Gut Microbiota Provides a New Mechanism for Explaining Agrochemical-Induced Synergistic Effects on Bee Mortality

Comprehensive analysis of neonicotinoids in Chinese commercial honey and pollen: A corresponding health risk assessment for non-targeted organisms

Neonicotinoids are broad-spectrum and highly effective insecticides that work by affecting neural activity in insects. Neonicotinoids are systemic pesticides that are absorbed by plants, transported, and accumulated in plant tissues, including nectar and pollen. Currently, there is a lack of a comprehensive assessment of the level of neonicotinoid contamination and the associated health risks to non-targeted organisms in commercial honey and pollen produced in China. This study collected 160 batches of honey and 26 batches of pollen from different regions and plant sources in China, analyzed the residue patterns of neonicotinoid pesticides, and comprehensively evaluated the exposure risks to non-targeted organisms including bees (adults and larvae) and humans. Furthermore, this study addresses this imperative by establishing a high-throughput, rapid, and ultra-sensitive indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) based on broad-spectrum monoclonal antibodies to detect and quantify neonicotinoids, with validation conducted using the LC-MS/MS method. The findings indicated that 59.4 % of honey samples contained at least one of eight neonicotinoids, and the ic-ELISA rapid detection and calculation method could detect all the samples containing neonicotinoids. Additionally, the dietary risk assessment for humans and honeybees indicates that the consumption of a specific quantity of honey may not pose a health risk to human due to neonicotinoid intake. However, the Risk Quotient values for imidacloprid to adult bees and bee larvae, as well as clothianidin to bee larvae, were determined to be 2.22, 5.03, and 1.01, respectively-each exceeding 1. This highlights the elevated risk of acute toxicity posed by imidacloprid and clothianidin residues to honey bees. The study bears significant implications for the safety evaluation of non-targeted organisms in the natural food chain. Moreover, it provides scientific guidance for protecting the diversity and health of the ecosystem.

Nanoplastics Affect the Bioaccumulation and Gut Toxicity of Emerging Perfluoroalkyl Acid Alternatives to Aquatic Insects (Chironomus kiinensis): Importance of Plastic Surface Charge

Persistent organic pollutants (POPs) have been widely suggested as contributors to the aquatic insect biomass decline, and their bioavailability is affected by engineered particles. However, the toxicity effects of emerging ionizable POPs mediated by differentially charged engineered nanoparticles on aquatic insects are unknown. In this study, 6:2 chlorinated polyfluoroalkyl ether sulfonate (F-53B, an emerging perfluoroalkyl acid alternative) was selected as a model emerging ionizable POP; the effect of differentially charged nanoplastics (NPs, 50 nm, 0.5 g/kg) on F-53B bioaccumulation and gut toxicity to Chironomus kiinensis were investigated through histopathology, biochemical index, and gut microbiota analysis. The results showed that when the dissolved concentration of F-53B remained constant, the presence of NPs enhanced the adverse effects on larval growth, emergence, gut oxidative stress and inflammation induced by F-53B, and the enhancement caused by positively charged NP-associated F-53B was stronger than that caused by the negatively charged one. This was mainly because positively charged NPs, due to their greater adsorption capacity and higher bioavailable fraction of associated F-53B, increased the bioaccumulation of F-53B in larvae more significantly than negatively charged NPs. In addition, positively charged NPs interact more easily with gut biomembranes and microbes with a negative charge, further increasing the probability of F-53B interacting with gut biomembranes and microbiota and thereby aggravating gut damage and key microbial dysbacteriosis related to gut health. These findings demonstrate that the surface charge of NPs can regulate the bioaccumulation and toxicity of ionizable POPs to aquatic insects.

How to analyse and account for interactions in mixture toxicity with toxicokinetic-toxicodynamic models

The assessment of chemical mixture toxicity is one of the major challenges in ecotoxicology. Chemicals can interact, leading to more or less effects than expected, commonly named synergism and antagonism respectively. The classic ad hoc approach for the assessment of mixture effects is based on dose-response curves at a single time point, and is limited to identifying a mixture interaction but cannot provide predictions for untested exposure durations, nor for scenarios where exposure varies in time. We here propose a new approach using toxicokinetic-toxicodynamic modelling: The General Unified Threshold model of Survival (GUTS) framework, recently extended for mixture toxicity assessment. We designed a dedicated mechanistic interaction module coupled with the GUTS mixture model to i) identify interactions, ii) test hypotheses to identify which chemical is likely responsible for the interaction, and finally iii) simulate and predict the effect of synergistic and antagonistic mixtures. We tested the modelling approach experimentally with two species (Enchytraeus crypticus and Mamestra brassicae) exposed to different potentially synergistic mixtures (composed of: prochloraz, imidacloprid, cypermethrin, azoxystrobin, chlorothalonil, and chlorpyrifos). Furthermore, we also tested the model with previously published experimental data on two other species (Bombus terrestris and Daphnia magna) exposed to pesticide mixtures (clothianidin, propiconazole, dimethoate, imidacloprid and thiacloprid) found to be synergistic or antagonistic with the classic approach. The results showed an accurate simulation of synergistic and antagonistic effects for the different tested species and mixtures. This modelling approach can identify interactions accounting for the entire time of exposure, and not only at one time point as in the classic approach, and provides predictions of the mixture effect for untested mixture exposure scenarios, including those with time-variable mixture composition.