Abstract
Background
Severe asthma is a heterogeneous condition encompassing multiple phenotypes. Understanding lung-specific mechanisms in children with severe asthma may enable the development of more precise therapeutic strategies. We previously reported that immune components in bronchoalveolar lavages (BALs) differentiate children with severe asthma from non-asthmatic disease-controls and, frequent from non-frequent exacerbators, among children with severe asthma.
Objective
To identify a local signature of severe asthma using complementary multi-omics analyses of BALs. A secondary objective was to evaluate whether bacterial taxa and metabolites discriminate severe asthma subtypes associated with distinct phenotypes or endotypes.
Methods
BAL microbiome and metabolome were investigated in 20 children with severe asthma and 10 non-asthmatic children using 16S rRNA gene amplicon sequencing and liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS), respectively. Data were analysed separately and through integrative multi-omics approaches.
Results
Compared with controls, BALs from children with severe asthma showed increased alpha-diversity, higher relative abundances of
Actinobacteriota
,
Streptococcus
,
Moraxella
,
Corynebacterium
,
Tropheryma
, and
Treponema
, and an altered polyamine pathway characterized by reduced arginine and increased spermine and spermidine levels. Integrated analyses revealed significant associations between
Streptococcus
and both spermine and spermidine. Independently, each dataset discriminated severe asthma phenotypes, notably exacerbation frequency and co-occurring atopic dermatitis. Unsupervised clustering of microbiome profiles identified four distinct clusters that may reflect severe asthma endotypes.
Conclusions
This study identifies a distinct airway microbiome–metabolome signature associated with pediatric severe asthma. Enrichment of specific bacterial taxa, particularly
Streptococcus
, together with altered polyamine metabolic pathway, highlights microbial–metabolic interactions potentially involved in disease pathophysiology. The ability of microbiome and metabolome profiles to independently and jointly discriminate clinical phenotypes underscores the relevance of multi-omics approaches for diagnosis and follow-up of severe asthma. Our findings support the importance of airway-level profiling to improve mechanistic understanding of severe asthma and inform on future targeted therapeutic strategies.