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Cited by 124Alzheimer's disease research and treatmentsNeuroinflammation and Neurodegeneration MechanismsNeurological Disease Mechanisms and Treatments

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

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Xia, Dan, Lianoglou, Steve, Sandmann, Thomas, Calvert, Meredith, Suh, Jung H., Thomsen, Elliot, Dugas, Jason, Pizzo, Michelle E. +40 more·2022·Molecular Neurodegeneration
DOI 10.1186/s13024-022-00547-7PMC PMC9188195
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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.

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  • Humanized App knockin (KI) and APP transgenic mouse models mimic certain aspects of AD pathogenesis, including progressive amyloidosis, gliosis, and neuroinflammation, as well as network hyperexcitability and cognitive impairment, albeit to different degrees of severity across models and paradigms.

    “Humanized App knockin (KI) and APP transgenic mouse models mimic certain aspects of AD pathogenesis, including progressive amyloidosis, gliosis, and neuroinflammation, as well as network hyperexcitability and cognitive impairment, albeit to different degrees of severity across models and paradigms.15-27”

    From Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models (2024) · cites this paper as [16]

    The Background and Discussion (page numbers unavailable) support progressive amyloid deposition and glial responses in App knock-in models, while the Results discuss learning and memory deficits reported in APP transgenic models. The paper does not establish network hyperexcitability in its AppSAA model; altered glutamate transport and locomotor hyperactivity are not the same finding. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Spontaneous behavioral abnormalities were age dependent and therefore tracked the progression of AD-related pathological and cognitive alterations.

    “Second, spontaneous behavioral abnormalities were age dependent and therefore tracked the progression of AD-related pathological and cognitive alterations.16,19,23”

    From Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models (2024) · cites this paper as [16]

    The Results section on behavioral phenotypes reports an age-dependent increase in spontaneous activity in AppSAA knock-in mice. The Results section on amyloid pathology reports progressive plaque deposition, but the paper does not establish that the behavioral abnormalities tracked the progression of pathology or cognitive alterations; it reports habituation deficits without demonstrating that relationship. Page numbers are unavailable in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • AD-related spontaneous behavioral alterations do not require APP overexpression and are likely linked to the pathologically elevated levels of amyloid in AppNL-G-F mice.

    “These results also indicate that AD-related spontaneous behavioral alterations do not require APP overexpression and are likely linked to the pathologically elevated levels of amyloid in AppNL-G-F mice.16,19”

    From Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models (2024) · cites this paper as [16]

    The Results section on behavioral phenotypes says that Aβ accumulation without APP overexpression can trigger brain dysfunction in App knock-in models. The Discussion reports spontaneous-activity changes without APP overexpression in this paper’s AppSAA mice and describes amyloid pathology as a likely driver in knock-in models. It does not establish that the behavioral changes in AppNL-G-F mice are linked to their elevated amyloid levels. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Authors
Xia, D., Lianoglou, S., Sandmann, T., Calvert, M., Suh, J. H., Thomsen, E., Dugas, J., Pizzo, M. E., DeVos, S. L., Earr, T. K., Lin, C.-C., Davis, S., Ha, C., Leung, A. W.-S., Nguyen, H., Chau, R., Yulyaningsih, E., Lopez, I., Solanoy, H., Masoud, S. T., Liang, C.-C., Lin, K., Astarita, G., Khoury, N., Zuchero, J. Y., Thorne, R. G., Shen, K., Miller, S., Palop, J. J., Garceau, D., Sasner, M., Whitesell, J. D., Harris, J. A., Hummel, S., Gnörich, J., Wind, K., Kunze, L., Zatcepin, A., Brendel, M., Willem, M., Haass, C., Barnett, D., Zimmer, T. S., Orr, A. G., Scearce-Levie, K., Lewcock, J. W., Di Paolo, G., & Sanchez, P. E.
Affiliations
  • Denali Therapeutics (United States) (US)
  • University of Minnesota (US)
  • Gladstone Institutes (US)
  • Jackson Laboratory (US)
  • Allen Institute for Brain Science (US)
  • German Center for Neurodegenerative Diseases (DE)
  • LMU Klinikum (DE)
  • Weill Cornell Medicine (US)
Corresponding author
Sanchez, Pascal E.
ORCID
Venue
Molecular Neurodegeneration
Publisher
Springer Science and Business Media LLC
Published
2022-06-11
Volume
17
Issue
1
Pages
41-41
ISSN
1750-1326 (ISSN-L)
DOI
10.1186/s13024-022-00547-7
PMC
PMC9188195
OpenAlex
W4282918923
Cited by
124
References
114
Citations by year
2026: 30 · 2025: 37 · 2024: 32 · 2023: 21 · 2022: 4
FWCI
14.53
Type
journal article
Language
en
Open access
Licence
Locations
  • Molecular Neurodegeneration (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion, cc-by)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
  • The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion, cc-by)
  • DZNE Pub (submittedVersion, other-oa)
Funders
  • National Institute on Aging (R01AG047589, RF1AG062234, R01AG062629, P01AG073082, R01AG068091)
  • Deutsche Forschungsgemeinschaft (390857198)
  • Koselleck Project (HA1737/16-1)
  • BrightFocus Foundation (A2019363S)
Topics
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
  • Life Sciences › Neuroscience › Neurology › Neurological Disease Mechanisms and Treatments
Keywords
Alzheimer's disease, amyloid-beta, microglia, lipid dysregulation, lysosomal dysfunction, foam cells, amyloid plaques, vascular amyloid deposits, astroglial response, cerebrospinal fluid biomarkers, neuroinflammation, TSPO, FDG-PET, familial Alzheimer's disease mutations
MeSH terms
Alzheimer Disease, Amyloidosis, Animals, Brain, Disease Models, Animal, Mice, Transgenic, Amyloid beta-Peptides, Amyloid beta-Protein Precursor, Microglia, Receptors, GABA, Mice, Plaque, Amyloid

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