Initiation of adaptive immunity involves distinct migratory cell populations coming together in a highly dynamic and spatially organized process. However, we lack a detailed spatiotemporal map of these events due to our inability to track the fate of cells between anatomically distinct locations or functionally identify cell populations as migratory. We used photo-convertible transgenic mice (Kaede) to spatiotemporally track the fate and composition of the cell populations that leave the site of priming and enter the draining lymph node to initiate immunity. We show that following skin priming, the lymph node migratory population is principally composed of cells recruited to the site of priming, with a minor contribution from tissue resident cells. In combination with the YAe/Eα system, we also show that the majority of cells presenting antigen are CD103CD11b dendritic cells that were recruited to the site of priming during the inflammatory response. This population has previously only been described in relation to mucosal tissues. Comprehensive phenotypic profiling of the cells migrating from the skin to the draining lymph node by mass cytometry revealed that in addition to dendritic cells, the migratory population also included CD4 and CD8 T cells, B cells, and neutrophils. Taking our complex spatiotemporal data set, we then generated a model of cell migration that quantifies and describes the dynamics of arrival, departure, and residence times of cells at the site of priming and in the draining lymph node throughout the time-course of the initiation of adaptive immunity. In addition, we have identified the mean migration time of migratory dendritic cells as they travel from the site of priming to the draining lymph node. These findings represent an unprecedented, detailed and quantitative map of cell dynamics and phenotypes during immunization, identifying where, when and which cells to target for immunomodulation in autoimmunity and vaccination strategies.
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http://dx.doi.org/10.3389/fimmu.2019.00598 | DOI Listing |
Nat Struct Mol Biol
January 2025
Heidelberg University Biochemistry Center (BZH), Heidelberg, Germany.
Intron removal during pre-mRNA splicing is of extraordinary complexity and its disruption causes a vast number of genetic diseases in humans. While key steps of the canonical spliceosome cycle have been revealed by combined structure-function analyses, structural information on an aberrant spliceosome committed to premature disassembly is not available. Here, we report two cryo-electron microscopy structures of post-B spliceosome intermediates from Schizosaccharomyces pombe primed for disassembly.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Biology, University of Konstanz, Konstanz, Germany.
Phosphorylation of substrates by cyclin-dependent kinases (CDKs) is the driving force of cell cycle progression. Several CDK-activating cyclins are involved, yet how they contribute to substrate specificity is still poorly understood. Here, we discover that a positively charged pocket in cyclin B1, which is exclusively conserved within B-type cyclins and binds phosphorylated serine- or threonine-residues, is essential for correct execution of mitosis.
View Article and Find Full Text PDFTrees (Berl West)
January 2025
Department of Geography, Johannes Gutenberg University, 55099 Mainz, Germany.
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Abstract: Tree-rings are the prime archive for high-resolution climate information over the past two millennia. However, the accuracy of annually resolved reconstructions from tree-rings can be constrained by what is known as climate signal age effects (CSAE), encompassing changes in the sensitivity of tree growth to climate over their lifespans. Here, we evaluate CSAE in from an upper tree line site in the Spanish central Pyrenees, Lake Gerber, which became a key location for reconstructing western Mediterranean summer temperatures at annual resolution.
CRISPR-Cas enzymes must recognize a protospacer-adjacent motif (PAM) to edit a genomic site, significantly limiting the range of targetable sequences in a genome. Machine learning-based protein engineering provides a powerful solution to efficiently generate Cas protein variants tailored to recognize specific PAMs. Here, we present Protein2PAM, an evolution-informed deep learning model trained on a dataset of over 45,000 CRISPR-Cas PAMs.
View Article and Find Full Text PDFMol Cell Proteomics
January 2025
Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, The Netherlands; Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, The Netherlands. Electronic address:
Protein kinases are prime targets for drug development due to their involvement in various cancers. However, selective inhibition of kinases, while avoiding off-target effects remains a significant challenge for the development of protein kinase inhibitors. Activity-based protein profiling (ABPP), in combination with pan-kinase activity-based probes (ABPs) and mass spectrometry-based proteomics, enables the identification of kinase drug targets.
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