Metabolomics-on-a-chip approach to study hepatotoxicity of DDT, permethrin and their mixtures.
Rachid JellaliFrançoise GilardVittoria PandolfiAudrey LegendreMarie-José FleuryPatrick PaullierCécile LegallaisEric LeclercPublished in: Journal of applied toxicology : JAT (2018)
Despite the diversity of studies on pesticide toxicities, there is a serious lack of information concerning the toxic effect of pesticides mixtures. Dichlorodiphenyl-trichloroethane (DDT) and permethrin (PMT) are among the most prevalent pesticides in the environment and have been the subject of several toxicological studies. However, there are no data on the toxicity of their mixtures. In this study, we used an approach combining cell culture in microfluidic biochips with gas chromatography-mass spectrometry metabolomics profiling to investigate the biomarkers of toxicity of DDT, PMT and their mixtures. All parameters observed indicated that no significant effect was observed in hepatocytes cultures exposed to low doses (15 μm) of DDT and PMT. Conversely, combined low doses induce moderate oxidative stress and cell death. The toxic signature of high doses of pesticides (150 μm) was illustrated by severe oxidative stress and cell mortality. Metabolomics profiling revealed that hepatocytes exposure to DDT150, PMT150 and DDT150 and PMT150 cause important modulation in intermediates of glutathione pathway and tricarboxylic acid cycle, amino acids and metabolites associated to hepatic necrosis and inflammation (α-ketoglutarate, arginine and 2-hydroxybutyrate). These changes were more striking in the combined group. Finally, DDT150 led to a significant increase of benzoate, decanoate, octanoate, palmitate, stearate and tetradecanoate, which illustrates the estrogen modulation. This study demonstrates the potential of metabolomics-on-a-chip approach to improve knowledge on the mode of action of pesticides.
Keyphrases
- oxidative stress
- risk assessment
- single cell
- mass spectrometry
- cell death
- ionic liquid
- gas chromatography
- healthcare
- circulating tumor cells
- amino acid
- cardiovascular disease
- ischemia reperfusion injury
- machine learning
- early onset
- coronary artery disease
- cardiovascular events
- liver injury
- cell proliferation
- mesenchymal stem cells
- social media
- drug induced
- deep learning
- electronic health record
- endoplasmic reticulum stress
- case control
- health information
- pi k akt
- oxide nanoparticles