The GMO90+ Project: Absence of Evidence for Biologically Meaningful Effects of Genetically Modified Maize-based Diets on Wistar Rats After 6-Months Feeding Comparative Trial.

The GMO90+ project was designed to identify biomarkers of exposure or health effects in Wistar Han RCC rats exposed in their diet to 2 genetically modified plants (GMP) and assess additional information with the use of metabolomic and transcriptomic techniques. Rats were fed for 6-months with 8 maize-based diets at 33% that comprised either MON810 (11% and 33%) or NK603 grains (11% and 33% with or without glyphosate treatment) or their corresponding near-isogenic controls. Extensive chemical and targeted analyses undertaken to assess each diet demonstrated that they could be used for the feeding trial. Rats were necropsied after 3 and 6 months. Based on the Organization for Economic Cooperation and Development test guideline 408, the parameters tested showed a limited number of significant differences in pairwise comparisons, very few concerning GMP versus non-GMP. In such cases, no biological relevance could be established owing to the absence of difference in biologically linked variables, dose-response effects, or clinical disorders. No alteration of the reproduction function and kidney physiology was found. Metabolomics analyses on fluids (blood, urine) were performed after 3, 4.5, and 6 months. Transcriptomics analyses on organs (liver, kidney) were performed after 3 and 6 months. Again, among the significant differences in pairwise comparisons, no GMP effect was observed in contrast to that of maize variety and culture site. Indeed, based on transcriptomic and metabolomic data, we could differentiate MON- to NK-based diets. In conclusion, using this experimental design, no biomarkers of adverse health effect could be attributed to the consumption of GMP diets in comparison with the consumption of their near-isogenic non-GMP controls.

Development and validation of a multiresidue method for the analysis of more than 500 pesticides and drugs in water based on on-line and liquid chromatography coupled to high resolution mass spectrometry.

Screening of a large number of emerging pollutants is highly desirable for the control of water quality. In this respect, a novel, fully automated contaminant screening method based on an integrated sample preconcentration and liquid chromatography coupled to high resolution mass spectrometry (SPE-UHPLC-HRMS) has been developed. The optimal chromatographic column and experimental conditions allowing the retention and subsequent elution of the maximum number of analytes were defined. Liquid chromatography and Q-exactive (Orbitrap™) parameters were optimized to obtain the best separation of molecules of interest, and the lowest detection limits. Due to the large amount of data to compare, a script written in R language was developed to evaluate the quality of the data generated by the comparison of 14 experimental conditions. The developed method enables the simultaneous semi quantitative analysis of 539 compounds (pesticides and drug residues), in 36 min with only 5 mL of water. Method validation was achieved through studies of repeatability, selectivity, linearity and matrix effect. Application to 20 tap water samples collected in and around Paris showed the presence of 34 different compounds all with concentrations below 0.1 µg/L, the European Union limit for drinking water. Pesticides and transformation products frequently found in water resources such as atrazine and its metabolites, hexazinone, oxadixyl, propazine and simazine were detected. Drug residues such as valsartan and carbamazepine, usually not monitored, were also found. The next step will be to assess the ability of this method to highlight the presence of unexpected contaminants not present in our database.

Annotation of the human cerebrospinal fluid lipidome using high resolution mass spectrometry and a dedicated data processing workflow.

INTRODUCTION: Due to its proximity with the brain, cerebrospinal fluid (CSF) could be a medium of choice for the discovery of biomarkers of neurological and psychiatric diseases using untargeted analytical approaches. OBJECTIVES: This study explored the CSF lipidome in order to generate a robust mass spectral database using an untargeted lipidomic approach. METHODS: Cerebrospinal fluid samples from 45 individuals were analyzed by liquid chromatography coupled to high-resolution mass spectrometry method (LC-HRMS). A dedicated data processing workflow was implemented using XCMS software and adapted filters to select reliable features. In addition, an automatic annotation using an in silico lipid database and several MS/MS experiments were performed to identify CSF lipid species. RESULTS: Using this complete workflow, 771 analytically relevant monoisotopic lipid species corresponding to 550 unique lipids which represent five major lipid families (i.e., free fatty acids, sphingolipids, glycerophospholipids, glycerolipids, and sterol lipids) were detected and annotated. In addition, MS/MS experiments enabled to improve the annotation of 304 lipid species. Thanks to LC-HRMS, it was possible to discriminate between isobaric and also isomeric lipid species; and interestingly, our study showed that isobaric ions represent about 50 % of the total annotated lipid species in the human CSF. CONCLUSION: This work provides an extensive LC/HRMS database of the human CSF lipidome which constitutes a relevant foundation for future studies aimed at finding biomarkers of neurological disorders.

High-resolution mass spectrometry associated with data mining tools for the detection of pollutants and chemical characterization of honey samples.

Analytical methods for food control are mainly focused on restricted lists of well-known contaminants. This paper shows that liquid chromatography-high-resolution mass spectrometry (LC/ESI-HRMS) associated with the data mining tools developed for metabolomics can address this issue by enabling (i) targeted analyses of pollutants, (ii) detection of untargeted and unknown xenobiotics, and (iii) detection of metabolites useful for the characterization of food matrices. A proof-of-concept study was performed on 76 honey samples. Targeted analysis indicated that 35 of 83 targeted molecules were detected in the 76 honey samples at concentrations below regulatory limits. Furthermore, untargeted metabolomic-like analyses highlighted 12 chlorinated xenobiotics, 1 of which was detected in lavender honey samples and identified as 2,6-dichlorobenzamide, a metabolite of dichlobenil, a pesticide banned in France since 2010. Lastly, multivariate statistical analyses discriminated honey samples according to their floral origin, and six discriminating metabolites were characterized thanks to the MS/MS experiments.