Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 860
Filter
1.
Cells ; 11(22)2022 Nov 18.
Article in English | MEDLINE | ID: covidwho-2142562

ABSTRACT

Firstly, I apologize for the delayed publication of this Special Issue in the form of a book title [...].


Subject(s)
Computational Biology , MicroRNAs , MicroRNAs/genetics
2.
Front Immunol ; 13: 1052850, 2022.
Article in English | MEDLINE | ID: covidwho-2142039

ABSTRACT

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged as a contemporary hazard to people. It has been known that COVID-19 can both induce heart failure (HF) and raise the risk of patient mortality. However, the mechanism underlying the association between COVID-19 and HF remains unclear. The common molecular pathways between COVID-19 and HF were identified using bioinformatic and systems biology techniques. Transcriptome analysis was performed to identify differentially expressed genes (DEGs). To identify gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, common DEGs were used for enrichment analysis. The results showed that COVID-19 and HF have several common immune mechanisms, including differentiation of T helper (Th) 1, Th 2, Th 17 cells; activation of lymphocytes; and binding of major histocompatibility complex class I and II protein complexes. Furthermore, a protein-protein interaction network was constructed to identify hub genes, and immune cell infiltration analysis was performed. Six hub genes (FCGR3A, CD69, IFNG, CCR7, CCL5, and CCL4) were closely associated with COVID-19 and HF. These targets were associated with immune cells (central memory CD8 T cells, T follicular helper cells, regulatory T cells, myeloid-derived suppressor cells, plasmacytoid dendritic cells, macrophages, eosinophils, and neutrophils). Additionally, transcription factors, microRNAs, drugs, and chemicals that are closely associated with COVID-19 and HF were identified through the interaction network.


Subject(s)
COVID-19 , Heart Failure , Humans , Systems Biology , Computational Biology , SARS-CoV-2 , Molecular Targeted Therapy , Heart Failure/genetics
3.
Front Immunol ; 13: 975848, 2022.
Article in English | MEDLINE | ID: covidwho-2142004

ABSTRACT

Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with a high mortality rate. In clinical practice, we have noted that many critically ill or critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. In addition, it has been demonstrated that severe COVID-19 has some pathological similarities with sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted and neutrophil dysfunction. Considering the parallels between COVID-19 and non-SARS-CoV-2 induced sepsis (hereafter referred to as sepsis), the aim of this study was to analyze the underlying molecular mechanisms between these two diseases by bioinformatics and a systems biology approach, providing new insights into the pathogenesis of COVID-19 and the development of new treatments. Specifically, the gene expression profiles of COVID-19 and sepsis patients were obtained from the Gene Expression Omnibus (GEO) database and compared to extract common differentially expressed genes (DEGs). Subsequently, common DEGs were used to investigate the genetic links between COVID-19 and sepsis. Based on enrichment analysis of common DEGs, many pathways closely related to inflammatory response were observed, such as Cytokine-cytokine receptor interaction pathway and NF-kappa B signaling pathway. In addition, protein-protein interaction networks and gene regulatory networks of common DEGs were constructed, and the analysis results showed that ITGAM may be a potential key biomarker base on regulatory analysis. Furthermore, a disease diagnostic model and risk prediction nomogram for COVID-19 were constructed using machine learning methods. Finally, potential therapeutic agents, including progesterone and emetine, were screened through drug-protein interaction networks and molecular docking simulations. We hope to provide new strategies for future research and treatment related to COVID-19 by elucidating the pathogenesis and genetic mechanisms between COVID-19 and sepsis.


Subject(s)
COVID-19 , Sepsis , Biomarkers , Computational Biology/methods , Critical Illness , Cytokines/genetics , Emetine , Gene Expression Profiling/methods , Humans , Molecular Docking Simulation , NF-kappa B/genetics , Progesterone , Receptors, Cytokine/genetics , SARS-CoV-2 , Sepsis/genetics , Sepsis/metabolism
4.
An Acad Bras Cienc ; 94(suppl 3): e20201380, 2022.
Article in English | MEDLINE | ID: covidwho-2140907

ABSTRACT

This study aimed to verify the action of bioactive compounds from Brazilian plants on the leader genes involved in the SARS-CoV-2 pathway. The main human genes involved were identified in GeneCards and UNIPROT platforms, and an interaction network between leader genes was established in the STRING database. To design chemo-biology interactome networks and elucidate the interplay between genes related to the disease and bioactive plant compounds, the metasearch engine STITCH 3.1 was used. The analysis revealed that SMAD3 and CASP3 genes are leader genes, suggesting that the mechanism of action of the virus on host cells is associated with the molecular effects of these genes. Furthermore, the bioactive plant compounds, such as ascorbate, benzoquinone, ellagic acid, and resveratrol was identified as a promising adjuvant for the treatment inhibiting CASP3-mediated apoptosis. Bioactive plant compounds were verified as the main pathways enriched with KEGG and related to viral infection, assessments/immune/infections, and cell proliferation, which are potentially used for respiratory viral infections. The best-ranked molecule docked in the CASP3 binding site was rutin, while the SMAD3 binding site was resveratrol. In conclusion, this work identified several bioactive compounds from Brazilian plants showing potential antiviral functions that can directly or indirectly inhibit the new coronavirus.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/genetics , Computational Biology , Caspase 3 , Resveratrol/pharmacology , COVID-19/drug therapy
5.
BMC Oral Health ; 22(1): 520, 2022 11 21.
Article in English | MEDLINE | ID: covidwho-2139248

ABSTRACT

BACKGROUND: 2019 Coronavirus disease (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The COVID-19 pandemic has already had a serious influence on human existence, causing a huge public health concern for countries all around the world. Because SARS-CoV-2 infection can be spread by contact with the oral cavity, the link between oral illness and COVID-19 is gaining traction. Through bioinformatics approaches, we explored the possible molecular mechanisms linking the COVID-19 and periodontitis to provide the basis and direction for future research. METHODS: Transcriptomic data from blood samples of patients with COVID-19 and periodontitis was downloaded from the Gene Expression Omnibus database. The shared differentially expressed genes were identified. The analysis of Gene Ontology, Kyoto Encyclopedia of Genesand Genomes pathway, and protein-protein interaction network was conducted for the shared differentially expressed genes. Top 5 hub genes were selected through Maximal Clique Centrality algorithm. Then mRNA-miRNA network of the hub genes was established based on miRDB database, miRTarbase database and Targetscan database. The Least absolute shrinkage and selection operator regression analysis was used to discover possible biomarkers, which were then investigated in relation to immune-related genes. RESULTS: Fifty-six shared genes were identified through differential expression analysis in COVID-19 and periodontitis. The function of these genes was enriched in regulation of hormone secretion, regulation of secretion by cell. Myozenin 2 was identified through Least absolute shrinkage and selection operator regression Analysis, which was down-regulated in both COVID-19 and periodontitis. There was a positive correlation between Myozenin 2 and the biomarker of activated B cell, memory B cell, effector memory CD4 T cell, Type 17 helper cell, T follicular helper cell and Type 2 helper cell. CONCLUSION: By bioinformatics analysis, Myozenin 2 is predicted to correlate to the pathogenesis and immune infiltrating of COVID-19 and periodontitis. However, more clinical and experimental researches are needed to validate the function of Myozenin 2.


Subject(s)
COVID-19 , Periodontitis , Humans , Computational Biology , Gene Regulatory Networks , Pandemics , SARS-CoV-2 , Periodontitis/genetics , Biomarkers/metabolism
6.
Clin Chem ; 68(2): 264-265, 2022 02 01.
Article in English | MEDLINE | ID: covidwho-2135117
7.
Front Endocrinol (Lausanne) ; 13: 935906, 2022.
Article in English | MEDLINE | ID: covidwho-2123396

ABSTRACT

Background: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a pandemic in many countries around the world. The virus is highly contagious and has a high fatality rate. Lung adenocarcinoma (LUAD) patients may have higher susceptibility and mortality to COVID-19. While Paxlovid is the first oral drug approved by the U.S. Food and Drug Administration (FDA) for COVID-19, its specific drug mechanism for lung cancer patients infected with COVID-19 remains to be further studied. Methods: COVID-19 related genes were obtained from NCBI, GeneCards, and KEGG, and then the transcriptome data for LUAD was downloaded from TCGA. The drug targets of Paxlovid were revealed through BATMAN-TCM, DrugBank, SwissTargetPrediction, and TargetNet. The genes related to susceptibility to COVID-19 in LUAD patients were obtained through differential analysis. The interaction of LUAD/COVID-19 related genes was evaluated and displayed by STRING, and a COX risk regression model was established to screen and evaluate the correlation between genes and clinical characteristics. The Venn diagram was drawn to select the candidate targets of Paxlovid against LUAD/COVID-19, and the functional analysis of the target genes was performed using KEGG and GO enrichment analysis. Finally, Cytoscape was used to screen and visualize the Hub Gene, and Autodock was used for molecular docking between the drug and the target. Result: Bioinformatics analysis was performed by combining COVID-19-related genes with the gene expression and clinical data of LUAD, including analysis of prognosis-related genes, survival rate, and hub genes screened out by the prognosis model. The key targets of Paxlovid against LUAD/COVID-19 were obtained through network pharmacology, the most important targets include IL6, IL12B, LBP. Furthermore, pathway analysis showed that Paxlovid modulates the IL-17 signaling pathway, the cytokine-cytokine receptor interaction, during LUAD/COVID-19 treatment. Conclusions: Based on bioinformatics and network pharmacology, the prognostic signature of LUAD/COVID-19 patients was screened. And identified the potential therapeutic targets and molecular pathways of Paxlovid Paxlovid in the treatment of LUAD/COVID. As promising features, prognostic signatures and therapeutic targets shed light on improving the personalized management of patients with LUAD.


Subject(s)
Adenocarcinoma of Lung , COVID-19 , Lung Neoplasms , Adenocarcinoma of Lung/genetics , Adenocarcinoma of Lung/metabolism , COVID-19/drug therapy , COVID-19/genetics , Computational Biology , Drug Combinations , Humans , Interleukin-17 , Interleukin-6 , Lactams , Leucine , Molecular Docking Simulation , Network Pharmacology , Nitriles , Proline , Receptors, Cytokine , Ritonavir , SARS-CoV-2/genetics , United States
8.
Eur Rev Med Pharmacol Sci ; 26(21): 8129-8143, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2118079

ABSTRACT

OBJECTIVE: A lack of objective biomarkers is preventing the screening and diagnosis of COVID-19 combined with major depression disorder (COVID-19-MDD). The purpose of this study was to identify diagnostic biomarkers and gene regulatory mechanisms associated with autophagy; a crucial process significantly involved in the pathogenesis of COVID-19-MDD. MATERIALS AND METHODS: In this study, differentially expressed genes (DEGs) were screened using GSE98793 from the GEO2R analysis (GEO) database, and intersected with the COVID-19-related gene (CRGs) and autophagy-related genes (ARGs) to obtain common genes involved in. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of these common genes were performed. Subsequently, the transcription factor (TF)-gene regulatory network and comorbidity network were constructed. In addition, 10 drug candidates were screened using the DSigDB database. To identify diagnostic markers, we used LASSO regression. RESULTS: In total, 13 common genes were screened, which were primarily enriched in lysosomes, endoplasmic reticulum membranes, and other endomembrane systems also associated with autophagy. Additionally, these genes were involved in neurological cell signaling and have a functional role in pathways related to vascular endothelial growth factor, tyrosine kinase, autophagy, inflammation, immunity, and carcinogenesis. Tumors and psychiatric disorders were the most highly linked diseases to COVID-19. Finally, ten drug candidates and eight diagnostic markers (STX17, NRG1, RRAGD, XPO1, HERC1, HSP90AB1, EPHB2, and S1PR3) were screened. CONCLUSIONS: This is the first study to screen eight diagnostic markers and construct a gene regulatory network for COVID-19-MDD from the perspective of autophagy. The findings of our study provide novel insights into the diagnosis and treatment of COVID-19-MDD.


Subject(s)
COVID-19 , Depressive Disorder, Major , Humans , Computational Biology , COVID-19/genetics , Vascular Endothelial Growth Factor A , Biomarkers , Machine Learning , Autophagy/genetics
9.
Int J Mol Sci ; 23(22)2022 Nov 17.
Article in English | MEDLINE | ID: covidwho-2116050

ABSTRACT

MSClustering is an efficient software package for visualizing and analyzing complex networks in Cytoscape. Based on the distance matrix of a network that it takes as input, MSClustering automatically displays the minimum span clustering (MSC) of the network at various characteristic levels. To produce a view of the overall network structure, the app then organizes the multi-level results into an MSC tree. Here, we demonstrate the package's phylogenetic applications in studying the evolutionary relationships of complex systems, including 63 beta coronaviruses and 197 GPCRs. The validity of MSClustering for large systems has been verified by its clustering of 3481 enzymes. Through an experimental comparison, we show that MSClustering outperforms five different state-of-the-art methods in the efficiency and reliability of their clustering.


Subject(s)
Computational Biology , Software , Computational Biology/methods , Phylogeny , Reproducibility of Results , Cluster Analysis
10.
Eur J Med Res ; 27(1): 251, 2022 Nov 15.
Article in English | MEDLINE | ID: covidwho-2115714

ABSTRACT

BACKGROUND: Patients with non-alcoholic fatty liver disease (NAFLD) may be more susceptible to coronavirus disease 2019 (COVID-19) and even more likely to suffer from severe COVID-19. Whether there is a common molecular pathological basis for COVID-19 and NAFLD remains to be identified. The present study aimed to elucidate the transcriptional alterations shared by COVID-19 and NAFLD and to identify potential compounds targeting both diseases. METHODS: Differentially expressed genes (DEGs) for COVID-19 and NAFLD were extracted from the GSE147507 and GSE89632 datasets, and common DEGs were identified using the Venn diagram. Subsequently, we constructed a protein-protein interaction (PPI) network based on the common DEGs and extracted hub genes. Then, we performed gene ontology (GO) and pathway analysis of common DEGs. In addition, transcription factors (TFs) and miRNAs regulatory networks were constructed, and drug candidates were identified. RESULTS: We identified a total of 62 common DEGs for COVID-19 and NAFLD. The 10 hub genes extracted based on the PPI network were IL6, IL1B, PTGS2, JUN, FOS, ATF3, SOCS3, CSF3, NFKB2, and HBEGF. In addition, we also constructed TFs-DEGs, miRNAs-DEGs, and protein-drug interaction networks, demonstrating the complex regulatory relationships of common DEGs. CONCLUSION: We successfully extracted 10 hub genes that could be used as novel therapeutic targets for COVID-19 and NAFLD. In addition, based on common DEGs, we propose some potential drugs that may benefit patients with COVID-19 and NAFLD.


Subject(s)
COVID-19 , MicroRNAs , Non-alcoholic Fatty Liver Disease , Humans , Non-alcoholic Fatty Liver Disease/genetics , Non-alcoholic Fatty Liver Disease/metabolism , Gene Regulatory Networks , Systems Biology , Gene Expression Profiling , Computational Biology , COVID-19/genetics , MicroRNAs/genetics
11.
J Vis Exp ; (188)2022 Oct 21.
Article in English | MEDLINE | ID: covidwho-2110320

ABSTRACT

Circular RNAs (circRNAs) are a class of non-coding RNAs that are formed via back-splicing. These circRNAs are predominantly studied for their roles as regulators of various biological processes. Notably, emerging evidence demonstrates that host circRNAs can be differentially expressed (DE) upon infection with pathogens (e.g., influenza and coronaviruses), suggesting a role for circRNAs in regulating host innate immune responses. However, investigations on the role of circRNAs during pathogenic infections are limited by the knowledge and skills required to carry out the necessary bioinformatic analysis to identify DE circRNAs from RNA sequencing (RNA-seq) data. Bioinformatics prediction and identification of circRNAs is crucial before any verification, and functional studies using costly and time-consuming wet-lab techniques. To solve this issue, a step-by-step protocol of in silico prediction and characterization of circRNAs using RNA-seq data is provided in this manuscript. The protocol can be divided into four steps: 1) Prediction and quantification of DE circRNAs via the CIRIquant pipeline; 2) Annotation via circBase and characterization of DE circRNAs; 3) CircRNA-miRNA interaction prediction through Circr pipeline; 4) functional enrichment analysis of circRNA parental genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). This pipeline will be useful in driving future in vitro and in vivo research to further unravel the role of circRNAs in host-pathogen interactions.


Subject(s)
MicroRNAs , RNA, Circular , RNA, Circular/genetics , Sequence Analysis, RNA , MicroRNAs/genetics , Computational Biology/methods , Host-Pathogen Interactions/genetics , Gene Expression Profiling/methods
12.
Front Immunol ; 13: 1008653, 2022.
Article in English | MEDLINE | ID: covidwho-2119881

ABSTRACT

Background: The severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. Human immunodeficiency virus (HIV) destroys immune system cells and weakens the body's ability to resist daily infections and diseases. Furthermore, HIV-infected individuals had double COVID-19 mortality risk and experienced worse COVID-related outcomes. However, the existing research still lacks the understanding of the molecular mechanism underlying crosstalk between COVID-19 and HIV. The aim of our work was to illustrate blood transcriptome crosstalk between COVID-19 and HIV and to provide potential drugs that might be useful for the treatment of HIV-infected COVID-19 patients. Methods: COVID-19 datasets (GSE171110 and GSE152418) were downloaded from Gene Expression Omnibus (GEO) database, including 54 whole-blood samples and 33 peripheral blood mononuclear cells samples, respectively. HIV dataset (GSE37250) was also obtained from GEO database, containing 537 whole-blood samples. Next, the "Deseq2" package was used to identify differentially expressed genes (DEGs) between COVID-19 datasets (GSE171110 and GSE152418) and the "limma" package was utilized to identify DEGs between HIV dataset (GSE37250). By intersecting these two DEG sets, we generated common DEGs for further analysis, containing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis, protein-protein interaction (PPI) analysis, transcription factor (TF) candidate identification, microRNAs (miRNAs) candidate identification and drug candidate identification. Results: In this study, a total of 3213 DEGs were identified from the merged COVID-19 dataset (GSE171110 and GSE152418), and 1718 DEGs were obtained from GSE37250 dataset. Then, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets. GO and KEGG enrichment analysis indicated that common DEGs were mainly gathered in chromosome-related and cell cycle-related signal pathways. Top ten hub genes (CCNA2, CCNB1, CDC20, TOP2A, AURKB, PLK1, BUB1B, KIF11, DLGAP5, RRM2) were ranked according to their scores, which were screened out using degree algorithm on the basis of common DEGs. Moreover, top ten drug candidates (LUCANTHONE, Dasatinib, etoposide, Enterolactone, troglitazone, testosterone, estradiol, calcitriol, resveratrol, tetradioxin) ranked by their P values were screened out, which maybe be beneficial for the treatment of HIV-infected COVID-19 patients. Conclusion: In this study, we provide potential molecular targets, signaling pathways, small molecular compounds, and promising biomarkers that contribute to worse COVID-19 prognosis in patients with HIV, which might contribute to precise diagnosis and treatment for HIV-infected COVID-19 patients.


Subject(s)
COVID-19 , HIV Infections , Humans , Transcriptome , COVID-19/genetics , Leukocytes, Mononuclear , Computational Biology/methods , SARS-CoV-2 , Gene Expression Profiling/methods , HIV Infections/drug therapy , HIV Infections/genetics
13.
Sci Rep ; 12(1): 19087, 2022 Nov 09.
Article in English | MEDLINE | ID: covidwho-2106475

ABSTRACT

The World Health Organization categorized SARS-CoV-2 as a variant of concern, having numerous mutations in spike protein, which have been found to evade the effect of antibodies stimulated by the COVID-19 vaccine. The susceptibility to omicron variant by immunization-induced antibodies are direly required for risk evaluation. To avoid the risk of arising viral illness, the construction of a specific vaccine that triggers the production of targeted antibodies to combat infection remains highly imperative. The aim of the present study is to develop a particular vaccine exploiting bioinformatics approaches which can target B- and T-cells epitopes. Through this approach, novel epitopes of the S protein-SARS-CoV-2 were predicted for the development of a multiple epitope vaccine. Multiple epitopes were selected on the basis of toxicity, immunogenicity and antigenicity, and vaccine subunit was constructed having potential immunogenic properties. The epitopes were linked with 3 types of linker EAAAK, AAY and GPGPG for vaccine construction. Subsequently, vaccine structure was docked with the receptor and cloned in a pET-28a (+) vector. The constructed vaccine was ligated in pET-28a (+) vector in E. coli using the SnapGene tool for the expression study and a good immune response was observed. Several computational tools were used to predict and analyze the vaccine constructed by using spike protein sequence of omicrons. The current study identified a Multi-Epitope Vaccine (MEV) as a significant vaccine candidate that could potentially help the global world to combat SARS-CoV-2 infections.


Subject(s)
COVID-19 , Viral Vaccines , Humans , SARS-CoV-2/genetics , COVID-19 Vaccines/genetics , Spike Glycoprotein, Coronavirus/chemistry , COVID-19/prevention & control , Computational Biology , Escherichia coli , Epitopes, B-Lymphocyte , Immunogenicity, Vaccine , Epitopes, T-Lymphocyte
14.
Immunobiology ; 227(6): 152287, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2105123

ABSTRACT

BACKGROUND: Epitope selection is the key to peptide vaccines development. Bioinformatics tools can efficiently improve the screening of antigenic epitopes and help to choose the right ones. OBJECTIVE: To predict, synthesize and testify peptide epitopes at spike protein, assess the effect of mutations on epitope humoral immunity, thus provide clues for the design and development of epitope peptide vaccines against SARS-CoV-2. METHODS: Bioinformatics servers and immunological tools were used to identify the helper T lymphocyte, cytotoxic T lymphocyte, and linear B lymphocyte epitopes on the S protein of SARS-CoV-2. Physicochemical properties of candidate epitopes were analyzed using IEDB, VaxiJen, and AllerTOP online software. Three candidate epitopes were synthesized and their antigenic responses were evaluated by binding antibody detection. RESULTS: A total of 20 antigenic, non-toxic and non-allergenic candidate epitopes were identified from 1502 epitopes, including 6 helper T-cell epitopes, 13 cytotoxic T-cell epitopes, and 1 linear B cell epitope. After immunization with antigen containing candidate epitopes S206-221, S403-425, and S1157-1170 in rabbits, the binding titers of serum antibody to the corresponding peptide, S protein, receptor-binding domain protein were (415044, 2582, 209.3), (852819, 45238, 457767) and (357897, 10528, 13.79), respectively. The binding titers to Omicron S protein were 642, 12,878 and 7750, respectively, showing that N211L, DEL212 and K417N mutations cause the reduction of the antibody binding activity. CONCLUSIONS: Bioinformatic methods are effective in peptide epitopes design. Certain mutations of the Omicron would lead to the loss of antibody affinity to Omicron S protein.


Subject(s)
COVID-19 , Viral Vaccines , Animals , Humans , Rabbits , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/chemistry , Computational Biology/methods , Epitopes, T-Lymphocyte/genetics , COVID-19 Vaccines/genetics , Immunity, Humoral , Epitopes, B-Lymphocyte/genetics , Vaccines, Subunit , Peptides
15.
Monoclon Antib Immunodiagn Immunother ; 41(5): 243-254, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-2097268

ABSTRACT

Increasing fungal infections in immunocompromised hosts are a growing concern for global public health. Along with treatments, preventive measures are required. The emergence of reverse vaccinology has opened avenues for using genomic and proteomic data from pathogens in the design of vaccines. In this work, we present a comprehensive collection of various computational tools and databases with potential to aid in vaccine development. The ongoing pandemic has directed attention toward the increasing number of mucormycosis infections in COVID-19 patients. As a case study, we developed a computational pipeline for assisting vaccine development for mucormycosis. We obtained 6 proteins from 29,447 sequences from UniProtKB as potential vaccine candidates against mucormycosis, fulfilling multiple criteria. These criteria included potential characteristics, namely adhesin properties, surface or extracellular localization, antigenicity, no similarity to any human proteins, nonallergenicity, stability in vitro, and expression in fungal cells. These six proteins were predicted to have B cell and T cell epitopes, proinflammatory inducing peptides, and orthologs in several mucormycosis-causing species. These data could aid in vaccine development against mucormycosis for at-risk individuals.


Subject(s)
COVID-19 , Mucormycosis , Humans , Vaccinology , Proteomics , Antibodies, Monoclonal , Epitopes, T-Lymphocyte/genetics , Computers , Computational Biology
16.
Arch Med Res ; 53(7): 694-710, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2095059

ABSTRACT

BACKGROUND: The mutations in SARS-CoV-2 variants of concern (VOC) facilitate the virus' escape from the neutralizing antibodies induced by vaccines. However, the protection from hospitalization and death is not significantly diminished. Both vaccine boosters and infection improve immune responses and provide protection, suggesting that conserved and/or cross-reactive epitopes could be involved. While several important T- and B-cell epitopes have been identified, mainly in the S protein, the M and N proteins and their potential cross-reactive epitopes with other coronaviruses remain largely unexplored. AIMS: To identify and map new potential B- and T-cell epitopes within the SARS-CoV-2 S, M and N proteins, as well as cross-reactive epitopes with human coronaviruses. METHODS: Different bioinformatics tools were used to: i) Identify new and compile previously-reported B-and T-cell epitopes from SARS-CoV-2 S, M and N proteins; ii) Determine the mutations in S protein from VOC that affect B- and T-cell epitopes, and; iii) Identify cross-reactive epitopes with coronaviruses relevant to human health. RESULTS: New, potential B- and T-cell epitopes from S, M and N proteins as well as cross-reactive epitopes with other coronaviruses were found and mapped within the proteins' structures. CONCLUSION: Numerous potential B- and T-cell epitopes were found in S, M and N proteins, some of which are conserved between coronaviruses. VOCs present mutations within important epitopes in the S protein; however, a significant number of other epitopes remain unchanged. The epitopes identified here may contribute to augmenting the protective response to SARS-CoV-2 and its variants induced by infection and/or vaccination, and may also be used for the rational design of novel broad-spectrum coronavirus vaccines.


Subject(s)
COVID-19 , Epitopes, T-Lymphocyte , Humans , Epitopes, T-Lymphocyte/genetics , Computational Biology , SARS-CoV-2
17.
Biochem Genet ; 60(6): 2052-2068, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2094662

ABSTRACT

Severe Acute Respiratory Syndrome Coronavirus Type 2 (SARS-CoV-2) is an enveloped single-stranded RNA virus that can lead to respiratory symptoms and damage many organs such as heart, kidney, intestine, brain and liver. It has not been clearly documented whether myocardial injury is caused by direct infection of cardiomyocytes, lung injury, or other unknown mechanisms. The gene expression profile of GSE150392 was obtained from the Gene Expression Omnibus (GEO) database. The processing of high-throughput sequencing data and the screening of differentially expressed genes (DEGs) were implemented by R software. The R software was employed to analyze the Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The protein-protein interaction (PPI) network of the DEGs was constructed by the STRING website. The Cytoscape software was applied for the visualization of PPI network and the identification of hub genes. The statistical analysis was performed by the GraphPad Prism software to verify the hub genes. A total of 516 up-regulated genes and 191 down-regulated genes were screened out. The top 1 enrichment items of GO in biological process (BP), Cellular Component (CC), and Molecular Function (MF) were type I interferon signaling pathway, sarcomere, and receptor ligand activity, respectively. The top 10 enrichment pathways, including TNF signaling pathway, were identified by KEGG enrichment analysis. A PPI network was established, consisting of 613 nodes and 3,993 edges. The 12 hub genes were confirmed as statistically significant, which was verified by GSE151879 dataset. In conclusion, the hub genes of human iPSC-cardiomyocytes infected with SARS-CoV-2 were identified through bioinformatics analysis, which may be used as biomarkers for further research.


Subject(s)
COVID-19 , Induced Pluripotent Stem Cells , Humans , SARS-CoV-2 , Gene Expression Profiling , Myocytes, Cardiac , COVID-19/genetics , Computational Biology , Signal Transduction/genetics
18.
Genes (Basel) ; 13(11)2022 10 28.
Article in English | MEDLINE | ID: covidwho-2090055

ABSTRACT

Currently, as an effect of the COVID-19 pandemic, bioinformatics, genomics, and biological computations are gaining increased attention. Genomes of viruses can be represented by character strings based on their nucleobases. Document similarity metrics can be applied to these strings to measure their similarities. Clustering algorithms can be applied to the results of their document similarities to cluster them. P systems or membrane systems are computation models inspired by the flow of information in the membrane cells. These can be used for various purposes, one of them being data clustering. This paper studies a novel and versatile clustering method for genomes and the utilization of such membrane clustering models using document similarity metrics, which is not yet a well-studied use of membrane clustering models.


Subject(s)
COVID-19 , Pandemics , Humans , COVID-19/genetics , Cluster Analysis , Algorithms , Computational Biology/methods
19.
BMC Med Genomics ; 15(Suppl 2): 94, 2022 04 23.
Article in English | MEDLINE | ID: covidwho-2089198

ABSTRACT

BACKGROUND: MicroRNAs (miRNAs) are a class of small non-coding RNA that can downregulate their targets by selectively binding to the 3' untranslated region (3'UTR) of most messenger RNAs (mRNAs) in the human genome. MiRNAs can interact with other molecules such as viruses and act as a mediator for viral infection. In this study, we examined whether, and to what extent, the SARS-CoV-2 virus can serve as a "sponge" for human miRNAs. RESULTS: We identified multiple potential miRNA/target pairs that may be disrupted during SARS-CoV-2 infection. Using miRNA expression profiles and RNA-seq from published studies, we further identified a highly confident list of 5 miRNA/target pairs that could be disrupted by the virus's miRNA sponge effect, namely hsa-miR-374a-5p/APOL6, hsa-let-7f-1-3p/EIF4A2, hsa-miR-374a-3p/PARP11, hsa-miR-548d-3p/PSMA2 and hsa-miR-23b-3p/ZNFX1 pairs. Using single-cell RNA-sequencing based data, we identified two important miRNAs, hsa-miR-302c-5p and hsa-miR-16-5p, to be potential virus targeting miRNAs across multiple cell types from bronchoalveolar lavage fluid samples. We further validated some of our findings using miRNA and gene enrichment analyses and the results confirmed with findings from previous studies that some of these identified miRNA/target pairs are involved in ACE2 receptor network, regulating pro-inflammatory cytokines and in immune cell maturation and differentiation. CONCLUSION: Using publicly available databases and patient-related expression data, we found that acting as a "miRNA sponge" could be one explanation for SARS-CoV-2-mediated pathophysiological changes. This study provides a novel way of utilizing SARS-CoV-2 related data, with bioinformatics approaches, to help us better understand the etiology of the disease and its differential manifestation across individuals.


Subject(s)
COVID-19 , MicroRNAs , SARS-CoV-2 , 3' Untranslated Regions , COVID-19/genetics , Computational Biology/methods , Humans , MicroRNAs/genetics , MicroRNAs/metabolism , RNA, Messenger/genetics , SARS-CoV-2/genetics , SARS-CoV-2/metabolism
20.
Biomed Res Int ; 2022: 1806427, 2022.
Article in English | MEDLINE | ID: covidwho-2088968

ABSTRACT

COVID-19 is still prevalent in more world regions and poses a severe threat to human health due to its high pathogenicity. The incidence of COPD patients is gradually increasing, especially in patients over 45 years old. COPD patients are susceptible to COVID-19 due to the specific lung receptor ACE2 of SARS-CoV-2. We attempt to reveal the genetic basis by analyzing the expression of common DEGs of the two diseases through bioinformatics approaches and find potential therapeutic agents based on the target genes. Thus, we search the GEO database for COVID-19 and COPD transcriptomic gene expression. We also study the enrichment of signaling regulatory pathways and hub genes for potential therapeutic treatments. There are 34 common DEGs in the two datasets. The signaling pathways are mainly enriched in intercellular junctions between virus and cytokine regulation. In the PPI network of common DEGs, we extract 5 hub genes. We find that artesunate CTD 00001840, dexverapamil MCF7 UP, and STOCK1N-35696 PC3 DOWN could be therapeutic agents for both diseases. We also analyze the regulatory network of differential genes with transcription factors and miRNAs. Therefore, we conclude that artesunate CTD 00001840, dexverapamil MCF7 UP, and STOCK1N-35696 PC3 DOWN can be therapeutic candidates in COPD combined with COVID-19.


Subject(s)
COVID-19 , Pulmonary Disease, Chronic Obstructive , Artesunate , COVID-19/genetics , Computational Biology , Gene Expression Profiling , Gene Regulatory Networks , Humans , Middle Aged , Pulmonary Disease, Chronic Obstructive/genetics , SARS-CoV-2 , Verapamil
SELECTION OF CITATIONS
SEARCH DETAIL