This study applied complementary multi-omics analyses to bronchoalveolar lavage (BAL) fluid to identify local signatures associated with pediatric severe asthma. The authors combined 16S rRNA gene amplicon sequencing to profile the lower airway microbiome with liquid chromatography–high-resolution mass spectrometry (LC-HRMS) metabolomics to measure BAL metabolites. Analyses were performed separately for each dataset and by integrating microbiome and metabolome information to explore microbial–metabolic interactions in the airway.
BAL samples were obtained from 20 children diagnosed with severe asthma and 10 non-asthmatic disease-controls. Details of BAL fluid sampling, measurements, data pre-treatment, and curation are available in a prior data paper and public repositories; the article provides accession numbers for the metabolite and microbiota datasets. The study focused on lower airway material to capture tissue-proximal signatures potentially more relevant to disease mechanisms than systemic measures.
Microbial composition in BALs was determined using 16S rRNA gene amplicon sequencing. Compared with non-asthmatic controls, children with severe asthma showed increased alpha-diversity in their BAL microbiome. Specific taxa with higher relative abundance in severe asthma included Actinobacteriota, Streptococcus, Moraxella, Corynebacterium, Tropheryma, and Treponema. These shifts mirror, in part, taxa previously associated with adult airway disease, though the study emphasizes lower airway profiling in children where prior data are more limited.
Untargeted metabolomic profiling of BAL fluid was performed using LC-HRMS. The metabolome of children with severe asthma was marked by perturbation of the polyamine metabolic pathway. Specifically, BALs from severe asthma patients showed decreased arginine levels and increased concentrations of the polyamines spermine and spermidine. These metabolite changes suggest altered local biochemical activity in the airways of children with severe asthma, reflecting combined host and microbial metabolic processes.
Integrative analyses combining microbial taxa and metabolite measures identified significant associations between bacterial abundance and specific polyamines. Notably, Streptococcus abundance was significantly associated with elevated spermine and spermidine levels in BAL fluid. The authors highlight this link as evidence of microbial–metabolic interactions that may participate in disease pathophysiology, though causality and mechanistic directionality are not established by the observational data reported.
When analysed independently, each dataset—microbiome or metabolome—could discriminate clinical features among children with severe asthma. The study reports discrimination of phenotypes notably including exacerbation frequency and the presence of co-occurring atopic dermatitis. These findings indicate that both taxonomic and metabolic profiles at the airway level carry information relevant to clinical heterogeneity within pediatric severe asthma.
Unsupervised clustering of BAL microbiome profiles identified four distinct clusters among the severe asthma cases. The authors propose that these clusters may reflect different severe asthma endotypes, representing underlying molecular or microbial-host interaction patterns that correspond to clinical heterogeneity. The study suggests that airway microbiome stratification could help delineate biologically meaningful subgroups within pediatric severe asthma.
The study identifies a distinct BAL microbiome–metabolome signature associated with pediatric severe asthma, characterized by enrichment of specific bacterial taxa—particularly Streptococcus—and perturbation of the polyamine pathway (lower arginine, higher spermine and spermidine). Integrated and separate analyses showed that microbial and metabolic profiles discriminate clinical phenotypes and may define endotype-related clusters. The authors conclude that airway-level multi-omics profiling is relevant to improve mechanistic understanding, diagnostic stratification, and future targeted therapeutic strategies for severe asthma in children.
The article states that microbiota and metabolite datasets are publicly available with specified accession numbers and that sampling and curation details were published in a data paper. The work was supported by a grant from the French National Research Agency (ANR) under the SevAsthma-children program; the funding source had no role in study design, data collection or analysis, manuscript preparation, or the decision to publish. The authors declared no competing interests.
The article emphasizes that findings describe associations observed in BAL samples from a limited cohort (20 severe asthma, 10 controls) and identify microbial–metabolic correlations rather than causal mechanisms. While the data suggest links between airway bacteria and altered polyamine metabolism, mechanistic validation and larger studies are needed to confirm these observations and assess their clinical utility for stratification or therapeutic targeting.