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Natural regulatory T (nT(reg)) cells recognize self-peptides with high affinity, yet the understanding of how affinity influences their selection in the thymus is incomplete. We use altered peptide ligands in transgenic mice and in organ culture to create thymic environments spanning a broad range of ligand affinity. We demonstrate that the nT(reg) TCR repertoire is shaped by affinity-based selection, similar to conventional T cells. The effect of each ligand on the two populations is distinct, consistent with early nT(reg) cell lineage specification. Foxp3 expression is an independent process that does not rely on "high affinity" binding per se, but requires a high-potency agonistic interaction for its induction. The timing of ligand exposure, TGFbeta signaling, and the organization of the thymic architecture are also important. The development of nT(reg) cells is therefore a multistep process in which ligand affinity, potency, and timing of presentation all play a role in determining cell fate.
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http://dx.doi.org/10.4049/jimmunol.182.3.1341 | DOI Listing |
Sci Rep
September 2025
Department of Pharmacy, College of Pharmacy and Research Institute for Drug Development, Pusan National University, Busan, Republic of Korea.
Drug-induced transcriptomic data are crucial for understanding molecular mechanisms of action (MOAs), predicting drug efficacy, and identifying off-target effects. However, their high dimensionality presents challenges for analysis and interpretation. Dimensionality reduction (DR) methods simplify such data, enabling efficient analysis and visualization.
View Article and Find Full Text PDFChem Commun (Camb)
August 2025
Department of Chemistry, Emory University, 1515 Dickey Drive, Atlanta, GA 30322, USA.
Natural products have served as a fruitful starting point for antibiotic drug development. Evolution has served both as a catalyst to optimize their structures and also as a hinderance to render them ineffective through a variety of resistance mechanisms. To combat this, there has been a significant effort to discover new antibiotics with non-conventional mechanisms of action.
View Article and Find Full Text PDFBrief Bioinform
July 2025
Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 T W Alexander Dr, Research Triangle Park, Durham, NC 27709, United States.
High-dimensional single-cell data analysis is crucial for understanding complex biological interactions, yet conventional dimensionality reduction methods (DRMs) often fail to preserve both global and local structures. Existing DRMs, such as t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), Principal Component Analysis (PCA), and Potential of Heat-diffusion for Affinity-based Transition Embedding (PHATE), optimize different visualization objectives, resulting in trade-offs between cluster separability, spatial organization, and temporal coherence. To overcome these limitations, we introduce GIBOOST, an AI-driven framework that integrates outputs from multiple DRMs using a Bayesian framework and an optimized autoencoder.
View Article and Find Full Text PDFOrg Lett
August 2025
Discovery Chemistry, Johnson & Johnson, San Diego, California 92121, United States.
DNA-encoded libraries (DELs) enable screening of billions of molecules in a single pool through affinity-based selection and have become an indispensable hit finding technology. Although DELs provide relatively easy access to billions of molecules, some methodologies to chemically diversify DELs remain adapted to partially aqueous conditions. To address this challenge, we recently demonstrated the use of surfactant-DNA (Surf-DNA) complexes as an effective approach to facilitate DEL-compatible reactions under anhydrous conditions.
View Article and Find Full Text PDFExpert Rev Proteomics
September 2025
Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, USA.
Introduction: The investigation of different proteoforms in clinical samples is a promising approach to elucidate the molecular mechanisms of diseases. Furthermore, proteoform analysis holds great potential for identifying disease-specific biomarkers and targets for personalized medicine. Despite advances in top-down proteomics (TDP) instrumentation, sample preparation and cleanup remain challenging.
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