1. Investigación

Background: The pathological mechanism of the gastrointestinal forms of food aller-gies is less understood in comparison to other clinical phenotypes, such as asthmaand anaphylaxis Importantly, high-IgE levels are a poor prognostic factor in gastroin-testinal allergies.Methods: This study investigated how high-IgE levels influence the development ofintestinal inflammation and the metabolome in allergic enteritis (AE), using IgE knock-in (IgEki) mice expressing high levels of IgE. In addition, correlation of the altered me-tabolome with gut microbiome was analysed.Results: Ovalbumin-sensitized and egg-white diet-fed (OVA/EW) BALB/c WT micedeveloped moderate AE, whereas OVA/EW IgEki mice induced more aggravated in-testinal inflammation with enhanced eosinophil accumulation. Untargeted metabo-lomics detected the increased levels of N-tau-methylhistamine and 2,3-butanediol,and reduced levels of butyric acid in faeces and/or sera of OVA/EW IgEki mice, whichwas accompanied with reduced Clostridium and increased Lactobacillus at the genus level. Non-sensitized and egg-white diet-fed (NC/EW) WT mice did not exhibit anysigns of AE, whereas NC/EW IgEki mice developed marginal degrees of AE. Comparedto NC/EW WT mice, enhanced levels of lysophospholipids, sphinganine and sphin-gosine were detected in serum and faecal samples of NC/EW IgEki mice. In addi-tion, several associations of altered metabolome with gut microbiome—for exampleAkkermansia with lysophosphatidylserine—were detected.Conclusions: Our results suggest that high-IgE levels alter intestinal and systemic levelsof endogenous and microbiota-associated metabolites in experimental AE. This studycontributes to deepening the knowledge of molecular mechanisms for the developmentof AE and provides clues to advance diagnostic and therapeutic strategies of allergicdiseases

The human gut microbiome establishes and matures during infancy, and dysregulation at this stage may lead to pathologies later in life. We conducted a multi-omics study comprising three generations of family members to investigate the early development of the gut microbiota. Fecal samples from 200 individuals, including infants (0-12 months old; 55% females, 45% males) and their respective mothers and grandmothers, were analyzed using two independent metabolomics platforms and metagenomics. For metabolomics, gas chromatography and capillary electrophoresis coupled to mass spectrometry were applied. For metagenomics, both 16S rRNA gene and shotgun sequencing were performed. Here we show that infants greatly vary from their elders in fecal microbiota populations, function, and metabolome. Infants have a less diverse microbiota than adults and present differences in several metabolite classes, such as short- and branched-chain fatty acids, which are associated with shifts in bacterial populations. These findings provide innovative biochemical insights into the shaping of the gut microbiome within the same generational line that could be beneficial in improving childhood health outcomes.

Oxylipins are bioactive lipid mediators that participate in a wide range of processes, from blood flow to inflammation. In recent years, their exhaustive characterization as potential biomarkers has been actively pursued. However, several analytical challenges and sources of variability, like the low abundance of oxylipins in samples, their structural diversity, and the lack of harmonized and validated protocols for sample selection, collection, storage, and preparation have hampered their translation into clinical settings. In this review, these sources of variability are addressed and are exemplified with their application in recent clinical studies in different diseases. Our overall analysis highlights the need to achieve harmonized protocols for generating reliable and reproducible results that could be integrated into clinical practice to reach their full diagnostic and prognostic potential.

Background: Antibodies to lipids are part of the first line of defense against microorganisms and regulate the pro/anti-inflammatory balance. Viruses modulate cellular lipid metabolism to enhance their replication, and some of these metabolites are proinflammatory. We hypothesized that antibodies to lipids would play a main role of in the defense against SARS-CoV-2 and thus, they would also avoid the hyperinflammation, a main problem in severe condition patients. Methods: Serum samples from COVID-19 patients with mild and severe course, and control group were included. IgG and IgM to different glycerophospholipids and sphingolipids were analyzed using a high-sensitive ELISA developed in our laboratory. A lipidomic approach for studying lipid metabolism was performed using ultra-high performance liquid chromatography coupled to electrospray ionization and quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS). Results: Mild and severe COVID-19 patients had higher levels of IgM to glycerophosphocholines than control group. Mild COVID-19 patients showed higher levels of IgM to glycerophosphoinositol, glycerophosphoserine and sulfatides than control group and mild cases. 82.5% of mild COVID-19 patients showed IgM to glycerophosphoinositol or glycerophosphocholines plus sulfatides or glycerophosphoserines. Only 35% of severe cases and 27.5% of control group were positive for IgM to these lipids. Lipidomic analysis identify a total of 196 lipids, including 172 glycerophospholipids and 24 sphingomyelins. Increased levels of lipid subclasses belonging to lysoglycerophospholipids, ether and/or vinyl-ether-linked glycerophospholipids, and sphingomyelins were observed in severe COVID-19 patients, when compared with those of mild cases and control group. Conclusion: Antibodies to lipids are essential for defense against SARS-CoV-2. Patients with low levels of anti-lipid antibodies have an elevated inflammatory response mediated by lysoglycerophospholipids. These findings provide novel prognostic biomarkers and therapeutic targets.

The role of the microbiome in the molecular mechanisms underlying allergy has become highly relevant in recent years. Studies are increasingly suggesting that altered composition of the microbiota, or dysbiosis, may result in local and systemic alteration of the immune response to specific allergens. In this regard, a link has been established between lung microbiota and respiratory allergy, between skin microbiota and atopic dermatitis, and between gut microbiota and food allergy. The composition of the human microbiota is dynamic and depends on host-associated factors such as diet, diseases, and lifestyle. Omics are the techniques of choice for the analysis and understanding of the microbiota. Microbiota analysis techniques have advanced considerably in recent decades, and the need for multiple approaches to explore and comprehend multifactorial diseases, including allergy, has increased. Thus, more and more studies are proposing mechanisms for intervention in the microbiota. In this review, we present the latest advances with respect to the human microbiota in the literature, focusing on the intestinal, cutaneous, and respiratory microbiota. We discuss the relationship between the microbiome and the immune system, with emphasis on allergic diseases. Finally, we discuss the main technologies for the study of the microbiome and interventions targeting the microbiota for prevention of allergy.

Glioblastoma (GBM) is one of the most malignant central nervous system tumor types. Comparative analysis of GBM tissues has rendered four major molecular subtypes. From them, two molecular subtypes are mainly found in their glioblastoma cancer stem-like cells (GSCs) derived in vitro: proneural (PN) and mesenchymal (MES) with nodular (MES-N) and semi-nodular (MES-SN) disseminations, which exhibit different metabolic, growth, and malignancy properties. Many studies suggest that cancer cells communicate between them, and the surrounding microenvironment, via exosomes. Identifying molecular markers that allow the specific isolation of GSC-derived exosomes is key in the development of new therapies. However, the differential exosome composition produced by main GSCs remains unknown. The aim of this study was to determine ceramide (Cer) composition, one of the critical lipids in both cells and their cell-derived exosomes, from the main three GSC phenotypes using mass spectrometry-based lipidomics. GSCs from human tissue samples and their cell-derived exosomes were measured using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS) in an untargeted analysis. Complete characterization of the ceramide profile, in both cells and cell-derived exosomes from GSC phenotypes, showed differential distributions among them. Results indicate that such differences of ceramide are chain-length dependent. Significant changes for the C16 Cer and C24:1 Cer and their ratio were observed among GSC phenotypes, being different for cells and their cell-derived exosomes.

Metabolites are the final products of the metabolism and are, therefore, directly related to phenotype. They constitute the metabolome of an organism. The wide diversity of the physicochemical properties of the metabolites in terms of molecular weight, concentration, polarity, volatility, solubility, pKa, and charge makes their analysis a remarkable challenge. With over 220,000 metabolites recorded in the HMDB database, there is no single analytical technique capable of analyzing all of them. Therefore, multiple analytical platforms are required to obtain a comprehensive picture of the metabolome. Among these platforms, mass spectrometry (MS)-based analytical techniques are among the most widely used. Capillary electrophoresis (CE) coupled to MS has been employed to analyze polar/ionic metabolites. Although this technique is not widely used, it has demonstrated unique capabilities for the detection of polar and ionic metabolites that are an essential part of the metabolome and are not usually detected by other techniques. This review highlights the role of CE-MS in untargeted metabolomics, particularly in comparison to the hydrophilic interaction chromatography (HILIC) separation mode. Additionally, we discuss the metabolomics workflow in CE-MS for untargeted metabolomics, including sample treatment and analysis, data treatment, and metabolite annotation. We notably present the annotation tools developed explicitly for CE-MS, as well as some computational alternatives, in-house libraries of relative migration times, effective mobility, MS/MS fragmentation, in-source fragmentation, and the CEU Mass Mediator online tool. Finally, we mention future perspectives of this technique, such as single cell-CE and ion mobility (IM)-MS. Overall, this review shows the important role of CE-MS in the studies of untargeted analysis published in the last five years to approach human diseases.

Background: Mechanisms causing the onset and perpetuation of inflammation in severe allergic patients remain unknown. Our previous studies suggested that severe allergic inflammation is linked to platelet dysfunction. Methods: Platelet-rich plasma (PRP) and platelet-poor plasma (PPP) samples were obtained by platelet-apheresis from severe (n = 7) and mild (n = 10) allergic patients and nonallergic subjects (n = 9) to perform platelet lipidomics by liquid chromatography coupled to mass spectrometry (LC–MS) and RNA-seq analysis. Significant metabolites and transcripts were used to identify compromised biological pathways in the severe phenotype. Platelet and inflammation-related proteins were quantified by Luminex. Results: Platelets from severe allergic patients were characterized by high levels of ceramides, phosphoinositols, phosphocholines, and sphingomyelins. In contrast, they showed a decrease in eicosanoid precursor levels. Biological pathway analysis performed with the significant lipids revealed the alteration of phospholipases, calcium-dependent events, and linolenic metabolism. RNAseq confirmed mRNA overexpression of genes related to platelet activation and arachidonic acid metabolism in the severe phenotypes. Pathway analysis indicated the alteration of NOD, MAPK, TLR, TNF, and IL-17 pathways in the severe phenotype. P-Selectin and IL-17AF proteins were increased in the severe phenotype. Conclusions: This study demonstrates that platelet lipid, mRNA, and protein content is different according to allergy severity. These findings suggest that platelet load is a potential source of biomarkers and a new chance for therapeutic targets in severe inflammatory pathologies.

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force “Omics technologies in allergic research” broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients’ stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

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.