Understanding uncontrolled severe allergic asthma by integration of omic and clinical data

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.

Development of a Novel Targeted Metabolomic LC-QqQ-MS Method in Allergic Inflammation

“…Regarding the female ICS versus female UC, we observed a clear separation of the groups using non-supervised and supervised models, indicating that these metabolites were able to differentiate both groups. The increase in LPE 18:0, LPC 17:0, LPC 14:0, and LPC 17:1 in the UC group followed the same trend as those observed previously in the non-targeted analysis [ 16 , 17 , 18 ]. Regarding these changes, LPCs have previously been associated with inflammatory signaling pathways in asthma, rhinitis, and eosinophilic asthma [ 31 , 32 , 33 ].…”

“…Metabolites from three non-targeted metabolomics studies were selected ( Table S1 ) [ 16 , 17 , 18 ]. These encompassed sphingolipids (sphinganine-1-phosphate (SPA-1P), sphinganine-C17, sphingosine and sphingosine-1-phosphate (S1P)), lysophospholipids (lysophosphocholine (LPC) 14:0, LPC 16:0, LPC 17:0, LPC 17:1, LPC 18:0, LPC 18:1, LPC 19:0, lysophosphoethanolamine (LPE) 18:0, lysophosphatidylinositol (LPI) 16:0 and LPI 20:4), amino acids (arginine, betaine, creatine, creatinine, leucine/isoleucine, phenylalanine and proline), fatty acids (arachidonic acid, lauric acid, oleic acid and palmitoleic acid), carnitines (carnitine, hexanoylcarnitine and propionylcarnitine) and others (adenosine, bilirubin, cortisol, hippuric acid, hypoxanthine, lactic acid, oleamide and urea).…”

“…In recent years, by using non-targeted metabolomics in combination with transcriptomics and proteomics in different allergic disease severity models, we have identified new biological systems and associated promising potential biomarker candidates [ 16 , 17 , 18 , 19 , 20 ]. Furthermore, the combined analysis of these studies has allowed us to identify metabolites potentially related to energy metabolism, sphingolipid, phospholipid pathways, and platelet function associated with the severe phenotype [ 21 , 22 ].…”

Contribution of allergy in the acquisition of uncontrolled severe asthma

“…Interestingly, deoxycholic acid was found reduced in UCA patients compared to UCNA. We have previously reported a decrease in deoxycholic acid in asthma according to severity (28). This secondary bile acid has been demonstrated to stimulate the production of inflammatory cytokines in an in vitro epithelial airway cell culture; and to promote inflammation in animal models either by inducing inflammatory cytokine production (65), activation of the inflammasome (66) and inducing the dysbiosis of gut microbiota (67).…”

“…The metabolomic analysis has already been thoroughly described elsewhere ( 27 , 28 ). Briefly, serum samples were measured in batches using an Agilent High Performance Liquid Chromatography system (Agilent 1200 series) coupled with a quadrupole- time of flight (QTOF) analyser system (Q-ToF MS 6520) (Agilent Technologies, Waldbronn, Germany), in positive and negative ionization modes (ESI + and ESI -, respectively).…”