Genome-wide analyses reveal the detrimental impacts of SARS-CoV-2 viral gene Orf9c on human pluripotent stem cell-derived cardiomyocytes.

Patients with coronavirus disease 2019 (COVID-19) commonly have manifestations of heart disease. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome encodes 27 proteins. Currently, SARS-CoV-2 gene-induced abnormalities of human heart muscle cells remain elusive. Here, we comprehensively characterized the detrimental effects of a SARS-CoV-2 gene, Orf9c, on human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) by preforming multi-omic analyses. Transcriptomic analyses of hPSC-CMs infected by SARS-CoV-2 with Orf9c overexpression (Orf9cOE) identified concordantly up-regulated genes enriched into stress-related apoptosis and inflammation signaling pathways, and down-regulated CM functional genes. Proteomic analysis revealed enhanced expressions of apoptotic factors, whereas reduced protein factors for ATP synthesis by Orf9cOE. Orf9cOE significantly reduced cellular ATP level, induced apoptosis, and caused electrical dysfunctions of hPSC-CMs. Finally, drugs approved by the U.S. Food and Drug Administration, namely, ivermectin and meclizine, restored ATP levels and ameliorated CM death and functional abnormalities of Orf9cOE hPSC-CMs. Overall, we defined the molecular mechanisms underlying the detrimental impacts of Orf9c on hPSC-CMs and explored potentially therapeutic approaches to ameliorate Orf9c-induced cardiac injury and abnormalities.

Virtual Screening and Molecular Docking to Study the Mechanism of Chinese Medicines in the Treatment of Coronavirus Infection.

BACKGROUND Heat-clearing and detoxifying herbs (HDHs) play an important role in the prevention and treatment of coronavirus infection. However, their mechanism of action needs further study. This study aimed to explore the anti-coronavirus basis and mechanism of HDHs. MATERIAL AND METHODS Database mining was performed on 7 HDHs. Core ingredients and targets were screened according to ADME rules combined with Neighborhood, Co-occurrence, Co-expression, and other algorithms. GO enrichment and KEGG pathway analyses were performed using the R language. Finally, high-throughput molecular docking was used for verification. RESULTS HDHs mainly acts on NOS3, EGFR, IL-6, MAPK8, PTGS2, MAPK14, NFKB1, and CASP3 through quercetin, luteolin, wogonin, indirubin alkaloids, ß-sitosterol, and isolariciresinol. These targets are mainly involved in the regulation of biological processes such as inflammation, activation of MAPK activity, and positive regulation of NF-kappaB transcription factor activity. Pathway analysis further revealed that the pathways regulated by these targets mainly include: signaling pathways related to viral and bacterial infections such as tuberculosis, influenza A, Ras signaling pathways; inflammation-related pathways such as the TLR, TNF, MAPK, and HIF-1 signaling pathways; and immune-related pathways such as NOD receptor signaling pathways. These pathways play a synergistic role in inhibiting lung inflammation and regulating immunity and antiviral activity. CONCLUSIONS HDHs play a role in the treatment of coronavirus infection by regulating the body's immunity, fighting inflammation, and antiviral activities, suggesting a molecular basis and new strategies for the treatment of COVID-19 and a foundation for the screening of new antiviral drugs.

Synergistic Interferon-Alpha-Based Combinations for Treatment of SARS-CoV-2 and Other Viral Infections.

BACKGROUND: There is an urgent need for new antivirals with powerful therapeutic potential and tolerable side effects. METHODS: Here, we tested the antiviral properties of interferons (IFNs), alone and with other drugs in vitro. RESULTS: While IFNs alone were insufficient to completely abolish replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), IFNα, in combination with remdesivir, EIDD-2801, camostat, cycloheximide, or convalescent serum, proved to be more effective. Transcriptome and metabolomic analyses revealed that the IFNα-remdesivir combination suppressed SARS-CoV-2-mediated changes in Calu-3 cells and lung organoids, although it altered the homeostasis of uninfected cells and organoids. We also demonstrated that IFNα combinations with sofosbuvir, telaprevir, NITD008, ribavirin, pimodivir, or lamivudine were effective against HCV, HEV, FLuAV, or HIV at lower concentrations, compared to monotherapies. CONCLUSIONS: Altogether, our results indicated that IFNα can be combined with drugs that affect viral RNA transcription, protein synthesis, and processing to make synergistic combinations that can be attractive targets for further pre-clinical and clinical development against emerging and re-emerging viral infections.

Corticosteroid treatment in COVID-19 modulates host inflammatory responses and transcriptional signatures of immune dysregulation.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease-2019 (COVID-19), a respiratory disease that varies in severity from mild to severe/fatal. Several risk factors for severe disease have been identified, notably age, male sex, and pre-existing conditions such as diabetes, obesity, and hypertension. Several advancements in clinical care have been achieved over the past year, including the use of corticosteroids (e.g., corticosteroids) and other immune-modulatory treatments that have now become standard of care for patients with acute severe COVID-19. While the understanding of the mechanisms that underlie increased disease severity with age has improved over the past few months, it remains incomplete. Furthermore, the molecular impact of corticosteroid treatment on host response to acute SARS-CoV-2 infection has not been investigated. In this study, a cross-sectional and longitudinal analysis of Ab, soluble immune mediators, and transcriptional responses in young (65 ≤ years) and aged (≥ 65 years) diabetic males with obesity hospitalized with acute severe COVID-19 was conducted. Additionally, the transcriptional profiles in samples obtained before and after corticosteroids became standard of care were compared. The analysis indicates that severe COVID-19 is characterized by robust Ab responses, heightened systemic inflammation, increased expression of genes related to inflammatory and pro-apoptotic processes, and reduced expression of those important for adaptive immunity regardless of age. In contrast, COVID-19 patients receiving steroids did not show high levels of systemic immune mediators and lacked transcriptional indicators of heightened inflammatory and apoptotic responses. Overall, these data suggest that inflammation and cell death are key drivers of severe COVID-19 pathogenesis in the absence of corticosteroid therapy.

SARS-CoV-2 promotes RIPK1 activation to facilitate viral propagation.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the ongoing global pandemic that poses substantial challenges to public health worldwide. A subset of COVID-19 patients experience systemic inflammatory response, known as cytokine storm, which may lead to death. Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) is an important mediator of inflammation and cell death. Here, we examined the interaction of RIPK1-mediated innate immunity with SARS-CoV-2 infection. We found evidence of RIPK1 activation in human COVID-19 lung pathological samples, and cultured human lung organoids and ACE2 transgenic mice infected by SARS-CoV-2. Inhibition of RIPK1 using multiple small-molecule inhibitors reduced the viral load of SARS-CoV-2 in human lung organoids. Furthermore, therapeutic dosing of the RIPK1 inhibitor Nec-1s reduced mortality and lung viral load, and blocked the CNS manifestation of SARS-CoV-2 in ACE2 transgenic mice. Mechanistically, we found that the RNA-dependent RNA polymerase of SARS-CoV-2, NSP12, a highly conserved central component of coronaviral replication and transcription machinery, promoted the activation of RIPK1. Furthermore, NSP12 323L variant, encoded by the SARS-CoV-2 C14408T variant first detected in Lombardy, Italy, that carries a Pro323Leu amino acid substitution in NSP12, showed increased ability to activate RIPK1. Inhibition of RIPK1 downregulated the transcriptional induction of proinflammatory cytokines and host factors including ACE2 and EGFR that promote viral entry into cells. Our results suggest that SARS-CoV-2 may have an unexpected and unusual ability to hijack the RIPK1-mediated host defense response to promote its own propagation and that inhibition of RIPK1 may provide a therapeutic option for the treatment of COVID-19.

Proteomics reveal cap-dependent translation inhibitors remodel the translation machinery and translatome.

Tactical disruption of protein synthesis is an attractive therapeutic strategy, with the first-in-class eIF4A-targeting compound zotatifin in clinical evaluation for cancer and COVID-19. The full cellular impact and mechanisms of these potent molecules are undefined at a proteomic level. Here, we report mass spectrometry analysis of translational reprogramming by rocaglates, cap-dependent initiation disruptors that include zotatifin. We find effects to be far more complex than simple "translational inhibition" as currently defined. Translatome analysis by TMT-pSILAC (tandem mass tag-pulse stable isotope labeling with amino acids in cell culture mass spectrometry) reveals myriad upregulated proteins that drive hitherto unrecognized cytotoxic mechanisms, including GEF-H1-mediated anti-survival RHOA/JNK activation. Surprisingly, these responses are not replicated by eIF4A silencing, indicating a broader translational adaptation than currently understood. Translation machinery analysis by MATRIX (mass spectrometry analysis of active translation factors using ribosome density fractionation and isotopic labeling experiments) identifies rocaglate-specific dependence on specific translation factors including eEF1ε1 that drive translatome remodeling. Our proteome-level interrogation reveals that the complete cellular response to these historical "translation inhibitors" is mediated by comprehensive translational landscape remodeling.

Antihypertensive drug treatment and susceptibility to SARS-CoV-2 infection in human PSC-derived cardiomyocytes and primary endothelial cells.

The pathogenicity of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been attributed to its ability to enter through the membrane-bound angiotensin-converting enzyme 2 (ACE2) receptor. Therefore, it has been heavily speculated that angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB) therapy may modulate SARS-CoV-2 infection. In this study, exposure of human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) and human endothelial cells (hECs) to SARS-CoV-2 identified significant differences in protein coding genes involved in immunity, viral response, and cardiomyocyte/endothelial structure. Specifically, transcriptome changes were identified in the tumor necrosis factor (TNF), interferon α/ß, and mitogen-activated protein kinase (MAPK) (hPSC-CMs) as well as nuclear factor kappa-B (NF-κB) (hECs) signaling pathways. However, pre-treatment of hPSC-CMs or hECs with two widely prescribed antihypertensive medications, losartan and lisinopril, did not affect the susceptibility of either cell type to SARS-CoV-2 infection. These findings demonstrate the toxic effects of SARS-CoV-2 in hPSC-CMs/hECs and, taken together with newly emerging multicenter trials, suggest that antihypertensive drug treatment alone does not alter SARS-CoV-2 infection.

A dynamic mucin mRNA signature associates with COVID-19 disease presentation and severity.

BACKGROUNDSARS-CoV-2 infection induces mucin overexpression, further promoting disease. Given that mucins are critical components of innate immunity, unraveling their expression profiles that dictate the course of disease could greatly enhance our understanding and management of COVID-19.METHODSUsing validated RT-PCR assays, we assessed mucin mRNA expression in the blood of patients with symptomatic COVID-19 compared with symptomatic patients without COVID-19 and healthy controls and correlated the data with clinical outcome parameters. Additionally, we analyzed mucin expression in mucus and lung tissue from patients with COVID-19 and investigated the effect of drugs for COVID-19 treatment on SARS-CoV-2-induced mucin expression in pulmonary epithelial cells.RESULTSWe identified a dynamic blood mucin mRNA signature that clearly distinguished patients with symptomatic COVID-19 from patients without COVID-19 based on expression of MUC1, MUC2, MUC4, MUC6, MUC13, MUC16, and MUC20 (AUCROC of 91.8%; sensitivity and specificity of 90.6% and 93.3%, respectively) and that discriminated between mild and critical COVID-19 based on the expression of MUC16, MUC20, and MUC21 (AUCROC of 89.1%; sensitivity and specificity of 90.0% and 85.7%, respectively). Differences in the transcriptional landscape of mucins in critical cases compared with mild cases identified associations with COVID-19 symptoms, respiratory support, organ failure, secondary infections, and mortality. Furthermore, we identified different mucins in the mucus and lung tissue of critically ill COVID-19 patients and showed the ability of baricitinib, tocilizumab, favipiravir, and remdesivir to suppress expression of SARS-CoV-2-induced mucins.CONCLUSIONThis multifaceted blood mucin mRNA signature showed the potential role of mucin profiling in diagnosing, estimating severity, and guiding treatment options in patients with COVID-19.FUNDINGThe Antwerp University Research and the Research Foundation Flanders COVID-19 funds.

DeepR2cov: deep representation learning on heterogeneous drug networks to discover anti-inflammatory agents for COVID-19.

Recent studies have demonstrated that the excessive inflammatory response is an important factor of death in coronavirus disease 2019 (COVID-19) patients. In this study, we propose a deep representation on heterogeneous drug networks, termed DeepR2cov, to discover potential agents for treating the excessive inflammatory response in COVID-19 patients. This work explores the multi-hub characteristic of a heterogeneous drug network integrating eight unique networks. Inspired by the multi-hub characteristic, we design 3 billion special meta paths to train a deep representation model for learning low-dimensional vectors that integrate long-range structure dependency and complex semantic relation among network nodes. Based on the representation vectors and transcriptomics data, we predict 22 drugs that bind to tumor necrosis factor-α or interleukin-6, whose therapeutic associations with the inflammation storm in COVID-19 patients, and molecular binding model are further validated via data from PubMed publications, ongoing clinical trials and a docking program. In addition, the results on five biomedical applications suggest that DeepR2cov significantly outperforms five existing representation approaches. In summary, DeepR2cov is a powerful network representation approach and holds the potential to accelerate treatment of the inflammatory responses in COVID-19 patients. The source code and data can be downloaded from https://github.com/pengsl-lab/DeepR2cov.git.

Transcriptomics-based drug repositioning pipeline identifies therapeutic candidates for COVID-19.

The novel SARS-CoV-2 virus emerged in December 2019 and has few effective treatments. We applied a computational drug repositioning pipeline to SARS-CoV-2 differential gene expression signatures derived from publicly available data. We utilized three independent published studies to acquire or generate lists of differentially expressed genes between control and SARS-CoV-2-infected samples. Using a rank-based pattern matching strategy based on the Kolmogorov-Smirnov Statistic, the signatures were queried against drug profiles from Connectivity Map (CMap). We validated 16 of our top predicted hits in live SARS-CoV-2 antiviral assays in either Calu-3 or 293T-ACE2 cells. Validation experiments in human cell lines showed that 11 of the 16 compounds tested to date (including clofazimine, haloperidol and others) had measurable antiviral activity against SARS-CoV-2. These initial results are encouraging as we continue to work towards a further analysis of these predicted drugs as potential therapeutics for the treatment of COVID-19.

JAK inhibitors dampen activation of interferon-stimulated transcription of ACE2 isoforms in human airway epithelial cells.

SARS-CoV-2 infection of human airway epithelium activates genetic programs leading to progressive hyperinflammation in COVID-19 patients. Here, we report on transcriptomes activated in primary airway cells by interferons and their suppression by Janus kinase (JAK) inhibitors. Deciphering the regulation of the angiotensin-converting enzyme 2 (ACE2), the receptor for SARS-CoV-2, is paramount for understanding the cell tropism of SARS-CoV-2 infection. ChIP-seq for activating histone marks and Pol II loading identified candidate enhancer elements controlling the ACE2 locus, including the intronic dACE2 promoter. Employing RNA-seq, we demonstrate that interferons activate expression of dACE2 and, to a lesser extent, the genuine ACE2 gene. Interferon-induced gene expression was mitigated by the JAK inhibitors baricitinib and ruxolitinib, used therapeutically in COVID-19 patients. Through integrating RNA-seq and ChIP-seq data we provide an in-depth understanding of genetic programs activated by interferons, and our study highlights JAK inhibitors as suitable tools to suppress these in bronchial cells.

Longitudinal saliva omics responses to immune perturbation: a case study.

Saliva omics has immense potential for non-invasive diagnostics, including monitoring very young or elderly populations, or individuals in remote locations. In this study, multiple saliva omics from an individual were monitored over three periods (100 timepoints) involving: (1) hourly sampling over 24 h without intervention, (2) hourly sampling over 24 h including immune system activation using the standard 23-valent pneumococcal polysaccharide vaccine, (3) daily sampling for 33 days profiling the post-vaccination response. At each timepoint total saliva transcriptome and proteome, and small RNA from salivary extracellular vesicles were profiled, including mRNA, miRNA, piRNA and bacterial RNA. The two 24-h periods were used in a paired analysis to remove daily variation and reveal vaccination responses. Over 18,000 omics longitudinal series had statistically significant temporal trends compared to a healthy baseline. Various immune response and regulation pathways were activated following vaccination, including interferon and cytokine signaling, and MHC antigen presentation. Immune response timeframes were concordant with innate and adaptive immunity development, and coincided with vaccination and reported fever. Overall, mRNA results appeared more specific and sensitive (timewise) to vaccination compared to other omics. The results suggest saliva omics can be consistently assessed for non-invasive personalized monitoring and immune response diagnostics.

A SARS-CoV-2 targeted siRNA-nanoparticle therapy for COVID-19.

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in humans. Despite several emerging vaccines, there remains no verifiable therapeutic targeted specifically to the virus. Here we present a highly effective small interfering RNA (siRNA) therapeutic against SARS-CoV-2 infection using a novel lipid nanoparticle (LNP) delivery system. Multiple siRNAs targeting highly conserved regions of the SARS-CoV-2 virus were screened, and three candidate siRNAs emerged that effectively inhibit the virus by greater than 90% either alone or in combination with one another. We simultaneously developed and screened two novel LNP formulations for the delivery of these candidate siRNA therapeutics to the lungs, an organ that incurs immense damage during SARS-CoV-2 infection. Encapsulation of siRNAs in these LNPs followed by in vivo injection demonstrated robust repression of virus in the lungs and a pronounced survival advantage to the treated mice. Our LNP-siRNA approaches are scalable and can be administered upon the first sign of SARS-CoV-2 infection in humans. We suggest that an siRNA-LNP therapeutic approach could prove highly useful in treating COVID-19 disease as an adjunctive therapy to current vaccine strategies.

Inferring molecular mechanisms of dexamethasone therapy in severe COVID-19 from existing transcriptomic data.

Dexamethasone, a synthetic glucocorticoid, has previously shown mortality benefit in severe coronavirus disease 2019 (COVID-19) in a randomized controlled trial. As the illness is considered to reflect a hyperinflammatory state, this therapeutic effectiveness is presumably ascribed to broad anti-inflammatory activities of glucocorticoids. Here, an unbiased analysis of available transcriptomic data on lung and blood immune cells from severe COVID-19 patients and matching cellular models of dexamethasone treatment is presented that supports this presumption. Comparison of differentially expressed genes in severe COVID-19 with that in dexamethasone treated cells reveals a small set of genes that are regulated in opposite direction between the disease and the drug, and are enriched for genes and processes related to glucocorticoid pathway and receptor binding. This expression signature differentiates as a whole various cytokines from a set of anti-cytokine/anti-inflammatory agents, with the former resembling COVID-19 and the latter dexamethasone in gene regulation. The signature apparently relates to TNF- α, IL-1α, IL-1ß, IFN-α, IFN-ß, and IFN-γ signaling, but not IL-6 signaling, suggesting that therapeutic effect of dexamethasone in COVID-19 does not involve IL-6 pathway. However, as all these observations are purely based on bioinformatic analysis, experimental evidence will be required to validate the inferences drawn. In conclusion, the present analysis seems to provide a proof of concept for therapeutic mechanisms of dexamethasone in COVID-19.

Identification of candidate repurposable drugs to combat COVID-19 using a signature-based approach.

The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. Identification of effective therapeutics is a crucial tool to treat those infected with SARS-CoV-2 and limit the spread of this novel disease globally. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an "omics" repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and publicly available SARS-CoV-2 infected cell lines to identify novel therapeutics. We identified a shortlist of 20 candidate drugs: 8 are already under trial for the treatment of COVID-19, the remaining 12 have antiviral properties and 6 have antiviral efficacy against coronaviruses specifically, in vitro. All candidate drugs are either FDA approved or are under investigation. Our candidate drug findings are discordant with (i.e., reverse) SARS-CoV-2 transcriptome signatures generated in vitro, and a subset are also identified in transcriptome signatures generated from COVID-19 patient samples, like the MEK inhibitor selumetinib. Overall, our findings provide additional support for drugs that are already being explored as therapeutic agents for the treatment of COVID-19 and identify promising novel targets that are worthy of further investigation.

Causal network inference from gene transcriptional time-series response to glucocorticoids.

Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2768 genes and 31,945 directed edges (FDR ≤ 0.2). We validate inferred causal network edges using two external data sources: Overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS.

Hypertension and renin-angiotensin system blockers are not associated with expression of angiotensin-converting enzyme 2 (ACE2) in the kidney.

AIMS: Angiotensin-converting enzyme 2 (ACE2) is the cellular entry point for severe acute respiratory syndrome coronavirus (SARS-CoV-2)-the cause of coronavirus disease 2019 (COVID-19). However, the effect of renin-angiotensin system (RAS)-inhibition on ACE2 expression in human tissues of key relevance to blood pressure regulation and COVID-19 infection has not previously been reported. METHODS AND RESULTS: We examined how hypertension, its major metabolic co-phenotypes, and antihypertensive medications relate to ACE2 renal expression using information from up to 436 patients whose kidney transcriptomes were characterized by RNA-sequencing. We further validated some of the key observations in other human tissues and/or a controlled experimental model. Our data reveal increasing expression of ACE2 with age in both human lungs and the kidney. We show no association between renal expression of ACE2 and either hypertension or common types of RAS inhibiting drugs. We demonstrate that renal abundance of ACE2 is positively associated with a biochemical index of kidney function and show a strong enrichment for genes responsible for kidney health and disease in ACE2 co-expression analysis. CONCLUSION: Our results indicate that neither hypertension nor antihypertensive treatment is likely to alter the expression of the key entry receptor for SARS-CoV-2 in the human kidney. Our data further suggest that in the absence of SARS-CoV-2 infection, kidney ACE2 is most likely nephro-protective but the age-related increase in its expression within lungs and kidneys may be relevant to the risk of SARS-CoV-2 infection.

Transcriptome network analyses in human coronavirus infections suggest a rational use of immunomodulatory drugs for COVID-19 therapy.

The recent outbreak of coronavirus disease 2019 (COVID-19) by SARS-CoV-2 has led to uptodate 24.3 M cases and 0.8 M deaths. It is thus in urgent need to rationalize potential therapeutic targets against the progression of diseases. An effective, feasible way is to use the pre-existing ΔORF6 mutant of SARS-CoV as a surrogate for SARS-CoV-2, since both lack the moiety responsible for interferon antagonistic effects. By analyzing temporal profiles of upregulated genes in ΔORF6-infected Calu-3 cells, we prioritized 55 genes and 238 ligands to reposition currently available medications for COVID-19 therapy. Eight of them are already in clinical trials, including dexamethasone, ritonavir, baricitinib, tofacitinib, naproxen, budesonide, ciclesonide and formoterol. We also pinpointed 16 drug groups from the Anatomical Therapeutic Chemical classification system, with the potential to mitigate symptoms of SARS-CoV-2 infection and thus to be repositioned for COVID-19 therapy.

Transcription-based drug repurposing for COVID-19.

We have utilised the transcriptional response of lung epithelial cells following infection by the original Severe Acute Respiratory Syndrome coronavirus (SARS) to identify repurposable drugs for COVID-19. Drugs best able to recapitulate the infection profile are highly enriched for antiviral activity. Nine of these have been tested against SARS-2 and found to potently antagonise SARS-2 infection/replication, with a number now being considered for clinical trials. It is hoped that this approach may serve to broaden the spectrum of approved drugs that should be further assessed as potential anti-COVID-19 agents and may help elucidate how this seemingly disparate collection of drugs are able to inhibit SARS-2 infection/replication.

Single-cell transcriptomic atlas of primate cardiopulmonary aging.

Aging is a major risk factor for many diseases, especially in highly prevalent cardiopulmonary comorbidities and infectious diseases including Coronavirus Disease 2019 (COVID-19). Resolving cellular and molecular mechanisms associated with aging in higher mammals is therefore urgently needed. Here, we created young and old non-human primate single-nucleus/cell transcriptomic atlases of lung, heart and artery, the top tissues targeted by SARS-CoV-2. Analysis of cell type-specific aging-associated transcriptional changes revealed increased systemic inflammation and compromised virus defense as a hallmark of cardiopulmonary aging. With age, expression of the SARS-CoV-2 receptor angiotensin-converting enzyme 2 (ACE2) was increased in the pulmonary alveolar epithelial barrier, cardiomyocytes, and vascular endothelial cells. We found that interleukin 7 (IL7) accumulated in aged cardiopulmonary tissues and induced ACE2 expression in human vascular endothelial cells in an NF-κB-dependent manner. Furthermore, treatment with vitamin C blocked IL7-induced ACE2 expression. Altogether, our findings depict the first transcriptomic atlas of the aged primate cardiopulmonary system and provide vital insights into age-linked susceptibility to SARS-CoV-2, suggesting that geroprotective strategies may reduce COVID-19 severity in the elderly.