Nuclear receptors regulate transcription in response to ligand signals and enable the pharmacological control of gene expression. However, many nuclear receptors are still poorly explored and are not accessible to ligand-based target identification studies. In particular, most members of the NR2 family are among the least studied proteins of the class, and apart from the retinoid X receptors (RXR), validated NR2 ligands are very rare. Here, we gathered the NR2 modulators reported in literature for comparative profiling in uniform test systems. Most candidate compounds displayed insufficient on-target activity or selectivity to be used as chemical tools for NR2 receptors underscoring the urgent need for further NR2 ligand development. Nevertheless, a small NR2 modulator set could be assembled for application in a chemogenomic fashion. There are 48 ligand-activated transcription factors in humans forming the superfamily of nuclear receptors (NRs, Figure 1a), which translate (endogenous) ligand signals into changes in gene expression patterns. The multifaceted roles of NRs span pivotal physiological processes, encompassing metabolism, inflammation, and cellular differentiation. Over decades, the NR1 and NR3 receptor families, including (steroid) hormone receptors and lipid sensors, have emerged as well-explored therapeutic targets of essential drugs like, for example, glucocorticoids as anti-inflammatory drugs, estrogen receptor modulators as contraceptives and anticancer agents, and PPAR agonists as oral antidiabetics. Despite this progress, a significant portion of the NR superfamily remains understudied, particularly within the NR2 family which comprises the hepatocyte nuclear factor-4 receptors (HNF4α/γ; NR2A1/2), the retinoid X receptors (RXRα/β/γ; NR2B1-3), the testicular receptors (TR2/4; NR2C1/2), the tailless-like receptors (TLX and PNR; NR2E1/3), and the COUP-TF-like receptors (COUP-TF1/2, V-erBA-related gene; NR2F1/2/6). Apart from RXR, all NR2 receptors are considered as orphan, and their ligands remain widely elusive. Therefore, chemical tools for most NR2 receptors are rare and poorly annotated posing an obstacle to target identification and validation studies. To enable chemogenomics on NR2 receptors and improve annotation, of the few available ligands, we gathered a scarce collection of NR2 modulators (agonists, antagonists, inverse agonists) for comparative evaluation and profiling. While the NR2B family (RXR) is well covered with high-quality ligands and a few early tools are available for NR2E1, we found the available ligands of the NR2A and NR2C families of insufficient quality to be used as chemical tools.
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http://dx.doi.org/10.1021/acsptsci.4c00719 | DOI Listing |
J Am Chem Soc
March 2025
Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-3290, United States.
Semiconductor devices often rely on high-purity materials and interfaces achieved through vapor- and vacuum-based fabrication methods, which can enable precise compositional control down to single atomic layers. Compared to groups IV and III-V semiconductors, hybrid perovskites (HPs) are an emergent class of semiconductor materials with remarkable solution processability and compositional variability that have facilitated rapid experimentation to achieve new properties and progress toward efficient devices, particularly for solar cells. Surprisingly, vapor deposition techniques for HPs are substantially less developed, despite the complementary benefits that have secured vapor methods as workhorse tools for semiconductor fabrication.
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March 2025
Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
With the rapid advancements in the field of fluorescent dyes, accurate prediction of optical properties and efficient retrieval of dye-related data are essential for effective dye design. However, there is a lack of tools for comprehensive data integration and convenient data retrieval. Moreover, existing prediction models mainly focus on a single property of fluorescent dyes and fail to account for the diverse fluorophores and solutions in a systematic manner.
View Article and Find Full Text PDFElife
March 2025
Department of Anatomy and Embryology, Leiden University Medical Center, Leiden, Netherlands.
Human autonomic neuronal cell models are emerging as tools for modelling diseases such as cardiac arrhythmias. In this systematic review, we compared thirty-three articles applying fourteen different protocols to generate sympathetic neurons and three different procedures to produce parasympathetic neurons. All methods involved the differentiation of human pluripotent stem cells, and none employed permanent or reversible cell immortalization.
View Article and Find Full Text PDFChem Sci
March 2025
Centre of Physiology and Pharmacology, Institute of Pharmacology, Medical University of Vienna Währinger Straβe 13A 1090 Vienna Austria
Fluorescent labeling techniques have enabled the visualization of various biomolecules, cellular structures, and their associated physiological processes. At the same time, there remains a demand for developing novel fluorescent compounds possessing unique chemical properties for biological imaging. A recently developed class of fluorophores, termed , displays optimal brightness and large Stokes shifts that are ideal for fluorescence microscopy.
View Article and Find Full Text PDFChem Sci
March 2025
Molecular AI, Discovery Sciences, R&D, AstraZeneca Gothenburg Pepparedsleden 1 43183 Mölndal Sweden
The regio- and site-selectivity of organic reactions is one of the most important aspects when it comes to synthesis planning. Due to that, massive research efforts were invested into computational models for regio- and site-selectivity prediction, and the introduction of machine learning to the chemical sciences within the past decade has added a whole new dimension to these endeavors. This review article walks through the currently available predictive tools for regio- and site-selectivity with a particular focus on machine learning models while being organized along the individual reaction classes of organic chemistry.
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