Understanding the underlying mechanisms behind IgE-mediated cow’s milk allergy (IgE-CMA) is imperative for the discovery of novel biomarkers and the design of innovative treatment and prevention strategies. Here, we report data on the gut microbiome, metabolome, and lipidome of 81 children affected by food allergies, including CMA and healthy controls. Moreover, we developed a mouse model that mimicked IgE CMA best, BALB/c mice sensitized with ß-lactoglobulin using cholera toxin. During sensitization, we observed multiple microbially derived metabolic alterations, most importantly bile acid and tryptophan metabolites, that preceded allergic inflammation, while this inflammation was reflected in a disturbed sphingomyelin and histamine metabolism. We endorsed the microbial origin of these metabolites by in vitro colonic digestions and confirmed the microbial dysbiosis in our patient cohort, which was accompanied by metabolic signatures of low-grade inflammation. Our results suggest that gut dysbiosis precedes allergic inflammation, opening new opportunities for future prevention and treatment strategies. Trial: NCT04249973.

In recent years, feces has surfaced as the matrix of choice for investigating the gut microbiome-health axis because of its noninvasive sampling and the unique reflection it offers of an individual's lifestyle. In cohort studies where the number of samples required is large, but availability is scarce, a clear need exists for high-throughput analyses. Such analyses should combine a wide physicochemical range of molecules with a minimal amount of sample and resources and downstream data processing workflows that are as automated and time efficient as possible. We present a dual fecal extraction and ultra high performance liquid chromatography-high resolution-quadrupole-orbitrap-mass spectrometry (UHPLC-HR-Q-Orbitrap-MS)-based workflow that enables widely targeted and untargeted metabolome and lipidome analysis. A total of 836 in-house standards were analyzed, of which 360 metabolites and 132 lipids were consequently detected in feces. Their targeted profiling was validated successfully with respect to repeatability (78% CV < 20%), reproducibility (82% CV < 20%), and linearity (81% R 2 > 0.9), while also enabling holistic untargeted fingerprinting (15,319 features, CV < 30%). To automate targeted processing, we optimized an R-based targeted peak extraction (TaPEx) algorithm relying on a database comprising retention time and mass-to-charge ratio (360 metabolites and 132 lipids), with batch-specific quality control curation. The latter was benchmarked toward vendor-specific targeted and untargeted software and our isotopologue parameter optimization/XCMS-based untargeted pipeline in LifeLines Deep cohort samples (n = 97). TaPEx clearly outperformed the untargeted approaches (81.3 vs 56.7−66.0% compounds detected). Finally, our novel dual fecal metabolomics−lipidomics−TaPEx method was successfully applied to Flemish Gut Flora Project cohort (n = 292) samples, leading to a sample-to-result time reduction of 60%.

In recent years, feces has surfaced as the matrix of choice for investigating the gut microbiome-health axis because of its non-invasive sampling and the unique reflection it offers of an individual’s lifestyle. In cohort studies where the number of samples required is large, but availability is scarce, a clear need exists for high-throughput analyses. Such analyses should combine a wide physicochemical range of molecules with a minimal amount of sample and resources, and downstream data processing workflows that are as automated and time efficient as possible. We present a dual fecal extraction and UHPLC-HR-Q-Orbitrap-MS-based workflow that enables widely targeted and untargeted metabolome and lipidome analy-sis. A total of 836 in-house standards were analyzed, of which 360 metabolites and 132 lipids were consequently detected in feces. Their targeted profiling was validated successfully with respect to repeatability (78% CV<20%), reproducibility (82% CV<20%) and linearity (81% R2>0.9), while also enabling holistic untargeted fingerprinting (15 319 features, CV<30%). To automate targeted processing, we optimized an R-based targeted peak extraction (TaPEx) algorithm relying on a database comprising retention time and mass-to-charge ratio (360 metabolites and 132 lipids), with batch-specific quality control curation. The latter was benchmarked towards vendor-specific targeted and untargeted software and our IPO/XCMS-based untargeted pipeline in Lifelines Deep cohort samples (n = 97). TaPEx clearly outperformed the untargeted approaches (81.3 vs. 56.7-66.0% compounds detected). Finally, our novel dual fecal metabolomics-lipidomics-TaPEx method was successful-ly applied to Flemish Gut Flora Project cohort (n = 292) samples, leading to a sample-to-result time reduction of 60%.

Background: IgE-mediated cow’s milk allergy (IgE-CMA) is one of the first allergies to arise in early childhood and may result from exposure to various milk allergens, of which β-lactoglobulin (BLG) and casein are the most important. Understanding the underlying mechanisms behind IgE-CMA is imperative for the discovery of novel biomarkers and the design of innovative treatment and prevention strategies. Methods: We report a longitudinal in vivo murine model, in which 2 mice strains (BALB/c and C57Bl/6) were sensitized to BLG using either cholera toxin or an oil emulsion (n=6 per group). After sensitization, mice were challenged orally, their clinical signs monitored, antibody (IgE and IgG1) and cytokine levels (IL-4 and IFN-γ) measured, and fecal samples subjected to metabolomics. The results of the murine models were further supported by fecal microbiome-metabolome data from our population of IgE-CMA (n=24) and healthy (n=23) children (Trial: NCT04249973), on which polar metabolomics, lipidomics and 16S rRNA metasequencing were performed. In vitro gastrointestinal digestions and multi-omics corroborated the microbial origin of proposed metabolic changes. Results: During sensitization, we observed multiple microbially derived metabolic alterations, most importantly bile acid, energy and tryptophan metabolites, that preceded allergic inflammation. The latter was reflected in a disturbed sphingolipid metabolism. We confirmed microbial dysbiosis, and its causal effect on metabolic alterations in our patient cohort, which was accompanied by metabolic signatures of low-grade inflammation. Conclusion: Our results indicate that gut dysbiosis precedes allergic inflammation and nurtures a chronic low-grade inflammation in children on elimination diets, opening important new opportunities for future prevention and treatment strategies.

In recent years, feces has surfaced as the matrix of choice for investigating the gut microbiome-health axis because of its non-invasive sampling and the unique reflection it offers of an individual’s lifestyle. In cohort studies where the number of samples required is large, but availability is scarce, a clear need exists for high-throughput analyses. Such analyses should combine a wide physicochemical range of molecules with a minimal amount of sample and resources, and downstream data processing workflows that are as automated and time efficient as possible. We present a dual fecal extraction and UHPLC-HR-Q-Orbitrap-MS-based workflow that enables widely targeted and untargeted metabolome and lipidome analy-sis. A total of 836 in-house standards were analyzed, of which 360 metabolites and 132 lipids were consequently detected in feces. Their targeted profiling was validated successfully with respect to repeatability (78% CV<20%), reproducibility (82% CV<20%) and linearity (81% R2>0.9), while also enabling holistic untargeted fingerprinting (15 319 features, CV<30%). To automate targeted processing, we optimized an R-based targeted peak extraction (TaPEx) algorithm relying on a database comprising retention time and mass-to-charge ratio (360 metabolites and 132 lipids), with batch-specific quality control curation. The latter was benchmarked towards vendor-specific targeted and untargeted software and our IPO/XCMS-based untargeted pipeline in Lifelines Deep cohort samples (n = 97). TaPEx clearly outperformed the untargeted approaches (81.3 vs. 56.7-66.0% compounds detected). Finally, our novel dual fecal metabolomics-lipidomics-TaPEx method was successful-ly applied to Flemish Gut Flora Project cohort (n = 292) samples, leading to a sample-to-result time reduction of 60%.