Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms

Machine Learning Methods for Endocrine Disrupting Potential Identification Based on Single-Cell Data

Humans are continuously exposed to a variety of toxicants and chemicals which is exacerbated during and after environmental catastrophes such as floods, earthquakes, and hurricanes. The hazardous chemical mixtures generated during these events threaten the health and safety of humans and other living organisms. This necessitates the development of rapid decision-making tools to facilitate mitigating the adverse effects of exposure on the key modulators of the endocrine system, such as the estrogen receptor alpha (ERα), for example. The mechanistic stages of the estrogenic transcriptional activity can be measured with high content/high throughput microscopy-based biosensor assays at the single-cell level, which generates millions of object-based minable data points. By combining computational modeling and experimental analysis, we built a highly accurate data-driven classification framework to assess the endocrine disrupting potential of environmental compounds. The effects of these compounds on the ERα pathway are predicted as being receptor agonists or antagonists using the principal component analysis (PCA) projections of high throughput, high content image analysis descriptors. The framework also combines rigorous preprocessing steps and nonlinear machine learning algorithms, such as the Support Vector Machines and Random Forest classifiers, to develop highly accurate mathematical representations of the separation between ERα agonists and antagonists. The results show that Support Vector Machines classify the unseen chemicals correctly with more than 96% accuracy using the proposed framework, where the preprocessing and the PCA steps play a key role in suppressing experimental noise and unraveling hidden patterns in the dataset.

Binary Classification of the Endocrine Disrupting Chemicals by Artificial Neural Networks

We develop a machine learning framework that integrates high content/high throughput image analysis and artificial neural networks (ANNs) to model the separation between chemical compounds based on their estrogenic receptor activity. Natural and man-made chemicals have the potential to disrupt the endocrine system by interfering with hormone actions in people and wildlife. Although numerous studies have revealed new knowledge on the mechanism through which these compounds interfere with various hormone receptors, it is still a very challenging task to comprehensively evaluate the endocrine disrupting potential of all existing chemicals and their mixtures by pure in vitro or in vivo approaches. Machine learning offers a unique advantage in the rapid evaluation of chemical toxicity through learning the underlying patterns in the experimental biological activity data. Motivated by this, we train and test ANN classifiers for modeling the activity of estrogen receptor-α agonists and antagonists at the single-cell level by using high throughput/high content microscopy descriptors. Our framework preprocesses the experimental data by cleaning, scaling, and feature engineering where only the middle 50% of the values from each sample with detectable receptor-DNA binding is considered in the dataset. Principal component analysis is also used to minimize the effects of experimental noise in modeling where these projected features are used in classification model building. The results show that our ANN-based nonlinear data-driven framework classifies the benchmark agonist and antagonist chemicals with 98.41% accuracy.

Environmental estrogens shape disease susceptibility

Along with industrialization, the environment is flooded with endocrine-disrupting chemicals, among which substances with estrogenic effects have attracted widespread attention in medical research. In terms of molecular mechanism, environmental estrogens can cause endocrine and metabolic disorders; interfere with multiple carcinogenic pathways; and lead to neurobehavioral disorders, reproductive toxicity, and multi- or trans-generational phenotypic abnormalities. However, many of the results from molecular and animal experiments were not supported by epidemiology, which may be related to the existence of a window of sensitivity to environmental estrogen exposure over the human life course, where the consequences of exposure vary greatly from other times. This paper will introduce the main sources of environmental estrogens, their toxicity and mechanisms of action, the status of research on several representative types, and current monitoring and treatment methods. We also discussed the extent of the risks to human health dialectically in the context of laboratory and epidemiological findings, with a view to better addressing these chemicals to which we are constantly exposed.

Autophagy attenuates placental apoptosis, oxidative stress and fetal growth restriction in pregnant ewes

Bisphenol A (BPA)-induced oxidative stress (OS) and its potentially associated autophagy and apoptosis have not been studied previously in pregnant ewes. Accordingly, this study investigated the underlying mechanisms of BPA-induced autophagy and apoptosis in the placenta and primary trophoblasts of pregnant ewes exposed to BPA both in vivo and in vitro. In vivo experiment, pregnant Hu ewes (n = 8) were exposed to 5 mg/kg/d of BPA compared to control ewes (n = 8) receiving only corn oil from day 40 through day 110 of gestation. Exposure to BPA during gestation resulted in placental insufficiency, fetal growth restriction (FGR), autophagy, endoplasmic reticulum stress (ERS), mitochondrial dysfunction, OS, and apoptosis in type A placentomes. Regarding in vitro model, primary ovine trophoblasts were exposed to BPA, BPA plus chloroquine (CQ; an autophagy inhibitor) or BPA plus rapamycin (RAP; an autophagy activator) for 12 h. Data illustrated that exposure to BPA enhanced autophagy (ULK1, Beclin-1, LC3, Parkin, and PINK1), ERS (GRP78, CHOP10, ATF4, and ATF6) and apoptosis (Caspase 3, Bcl-2, Bax, P53) but decreased the antioxidant (CAT, Nrf2, HO-1, and NQO1)-related mRNA and protein expressions as well as impaired the mitochondrial function. Moreover, treatment with CQ exacerbated the BPA-mediated OS, mitochondrial dysfunction, apoptosis, and ERS. On the contrary, RAP treatment counteracted the BPA-induced trophoblast dysfunctions mentioned above. Overall, the findings illustrated that BPA exposure could contribute to autophagy in the ovine placenta and trophoblasts and that autophagy, in turn, could alleviate BPA-induced apoptosis, mitochondrial dysfunction, ERS, and OS. These results offer new mechanistic insights into the role of autophagy in mitigating BPA-induced placental dysfunctions and FGR.

Research Advances in the Analysis of Estrogenic Endocrine Disrupting Compounds in Milk and Dairy Products

Milk and dairy products are sources of exposure to estrogenic endocrine disrupting compounds (e-EDCs). Estrogenic disruptors can accumulate in organisms through the food chain and may negatively affect ecosystems and organisms even at low concentrations. Therefore, the analysis of e-EDCs in dairy products is of practical significance. Continuous efforts have been made to establish effective methods to detect e-EDCs, using convenient sample pretreatments and simple steps. This review aims to summarize the recently reported pretreatment methods for estrogenic disruptors, such as solid-phase extraction (SPE) and liquid phase microextraction (LPME), determination methods including gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), Raman spectroscopy, and biosensors, to provide a reliable theoretical basis and operational method for e-EDC analysis in the future.

Predicting the Estrogen Receptor Activity of Environmental Chemicals by Single-Cell Image Analysis and Data-driven Modeling

A comprehensive evaluation of toxic chemicals and understanding their potential harm to human physiology is vital in mitigating their adverse effects following exposure from environmental emergencies. In this work, we develop data-driven classification models to facilitate rapid decision making in such catastrophic events and predict the estrogenic activity of environmental toxicants as estrogen receptor-α (ERα) agonists or antagonists. By combining high-content analysis, big-data analytics, and machine learning algorithms, we demonstrate that highly accurate classifiers can be constructed for evaluating the estrogenic potential of many chemicals. We follow a rigorous, high throughput microscopy-based high-content analysis pipeline to measure the single cell-level response of benchmark compounds with known in vivo effects on the ERα pathway. The resulting high-dimensional dataset is then pre-processed by fitting a non-central gamma probability distribution function to each feature, compound, and concentration. The characteristic parameters of the distribution, which represent the mean and the shape of the distribution, are used as features for the classification analysis via Random Forest (RF) and Support Vector Machine (SVM) algorithms. The results show that the SVM classifier can predict the estrogenic potential of benchmark chemicals with higher accuracy than the RF algorithm, which misclassifies two antagonist compounds.

Combining Experimental Isotherms, Minimalistic Simulations, and a Model to Understand and Predict Chemical Adsorption onto Montmorillonite Clays

An attractive approach to minimize human and animal exposures to toxic environmental contaminants is the use of safe and effective sorbent materials to sequester them. Montmorillonite clays have been shown to tightly bind diverse toxic chemicals. Due to their promise as sorbents to mitigate chemical exposures, it is important to understand their function and rapidly screen and predict optimal clay-chemical combinations for further testing. We derived adsorption free-energy values for a structurally and physicochemically diverse set of toxic chemicals using experimental adsorption isotherms performed in the current and previous studies. We studied the diverse set of chemicals using minimalistic MD simulations and showed that their interaction energies with calcium montmorillonite clays calculated using simulation snapshots in combination with their net charge and their corresponding solvent's dielectric constant can be used as inputs to a minimalistic model to predict adsorption free energies in agreement with experiments. Additionally, experiments and computations were used to reveal structural and physicochemical properties associated with chemicals that can be adsorbed to calcium montmorillonite clay. These properties include positively charged groups, phosphine groups, halide-rich moieties, hydrogen bond donor/acceptors, and large, rigid structures. The combined experimental and computational approaches used in this study highlight the importance and potential applicability of analogous methods to study and design novel advanced sorbent systems in the future, broadening their applicability for environmental contaminants.