Identification of biomarkers and pathways for the SARS-CoV-2 infections that make complexities in pulmonary arterial hypertension patients

Advances in the study of S100A9 in cardiovascular diseases

Cardiovascular disease (CVD) is a group of diseases that primarily affect the heart or blood vessels, with high disability and mortality rates, posing a serious threat to human health. The causative factors, pathogenesis, and characteristics of common CVD differ, but they all involve common pathological processes such as inflammation, oxidative stress, and fibrosis. S100A9 belongs to the S100 family of calcium-binding proteins, which are mainly secreted by myeloid cells and bind to the Toll-like receptor 4 and receptor for advanced glycation end products and is involved in regulating pathological processes such as inflammatory response, fibrosis, vascular calcification, and endothelial barrier function in CVD. The latest research has found that S100A9 is a key biomarker for diagnosing and predicting various CVD. Therefore, this article reviews the latest research progress on the diagnostic and predictive, and therapeutic value of S100A9 in inflammatory-related CVD such as atherosclerosis, myocardial infarction, and arterial aneurysm and summarizes its molecular mechanisms in the progression of CVD, aiming to explore new predictive methods and to identify potential intervention targets for CVD in clinical practice.

Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR-mutated lung adenocarcinoma

Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.

Mast cell activation triggered by SARS-CoV-2 causes inflammation in brain microvascular endothelial cells and microglia

SARS-CoV-2-induced excessive inflammation in brain leads to damage of blood-brain barrier, hypoxic-ischemic injury, and neuron degeneration. The production of inflammatory cytokines by brain microvascular endothelial cells and microglia is reported to be critically associated with the brain pathology of COVID-19 patients. However, the cellular mechanisms for SARS-CoV-2-inducing activation of brain cells and the subsequent neuroinflammation remain to be fully delineated. Our research, along with others', has recently demonstrated that SARS-CoV-2-induced accumulation and activation of mast cells (MCs) in mouse lung could further induce inflammatory cytokines and consequent lung damages. Intracerebral MCs activation and their cross talk with other brain cells could induce neuroinflammation that play important roles in neurodegenerative diseases including virus-induced neuro-pathophysiology. In this study, we investigated the role of MC activation in SARS-CoV-2-induced neuroinflammation. We found that (1) SARS-CoV-2 infection triggered MC accumulation in the cerebrovascular region of mice; (2) spike/RBD (receptor-binding domain) protein-triggered MC activation induced inflammatory factors in human brain microvascular endothelial cells and microglia; (3) MC activation and degranulation destroyed the tight junction proteins in brain microvascular endothelial cells and induced the activation and proliferation of microglia. These findings reveal a cellular mechanism of SARS-CoV-2-induced neuroinflammation.

Discovery of common molecular signatures and drug repurposing for COVID-19/Asthma comorbidity: ACE2 and multi-partite networks

Angiotensin-converting enzyme 2 (ACE2) is identified as the functional receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the ongoing global coronavirus disease-2019 (COVID-19) pandemic. This study aimed to elucidate potential therapeutic avenues by scrutinizing approved drugs through the identification of the genetic signature associated with SARS-CoV-2 infection in individuals with asthma. This exploration was conducted through an integrated analysis, encompassing interaction networks between the ACE2 receptor and common host (co-host) factors implicated in COVID-19/asthma comorbidity. The comprehensive analysis involved the identification of common differentially expressed genes (cDEGs) and hub-cDEGs, functional annotations, interaction networks, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and module construction. Interaction networks were used to identify overlapping disease modules and potential drug targets. Computational biology and molecular docking analyzes were utilized to discern functional drug modules. Subsequently, the impact of the identified drugs on the expression of hub-cDEGs was experimentally validated using a mouse model. A total of 153 cDEGs or co-host factors associated with ACE2 were identified in the COVID-19 and asthma comorbidity. Among these, seven significant cDEGs and proteins - namely, HRAS, IFNG, JUN, CDH1, TLR4, ICAM1, and SCD-were recognized as pivotal host factors linked to ACE2. Regulatory network analysis of hub-cDEGs revealed eight top-ranked transcription factors (TFs) proteins and nine microRNAs as key regulatory factors operating at the transcriptional and post-transcriptional levels, respectively. Molecular docking simulations led to the proposal of 10 top-ranked repurposable drug molecules (Rapamycin, Ivermectin, Everolimus, Quercetin, Estradiol, Entrectinib, Nilotinib, Conivaptan, Radotinib, and Venetoclax) as potential treatment options for COVID-19 in individuals with comorbid asthma. Validation analysis demonstrated that Rapamycin effectively inhibited ICAM1 expression in the HDM-stimulated mice group (p < 0.01). This study unveils the common pathogenesis and genetic signature underlying asthma and SARS-CoV-2 infection, delineated by the interaction networks of ACE2-related host factors. These findings provide valuable insights for the design and discovery of drugs aimed at more effective therapeutics within the context of lung disease comorbidities.

Multi-omics analysis reveals overactive inflammation and dysregulated metabolism in severe community-acquired pneumonia patients

BACKGROUND

Severe community-acquired pneumonia (S-CAP) is a public health threat, making it essential to identify novel biomarkers and investigate the underlying mechanisms of disease severity.

METHODS

Here, we profiled host responses to S-CAP through proteomics analysis of plasma samples from a cohort of S-CAP patients, non-severe (NS)-CAP patients, diseases controls (DCs), and healthy controls (HCs). Then, typical differentially expressed proteins were then validated by ELISA in an independent cohort. Metabolomics analysis was further performed on both the cohort 1 and cohort 2. Then, the proteomic and metabolomic signatures were compared between the adult and child cohorts to explore the characteristics of severe pneumonia patients.

RESULTS

There were clear differences between CAP patients and controls, as well as substantial differences between the S-CAP and NS-CAP. Pathway analysis of changes revealed excessive inflammation, suppressed immunity, and lipid metabolic disorders in S-CAP cases. Interestingly, comparing these signatures between the adult and child cohorts confirmed that overactive inflammation and dysregulated lipid metabolism were common features of S-CAP patients, independent of age. The change proportion of glycerophospholipids, glycerolipids, and sphingolipids were obviously different in the adult and child S-CAP cases.

CONCLUSION

The plasma multi-omics profiling revealed that excessive inflammation, suppressed humoral immunity, and disordered metabolism are involved in S-CAP pathogenesis.

In silico Evaluation of NO-Sartans against SARS-CoV-2

INTRODUCTION

Numerous clinical trials are currently investigating the potential of nitric oxide (NO) as an antiviral agent against coronaviruses, including SARS-CoV-2. Additionally, some researchers have reported positive effects of certain Sartans against SARS-CoV-2.

METHOD

Considering the impact of NO-Sartans on the cardiovascular system, we have compiled information on the general structure, synthesis methods, and biological studies of synthesized NOSartans. In silico evaluation of all NO-Sartans and approved sartans against three key SARS-CoV- -2 targets, namely Mpro (PDB ID: 6LU7), NSP16 (PDB ID: 6WKQ), and ACE-2 (PDB ID: 1R4L), was performed using MOE.

RESULTS

Almost all NO-Sartans and approved sartans demonstrated promising results in inhibiting these SARS-CoV-2 targets. Compound 36 (CLC-1280) showed the best docking scores against the three evaluated targets and was further evaluated using molecular dynamics (MD) simulations.

CONCLUSION

Based on our in silico studies, CLC-1280 (a Valsartan dinitrate) has the potential to be considered as an inhibitor of the SARS-CoV-2 virus. However, further in vitro and in vivo evaluations are necessary for the drug development process.

Systematical analyses of large-scale transcriptome reveal viral infection-related genes and disease comorbidities

Perturbation of transcriptome in viral infection patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent transcriptome and identification of robust biomarkers is not complete. In this study, we manually collected 23 datasets related to 6,197 blood transcriptomes across 16 types of respiratory virus infections. We applied a comprehensive systems biology approach starting with whole-blood transcriptomes combined with multilevel bioinformatics analyses to characterize the expression, functional pathways, and protein-protein interaction (PPI) networks to identify robust biomarkers and disease comorbidities. Robust gene markers of infection with different viruses were identified, which can accurately classify the normal and infected patients in train and validation cohorts. The biological processes (BP) of different viruses showed great similarity and enriched in infection and immune response pathways. Network-based analyses revealed that a variety of viral infections were associated with nervous system diseases, neoplasms and metabolic diseases, and significantly correlated with brain tissues. In summary, our manually collected transcriptomes and comprehensive analyses reveal key molecular markers and disease comorbidities in the process of viral infection, which could provide a valuable theoretical basis for the prevention of subsequent public health events for respiratory virus infections.

Prevalence of covid-19 among patients with chronic obstructive pulmonary disease and tuberculosis

BACKGROUND

The exhaustive information about non-communicable diseases associated with COVID-19 and severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) are getting easier to find in the literature. However, there is a lack of knowledge regarding tuberculosis (TB) and chronic obstructed pulmonary disease (COPD), with numerous infections in COVID-19 patients.

OBJECTIVES

Priority is placed on determining the patient's prognosis based on the presence or absence of TB and COPD. Additionally, a comparison is made between the risk of death and the likelihood of recovery in terms of time in COVID-19 patients who have either COPD or TB.

METHODOLOGY

At the DHQ Hospital in Muzaffargarh, Punjab, Pakistan, 498 COVID-19 patients with TB and COPD were studied retrospectively. The duration of study started in February 2022 and concluded in August 2022. The Kaplan-Meier curves described time-to-death and time-to-recovery stratified by TB and COPD status. The Wilcoxon test compared the survival rates of people with TB and COPD in two matched paired groups and their status differences with their standard of living.

RESULTS

The risk of death in COVID-19 patients with TB was 1.476 times higher than in those without (95% CI: 0.949-2.295). The recovery risk in COVID-19 patients with TB was 0.677 times lower than in those without (95% CI: 0.436-1.054). Similarly, patients with TB had a significantly shorter time to death (p=.001) and longer time to recovery (p=.001).

CONCLUSIONS

According to the findings, the most significant contributor to an increased risk of morbidity and mortality in TB and COPD patients was the COVID-19.KEY MESSAGESSARS-Cov-19 is a new challenge for the universe in terms of prevention and treatment for people with tuberculosis and chronic obstructive pulmonary disease, among other diseases.Propensity score matching to control for potential biases.Compared to hospitalized patients with and without (TB and COPD) had an equivalently higher mortality rate.

Over-representation analysis of angiogenic factors in immunosuppressive mechanisms in neoplasms and neurological conditions during COVID-19

BACKGROUND

Recent studies emphasized the necessity to identify key (human) biological processes and pathways targeted by the Coronaviridae family of viruses, especially Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Coronavirus Disease (COVID-19) caused up to 33-55 % death rates in COVID-19 patients with malignant neoplasms and Alzheimer's disease. Given this scenario, we identified biological processes and pathways involved in various diseases which are most likely affected by COVID-19.

METHODS

The COVID-19 DisGeNET data set (v4.0) contains the associations between various diseases and human genes known to interact with viruses from Coronaviridae family and were obtained from the IntAct Coronavirus data set annotated with DisGeNET data. We constructed the disease-gene network to identify genes that are involved in various comorbid diseased states. Communities from the disease-gene network were identified using Louvain method and functional enrichment through over-representation analysis methodology was used to discover significant biological processes and pathways shared between COVID-19 and other diseases.

RESULT

The COVID-19 DisGeNET data set (v4.0) comprised of 828 human genes and 10,473 diseases (including various phenotypes) that together constituted nodes in the disease-gene network. Each of the 70,210 edges connects a human gene with an associated disease. The top 10 genes linked to most number of diseases were VEGFA, BCL2, CTNNB1, ALB, COX2, AGT, HLA-A, HMOX1, FGF2 and COMT. The most vulnerable group of patients thus discovered had comorbid conditions such as carcinomas, malignant neoplasms and Alzheimer's disease. Finally, we identified 15 potentially useful biological processes and pathways for improved therapies. Vascular endothelial growth factor (VEGF) is the key mediator of angiogenesis in cancer. It is widely distributed in the brain and plays a crucial role in brain inflammation regulating the level of angiopoietins. With a degree of 1899, VEGFA was associated with maximum number of diseases in the disease-gene network. Previous studies have indicated that increased levels of VEGFA in the blood results in dyspnea, Pulmonary Edema (PE), Acute Lung Injury (ALI) and Acute Respiratory Distress Syndrome (ARDS). In case of COVID-19 patients with neoplasms and other neurological symptoms, our results indicate VEGFA as a therapeutic target for inflammation suppression. As VEGFs are known to disproportionately affect cancer patients, improving endothelial permeability and vasodilation with anti-VEGF therapy could lead to suppression of inflammation and also improve oxygenation. As an outcome of our study, we make case for clinical investigations towards anti-VEGF therapies for such comorbid conditions affected by COVID-19 for better therapeutic outcomes.

Bioinformatics and system biology analysis revealed the crosstalk between COVID-19 and osteoarthritis

BACKGROUND

The global coronavirus disease 2019 (COVID-19) outbreak has significantly impacted public health. Moreover, there has been an association between the incidence and severity of osteoarthritis (OA) and the onset of COVID-19. However, the optimal diagnosis and treatment strategies for patients with both diseases remain uncertain. Bioinformatics is a novel approach that may help find the common pathology between COVID-19 and OA.

METHODS

Differentially expressed genes (DEGs) were screened by R package "limma." Functional enrichment analyses were performed to find key biological functions. Protein-protein interaction (PPI) network was constructed by STRING database and then Cytoscape was used to select hub genes. External data sets and OA mouse model validated and identified the hub genes in both mRNA and protein levels. Related transcriptional factors (TF) and microRNAs (miRNAs) were predicted with miRTarBase and JASPR database. Candidate drugs were obtained from Drug Signatures database. The immune infiltration levels of COVID-19 and OA were evaluated by CIBERSORT and scRNA-seq.

RESULTS

A total of 74 common DEGs were identified between COVID-19 and OA. Receiver operating characteristic curves validated the effective diagnostic values (area under curve > 0.7) of four hub genes (matrix metalloproteinases 9, ATF3, CCL4, and RELA) in both the training and validation data sets of COVID-19 and OA. Quantitative polymerase chain reaction and Western Blot showed significantly higher hub gene expression in OA mice than in healthy controls. A total of 84 miRNAs and 28 TFs were identified to regulate the process of hub gene expression. The top 10 potential drugs were screened including "Simvastatin," "Hydrocortisone," and "Troglitazone" which have been proven by Food and Drug Administration. Correlated with hub gene expression, Macrophage M0 was highly expressed while Natural killer cells and Mast cells were low in both COVID-19 and OA.

CONCLUSION

Four hub genes, disease-related miRNAs, TFs, drugs, and immune infiltration help to understand the pathogenesis and perform further studies, providing a potential therapy target for COVID-19 and OA.

Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing

Some recent studies showed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other through the shared genes. Therefore, in this study, an attempt was made to explore common genomic biomarkers for SARS-CoV-2 infections and IPF disease highlighting their functions, pathways, regulators and associated drug molecules. At first, we identified 32 statistically significant common differentially expressed genes (cDEGs) between disease (SARS-CoV-2 and IPF) and control samples of RNA-Seq profiles by using a statistical r-package (edgeR). Then we detected 10 cDEGs (CXCR4, TNFAIP3, VCAM1, NLRP3, TNFAIP6, SELE, MX2, IRF4, UBD and CH25H) out of 32 as the common hub genes (cHubGs) by the protein-protein interaction (PPI) network analysis. The cHubGs regulatory network analysis detected few key TFs-proteins and miRNAs as the transcriptional and post-transcriptional regulators of cHubGs. The cDEGs-set enrichment analysis identified some crucial SARS-CoV-2 and IPF causing common molecular mechanisms including biological processes, molecular functions, cellular components and signaling pathways. Then, we suggested the cHubGs-guided top-ranked 10 candidate drug molecules (Tegobuvir, Nilotinib, Digoxin, Proscillaridin, Simeprevir, Sorafenib, Torin 2, Rapamycin, Vancomycin and Hesperidin) for the treatment against SARS-CoV-2 infections with IFP diseases as comorbidity. Finally, we investigated the resistance performance of our proposed drug molecules compare to the already published molecules, against the state-of-the-art alternatives publicly available top-ranked independent receptors by molecular docking analysis. Molecular docking results suggested that our proposed drug molecules would be more effective compare to the already published drug molecules. Thus, the findings of this study might be played a vital role for diagnosis and therapies of SARS-CoV-2 infections with IPF disease as comorbidity risk.

Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics

Objective

To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment.

Methods

Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein-protein interaction (PPI) network was constructed by STRING online software for further study, and the core genes were determined by Cytoscape software and KEGG pathway enrichment analysis. The clustering heat map was drawn by Excel software to verify its accuracy. In addition, we used the University of Alabama at Birmingham Cancer (UALCAN) website to analyze the expression of core genes in P53 mutation status, confirmed the expression of crucial core genes in lung cancer tissues with Gene Expression Profiling Interactive Analysis (GEPIA) and GEPIA2 online software, and evaluated their prognostic value in lung cancer patients with the Kaplan-Meier online plotter tool.

Results

CHEK1, CCNB1, CCNB2, and CDK1 were selected. The expression levels of these four genes in lung cancer tissues were significantly higher than those in normal tissues. Their increased expression was negatively correlated with lung cancer patients (including LUAD and LUSC) prognosis and survival rate.

Conclusion

CHEK1, CCNB1, CCNB2, and CDK1 are the critical core genes of lung cancer and are highly expressed in lung cancer. They are negatively correlated with the prognosis of lung cancer patients (including LUAD and LUSC) and closely related to the formation and prediction of lung cancer. They are valuable predictors and may be predictive biomarkers of lung cancer.

Recent Advances in Biosensors for Detection of COVID-19 and Other Viruses

This century has introduced very deadly, dangerous, and infectious diseases to humankind such as the influenza virus, Ebola virus, Zika virus, and the most infectious SARS-CoV-2 commonly known as COVID-19 and have caused epidemics and pandemics across the globe. For some of these diseases, proper medications, and vaccinations are missing and the early detection of these viruses will be critical to saving the patients. And even the vaccines are available for COVID-19, the new variants of COVID-19 such as Delta, and Omicron are spreading at large. The available virus detection techniques take a long time, are costly, and complex and some of them generates false negative or false positive that might cost patients their lives. The biosensor technique is one of the best qualified to address this difficult challenge. In this systematic review, we have summarized recent advancements in biosensor-based detection of these pandemic viruses including COVID-19. Biosensors are emerging as efficient and economical analytical diagnostic instruments for early-stage illness detection. They are highly suitable for applications related to healthcare, wearable electronics, safety, environment, military, and agriculture. We strongly believe that these insights will aid in the study and development of a new generation of adaptable virus biosensors for fellow researchers.

Identifying the Effect of COVID-19 Infection in Multiple Myeloma and Diffuse Large B-Cell Lymphoma Patients Using Bioinformatics and System Biology

The severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), also referred to as COVID-19, has spread to several countries and caused a serious threat to human health worldwide. Patients with confirmed COVID-19 infection spread the disease rapidly throughout the region. Multiple myeloma (MM) and diffuse large B-cell lymphoma (DLBCL) are risk factors for COVID-19, although the molecular mechanisms underlying the relationship among MM, DLBCL, and COVID-19 have not been elucidated so far. In this context, transcriptome analysis was performed in the present study to identify the shared pathways and molecular indicators of MM, DLBCL, and COVID-19, which benefited the overall understanding of the effect of COVID-19 in patients with MM and DLBCL. Three datasets (GSE16558, GSE56315, and GSE152418) were downloaded from the Gene Expression Omnibus (GEO) and searched for the shared differentially expressed genes (DEGs) in patients with MM and DLBCL who were infected with SARS-CoV-2. The objective was to detect similar pathways and prospective medicines. A total of 29 DEGs that were common across these three datasets were selected. A protein-protein interaction (PPI) network was constructed using data from the STRING database followed by the identification of hub genes. In addition, the association of MM and DLBCL with COVID-19 infection was analyzed through functional analysis using ontologies terms and pathway analysis. Three relationships were observed in the evaluated datasets: transcription factor-gene interactions, protein-drug interactions, and an integrated regulatory network of DEGs and miRNAs with mutual DEGs. The findings of the present study revealed potential pharmaceuticals that could be beneficial in the treatment of COVID-19.

Effect of SARS-CoV-2 infection on asthma patients

Background

SARS-CoV-2 causes coronavirus disease 2019 (COVID-19), a new coronavirus pneumonia, and containing such an international pandemic catastrophe remains exceedingly difficult. Asthma is a severe chronic inflammatory airway disease that is becoming more common around the world. However, the link between asthma and COVID-19 remains unknown. Through bioinformatics analysis, this study attempted to understand the molecular pathways and discover potential medicines for treating COVID-19 and asthma.

Methods

To investigate the relationship between SARS-CoV-2 and asthma patients, a transcriptome analysis was used to discover shared pathways and molecular signatures in asthma and COVID-19. Here, two RNA-seq data (GSE147507 and GSE74986) from the Gene Expression Omnibus were used to detect differentially expressed genes (DEGs) in asthma and COVID-19 patients to find the shared pathways and the potential drug candidates.

Results

There were 66 DEGs in all that were classified as common DEGs. Using a protein-protein interaction (PPI) network created using various bioinformatics techniques, five hub genes were found. We found that asthma has some shared links with the progression of COVID-19. Additionally, protein-drug interactions with common DEGs were also identified in the datasets.

Conclusion

We investigated possible links between COVID-19 and asthma using bioinformatics databases, which might be useful in treating COVID-19 patients. More studies on populations affected by these diseases are needed to elucidate the molecular mechanism behind their association.

Enrichment analysis on regulatory subspaces: A novel direction for the superior description of cellular responses to SARS-CoV-2

STATEMENT

Enrichment analysis of cell transcriptional responses to SARS-CoV-2 infection from biclustering solutions yields broader coverage and superior enrichment of GO terms and KEGG pathways against alternative state-of-the-art machine learning solutions, thus aiding knowledge extraction.

MOTIVATION AND METHODS

The comprehensive understanding of the impacts of SARS-CoV-2 virus on infected cells is still incomplete. This work aims at comparing the role of state-of-the-art machine learning approaches in the study of cell regulatory processes affected and induced by the SARS-CoV-2 virus using transcriptomic data from both infectable cell lines available in public databases and in vivo samples. In particular, we assess the relevance of clustering, biclustering and predictive modeling methods for functional enrichment. Statistical principles to handle scarcity of observations, high data dimensionality, and complex gene interactions are further discussed. In particular, and without loos of generalization ability, the proposed methods are applied to study the differential regulatory response of lung cell lines to SARS-CoV-2 (α-variant) against RSV, IAV (H1N1), and HPIV3 viruses.

RESULTS

Gathered results show that, although clustering and predictive algorithms aid classic stances to functional enrichment analysis, more recent pattern-based biclustering algorithms significantly improve the number and quality of enriched GO terms and KEGG pathways with controlled false positive risks. Additionally, a comparative analysis of these results is performed to identify potential pathophysiological characteristics of COVID-19. These are further compared to those identified by other authors for the same virus as well as related ones such as SARS-CoV-1. The findings are particularly relevant given the lack of other works utilizing more complex machine learning algorithms within this context.

Uncovering the genetic links of SARS-CoV-2 infections on heart failure co-morbidity by a systems biology approach

Aims

The co‐morbidities contribute to the inferior prognosis of COVID‐19 patients. Recent reports suggested that the higher co‐morbidity rate between COVID‐19 and heart failure (HF) leads to increased mortality. However, the common pathogenic mechanism between them remained elusive. Here, we aimed to reveal underlying molecule mechanisms and genetic correlation between COVID‐19 and HF, providing a new perspective on current clinical management for patients with co‐morbidity.

Methods

The gene expression profiles of HF (GSE26887) and COVID‐19 (GSE147507) were retrieved from the GEO database. After identifying the common differentially expressed genes (|log2FC| > 1 and adjusted P < 0.05), integrated analyses were performed, namely, enrichment analyses, protein–protein interaction network, module construction, critical gene identification, and functional co‐expression analysis. The performance of critical genes was validation combining hierarchical clustering, correlation, and principal component analysis in external datasets (GSE164805 and GSE9128). Potential transcription factors and miRNAs were obtained from the JASPER and RegNetwork repository used to construct co‐regulatory networks. The candidate drug compounds in potential genetic link targets were further identified using the DSigDB database.

Results

The alteration of 12 genes was identified as a shared transcriptional signature, with the role of immune inflammatory pathway, especially Toll‐like receptor, NF‐kappa B, chemokine, and interleukin‐related pathways that primarily emphasized in response to SARS‐CoV‐2 complicated with HF. Top 10 critical genes (TLR4, TLR2, CXCL8, IL10, STAT3, IL1B, TLR1, TP53, CCL20, and CXCL10) were identified from protein–protein interaction with topological algorithms. The unhealthy microbiota status and gut–heart axis in co‐morbidity were identified as potential disease roads in bridging pathogenic mechanism, and lipopolysaccharide acts as a potential marker for monitoring HF during COVID‐19. For transcriptional and post‐transcriptional levels, regulation networks tightly coupling with both disorders were constructed, and significant regulator signatures with high interaction degree, especially FOXC1, STAT3, NF‐κB1, miR‐181, and miR‐520, were detected to regulate common differentially expressed genes. According to genetic links targets, glutathione‐based antioxidant strategy combined with muramyl dipeptide‐based microbe‐derived immunostimulatory therapies was identified as promising anti‐COVID‐19 and anti‐HF therapeutics.

Conclusions

This study identified shared transcriptomic and corresponding regulatory signatures as emerging therapeutic targets and detected a set of pharmacologic agents targeting genetic links. Our findings provided new insights for underlying pathogenic mechanisms between COVID‐19 and HF.

Discovering Common Pathophysiological Processes between COVID-19 and Cystic Fibrosis by Differential Gene Expression Pattern Analysis

Coronaviruses are a family of viruses that infect mammals and birds. Coronaviruses cause infections of the respiratory system in humans, which can be minor or fatal. A comparative transcriptomic analysis has been performed to establish essential profiles of the gene expression of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) linked to cystic fibrosis (CF). Transcriptomic studies have been carried out in relation to SARS-CoV-2 since a number of people have been diagnosed with CF. The recognition of differentially expressed genes demonstrated 8 concordant genes shared between the SARS-CoV-2 and CF. Extensive gene ontology analysis and the discovery of pathway enrichment demonstrated SARS-CoV-2 response to CF. The gene ontological terms and pathway enrichment mechanisms derived from this research may affect the production of successful drugs, especially for the people with the following disorder. Identification of TF-miRNA association network reveals the interconnection between TF genes and miRNAs, which may be effective to reveal the other influenced disease that occurs for SARS-CoV-2 to CF. The enrichment of pathways reveals SARS-CoV-2-associated CF mostly engaged with the type of innate immune system, Toll-like receptor signaling pathway, pantothenate and CoA biosynthesis, allograft rejection, graft-versus-host disease, intestinal immune network for IgA production, mineral absorption, autoimmune thyroid disease, legionellosis, viral myocarditis, inflammatory bowel disease (IBD), etc. The drug compound identification demonstrates that the drug targets of IMIQUIMOD and raloxifene are the most significant with the significant hub DEGs.

Causal Association and Shared Genetics Between Asthma and COVID-19

Objectives

Recent studies suggest that asthma may have a protective effect on COVID-19.We aimed to investigate the causality between asthma and two COVID-19 outcomes and explore the mechanisms underlining this connection.

Methods

Summary results of GWAS were used for the analyses, including asthma (88,486 cases and 447,859 controls), COVID-19 hospitalization (6,406 hospitalized COVID-19 cases and 902,088 controls), and COVID-19 infection (14,134 COVID-19 cases and 1,284,876 controls). The Mendelian randomization (MR) analysis was performed to evaluate the causal effects of asthma on the two COVID-19 outcomes. A cross-trait meta-analysis was conducted to analyze genetic variants within two loci shared by COVID-19 hospitalization and asthma.

Results

Asthma is associated with decreased risk both for COVID-19 hospitalization (odds ratio (OR): 0.70, 95% confidence interval (CI): 0.70-0.99) and for COVID-19 infection (OR: 0.83, 95%CI: 0.51-0.95). Asthma and COVID-19 share two genome-wide significant genes, including ABO at the 9q34.2 region and OAS2 at the 12q24.13 region. The meta-analysis revealed that ABO and ATXN2 contain variants with pleiotropic effects on both COVID-19 and asthma.

Conclusion

In conclusion, our results suggest that genetic liability to asthma is associated with decreased susceptibility to SARS-CoV-2 and to severe COVID-19 disease, which may be due to the protective effects of ongoing inflammation and, possibly, related compensatory responses against COVID-19 in its early stage.

Identification of Crucial Genes and Key Functions in Type 2 Diabetic Hearts by Bioinformatic Analysis

Type 2 diabetes (T2D) patients with SARS-CoV-2 infection hospitalized develop an acute cardiovascular syndrome. It is urgent to elucidate underlying mechanisms associated with the acute cardiac injury in T2D hearts. We performed bioinformatic analysis on the expression profiles of public datasets to identify the pathogenic and prognostic genes in T2D hearts. Cardiac RNA-sequencing datasets from db/db or BKS mice (GSE161931) were updated to NCBI-Gene Expression Omnibus (NCBI-GEO), and used for the transcriptomics analyses with public datasets from NCBI-GEO of autopsy heart specimens with COVID-19 (5/6 with T2D, GSE150316), or dead healthy persons (GSE133054). Differentially expressed genes (DEGs) and overlapping homologous DEGs among the three datasets were identified using DESeq2. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses were conducted for event enrichment through clusterProfile. The protein-protein interaction (PPI) network of DEGs was established and visualized by Cytoscape. The transcriptions and functions of crucial genes were further validated in db/db hearts. In total, 542 up-regulated and 485 down-regulated DEGs in mice, and 811 up-regulated and 1399 down-regulated DEGs in human were identified, respectively. There were 74 overlapping homologous DEGs among all datasets. Mitochondria inner membrane and serine-type endopeptidase activity were further identified as the top-10 GO events for overlapping DEGs. Cardiac CAPNS1 (calpain small subunit 1) was the unique crucial gene shared by both enriched events. Its transcriptional level significantly increased in T2D mice, but surprisingly decreased in T2D patients with SARS-CoV-2 infection. PPI network was constructed with 30 interactions in overlapping DEGs, including CAPNS1. The substrates Junctophilin2 (Jp2), Tnni3, and Mybpc3 in cardiac calpain/CAPNS1 pathway showed less transcriptional change, although Capns1 increased in transcription in db/db mice. Instead, cytoplasmic JP2 significantly reduced and its hydrolyzed product JP2NT exhibited nuclear translocation in myocardium. This study suggests CAPNS1 is a crucial gene in T2D hearts. Its transcriptional upregulation leads to calpain/CAPNS1-associated JP2 hydrolysis and JP2NT nuclear translocation. Therefore, attenuated cardiac CAPNS1 transcription in T2D patients with SARS-CoV-2 infection highlights a novel target in adverse prognostics and comprehensive therapy. CAPNS1 can also be explored for the molecular signaling involving the onset, progression and prognostic in T2D patients with SARS-CoV-2 infection.

Integrated bioinformatic analysis reveals immune molecular markers and potential drugs for diabetic cardiomyopathy

Diabetic cardiomyopathy (DCM) is a pathophysiological condition induced by diabetes mellitus that often causes heart failure (HF). However, their mechanistic relationships remain unclear. This study aimed to identify immune gene signatures and molecular mechanisms of DCM. Microarray data from the Gene Expression Omnibus (GEO) database from patients with DCM were subjected to weighted gene co-expression network analysis (WGCNA) identify co-expression modules. Core expression modules were intersected with the immune gene database. We analyzed and mapped protein-protein interaction (PPI) networks using the STRING database and MCODE and filtering out 17 hub genes using cytoHubba software. Finally, potential transcriptional regulatory factors and therapeutic drugs were identified and molecular docking between gene targets and small molecules was performed. We identified five potential immune biomarkers: proteosome subunit beta type-8 (PSMB8), nuclear factor kappa B1 (NFKB1), albumin (ALB), endothelin 1 (EDN1), and estrogen receptor 1 (ESR1). Their expression levels in animal models were consistent with the changes observed in the datasets. EDN1 showed significant differences in expression in both the dataset and the validation model by real-time quantitative PCR (qPCR) and Western blotting(WB). Subsequently, we confirmed that the potential transcription factors upstream of EDN1 were PRDM5 and KLF4, as its expression was positively correlated with the expression of the two transcription factors. To repurpose known therapeutic drugs, a connectivity map (CMap) database was retrieved, and nine candidate compounds were identified. Finally, molecular docking simulations of the proteins encoded by the five genes with small-molecule drugs were performed. Our data suggest that EDN1 may play a key role in the development of DCM and is a potential DCM biomarker.

Pannexin-1 channel opening is critical for COVID-19 pathogenesis

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rapidly rampaged worldwide, causing a pandemic of coronavirus disease (COVID -19), but the biology of SARS-CoV-2 remains under investigation. We demonstrate that both SARS-CoV-2 spike protein and human coronavirus 229E (hCoV-229E) or its purified S protein, one of the main viruses responsible for the common cold, induce the transient opening of Pannexin-1 (Panx-1) channels in human lung epithelial cells. However, the Panx-1 channel opening induced by SARS-CoV-2 is greater and more prolonged than hCoV-229E/S protein, resulting in an enhanced ATP, PGE₂, and IL-1β release. Analysis of lung lavages and tissues indicate that Panx-1 mRNA expression is associated with increased ATP, PGE₂, and IL-1β levels. Panx-1 channel opening induced by SARS-CoV-2 spike protein is angiotensin-converting enzyme 2 (ACE-2), endocytosis, and furin dependent. Overall, we demonstrated that Panx-1 channel is a critical contributor to SARS-CoV-2 infection and should be considered as an alternative therapy.

Effects of Bacille Calmette Guerin (BCG) vaccination during COVID-19 infection

The coronavirus disease 2019 (COVID-19) is caused by the infection of highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as the novel coronavirus. In most countries, the containment of this virus spread is not controlled, which is driving the pandemic towards a more difficult phase. In this study, we investigated the impact of the Bacille Calmette Guerin (BCG) vaccination on the severity and mortality of COVID-19 by performing transcriptomic analyses of SARS-CoV-2 infected and BCG vaccinated samples in peripheral blood mononuclear cells (PBMC). A set of common differentially expressed genes (DEGs) were identified and seeded into their functional enrichment analyses via Gene Ontology (GO)-based functional terms and pre-annotated molecular pathways databases, and their Protein-Protein Interaction (PPI) network analysis. We further analysed the regulatory elements, possible comorbidities and putative drug candidates for COVID-19 patients who have not been BCG-vaccinated. Differential expression analyses of both BCG-vaccinated and COVID-19 infected samples identified 62 shared DEGs indicating their discordant expression pattern in their respected conditions compared to control. Next, PPI analysis of those DEGs revealed 10 hub genes, namely ITGB2, CXCL8, CXCL1, CCR2, IFNG, CCL4, PTGS2, ADORA3, TLR5 and CD33. Functional enrichment analyses found significantly enriched pathways/GO terms including cytokine activities, lysosome, IL-17 signalling pathway, TNF-signalling pathways. Moreover, a set of identified TFs, miRNAs and potential drug molecules were further investigated to assess their biological involvements in COVID-19 and their therapeutic possibilities. Findings showed significant genetic interactions between BCG vaccination and SARS-CoV-2 infection, suggesting an interesting prospect of the BCG vaccine in relation to the COVID-19 pandemic. We hope it may potentially trigger further research on this critical phenomenon to combat COVID-19 spread.

Covid-19 and development of heart failure: mystery and truth

Coronavirus disease 2019 (Covid-19) is a novel worldwide pandemic caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). During Covid-19 pandemic, socioeconomic deprivation, social isolation, and reduced physical activities may induce heart failure (HF), destabilization, and cause more complications. HF appears as a potential hazard due to SARS-CoV-2 infection, chiefly in elderly patients with underlying comorbidities. In reality, the expression of cardiac ACE2 is implicated as a target point for SARS-CoV-2-induced acute cardiac injury. In SARS-CoV-2 infection, like other febrile illnesses, high blood viscosity, exaggerated pro-inflammatory response, multisystem inflammatory syndrome, and endothelial dysfunction-induced coagulation disorders may increase risk of HF development. Hypoxic respiratory failure, as in pulmonary edema, severe acute lung injury (ALI), and acute respiratory distress syndrome (ARDS) may affect heart hemodynamic stability due to the development of pulmonary hypertension. Indeed, Covid-19-induced HF could be through the development of cytokine storm, characterized by high proliferation pro-inflammatory cytokines. In cytokine storm-mediated cardiac dysfunction, there is a positive correlation between levels of pro-inflammatory cytokine and myocarditis-induced acute cardiac injury biomarkers. Therefore, Covid-19-induced HF is more complex and related from a molecular background in releasing pro-inflammatory cytokines to the neuro-metabolic derangements that together affect cardiomyocyte functions and development of HF. Anti-heart failure medications, mainly digoxin and carvedilol, have potent anti-SARS-CoV-2 and anti-inflammatory properties that may mitigate Covid-19 severity and development of HF. In conclusion, SARS-CoV-2 infection may lead to the development of HF due to direct acute cardiac injury or through the development of cytokine storms, which depress cardiomyocyte function and cardiac contractility. Anti-heart failure drugs, mainly digoxin and carvedilol, may attenuate severity of HF by reducing the infectivity of SARS-CoV-2 and prevent the development of cytokine storms in severely affected Covid-19 patients.

SARS-CoV-2 host cell entry: an in silico investigation of potential inhibitory roles of terpenoids

BACKGROUND

Targeting viral cell entry proteins is an emerging therapeutic strategy for inhibiting the first stage of SARS-CoV-2 infection. In this study, 106 bioactive terpenoids from African medicinal plants were screened through molecular docking analysis against human angiotensin-converting enzyme 2 (hACE2), human transmembrane protease serine 2 (TMPRSS2), and the spike (S) proteins of SARS-CoV-2, SARS-CoV, and MERS-CoV. In silico absorption-distribution-metabolism-excretion-toxicity (ADMET) and drug-likeness prediction, molecular dynamics (MD) simulation, binding free energy calculations, and clustering analysis of MD simulation trajectories were performed on the top docked terpenoids to respective protein targets.

RESULTS

The results revealed eight terpenoids with high binding tendencies to the catalytic residues of different targets. Two pentacyclic terpenoids (24-methylene cycloartenol and isoiguesteri) interacted with the hACE2 binding hotspots for the SARS-CoV-2 spike protein, while the abietane diterpenes were found accommodated within the S1-specificity pocket, interacting strongly with the active site residues TMPRSS2. 3-benzoylhosloppone and cucurbitacin interacted with the RBD and S2 subunit of SARS-CoV-2 spike protein respectively. These interactions were preserved in a simulated dynamic environment, thereby, demonstrating high structural stability. The MM-GBSA binding free energy calculations corroborated the docking interactions. The top docked terpenoids showed favorable drug-likeness and ADMET properties over a wide range of molecular descriptors.

CONCLUSION

The identified terpenoids from this study provides core structure that can be exploited for further lead optimization to design drugs against SARS-CoV-2 cell-mediated entry proteins. They are therefore recommended for further in vitro and in vivo studies towards developing entry inhibitors against the ongoing COVID-19 pandemic.

Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes

The ongoing COVID-19 outbreak, caused by SARS-CoV-2, has posed a massive threat to global public health, especially to people with underlying health conditions. Type 2 diabetes (T2D) is lethal comorbidity of COVID-19. However, its pathogenetic link remains unclear. This research aims to determine the genetic factors and processes contributing to the synergistic severity of SARS-CoV-2 infection among T2D patients through bioinformatics approaches. We analyzed two sets of transcriptomic data of SARS-CoV-2 infection obtained from lung epithelium cells and PBMCs, and two sets of T2D data from pancreatic islet cells and PBMCs to identify the associated differentially expressed genes (DEGs) followed by their functional enrichment analyses in terms of protein-protein interaction (PPI) to detect hub-proteins and associated comorbidities, transcription factors (TFs), microRNAs (miRNAs) as well as the potential drug candidates. In PPI analysis, four potential hub-proteins (i.e., BIRC3, C3, MME, and IL1B) were identified among 25 DEGs shared between the disease pair. Enrichment analyses using the mutually overlapped DEGs revealed the most prevalent GO and cell signalling pathways, including TNF signalling, cytokine-cytokine receptor interaction, and IL-17 signalling, which are related to cytokine activities. Furthermore, as significant TFs, we identified IRF1, KLF11, FOSL1, and CREB3L1 while miRNAs including miR-1-3p, 34a-5p, 16–5p, 155–5p, 20a-5p, and let-7b-5p were found to be noteworthy. The findings illustrated the significant association between COVID-19 and T2D at the molecular level. These genetic determinants can further be explored for their specific roles in disease progression and therapeutic intervention, while significant pathways can also be studied as molecular checkpoints. Finally, the identified drug candidates may be evaluated for their potency to minimize the severity of COVID-19 patients with pre-existing T2D.

Molecular Characterization and Elucidation of Pathways to Identify Novel Therapeutic Targets in Pulmonary Arterial Hypertension

Background: Pulmonary arterial hypertension (PAH) is a life-threatening chronic cardiopulmonary disease. However, there are limited studies reflecting the available biomarkers from separate gene expression profiles in PAH. This study explored two microarray datasets by an integrative analysis to estimate the molecular signatures in PAH. Methods: Two microarray datasets (GSE53408 and GSE113439) were exploited to compare lung tissue transcriptomes of patients and controls with PAH and to estimate differentially expressed genes (DEGs). According to common DEGs of datasets, gene and protein overrepresentation analyses, protein-protein interactions (PPIs), DEG-transcription factor (TF) interactions, DEG-microRNA (miRNA) interactions, drug-target protein interactions, and protein subcellular localizations were conducted in this study. Results: We obtained 38 common DEGs for these two datasets. Integration of the genome transcriptome datasets with biomolecular interactions revealed hub genes (HSP90AA1, ANGPT2, HSPD1, HSPH1, TTN, SPP1, SMC4, EEA1, and DKC1), TFs (FOXC1, FOXL1, GATA2, YY1, and SRF), and miRNAs (hsa-mir-17-5p, hsa-mir-26b-5p, hsa-mir-122-5p, hsa-mir-20a-5p, and hsa-mir-106b-5p). Protein-drug interactions indicated that two compounds, namely, nedocromil and SNX-5422, affect the identification of PAH candidate biomolecules. Moreover, the molecular signatures were mostly localized in the extracellular and nuclear areas. Conclusions: In conclusion, several lung tissue-derived molecular signatures, highlighted in this study, might serve as novel evidence for elucidating the essential mechanisms of PAH. The potential drugs associated with these molecules could thus contribute to the development of diagnostic and therapeutic strategies to ameliorate PAH.

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein–protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors–genes interaction, protein–drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.

Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development

Background

Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction.

Objective

Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes.

Methods

We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods.

Results

Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19–positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores >90% for disease severity prediction.

Conclusions

We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.

COVID-19 patient transcriptomic and genomic profiling reveals comorbidity interactions with psychiatric disorders

Psychiatric symptoms are seen in some COVID-19 patients, as direct or indirect sequelae, but it is unclear whether SARS-CoV-2 infection interacts with underlying neuronal or psychiatric susceptibilities. Such interactions might arise from COVID-19 immune responses, from infection of neurons themselves or may reflect social-psychological causes. To clarify this we sought the key gene expression pathways altered in COVID-19 also affected in bipolar disorder, post-traumatic stress disorder (PTSD) and schizophrenia, since this may identify pathways of interaction that could be treatment targets. We performed large scale comparisons of whole transcriptome data and immune factor transcript data in peripheral blood mononuclear cells (PBMC) from COVID-19 patients and patients with psychiatric disorders. We also analysed genome-wide association study (GWAS) data for symptomatic COVID-19 patients, comparing GWAS and whole-genome sequence data from patients with bipolar disorder, PTSD and schizophrenia patients. These studies revealed altered signalling and ontology pathways shared by COVID-19 patients and the three psychiatric disorders. Finally, co-expression and network analyses identified gene clusters common to the conditions. COVID-19 patients had peripheral blood immune system profiles that overlapped with those of patients with psychiatric conditions. From the pathways identified, PTSD profiles were the most highly correlated with COVID-19, perhaps consistent with stress-immune system interactions seen in PTSD. We also revealed common inflammatory pathways that may exacerbate psychiatric disorders, which may support the usage of anti-inflammatory medications in these patients. It also highlights the potential clinical application of multi-level dataset studies in difficult-to-treat psychiatric disorders in this COVID-19 pandemic.