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.

In meiosis I, chromosomes become paired with their homologous partners and then are pulled towards opposite poles of the spindle. In the budding yeast, Saccharomyces cerevisiae, in early meiotic prophase, centromeres are observed to associate in pairs in a homology-independent manner, a process called centromere coupling. Later, as homologous chromosomes align, their centromeres associate in a process called centromere pairing. The synaptonemal complex protein Zip1 is necessary for both types of centromere association. We aimed to test the role of centromere-coupling in modulating recombination at centromeres, and to test whether the two types of centromere associations depend upon the same sets of genes. The zip1-S75E mutation, which blocks centromere-coupling, but no other known functions of Zip1, was used to show that in the absence of centromere coupling, centromere-proximal recombination was unchanged. Further, this mutation did not diminish centromere pairing, demonstrating that these two processes have different genetic requirements. In addition, we tested other synaptonemal complex components, Ecm11 and Zip4, for their contributions to centromere pairing. ECM11 was dispensable for centromere pairing and segregation of achiasmate partner chromosomes, while ZIP4 was not required for centromere pairing during pachytene, but was required for proper segregation of achiasmate chromosomes. These findings help differentiate the two mechanisms that allow centromeres to interact in meiotic prophase, and illustrate that centromere pairing, which was previously shown to be necessary to ensure disjunction of achiasmate chromosomes, is not sufficient for ensure their disjunction.

Background: Allergic diseases begin early in life and are often chronic, thus creating an inflammatory environment that may precede or exacerbate other pathologies. In this regard, allergy has been associated to metabolic disorders and with a higher risk of cardiovascular disease, but the underlying mechanisms remain incompletely understood. Methods: We used a murine model of allergy and atherosclerosis, different diets and sensitization methods, and cell‐depleting strategies to ascertain the contribution of acute and late phase inflammation to dyslipidemia. Untargeted lipidomic analyses were applied to define the lipid fingerprint of allergic inflammation at different phases of allergic pathology. Expression of genes related to lipid metabolism was assessed in liver and adipose tissue at different times post‐allergen challenge. Also, changes in serum triglycerides (TGs) were evaluated in a group of 59 patients ≥14 days after the onset of an allergic reaction. Results: We found that allergic inflammation induces a unique lipid signature that is characterized by increased serum TGs and changes in the expression of genes related to lipid metabolism in liver and adipose tissue. Alterations in blood TGs following an allergic reaction are independent of T‐cell‐driven late phase inflammation. On the contrary, the IgG‐mediated alternative pathway of anaphylaxis is sufficient to induce a TG increase and a unique lipid profile. Lastly, we demonstrated an increase in serum TGs in 59 patients after undergoing an allergic reaction. Conclusion: Overall, this study reveals that IgG‐mediated allergic inflammation regulates lipid metabolism.

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.