Sputum Metabolomic Profiling Reveals Metabolic Pathways and Signatures Associated With Inflammatory Phenotypes in Patients With Asthma

• Published in: Allergy, asthma & immunology research Vol. 14; no. 4; pp. 393 - 411
• Main Authors: Liu, Ying, Zhang, Xin, Zhang, Li, Oliver, Brian G, Wang, Hong Guang, Liu, Zhi Peng, Chen, Zhi Hong, Wood, Lisa, Hsu, Alan Chen-Yu, Xie, Min, McDonald, Vanessa, Wan, Hua Jing, Luo, Feng Ming, Liu, Dan, Li, Wei Min, Wang, Gang
• Format: Journal Article
• Language: English
• Published: Seoul Korean Academy of Asthma, Allergy and Clinical Immunology 01.07.2022; The Korean Academy of Asthma, Allergy and Clinical Immunology; The Korean Academy of Pediatric Allergy and Respiratory Disease; 대한천식알레르기학회
• Subjects: Adenosine monophosphate; Allantoin; AMP; Asthma; Biosynthesis; Confidence intervals; Discriminant analysis; High performance liquid chromatography; Histidine; Inflammation; Linoleic acid; Liquid chromatography; Mass spectrometry; Mass spectroscopy; Metabolic pathways; Metabolism; Metabolites; Metabolomics; Nicotinamide; Original; Pathogenesis; Patients; Phenotypes; Phenylalanine; Regression analysis; Regression models; Sputum; Statistical analysis; Target recognition; Therapeutic targets; Topology; Tryptophan; Tyrosine; 내과학
• ISSN: 2092-7355; 2092-7363
• DOI: 10.4168/aair.2022.14.4.393

Purpose: The molecular links between metabolism and inflammation that drive different inflammatory phenotypes in asthma are poorly understood. We aimed to identify the metabolic signatures and underlying molecular pathways of different inflammatory asthma phenotypes. Methods: In the discovery set (n = 119), untargeted ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) was applied to characterize the induced sputum metabolic profiles of asthmatic patients with different inflammatory phenotypes using orthogonal partial least-squares discriminant analysis (OPLS-DA), and pathway topology enrichment analysis. In the validation set (n = 114), differential metabolites were selected to perform targeted quantification. Correlations between targeted metabolites and clinical indices in asthmatic patients were analyzed. Logistic and negative binomial regression models were established to assess the association between metabolites and severe asthma exacerbations. Results: Seventy-seven differential metabolites were identified in the discovery set. Pathway topology analysis uncovered that histidine metabolism, glycerophospholipid metabolism, nicotinate and nicotinamide metabolism, linoleic acid metabolism as well as phenylalanine, tyrosine and tryptophan biosynthesis were involved in the pathogenesis of different asthma phenotypes. In the validation set, 24 targeted quantification metabolites were significantly expressed between asthma inflammatory phenotypes. Finally, adenosine 5′-monophosphate (adjusted relative risk [adj RR] = 1.000; 95% confidence interval [CI] = 1.000–1.000; P = 0.050), allantoin (adj RR = 1.000; 95% CI = 1.000–1.000; P = 0.043) and nicotinamide (adj RR = 1.001; 95% CI = 1.000–1.002; P = 0.021) were demonstrated to predict severe asthma exacerbation rates. Conclusions: Different inflammatory asthma phenotypes have specific metabolic profiles in induced sputum. The potential metabolic signatures may identify therapeutic targets in different inflammatory asthma phenotypes.