ABSTRACT
The removal of Cr(III)-organic complexes, encompassing both decomplexation and ligand degradation, presents significant challenges in industrial wastewater treatment. As one of the most common anions in wastewater, Cl- significantly improves the efficiency of electrochemically removing Cr(III)-organic complexes through generated reactive chlorine species (RCS). In the electrochemical chlorine (EC/Cl2) process, extensive experimentation revealed that ClO⢠plays a dominant role in the degradation of Cr(III)-EDTA, surpassing the effects of free chlorine, direct electrooxidation, HOâ¢, and other RCS. Density functional theory calculations indicated that RCS, primarily Cl⢠and ClOâ¢, preferentially oxidize the ligand in Cr(III)-EDTA via H-abstraction, whereas HO⢠trends to attack the Cr atom through electron transfer. The influential factors on the degradation efficiency of Cr(III)-EDTA, Cr(VI) yield, and total organic carbon removal in EC/Cl2 were also assessed, including Cl- concentration, current density, and pH. Real industrial wastewater was employed as a reaction matrix to evaluate the application of the EC/Cl2 process for treating Cr(III)-EDTA, accompanied by energy efficiency calculations. Additionally, a two-chamber reactor was established to simultaneously oxidize Cr(III)-EDTA at the anode and reduce Cr(VI) at the cathode. This study provided insight into developing RCS-dominated AOPs to effectively decomplex and decompose organic Cr(III)-complexes in Cl--containing industrial wastewater.
ABSTRACT
Fluorene-9-bisphenol (BHPF) is an emerging contaminant. Presently, there is no report on its interaction with G protein-coupled estrogen receptor 1 (GPER). By using an integrated toxicity research scenario that combined theoretical study with experimental methods, BHPF was found to inhibit the GPER-mediated effect via direct receptor binding. Molecular dynamics simulations found that Trp2726.48 and Glu2756.51 be the key amino acids of BHPF binding with GPER. Moreover, the calculation indicated that BHPF was a suspected GPER inhibitor, which neither can activate GPER nor is able to form water channels of GPER. The role of two residues was successfully verified by following gene knockout and site-directed mutagenesis assays. Further in vitro assays showed that BHPF could attenuate the increase in intracellular concentration of free Ca2+ induced by G1-activated GPER. Besides, BHPF showed an enhanced cytotoxicity compared with G15, indicating that BHPF might be a more potent GPER inhibitor than G15. In addition, a statistically significant effect on the mRNA level of GPER was observed for BHPF. In brief, the present study proposes that BHPF be a GPER inhibitor, and its GPER molecular recognition mechanism has been revealed, which is of great significance for the health risk and assessment of BHPF.
Subject(s)
Apoptosis , Humans , Apoptosis/drug effects , Neuroblastoma/metabolism , Neuroblastoma/pathology , Cell Line, Tumor , Fluorenes , Phenols/pharmacology , Phenols/metabolism , Receptors, G-Protein-Coupled/metabolism , Receptors, Estrogen/metabolismABSTRACT
Lithium (Li) is an important resource that drives sustainable mobility and renewable energy. Its demand is projected to continue to increase in the coming decades. However, the risk of Li pollution has also emerged as a global concern. Here, we investigated the pollution characteristics, sources, exposure levels, and associated health risks of Li in the Jinjiang River basin, the largest area for Li2CO3 production in China. Our results revealed the dominant role of Li extraction activities in the pollution of the river, with over 95% of dissolved Li in downstream river water being emitted from this source. Moreover, the Li concentration in aquatic plants (i.e., water hyacinth) and animals (i.e., fish) significantly increased from upstream to downstream areas, indicating a significant risk to local aquatic ecosystems. More importantly, our study found that local residents were suffering potential chronic noncarcinogenic health risks primarily from consuming contaminated water and vegetables. We also investigated the pollution characteristics of associated elements present in Li ores (e.g., Rb, Cs, Ni, and F-). By uncovering the remarkable impact of Li extraction activities on the Li content in ecosystems for the first time, our study emphasizes the importance of evaluating Li pollution from Li-related industrial activities, including mining, extraction, and recovery.
Subject(s)
Lithium , Lithium/analysis , China , Water Pollutants, Chemical/analysis , Humans , Rivers/chemistry , Risk Assessment , Environmental Monitoring , AnimalsABSTRACT
Organic contaminants are ubiquitous in the environment, with mounting evidence unequivocally connecting them to aquatic toxicity, illness, and increased mortality, underscoring their substantial impacts on ecological security and environmental health. The intricate composition of sample mixtures and uncertain physicochemical features of potential toxic substances pose challenges to identify key toxicants in environmental samples. Effect-directed analysis (EDA), establishing a connection between key toxicants found in environmental samples and associated hazards, enables the identification of toxicants that can streamline research efforts and inform management action. Nevertheless, the advancement of EDA is constrained by the following factors: inadequate extraction and fractionation of environmental samples, limited bioassay endpoints and unknown linkage to higher order impacts, limited coverage of chemical analysis (i.e., high-resolution mass spectrometry, HRMS), and lacking effective linkage between bioassays and chemical analysis. This review proposes five key advancements to enhance the efficiency of EDA in addressing these challenges: (1) multiple adsorbents for comprehensive coverage of chemical extraction, (2) high-resolution microfractionation and multidimensional fractionation for refined fractionation, (3) robust in vivo/vitro bioassays and omics, (4) high-performance configurations for HRMS analysis, and (5) chemical-, data-, and knowledge-driven approaches for streamlined toxicant identification and validation. We envision that future EDA will integrate big data and artificial intelligence based on the development of quantitative omics, cutting-edge multidimensional microfractionation, and ultraperformance MS to identify environmental hazard factors, serving for broader environmental governance.
Subject(s)
Environmental Monitoring , Environmental Monitoring/methods , Environmental Pollutants , Chemical FractionationABSTRACT
Persistent organic pollutants (POPs) tend to accumulate in cold regions by cold condensation and global distillation. Soil organic matter is the main storage compartment for POPs in terrestrial ecosystems due to deposition and repeated air-surface exchange processes. Here, physicochemical properties and environmental factors were investigated for their role in influencing POPs accumulation in soils of the Tibetan Plateau and Antarctic and Arctic regions. The results showed that the soil burden of most POPs was closely coupled to stable mineral-associated organic carbon (MAOC). Combining the proportion of MAOC and physicochemical properties can explain much of the soil distribution characteristics of the POPs. The background levels of POPs were estimated in conjunction with the global soil database. It led to the proposition that the stable soil carbon pools are key controlling factors affecting the ultimate global distribution of POPs, so that the dynamic cycling of soil carbon acts to counteract the cold-trapping effects. In the future, soil carbon pool composition should be fully considered in a multimedia environmental model of POPs, and the risk of secondary release of POPs in soils under conditions such as climate change can be further assessed with soil organic carbon models.
Subject(s)
Carbon , Soil Pollutants , Soil , Soil/chemistry , Persistent Organic Pollutants , Environmental Monitoring , Arctic Regions , EcosystemABSTRACT
Knowledge regarding the occurrence of short-chain and medium-chain chlorinated paraffins (SCCPs and MCCPs) in foodstuffs and their dietary exposure risks for rural Tibetan residents remains largely unknown. Herein, we collected main foodstuffs (including highland barley, vegetables, Tibetan butter, mutton, and yak beef) across the rural Tibetan Plateau and characterized the CP profiles and concentrations. The highest SCCPs concentrations were detected in Tibetan butter (geometric mean (GM): 240.6 ng/g wet weight (ww)), followed by vegetables (59.4 ng/g ww), mutton (51.4 ng/g ww), highland barley (46.3 ng/g ww), and yak beef (31.7 ng/g ww). For MCCPs, the highest concentrations were also detected in Tibetan butter (319.5 ng/g ww), followed by mutton (181.9 ng/g ww), vegetables (127.0 ng/g ww), yak beef (71.2 ng/g ww), and highland barley (30.3 ng/g ww). The predominant congener profiles of SCCPs were C13Cl7-8 in mutton and yak beef, C10Cl7-8 in Tibetan butter, and C10-11Cl6-7 in highland barley and vegetables. The predominant congener profiles of MCCPs were C14Cl7-9 in all sample types. Combined with our previous results of free-range chicken eggs, the median estimated daily intakes (EDIs) of SCCPs and MCCPs via diet for Tibetan rural adults and children was estimated to be 728.8 and 1853.9 ng/kg bw/day and 2565.6 and 5952.8 ng/kg bw/day, respectively. In the worst scenario, MCCPs might induce potential health risks for rural Tibetan population. To our knowledge, this is the first systematic dietary exposure research of SCCPs and MCCPs in the remote rural areas.
Subject(s)
Dietary Exposure , Paraffin , Rural Population , Tibet , Humans , Dietary Exposure/statistics & numerical data , Dietary Exposure/analysis , Paraffin/analysis , Rural Population/statistics & numerical data , Adult , Food Contamination/analysis , Food Contamination/statistics & numerical data , Child , Middle Aged , Hydrocarbons, Chlorinated/analysis , Risk Assessment , Female , Male , China , Child, Preschool , Adolescent , Young Adult , Diet/statistics & numerical data , Environmental Pollutants/analysisABSTRACT
The pervasive contamination of novel brominated flame retardants (NBFRs) in remote polar ecosystems has attracted great attention in recent research. However, understanding regarding the trophic transfer behavior of NBFRs in the Arctic and Antarctic marine food webs is limited. In this study, we examined the occurrence and trophodynamics of NBFRs in polar benthic marine sediment and food webs collected from areas around the Chinese Arctic Yellow River Station (n = 57) and Antarctic Great Wall Station (n = 94). ∑7NBFR concentrations were in the range of 1.27-7.47 ng/g lipid weight (lw) and 0.09-1.56 ng/g lw in the Arctic and Antarctic marine biota, respectively, among which decabromodiphenyl ethane (DBDPE) was the predominant compound in all sample types. The biota-sediment bioaccumulation factors (g total organic carbon/g lipid) of NBFRs in the Arctic (0.85-3.40) were 4-fold higher than those in the Antarctica (0.13-0.61). Trophic magnification factors (TMFs) and their 95% confidence interval (95% CI) of individual NBFRs ranged from 0.43 (95% CI: 0.32, 0.60) to 1.32 (0.92, 1.89) and from 0.34 (0.24, 0.49) to 0.92 (0.56, 1.51) in the Arctic and Antarctic marine food webs, respectively. The TMFs of most congeners were significantly lower than 1, indicating a trophic dilution potential. This is one of the very few investigations on the trophic transfer of NBFRs in remote Arctic and Antarctic marine ecosystems, which provides a basis for exploring the ecological risks of NBFRs in polar regions.
Subject(s)
Flame Retardants , Antarctic Regions , Flame Retardants/analysis , Food Chain , Ecosystem , Bioaccumulation , Arctic Regions , Environmental Monitoring , Lipids , Halogenated Diphenyl Ethers/analysisABSTRACT
Metal oxides with oxygen vacancies have a significant impact on catalytic activity for the transformation of organic pollutants in waste-to-energy (WtE) incineration processes. This study aims to investigate the influence of hematite surface oxygen point defects on the formation of environmentally persistent free radicals (EPFRs) from phenolic compounds based on the first-principles calculations. Two oxygen-deficient conditions were considered: oxygen vacancies at the top surface and on the subsurface. Our simulations indicate that the adsorption strength of phenol on the α-Fe2O3(0001) surface is enhanced by the presence of oxygen vacancies. However, the presence of oxygen vacancies has a negative impact on the dissociation of the phenol molecule, particularly for the surface with a defective point at the top layer. Thermo-kinetic parameters were established over a temperature range of 300-1000 K, and lower reaction rate constants were observed for the scission of phenolic O-H bonds over the oxygen-deficient surfaces compared to the pristine surface. The negative effects caused by the oxygen-deficient conditions could be attributed to the local reduction of FeIII to FeII, which lower the oxidizing ability of surface reaction sites. The findings of this study provide us a promising approach to regulate the formation of EPFRs.
Subject(s)
Ferric Compounds , Oxygen , Ferric Compounds/chemistry , Free Radicals/chemistry , Phenols , Phenol/chemistryABSTRACT
Artificial CO2 removal from the atmosphere (also referred to as negative CO2 emissions) has been proposed as a potential means to counteract anthropogenic climate change. Here we use an Earth system model to examine the response of ocean acidification to idealized atmospheric CO2 removal scenarios. In our simulations, atmospheric CO2 is assumed to increase at a rate of 1% per year to four times its pre-industrial value and then decreases to the pre-industrial level at a rate of 0.5%, 1%, 2% per year, respectively. Our results show that the annual mean state of surface ocean carbonate chemistry fields including hydrogen ion concentration ([H+]), pH and aragonite saturation state respond quickly to removal of atmospheric CO2. However, the change of seasonal cycle in carbonate chemistry lags behind the decline in atmospheric CO2. When CO2 returns to the pre-industrial level, over some parts of the ocean, relative to the pre-industrial state, the seasonal amplitude of carbonate chemistry fields is substantially larger. Simulation results also show that changes in deep ocean carbonate chemistry substantially lag behind atmospheric CO2 change. When CO2 returns to its pre-industrial value, the whole-ocean acidity measured by [H+] is 15%-18% larger than the pre-industrial level, depending on the rate of CO2 decrease. Our study demonstrates that even if atmospheric CO2 can be lowered in the future as a result of net negative CO2 emissions, the recovery of some aspects of ocean acidification would take decades to centuries, which would have important implications for the resilience of marine ecosystems.
Subject(s)
Carbon Dioxide , Seawater , Ecosystem , Hydrogen-Ion Concentration , Ocean Acidification , CarbonatesABSTRACT
Rapid social-economic development introduces modern lifestyles into rural areas, not only bringing numerous modern products but also new pollutants, such as chlorinated paraffins (CPs). The rural Tibetan Plateau has limited industrial activities and is a unique place to investigate this issue. Herein we collected 90 free-range chicken egg pool samples across the rural Tibetan Plateau to evaluate the pollution status of CPs. Meanwhile, CPs in related soils, free-range chicken eggs from Jiangxi, and farmed eggs from markets were also analyzed. The median concentrations of SCCPs (159 ng g-1 wet weight (ww)) and MCCPs (1390 ng g-1 ww) in Tibetan free-range chicken eggs were comparable to those from Jiangxi (259 and 938 ng g-1 ww) and significantly higher than those in farmed eggs (22.0 and 81.7 ng g-1 ww). In the rural Tibetan Plateau, the median EDI of CPs via egg consumption by adults and children were estimated to be 81.6 and 220.2 ng kg-1 bw day-1 for SCCPs and 483.4 and 1291 ng kg-1 bw day-1 for MCCPs, respectively. MCCPs might pose potential health risks for both adults and children in the worst scenario. Our study demonstrates that new pollutants should not be ignored and need further attention in remote rural areas.
Subject(s)
Environmental Pollutants , Hydrocarbons, Chlorinated , Animals , Child , Humans , Tibet , Hydrocarbons, Chlorinated/analysis , Paraffin/analysis , Chickens , Environmental Monitoring , ChinaABSTRACT
Cancer remains a significant global health concern, with millions of deaths attributed to it annually. Environmental pollutants play a pivotal role in cancer etiology and contribute to the growing prevalence of this disease. The carcinogenic assessment of these pollutants is crucial for chemical health evaluation and environmental risk assessments. Traditional experimental methods are expensive and time-consuming, prompting the development of alternative approaches such as in silico methods. In this regard, deep learning (DL) has shown potential but lacks optimal performance and interpretability. This study introduces an interpretable DL model called CarcGC for chemical carcinogenicity prediction, utilizing a graph convolutional neural network (GCN) that employs molecular structural graphs as inputs. Compared to existing models, CarcGC demonstrated enhanced performance, with the area under the receiver operating characteristic curve (AUCROC) reaching 0.808 on the test set. Due to air pollution is closely related to the incidence of lung cancers, we applied the CarcGC to predict the potential carcinogenicity of chemicals listed in the United States Environmental Protection Agency's Hazardous Air Pollutants (HAPs) inventory, offering a foundation for environmental carcinogenicity screening. This study highlights the potential of artificially intelligent methods in carcinogenicity prediction and underscores the value of CarcGC interpretability in revealing the structural basis and molecular mechanisms underlying chemical carcinogenicity.
Subject(s)
Air Pollutants , Deep Learning , Environmental Pollutants , Neoplasms , United States , Humans , Carcinogens/chemistryABSTRACT
Endocrine-disrupting chemicals (EDCs) pose significant environmental and health risks due to their potential to interfere with nuclear receptors (NRs), key regulators of physiological processes. Despite the evident risks, the majority of existing research narrows its focus on the interaction between compounds and the individual NR target, neglecting a comprehensive assessment across the entire NR family. In response, this study assembled a comprehensive human NR dataset, capturing 49,244 interactions between 35,467 unique compounds and 42 NRs. We introduced a cross-attention network framework, "CatNet", innovatively integrating compound and protein representations through cross-attention mechanisms. The results showed that CatNet model achieved excellent performance with an area under the receiver operating characteristic curve (AUCROC) = 0.916 on the test set, and exhibited reliable generalization on unseen compound-NR pairs. A distinguishing feature of our research is its capacity to expand to novel targets. Beyond its predictive accuracy, CatNet offers a valuable mechanistic perspective on compound-NR interactions through feature visualization. Augmenting the utility of our research, we have also developed a graphical user interface, empowering researchers to predict chemical binding to diverse NRs. Our model enables the prediction of human NR-related EDCs and shows the potential to identify EDCs related to other targets.
Subject(s)
Deep Learning , Endocrine Disruptors , Humans , Endocrine Disruptors/chemistryABSTRACT
The investigation of pollutant behavior at water interfaces is critical to understand pollution in aquatic systems. Computational methods allow us to overcome the limitations of experimental analysis, delivering valuable insights into the chemical mechanisms and structural characteristics of pollutant behavior at interfaces across a range of scales, from microscopic to mesoscopic. Quantum mechanics, all-atom molecular dynamics simulations, coarse-grained molecular dynamics simulations, and dissipative particle dynamics simulations represent diverse molecular interaction calculation methods that can effectively model pollutant behavior at environmental interfaces from atomic to mesoscopic scales. These methods provide a rich variety of information on pollutant interactions with water surfaces. This review synthesizes the advancements in applying typical computational methods to the formation, adsorption, binding, and catalytic conversion of pollutants at water interfaces. By drawing on recent advancements, we critically examine the current challenges and offer our perspective on future directions. This review seeks to advance our understanding of computational techniques for elucidating pollutant behavior at water interfaces, a critical aspect of water research.
Subject(s)
Environmental Pollutants , Water , Water/chemistry , Molecular Dynamics SimulationABSTRACT
Investigating soil organic matter's (SOM) microscale assembly and functionality is challenging due to its complexity. This study constructs comparatively realistic SOM models, including diverse components such as Leonardite humic acid (LHA), lipids, peptides, carbohydrates, and lignin, to unveil their spontaneous self-assembly behavior at the mesoscopic scale through microsecond coarse-grained molecular dynamics simulations. We discovered an ordered SOM aggregation creating a layered phase from its hydrophobic core to the aqueous phase, resulting in an increasing O/C ratio and declining structural amphiphilicity. Notably, the amphiphilic lipids formed a bilayer membrane, partnering with lignin to constitute SOM's hydrophobic core. LHA, despite forming a layer, was embedded within this structure. The formation of such complex architectures was driven by nonbonded interactions between components. Our analysis revealed component-dependent diffusion effects within the SOM system. Lipids, peptides, and lignin showed inhibitory effects on self-diffusion, while carbohydrates facilitated diffusion. This study offers novel insights into the dynamic behavior and assembly of SOM components, introducing an effective approach for studying dynamic SOM mechanisms in aquatic environments.
Subject(s)
Molecular Dynamics Simulation , Soil , Soil/chemistry , Water/chemistry , Lignin , Humic Substances , Peptides/chemistry , Lipids , CarbohydratesABSTRACT
To elucidate the potential risks of the toxic pollutant mercury (Hg) in polar waters, the study of accumulated Hg in fish is compelling for understanding the cycling and fate of Hg on a regional scale in Antarctica. Herein, the Hg isotopic compositions of Antarctic cod Notothenia coriiceps were assessed in skeletal muscle, liver, and heart tissues to distinguish the differences in Hg accumulation in isolated coastal environments of the eastern (Chinese Zhongshan Station, ZSS) and the antipode western Antarctica (Chinese Great Wall Station, GWS), which are separated by over 4000 km. Differences in odd mass-independent isotope fractionation (odd-MIF) and mass-dependent fractionation (MDF) across fish tissues were reflection of the specific accumulation of methylmercury (MeHg) and inorganic Hg (iHg) with different isotopic fingerprints. Internal metabolism including hepatic detoxification and processes related to heart may also contribute to MDF. Regional heterogeneity in iHg end-members further provided evidence that bioaccumulated Hg origins can be largely influenced by polar water circumstances and foraging behavior. Sea ice was hypothesized to play critical roles in both the release of Hg with negative odd-MIF derived from photoreduction of Hg2+ on its surface and the impediment of photochemical transformation of Hg in water layers. Overall, the multitissue isotopic compositions in local fish species and prime drivers of the heterogeneous Hg cycling and bioaccumulation patterns presented here enable a comprehensive understanding of Hg biogeochemical cycling in polar coastal waters.
Subject(s)
Mercury , Methylmercury Compounds , Water Pollutants, Chemical , Animals , Mercury/analysis , Antarctic Regions , Mercury Isotopes/analysis , Bioaccumulation , Ice Cover , Environmental Monitoring , Methylmercury Compounds/metabolism , Fishes/metabolism , Isotopes , Water/metabolism , Water Pollutants, Chemical/analysisABSTRACT
Persistent organic pollutants (POPs) could pose adverse risks towards fish in aquatic environments. However, related risk assessments in remote regions are lacking. In this study we investigated three kinds of POPs in four common fish species (n = 62) from high-altitude rivers and lakes on the Tibetan Plateau. The results showed that the lipid weight concentrations of organochlorine pesticides (OCPs), polycyclic aromatic hydrocarbons (PAHs), and perfluoroalkyl substances (PFAS) in fish muscle followed the order Σ13PAHs (24.5-3354 ng/g) > Σ11PFAS (2.48-164 ng/g) > Σ7OCPs (1.61-82.2 ng/g), which is comparable to that found in other remote regions. The physiologically based pharmacokinetic (PBPK) model was optimized using physiological parameters specific to the sampled Tibetan fish to generate accurate effective concentration (EC) thresholds. Based on these measured concentrations and newly simulated EC thresholds, the ecological risk ratios for selected toxic POPs (dichlorodiphenyltrichloroethane (DDT), pyrene (Pyr), and perfluorooctane sulfonate (PFOS)) ranged from 8.53 × 10-8 to 2.03 × 10-5. Racoma tibetanus and Schizothorax macropogon were the most vulnerable Tibetan fish species. All the risk ratios were far below 1, indicating that there was no risk of POPs towards Tibetan fish. However, the risk ratios for emerging POPs (i.e., PFOS) were 2-3 orders of magnitude higher than for legacy POPs (i.e., DDT and Pyr), suggesting that monitoring of emerging POPs should be reinforced. Our study sheds light on the risk assessment of wildlife exposed to POPs in remote regions with limited toxicity data.
Subject(s)
Environmental Pollutants , Hydrocarbons, Chlorinated , Pesticides , Polycyclic Aromatic Hydrocarbons , Water Pollutants, Chemical , Animals , DDT/analysis , Tibet , Persistent Organic Pollutants , Pesticides/analysis , Hydrocarbons, Chlorinated/analysis , Fishes , Polycyclic Aromatic Hydrocarbons/analysis , Environmental Monitoring , Water Pollutants, Chemical/analysisABSTRACT
Heterogeneous transformation of organic pollutants into more toxic chemicals poses substantial health risks to humans. Activation energy is an important indicator that help us to understand transformation efficacy of environmental interfacial reactions. However, the determination of activation energies for large numbers of pollutants using either the experimental or high-accuracy theoretical methods is expensive and time-consuming. Alternatively, the machine learning (ML) method shows the strength in predictive performance. In this study, using the formation of a typical montmorillonite-bound phenoxy radical as an example, a generalized ML framework RAPID was proposed for activation energy prediction of environmental interfacial reactions. Accordingly, an explainable ML model was developed to predict the activation energy via easily accessible properties of the cations and organics. The model developed by decision tree (DT) performed best with the lowest root-mean-squared error (RMSE = 0.22) and the highest coefficient of determination values (R2 score = 0.93), the underlying logic of which was well understood by combining model visualization and SHapley Additive exPlanations (SHAP) analysis. The performance and interpretability of the established model suggest that activation energies can be predicted by the well-designed ML strategy, and this would allow us to predict more heterogeneous transformation reactions in the environmental field.
ABSTRACT
Large numbers of pollutants competitively inhibit the binding between thyroid hormones and transthyretin (TTR) in vitro. However, the impact of this unintended binding on free thyroid hormones in vivo has not yet been characterized. Herein, we established a quantitative in vitro to in vivo extrapolation (QIVIVE) method based on a competitive binding model to quantify the effect of TTR-binding chemicals on free thyroid hormones in human blood. Twenty-five TTR-binding chemicals including 6 hydroxyl polybromodiphenyl ethers (OH-PDBEs), 6 hydroxyl polychlorobiphenyls (OH-PCBs), 4 halogenphenols, 5 per- and polyfluorinated substances (PFASs), and 4 phenols were selected for investigation. Incorporating the in vitro binding parameters and human exposure data, the QIVIVE model could well predict the in vivo effect on free thyroid hormones. Co-exposure to twenty-five typical TTR-binding chemicals resulted in median increases of 0.080 and 0.060% in circulating levels of free thyroxine (FT4) and free triiodothyronine (FT3) in the general population. Individuals with occupational exposure to TTR-binding chemicals suffered 1.88-32.2% increases in free thyroid hormone levels. This study provides a quantitative tool to evaluate the thyroid-disrupting risks of TTR-binding chemicals and proposes a new framework for assessing the in vivo effects of chemical exposures on endogenous molecules.
Subject(s)
Environmental Pollutants , Polychlorinated Biphenyls , Prealbumin , Thyroid Hormones , Humans , Binding, Competitive , Polychlorinated Biphenyls/metabolism , Prealbumin/metabolism , Thyroid Hormones/metabolism , Thyroxine/metabolism , Triiodothyronine/metabolismABSTRACT
Benzotriazole ultraviolet stabilizers (BZT-UVs), substituted diphenylamine antioxidants (S-DPAs), and synthetic phenolic antioxidants (SPAs) are three types of synthetic additive pollutants that are received increasing attention in the recent decade. In this study, a total of twenty-seven target analytes were measured in sediment cores and surface sediments collected from a lake with long sedimentary history. All target analytes were detected in the sediment samples, and mean values of the total ∑BZT-UVs, ∑S-DPAs and ∑SPAs concentrations were 220 ± 552 ng/g dry weight (d.w.), 20.8 ± 27.9 ng/g d. w., and 95.8 ± 146 ng/g d. w., respectively. Based on the dating results of two sediment cores, the total concentrations of BZT-UVs, S-DPA, and SPAs showed a fluctuating but overall increasing trend over time, which intuitively reflects the change of historical emissions. Meanwhile, fecal and sewage markers (i.e., sterols and pharmaceuticals, respectively) were jointly used to trace the probable sources of these pollutants. Results from the correlation and clustering analyses suggest that unregular fecal discharges and point-source domestic sewage need to be considered if pollution in the investigated area is to be controlled. To our knowledge, this study is the initial attempt to reveal the temporal variations of these synthetic additive pollutants in an aquatic environment in China and to demonstrate the feasibility of using markers to trace the sources of emerging pollutant analogues.
Subject(s)
Environmental Pollutants , Water Pollutants, Chemical , Sewage/analysis , Antioxidants/analysis , Environmental Monitoring , Environmental Pollutants/analysis , Lakes/analysis , Water Pollutants, Chemical/analysis , Phenols/analysis , Diphenylamine , China , Geologic SedimentsABSTRACT
Despite the fact that coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been disrupting human life and health worldwide since the outbreak in late 2019, the impact of exogenous substance exposure on the viral infection remains unclear. It is well-known that, during viral infection, organism receptors play a significant role in mediating the entry of viruses to enter host cells. A major receptor of SARS-CoV-2 is the angiotensin-converting enzyme 2 (ACE2). This study proposes a deep learning model based on the graph convolutional network (GCN) that enables, for the first time, the prediction of exogenous substances that affect the transcriptional expression of the ACE2 gene. It outperforms other machine learning models, achieving an area under receiver operating characteristic curve (AUROC) of 0.712 and 0.703 on the validation and internal test set, respectively. In addition, quantitative polymerase chain reaction (qPCR) experiments provided additional supporting evidence for indoor air pollutants identified by the GCN model. More broadly, the proposed methodology can be applied to predict the effect of environmental chemicals on the gene transcription of other virus receptors as well. In contrast to typical deep learning models that are of black box nature, we further highlight the interpretability of the proposed GCN model and how it facilitates deeper understanding of gene change at the structural level.