Metabolomic analysis of an allergic enteritis murine model by GC-MS

BARKER TEJEDA TC; ZUBELDIA-VARELA E; BLANCO F; ROJO D; VILLASEÑOR A; LAIÑO J; VIETHS S; YU P; PÉREZ-GORDO M; TODA M

Lisboa

Congreso; European Academy of Allergy and Clinical Immunology Congress; 2019

European Academy of Allergy and Clinical Immunology

Background : Allergic diseases are on the rise, and food allergies are one of the most prevalent and concerning. From these, allergic enteritis (AE) − the inflammation of the small intestine caused by food allergy – is gathering increasing interest. Concerning allergic mechanisms, allergen- specific IgE is one of the proven effectors of allergy, although food- allergic phenotypes that are independent of IgE also exist. Moreover, metabolomics is a new science that seeks altered metabolic changes in living organisms. Thus, the aim of this work is to shed light on the molecular mechanisms of AE, specifically the role of IgE, by means of metabolome analysis using an animal model. Method : To induce AE, wild- type (WT) mice and IgE knock- in (IgEki) mice expressing IgE instead of IgG were used. These were sensitized with ovalbumin (OVA, an egg white allergen) and challenged with a diet containing egg whites. Control mice received egg white diet without OVA sensitization. From these mice, whole faeces and serum samples were extracted and used for metabolomic analysis by Gas Chromatography coupled to Mass Spectrometry (GC- MS). GCMS is a technique that allows the detection of volatile compounds or those that are volatile after derivatization, such as TCA intermediates, fatty acids, phenolic compounds, and amino acids, among others. Significant compounds were obtained for all the comparisons between the four groups. Results : Metabolic profiles of serum and total faeces were characterized, obtaining 91 metabolites for serum and 265 for faeces. From these, after a statistical analysis, 129 compounds were found to be significative for faeces and 15 for serum, in total for all comparisons. The comparisons with the highest number of compounds were between the most extreme groups (“allergic IgEki vs non- allergic WT” & “allergic WT vs non- allergic IgEki”). IgEki mice displayed more severe symptoms and their metabolic signatures were significantly different from WT mice. It is also notable that the high levels of IgE have an impact on the metabolome as well as the allergic condition. Conclusion : We have analysed the metabolome of a murine model of AE. It appears that the metabolic changes of AE are affected by IgE levels, which suggests a role of IgE in the pathology. Further studies for the validation of these results and the identification of a metabolomic signature associated with AE development would advance in the search for the molecular mechanism of AE and biomarkers for allergic conditions.