Understanding uncontrolled severe allergic asthma by integration of omic and clinical data. Issue 6 (9th December 2021)

• Record Type: Journal Article
• Authors: Delgado‐Dolset, María Isabel; Obeso, David; Rodríguez‐Coira, Juan; Tarin, Carlos; Tan, Ge; Cumplido, José A.; Cabrera, Ana; Angulo, Santiago; Barbas, Coral; Sokolowska, Milena; Barber, Domingo; Carrillo, Teresa; Villaseñor, Alma; Escribese, María M.
• Is Part Of: Allergy. Volume 77:Issue 6(2022)
• Page Start: 1772
• Page End: 1785
• Publication Date: 2021-12-09
• Subjects: allergy -- asthma -- machine learning -- metabolomics -- proteomics; Allergy -- Periodicals; 616.97
• DOI: 10.1111/all.15192
• Languages: English
• ISSNs: 0105-4538

Abstract

Abstract: Background: Asthma is a complex, multifactorial disease often linked with sensitization to house dust mites (HDM). There is a subset of patients that does not respond to available treatments, who present a higher number of exacerbations and a worse quality of life. To understand the mechanisms of poor asthma control and disease severity, we aim to elucidate the metabolic and immunologic routes underlying this specific phenotype and the associated clinical features. Methods: Eighty‐seven patients with a clinical history of asthma were recruited and stratified in 4 groups according to their response to treatment: corticosteroid‐controlled (ICS), immunotherapy‐controlled (IT), biologicals‐controlled (BIO) or uncontrolled (UC). Serum samples were analysed by metabolomics and proteomics; and classifiers were built using machine‐learning algorithms. Results: Metabolomic analysis showed that ICS and UC groups cluster separately from one another and display the highest number of significantly different metabolites among all comparisons. Metabolite identification and pathway enrichment analysis highlighted increased levels of lysophospholipids related to inflammatory pathways in the UC patients. Likewise, 8 proteins were either upregulated (CCL13, ARG1, IL15 and TNFRSF12A) or downregulated (sCD4, CCL19 and IFNγ) in UC patients compared to ICS, suggesting a significant activation of T cells in these patients. Finally, the machine‐learning model built including metabolomic and clinical data was able to classify the patients with an 87.5% accuracy. Conclusions: UC patients display a unique fingerprint characterized by inflammatory‐related metabolites and proteins, suggesting a pro‐inflammatory environment. Moreover, the integration of clinical and experimental data led to a deeper understanding of the mechanisms underlying UC phenotype. Abstract : Severe uncontrolled HDM‐allergic asthma (UC) displays an increased T‐cell activation and proliferation (IL‐15, CASP‐8, S1P, Leu) and an increased T‐cell tissue recruitment (CCL13) compared to corticosteroid‐controlled HDM‐allergic asthma (ICS). UC shows an exacerbated inflammatory response with increased levels of inflammatory mediators (AA, EPA, DHA, …). Integration of clinical and metabolomic data is the best strategy to stratify patients by severity.Abbreviations: AA, arachidonic acid; CASP‐8, caspase‐8; CCL13, chemokine (C‐C motif) ligand 13; CCL19, chemokine (C‐C motif) ligand 19; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; HDM, house dust mites; ICS, inhaled corticosteroid‐controlled HDM‐allergic asthma; IL‐15, interleukin‐15; LC‐MS, liquid chromatography coupled to mass spectrometry; Leu, leukine; PEA, proximity extension assay; S1P, sphingosine‐1‐phosphate; UC, uncontrolled HDM‐allergic asthma