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.

Precisely Identifying the Sources of Magnetic Particles by Hierarchical Classification-Aided Isotopic Fingerprinting.

Magnetic particles (MPs), with magnetite (Fe3O4) and maghemite (γ-Fe2O3) as the most abundant species, are ubiquitously present in the natural environment. MPs are among the most applied engineered particles and can be produced incidentally by various human activities. Identification of the sources of MPs is crucial for their risk assessment and regulation, which, however, is still an unsolved problem. Here, we report a novel approach, hierarchical classification-aided stable isotopic fingerprinting, to address this problem. We found that naturally occurring, incidental, and engineered MPs have distinct Fe and O isotopic fingerprints due to significant Fe/O isotope fractionation during their generation processes, which enables the establishment of an Fe-O isotopic library covering complex sources. Furthermore, we developed a three-level machine learning model that not only can distinguish the sources of MPs with a high precision (94.3%) but also can identify the multiple species (Fe3O4 or γ-Fe2O3) and synthetic routes of engineered MPs with a precision of 81.6%. This work represents the first reliable strategy for the precise source tracing of particles with multiple species and complex sources.

RBM8A Depletion Decreases the Cisplatin Resistance and Represses the Proliferation and Metastasis of Breast Cancer Cells via AKT/mTOR Pathway.

Background: Breast cancer (BC) is the most prevalent malignancy in women. This study is aimed to explore the role and regulatory mechanism of RNA-binding motif protein 8A (RBM8A) in BC. Methods: We detected the expression of RBM8A in BC tissues and cell lines (MCF-7, MDA-MB-231, and MDA-MB-436), and explored the correlation of RBM8A expression with clinicopathological features in patients. The function of RBM8A deficiency in MCF-7 and MDA-MB-231 cells was determined using MTT, wound healing, and transwell assay. The effect of RBM8A suppression on the cisplatin (DDP) resistance in MCF-7 and MDA-MB-231 cells was also evaluated. Besides, western blotting was used to examine AKT/mTOR pathway-related proteins. The mouse model was constructed to confirm the effect of RBM8A on tumor growth. Results: The expression of RBM8A was elevated in BC tissues and cell lines. RBM8A silencing restrained the malignant behaviors of MCF-7 and MDA-MB-231 cells, including viability, migration, and invasion, while promoting apoptosis. Silencing of RBM8A overcame resistance to DDP in MCF-7 and MDA-MB-231 cells. Furthermore, RBM8A suppression restrained the activation of the AKT/mTOR pathway in both MCF-7 and MDA-MB-231 cells. Feedback experiments revealed that SC79 treatment reversed the reduction effects of RBM8A knockdown on viability, DDP resistance, migration, and invasion of MDA-MB-231 cells. Moreover, the silencing of RBM8A inhibited the growth of tumor xenograft in vivo. Conclusions: RBM8A knockdown may reduce DDP resistance in BC to repress the development of BC via the AKT/mTOR pathway, suggesting that RBM8A may serve as a new therapeutic target in BC.

Unified Probability Distribution and Dynamics of Lead Contents in Human Erythrocytes Revealed by Single-Cell Analysis.

Understanding the presence and dynamics of chemical pollutants in individual cells is fundamentally important for their trafficking, fate, and toxicity in humans. The presence of molecular components (i.e., proteins and mRNA) in individual cells of higher organisms is considered a stochastic event. The characteristics of chemical pollutants, as extrinsic compounds, in subpopulation of human cells on single-cell basis have not been explored yet. Here, we demonstrated the lead (Pb) content in individual mature erythrocytes (m-erythrocytes) of Pb-intoxicated patients, and healthy subjects exhibited a unified pattern in probability distribution (gamma distribution) and dynamics, despite being highly heterogeneous. The Pb content in individual m-erythrocytes decreased with the lifetime of m-erythrocytes. Meanwhile, the distribution and dynamics were found to be highly related to the Pb content in m-erythrocytes and was independent of patients and their status. This is the first study to analyze the distribution pattern of chemical pollutants at a single-cell level in higher organisms. This study sheds light on the molecular mechanism of Pb trafficking and fate in humans and the search for an efficient strategy to improve Pb excretion during Pb treatment.

A novel computational solution to the health risk assessment of air pollution via joint toxicity prediction: A case study on selected PAH binary mixtures in particulate matters.

Regional haze episode has already caused overwhelming public concern. Unraveling the health effects of the representative composition mixtures of atmospheric fine particulate matters (PM2.5) becomes a top priority. In this study, a novel computational solution integrating chemical-induced genomic residual effect prediction with in vitro-based risk assessment is proposed to obtain the cumulative health risk of typical chemical mixtures of particulate matters (PM). The joint toxicity of binary mixtures is estimated by analyzing both genomic similarity and dose-response curve of relevant pollutants for the chemical-induced genomic residual effect. Specifically, the modified relative potency factor (mRPF) of mixtures is introduced for this purpose, and the ratio of activation (RA) value is defined to assess the corresponding health risks of the mixtures. As a methodology demonstration, the health risk of typical binary polycyclic aromatic hydrocarbon (PAH) mixtures in PM, containing Benzo[a]pyrene (BaP) as a component, is assessed using the proposed solution. Our results indicate that the combined effect of pairwise PAHs of BaP with Benzo[b]fluoranthene (BbF) and Benz[a]anthracene (BaA) is synergistic on p53 pathway, and that the health risk of the such mixtures increases compared to that of the individual ones. Obviously, the cumulative health risk of environmental mixtures will be underestimated when the synergistic effect is wrongly assumed to be additive. To our knowledge, this is the first study ever report on a computational solution to the health risk assessment of environmental pollution via joint toxicity prediction. The novel methodology proposed here makes full use of the open-access in vitro assay data and transcriptomic information in literatures and provides a successful demonstration of the concept of systems biology and translational science.

CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals.

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.

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.

Plasma-Promoted Ammonia Decomposition over Supported Ruthenium Catalysts for COx -Free H2 Production.

The efficient decomposition of ammonia to produce COx -free hydrogen at low temperatures has been extensively investigated as a potential method for supplying hydrogen to mobile devices based on fuel cells. In this study, we employed dielectric barrier discharge (DBD) plasma, a non-thermal plasma, to enhance the catalytic ammonia decomposition over supported Ru catalysts (Ru/Y2 O3 , Ru/La2 O3 , Ru/CeO2 and Ru/SiO2 ). The plasma-catalytic reactivity of Ru/La2 O3 was found to be superior to that of the other three catalysts. It was observed that both the physicochemical properties of the catalyst (such as support acidity) and the plasma discharge behaviours exerted significant influence on plasma-catalytic reactivity. Combining plasma with a Ru catalyst significantly enhanced ammonia conversion at low temperatures, achieving near complete NH3 conversion over the 1.5 %-Ru/La2 O3 catalyst at temperatures as low as 380 °C. Under a weight gas hourly space velocity of 2400 mL gcat -1 h-1 and an AC supply power of 20 W, the H2 formation rate and energy efficiency achieved were 10.7 mol gRu -1 h-1 and 535 mol gRu -1 (kWh)-1 , respectively, using a 1.5 %-Ru/La2 O3 catalyst.

Metallomic profiling and natural copper isotopic signatures of childhood autism in serum and red blood cells.

Excessive exposure to metals directly threatens human health, including neurodeve lopment. Autism spectrum disorder (ASD) is a neurodevelopmental disorder, leaving great harms to children themselves, their families, and even society. In view of this, it is critical to develop reliable biomarkers for ASD in early childhood. Here we used inductively coupled plasma mass spectrometry (ICP-MS) to identify the abnormalities in ASD-associated metal elements in children blood. Multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) was applied to detect isotopic differences in copper (Cu) for further assessment on account of its core role in the brain. We also developed a machine learning classification method for unknown samples based on a support vector machine (SVM) algorithm. The results indicated significant differences in the blood metallome (chromium (Cr), manganese (Mn), cobalt (Co), magnesium (Mg), and arsenic (As)) between cases and controls, and a significantly lower Zn/Cu ratio was observed in the ASD cases. Interestingly, we found a strong association of serum copper isotopic composition (δ65Cu) with autistic serum. SVM was successfully applied to discriminate cases and controls based on the two-dimensional Cu signatures (Cu concentration and δ65Cu) with a high accuracy (94.4%). Overall, our findings revealed a new biomarker for potential early diagnosis and screening of ASD, and the significant alterations in the blood metallome also helped to understand the potential pathogenesis of ASD in terms of metallomics.

Structural basis for molecular recognition of G protein-coupled estrogen receptor by selected bisphenols.

Complicated ligand-dependent signaling pathways of bisphenol A (BPA) and its analogues involve not only intranuclear estrogen receptor but also membrane receptor G protein-coupled estrogen receptor (GPER). However, the structural basis for molecular recognition of GPER by the environmental chemicals remains unknown. To reveal the structural dependence of GPER recognition by bisphenols, a systematic molecular dynamics simulation study was performed for selected bisphenols with different electron hybrid orbitals and substituents on their C atoms connecting two phenol rings. BPA was used as a control, bisphenol C(BPC) as an example for a connecting C with sp2 hybrid orbitals to provide more ligand rigidity, bisphenol E(BPE) and bisphenol F(BPF) for decreased steric hindrance and hydrophobicity around the connecting C, and bisphenol B(BPB) and bisphenol AF(BPAF) for increased hydrophobicity and steric hindrance. All the tested bisphenols can bind with GPER at its classic orthosteric site to obtain GPER-ligand complexes, while van der Waals interactions and direct inter-molecular electrostatic energies provide the driving forces for ligand binding. Bulky substituents and structural rigidity of the connecting C dramatically impair hydrogen bonding between GPER and the bisphenols, which results in decreased contribution of both favorable intermolecular hydrogen bonds and unfavorable polar solvation effect to complex stability of BPB and BPC since decreased number of key residues is expected. Increase in substituent lipophilicity enhances the van der Waals interactions and favorable non-polar solvation effect. The six bisphenols of high structural similarity shared two key recognition residues, Leu137TM3 and Trp272TM6, the latter of which was in the highly conserved CWxP motif of TM6 and has been reported as key residue for G protein-coupled receptor activation. Based on the obtained knowledge, GPER affinity and relevant toxicity of BPA alternatives can be easily predicted, and the calculated binding free energies are consistent with the available experimental observations.

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.