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1.
Front Immunol ; 13: 997851, 2022.
Article in English | MEDLINE | ID: covidwho-2115356

ABSTRACT

The immune system is highly networked and complex, which is continuously changing as encountering old and new pathogens. However, reductionism-based researches do not give a systematic understanding of the molecular mechanism of the immune response and viral pathogenesis. Here, we present HUMPPI-2022, a high-quality human protein-protein interaction (PPI) network, containing > 11,000 protein-coding genes with > 78,000 interactions. The network topology and functional characteristics analyses of the immune-related genes (IRGs) reveal that IRGs are mostly located in the center of the network and link genes of diverse biological processes, which may reflect the gene pleiotropy phenomenon. Moreover, the virus-human interactions reveal that pan-viral targets are mostly hubs, located in the center of the network and enriched in fundamental biological processes, but not for coronavirus. Finally, gene age effect was analyzed from the view of the host network for IRGs and virally-targeted genes (VTGs) during evolution, with IRGs gradually became hubs and integrated into host network through bridging functionally differentiated modules. Briefly, HUMPPI-2022 serves as a valuable resource for gaining a better understanding of the composition and evolution of human immune system, as well as the pathogenesis of viruses.


Subject(s)
Viruses , Humans , Viruses/genetics , Protein Interaction Maps , Immune System
2.
Brief Bioinform ; 23(6)2022 Nov 19.
Article in English | MEDLINE | ID: covidwho-2097311

ABSTRACT

The ongoing coronavirus disease 2019 (COVID-19) pandemic has highlighted the need to better understand virus-host interactions. We developed a network-based method that expands the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-host protein interaction network and identifies host targets that modulate viral infection. To disrupt the SARS-CoV-2 interactome, we systematically probed for potent compounds that selectively target the identified host proteins with high expression in cells relevant to COVID-19. We experimentally tested seven chemical inhibitors of the identified host proteins for modulation of SARS-CoV-2 infection in human cells that express ACE2 and TMPRSS2. Inhibition of the epigenetic regulators bromodomain-containing protein 4 (BRD4) and histone deacetylase 2 (HDAC2), along with ubiquitin-specific peptidase (USP10), enhanced SARS-CoV-2 infection. Such proviral effect was observed upon treatment with compounds JQ1, vorinostat, romidepsin and spautin-1, when measured by cytopathic effect and validated by viral RNA assays, suggesting that the host proteins HDAC2, BRD4 and USP10 have antiviral functions. We observed marked differences in antiviral effects across cell lines, which may have consequences for identification of selective modulators of viral infection or potential antiviral therapeutics. While network-based approaches enable systematic identification of host targets and selective compounds that may modulate the SARS-CoV-2 interactome, further developments are warranted to increase their accuracy and cell-context specificity.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , COVID-19/drug therapy , Protein Interaction Maps , Nuclear Proteins , Transcription Factors , Antiviral Agents/pharmacology , Ubiquitin Thiolesterase , Cell Cycle Proteins
3.
PLoS Pathog ; 18(7): e1010660, 2022 07.
Article in English | MEDLINE | ID: covidwho-1993526

ABSTRACT

Coxiella burnetii is the etiological agent of the zoonotic disease Q fever, which is featured by its ability to replicate in acid vacuoles resembling the lysosomal network. One key virulence determinant of C. burnetii is the Dot/Icm system that transfers more than 150 effector proteins into host cells. These effectors function to construct the lysosome-like compartment permissive for bacterial replication, but the functions of most of these effectors remain elusive. In this study, we used an affinity tag purification mass spectrometry (AP-MS) approach to generate a C. burnetii-human protein-protein interaction (PPI) map involving 53 C. burnetii effectors and 3480 host proteins. This PPI map revealed that the C. burnetii effector CBU0425 (designated CirB) interacts with most subunits of the 20S core proteasome. We found that ectopically expressed CirB inhibits hydrolytic activity of the proteasome. In addition, overexpression of CirB in C. burnetii caused dramatic inhibition of proteasome activity in host cells, while knocking down CirB expression alleviated such inhibitory effects. Moreover, we showed that a region of CirB that spans residues 91-120 binds to the proteasome subunit PSMB5 (beta 5). Finally, PSMB5 knockdown promotes C. burnetii virulence, highlighting the importance of proteasome activity modulation during the course of C. burnetii infection.


Subject(s)
Coxiella burnetii , Q Fever , Bacterial Proteins/genetics , Bacterial Proteins/metabolism , Host-Pathogen Interactions , Humans , Proteasome Endopeptidase Complex/genetics , Proteasome Endopeptidase Complex/metabolism , Protein Interaction Maps , Q Fever/metabolism , Vacuoles/metabolism
4.
BMC Infect Dis ; 21(1): 852, 2021 Aug 21.
Article in English | MEDLINE | ID: covidwho-1363547

ABSTRACT

BACKGROUND AND AIMS: Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is one of the most common acute thoracopathy with complicated pathogenesis in ICU. The study is to explore the differentially expressed genes (DEGs) in the lung tissue and underlying altering mechanisms in ARDS. METHODS: Gene expression profiles of GSE2411 and GSE130936 were available from GEO database, both of them included in GPL339. Then, an integrated analysis of these genes was performed, including gene ontology (GO) and KEGG pathway enrichment analysis in DAVID database, protein-protein interaction (PPI) network construction evaluated by the online database STRING, Transcription Factors (TFs) forecasting based on the Cytoscape plugin iRegulon, and their expression in varied organs in The Human Protein Atlas. RESULTS: A total of 39 differential expressed genes were screened from the two datasets, including 39 up-regulated genes and 0 down-regulated genes. The up-regulated genes were mainly enriched in the biological process, such as immune system process, innate immune response, inflammatory response, and also involved in some signal pathways, including cytokine-cytokine receptor interaction, Salmonella infection, Legionellosis, Chemokine, and Toll-like receptor signal pathway with an integrated analysis. GBP2, IFIT2 and IFIT3 were identified as hub genes in the lung by PPI network analysis with MCODE plug-in, as well as GO and KEGG re-enrichment. All of the three hub genes were regulated by the predictive common TFs, including STAT1, E2F1, IRF1, IRF2, and IRF9. CONCLUSIONS: This study implied that hub gene GBP2, IFIT2 and IFIT3, which might be regulated by STAT1, E2F1, IRF1, IRF2, or IRF9, played significant roles in ARDS. They could be potential diagnostic or therapeutic targets for ARDS patients.


Subject(s)
Lipopolysaccharides , Respiratory Distress Syndrome , Computational Biology , Gene Expression Profiling , Humans , Protein Interaction Maps , Respiratory Distress Syndrome/genetics
5.
Sci Rep ; 12(1): 5867, 2022 04 07.
Article in English | MEDLINE | ID: covidwho-1921658

ABSTRACT

SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 298 million individuals and caused over 5 million deaths globally. The identification of essential proteins in a protein-protein interaction network (PPIN) is not only crucial in understanding the process of cellular life but also useful in drug discovery. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common human proteins. Analyzing influential proteins and comparing these networks together can be an effective step in helping biologists for drug-target prediction. We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. We applied principal component analysis and unsupervised machine learning methods to reveal the most informative measures. Appealingly, some measures had a high level of contribution in comparison to others in both PPINs, namely Decay, Residual closeness, Markov, Degree, closeness (Latora), Barycenter, Closeness (Freeman), and Lin centralities. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Both PPINs tended to properties of scale-free networks that expose their nature of heterogeneity. Dimensionality reduction and unsupervised learning methods were so effective to uncover appropriate centrality measures.


Subject(s)
COVID-19 , Influenza A Virus, H1N1 Subtype , Influenza, Human , Humans , Influenza A Virus, H1N1 Subtype/metabolism , Protein Interaction Maps , Proteins/metabolism , SARS-CoV-2
6.
PLoS One ; 17(6): e0269386, 2022.
Article in English | MEDLINE | ID: covidwho-1910661

ABSTRACT

BACKGROUND: There is growing evidence of a strong relationship between COVID-19 and myocarditis. However, there are few bioinformatics-based analyses of critical genes and the mechanisms related to COVID-19 Myocarditis. This study aimed to identify critical genes related to COVID-19 Myocarditis by bioinformatic methods, explore the biological mechanisms and gene regulatory networks, and probe related drugs. METHODS: The gene expression data of GSE150392 and GSE167028 were obtained from the Gene Expression Omnibus (GEO), including cardiomyocytes derived from human induced pluripotent stem cells infected with SARS-CoV-2 in vitro and GSE150392 from patients with myocarditis infected with SARS-CoV-2 and the GSE167028 gene expression dataset. Differentially expressed genes (DEGs) (adjusted P-Value <0.01 and |Log2 Fold Change| ≥2) in GSE150392 were assessed by NetworkAnalyst 3.0. Meanwhile, significant modular genes in GSE167028 were identified by weighted gene correlation network analysis (WGCNA) and overlapped with DEGs to obtain common genes. Functional enrichment analyses were performed by using the "clusterProfiler" package in the R software, and protein-protein interaction (PPI) networks were constructed on the STRING website (https://cn.string-db.org/). Critical genes were identified by the CytoHubba plugin of Cytoscape by 5 algorithms. Transcription factor-gene (TF-gene) and Transcription factor-microRibonucleic acid (TF-miRNA) coregulatory networks construction were performed by NetworkAnalyst 3.0 and displayed in Cytoscape. Finally, Drug Signatures Database (DSigDB) was used to probe drugs associated with COVID-19 Myocarditis. RESULTS: Totally 850 DEGs (including 449 up-regulated and 401 down-regulated genes) and 159 significant genes in turquoise modules were identified from GSE150392 and GSE167028, respectively. Functional enrichment analysis indicated that common genes were mainly enriched in biological processes such as cell cycle and ubiquitin-protein hydrolysis. 6 genes (CDK1, KIF20A, PBK, KIF2C, CDC20, UBE2C) were identified as critical genes. TF-gene interactions and TF-miRNA coregulatory network were constructed successfully. A total of 10 drugs, (such as Etoposide, Methotrexate, Troglitazone, etc) were considered as target drugs for COVID-19 Myocarditis. CONCLUSIONS: Through bioinformatics method analysis, this study provides a new perspective to explore the pathogenesis, gene regulatory networks and provide drug compounds as a reference for COVID-19 Myocarditis. It is worth highlighting that critical genes (CDK1, KIF20A, PBK, KIF2C, CDC20, UBE2C) may be potential biomarkers and treatment targets of COVID-19 Myocarditis for future study.


Subject(s)
COVID-19 , Induced Pluripotent Stem Cells , MicroRNAs , Myocarditis , COVID-19/genetics , Computational Biology/methods , Gene Expression Profiling/methods , Gene Expression Regulation, Neoplastic , Gene Regulatory Networks , Humans , Induced Pluripotent Stem Cells/metabolism , MicroRNAs/genetics , Myocarditis/genetics , Protein Interaction Maps/genetics , SARS-CoV-2/genetics , Transcription Factors/metabolism
7.
Front Immunol ; 13: 882651, 2022.
Article in English | MEDLINE | ID: covidwho-1903017

ABSTRACT

Purpose: The purpose of this article was to investigate the mechanism of immune dysregulation of COVID-19-related proteins in spinal tuberculosis (STB). Methods: Clinical data were collected to construct a nomogram model. C-index, calibration curve, ROC curve, and DCA curve were used to assess the predictive ability and accuracy of the model. Additionally, 10 intervertebral disc samples were collected for protein identification. Bioinformatics was used to analyze differentially expressed proteins (DEPs), including immune cells analysis, Gene Ontology (GO) and KEGG pathway enrichment analysis, and protein-protein interaction networks (PPI). Results: The nomogram predicted risk of STB ranging from 0.01 to 0.994. The C-index and AUC in the training set were 0.872 and 0.862, respectively. The results in the external validation set were consistent with the training set. Immune cells scores indicated that B cells naive in STB tissues were significantly lower than non-TB spinal tissues. Hub proteins were calculated by Degree, Closeness, and MCC methods. The main KEGG pathway included Coronavirus disease-COVID-19. There were 9 key proteins in the intersection of COVID-19-related proteins and hub proteins. There was a negative correlation between B cells naive and RPL19. COVID-19-related proteins were associated with immune genes. Conclusion: Lymphocytes were predictive factors for the diagnosis of STB. Immune cells showed low expression in STB. Nine COVID-19-related proteins were involved in STB mechanisms. These nine key proteins may suppress the immune mechanism of STB by regulating the expression of immune genes.


Subject(s)
COVID-19 , Tuberculosis, Spinal , Computational Biology/methods , Gene Ontology , Humans , Protein Interaction Maps/genetics
8.
Biomolecules ; 12(5)2022 05 11.
Article in English | MEDLINE | ID: covidwho-1855502

ABSTRACT

Coronavirus disease 2019 (COVID-19) is still an active global public health issue. Although vaccines and therapeutic options are available, some patients experience severe conditions and need critical care support. Hence, identifying key genes or proteins involved in immune-related severe COVID-19 is necessary to find or develop the targeted therapies. This study proposed a novel construction of an immune-related protein interaction network (IPIN) in severe cases with the use of a network diffusion technique on a human interactome network and transcriptomic data. Enrichment analysis revealed that the IPIN was mainly associated with antiviral, innate immune, apoptosis, cell division, and cell cycle regulation signaling pathways. Twenty-three proteins were identified as key proteins to find associated drugs. Finally, poly (I:C), mitomycin C, decitabine, gemcitabine, hydroxyurea, tamoxifen, and curcumin were the potential drugs interacting with the key proteins to heal severe COVID-19. In conclusion, IPIN can be a good representative network for the immune system that integrates the protein interaction network and transcriptomic data. Thus, the key proteins and target drugs in IPIN help to find a new treatment with the use of existing drugs to treat the disease apart from vaccination and conventional antiviral therapy.


Subject(s)
COVID-19 , Protein Interaction Maps , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , COVID-19/drug therapy , Drug Repositioning , Humans , Signal Transduction , Transcriptome
9.
Comput Biol Med ; 146: 105601, 2022 07.
Article in English | MEDLINE | ID: covidwho-1850901

ABSTRACT

BACKGROUND: The 2019 novel coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently a major challenge threatening the global healthcare system. Respiratory virus infection is the most common cause of asthma attacks, and thus COVID-19 may contribute to an increase in asthma exacerbations. However, the mechanisms of COVID-19/asthma comorbidity remain unclear. METHODS: The "Limma" package or "DESeq2" package was used to screen differentially expressed genes (DEGs). Alveolar lavage fluid datasets of COVID-19 and asthma were obtained from the GEO and GSV database. A series of analyses of common host factors for COVID-19 and asthma were conducted, including PPI network construction, module analysis, enrichment analysis, inference of the upstream pathway activity of host factors, tissue-specific analysis and drug candidate prediction. Finally, the key host factors were verified in the GSE152418 and GSE164805 datasets. RESULTS: 192 overlapping host factors were obtained by analyzing the intersection of asthma and COVID-19. FN1, UBA52, EEF1A1, ITGB1, XPO1, NPM1, EGR1, EIF4E, SRSF1, CCR5, PXN, IRF8 and DDX5 as host factors were tightly connected in the PPI network. Module analysis identified five modules with different biological functions and pathways. According to the degree values ranking in the PPI network, EEF1A1, EGR1, UBA52, DDX5 and IRF8 were considered as the key cohost factors for COVID-19 and asthma. The H2O2, VEGF, IL-1 and Wnt signaling pathways had the strongest activities in the upstream pathways. Tissue-specific enrichment analysis revealed the different expression levels of the five critical host factors. LY294002, wortmannin, PD98059 and heparin might have great potential to evolve into therapeutic drugs for COVID-19 and asthma comorbidity. Finally, the validation dataset confirmed that the expression of five key host factors were statistically significant among COVID-19 groups with different severity and healthy control subjects. CONCLUSIONS: This study constructed a network of common host factors between asthma and COVID-19 and predicted several drugs with therapeutic potential. Therefore, this study is likely to provide a reference for the management and treatment for COVID-19/asthma comorbidity.


Subject(s)
Asthma , COVID-19 , Asthma/genetics , Bronchoalveolar Lavage Fluid , COVID-19/genetics , Computational Biology , DEAD-box RNA Helicases , Gene Expression Profiling , Humans , Hydrogen Peroxide , Interferon Regulatory Factors/genetics , Protein Interaction Maps/genetics , SARS-CoV-2 , Serine-Arginine Splicing Factors/genetics
10.
Comput Math Methods Med ; 2022: 3386999, 2022.
Article in English | MEDLINE | ID: covidwho-1840653

ABSTRACT

Background: Systemic lupus erythematosus (SLE) is an autoimmune disease involving multiple organs, with atypical clinical manifestations and indefinite diagnosis and treatment. So far, the etiology of the disease is not completely clear. Current studies have known the interaction of genetic system, endocrine system, infection, environment, and other factors. Due to abnormal immune function, the human body, with the participation of various immune cells such as T cells and B cells, abnormally recognizes autoantigens, so as to produce a variety of autoantibodies and combine them to form immune complexes. These complexes will stay in the skin, kidney, serosa cavity, large joints, and even the central nervous system, resulting in multisystem damage of the body. The disease is heterogeneous, and it can show different symptoms in different populations and different disease stages; patients with systemic lupus erythematosus need individualized diagnosis and treatment. Therefore, we aimed to search for SLE immune-related hub genes and determine appropriate diagnostic genes to provide help for the detection and treatment of the disease. Methods: Gene expression data of whole blood samples of SLE patients and healthy controls were downloaded from the GEO database. Firstly, we analyzed and identified the differentially expressed genes between SLE and the normal population. Meanwhile, the single-sample gene set enrichment analysis (ssGSEA) was used to identify the activation degree of immune-related pathways based on gene expression profile of different patients, and weighted gene coexpression network analysis (WGCNA) was used to search for coexpressed gene modules associated with immune cells. Then, key networks and corresponding genes were found in the protein-protein interaction (PPI) network. The above corresponding genes were hub genes. After that, this study used receiver operating characteristic (ROC) curve to evaluate hub gene in order to verify its ability to distinguish SLE from the healthy control group, and miRNA and transcription factor regulatory network analyses were performed for hub genes. Results: Through bioinformatics technology, compared with the healthy control group, 2996 common differentially expressed genes (DEGs) were found in SLE patients, of which 1639 genes were upregulated and 1357 genes were downregulated. These differential genes were analyzed by ssGSEA to obtain the enrichment fraction of immune-related pathways. Next, the samples were selected by WGCNA analysis, and a total of 18 functional modules closely related to the pathogenesis of SLE were obtained. Thirdly, the correlation between the above modules and the enrichment fraction of immune-related pathways was analyzed, and the turquoise module with the highest correlation was selected. The 290 differential genes of this module were analyzed by GO and KEGG. The results showed that these genes were mainly enriched in coronavirus disease (COVID-19), ribosome, and human T cell leukemia virus 1 infection pathway. The 290 DEGs with PPI network and 28 genes of key networks were selected. ROC curve showed that 28 hub genes are potential biomarkers of SLE. Conclusion: The 28 hub genes such as RPS7, RPL19, RPS17, and RPS19 may play key roles in the advancement of SLE. The results obtained in this study can provide a reference in a certain direction for the diagnosis and treatment of SLE in the future and can also be used as a new biomarker in clinical practice or drug research.


Subject(s)
COVID-19 , Lupus Erythematosus, Systemic , Biomarkers , Computational Biology/methods , Gene Expression Profiling , Gene Regulatory Networks , Humans , Lupus Erythematosus, Systemic/diagnosis , Lupus Erythematosus, Systemic/genetics , Protein Interaction Maps/genetics
11.
Bioengineered ; 12(1): 2274-2287, 2021 12.
Article in English | MEDLINE | ID: covidwho-1769071

ABSTRACT

Xuebijing Injection have been found to improve the clinical symptoms of COVID-19 and alleviate disease severity, but the mechanisms are currently unclear. This study aimed to investigate the potential molecular targets and mechanisms of the Xuebijing injection in treating COVID-19 via network pharmacology and molecular docking analysis. The main active ingredients and therapeutic targets of the Xuebijing injection, and the pathogenic targets of COVID-19 were screened using the TCMSP, UniProt, and GeneCard databases. According to the 'Drug-Ingredients-Targets-Disease' network built by STRING and Cytoscape, AKT1 was identified as the core target, and baicalein, luteolin, and quercetin were identified as the active ingredients of the Xuebijing injection in connection with AKT1. R language was used for enrichment analysis that predict the mechanisms by which the Xuebijing injection may inhibit lipopolysaccharide-mediated inflammatory response, modulate NOS activity, and regulate the TNF signal pathway by affecting the role of AKT1. Based on the results of network pharmacology, a molecular docking was performed with AKT1 and the three active ingredients, the results indicated that all three active ingredients could stably bind with AKT1. These findings identify potential molecular mechanisms by which Xuebijing Injection inhibit COVID-19 by acting on AKT1.


Subject(s)
Antiviral Agents/administration & dosage , COVID-19/drug therapy , COVID-19/metabolism , Drugs, Chinese Herbal/administration & dosage , SARS-CoV-2 , Antiviral Agents/pharmacokinetics , Antiviral Agents/pharmacology , Biomedical Engineering , Drugs, Chinese Herbal/pharmacokinetics , Drugs, Chinese Herbal/pharmacology , Flavanones/administration & dosage , Humans , Injections , Luteolin/administration & dosage , Molecular Docking Simulation , Pandemics , Protein Binding , Protein Interaction Maps , Proto-Oncogene Proteins c-akt/chemistry , Proto-Oncogene Proteins c-akt/drug effects , Proto-Oncogene Proteins c-akt/metabolism , Quercetin/administration & dosage , Signal Transduction/drug effects
12.
Sci Rep ; 12(1): 4279, 2022 03 11.
Article in English | MEDLINE | ID: covidwho-1740476

ABSTRACT

The pandemic threat of COVID-19 has severely destroyed human life as well as the economy around the world. Although, the vaccination has reduced the outspread, but people are still suffering due to the unstable RNA sequence patterns of SARS-CoV-2 which demands supplementary drugs. To explore novel drug target proteins, in this study, a transcriptomics RNA-Seq data generated from SARS-CoV-2 infection and control samples were analyzed. We identified 109 differentially expressed genes (DEGs) that were utilized to identify 10 hub-genes/proteins (TLR2, USP53, GUCY1A2, SNRPD2, NEDD9, IGF2, CXCL2, KLF6, PAG1 and ZFP36) by the protein-protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of hub-DEGs revealed some important functions and signaling pathways that are significantly associated with SARS-CoV-2 infections. The interaction network analysis identified 5 TFs proteins and 6 miRNAs as the key regulators of hub-DEGs. Considering 10 hub-proteins and 5 key TFs-proteins as drug target receptors, we performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 FDA approved drugs. We found Torin-2, Rapamycin, Radotinib, Ivermectin, Thiostrepton, Tacrolimus and Daclatasvir as the top ranked seven candidate drugs. We investigated their resistance performance against the already published COVID-19 causing top-ranked 11 independent and 8 protonated receptor proteins by molecular docking analysis and found their strong binding affinities, which indicates that the proposed drugs are effective against the state-of-the-arts alternatives independent receptor proteins also. Finally, we investigated the stability of top three drugs (Torin-2, Rapamycin and Radotinib) by using 100 ns MD-based MM-PBSA simulations with the two top-ranked proposed receptors (TLR2, USP53) and independent receptors (IRF7, STAT1), and observed their stable performance. Therefore, the proposed drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections.


Subject(s)
COVID-19/drug therapy , COVID-19/genetics , Drug Repositioning , SARS-CoV-2/drug effects , Case-Control Studies , Gene Regulatory Networks/genetics , Genetic Markers/genetics , Humans , Molecular Docking Simulation , Protein Interaction Maps/genetics
13.
Funct Integr Genomics ; 22(3): 429-433, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1739358

ABSTRACT

Although extrapulmonary manifestations of coronavirus disease 2019 (COVID-19) are increasingly reported, no effective therapeutic strategy for these multisystemic complications is available due to a poor understanding of the pathophysiology of COVID-19 multiorgan involvement. In this study, differentially expressed genes (DEGs) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected extrapulmonary organs including human pluripotent stem cells (hPSCs)-derived liver organoids and choroid plexus organoids besides transformed lung alveolar (A549) cells were analyzed. First, pathway enrichment analysis was done to compare the underlying biological pathways enriched upon SARS-CoV-2 infection in different organs. Then, these lists of DEGs were used in a connectivity map (CMap)-based drug repurposing experiment. Also, protein-protein interaction (PPI) network analysis was done to compare the associated hub genes. The results revealed different biological pathways and genes responsible for SARS-CoV-2 multisystemic pathogenesis based on the organ involved that highlighted the need for considering organ-specific treatments or even personalized therapy. Besides, some FDA-approved drugs were proposed as the potential therapeutic candidates for each infected cell line.


Subject(s)
COVID-19 , COVID-19/drug therapy , Cell Line , Humans , Precision Medicine , Protein Interaction Maps , SARS-CoV-2
14.
Protein J ; 39(3): 198-216, 2020 06.
Article in English | MEDLINE | ID: covidwho-1718840

ABSTRACT

The devastating effects of the recent global pandemic (termed COVID-19 for "coronavirus disease 2019") caused by the severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) are paramount with new cases and deaths growing at an exponential rate. In order to provide a better understanding of SARS CoV-2, this article will review the proteins found in the SARS CoV-2 that caused this global pandemic.


Subject(s)
Betacoronavirus/chemistry , Betacoronavirus/physiology , Coronavirus Infections/virology , Pneumonia, Viral/virology , Viral Proteins/chemistry , Viral Proteins/metabolism , Amino Acid Sequence , Betacoronavirus/genetics , COVID-19 , Coronavirus Envelope Proteins , Coronavirus Infections/drug therapy , Coronavirus Infections/metabolism , Coronavirus Nucleocapsid Proteins , Drug Discovery/methods , Genome, Viral , Host-Pathogen Interactions/drug effects , Humans , Nucleocapsid Proteins/chemistry , Nucleocapsid Proteins/genetics , Nucleocapsid Proteins/metabolism , Pandemics , Phosphoproteins , Pneumonia, Viral/drug therapy , Pneumonia, Viral/metabolism , Polyproteins , Protein Interaction Maps/drug effects , SARS-CoV-2 , Sequence Alignment , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/genetics , Spike Glycoprotein, Coronavirus/metabolism , Viral Envelope Proteins/chemistry , Viral Envelope Proteins/genetics , Viral Envelope Proteins/metabolism , Viral Matrix Proteins/chemistry , Viral Matrix Proteins/genetics , Viral Matrix Proteins/metabolism , Viral Nonstructural Proteins/chemistry , Viral Nonstructural Proteins/genetics , Viral Nonstructural Proteins/metabolism , Viral Proteins/genetics , Viral Regulatory and Accessory Proteins/chemistry , Viral Regulatory and Accessory Proteins/genetics , Viral Regulatory and Accessory Proteins/metabolism , Viroporin Proteins
15.
Front Immunol ; 13: 798538, 2022.
Article in English | MEDLINE | ID: covidwho-1699559

ABSTRACT

Existing evidence demonstrates that coronavirus disease 2019 (COVID-19) leads to psychiatric illness, despite its main clinical manifestations affecting the respiratory system. People with mental disorders are more susceptible to COVID-19 than individuals without coexisting mental health disorders, with significantly higher rates of severe illness and mortality in this population. The incidence of new psychiatric diagnoses after infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is also remarkably high. SARS-CoV-2 has been reported to use angiotensin-converting enzyme-2 (ACE2) as a receptor for infecting susceptible cells and is expressed in various tissues, including brain tissue. Thus, there is an urgent need to investigate the mechanism linking psychiatric disorders to COVID-19. Using a data set of peripheral blood cells from patients with COVID-19, we compared this to data sets of whole blood collected from patients with psychiatric disorders and used bioinformatics and systems biology approaches to identify genetic links. We found a large number of overlapping immune-related genes between patients infected with SARS-CoV-2 and differentially expressed genes of bipolar disorder (BD), schizophrenia (SZ), and late-onset major depressive disorder (LOD). Many pathways closely related to inflammatory responses, such as MAPK, PPAR, and TGF-ß signaling pathways, were observed by enrichment analysis of common differentially expressed genes (DEGs). We also performed a comprehensive analysis of protein-protein interaction network and gene regulation networks. Chemical-protein interaction networks and drug prediction were used to screen potential pharmacologic therapies. We hope that by elucidating the relationship between the pathogenetic processes and genetic mechanisms of infection with SARS-CoV-2 with psychiatric disorders, it will lead to innovative strategies for future research and treatment of psychiatric disorders linked to COVID-19.


Subject(s)
Bipolar Disorder/genetics , COVID-19/pathology , Depressive Disorder, Major/genetics , Mental Disorders/epidemiology , Protein Interaction Maps/genetics , Schizophrenia/genetics , COVID-19/epidemiology , Comorbidity , Gene Expression Profiling , Humans , Mental Disorders/genetics , SARS-CoV-2/immunology , Severity of Illness Index
16.
Front Endocrinol (Lausanne) ; 12: 802447, 2021.
Article in English | MEDLINE | ID: covidwho-1699427

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a serious epidemic, characterized by potential mutation and can bring about poor vaccine efficiency. It is evidenced that patients with malignancies, including prostate cancer (PC), may be highly vulnerable to the SARS-CoV-2 infection. Currently, there are no existing drugs that can cure PC and COVID-19. Luteolin can potentially be employed for COVID-19 treatment and serve as a potent anticancer agent. Our present study was conducted to discover the possible drug target and curative mechanism of luteolin to serve as treatment for PC and COVID-19. The differential gene expression of PC cases was determined via RNA sequencing. The application of network pharmacology and molecular docking aimed to exhibit the drug targets and pharmacological mechanisms of luteolin. In this study, we found the top 20 up- and downregulated gene expressions in PC patients. Enrichment data demonstrated anti-inflammatory effects, where improvement of metabolism and enhancement of immunity were the main functions and mechanism of luteolin in treating PC and COVID-19, characterized by associated signaling pathways. Additional core drug targets, including MPO and FOS genes, were computationally identified accordingly. In conclusion, luteolin may be a promising treatment for PC and COVID-19 based on bioinformatics findings, prior to future clinical validation and application.


Subject(s)
COVID-19/drug therapy , Drug Discovery/methods , Luteolin/therapeutic use , Prostatic Neoplasms/drug therapy , COVID-19/pathology , Computational Biology/methods , High-Throughput Screening Assays/methods , Humans , Luteolin/pharmacology , Male , Metabolic Networks and Pathways/drug effects , Models, Molecular , Molecular Docking Simulation , Molecular Targeted Therapy/methods , Prostatic Neoplasms/pathology , Protein Interaction Maps/drug effects , Protein Interaction Maps/physiology , SARS-CoV-2/drug effects , SARS-CoV-2/physiology
17.
Stem Cell Reports ; 17(3): 522-537, 2022 03 08.
Article in English | MEDLINE | ID: covidwho-1692862

ABSTRACT

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


Subject(s)
COVID-19/pathology , Coronavirus Nucleocapsid Proteins/genetics , Genome-Wide Association Study/methods , SARS-CoV-2/genetics , Action Potentials/drug effects , Adenosine Triphosphate/metabolism , Apoptosis/drug effects , Apoptosis/genetics , COVID-19/virology , Down-Regulation , Humans , Ivermectin/pharmacology , Meclizine/pharmacology , Myocytes, Cardiac/cytology , Myocytes, Cardiac/metabolism , Phosphoproteins/genetics , Pluripotent Stem Cells/cytology , Pluripotent Stem Cells/metabolism , Protein Interaction Maps/genetics , RNA, Messenger/chemistry , RNA, Messenger/metabolism , SARS-CoV-2/isolation & purification , Signal Transduction/genetics , Transcriptome/drug effects , Up-Regulation
18.
Int J Med Sci ; 19(2): 402-415, 2022.
Article in English | MEDLINE | ID: covidwho-1662815

ABSTRACT

Hypertension, diabetes mellitus, and coronary artery disease are common comorbidities and dangerous factors for infection and serious COVID-19. Polymorphisms in genes associated with comorbidities may help observe susceptibility and disease severity variation. However, specific genetic factors and the extent to which they can explain variation in susceptibility of severity are unclear. Therefore, we evaluated candidate genes associated with COVID-19 and hypertension, diabetes mellitus, and coronary artery disease. In particular, we performed searches against OMIM, NCBI, and other databases, protein-protein interaction network construction, and GO and KEGG pathway enrichment analyses. Results showed that the associated overlapping genes were TLR4, NLRP3, MBL2, IL6, IL1RN, IL1B, CX3CR1, CCR5, AGT, ACE, and F2. GO and KEGG analyses yielded 302 GO terms (q < 0.05) and 29 signaling pathways (q < 0.05), respectively, mainly including coronavirus disease-COVID-19 and cytokine-cytokine receptor interaction. IL6 and AGT were central in the PPI, with 8 and 5 connections, respectively. In this study, we identified 11 genes associated with both COVID-19 and three comorbidities that may contribute to infection and disease severity. The key genes IL6 and AGT are involved in regulating immune response, cytokine activity, and viral infection. Therefore, RAAS inhibitors, AGT antisense nucleotides, cytokine inhibitors, vitamin D, fenofibrate, and vaccines regulating non-immune and immune factors could be potential strategies to prevent and cure COVID-19. The study provides a basis for further investigation of genes and pathways with predictive value for the risk of infection and prognosis and could help guide drug and vaccine development to improve treatment efficacy and the development of personalised treatments, especially for COVID-19 individuals with common comorbidities.


Subject(s)
COVID-19/genetics , COVID-19/epidemiology , Comorbidity , Coronary Artery Disease/complications , Coronary Artery Disease/epidemiology , Coronary Artery Disease/genetics , Diabetes Complications/epidemiology , Diabetes Complications/genetics , Humans , Hypertension/complications , Hypertension/epidemiology , Hypertension/genetics , Mutation , Protein Interaction Maps
19.
Med Sci Monit ; 28: e934102, 2022 Jan 25.
Article in English | MEDLINE | ID: covidwho-1651076

ABSTRACT

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


Subject(s)
COVID-19/drug therapy , Coronavirus/drug effects , Drugs, Chinese Herbal/pharmacology , SARS-CoV-2/drug effects , Alkaloids/chemistry , Alkaloids/pharmacology , Caspase 3/drug effects , Caspase 3/genetics , Coronavirus/metabolism , Coronavirus Infections/drug therapy , Cyclooxygenase 2/drug effects , Cyclooxygenase 2/genetics , Databases, Pharmaceutical , Drugs, Chinese Herbal/chemistry , Drugs, Chinese Herbal/therapeutic use , Flavanones/chemistry , Flavanones/pharmacology , Humans , Indoles/chemistry , Indoles/pharmacology , Interleukin-6/genetics , Lignin/chemistry , Lignin/pharmacology , Luteolin/chemistry , Luteolin/pharmacology , Mitogen-Activated Protein Kinase 14/drug effects , Mitogen-Activated Protein Kinase 14/genetics , Mitogen-Activated Protein Kinase 8/drug effects , Mitogen-Activated Protein Kinase 8/genetics , Molecular Docking Simulation , NF-kappa B p50 Subunit/drug effects , NF-kappa B p50 Subunit/genetics , Naphthols/chemistry , Naphthols/pharmacology , Nitric Oxide Synthase Type III/drug effects , Nitric Oxide Synthase Type III/genetics , Protein Interaction Maps , Quercetin/chemistry , Quercetin/pharmacology , SARS-CoV-2/metabolism , Signal Transduction , Sitosterols/chemistry , Sitosterols/pharmacology , Transcriptome/drug effects , Transcriptome/genetics
20.
Front Immunol ; 12: 769011, 2021.
Article in English | MEDLINE | ID: covidwho-1650341

ABSTRACT

Asthma patients may increase their susceptibility to SARS-CoV-2 infection and the poor prognosis of coronavirus disease 2019 (COVID-19). However, anti-COVID-19/asthma comorbidity approaches are restricted on condition. Existing evidence indicates that luteolin has antiviral, anti-inflammatory, and immune regulation capabilities. We aimed to evaluate the possibility of luteolin evolving into an ideal drug and explore the underlying molecular mechanisms of luteolin against COVID-19/asthma comorbidity. We used system pharmacology and bioinformatics analysis to assess the physicochemical properties and biological activities of luteolin and further analyze the binding activities, targets, biological functions, and mechanisms of luteolin against COVID-19/asthma comorbidity. We found that luteolin may exert ideal physicochemical properties and bioactivity, and molecular docking analysis confirmed that luteolin performed effective binding activities in COVID-19/asthma comorbidity. Furthermore, a protein-protein interaction network of 538 common targets between drug and disease was constructed and 264 hub targets were obtained. Then, the top 6 hub targets of luteolin against COVID-19/asthma comorbidity were identified, namely, TP53, AKT1, ALB, IL-6, TNF, and VEGFA. Furthermore, the enrichment analysis suggested that luteolin may exert effects on virus defense, regulation of inflammation, cell growth and cell replication, and immune responses, reducing oxidative stress and regulating blood circulation through the Toll-like receptor; MAPK, TNF, AGE/RAGE, EGFR, ErbB, HIF-1, and PI3K-AKT signaling pathways; PD-L1 expression; and PD-1 checkpoint pathway in cancer. The possible "dangerous liaison" between COVID-19 and asthma is still a potential threat to world health. This research is the first to explore whether luteolin could evolve into a drug candidate for COVID-19/asthma comorbidity. This study indicated that luteolin with superior drug likeness and bioactivity has great potential to be used for treating COVID-19/asthma comorbidity, but the predicted results still need to be rigorously verified by experiments.


Subject(s)
Anti-Inflammatory Agents/metabolism , Antioxidants/metabolism , Antiviral Agents/metabolism , Asthma/epidemiology , Asthma/metabolism , COVID-19/epidemiology , COVID-19/metabolism , Immunologic Factors/metabolism , Luteolin/metabolism , SARS-CoV-2/metabolism , Anti-Inflammatory Agents/chemistry , Antioxidants/chemistry , Antiviral Agents/chemistry , Comorbidity , Computational Biology/methods , Drug Discovery/methods , Humans , Immunologic Factors/chemistry , Interleukin-6/metabolism , Luteolin/chemistry , Molecular Docking Simulation , Protein Interaction Maps/drug effects , Proto-Oncogene Proteins c-akt/metabolism , Serum Albumin, Human/metabolism , Signal Transduction/drug effects , Tumor Necrosis Factor-alpha/metabolism , Tumor Suppressor Protein p53/metabolism , Vascular Endothelial Growth Factor A/metabolism
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