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1.
Int J Mol Sci ; 23(6)2022 Mar 16.
Article in English | MEDLINE | ID: covidwho-1742493

ABSTRACT

Advanced prostate cancer (PCa) patients with bone metastases are treated with androgen pathway directed therapy (APDT). However, this treatment invariably fails and the cancer becomes castration resistant. To elucidate resistance mechanisms and to provide a more predictive pre-clinical research platform reflecting tumor heterogeneity, we established organoids from a patient-derived xenograft (PDX) model of bone metastatic prostate cancer, PCSD1. APDT-resistant PDX-derived organoids (PDOs) emerged when cultured without androgen or with the anti-androgen, enzalutamide. Transcriptomics revealed up-regulation of neurogenic and steroidogenic genes and down-regulation of DNA repair, cell cycle, circadian pathways and the severe acute respiratory syndrome (SARS)-CoV-2 host viral entry factors, ACE2 and TMPRSS2. Time course analysis of the cell cycle in live cells revealed that enzalutamide induced a gradual transition into a reversible dormant state as shown here for the first time at the single cell level in the context of multi-cellular, 3D living organoids using the Fucci2BL fluorescent live cell cycle tracker system. We show here a new mechanism of castration resistance in which enzalutamide induced dormancy and novel basal-luminal-like cells in bone metastatic prostate cancer organoids. These PDX organoids can be used to develop therapies targeting dormant APDT-resistant cells and host factors required for SARS-CoV-2 viral entry.


Subject(s)
Bone Neoplasms/genetics , Gene Expression Profiling/methods , Gene Expression Regulation, Neoplastic/genetics , Organoids/metabolism , Prostatic Neoplasms, Castration-Resistant/genetics , Androgens/pharmacology , Angiotensin-Converting Enzyme 2/genetics , Angiotensin-Converting Enzyme 2/metabolism , Animals , Benzamides/pharmacology , Bone Neoplasms/metabolism , Bone Neoplasms/secondary , COVID-19/genetics , COVID-19/metabolism , COVID-19/virology , Drug Resistance, Neoplasm/drug effects , Drug Resistance, Neoplasm/genetics , Gene Expression Regulation, Neoplastic/drug effects , Humans , Male , Mice , Nitriles/pharmacology , Phenylthiohydantoin/pharmacology , Prostatic Neoplasms/genetics , Prostatic Neoplasms/metabolism , Prostatic Neoplasms/pathology , Prostatic Neoplasms, Castration-Resistant/metabolism , Prostatic Neoplasms, Castration-Resistant/pathology , Receptors, Virus/genetics , Receptors, Virus/metabolism , SARS-CoV-2/metabolism , SARS-CoV-2/physiology , Serine Endopeptidases/genetics , Serine Endopeptidases/metabolism , Transplantation, Heterologous , Virus Internalization
2.
Int J Mol Sci ; 23(5)2022 Feb 24.
Article in English | MEDLINE | ID: covidwho-1715406

ABSTRACT

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


Subject(s)
COVID-19/genetics , Gene Expression Profiling/methods , Machine Learning , Neural Networks, Computer , RNA-Seq/methods , Transcriptome/genetics , Algorithms , COVID-19/classification , COVID-19/virology , Gene Ontology , Humans , Reproducibility of Results , SARS-CoV-2/physiology
3.
JCI Insight ; 7(7)2022 Apr 08.
Article in English | MEDLINE | ID: covidwho-1691177

ABSTRACT

Studying temporal gene expression shifts during disease progression provides important insights into the biological mechanisms that distinguish adaptive and maladaptive responses. Existing tools for the analysis of time course transcriptomic data are not designed to optimally identify distinct temporal patterns when analyzing dynamic differentially expressed genes (DDEGs). Moreover, there are not enough methods to assess and visualize the temporal progression of biological pathways mapped from time course transcriptomic data sets. In this study, we developed an open-source R package TrendCatcher (https://github.com/jaleesr/TrendCatcher), which applies the smoothing spline ANOVA model and break point searching strategy, to identify and visualize distinct dynamic transcriptional gene signatures and biological processes from longitudinal data sets. We used TrendCatcher to perform a systematic temporal analysis of COVID-19 peripheral blood transcriptomes, including bulk and single-cell RNA-Seq time course data. TrendCatcher uncovered the early and persistent activation of neutrophils and coagulation pathways, as well as impaired type I IFN (IFN-I) signaling in circulating cells as a hallmark of patients who progressed to severe COVID-19, whereas no such patterns were identified in individuals receiving SARS-CoV-2 vaccinations or patients with mild COVID-19. These results underscore the importance of systematic temporal analysis to identify early biomarkers and possible pathogenic therapeutic targets.


Subject(s)
COVID-19 , COVID-19/genetics , Gene Expression Profiling/methods , Humans , Neutrophil Activation , SARS-CoV-2/genetics , Transcriptome
4.
Front Immunol ; 12: 700705, 2021.
Article in English | MEDLINE | ID: covidwho-1686468

ABSTRACT

A novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), arose late in 2019, with disease pathology ranging from asymptomatic to severe respiratory distress with multi-organ failure requiring mechanical ventilator support. It has been found that SARS-CoV-2 infection drives intracellular complement activation in lung cells that tracks with disease severity. However, the cellular and molecular mechanisms responsible remain unclear. To shed light on the potential mechanisms, we examined publicly available RNA-Sequencing data using CIBERSORTx and conducted a Ingenuity Pathway Analysis to address this knowledge gap. In complement to these findings, we used bioinformatics tools to analyze publicly available RNA sequencing data and found that upregulation of complement may be leading to a downregulation of T-cell activity in lungs of severe COVID-19 patients. Thus, targeting treatments aimed at the modulation of classical complement and T-cell activity may help alleviate the proinflammatory effects of COVID-19, reduce lung pathology, and increase the survival of COVID-19 patients.


Subject(s)
COVID-19/genetics , Complement Activation/genetics , Complement System Proteins/genetics , Gene Expression Profiling/methods , Lung/metabolism , T-Lymphocytes/metabolism , COVID-19/immunology , COVID-19/virology , Gene Regulatory Networks/genetics , Humans , Intracellular Space/genetics , Lung/immunology , Lung/microbiology , Lymphocyte Count , SARS-CoV-2/physiology , T-Lymphocyte Subsets/metabolism
5.
Sci Rep ; 12(1): 1626, 2022 01 31.
Article in English | MEDLINE | ID: covidwho-1661980

ABSTRACT

The ongoing COVID-19 pandemic is one of the biggest health challenges of recent decades. Among the causes of mortality triggered by SARS-CoV-2 infection, the development of an inflammatory "cytokine storm" (CS) plays a determinant role. Here, we used transcriptomic data from the bronchoalveolar lavage fluid (BALF) of COVID-19 patients undergoing a CS to obtain gene-signatures associated to this pathology. Using these signatures, we interrogated the Connectivity Map (CMap) dataset that contains the effects of over 5000 small molecules on the transcriptome of human cell lines, and looked for molecules which effects on transcription mimic or oppose those of the CS. As expected, molecules that potentiate immune responses such as PKC activators are predicted to worsen the CS. In addition, we identified the negative regulation of female hormones among pathways potentially aggravating the CS, which helps to understand the gender-related differences in COVID-19 mortality. Regarding drugs potentially counteracting the CS, we identified glucocorticoids as a top hit, which validates our approach as this is the primary treatment for this pathology. Interestingly, our analysis also reveals a potential effect of MEK inhibitors in reverting the COVID-19 CS, which is supported by in vitro data that confirms the anti-inflammatory properties of these compounds.


Subject(s)
Anti-Inflammatory Agents/therapeutic use , COVID-19/complications , COVID-19/drug therapy , Computer Simulation , Cytokine Release Syndrome/etiology , Cytokine Release Syndrome/prevention & control , Glucocorticoids/therapeutic use , Pandemics , Protein Kinase Inhibitors/therapeutic use , SARS-CoV-2 , Anti-Inflammatory Agents/pharmacology , Bronchoalveolar Lavage Fluid/virology , COVID-19/blood , COVID-19/epidemiology , Cytokine Release Syndrome/mortality , Cytokines/blood , Female , Gene Expression Profiling/methods , Glucocorticoids/pharmacology , Humans , MAP Kinase Kinase Kinases/antagonists & inhibitors , MAP Kinase Signaling System/drug effects , Male , Protein Kinase Inhibitors/pharmacology , Sex Factors , Transcriptome/genetics
6.
Nat Commun ; 13(1): 440, 2022 01 21.
Article in English | MEDLINE | ID: covidwho-1641960

ABSTRACT

Dysregulated immune responses against the SARS-CoV-2 virus are instrumental in severe COVID-19. However, the immune signatures associated with immunopathology are poorly understood. Here we use multi-omics single-cell analysis to probe the dynamic immune responses in hospitalized patients with stable or progressive course of COVID-19, explore V(D)J repertoires, and assess the cellular effects of tocilizumab. Coordinated profiling of gene expression and cell lineage protein markers shows that S100Ahi/HLA-DRlo classical monocytes and activated LAG-3hi T cells are hallmarks of progressive disease and highlights the abnormal MHC-II/LAG-3 interaction on myeloid and T cells, respectively. We also find skewed T cell receptor repertories in expanded effector CD8+ clones, unmutated IGHG+ B cell clones, and mutated B cell clones with stable somatic hypermutation frequency over time. In conclusion, our in-depth immune profiling reveals dyssynchrony of the innate and adaptive immune interaction in progressive COVID-19.


Subject(s)
Adaptive Immunity/immunology , COVID-19/immunology , Gene Expression Profiling/methods , Immunity, Innate/immunology , SARS-CoV-2/immunology , Single-Cell Analysis/methods , Adaptive Immunity/drug effects , Adaptive Immunity/genetics , Aged , Antibodies, Monoclonal, Humanized/therapeutic use , CD4-Positive T-Lymphocytes/drug effects , CD4-Positive T-Lymphocytes/immunology , CD4-Positive T-Lymphocytes/metabolism , CD8-Positive T-Lymphocytes/drug effects , CD8-Positive T-Lymphocytes/immunology , CD8-Positive T-Lymphocytes/metabolism , COVID-19/drug therapy , COVID-19/genetics , Cells, Cultured , Female , Gene Expression Regulation/drug effects , Gene Expression Regulation/immunology , Humans , Immunity, Innate/drug effects , Immunity, Innate/genetics , Male , RNA-Seq/methods , Receptors, Antigen, B-Cell/genetics , Receptors, Antigen, B-Cell/immunology , Receptors, Antigen, T-Cell/genetics , Receptors, Antigen, T-Cell/immunology , SARS-CoV-2/drug effects , SARS-CoV-2/physiology
7.
Int J Mol Sci ; 23(2)2022 Jan 14.
Article in English | MEDLINE | ID: covidwho-1633064

ABSTRACT

Peripheral blood mononuclear cells (PBMCs) belong to the innate and adaptive immune system and are highly sensitive and responsive to changes in their systemic environment. In this study, we focused on the time course of transcriptional changes in freshly isolated human PBMCs 4, 8, 24 and 48 h after onset of stimulation with the active vitamin D metabolite 1α,25-dihydroxyvitamin D3 (1,25(OH)2D3). Taking all four time points together, 662 target genes were identified and segregated either by time of differential gene expression into 179 primary and 483 secondary targets or by driver of expression change into 293 direct and 369 indirect targets. The latter classification revealed that more than 50% of target genes were primarily driven by the cells' response to ex vivo exposure than by the nuclear hormone and largely explained its down-regulatory effect. Functional analysis indicated vitamin D's role in the suppression of the inflammatory and adaptive immune response by down-regulating ten major histocompatibility complex class II genes, five alarmins of the S100 calcium binding protein A family and by affecting six chemokines of the C-X-C motif ligand family. Taken together, studying time-resolved responses allows to better contextualize the effects of vitamin D on the immune system.


Subject(s)
Adaptive Immunity/genetics , Gene Expression Profiling , Gene Expression Regulation , Inflammation Mediators/metabolism , Transcriptome , Vitamin D/metabolism , Computational Biology/methods , Gene Expression Profiling/methods , Gene Expression Regulation/drug effects , Humans , Inflammation/etiology , Inflammation/metabolism , Inflammation/pathology , Leukocytes, Mononuclear/drug effects , Leukocytes, Mononuclear/immunology , Leukocytes, Mononuclear/metabolism , Molecular Sequence Annotation , Vitamin D/analogs & derivatives , Vitamin D/pharmacology
8.
Nat Commun ; 13(1): 21, 2022 01 10.
Article in English | MEDLINE | ID: covidwho-1616983

ABSTRACT

While the seroprevalence of SARS-CoV-2 in healthy people does not differ significantly among age groups, those aged 65 years or older exhibit strikingly higher COVID-19 mortality compared to younger individuals. To further understand differing COVID-19 manifestations in patients of different ages, three age groups of ferrets are infected with SARS-CoV-2. Although SARS-CoV-2 is isolated from all ferrets regardless of age, aged ferrets (≥3 years old) show higher viral loads, longer nasal virus shedding, and more severe lung inflammatory cell infiltration, and clinical symptoms compared to juvenile (≤6 months) and young adult (1-2 years) groups. Furthermore, direct contact ferrets co-housed with the virus-infected aged group shed more virus than direct-contact ferrets co-housed with virus-infected juvenile or young adult ferrets. Transcriptome analysis of aged ferret lungs reveals strong enrichment of gene sets related to type I interferon, activated T cells, and M1 macrophage responses, mimicking the gene expression profile of severe COVID-19 patients. Thus, SARS-CoV-2-infected aged ferrets highly recapitulate COVID-19 patients with severe symptoms and are useful for understanding age-associated infection, transmission, and pathogenesis of SARS-CoV-2.


Subject(s)
Antibodies, Viral/immunology , COVID-19/immunology , Disease Models, Animal , SARS-CoV-2/immunology , Virus Shedding/immunology , Age Factors , Animals , Antibodies, Neutralizing/blood , Antibodies, Neutralizing/immunology , Antibodies, Viral/blood , COVID-19/genetics , COVID-19/transmission , Chlorocebus aethiops , Female , Ferrets , Gene Expression Profiling/methods , Humans , Immunoglobulin G/blood , Immunoglobulin G/immunology , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity , Vero Cells , Virulence
9.
BJOG ; 129(2): 256-266, 2022 01.
Article in English | MEDLINE | ID: covidwho-1591639

ABSTRACT

BACKGROUND: Pregnant women have been identified as a potentially at-risk group concerning COVID-19 infection, but little is known regarding the susceptibility of the fetus to infection. Co-expression of ACE2 and TMPRSS2 has been identified as a prerequisite for infection, and expression across different tissues is known to vary between children and adults. However, the expression of these proteins in the fetus is unknown. METHODS: We performed a retrospective analysis of a single cell data repository. The data were then validated at both gene and protein level by performing RT-qPCR and two-colour immunohistochemistry on a library of second-trimester human fetal tissues. FINDINGS: TMPRSS2 is present at both gene and protein level in the predominantly epithelial fetal tissues analysed. ACE2 is present at significant levels only in the fetal intestine and kidney, and is not expressed in the fetal lung. The placenta also does not co-express the two proteins across the second trimester or at term. INTERPRETATION: This dataset indicates that the lungs are unlikely to be a viable route of SARS-CoV2 fetal infection. The fetal kidney, despite presenting both the proteins required for the infection, is anatomically protected from the exposure to the virus. However, the gastrointestinal tract is likely to be susceptible to infection due to its high co-expression of both proteins, as well as its exposure to potentially infected amniotic fluid. TWEETABLE ABSTRACT: This work provides detailed mechanistic insight into the relative protection & vulnerabilities of the fetus & placenta to SARS-CoV-2 infection by scRNAseq & protein expression analysis for ACE2 & TMPRSS2. The findings help to explain the low rate of vertical transmission.


Subject(s)
Angiotensin-Converting Enzyme 2/genetics , COVID-19 , Gene Expression Profiling , Placenta/metabolism , Serine Endopeptidases/genetics , Adult , COVID-19/epidemiology , COVID-19/genetics , COVID-19/transmission , Databases, Nucleic Acid , Disease Susceptibility/metabolism , Female , Fetal Research , Gene Expression Profiling/methods , Gene Expression Profiling/statistics & numerical data , Genetic Testing/methods , Gestational Age , Humans , Immunohistochemistry , Infectious Disease Transmission, Vertical , Pregnancy , Protective Factors , Ribonucleoproteins, Small Cytoplasmic/analysis , SARS-CoV-2/physiology
10.
PLoS Biol ; 19(12): e3001065, 2021 12.
Article in English | MEDLINE | ID: covidwho-1594053

ABSTRACT

The pandemic spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiological agent of Coronavirus Disease 2019 (COVID-19), represents an ongoing international health crisis. A key symptom of SARS-CoV-2 infection is the onset of fever, with a hyperthermic temperature range of 38 to 41°C. Fever is an evolutionarily conserved host response to microbial infection that can influence the outcome of viral pathogenicity and regulation of host innate and adaptive immune responses. However, it remains to be determined what effect elevated temperature has on SARS-CoV-2 replication. Utilizing a three-dimensional (3D) air-liquid interface (ALI) model that closely mimics the natural tissue physiology of SARS-CoV-2 infection in the respiratory airway, we identify tissue temperature to play an important role in the regulation of SARS-CoV-2 infection. Respiratory tissue incubated at 40°C remained permissive to SARS-CoV-2 entry but refractory to viral transcription, leading to significantly reduced levels of viral RNA replication and apical shedding of infectious virus. We identify tissue temperature to play an important role in the differential regulation of epithelial host responses to SARS-CoV-2 infection that impact upon multiple pathways, including intracellular immune regulation, without disruption to general transcription or epithelium integrity. We present the first evidence that febrile temperatures associated with COVID-19 inhibit SARS-CoV-2 replication in respiratory epithelia. Our data identify an important role for tissue temperature in the epithelial restriction of SARS-CoV-2 independently of canonical interferon (IFN)-mediated antiviral immune defenses.


Subject(s)
Epithelial Cells/immunology , Hot Temperature , Immunity, Innate/immunology , Interferons/immunology , Respiratory Mucosa/immunology , SARS-CoV-2/immunology , Virus Replication/immunology , Adolescent , Animals , COVID-19/genetics , COVID-19/immunology , COVID-19/virology , Chlorocebus aethiops , Epithelial Cells/metabolism , Epithelial Cells/virology , Female , Gene Expression Profiling/methods , Host-Pathogen Interactions/genetics , Host-Pathogen Interactions/immunology , Humans , Immunity, Innate/genetics , Interferons/genetics , Interferons/metabolism , Male , Middle Aged , Models, Biological , RNA-Seq/methods , Respiratory Mucosa/metabolism , Respiratory Mucosa/virology , SARS-CoV-2/genetics , SARS-CoV-2/physiology , Tissue Culture Techniques , Vero Cells , Virus Replication/genetics , Virus Replication/physiology
11.
Front Immunol ; 12: 789317, 2021.
Article in English | MEDLINE | ID: covidwho-1593957

ABSTRACT

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


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

ABSTRACT

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


Subject(s)
COVID-19/genetics , Gene Expression Profiling/methods , Gene Expression Regulation , Immunity/genetics , Aged , COVID-19/epidemiology , COVID-19/immunology , Female , Gene Ontology , Gene Regulatory Networks , Humans , Male , Middle Aged , Pandemics/prevention & control , Protein Interaction Maps/genetics , SARS-CoV-2/immunology , SARS-CoV-2/physiology , Signal Transduction/genetics , Signal Transduction/immunology
13.
Sci Rep ; 11(1): 23928, 2021 12 14.
Article in English | MEDLINE | ID: covidwho-1585797

ABSTRACT

Identification of transcriptional regulatory mechanisms and signaling networks involved in the response of host cells to infection by SARS-CoV-2 is a powerful approach that provides a systems biology view of gene expression programs involved in COVID-19 and may enable the identification of novel therapeutic targets and strategies to mitigate the impact of this disease. In this study, our goal was to identify a transcriptional regulatory network that is associated with gene expression changes between samples infected by SARS-CoV-2 and those that are infected by other respiratory viruses to narrow the results on those enriched or specific to SARS-CoV-2. We combined a series of recently developed computational tools to identify transcriptional regulatory mechanisms involved in the response of epithelial cells to infection by SARS-CoV-2, and particularly regulatory mechanisms that are specific to this virus when compared to other viruses. In addition, using network-guided analyses, we identified kinases associated with this network. The results identified pathways associated with regulation of inflammation (MAPK14) and immunity (BTK, MBX) that may contribute to exacerbate organ damage linked with complications of COVID-19. The regulatory network identified herein reflects a combination of known hits and novel candidate pathways supporting the novel computational pipeline presented herein to quickly narrow down promising avenues of investigation when facing an emerging and novel disease such as COVID-19.


Subject(s)
COVID-19/genetics , Gene Expression Profiling/methods , SARS-CoV-2/pathogenicity , Sequence Analysis, RNA/methods , A549 Cells , Cell Line , Epithelial Cells/chemistry , Epithelial Cells/cytology , Epithelial Cells/virology , Gene Expression Regulation , Humans , Models, Biological , Systems Biology
14.
Cells ; 11(1)2021 12 22.
Article in English | MEDLINE | ID: covidwho-1580996

ABSTRACT

Patients with COPD may be at an increased risk for severe illness from COVID-19 because of ACE2 upregulation, the entry receptor for SARS-CoV-2. Chronic exposure to cigarette smoke, the main risk factor for COPD, increases pulmonary ACE2. How ACE2 expression is controlled is not known but may involve HuR, an RNA binding protein that increases protein expression by stabilizing mRNA. We hypothesized that HuR would increase ACE2 protein expression. We analyzed scRNA-seq data to profile ELAVL1 expression in distinct respiratory cell populations in COVID-19 and COPD patients. HuR expression and cellular localization was evaluated in COPD lung tissue by multiplex immunohistochemistry and in human lung cells by imaging flow cytometry. The regulation of ACE2 expression was evaluated using siRNA-mediated knockdown of HuR. There is a significant positive correlation between ELAVL1 and ACE2 in COPD cells. HuR cytoplasmic localization is higher in smoker and COPD lung tissue; there were also higher levels of cleaved HuR (CP-1). HuR binds to ACE2 mRNA but knockdown of HuR does not change ACE2 protein levels in primary human lung fibroblasts (HLFs). Our work is the first to investigate the association between ACE2 and HuR. Further investigation is needed to understand the mechanistic underpinning behind the regulation of ACE2 expression.


Subject(s)
Angiotensin-Converting Enzyme 2/genetics , COVID-19/genetics , ELAV-Like Protein 1/genetics , Gene Expression Regulation , Lung/metabolism , Aged , Angiotensin-Converting Enzyme 2/metabolism , COVID-19/metabolism , COVID-19/virology , Cells, Cultured , ELAV-Like Protein 1/metabolism , Female , Fibroblasts/metabolism , Gene Expression Profiling/methods , Humans , Lung/pathology , Lung/virology , Male , Middle Aged , Pulmonary Disease, Chronic Obstructive/genetics , Pulmonary Disease, Chronic Obstructive/metabolism , Pulmonary Disease, Chronic Obstructive/virology , RNA Interference , RNA-Seq/methods , SARS-CoV-2/physiology , Single-Cell Analysis/methods
15.
Int J Mol Sci ; 22(24)2021 Dec 19.
Article in English | MEDLINE | ID: covidwho-1580688

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) triggered the pandemic Coronavirus Disease 19 (COVID-19), causing millions of deaths. The elderly and those already living with comorbidity are likely to die after SARS-CoV-2 infection. People suffering from Alzheimer's disease (AD) have a higher risk of becoming infected, because they cannot easily follow health roles. Additionally, those suffering from dementia have a 40% higher risk of dying from COVID-19. Herein, we collected from Gene Expression Omnibus repository the brain samples of AD patients who died of COVID-19 (AD+COVID-19), AD without COVID-19 (AD), COVID-19 without AD (COVID-19) and control individuals. We inspected the transcriptomic and interactomic profiles by comparing the COVID-19 cohort against the control cohort and the AD cohort against the AD+COVID-19 cohort. SARS-CoV-2 in patients without AD mainly activated processes related to immune response and cell cycle. Conversely, 21 key nodes in the interactome are deregulated in AD. Interestingly, some of them are linked to beta-amyloid production and clearance. Thus, we inspected their role, along with their interactors, using the gene ontologies of the biological process that reveals their contribution in brain organization, immune response, oxidative stress and viral replication. We conclude that SARS-CoV-2 worsens the AD condition by increasing neurotoxicity, due to higher levels of beta-amyloid, inflammation and oxidative stress.


Subject(s)
Alzheimer Disease/genetics , COVID-19/complications , COVID-19/genetics , Alzheimer Disease/complications , Alzheimer Disease/virology , Amyloid beta-Peptides/metabolism , Brain/virology , COVID-19/physiopathology , Comorbidity/trends , Databases, Factual , Gene Expression/genetics , Gene Expression Profiling/methods , Humans , Inflammation/metabolism , Neurotoxicity Syndromes/metabolism , Oxidative Stress/physiology , Pandemics , SARS-CoV-2/metabolism , SARS-CoV-2/pathogenicity , Transcriptome/genetics
16.
Front Immunol ; 12: 733539, 2021.
Article in English | MEDLINE | ID: covidwho-1572288

ABSTRACT

The response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is largely impacted by the level of virus exposure and status of the host immunity. The nature of protection shown by direct asymptomatic contacts of coronavirus disease 2019 (COVID-19)-positive patients is quite intriguing. In this study, we have characterized the antibody titer, SARS-CoV-2 surrogate virus neutralization, cytokine levels, single-cell T-cell receptor (TCR), and B-cell receptor (BCR) profiling in asymptomatic direct contacts, infected cases, and controls. We observed significant increase in antibodies with neutralizing amplitude in asymptomatic contacts along with cytokines such as Eotaxin, granulocyte-colony stimulating factor (G-CSF), interleukin 7 (IL-7), migration inhibitory factor (MIF), and macrophage inflammatory protein-1α (MIP-1α). Upon single-cell RNA (scRNA) sequencing, we explored the dynamics of the adaptive immune response in few representative asymptomatic close contacts and COVID-19-infected patients. We reported direct asymptomatic contacts to have decreased CD4+ naive T cells with concomitant increase in CD4+ memory and CD8+ Temra cells along with expanded clonotypes compared to infected patients. Noticeable proportions of class switched memory B cells were also observed in them. Overall, these findings gave an insight into the nature of protection in asymptomatic contacts.


Subject(s)
Adaptive Immunity/immunology , COVID-19/immunology , Genomics/methods , SARS-CoV-2/immunology , Single-Cell Analysis/methods , Adaptive Immunity/genetics , Adult , Antibodies, Viral/immunology , COVID-19/genetics , COVID-19/virology , Cytokines/immunology , Cytokines/metabolism , Female , Gene Expression Profiling/methods , Humans , Male , /metabolism , Middle Aged , SARS-CoV-2/physiology , Sequence Analysis, RNA/methods , T-Lymphocytes/immunology , T-Lymphocytes/metabolism , T-Lymphocytes/virology , Young Adult
17.
Brief Bioinform ; 22(5)2021 09 02.
Article in English | MEDLINE | ID: covidwho-1528156

ABSTRACT

The low capture rate of expressed RNAs from single-cell sequencing technology is one of the major obstacles to downstream functional genomics analyses. Recently, a number of imputation methods have emerged for single-cell transcriptome data, however, recovering missing values in very sparse expression matrices remains a substantial challenge. Here, we propose a new algorithm, WEDGE (WEighted Decomposition of Gene Expression), to impute gene expression matrices by using a biased low-rank matrix decomposition method. WEDGE successfully recovered expression matrices, reproduced the cell-wise and gene-wise correlations and improved the clustering of cells, performing impressively for applications with sparse datasets. Overall, this study shows a potent approach for imputing sparse expression matrix data, and our WEDGE algorithm should help many researchers to more profitably explore the biological meanings embedded in their single-cell RNA sequencing datasets. The source code of WEDGE has been released at https://github.com/QuKunLab/WEDGE.


Subject(s)
Algorithms , Computational Biology/methods , Gene Expression Profiling/methods , RNA-Seq/methods , Single-Cell Analysis/methods , COVID-19/blood , COVID-19/genetics , COVID-19/virology , Cluster Analysis , Computer Simulation , Genomics/methods , Humans , Leukocytes, Mononuclear/classification , Leukocytes, Mononuclear/metabolism , Reproducibility of Results , SARS-CoV-2/physiology , Severity of Illness Index
18.
J Alzheimers Dis ; 85(2): 729-744, 2022.
Article in English | MEDLINE | ID: covidwho-1518457

ABSTRACT

BACKGROUND: COVID-19 pandemic is a global crisis which results in millions of deaths and causes long-term neurological sequelae, such as Alzheimer's disease (AD). OBJECTIVE: We aimed to explore the interaction between COVID-19 and AD by integrating bioinformatics to find the biomarkers which lead to AD occurrence and development with COVID-19 and provide early intervention. METHODS: The differential expressed genes (DEGs) were found by GSE147507 and GSE132903, respectively. The common genes between COVID-19 and AD were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interactions (PPI) network analysis were carried out. Hub genes were found by cytoscape. A multivariate logistic regression model was constructed. NetworkAnalyst was used for the analysis of TF-gene interactions, TF-miRNA coregulatory network, and Protein-chemical Interactions. RESULTS: Forty common DEGs for AD and COVID-19 were found. GO and KEGG analysis indicated that the DEGs were enriched in the calcium signal pathway and other pathways. A PPI network was constructed, and 5 hub genes were identified (ITPR1, ITPR3, ITPKB, RAPGEF3, MFGE8). Four hub genes (ITPR1, ITPR3, ITPKB, RAPGEF3) which were considered as important factors in the development of AD that were affected by COVID-19 were shown by nomogram. Utilizing NetworkAnalyst, the interaction network of 4 hub genes and TF, miRNA, common AD risk genes, and known compounds is displayed, respectively. CONCLUSION: COVID-19 patients are at high risk of developing AD. Vaccination is required. Four hub genes can be considered as biomarkers for prediction and treatment of AD development caused by COVID-19. Compounds with neuroprotective effects can be used as adjuvant therapy for COVID-19 patients.


Subject(s)
Alzheimer Disease/genetics , COVID-19/virology , Protein Interaction Maps/genetics , SARS-CoV-2/pathogenicity , Alzheimer Disease/complications , Alzheimer Disease/metabolism , Alzheimer Disease/virology , Computational Biology/methods , Databases, Genetic , Gene Expression Profiling/methods , Humans , SARS-CoV-2/genetics
19.
Cell Rep ; 37(7): 110020, 2021 11 16.
Article in English | MEDLINE | ID: covidwho-1509641

ABSTRACT

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types.


Subject(s)
COVID-19/genetics , SARS-CoV-2/genetics , Chromosome Mapping/methods , Computational Biology/methods , Databases, Genetic , Gene Expression/genetics , Gene Expression Profiling/methods , Genetic Predisposition to Disease/genetics , Genetic Variation/genetics , Genome-Wide Association Study/methods , Humans , Organ Specificity/genetics , Polymorphism, Single Nucleotide/genetics , Quantitative Trait Loci/genetics , SARS-CoV-2/pathogenicity , Severity of Illness Index , Transcriptome/genetics
20.
Front Biosci (Landmark Ed) ; 26(10): 740-751, 2021 10 30.
Article in English | MEDLINE | ID: covidwho-1498507

ABSTRACT

Objectives: To quantify the integrated levels of ACE2 and TMPRSS2, the two well-recognized severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entry-related genes, and to further identify key factors contributing to SARS-CoV-2 susceptibility in head and neck squamous cell carcinoma (HNSC). Methods: We developed a metric of the potential for tissue infected with SARS-CoV-2 ("TPSI") based on ACE2 and TMPRSS2 transcript levels and compared TPSI levels between tumor and matched normal tissues across 11 tumor types. For further analysis of HNSC, weighted gene co-expression network analysis (WGCNA), functional analysis, and single sample gene set enrichment analysis (ssGSEA) were conducted to investigate TPSI-relevant biological processes and their relationship with the immune landscape. TPSI-related factors were identified from clinical and mutational domains, followed by lasso regression to determine their relative effects on TPSI levels. Results: TPSI levels in tumors were generally lower than in the normal tissues. In HNSC, the genes highly associated with TPSI were enriched in viral entry-related processes, and TPSI levels were positively correlated with both eosinophils and T helper 17 (Th17) cell infiltration. Furthermore, the site of onset, human papillomaviruses (HPV) status, and nuclear receptor binding SET domain protein 1 (NSD1) mutations were identified as the most important factors shaping TPSI levels. Conclusions: This study identified the infection risk of SARS-CoV-2 between tumor and normal tissues, and provided evidence for the risk stratification of HNSC.


Subject(s)
Angiotensin-Converting Enzyme 2/genetics , COVID-19/genetics , Carcinoma, Squamous Cell/genetics , Head and Neck Neoplasms/genetics , Serine Endopeptidases/genetics , Angiotensin-Converting Enzyme 2/metabolism , COVID-19/metabolism , COVID-19/virology , Carcinoma, Squamous Cell/metabolism , Carcinoma, Squamous Cell/virology , Gene Expression Profiling/methods , Gene Expression Regulation, Neoplastic , Gene Regulatory Networks , Head and Neck Neoplasms/metabolism , Head and Neck Neoplasms/virology , Humans , Reverse Transcriptase Polymerase Chain Reaction/methods , SARS-CoV-2/metabolism , SARS-CoV-2/physiology , Serine Endopeptidases/metabolism , Virus Internalization
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