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      Novel App knock-in mouse model shows key features of amyloid pathology and reveals profound metabolic dysregulation of microglia

      research-article
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      Molecular Neurodegeneration
      BioMed Central
      Neuritic plaques, Vascular amyloid, Neurodegeneration, Astrogliosis, Phagocytic microglia, Lipid dyshomeostasis

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          Abstract

          Background

          Genetic mutations underlying familial Alzheimer’s disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those models are subject to artifacts, including random genetic integration of the transgene, ectopic expression and non-physiological protein levels. The genetic engineering of novel mouse models using knock-in approaches addresses some of those limitations. With mounting evidence of the role played by microglia in AD, high-dimensional approaches to phenotype microglia in those models are critical to refine our understanding of the immune response in the brain.

          Methods

          We engineered a novel App knock-in mouse model ( App SAA) using homologous recombination to introduce three disease-causing coding mutations (Swedish, Arctic and Austrian) to the mouse App gene. Amyloid-β pathology, neurodegeneration, glial responses, brain metabolism and behavioral phenotypes were characterized in heterozygous and homozygous App SAA mice at different ages in brain and/ or biofluids. Wild type littermate mice were used as experimental controls. We used in situ imaging technologies to define the whole-brain distribution of amyloid plaques and compare it to other AD mouse models and human brain pathology. To further explore the microglial response to AD relevant pathology, we isolated microglia with fibrillar Aβ content from the brain and performed transcriptomics and metabolomics analyses and in vivo brain imaging to measure energy metabolism and microglial response. Finally, we also characterized the mice in various behavioral assays.

          Results

          Leveraging multi-omics approaches, we discovered profound alteration of diverse lipids and metabolites as well as an exacerbated disease-associated transcriptomic response in microglia with high intracellular Aβ content. The App SAA knock-in mouse model recapitulates key pathological features of AD such as a progressive accumulation of parenchymal amyloid plaques and vascular amyloid deposits, altered astroglial and microglial responses and elevation of CSF markers of neurodegeneration. Those observations were associated with increased TSPO and FDG-PET brain signals and a hyperactivity phenotype as the animals aged.

          Discussion

          Our findings demonstrate that fibrillar Aβ in microglia is associated with lipid dyshomeostasis consistent with lysosomal dysfunction and foam cell phenotypes as well as profound immuno-metabolic perturbations, opening new avenues to further investigate metabolic pathways at play in microglia responding to AD-relevant pathogenesis. The in-depth characterization of pathological hallmarks of AD in this novel and open-access mouse model should serve as a resource for the scientific community to investigate disease-relevant biology.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13024-022-00547-7.

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          Most cited references114

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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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            Fiji: an open-source platform for biological-image analysis.

            Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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              STAR: ultrafast universal RNA-seq aligner.

              Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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                Author and article information

                Contributors
                xia@dnli.com
                lianoglou@dnli.com
                sandmann@dnli.com
                calvert@dnli.com
                suh@dnli.com
                thomsen@dnli.com
                dugas@dnli.com
                pizzo@dnli.com
                devos@dnli.com
                earr@dnli.com
                zouklin@hotmail.com
                davis@dnli.com
                ha@dnli.com
                aleung@dnli.com
                nguyen@dnli.com
                chau@dnli.com
                yulyaningsih@dnli.com
                ilopez@dnli.com
                solanoy@dnli.com
                masoud@dnli.com
                richardliangphd@gmail.com
                karinlin28@gmail.com
                gastarita@gmail.com
                khoury@dnli.com
                zuchero@dnli.com
                thorne@dnli.com
                kevin.shen@gladstone.ucsf.edu
                stephanie.miller@gladstone.ucsf.edu
                jorge.palop@gladstone.ucsf.edu
                Dylan.Garceau@jax.org
                Mike.Sasner@jax.org
                jennifer@cajalneuro.com
                julie@cajalneuro.com
                Selina.Hummel@med.uni-muenchen.de
                Johannes.Gnoerich@med.uni-muenchen.de
                Karin.Wind@med.uni-muenchen.de
                Lea.Kunze@med.uni-muenchen.de
                Artem.Zatcepin@med.uni-muenchen.de
                Matthias.Brendel@med.uni-muenchen.de
                Michael.Willem@mail03.med.uni-muenchen.de
                Christian.Haass@mail03.med.uni-muenchen.de
                dmb4001@med.cornell.edu
                tsz4002@med.cornell.edu
                ago2002@med.cornell.edu
                scearce-levie@dnli.com
                lewcock@dnli.com
                dipaolo@dnli.com
                sanchez@dnli.com
                Journal
                Mol Neurodegener
                Mol Neurodegener
                Molecular Neurodegeneration
                BioMed Central (London )
                1750-1326
                11 June 2022
                11 June 2022
                2022
                : 17
                : 41
                Affiliations
                [1 ]GRID grid.491115.9, ISNI 0000 0004 5912 9212, Denali Therapeutics, Inc., ; 161 Oyster Point Blvd, South San Francisco, California, 94080 USA
                [2 ]GRID grid.17635.36, ISNI 0000000419368657, Department of Pharmaceutics, University of Minnesota, ; 9-177 Weaver-Densford Hall, 308 Harvard St. SE, Minneapolis, MN 55455 USA
                [3 ]GRID grid.249878.8, ISNI 0000 0004 0572 7110, Gladstone Institute of Neurological Disease, ; San Francisco, CA 94158 USA
                [4 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Neurology, University of California, ; San Francisco, CA 94158 USA
                [5 ]GRID grid.249880.f, ISNI 0000 0004 0374 0039, The Jackson Lab, ; Bar Harbor, Maine USA
                [6 ]GRID grid.417881.3, ISNI 0000 0001 2298 2461, Allen Institute for Brain Science, ; Seattle, Washington, USA
                [7 ]GRID grid.424247.3, ISNI 0000 0004 0438 0426, German Center for Neurodegenerative Diseases (DZNE) Munich, ; 81377 Munich, Germany
                [8 ]GRID grid.411095.8, ISNI 0000 0004 0477 2585, Department of Nuclear Medicine, , University Hospital of Munich, ; LMU Munich, Munich, Germany
                [9 ]GRID grid.5252.0, ISNI 0000 0004 1936 973X, Metabolic Biochemistry, Biomedical Center (BMC), Faculty of Medicine, Ludwig- Maximilians-Universität, ; München, 81377 Munich, Germany
                [10 ]GRID grid.452617.3, Munich Cluster for Systems Neurology (SyNergy), ; 81377 Munich, Germany
                [11 ]GRID grid.5386.8, ISNI 000000041936877X, Appel Alzheimer’s Disease Research Institute, Weill Cornell Medicine, ; New York, NY USA
                [12 ]GRID grid.5386.8, ISNI 000000041936877X, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, ; New York, NY USA
                [13 ]GRID grid.5386.8, ISNI 000000041936877X, Neuroscience Graduate Program, Weill Cornell Medicine, ; New York, NY USA
                Author information
                http://orcid.org/0000-0002-2620-5165
                Article
                547
                10.1186/s13024-022-00547-7
                9188195
                35690868
                3610134d-5adb-4b84-a4d0-a7fcc7325ff6
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 22 December 2021
                : 25 May 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000049, National Institute on Aging;
                Award ID: R01AG047589
                Award ID: RF1AG062234
                Award ID: R01AG062629
                Award ID: P01AG073082
                Award ID: R01AG068091
                Award Recipient :
                Funded by: Deutsche Forschungsgemeinschaft
                Award ID: 390857198
                Award Recipient :
                Funded by: Koselleck Project
                Award ID: HA1737/16-1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006312, BrightFocus Foundation;
                Award ID: A2019363S
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2022

                Neurosciences
                neuritic plaques,vascular amyloid,neurodegeneration,astrogliosis,phagocytic microglia,lipid dyshomeostasis

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