Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

MiR-155 deficiency alters heterogeneity of lung immune cells in a mouse model of severe asthma

ABSTRACT: MiR-155 deficiency alters heterogeneity of lung immune cells in a mouse model of severe asthma

PROVIDER: PRJNA841824 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR19371714_1.fastq.gz	Fastqsanger.gz
	SRR19371714_2.fastq.gz	Fastqsanger.gz
	SRR19371715_1.fastq.gz	Fastqsanger.gz
	SRR19371715_2.fastq.gz	Fastqsanger.gz
	SRR19371716_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 24

Similar Datasets

MiR-155 deficiency alters heterogeneity of lung immune cells in a mouse model of severe asthma

Project description:The lung is a complex tissue comprised of more than 40 cell populations. The interplay among the heterogeneous pulmonary cells is required for proper function and long-term maintenance of lung homeostasis. Because each of the cell populations has a distinct transcriptome, bulk RNA-seq could mask cell-cell crosstalk, cell-type-specific contributions, and relevant networking changes in the lung. To overcome the limitations of bulk tissue analysis, we performed single cell RNA sequencing (scRNA-seq) to investigate the transcriptional profiles of lungs affected by miR-155. Unsupervised clustering analysis revealed 27 different clusters corresponding to distinct cell types of the lung, including epithelial, stromal, endothelial, and leukocyte lineages with specific molecular markers. The miR-155 knockout mice subjected to the severe asthma model showed a decrease in lung immune cells, whereas the numbers of structural cells such as epithelial and endothelial cells were slightly increased compared to their wild type counterparts. We also showed that the transcripts of extracellular trap formation-related genes were substantially down-regulated in miR-155 knockout mice subjected to the severe asthma model.

2022-07-21 | GSE203656 | GEO

Severe Asthma Microbiome

Project description:Airway microbiota in severe asthma and relationship to asthma clusters and severity

| PRJEB11495 | ENA

Severe Asthma Research Program (SARP)

Project description:Severe Asthma Research Program (SARP)

| PRJNA801394 | ENA

Nasal DNA methylation differentiates severe from non-severe asthma in African-American (AA) children

Project description:Background: Asthma is highly heterogeneous and severity evaluation is key to asthma management. DNA methylation (DNAm) contributes to asthma pathogenesis. This study aimed to identify nasal epithelial DNAm differences between severe and non-severe asthmatic children and evaluate the impact of environmental exposures. Methods: Thirty-three non-severe and 22 severe asthmatic African-American children were included in an epigenome-wide association study. Genome-wide nasal epithelial DNAm and gene expression were measured. CpG sites associated with asthma severity and environmental exposures and predictive of severe asthma were identified. DNAm was correlated with gene expression. Enrichment for transcription factor (TF) binding sites or histone modifications surrounding DNAm differences were determined. Results: We identified 816 differentially methylated CpG positions (DMPs) and 10 differentially methylated regions (DMRs) associated with asthma severity. Three DMPs exhibited discriminatory ability for severe asthma. Intriguingly, six DMPs were simultaneously associated with asthma, allergic asthma, total IgE, environmental IgE, and FeNO in an independent cohort of children. 27 DMPs were associated with traffic-related air pollution or secondhand smoke. DNAm at 22 DMPs were altered by diesel particles or allergen in human bronchial epithelial cells. DNAm levels at 39 DMPs were correlated with mRNA expression. Proximal to 816 DMPs, three histone marks and several TFs involved in asthma pathogenesis were enriched. Conclusions: Significant differences in nasal epithelial DNAm were observed between non-severe and severe asthma in African-American children, a subset of which may be useful to predict disease severity. These CpG sites are subject to the influences of environmental exposures and may regulate gene expression.

2022-04-15 | GSE161485 | GEO

Altered epithelial gene expression in peripheral airways of severe asthma

Project description:Management of severe asthma remains a challenge despite treatment with glucocorticosteroid therapy. The majority of studies investigating disease mechanisms in treatment-resistant severe asthma have previously focused on the large central airways, with very few utilizing transcriptomic approaches. The small peripheral airways, which comprise the majority of the airway surface area, remain an unexplored area in severe asthma and were targeted for global epithelial gene expression profiling in this study.

2017-01-04 | GSE64913 | GEO

Transcriptomic analysis of induced sputum samples in severe asthma

Project description:Gene expression profiles were generated from induced sputum samples in asthma and healthy controls. The study identified differential gene expression and pathways in severe asthma.

2019-09-12 | GSE137268 | GEO

Targeting the IL-5 pathway in severe asthma

Project description:Therapies targeting interleukin-5 (IL-5) or its receptor (IL-5Rα) are used to treat patients with severe eosinophilic asthma. The therapeutic effect are considered being the suppression of eosinophil activation and elimination. However, the impact of these therapies on mast cells (MCs) and their progenitors has remained largely unexplored. We investigate whether targeting the IL-5 pathway in severe asthma patients could reduce blood MC progenitors

2024-12-05 | GSE263241 | GEO

Transcriptomic profiling of inflammatory phenotypes of severe asthma using endobronchial biopsies

Project description:Severe asthma is a complex disease with different inflammatory phenotypes. Transcriptomic profiling has contributed to understanding the pathogenesis of asthma, especially type-2 inflammation; however, there is still poor understanding of non-eosinophilic asthma, and consequently there are limited treatment options. The aim of this study was to determine transcriptomic profiles in endobronchial biopsies of adults with severe asthma and different inflammatory phenotypes (neutrophilic, eosinophilic and paucigranulocytic) compared with healthy controls.

2021-07-31 | GSE143303 | GEO

Gene signature profiles in whole blood cells from severe asthma patinets treated with benralizumab

Project description:Severe asthma is a clinically and physiologically heterogeneous disease. Benralizumab is a monoclonal antibody which binds the alpha chain of the interleukin-5 receptor and used for severe eosinophilic asthma worldwide. However, not all eosinophilic asthma patients will benefit from benralizumab due to heterogeneity of this disease. Therefore, we performed comprehensive gene expression analysis of whole blood cells that examine severe asthma disease heterogeneity in response to benralizumab. This study is the first to perform comprehensive transcriptome analysis of whole blood cells to identify transcriptomic endotypes of severe asthma clusters that correlate with benralizumab response. The identified transcriptomic endotypes of severe asthma clusters are associated with gene signatures of eosinophils and neutrophilis.

2023-04-15 | GSE148725 | GEO

Chronic allergic asthma alters m6A epitranscriptomic tagging of mRNAs and lncRNAs in the lung

Project description:Chronic allergic asthma alters the lung m6A epitranscriptome, suggesting functional implications in the pathophysiology of refractory asthma. Methylated IL17RB mRNA possibly become a new therapeutic target for chronic allergic asthma.

2022-06-08 | GSE205471 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

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