key: cord-0749768-om4e8bs8
authors: Ghandikota, Sudhir; Sharma, Mihika; Jegga, Anil G.
title: Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19
date: 2020-08-28
journal: bioRxiv
DOI: 10.1101/2020.08.27.270835
sha: 5b0450db6f755253cd39057a5030e450223f4e53
doc_id: 749768
cord_uid: om4e8bs8

Knowledge about the molecular mechanisms driving COVID-19 pathophysiology and outcomes is still limited. To learn more about COVID-19 pathophysiology we performed secondary analyses of transcriptomic data from two in vitro (Calu-3 and Vero E6 cells) and one in vivo (Ad5-hACE2-sensitized mice) models of SARS-CoV-2 infection. We found 1467 conserved differentially expressed host genes (differentially expressed in at least two of the three model system transcriptomes compared) in SARS-CoV-2 infection. To find potential genetic factors associated with COVID-19, we analyzed these conserved differentially expressed genes using known human genotype-phenotype associations. Genome-wide association study enrichment analysis showed evidence of enrichment for GWA loci associated with platelet functions, blood pressure, body mass index, respiratory functions, and neurodegenerative and neuropsychiatric diseases, among others. Since human protein complexes are known to be directly related to viral infection, we combined and analyzed the conserved transcriptomic signature with SARS-CoV-2-host protein-protein interaction data and found more than 150 gene clusters. Of these, 29 clusters (with 5 or more genes in each cluster) had at least one gene encoding protein that interacts with SARS-CoV-2 proteome. These clusters were enriched for different cell types in lung including epithelial, endothelial, and immune cell types suggesting their pathophysiological relevancy to COVID-19. Finally, pathway analysis on the conserved differentially expressed genes and gene clusters showed alterations in several pathways and biological processes that could enable in understanding or hypothesizing molecular signatures inducing pathophysiological changes, risks, or sequelae of COVID-19.

Of the seven coronaviruses known to infect human beings, four (229E, HKU1, NL63, and OC43) are associated with several respiratory conditions ranging from common cold to bronchiolitis and pneumonia but rarely result in any mortality. The other three, namely, MERS-CoV, SARS-CoV, and the recent SARS-CoV-2, have different degrees of lethality. The SARS-CoV outbreak in [2002] [2003] led to about 10 % mortality among the infected [1, 2] . The MERS-CoV, on the other hand killed about 23% of the infected people between 2012-2019 [3] . SARS-CoV-2 [4] which emerged in December 2019 in Wuhan of China, has already killed several more people than the SARS-CoV and MERS-CoV outbreaks combined. Since the initial report of coronavirus disease 2019 in China on 30th December 2019 [5] , as of August 26, 2020, there have been more than 24 million confirmed infections and >820 thousand deaths reported worldwide from 216 different countries (World Health Organization, 2020) including ~5.7 million confirmed infections and ~177,000 deaths in the USA. The COVID-19 pandemic dealt a devastating blow to global financial and social fabric and the impact on human health is expected to loom over several years to come if not decades. The limited and emerging stages of data and information surrounding this disease, and the necessity to find effective interventions (vaccines, small molecules, etc.), supplies a strong rationale for secondary analysis of existing data collected from different models and studies.

In the past 8 months, studies on COVID-19 included observational studies (e.g., clinical cohorts, epidemiological investigations and forecasts), in-silico genomic and structural analyses, and studies addressing basic virologic questions of SARS-CoV-2 (host cell tropism, viral replication kinetics, and cell damage profiles). Few studies have focused on the COVID-19 sequelae in surviving or recovered patients, genetic predisposition, and long-term effects if any in infected but asymptomatic individuals. Leveraging the available repository of datasets and information, even if they were not designed specifically to study COVID-19, can supply a jump start to discover different sides of this disease. Our study is based on the premise that combining information from multiple layers of data may result in new biologically interpretable associations in several ways. Indeed, some of the noteworthy discoveries surrounding SARS-CoV-2 are direct offshoots of secondary data analysis using available omics data generated in pre-COVID -19 times. For instance, in two seminal studies, Sungnak et al. [6] and Ziegler et al. [7] surveyed expression of SARS-CoV-2 entry-associated genes (ACE2, TMPRSS2, CTSB, and CTSL) in single-cell RNA-sequencing (scRNA-seq) data from multiple tissues from healthy human donors generated by the Human Cell Atlas consortium and other published and unpublished studies to investigate SARS-CoV-2 tropism. Likewise, there have been several other studies assessing the expression patterns of SARS-CoV-2 [8] [9] [10] [11] and other coronavirus entry proteins [12] in barrier tissues. In yet another study, Genotype Tissue Expression (GTEx) database [13] was used to compare the genomic characteristics of ACE2 among different populations and systematically analyze coding-region variants in ACE2 and the eQTL variants that may affect the expression of ACE2 [14] . Characterizing COVID-19 pathophysiology and understanding the molecular effects of SARS-CoV-2 on human proteins is critical to discover, guide, and prioritize therapeutic strategies. In the current study, we leverage transcriptomic data from 3 model systems (two in vitro and one in vivo) of SARS-CoV-2 infection, SARS-CoV-2 viral-host protein interaction data, and analyze them jointly with non-COVID-19/SARS-CoV-2 data. For the latter, we used the normal and aging lung transcriptome (both bulk RNA-Seq and single cell RNA-seq), proteinprotein interactions, and genome-wide association study (GWAS) data.

We use transcriptomic data from human (Calu-3) and non-human primate (VeroE6) cell lines and from a mouse model (Ad5-hACE2) of SARS-CoV-2 infection. The SARS-CoV-2 infection triggered transcriptome in Calu-3 cell lines is from a recently published study (GSE147507; [15] ) wherein 3 samples each were either mock treated or infected with SARS-CoV-2. The second transcriptome signature is based on mRNA profiles of control (mock-infected) and 24h post-SARS-CoV-2-infection (USA-WA1/2020, MOI = 0.3) in Vero E6 cells (kidney epithelial cells extracted from an African green monkey (GSE153940; [16] ). The third data set is from a mouse model using Ad5-hACE2-sensitized mice (GSE150847; [17] ) that develop pneumonia after infection with SARS-CoV-2, overcoming the natural resistance of mice to the infection.

Raw data from GSE147507 [15] , GSE153940 [16] and GSE150847 [17] were obtained and analyzed using the Computational Suite for Bioinformatics and Biology (CSBB v3.0) [18] . The raw data was downloaded from NCBI Sequence Read Archive (ProcessPublicData module) and the technical replicates were merged for individual samples before processing them (Process-RNASeq_SingleEnd module). Quality checks [19] and quality trimming [20] were conducted prior to the transcript mapping/quantification step using the RSEM package [21] . Raw counts and transcript per million (TPM) were generated for all samples for further downstream analysis.

Within each sample series, differential expression (DE) analysis was carried out based on treatment vs. mock samples using CSBB-Shiny server [22] . RUVSeq [23] was used to remove potential variation and sequencing effects from the data before performing DE analysis using edgeR [24] . Differentially expressed genes (DEGs) were obtained by applying a 1. 5 

The SARS-CoV-2-human virus-host protein-protein interaction data included a set of 332 human proteins involved in assembly and trafficking of RNA viruses and shown recently through affinity-purification and by mass spectrometry to interact physically with 26 of 29 SARS-CoV-2 structural proteins [25] . These are in addition to the SARS-CoV-2 entry receptor ACE2, and SARS-CoV-2 entry associated proteases, namely, TMPRSS2, CTSB, and CTSL.

For all conserved DEGs (genes differentially expressed unambiguously in at least 2 of the 3 transcriptomic sets compared, i.e., Calu-3, VeroE6, or mouse model -Ad5-hACE2) of SARS-CoV-2 infection models and 336 SARS-CoV-2-human PPI genes, we extracted interactions with a score of ≥ 0.9 or experimental interaction score of 0.7 or more from STRING v11 [26] . This DEG-PPI network was clustered (through Cytoscape v3.8.0 [27] ) using Markov Clustering Algorithm (MCL) (available as part of the ClusterMaker Plugin [28] in Cytoscape) to identify gene clusters. Briefly, MCL clusters a network to determine modules or clusters of genes with more interactions within the module than to those in the rest of the network [29] . We used the default 2.5 as the inflation factor which determines the granularity (or 'tightness') of the clusters and thereby the cluster size. In MCL, each gene can only be assigned to a single module.

Modules with 5 or more genes were considered for enrichment analyses while the remaining clusters (with 4 or lesser number of genes) were excluded from further analysis.

Functional enrichment analysis was carried out on the various DEG sets and gene clusters (from MCL clustering) using the ToppFun application of the ToppGene suite [30] and Enrichr [31] .

Gene Ontology biological processes, mouse phenotypes, pathways, and 4872 immunologic [32] and 50 hallmark [33] gene sets from MSigDB [34] that are significantly enriched (FDR Benjamini and Hochberg 0.05) within each of the signature gene sets were clustered together based on their size of enrichments. Similarly, significantly enriched terms from the gene clusters (described above) were integrated and analyzed further. We also performed pathway enrichment analysis using the Elsevier Pathway Collection available as part of the Enrichr application [31] .

To detect specific cell-types potentially perturbed or affected in COVID-19, we intersected the DEGs and gene clusters from SARS-CoV-2 infection models with cell type markers from normal and aging lung. To do this, we compiled cell type marker genes from normal adult human [35] [36] [37] and aging mouse lung single cell [38] transcriptomic studies and also bulk RNA-seq data from Genotype-Tissue Expression (GTEx) [39, 40] . We used significant markers (FDR p-value 0.05; logFC 0.5) for the enrichment analysis. In case of the aging mouse lung signatures, upregulated (logFC 0 . 5

) and downregulated (logFC െ 0 . 5

) gene sets were used separately for the enrichment analyses. We also generated aging human lung and liver DEG sets from the GTEx data using the BioJupies tool [41] . To do this, we compared samples in the age group 50-79 with those from 20-29 years (lung) and 50-69 years vs. 20-29 years (liver; there were no significant DEGs in 70-79 age group for liver samples). A total of 1683 genes in aging lung (934 upregulated and 749 downregulated) and 745 genes in liver (289 upregulated and 456 downregulated) were significantly dysregulated and were used for the enrichment analysis.

Additionally, we also used a curated set of 307 human aging genes from GenAge database [42] (Build February 2020) for enrichment analysis (Supplementary File 2).

To assess the value of our conserved DEGs from SARS-CoV-2 infection models, we intersected them with loci found in GWA studies. To do this we obtained published gene and phenotype trait associations data from the NCBI's Phenotype-Genotype Integrator (PheGenI) [43] and the NHGRI-EBI GWAS catalog [44] . We used both vulnerability loci of various human physiological traits and the human disease loci. We used an association p-value threshold of

(for PheGenI associations) and excluded all the variants in intergenic regions from the enrichment analysis. We also included child trait associations for the mapped traits from GWAS Catalog. The child terms for each trait were obtained by parsing the experimental factor ontology (EFO) hierarchy [45] . We applied Fisher's exact test to determine the enrichment.

To assess the transcriptomic concordance between in vitro and in vivo models of SARS-CoV-2 infection, we computed pairwise overlaps of the differentially expressed genes (DEGs) from the two in vitro models and the one in vivo model (Table 1) . A strong concordance was seen among the upregulated genes and downregulated gene signatures from the three models based on the extent and the significance of the DEG overlaps ( Figures 1A, 1B, and 1C) . A total of 732 DEGs (537 upregulated and 195 downregulated) were shared between the SARS-CoV-2 infected human Calu-3 and non-human primate VeroE6 cell lines ( Figure 1B) . Similarly, we found 325 upregulated and 369 downregulated genes common between DEGs from Calu-3 model and Ad5-hACE2-sensitized mice. While there was an overall concordance between the three SARS-CoV-2 infection models, each of the models also had many DEGs unique to the model ( Figure 1C ).

These results highlight the complexity of SARS-CoV-2-host interactions, potential heterogeneity in host response and a dynamic environment driven by the virus replication and the infected cell type.

To obtain a "conserved" transcriptomic signature from the SARS-CoV-2 infection models, we filtered out all ambiguous differentially regulated genes in different models. Specifically, we considered differentially expressed genes in at least 2 of the 3 models compared (i.e., 2 cell lines, instance, is reported to be 4-fold higher compared to patients without COPD [47] .

The 634 conserved downregulated genes of the SARS-CoV-2 infection models were enriched for several cell cycle associated and other metabolic processes (Supplementary File 5).

We also saw enrichment of mitochondrion organization (50 genes) and transport (25 genes).

There were more than 60 genes associated with various mitochondrial functions downregulated.

Viruses are known to tweak host mitochondrial metabolism to sustain an appropriate viral replication niche [48] . Additionally, several studies have also established a crosstalk between innate immune receptor mediated signaling and the mitochondria [48] . How can SARS-CoV-2 manipulate host mitochondrial morphology and perturb mitochondrial metabolism and how these mechanisms are used to evade the host defense response are some of the outstanding questions that call for further investigation. Equally important, can the immune responses against SARS-CoV-2 be elicited via mitochondria-targeting small intervening agents? Additionally, we found strong enrichments of genes from pathways involved in neurodegenerative diseases such as Alzheimer's (30 genes), Parkinson's (27 genes) and Huntington's (32 genes).

We next evaluated the 1467 conserved SARS-CoV-2 genes for lung single cell associations by performing enrichment analysis of the 634 downregulated and 833 upregulated DEGs against single cell marker gene sets compiled from three different human lung scRNA-seq studies.

Additionally, to explore the correlation between age and COVID-19 morbidity and mortality, we also used aging lung cell markers in mice. Both the upregulated and downregulated gene sets showed enrichment for several epithelial, immune, and stromal cell marker genes ( Figure 2A ;

. The marker enrichment also showed concordance between the three models of SARS-CoV-2 infection ( Figure 2B ). There was a higher incidence of endothelial cell types and its marker genes in the conserved upregulated gene set while the downregulated genes showed enrichment for epithelial cells (proliferating basal, ionocytes, goblet cells) ( Figure 2C -

Conserved upregulated genes were primarily enriched for both classical (64 genes) and nonclassical (28 genes) monocyte markers ( Figure 2F ; Table 2 ). They also showed exclusive enrichments for endothelial cells in addition to a significant number of adventitial (52 genes) and

lipofibroblast (37 genes IL11, a hematopoietic cytokine and member of IL6 family of cytokines, is known to have potent thrombopoietic activity. The PLAU-PLAUR (uPA-UPAR) system is reported to contribute to fibrinolysis, inflammation, innate and adaptive immune responses, and tissue remodeling [50, 51] . PLAU-PLAUR are known to take part in the process of coagulation disorder in patients with systemic inflammatory response syndrome (SIRS) and increased levels of PLAUR are reported to promote the development of SIRS to multiple organ dysfunction syndrome (MODS) [52] .

Further, retrospective studies [53, 54] have also shown that the measurement of soluble uPAR (suPAR) levels in serum, tissue and urine of patients can predict disease severity and outcome.

Intriguingly, SERPINE1, the gene encoding PAI-1, the endogenous inhibitor of PLAU-PLAUR system was also found to be upregulated in COVID-19. Further studies are needed to understand the molecular mechanisms that promote the expansion of distinct cell subpopulations that may be involved in regenerating a functional respiratory system in COVID-19 patients with severe pneumonia. Finally, the conserved upregulated DEGs showed an enrichment for human aging genes from GenAge (benchmark database of aging genes -Build February 2020) with an overlap of 32/833 (p-value <0.05) genes.

The vast majority of SARS-CoV-2 infected individuals experience mild or no symptoms and mortality in a subset of patients is primarily due to acute respiratory distress syndrome, bilateral interstitial pneumonia followed by severe respiratory failure. (Table 3) . Surprisingly, we also found enrichment for child developmental disorders and attention deficit disorder with hyperactivity. TRANK1 (tetratricopeptide repeat and ankyrin repeat containing 1), one of the genes associated with child developmental disorder has been shown to be an interferon-stimulated gene (ISG) in both humans and P. vampyrus (large fruit bat) cells. We found this gene upregulated in our study. A recent study reported that a polymorphism in the TRANK1 region affects brain development in the presence of a perinatal injury, with pathophysiological consequences such as KLS (Kleine-Levin Syndrome), bipolar disorder and schizophrenia [57] . KLS, a rare disease affecting adolescents is characterized by relapsing-remitting episodes of severe hypersomnia, cognitive impairment, and behavioral disturbances [58, 59] . Interestingly, although the etiology of KLS is still unknown, a strong positive correlation between upper respiratory infections or flu-like illness and symptomatic episodes of KLS has been found [60, 61] .

On similar lines, we found enrichment for other cognition impairment and neurodegenerative disorders: Alzheimer's (25 genes; 20 upregulated and 5 downregulated), Schizophrenia (47 genes; 29 upregulated and 18 downregulated), and Bipolar (13 genes; 11 upregulated and 2 downregulated). Both acute and delayed neurological and neuropsychiatric effects have been associated with previous viral pandemics [62] . For instance, earlier studies reported infection with SARS-CoV-1 as a risk factor for developing Parkinson's disease [63] .

Further, results from autopsy of COVID-19 patients showed edema and hyperemia in brain along with neuron degeneration [64] . However, long-term neurological and neuropsychiatric sequelae for SARS-CoV-2 infection are currently unknown and may only be revealed in coming months or even years. Even though the role of immune dysregulation in classical autoimmune brain diseases (e.g., multiple sclerosis, autoimmune encephalitis), psychiatric disorders (e.g., schizophrenia, autism spectrum disorder, bipolar disorder, and depression) is well-documented [65] there are relatively few reports of viral infections as risk factors for psychiatric disorders [66, 67] . We also found a strong enrichment for sleep and sleep measurement traits (19 genes) among the downregulated genes. In case of COVID-19, the unique psychosocial stressors associated with social distancing may further add to the neuropsychiatric symptom burden.

Although, a recent study assessing the neuropsychiatric presentations of SARS, MERS, and COVID-19 using systematic reviews and meta-analysis found little evidence to support the plausibility of neuropsychiatric complications as COVID-19 sequelae, given the early stages of this pandemic, it is too early to rule out the long-term neuropsychiatric effects. Unlike SARS-CoV, SARS-CoV-2 was reported to modestly replicate in neuronal (U251) cells [68] , lending further credence to the possibility that this virus can lead to neurological manifestations in the COVID-19 patients. Furthermore, beta coronavirus (HCoV-OC43) has been previously associated with fatal encephalitis in an 11-month-old boy with severe combined immunodeficiency (SCID) [69] . Prospective monitoring of COVID-19 patients including children and younger adults who have recovered from COVID-19 must determine potentially long-term neuropsychiatric outcomes. Further exploration of our findings with regards to their utility in mechanistic understanding and characterizing of COVID-19, and their potential in clinical risk profiling of COVID-19 patients is warranted.

To Figure 4A ; Table 4 ).

It is well proven that viral infection is tightly associated with host protein complexes which are manipulated by viruses to hijack the host cell biological processes. To better understand this phenomenon in COVID-19, we next combined the conserved transcriptomic signature (833 upregulated and 634 downregulated genes) from the three SARS-CoV-2 infection models with SARS-CoV-2-human interaction map [25] (336 genes). We then queried the STRING (v11) [26] database using this combined set of genes to build a SARS-CoV-2 DEG and interaction map.

STRING database collects the available PPI information from multiple sources and assigns a confidence score by considering both direct and indirect interactions. Using highest confidence score (0.9) or experimental interaction score of 0.7 or more, we aimed to filter out the PPIs with lower probability to be interactions. With this criterion, there was an enrichment for PPIs (p- 

Gene clusters -lung single cell markers proliferating macrophages, and adventitial fibroblasts ( Figure 5A ) ( Table 5 ). These clusters also shared strong enrichment for genes that are downregulated in human aging lungs (DEGs from GTEx). Cluster C-9 (18 genes) consisted of gene markers in fibroblasts, myofibroblasts and smooth muscle cells and shared enrichments with clusters C-1, C-2, and C-3 ( Figure 5B ).

Ionocyte cell marker [35] genes were confined to Clusters C-5 (40 genes; 12 markers) and C-22 

To further characterize the 35 selected clusters, we next performed functional enrichment analysis against several known gene sets (Supplementary File 12) . Cluster C-1 (190 genes) was enriched for innate immune response (48 genes) and type I interferon signaling (26 genes) while genes from cluster C-2 (92 genes) were involved in transport regulation (31 genes) and tube development (31 genes). We also found genes associated with abnormal cardiovascular development (21 genes) in cluster C-2. Clusters C-7 (20 genes) and C-8 (20 genes) had genes associated with abnormal interleukin and cytokine secretion phenotypes. Similarly, clusters C-12 (14 genes), C-28 (6 genes) and C-23 (8 genes) were jointly enriched for mitochondrion translation, organization, and transport. We also saw high concordance between the functional and cell type marker enrichments among the candidate clusters. Cluster C-4 (73 genes) was found to consist of markers (9 genes 

As described in earlier sections, we repeated the enrichment for genotype-phenotype associations but this time using each of the 35 clusters. The clusters revealed enrichment for several physiological and phenotypic traits that provide several insights into the underlying pathophysiology of COVID-19 ( Supplementary Files 13 and 14) . For example, among the most significantly enriched traits were respiratory system disease (clusters C-7 and C-8), asthma (C-7), autoimmune disease (clusters C-7 and C-29), allergic rhinitis (C-7), immune system disease (cluster C-7 and C-8) and diabetes (C-15). We also observed risk genes associated with several inflammatory system disorders like inflammatory bowel disease and Crohn's disease (C-7), ulcerative colitis (C-8), rheumatoid arthritis (clusters C-7 and C-8) and ankylosing spondylitis (C-8). Apart from helping in understanding the underlying pathophysiology of COVID-19, the enriched traits can potentially help the researchers to understand or formulate hypotheses surrounding the long-hauler patients or survivors. For instance, apart from being risk factors for COVID-19, could COVID-19 conversely be a risk factor for an autoimmune or neurodegenerative disease? A plausible mechanism could be through an over-activated innate immune system [70] [71] [72] . Both acute and delayed neurological and neuropsychiatric effects have been associated with previous viral pandemics [62, 63] .

The overall concordance between the enriched cell types and functional enrichments for different gene clusters motivated us to perform a more general analysis across all enrichments (i.e., cell type, phenotypic traits, biological processes, and pathways). To do this, we selected a subset of Gephi) [74] . With a resolution set to 0.3, we found 31 communities of highly interconnected biological processes, pathways, cell types, and phenotypic traits and a high modularity score of 0.676 ( Figure 6 ). Since sub-units of a functional complex (a cluster of pathways, biological processes, phenotype, etc.) work towards the same biological goal, prediction of an unknown pathway or biological process or a phenotype as part of this complex also allows increased confidence in the annotation of that functional cluster. Additionally, by doing this, potential redundancies across different sources (e.g., ontology or cell types) could be reduced, apart from enabling interpretation of the enrichment results through intra-cluster and inter-cluster similarities of enriched terms [75] . For instance, cluster C-9 ( The STRING-based PPI network data, as is the case with any omics data, suffers from data incompleteness and certain degree of noise. Further, there are no clear guidelines on what to use for the STRING interaction score cutoff. Similarly, although Markov clustering is recommended for module detection [79] , there exist no guidelines for inflation factor threshold nor for the functional annotation of modules. Nevertheless, to overcome some of these limitations, we used a very stringent cut-off score of 0.9 for STRING interactions. Similarly, we selected 2.5 (default) as the MCL inflation factor which generated 153 clusters of 2-190 genes.

The cluster composition can vary depending on the clustering algorithm and parameters. For enrichment analysis we used 35 of those clusters that have 5 or more genes. Lastly, our study does not include any experiment validations. However, the gene clusters, enriched biological processes and pathways, the normal and aging lung marker and human genotype-phenotype associations emerging from our joint analysis of COVID-19 and non-COVID-19 related data can serve as valuable resource for the scientific community to formulate or further investigate hypotheses. However, we caution against interpreting our results from genotype-phenotype enrichment analysis as causal effects. Although we found enrichment for smoking and COPD, blood pressure, and body mass index (along with several other traits), as a recent study pointed out [56] , carefully designed causal analyses focusing on the causal effects of enriched traits and COVID-19 outcomes are needed.

In conclusion, with no therapeutic prevention or intervention methods available, preclinical research using in vitro and in vivo model organisms is needed to understand SARS-CoV-2 infection, clinical manifestations of COVID-19, and to test therapeutic and preventive agents for safety and efficacy. Based on current in vitro and in vivo models of SARS-CoV-2 infection, we believe that our conserved gene signature from SARS-CoV-2 infection models, SARS-CoV-2 targeted human protein clusters, and their downstream analyses will help researchers to further understand and formulate testable hypotheses for COVID-19 risk factors, pathophysiology, potential sequelae, and support development of therapeutic agents.

Not applicable.

Not applicable.

All data generated or analyzed during this study are included in this published article and its supplementary information files.

The authors declare that they have no competing interests.

This study was supported in part by the Cincinnati Children's Hospital and Medical Center. Heatmap showing the correlation between the three SARS-CoV-2 infection models based on the shared enriched cell markers. (C). Network of enriched cell types from normal human lung [35] and aging mice [38] in the conserved differentially expressed genes from the 3 models of SARS- Table 4 : List of 29 differentially expressed genes in SARS-CoV-2 infection models that encode proteins interacting with SARS-CoV-2 proteins. 16 upregulated and 13 downregulated genes in SARS-CoV-2-triggered transcriptome whose encoded proteins interact directly with SARS-CoV-2 proteins are presented along with their function and potential druggability [25] . Host genes highlighted in bold (and with asterisk) are potential drug candidates. 

Proliferating NK/T, Proliferating Basal, Mki67+ proliferating cells-UP (aging), Proliferating Macrophage, Adventitial Fibroblasts, alveolar epithelial type 1

Epidemiology and cause of severe acute respiratory syndrome (SARS) in Guangdong, People's Republic of China

Identification of a Novel Coronavirus in Patients with Severe Acute Respiratory Syndrome

Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia

A pneumonia outbreak associated with a new coronavirus of probable bat origin

A new coronavirus associated with human respiratory disease in China

SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes

SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues

Digestive system is a potential route of COVID-19: an analysis of single-cell coexpression pattern of key proteins in viral entry process

Identification of a potential mechanism of acute kidney injury during the COVID-19 outbreak: a study based on single-cell transcriptome analysis

Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia

SARS-CoV-2 receptor ACE2 and TMPRSS2 are primarily expressed in bronchial transient secretory cells

Single cell RNA sequencing of 13 human tissues identify cell types and receptors of human coronaviruses. Biochemical and Biophysical Research Communications

Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans

Comparative genetic analysis of the novel coronavirus (2019-nCoV/SARS-CoV-2) receptor ACE2 in different populations

Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19

Discovery of SARS-CoV-2 antiviral drugs through large-scale compound repurposing

Generation of a Broadly Useful Model for COVID-19 Pathogenesis, Vaccination, and Treatment

Computational Suite For Bioinformaticians and Biologists (v3.0). n.d

FastQC: A quality control tool for high throughput sequence data

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Normalization of RNA-seq data using factor analysis of control genes or samples

Bioconductor package for differential expression analysis of digital gene expression data

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Cytoscape: a software environment for integrated models of biomolecular interaction networks

clusterMaker: a multi-algorithm clustering plugin for Cytoscape

An efficient algorithm for large-scale detection of protein families

ToppGene Suite for gene list enrichment analysis and candidate gene prioritization

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Compendium of Immune Signatures Identifies Conserved and Species-Specific Biology in Response to Inflammation

The Molecular Signatures Database (MSigDB) hallmark gene set collection

Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles

A molecular cell atlas of the human lung from single cell RNA sequencing. bioRxiv

Single-cell RNA sequencing reveals profibrotic roles of distinct epithelial and mesenchymal lineages in pulmonary fibrosis

Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis

An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics

The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans

The Genotype-Tissue Expression (GTEx) project

BioJupies: Automated Generation of Interactive Notebooks for RNA-Seq Data Analysis in the Cloud

Human Ageing Genomic Resources: new and updated databases

Phenotype-Genotype Integrator (PheGenI): synthesizing genomewide association study (GWAS) data with existing genomic resources

The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019

Modeling sample variables with an Experimental Factor Ontology

Multimorbidity and Functional Limitations Among Adults 65 or Older

The impact of COPD and smoking history on the severity of COVID-19: A systemic review and meta-analysis

Mitochondrial Functions in Infection and Immunity

Distinct stem/progenitor cells proliferate to regenerate the trachea, intrapulmonary airways and alveoli in COVID-19 patients

uPA and uPAR in fibrinolysis, immunity and pathology

The urokinase receptor system, a key regulator at the intersection between inflammation, immunity, and coagulation

Urokinase-type plasminogen activator receptor as a predictor of poor outcome in patients with systemic inflammatory response syndrome

suPAR as a prognostic biomarker in sepsis

Circulating soluble urokinase plasminogen activator receptor is stably elevated during the first week of treatment in the intensive care unit and predicts mortality in critically ill patients

Genetic risk factors for death with SARS-CoV-2 from the UK Biobank. medRxiv

OpenSAFELY: factors associated with COVID-19 death in 17 million patients

0026 Strong Gene-Environment Interactions of Trank1 Gene Polymorphisms with Birth Difficulties in Kleine Levin Syndrome

The Syndrome of Periodic Somnolence and Morbid Hunger (Kleine-Levin Syndrome)

Periodic hypersomnia and megaphagia in adolescent males

Relationship between Kleine-Levin syndrome and upper respiratory infection in Taiwan

Diagnosis, disease course, and management of patients with Kleine-Levin syndrome

Are we facing a crashing wave of neuropsychiatric sequelae of COVID-19? Neuropsychiatric symptoms and potential immunologic mechanisms

Cerebrospinal fluid antibodies to coronavirus in patients with Parkinson's disease

Neurologic Manifestations of Hospitalized Patients With Coronavirus Disease

Immunoneuropsychiatry -novel perspectives on brain disorders

Schizophrenia is Associated With an Aberrant Immune Response to Epstein-Barr Virus

Large-scale study of Toxoplasma and Cytomegalovirus shows an association between infection and serious psychiatric disorders

Comparative tropism, replication kinetics, and cell damage profiling of SARS-CoV-2 and SARS-CoV with implications for clinical manifestations, transmissibility, and laboratory studies of COVID-19: an observational study. The Lancet Microbe

Human Coronavirus OC43 Associated with Fatal Encephalitis

Activation of innate immunity in the CNS triggers neurodegeneration through a Toll-like receptor 4-dependent pathway

Exaggerated neuroinflammation and sickness behavior in aged mice following activation of the peripheral innate immune system

Experimental models of inflammatory bowel disease reveal innate, adaptive, and regulatory mechanisms of host dialogue with the microbiota

Gephi: An Open Source Software for Exploring and Manipulating Networks

Fast unfolding of communities in large networks

Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

Anosmia and Ageusia: Common Findings in COVID-19 Patients

Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study

Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in

Evaluation of clustering algorithms for protein-protein interaction networks

Enhanced isolation of SARS-CoV-2 by TMPRSS2-expressing cells

SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor

Not applicable.

Innate immune response; response to type I interferon; Cell cycle; Proliferating NK/T; Proliferating macrophages; AT1; Adventitial fibroblasts; Platelet function tests

Toll-like receptor signaling pathway; Abnormal blood vessel morphology; Arteriosclerosis; Goblet; AT2; Capillary; C-X-C motif chemokine 11 measurement C-3 (81 genes) DNA-templated transcription, initiation; RNA splicing; abnormal heart ventricle morphology; Basal; Fibroblasts; Adventitial fibroblasts; monocytes; macrophages C-4 (73 genes)Golgi vesicle transport; membrane docking; Ciliated; Capillary; abnormal neural tube ventricular layer morphology C-5 (40 genes), C-12 (14 genes) and C-28 ( Cellular amino acid metabolic process; glycine metabolic process; decreased urine creatine level; abnormal amino acid level; urinary potassium to creatinine ratio C-33 (5 genes) activin receptor signaling pathway; RNA degradation; TGF-beta signaling pathway; Calcium signaling in the CD4+ TCR pathway