Mitochondrial Bioenergetics in Brain Following Ozone Exposure in Rats Maintained on Coconut, Fish and Olive Oil-Rich Diets.

Dietary supplementation with omega-3 and omega-6 fatty acids offer cardioprotection against air pollution, but these protections have not been established in the brain. We tested whether diets rich in omega-3 or -6 fatty acids offered neuroprotective benefits, by measuring mitochondrial complex enzyme I, II and IV activities and oxidative stress measures in the frontal cortex, cerebellum, hypothalamus, and hippocampus of male rats that were fed either a normal diet, or a diet enriched with fish oil olive oil, or coconut oil followed by exposure to either filtered air or ozone (0.8 ppm) for 4 h/day for 2 days. Results show that mitochondrial complex I enzyme activity was significantly decreased in the cerebellum, hypothalamus and hippocampus by diets. Complex II enzyme activity was significantly lower in frontal cortex and cerebellum of rats maintained on all test diets. Complex IV enzyme activity was significantly lower in the frontal cortex, hypothalamus and hippocampus of animals maintained on fish oil. Ozone exposure decreased complex I and II activity in the cerebellum of rats maintained on the normal diet, an effect blocked by diet treatments. While diet and ozone have no apparent influence on endogenous reactive oxygen species production, they do affect antioxidant levels in the brain. Fish oil was the only diet that ozone exposure did not alter. Microglial morphology and GFAP immunoreactivity were assessed across diet groups; results indicated that fish oil consistently decreased reactive microglia in the hypothalamus and hippocampus. These results indicate that acute ozone exposure alters mitochondrial bioenergetics in brain and co-treatment with omega-6 and omega-3 fatty acids alleviate some adverse effects within the brain.

Burst and principal components analyses of MEA data for 16 chemicals describe at least three effects classes.

Microelectrode arrays (MEAs) can be used to detect drug and chemical induced changes in neuronal network function and have been used for neurotoxicity screening. As a proof-of-concept, the current study assessed the utility of analytical "fingerprinting" using principal components analysis (PCA) and chemical class prediction using support vector machines (SVMs) to classify chemical effects based on MEA data from 16 chemicals. Spontaneous firing rate in primary cortical cultures was increased by bicuculline (BIC), lindane (LND), RDX and picrotoxin (PTX); not changed by nicotine (NIC), acetaminophen (ACE), and glyphosate (GLY); and decreased by muscimol (MUS), verapamil (VER), fipronil (FIP), fluoxetine (FLU), chlorpyrifos oxon (CPO), domoic acid (DA), deltamethrin (DELT) and dimethyl phthalate (DMP). PCA was performed on mean firing rate, bursting parameters and synchrony data for concentrations above each chemical's EC50 for mean firing rate. The first three principal components accounted for 67.5, 19.7, and 6.9% of the data variability and were used to identify separation between chemical classes visually through spatial proximity. In the PCA, there was clear separation of GABAA antagonists BIC, LND, and RDX from other chemicals. For the SVM prediction model, the experiments were classified into the three chemical classes of increasing, decreasing or no change in activity with a mean accuracy of 83.8% under a radial kernel with 10-fold cross-validation. The separation of different chemical classes through PCA and high prediction accuracy in SVM of a small dataset indicates that MEA data may be useful for separating chemicals into effects classes using these or other related approaches.