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Fluorinated biphenyls and their analogues (FBAs) are considered new persistent organic pollutants, but their endocrine-disrupting effects are still unknown. To fill this gap, the binding probability of 44 FBAs to different nuclear hormone receptors (NHRs) was predicted using Endocrine Disruptome. And molecular similarity and network toxicology analysis were used to strengthen the docking screening. The docking results showed that FBAs could have high binding potential for various NHRs, such as estrogen receptors β antagonism (ERβ an), liver X receptors α (LXRα), estrogen receptors α (ERα), and liver X receptors β (LXRβ). The similarity analysis found that the degree of overlap of the NHR repertoire was related to the Tanimoto coefficient of FBAs. Network toxicology verified a part of docking screening results and identified endocrine-disrupting pathways worthy of attention. This study found out potential endocrine-disrupting FBAs and their vulnerable, and developed a workflow that would leverage in silico approaches including molecular docking, similarity, and network toxicology for risk prioritization of potential endocrine-disrupting compounds.
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http://dx.doi.org/10.1016/j.chemosphere.2022.137701 | DOI Listing |
Reprod Biol
September 2025
Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Engineering Research Center of Biopreservation and Artificial Organs, Ministry of Education, No 218 Jixi Road, Hefei Anhui230022, China; Key Laboratory of Population Health Across
Current research indicates that polyethylene terephthalate microplastics (PET-MPs) may significantly impair male reproductive function. This study aimed to investigate the potential molecular mechanisms underlying this impairment. Potential gene targets of PET-MPs were predicted via the SwissTargetPrediction database.
View Article and Find Full Text PDFSci Adv
September 2025
Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here, we performed single-nucleus multiomics and spatial transcriptomics in up to 32 nondiabetic (ND), autoantibody-positive (AAB), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine subtypes.
View Article and Find Full Text PDFRSC Chem Biol
July 2025
Institute for Pharmaceutical Chemistry, Johann Wolfgang Goethe-University Max-von-Laue-Str. 9 D-60438 Frankfurt am Main Germany
Herein we present the rapid development of LH168, a potent and highly selective chemical probe for WDR5, streamlined by utilizing a DEL-ML (DNA encoded library-machine learning) hit as the chemical starting point. LH168 was comprehensively characterized in bioassays and demonstrated potent target engagement at the WIN-site pocket of WDR5, with an EC of approximately 10 nM, a long residence time, and exceptional proteome-wide selectivity for WDR5. In addition, we present the X-ray co-crystal structure and provide insights into the structure-activity relationships (SAR).
View Article and Find Full Text PDFMost of the United States (US) population resides in cities, where they are subjected to the urban heat island effect. In this study, we develop a method to estimate hourly air temperatures at resolution, improving exposure assessment of US population when compared to existing gridded products. We use an extensive network of personal weather stations to capture the intra-urban variability.
View Article and Find Full Text PDFMetabolomics
September 2025
Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
Introduction: Initially developed for transcriptomics data, pathway analysis (PA) methods can introduce biases when applied to metabolomics data, especially if input parameters are not chosen with care. This is particularly true for exometabolomics data, where there can be many metabolic steps between the measured exported metabolites in the profile and internal disruptions in the organism. However, evaluating PA methods experimentally is practically impossible when the sample's "true" metabolic disruption is unknown.
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