Dataset Information

Transcriptomic evaluation of skin tape-strips in children with allergic asthma uncovers epidermal barrier dysfunction and asthma-associated biomarkers abnormalities

ABSTRACT: Tape-strips were obtained from skin of 18 pediatric patients with moderate allergic asthma (MAA) (n=11), severe allergic asthma (SAA) (n=9), and from healthy children (n=12) and analyzed with bulk RNA-sequencing (RNA-seq). Differential expression was defined as |fold change|≥2 and false discovery rate<0.05. MAA and SAA shared downregulation of G-protein-coupled receptor- (GPCR), mitochondria-, and Wnt-signaling-related genes. Terminal differentiation (FLG/filaggrin), cell adhesion (CDH19, JAM2), lipid biosynthesis/metabolism (ACOT2, LOXL2) markers were significantly downregulated in MAA and SAA (FDR<0.05). Many of these markers additionally correlated with metrics of asthma severity (e.g. exacerbation rate per year, forced expiratory volume in 1 second [FEV1] [% predicted], impulse oscillometry [IOS] R5-20 Hz, fractional excretion of nitric oxide [FeNO]).

ORGANISM(S): Homo sapiens

PROVIDER: GSE245608 | GEO | 2024/07/25

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

Project description:Rationale: MicroRNAs have emerged as crucial post-transcriptional and network regulators in inflammatory diseases, including asthma. We hypothesized that peripheral blood miRNA would be associated with airflow obstruction in children with asthma, and that some of these effects would also be observable in adults with COPD. Methods: We analyzed small RNA-Seq data from 365 peripheral blood samples from the Genetics of Asthma in Costa Rica Study (GACRS). GACRS comprised children from the Central Valley of Costa Rica age 6-14 years with physician-diagnosed asthma and ≥2 respiratory symptoms or asthma attacks in the prior year. FEV1/FVC percent-predicted was dichotomized at 100%, splitting the cohort into those with and without evidence of airflow obstruction and used as our primary outcome. Differentially expressed (DE) miRs were identified using the DESeq2 package in R with a 10% FDR and adjustment for age, gender, and inhaled corticosteroid (ICS) use. We attempted to replicate the top airflow obstruction-associated microRNAs from the GACRS study in the COPDGene study, in which blood microRNA data were available in 439 current and former smoking adults with and without airflow obstruction (defined as raw FEV1/FVC < 0.7). Results: After QC, we had 361 samples and 649 miRs for DE analysis. Of the 361 samples, 220 and 141 were from subjects without and with airflow obstruction, respectively. We found 1 upregulated (let-7e-5p p=0.0004) and 2 downregulated (miR-342-3p p=0.0002; miR-671-5p p=0.0001) miRs in subjects with airflow obstruction compared to those without airflow obstruction. These three miRNAs were then tested for association with airflow obstruction in the COPDGene study, in which let-7e-5p was upregulated (p = 0.064) and miR-342-3p (p =0.085) was downregulated in participants with FEV1/FVC < 0.7 (n=196) compared to those with FEV1/FVC > 0.7 (n=243). Differentially expressed miR’s target genes were enriched for PI3K-Akt, Hippo, WNT, MAPK, and focal adhesion signaling pathways. We also separately considered the targets of only the two miRNAs that were also associated with FEV1/FVC in the adult current and former smokers, where PI3K-Akt, MAPK and Hippo signaling pathways were among the top five most enriched pathways. Conclusion: Three DE miRs were linked to airflow obstruction in children with asthma. Two miRs were associated with FEV1/FVC in current and former smoking adults. These miRs were involved in asthma and COPD-related pathways: PI3K-Akt, Hippo, MAPK, and focal adhesion signaling pathways. Together these findings provide important evidence that these two disorders may share genetic regulatory systems that contribute to airflow obstruction.

Dataset Information

Transcriptomic evaluation of skin tape-strips in children with allergic asthma uncovers epidermal barrier dysfunction and asthma-associated biomarkers abnormalities

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure