1. Investigación

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.

Background: Sublingual allergen-specific immunotherapy (SLIT) intervention improves the control of grass pollen allergy by maintaining allergen tolerance after cessation. Despite its widespread use, little is known about systemic effects and kinetics associated to SLIT, as well as the influence of the patient sensitization phenotype (Mono- or Poly-sensitized). In this quest, omics sciences could help to gain new insights to understand SLIT effects. Methods: 47 grass-pollen-allergic patients were enrolled in a double-blind, placebocontrolled, multicenter trial using GRAZAX® during 2 years. Immunological assays (sIgE, sIgG4, and ISAC) were carried out to 31 patients who finished the trial. Additionally, serum and PBMCs samples were analyzed by metabolomics and transcriptomics, respectively. Based on their sensitization level, 22 patients were allocated in Mono- or Poly-sensitized groups, excluding patients allergic to epithelia. Individuals were compared based on their treatment (Active/Placebo) and sensitization level (Mono/Poly). Results: Kinetics of serological changes agreed with those previously described. At two years of SLIT, there are scarce systemic changes that could be associated to improvement in systemic inflammation. Poly-sensitized patients presented a higher inflammation at inclusion, while Mono-sensitized patients presented a reduced activity of mast cells and phagocytes as an effect of the treatment. Conclusions: The most relevant systemic change detected after two years of SLIT was the desensitization of effector cells, which was only detected in Mono-sensitized patients. This change may be related to the clinical improvement, as previously reported, and, together with the other results, may explain why clinical effect is lost if SLIT is discontinued at this point.

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: Prevalence and severity of allergic diseases have increased worldwide. To date, respiratory allergy phenotypes are not fully characterized and, along with inflammation progression, treatment is increasingly complex and expensive. Profilin sensitization constitutes a good model to study the progression of allergic inflammation. Our aim was to identify the underlying mechanisms and the associated biomarkers of this progression, focusing on severe phenotypes, using transcriptomics and metabolomics. Methods: 25 subjects were included in the study. Plasma samples were analyzed using Gas and Liquid Chromatography coupled to Mass Spectrometry (GC -MS and LC -MS, respectively). Individuals were classified in 4 groups – “non -allergic”, “mild”, “moderate” and “severe” – based on their clinical history, their response to an oral challenge test with profilin , and after a refinement using a mathematical metabolomic model. PBMCs were used for microarray analysis. Results: We found a set of transcripts and metabolites that were specific for the “severe” phenotype. By metabolomics, it was detected a decrease in carbohydrate s and pyruvate and an increase in lactate , suggesting aerobic glycolysis. Ohter metabolites were incremented in severe group: lysophospholipids, sphingosine - 1 -phosphate, sphinganine - 1 -phosphate, as well as lauric, myristic, palmitic, and oleic fatty acids. On the other hand, carnitines were decreased along severity. Significant transcripts in the “severe” group were downregulated and were associated to platelet functions, protein synthesis, histone modification and fatty acid metabolism. Conclusions: We have found evidence that points to the association of severe allergic inflammation with platelet functions alteration, together with reduced protein synthesis, and switch of immune cells to aerobic glycolysis.