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.

Metabolomics, understood as the science that manages the study of compounds from the metabolism, is an essential tool for deciphering metabolic changes in disease. The experiments rely on the use of high-throughput analytical techniques such as liquid chromatography coupled to mass spectrometry (LC-ToF MS). This hyphenation has brought positive aspects such as higher sensitivity, specificity and the extension of the metabolome coverage in a single run. The analysis of a high number of samples in a single batch is currently not always feasible due to technical and practical issues (i.e., a drop of the MS signal) which result in the MS stopping during the experiment obtaining more than a single sample batch. In this situation, careful data treatment is required to enable an accurate joint analysis of multi-batch data sets. This paper summarizes the analytical strategies in large-scale metabolomic experiments; special attention has been given to QC preparation troubleshooting and data treatment. Moreover, labeled internal standards analysis and their aim in data treatment, and data normalization procedures (intra- and inter-batch) are described. These concepts are exemplified using a cohort of 165 patients from a study in asthma.

Background: In previous studies with peripheral blood cells, platelet factors were found to be associated with severe allergic phenotypes. A reliable method yielding highly concentrated and pure platelet samples is usually not available for immunological studies. Plateletpheresis is widely used in the clinics for donation purposes. In this study, we designed a protocol based on plateletpheresis to obtain Platelet‐Rich Plasma (PRP), Platelet‐Poor Plasma (PPP) as well as CD3⁺ and CD14⁺ cells matched samples from a waste plateletpheresis product for immunological studies.Methods: Twenty‐seven subjects were voluntarily subjected to plateletpheresis. PRP, PPP and blood cell concentrate contained in a leukocyte reduction system chamber (LRSC) were obtained in this process. CD3⁺ and CD14⁺ cells were isolated from the LRSC by density‐gradient centrifugation and positive magnetic bead isolation. RNA was isolated from PRP, CD3⁺ and CD14⁺ cell samples and used for transcriptomic studies by Affymetrix. PRP and PPP samples were used for platelet protein quantification by multiplex assays.Results: A reliable high yield method to obtain matched samples of PRP, PPP, CD3⁺ and CD14⁺ from a single donor for RNA and protein analyses has been designed. The RNA quality indicators (RQI) routinely used for other cell types were not suitable for platelet RNA characterization. Despite this, the platelet RNA was valid for transcriptomic studies by Affymetrix, as platelet transcripts obtained in our previous studies were confirmed in PRP samples. Platelet samples were enriched in platelet factors as determined in protein multiplex analysis.Conclusions: We have developed a method that yields not only high content and pure platelet samples from a single donor but also CD3⁺ and CD14⁺ matched samples that can be used for RNA and protein analyses in immunological studies.

Rationale: Biologics are becoming increasingly important in the management of severe asthma. However, little is known about the systemic immunometabolic consequences of Th2 response blockage. Objectives: To provide a better immunometabolic understanding of the effects of mepolizumab and omalizumab treatments by identifying potential biomarkers for monitoring. Methods: In this exploratory longitudinal study severe asthmatic patients were followed for 18 months after initiating mepolizumab ( n = 36) or Omalizumab ( n = 20) treatment. Serum samples were collected before, 6, and 18 months after treatment. Targeted omic approaches were performed to analyze inflammatory metabolites ( n = 35) and proteins ( n = 45). Multiomic integration was performed individually for each treatment applying supervised analysis Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) framework. Then, potential biomarkers were confirmed using multivariate ROC analyses and correlated with clinical variables along treatment. Measurements and Main Results: Mepolizumab and omalizumab were both effective (improved clinical variables) and showed different and specific metabolic and protein profiles in severe asthmatic patients during treatment. Multiomic integration and multivariate ROC analyses identified specific biomarkers, such as arachidonic acid, palmitoleic acid, oleic acid, propionylcarnitine, bilirubin, CCL11, and TNFSF10, which can explain the differences observed with Mepolizumab treatment over 18 months and significantly correlate with clinical improvement. However, no significant biomolecules and no discriminative multivariate ROC curves were found for Omalizumab treatment. Conclusions: Our results provide a comprehensive insight into the differential effects of mepolizumab and omalizumab on the immunometabolic kinetics of the inflammatory response in severe asthma. We identified a set of biomolecules with potential for monitoring mepolizumab treatment which could be useful for personalized medicine.