Novel Comprehensive Bioinformatics Approaches to Determine the Molecular Genetic Susceptibility Profile of Moderate and Severe Asthma.
Asthma
/ diagnosis
Biomarkers
Computational Biology
/ methods
Gene Expression Profiling
Genetic Association Studies
Genetic Predisposition to Disease
Humans
Polymorphism, Single Nucleotide
Protein Interaction Mapping
Protein Interaction Maps
Severity of Illness Index
Signal Transduction
Transcriptome
bioinformatics
gene networks
gene ontology
genotype-phenotype correlations
protein-protein interaction
severe and mild asthma
Journal
International journal of molecular sciences
ISSN: 1422-0067
Titre abrégé: Int J Mol Sci
Pays: Switzerland
ID NLM: 101092791
Informations de publication
Date de publication:
04 Jun 2020
04 Jun 2020
Historique:
received:
17
03
2020
revised:
19
05
2020
accepted:
26
05
2020
entrez:
10
6
2020
pubmed:
10
6
2020
medline:
20
2
2021
Statut:
epublish
Résumé
Asthma is a chronic inflammatory condition linked to hyperresponsiveness in the airways. There is currently no cure available for asthma, and therapy choices are limited. Asthma is the result of the interplay between genes and the environment. The exact molecular genetic mechanism of asthma remains elusive. The aim of this study is to provide a comprehensive, detailed molecular etiology profile for the molecular factors that regulate the severity of asthma and pathogenicity using integrative bioinformatics tools. The GSE43696 omnibus gene expression dataset, which contains 50 moderate cases, 38 severe cases, and 20 healthy controls, was used to investigate differentially expressed genes (DEGs), susceptible chromosomal loci, gene networks, pathways, gene ontologies, and protein-protein interactions (PPIs) using an intensive bioinformatics pipeline. The PPI network analysis yielded DEGs that contribute to interactions that differ from moderate-to-severe asthma. The combined interaction scores resulted in higher interactions for the genes New genetic risk factors for the development of moderate-to-severe asthma were identified in this study, and these could provide a better understanding of the molecular pathology of asthma and might provide a platform for the treatment of asthma.
Sections du résumé
BACKGROUND
BACKGROUND
Asthma is a chronic inflammatory condition linked to hyperresponsiveness in the airways. There is currently no cure available for asthma, and therapy choices are limited. Asthma is the result of the interplay between genes and the environment. The exact molecular genetic mechanism of asthma remains elusive.
AIMS
OBJECTIVE
The aim of this study is to provide a comprehensive, detailed molecular etiology profile for the molecular factors that regulate the severity of asthma and pathogenicity using integrative bioinformatics tools.
METHODS
METHODS
The GSE43696 omnibus gene expression dataset, which contains 50 moderate cases, 38 severe cases, and 20 healthy controls, was used to investigate differentially expressed genes (DEGs), susceptible chromosomal loci, gene networks, pathways, gene ontologies, and protein-protein interactions (PPIs) using an intensive bioinformatics pipeline.
RESULTS
RESULTS
The PPI network analysis yielded DEGs that contribute to interactions that differ from moderate-to-severe asthma. The combined interaction scores resulted in higher interactions for the genes
CONCLUSION
CONCLUSIONS
New genetic risk factors for the development of moderate-to-severe asthma were identified in this study, and these could provide a better understanding of the molecular pathology of asthma and might provide a platform for the treatment of asthma.
Identifiants
pubmed: 32512817
pii: ijms21114022
doi: 10.3390/ijms21114022
pmc: PMC7312607
pii:
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Qatar University
ID : QUST-1-CHS-2020-2
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