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      Systematic phenotyping and characterization of the 5xFAD mouse model of Alzheimer’s disease

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          Abstract

          Mouse models of human diseases are invaluable tools for studying pathogenic mechanisms and testing interventions and therapeutics. For disorders such as Alzheimer’s disease in which numerous models are being generated, a challenging first step is to identify the most appropriate model and age to effectively evaluate new therapeutic approaches. Here we conducted a detailed phenotypic characterization of the 5xFAD model on a congenic C57BL/6 J strain background, across its lifespan – including a seldomly analyzed 18-month old time point to provide temporally correlated phenotyping of this model and a template for characterization of new models of LOAD as they are generated. This comprehensive analysis included quantification of plaque burden, Aβ biochemical levels, and neuropathology, neurophysiological measurements and behavioral and cognitive assessments, and evaluation of microglia, astrocytes, and neurons. Analysis of transcriptional changes was conducted using bulk-tissue generated RNA-seq data from microdissected cortices and hippocampi as a function of aging, which can be explored at the MODEL-AD Explorer and AD Knowledge Portal. This deep-phenotyping pipeline identified novel aspects of age-related pathology in the 5xFAD model.

          Abstract

          Measurement(s) Protein Expression • gene expression • electrophysiology data • protein measurement • behavior
          Technology Type(s) immunofluorescence microscopy assay • RNA sequencing • electrophysiology assay • Electrochemiluminescence Immunoassay • animal activity monitoring system
          Factor Type(s) genotype • age • sex
          Sample Characteristic - Organism Mus musculus

          Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.15176109

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          Most cited references40

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          Is Open Access

          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
            • Record: found
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            A Rapid and Sensitive Method for the Quantitation of Microgram Quantities of Protein Utilizing the Principle of Protein-Dye Binding

              • Record: found
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              Neuroinflammation in Alzheimer's disease.

              Increasing evidence suggests that Alzheimer's disease pathogenesis is not restricted to the neuronal compartment, but includes strong interactions with immunological mechanisms in the brain. Misfolded and aggregated proteins bind to pattern recognition receptors on microglia and astroglia, and trigger an innate immune response characterised by release of inflammatory mediators, which contribute to disease progression and severity. Genome-wide analysis suggests that several genes that increase the risk for sporadic Alzheimer's disease encode factors that regulate glial clearance of misfolded proteins and the inflammatory reaction. External factors, including systemic inflammation and obesity, are likely to interfere with immunological processes of the brain and further promote disease progression. Modulation of risk factors and targeting of these immune mechanisms could lead to future therapeutic or preventive strategies for Alzheimer's disease. Copyright © 2015 Elsevier Ltd. All rights reserved.

                Author and article information

                Contributors
                kngreen@uci.edu
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                15 October 2021
                15 October 2021
                2021
                : 8
                : 270
                Affiliations
                [1 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Institute for Memory Impairments and Neurological Disorders (UCI MIND), , University of California, ; Irvine, CA 92697 USA
                [2 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Transgenic Mouse Facility, University Laboratory Animal Resources, Office of Research, , University of California, ; Irvine, CA 92697 USA
                [3 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Department of Developmental and Cell Biology, , University of California, ; Irvine, CA 92697 USA
                [4 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Center for Complex Biological Systems, , University of California, ; Irvine, CA 92697 USA
                [5 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Department of Neurobiology and Behavior, , University of California, ; Irvine, CA 92697 USA
                [6 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Department of Molecular Biology and Biochemistry, , University of California, ; Irvine, CA 92697 USA
                [7 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Department of Pathology and Laboratory Medicine, , University of California, ; Irvine, CA 92697 USA
                Author information
                http://orcid.org/0000-0001-7977-4866
                http://orcid.org/0000-0001-9326-9954
                http://orcid.org/0000-0001-7598-9501
                http://orcid.org/0000-0002-4259-6362
                http://orcid.org/0000-0003-3000-024X
                http://orcid.org/0000-0003-2324-6911
                Article
                1054
                10.1038/s41597-021-01054-y
                8519958
                34654824
                0eab30ca-7cd4-40ec-b05a-78bb9cc2402d
                © The Author(s) 2021

                Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, 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 metadata files associated with this article.

                History
                : 16 March 2021
                : 2 September 2021
                Categories
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                © The Author(s) 2021

                cognitive neuroscience,neuroscience
                cognitive neuroscience, neuroscience

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