Similarity assessment of metallic nanoparticles within a risk assessment framework: A case study on metallic nanoparticles and lettuce

Bioinformatics and machine learning to support nanomaterial grouping

Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and risk assessment strategies building on New Approach Methodologies (NAMs) become indispensable. Indeed, the design, the development and implementation of NAMs has been a major topic in a substantial number of research projects. One of the promising strategies that can help to deal with the high number of NMs variants is grouping and read-across. Based on demonstrated structural and physicochemical similarity, NMs can be grouped and assessed together. Within an established NM group, read-across may be performed to fill in data gaps for data-poor variants using existing data for NMs within the group. Establishing a group requires a sound justification, usually based on a grouping hypothesis that links specific physicochemical properties to well-defined hazard endpoints. However, for NMs these interrelationships are only beginning to be understood. The aim of this review is to demonstrate the power of bioinformatics with a specific focus on Machine Learning (ML) approaches to unravel the NM Modes-of-Action (MoA) and identify the properties that are relevant to specific hazards, in support of grouping strategies. This review emphasizes the following messages: 1) ML supports identification of the most relevant properties contributing to specific hazards; 2) ML supports analysis of large omics datasets and identification of MoA patterns in support of hypothesis formulation in grouping approaches; 3) omics approaches are useful for shifting away from consideration of single endpoints towards a more mechanistic understanding across multiple endpoints gained from one experiment; and 4) approaches from other fields of Artificial Intelligence (AI) like Natural Language Processing or image analysis may support automated extraction and interlinkage of information related to NM toxicity. Here, existing ML models for predicting NM toxicity and for analyzing omics data in support of NM grouping are reviewed. Various challenges related to building robust models in the field of nanotoxicology exist and are also discussed.

Dynamic interplay of metal and metal oxide nanoparticles with plants: Influencing factors, action mechanisms, and assessment of stimulatory and inhibitory effects

Nanoparticles (NPs) of metals and metal oxides have received increasing attention regarding their characteristic behavior in plant systems. The fate and transport of metal NPs and metal oxide NPs in plants is of emerging concern for researchers because they ultimately become part of the food chain. The widespread use of metal-based NPs (MBNPs) in plants has revealed their beneficial and harmful effects. This review addresses the main factors affecting the uptake, translocation, absorption, bioavailability, toxicity, and accumulation of MBNPs in different plant species. It appraises the mechanism of nanoparticle-plant interaction in detail and provides understanding of the estimation strategies for the associated pros and cons with this interplay. Critical parameters of NPs include, but are not limited to, particle size and shape, surface chemistry, surface charge, concentration, solubility, and exposure route. On exposure to MBNPs, the molecular, physiological, and biochemical reactions of plants have been assessed. We have filled knowledge gaps and answered research questions regarding the positive and negative effects of metal and metal oxide NPs on seed germination, callus induction, growth and yield of plant, nutritional content, antioxidants, and enzymes. Besides, the phytotoxicity, cytotoxicity, genotoxicity, and detoxification studies of MBNPs in plants have been outlined. Furthermore, the recent developments and future perspectives of the two-way traffic of interplay of MBNPs and plants have been provided in this comprehensive review.

Trimetal-based nanomaterials induced toxicity to plants: Does it differ from the toxicity of mixed and single-element nanoparticles?

Advanced materials comprising multiple metal alloys have made their way into the market. Trimetal-based nanomaterials (TNMs) are an example of advanced materials which have gained significant traction and are now employed in a wide array of products. It is essential to raise the question if the toxicity of advanced nanomaterials like TNMs differs from the joint effects as manifested by exposure to the single component nanoparticles (NPs). To answer this question, a trimetal-based nanomaterial: bismuth cobalt zinc oxide (BiCoZnO) was tested. This TNM had a mass ratio of 90 % ZnO NPs, 7 % Bi2O3 NPs and 3 % Co3O4 NPs. Nanoparticle-exposed lettuce seedlings (Lactuca sativa L.) showed decreases in relative root elongation (RRE) and biomass production after 21 days of exposure. The 50 % of maximal effective concentration (EC50) value of the TNMs for biomass production was 1.2 mg L-1 when the exposure period was 240 h. This is of the same magnitude as the EC50 values found for ZnO NPs (EC50 = 1.5 mg L-1) and for the mixture of components NPs (MCNPs) which jointly form the TNMs (EC50 = 3.7 mg L-1) after 10 d of exposure. The inhibition of plant root elongation by the TNMs was partially (65 %) attributed to the release of Zn ions, with the actual concentration of released Zn ions being lower in TNMs compared to the actual concentration of Zn ions in case of ZnO NPs. It is therefore to be concluded that the concentration of Zn ions cannot be used as a direct measure to compare the toxicity between traditional and advanced Zn-related nanomaterials. The EC50 values could be assessed within a factor of two; which is helpful when developing advanced alloy nanomaterials and assessing prospective the effects of trimetal-based nanomaterials.

Graphene oxide affects the symbiosis of legume-rhizobium and associated rhizosphere rhizobial communities

The large-scale production and utilization of graphene oxide (GO) have raised concerns regarding its environmental exposure and potential risks. However, existing research on GO toxicity has primarily focused on individual organisms. Little attention has been given to the interaction between GO and the nitrogen-fixing symbiosis of legume-rhizobium. In this study, we focused on alfalfa (Medicago sativa L.), a typical leguminous nitrogen-fixing plant, to investigate the effects of GO on various aspects of this symbiotic relationship, including root nodulation, rhizobial viability, nodule nitrogen fixation, DNA damage, and the composition of the rhizobial community in the rhizosphere. As the dosage of GO increased, a significant inhibition in nodulation development was observed. Exposure to GO resulted in decreased growth and viability of rhizobia, as well as induced DNA damage in nodule cells. Furthermore, with increasing GO dosage, there were significant reductions in nitrogenase activity, leghemoglobin level, and cytoplasmic ammonia content within the root nodules. Additionally, the presence of GO led to notable changes in the rhizobial community in the rhizosphere. Our findings support the existence of the damage promoted by GO in the symbiosis of nitrogen fixing rhizobia with legumes. This underscores the importance of careful soil GO management.

Soil properties influence the toxicity and availability of Zn from ZnO nanoparticles to earthworms

To develop models that support site-specific risk assessment for nanoparticles (NPs), a better understanding of how NP transformation processes, bioavailability and toxicity are influenced by soil properties is needed. In this study, the influence of differing soil properties on the bioavailability and toxicity of zinc oxide (ZnO) NPs and ionic Zn to the earthworm Eisenia fetida was investigated. Earthworms were exposed to ZnO_NPs and ionic Zn, between 100 and 4400 mg Zn/kg, in four different natural soils (organic matter content: 1.8-16.7%, soil pH: 5.4-8.3, representing sandy loam to calcareous soils). Survival and reproduction were assessed after 28 and 56 days, respectively. Zn concentrations in soil pore waters were measured while labile concentrations of Zn were measured using an in-situ dynamic speciation technique (diffusive gradient in thin films, DGT). Earthworm Zn tissue concentrations were also measured. Soil properties influenced earthworm reproduction between soil controls, with highest reproductive output in soils with pH values of 6-7. Toxicity was also influenced by soil properties, with EC₅₀s based on total Zn in soil ranging from 694 to >2200 mg Zn/kg for ZnO_NP and 277-734 mg Zn/kg for ionic Zn. Soil pore water and DGT measurements showed good agreement in the relative amount of Zn extracted across the four soils. Earthworms exposed to ZnO_NPs survived higher Zn concentrations in the soils and had higher tissue concentrations compared with ionic Zn exposures, particularly in the high organic content calcareous soil. These higher tissue concentrations in ZnO_NP exposed earthworm could have consequences for the persistence and trophic mobility of Zn in terrestrial systems and need to be further investigated to elucidate if there any longer-term risks associated with sustained input of ZnO_NP to soil.