New Mechanism for the Apoptosis of Human Neuroblastoma Cells by the Interaction between Fluorene-9-Bisphenol and the G Protein-Coupled Estrogen Receptor 1.

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.

Deciphering exogenous chemical carcinogenicity through interpretable deep learning: A novel approach for evaluating atmospheric pollutant hazards.

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.

Dynamic Behavior and Interaction Mechanism of Soil Organic Matter in Water Systems: A Coarse-Grained Molecular Dynamics Study.

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.

Insight into the formation and biological effects of natural organic matter corona on silver nanoparticles in water environment using biased cyclical electrical field-flow fractionation.

Natural organic matter (NOM) readily interacts with nanoparticles, leading to the formation of NOM corona structures on their surface. NOM corona formation is closely related to the surface coatings and bioavailability of nanoparticles. However, the mechanism underlying NOM corona formation on silver nanoparticles (AgNPs) remains largely unknown due to the lack of effective analytical methods for identifying the changes in the AgNP surface. Herein, the separation ability of biased cyclical electrical field-flow fractionation (BCyElFFF) for same-sized polyvinyl pyrrolidone-coated and poly(ethylene glycol)-coated silver nanoparticles (AgNPs) with different electrophoretic mobilities was evaluated under various electrical conditions. Then, the mechanism behind the NOM corona formation on these AgNP surfaces was elucidated based on the changes in the elution time and off-line characterization of the collected fractions during their elution time in a BCyElFFF run. Finally, the survival rates of E. coli exposed to polyvinyl pyrrolidone-coated and poly(ethylene glycol)-coated AgNPs with or without NOM collected during repeated BCyElFFF runs were observed to increase with increasing NOM concentration, clearly demonstrating the negative effect of NOM corona structures on the bioavailability of AgNPs. These findings highlight the powerful separation and isolation ability of BCyElFFF in studying the transformation and fate of nanoparticles in aqueous environments.

Evaluation of the binding performance of flavonoids to estrogen receptor alpha by Autodock, Autodock Vina and Surflex-Dock.

Molecular docking is a widely used method to predict the binding modes of small-molecule ligands to the target binding site. However, it remains a challenge to identify the correct binding conformation and the corresponding binding affinity for a series of structurally similar ligands, especially those with weak binding. An understanding of the various relative attributes of popular docking programs is required to ensure a successful docking outcome. In this study, we systematically compared the performance of three popular docking programs, Autodock, Autodock Vina, and Surflex-Dock for a series of structurally similar weekly binding flavonoids (22) binding to the estrogen receptor alpha (ERα). For these flavonoids-ERα interactions, Surflex-Dock showed higher accuracy than Autodock and Autodock Vina. The hydrogen bond overweighting by Autodock and Autodock Vina led to incorrect binding results, while Surflex-Dock effectively balanced both hydrogen bond and hydrophobic interactions. Moreover, the selection of initial receptor structure is critical as it influences the docking conformations of flavonoids-ERα complexes. The flexible docking method failed to further improve the docking accuracy of the semi-flexible docking method for such chemicals. In addition, binding interaction analysis revealed that 8 residues, including Ala350, Glu353, Leu387, Arg394, Phe404, Gly521, His524, and Leu525, are the key residues in ERα-flavonoids complexes. This work provides reference for assessing molecular interactions between ERα and flavonoid-like chemicals and provides instructive information for other environmental chemicals.

In silico health effect prioritization of environmental chemicals through transcriptomics data exploration from a chemo-centric view.

With the explosive growth of synthetic compounds, the health effects caused by exogenous chemical exposure have attracted more and more public attention. The prediction of health effect is a never-ending story. Collective resource of transcriptomics data offers an opportunity to understand and identify the multiple health effects of small molecule. Inspired by the fact that environmental chemicals of high health risk frequently share both similar gene expression profile and common structural feature of certain drugs, we here propose a novel computational effect prioritization method for environmental chemicals through transcriptomics data exploration from a chemo-centric view. Specifically, non-negative matrix factorization (NMF) method has been adopted to get the association network linking structural features with transcriptomics characteristics of drugs with specific effects. The model yields 13 pivotal types of effects, so-called components, that represent drug categories with common chemo- and geno- type features. Moreover, the established model effectively prioritizes potential toxic effects for the external chemicals from the endocrine disruptor screening program (EDSP) for their potential estrogenicity and other verified risks. Even if only the highest priority is set for the estrogenic effect, the precision and recall can reach 0.76 and 0.77 respectively for these chemicals. Our effort provides a successful endeavor as to profile potential toxic effects simultaneously for environmental chemicals using both chemical and omics data.

The Effect of Structural Diversity on Ligand Specificity and Resulting Signaling Differences of Estrogen Receptor α.

Numerous chemicals have been reported to exert estrogen-like endocrine disrupting effects via a receptor binding mechanism that directly interacts with the ligand binding domain of estrogen receptor α (ERα). However, not only their binding affinities to ERα but also their interference in specific cell and tissue functions are clearly different. In this regard, significant regulation differences among three representative estrogenic chemicals (diethylstilbestrol (DES), bisphenol A (BPA), and diarylpropionitrile (DPN)), well-known ERα agonists with very similar structures, have been observed. Molecular dynamics simulation is used to explore the underlying mechanism of different regulation effects induced by the similar estrogen-like chemicals. The DES-induced 12 Å motion of the H9-H10 loop markedly expands the negative electrostatic potential surface of the AF-2 domain, which is consistent with the over-regulation effect of the agonist. In comparison, the 3 Å motion induced by BPA and DPN corresponds to the low-regulation effect of the chemicals. Cross-correlation analysis indicates that the different ERα motions and resulting surface feature of AF-2 domain are brought by the distinguished binding modes of the agonists. Moreover, only hydrophobic DES with estrogen-like size and flexibility has a high binding affinity of -23.47 kcal/mol binding free energy. Both the hydrophilic group in DPN and the small molecular size of BPA dramatically decrease the agonist binding ability, and their binding free energies are only -12.43 kcal/mol and -11.82 kcal/mol, respectively. Our study demonstrates that similar chemicals interact differently with ERα and induce different allosteric effects, which explains the observed regulation diversity.

Determination of short-chain chlorinated paraffins in multiple matrices of Arctic using gas chromatography-electron capture negative ion-low resolution mass spectrometry.

Gas chromatography-electron capture negative ion-low resolution mass spectrometry (GC-ECNI-MS) was used for the quantification of short-chain chlorinated paraffins (SCCPs) in multiple matrices of Arctic. Samples were spiked with surrogate standards (13C10-trans-chlordane) and extracted with dichloromethane and hexane (1:1, v/v) using accelerated solvent extraction (ASE). The extract was cleaned using a multilayer Silica-Florisil column and ε-HCH was added before instrument analysis. The SCCPs were analyzed using a gas chromatograph (GC) in electron capture negative ion (ECNI) mode coupled with a 7000B triple quadruple mass spectrometer (MS) in single quad mode. The calibration is performed using three commercial standards (chlorine contents of 51.5%, 55.5%, and 63.0%). A reasonable linear correlation for commercial standards was found between chlorine content and total response factor (R2 = 0.96). To ensure instrument sensitivity, the SCCP congeners were divided into four groups by the optimized combinations (C10, C11, C12 and C13) and subjected to analysis by four individual injections. This method is suited to analyse the total concentration of SCCPs and individual SCCP congener groups in the environmental analysis. • Accelerated solvent extraction offers a lower cost of time for per sample and reducing solvent consumption. • The presented quantification procedure makes the quantification of SCCPs independent from the chlorine content of the used standard mixtures. • The samples were subjected to analysis by four individual injections for the instrument sensitivity, and the total concentration of SCCPs and CP congener groups can be obtained at the same time.

Morphology-dependent gas-sensing properties of ZnO nanostructures for chlorophenol.

The crystal-plane effect of ZnO nanostructures on the toxic 2-chlorophenol gas-sensing properties was examined. Three kinds of single-crystalline ZnO nanostructures including nanoawls, nanorods, and nanodisks were synthesized by using different capping agents via simple hydrothermal routes. Different crystal surfaces were expected for these ZnO nanostructures. The sensing tests results showed that ZnO nanodisks exhibited the greatest sensitivity for the detection of toxic 2-chlorophenol. The results revealed that the sensitivity of these ZnO samples was heavily dependent on their exposed surfaces. The polar (0001) planes were most reactive and could be considered as the critical factor for the gas-sensing performance. In addition, calculations using density functional theory were employed to simulate the gas-sensing reaction involving surface reconstruction and charge transfer both of which result in the change of electronic conductance of ZnO.

Low-power Helium-Neon laser irradiation enhances the expression of VEGF in murine myocardium.

BACKGROUND: Low-power helium-neon (He-Ne) lasers have been increasingly widely applied in the treatment of cardiovascular diseases, and its vasodilation effect has been proven. The aim of this study was to determine the effects of low-power He-Ne laser irradiation directed at the precardial region of Wistar rats on capillary permeability in the myocardium and the expression of myocardial vascular endothelial growth factor (VEGF). METHODS: Sixteen rats were divided randomly into control and irradiated groups (n = 8, each). A He-Ne laser (632.8 nm) was applied to the irradiated group with a dose of 60.5 J/cm(2). Ferritin was perfused into the left femoral vein and capillary permeability was examined under an electron microscope. VEGF expression in the myocardium was investigated by immunohistochemical methods, RT-PCR, and image analysis. RESULTS: The ultrastructures of the myocardial capillaries were examined. Compared to the control group, more high-density granules (ferritin), which were present within the capillary endothelium and the mitochondrions of myocardial cells in the internal layer of the myocardium, were observed in the irradiated group. VEGF staining of the myocardium was stronger in the irradiated group than that in the control group. The optic density of the irradiated group (0.246 +/- 0.015) was significantly higher than that of the control group (0.218 +/- 0.012, P < 0.05). Finally, the levels of RT-PCR products of VEGF165 mRNA were 2.79 times higher in irradiated rats than in the control rats. CONCLUSIONS: Our study demonstrates that He-Ne laser irradiation (in doses of 60.5 J/cm(2)) increases myocardial capillary permeability and the production of VEGF in myocardial microvessels and in myocardium. Our study provides experimental morphological evidence that myocardial microcirculation can be improved using He-Ne laser irradiation.