Gene Expression Profiling Reveals the Shared and Distinct Transcriptional Signatures in Human Lung Epithelial Cells Infected With SARS-CoV-2, MERS-CoV, or SARS-CoV: Potential Implications in Cardiovascular Complications of COVID-19

Obesity and age are transmission risk factors for SARS-CoV-2 infection among exposed individuals

The coronavirus disease (COVID-19) pandemic has occurred in Massachusetts in multiple waves led by a series of emerging variants. While the evidence has linked obesity with severe symptoms of COVID-19, the effect of obesity on susceptibility to SARS-CoV-2 infection remains unclear. Identification of intrinsic factors, which increase the likelihood of exposed individuals succumbing to productive SARS-CoV-2 infection could help plan mitigation efforts to curb the illness. We aim to investigate whether obese individuals have a higher susceptibility to developing productive SARS-CoV-2 infection given comparable exposure to nonobese individuals. This case-control study leveraged data from the Mass General Brigham's (MGB) electronic medical records (EMR), containing 687,813 patients, to determine whether obesity at any age increases the proportion of infections. We used PCR results of 72,613 subjects who tested positive to SARS-CoV-2 or declared exposure to the virus independently of the result of the test. For this study, we defined susceptibility as the likelihood of testing positive upon suspected exposure. We demonstrate evidence that SARS-CoV-2 exposed obese individuals were more prone to become COVID positive than nonobese individuals [adjusted odds ratio = 1.34 (95% CI: 1.29-1.39)]. Temporal analysis showed significantly increased susceptibility in obese individuals across the duration of the pandemic in Massachusetts. Obese exposed individuals are at a higher risk of getting infected with SARS-CoV-2. This indicates that obesity is not only a risk factor for worsened outcomes but also increases the risk for infection upon exposure. Identifying such populations early will be crucial for curbing the spread of this infectious disease.

SARS-CoV-2-infected human airway epithelial cell cultures uniquely lack interferon and immediate early gene responses caused by other coronaviruses

Objectives

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a member of a class of highly pathogenic coronaviruses. The large family of coronaviruses, however, also includes members that cause only mild symptoms, like human coronavirus-229E (HCoV-229E) or OC43 (HCoV-OC43). Unravelling how molecular (and cellular) pathophysiology differs between highly and low pathogenic coronaviruses is important for the development of therapeutic strategies.

Methods

Here, we analysed the transcriptome of primary human bronchial epithelial cells (PBEC), differentiated at the air-liquid interface (ALI) after infection with SARS-CoV-2, SARS-CoV, Middle East Respiratory Syndrome (MERS)-CoV and HCoV-229E using bulk RNA sequencing.

Results

ALI-PBEC were efficiently infected by all viruses, and SARS-CoV, MERS-CoV and HCoV-229E infection resulted in a largely similar transcriptional response. The response to SARS-CoV-2 infection differed markedly as it uniquely lacked the increase in expression of immediate early genes, including FOS, FOSB and NR4A1 that was observed with all other coronaviruses. This finding was further confirmed in publicly available experimental and clinical datasets. Interfering with NR4A1 signalling in Calu-3 lung epithelial cells resulted in a 100-fold reduction in extracellular RNA copies of SARS-CoV-2 and MERS-CoV, suggesting an involvement in virus replication. Furthermore, a lack in induction of interferon-related gene expression characterised the main difference between the highly pathogenic coronaviruses and low pathogenic viruses HCoV-229E and HCoV-OC43.

Conclusion

Our results demonstrate a previously unknown suppression of a host response gene set by SARS-CoV-2 and confirm a difference in interferon-related gene expression between highly pathogenic and low pathogenic coronaviruses.

Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology

PURPOSE

Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations.

METHODS

To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins.

RESULTS

We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences.

CONCLUSION

Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

CompCorona: A web application for comparative transcriptome analyses of coronaviruses reveals SARS-CoV-2-specific host response

Background/aim

Understanding the mechanism of host transcriptomic response to infection by the SARS-CoV-2 virus is crucial, especially for patients suffering from long-term effects of COVID-19, such as long COVID or pericarditis inflammation, potentially linked to side effects of the SARS-CoV-2 spike proteins. We conducted comprehensive transcriptome and enrichment analyses on lung and peripheral blood mononuclear cells (PBMCs) infected with SARS-CoV-2, as well as on SARS-CoV and MERS-CoV, to uncover shared pathways and elucidate their common disease progression and viral replication mechanisms.

Materials and methods

We developed CompCorona, the first interactive online tool for visualizing gene response variance among the family Coronaviridae through 2D and 3D principal component analysis (PCA) and exploring systems biology variance using pathway plots. We also made preprocessed datasets of lungs and PBMCs infected by SARS-CoV-2, SARS-CoV, and MERS-CoV publicly available through CompCorona.

Results

One remarkable finding from the lung and PBMC datasets for infections by SARS-CoV-2, but not infections by other coronaviruses (CoVs), was the significant downregulation of the angiogenin (ANG) and vascular endothelial growth factor A (VEGFA) genes, both directly involved in epithelial and vascular endothelial cell dysfunction. Suppression of the TNF signaling pathway was also observed in cells infected by SARS-CoV-2, along with simultaneous activation of complement and coagulation cascades and pertussis pathways. The ribosome pathway was found to be universally suppressed across all three viruses. The CompCorona online tool enabled the comparative analysis of 9 preprocessed host transcriptome datasets of cells infected by CoVs, revealing the specific host response differences in cases of SARS-CoV-2 infection. This included identifying markers of epithelial dysfunction via interactive 2D and 3D PCA, Venn diagrams, and pathway plots.

Conclusion

Our findings suggest that infection by SARS-CoV-2 might induce pulmonary epithelial dysfunction, a phenomenon not observed in cells infected by other CoVs. The publicly available CompCorona tool, along with the preprocessed datasets of cells infected by various CoVs, constitutes a valuable resource for further research into CoV-associated syndromes.

Graphical Learning and Causal Inference for Drug Repurposing

Gene expression profiles that connect drug perturbations, disease gene expression signatures, and clinical data are important for discovering potential drug repurposing indications. However, the current approach to gene expression reversal has several limitations. First, most methods focus on validating the reversal expression of individual genes. Second, there is a lack of causal approaches for identifying drug repurposing candidates. Third, few methods for passing and summarizing information on a graph have been used for drug repurposing analysis, with classical network propagation and gene set enrichment analysis being the most common. Fourth, there is a lack of graph-valued association analysis, with current approaches using real-valued association analysis one gene at a time to reverse abnormal gene expressions to normal gene expressions. To overcome these limitations, we propose a novel causal inference and graph neural network (GNN)-based framework for identifying drug repurposing candidates. We formulated a causal network as a continuous constrained optimization problem and developed a new algorithm for reconstructing large-scale causal networks of up to 1,000 nodes. We conducted large-scale simulations that demonstrated good false positive and false negative rates. To aggregate and summarize information on both nodes and structure from the spatial domain of the causal network, we used directed acyclic graph neural networks (DAGNN). We also developed a new method for graph regression in which both dependent and independent variables are graphs. We used graph regression to measure the degree to which drugs reverse altered gene expressions of disease to normal levels and to select potential drug repurposing candidates. To illustrate the application of our proposed methods for drug repurposing, we applied them to phase I and II L1000 connectivity map perturbational profiles from the Broad Institute LINCS, which consist of gene-expression profiles for thousands of perturbagens at a variety of time points, doses, and cell lines, as well as disease gene expression data under-expressed and over-expressed in response to SARS-CoV-2.

Molecular crosstalk between COVID-19 and Alzheimer's disease using microarray and RNA-seq datasets: A system biology approach

Objective

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The clinical and epidemiological analysis reported the association between SARS-CoV-2 and neurological diseases. Among neurological diseases, Alzheimer's disease (AD) has developed as a crucial comorbidity of SARS-CoV-2. This study aimed to understand the common transcriptional signatures between SARS-CoV-2 and AD.

Materials and methods

System biology approaches were used to compare the datasets of AD and COVID-19 to identify the genetic association. For this, we have integrated three human whole transcriptomic datasets for COVID-19 and five microarray datasets for AD. We have identified differentially expressed genes for all the datasets and constructed a protein-protein interaction (PPI) network. Hub genes were identified from the PPI network, and hub genes-associated regulatory molecules (transcription factors and miRNAs) were identified for further validation.

Results

A total of 9,500 differentially expressed genes (DEGs) were identified for AD and 7,000 DEGs for COVID-19. Gene ontology analysis resulted in 37 molecular functions, 79 cellular components, and 129 biological processes were found to be commonly enriched in AD and COVID-19. We identified 26 hub genes which includes AKT1, ALB, BDNF, CD4, CDH1, DLG4, EGF, EGFR, FN1, GAPDH, INS, ITGB1, ACTB, SRC, TP53, CDC42, RUNX2, HSPA8, PSMD2, GFAP, VAMP2, MAPK8, CAV1, GNB1, RBX1, and ITGA2B. Specific miRNA targets associated with Alzheimer's disease and COVID-19 were identified through miRNA target prediction. In addition, we found hub genes-transcription factor and hub genes-drugs interaction. We also performed pathway analysis for the hub genes and found that several cell signaling pathways are enriched, such as PI3K-AKT, Neurotrophin, Rap1, Ras, and JAK-STAT.

Conclusion

Our results suggest that the identified hub genes could be diagnostic biomarkers and potential therapeutic drug targets for COVID-19 patients with AD comorbidity.

Coagulopathy and thromboembolic events a pathogenic mechanism of COVID-19 associated with mortality: An updated review

During 2019, the SARS-CoV-2 emerged from China, and during months, COVID-19 spread in many countries around the world. The expanding data about pathogenesis of this virus could elucidate the exact mechanism by which COVID-19 caused death in humans. One of the pathogenic mechanisms of this disease is coagulation. Coagulation disorders that affect both venous and arterial systems occur in patients with COVID-19. The possible mechanism involved in the coagulation could be excessive inflammation induced by SARS-CoV-2. However, it is not yet clear well how SARS-CoV-2 promotes coagulopathy. However, some factors, such as pulmonary endothelial cell damage and some anticoagulant system disorders, are assumed to have an important role. In this study, we assessed conducted studies about COVID-19-induced coagulopathy to obtain clearer vision of the wide range of manifestations and possible pathogenesis mechanisms.

Innate and adaptive immunity to SARS-CoV-2 and predisposing factors

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2), has affected all countries worldwide. Although some symptoms are relatively mild, others are still associated with severe and even fatal clinical outcomes. Innate and adaptive immunity are important for the control of SARS-CoV-2 infections, whereas a comprehensive characterization of the innate and adaptive immune response to COVID-19 is still lacking and the mechanisms underlying immune pathogenesis and host predisposing factors are still a matter of scientific debate. Here, the specific functions and kinetics of innate and adaptive immunity involved in SARS-CoV-2 recognition and resultant pathogenesis are discussed, as well as their immune memory for vaccinations, viral-mediated immune evasion, and the current and future immunotherapeutic agents. We also highlight host factors that contribute to infection, which may deepen the understanding of viral pathogenesis and help identify targeted therapies that attenuate severe disease and infection.

Shared miRNA landscapes of COVID-19 and neurodegeneration confirm neuroinflammation as an important overlapping feature

Introduction

Development and worsening of most common neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis, have been associated with COVID-19 However, the mechanisms associated with neurological symptoms in COVID-19 patients and neurodegenerative sequelae are not clear. The interplay between gene expression and metabolite production in CNS is driven by miRNAs. These small non-coding molecules are dysregulated in most common neurodegenerative diseases and COVID-19.

Methods

We have performed a thorough literature screening and database mining to search for shared miRNA landscapes of SARS-CoV-2 infection and neurodegeneration. Differentially expressed miRNAs in COVID-19 patients were searched using PubMed, while differentially expressed miRNAs in patients with five most common neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, and multiple sclerosis) were searched using the Human microRNA Disease Database. Target genes of the overlapping miRNAs, identified with the miRTarBase, were used for the pathway enrichment analysis performed with Kyoto Encyclopedia of Genes and Genomes and Reactome.

Results

In total, 98 common miRNAs were found. Additionally, two of them (hsa-miR-34a and hsa-miR-132) were highlighted as promising biomarkers of neurodegeneration, as they are dysregulated in all five most common neurodegenerative diseases and COVID-19. Additionally, hsa-miR-155 was upregulated in four COVID-19 studies and found to be dysregulated in neurodegeneration processes as well. Screening for miRNA targets identified 746 unique genes with strong evidence for interaction. Target enrichment analysis highlighted most significant KEGG and Reactome pathways being involved in signaling, cancer, transcription and infection. However, the more specific identified pathways confirmed neuroinflammation as being the most important shared feature.

Discussion

Our pathway based approach has identified overlapping miRNAs in COVID-19 and neurodegenerative diseases that may have a valuable potential for neurodegeneration prediction in COVID-19 patients. Additionally, identified miRNAs can be further explored as potential drug targets or agents to modify signaling in shared pathways. Graphical AbstractShared miRNA molecules among the five investigated neurodegenerative diseases and COVID-19 were identified. The two overlapping miRNAs, hsa-miR-34a and has-miR-132, present potential biomarkers of neurodegenerative sequelae after COVID-19. Furthermore, 98 common miRNAs between all five neurodegenerative diseases together and COVID-19 were identified. A KEGG and Reactome pathway enrichment analyses was performed on the list of shared miRNA target genes and finally top 20 pathways were evaluated for their potential for identification of new drug targets. A common feature of identified overlapping miRNAs and pathways is neuroinflammation. AD, Alzheimer's disease; ALS, amyotrophic lateral sclerosis; COVID-19, coronavirus disease 2019; HD, Huntington's disease; KEGG, Kyoto Encyclopedia of Genes and Genomes; MS, multiple sclerosis; PD, Parkinson's disease.

The use of bioinformatics methods to identify the effects of SARS-CoV-2 and influenza viruses on the regulation of gene expression in patients

Background

SARS-CoV-2 infection is a respiratory infectious disease similar to influenza virus infection. Numerous studies have reported similarities and differences in the clinical manifestations, laboratory tests, and mortality between these two infections. However, the genetic effects of coronavirus and influenza viruses on the host that lead to these characteristics have rarely been reported.

Methods

COVID-19 (GSE157103) and influenza (GSE111368, GSE101702) datasets were downloaded from the Gene Expression Ominbus (GEO) database. Differential gene, gene set enrichment, protein-protein interaction (PPI) network, gene regulatory network, and immune cell infiltration analyses were performed to identify the critical impact of COVID-19 and influenza viruses on the regulation of host gene expression.

Results

The number of differentially expressed genes in the COVID-19 patients was significantly higher than in the influenza patients. 22 common differentially expressed genes (DEGs) were identified between the COVID-19 and influenza datasets. The effects of the viruses on the regulation of host gene expression were determined using gene set enrichment and PPI network analyses. Five HUB genes were finally identified: IFI27, OASL, RSAD2, IFI6, and IFI44L.

Conclusion

We identified five HUB genes between COVID-19 and influenza virus infection, which might be helpful in the diagnosis and treatment of COVID-19 and influenza. This knowledge may also guide future mechanistic studies that aim to identify pathogen-specific interventions.

The Natural Products Withaferin A and Withanone from the Medicinal Herb Withania somnifera Are Covalent Inhibitors of the SARS-CoV-2 Main Protease

The current COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) created a global health crisis. The ability of vaccines to protect immunocompromised individuals and from emerging new strains are major concerns. Hence antiviral drugs against SARS-CoV-2 are essential. The SARS-CoV-2 main protease Mpro is vital for replication and an important target for antivirals. Using CMap analysis and docking studies, withaferin A (wifA) and withanone (win), two natural products from the medicinal herb Withania somnifera (ashwagandha), were identified as promising candidates that can covalently inhibit the viral protease Mpro. Cell culture, enzymatic, LC-MS/MS, computational, and equilibrium dialysis based assays were performed. DFT calculations indicated that wifA and win can form stable adducts with thiols. The cytotoxicity of Mpro was significantly reduced by wifA and win. Both wifA and win were found to irreversibly inhibit 0.5 μM Mpro with IC50 values of 0.54 and 1.8 μM, respectively. LC-MS/MS analysis revealed covalent adduct formation with wifA at cysteines 145 and 300 of Mpro. The natural products wifA and win can irreversibly inhibit the SARS-CoV-2 main protease Mpro. Based on the work presented here we propose that both wifA and win have the potential to be safely used as preventative and therapeutic interventions for COVID-19.

The current status of gene expression profilings in COVID-19 patients

Background

The global pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has swept through every part of the world. Because of its impact, international efforts have been underway to identify the variants of SARS-CoV-2 by genome sequencing and to understand the gene expression changes in COVID-19 patients compared to healthy donors using RNA sequencing (RNA-seq) assay. Within the last two and half years since the emergence of SARS-CoV-2, a large number of OMICS data of COVID-19 patients have accumulated. Yet, we are still far from understanding the disease mechanism. Further, many people suffer from long-term effects of COVID-19; calling for a more systematic way to data mine the generated OMICS data, especially RNA-seq data.

Methods

By searching gene expression omnibus (GEO) using the key terms, COVID-19 and RNA-seq, 108 GEO entries were identified. Each of these studies was manually examined to categorize the studies into bulk or single-cell RNA-seq (scRNA-seq) followed by an inspection of their original articles.

Results

The currently available RNA-seq data were generated from various types of patients' samples, and COVID-19 related sample materials have been sequenced at the level of RNA, including whole blood, different components of blood [e.g., plasma, peripheral blood mononuclear cells (PBMCs), leukocytes, lymphocytes, monocytes, T cells], nasal swabs, and autopsy samples (e.g., lung, heart, liver, kidney). Of these, RNA-seq studies using whole blood, PBMCs, nasal swabs and autopsy/biopsy samples were reviewed to highlight the major findings from RNA-seq data analysis.

Conclusions

Based on the bulk and scRNA-seq data analysis, severe COVID-19 patients display shifts in cell populations, especially those of leukocytes and monocytes, possibly leading to cytokine storms and immune silence. These RNA-seq data form the foundation for further gene expression analysis using samples from individuals suffering from long COVID.

Differential gene expression profiling reveals potential biomarkers and pharmacological compounds against SARS-CoV-2: Insights from machine learning and bioinformatics approaches

The COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has created an urgent global situation. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability and disease comorbidities. The pandemic continues to spread worldwide, despite intense efforts to develop multiple vaccines and therapeutic options against COVID-19. However, the precise role of SARS-CoV-2 in the pathophysiology of the nasopharyngeal tract (NT) is still unfathomable. This study utilized machine learning approaches to analyze 22 RNA-seq data from COVID-19 patients (n = 8), recovered individuals (n = 7), and healthy individuals (n = 7) to find disease-related differentially expressed genes (DEGs). We compared dysregulated DEGs to detect critical pathways and gene ontology (GO) connected to COVID-19 comorbidities. We found 1960 and 153 DEG signatures in COVID-19 patients and recovered individuals compared to healthy controls. In COVID-19 patients, the DEG–miRNA, and DEG–transcription factors (TFs) interactions network analysis revealed that E2F1, MAX, EGR1, YY1, and SRF were the highly expressed TFs, whereas hsa-miR-19b, hsa-miR-495, hsa-miR-340, hsa-miR-101, and hsa-miR-19a were the overexpressed miRNAs. Three chemical agents (Valproic Acid, Alfatoxin B1, and Cyclosporine) were abundant in COVID-19 patients and recovered individuals. Mental retardation, mental deficit, intellectual disability, muscle hypotonia, micrognathism, and cleft palate were the significant diseases associated with COVID-19 by sharing DEGs. Finally, the detected DEGs mediated by TFs and miRNA expression indicated that SARS-CoV-2 infection might contribute to various comorbidities. Our results provide the common DEGs between COVID-19 patients and recovered humans, which suggests some crucial insights into the complex interplay between COVID-19 progression and the recovery stage, and offer some suggestions on therapeutic target identification in COVID-19 caused by the SARS-CoV-2.

Meta-Data Analysis to Explore the Hub of the Hub-Genes That Influence SARS-CoV-2 Infections Highlighting Their Pathogenetic Processes and Drugs Repurposing

The pandemic of SARS-CoV-2 infections is a severe threat to human life and the world economic condition. Although vaccination has reduced the outspread, but still the situation is not under control because of the instability of RNA sequence patterns of SARS-CoV-2, which requires effective drugs. Several studies have suggested that the SARS-CoV-2 infection causing hub differentially expressed genes (Hub-DEGs). However, we observed that there was not any common hub gene (Hub-DEGs) in our analyses. Therefore, it may be difficult to take a common treatment plan against SARS-CoV-2 infections globally. The goal of this study was to examine if more representative Hub-DEGs from published studies by means of hub of Hub-DEGs (hHub-DEGs) and associated potential candidate drugs. In this study, we reviewed 41 articles on transcriptomic data analysis of SARS-CoV-2 and found 370 unique hub genes or studied genes in total. Then, we selected 14 more representative Hub-DEGs (AKT1, APP, CXCL8, EGFR, IL6, INS, JUN, MAPK1, STAT3, TNF, TP53, UBA52, UBC, VEGFA) as hHub-DEGs by their protein-protein interaction analysis. Their associated biological functional processes, transcriptional, and post-transcriptional regulatory factors. Then we detected hHub-DEGs guided top-ranked nine candidate drug agents (Digoxin, Avermectin, Simeprevir, Nelfinavir Mesylate, Proscillaridin, Linifanib, Withaferin, Amuvatinib, Atazanavir) by molecular docking and cross-validation for treatment of SARS-CoV-2 infections. Therefore, the findings of this study could be useful in formulating a common treatment plan against SARS-CoV-2 infections globally.

Attenuation of SARS-CoV-2 replication and associated inflammation by concomitant targeting of viral and host cap 2'-O-ribose methyltransferases

The SARS‐CoV‐2 infection cycle is a multistage process that relies on functional interactions between the host and the pathogen. Here, we repurposed antiviral drugs against both viral and host enzymes to pharmaceutically block methylation of the viral RNA 2'‐O‐ribose cap needed for viral immune escape. We find that the host cap 2'‐O‐ribose methyltransferase MTr1 can compensate for loss of viral NSP16 methyltransferase in facilitating virus replication. Concomitant inhibition of MTr1 and NSP16 efficiently suppresses SARS‐CoV‐2 replication. Using in silico target‐based drug screening, we identify a bispecific MTr1/NSP16 inhibitor with anti‐SARS‐CoV‐2 activity in vitro and in vivo but with unfavorable side effects. We further show antiviral activity of inhibitors that target independent stages of the host SAM cycle providing the methyltransferase co‐substrate. In particular, the adenosylhomocysteinase (AHCY) inhibitor DZNep is antiviral in in vitro, in ex vivo, and in a mouse infection model and synergizes with existing COVID‐19 treatments. Moreover, DZNep exhibits a strong immunomodulatory effect curbing infection‐induced hyperinflammation and reduces lung fibrosis markers ex vivo. Thus, multispecific and metabolic MTase inhibitors constitute yet unexplored treatment options against COVID‐19.

Therapy Targets SARS-CoV-2 Infection-Induced Cell Death

Coronavirus Disease 2019 (COVID-19) caused by SARS-CoV-2 has become a global health issue. The clinical presentation of COVID-19 is highly variable, ranging from asymptomatic and mild disease to severe. However, the mechanisms for the high mortality induced by SARS-CoV-2 infection are still not well understood. Recent studies have indicated that the cytokine storm might play an essential role in the disease progression in patients with COVID-19, which is characterized by the uncontrolled release of cytokines and chemokines leading to acute respiratory distress syndrome (ARDS), multi-organ failure, and even death. Cell death, especially, inflammatory cell death, might be the initiation of a cytokine storm caused by SARS-CoV-2 infection. This review summarizes the forms of cell death caused by SARS-CoV-2 in vivo or in vitro and elaborates on the dedication of apoptosis, necroptosis, NETosis, pyroptosis of syncytia, and even SARS-CoV-2 E proteins forming channel induced cell death, providing insights into targets on the cell death pathway for the treatment of COVID-19.

The effects of SARS-CoV-2 infection on modulating innate immunity and strategies of combating inflammatory response for COVID-19 therapy

The global pandemic of COVID-19 has caused huge causality and unquantifiable loss of social wealth. The innate immune response is the first line of defense against SARS-CoV-2 infection. However, strong inflammatory response associated with dysregulation of innate immunity causes severe acute respiratory syndrome (SARS) and death. In this review, we update the current knowledge on how SARS-CoV-2 modulates the host innate immune response for its evasion from host defense and its corresponding pathogenesis caused by cytokine storm. We emphasize Type I interferon response and the strategies of evading innate immune defense used by SARS-CoV-2. We also extensively discuss the cells and their function involved in the innate immune response and inflammatory response, as well as the promises and challenges of drugs targeting excessive inflammation for antiviral treatment. This review would help us to figure out the current challenge questions of SARS-CoV-2 infection on innate immunity and directions for future studies.

Potential benefits of ginseng against COVID-19 by targeting inflammasomes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogenic virus that causes coronavirus disease 2019 (COVID-19), with major symptoms including hyper-inflammation and cytokine storm, which consequently impairs the respiratory system and multiple organs, or even cause death. SARS-CoV-2 activates inflammasomes and inflammasome-mediated inflammatory signaling pathways, which are key determinants of hyperinflammation and cytokine storm in COVID-19 patients. Additionally, SARS-CoV-2 inhibits inflammasome activation to evade the host's antiviral immunity. Therefore, regulating inflammasome initiation has received increasing attention as a preventive measure in COVID-19 patients. Ginseng and its major active constituents, ginsenosides and saponins, improve the immune system and exert anti-inflammatory effects by targeting inflammasome stimulation. Therefore, this review discussed the potential preventive and therapeutic roles of ginseng in COVID-19 based on its regulatory role in inflammasome initiation and the host's antiviral immunity.

In silico Drug Screening Approach Using L1000-Based Connectivity Map and Its Application to COVID-19

Conventional drug screening methods search for a limited number of small molecules that directly interact with the target protein. This process can be slow, cumbersome and has driven the need for developing new drug screening approaches to counter rapidly emerging diseases such as COVID-19. We propose a pipeline for drug repurposing combining in silico drug candidate identification followed by in vitro characterization of these candidates. We first identified a gene target of interest, the entry receptor for the SARS-CoV-2 virus, angiotensin converting enzyme 2 (ACE2). Next, we employed a gene expression profile database, L1000-based Connectivity Map to query gene expression patterns in lung epithelial cells, which act as the primary site of SARS-CoV-2 infection. Using gene expression profiles from 5 different lung epithelial cell lines, we computationally identified 17 small molecules that were predicted to decrease ACE2 expression. We further performed a streamlined validation in the normal human epithelial cell line BEAS-2B to demonstrate that these compounds can indeed decrease ACE2 surface expression and to profile cell health and viability upon drug treatment. This proposed pipeline combining in silico drug compound identification and in vitro expression and viability characterization in relevant cell types can aid in the repurposing of FDA-approved drugs to combat rapidly emerging diseases.

Programmed cell death: the pathways to severe COVID-19?

Two years after the emergence of SARS-CoV-2, our understanding of COVID-19 disease pathogenesis is still incomplete. Despite unprecedented global collaborative scientific efforts and rapid vaccine development, an uneven vaccine roll-out and the emergence of novel variants of concern such as omicron underscore the critical importance of identifying the mechanisms that contribute to this disease. Overt inflammation and cell death have been proposed to be central drivers of severe pathology in COVID-19 patients and their pathways and molecular components therefore present promising targets for host-directed therapeutics. In our review, we summarize the current knowledge on the role and impact of diverse programmed cell death (PCD) pathways on COVID-19 disease. We dissect the complex connection of cell death and inflammatory signaling at the cellular and molecular level and identify a number of critical questions that remain to be addressed. We provide rationale for targeting of cell death as potential COVID-19 treatment and provide an overview of current therapeutics that could potentially enter clinical trials in the near future.

Systems analysis shows that thermodynamic physiological and pharmacological fundamentals drive COVID-19 and response to treatment

Why a systems analysis view of this pandemic? The current pandemic has inflicted almost unimaginable grief, sorrow, loss, and terror at a global scale. One of the great ironies with the COVID‐19 pandemic, particularly early on, is counter intuitive. The speed at which specialized basic and clinical sciences described the details of the damage to humans in COVID‐19 disease has been impressive. Equally, the development of vaccines in an amazingly short time interval has been extraordinary. However, what has been less well understood has been the fundamental elements that underpin the progression of COVID‐19 in an individual and in populations. We have used systems analysis approaches with human physiology and pharmacology to explore the fundamental underpinnings of COVID‐19 disease. Pharmacology powerfully captures the thermodynamic characteristics of molecular binding with an exogenous entity such as a virus and its consequences on the living processes well described by human physiology. Thus, we have documented the passage of SARS‐CoV‐2 from infection of a single cell to species jump, to tropism, variant emergence and widespread population infection. During the course of this review, the recurrent observation was the efficiency and simplicity of one critical function of this virus. The lethality of SARS‐CoV‐2 is due primarily to its ability to possess and use a variable surface for binding to a specific human target with high affinity. This binding liberates Gibbs free energy (GFE) such that it satisfies the criteria for thermodynamic spontaneity. Its binding is the prelude to human host cellular entry and replication by the appropriation of host cell constituent molecules that have been produced with a prior energy investment by the host cell. It is also a binding that permits viral tropism to lead to high levels of distribution across populations with newly formed virions. This thermodynamic spontaneity is repeated endlessly as infection of a single host cell spreads to bystander cells, to tissues, to humans in close proximity and then to global populations. The principal antagonism of this process comes from SARS‐CoV‐2 itself, with its relentless changing of its viral surface configuration, associated with the inevitable emergence of variants better configured to resist immune sequestration and importantly with a greater affinity for the host target and higher infectivity. The great value of this physiological and pharmacological perspective is that it reveals the fundamental thermodynamic underpinnings of SARS‐CoV‐2 infection.

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Background

The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.

Methods

RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.

Results

Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.

Conclusion

This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

Transcriptional Profiling and Machine Learning Unveil a Concordant Biosignature of Type I Interferon-Inducible Host Response Across Nasal Swab and Pulmonary Tissue for COVID-19 Diagnosis

Background

COVID-19, caused by SARS-CoV-2 virus, is a global pandemic with high mortality and morbidity. Limited diagnostic methods hampered the infection control. Since the direct detection of virus mainly by RT-PCR may cause false-negative outcome, host response-dependent testing may serve as a complementary approach for improving COVID-19 diagnosis.

Objective

Our study discovered a highly-preserved transcriptional profile of Type I interferon (IFN-I)-dependent genes for COVID-19 complementary diagnosis.

Methods

Computational language R-dependent machine learning was adopted for mining highly-conserved transcriptional profile (RNA-sequencing) across heterogeneous samples infected by SARS-CoV-2 and other respiratory infections. The transcriptomics/high-throughput sequencing data were retrieved from NCBI-GEO datasets (GSE32155, GSE147507, GSE150316, GSE162835, GSE163151, GSE171668, GSE182569). Mathematical approaches for homological analysis were as follows: adjusted rand index-related similarity analysis, geometric and multi-dimensional data interpretation, UpsetR, t-distributed Stochastic Neighbor Embedding (t-SNE), and Weighted Gene Co-expression Network Analysis (WGCNA). Besides, Interferome Database was used for predicting the transcriptional factors possessing IFN-I promoter-binding sites to the key IFN-I genes for COVID-19 diagnosis.

Results

In this study, we identified a highly-preserved gene module between SARS-CoV-2 infected nasal swab and postmortem lung tissue regulating IFN-I signaling for COVID-19 complementary diagnosis, in which the following 14 IFN-I-stimulated genes are highly-conserved, including BST2, IFIT1, IFIT2, IFIT3, IFITM1, ISG15, MX1, MX2, OAS1, OAS2, OAS3, OASL, RSAD2, and STAT1. The stratified severity of COVID-19 may also be identified by the transcriptional level of these 14 IFN-I genes.

Conclusion

Using transcriptional and computational analysis on RNA-seq data retrieved from NCBI-GEO, we identified a highly-preserved 14-gene transcriptional profile regulating IFN-I signaling in nasal swab and postmortem lung tissue infected by SARS-CoV-2. Such a conserved biosignature involved in IFN-I-related host response may be leveraged for COVID-19 diagnosis.

Bioinformatics analyses reveal cell-barrier junction modulations in lung epithelial cells on SARS-CoV-2 infection

Cell junctions maintain the blood-tissue barriers to preserve vascular and tissue integrity. Viral infections reportedly modulate cell-cell junctions to facilitate their invasion. However, information on the effect of COVID-19 infection on the gene expression of cell junction and cytoskeletal proteins is limited. Using the Gene Expression Omnibus and Reactome databases, we analyzed the data on human lung A549, NHBE, and Calu-3 cells for the expression changes in cell junction and cytoskeletal proteins by SARS-CoV-2 (CoV-2) infection. The analysis revealed changes in 3,660 genes in A549, 100 genes in NHBE, and 592 genes in Calu-3 cells with CoV-2 infection. Interestingly, EGOT (9.8-, 3- and 8.3-fold; p < .05) and CSF3 (4.3-, 33- and 56.3-fold; p < .05) were the only two genes significantly elevated in all three cell lines (A549, NHBE and Calu-3, respectively). On the other hand, 39 genes related to cell junctions and cytoskeleton were modulated in lung cells, with DLL1 demonstrating alterations in all cells. Alterations were also seen in several miRNAs associated with the cell junction and cytoskeleton genes modulated in the analysis. Further, matrix metalloproteinases involved in disease pathologies, including MMP-3, -9, and -12 demonstrated elevated expression on CoV-2 infection (p < .05). The study findings emphasize the integral role of cell junction and cytoskeletal genes in COVID-19, suggesting their therapeutic potential. Our analysis also identified a distinct EGOT gene that has not been previously implicated in COVID-19. Further studies on these newly identified genes and miRNAs could lead to advances in the pathogenesis and therapeutics of COVID-19.

Molecular mechanisms of Na,K-ATPase dysregulation driving alveolar epithelial barrier failure in severe COVID-19

A significant number of patients with coronavirus disease 2019 (COVID-19) develop acute respiratory distress syndrome (ARDS) that is associated with a poor outcome. The molecular mechanisms driving failure of the alveolar barrier upon severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection remain incompletely understood. The Na,K-ATPase is an adhesion molecule and a plasma membrane transporter that is critically required for proper alveolar epithelial function by both promoting barrier integrity and resolution of excess alveolar fluid, thus enabling appropriate gas exchange. However, numerous SARS-CoV-2-mediated and COVID-19-related signals directly or indirectly impair the function of the Na,K-ATPase, thereby potentially contributing to disease progression. In this Perspective, we highlight some of the putative mechanisms of SARS-CoV-2-driven dysfunction of the Na,K-ATPase, focusing on expression, maturation, and trafficking of the transporter. A therapeutic mean to selectively inhibit the maladaptive signals that impair the Na,K-ATPase upon SARS-CoV-2 infection might be effective in reestablishing the alveolar epithelial barrier and promoting alveolar fluid clearance and thus advantageous in patients with COVID-19-associated ARDS.