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      Inhibition of the Eukaryotic Initiation Factor-2-α Kinase PERK Decreases Risk of Autoimmune Diabetes in Mice

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          Abstract

          Preventing the onset of autoimmune type 1 diabetes (T1D) is feasible through pharmacological interventions that target molecular stress-responsive mechanisms. Cellular stresses, such as nutrient deficiency, viral infection, or unfolded proteins, trigger the integrated stress response (ISR), which curtails protein synthesis by phosphorylating eIF2α. In T1D, maladaptive unfolded protein response (UPR) in insulin-producing β cells renders these cells susceptible to autoimmunity. We show that inhibition of the eIF2α kinase PERK, a common component of the UPR and ISR, reverses the mRNA translation block in stressed human islets and delays the onset of diabetes, reduces islet inflammation, and preserves β cell mass in T1D-susceptible mice. Single-cell RNA sequencing of islets from PERK-inhibited mice shows reductions in the UPR and PERK signaling pathways and alterations in antigen processing and presentation pathways in β cells. Spatial proteomics of islets from these mice shows an increase in the immune checkpoint protein PD-L1 in β cells. Golgi membrane protein 1, whose levels increase following PERK inhibition in human islets and EndoC-βH1 human β cells, interacts with and stabilizes PD-L1. Collectively, our studies show that PERK activity enhances β cell immunogenicity, and inhibition of PERK may offer a strategy to prevent or delay the development of T1D.

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Comprehensive Integration of Single-Cell Data

            Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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              Fast, sensitive, and accurate integration of single cell data with Harmony

              The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                03 June 2024
                : 2023.10.06.561126
                Affiliations
                [1 ]Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, IL, USA
                [2 ]HiberCell Inc., New York, NY, USA
                [3 ]ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels, Belgium
                [4 ]Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
                [5 ]Department of Pathology, The University of Chicago, Chicago, IL, USA
                [6 ]Department of Pediatrics, Center for Diabetes and Metabolic Diseases, and the Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN
                [7 ]Department of Biochemistry and Molecular Biology and the Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, IN, USA
                Author notes

                AUTHOR CONTRIBUTIONS

                CM, VC, SAO, MES, RGM, and SAT conceptualized the research; CM, FH, JRE, JEW, JBN, TN, AC, KTF, SN, CMA, SS, EN, XY, DLE, BJWR, KAS, and SAT performed investigation; RGM and SAT provided project supervision; CM, SAT, and RGM wrote the original draft; all authors contributed to discussion, edited the manuscript, and approved the final version of the manuscript.

                [* ]Corresponding author: Raghavendra G Mirmira, 900 E. 57 th Street, KCBD 8130, Chicago, IL 60637, USA; mirmira@ 123456uchicago.edu
                Author information
                http://orcid.org/0000-0002-5858-6759
                http://orcid.org/0000-0002-4393-954X
                http://orcid.org/0000-0002-4056-2695
                http://orcid.org/0000-0002-5013-6075
                Article
                10.1101/2023.10.06.561126
                11185543
                38895427
                8ad9bcab-75ae-4185-89c7-3a74c72435ab

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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                Article

                unfolded protein response,integrated stress response,type 1 diabetes,islet,mrna translation

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