Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection.
ABSTRACT: The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.
Project description:COVID-19, a severe respiratory disease caused by a new type of coronavirus SARS-CoV-2, has been spreading all over the world. Patients infected with SARS-CoV-2 may have no pathogenic symptoms, i.e., presymptomatic patients and asymptomatic patients. Both patients could further spread the virus to other susceptible people, thereby making the control of COVID-19 difficult. The two major challenges for COVID-19 diagnosis at present are as follows: (1) patients could share similar symptoms with other respiratory infections, and (2) patients may not have any symptoms but could still spread the virus. Therefore, new biomarkers at different omics levels are required for the large-scale screening and diagnosis of COVID-19. Although some initial analyses could identify a group of candidate gene biomarkers for COVID-19, the previous work still could not identify biomarkers capable for clinical use in COVID-19, which requires disease-specific diagnosis compared with other multiple infectious diseases. As an extension of the previous study, optimized machine learning models were applied in the present study to identify some specific qualitative host biomarkers associated with COVID-19 infection on the basis of a publicly released transcriptomic dataset, which included healthy controls and patients with bacterial infection, influenza, COVID-19, and other kinds of coronavirus. This dataset was first analysed by Boruta, Max-Relevance and Min-Redundancy feature selection methods one by one, resulting in a feature list. This list was fed into the incremental feature selection method, incorporating one of the classification algorithms to extract essential biomarkers and build efficient classifiers and classification rules. The capacity of these findings to distinguish COVID-19 with other similar respiratory infectious diseases at the transcriptomic level was also validated, which may improve the efficacy and accuracy of COVID-19 diagnosis.
Project description:The latest threat to global health is the form of the ongoing Coronavirus Disease 2019 (COVID-19) pandemic. This new coronavirus (SARS-COV-2) started as a local outbreak in Wuhan, China but soon tightened its grip on human lives around the globe. So far, we do not have a particularly effective anti-SARS-COV-2 vaccine or antiviral agent against COVID-19. Across the globe, many research organizations such as the National Institutes of Health (NIH), United States are studying and testing various drugs and vaccines for their effectiveness against SARS-COV-2. Currently, the principle fighting tool being promoted by the World Health Organization (WHO) is the prevention of acquiring SARS-COV-2 infection by following basic health hygiene rules and social distancing. We hereby discuss major non-pharmacological and pharmacological interventions.
Project description:mRNA-1273, an mRNA-based vaccine for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is efficacious in mouse and non-human primate models of mild COVID-19. Hamsters exhibit more severe clinical disease from SARS-CoV-2, making it an important model to evaluate vaccine efficacy. Here we show that mock-vaccine treated hamsters infected with SARS-CoV-2 show weight loss and pulmonary infection with pathological changes and increased activated interstitial macrophages and other immune cell types. Prime-boost administration of mRNA-1273 elicited dose-independent robust binding and neutralizing antibodies, ameliorated weight loss and suppressed virus replication in the upper and lower airways. Vaccination largely prevented pulmonary pathological changes, antiviral immune cell activation and changes in cell type composition although increases in some immune cell types and activation of regulatory pathways were found and coincided with an anamnestic antibody response. The results characterize unexpected pulmonary cellular responses to SARS-CoV-2 in vaccinated animals that were shared but greatly attenuated, when compared to mock-vaccinated animals. The efficacy of mRNA-1273 is demonstrated as a two-dose vaccination schedule in a model of severe COVID-19. Overall design: Single cell RNA sequencing was conducted on fourteen samples of the Syrian golden hamster lung tissue. Samples were from naïve, mock-vaccinated + SARS-CoV-2 infected, and mRNA-1273 vaccinated + SARS-CoV-2 infected hamsters.
Project description:To evaluate the gene expression profiling of peripheral leukocytes in different outcomes of SARS-CoV-2 infections, whole blood samples were collected from individuals with positive SARS-CoV-2 nasopharyngeal swab by RT-PCR (54 patients) and healthy uninfected individuals (12 volunteers). Infected patients were classified into mild, moderate, severe and critical groups according to a modified statement in the Novel Coronavirus Pneumonia Diagnosis and Treatment Guideline. Blood were collected into EDTA tubes and the buffy coat samples were stored in TRIzol reagent at -80 °C until use for RNA extraction. Affymetrix Clariom S array was used to perform the high-throughput gene expression profiling. Microarray analyses were performed using APT Affymetrix software, R packages and Bioconductor libraries. This systemic view of SARS-CoV-2 infections through blood transcriptomics will foster the understanding about molecular mechanisms and immunopathological processes involved in COVID-19 disease and its different outcomes.
Project description:Differential expression was determined in Calu-3 cells between mock infected and infection with either Human coronavirus EMC and SARS coronavirus at different times post infection. Calu-3 2B4 cells were infected with Human Coronavirus EMC 2012 (HCoV-EMC) or mock infected. Samples were collected 0, 3, 7, 12, 18 and 24 hpi. There are 3 mock and 3 infected replicates for each time point, except for 12 hpi for which there are only 2 infected replicates (one replicate did not pass RNA quality check). There were no mock sampes at 18 hpi, and therefore infected samples at 18 hpi were compared with mocks at 24 hpi. For direct comparison with SARS-CoV infected cells, raw data from HCoV-EMC experiments were quantile normalized together with the SARS-CoV dataset (GEO Series accession number GSE33267).
Project description:Background: The recent emergence of a novel coronavirus in the Middle East (designated MERS-CoV) is a reminder of the zoonotic potential of coronaviruses and the severe disease these etiologic agents can cause in humans. Clinical features of Middle East respiratory syndrome (MERS) include severe acute pneumonia and renal failure that is highly reminiscent of severe acute respiratory syndrome (SARS) caused by SARS-CoV. The host response is a key component of highly pathogenic respiratory virus infection. Here, we computationally analyzed gene expression changes in a human airway epithelial cell line infected with two genetically distinct MERS-CoV strains obtained from human patients, MERS-CoV-EMC (designated EMC) and MERS-CoV-London (designated LoCoV). Results: Using topological techniques, such as persistence homology and filtered clustering, we characterized the host response system to the different MERS-CoVs, with LoCoV inducing early kinetic changes, between 3 and 12 hours post infection, compared to EMC. Robust transcriptional changes distinguished the two MERS-CoV strains predominantly at the late time points. Combining statistical analysis of infection and cytokine-stimulated treatment transcriptomics, we identified differential innate and pro-inflammatory responses between the two virus strains, including up-regulation of extracellular remodeling genes following LoCoV infection and differential pro-inflammatory responses between the two strains. Conclusions: These transcriptional differences may be the result of amino acid differences in viral proteins known to modulate innate immunity against MERS infection. Triplicate wells of Calu-3 2B4 cells were infected with Human Coronavirus EMC 2012 (HCoV-EMC) or time-matched mock infected. Cells were harvested at 0, 3, 7, 12, 18 and 24 hours post-infection (hpi), RNA extracted and transcriptomics analyzed by microarray.
Project description:<h4>Background</h4>Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 19 (COVID-19) that was emerged as a new member of coronaviruses since December 2019 in Wuhan, China and then after was spread in all continentals. Since SARS-CoV-2 has shown about 77.5% similarity to SARS-CoV, the transcriptome and immunological regulations of SARS-CoV-2 was expected to have high percentage of overlap with SARS-CoV.<h4>Results</h4>In this study, we applied the single cell transcriptomics data of human bronchial epithelial cells (2B4 cell line) infected with SARS-CoV, which was annotated in the Expression Atlas database to expand this data to COVID-19. In addition, we employed system biology methods including gene ontology (GO) and Reactome pathway analyses to define functional genes and pathways in the infected cells with SARS-CoV. The transcriptomics analysis on the Expression Atlas database revealed that most genes from infected 2B4 cell line with SARS-CoV were downregulated leading to immune system hyperactivation, induction of signaling pathways, and consequently a cytokine storm. In addition, GO:0016192 (vesicle-mediated transport), GO:0006886 (intracellular protein transport), and GO:0006888 (ER to Golgi vesicle-mediated transport) were shown as top three GOs in the ontology network of infected cells with SARS-CoV. Meanwhile, R-HAS-6807070 (phosphatase and tensin homolog or PTEN regulation) showed the highest association with other Reactome pathways in the network of infected cells with SARS-CoV. PTEN plays a critical role in the activation of dendritic cells, B- and T-cells, and secretion of proinflammatory cytokines, which cooperates with downregulated genes in the promotion of cytokine storm in the COVID-19 patients.<h4>Conclusions</h4>Based on the high similarity percentage of the transcriptome of SARS-CoV with SARS-CoV-2, the data of immunological regulations, signaling pathways, and proinflammatory cytokines in SARS-CoV infection can be expanded to COVID-19 to have a valid platform for future pharmaceutical and vaccine studies.
Project description:The first ever US Food and Drug Administration-approved messenger RNA vaccines are highly protective against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)<sup>1-3</sup>. However, the contribution of each dose to the generation of antibodies against SARS-CoV-2 spike (S) protein and the degree of protection against novel variants warrant further study. Here, we investigated the B cell response to the BNT162b2 vaccine by integrating B cell repertoire analysis with single-cell transcriptomics pre- and post-vaccination. The first vaccine dose elicits a recall response of IgA<sup>+</sup> plasmablasts targeting the S subunit S2. Three weeks after the first dose, we observed an influx of minimally mutated IgG<sup>+</sup> memory B cells that targeted the receptor binding domain on the S subunit S1 and likely developed from the naive B cell pool. This response was strongly boosted by the second dose and delivers potently neutralizing antibodies against SARS-CoV-2 and several of its variants.
Project description:The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late 2019 has spread globally, causing a pandemic of respiratory illness designated coronavirus disease 2019 (COVID-19). A better definition of the pulmonary host response to SARS-CoV-2 infection is required to understand viral pathogenesis and to validate putative COVID-19 biomarkers that have been proposed in clinical studies. Here, we use targeted transcriptomics of FFPE tissue using the Nanostring GeoMX™ platform to generate an in-depth picture of the pulmonary transcriptional landscape of COVID-19, pandemic H1N1 influenza and uninfected control patients. Host transcriptomics showed a significant upregulation of genes associated with inflammation, type I interferon production, coagulation and angiogenesis in the lungs of COVID-19 patients compared to non-infected controls. SARS-CoV-2 was non-uniformly distributed in lungs (emphasising the advantages of spatial transcriptomics) with the areas of high viral load associated with an increased type I interferon response. Once the dominant cell type present in the sample, within patient correlations and patient-patient variation had been controlled for, only a very limited number of genes were differentially expressed between the lungs of fatal influenza and COVID-19 patients. Strikingly, the interferon-associated gene IFI27, previously identified as a useful blood biomarker to differentiate bacterial and viral lung infections, was significantly upregulated in the lungs of COVID-19 patients compared to patients with influenza. Collectively, these data demonstrate that spatial transcriptomics is a powerful tool to identify novel gene signatures within tissues, offering new insights into the pathogenesis of SARS-COV-2 to aid in patient triage and treatment Overall design: In this dataset, we capture GeoMx DSP RNA profiles of lung tissue from SARS-CoV2 infected patients aswell as pH1N1 and control specimens
Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N) protein is highly expressed upon infection and is essential for viral replication, making it a promising target for antiviral drug and vaccine development. Here, starting from a functional proteomics workflow, we catalogued the protein-protein interactions of 28 SARS-CoV-2 proteins in HEK293 cells, including an evolutionarily conserved specific interaction of N with the stress granule resident proteins G3BP1 and G3BP2. N protein localizes to stress granules and sequesters G3BP1 and G3BP2 away from their normal interaction partners, thus attenuating stress granule formation. N also binds directly to host mRNAs, with a preference for 3´ UTRs, and modulates target mRNA stability. We show that SARS-CoV-2 N protein rewires the G3BP1 mRNA-binding profile, thereby suppressing host gene expression changes induced in response to cellular stress. Overall design: RNA-seq analysis of WT type and SARS-CoV-2 N expressing HEK293 cells treated with or without Sodium Arsenite