Distinct Airway Microbiome and Metabolite Profiles in Eosinophilic and Neutrophilic Asthma

Abstract

Background: Asthma is a chronic, heterogeneous disease driven by inflammatory phenotypes, primarily eosinophilic asthma (EA) and neutrophilic asthma (NEA). While allergen triggers are well-known, the role of the airway microbiome and metabolites in asthma exacerbations remains poorly understood.

Methods: We recruited 64 participants (24 EA, 20 NEA, 20 healthy controls [HC]) for the discovery cohort, with validation in an external cohort (10 EA, 8 NEA, 8 HC). Induced sputum samples were analyzed using 16S rRNA sequencing to profile bacterial composition and non-targeted metabolomics to assess airway metabolites. Random forest models identified diagnostic markers, validated in the external cohort.

Results: Significant shifts in airway microbiota were observed, particularly between NEA and HC, and between EA and NEA. Four bacterial general-Stenotrophomonas, Streptococcus, Achromobacter, and Neisseria-were consistently identified across groups. Veillonella was more abundant in NEA vs HC, while Achromobacter was enriched in NEA vs EA, indicating distinct microbial signatures. Metabolomic profiling revealed distinct pathways: pyrimidine metabolism (EA vs HC), tryptophan metabolism (NEA vs HC), and arachidonic acid metabolism (EA vs NEA). Microbial-metabolite correlations indicated microbiota-driven metabolic activity. Biomarker candidates were validated in the external cohort.

Conclusion: The airway microbiota and metabolites are intricately linked to asthma exacerbations, with distinct patterns between EA and NEA. These findings highlight their potential as diagnostic biomarkers and therapeutic targets for personalized asthma management.

Keywords: 16S rRNA amplicon sequencing; biomarker; eosinophilic; metabolomics; neutrophils asthma.

Figure 1

Visual representation of the study design. A cohort study combining microbiome analysis, metabolomics analysis, and machine learning applications to identify and validate biomarkers and pathways associated with asthma phenotypes and disease mechanisms.

Figure 2

Characteristics of the airway microbiome composition. (A) Alpha diversity analysis. (B) Microbial community beta-diversity. (C) Venn diagram of OTUs. (D) Relative abundance of top 10 phylum and genus. (E) Heatmap of microbial relative abundances of top 10 phylum. (F) Heatmap of microbial relative abundances of top 10 genus. *P <0.05, **P<0.01.

Figure 3

Prediction model of airway microbiota for EA and NEA based on genus-level abundances using random forests. (A–C) Genus-level taxa differed between groups. (D) Variable importance ranking chart. (E) ROC curve of the model. (F) COG functional analysis of microbiota between NEA and HC. (G) COG functional analysis of microbiota between EA and NEA. *P <0.05, **P<0.01.

Figure 4

Metabolic profiles among three groups. (A–C) PLS-DA plot for pairwise comparison between groups (D–F) Volcano plot of metabolites of pairwise comparison between groups. (G–I) Matchstick chart of the top 20 different metabolites between EA and HC, NEA and HC, EA and NEA.

Figure 5

Enrichment Analysis and correlation of metabolites in sputum. (A–C) KEGG pathway enrichment analysis of the top 20 differential metabolites between EA and HC, NEA and HC, EA and NEA. (D–F) Correlation analysis of the top 20 differential metabolites between EA and HC, NEA and HC, EA and NEA.

Figure 6

Metabolic markers of EA and NEA by random forest models. (A–C) Candidate metabolites classifying EA and HC. (D–F) Candidate metabolites classifying NEA and HC. (G–I) Candidate metabolites classifying EA and NEA. (J) ROC curve analysis of the candidate biomarkers.

Figure 7

Spearman correlation heatmap for significantly altered metabolites and general among the three groups. (A) Correlation heatmap between EA and HC. (B) Correlation heatmap between NEA and HC. (C) Correlation heatmap between EA and NEA. *P < 0.05, **P < 0.01.