Scalable Hypothalamic Arcuate Neuron Differentiation from Human Pluripotent Stem Cells Suitable for Modeling Metabolic and Reproductive Disorders.

The hypothalamus, composed of several nuclei, is essential for maintaining our body's homeostasis. The arcuate nucleus (ARC), located in the mediobasal hypothalamus, contains neuronal populations with eminent roles in energy and glucose homeostasis as well as reproduction. These neuronal populations are of great interest for translational research. To fulfill this promise, we used a robotic cell culture platform to provide a scalable and chemically defined approach for differentiating human pluripotent stem cells (hPSCs) into pro-opiomelanocortin (POMC), somatostatin (SST), tyrosine hydroxylase (TH) and gonadotropin-releasing hormone (GnRH) neuronal subpopulations with an ARC-like signature. This robust approach is reproducible across several distinct hPSC lines and exhibits a stepwise induction of key ventral diencephalon and ARC markers in transcriptomic profiling experiments. This is further corroborated by direct comparison to human fetal hypothalamus, and the enriched expression of genes implicated in obesity and type 2 diabetes (T2D). Genome-wide chromatin accessibility profiling by ATAC-seq identified accessible regulatory regions that can be utilized to predict candidate enhancers related to metabolic disorders and hypothalamic development. In depth molecular, cellular, and functional experiments unveiled the responsiveness of the hPSC-derived hypothalamic neurons to hormonal stimuli, such as insulin, neuropeptides including kisspeptin, and incretin mimetic drugs such as Exendin-4, highlighting their potential utility as physiologically relevant cellular models for disease studies. In addition, differential glucose and insulin treatments uncovered adaptability within the generated ARC neurons in the dynamic regulation of POMC and insulin receptors. In summary, the establishment of this model represents a novel, chemically defined, and scalable platform for manufacturing large numbers of hypothalamic arcuate neurons and serves as a valuable resource for modeling metabolic and reproductive disorders.

Development and validation of CYP26A1 inhibition assay for high-throughput screening.

All-trans retinoic acid (atRA) is an endogenous ligand of the retinoic acid receptors, which heterodimerize with retinoid X receptors. AtRA is generated in tissues from vitamin A (retinol) metabolism to form a paracrine signal and is locally degraded by cytochrome P450 family 26 (CYP26) enzymes. The CYP26 family consists of three subtypes: A1, B1, and C1, which are differentially expressed during development. This study aims to develop and validate a high throughput screening assay to identify CYP26A1 inhibitors in a cell-free system using a luminescent P450-Glo assay technology. The assay performed well with a signal to background ratio of 25.7, a coefficient of variation of 8.9%, and a Z-factor of 0.7. To validate the assay, we tested a subset of 39 compounds that included known CYP26 inhibitors and retinoids, as well as positive and negative control compounds selected from the literature and/or the ToxCast/Tox21 portfolio. Known CYP26A1 inhibitors were confirmed, and predicted CYP26A1 inhibitors, such as chlorothalonil, prochloraz, and SSR126768, were identified, demonstrating the reliability and robustness of the assay. Given the general importance of atRA as a morphogenetic signal and the localized expression of Cyp26a1 in embryonic tissues, a validated CYP26A1 assay has important implications for evaluating the potential developmental toxicity of chemicals.

Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development.

The opioid crisis in the United States is a significant public health issue, with a nearly threefold increase in opioid-related fatalities between 1999 and 2014. In response to this crisis, society has made numerous efforts to mitigate its impact. Recent advancements in understanding the structural intricacies of the κ opioid receptor (KOR) have improved our knowledge of how opioids interact with their receptors, triggering downstream signaling pathways that lead to pain relief. This review concentrates on the KOR, offering crucial structural insights into the binding mechanisms of both agonists and antagonists to the receptor. Through comparative analysis of the atomic details of the binding site, distinct interactions specific to agonists and antagonists have been identified. These insights not only enhance our understanding of ligand binding mechanisms but also shed light on potential pathways for developing new opioid analgesics with an improved risk-benefit profile.

Use of Tox21 screening data to profile PFAS bioactivities on nuclear receptors, cellular stress pathways, and cytochrome p450 enzymes.

Per- and poly-fluoroalkyl substances (PFAS) are synthetic chemicals widely used in commercial products. PFAS are a global concern due to their persistence in the environment and extensive associations with adverse health outcomes. While legacy PFAS have been extensively studied, many non-legacy PFAS lack sufficient toxicity information. In this study, we first analyzed the bioactivity of PFAS using Tox21 screening data surveying more than 75 assay endpoints (e.g., nuclear receptors, stress response, and metabolism) to understand the toxicity of non-legacy PFAS and investigate potential new targets of PFAS. From the Tox21 screening data analysis, we confirmed several known PFAS targets/pathways and identified several potential novel targets/pathways of PFAS. To confirm the effect of PFAS on these novel targets/pathways, we conducted several cell- and enzyme-based assays in the follow-up studies. We found PFAS inhibited cytochromes P450s (CYPs), especially CYP2C9 with IC50 values of < 1 µM. Considering PFAS affected other targets/pathways at > 10 µM, PFAS have a higher affinity to CYP2C9. This PFAS-CYP2C9 interaction was further investigated using molecular docking analysis. The result suggested that PFAS directly bind to the active sites of CYP2C9. These findings have important implications to understand the mechanism of PFAS action and toxicity.

Use of in vitro methods combined with in silico analysis to identify potential skin sensitizers in the Tox21 10K compound library.

Introduction: Skin sensitization, which leads to allergic contact dermatitis, is a key toxicological endpoint with high occupational and consumer prevalence. This study optimized several in vitro assays listed in OECD skin sensitization test guidelines for use on a quantitative high-throughput screening (qHTS) platform and performed in silico model predictions to assess the skin sensitization potential of prioritized compounds from the Tox21 10K compound library. Methods: First, we screened the entire Tox21 10K compound library using a qHTS KeratinoSensTM (KS) assay and built a quantitative structure-activity relationship (QSAR) model based on the KS results. From the qHTS KS screening results, we prioritized 288 compounds to cover a wide range of structural chemotypes and tested them in the solid phase extraction-tandem mass spectrometry (SPE-MS/MS) direct peptide reactivity assay (DPRA), IL-8 homogeneous time-resolved fluorescence (HTRF) assay, CD86 and CD54 surface expression in THP1 cells, and predicted in silico sensitization potential using the OECD QSAR Toolbox (v4.5). Results: Interpreting tiered qHTS datasets using a defined approach showed the effectiveness and efficiency of in vitro methods. We selected structural chemotypes to present this diverse chemical collection and to explore previously unidentified structural contributions to sensitization potential. Discussion: Here, we provide a skin sensitization dataset of unprecedented size, along with associated tools, and analysis designed to support chemical assessments.

High-throughput screening of novel TFEB agonists in protecting against acetaminophen-induced liver injury in mice.

Macroautophagy (referred to as autophagy hereafter) is a major intracellular lysosomal degradation pathway that is responsible for the degradation of misfolded/damaged proteins and organelles. Previous studies showed that autophagy protects against acetaminophen (APAP)-induced injury (AILI) via selective removal of damaged mitochondria and APAP protein adducts. The lysosome is a critical organelle sitting at the end stage of autophagy for autophagic degradation via fusion with autophagosomes. In the present study, we showed that transcription factor EB (TFEB), a master transcription factor for lysosomal biogenesis, was impaired by APAP resulting in decreased lysosomal biogenesis in mouse livers. Genetic loss-of and gain-of function of hepatic TFEB exacerbated or protected against AILI, respectively. Mechanistically, overexpression of TFEB increased clearance of APAP protein adducts and mitochondria biogenesis as well as SQSTM1/p62-dependent non-canonical nuclear factor erythroid 2-related factor 2 (NRF2) activation to protect against AILI. We also performed an unbiased cell-based imaging high-throughput chemical screening on TFEB and identified a group of TFEB agonists. Among these agonists, salinomycin, an anticoccidial and antibacterial agent, activated TFEB and protected against AILI in mice. In conclusion, genetic and pharmacological activating TFEB may be a promising approach for protecting against AILI.

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in ß2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.