Microplastics (MPs; <5 mm) are a macro issue recognised worldwide as a threat to biodiversity and ecosystems. Widely distributed in marine ecosystems, MPs have already been found in the deep-sea environment. However, there is little information on ecological mechanisms driving MP uptake by deep-sea species. For the first time, this study generates data on MP contamination in mesopelagic fishes from the Southwestern Tropical Atlantic (SWTA) to help understand the deep-sea contamination patterns. An alkaline digestion protocol was applied to extract MPs from the digestive tract of four mesopelagic fish species: Argyropelecus sladeni, Sternoptyx diaphana (Sternoptychidae), Diaphus brachycephalus, and Hygophum taaningi (Myctophidae). A total of 213 particles were recovered from 170 specimens, and MPs were found in 67% of the specimens. Fibres were the most common shape found in all species, whereas polyamide, polyethylene, and polyethylene terephthalate were the most frequent polymers. The most contaminated species was A. sladeni (93%), and the least contaminated was S. diaphana (45%). Interestingly, individuals caught in the lower mesopelagic zone (500–1000 m depth) were less contaminated with MPs than those captured in the upper mesopelagic layer (200–500 m). Our results highlight significant contamination levels and reveal the influence of mesopelagic fishes on MPs transport in the deep waters of the SWTA.

In fish, the gut microbiome plays a crucial role in homeostasis and health and is affected by several organic and inorganic environmental contaminants. Amphidromous fish are sentinel species, particularly exposed to these stressors. We used whole metagenome sequencing to characterize the gut microbiome of wild European eels (Anguilla anguilla) at a juvenile stage captured from three sites with contrasted pollution levels in term of heavy metals and persistent organic pollutants. The objectives were to identify what parameters could alter the gut microbiome of this catadromous fish and to explore the potential use of microbiota as bioindicators of environment quality. We identified a total of 1079 microbial genera. Overall, gut microbiome was dominated by Proteobacteria, Firmicutes and Actinobacteria. Alpha and beta diversity were different amongst sites and could be explained by a reduced number of environmental and biological factors, specifically the relative abundance of fish preys in eels’ diet, PCB101, γHCH (lindane), transnonachlor and arsenic. Furthermore, we identified a series of indicator taxa with differential abundance between the three sites. Changes in the microbial communities in the gut caused by environmental pollutants were previously undocumented in European eels. Our results indicate that microbiota might represent another route by which pollutants affect the health of these aquatic sentinel organisms.

The impact of microplastics (MP) has attracted much attention from the scientific community and many laboratory assessments have been made of their effects on aquatic organisms. To produce MP from real environmental plastic waste, which would enable more realistic experiments, we used plastic pearl farming equipment from French Polynesian lagoons. Here, the pearl oyster Pinctada margaritifera could encounter MP coming from their breakdown in its surrounding environment. We tested an established method based on mechanical cryogenic grinding and liquid sieving. Our desired size range was 20–60 μm, corresponding to the optimal particle size ingested by P. margaritifera. The protocol was effective, generating MP particles of 20–60 μm (∼17,000–28,000 MP μg−1), but also produced too many smaller particles. The peak in the desired size range was thus flattened by the many small particles <3 μm (∼82,000–333,000 MP μg−1; 53–70% of total analysed particles), visible at the limit of Coulter counter analysis (cut-off point: 2 μm). Laser diffraction analysis (cut-off point: 0.4 μm) provided greater detail, showing that ∼80–90% of the total analysed particles were <1 μm. Diverging particle size distributions between those expected based on sieving range and those really observed, highlight the need to perform fine-scaled particle size distribution analyses to avoid underestimating the number of small micro- and nanoplastics (MNP) and to obtain an exact estimation of the fractions produced. Size and microstructure characterization by scanning electron microscopy suggested spontaneous particle self-assembly into crystal superstructures, which is the supposed cause of the divergence we observed. Overall, our results emphasize that particle self-assembly is a technical hurdle requiring further work and highlight the specific need to finely characterize the size distribution of MNP used in ecotoxicological experiments to avoid overestimating effects.

International audience | Characterizing waste ecotoxicity is laborious because of both the undefined nature of environmental samples and the diversity of contaminants that can be present. With regard to these limitations, traditional approaches do not provide information about the nature of the pollution encountered. To improve such assessments, a fluorescent library of 1870 transcriptomic reporters from Escherichia coli K12 MG1655 was used to report the ecotoxic status of environmental samples. The reliability of the approach was evaluated with 6 metallic pollutants (As, Cu, Cd, Hg, Pb, Zn) used alone and in mixture in pure and complex matrices. A total of 18 synthetic samples were used to characterize the specificity of the resulting metallic contamination fingerprints. Metallic contamination impacted 4.5 to 10.2% of the whole transcriptomic fingerprint of E. coli. The analysis revealed that a subset of 175 transcriptomic reporters is sufficient to characterize metallic contamination, regardless of the nature of the sample. A statistical model distinguished patterns due to metallic contamination and provided information about the level of toxicity with 93 to 98% confidence. The use of the transcriptomic assessment was validated for 17 complex matrices with various toxicities and metal contaminants, such as activated sludge, wastewater effluent, soil, wood and river water. The presence of metals and their associated toxicity, which seems linked to their bioavailabilities, were thereby determined. This method constitutes a possible tool to screen unknown complex samples for their metallic status and identify those for which a deeper characterization must be achieved by the use of traditional biosensors and analytical methods.

International audience | The purpose of this study was to assess heavy metal contamination in soil, plants, earthworms, and chicken in farmlands adjacent to an old mining site and to evaluate the potential exposure risks to humans through the consumption of chicken. For this purpose, soil, earthworms, plant, chickens, and eggs were sampled from 5 sites following a gradient of contamination. All samples were analyzed for heavy metals (Pb, Cd, Cu, and Zn). A food chain model was used in order to characterize heavy metal transfer between soil-plant-earthworm and chicken organs. Furthermore, target hazard quotient (THQ), estimated daily intake (EDI), and hazard index (HI) were employed to assess human health risks posed by heavy metal contamination. Despite the higher level of Pb, our data related to the calculation of EDI and THQ suggested that local consumers are more at risk of Cd contamination. The calculated HI showed values ranging from 2.58 to 4.74 for adults, and up to 12.34 for children, indicating a considerable risk to the health of local inhabitants, especially children. This study highlighted the crucial role of diets based on chickens grown in contaminated areas, on health risks especially for children.

International audience | A wide diversity of plant protection products (PPP) is used for crop protection leading to the contamination of soil, water, and air, which can have ecotoxicological impacts on living organisms. It is inconceivable to study the effects of each compound on each species from each compartment, experimental studies being time consuming and cost prohibitive, and animal testing having to be avoided. Therefore, numerous models are developed to assess PPP ecotoxicological effects. Our objective was to provide an overview of the modeling approaches enabling the assessment of PPP effects (including biopesticides) on the biota. Six categories of models were inventoried: (Q)SAR, DR and TKTD, population, multi-species, landscape, and mixture models. They were developed for various species (terrestrial and aquatic vertebrates and invertebrates, primary producers, microorganisms) belonging to diverse environmental compartments, to address different goals (e.g., species sensitivity or PPP bioaccumulation assessment, ecosystem services protection). Among them, mechanistic models are increasingly recognized by EFSA for PPP regulatory risk assessment but, to date, remain not considered in notified guidance documents. The strengths and limits of the reviewed models are discussed together with improvement avenues (multigenerational effects, multiple biotic and abiotic stressors). This review also underlines a lack of model testing by means of field data and of sensitivity and uncertainty analyses. Accurate and robust modeling of PPP effects and other stressors on living organisms, from their application in the field to their functional consequences on the ecosystems at different scales of time and space, would help going toward a more sustainable management of the environment.

International audience