In vivo metabolic disorders induced by a pesticide cocktail at the Acceptable Daily Intake level

Consumers are exposed through food intake to a cocktail of pesticides at low doses. Epidemiological evidence suggested a link between pesticide exposure and the development of the metabolic syndrome. We used a mouse model to mimic consumer exposure and assessed the metabolic consequences of a chronic dietary exposure to a cocktail of 6 commonly used pesticides (boscalid, captan, chlorpyrifos, thiofanate, thiacloprid and ziram) at non-toxic doses (acceptable daily intake ADI). One year of exposure induced body weight and adiposity gain, hepatic steatosis and glucose intolerance in males. Females did not display significant changes in body weight but displayed fasted hyperglycemia and perturbations of gut-microbiota related urinary metabolites. Exposure of mice invalidated for the constitutive androstane receptor (CAR) demonstrated that this nuclear receptor was involved in the observed sexual dimorphic response to pesticide exposure. These results demonstrate for the first time that chronic dietary exposure to a pesticide cocktail at the ADI levels induces metabolic perturbations favouring obesity and diabetic state and raise the questions of the relevance of the ADI levels of individual pesticides when present in mixture. Thirty-six males and 36 females were housed in the laboratory animal room (23 ± 2 ºC) and segregated into 2 groups, each including 18 females and 18 males, fed the pesticides-enriched or the control diet for 52 weeks. Body weight, glucose tolerance were serially measured. At death, blood, liver, epididymal adipose tissue (WAT) were collected and serum biochemical parameters were measured. Liver were analyzed for fatty acid content, histology. Hepatic gene expression studies were performed by microarray ..Gene expression profiles were performed on 6 liver samples per group at the GeT‐TRiX facility (GénoToul, Génopole Toulouse Midi-Pyrénées) using Agilent Sureprint G3 Mouse GE v2 microarrays (8x60K, design 074809) following the manufacturer's instructions. For each sample, Cyanine-3 (Cy3) labeled cRNA was prepared from 200 ng of total RNA using the One-Color Quick Amp Labeling kit (Agilent) according to the manufacturer's instructions, followed by Agencourt RNAClean XP (Agencourt Bioscience Corporation, Beverly, Massachusetts). Dye incorporation and cRNA yield were checked using Dropsense™ 96 UV/VIS droplet reader (Trinean, Belgium). 600 ng of Cy3-labelled cRNA were hybridized on the microarray slides following the manufacturer’s instructions. Immediately after washing, the slides were scanned on Agilent G2505C Microarray Scanner using Agilent Scan Control A.8.5.1 software and fluorescence signal extracted using Agilent Feature Extraction software v10.10.1.1 with default parameters. Microarray data were analyzed using R (www.r-project.org, R v. 3.1.2), using Bioconductor packages (www.bioconductor.org, v 3.0, as described in GEO accession GSE. Raw data (median signal intensity) were filtered, log2 transformed and normalized using quantile method3 . A model was fitted using the limma lmFit function4. Pair-wise comparisons between biological conditions were applied using specific contrasts. A correction for multiple testing was applied using Benjamini-Hochberg procedure 5for False Discovery Rate (FDR). Hierarchical clustering was applied to the samples and the differentially expressed probes using 1-Pearson correlation coefficient as distance and Ward’s criterion for agglomeration. The clustering results are illustrated as a heatmap of expression signals. Functional annotation clustering of GO Biological Process were performed using “Search Tool for the Retrieval of Interacting Genes” (String V10)6 and DAVID Bioinformatics Resources 6.7