Bronchiolitis is the leading cause of infant hospitalization in the U.S., accounting for 110,000 hospitalizations each year (1). Its chronic morbidity is also substantial. Among infants hospitalized with bronchiolitis (i.e., severe bronchiolitis), ~30% subsequently develop childhood asthma (2–6). Yet, the mechanisms underlying the bronchiolitis-asthma link remain unclear. Our limited understanding has hindered the development of asthma prevention strategies.

Asthma is a complex syndrome that is influenced by both genetic and environmental factors (e.g., early-life virus infection) (7). Metabolomics systematically profiles small molecules in a biological system, which represent the downstream functional products of these genetic and environmental interactions. Studies have suggested metabolites involved in asthma pathobiology—e.g., sphingolipids (e.g., sphingomyelins) (8, 9), phospholipids (e.g., phosphatidylglycerol [PG]) (10), and fatty acids (e.g., docosapentaenoate [DPA]) (11). In addition to metabolomics, genome-wide association studies (GWASs) have identified genetic loci for childhood asthma susceptibility (12–17). For example, ORMDL3 located at chromosome 17q21—a major regulator of sphingolipid metabolism—plays an important role in asthma development (12, 15). Metabolomics and genetics studies have independently attempted to elucidate the mechanisms underlying asthma pathobiology. However, no study has yet examined the integrated relationship of genetics, airway metabolome, and asthma development in children—let alone in infants at high risk for asthma development.

To address this knowledge gap, we applied an integrative genetics-metabolomics approach to data from a multicenter prospective cohort study of infants with severe bronchiolitis. We sought to identify the genetically driven metabolites and the genetic loci regulating those metabolites associated with the development of childhood asthma. 

Of 1,016 infants enrolled in the MARC-35 cohort, the current study focused on 744 infants with severe bronchiolitis who underwent both genotyping and nasopharyngeal metabolome profiling. The analytic (n=744) and non-analytic (n=272) cohorts did not differ in the patient characteristics (P≥0.05; Table S2 ), except for the proportion of racial/ethnicity and rhinovirus infection. Of the infants in the analytic cohort, the median age was 3 (interquartile range [IQR], 2-6) months and 40% were female; 45% were non-Hispanic white, 25% were non-Hispanic black, and 31% were Hispanics ( Table 1 ). Overall, 26% subsequently developed asthma by age 6 years ( Table S3 ).

By applying an integrated genetics-metabolomics approach to multicenter prospective cohort data of 744 infants with severe bronchiolitis, we identified 28 metabolites associated with asthma development and 349 independent genetic loci associated with these metabolites. Additionally, of these loci, colocalization analysis (with independent GWAS datasets for asthma) revealed 30 shared loci between 16 metabolites and asthma risk. Furthermore, the significant SNPs within two loci were aligned with known asthma-susceptibility genes (e.g., ADORA1, MUC16). To the best of our knowledge, this is the first study that has investigated infant bronchiolitis-childhood asthma link with an integrative genetics-metabolomics approach and demonstrated genetically driven metabolites and related genetic loci associated with asthma risk.

Childhood asthma is a heterogeneous syndrome that results from complex interactions between genetic and environmental factors in early childhood (7). Recent research has studied the mechanisms by applying high-throughput approaches to survey the metabolome—the downstream functional products of a child’s genetic make-up and environmental exposures (e.g., virus respiratory infection) (21, 31–40). Consistent with our findings, for example, recent studies of infants with acute respiratory infection have reported associations of the upper airway (21, 34–38), serum (36, 38), and urine (39) metabolome signature (e.g., altered sphingolipid, phospholipid, and fatty acid metabolism)—with the subsequent development of asthma. Independent from these metabolomics investigations, GWASs have identified genetic regions associated with respiratory syncytial virus (RSV) infection (41–43), severe bronchiolitis (41, 42, 44), and asthma risk (12–17, 43, 45–49). For example, multiple studies have identified ORMDL3 as an asthma susceptibility gene (12, 15, 46, 48) with asthma risk. ORMDL3 is a major regulator of serine palmitoyltransferase—the rate-limiting enzyme of sphingolipid biosynthesis (50). While most research independently have applied genetics and metabolomics, few studies have examined the integrated relationships of genetic variants and altered metabolism with prevalent asthma in adults (51, 52). For example, Johnson et al. recently conducted an mtQTL analysis of 348 adults (59 with prevalent asthma) from Tangier Island and found several serum metabolites (e.g., linoleoyl ethanolamide) associated with asthma risk (51). Our multicenter prospective study—integrating the genome and nasopharyngeal airway metabolome data from high-risk infants—corroborates these earlier reports, and extends them by demonstrating genetic loci and genetically driven metabolites associated with the risk of developing asthma.

The exact mechanisms underlying the observed relationship of genetic loci and metabolites—e.g., genes within chromosome 1q32 (e.g., ADORA1, PPFIA4) and sphingomyelin (d17:1/16:0, d18:1/15:0, d16:1/17:0)—with asthma risk warrant further clarification. Studies have shown that genes on chromosome 1q32, including ADORA1 and PPFIA4 are associated with asthma risk (13, 53, 54), and ADORA1 may interact with sphingolipids to enhance airway inflammation (55–59). For example, adenosine receptor A1 (ADORA1) encoded by the ADORA1 gene contributes to bronchoconstriction, mucus secretion, and inflammation in bronchial epithelial cells through the sphingolipid signaling pathway (55, 56). Experimental studies have also shown that ADORA1 regulated cyclic adenosine monophosphate (cAMP) and sphingomyelin-derived lipids to mobilize intracellular calcium stores in bronchial smooth muscle cells, leading to the contraction of the bronchial smooth muscles and airway remodeling (57–59). Sphingolipids are not only integrated components of the human cell membrane (60) but also have molecular signaling functions with roles in the immune response to infections, inflammation, and cell proliferation, thereby contributing to asthma pathobiology (61). Accordingly, studies have suggested ADORA1 (62) and sphingolipids (63) as therapeutic targets for asthma.

In addition to sphingomyelins, we also observed a relationship between genes on chromosome 19p13 (e.g., MUC16), 1,2-dioleoyl-GPG, and asthma risk. 1,2-dioleoyl-GPG is one of the phosphatidylglycerols (PGs)—a main component of pulmonary surfactant (64). An experimental study has suggested that PG inhibits proinflammatory protein expression in alveolar macrophages through downregulation of NF-κB activation (65, 66). In contrast, the depletion of pulmonary surfactant PG leads to asthma-associated surfactant dysfunction (10). Another study has also reported that PG inhibits RSV infection by blocking viral attachment to epithelial cells (67). Besides, Mucin-16—also known as CA125 and encoded regulated by the MUC16 gene—is the largest membrane-associated mucin synthesized in the endoplasmic reticulum (ER) of the bronchial epithelial cells (68–70). Research has demonstrated that the expression of mucin-16 was promoted by NF-κB activation and protects against ER stress (71). ER stress regulates proinflammatory signaling in epithelial cells through pulmonary surfactant dysfunction that is also induced by PG depletion (72). Accordingly, MUC16 gene, mucin-16, and GPG jointly play roles in ER stress (73). Notwithstanding the complexity of these potential mechanisms, the identification of genetically driven metabolites associated with the development of childhood asthma is an important finding. Our data—in conjunction with the literature—should advance further research into the pathobiological mechanisms underlying the bronchiolitis-asthma link.

The current study has several potential limitations. First, the study did not have “healthy controls.” Yet, the objective of the study was not to identify the genetic loci and metabolites related to incident bronchiolitis (i.e., bronchiolitis yes vs. no) but to investigate the functional consequences of genetic risk factors for asthma (i.e., genetically driven metabolites) in this high-risk population. Second, compared to the non-analytic cohort, the analytic cohort had an overrepresentation of solo rhinovirus infection (3% vs. 7%), which might have led to selection bias. Third, it is possible that asthma diagnosis is misclassified and that some children are going to develop asthma at a later age. To address these potential limitations, the cohort is currently being followed up to age 9 years. In addition, in the current study, children who had asthma-related symptoms but did not receive asthma medication might have been over-diagnosed with asthma. Fourth, the sample size of the current analysis was relatively small, partially because of the stratified analysis across the different racial/ethnic subgroups. To accommodate the limited statistical power, we performed the mtQTL analyses with the significance threshold (P <1×10^-6) proposed in a previous study (28). The statistical power of the analyses calculated using the GAS Power Calculator (74) was 0.72 in non-Hispanic white, 0.71 in non-Hispanic black, and 0.71 in Hispanics. Fifth, the CAAPA dataset provided summary statistics for an African-admixed population with asthma (26). Therefore, the sample from the other racial/ethnic groups might have affected the colocalization estimates of our non-Hispanic black sample due to population stratification (75). Sixth, the lack of publicly available asthma GWAS data in the Hispanic sample precluded us from conducting colocalization analysis for our Hispanic sample. Lastly, our inferences may not be generalizable to infants without severe bronchiolitis (i.e., infants with mild-to-moderate bronchiolitis). Nonetheless, our observations remain directly relevant to the 110,000 infants hospitalized annually in the U.S (1).—a large population with a substantial morbidity burden.

By integrating the genetics and nasopharyngeal airway metabolomics data from a multicenter, prospective cohort study of infants hospitalized for bronchiolitis, we identified genetically driven metabolites (e.g., 1,2-dioleoyl-GPG, sphingomyelin) associated with asthma development and genetic loci associated with both these metabolites and asthma susceptibility genes (e.g., ADORA1, MUC16). These associations were also confirmed by colocalization analyses with independent GWAS datasets for asthma. Identifying these metabolites and genetic loci should advance research into the functional consequences of genetic risk factors for the development of asthma. Furthermore, these findings will, in turn, accelerate the understanding of the bronchiolitis-asthma link and the development of prevention strategies for childhood asthma. 

The data presented in the study are deposited in the ImmPort repository, accession number SDY2157.

The studies involving human participants were reviewed and approved by Alfred I. duPont Hospital for Children, Wilmington, DE; Arnold Palmer Hospital for Children, Orlando, FL; Boston Children’s Hospital, Boston, MA; Children’s Hospital of Los Angeles, Los Angeles, CA; Children’s Hospital of Philadelphia, Philadelphia, PA; Children’s Hospital of Pittsburgh, Pittsburgh, PA; The Children’s Hospital at St. Francis, Tulsa, OK; The Children’s Mercy Hospital & Clinics, Kansas City, MO; Children’s National Medical Center, Washington, D.C.; Cincinnati Children’s Hospital and Medical Center, Cincinnati, OH; Connecticut Children’s Medical Center, Hartford, CT; Dell Children’s Medical Center of Central Texas, Austin, TX; Norton Children’s Hospital, Louisville, KY; Massachusetts General Hospital, Boston, MA; Phoenix Children’s Hospital, Phoenix, AZ; Seattle Children’s Hospital, Seattle, WA; Texas Children’s Hospital, Houston, TX. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

TO carried out the main statistical analysis, drafted the initial manuscript, and approved the final manuscript as submitted. ZZ carried out the data processing, developed the methodology, analyzed and interpreted data, reviewed and revised the initial manuscript, and approved the final manuscript as submitted. LL, JC, BH, AH, ER, and RF collected the study data, reviewed and revised the initial manuscript, and approved the final manuscript as submitted. CC and KH conceptualized the study, obtained funding, supervised the statistical analysis, reviewed and revised the initial manuscript, and approved the final manuscript as submitted. All authors contributed to the article and approved the submitted version.