Global prevalence of microplastics in tap water systems: Abundance, characteristics, drivers and knowledge gaps.

Tap water is a main route for human direct exposure to microplastics (MPs). This study recompiled baseline data from 34 countries to assess the current status and drivers of MP contamination in global tap water systems (TWS). It was shown that MPs were detected in 87 % of 1148 samples, suggesting the widespread occurrence of MPs in TWS. The detected concentrations of MPs spanned seven orders of magnitude and followed the linearized log-normal distribution (MSE = 0.035, R2 = 0.965), with cumulative concentrations at 5th, 50th and 95th percentiles of 0.028, 4.491 and 728.105 items/L, respectively. The morphological characteristics were further investigated, indicating that particles smaller than 50 µm dominated in global TWS, with fragment, polyester and transparent as the most common shape, composition and color of MPs, respectively. Subsequently, the SHapley Additive exPlanations (SHAP) algorithm was implemented to quantify the importance of variables affecting the MP abundance in global TWS, showing that the lower particle size limit was the most important variables. Subgroup analysis revealed that the concentration of MPs counted at the size limit of 1 µm was >20 times higher than that above 1 µm. Ultimately, current knowledge gaps and future research needs were elucidated.

Characterization of oxidative damage induced by nanoparticles via mechanism-driven machine learning approaches.

The wide application of TiO2-based engineered nanoparticles (nTiO2) inevitably led to release into aquatic ecosystems. Importantly, increasing studies have emphasized the high risks of nTiO2 to coastal environments. Bivalves, the representative benthic filter feeders in coastal zones, acted as important roles to assess and monitor the toxic effects of nanoparticles. Oxidative damage was one of the main toxic mechanisms of nTiO2 on bivalves, but the experimental variables/nanomaterial characteristics were diverse and the toxicity mechanism was complex. Therefore, it was very necessary to develop machine learning model to characterize and predict the potential toxicity. In this study, thirty-six machine learning models were built by nanodescriptors combined with six machine learning algorithms. Among them, random forest (RF) - catalase (CAT), k-neighbors classifier (KNN) - glutathione peroxidase (GPx), neural networks - multilayer perceptron (ANN) - glutathione s-transferase (GST), random forest (RF) - malondialdehyde (MDA), random forest (RF) - reactive oxygen species (ROS), and extreme gradient boosting decision tree (XGB) - superoxide dismutase (SOD) models performed good with high accuracy and balanced accuracy for both training sets and external validation sets. Furthermore, the best model revealed the predominant factors (exposure concentration, exposure periods, and exposure matrix) influencing the oxidative stress induced by nTiO2. These results showed that high exposure concentrations and short exposure-intervals tended to cause oxidative damage to bivalves. In addition, gills and digestive glands could be vulnerable to nTiO2-induced oxidative damage as tissues/organs differences were the important factors controlling MDA activity. This study provided insights into important nano-features responsible for the different indicators of oxidative stress and thereby extended the application of machine learning approaches in toxicological assessment for nanoparticles.

Potential adverse outcome pathways with hazard identification of organophosphate esters.

Data-driven analysis and pathway-based approaches contribute to reasonable arrangements of limited resources and laboratory tests for continuously emerging commercial chemicals, which provides opportunities to save time and effort for toxicity research. With the widespread usage of organophosphate esters (OPEs) on a global scale, the concentrations generally reached up to micromolar range in environmental media and even in organisms. However, potential adverse effects and toxicity pathways of OPEs have not been systematically assessed. Therefore, it is necessary to review the current situation, formulate the future research priorities, and characterize toxicity mechanisms via data-driven analysis. Results showed that the early toxicity studies focused on neurotoxicity, cytotoxicity, and metabolic disorders. Then the main focus shifted to the mechanisms of cardiotoxicity, endocrine disruption, hepatocytes, reproductive and developmental toxicity to vulnerable sub-populations, such as infants and embryos, affected by OPEs. In addition, several novel OPEs have been emerging, such as bis(2-ethylhexyl)-phenyl phosphate (HDEHP) and oxidation derivatives (OPAsO) generated from organophosphite antioxidants (OPAs), leading to multiple potential ecological and human health risks (neurotoxicity, hepatotoxicity, developmental toxicity, etc.). Notably, in-depth statistical analysis was promising in encapsulating toxicological information to develop adverse outcome pathways (AOPs) frameworks. Subsequently, network-centric analysis and quantitative weight-of-evidence (QWOE) approaches were utilized to construct and evaluate the putative AOPs frameworks of OPEs, showing the moderate confidences of the developed AOPs. In addition, frameworks demonstrated that several events, such as nuclear receptor activation, reactive oxygen species (ROS) production, oxidative stress, and DNA damage, were involved in multiple different adverse outcome (AO), and these AOs had certain degree of connectivity. This study brought new insights into facilitating the complement of AOP efficiently, as well as establishing toxicity pathways framework to inform risk assessment of emerging OPEs.

Critical features identification for chemical chronic toxicity based on mechanistic forecast models.

Facing billions of tons of pollutants entering the ocean each year, aquatic toxicity is becoming a crucial endpoint for evaluating chemical adverse effects on ecosystems. Notably, huge amount of toxic chemicals at environmental relevant doses can cause potential adverse effects. However, chronic aquatic toxicity effects of chemicals are much scarcer, especially at population level. Rotifers are highly sensitive to toxicants even at chronic low-doses and their communities are usually considered as effective indicators for assessing the status of aquatic ecosystems. Therefore, the no observed effect concentration (NOEC) for population abundance of rotifers were selected as endpoints to develop machine learning models for the prediction of chemical aquatic chronic toxicity. In this study, forty-eight binary models were built by eight types of chemical descriptors combined with six machine learning algorithms. The best binary model was 1D & 2D molecular descriptors - random trees model (RT) with high balanced accuracy (BA) (0.83 for training and 0.83 for validation set), and Matthews correlation coefficient (MCC) (0.72 for training set and 0.67 for validation set). Moreover, the optimal model identified the primary factors (SpMAD_Dzp, AMW, MATS2v) and filtered out three high alerting substructures [c1cc(Cl)cc1, CNCO, CCOP(=S)(OCC)O] influencing the chronic aquatic toxicity. These results showed that the compounds with low molecular volume, high polarity and molecular weight could contribute to adverse effects on rotifers, facilitating the deeper understanding of chronic toxicity mechanisms. In addition, forecast models had better performances than the common models embedded into ECOSAR software. This study provided insights into structural features responsible for the toxicity of different groups of chemicals and thereby allowed for the rational design of green and safer alternatives.

Critical target identification and human health risk ranking of metal ions based on mechanism-driven modeling.

Huge amounts of metals have been released into environment due to various anthropogenic activities, such as smelting and processing of metals and subsequent application in construction, automobiles, batteries, optoelectronic devices, and so on, resulting in widespread detection in environmental media. However, some metal ions are considered as "Environmental health hazards", leading to serious human health concerns through affecting critical targets. Hence, it is necessary to quickly and effectively recognize the key target of metal ions in living organisms. Fortunately, the development of high-throughput analysis and in silico approaches offer a promising tool for target identification. In this study, the key oncogenic target (tumor suppressor protein, p53) was screened by network analysis based on the comparative toxicogenomics database (CTD). Some metal ions could bind to p53 core domain, impair its function and induce the development of cancer risk, but its mechanisms were still unclear. Therefore, a quantitative structure-activity relationship (QSAR) model was constructed to characterize the binding constants (Ka) between DNA binding domain of p53 (p53 DBD) and nine metal ions (Mg2+, Ca2+, Cu2+, Zn2+, Co2+, Ni2+, Mn2+, Fe3+ and Ba2+). It had good robustness and predictive ability, which could be used to predict the Ka values of other six metal ions (Li+, Ag+, Cs+, Cd2+, Hg2+ and Pb2+) within application domain. The results showed strong binding affinity between Cd2+/Hg2+/Pb2+ and p53 DBD. Subsequent mechanism analyses revealed that first hydrolysis constant (|logKOH|) and polarization force (Z2/r) were key metal ion-characteristic parameters. The metal ions with weak hydrolysis constants and strong polarization forces could readily interact with N-containing histidine and S-containing cysteine of p53 DBD, which resulted in high Ka values. This study identified p53 as potential target for metal ions, revealed the key characteristics affecting the actions and provide a basic understanding of metal ions-p53 DBD interaction.

Toxicity pathways of lipid metabolic disorders induced by typical replacement flame retardants via data-driven analysis, in silico and in vitro approaches.

Endocrine-disrupting chemicals can interfere with hormone action via various pathways, thereby increasing the risk of adverse health outcomes. Organophosphorus ester (OPEs) retardants, a group of new emerging endocrine disruption chemicals, have been referred to as metabolism disruptors and reported to induce chronic health problems. However, the toxicity pathways were mainly focused on nuclear receptor signaling pathways. Significantly, the membrane receptor pathway (such as G protein-coupled estrogen receptor 1 (GPER) signaling pathway) had been gradually realized as the important role in respond more effective to lipid metabolism disorder than traditional nuclear receptors, whereas the detailed mechanism was unclear yet. Therefore, this study innovatively integrated the bibliometric analysis, in silico and in vitro approach to develop toxicity pathways for the mechanism interpretation. Bibliometric analysis found that the typical OPEs - triphenyl phosphate was a major concern of lipid metabolism abnormality. Results verified that TPP could damage the structures of cell membranes and exert an agonistic effect of GPER as the molecular initiating event. Then, the activated GPER could trigger the PI3K-Akt/NCOR1 and mTOR/S6K2/PPARα transduction pathways as key event 1 (KE1) and affect the process of lipid metabolism and synthesis (CPT1A, CPT2, SREBF2 and SCD) as KE2. As a result, these alterations led to lipid accumulation as adverse effect at cellular-levels. Furthermore, the potential outcomes (such as immunity damage, weight change and steatohepatitis) at high biological levels were expanded. These findings improved knowledge to deeply understand toxicity pathways of phosphorus flame retardants and then provided a theoretical basis for risk assessments.