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13 th NuGOwee: PHENOTYPES AND PREVENTION -THE INTERPLAY OF GENES, LIFE-STYLES FACTORS AND GUT ENVIRONMENT Session 1 board 2: Biomarkers13 th NuGOwee: PHENOTYPES AND PREVENTION -THE INTERPLAY OF GENES, LIFE-STYLES FACTORS AND GUT ENVIRONMENTSession 1 board 2: Biomarkers; Two complementary metabolomics studies to identify biomarkers of banana intake. NuGOweek 2016

The overall objective of the project is to develop accurate and robust markers of the evolution of health status toward metabolic syndrome (MetS), and to determine to what extent nutrition is a major determinant, by using a multidisciplinary approach, putting together sociology, epidemiology, nutrition, statistics, and computer science. The project uses the French population-based cohort GAZEL, an on-going epidemiological study set up in 1989 (around 20,000 volunteers) among employees of the French national Gas and Electricity Company. The study consists in integrating demographic, socioeconomic, clinical, and biological data (from annual questionnaires, including food frequency questionnaires (FFQs)) to analyse food trajectories between 1998 and 2009. A case-control approach is used within a sub-cohort to characterize metabolic signatures and identify early discriminant factors predictive of MetS development. Male subjects between 52 and 64 years old, with high BMI (25≤BMI

International audience; PATHWAY-27 will evaluate the effectiveness of docosahexaenoic acid (DHA) alone or in combination with two other bioactives, beta-glucan (BG) and anthocyanins (AC) in reducing some risk factors of Metabolic Syndrome MS. These compounds will be used as ingredients of bioactive-enriched foods (BEF), enriching 3 different widely-consumed food matrices (dairy-, bakery-, egg products) and not as pure compounds. This will allow a better understanding of possible synergisms and bioactive-matrix interactions. BEFs to be tested in clinical studies have been designed, selected and produced by different Pathway’ partners. The aim of this multi-centre, randomized, double- blind, parallel pilot study is to identify the BEF achieving the greatest effect on lipid parameters (reduction in serum triglycerides or increase in HDL-C). The selected BEF will then be tested in a subsequent, larger interventional study. Three different matrices containing DHA, BG and AC given alone or of DHA associated with BG or AC will be tested. 300 men and women at risk for MS will be investigated (either one or two of the following criteria should be met: elevated waist circumference, elevated fasting trigycerides, reduced fasting HDL-C, elevated blood pressure or hypotensive treatment or elevated fasting glucose). MRI, ULE, CRNH will investigate BEFs based on either bakery, dairy or egg products, representing a different food matrix. Each pilot study will be conducted on 100 volunteers for a period of 4 weeks. Participants will be divided in 5 groups receiving BEF enriched with DHA, BG, or AC alone or DHA+BG, or DHA+AC. At baseline and after 4 weeks of intervention, fasting blood samples will be collected for further analysis. Additionally, blood pressure and anthropometric data will be determined. The 3 most effective BEF (one for each matrix) having the most significant impact on end-points selected for this study will be used in a larger randomized, double-blind, placebo-controlled study. The aim will be to understand the mechanisms underlying the effects observed on primary and secondary endpoints related to the consumption of BEF. Omics approaches will be used to examine metabolic changes and potentially identify new markers of effects.

International audience

The 1st international workshop on the ‘Food metabolome and biomarkers for dietary exposure’, organized in Glasgow last year, identified as a priority the development of databases and libraries of spectra for the food metabolome. The food metabolome comprises all metabolites present in human biofluids and tissues that directly derive from the digestion and metabolism of food chemicals. Exploration of the food metabolome through mass spectrometry untargeted profiling has opened new avenues for the discovery of intake biomarkers, also a key priority in nutrition research as described in the JPI HDHL strategic agenda and recent BioNH call. A large proportion of the food metabolome consists of metabolites of phytochemicals such as polyphenols and terpenes. Identification of these metabolites in metabolomic profiles is often a bottleneck in biomarker discovery, as their standards are not commercially available and their integration in online databases and libraries of spectra is still very limited. As part of the ANR PhenoMeNep project, we have designed PhytoHub (www.phytohub.eu), dedicated to all phytochemicals commonly ingested with the diet. Chemical data include name, chemical structure and identifiers, synonyms, physico- chemical properties. Data also include the taxonomy, foods of origin (linked to FooDB), compound of origin (if a metabolite), biofluid location and literature references. Known metabolites are manually extracted from the literature and references are attached. Since the metabolism of many phytochemicals has not been studied yet, in silico prediction of metabolism will be used. We are developing an in-house tool from (1) a compilation of all biotransformations (and combinations) occurring in humans (including gut microbiota biotransformations) for each chemical class and (2) analysis of functional groups on precursor phytochemicals. A list of predicted metabolites will be generated for each phytochemical. Mass spectral data come from literature, other databases on phytochemicals and experimental data from our collaborative platforms. Hopefully in silico prediction of mass fragmentation will be added in the future. An efficient relational design supports a powerful and intuitive web interface. For a queried monoisotopic mass or molecular formula, the database will return a list of metabolites or phytochemical precursors, along with their spectral data and possible dietary and metabolic origins. For a queried food, it will return a list of metabolites likely to be present in biofluids after consumption. MS-MS and custom advanced searches are also possible. PhytoHub is the first database to collate information on phytochemical metabolites from a metabolomics standpoint, and should facilitate identification of unknowns in non-targeted profiling. The version 1.0 of PhytoHub is online, with a first dataset on the 240 most consumed terpenes and their known metabolites. It will be updated monthly. Any willingness to contribute to this freely accessible resource is welcome so that the database can be as complete, accurate and useful as possible.

Shifts in dietary pattern, food composition and processing as well as a modification in physical activity are linked to nutritional transitions. In the context of deleterious behavior, transition has led to an energy imbalance not only in the Western world but also in emerging countries with an increasing prevalence of overweight and obesity. Obesity is associated with increased risk of metabolic syndrome which is a complex disease involving dysregulation of several metabolic pathways. The purpose of the present work was to compare metabolic trajectories followed by free living non-obese overweight and lean subjects during a moderate weight gain induced by a lipid-enriched supplementation of the diet. Metabolomics allowed us determining early changes in metabolic signatures with indicators of metabolic alterations. Thirty eight male adult subjects, 19 normal weight (BMI: 20-24.9 kg.m2, no family history of obesity; NW) and 19 overweight (BMI: 25-29.9 kg.m2, known family history of obesity; OW) were recruited and submitted for 56 days to an overfeeding protocol consisting in adding an excess of 3,300 kJ/d to their usual diet. Conventional anthropometric and metabolic parameters were measured over time. Plasma and urine samples were collected at different time points for an untargeted metabolomic approach. Deproteinized plasma and diluted urine samples were analyzed using UPLC-QToF-Micro mass spectrometer. Univariate (ANOVA) and multivariate (OSC-PLS) models were used to explore the kinetic differences between OW and NW subjects. Metabolite identifications were performed using public databases and complementary analyses on a LTQ Orbitrap Velos high resolution mass spectrometer. For most of the anthropometric, biochemical, metabolic parameters and for some plasma metabolomic data, the phenotypes were discriminated at any time of the kinetic. On the whole, plasma metabolite signatures evolve similarly in NW and OW subjects. In comparison with the NW, lower abundances of plasma highly unsaturated lysophospholipids were observed in OW while increased levels of saturated isomers occurred with the intervention in both groups. Urinary metabolomic profiles clearly evidenced different metabolic trajectories between groups, generally characterized in OW by an increase over time of short- and medium-chain acylcarnitines and bile acids. Changes in metabolic signatures induced by the overfeeding protocol occurred at different time points for the NW and OW volunteers. Therefore, the comprehensive metabolic profiling provided by metabolomics revealed a differential response to the nutritional intervention in urine metabolomes of NW and OW, indicating dissimilar metabolic flexibility. Changes with weight status of plasma and urine metabolites, mostly related to modifications in fatty acid β-oxidation and metabolism, and inflammation indicate a greater metabolic flexibility in NW subjects. In conclusion, this study demonstrates that untargeted metabolomics allows detecting subtle metabolic changes linked to differences in metabolic flexibility that were not evidenced by a classical approach.

Insulin resistance (IR) is one of the main components of metabolic syndrome. However, the early phases of its development such as the first metabolic alterations preceding its installation remain unclear. In order to analyse the early processes over time from the gene to the metabolite levels, we have pair-fed two groups of rats with either a control (65% starch) or a high-fructose (65% fructose) diets for 45 days. The final phenotype of the fructose-fed group consisted in fasting hyperglycaemia, hyperinsulinemia, and IR at the muscle level. Several major changes were also noticed over time before the IR installation: at the molecular level, the liver expression profile (90 genes related to lipid and glucose metabolism and insulin signalling) was strongly modified at D5. This drift was quickly compensated and the differences were no longer visible between D12 and D30. Nevertheless, this phenotypic flexibility was limited over time, and after 45 days of fructose feeding, the observed profile was identical as at D5, suggesting a final drift of the molecular phenotype probably linked to IR installation. This hypothesis was further supported by the metabolic trajectory observed in urine metabolomics analysis of the fructose-fed rats, which was completely different from that of the control group. This trajectory, together with the identified metabolites between day 30 and 45 (when the IR was installed) allowed us to better characterise the metabolic flexibility of the fructose-fed rats. Finally, at the metabolic level, the fructose-fed phenotype was characterized by a rapid (D5) increase in the lipogenic and gluconeogenic potentials (enzyme activities and gene expression). All together, the events preceding the fructose-induced IR were characterized by an early (D5) shift of the metabolic phenotype (at the molecular and metabolite levels) mainly reflected in a highly- induced hepatic lipogenesis. While the global phenotype partially recovered the initial profile in the middle of the trial (D12-30, concomitantly with hyperglycaemia), a final shift was observed at D45 when IR was fully installed. This final phenotype was characterized (despite fasting hyperinsulinemia) by uncontrolled beta-oxidation and gluconeogenesis and impaired insulin signalling.

Nutrigenomic effect of omega-3 fatty acids in the aorta of LDLR-/- mice. 10. NuGO week 2013