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1.
Int J Mol Sci ; 23(6)2022 Mar 18.
Article in English | MEDLINE | ID: covidwho-1760651

ABSTRACT

PDCoV is an emerging enteropathogenic coronavirus that mainly causes acute diarrhea in piglets, seriously affecting pig breeding industries worldwide. To date, the molecular mechanisms of PDCoV-induced immune and inflammatory responses or host responses in LLC-PK cells in vitro are not well understood. HSP90 plays important roles in various viral infections. In this study, HSP90AB1 knockout cells (HSP90AB1KO) were constructed and a comparative transcriptomic analysis between PDCoV-infected HSP90AB1WT and HSP90AB1KO cells was conducted using RNA sequencing to explore the effect of HSP90AB1 on PDCoV infection. A total of 1295 and 3746 differentially expressed genes (DEGs) were identified in PDCoV-infected HSP90AB1WT and HSP90AB1KO cells, respectively. Moreover, most of the significantly enriched pathways were related to immune and inflammatory response-associated pathways upon PDCoV infection. The DEGs enriched in NF-κB pathways were specifically detected in HSP90AB1WT cells, and NF-κB inhibitors JSH-23, SC75741 and QNZ treatment reduced PDCoV infection. Further research revealed most cytokines associated with immune and inflammatory responses were upregulated during PDCoV infection. Knockout of HSP90AB1 altered the upregulated levels of some cytokines. Taken together, our findings provide new insights into the host response to PDCoV infection from the transcriptome perspective, which will contribute to illustrating the molecular basis of the interaction between PDCoV and HSP90AB1.


Subject(s)
Coronavirus Infections/veterinary , Deltacoronavirus , Gene Expression Profiling , HSP90 Heat-Shock Proteins/genetics , Immunity/genetics , Swine Diseases/etiology , Transcriptome , Animals , Computational Biology/methods , Disease Susceptibility , Gene Knockdown Techniques , Gene Ontology , Host-Pathogen Interactions/genetics , Host-Pathogen Interactions/immunology , NF-kappa B/metabolism , Swine
2.
Int J Mol Sci ; 23(5)2022 Feb 24.
Article in English | MEDLINE | ID: covidwho-1715406

ABSTRACT

To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means of DNA microarray or RNA-seq technology. Such data can also be useful to classify infected individuals by molecular signatures in the form of machine-learning models with genes as predictor variables. Early diagnosis of patients by molecular signatures could also contribute to better treatments. An approach that has rarely been considered for machine-learning models in the context of transcriptomics is data augmentation. For other data types it has been shown that augmentation can improve classification accuracy and prevent overfitting. Here, we compare three strategies for data augmentation of DNA microarray and RNA-seq data from two selected studies on respiratory diseases of viral origin. The first study involves samples of patients with either viral or bacterial origin of the respiratory disease, the second study involves patients with either SARS-CoV-2 or another respiratory virus as disease origin. Specifically, we reanalyze these public datasets to study whether patient classification by transcriptomic signatures can be improved when adding artificial data for training of the machine-learning models. Our comparison reveals that augmentation of transcriptomic data can improve the classification accuracy and that fewer genes are necessary as explanatory variables in the final models. We also report genes from our signatures that overlap with signatures presented in the original publications of our example data. Due to strict selection criteria, the molecular role of these genes in the context of respiratory infectious diseases is underlined.


Subject(s)
COVID-19/genetics , Gene Expression Profiling/methods , Machine Learning , Neural Networks, Computer , RNA-Seq/methods , Transcriptome/genetics , Algorithms , COVID-19/classification , COVID-19/virology , Gene Ontology , Humans , Reproducibility of Results , SARS-CoV-2/physiology
3.
Comput Math Methods Med ; 2022: 9604456, 2022.
Article in English | MEDLINE | ID: covidwho-1704361

ABSTRACT

Objective: To investigate the potential pharmacological value of extracts from honeysuckle on patients with mild coronavirus disease 2019 (COVID-19) infection. Methods: The active components and targets of honeysuckle were screened by Traditional Chinese Medicine Database and Analysis Platform (TCMSP). SwissADME and pkCSM databases predict pharmacokinetics of ingredients. The Gene Expression Omnibus (GEO) database collected transcriptome data for mild COVID-19. Data quality control, differentially expressed gene (DEG) identification, enrichment analysis, and correlation analysis were implemented by R toolkit. CIBERSORT evaluated the infiltration of 22 immune cells. Results: The seven active ingredients of honeysuckle had good oral absorption and medicinal properties. Both the active ingredient targets of honeysuckle and differentially expressed genes of mild COVID-19 were significantly enriched in immune signaling pathways. There were five overlapping immunosignature genes, among which RELA and MAP3K7 expressions were statistically significant (P < 0.05). Finally, immune cell infiltration and correlation analysis showed that RELA, MAP3K7, and natural killer (NK) cell are with highly positive correlation and highly negatively correlated with hematopoietic stem cells. Conclusion: Our analysis suggested that honeysuckle extract had a safe and effective protective effect against mild COVID-19 by regulating a complex molecular network. The main mechanism was related to the proportion of infiltration between NK cells and hematopoietic stem cells.


Subject(s)
COVID-19/drug therapy , Drugs, Chinese Herbal/therapeutic use , Lonicera , Phytotherapy , SARS-CoV-2 , Antiviral Agents/chemistry , Antiviral Agents/pharmacokinetics , Antiviral Agents/therapeutic use , COVID-19/genetics , COVID-19/immunology , Computational Biology , Databases, Pharmaceutical/statistics & numerical data , Drug Evaluation, Preclinical , Drugs, Chinese Herbal/chemistry , Drugs, Chinese Herbal/pharmacokinetics , Gene Expression/drug effects , Gene Ontology , Gene Regulatory Networks/drug effects , Gene Regulatory Networks/immunology , Hematopoietic Stem Cells/drug effects , Hematopoietic Stem Cells/immunology , Humans , Killer Cells, Natural/drug effects , Killer Cells, Natural/immunology , Lonicera/chemistry , Medicine, Chinese Traditional , Pandemics , SARS-CoV-2/drug effects
4.
J Cell Biochem ; 123(3): 673-690, 2022 03.
Article in English | MEDLINE | ID: covidwho-1626208

ABSTRACT

COVID-19 is a sneaking deadly disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The rapid increase in the number of infected patients worldwide enhances the exigency for medicines. However, precise therapeutic drugs are not available for COVID-19; thus, exhaustive research is critically required to unscramble the pathogenic tools and probable therapeutic targets for the development of effective therapy. This study utilizes a chemogenomics strategy, including computational tools for the identification of viral-associated differentially expressed genes (DEGs), and molecular docking of potential chemical compounds available in antiviral, anticancer, and natural product-based libraries against these DEGs. We scrutinized the messenger RNA expression profile of SARS-CoV-2 patients, publicly available on the National Center for Biotechnology Information-Gene Expression Omnibus database, stratified them into different groups based on the severity of infection, superseded by identification of overlapping mild and severe infectious (MSI)-DEGs. The profoundly expressed MSI-DEGs were then subjected to trait-linked weighted co-expression network construction and hub module detection. The hub module MSI-DEGs were then exposed to enrichment (gene ontology + pathway) and protein-protein interaction network analyses where Rho guanine nucleotide exchange factor 1 (ARHGEF1) gene conjectured in all groups and could be a probable target of therapy. Finally, we used the molecular docking and molecular dynamics method to identify inherent hits against the ARHGEF1 gene from antiviral, anticancer, and natural product-based libraries. Although the study has an identified significant association of the ARHGEF1 gene in COVID19; and probable compounds targeting it, using in silico methods, these targets need to be validated by both in vitro and in vivo methods to effectively determine their therapeutic efficacy against the devastating virus.


Subject(s)
COVID-19 , COVID-19/drug therapy , COVID-19/genetics , Gene Ontology , Humans , Molecular Docking Simulation , Rho Guanine Nucleotide Exchange Factors , SARS-CoV-2/genetics
5.
Int J Chron Obstruct Pulmon Dis ; 17: 13-24, 2022.
Article in English | MEDLINE | ID: covidwho-1623677

ABSTRACT

Purpose: Chronic obstructive pulmonary disease (COPD) is a major cause of death and morbidity worldwide. A better understanding of new biomarkers for COPD patients and their complex mechanisms in the progression of COPD are needed. Methods: An algorithm was conducted to reveal the proportions of 22 subsets of immune cells in COPD samples. Differentially expressed immune-related genes (DE-IRGs) were obtained based on the differentially expressed genes (DEGs) of the GSE57148 dataset, and 1509 immune-related genes (IRGs) were downloaded from the ImmPort database. Functional enrichment analyses of DE-IRGs were conducted by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and Ingenuity Pathway Analysis (IPA). We defined the DE-IRGs that had correlations with immune cells as hub genes. The potential interactions among the hub genes were explored by a protein-protein interaction (PPI) network. Results: The CIBERSORT results showed that lung tissue of COPD patients contained a greater number of resting NK cells, activated dendritic cells, and neutrophils than normal samples. However, the fractions of follicular helper T cells and resting dendritic cells were relatively lower. Thirty-eight DE-IRGs were obtained for further analysis. Functional enrichment analysis revealed that these DE-IRGs were significantly enriched in several immune-related biological processes and pathways. Notably, we also observed that DE-IRGs were associated with the coronavirus disease COVID-19 in the progression of COPD. After correlation analysis, six DE-IRGs associated with immune cells were considered hub genes, including AHNAK, SLIT2 TNFRRSF10C, CXCR1, CXCR2, and FCGR3B. Conclusion: In the present study, we investigated immune-related genes as novel diagnostic biomarkers and explored the potential mechanism for COPD based on CIBERSORT analysis, providing a new understanding for COPD treatment.


Subject(s)
COVID-19 , Pulmonary Disease, Chronic Obstructive , Gene Ontology , Humans , Protein Interaction Maps , Pulmonary Disease, Chronic Obstructive/diagnosis , Pulmonary Disease, Chronic Obstructive/genetics , SARS-CoV-2
6.
Front Immunol ; 12: 789317, 2021.
Article in English | MEDLINE | ID: covidwho-1593957

ABSTRACT

Background: The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches. Methods: RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules. Results: Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis. Conclusion: This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.


Subject(s)
COVID-19/genetics , Gene Expression Profiling/methods , Signal Transduction/genetics , Transcription Factors/genetics , Transcriptome/genetics , COVID-19/epidemiology , COVID-19/virology , Cluster Analysis , Gene Ontology , Gene Regulatory Networks , Humans , Immunity/genetics , Models, Genetic , Pandemics , Protein Interaction Maps/genetics , SARS-CoV-2/physiology
7.
Front Immunol ; 12: 724936, 2021.
Article in English | MEDLINE | ID: covidwho-1592205

ABSTRACT

The COVID-19 pandemic has created an urgent situation throughout the globe. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability. The DEGs will help understand the disease's potential underlying molecular mechanisms and genetic characteristics, including the regulatory genes associated with immune response elements and protective immunity. This study aimed to determine the DEGs in mild and severe COVID-19 patients versus healthy controls. The Agilent-085982 Arraystar human lncRNA V5 microarray GEO dataset (GSE164805 dataset) was used for this study. We used statistical tools to identify the DEGs. Our 15 human samples dataset was divided into three groups: mild, severe COVID-19 patients and healthy control volunteers. We compared our result with three other published gene expression studies of COVID-19 patients. Along with significant DEGs, we developed an interactome map, a protein-protein interaction (PPI) pattern, a cluster analysis of the PPI network, and pathway enrichment analysis. We also performed the same analyses with the top-ranked genes from the three other COVID-19 gene expression studies. We also identified differentially expressed lncRNA genes and constructed protein-coding DEG-lncRNA co-expression networks. We attempted to identify the regulatory genes related to immune response elements and protective immunity. We prioritized the most significant 29 protein-coding DEGs. Our analyses showed that several DEGs were involved in forming interactome maps, PPI networks, and cluster formation, similar to the results obtained using data from the protein-coding genes from other investigations. Interestingly we found six lncRNAs (TALAM1, DLEU2, and UICLM CASC18, SNHG20, and GNAS) involved in the protein-coding DEG-lncRNA network; which might be served as potential biomarkers for COVID-19 patients. We also identified three regulatory genes from our study and 44 regulatory genes from the other investigations related to immune response elements and protective immunity. We were able to map the regulatory genes associated with immune elements and identify the virogenomic responses involved in protective immunity against SARS-CoV-2 infection during COVID-19 development.


Subject(s)
COVID-19/genetics , Gene Expression Profiling/methods , Gene Expression Regulation , Immunity/genetics , Aged , COVID-19/epidemiology , COVID-19/immunology , Female , Gene Ontology , Gene Regulatory Networks , Humans , Male , Middle Aged , Pandemics/prevention & control , Protein Interaction Maps/genetics , SARS-CoV-2/immunology , SARS-CoV-2/physiology , Signal Transduction/genetics , Signal Transduction/immunology
8.
Eur J Med Res ; 26(1): 146, 2021 Dec 17.
Article in English | MEDLINE | ID: covidwho-1582003

ABSTRACT

BACKGROUND: At the end of 2019, the world witnessed the emergence and ravages of a viral infection induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Also known as the coronavirus disease 2019 (COVID-19), it has been identified as a public health emergency of international concern (PHEIC) by the World Health Organization (WHO) because of its severity. METHODS: The gene data of 51 samples were extracted from the GSE150316 and GSE147507 data set and then processed by means of the programming language R, through which the differentially expressed genes (DEGs) that meet the standards were screened. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the selected DEGs to understand the functions and approaches of DEGs. The online tool STRING was employed to construct a protein-protein interaction (PPI) network of DEGs and, in turn, to identify hub genes. RESULTS: A total of 52 intersection genes were obtained through DEG identification. Through the GO analysis, we realized that the biological processes (BPs) that have the deepest impact on the human body after SARS-CoV-2 infection are various immune responses. By using STRING to construct a PPI network, 10 hub genes were identified, including IFIH1, DDX58, ISG15, EGR1, OASL, SAMD9, SAMD9L, XAF1, IFITM1, and TNFSF10. CONCLUSION: The results of this study will hopefully provide guidance for future studies on the pathophysiological mechanism of SARS-CoV-2 infection.


Subject(s)
COVID-19/genetics , Computational Biology/methods , Gene Expression Regulation/genetics , Lung/pathology , Protein Interaction Maps/genetics , COVID-19/pathology , Databases, Genetic , Gene Expression Profiling , Gene Ontology , Humans , Immunity, Humoral/genetics , Immunity, Humoral/immunology , Lung/virology , Neutrophil Activation/genetics , Neutrophil Activation/immunology , Neutrophils/immunology , SARS-CoV-2 , Transcriptome/genetics
9.
Cells ; 10(12)2021 12 10.
Article in English | MEDLINE | ID: covidwho-1572376

ABSTRACT

To assess the biology of the lethal endpoint in patients with SARS-CoV-2 infection, we compared the transcriptional response to the virus in patients who survived or died during severe COVID-19. We applied gene expression profiling to generate transcriptional signatures for peripheral blood mononuclear cells (PBMCs) from patients with SARS-CoV-2 infection at the time when they were placed in the Intensive Care Unit of the Pavlov First State Medical University of St. Petersburg (Russia). Three different bioinformatics approaches to RNA-seq analysis identified a downregulation of three common pathways in survivors compared with nonsurvivors among patients with severe COVID-19, namely, low-density lipoprotein (LDL) particle receptor activity (GO:0005041), important for maintaining cholesterol homeostasis, leukocyte differentiation (GO:0002521), and cargo receptor activity (GO:0038024). Specifically, PBMCs from surviving patients were characterized by reduced expression of PPARG, CD36, STAB1, ITGAV, and ANXA2. Taken together, our findings suggest that LDL particle receptor pathway activity in patients with COVID-19 infection is associated with poor disease prognosis.


Subject(s)
COVID-19/genetics , Down-Regulation/genetics , Gene Expression Profiling , Receptors, LDL/genetics , Aged , COVID-19/virology , Gene Ontology , Gene Regulatory Networks , Humans , Leukocytes, Mononuclear/metabolism , Male , Middle Aged , RNA-Seq , SARS-CoV-2/physiology
10.
Nucleic Acids Res ; 50(D1): D632-D639, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1506219

ABSTRACT

Network medicine has proven useful for dissecting genetic organization of complex human diseases. We have previously published HumanNet, an integrated network of human genes for disease studies. Since the release of the last version of HumanNet, many large-scale protein-protein interaction datasets have accumulated in public depositories. Additionally, the numbers of research papers and functional annotations for gene-phenotype associations have increased significantly. Therefore, updating HumanNet is a timely task for further improvement of network-based research into diseases. Here, we present HumanNet v3 (https://www.inetbio.org/humannet/, covering 99.8% of human protein coding genes) constructed by means of the expanded data with improved network inference algorithms. HumanNet v3 supports a three-tier model: HumanNet-PI (a protein-protein physical interaction network), HumanNet-FN (a functional gene network), and HumanNet-XC (a functional network extended by co-citation). Users can select a suitable tier of HumanNet for their study purpose. We showed that on disease gene predictions, HumanNet v3 outperforms both the previous HumanNet version and other integrated human gene networks. Furthermore, we demonstrated that HumanNet provides a feasible approach for selecting host genes likely to be associated with COVID-19.


Subject(s)
Algorithms , COVID-19/genetics , Communicable Diseases/genetics , Databases, Genetic , Gene Regulatory Networks , Software , COVID-19/virology , Communicable Diseases/classification , Gene Ontology , Humans , Internet , Molecular Sequence Annotation , Protein Interaction Mapping , SARS-CoV-2/pathogenicity
11.
Nucleic Acids Res ; 50(D1): D817-D827, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1493884

ABSTRACT

Virus infections are huge threats to living organisms and cause many diseases, such as COVID-19 caused by SARS-CoV-2, which has led to millions of deaths. To develop effective strategies to control viral infection, we need to understand its molecular events in host cells. Virus related functional genomic datasets are growing rapidly, however, an integrative platform for systematically investigating host responses to viruses is missing. Here, we developed a user-friendly multi-omics portal of viral infection named as MVIP (https://mvip.whu.edu.cn/). We manually collected available high-throughput sequencing data under viral infection, and unified their detailed metadata including virus, host species, infection time, assay, and target, etc. We processed multi-layered omics data of more than 4900 viral infected samples from 77 viruses and 33 host species with standard pipelines, including RNA-seq, ChIP-seq, and CLIP-seq, etc. In addition, we integrated these genome-wide signals into customized genome browsers, and developed multiple dynamic charts to exhibit the information, such as time-course dynamic and differential gene expression profiles, alternative splicing changes and enriched GO/KEGG terms. Furthermore, we implemented several tools for efficiently mining the virus-host interactions by virus, host and genes. MVIP would help users to retrieve large-scale functional information and promote the understanding of virus-host interactions.


Subject(s)
Databases, Factual , Host Microbial Interactions , Virus Diseases , Animals , Chromatin Immunoprecipitation Sequencing , Gene Ontology , Genome, Viral , High-Throughput Nucleotide Sequencing , Host Microbial Interactions/genetics , Humans , Metadata , Sequence Analysis, RNA , Software , Transcriptome , User-Computer Interface , Virus Diseases/genetics , Virus Diseases/metabolism , Web Browser
12.
Int J Lab Hematol ; 43(6): 1325-1333, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1462811

ABSTRACT

BACKGROUND: Multiple myeloma (MM) is a hematological malignancy. Coronavirus disease 2019 (COVID-19) infection correlates with MM features. This study aimed to identify MM prognostic biomarkers with potential association with COVID-19. METHODS: Differentially expressed genes (DEGs) in five MM data sets (GSE47552, GSE16558, GSE13591, GSE6477, and GSE39754) with the same expression trends were screened out. Functional enrichment analysis and the protein-protein interaction network were performed for all DEGs. Prognosis-associated DEGs were screened using the stepwise Cox regression analysis in the cancer genome atlas (TCGA) MMRF-CoMMpass cohort and the GSE24080 data set. Prognosis-associated DEGs associated with COVID-19 infection in the GSE164805 data set were also identified. RESULTS: A total of 98 DEGs with the same expression trends in five data sets were identified, and 83 DEGs were included in the protein-protein interaction network. Cox regression analysis identified 16 DEGs were associated with MM prognosis in the TCGA cohort, and only the cytochrome c oxidase subunit 6C (COX6C) gene (HR = 1.717, 95% CI 1.231-2.428, p = .002) and the nucleotide-binding oligomerization domain containing 2 (NOD2) gene (HR = 0.882, 95% CI 0.798-0.975, p = .014) were independent factors related to MM prognosis in the GSE24080 data set. Both of them were downregulated in patients with mild COVID-19 infection compared with controls but were upregulated in patients with severe COVID-19 compared with patients with mild illness. CONCLUSIONS: The NOD2 and COX6C genes might be used as prognostic biomarkers in MM. The two genes might be associated with the development of COVID-19 infection.


Subject(s)
COVID-19/genetics , Computational Biology/methods , Gene Expression Profiling , Multiple Myeloma/genetics , SARS-CoV-2 , COVID-19/mortality , Datasets as Topic , Electron Transport Complex IV/genetics , Gene Expression Regulation, Neoplastic , Gene Expression Regulation, Viral , Gene Ontology , Humans , Kaplan-Meier Estimate , Microarray Analysis , Neoplasm Proteins/biosynthesis , Neoplasm Proteins/genetics , Nod2 Signaling Adaptor Protein/genetics , Prognosis , Proportional Hazards Models , Protein Interaction Maps/genetics
13.
Dis Markers ; 2021: 4129993, 2021.
Article in English | MEDLINE | ID: covidwho-1440848

ABSTRACT

Hyperinflammation is related to the development of COVID-19. Resveratrol is considered an anti-inflammatory and antiviral agent. Herein, we used a network pharmacological approach and bioinformatic gene analysis to explore the pharmacological mechanism of Resveratrol in COVID-19 therapy. Potential targets of Resveratrol were obtained from public databases. SARS-CoV-2 differentially expressed genes (DEGs) were screened out via bioinformatic analysis Gene Expression Omnibus (GEO) datasets GSE147507, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis; then, protein-protein interaction network was constructed. The common targets, GO terms, and KEGG pathways of Resveratrol targets and SARS-CoV-2 DEGs were confirmed. KEGG Mapper queried the location of common targets in the key pathways. A notable overlap of the GO terms and KEGG pathways between Resveratrol targets and SARS-CoV-2 DEGs was revealed. The shared targets between Resveratrol targets and SARS-CoV-2 mainly involved the IL-17 signaling pathway, NF-kappa B signaling pathway, and TNF signaling pathway. Our study uncovered that Resveratrol is a promising therapeutic candidate for COVID-19 and we also revealed the probable key targets and pathways involved. Ultimately, we bring forward new insights and encourage more studies on Resveratol to benefit COVID-19 patients.


Subject(s)
Anti-Inflammatory Agents/therapeutic use , COVID-19/complications , Inflammation/drug therapy , Resveratrol/therapeutic use , COVID-19/virology , Gene Ontology , Genes, Viral , Humans , Inflammation/etiology , Molecular Docking Simulation , Protein Interaction Maps , Resveratrol/chemistry , SARS-CoV-2/genetics , SARS-CoV-2/isolation & purification
14.
J Virol ; 95(20): e0101021, 2021 09 27.
Article in English | MEDLINE | ID: covidwho-1440800

ABSTRACT

The host response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is poorly understood due to a lack of an animal model that recapitulates severe human disease. Here, we report a Syrian hamster model that develops progressive lethal pulmonary disease that closely mimics severe coronavirus disease 2019 (COVID-19). We evaluated host responses using a multi-omic, multiorgan approach to define proteome, phosphoproteome, and transcriptome changes. These data revealed both type I and type II interferon-stimulated gene and protein expression along with a progressive increase in chemokines, monocytes, and neutrophil-associated molecules throughout the course of infection that peaked in the later time points correlating with a rapidly developing diffuse alveolar destruction and pneumonia that persisted in the absence of active viral infection. Extrapulmonary proteome and phosphoproteome remodeling was detected in the heart and kidneys following viral infection. Together, our results provide a kinetic overview of multiorgan host responses to severe SARS-CoV-2 infection in vivo. IMPORTANCE The current pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has created an urgent need to understand the pathogenesis of this infection. These efforts have been impaired by the lack of animal models that recapitulate severe coronavirus disease 2019 (COVID-19). Here, we report a hamster model that develops severe COVID-19-like disease following infection with human isolates of SARS-CoV-2. To better understand pathogenesis, we evaluated changes in gene transcription and protein expression over the course of infection to provide an integrated multiorgan kinetic analysis of the host response to infection. These data reveal a dynamic innate immune response to infection and corresponding immune pathologies consistent with severe human disease. Altogether, this model will be useful for understanding the pathogenesis of severe COVID-19 and for testing interventions.


Subject(s)
COVID-19/immunology , COVID-19/metabolism , Immunity, Innate , Proteome , Transcriptome , Animals , COVID-19/genetics , COVID-19/virology , Disease Models, Animal , Gene Ontology , Heart/virology , Kidney/metabolism , Kidney/virology , Lung/immunology , Lung/metabolism , Lung/pathology , Lung/virology , Male , Mesocricetus , Myocardium/metabolism , Phosphoproteins/metabolism , Proteomics , SARS-CoV-2/genetics , SARS-CoV-2/physiology , Severity of Illness Index , Viral Load
15.
Cells ; 10(9)2021 09 09.
Article in English | MEDLINE | ID: covidwho-1408628

ABSTRACT

The present study sought to identify gene networks that are hallmarks of the developing inguinal subcutaneous adipose tissue (iWAT) and the interscapular brown adipose tissue (BAT) in the mouse. RNA profiling revealed that the iWAT of postnatal (P) day 6 mice expressed thermogenic and lipid catabolism transcripts, along with the abundance of transcripts associated with the beige adipogenesis program. This was an unexpected finding, as thermogenic BAT was believed to be the only site of nonshivering thermogenesis in the young mouse. However, the transcriptional landscape of BAT in P6 mice suggests that it is still undergoing differentiation and maturation, and that the iWAT temporally adopts thermogenic and lipolytic potential. Moreover, P6 iWAT and adult (P56) BAT were similar in their expression of immune gene networks, but P6 iWAT was unique in the abundant expression of antimicrobial proteins and virus entry factors, including a possible receptor for SARS-CoV-2. In summary, postnatal iWAT development is associated with a metabolic shift from thermogenesis and lipolysis towards fat storage. However, transcripts of beige-inducing signal pathways including ß-adrenergic receptors and interleukin-4 signaling were underrepresented in young iWAT, suggesting that the signals for thermogenic fat differentiation may be different in early postnatal life and in adulthood.


Subject(s)
Adipocytes, Beige/metabolism , Transcription, Genetic , Adipose Tissue, Brown/metabolism , Adipose Tissue, White/metabolism , Animals , Animals, Newborn , Biomarkers/metabolism , Cell Cycle/genetics , Gene Expression Regulation, Developmental , Gene Ontology , Gene Regulatory Networks , Male , Mice, Inbred C57BL , Models, Biological , Muscle Development/genetics , Neuropeptides/metabolism , RNA, Messenger/genetics , RNA, Messenger/metabolism , Signal Transduction
16.
Front Immunol ; 12: 729776, 2021.
Article in English | MEDLINE | ID: covidwho-1403478

ABSTRACT

Coronavirus disease 2019 (COVID-19) pandemic is caused by the novel coronavirus that has spread rapidly around the world, leading to high mortality because of multiple organ dysfunction; however, its underlying molecular mechanism is unknown. To determine the molecular mechanism of multiple organ dysfunction, a bioinformatics analysis method based on a time-order gene co-expression network (TO-GCN) was performed. First, gene expression profiles were downloaded from the gene expression omnibus database (GSE161200), and a TO-GCN was constructed using the breadth-first search (BFS) algorithm to infer the pattern of changes in the different organs over time. Second, Gene Ontology enrichment analysis was used to analyze the main biological processes related to COVID-19. The initial gene modules for the immune response of different organs were defined as the research object. The STRING database was used to construct a protein-protein interaction network of immune genes in different organs. The PageRank algorithm was used to identify five hub genes in each organ. Finally, the Comparative Toxicogenomics Database played an important role in exploring the potential compounds that target the hub genes. The results showed that there were two types of biological processes: the body's stress response and cell-mediated immune response involving the lung, trachea, and olfactory bulb (olf) after being infected by COVID-19. However, a unique biological process related to the stress response is the regulation of neuronal signals in the brain. The stress response was heterogeneous among different organs. In the lung, the regulation of DNA morphology, angiogenesis, and mitochondrial-related energy metabolism are specific biological processes related to the stress response. In particular, an effect on tracheal stress response was made by the regulation of protein metabolism and rRNA metabolism-related biological processes, as biological processes. In the olf, the distinctive stress responses consist of neural signal transmission and brain behavior. In addition, myeloid leukocyte activation and myeloid leukocyte-mediated immunity in response to COVID-19 can lead to a cytokine storm. Immune genes such as SRC, RHOA, CD40LG, CSF1, TNFRSF1A, FCER1G, ICAM1, LAT, LCN2, PLAU, CXCL10, ICAM1, CD40, IRF7, and B2M were predicted to be the hub genes in the cytokine storm. Furthermore, we inferred that resveratrol, acetaminophen, dexamethasone, estradiol, statins, curcumin, and other compounds are potential target drugs in the treatment of COVID-19.


Subject(s)
COVID-19/complications , Multiple Organ Failure/genetics , Antiviral Agents/therapeutic use , Brain/metabolism , Brain/virology , COVID-19/drug therapy , COVID-19/genetics , COVID-19/virology , Gene Expression Profiling , Gene Ontology , Humans , Lung/metabolism , Lung/virology , Multiple Organ Failure/drug therapy , Multiple Organ Failure/etiology , Multiple Organ Failure/metabolism , Olfactory Bulb/metabolism , Olfactory Bulb/virology , Protein Interaction Maps , SARS-CoV-2/physiology , Trachea/metabolism , Trachea/virology , Transcriptome
17.
Infect Genet Evol ; 89: 104733, 2021 04.
Article in English | MEDLINE | ID: covidwho-1386288

ABSTRACT

OBJECTIVE: A recent study on the effects of SARS-CoV-2 infection on the host's transcriptome indicated the perturbation of several pathways associated with neurodegeneration, including but not limited to Parkinson's and Huntington's diseases. The purpose of this study was to determine overlapping pathways between iPD vs. Controls and those associated with SARS-CoV-2 infection. METHODS: Gene set enrichment analyses (GSEA) were performed on gene expression data from tissues donated by idiopathic Parkinson's disease patients (iPD). These included dorsal motor nucleus of the vagus (DMNV), substantia nigra (SN), whole blood (WB) and peripheral blood mononuclear cell samples (PBMC). Enriched pathways detected by GSEA results were subsequently compared to (a) those retrieved by two independently constructed SARS-CoV-2 - host interactomes, as well as (b) previously published pathway data. For all analyses, a false discovery rate (FDR) <0.05 was considered statistically significant. RESULTS: Analysis of iPD data revealed multiple immune response and viral parasitism -related pathways (FDR < 0.05). Head-to-head comparisons as well as confirmatory analyses revealed several pathways and gene ontology (GO) terms overlapping between iPD tissues and SARS-CoV-2 induced transcriptomic changes: "Parkinson's Disease" and "Huntington's Disease" (overlapping in DMNV, ION, SN, and WB; FDR < 0.05), "NAFLD" (overlapping in DMNV, SN, PBMC and WB; FDR < 0.05), mRNA surveillance and proteostasis pathways (All datasets; FDR < 0.5), among others. CONCLUSION: The overlap noted in this comparative transcriptomic study outlines the potential contribution of human coronaviruses in the pathogenesis of iPD. Furthermore, given SARS-CoV-2's neuroinvasive potential, closer scrutiny is warranted towards its contribution in the long-term development of neurodegenerative disease.


Subject(s)
COVID-19/virology , Parkinson Disease/virology , SARS-CoV-2/physiology , Transcriptome , Case-Control Studies , Gene Expression , Gene Ontology , Humans , Parkinson Disease/genetics
18.
Brief Bioinform ; 22(2): 1451-1465, 2021 03 22.
Article in English | MEDLINE | ID: covidwho-1352119

ABSTRACT

This study aimed to identify significant gene expression profiles of the human lung epithelial cells caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We performed a comparative genomic analysis to show genomic observations between SARS-CoV and SARS-CoV-2. A phylogenetic tree has been carried for genomic analysis that confirmed the genomic variance between SARS-CoV and SARS-CoV-2. Transcriptomic analyses have been performed for SARS-CoV-2 infection responses and pulmonary arterial hypertension (PAH) patients' lungs as a number of patients have been identified who faced PAH after being diagnosed with coronavirus disease 2019 (COVID-19). Gene expression profiling showed significant expression levels for SARS-CoV-2 infection responses to human lung epithelial cells and PAH lungs as well. Differentially expressed genes identification and integration showed concordant genes (SAA2, S100A9, S100A8, SAA1, S100A12 and EDN1) for both SARS-CoV-2 and PAH samples, including S100A9 and S100A8 genes that showed significant interaction in the protein-protein interactions network. Extensive analyses of gene ontology and signaling pathways identification provided evidence of inflammatory responses regarding SARS-CoV-2 infections. The altered signaling and ontology pathways that have emerged from this research may influence the development of effective drugs, especially for the people with preexisting conditions. Identification of regulatory biomolecules revealed the presence of active promoter gene of SARS-CoV-2 in Transferrin-micro Ribonucleic acid (TF-miRNA) co-regulatory network. Predictive drug analyses provided concordant drug compounds that are associated with SARS-CoV-2 infection responses and PAH lung samples, and these compounds showed significant immune response against the RNA viruses like SARS-CoV-2, which is beneficial in therapeutic development in the COVID-19 pandemic.


Subject(s)
COVID-19/complications , Hypertension, Pulmonary/complications , SARS-CoV-2/isolation & purification , Algorithms , Biomarkers/metabolism , COVID-19/metabolism , COVID-19/virology , Gene Ontology , Humans , Hypertension, Pulmonary/metabolism , Information Storage and Retrieval , MicroRNAs/metabolism , Phylogeny , Protein Interaction Maps , Transcription Factors/metabolism
19.
Brief Bioinform ; 22(2): 1415-1429, 2021 03 22.
Article in English | MEDLINE | ID: covidwho-1352118

ABSTRACT

With the increasing number of immunoinflammatory complexities, cancer patients have a higher risk of serious disease outcomes and mortality with SARS-CoV-2 infection which is still not clear. In this study, we aimed to identify infectome, diseasome and comorbidities between COVID-19 and cancer via comprehensive bioinformatics analysis to identify the synergistic severity of the cancer patient for SARS-CoV-2 infection. We utilized transcriptomic datasets of SARS-CoV-2 and different cancers from Gene Expression Omnibus and Array Express Database to develop a bioinformatics pipeline and software tools to analyze a large set of transcriptomic data and identify the pathobiological relationships between the disease conditions. Our bioinformatics approach revealed commonly dysregulated genes (MARCO, VCAN, ACTB, LGALS1, HMOX1, TIMP1, OAS2, GAPDH, MSH3, FN1, NPC2, JUND, CHI3L1, GPNMB, SYTL2, CASP1, S100A8, MYO10, IGFBP3, APCDD1, COL6A3, FABP5, PRDX3, CLEC1B, DDIT4, CXCL10 and CXCL8), common gene ontology (GO), molecular pathways between SARS-CoV-2 infections and cancers. This work also shows the synergistic complexities of SARS-CoV-2 infections for cancer patients through the gene set enrichment and semantic similarity. These results highlighted the immune systems, cell activation and cytokine production GO pathways that were observed in SARS-CoV-2 infections as well as breast, lungs, colon, kidney and thyroid cancers. This work also revealed ribosome biogenesis, wnt signaling pathway, ribosome, chemokine and cytokine pathways that are commonly deregulated in cancers and COVID-19. Thus, our bioinformatics approach and tools revealed interconnections in terms of significant genes, GO, pathways between SARS-CoV-2 infections and malignant tumors.


Subject(s)
COVID-19/complications , Neoplasms/complications , COVID-19/virology , Gene Ontology , Humans , SARS-CoV-2/isolation & purification , Signal Transduction , Transcriptome
20.
Brief Bioinform ; 22(2): 1254-1266, 2021 03 22.
Article in English | MEDLINE | ID: covidwho-1343630

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is accountable for the cause of coronavirus disease (COVID-19) that causes a major threat to humanity. As the spread of the virus is probably getting out of control on every day, the epidemic is now crossing the most dreadful phase. Idiopathic pulmonary fibrosis (IPF) is a risk factor for COVID-19 as patients with long-term lung injuries are more likely to suffer in the severity of the infection. Transcriptomic analyses of SARS-CoV-2 infection and IPF patients in lung epithelium cell datasets were selected to identify the synergistic effect of SARS-CoV-2 to IPF patients. Common genes were identified to find shared pathways and drug targets for IPF patients with COVID-19 infections. Using several enterprising Bioinformatics tools, protein-protein interactions (PPIs) network was designed. Hub genes and essential modules were detected based on the PPIs network. TF-genes and miRNA interaction with common differentially expressed genes and the activity of TFs are also identified. Functional analysis was performed using gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathway and found some shared associations that may cause the increased mortality of IPF patients for the SARS-CoV-2 infections. Drug molecules for the IPF were also suggested for the SARS-CoV-2 infections.


Subject(s)
COVID-19/complications , Idiopathic Pulmonary Fibrosis/complications , SARS-CoV-2/genetics , COVID-19/genetics , COVID-19/virology , Datasets as Topic , Epithelial Cells/virology , Gene Ontology , Genes, Viral , Humans , Lung/cytology , Lung/virology , Transcriptome
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