Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

SARS-CoV-2 genomes from upper and lower respiratory tract

ABSTRACT: Pairs of SARS-CoV-2 genomes from upper and lower respiratory tract of different individuals

PROVIDER: PRJEB42830 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Nasally-delivered interferon-λ in mice protects against upper and lower respiratory tract infection of SARS-CoV-2 variants including Omicron

Project description:The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has had devastating impacts on our global society. Although vaccines and monoclonal antibody countermeasures have reduced the morbidity and mortality associated with SARS-CoV-2 infection, variants with constellations of mutations in the spike gene threaten their efficacy. Therefore, antiviral interventions that are resistant to further virus evolution may be needed. Here, we show IFN-λ protects against SARS-CoV-2 B.1.351 (Beta) and B.1.1529 (Omicron) variants in three strains of conventional and human ACE2 transgenic mice. Prophylaxis or therapy with nasally-delivered IFN-λ2 limited infection of historical or variant (B.1.351 and B.1.1.529) SARS-CoV-2 strains in both the upper and lower respiratory tracts without causing excessive inflammation. In the lung, IFN-λ was produced preferentially in epithelial cells and acted on radio-resistant cells to protect against of SARS-CoV-2 infection. Thus, inhaled IFN-λ may have promise as a treatment for evolving SARS-CoV-2 variants that develop resistance to antibody-based countermeasures.

2022-02-08 | GSE193990 | GEO

Analysis of blood and nasal epithelial transcriptomes to identify mechanisms associated with control of SARS-CoV-2 viral load in the upper respiratory tract

Project description:The amount of SARS-CoV-2 detected in the upper respiratory tract (URT viral load) is a key driver of transmission of infection. Current evidence suggests that mechanisms constraining URT viral load are different from those controlling lower respiratory tract viral load and disease severity. Understanding such mechanisms may help to develop treatments and vaccine strategies to reduce transmission. Combining mathematical modelling of URT viral load dynamics with transcriptome analyses we aimed to identify mechanisms controlling URT viral load. COVID-19 patients were recruited in Spain during the first wave of the pandemic. RNA sequencing of peripheral blood and targeted NanoString nCounter transcriptome analysis of nasal epithelium were performed and gene expression analysed in relation to paired URT viral load samples collected within 15 days of symptom onset. Proportions of major immune cells in blood were estimated from transcriptional data using computational differential estimation. Weighted correlation network analysis (adjusted for cell proportions) and fixed transcriptional repertoire analysis were used to identify associations with URT viral load, quantified as standard deviations (z-scores) from an expected trajectory over time.

2023-04-01 | E-MTAB-12791 | biostudies-arrayexpress

Microbiota differs in upper and lower respiratory tract of LTRs

Project description:Comparison of the upper and lower airway microbiota in early postoperative lung transplant recipients.

| PRJEB44751 | ENA

upper respiratory tract metagenome

Project description:Upper respiratory tract microbiota Metagenome

| PRJNA209575 | ENA

upper respiratory tract metagenome

Project description:upper respiratory tract metagenome Raw sequence reads

| PRJNA434486 | ENA

upper respiratory tract metagenome

Project description:Upper respiratory tract metagenome Raw sequence reads

| PRJNA401637 | ENA

Upper vs lower airway microbiota

Project description:Comparison of the upper and lower airway microbiota

| PRJEB14074 | ENA

upper respiratory tract metagenome

Project description:Upper respiratory tract microbiomes of pandemic H1N1 patients

| PRJNA200951 | ENA

Upper respiratory tract microbiota.

Project description:Isolates cultured from the human upper respiratory tract. Raw sequence reads.

| PRJNA381557 | ENA

Upper airway gene expression is an effective surrogate biomarker for Th2-driven inflammation in the lower airway

Project description:Molecular profiling studies in asthma cohorts have identified a Th2-driven asthma subtype, characterized by elevated lower airway expression of POSTN, CLCA1 and SERPINB2. To assess upper airway gene expression as a potential biomarker for lower airway Th2 inflammation, we assayed upper airway (nasal) and lower airway (bronchial) epithelial gene expression, serum total IgE, blood eosinophils and serum periostin in a cohort of 54 allergic asthmatics and 30 matched healthy controls. 23 of 51 asthmatics in our cohort were classified as âTh2 highâ based on lower airway Th2 gene signature expression. Consistent with this classification, âTh2 highâ subjects displayed elevated total IgE and blood eosinophil levels relative to âTh2 lowâ subjects. Upper airway Th2 signature expression was significantly correlated with lower airway Th2 signature expression (r=0.44), with similar strength of association as serum total IgE and blood eosinophils, known biomarkers of Th2 inflammation. In an unbiased genome-wide scan, we identified 8 upper airway genes more strongly correlated with lower airway Th2 gene signature expression (r=0.58), including Eotaxin-3 (CCL26), Galectin-10 (CLC) and Cathepsin-C (CTSC). Asthmatics classified as âTh2 highâ using this 8-gene signature show similar serum total IgE and blood eosinophil levels as âTh2 highâ asthmatics classified using lower airway Th2 gene signature expression. We have identified an 8-gene upper airway signature correlated with lower airway Th2 inflammation, which may be used as a diagnostic biomarker for Th2-driven asthma. Upper airway (nasal) and lower airway (bronchial) epithelial brushings obtained from a cohort of 54 allergic asthmatics and 30 matched healthy controls were profiled by gene expression by microarray. Subjects were assayed for gene expression, serum total IgE, blood eosinophils and serum periostin.

2015-10-23 | E-GEOD-41861 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data