Does the exposure mode to ENPs influence their toxicity to aquatic species? A case study with TiO2 nanoparticles and Daphnia magna

Developing random forest based QSAR models for predicting the mixture toxicity of TiO2 based nano-mixtures to Daphnia magna

During emission, TiO₂ nanoparticles (NPs) might meet various chemicals, including metal ions and organic compounds in aquatic environments (e.g., surface water, sediments). At environmentally safe concentrations, combinations of both TiO₂ NPs and those chemicals might cause cocktail effects (i.e., mixture toxicity) to aquatic organisms. Previous models such as concentration addition and independent action require dose-response curves of single components in the mixtures to predict the mixture toxicity. Structure-activity relationship (QSAR) models might predict the toxicity of nano-mixtures without dose-response curves of single components in the mixtures. However, current quantitative structure-activity relationship (QSAR) models are mainly focused on predicting cytotoxicity (i.e., cell viability) of heterogeneous metallic TiO₂ nanoparticles (NPs) or mixtures of TiO₂ NPs and four metal ions (Cu²⁺, Cd²⁺, Ni^2+, and Zn²⁺). To minimize the experimental cost of nano-mixture risk assessment, in this study, we developed novel nano-mixture QSAR models to predict i) EC₅₀ of 76 nano-mixtures containing TiO₂ NPs and one of eight inorganic/organic compounds (i.e., AgNO₃, Cd(NO₃)₂, Cu(NO₃)₂, CuSO₄, Na₂HAsO₄, NaAsO₂, Benzylparaben and Benzophenone-3), to Daphnia magna(D. magna), and ii) immobilization of D. magna exposed to one of 98 mixtures containing TiO₂ NPs and one of eleven inorganic/organic compounds (i.e., AgNO₃, Cd(NO₃)₂, Cu(NO₃)₂, CuSO₄, Na₂HAsO₄, NaAsO₂, Benzylparaben Benzophenone-3, Pirimicarb, Pentabromodiphenyl Ether and Triton X-100). The nano-mixture QSAR models were developed with mixture descriptors (D_mix) combing quantum descriptors of mixture components (e.g., TiO₂ NPs and its partners) by using different machine learning techniques (i.e., random forest, neural network, support vector machine, and multiple linear regression). Nano-mixture QSAR models built with the random forest algorithm and proposed mixture descriptors exhibited good performance for predicting logEC₅₀ (Adj.R²_test = 0.955 ± 0.003, RMSE_test = 0.016 ± 0.002, and MAE_test = 0.008 ± 0.001) and immobilization (Adj.R²_test = 0.888 ± 0.011, RMSE_test = 11.327 ± 0.730, and MAE_test = 5.933 ± 0.442). The models developed in this study were implemented in a user-friendly application for assessing the aquatic toxicity of TiO₂ based nano-mixtures.

The Toxicity of Nanoparticles to Organisms in Freshwater

Nanotechnology is a rapidly growing industry yielding many benefits to society. However, aquatic environments are at risk as increasing amounts of nanoparticles (NPs) are contaminating waterbodies causing adverse effects on aquatic organisms. In this review, the impacts of environmental exposure to NPs, the influence of the physicochemical characteristics of NPs and the surrounding environment on toxicity and mechanisms of toxicity together with NP bioaccumulation and trophic transfer are assessed with a focus on their impacts on bacteria, algae and daphnids. We identify several gaps which need urgent attention in order to make sound decisions to protect the environment. These include uncertainty in both estimated and measured environmental concentrations of NPs for reliable risk assessment and for regulating the NP industry. In addition toxicity tests and risk assessment methodologies specific to NPs are still at the research and development stage. Also conflicting and inconsistent results on physicochemical characteristics and the fate and transport of NPs in the environment suggest the need for further research. Finally, improved understanding of the mechanisms of NP toxicity is crucial in risk assessment of NPs, since conventional toxicity tests may not reflect the risks associated with NPs. Behavioural effects may be more sensitive and would be efficient in certain situations compared with conventional toxicity tests due to low NP concentrations in field conditions. However, the development of such tests is still lacking, and further research is recommended.

Environmental risks of ZnO nanoparticle exposure on Microcystis aeruginosa: Toxic effects and environmental feedback

The vast majority of studies measure the toxic effect of organisms exposed to nanoparticles (NPs) while there is still a lack of knowledge about the influence of NPs on the aquatic environment. It is unknown whether or not the interaction between NPs and algae will result in the variation of algal organic matter (AOM) and stimulate the production of more algal toxins. In this study, zinc oxide nanoparticles (nano-ZnO) as a typical representative of metal oxide NPs were used to evaluate the toxic effects and environmental feedback of Microcystis aeruginosa. Reactive oxygen species (ROS) and malondialdehyde (MDA) were measured to explain the toxicity mechanism. Changes of AOM, including the production of toxins, the molecular weight distribution and the excitation-emission matrices of algal solution were also studied as environmental feedback indicators after nano-ZnO destroyed the algae. As the nano-ZnO exceeded the comparable critical concentration (1.0 mg/L), the algae were destroyed and intracellular organic matters were released into the aquatic environment, which stimulated the generation of microcystin-LR (MC-LR). However, it is worth noting that the concentration of nano-ZnO would need to be high (at mg/L range) to stimulate more MC-LR production. These findings are expected to be beneficial in interpreting the toxicity and risks of the releasing of NPs through the feedback between algae and the aquatic environment.

Molecular and physiological responses to titanium dioxide and cerium oxide nanoparticles in Arabidopsis

Changes in tissue transcriptomes and productivity of Arabidopsis thaliana were investigated during exposure of plants to 2 widely used engineered metal oxide nanoparticles, titanium dioxide (nano-titania) and cerium dioxide (nano-ceria). Microarray analyses confirmed that exposure to either nanoparticle altered the transcriptomes of rosette leaves and roots, with comparatively larger numbers of differentially expressed genes found under nano-titania exposure. Nano-titania induced more differentially expressed genes in rosette leaves, whereas roots had more differentially expressed genes under nano-ceria exposure. MapMan analyses indicated that although nano-titania up-regulated overall metabolism in both tissues, metabolic processes under nano-ceria remained mostly unchanged. Gene enrichment analysis indicated that both nanoparticles mainly enriched ontology groups such as responses to stress (abiotic and biotic), and defense responses (pathogens), and responses to endogenous stimuli (hormones). Nano-titania specifically induced genes associated with photosynthesis, whereas nano-ceria induced expression of genes related to activating transcription factors, most notably those belonging to the ethylene responsive element binding protein family. Interestingly, there were also increased numbers of rosette leaves and plant biomass under nano-ceria exposure, but not under nano-titania. Other transcriptomic responses did not clearly relate to responses observed at the organism level, possibly because of functional and genomic redundancy in Arabidopsis, which may mask expression of morphological changes, despite discernable responses at the transcriptome level. In addition, transcriptomic changes often relate to transgenerational phenotypic development, and hence it may be productive to direct further experimental work to integrate high-throughput genomic results with longer term changes in subsequent generations. Environ Toxicol Chem 2017;36:71-82. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

Saltwater ecotoxicology of Ag, Au, CuO, TiO2, ZnO and C60 engineered nanoparticles: An overview

This review paper examined 529 papers reporting experimental nanoecotoxicological original data. Only 126 papers referred to saltwater environments (water column and sediment) including a huge variety of species (n=51), their relative endpoints and engineered nanoparticles (ENPs) (n=38). We tried to provide a synthetic overview of the ecotoxicological effects of ENPs from existing data, refining papers on the basis of cross-cutting selection criteria and supporting a "mind the gap" approach stressing on missing data for hazard and risk assessment. After a codified selection procedure, attention was paid to Ag, Au, CuO, TiO2, ZnO and C60 ENPs, evidencing and comparing the observed nanoecotoxicity range of effect. Several criticisms were evidenced: i) some model organisms are overexploited like microalgae and molluscs compared to annelids, echinoderms and fish; ii) underexploited model organisms: mainly bacteria and fish; iii) exposure scenario variability: high species-specific and ENP scenarios including organism life stage and way of administration/spiking of toxicants; iv) scarce comparability between results due to exposure scenario variability; v) micro- and mesocosms substantially unexplored; vi) mixture effects: few examples are available only for ENPs and traditional pollutants; mixtures of ENPs have not been investigated yet; vii) effects of ions and ENPs: nAg, nCuO and nZnO toxicity aetiology is still a matter of discussion; viii) size and morphology effects of ENPs: scarcely investigated, justified and understood. Toxicity results evidenced that: nAu>nZnO>nAg>nCuO>nTiO2>C60.