A Data-Driven Computational Framework for Assessing the Risk of Placental Exposure to Environmental Chemicals.

A computational framework based on placental gene networks was proposed in this work to improve the accuracy of the placental exposure risk assessment of environmental compounds. The framework quantitatively characterizes the ability of compounds to cross the placental barrier by systematically considering the interaction and pathway-level information on multiple placental transporters. As a result, probability scores were generated for 307 compounds crossing the placental barrier based on this framework. These scores were then used to categorize the compounds into different levels of transplacental transport range, creating a gradient partition. These probability scores not only facilitated a more intuitive understanding of a compound's ability to cross the placental barrier but also provided valuable information for predicting potential placental disruptors. Compounds with probability scores greater than 90% were considered to have significant transplacental transport potential, whereas those with probability scores less than 80% were classified as unlikely to cross the placental barrier. Furthermore, external validation set results showed that the probability score could accurately predict the compounds known to cross the placental barrier. In conclusion, the computational framework proposed in this study enhances the intuitive understanding of the ability of compounds to cross the placental barrier and opens up new avenues for assessing the placental exposure risk of compounds.

Assessment of Fetal Exposure and Elimination of Perfluoroalkyl and Polyfluoroalkyl Substances: New Evidence from Paired Serum, Placenta, and Meconium Samples.

Multiple pieces of evidence have shown that prenatal exposure to perfluoroalkyl and polyfluoroalkyl substances (PFASs) is closely related to adverse birth outcomes for infants. However, difficult access to human samples limits our understanding of PFASs transport and metabolism across the human placental barrier, as well as the accurate assessment of fetal PFASs exposure. Herein, we assess fetal exposure to 28 PFASs based on paired serum, placenta, and meconium samples. Overall, 21 PFASs were identified first to be exposed to the fetus prenatally and to be metabolized and excreted by the fetus. In meconium samples, 25 PFASs were detected, with perfluorooctane sulfonate and perfluorohexane sulfonic acid being the dominant congeners, suggesting the metabolism and excretion of PFASs through meconium. Perfluoroalkyl sulfonic acids might be more easily eliminated through the meconium than perfluorinated carboxylic acids. Importantly, based on molecular docking, MRP1, OATP2B1, ASCT1, and P-gp were identified as crucial transporters in the dynamic placental transfer of PFASs between the mother and the fetus. ATSC5p and PubchemFP679 were recognized as critical structural features that affect the metabolism and secretion of PFASs through meconium. With increasing carbon chain length, both the transplacental transfer efficiency and meconium excretion efficiency of PFASs showed a structure-dependent manner. This study reports, for the first time, that meconium, which is a noninvasive and stable biological matrix, can be strong evidence of prenatal PFASs exposure.

Machine learning models based on residue interaction network for ABCG2 transportable compounds recognition.

As the one of the most important protein of placental transport of environmental substances, the identification of ABCG2 transport molecules is the key step for assessing the risk of placental exposure to environmental chemicals. Here, residue interaction network (RIN) was used to explore the difference of ABCG2 binding conformations between transportable and non-transportable compounds. The RIN were treated as a kind of special quantitative data of protein conformation, which not only reflected the changes of single amino acid conformation in protein, but also indicated the changes of distance and action type between amino acids. Based on the quantitative RIN, four machine learning algorithms were applied to establish the classification and recognition model for 1100 compounds with transported by ABCG2 potential. The random forest (RF) models constructed with RIN presented the best and satisfied predictive ability with an accuracy of training set of 0.97 and the test set of 0.96 respectively. In conclusion, the construction of residue interaction network provided a new perspective for the quantitative characterization of protein conformation and the establishment of prediction models for transporter molecular recognition. The ABCG2 transport molecular recognition model based on residue interaction network provides a possible way for screening environmental chemistry transported through placenta.

Identification of molecular initiating events and key events leading to endocrine disrupting effects of PFOA: Integrated molecular dynamic, transcriptomic, and proteomic analyses.

Perfluorooctanoic acid (PFOA) can rapidly activate signaling pathways independent of nuclear hormone receptors through membrane receptor regulation, which leads to endocrine disrupting effects. In the present work, the molecular initiating event (MIE) and the key events (KEs) which cause the endocrine disrupting effects of PFOA have been explored and determined based on molecular dynamics simulation (MD), fluorescence analysis, transcriptomics, and proteomics. MD modeling and fluorescence analysis proved that, on binding to the G protein-coupled estrogen receptor-1 (GPER), PFOA could induce a conformational change in the receptor, turning it into an active state. The results also indicated that the binding to GPER was the MIE that led to the adverse outcome (AO) of PFOA. In addition, the downstream signal transduction pathways of GPER, as regulated by PFOA, were further investigated through genomics and proteomics to identify the KEs leading to thr endocrine disrupting effects. Two pathways (Endocrine resistance, ERP and Estrogen signaling pathway, ESP) containing GPER were regulated by different concentration of PFOA and identified as the KEs. The knowledge of MIE, KEs, and AO of PFOA is necessary to understand the links between PFOA and the possible pathways that lead to its negative effects.

The adverse outcome pathway (AOP) of estrogen interference effect induced by triphenyl phosphate (TPP): Integrated multi-omics and molecular dynamics approaches.

Triphenyl phosphate (TPP) has been detected with increasing frequency in various biota and environmental media, and it has been confirmed that G protein-coupled estrogen receptor (GPER) was involved in the estrogenic activity of TPP. Therefore, it is necessary to link the estrogen-interfering effects and possible mechanisms of action of TPP with the molecular initiation event (MIE) to improve its adverse outcome pathway framework. In this study, transcriptomic and proteomic methods were used to analyze the estrogen interference effect of TPP mediated by GPER, and the causal relationship was supplemented by molecular dynamics simulation and fluorescence analysis. The omics results showed that TPP could regulate the response of key GPER signaling factors and the activation of downstream pathways including PI3K-Akt signaling pathway, MAPK signaling pathway, and estrogen signaling pathway. The similar activation effect of TPP and agonist G1 change of GPER was proved by molecular dynamics simulation. After TPP binding, the conformation of GPER will change from the inactive to active state. Therefore, TPP may affect cell proliferation, metastasis, and apoptosis and regulate gene transcription and kinase activity, leading to abnormal immune function and other estrogen-dependent cell processes and cancer through GPER, ultimately causing the estrogen interference effect.

Effect of short-chain chlorinated paraffins (SCCPs) on lipid membranes: Combination of molecular dynamics and membrane damage experiments.

In recent years, more attention has been paid to the biological effects of short-chain chlorinated paraffin (SCCP). Studies have shown that SCCPs exposure could cause metabolic damage and lipid metabolic damage. In the present work, based on E. coli membrane damage experiments and molecular dynamics (MD) simulation, the effects of SCCPs on the membrane structure and membrane properties were studied to explore the possible toxic damage effects of SCCPs on cell membrane. Experiments results showed that SCCPs had a significant inhibitory effect on E. coli. The E. coli cell membrane of the bacteria was broken and the macromolecules of the cell flowed out when exposed to SCCPs. SCCPs would lead to the decrease and depolarization of cell membrane potential, and then affect the integrity and permeability of cell membrane. The further molecular dynamic simulation revealed that SCCP molecules can easily enter the lipid DPPC membranes from the aqueous phase and tended to aggregate inside bilayer stably. The bound of SCCPs could lead to significant variations in DPPC bilayer with a less dense, more disorder and rougher layer, which thus made the damage of cell membrane. In a word, although the overall toxicity of SCCPs to cell was relatively weak, the damage to the cell membrane may be one of the mechanisms of its toxicity. MAIN FINDING OF THE WORK: The exposure of SCCPs could cause structural change of cell membrane in E. coli, which verified the damage to the cell membrane may be one of the mechanisms of its toxicity.