Publications by authors named "D Reiman"

Because large brains are energetically expensive, they are associated with metabolic traits that facilitate energy availability across vertebrates. However, the biological underpinnings driving these traits are not known. Given its role in regulating host metabolism in disease studies, we hypothesized that the gut microbiome contributes to variation in normal cross-vertebrate species differences in metabolism, including those associated with the brain's energetic requirements.

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Autoimmune diseases such as systemic lupus erythematosus (SLE) display a strong female bias. Although sex hormones have been associated with protecting males from autoimmunity, the molecular mechanisms are incompletely understood. Here we report that androgen receptor (AR) expressed in T cells regulates genes involved in T cell activation directly, or indirectly via controlling other transcription factors.

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Article Synopsis
  • Single-cell RNA sequencing (scRNA-seq) allows in-depth analysis of B cell evolution during immune responses, highlighting processes like somatic hypermutation (SHM) and class switch recombination (CSR).
  • The newly introduced TRIBAL algorithm focuses on accurately reconstructing B cell evolutionary histories by considering both SHM and CSR, addressing limitations of prior phylogenetic methods.
  • Simulations and real-world applications show TRIBAL outperforms existing approaches, offering more accurate lineage trees and insights into B cell responses, which can enhance vaccine development and therapeutic antibody identification.
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The introduction of AlphaFold 2 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.

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