PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 3, 2026Journal of Inflammation Research0 citationsOpen Access

Glycolytic-Cholesterol Subtypes of Severe Asthma Reveal Distinct Immune-Inflammatory and Metabolic Phenotypes

QLQibin LinHNHaiyang NiJZJiudan Zhang

Key Points

  • This research aims to profile glycolytic-cholesterol dysregulation in severe asthma to identify metabolic subtypes and explore their therapeutic implications.
  • Analyzed sputum mRNA from 93 severe asthma patients and 16 healthy controls.
  • Identified differentially expressed genes in the glycolysis-cholesterol synthesis axis.
  • Applied unsupervised consensus clustering to define metabolic subtypes from DEGs.
  • Characterized immune microenvironment features using gene set enrichment analysis.
  • Built a random forest model to distinguish subtypes and screened candidate therapies via CMap.
  • Identified 47 dysregulated genes in the glycolysis-cholesterol synthesis axis in severe asthma.
  • Defined two distinct metabolic subtypes based on clustering of DEGs.
  • Cluster 1 showed an up-regulated axis linked to NK and gamma-delta T cells; Cluster 2 showed a down-regulated axis related to eosinophils and Th17 cells.
  • Achieved a random forest model accuracy with AUC=0.888 for subtype discrimination.
  • Suggested potential therapeutics, including dipeptidyl peptidase inhibitors for Cluster 1 and leukotriene receptor antagonists for Cluster 2.

Abstract

Purpose: Severe asthma (SA) is a heterogeneous disease with unmet therapeutic needs. Metabolic dysregulation involving glycolysis and cholesterol synthesis is implicated in its pathogenesis. This study aimed to systematically profile the glycolysis-cholesterol synthesis axis in SA to identify distinct metabolic subtypes and explore their potential therapeutic implications. Patients and Methods: We analyzed sputum mRNA expression from 93 SA patients and 16 healthy controls (HC) in the U-BIOPRED cohort (GSE76262). First, differentially expressed genes (DEGs) in the glycolysis-cholesterol synthesis axis were identified between SA patients and HC. Unsupervised consensus clustering was then applied to these DEGs within the SA cohort to define metabolic subtypes. Immune microenvironment features were characterized using single-sample gene set enrichment analysis. A random forest (RF) model was built to distinguish subtypes, and candidate therapeutic compounds were screened for each subtype via the Connectivity Map (CMap) database based on subtype-specific gene signatures. Results: This study revealed that, 47 genes in the glycolysis-cholesterol synthesis axis were dysregulated in SA when compared to HC. Clustering of SA based on these DEGs revealed two distinct metabolic subtypes (Cluster 1, n=52; Cluster 2, n=41). Cluster 1 exhibited an up-regulated axis with an immune landscape enriched for NK and gamma-delta T cells, linked to oxidative phosphorylation. Conversely, Cluster 2 displayed a down-regulated axis with enrichment of eosinophils, neutrophils, mast cells, and Th17 cells, associated with calcium and cAMP signaling. An RF model utilizing PPP2CB and SEH1L achieved accurate subtype discrimination (AUC=0.888), validated by decreased protein expression in an SA murine model. Exploratory drug screening suggested dipeptidyl peptidase inhibitor and mTOR inhibitor were identified as candidate compounds for Cluster 1, while leukotriene receptor antagonist and calcium channel blocker were suggested for Cluster 2 via CMap analysis. Conclusion: This study reveals two novel metabolic subtypes in SA, which offers a classification model and suggests potential targeted therapeutics for personalized management. On the left, Cluster 1 features an upregulated glycolysis-cholesterol synthesis axis, enriched NK and γδ T cells, and enhanced oxidative phosphorylation/TCA cycle, with DPP4/mTOR inhibitors identified as potential therapeutics. On the right, Cluster 2 exhibits a downregulated axis, mixed granulocytic and Th17 inflammation, and enriched calcium/cAMP signaling, for which leukotriene receptor antagonists and calcium channel blockers are proposed. The two subtypes are distinguished using PPP2CB and SEH1L via a random forest model, enabling personalized therapy.Diagram of glycolytic-cholesterol subtypes in severe asthma with immune-inflammatory and metabolic phenotypes. Keywords: cholesterol biosynthesis, glycolysis, metabolic subtype, personalized treatment, severe asthma

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Lin et al. (2026) studied this question.

synapsesocial.com/papers/69cf59635a333a821460a027https://doi.org/10.2147/jir.s585368
Ask AI
Helpful
Bookmark
Share
View Full Paper