Visualized Networking of Co-Regulated Lipids in Human Blood Based on High-Throughput Screening Data: Implications for Exposure Assessment

Sustained oral intake of nano-iron oxide perturbs the gut-liver axis

Nanomaterial have shown excellent properties in the food industry. Although iron oxides are often considered safe and widely used as food additives, the toxicity of nano‑iron oxide remains unclear. Here we established a subchronic exposure mouse model by gavage, tested the biodistribution of nano‑iron oxide, and explored the mechanism of liver injury caused by it through disturbance of the gut-liver axis. Oral intake of nano‑iron oxide will likely disrupt the small intestinal epithelial barrier, induce hepatic lipid metabolism disorders through the gut-liver axis, and cause hepatic damage accompanied with hepatic iron deposition. Nano‑iron oxide mainly caused hepatic lipid metabolism disorder by perturbing glycerophospholipid metabolism and the sphingolipid metabolism pathways, with the total abundance of phosphatidylcholine (PC) and phosphatidylethanolamine (PE) tending to decrease while that of triglyceride tended to increase, in a time- and dose-dependent manner. The imbalanced lipid homeostasis could cause damage via membrane disruption, lipid accumulation, and lipotoxicity. This data provides information about the subchronic toxicity of nano‑iron oxide, highlights the importance of gut-liver axis in the hepatotoxicity.

Characterization of non-volatile organic contaminants in coking wastewater using non-target screening: Dominance of nitrogen, sulfur, and oxygen-containing compounds in biological effluents

While abundant volatile compounds (VOCs) have been identified in coking wastewater, the structures and occurrence of non-volatile organic compounds (non-VOCs) have remained unknown. In this study, 3966 non-VOCs belonging to 24 groups were tentatively identified for the first time in wastewater from four biological coking wastewater treatment systems in northern China using a non-target screening technique. A total of 227 compounds with CHNO, CHO, CHOS, and CHNOS elemental compositions were assigned with level 2 identification confidence, and 19 of them were confirmed with authentic standards, with 9-methyl-9H-carbazole-3-carbaldehyde (1706.3-2032.7 μg/L) and 3-Indolyl acetic acid monomethyl terephthalate (773.7-1449.9 μg/L) as the top two compounds in the influents, and 9-methyl-9H-carbazole-3-carbaldehyde (31.8-130.1 μg/L) and monomethyl terephthalate (13.9-196.6 μg/L) as the top two in the effluents. The four groups of substances accounted for 93.4% and 71.5% of the total responses of tentatively identified compounds in the influents and biological effluents, respectively, and were estimated to contribute 32.3-48.9% of the chemical oxygen demand in the biological effluents. In comparison with those in the influent, abundant S-containing compounds (CHOS and CHNOS, 35.2% of the total responses) were observed in the biological effluents, suggesting their highly bio-refractory characteristics. The advanced treatment process using synchronized oxidation-adsorption could almost completely remove the CHOS and CHNOS compounds from the biological effluents.

Identifying active xenobiotics in humans by use of a suspect screening technique coupled with lipidomic analysis

Lipidomic analysis has been proven to be a powerful technique to explore the underlying associations between xenobiotics and health status of organisms. Here, we established a strategy that combined the lipidomic analysis with high-throughput suspect contaminant screening technique with an aim to efficiently identify active xenobiotics in humans. Firstly, in the light of single liquid phase equilibrium of chloroform-methanol-water (15:14:2, v/v/v), we developed an efficient method that was able to simultaneously extract both polar and nonpolar lipids in serum samples. By use of this method, targeted and non-targeted lipid analyses were conducted for n = 120 serum samples collected from Wuxi city, China. Secondly, we established a suspect database containing 1450 contaminants that have been previously reported in human samples, and contaminants in this database were screened in the same batch of serum samples by use of high-resolution mass spectrometry (HR-MS). Thirdly, the underlying associations between suspect contaminants and lipids were explored and discussed, and we observed that levels of some lipids were statistically correlated with concentrations of numerous contaminants. Among these active contaminants, 23 ones were identified on the basis of HR MS¹ and MS² characteristics, and these contaminants belonged to the classes of phthalates, phenols, parabens, or perfluorinated compounds (PFCs). Three active xenobiotics were fully validated by comparison with authentic standards, and they were perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), and diethyl phthalate (DEP). There were statistically significant changes in levels of triglyceride (TG), lysophosphocholine (LPC), and sphingomyelin (SM) as peak areas of xenobiotics increase. We also observed that, among target lipid molecules, 18:0 lysophosphatidylethanolamine (LPE(18:0)) was very sensitive, and this lipid responded to exposure of various contaminants. Our present study provides novel knowledge on potential alteration of lipid metabolism in humans following exposure to xenobiotics, and provides an efficient strategy for efficiently identifying active xenobiotics in humans.

Xenobiotics Targeting Cardiolipin Metabolism to Promote Thrombosis in Zebrafish

Exposure to environmental pollutants is an important factor contributing to the development and severity of thrombosis. However, the important physiological molecules in the thrombotic processes affected by environmental exposures remain unknown. In this study, we show that exposure to environmental chemicals disrupts the equilibrium of cardiolipins (CLs), and directing CL synthesis promotes thrombosis. Using an untargeted metabolomics approach, approximately 3030 molecules were detected in zebrafish embryos exposed to 11 environmental chemicals and automatically clustered into a network. Interconnectivity among CLs and linoleates or isoxanthopterin was discovered through the highly consistent variations in the coregulated metabolites in the network. The chemical exposure resulted in significant upregulation of CLs through influencing the enzymatic activities of phospholipase A₂, cardiolipin synthase, and lysocardiolipin acyltransferase. Consequently, metabolic disorders of CLs affected the levels of anticardiolipin antibodies, disrupted the homeostasis between platelet thromboxane A₂ and endothelial prostacyclin, and promoted thrombotic events including heart ischemia and tachycardia. Our study thus reveals the common molecular mechanisms underlying the CL-induced thrombosis targeted by environmental exposures.

Protein corona formation of human serum albumin with carbon quantum dots from roast salmon

When food-borne nanoparticles enter biological systems, they can interact with various proteins to form protein coronas, which can affect their physicochemical properties and biological identity. In this study, the protein corona formation of carbon quantum dots (CQDs) from roast salmon with human serum albumin (HSA) was explored. Furthermore, the biological identity of the HSA-CQD coronas, in relation to cell apoptosis, energy, glucose and lipid metabolism and acute toxicity in mice, was also investigated. The HSA-CQD coronas were formed between HSA and CQDs via a static binding mechanism, and the binding site of CQDs on HSA was located at both Sudlow's site I and site II. After entering the cytoplasm, the HSA-CQD coronas became localized in the lysosomes and autolysosomes. Importantly, the HSA coronas reduced the cytotoxicity of the CQDs from 18.65% to 9.26%, and the energy metabolism was rectified by changing from glycolytic to aerobic metabolism. The glucose and lipid metabolite profile of cells exposed to the HSA-CQD coronas differed from that of those treated with CQDs, indicating that the HSA-CQD coronas rectified metabolic disturbances caused by CQDs. Histopathological and blood biochemical analysis revealed no statistically significant differences between the treated and control mice after a single CQDs dose of 2000 mg per kg body weight. Overall, the results confirmed the formation of protein coronas between HSA and food-borne fluorescent CQDs, and could be helpful for evaluating the safety of fluorescent CQDs in cooked food items.

Visualized Metabolic Disorder and Its Chemical Inducer in Wild Crucian Carp from Taihu Lake, China

A variety of anthropogenic chemicals can disrupt the equilibrium of intrinsic biological metabolites in organisms, leading to metabolic disorders and an increased risk of metabolic syndromes. However, exposure to pollutants that induce metabolic disorders in wildlife as a cause of adverse effects is unknown. In this study, approximately 3108 compounds, including 11 groups of metabolites and 388 pollutants, were simultaneously identified in the blood of wild crucian carp (Carassius auratus) captured in three bays of Taihu Lake, China. A visualized network linking thousands of co-regulated metabolites was automatically produced for the screened signals. This comprehensive view of the differences in blood metabolite profiles in carp from the north and south bays showed that triglycerides (TGs) were the intrinsic molecules most affected by differing environmental pollution in each bay. The regional differences in metabolite profiles were linked to exposure to screened perfluorinated compounds that displayed corresponding regional differences in concentrations and effects on TGs in in vivo exposure tests. Perfluoroundecanoic acid (PFUnDA) was the key pollutant responsible for the variation in blood TGs in wild crucian carp, and exposure to PFUnDA resulted in extremely high biological activity on lipid deposition in the liver tissues of crucian carp at environmental levels.

Predicting the acute ecotoxicity of chemical substances by machine learning using graph theory

Accurate in silico predictions of chemical substance ecotoxicity has become an important issue in recent years. Most conventional methods, such as the Ecological Structure-Activity Relationship (ECOSAR) model, cluster chemical substances empirically based on structural information and then predict toxicity by employing a log P linear regression model. Due to empirical classification, the prediction accuracy does not improve even if new ecotoxicity test data are added. In addition, most of the conventional methods are not appropriate for predicting the ecotoxicity on inorganic and/or ionized compounds. Furthermore, a user faces difficulty in handling multiple Quantitative Structure-Activity Relationship (QSAR) formulas with one chemical substance. To overcome the flaws of the conventional methods, in this study a new method was developed that applied unsupervised machine learning and graph theory to predict acute ecotoxicity. The proposed machine learning technique is based on the large AIST-MeRAM ecotoxicity test dataset, a software program developed by the National Institute of Advanced Industry Science and Technology for Multi-purpose Ecological Risk Assessment and Management, and the Molecular ACCess System (MACCS) keys that vectorize a chemical structure to 166-bit binary information. The acute toxicity of fish, daphnids, and algae can be predicted with good accuracy, without requiring log P and linear regression models in existing methods. Results from the new method were cross-validated and compared with ECOSAR predictions and show that the new method provides better accuracy for a wider range of chemical substances, including inorganic and ionized compounds.