Репозиторий Университета

© 2020 Elsevier B.V. Ecosystems are facing increased pressure due to the emission of many classes of emerging contaminants. However, very little is known about the interactions of these pollutants, such as bisphenols (BPs), plasticizers or pharmaceuticals. By employing bioluminescent bacteria (Microtox assay), we were able to define interactions between selected emerging pollutants (namely BPA, BPS, BPF, BADGE, BADGE·2HCl, DEP, DBP) in ternary mixtures, at environmentally relevant concentration levels (down to as low as 1.89, 1.42, 3.08, and 0.326 μM for, respectively, BPA, BPF, BPS and BADGE·2HCl). We provide the first systematic analysis of bisphenols and phthalates in three component mixtures. Using this system, we performed toxicity modelling with concentration addition (CA) and independent action (IA) approaches, followed by data interpretation using Model Deviation Ratio (MDR) evaluation. Interestingly, we mathematically and experimentally confirmed a novel synergy between BPA, BADGE and BADGE·2HCl. The synergy of BPA, BADGE and BADGE·2HCl is distinct, with both models suggesting these analytes have a similar mode of action (MOA). Moreover, we unexpectedly found a strong antagonistic impact with DEP, in mixtures containing BPA and BADGE analogues, which is confirmed with both mathematical models. Our study also shows that the impact of BPS and BPF in many mixtures is highly concentration dependent, justifying the necessity to perform mixture studies using wide concentration ranges. Overall, this study demonstrates that bioluminescent bacteria are a relevant model for detecting the synergistic and antagonist actions of environmental pollutants in mixtures, and highlights the importance of analyzing combinations of pollutants in higher order mixtures.

© 2020 Elsevier B.V. Pesticides can potentially contribute to the development of numerous neurodegenerative diseases. This study evaluates the effects of a six-pesticide mixture at doses around the no-observed-adverse-effectlevels (0 × NOAEL, control) and 0.25, 1 and 5 × NOAEL on behavior of Wistar rats. After 3, 6 and 12 months, rats were observed for neurobehavioral changes using the techniques of elevated plus maze and universal problemchamber, and the experiment was conducted thrice. The 3-month exposure revealed a decrease in the cognitive ability at the dose of 5 × NOAEL, and a dose-dependent research activity and anxiety. The 6-month exposurerevealed non-monotonic effects on the cognitive ability, with a decrease by 0.25 and 5 × NOAEL, as well as non-monotonic effects on anxiety, withan increase by 0.25 and 1 × NOAEL. A decrease was also observed in research activity at 5 × NOAEL. However, the 12-month exposure resulted to an increase in cognitive ability by 0.25 × NOAEL and in anxiety by 1 × NOAEL, as well as to a dose-dependent research activity. Repeating the trial showed that the cognitive ability increased from one trial to another, while the researching activity decreased and the anxiety increased by 0× NOAEL. In the groups exposed to pesticides mixture, the trends were different, showing that the exposure to pesticides combined with repeated trials, also influence the response of the animals. The resultsdemonstrate the occurrence of several dose-dependent behavioral responses, with negative effects occurring at doses that are considered safe. This study provides novel insights about time-dependent mixtures biology, and an important perspective to consider when conducting risk assessments.

© 2019, The Author(s). Humans are exposed to multiple chemicals on a daily basis instead of to just a single chemical, yet the majority of existing toxicity data comes from single-chemical exposure. Multiple factors must be considered such as the route, concentration, duration, and the timing of exposure when determining toxicity to the organism. The need for adequate model systems (in vivo, in vitro, in silico and mathematical) is paramount for better understanding of chemical mixture toxicity. Currently, shortcomings plague each model system as investigators struggle to find the appropriate balance of rigor, reproducibility and appropriateness in mixture toxicity studies. Significant questions exist when comparing single-to mixture-chemical toxicity concerning additivity, synergism, potentiation, or antagonism. Dose/concentration relevance is a major consideration and should be subthreshold for better accuracy in toxicity assessment. Previous work was limited by the technology and methodology of the time, but recent advances have resulted in significant progress in the study of mixture toxicology. Novel technologies have added insight to data obtained from in vivo studies for predictive toxicity testing. These include new in vitro models: omics-related tools, organs-on-a-chip and 3D cell culture, and in silico methods. Taken together, all these modern methodologies improve the understanding of the multiple toxicity pathways associated with adverse outcomes (e.g., adverse outcome pathways), thus allowing investigators to better predict risks linked to exposure to chemical mixtures. As technology and knowledge advance, our ability to harness and integrate separate streams of evidence regarding outcomes associated with chemical mixture exposure improves. As many national and international organizations are currently stressing, studies on chemical mixture toxicity are of primary importance.

© 2019 Elsevier B.V. Human populations are chronically exposed to mixtures of toxic chemicals. Predicting the health effects of these mixtures require a large amount of information on the mode of action of their components. Xenobiotic metabolism by bacteria inhabiting the gastrointestinal tract has a major influence on human health. Our review aims to explore the literature for studies looking to characterize the different modes of action and outcomes of major chemical pollutants, and some components of cosmetics and food additives, on gut microbial communities in order to facilitate an estimation of their potential mixture effects. We identified good evidence that exposure to heavy metals, pesticides, nanoparticles, polycyclic aromatic hydrocarbons, dioxins, furans, polychlorinated biphenyls, and non-caloric artificial sweeteners affect the gut microbiome and which is associated with the development of metabolic, malignant, inflammatory, or immune diseases. Answering the question ‘Who is there?’ is not sufficient to define the mode of action of a toxicant in predictive modeling of mixture effects. Therefore, we recommend that new studies focus to simulate real-life exposure to diverse chemicals (toxicants, cosmetic/food additives), including as mixtures, and which combine metagenomics, metatranscriptomics and metabolomic analytical methods achieving in that way a comprehensive evaluation of effects on human health.