Effects of polystyrene nanoplastics exposure on in vitro-grown Stevia rebaudiana plants

Nanoplastics (NPs) as environmental contaminants have received increased attention in recent years. Numerous studies have suggested possible negative effects of plants exposure to NPs, but more data are needed with various plants under different exposure conditions to clarify the underlying phytotoxicity mechanisms. In this study, we investigated the effect of polystyrene nanoplastics (PSNPs; 28.65 nm average diameter) exposure (10, 100 and 250 mg/L) on plant morphology and production of relevant metabolites (steviol glycosides, chlorophylls, carotenoids, and vitamins) of in vitro-grown Stevia rebaudiana plantlets. Additionally, we used dark field microscopy combined with fluorescence hyperspectral imaging for the visualization of internalized PSNPs inside plant tissues. At higher concentrations (>100 mg/L), PSNPs were shown to aggregate in roots and to be transported to leaves, having a significantly negative impact on plant growth (reduced size and biomass), while increasing the production of metabolites compared to controls, most probably because of response to stress. The production of steviol glycosides presented a biphasic dose-response suggestive of hormesis, with the highest values at 10 mg/L PSNPs (1.5-2.2-fold increase compared to controls), followed by a decline in production at higher concentrations (100 and 250 mg/L), but with values comparable to controls. These results are promising for future in vivo studies evaluating the effect of NP exposure on the production of steviol glycosides, the natural sweeteners from stevia.

The effect of 100-200 nm ZnO and TiO2 nanoparticles on the in vitro-grown soybean plants

Engineered nanomaterials are increasingly used in everyday life applications and, in consequence, significant amounts are being released into the environment. From soil, water, and air they can reach the organelles of edible plants, potentially impacting the food chain and human health. The potential environmental and health impact of these nanoscale materials is of public concern. TiO2 and ZnO are among the most significant nanomaterials in terms of production amounts. Our study aimed at evaluating the effects of large-scale TiO2 (~100 nm) and ZnO (~200 nm) nanoparticles on soybean plants grown in vitro. The effect of different concentrations of nanoparticles (10, 100, 1000 mg/L) was evaluated regarding plant morphology and metabolic changes. ZnO nanoparticles showed higher toxicity compared to TiO2 in the experimental set-up. Overall, elevated levels of chlorophylls and proteins were observed, as well as increased concentrations of ascorbic and dehydroascorbic acids. Also, the decreasing stomatal conductance to water vapor and net CO2 assimilation rate show higher plant stress levels. In addition, ZnO nanoparticle treatments severely affected plant growth, while TEM analysis revealed ultrastructural changes in chloroplasts and rupture of leaf cell walls. By combining ICP-OES and TEM results, we were able to show that the nanoparticles were metabolized, and their internalization in the soybean plant tissues occurred in ionic forms. This behavior most likely is the main driving force of nanoparticle toxicity.

The Use of Machine Learning Algorithms and the Mass Spectrometry Lipidomic Profile of Serum for the Evaluation of Tacrolimus Exposure and Toxicity in Kidney Transplant Recipients

Tacrolimus has a narrow therapeutic window; a whole-blood trough target concentration of between 5 and 8 ng/mL is considered a safe level for stable kidney transplant recipients. Tacrolimus serum levels must be closely monitored to obtain a balance between maximizing efficacy and minimizing dose-related toxic effects. Currently, there is no specific tacrolimus toxicity biomarker except a graft biopsy. Our study aimed to identify specific serum metabolites correlated with tacrolinemia levels using serum high-precision liquid chromatography–mass spectrometry and standard laboratory evaluation. Three machine learning algorithms were used (Naïve Bayes, logistic regression, and Random Forest) in 19 patients with high tacrolinemia (8 ng/mL) and 23 patients with low tacrolinemia (5 ng/mL). Using a selected panel of five lipid metabolites (phosphatidylserine, phosphatidylglycerol, phosphatidylethanolamine, arachidyl palmitoleate, and ceramide), Mg2+, and uric acid, all three machine learning algorithms yielded excellent classification accuracies between the two groups. The highest classification accuracy was obtained by Naïve Bayes, with an area under the curve of 0.799 and a classification accuracy of 0.756. Our results show that using our identified five lipid metabolites combined with Mg2+ and uric acid serum levels may provide a novel tool for diagnosing tacrolimus toxicity in kidney transplant recipients. Further validation with targeted MS and biopsy-proven TAC toxicity is needed.