Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

MicroRNAs in Nasal Lavage Extracellular Vesicles as Potential Therapeutic Targets for Mucus Hypersecretion

ABSTRACT: MicroRNAs in Nasal Lavage Extracellular Vesicles as Potential Therapeutic Targets for Mucus Hypersecretion

PROVIDER: PRJNA1046545 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR26990618.fastq.gz	Fastqsanger.gz
	SRR26990619.fastq.gz	Fastqsanger.gz
	SRR26990620.fastq.gz	Fastqsanger.gz
	SRR26990621.fastq.gz	Fastqsanger.gz
	SRR26990622.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 18

Similar Datasets

MicroRNAs in Nasal Lavage Extracellular Vesicles as Potential Therapeutic Targets for Mucus Hypersecretion

Project description:Small RNA sequencing was conducted to identify the miRNAs responsible for AR using exosomes isolated from the nasal lavage (NAL) fluid of the control (n=9) and AR patient groups (n=8).

2023-12-04 | GSE248991 | GEO

Proteomics and metabolomics analysis of nasal lavage fluid in chronic rhinosinusitis with nasal polyps

Project description:Induced sputum cells (ISCs) and nasal lavage fluid cells (NLFCs) from 6 patients with CRSwNP, 6 patients suffering from CRSwNP with asthma, and 6 control subjects were procured in the study.

2023-02-20 | PXD040299 |

Protoemics of nasal and bronchial mucus from primary human airway epithelial cells grown at air-liquid interface.

Project description:Differentiated primary human respiratory epithelial cells grown at air-liquid interface have become a widely used cell culture model of the human conducting airways. These cultures contain secretory cells such as goblet and club cells, which produce and secrete mucus. Here, we characterize the composition of mucus harvested from airway cultures of nasal and bronchial origin. We find that despite inter-donor variability, the protein content and composition is very similar between nasal and bronchial mucus. However, subtle differences in the abundance of individual components in nasal versus bronchial mucus can influence its antimicrobial properties.

2025-08-13 | PXD066146 | Pride

Label free Quantitative Proteomics analysis of Nasal lavage fluid rhinosinusitis

Project description:The study aimed to identify proteins associated with chronic rhinosinusitis with nasal polyps. Samples from nasal Lavage fluid from CRSwNP patients and controls were analysed and revealed a significant difference in protein expression. Dysregulated proteins were linked to airway inflammation, immune response and oxidative stress.

2024-10-22 | MSV000096171 | MassIVE

Proteomic analysis of mucus from chronic rhinosinusitis patients with or without nasal polyps

Project description:Patients with chronic rhinosinusitis (CRS) have abnormal immune responses triggered by a variety of infectious agents, airborne toxins and fungi. Respiratory epithelial cells serve as relay stations capable of amplifying or augmenting cues received from external stimuli to nearby immune cells located in the sinus mucosa. Previous studies have identified increases in complement components and complement gene expression in the mucosa of patients with atopic CRS with nasal polyps (CRSwNP). As part of a larger study, we used shotgun proteomics to quantify changes in mucus proteins between patients with and without nasal polyps.

2016-05-26 | PXD004144 | Pride

Application of text mining to build AOP-based mucus hypersecretion genesets and validation with in vitro and clinical samples

Project description:Purpose: Build genesets using text mining and validate the geneset with data from public repository and a dataset generated in house using in vitro models and RNA-seq Methods: Mucus hypersecretion and mucociliary dysfucntion adverse outcome pathways (AOPs) genesets were build using the text mining method described by Rani et al., 2015. Validation was performed using RNA expression data from cigarette and e-cigarette aerosol treated cells, IL-13 treated airway cells, and COPD-lung biopsies. The cigarette and e-cigarette aerosol RNA-samples from airway cells were generated and sequenced in house. The other dataset were publically available. Results: Using unsupervised clustering, the mucus hypersecretion and mucociliary dysfunction genesets were able to discriminate the cigartte treated cells from the e-cigarettes and the air control. The e-cigarette and the air control clustered together. Clustering was also observed with IL-13 treated cells. IL-13 is an induced of mucus hypersecretion. Clustering was not observed when COPD RNA-seq samples were used. PCA analysis revealed some degree of grouping based on disease status, but this was also heavily confounded by other parameters. Conclusions: Our study described the first application of text mining to build genesets relevant to AOPs. In vitro validation confirmed the genesets could discriminates between treatment that induce mucus hypersecretion phenotypes, however this could not be confirmed with COPD biopsy samples. This could be due to a series of technical confouding factors and the heterogeneity of the COPD disease.

2020-04-30 | GSE142100 | GEO

Proteomic analysis of mucus from chronic rhinosinusitis patients with or without nasal polyps

2016-07-22 | MSV000079984 | MassIVE

Proteomic Profiling of Equine Airway Mucus Reveals Compositional Changes in Asthmatic Phenotypes

Project description:Mucus hypersecretion and accumulation are hallmark features of equine asthma (EA), a meaningful respiratory disorder in horses occurring in mild to moderate (MEA) and severe (SEA) forms. Changes of the proteomic composition of airway mucus in EA are poorly understood. Using label-free quantitative liquid chromatography-mass spectrometry, we analyzed airway mucus from SEA (n=10), MEA (n=6), and healthy (n=8) horses. We identified and quantified 2,275 proteins including gel-forming mucins MUC5AC and MUC5B and membrane-bound mucins MUC1 and MUC4. Compared with healthy controls, 130 proteins (SEA) and 103 (MEA) were significantly increased. 38 were elevated in SEA relative to MEA, 10 were higher in MEA. MUC4 was markedly increased in both, correlated with bronchoalveolar lavage neutrophils (ρ=0.790, p= 4.9E-06), and distinguished excellently between healthy and asthmatics (AUC=1.0, 95 % CI: 1-1), similar to 23 other proteins. MUC5AC was elevated in both, whereas MUC5B only in SEA. MUC1 did not differ between groups. Changes in mucus-modifying proteins, including glycosyltransferases and aquaporins, suggest altered mucus properties in EA. Functional enrichment analyses revealed inflammation-, tissue remodeling- and coagulation-linked GO terms and pathways in EA. The distinct proteomic profiles add to the understanding of EA and may offer novel targets for phenotype-specific biomarkers and therapy.

2025-12-18 | PXD070776 | Pride

Pasteurella multocida from deep nasal swabs and tracheobronchial lavage in calves

Project description:Pasteurella multocida from deep nasal swabs and tracheobronchial lavage in calves

| PRJEB73847 | ENA

Proteomics and microbiotas conjoint analysis in the nasal mucus: revelation of differences in immunological function in Manis javanica and Manis pentadactyla

Project description:This study aimed to investigate the differences in the immunity of Manis javanica and Manis pentadactyla through proteomics and microbiotas conjoint analysis.The nasal mucus of 10 pangolins (weight 5.71 kg ± 0.96 kg) was collected from the Wildlife Rescue Center of Shenzhen (Shenzhen City) and Guangdong Province (Guangzhou City). The pangolins from two different Wildlife Rescue Center were all in a healthy condition, although their feeding diets were not completely consistent.

2025-05-07 | PXD054168 | Pride

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