Diet-Related Metabolites Associated with Cognitive Decline Revealed by Untargeted Metabolomics in a Prospective Cohort

Scope: Untargeted metabolomics may reveal preventive targets in cognitive aging, including within the food metabolome. Methods and results: A case-control study nested in the prospective Three-City study includes participants aged &65 years and initially free of dementia. A total of 209 cases of cognitive decline and 209 controls (matched for age, gen- der, education) with slower cognitive decline over up to 12 years are contrasted. Using untargeted metabolomics and bootstrap-enhanced penalized regression, a baseline serum signature of 22 metabolites associated with subsequent cognitive decline is identified. The signature includes three coffee metabolites, a biomarker of citrus intake, a cocoa metabolite, two metabolites putatively derived from fish and wine, three medium-chain acylcarnitines, glycodeoxycholic acid, lysoPC(18:3), trimethyllysine, glucose, cortisol, creatinine, and arginine. Adding the 22 metabolites to a reference predictive model for cognitive decline (conditioned on age, gender, education and including ApoE-ε4, diabetes, BMI, and number of medications) substantially increases the predictive performance: cross-validated Area Under the Receiver Operating Curve = 75% [95% CI 70-80%] compared to 62% [95% CI 56-67%]. Conclusions: The untargeted metabolomics study supports a protective role of specific foods (e.g., coffee, cocoa, fish) and various alterations in the endogenous metabolism responsive to diet in cognitive aging

IN SILICO PREDICTION OF METABOLISM AS A TOOL TO IDENTIFY NEW METABOLITES OF DIETARY MONOTERPENES IN RATS

Dietary terpenes have not been studied enough, despite the fact that they are absorbed well and display a range of biological properties. Better knowledge about their metabolism will help understanding the health effects of plant foods, herbs and spices and may provide new biomarkers of food intake. As part of the FoodBAll project we are investigating the metabolism of terpenes, identifying metabolites and biotransformations involved in their metabolism. PhytoHub (database that compiles all known metabolites of dietary phytochemicals, including terpenes) and Nexus Meteor (in silico prediction of metabolism), were used to identify biotransformations involved in the metabolism of monoterpenoids. Twenty-two selected biotransformations were used to predict the metabolism of camphene, camphor, carvacrol, carvone, 1,4-cineole, 1,8-cineole, citral, citronellal, cuminaldehyde, p-cymene, fenchone, geraniol, limonene, linalool, menthol, myrcene, nootkatone, perillyl alcohol, pinene, pulegone, terpinen-4-ol and thymol. In parallel, Wistar rats received a chemically defined diet with or without 0.05% of the referred compounds. Before and after 5 days of the exposure to the dietary monoterpenes, urine was collected and untargeted metabolomics analysis performed using high-resolution mass spectrometry (UPLC-QToF).On average, 10 metabolites per compound were identified in rat urine, including new and known ones. Identification of metabolites was based on monoisotopic mass and formula match, presence of adducts and specific mass losses indicative of glucuronidation and conjugation to amino acids. Validation of identification is being done using orbitrap MS/MS and hydrogen-deuterium exchange experiments.The combination of in silico prediction and in vivo experiment allowed the identification of known and new metabolites of different dietary terpenoids. Predicted metabolites of terpenes will be added in PhytoHub to complement the database of known metabolites.Acknowledgement: Agence Nationale de la Recherche - FoodBAll project (JPI HDHL

TOWARDS A DIAGNOSIS OF NON-CELIAC GLUTEN SENSITIVITY: the contribution of metabolomics for monitoring metabolites produced by in vitro digestates of bread

International audienceBody fluid metabolomics is a large-scale approach allowing exploring the mechanisms that might underlie specific diseases or sensitivity to processed foods, and identifying associated biomarkers for diagnostics or stratification. Over the past decade, the non-celiac gluten sensitivity (NCGS) is more and more self-diagnosed, which makes the gluten-free diet more frequent, without objective clinical criteria. In fact, because of a lack of clinical indicators, NCGS is poorly understood and challenging to diagnose in contrast to celiac disease. Therefore, finding biomarkers associated with this phenotype is critical for an accurate diagnosis and innovative patient management.To understand the relationship between bread digestion mechanisms and the occurrence of NCGS, a recent approach with in vitro investigation was applied to study the overall digestive process of different breads, combining tools from the oral step thanks to the AM2 masticator apparatus, until the end of digestion thanks to a dynamic digester (DIDGI©) mimicking the physiology of the adult gastrointestinal tract "GIT". One objective in this study was to monitor metabolites produced by in vitro digestates using an untargeted metabolomics approach.In this study, we will outline the methodological strategy taken from preparation of the stomach and intestinal digestates, to acquisition, processing, and annotation of the LC-HRMS data.Interestingly, the first results show fluctuations in certain metabolites identified according to the type of bread digested. This reveals the impact of type of bread on the digestibility and allowed us to emphasize the contribution of metabolomic approach for monitoring the metabolites produced by in vitro digestates

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update

Abstract Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations