Respiratory microbiome profiles are associated with distinct inflammatory phenotype and lung function in children with asthma
Kim YH1,2, Park MR1,2, Kim SY2,3, Kim MY2,4, Kim KW2,3, Sohn MH2,3
1Department of Pediatrics, Gangnam Severance Hospital, Seoul
2Institute of Allergy, Severance Biomedical Science Institute, Brain Korea 21 Project for Medical Science, Yonsei University College of Medicine, Seoul
3Department of Pediatrics, Severance Hospital, Seoul
4Department of Pediatrics, Yongin Severance Hospital, Yongin, Korea
J Investig Allergol Clin Immunol 2024; Vol. 34(4)
Background: Respiratory microbiome studies have fostered our understanding of various phenotypes and endotypes of heterogeneous asthma. However, the relationship between the respiratory microbiome and clinical phenotypes in children with asthma remains unclear. We aimed to identify microbiome-driven clusters reflecting the clinical features of asthma and their dominant microbiotas in children with asthma.
Methods: Induced sputum was collected from children with asthma, and microbiome profiles were generated via sequencing of the V3–V4 region of the 16S rRNA gene. Cluster analysis was performed using the partitioning around medoid clustering method. The dominant microbiota in each cluster was determined using the Linear Discriminant Effect Size analysis. Each cluster was analyzed for association among the dominant microbiota, clinical phenotype, and inflammatory cytokine.
Results: Eighty-three children diagnosed with asthma were evaluated. Among four clusters reflecting the clinical characteristics of asthma, cluster 1, dominated by Haemophilus and Neisseria, demonstrated lower post-bronchodilator (BD) forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC) than that in the other clusters and more mixed granulocytic asthma. Neisseria negatively correlated with pre-BD and post-BD FEV1/FVC. Haemophilus and Neisseria positively correlated with programmed death-ligand (PD-L)1.
Conclusion: To our knowledge, this study is the first to analyze the relationship between an unbiased microbiome-driven cluster and clinical phenotype in children with asthma. The cluster dominated by Haemophilus and Neisseria showed fixed airflow obstruction and mixed granulocytic asthma, which correlated with PD-L1 levels. Thus, microbiome-driven unbiased clustering can help identify new asthma phenotypes related to endotypes in childhood asthma.
Key words: Asthma, Children, Cluster analysis, Cytokines, Microbiota, Phenotype