Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 313
Filter
1.
Front Immunol ; 14: 1152186, 2023.
Article in English | MEDLINE | ID: covidwho-20238642

ABSTRACT

Background Severe coronavirus disease 2019 (COVID -19) has led to severe pneumonia or acute respiratory distress syndrome (ARDS) worldwide. we have noted that many critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. The molecular mechanisms that underlie COVID-19, ARDS and sepsis are not well understood. The objectives of this study were to analyze potential molecular mechanisms and identify potential drugs for the treatment of COVID-19, ARDS and sepsis using bioinformatics and a systems biology approach. Methods Three RNA-seq datasets (GSE171110, GSE76293 and GSE137342) from Gene Expression Omnibus (GEO) were employed to detect mutual differentially expressed genes (DEGs) for the patients with the COVID-19, ARDS and sepsis for functional enrichment, pathway analysis, and candidate drugs analysis. Results We obtained 110 common DEGs among COVID-19, ARDS and sepsis. ARG1, FCGR1A, MPO, and TLR5 are the most influential hub genes. The infection and immune-related pathways and functions are the main pathways and molecular functions of these three diseases. FOXC1, YY1, GATA2, FOXL, STAT1 and STAT3 are important TFs for COVID-19. mir-335-5p, miR-335-5p and hsa-mir-26a-5p were associated with COVID-19. Finally, the hub genes retrieved from the DSigDB database indicate multiple drug molecules and drug-targets interaction. Conclusion We performed a functional analysis under ontology terms and pathway analysis and found some common associations among COVID-19, ARDS and sepsis. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs were also identified on the datasets. We believe that the candidate drugs obtained in this study may contribute to the effective treatment of COVID-19.


Subject(s)
COVID-19 , MicroRNAs , Respiratory Distress Syndrome , Sepsis , Humans , Gene Expression Profiling/methods , COVID-19/genetics , MicroRNAs/genetics , Computational Biology/methods , Respiratory Distress Syndrome/drug therapy , Respiratory Distress Syndrome/genetics , Sepsis/complications , Sepsis/drug therapy , Sepsis/genetics
2.
Medicine (Baltimore) ; 102(23): e33912, 2023 Jun 09.
Article in English | MEDLINE | ID: covidwho-20234985

ABSTRACT

Nonalcoholic fatty liver disease (NAFLD) is considered a risk factor for severe COVID-19, but the mechanism remains unknown. This study used bioinformatics to help define the relationship between these diseases. The GSE147507 (COVID-19), GSE126848 (NAFLD), and GSE63067 (NAFLD-2) datasets were screened using the Gene Expression Omnibus. Common differentially expressed genes were then identified using a Venn diagram. Gene ontology analysis and KEGG pathway enrichment were performed on the differentially expressed genes. A protein-protein interaction network was also constructed using the STRING platform, and key genes were identified using the Cytoscape plugin. GES63067 was selected for validation of the results. Analysis of ferroptosis gene expression during the development of the 2 diseases and prediction of their upstream miRNAs and lncRNAs. In addition, transcription factors (TFs) and miRNAs related to key genes were identified. Effective drugs that act on target genes were found in the DSigDB. The GSE147507 and GSE126848 datasets were crossed to obtain 28 co-regulated genes, 22 gene ontology terms, 3 KEGG pathways, and 10 key genes. NAFLD may affect COVID-19 progression through immune function and inflammatory signaling pathways. CYBB was predicted to be a differential ferroptosis gene associated with 2 diseases, and the CYBB-hsa-miR-196a/b-5p-TUG1 regulatory axis was identified. TF-gene interactions and TF-miRNA coregulatory network were constructed successfully. A total of 10 drugs, (such as Eckol, sulfinpyrazone, and phenylbutazone) were considered as target drugs for Patients with COVID-19 and NAFLD. This study identified key gene and defined molecular mechanisms associated with the progression of COVID-19 and NAFLD. COVID-19 and NAFLD progression may regulate ferroptosis through the CYBB-hsa-miR-196a/b-5p-TUG1 axis. This study provides additional drug options for the treatment of COVID-19 combined with NAFLD disease.


Subject(s)
COVID-19 , MicroRNAs , Non-alcoholic Fatty Liver Disease , Humans , Non-alcoholic Fatty Liver Disease/genetics , Non-alcoholic Fatty Liver Disease/metabolism , Systems Biology , Gene Expression Profiling/methods , COVID-19/genetics , MicroRNAs/genetics , Computational Biology/methods , Gene Regulatory Networks
3.
Funct Integr Genomics ; 23(3): 199, 2023 Jun 06.
Article in English | MEDLINE | ID: covidwho-20243808

ABSTRACT

Silicosis is an occupational lung disease that is common worldwide. In recent years, coronavirus disease 2019 (COVID-19) has provided daunting challenges to public healthcare systems globally. Although multiple studies have shown a close link between COVID-19 and other respiratory diseases, the inter-relational mechanisms between COVID-19 and silicosis remain unclear. This study aimed to explore the shared molecular mechanisms and drug targets of COVID-19 and silicosis. Gene expression profiling identified four modules that were most closely associated with both diseases. Furthermore, we performed functional analysis and constructed a protein-protein interaction network. Seven hub genes (budding uninhibited by benzimidazoles 1 [BUB1], protein regulator of cytokinesis 1 [PRC1], kinesin family member C1 [KIFC1], ribonucleotide reductase regulatory subunit M2 [RRM2], cyclin-dependent kinase inhibitor 3 [CDKN3], Cyclin B2 [CCNB2], and minichromosome maintenance complex component 6 [MCM6]) were involved in the interaction between COVID-19 and silicosis. We investigated how diverse microRNAs and transcription factors regulate these seven genes. Subsequently, the correlation between the hub genes and infiltrating immune cells was explored. Further in-depth analyses were performed based on single-cell transcriptomic data from COVID-19, and the expression of hub-shared genes was characterized and located in multiple cell clusters. Finally, molecular docking results reveal small molecular compounds that may improve COVID-19 and silicosis. The current study reveals the common pathogenesis of COVID-19 and silicosis, which may provide a novel reference for further research.


Subject(s)
COVID-19 , Silicosis , Humans , COVID-19/genetics , Molecular Docking Simulation , Protein Interaction Maps/genetics , Computational Biology/methods , Gene Expression Profiling , Silicosis/genetics
4.
Math Biosci Eng ; 20(6): 10659-10674, 2023 Apr 13.
Article in English | MEDLINE | ID: covidwho-2324457

ABSTRACT

To comprehend the etiology and pathogenesis of many illnesses, it is essential to identify disease-associated microRNAs (miRNAs). However, there are a number of challenges with current computational approaches, such as the lack of "negative samples", that is, confirmed irrelevant miRNA-disease pairs, and the poor performance in terms of predicting miRNAs related with "isolated diseases", i.e. illnesses with no known associated miRNAs, which presents the need for novel computational methods. In this study, for the purpose of predicting the connection between disease and miRNA, an inductive matrix completion model was designed, referred to as IMC-MDA. In the model of IMC-MDA, for each miRNA-disease pair, the predicted marks are calculated by combining the known miRNA-disease connection with the integrated disease similarities and miRNA similarities. Based on LOOCV, IMC-MDA had an AUC of 0.8034, which shows better performance than previous methods. Furthermore, experiments have validated the prediction of disease-related miRNAs for three major human diseases: colon cancer, kidney cancer, and lung cancer.


Subject(s)
Colonic Neoplasms , MicroRNAs , Humans , MicroRNAs/genetics , Genetic Predisposition to Disease , Algorithms , Computational Biology/methods , Colonic Neoplasms/genetics
5.
Biotechnol Lett ; 45(7): 779-797, 2023 Jul.
Article in English | MEDLINE | ID: covidwho-2317808

ABSTRACT

BACKGROUND: COVID-19 has proved to be a fatal disease of the year 2020, due to which thousands of people globally have lost their lives, and still, the infection cases are at a high rate. Experimental studies suggested that SARS-CoV-2 interacts with various microorganisms, and this coinfection is accountable for the augmentation of infection severity. METHODS AND RESULTS: In this study, we have designed a multi-pathogen vaccine by involving the immunogenic proteins from S. pneumonia, H. influenza, and M. tuberculosis, as they are dominantly associated with SARS-CoV-2. A total of 8 antigenic protein sequences were selected to predict B-cell, HTL, and CTL epitopes restricted to the most prevalent HLA alleles. The selected epitopes were antigenic, non-allergenic, and non-toxic and were linked with adjuvant and linkers to make the vaccine protein more immunogenic, stable, and flexible. The tertiary structure, Ramachandran plot, and discontinuous B-cell epitopes were predicted. Docking and MD simulation study has shown efficient binding of the chimeric vaccine with the TLR4 receptor. CONCLUSION: The in silico immune simulation analysis has shown a high level of cytokines and IgG after a three-dose injection. Hence, this strategy could be a better way to decrease the disease's severity and could be used as a weapon to prevent this pandemic.


Subject(s)
COVID-19 , Coinfection , Viral Vaccines , Humans , COVID-19/prevention & control , SARS-CoV-2 , COVID-19 Vaccines , Epitopes, T-Lymphocyte/genetics , Molecular Docking Simulation , Vaccines, Subunit , Epitopes, B-Lymphocyte/genetics , Epitopes, B-Lymphocyte/chemistry , Computational Biology/methods
6.
Int J Mol Sci ; 23(22)2022 Nov 17.
Article in English | MEDLINE | ID: covidwho-2295420

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
7.
OMICS ; 27(4): 141-152, 2023 04.
Article in English | MEDLINE | ID: covidwho-2297045

ABSTRACT

Omics data are multidimensional, heterogeneous, and high throughput. Robust computational methods and machine learning (ML)-based models offer new prospects to accelerate the data-to-knowledge trajectory. Deep learning (DL) is a powerful subset of ML inspired by brain structure and has created unprecedented momentum in bioinformatics and computational biology research. This article provides an overview of the current DL models applied to multi-omics data for both the beginner and the expert user. Additionally, COVID-19 will continue to impact planetary health as a pandemic and an endemic disease, with genomic and multi-omic pathophysiology. DL offers, therefore, new ways of harnessing systems biology research on COVID-19 diagnostics and therapeutics. Herein, we discuss, first, the statistical ML algorithms and essential deep architectures. Then, we review DL applications in multi-omics data analysis and their intersection with COVID-19. Finally, challenges and several promising directions are highlighted going forward in the current era of COVID-19.


Subject(s)
COVID-19 , Deep Learning , Humans , Genomics/methods , Computational Biology/methods , Machine Learning
8.
Ther Adv Cardiovasc Dis ; 17: 17539447231168471, 2023.
Article in English | MEDLINE | ID: covidwho-2295311

ABSTRACT

BACKGROUND: Heart failure (HF) is the most common cardiovascular diseases and the leading cause of cardiovascular diseases related deaths. Increasing molecular targets have been discovered for HF prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might aid the diagnosis and treatment of HF. METHODS: We searched next-generation sequencing (NGS) dataset (GSE161472) and identified differentially expressed genes (DEGs) by comparing 47 HF samples and 37 normal control samples using limma in R package. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g: Profiler database. The protein-protein interaction (PPI) network was plotted with Human Integrated Protein-Protein Interaction rEference (HiPPIE) and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. Then, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed by Cytoscape software. Finally, we performed receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes. RESULTS: A total of 930 DEGs, 464 upregulated genes and 466 downregulated genes, were identified in HF. GO and REACTOME pathway enrichment results showed that DEGs mainly enriched in localization, small molecule metabolic process, SARS-CoV infections, and the citric acid tricarboxylic acid (TCA) cycle and respiratory electron transport. After combining the results of the PPI network miRNA-hub gene regulatory network and TF-hub gene regulatory network, 10 hub genes were selected, including heat shock protein 90 alpha family class A member 1 (HSP90AA1), arrestin beta 2 (ARRB2), myosin heavy chain 9 (MYH9), heat shock protein 90 alpha family class B member 1 (HSP90AB1), filamin A (FLNA), epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), cullin 4A (CUL4A), YEATS domain containing 4 (YEATS4), and lysine acetyltransferase 2B (KAT2B). CONCLUSIONS: This discovery-driven study might be useful to provide a novel insight into the diagnosis and treatment of HF. However, more experiments are needed in the future to investigate the functional roles of these genes in HF.


Subject(s)
Cardiovascular Diseases , Heart Failure , MicroRNAs , Humans , Gene Expression Profiling/methods , Biomarkers , MicroRNAs/genetics , Computational Biology/methods , High-Throughput Nucleotide Sequencing , Heat-Shock Proteins/genetics , Cullin Proteins/genetics
9.
Biomed Res Int ; 2023: 3728131, 2023.
Article in English | MEDLINE | ID: covidwho-2294565

ABSTRACT

Purpose: As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion: The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other.


Subject(s)
Bibliometrics , Computational Biology , Iran , Computational Biology/methods , Natural Language Processing , Algorithms
10.
Front Cell Infect Microbiol ; 13: 1139998, 2023.
Article in English | MEDLINE | ID: covidwho-2301324

ABSTRACT

Background: The coronavirus disease 2019 (COVID-19) has been spreading astonishingly and caused catastrophic losses worldwide. The high mortality of severe COVID-19 patients is an serious problem that needs to be solved urgently. However, the biomarkers and fundamental pathological mechanisms of severe COVID-19 are poorly understood. The aims of this study was to explore key genes related to inflammasome in severe COVID-19 and their potential molecular mechanisms using random forest and artificial neural network modeling. Methods: Differentially expressed genes (DEGs) in severe COVID-19 were screened from GSE151764 and GSE183533 via comprehensive transcriptome Meta-analysis. Protein-protein interaction (PPI) networks and functional analyses were conducted to identify molecular mechanisms related to DEGs or DEGs associated with inflammasome (IADEGs), respectively. Five the most important IADEGs in severe COVID-19 were explored using random forest. Then, we put these five IADEGs into an artificial neural network to construct a novel diagnostic model for severe COVID-19 and verified its diagnostic efficacy in GSE205099. Results: Using combining P value < 0.05, we obtained 192 DEGs, 40 of which are IADEGs. The GO enrichment analysis results indicated that 192 DEGs were mainly involved in T cell activation, MHC protein complex and immune receptor activity. The KEGG enrichment analysis results indicated that 192 GEGs were mainly involved in Th17 cell differentiation, IL-17 signaling pathway, mTOR signaling pathway and NOD-like receptor signaling pathway. In addition, the top GO terms of 40 IADEGs were involved in T cell activation, immune response-activating signal transduction, external side of plasma membrane and phosphatase binding. The KEGG enrichment analysis results indicated that IADEGs were mainly involved in FoxO signaling pathway, Toll-like receptor, JAK-STAT signaling pathway and Apoptosis. Then, five important IADEGs (AXL, MKI67, CDKN3, BCL2 and PTGS2) for severe COVID-19 were screened by random forest analysis. By building an artificial neural network model, we found that the AUC values of 5 important IADEGs were 0.972 and 0.844 in the train group (GSE151764 and GSE183533) and test group (GSE205099), respectively. Conclusion: The five genes related to inflammasome, including AXL, MKI67, CDKN3, BCL2 and PTGS2, are important for severe COVID-19 patients, and these molecules are related to the activation of NLRP3 inflammasome. Furthermore, AXL, MKI67, CDKN3, BCL2 and PTGS2 as a marker combination could be used as potential markers to identify severe COVID-19 patients.


Subject(s)
COVID-19 , Inflammasomes , Humans , Inflammasomes/genetics , Cyclooxygenase 2 , Random Forest , Gene Expression Profiling/methods , Computational Biology/methods , Proto-Oncogene Proteins c-bcl-2
11.
BMC Neurol ; 22(1): 139, 2022 Apr 12.
Article in English | MEDLINE | ID: covidwho-2268723

ABSTRACT

BACKGROUND: Glioblastoma multiforme (GBM) is the most common aggressive malignant brain tumor. However, the molecular mechanism of glioblastoma formation is still poorly understood. To identify candidate genes that may be connected to glioma growth and development, weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between gene sets and clinical characteristics. We also explored the function of the key candidate gene. METHODS: Two GBM datasets were selected from GEO Datasets. The R language was used to identify differentially expressed genes. WGCNA was performed to construct a gene co-expression network in the GEO glioblastoma samples. A custom Venn diagram website was used to find the intersecting genes. The GEPIA website was applied for survival analysis to determine the significant gene, FUBP3. OS, DSS, and PFI analyses, based on the UCSC Cancer Genomics Browser, were performed to verify the significance of FUBP3. Immunohistochemistry was performed to evaluate the expression of FUBP3 in glioblastoma and adjacent normal tissue. KEGG and GO enrichment analyses were used to reveal possible functions of FUBP3. Microenvironment analysis was used to explore the relationship between FUBP3 and immune infiltration. Immunohistochemistry was performed to verify the results of the microenvironment analysis. RESULTS: GSE70231 and GSE108474 were selected from GEO Datasets, then 715 and 694 differentially expressed genes (DEGs) from GSE70231 and GSE108474, respectively, were identified. We then performed weighted gene co-expression network analysis (WGCNA) and identified the most downregulated gene modules of GSE70231 and GSE108474, and 659 and 3915 module genes from GSE70231 and GSE108474, respectively, were selected. Five intersection genes (FUBP3, DAD1, CLIC1, ABR, and DNM1) were calculated by Venn diagram. FUBP3 was then identified as the only significant gene by survival analysis using the GEPIA website. OS, DSS, and PFI analyses verified the significance of FUBP3. Immunohistochemical analysis revealed FUBP3 expression in GBM and adjacent normal tissue. KEGG and GO analyses uncovered the possible function of FUBP3 in GBM. Tumor microenvironment analysis showed that FUBP3 may be connected to immune infiltration, and immunohistochemistry identified a positive correlation between immune cells (CD4 + T cells, CD8 + T cells, and macrophages) and FUBP3. CONCLUSION: FUBP3 is associated with immune surveillance in GBM, indicating that it has a great impact on GBM development and progression. Therefore, interventions involving FUBP3 and its regulatory pathway may be a new approach for GBM treatment.


Subject(s)
Glioblastoma , Biomarkers, Tumor , Chloride Channels/genetics , Computational Biology/methods , DNA-Binding Proteins/genetics , Gene Expression Profiling/methods , Gene Expression Regulation, Neoplastic/genetics , Glioblastoma/pathology , Humans , Prognosis , Transcription Factors/genetics , Tumor Microenvironment
12.
Ren Fail ; 44(1): 204-216, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2256901

ABSTRACT

The antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systematic of relatively rare autoimmune diseases with unknown cause. Kidney involvement is one of the most common clinical manifestations, and the degree of renal damage is closely associated with the development and prognosis of AAV. In this study, we utilized the Robust Rank Aggreg (RRA) method in R to integrate GSE104948, GSE104954, GSE108109, GSE108112, and GSE108113 profile datasets loaded from Gene Expression Omnibus (GEO) database and identified a set of differentially expressed genes (DEGs) in kidney between AAV patients and living donors. Then, the results of gene ontology (GO) functional annotation showed that immunity and metabolism involved process of AAV both in glomerulus and tubulointerstitial. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that following pathways, such as complement and coagulation cascades pathway; Staphylococcus aureus infection; disease-COVID-19; and systemic lupus erythematosus (SLE) pathway play a crucial role in AAV. Next, the results analyzed by protein-protein interaction (PPI) network and Cytoscape software exhibited the hub genes ALB, TYROBP, and CYBB existed in both glomerular and tubulointerstitial compartments datasets. Finally, KEGG analysis using genes of two most important modules also further validated complement and coagulation cascades pathway and S. aureus infection existed both in glomerulus and tubulointerstitial compartments datasets. In conclusion, this study identified key genes and pathways involved in kidney of AAV, which was benefit to further uncover the mechanisms underlying the development and progress of AAV, biomarkers, and potential therapeutic targets as well.


Subject(s)
Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis/genetics , Computational Biology/methods , Gene Expression Regulation, Neoplastic , Kidney/pathology , Protein Interaction Maps/genetics , Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis/pathology , Biomarkers, Tumor/genetics , Gene Expression Profiling , Gene Regulatory Networks , Humans , Prognosis , Software
13.
Cytokine ; 166: 156187, 2023 06.
Article in English | MEDLINE | ID: covidwho-2279243

ABSTRACT

COVID-19 is associated with dysregulation of several genes and signaling pathways. Based on the importance of expression profiling in identification of the pathogenesis of COVID-19 and proposing novel therapies for this disorder, we have employed an in silico approach to find differentially expressed genes between COVID-19 patients and healthy controls and their relevance with cellular functions and signaling pathways. We obtained 630 DEmRNAs, including 486 down-regulated DEGs (such as CCL3 and RSAD2) and 144 up-regulated DEGs (such as RHO and IQCA1L), and 15 DElncRNAs, including 9 down-regulated DElncRNAs (such as PELATON and LINC01506) and 6 up-regulated DElncRNAs (such as AJUBA-DT and FALEC). The PPI network of DEGs showed the presence of a number immune-related genes such as those coding for HLA molecules and interferon regulatory factors. Taken together, these results highlight the importance of immune-related genes and pathways in the pathogenesis of COVID-19 and suggest novel targets for treatment of this disorder.


Subject(s)
COVID-19 , Gene Expression Profiling , Humans , Gene Expression Profiling/methods , Systems Biology , SARS-CoV-2/genetics , Computational Biology/methods , COVID-19/genetics , RNA-Seq , LIM Domain Proteins
14.
Int J Mol Sci ; 24(5)2023 Mar 02.
Article in English | MEDLINE | ID: covidwho-2281145

ABSTRACT

The COVID-19 pandemic has caused millions of deaths and remains a major public health burden worldwide. Previous studies found that a large number of COVID-19 patients and survivors developed neurological symptoms and might be at high risk of neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD). We aimed to explore the shared pathways between COVID-19, AD, and PD by using bioinformatic analysis to reveal potential mechanisms, which may explain the neurological symptoms and degeneration of brain that occur in COVID-19 patients, and to provide early intervention. In this study, gene expression datasets of the frontal cortex were employed to detect common differentially expressed genes (DEGs) of COVID-19, AD, and PD. A total of 52 common DEGs were then examined using functional annotation, protein-protein interaction (PPI) construction, candidate drug identification, and regulatory network analysis. We found that the involvement of the synaptic vesicle cycle and down-regulation of synapses were shared by these three diseases, suggesting that synaptic dysfunction might contribute to the onset and progress of neurodegenerative diseases caused by COVID-19. Five hub genes and one key module were obtained from the PPI network. Moreover, 5 drugs and 42 transcription factors (TFs) were also identified on the datasets. In conclusion, the results of our study provide new insights and directions for follow-up studies of the relationship between COVID-19 and neurodegenerative diseases. The hub genes and potential drugs we identified may provide promising treatment strategies to prevent COVID-19 patients from developing these disorders.


Subject(s)
Alzheimer Disease , COVID-19 , Neurodegenerative Diseases , Parkinson Disease , Humans , Pandemics , Protein Interaction Maps/genetics , Parkinson Disease/genetics , Alzheimer Disease/metabolism , Computational Biology/methods , Gene Expression Profiling , Gene Regulatory Networks
15.
J Infect Public Health ; 16(5): 746-753, 2023 May.
Article in English | MEDLINE | ID: covidwho-2281062

ABSTRACT

BACKGROUND: Coronavirus disease 2019(COVID-19) caused a large number of infections worldwide. Although some patients recovered from the disease, some of the other problems that accompanied it, such as cardiac injury, could affect the patient's subsequent quality of life and prognosis. OBJECTIVES: To clarify the molecular mechanism of cardiac injury in SARS-CoV-2 Infection. METHODS: The RNA-Seq dataset (GSE184715) comparing expression profiling of Mock human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and SARS-CoV-2-infected hiPSC-CMs was downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes(DEGs) were performed by the R software. Degs were analyzed by enrichment analysis to clarify the affected pathways. Hub genes were screened out by a PPI network constructed from Degs. Finally, Connectivity Map was used to screen for the treatment of COVID-19 induced cardiac injury. RESULTS: 2705 differentially expressed genes were identified. Enrichment analysis confirmed that mitochondrial dysfunction was caused by SARS-CoV-2, meanwhile, cardiac muscle contraction was suppressed and NF-κB was activated. Based on the PPI network, 15 hub genes were identified. These 15 down-regulated hub genes were mainly involved in the reduced activity of complexes in the mitochondrial respiratory chain associated with mitochondrial dysfunction. Moreover, 5 candidate drugs were identified to treat cardiac injury. CONCLUSION: In conclusion, SARS-CoV-2 infection of cardiomyocytes causes mitochondrial dysfunction, including reduced mitochondrial respiratory chain complex activity and decreased ATP synthesis, leading to cardiomyocyte apoptosis, while the activated NF-κB also induced cytokine storms, ultimately resulting in cardiac injury.


Subject(s)
COVID-19 , Induced Pluripotent Stem Cells , Humans , SARS-CoV-2 , Gene Expression Profiling/methods , NF-kappa B , Quality of Life , Computational Biology/methods
16.
BMC Bioinformatics ; 24(1): 67, 2023 Feb 24.
Article in English | MEDLINE | ID: covidwho-2280689

ABSTRACT

BACKGROUND: Streptococcus pneumoniae (Pneumococcus) has remained a leading cause of fatal infections such as pneumonia, meningitis, and sepsis. Moreover, this pathogen plays a major role in bacterial co-infection in patients with life-threatening respiratory virus diseases such as influenza and COVID-19. High morbidity and mortality in over one million cases, especially in very young children and the elderly, are the main motivations for pneumococcal vaccine development. Due to the limitations of the currently marketed polysaccharide-based vaccines, non-serotype-specific protein-based vaccines have received wide research interest in recent years. One step further is to identify high antigenic regions within multiple highly-conserved proteins in order to develop peptide vaccines that can affect various stages of pneumococcal infection, providing broader serotype coverage and more effective protection. In this study, immunoinformatics tools were used to design an effective multi-epitope vaccine in order to elicit neutralizing antibodies against multiple strains of pneumococcus. RESULTS: The B- and T-cell epitopes from highly protective antigens PspA (clades 1-5) and PhtD were predicted and immunodominant peptides were linked to each other with proper linkers. The domain 4 of Ply, as a potential TLR4 agonist adjuvant candidate, was attached to the end of the construct to enhance the immunogenicity of the epitope vaccine. The evaluation of the physicochemical and immunological properties showed that the final construct was stable, soluble, antigenic, and non-allergenic. Furthermore, the protein was found to be acidic and hydrophilic in nature. The protein 3D-structure was built and refined, and the Ramachandran plot, ProSA-web, ERRAT, and Verify3D validated the quality of the final model. Molecular docking analysis showed that the designed construct via Ply domain 4 had a strong interaction with TLR4. The structural stability of the docked complex was confirmed by molecular dynamics. Finally, codon optimization was performed for gene expression in E. coli, followed by in silico cloning in the pET28a(+) vector. CONCLUSION: The computational analysis of the construct showed acceptable results, however, the suggested vaccine needs to be experimentally verified in laboratory to ensure its safety and immunogenicity.


Subject(s)
COVID-19 , Streptococcus pneumoniae , Child , Humans , Child, Preschool , Aged , Molecular Docking Simulation , Escherichia coli , Toll-Like Receptor 4 , Epitopes, T-Lymphocyte/chemistry , Vaccines, Subunit/chemistry , Vaccines, Subunit/genetics , Epitopes, B-Lymphocyte , Computational Biology/methods
17.
PLoS Comput Biol ; 19(1): e1010752, 2023 01.
Article in English | MEDLINE | ID: covidwho-2262899

ABSTRACT

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.


Subject(s)
Computational Biology , Software , Humans , Computational Biology/methods , Data Analysis , Research Personnel
18.
Biotechnol Appl Biochem ; 70(3): 1189-1205, 2023 Jun.
Article in English | MEDLINE | ID: covidwho-2172675

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shown rapid global spread and has resulted in a significant death toll worldwide. In this study, we aimed to design a multi-epitope vaccine against SARS-CoV-2 based on structural proteins S, M, N, and E. We identified B- and T-cell epitopes and then the antigenicity, toxicity, allergenicity, and similarity of predicted epitopes were analyzed. T-cell epitopes were docked with corresponding HLA alleles. Consequently, the selected T- and B-cell epitopes were included in the final construct. All selected epitopes were connected with different linkers and flagellin and pan-HLA DR binding epitopes (PADRE) as an adjuvant were used in the vaccine construct. Furthermore, molecular docking was used to evaluate the complex between the final vaccine construct and two alleles, HLA-A*02:01 and HLA-DRB1*01:01. Finally, codons were optimized for in silico cloning into pET28a(+) vector using SnapGene. The final vaccine construct comprised 11 CTL, HTL, and B-cell epitopes corresponding to 394 amino acid residues. In silico evaluation showed that the designed vaccine might potentially promote an immune response. Further in vivo preclinical and clinical testing is required to determine the safety and efficacy of the designed vaccine.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/genetics , COVID-19/prevention & control , Immunodominant Epitopes/genetics , Epitopes, T-Lymphocyte/genetics , Epitopes, T-Lymphocyte/chemistry , Epitopes, B-Lymphocyte/genetics , Epitopes, B-Lymphocyte/chemistry , COVID-19 Vaccines/genetics , Molecular Docking Simulation , Computational Biology/methods
19.
Brief Bioinform ; 24(1)2023 01 19.
Article in English | MEDLINE | ID: covidwho-2188256

ABSTRACT

The proliferation of single-cell multimodal sequencing technologies has enabled us to understand cellular heterogeneity with multiple views, providing novel and actionable biological insights into the disease-driving mechanisms. Here, we propose a comprehensive end-to-end single-cell multimodal analysis framework named Deep Parametric Inference (DPI). DPI transforms single-cell multimodal data into a multimodal parameter space by inferring individual modal parameters. Analysis of cord blood mononuclear cells (CBMC) reveals that the multimodal parameter space can characterize the heterogeneity of cells more comprehensively than individual modalities. Furthermore, comparisons with the state-of-the-art methods on multiple datasets show that DPI has superior performance. Additionally, DPI can reference and query cell types without batch effects. As a result, DPI can successfully analyze the progression of COVID-19 disease in peripheral blood mononuclear cells (PBMC). Notably, we further propose a cell state vector field and analyze the transformation pattern of bone marrow cells (BMC) states. In conclusion, DPI is a powerful single-cell multimodal analysis framework that can provide new biological insights into biomedical researchers. The python packages, datasets and user-friendly manuals of DPI are freely available at https://github.com/studentiz/dpi.


Subject(s)
COVID-19 , Leukocytes, Mononuclear , Humans , Single-Cell Analysis/methods , Computational Biology/methods
20.
Brief Bioinform ; 24(1)2023 01 19.
Article in English | MEDLINE | ID: covidwho-2188253

ABSTRACT

Accurate in silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the methods achieve very low performances, and some do not perform better than randomly generated patches of surface residues. In addition, we also found that commonly used consensus strategies that combine the results from multiple webservers are at best only marginally better than random. Finally, we applied all the predictors to the SARS-CoV-2 spike protein as an independent case study, and showed that they perform poorly in general, which largely recapitulates our benchmarking conclusions. We hope that these results will lead to greater caution when using these tools until the biases and issues that limit current methods have been addressed, promote the use of state-of-the-art evaluation methodologies in future publications and suggest new strategies to improve the performance of conformational B-cell epitope prediction methods.


Subject(s)
Epitopes, B-Lymphocyte , Spike Glycoprotein, Coronavirus , Humans , Computational Biology/methods , Epitopes, B-Lymphocyte/immunology , SARS-CoV-2 , Spike Glycoprotein, Coronavirus/immunology
SELECTION OF CITATIONS
SEARCH DETAIL