Characterisation of Fasting and Postprandial NMR Metabolites: Insights from the ZOE PREDICT 1 Study

Background: Postprandial metabolomic profiles and their inter-individual variability are not well characterised. Here, we describe postprandial metabolite changes, their correlations with fasting values and their inter- and intra-individual variability, following a standardised meal in the ZOE PREDICT 1 cohort. Methods: In the ZOE PREDICT 1 study (n = 1002 (NCT03479866)), 250 metabolites, mainly lipids, were measured by a Nightingale NMR panel in fasting and postprandial (4 and 6 h after a 3.7 MJ mixed nutrient meal, with a second 2.2 MJ mixed nutrient meal at 4 h) serum samples. For each metabolite, inter- and intra-individual variability over time was evaluated using linear mixed modelling and intraclass correlation coefficients (ICC) were calculated. Results: Postprandially, 85% (of 250 metabolites) significantly changed from fasting at 6 h (47% increased, 53% decreased; Kruskal–Wallis), with 37 measures increasing by >25% and 14 increasing by >50%. The largest changes were observed in very large lipoprotein particles and ketone bodies. Seventy-one percent of circulating metabolites were strongly correlated (Spearman’s rho >0.80) between fasting and postprandial timepoints, and 5% were weakly correlated (rho <0.50). The median ICC of the 250 metabolites was 0.91 (range 0.08–0.99). The lowest ICCs (ICC <0.40, 4% of measures) were found for glucose, pyruvate, ketone bodies (β-hydroxybutyrate, acetoacetate, acetate) and lactate. Conclusions: In this large-scale postprandial metabolomic study, circulating metabolites were highly variable between individuals following sequential mixed meals. Findings suggest that a meal challenge may yield postprandial responses divergent from fasting measures, specifically for glycolysis, essential amino acid, ketone body and lipoprotein size metabolites

Manipulating the Mosquito Microbiota to Study Its Function

Date de fin d'embargo : le 28 janvier 2021International audienceAedes aegypti mosquitoes are the main vectors of several arboviruses and are commonly used as models in mosquito biology and vector competence studies. The mosquito microbiota has an impact on different aspects of host physiology, including development, immunity, and fecundity, in turn influencing the capability of the mosquito to transmit diseases. The composition of the microbiota is relatively simple in field mosquitoes, and many of its bacterial members are culturable in the laboratory. Being able to manipulate the composition of the mosquito microbiota is essential to effectively investigate its effect on host physiology and vector competence. This protocol describes how to obtain gnotobiotic mosquitoes, i.e., mosquitoes with a known microbiota composition, and how to monitor the effect of a manipulated microbiota on mosquito development