \u0026amp; Richards, G. A. COVID-19: NAD + deficiency may predispose the aged, obese and type2 diabetics to mortality through its effect on SIRT1 activity. \u003cem\u003eMed. Hypotheses\u003c/em\u003e \u003cb\u003e144\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhilips, A. M. \u0026amp; Khan, N. Amino acid sensing pathway: A major check point in the pathogenesis of obesity and COVID-19. \u003cem\u003eObes. Rev.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelančić, A., Kresović, A. \u0026amp; Rački, V. Potential pathophysiological mechanisms leading to increased COVID-19 susceptibility and severity in obesity. \u003cem\u003eObesity Medicine\u003c/em\u003e vol.\u0026nbsp;19 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbell, R. A., Boilard, E. \u0026amp; Rondina, M. T. Is there a role for the ACE2 receptor in SARS-CoV-2 interactions with platelets? \u003cem\u003eJ. Thromb. Haemost.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRapkiewicz, A. V. \u003cem\u003eet al.\u003c/em\u003e Megakaryocytes and platelet-fibrin thrombi characterize multi-organ thrombosis at autopsy in COVID-19: A case series. \u003cem\u003eEClinicalMedicine\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKato, A., Favoreto, S., Avila, P. C. \u0026amp; Schleimer, R. P. TLR3- and Th2 Cytokine-Dependent Production of Thymic Stromal Lymphopoietin in Human Airway Epithelial Cells. \u003cem\u003eJ. Immunol.\u003c/em\u003e \u003cb\u003e179\u003c/b\u003e, (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChore\u0026ntilde;o-Parra, J. A. \u003cem\u003eet al.\u003c/em\u003e Clinical and Immunological Factors That Distinguish COVID-19 From Pandemic Influenza A(H1N1). \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaterino, M. \u003cem\u003eet al.\u003c/em\u003e Dysregulation of lipid metabolism and pathological inflammation in patients with COVID-19. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelley, N., Jeltema, D., Duan, Y. \u0026amp; He, Y. The NLRP3 Inflammasome: An Overview of Mechanisms of Activation and Regulation. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVora, S. M., Lieberman, J. \u0026amp; Wu, H. Inflammasome activation at the crux. \u003cem\u003eNat. Rev. Immunol.\u003c/em\u003e doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41577-021-00588-x\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, S., Channappanavar, R. \u0026amp; Kanneganti, T.-D. Coronaviruses: Innate Immunity, Inflammasome Activation, Inflammatory Cell Death, and Cytokines. \u003cem\u003eTrends Immunol.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, 1083\u0026ndash;1099 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi, C.-S., Nabar, N. R., Huang, N.-N. \u0026amp; Kehrl, J. H. SARS-Coronavirus Open Reading Frame-8b triggers intracellular stress pathways and activates NLRP3 inflammasomes. \u003cem\u003eCell Death Discov.\u003c/em\u003e 2019 \u003cem\u003e51\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 1\u0026ndash;12 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodrigues, T. S. \u003cem\u003eet al.\u003c/em\u003e Inflammasome activation in COVID-19 patients. \u003cem\u003emedRxiv\u003c/em\u003e 2020.08.05.20168872 (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2020.08.05.20168872\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVidal, M., Cusick, M. E. \u0026amp; Barab\u0026aacute;si, A. L. Interactome networks and human disease. \u003cem\u003eCell\u003c/em\u003e (2011) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2011.02.016\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan, A., Lahiri, C., Rajendiran, A. \u0026amp; Shanmugham, B. Computational analysis of protein interaction networks for infectious diseases. \u003cem\u003eBrief. Bioinform.\u003c/em\u003e (2016) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bib/bbv059\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar, N., Mishra, B., Mehmood, A., Mohammad Athar \u0026amp; M Shahid Mukhtar. Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis. \u003cem\u003eiScience\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.isci.2020.101526\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaini, K. S. \u003cem\u003eet al.\u003c/em\u003e Repurposing anticancer drugs for COVID-19-induced inflammation, immune dysfunction, and coagulopathy. \u003cem\u003eBritish Journal of Cancer\u003c/em\u003e (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41416-020-0948-x\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, S. J., Channappanavar, R. \u0026amp; Kanneganti, T. D. Coronaviruses: Innate Immunity, Inflammasome Activation, Inflammatory Cell Death, and Cytokines. \u003cem\u003eTrends Immunol.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, 1083\u0026ndash;1099 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, S. \u003cem\u003eet al.\u003c/em\u003e SARS-CoV-2 triggers inflammatory responses and cell death through caspase-8 activation. \u003cem\u003eSignal Transduct. Target. Ther.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAckermann, M., Mentzer, S. J., Kolb, M. \u0026amp; Jonigk, D. Inflammation and intussusceptive angiogenesis in COVID-19: Everything in and out of flow. \u003cem\u003eEur. Respir. J.\u003c/em\u003e \u003cb\u003e56\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArdestani, A. \u0026amp; Azizi, Z. Targeting glucose metabolism for treatment of COVID-19. \u003cem\u003eSignal Transduct. Target. Ther.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 1\u0026ndash;2 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmaral, M. P. \u0026amp; Bortoluci, K. R. Caspase-8 and FADD: Where Cell Death and Inflammation Collide. \u003cem\u003eImmunity\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCodo, A. C. \u003cem\u003eet al.\u003c/em\u003e Elevated Glucose Levels Favor SARS-CoV-2 Infection and Monocyte Response through a HIF-1α/Glycolysis-Dependent Axis. \u003cem\u003eCell Metab.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtiz-Prado, E., Dunn, J. F., Vasconez, J., Castillo, D. \u0026amp; Viscor, G. Partial pressure of oxygen in the human body: a general review. \u003cem\u003eAm. J. Blood Res.\u003c/em\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrice, L. C., McCabe, C., Garfield, B. \u0026amp; Wort, S. J. Thrombosis and COVID-19 pneumonia: The clot thickens! \u003cem\u003eEuropean Respiratory Journal\u003c/em\u003e vol.\u0026nbsp;56 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuertas, A. \u003cem\u003eet al.\u003c/em\u003e Endothelial cell dysfunction: A major player in SARS-CoV-2 infection (COVID-19)? \u003cem\u003eEuropean Respiratory Journal\u003c/em\u003e vol.\u0026nbsp;56 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalz, L. \u003cem\u003eet al.\u003c/em\u003e JAK-inhibitor and type I interferon ability to produce favorable clinical outcomes in COVID-19 patients: a systematic review and meta-analysis. \u003cem\u003eBMC Infect. Dis.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan, B. \u003cem\u003eet al.\u003c/em\u003e SARS-CoV-2 drives JAK1/2-dependent local complement hyperactivation. \u003cem\u003eSci. Immunol.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 1\u0026ndash;20 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNct. Phase 3 Randomized, Double-blind, Placebo-controlled Multi-center Study to Assess the Efficacy and Safety of Ruxolitinib in Patients With COVID-19 Associated Cytokine Storm (RUXCOVID). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clinicaltrials.gov/show/NCT04362137\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFridman, J. S. \u003cem\u003eet al.\u003c/em\u003e Selective Inhibition of JAK1 and JAK2 Is Efficacious in Rodent Models of Arthritis: Preclinical Characterization of INCB028050. \u003cem\u003eJ. Immunol.\u003c/em\u003e \u003cb\u003e184\u003c/b\u003e, (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCantini, F. \u003cem\u003eet al.\u003c/em\u003e Immune Therapy, or Antiviral Therapy, or Both for COVID-19: A Systematic Review. \u003cem\u003eDrugs\u003c/em\u003e vol.\u0026nbsp;80 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCOVID-19 Treatment Guidelines Panel. Coronavirus Disease 2019 (COVID-19) Treatment Guidelines. Disponible en: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://covid19treatmentguidelines.nih.gov/\u003c/span\u003e\u003c/span\u003e. \u003cem\u003eNatl. Inst. Heal.\u003c/em\u003e 2019, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarconi, V. C. \u003cem\u003eet al.\u003c/em\u003e Baricitinib plus Standard of Care for Hospitalized Adults with COVID-19. \u003cem\u003emedRxiv\u003c/em\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliam, A. D. \u003cem\u003eet al.\u003c/em\u003e Discovery of the macrocycle 11-(2-pyrrolidin-1-yl-ethoxy)-14,19-dioxa-5,7, 26-triaza-tetracyclo[19.3.1.1(2,6).1(8,12)]heptacosa-1(25),2(26),3,5,8,10,12(27),16,21,23-decaene (SB1518), a potent Janus Kinase 2/Fms-like tyrosine kinase-3 (JAK2/FLT3) inhibitor for the treatment of myelofibrosis and lymphoma. \u003cem\u003eJ. Med. Chem.\u003c/em\u003e \u003cb\u003e54\u003c/b\u003e, (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWillette, R. N. \u003cem\u003eet al.\u003c/em\u003e Differential effects of p38 mitogen-activated protein kinase and cyclooxygenase 2 inhibitors in a model of cardiovascular disease. \u003cem\u003eJ. Pharmacol. Exp. Ther.\u003c/em\u003e \u003cb\u003e330\u003c/b\u003e, (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShirey, K. A., Blanco, J. C. G. \u0026amp; Vogel, S. N. Targeting TLR4 Signaling to Blunt Viral-Mediated Acute Lung Injury. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e vol.\u0026nbsp;12 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark, J. J. H. \u003cem\u003eet al.\u003c/em\u003e How COVID-19 has fundamentally changed clinical research in global health. \u003cem\u003eThe Lancet Global Health\u003c/em\u003e vol.\u0026nbsp;9 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal coalition to accelerate COVID-19 clinical research in resource-limited settings. \u003cem\u003eThe Lancet\u003c/em\u003e vol. 395 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. \u003cem\u003eWHO Coronavirus (COVID-19) Dashboard\u003c/em\u003e. \u003cem\u003eWorld Health Organisation\u003c/em\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScoggins, J. F. \u0026amp; Ramsey, S. D. A National cancer clinical trials system for the 21st century: Reinvigorating the NCI cooperative group program. \u003cem\u003eJournal of the National Cancer Institute\u003c/em\u003e vol.\u0026nbsp;102 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAltay, O. \u003cem\u003eet al.\u003c/em\u003e Current Status of COVID-19 Therapies and Drug Repositioning Applications. \u003cem\u003eiScience\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 101303 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED)","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"lethal COVID-19, inflammatory proteins, drugs, clinical trials ","lastPublishedDoi":"10.21203/rs.3.rs-808746/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-808746/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThere is pressing urgency to identify drugs that allow treating COVID-19 patients effectively. Respiratory failure is the leading cause of death in patients with severe COVID-19, and the host inflammatory response at the lungs remains poorly understood. Therefore, we retrieved data from postmortem lungs from COVID-19 patients and performed in-depth \u003cem\u003ein silico\u003c/em\u003e analyses of single-nucleus RNA sequencing data, inflammatory protein interactome network, functional enrichment, and shortest pathways to cancer hallmark phenotypes to reveal potential therapeutic targets and drugs in advanced-stage COVID-19 clinical trials. Herein, we analyzed transcriptomics data of 719 inflammatory response genes across 19 cell types (116,313 nuclei) from lung autopsies. The functional enrichment analysis of the 233 significantly expressed genes showed that the most relevant biological annotations were: inflammatory response, innate immune response, cytokine production, interferon production, macrophage activation, thymic stromal lymphopoietin, blood coagulation, IL-1 and megakaryocytes in obesity, NLRP3 inflammasome complex, and the TLR, JAK-STAT, NF-\u003cem\u003eκ\u003c/em\u003eB, TNF, oncostatin M, AGE-RAGE signaling pathways. Subsequently, we identified 34 essential inflammatory proteins with both high-confidence protein interactions and shortest pathways to inflammation, cell death, glycolysis, and angiogenesis. Lastly, we propose five small molecules involved in advanced-stage COVID-19 clinical trials: baricitinib, pacritinib, and ruxolitinib are tyrosine-protein kinase JAK2 inhibitors, losmapimod is a MAP kinase p38 alpha inhibitor, and eritoran is a TLR4/MD-2 antagonist. After being thoroughly analyzed in COVID-19 clinical trials, these drugs can be considered for treating severe COVID-19 patients.\u003c/p\u003e","manuscriptTitle":"Single-nucleus lung transcriptomics and inflammatory responses in lethal COVID-19 reveal potential drugs in advanced-stage clinical trials","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-08-17 15:30:36","doi":"10.21203/rs.3.rs-808746/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b72d2380-136d-4764-b97b-fb303da55920","owner":[],"postedDate":"August 17th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":6495867,"name":"Immunology"},{"id":6495868,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2021-08-17T15:30:37+00:00","versionOfRecord":[],"versionCreatedAt":"2021-08-17 15:30:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-808746","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-808746","identity":"rs-808746","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}