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1.
Nucleic Acids Res ; 50(D1): D1255-D1261, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-2062939

ABSTRACT

The Human Disease Ontology (DO) (www.disease-ontology.org) database, has significantly expanded the disease content and enhanced our userbase and website since the DO's 2018 Nucleic Acids Research DATABASE issue paper. Conservatively, based on available resource statistics, terms from the DO have been annotated to over 1.5 million biomedical data elements and citations, a 10× increase in the past 5 years. The DO, funded as a NHGRI Genomic Resource, plays a key role in disease knowledge organization, representation, and standardization, serving as a reference framework for multiscale biomedical data integration and analysis across thousands of clinical, biomedical and computational research projects and genomic resources around the world. This update reports on the addition of 1,793 new disease terms, a 14% increase of textual definitions and the integration of 22 137 new SubClassOf axioms defining disease to disease connections representing the DO's complex disease classification. The DO's updated website provides multifaceted etiology searching, enhanced documentation and educational resources.


Subject(s)
Biological Ontologies , Databases, Factual , Databases, Genetic , Genetic Diseases, Inborn/classification , Genetic Diseases, Inborn/genetics , Genomics/classification , Humans
2.
Nucleic Acids Res ; 50(D1): D777-D784, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-2062936

ABSTRACT

GMrepo (data repository for Gut Microbiota) is a database of curated and consistently annotated human gut metagenomes. Its main purposes are to increase the reusability and accessibility of human gut metagenomic data, and enable cross-project and phenotype comparisons. To achieve these goals, we performed manual curation on the meta-data and organized the datasets in a phenotype-centric manner. GMrepo v2 contains 353 projects and 71,642 runs/samples, which are significantly increased from the previous version. Among these runs/samples, 45,111 and 26,531 were obtained by 16S rRNA amplicon and whole-genome metagenomics sequencing, respectively. We also increased the number of phenotypes from 92 to 133. In addition, we introduced disease-marker identification and cross-project/phenotype comparison. We first identified disease markers between two phenotypes (e.g. health versus diseases) on a per-project basis for selected projects. We then compared the identified markers for each phenotype pair across datasets to facilitate the identification of consistent microbial markers across datasets. Finally, we provided a marker-centric view to allow users to check if a marker has different trends in different diseases. So far, GMrepo includes 592 marker taxa (350 species and 242 genera) for 47 phenotype pairs, identified from 83 selected projects. GMrepo v2 is freely available at: https://gmrepo.humangut.info.


Subject(s)
Databases, Genetic , Intestinal Neoplasms/microbiology , Metagenome , Microbiota , Biomarkers/blood , Datasets as Topic , Gastrointestinal Microbiome/genetics , High-Throughput Nucleotide Sequencing , Humans , Internet , Intestinal Neoplasms/blood , Intestinal Neoplasms/genetics , Intestinal Neoplasms/pathology , Molecular Sequence Annotation , Phenotype , RNA, Ribosomal, 16S , Software
3.
Comput Math Methods Med ; 2022: 9914927, 2022.
Article in English | MEDLINE | ID: covidwho-2020562

ABSTRACT

Introduction: Novel coronavirus pneumonia (COVID-19) is an acute respiratory disease caused by the novel coronavirus SARS-CoV-2. Severe and critical illness, especially secondary bacterial infection (SBI) cases, accounts for the vast majority of COVID-19-related deaths. However, the relevant biological indicators of COVID-19 and SBI are still unclear, which significantly limits the timely diagnosis and treatment. Methods: The differentially expressed genes (DEGs) between severe COVID-19 patients with SBI and without SBI were screened through the analysis of GSE168017 and GSE168018 datasets. By performing Gene Ontology (GO) enrichment analysis for significant DEGs, significant biological processes, cellular components, and molecular functions were selected. To understand the high-level functions and utilities of the biological system, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed. By analyzing protein-protein interaction (PPI) and key subnetworks, the core DEGs were found. Results: 85 DEGs were upregulated, and 436 DEGs were downregulated. The CD14 expression was significantly increased in the SBI group of severe COVID-19 patients (P < 0.01). The area under the curve (AUC) of CD14 in the SBI group in severe COVID-19 patients was 0.9429. The presepsin expression was significantly higher in moderate to severe COVID-19 patients (P < 0.05). Presepsin has a diagnostic value for moderate to severe COVID-19 with the AUC of 0.9732. The presepsin expression of COVID-19 patients in the nonsurvivors was significantly higher than that in the survivors (P < 0.05). Conclusion: Presepsin predicts severity and SBI in COVID-19 and may be associated with prognosis in COVID-19.


Subject(s)
Bacterial Infections , COVID-19 , Computational Biology , Databases, Genetic , Gene Expression Profiling , Gene Expression Regulation, Neoplastic , Gene Regulatory Networks , Humans , Lipopolysaccharide Receptors/genetics , Peptide Fragments/genetics , SARS-CoV-2 , Signal Transduction/genetics
4.
Cytogenet Genome Res ; 162(1-2): 40-45, 2022.
Article in English | MEDLINE | ID: covidwho-1938106

ABSTRACT

The 16p11.2 duplication is a well-known cause of developmental delay and autism, but there are only 2 previously reported cases of 16p11.2 triplication. Both of the previously reported cases exhibited tandem triplication on a 16p11.2 duplication inherited from 1 parent. We report fraternal twins presenting with developmental delay and 16p11.2 triplication resulting from inheritance of a 16p11.2 duplicated homolog from each parent. This report also reviews the overlapping features in previously published cases of 16p11.2 triplication, and possible implications are discussed.


Subject(s)
Autistic Disorder , Autistic Disorder/genetics , Chromosome Duplication/genetics , Chromosomes, Human, Pair 16/genetics , Databases, Genetic , Female , Humans , Male , Parents , Phenotype
5.
Sci Rep ; 12(1): 1716, 2022 02 02.
Article in English | MEDLINE | ID: covidwho-1900583

ABSTRACT

The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. We show that the combination of features derived from the chest CT volumes improve the AUC performance to 0.80 from the 0.52 obtained by using patients' clinical data alone. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19 patients.


Subject(s)
COVID-19/diagnosis , COVID-19/virology , Deep Learning , SARS-CoV-2 , Thorax/diagnostic imaging , Thorax/pathology , Tomography, X-Ray Computed , Algorithms , COVID-19/mortality , Databases, Genetic , Humans , Image Interpretation, Computer-Assisted/methods , Image Processing, Computer-Assisted/methods , Prognosis , Tomography, X-Ray Computed/methods , Tomography, X-Ray Computed/standards
6.
Int J Mol Sci ; 23(7)2022 Apr 02.
Article in English | MEDLINE | ID: covidwho-1785741

ABSTRACT

The understanding of how genetic information may be inherited through generations was established by Gregor Mendel in the 1860s when he developed the fundamental principles of inheritance. The science of genetics, however, began to flourish only during the mid-1940s when DNA was identified as the carrier of genetic information. The world has since then witnessed rapid development of genetic technologies, with the latest being genome-editing tools, which have revolutionized fields from medicine to agriculture. This review walks through the historical timeline of genetics research and deliberates how this discipline might furnish a sustainable future for humanity.


Subject(s)
Heredity , Databases, Genetic , Inheritance Patterns
7.
Nucleic Acids Res ; 50(D1): D27-D38, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1758797

ABSTRACT

The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global research in both academia and industry. With the explosively accumulated multi-omics data at ever-faster rates, CNCB-NGDC is constantly scaling up and updating its core database resources through big data archive, curation, integration and analysis. In the past year, efforts have been made to synthesize the growing data and knowledge, particularly in single-cell omics and precision medicine research, and a series of resources have been newly developed, updated and enhanced. Moreover, CNCB-NGDC has continued to daily update SARS-CoV-2 genome sequences, variants, haplotypes and literature. Particularly, OpenLB, an open library of bioscience, has been established by providing easy and open access to a substantial number of abstract texts from PubMed, bioRxiv and medRxiv. In addition, Database Commons is significantly updated by cataloguing a full list of global databases, and BLAST tools are newly deployed to provide online sequence search services. All these resources along with their services are publicly accessible at https://ngdc.cncb.ac.cn.


Subject(s)
Databases, Factual , Animals , China , Computational Biology , Databases, Genetic , Databases, Pharmaceutical , Dogs , Epigenome , Genome, Human , Genome, Viral , Genomics , Humans , Methylation , Neoplasms/genetics , Neoplasms/pathology , Regeneration , SARS-CoV-2/genetics , Single-Cell Analysis , Software , Synthetic Biology
8.
Mamm Genome ; 33(1): 66-80, 2022 03.
Article in English | MEDLINE | ID: covidwho-1756794

ABSTRACT

Model organism research is essential for discovering the mechanisms of human diseases by defining biologically meaningful gene to disease relationships. The Rat Genome Database (RGD, ( https://rgd.mcw.edu )) is a cross-species knowledgebase and the premier online resource for rat genetic and physiologic data. This rich resource is enhanced by the inclusion and integration of comparative data for human and mouse, as well as other human disease models including chinchilla, dog, bonobo, pig, 13-lined ground squirrel, green monkey, and naked mole-rat. Functional information has been added to records via the assignment of annotations based on sequence similarity to human, rat, and mouse genes. RGD has also imported well-supported cross-species data from external resources. To enable use of these data, RGD has developed a robust infrastructure of standardized ontologies, data formats, and disease- and species-centric portals, complemented with a suite of innovative tools for discovery and analysis. Using examples of single-gene and polygenic human diseases, we illustrate how data from multiple species can help to identify or confirm a gene as involved in a disease and to identify model organisms that can be studied to understand the pathophysiology of a gene or pathway. The ultimate aim of this report is to demonstrate the utility of RGD not only as the core resource for the rat research community but also as a source of bioinformatic tools to support a wider audience, empowering the search for appropriate models for human afflictions.


Subject(s)
Biomedical Research , Databases, Genetic , Animals , Chlorocebus aethiops , Dogs , Genome/genetics , Genomics , Mice , Oligopeptides , Swine
9.
PLoS One ; 17(3): e0262373, 2022.
Article in English | MEDLINE | ID: covidwho-1753184

ABSTRACT

Human genetics has been proposed to play an essential role in inter-individual differences in respiratory virus infection occurrence and outcomes. To systematically understand human genetic contributions to respiratory virus infection, we developed the database dbGSRV, a manually curated database that integrated the host genetic susceptibility and severity studies of respiratory viruses scattered over literatures in PubMed. At present, dbGSRV contains 1932 records of genetic association studies relating 1010 unique variants and seven respiratory viruses, manually curated from 168 published articles. Users can access the records by quick searching, batch searching, advanced searching and browsing. Reference information, infection status, population information, mutation information and disease relationship are provided for each record, as well as hyperlinks to public databases in convenient of users accessing more information. In addition, a visual overview of the topological network relationship between respiratory viruses and associated genes is provided. Therefore, dbGSRV offers a convenient resource for researchers to browse and retrieve genetic associations with respiratory viruses, which may inspire future studies and provide new insights in our understanding and treatment of respiratory virus infection. Database URL: http://www.ehbio.com/dbGSRV/front/.


Subject(s)
Virus Diseases , Viruses , Databases, Factual , Databases, Genetic , Genetic Association Studies , Genetic Predisposition to Disease , Humans , Virus Diseases/genetics , Viruses/genetics
10.
Nucleic Acids Res ; 50(D1): D387-D390, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1705079

ABSTRACT

The Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra/) stores raw sequencing data and alignment information to enhance reproducibility and facilitate new discoveries through data analysis. Here we note changes in storage designed to increase access and highlight analyses that augment metadata with taxonomic insight to help users select data. In addition, we present three unanticipated applications of taxonomic analysis.


Subject(s)
Bacteria/genetics , Databases, Genetic , Metadata/statistics & numerical data , Software , Viruses/genetics , Bacteria/classification , Base Sequence , High-Throughput Nucleotide Sequencing , Internet , Phylogeny , Reproducibility of Results , SARS-CoV-2/genetics , Sequence Analysis, RNA , Viruses/classification
11.
Curr Protoc ; 2(1): e355, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1653213

ABSTRACT

The Illuminating the Druggable Genome (IDG) consortium is a National Institutes of Health (NIH) Common Fund program designed to enhance our knowledge of under-studied proteins, more specifically, proteins unannotated within the three most commonly drug-targeted protein families: G-protein coupled receptors, ion channels, and protein kinases. Since 2014, the IDG Knowledge Management Center (IDG-KMC) has generated several open-access datasets and resources that jointly serve as a highly translational machine-learning-ready knowledgebase focused on human protein-coding genes and their products. The goal of the IDG-KMC is to develop comprehensive integrated knowledge for the druggable genome to illuminate the uncharacterized or poorly annotated portion of the druggable genome. The tools derived from the IDG-KMC provide either user-friendly visualizations or ways to impute the knowledge about potential targets using machine learning strategies. In the following protocols, we describe how to use each web-based tool to accelerate illumination in under-studied proteins. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Interacting with the Pharos user interface Basic Protocol 2: Accessing the data in Harmonizome Basic Protocol 3: The ARCHS4 resource Basic Protocol 4: Making predictions about gene function with PrismExp Basic Protocol 5: Using Geneshot to illuminate knowledge about under-studied targets Basic Protocol 6: Exploring under-studied targets with TIN-X Basic Protocol 7: Interacting with the DrugCentral user interface Basic Protocol 8: Estimating Anti-SARS-CoV-2 activities with DrugCentral REDIAL-2020 Basic Protocol 9: Drug Set Enrichment Analysis using Drugmonizome Basic Protocol 10: The Drugmonizome-ML Appyter Basic Protocol 11: The Harmonizome-ML Appyter Basic Protocol 12: GWAS target illumination with TIGA Basic Protocol 13: Prioritizing kinases for lists of proteins and phosphoproteins with KEA3 Basic Protocol 14: Converting PubMed searches to drug sets with the DrugShot Appyter.


Subject(s)
Databases, Genetic , Genome , COVID-19 , Humans , Machine Learning , Proteins , SARS-CoV-2
13.
PLoS One ; 17(1): e0262737, 2022.
Article in English | MEDLINE | ID: covidwho-1631070

ABSTRACT

INTRODUCTION: The coronavirus disease 2019 (COVID-19), emerged in late 2019, was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The risk factors for idiopathic pulmonary fibrosis (IPF) and COVID-19 are reported to be common. This study aimed to determine the potential role of differentially expressed genes (DEGs) common in IPF and COVID-19. MATERIALS AND METHODS: Based on GEO database, we obtained DEGs from one SARS-CoV-2 dataset and five IPF datasets. A series of enrichment analysis were performed to identify the function of upregulated and downregulated DEGs, respectively. Two plugins in Cytoscape, Cytohubba and MCODE, were utilized to identify hub genes after a protein-protein interaction (PPI) network. Finally, candidate drugs were predicted to target the upregulated DEGs. RESULTS: A total of 188 DEGs were found between COVID-19 and IPF, out of which 117 were upregulated and 71 were downregulated. The upregulated DEGs were involved in cytokine function, while downregulated DEGs were associated with extracellular matrix disassembly. Twenty-two hub genes were upregulated in COVID-19 and IPF, for which 155 candidate drugs were predicted (adj.P.value < 0.01). CONCLUSION: Identifying the hub genes aberrantly regulated in both COVID-19 and IPF may enable development of molecules, encoded by those genes, as therapeutic targets for preventing IPF progression and SARS-CoV-2 infections.


Subject(s)
COVID-19/genetics , Idiopathic Pulmonary Fibrosis/genetics , COVID-19/pathology , COVID-19/virology , Databases, Genetic , Down-Regulation/drug effects , Down-Regulation/genetics , Humans , Idiopathic Pulmonary Fibrosis/drug therapy , Idiopathic Pulmonary Fibrosis/pathology , Protein Interaction Maps/drug effects , Protein Interaction Maps/genetics , SARS-CoV-2/isolation & purification , Suloctidil/pharmacology , Suloctidil/therapeutic use , Up-Regulation/drug effects , Up-Regulation/genetics , Vasodilator Agents/pharmacology , Vasodilator Agents/therapeutic use
14.
Brief Bioinform ; 23(2)2022 03 10.
Article in English | MEDLINE | ID: covidwho-1639367

ABSTRACT

Genomic epidemiology is important to study the COVID-19 pandemic, and more than two million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic sequences were deposited into public databases. However, the exponential increase of sequences invokes unprecedented bioinformatic challenges. Here, we present the Coronavirus GenBrowser (CGB) based on a highly efficient analysis framework and a node-picking rendering strategy. In total, 1,002,739 high-quality genomic sequences with the transmission-related metadata were analyzed and visualized. The size of the core data file is only 12.20 MB, highly efficient for clean data sharing. Quick visualization modules and rich interactive operations are provided to explore the annotated SARS-CoV-2 evolutionary tree. CGB binary nomenclature is proposed to name each internal lineage. The pre-analyzed data can be filtered out according to the user-defined criteria to explore the transmission of SARS-CoV-2. Different evolutionary analyses can also be easily performed, such as the detection of accelerated evolution and ongoing positive selection. Moreover, the 75 genomic spots conserved in SARS-CoV-2 but non-conserved in other coronaviruses were identified, which may indicate the functional elements specifically important for SARS-CoV-2. The CGB was written in Java and JavaScript. It not only enables users who have no programming skills to analyze millions of genomic sequences, but also offers a panoramic vision of the transmission and evolution of SARS-CoV-2.


Subject(s)
COVID-19/epidemiology , COVID-19/virology , Public Health Surveillance/methods , SARS-CoV-2/genetics , Software , Web Browser , Computational Biology/methods , DNA Mutational Analysis , Databases, Genetic , Genome, Viral , Genomics , Humans , Molecular Epidemiology/methods , Molecular Sequence Annotation , Mutation
15.
Nucleic Acids Res ; 50(3): 1551-1561, 2022 02 22.
Article in English | MEDLINE | ID: covidwho-1636373

ABSTRACT

During the course of the COVID-19 pandemic, large-scale genome sequencing of SARS-CoV-2 has been useful in tracking its spread and in identifying variants of concern (VOC). Viral and host factors could contribute to variability within a host that can be captured in next-generation sequencing reads as intra-host single nucleotide variations (iSNVs). Analysing 1347 samples collected till June 2020, we recorded 16 410 iSNV sites throughout the SARS-CoV-2 genome. We found ∼42% of the iSNV sites to be reported as SNVs by 30 September 2020 in consensus sequences submitted to GISAID, which increased to ∼80% by 30th June 2021. Following this, analysis of another set of 1774 samples sequenced in India between November 2020 and May 2021 revealed that majority of the Delta (B.1.617.2) and Kappa (B.1.617.1) lineage-defining variations appeared as iSNVs before getting fixed in the population. Besides, mutations in RdRp as well as RNA-editing by APOBEC and ADAR deaminases seem to contribute to the differential prevalence of iSNVs in hosts. We also observe hyper-variability at functionally critical residues in Spike protein that could alter the antigenicity and may contribute to immune escape. Thus, tracking and functional annotation of iSNVs in ongoing genome surveillance programs could be important for early identification of potential variants of concern and actionable interventions.


Subject(s)
Evolution, Molecular , Genetic Variation/genetics , Genome, Viral/genetics , Host-Pathogen Interactions/genetics , SARS-CoV-2/genetics , APOBEC-1 Deaminase/genetics , Adenosine Deaminase/genetics , Animals , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/virology , Chlorocebus aethiops , Coronavirus RNA-Dependent RNA Polymerase/genetics , Databases, Genetic , Immune Evasion/genetics , India/epidemiology , Phylogeny , RNA-Binding Proteins/genetics , SARS-CoV-2/classification , SARS-CoV-2/growth & development , Spike Glycoprotein, Coronavirus/genetics , Vero Cells
16.
Gene ; 813: 146113, 2022 Mar 01.
Article in English | MEDLINE | ID: covidwho-1616498

ABSTRACT

Since late 2019, when SARS-CoV-2 was reported at Wuhan, several sequence analyses have been performed and SARS-CoV-2 genome sequences have been submitted in various databases. Moreover, the impact of these variants on infectivity and response to neutralizing antibodies has been assessed. In the present study, we retrieved a total number of 176 complete and high-quality S glycoprotein sequences of Iranian SARS-COV-2 in public database of the GISAID and GenBank from April 2020 up to May 2021. Then, we identified the number of variables, singleton and parsimony informative sites at both gene and protein levels and discussed the possible functional consequences of important mutations on the infectivity and response to neutralizing antibodies. Phylogenetic tree was constructed to represent the relationship between Iranian SARS-COV2 and variants of concern (VOC), variants of interest (VOI) and reference sequence. We found that the four current VOCs - Alpha, Beta, Gamma and Delta - are circulated in different regions in Iran. The Delta variant is notably more transmissible than other variants, and is expected to become a dominant variant. However, some of the Delta variants in Iran carry an additional mutation, namely E1202Q in the HR2 subdomain that might confer an advantage to viral/cell membrane fusion process. We also observed some more common mutations such as an N-terminal domain (NTD) deletion at position I210 and P863H in fusion peptide-heptad repeat 1 span region in Iranian SARS-COV-2. The reported mutations in the current project have practical significance in prediction of disease spread as well as design of vaccines and drugs.


Subject(s)
COVID-19/genetics , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/genetics , Antibodies, Neutralizing/immunology , Antibodies, Viral/genetics , COVID-19/epidemiology , COVID-19/metabolism , Databases, Genetic , Humans , Iran/epidemiology , Mutation/genetics , Phylogeny , Protein Binding , RNA, Viral , SARS-CoV-2/metabolism , SARS-CoV-2/pathogenicity , Sequence Analysis, DNA/methods , Spike Glycoprotein, Coronavirus/metabolism
17.
Front Immunol ; 12: 796379, 2021.
Article in English | MEDLINE | ID: covidwho-1604322

ABSTRACT

Whole genome sequencing of Epstein-Barr virus (EBV) isolates from around the world has uncovered pervasive strain heterogeneity, but the forces driving strain diversification and the impact on immune recognition remained largely unknown. Using a data mining approach, we analyzed more than 300 T-cell epitopes in 168 published EBV strains. Polymorphisms were detected in approximately 65% of all CD8+ and 80% of all CD4+ T-cell epitopes and these numbers further increased when epitope flanking regions were included. Polymorphisms in CD8+ T-cell epitopes often involved MHC anchor residues and resulted in changes of the amino acid subgroup, suggesting that only a limited number of conserved T-cell epitopes may represent generic target antigens against different viral strains. Although considered the prototypic EBV strain, the rather low degree of overlap with most other viral strains implied that B95.8 may not represent the ideal reference strain for T-cell epitopes. Instead, a combinatorial library of consensus epitopes may provide better targets for diagnostic and therapeutic purposes when the infecting strain is unknown. Polymorphisms were significantly enriched in epitope versus non-epitope protein sequences, implicating immune selection in driving strain diversification. Remarkably, CD4+ T-cell epitopes in EBNA2, EBNA-LP, and the EBNA3 family appeared to be under negative selection pressure, hinting towards a beneficial role of immune responses against these latency type III antigens in virus biology. These findings validate this immunoinformatics approach for providing novel insight into immune targets and the intricate relationship of host defense and virus evolution that may also pertain to other pathogens.


Subject(s)
Antigenic Variation , Antigens, Viral/genetics , Epitopes, T-Lymphocyte/genetics , Genetic Heterogeneity , Herpesvirus 4, Human/genetics , Polymorphism, Genetic , Algorithms , Antigens, Viral/immunology , CD4-Positive T-Lymphocytes/immunology , CD4-Positive T-Lymphocytes/virology , CD8-Positive T-Lymphocytes/immunology , CD8-Positive T-Lymphocytes/virology , Data Mining , Databases, Genetic , Epitopes, T-Lymphocyte/immunology , Herpesvirus 4, Human/immunology
18.
Nucleic Acids Res ; 50(1): 333-349, 2022 01 11.
Article in English | MEDLINE | ID: covidwho-1591186

ABSTRACT

A promising approach to tackle the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) could be small interfering (si)RNAs. So far it is unclear, which viral replication steps can be efficiently inhibited with siRNAs. Here, we report that siRNAs can target genomic RNA (gRNA) of SARS-CoV-2 after cell entry, and thereby terminate replication before start of transcription and prevent virus-induced cell death. Coronaviruses replicate via negative sense RNA intermediates using a unique discontinuous transcription process. As a result, each viral RNA contains identical sequences at the 5' and 3' end. Surprisingly, siRNAs were not active against intermediate negative sense transcripts. Targeting common sequences shared by all viral transcripts allowed simultaneous suppression of gRNA and subgenomic (sg)RNAs by a single siRNA. The most effective suppression of viral replication and spread, however, was achieved by siRNAs that targeted open reading frame 1 (ORF1) which only exists in gRNA. In contrast, siRNAs that targeted the common regions of transcripts were outcompeted by the highly abundant sgRNAs leading to an impaired antiviral efficacy. Verifying the translational relevance of these findings, we show that a chemically modified siRNA that targets a highly conserved region of ORF1, inhibited SARS-CoV-2 replication ex vivo in explants of the human lung. Our work encourages the development of siRNA-based therapies for COVID-19 and suggests that early therapy start, or prophylactic application, together with specifically targeting gRNA, might be key for high antiviral efficacy.


Subject(s)
COVID-19/virology , Lung/virology , RNA, Small Interfering , RNA, Viral , SARS-CoV-2/genetics , Virus Replication , 3' Untranslated Regions , Animals , Antiviral Agents/pharmacology , COVID-19/drug therapy , Cell Survival , Databases, Genetic , HEK293 Cells , Humans , Nucleic Acid Conformation , Oligonucleotides , Open Reading Frames , RNA, Small Interfering/metabolism
19.
Infect Genet Evol ; 97: 105195, 2022 01.
Article in English | MEDLINE | ID: covidwho-1586990

ABSTRACT

SARS-CoV-2 is the RNA virus responsible for COVID-19, the prognosis of which has been found to be slightly worse in men. The present study aimed to analyze the expression of different mRNAs and their regulatory molecules (miRNAs and lncRNAs) to consider the potential existence of sex-specific expression patterns and COVID-19 susceptibility using bioinformatics analysis. The binding sites of all human mature miRNA sequences on the SARS-CoV-2 genome nucleotide sequence were predicted by the miRanda tool. Sequencing data was excavated using the Galaxy web server from GSE157103, and the output of feature counts was analyzed using DEseq2 packages to obtain differentially expressed genes (DEGs). Gene set enrichment analysis (GSEA) and DEG annotation analyses were performed using the ToppGene and Metascape tools. Using the RNA Interactome Database, we predicted interactions between differentially expressed lncRNAs and differentially expressed mRNAs. Finally, their networks were constructed with top miRNAs. We identified 11 miRNAs with three to five binding sites on the SARS-COVID-2 genome reference. MiR-29c-3p, miR-21-3p, and miR-6838-5p occupied four binding sites, and miR-29a-3p had five binding sites on the SARS-CoV-2 genome. Moreover, miR-29a-3p, and miR-29c-3p were the top miRNAs targeting DEGs. The expression levels of miRNAs (125, 181b, 130a, 29a, b, c, 212, 181a, 133a) changed in males with COVID-19, in whom they regulated ACE2 expression and affected the immune response by affecting phagosomes, complement activation, and cell-matrix adhesion. Our results indicated that XIST lncRNA was up-regulated, and TTTY14, TTTY10, and ZFY-AS1 lncRN as were down-regulated in both ICU and non-ICU men with COVID-19. Dysregulation of noncoding-RNAs has critical effects on the pathophysiology of men with COVID-19, which is why they may be used as biomarkers and therapeutic agents. Overall, our results indicated that the miR-29 family target regulation patterns and might become promising biomarkers for severity and survival outcome in men with COVID-19.


Subject(s)
Angiotensin-Converting Enzyme 2/genetics , COVID-19/genetics , MicroRNAs/genetics , RNA, Long Noncoding/genetics , SARS-CoV-2/genetics , Angiotensin-Converting Enzyme 2/metabolism , COVID-19/epidemiology , COVID-19/pathology , COVID-19/virology , Computational Biology/methods , Coronavirus Envelope Proteins/genetics , Coronavirus Envelope Proteins/metabolism , Coronavirus M Proteins/genetics , Coronavirus M Proteins/metabolism , Coronavirus Nucleocapsid Proteins/genetics , Coronavirus Nucleocapsid Proteins/metabolism , Databases, Genetic , Female , Gene Expression Regulation , Host-Pathogen Interactions/genetics , Humans , Male , MicroRNAs/classification , MicroRNAs/metabolism , Phosphoproteins/genetics , Phosphoproteins/metabolism , Protein Binding , RNA, Long Noncoding/classification , RNA, Long Noncoding/metabolism , RNA, Messenger/genetics , RNA, Messenger/metabolism , SARS-CoV-2/classification , SARS-CoV-2/pathogenicity , Severity of Illness Index , Sex Factors , Signal Transduction , Spike Glycoprotein, Coronavirus/genetics , Spike Glycoprotein, Coronavirus/metabolism
20.
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
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