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1.
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
2.
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
3.
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
4.
BMC Med Genomics ; 14(1): 226, 2021 09 17.
Article in English | MEDLINE | ID: covidwho-1542114

ABSTRACT

BACKGROUND: Higher mortality of COVID-19 patients with lung disease is a formidable challenge for the health care system. Genetic association between COVID-19 and various lung disorders must be understood to comprehend the molecular basis of comorbidity and accelerate drug development. METHODS: Lungs tissue-specific neighborhood network of human targets of SARS-CoV-2 was constructed. This network was integrated with lung diseases to build a disease-gene and disease-disease association network. Network-based toolset was used to identify the overlapping disease modules and drug targets. The functional protein modules were identified using community detection algorithms and biological processes, and pathway enrichment analysis. RESULTS: In total, 141 lung diseases were linked to a neighborhood network of SARS-CoV-2 targets, and 59 lung diseases were found to be topologically overlapped with the COVID-19 module. Topological overlap with various lung disorders allows repurposing of drugs used for these disorders to hit the closely associated COVID-19 module. Further analysis showed that functional protein-protein interaction modules in the lungs, substantially hijacked by SARS-CoV-2, are connected to several lung disorders. FDA-approved targets in the hijacked protein modules were identified and that can be hit by exiting drugs to rescue these modules from virus possession. CONCLUSION: Lung diseases are clustered with COVID-19 in the same network vicinity, indicating the potential threat for patients with respiratory diseases after SARS-CoV-2 infection. Pathobiological similarities between lung diseases and COVID-19 and clinical evidence suggest that shared molecular features are the probable reason for comorbidity. Network-based drug repurposing approaches can be applied to improve the clinical conditions of COVID-19 patients.


Subject(s)
COVID-19/drug therapy , COVID-19/epidemiology , Drug Repositioning , Lung Diseases/epidemiology , Pandemics , SARS-CoV-2 , Algorithms , Antiviral Agents/therapeutic use , COVID-19/genetics , Comorbidity , Drug Discovery , Drug Repositioning/methods , Gene Regulatory Networks/drug effects , Host Microbial Interactions/drug effects , Host Microbial Interactions/genetics , Humans , Lung Diseases/drug therapy , Lung Diseases/genetics , Protein Interaction Maps/drug effects , Protein Interaction Maps/genetics , Systems Biology
5.
Sci Rep ; 11(1): 20687, 2021 10 19.
Article in English | MEDLINE | ID: covidwho-1475486

ABSTRACT

This analysis presents a systematic evaluation of the extent of therapeutic opportunities that can be obtained from drug repurposing by connecting drug targets with disease genes. When using FDA-approved indications as a reference level we found that drug repurposing can offer an average of an 11-fold increase in disease coverage, with the maximum number of diseases covered per drug being increased from 134 to 167 after extending the drug targets with their high confidence first neighbors. Additionally, by network analysis to connect drugs to disease modules we found that drugs on average target 4 disease modules, yet the similarity between disease modules targeted by the same drug is generally low and the maximum number of disease modules targeted per drug increases from 158 to 229 when drug targets are neighbor-extended. Moreover, our results highlight that drug repurposing is more dependent on target proteins being shared between diseases than on polypharmacological properties of drugs. We apply our drug repurposing and network module analysis to COVID-19 and show that Fostamatinib is the drug with the highest module coverage.


Subject(s)
COVID-19/drug therapy , Drug Repositioning/methods , Gene Regulatory Networks/drug effects , Protein Interaction Maps/genetics , SARS-CoV-2 , Antiviral Agents/pharmacology , Bayes Theorem , Computational Biology/methods , Drug Delivery Systems , Drug Discovery , Humans , Polypharmacology , Protein Interaction Mapping , United States , United States Food and Drug Administration
6.
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
7.
FEBS J ; 288(17): 5130-5147, 2021 09.
Article in English | MEDLINE | ID: covidwho-1388264

ABSTRACT

SARS-CoV-2 virus has triggered a global pandemic with devastating consequences. The understanding of fundamental aspects of this virus is of extreme importance. In this work, we studied the viral ribonuclease nsp14, one of the most interferon antagonists from SARS-CoV-2. Nsp14 is a multifunctional protein with two distinct activities, an N-terminal 3'-to-5' exoribonuclease (ExoN) and a C-terminal N7-methyltransferase (N7-MTase), both critical for coronaviruses life cycle, indicating nsp14 as a prominent target for the development of antiviral drugs. In coronaviruses, nsp14 ExoN activity is stimulated through the interaction with the nsp10 protein. We have performed a biochemical characterization of nsp14-nsp10 complex from SARS-CoV-2. We confirm the 3'-5' exoribonuclease and MTase activities of nsp14 and the critical role of nsp10 in upregulating the nsp14 ExoN activity. Furthermore, we demonstrate that SARS-CoV-2 nsp14 N7-MTase activity is functionally independent of the ExoN activity and nsp10. A model from SARS-CoV-2 nsp14-nsp10 complex allowed mapping key nsp10 residues involved in this interaction. Our results show that a stable interaction between nsp10 and nsp14 is required for the nsp14-mediated ExoN activity of SARS-CoV-2. We studied the role of conserved DEDD catalytic residues of SARS-CoV-2 nsp14 ExoN. Our results show that motif I of ExoN domain is essential for the nsp14 function, contrasting to the functionality of these residues in other coronaviruses, which can have important implications regarding the specific pathogenesis of SARS-CoV-2. This work unraveled a basis for discovering inhibitors targeting specific amino acids in order to disrupt the assembly of this complex and interfere with coronaviruses replication.


Subject(s)
COVID-19/genetics , Exoribonucleases/genetics , SARS-CoV-2/genetics , Viral Nonstructural Proteins/genetics , Viral Regulatory and Accessory Proteins/genetics , Antiviral Agents/chemistry , Antiviral Agents/therapeutic use , COVID-19/drug therapy , COVID-19/virology , Drug Design , Exoribonucleases/antagonists & inhibitors , Humans , Multiprotein Complexes/drug effects , Multiprotein Complexes/genetics , Protein Interaction Maps/genetics , SARS-CoV-2/drug effects , SARS-CoV-2/pathogenicity , Viral Nonstructural Proteins/antagonists & inhibitors , Viral Regulatory and Accessory Proteins/antagonists & inhibitors , Virus Replication/genetics
8.
Brief Bioinform ; 22(6)2021 11 05.
Article in English | MEDLINE | ID: covidwho-1309589

ABSTRACT

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the newly discovered coronavirus, SARS-CoV-2. Increased severity of COVID-19 has been observed in patients with diabetes mellitus (DM). This study aimed to identify common transcriptional signatures, regulators and pathways between COVID-19 and DM. We have integrated human whole-genome transcriptomic datasets from COVID-19 and DM, followed by functional assessment with gene ontology (GO) and pathway analyses. In peripheral blood mononuclear cells (PBMCs), among the upregulated differentially expressed genes (DEGs), 32 were found to be commonly modulated in COVID-19 and type 2 diabetes (T2D), while 10 DEGs were commonly downregulated. As regards type 1 diabetes (T1D), 21 DEGs were commonly upregulated, and 29 DEGs were commonly downregulated in COVID-19 and T1D. Moreover, 35 DEGs were commonly upregulated in SARS-CoV-2 infected pancreas organoids and T2D islets, while 14 were commonly downregulated. Several GO terms were found in common between COVID-19 and DM. Prediction of the putative transcription factors involved in the upregulation of genes in COVID-19 and DM identified RELA to be implicated in both PBMCs and pancreas. Here, for the first time, we have characterized the biological processes and pathways commonly dysregulated in COVID-19 and DM, which could be in the next future used for the design of personalized treatment of COVID-19 patients suffering from DM as comorbidity.


Subject(s)
COVID-19/genetics , Diabetes Mellitus/genetics , SARS-CoV-2/genetics , Transcriptome/genetics , COVID-19/pathology , COVID-19/virology , Computational Biology , Diabetes Mellitus/pathology , Gene Expression Profiling , Gene Expression Regulation/genetics , Humans , Leukocytes, Mononuclear/pathology , Leukocytes, Mononuclear/virology , Protein Interaction Maps/genetics , SARS-CoV-2/pathogenicity
9.
EMBO J ; 40(17): e107776, 2021 09 01.
Article in English | MEDLINE | ID: covidwho-1299728

ABSTRACT

Host-virus protein-protein interactions play key roles in the life cycle of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We conducted a comprehensive interactome study between the virus and host cells using tandem affinity purification and proximity-labeling strategies and identified 437 human proteins as the high-confidence interacting proteins. Further characterization of these interactions and comparison to other large-scale study of cellular responses to SARS-CoV-2 infection elucidated how distinct SARS-CoV-2 viral proteins participate in its life cycle. With these data mining, we discovered potential drug targets for the treatment of COVID-19. The interactomes of two key SARS-CoV-2-encoded viral proteins, NSP1 and N, were compared with the interactomes of their counterparts in other human coronaviruses. These comparisons not only revealed common host pathways these viruses manipulate for their survival, but also showed divergent protein-protein interactions that may explain differences in disease pathology. This comprehensive interactome of SARS-CoV-2 provides valuable resources for the understanding and treating of this disease.


Subject(s)
COVID-19/genetics , Coronavirus Nucleocapsid Proteins/genetics , SARS-CoV-2/genetics , Viral Nonstructural Proteins/genetics , COVID-19/pathology , COVID-19/virology , Host-Pathogen Interactions/genetics , Humans , Protein Interaction Maps/genetics , SARS-CoV-2/pathogenicity , Virus Replication/genetics
10.
Genomics ; 113(4): 2158-2170, 2021 07.
Article in English | MEDLINE | ID: covidwho-1230819

ABSTRACT

Recently, the SARS-CoV-2 variants from the United Kingdom (UK), South Africa, and Brazil have received much attention for their increased infectivity, potentially high virulence, and possible threats to existing vaccines and antibody therapies. The question remains if there are other more infectious variants transmitted around the world. We carry out a large-scale study of 506,768 SARS-CoV-2 genome isolates from patients to identify many other rapidly growing mutations on the spike (S) protein receptor-binding domain (RBD). We reveal that essentially all 100 most observed mutations strengthen the binding between the RBD and the host angiotensin-converting enzyme 2 (ACE2), indicating the virus evolves toward more infectious variants. In particular, we discover new fast-growing RBD mutations N439K, S477N, S477R, and N501T that also enhance the RBD and ACE2 binding. We further unveil that mutation N501Y involved in United Kingdom (UK), South Africa, and Brazil variants may moderately weaken the binding between the RBD and many known antibodies, while mutations E484K and K417N found in South Africa and Brazilian variants, L452R and E484Q found in India variants, can potentially disrupt the binding between the RBD and many known antibodies. Among these RBD mutations, L452R is also now known as part of the California variant B.1.427. Finally, we hypothesize that RBD mutations that can simultaneously make SARS-CoV-2 more infectious and disrupt the existing antibodies, called vaccine escape mutations, will pose an imminent threat to the current crop of vaccines. A list of most likely vaccine escape mutations is given, including S494P, Q493L, K417N, F490S, F486L, R403K, E484K, L452R, K417T, F490L, E484Q, and A475S. Mutation T478K appears to make the Mexico variant B.1.1.222 the most infectious one. Our comprehensive genetic analysis and protein-protein binding study show that the genetic evolution of SARS-CoV-2 on the RBD, which may be regulated by host gene editing, viral proofreading, random genetic drift, and natural selection, gives rise to more infectious variants that will potentially compromise existing vaccines and antibody therapies.


Subject(s)
Angiotensin-Converting Enzyme 2/genetics , COVID-19 Vaccines/genetics , COVID-19/genetics , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/genetics , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/virology , COVID-19 Vaccines/adverse effects , Humans , Mutation , Protein Binding/genetics , Protein Interaction Maps/genetics , SARS-CoV-2/pathogenicity
11.
Infect Genet Evol ; 93: 104921, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1230672

ABSTRACT

The development of therapeutic targets for COVID-19 relies on understanding the molecular mechanism of pathogenesis. Identifying genes or proteins involved in the infection mechanism is the key to shedding light on the complex molecular mechanisms. The combined effort of many laboratories distributed throughout the world has produced protein and genetic interactions. We integrated available results and obtained a host protein-protein interaction network composed of 1432 human proteins. Next, we performed network centrality analysis to identify critical proteins in the derived network. Finally, we performed a functional enrichment analysis of central proteins. We observed that the identified proteins are primarily associated with several crucial pathways, including cellular process, signaling transduction, neurodegenerative diseases. We focused on the proteins that are involved in human respiratory tract diseases. We highlighted many potential therapeutic targets, including RBX1, HSPA5, ITCH, RAB7A, RAB5A, RAB8A, PSMC5, CAPZB, CANX, IGF2R, and HSPA1A, which are central and also associated with multiple diseases.


Subject(s)
COVID-19/metabolism , Host-Pathogen Interactions/physiology , Protein Interaction Maps , SARS-CoV-2/pathogenicity , Gene Ontology , Humans , Protein Interaction Maps/genetics , Proteins/genetics , Proteins/metabolism , Viral Proteins/metabolism
12.
Brief Bioinform ; 22(6)2021 11 05.
Article in English | MEDLINE | ID: covidwho-1228438

ABSTRACT

Coronavirus Disease 2019 (COVID-19), although most commonly demonstrates respiratory symptoms, but there is a growing set of evidence reporting its correlation with the digestive tract and faeces. Interestingly, recent studies have shown the association of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection with gastrointestinal symptoms in infected patients but any sign of respiratory issues. Moreover, some studies have also shown that the presence of live SARS-CoV-2 virus in the faeces of patients with COVID-19. Therefore, the pathophysiology of digestive symptoms associated with COVID-19 has raised a critical need for comprehensive investigative efforts. To address this issue we have developed a bioinformatics pipeline involving a system biological framework to identify the effects of SARS-CoV-2 messenger RNA expression on deciphering its association with digestive symptoms in COVID-19 positive patients. Using two RNA-seq datasets derived from COVID-19 positive patients with celiac (CEL), Crohn's (CRO) and ulcerative colitis (ULC) as digestive disorders, we have found a significant overlap between the sets of differentially expressed genes from SARS-CoV-2 exposed tissue and digestive tract disordered tissues, reporting 7, 22 and 13 such overlapping genes, respectively. Moreover, gene set enrichment analysis, comprehensive analyses of protein-protein interaction network, gene regulatory network, protein-chemical agent interaction network revealed some critical association between SARS-CoV-2 infection and the presence of digestive disorders. The infectome, diseasome and comorbidity analyses also discover the influences of the identified signature genes in other risk factors of SARS-CoV-2 infection to human health. We hope the findings from this pathogenetic analysis may reveal important insights in deciphering the complex interplay between COVID-19 and digestive disorders and underpins its significance in therapeutic development strategy to combat against COVID-19 pandemic.


Subject(s)
COVID-19/drug therapy , Gastrointestinal Tract/virology , SARS-CoV-2/drug effects , COVID-19/virology , Comorbidity , Computational Biology , Gastrointestinal Tract/pathology , Gene Regulatory Networks/genetics , Humans , Pandemics , Protein Interaction Maps/genetics , SARS-CoV-2/pathogenicity , Systems Biology
13.
Mol Cell ; 81(13): 2838-2850.e6, 2021 07 01.
Article in English | MEDLINE | ID: covidwho-1202181

ABSTRACT

SARS-CoV-2 is an RNA virus whose success as a pathogen relies on its abilities to repurpose host RNA-binding proteins (RBPs) and to evade antiviral RBPs. To uncover the SARS-CoV-2 RNA interactome, we here develop a robust ribonucleoprotein (RNP) capture protocol and identify 109 host factors that directly bind to SARS-CoV-2 RNAs. Applying RNP capture on another coronavirus, HCoV-OC43, revealed evolutionarily conserved interactions between coronaviral RNAs and host proteins. Transcriptome analyses and knockdown experiments delineated 17 antiviral RBPs, including ZC3HAV1, TRIM25, PARP12, and SHFL, and 8 proviral RBPs, such as EIF3D and CSDE1, which are responsible for co-opting multiple steps of the mRNA life cycle. This also led to the identification of LARP1, a downstream target of the mTOR signaling pathway, as an antiviral host factor that interacts with the SARS-CoV-2 RNAs. Overall, this study provides a comprehensive list of RBPs regulating coronaviral replication and opens new avenues for therapeutic interventions.


Subject(s)
Autoantigens/genetics , COVID-19/genetics , RNA, Viral/genetics , Ribonucleoproteins/genetics , SARS-CoV-2/genetics , COVID-19/virology , Coronavirus OC43, Human/genetics , Coronavirus OC43, Human/pathogenicity , HEK293 Cells , Host-Pathogen Interactions/genetics , Humans , Protein Binding/genetics , Protein Interaction Maps/genetics , RNA-Binding Proteins/genetics , SARS-CoV-2/pathogenicity , TOR Serine-Threonine Kinases/genetics , Transcription Factors/genetics , Transcriptome/genetics , Tripartite Motif Proteins/genetics , Ubiquitin-Protein Ligases/genetics , Virus Replication/genetics
14.
Mediators Inflamm ; 2021: 6635925, 2021.
Article in English | MEDLINE | ID: covidwho-1175215

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was initially identified in China and currently worldwide dispersed, resulting in the coronavirus disease 2019 (COVID-19) pandemic. Notably, COVID-19 is characterized by systemic inflammation. However, the potential mechanisms of the "cytokine storm" of COVID-19 are still limited. In this study, fourteen peripheral blood samples from COVID-19 patients (n = 10) and healthy donors (n = 4) were collected to perform the whole-transcriptome sequencing. Lung tissues of COVID-19 patients (70%) presenting with ground-glass opacity. Also, the leukocytes and lymphocytes were significantly decreased in COVID-19 compared with the control group (p < 0.05). In total, 25,482 differentially expressed messenger RNAs (DE mRNA), 23 differentially expressed microRNAs (DE miRNA), and 410 differentially expressed long noncoding RNAs (DE lncRNAs) were identified in the COVID-19 samples compared to the healthy controls. Gene Ontology (GO) analysis showed that the upregulated DE mRNAs were mainly involved in antigen processing and presentation of endogenous antigen, positive regulation of T cell mediated cytotoxicity, and positive regulation of gamma-delta T cell activation. The downregulated DE mRNAs were mainly concentrated in the glycogen biosynthetic process. We also established the protein-protein interaction (PPI) networks of up/downregulated DE mRNAs and identified 4 modules. Functional enrichment analyses indicated that these module targets were associated with positive regulation of cytokine production, cytokine-mediated signaling pathway, leukocyte differentiation, and migration. A total of 6 hub genes were selected in the PPI module networks including AKT1, TNFRSF1B, FCGR2A, CXCL8, STAT3, and TLR2. Moreover, a competing endogenous RNA network showed the interactions between lncRNAs, mRNAs, and miRNAs. Our results highlight the potential pathogenesis of excessive cytokine production such as MSTRG.119845.30/hsa-miR-20a-5p/TNFRSF1B, MSTRG.119845.30/hsa-miR-29b-2-5p/FCGR2A, and MSTRG.106112.2/hsa-miR-6501-5p/STAT3 axis, which may also play an important role in the development of ground-glass opacity in COVID-19 patients. This study gives new insights into inflammation regulatory mechanisms of coding and noncoding RNAs in COVID-19, which may provide novel diagnostic biomarkers and therapeutic avenues for COVID-19 patients.


Subject(s)
COVID-19/blood , COVID-19/genetics , RNA/blood , RNA/genetics , SARS-CoV-2 , Adult , Aged , COVID-19/complications , Case-Control Studies , Cytokine Release Syndrome/blood , Cytokine Release Syndrome/etiology , Cytokine Release Syndrome/genetics , Cytokines/biosynthesis , Cytokines/genetics , Female , Gene Expression , Humans , Inflammation Mediators/blood , Male , MicroRNAs/blood , MicroRNAs/genetics , Middle Aged , Pandemics , Protein Interaction Maps/genetics , RNA, Long Noncoding/blood , RNA, Long Noncoding/genetics , RNA, Messenger/blood , RNA, Messenger/genetics , Sequence Analysis, RNA , Signal Transduction , Whole Exome Sequencing , Young Adult
15.
Front Immunol ; 12: 597399, 2021.
Article in English | MEDLINE | ID: covidwho-1167337

ABSTRACT

There exists increasing evidence that people with preceding medical conditions, such as diabetes and cancer, have a higher risk of infection with SARS-CoV-2 and are more vulnerable to severe disease. To get insights into the possible role of the immune system upon COVID-19 infection, 2811 genes of the gene ontology term "immune system process GO: 0002376" were selected for coexpression analysis of the human targets of SARS-CoV-2 (HT-SARS-CoV-2) ACE2, TMPRSS2, and FURIN in tissue samples from patients with cancer and diabetes mellitus. The network between HT-SARS-CoV-2 and immune system process genes was analyzed based on functional protein associations using STRING. In addition, STITCH was employed to determine druggable targets. DPP4 was the only immune system process gene, which was coexpressed with the three HT-SARS-CoV-2 genes, while eight other immune genes were at least coexpressed with two HT-SARS-CoV-2 genes. STRING analysis between immune and HT-SARS-CoV-2 genes plotted 19 associations of which there were eight common networking genes in mixed healthy (323) and pan-cancer (11003) tissues in addition to normal (87), cancer (90), and diabetic (128) pancreatic tissues. Using this approach, three commonly applicable druggable connections between HT-SARS-CoV-2 and immune system process genes were identified. These include positive associations of ACE2-DPP4 and TMPRSS2-SRC as well as a negative association of FURIN with ADAM17. Furthermore, 16 drugs were extracted from STITCH (score <0.8) with 32 target genes. Thus, an immunological network associated with HT-SARS-CoV-2 using bioinformatics tools was identified leading to novel therapeutic opportunities for COVID-19.


Subject(s)
Diabetes Mellitus/metabolism , Neoplasms/metabolism , SARS-CoV-2/drug effects , SARS-CoV-2/metabolism , Spike Glycoprotein, Coronavirus/metabolism , Angiotensin-Converting Enzyme 2/genetics , Angiotensin-Converting Enzyme 2/metabolism , Antiviral Agents/chemistry , Antiviral Agents/pharmacology , COVID-19/drug therapy , COVID-19/genetics , COVID-19/immunology , COVID-19/metabolism , Databases, Genetic , Diabetes Mellitus/genetics , Diabetes Mellitus/immunology , Diabetes Mellitus/virology , Dipeptidyl Peptidase 4/genetics , Dipeptidyl Peptidase 4/metabolism , Furin/genetics , Furin/metabolism , Gene Expression Regulation/immunology , Gene Ontology , Genome-Wide Association Study , Genomics , Humans , Lymphocytes/immunology , Lymphocytes/metabolism , Neoplasms/genetics , Neoplasms/immunology , Neoplasms/virology , Pancreas/immunology , Pancreas/metabolism , Pancreas/virology , Protein Interaction Maps/genetics , Protein Interaction Maps/immunology , SARS-CoV-2/genetics , SARS-CoV-2/immunology , Serine Endopeptidases/genetics , Serine Endopeptidases/metabolism
16.
Hum Genomics ; 15(1): 18, 2021 03 16.
Article in English | MEDLINE | ID: covidwho-1136250

ABSTRACT

BACKGROUND: In the novel coronavirus pandemic, the high infection rate and high mortality have seriously affected people's health and social order. To better explore the infection mechanism and treatment, the three-dimensional structure of human bronchus has been employed in a better in-depth study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: We downloaded a separate microarray from the Integrated Gene Expression System (GEO) on a human bronchial organoids sample to identify differentially expressed genes (DEGS) and analyzed it with R software. After processing with R software, Gene Ontology (GO) and Kyoto PBMCs of Genes and Genomes (KEGG) were analyzed, while a protein-protein interaction (PPI) network was constructed to show the interactions and influence relationships between these differential genes. Finally, the selected highly connected genes, which are called hub genes, were verified in CytoHubba plug-in. RESULTS: In this study, a total of 966 differentially expressed genes, including 490 upregulated genes and 476 downregulated genes were used. Analysis of GO and KEGG revealed that these differentially expressed genes were significantly enriched in pathways related to immune response and cytokines. We construct protein-protein interaction network and identify 10 hub genes, including IL6, MMP9, IL1B, CXCL8, ICAM1, FGF2, EGF, CXCL10, CCL2, CCL5, CXCL1, and FN1. Finally, with the help of GSE150728, we verified that CXCl1, CXCL8, CXCL10, CCL5, EGF differently expressed before and after SARS-CoV-2 infection in clinical patients. CONCLUSIONS: In this study, we used mRNA expression data from GSE150819 to preliminarily confirm the feasibility of hBO as an in vitro model to further study the pathogenesis and potential treatment of COVID-19. Moreover, based on the mRNA differentiated expression of this model, we found that CXCL8, CXCL10, and EGF are hub genes in the process of SARS-COV-2 infection, and we emphasized their key roles in SARS-CoV-2 infection. And we also suggested that further study of these hub genes may be beneficial to treatment, prognostic prediction of COVID-19.


Subject(s)
Bronchi/virology , COVID-19/genetics , Gene Expression Regulation , Bronchi/physiology , Chemokine CXCL10/genetics , Epidermal Growth Factor/genetics , Host-Pathogen Interactions/genetics , Humans , Interleukin-8/genetics , Organoids , Protein Interaction Maps/genetics , Software
17.
Biotechniques ; 69(4): 239-241, 2020 10.
Article in English | MEDLINE | ID: covidwho-1067503

ABSTRACT

There are up to 650,000 'undruggable' protein-protein interactions (PPIs) in the human interactome that can be potentially considered as novel therapeutic targets. How does the 'undruggable' become 'druggable'?


Subject(s)
Drug Discovery , Molecular Targeted Therapy/trends , Pharmaceutical Preparations , Protein Interaction Maps/genetics , Humans , Protein Binding/genetics , Protein Interaction Maps/drug effects
18.
Chem Biol Interact ; 335: 109370, 2021 Feb 01.
Article in English | MEDLINE | ID: covidwho-1014379

ABSTRACT

The aberrant expression level of SARS-CoV-2 cell receptor gene ACE2 was reported in lung adenocarcinoma (LUAD) comorbidity of COVID-19. However, the association of ACE2 expression levels with immunosuppression and metabolic reprogramming in LUAD remains lacking. We investigated the expression level of ACE2, an association of ACE2 expression level with various types of immune signatures, immune ratios, and pathways. We employed a weighted gene co-expression network analysis (WGCNA) R package to identify the gene modules and investigated prognostic roles of hub genes in LUAD. Overexpression of ACE2 level was found in LUAD and ACE2 expression was negatively associated with various types of immune signatures including CD8+ T cells, CD4+ regulatory T cells, NK cells, and T cell activation. Besides, ACE2 upregulation was not only associated with CD8+ T cell/CD4+ regulatory T cell ratios but also linked with downregulation of immune-markers including CD8A, KLRC1, GZMA, GZMB, NKG7, CCL4, and IFNG. Moreover, the ACE2 expression level was found to be associated with the enrichment level of various metabolic pathways and it was also found that the metabolic pathways are directly positively correlated with the increased expression levels of ACE2, indicating that the overexpression of ACE2 is associated with metabolic reprogramming in LUAD. Furthermore, WGCNA based analysis revealed the gene modules in the high-ACE2-expression-level group of LUAD and identified GCLC and SLC7A11 hub genes which are not only highly expressed in lung adenocarcinoma but also correlated with the poor survival prognosis. Our analysis of ACE2 in LUAD tissues suggests that ACE2 is not only a receptor but is also associated with immunosuppression and metabolic reprogramming. This study underlines the clue for understanding the clinical significance of ACE2 in COVID-19 patients with LUAD comorbidity.


Subject(s)
Adenocarcinoma of Lung/metabolism , Angiotensin-Converting Enzyme 2/metabolism , Immunity, Cellular/genetics , Immunity, Innate/genetics , Lung Neoplasms/metabolism , Adenocarcinoma of Lung/epidemiology , Amino Acid Transport System y+/genetics , Angiotensin-Converting Enzyme 2/genetics , COVID-19/epidemiology , Comorbidity , Computational Biology , Databases, Genetic/statistics & numerical data , Female , Gene Expression Regulation, Neoplastic , Glutamate-Cysteine Ligase/genetics , Humans , Lung Neoplasms/epidemiology , Lymphocyte Activation/genetics , Male , Non-Smokers , Protein Interaction Maps/genetics , SARS-CoV-2 , Smokers , T-Lymphocytes/metabolism , Transcriptome , Up-Regulation
19.
J Proteome Res ; 19(11): 4553-4566, 2020 11 06.
Article in English | MEDLINE | ID: covidwho-974862

ABSTRACT

While the COVID-19 pandemic is causing important loss of life, knowledge of the effects of the causative SARS-CoV-2 virus on human cells is currently limited. Investigating protein-protein interactions (PPIs) between viral and host proteins can provide a better understanding of the mechanisms exploited by the virus and enable the identification of potential drug targets. We therefore performed an in-depth computational analysis of the interactome of SARS-CoV-2 and human proteins in infected HEK 293 cells published by Gordon et al. (Nature 2020, 583, 459-468) to reveal processes that are potentially affected by the virus and putative protein binding sites. Specifically, we performed a set of network-based functional and sequence motif enrichment analyses on SARS-CoV-2-interacting human proteins and on PPI networks generated by supplementing viral-host PPIs with known interactions. Using a novel implementation of our GoNet algorithm, we identified 329 Gene Ontology terms for which the SARS-CoV-2-interacting human proteins are significantly clustered in PPI networks. Furthermore, we present a novel protein sequence motif discovery approach, LESMoN-Pro, that identified 9 amino acid motifs for which the associated proteins are clustered in PPI networks. Together, these results provide insights into the processes and sequence motifs that are putatively implicated in SARS-CoV-2 infection and could lead to potential therapeutic targets.


Subject(s)
Betacoronavirus , Coronavirus Infections , Host-Pathogen Interactions/genetics , Pandemics , Pneumonia, Viral , Protein Interaction Maps , Algorithms , Amino Acid Motifs , Betacoronavirus/chemistry , Betacoronavirus/metabolism , Betacoronavirus/pathogenicity , COVID-19 , Cluster Analysis , Coronavirus Infections/metabolism , Coronavirus Infections/virology , Gene Ontology , HEK293 Cells , Humans , Molecular Sequence Annotation , Pneumonia, Viral/metabolism , Pneumonia, Viral/virology , Protein Binding , Protein Interaction Maps/genetics , Protein Interaction Maps/physiology , Proteins/chemistry , Proteins/classification , Proteins/genetics , Proteins/metabolism , SARS-CoV-2 , Viral Proteins/chemistry , Viral Proteins/genetics , Viral Proteins/metabolism
20.
Med Sci Monit ; 26: e928861, 2020 Dec 14.
Article in English | MEDLINE | ID: covidwho-976582

ABSTRACT

BACKGROUND Rhinovirus (RV) is the most common pathogen involved in asthma, and COVID-19, caused by SARS-COV-2, may be more severe in asthma patients. Here, we applied integrated bioinformatics to identify potential key genes and cytokine pathways after RV infection in asthma, and analyzed changes in angiotensin-converting enzyme 2 (ACE2), the cellular receptor of SARS-COV-2. MATERIAL AND METHODS The gene expression profile dataset GSE149273 was downloaded from NCBI-GEO, which included 90 samples of non-infected, RVA, and RVC. Differentially expressed genes (DEGs) were identified using t tests in the limma R package, and subsequently investigated by GO, KEGG, and DO analysis. Moreover, the expression of ACE2 and the proportion of immune cells were further analyzed to determine the effects of RV on cytokines. RESULTS A total of 555 DEGs of RVA and 421 of RVC were identified. There were 415 DEGs in RVA and RVC, of which 406 were upregulated and 9 were downregulated. The functional enrichment analysis showed that most DEGs were obviously enriched in cytokines, and were mainly enriched in "influenza" and "hepatitis C, chronic". In addition, the expression of ACE2 increased significantly and the proportion of immune cytokines significantly changed after RV infection. Our results suggest that RV can activate the cytokine pathway associated with COVID-19 by increasing ACE2. CONCLUSIONS The DEGs and related cytokine pathways after asthma RV infection identified using integrated bioinformatics in this study elucidate the potential link between RV and COVID-19.


Subject(s)
Angiotensin-Converting Enzyme 2/metabolism , Asthma/immunology , COVID-19/immunology , Cytokines/metabolism , Picornaviridae Infections/immunology , Protein Interaction Maps/genetics , Asthma/complications , COVID-19/genetics , COVID-19/virology , Computational Biology , Datasets as Topic , Gene Expression Profiling , Gene Expression Regulation/immunology , Host-Pathogen Interactions/genetics , Host-Pathogen Interactions/immunology , Humans , Picornaviridae Infections/genetics , Protein Interaction Maps/immunology , Rhinovirus/immunology , SARS-CoV-2/immunology , Signal Transduction/genetics , Signal Transduction/immunology
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