Allergy Asthma Immunol Res.  2019 Jan;11(1):104-115. 10.4168/aair.2019.11.1.104.

Different Biological Pathways Are Up-regulated in the Elderly With Asthma: Sputum Transcriptomic Analysis

Affiliations
  • 1Department of Internal Medicine, Korea University Medical Center Anam Hospital, Seoul, Korea.
  • 2Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea. guinea71@snu.ac.kr
  • 3Institute of Allergy and Clinical Immunology, Seoul National University Medical Research Center, Seoul, Korea.
  • 4Department of Internal Medicine, KyungHee University Medical center, Seoul, Korea.

Abstract

BACKGROUND
Elderly asthma (EA) is increasing, but the pathogenesis is unclear. This study aimed to identify EA-related biological pathways by analyzing genome-wide gene expression profiles in sputum cells.
METHODS
A total of 3,156 gene probes with significantly differential expressions between EA and healthy elderly controls were used for a hierarchical clustering of genes to identify gene clusters. Gene set enrichment analysis provided biological information, with replication from Gene Expression Omnibus expression profiles.
RESULTS
Fifty-five EA patients and 10 elderly control subjects were enrolled. Two distinct gene clusters were found. Cluster 1 (n = 35) showed a lower eosinophil proportion in sputum and less severe airway obstruction compared to cluster 2 (n = 20). The replication data set also identified 2 gene clusters (clusters 1' and 2'). Among 5 gene sets significantly enriched in cluster 1 and 3 gene sets significantly enriched in cluster 2, we confirmed that 2 were significantly enriched in the replication data set (OXIDATIVE_PHOSPHORYLATION gene set in cluster 1 and EPITHELIAL MESENCHYMAL TRANSITION gene set in cluster 2').
CONCLUSIONS
The findings of 2 distinct gene clusters in EA and different biological pathways in each gene cluster suggest 2 different pathogenesis mechanisms underlying EA.

Keyword

Asthma; Cluster analysis; Elderly; Gene expression

MeSH Terms

Aged*
Airway Obstruction
Asthma*
Cluster Analysis
Dataset
Eosinophils
Epithelial-Mesenchymal Transition
Gene Expression
Humans
Multigene Family
Sputum*
Transcriptome

Figure

  • Fig. 1 Two gene clusters identified in the discovery and replication dataset. (A) Discovery dataset. Three outliers (Pt5, Pt21, and Pt28) were excluded from analysis. (B) Replication dataset. Pt, Patient.

  • Fig. 2 Gene sets enriched in each cluster identified in the discovery dataset with FDR P values less than 0.001. (A) Cluster 1, (B) Cluster 2. FDR, false discovery rate; OXPHOS, OXIDATIVE_PHOSPHORYLATION; UPR, UNFOLDED_PROTEIN_RESPONSE, EMT, EPITHELIAL_MESENCHYMAL_TRANSITION.

  • Fig. 3 Association between clinical variables and PC1 of the leading edge genes from gene sets enriched in both discovery and replication datasets. (A) Cluster 1, (B) Cluster 2. Figures without P values denote statistically insignificant associations. PC, principal component; OXPHOS, OXIDATIVE_PHOSPHORYLATION; EMT, EPITHELIAL_MESENCHYMAL_TRANSITION.

  • Fig. 4 Volcano plot displaying differential expressed genes. The 4 genes (MRPS11, HSPA9, NUDF4, and ACTA1) belong to the leading edge genes of the OXPHOS gene set and 2 genes (SNTB1 and FUCA1) belong to the leading edge genes of the EMT gene set. OXPHOS, OXIDATIVE_PHOSPHORYLATION; EMT, EPITHELIAL_MESENCHYMAL_TRANSITION.


Cited by  1 articles

Understanding the Molecular Mechanisms of Asthma through Transcriptomics
Heung-Woo Park, Scott T. Weiss
Allergy Asthma Immunol Res. 2020;12(3):399-411.    doi: 10.4168/aair.2020.12.3.399.


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