Filling the Blank Space: Branched 4-Nonylphenol Isomers Are Responsible for Robust Constitutive Androstane Receptor (CAR) Activation by Nonylphenol.

4-Nonylphenol (4-NP), a para-substituted phenolic compound with a straight or branched carbon chain, is a ubiquitous environmental pollutant and food contaminant. 4-NP, particularly the branched form, has been identified as an endocrine disruptor (ED) with potent activities on estrogen receptors. Constitutive Androstane Receptor (CAR) is another crucial nuclear receptor that regulates hepatic lipid, glucose, and steroid metabolism and is involved in the ED mechanism of action. An NP mixture has been described as an extremely potent activator of both human and rodent CAR. However, detailed mechanistic aspects of CAR activation by 4-NP are enigmatic, and it is not known if 4-NP can directly interact with the CAR ligand binding domain (LBD). Here, we examined interactions of individual branched (22NP, 33NP, and 353NP) and linear 4-NPs with CAR variants using molecular dynamics (MD) simulations, cellular experiments with various CAR expression constructs, recombinant CAR LBD in a TR-FRET assay, or a differentiated HepaRG hepatocyte cellular model. Our results demonstrate that branched 4-NPs display more stable poses to activate both wild-type CAR1 and CAR3 variant LBDs in MD simulations. Consistently, branched 4-NPs activated CAR3 and CAR1 LBD more efficiently than linear 4-NP. Furthermore, in HepaRG cells, we observed that all 4-NPs upregulated CYP2B6 mRNA, a relevant hallmark for CAR activation. This is the first study to provide detailed insights into the direct interaction between individual 4-NPs and human CAR-LBD, as well as its dominant variant CAR3. The work could contribute to the safer use of individual 4-NPs in many areas of industry.

Market analysis of the presence of endocrine disrupting chemicals in cosmetic products intended for oncological patients and other vulnerable groups.

There is growing concern about the presence of endocrine disrupting chemicals (EDCs) in cosmetics. We aimed to identify the main cosmetic ingredients with suspected endocrine-disrupting properties, and analyse their presence in current marketed products. Particular attention was given to products intended for susceptible (due to physiological status) and vulnerable (due to specific pathologies) groups with a view to informing cosmetologists and related health professionals of the scientific basis and current status of any concerns. Suspected EDCs used as cosmetic ingredients, included in lists published by regulatory agencies, were documented and investigated by weight of evidence analysis based on endocrine-related toxicity studies. In total, 49 suspected EDCs were identified from a sample of over a thousand cosmetic products marketed in the European Union. Suspected EDCs were found in approximately one third of products, with a similar frequency in products intended for susceptible and vulnerable groups. Avobenzone (CAS number:70356-09-1), octisalate (CAS number: 118-60-5), and butylated hydroxytoluene (CAS number: 128-37-0) were mostly commonly identified. The presence of EDCs was particularly high for sun care cosmetic products. Our results highlight potentially significant exposure through cosmetics to substances currently studied by regulatory institutions as suspected endocrine disrupters. EDCs are not yet universally regulated, and informing health professionals and educating the population as a precaution are options to reduce individual exposure levels, especially in vulnerable and susceptible groups. Special recommendations are needed for products intended for oncological patients.

Nitrogen-doped titanium dioxide/schwertmannite nanocomposites as heterogeneous photo-Fenton catalysts with enhanced efficiency for the degradation of bisphenol A.

Potential health risks related to environmental endocrine disruptors (EEDs) have aroused research hotspots at the forefront of water treatment technologies. Herein, nitrogen-doped titanium dioxide/schwertmannite nanocomposites (N-TiO2/SCH) have been successfully developed as heterogeneous catalysts for the degradation of typical EEDs via photo-Fenton processes. Due to the sustainable Fe(III)/Fe(II) conversion induced by photoelectrons, as-prepared N-TiO2/SCH nanocomposites exhibit much enhanced efficiency for the degradation of bisphenol A (BPA; ca. 100% within 60 min under visible irradiation) in a wide pH range of 3.0-7.8, which is significantly higher than that of the pristine schwertmannite (ca. 74.5%) or N-TiO2 (ca. 10.8%). In this photo-Fenton system, the efficient degradation of BPA is mainly attributed to the oxidation by hydroxyl radical (•OH) and singlet oxygen (1O2). Moreover, the possible catalytic mechanisms and reaction pathway of BPA degradation are systematically investigated based on analytical and photoelectrochemical analyses. This work not only provides a feasible means for the development of novel heterogeneous photo-Fenton catalysts, but also lays a theoretical foundation for the potential application of mineral-based materials in wastewater treatment.

Plastic Food Packaging from Five Countries Contains Endocrine- and Metabolism-Disrupting Chemicals.

Plastics are complex chemical mixtures of polymers and various intentionally and nonintentionally added substances. Despite the well-established links between certain plastic chemicals (bisphenols and phthalates) and adverse health effects, the composition and toxicity of real-world mixtures of plastic chemicals are not well understood. To assess both, we analyzed the chemicals from 36 plastic food contact articles from five countries using nontarget high-resolution mass spectrometry and reporter-gene assays for four nuclear receptors that represent key components of the endocrine and metabolic system. We found that chemicals activating the pregnane X receptor (PXR), peroxisome proliferator receptor γ (PPARγ), estrogen receptor α (ERα), and inhibiting the androgen receptor (AR) are prevalent in plastic packaging. We detected up to 9936 chemical features in a single product and found that each product had a rather unique chemical fingerprint. To tackle this chemical complexity, we used stepwise partial least-squares regressions and prioritized and tentatively identified the chemical features associated with receptor activity. Our findings demonstrate that most plastic food packaging contains endocrine- and metabolism-disrupting chemicals. Since samples with fewer chemical features induce less toxicity, chemical simplification is key to producing safer plastic packaging.

Mechanistic insight into human androgen receptor-mediated endocrine disrupting potential of cyclic depsipeptide mycotoxin, beauvericin, and influencing environmental factors for its biosynthesis in Fusarium oxysporum KFCC 11363P on rice cereal.

In current study, Fusarium mycotoxin, beauvericin (BEA), has endocrine disrupting potential through suppressing the exogenous androgen receptor (AR)-mediated transcriptional activation. BEA was classified as an AR antagonist, with IC30 and IC50 values indicating that it suppressed AR dimerization in the cytosol. BEA suppress the translocation of cytosolic activated ARs to the nucleus via exogenous androgens. Furthermore, we investigated the impact of environmental conditions for BEA production on rice cereal using response surface methodology. The environmental factors affecting the production of BEA, namely temperature, initial moisture content, and growth time were optimized at 20.28 °C, 42.79 % (w/w), and 17.31 days, respectively. To the best of our knowledge, this is the first report showing that BEA has endocrine disrupting potential through suppressing translocation of cytosolic ARs to nucleus, and temperature, initial moisture content, and growth time are important influencing environmental factors for its biosynthesis in Fusarium strains on cereal.

Predicting the Activity of Unidentified Chemicals in Complementary Bioassays from the HRMS Data to Pinpoint Potential Endocrine Disruptors.

The majority of chemicals detected via nontarget liquid chromatography high-resolution mass spectrometry (HRMS) in environmental samples remain unidentified, challenging the capability of existing machine learning models to pinpoint potential endocrine disruptors (EDs). Here, we predict the activity of unidentified chemicals across 12 bioassays related to EDs within the Tox21 10K dataset. Single- and multi-output models, utilizing various machine learning algorithms and molecular fingerprint features as an input, were trained for this purpose. To evaluate the models under near real-world conditions, Monte Carlo sampling was implemented for the first time. This technique enables the use of probabilistic fingerprint features derived from the experimental HRMS data with SIRIUS+CSI:FingerID as an input for models trained on true binary fingerprint features. Depending on the bioassay, the lowest false-positive rate at 90% recall ranged from 0.251 (sr.mmp, mitochondrial membrane potential) to 0.824 (nr.ar, androgen receptor), which is consistent with the trends observed in the models' performances submitted for the Tox21 Data Challenge. These findings underscore the informativeness of fingerprint features that can be compiled from HRMS in predicting the endocrine-disrupting activity. Moreover, an in-depth SHapley Additive exPlanations analysis unveiled the models' ability to pinpoint structural patterns linked to the modes of action of active chemicals. Despite the superior performance of the single-output models compared to that of the multi-output models, the latter's potential cannot be disregarded for similar tasks in the field of in silico toxicology. This study presents a significant advancement in identifying potentially toxic chemicals within complex mixtures without unambiguous identification and effectively reducing the workload for postprocessing by up to 75% in nontarget HRMS.

Screening androgen receptor agonists of fish species using machine learning and molecular model in NORMAN water-relevant list.

Androgen receptor (AR) agonists have strong endocrine disrupting effects in fish. Most studies mainly investigate AR binding capacity using human AR in vitro. However, there is still few methods to rapidly predict AR agonists in aquatic organisms. This study aimed to screen AR agonists of fish species using machine learning and molecular models in water-relevant list from NORMAN, a network of reference laboratories for monitoring contaminants of emerging concern in the environment. In this study, machine learning approaches (e.g., Deep Forest (DF)), Random Forests and artificial neural networks) were applied to predict AR agonists. Zebrafish, fathead minnow, mosquitofish, medaka fish and grass carp are all important aquatic model organisms widely used to evaluate the toxicity of new pollutants, and the molecular models of ARs from these five fish species were constructed to further screen AR agonists using AlphaFold2. The DF method showed the best performances with 0.99 accuracy, 0.97 sensitivity and 1 precision. The Asn705, Gln711, Arg752, and Thr877 residues in human AR and the corresponding sites in ARs from the five fish species were responsible for agonist binding. Overall, 245 substances were predicted as suspect AR agonists in the five fish species, including, certain glucocorticoids, cholesterol metabolites, and cardiovascular drugs in the NORMAN list. Using machine learning and molecular modeling hybrid methods rapidly and accurately screened AR agonists in fish species, and helping evaluate their ecological risk in fish populations.

Prediction of the Interactions of a Large Number of Per- and Poly-Fluoroalkyl Substances with Ten Nuclear Receptors.

Per- and poly-fluoroalkyl substances (PFASs) are persistent, toxic chemicals that pose significant hazards to human health and the environment. Screening large numbers of chemicals for their ability to act as endocrine disruptors by modulating the activity of nuclear receptors (NRs) is challenging because of the time and cost of in vitro and in vivo experiments. For this reason, we need computational approaches to screen these chemicals and quickly prioritize them for further testing. Here, we utilized molecular modeling and machine-learning predictions to identify potential interactions between 4545 PFASs with ten different NRs. The results show that some PFASs can bind strongly to several receptors. Further, PFASs that bind to different receptors can have very different structures spread throughout the chemical space. Biological validation of these in silico findings should be a high priority.

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.

Effect of bisphenol A on the neurological system: a review update.

Bisphenol A (BPA) is an endocrine-disrupting chemical (EDC) and one of the most produced synthetic compounds worldwide. BPA can be found in epoxy resins and polycarbonate plastics, which are frequently used in food storage and baby bottles. However, BPA can bind mainly to estrogen receptors, interfering with various neurologic functions, its use is a topic of significant concern. Nonetheless, the neurotoxicity of BPA has not been fully understood despite numerous investigations on its disruptive effects. Therefore, this review aims to highlight the most recent studies on the implications of BPA on the neurologic system. Our findings suggest that BPA exposure impairs various structural and molecular brain changes, promoting oxidative stress, changing expression levels of several crucial genes and proteins, destructive effects on neurotransmitters, excitotoxicity and neuroinflammation, damaged blood-brain barrier function, neuronal damage, apoptosis effects, disruption of intracellular Ca2+ homeostasis, increase in reactive oxygen species, promoted apoptosis and intracellular lactate dehydrogenase release, a decrease of axon length, microglial DNA damage, astrogliosis, and significantly reduced myelination. Moreover, BPA exposure increases the risk of developing neurologic diseases, including neurovascular (e.g. stroke) and neurodegenerative (e.g. Alzheimer's and Parkinson's) diseases. Furthermore, epidemiological studies showed that the adverse effects of BPA on neurodevelopment in children contributed to the emergence of serious neurological diseases like attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), depression, emotional problems, anxiety, and cognitive disorders. In summary, BPA exposure compromises human health, promoting the development and progression of neurologic disorders. More research is required to fully understand how BPA-induced neurotoxicity affects human health.

Evaluation of new alternative methods for the identification of estrogenic, androgenic and steroidogenic effects: a comparative in vitro/in silico study.

A suite of in vitro assays and in silico models were evaluated to identify which best detected the endocrine-disrupting (ED) potential of 10 test chemicals according to their estrogenic, androgenic and steroidogenic (EAS) potential compared to the outcomes from ToxCast. In vitro methods included receptor-binding, CALUX transactivation, H295R steroidogenesis, aromatase activity inhibition and the Yeast oestrogen (YES) and Yeast androgen screen (YAS) assays. The impact of metabolism was also evaluated. The YES/YAS assays exhibited a high sensitivity for ER effects and, despite some challenges in predicting AR effects, is a good initial screening assay. Results from receptor-binding and CALUX assays generally correlated and were in accordance with classifications based on ToxCast assays. ER agonism and AR antagonism of benzyl butyl phthalate were abolished when CALUX assays included liver S9. In silico final calls were mostly in agreement with the in vitro assays, and predicted ER and AR effects well. The efficiency of the in silico models (reflecting applicability domains or inconclusive results) was 43-100%. The percentage of correct calls for ER (50-100%), AR (57-100%) and aromatase (33-100%) effects when compared to the final ToxCast call covered a wide range from highly reliable to less reliable models. In conclusion, Danish (Q)SAR, Opera, ADMET Lab LBD and ProToxII models demonstrated the best overall performance for ER and AR effects. These can be combined with the YES/YAS assays in an initial screen of chemicals in the early tiers of an NGRA to inform on the MoA and the design of mechanistic in vitro assays used later in the assessment. Inhibition of aromatase was best predicted by the Vega, AdmetLab and ProToxII models. Other mechanisms and exposure should be considered when making a conclusion with respect to ED effects.

Nontargeted Analysis Reveals a Broad Range of Bioactive Pollutants in Drinking Water by Estrogen Receptor Affinity-Mass Spectrometry.

Exposure to environmental endocrine-disrupting chemicals (EDCs) can cause extensive health issues. However, specific EDCs remain elusive. This work aimed at performing nontargeted identification of estrogen receptor α (ERα)-active compounds using an ERα protein affinity assay combined with high-resolution mass spectrometry in the source and drinking water sampled from major rivers in China. Fifty-one potential ERα-active compounds across 13 categories were identified. For the first time, diisodecyl phenyl phosphate was found to have antiestrogenic activity, and three chemicals (galaxolidone, bensulfuron methyl, and UV234) were plausible ERα ligands. Among the 51 identified compounds, 12 were detected in the aquatic environment for the first time, and the concentration of N-phenyl-2-naphthylamine, a widely used antioxidant in rubber products, was up to 1469 and 1190 ng/L in source and drinking water, respectively. This study demonstrated the widespread presence of known and unknown ERα estrogenic and antiestrogenic pollutants in the major rivers that serve as key sources of drinking water in China and the low removal efficiency of these chemicals in drinking water treatment plants.

Priority list of potential endocrine-disrupting chemicals in food chemical contaminants: a docking study and in vitro/epidemiological evidence integration.

Diet is an important exposure route of endocrine-disrupting chemicals (EDCs), but many unfiltered potential EDCs remain in food. The in silico prediction of EDCs is a popular method for preliminary screening. Potential EDCs in food were screened using Endocrine Disruptome, an open-source platform for inverse docking, to predict the binding probabilities of 587 food chemical contaminants with 18 human nuclear hormone receptor (NHR) conformations. In total, 25 contaminants were bound to multiple NHRs such as oestrogen receptor α/ß and androgen receptor. These 25 compounds mainly include pesticides and per- and polyfluoroalkyl substances (PFASs). The prediction results were validated with the in vitro data. The structural features and the crucial amino acid residues of the four NHRs were also validated based on previous literature. The findings indicate that the screening has good prediction efficiency. In addition, the epidemic evidence about endocrine interference of PFASs in food on children was further validated through this screening. This study provides preliminary screening results for EDCs in food and a priority list for in vitro and in vivo research.

A 4-year study of bovine reproductive hormones that are induced by pharmaceuticals and appear as steroid estrogenic pollutants in the resulting slurry, using in vitro and instrumental analytical methods.

The main objective of the research was to study the environmental "price" of the large-scale, milk production from a rarely known perspective, from the mapping of the estrogenic footprint (the amount of oestrus-inducer hormonal products, and the generated endoestrogens) in the resulting slurry in a dairy cow farm. These micropollutants are endocrine-disrupting chemicals (EDCs) and can be dangerous to the normal reproductive functions even at ng/kg concentration. One of them, 17ß-estradiol, has a 20,000 times stronger estrogenic effect than bisphenol-A, a widely known EDC of industrial origin. While most studies on EDCs are short-term and/or laboratory based, this study is longitudinal and field-based. We sampled the slurry pool on a quarterly basis between 2017 and 2020. Our purpose was testing the estrogenic effects using a dual approach. As an effect-based, holistic method, we developed and used the YES (yeast estrogen screen) test employing the genetically modified Saccharomyces cerevisiae BJ3505 strain which contains human estrogenic receptor. For testing exact molecules, UHPLC-FLD was used. Our study points out that slurry contains a growing amount of EDCs with the risk of penetrating into the soil, crops and the food chain. Considering the Green Chemistry concept, the most benign ways to prevent of the pollution of the slurry is choosing appropriate oestrus-inducing veterinary pharmaceuticals (OIVPs) and the separation of the solid and liquid parts with adequate treatment methods. To our knowledge, this is the first paper on the adaptation of the YES test for medicine and slurry samples, extending its applicability. The adapted YES test turned out to be a sensitive, robust and reliable method for testing samples with potential estrogenic effect. Our dual approach was successful in evaluating the estrogenic effect of the slurry samples.

Combining computational tools and experimental studies towards endocrine disruptors mitigation: A review of biocatalytic and adsorptive processes.

The endocrine disruptors (EDCs) are an important group of emerging contaminants, and their mitigation has been a huge challenge due to their chemistry complexity and variety of these compounds. The traditional treatments are inefficient to completely remove EDCs, and adsorptive processes are the major alternative investigated on their removal. Also, the use of EDCs degrading enzymes has been encouraged due to ecofriendly approach of biocatalytic processes. This paper highlights the occurrence, classification, and toxicity of EDCs with special focus in the use of enzyme-based and adsorptive technologies in the elimination of EDCs from ambiental matrices. Numerous prior reviews have focused on the discussions toward these technologies. However, the literature lacks theoretical discussions about important aspects of these methods such as the mechanisms of EDCs adsorption on the adsorbent surface or the interactions between degrading enzymes - EDCs. In this sense, theoretical calculations combined to experimental studies may help in the development of more efficient technologies to EDCs mitigation. In this review, we point out how computational tools such as molecular docking and molecular dynamics have to contribute to the design of new adsorbents and efficient catalytic processes towards endocrine disruptors mitigation.

Endocrine disrupting chemicals and male fertility: from physiological to molecular effects.

The deleterious effects of chemical or non-chemical endocrine disruptors (EDs) on male fertility potential is well documented but still not fully elucidated. For example, the detection of industrial chemicals' metabolites in seminal plasma and follicular fluid can affect efficiency of the gametogenesis, the maturation and competency of gametes and has guided scientists to hypothesize that endocrine disrupting chemicals (EDCs) may disrupt hormonal homoeostasis by leading to a wide range of hormonal control impairments. The effects of EDCs exposure on reproductive health are highly dependent on factors including the type of EDCs, the duration of exposure, individual susceptibility, and the presence of other co-factors. Research and scientists continue to study these complex interactions. The aim of this review is to summarize the literature to better understand the potential reproductive health risks of EDCs in France.

Melatonin and Vitamins as Protectors against the Reproductive Toxicity of Bisphenols: Which Is the Most Effective? A Systematic Review and Meta-Analysis.

Bisphenols such as bisphenol A (BPA), S (BPS), C (BPC), F (BPF), AF (BPAF), tetrabromobisphenol, nonylphenol, and octylphenol are plasticizers used worldwide to manufacture daily-use articles. Exposure to these compounds is related to many pathologies of public health importance, such as infertility. Using a protector compound against the reproductive toxicological effects of bisphenols is of scientific interest. Melatonin and vitamins have been tested, but the results are not conclusive. To this end, this systematic review and meta-analysis compared the response of reproductive variables to melatonin and vitamin administration as protectors against damage caused by bisphenols. We search for controlled studies of male rats exposed to bisphenols to induce alterations in reproduction, with at least one intervention group receiving melatonin or vitamins (B, C, or E). Also, molecular docking simulations were performed between the androgen (AR) and estrogen receptors (ER), melatonin, and vitamins. About 1234 records were initially found; finally, 13 studies were qualified for review and meta-analysis. Melatonin plus bisphenol improves sperm concentration and viability of sperm and increases testosterone serum levels compared with control groups; however, groups receiving vitamins plus bisphenols had lower sperm concentration, total testis weight, and testosterone serum levels than the control. In the docking analysis, vitamin E had the highest negative MolDock score, representing the best binding affinity with AR and ER, compared with other vitamins and melatonin in the docking. Our findings suggest that vitamins could act as an endocrine disruptor, and melatonin is most effective in protecting against the toxic effects of bisphenols.

Machine Learning Model for Screening Thyroid Stimulating Hormone Receptor Agonists Based on Updated Datasets and Improved Applicability Domain Metrics.

Machine learning (ML) models for screening endocrine-disrupting chemicals (EDCs), such as thyroid stimulating hormone receptor (TSHR) agonists, are essential for sound management of chemicals. Previous models for screening TSHR agonists were built on imbalanced datasets and lacked applicability domain (AD) characterization essential for regulatory application. Herein, an updated TSHR agonist dataset was built, for which the ratio of active to inactive compounds greatly increased to 1:2.6, and chemical spaces of structure-activity landscapes (SALs) were enhanced. Resulting models based on 7 molecular representations and 4 ML algorithms were proven to outperform previous ones. Weighted similarity density (ρs) and weighted inconsistency of activities (IA) were proposed to characterize the SALs, and a state-of-the-art AD characterization methodology ADSAL{ρs, IA} was established. An optimal classifier developed with PubChem fingerprints and the random forest algorithm, coupled with ADSAL{ρs ≥ 0.15, IA ≤ 0.65}, exhibited good performance on the validation set with the area under the receiver operating characteristic curve being 0.984 and balanced accuracy being 0.941 and identified 90 TSHR agonist classes that could not be found previously. The classifier together with the ADSAL{ρs, IA} may serve as efficient tools for screening EDCs, and the AD characterization methodology may be applied to other ML models.

Weight of evidence for cross-species conservation of androgen receptor-based biological activity.

The U.S. Environmental Protection Agency's Endocrine Disruptor Screening Program (EDSP) is tasked with assessing chemicals for their potential to perturb endocrine pathways, including those controlled by androgen receptor (AR). To address challenges associated with traditional testing strategies, EDSP is considering in vitro high-throughput screening assays to screen and prioritize chemicals more efficiently. The ability of these assays to accurately reflect chemical interactions in nonmammalian species remains uncertain. Therefore, a goal of the EDSP is to evaluate how broadly results can be extrapolated across taxa. To assess the cross-species conservation of AR-modulated pathways, computational analyses and systematic literature review approaches were used to conduct a comprehensive analysis of existing in silico, in vitro, and in vivo data. First, molecular target conservation was assessed across 585 diverse species based on the structural similarity of ARs. These results indicate that ARs are conserved across vertebrates and are predicted to share similarly susceptibility to chemicals that interact with the human AR. Systematic analysis of over 5000 published manuscripts was used to compile in vitro and in vivo cross-species toxicity data. Assessment of in vitro data indicates conservation of responses occurs across vertebrate ARs, with potential differences in sensitivity. Similarly, in vivo data indicate strong conservation of the AR signaling pathways across vertebrate species, although sensitivity may vary. Overall, this study demonstrates a framework for utilizing bioinformatics and existing data to build weight of evidence for cross-species extrapolation and provides a technical basis for extrapolating hAR-based data to prioritize hazard in nonmammalian vertebrate species.

Mixed and single effects of endocrine disrupting chemicals in follicular fluid on likelihood of diminished ovarian reserve: A case-control study.

Endocrine-disrupting chemicals (EDCs) are a group of the most widely spread pollutants. Their impacts on reproductive health have become public concerns. Diminished ovarian reserve (DOR) is a disorder of ovarian function. Associations between EDC and DOR have been inconsistent. Very little research investigated the joint effects of multiple EDCs. Here, we performed a case-control study among 64 DOR women and 86 controls. Twenty-one EDC chemicals were assessed in follicular fluid, including parabens, phenols, phthalates and poly-fluoroalkyl substances. Both mixed and single effects of EDCs on DOR were evaluated and validated with a Bayesian kernel machine and logistic regressions. We found that the likelihood of DOR significantly increased with rising levels of the 21-EDC mixture, with an odds ratio (OR) and 95% confidence interval (CI) of 2.12 (1.17-3.83) for the 75th percentile compared to its median level. The overall effect was higher than effects of each subgroup. BP4, MECPP, and PFHxA were driving the association to the mixture, and their single effects were validated, with individual ORs of 8.25 (95%CI:3.45-12.21), 1.92 (95%CI:1.02-4.09), and 1.84 (95%CI:1.08-3.86), respectively. In conclusion, we provided new pollutant markers for DOR and emphasized the importance of the effects of EDC mixtures on female reproductive health.