APP mouse models for Alzheimer’s disease preclinical studies.
Open accessAbstract
Abstract Animal models of human diseases that accurately recapitulate clinical pathology are indispensable for understanding molecular mechanisms and advancing preclinical studies. The Alzheimer's disease (AD) research community has historically used first‐generation transgenic (Tg) mouse models that overexpress proteins linked to familial AD (FAD), mutant amyloid precursor protein (APP), or APP and presenilin (PS). These mice exhibit AD pathology, but the overexpression paradigm may cause additional phenotypes unrelated to AD. Second‐generation mouse models contain humanized sequences and clinical mutations in the endogenous mouse App gene. These mice show Aβ accumulation without phenotypes related to overexpression but are not yet a clinical recapitulation of human AD. In this review, we evaluate different APP mouse models of AD, and review recent studies using the second‐generation mice. We advise AD researchers to consider the comparative strengths and limitations of each model against the scientific and therapeutic goal of a prospective preclinical study.
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The general behavioral phenotypes of App-KI mice described in many published studies are minimal, with the exception of robust alterations in emotional domains.
“However, the general behavioral phenotypes of App-KI mice described in many published studies are minimal [9], with the exception of robust alterations in emotional domains [14, 21, 22].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [9]
The citation to this paper [9] supports the claim that App-KI mice generally have minimal behavioral phenotypes; the emotional-domain exception is cited to other sources. In “Second-generation mouse models” (page unavailable), the paper instead reports various cognitive dysfunctions in App NL-F mice, including spatial-memory, learning, and attention deficits, and greater cognitive abnormalities in App NL-G-F mice. (LLM, conf=0.76)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredParental lines rely on transgenic overexpression of familial AD or FTD mutations and do not fully recapitulate the slow accumulation of amyloid and tau in humans.
“First, although 5XFAD;PS19 provides an experimentally tractable system for studying Aβ–tau interactions, parental lines rely on transgenic overexpression of familial AD or frontotemporal dementia (FTD) mutations and do not fully recapitulate the slow accumulation of amyloid and tau in humans. 70 , 71”
From Unique transcriptomic alterations in 5XFAD;PS19 mouse model identify glial lipid dysregulation and coordinated microglial–oligodendrocyte responses (2026) · cites this paper as [70]
The First-generation mouse models section identifies 5XFAD as carrying APP and PS1 familial-AD transgenes, and describes overexpressed mutant tau in another model as deriving from frontotemporal dementia rather than AD. It reports Aβ pathology in 5XFAD by two months, while the Limitations of second-generation mouse models section says human Aβ amyloidosis may take more than two decades to induce cortical tauopathy. Our copy does not identify PS19 specifically, so it does not establish that both parental lines have the stated characteristics. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThese mouse models suffered from potential off-target effects caused by non-physiological overexpression of APP/PSEN1 genes and/or combinations of familial AD mutations, which are not observed in AD patients.
“they also suffered from potential off-target effects caused by either non-physiological overexpression of APP/PSEN1 genes and/or combinations of familial AD mutations, which are not observed in AD patients [2, 9].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [9]
The “Limitations of first-generation mouse models” section warns that APP or APP/PS overexpression can produce artifacts and phenotypes outside normal disease pathology. The paper also describes models combining several familial AD mutations, but the cited passages do not establish that those combinations are never observed in AD patients or that the combinations themselves cause the asserted off-target effects. Page numbers were not available in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThese studies all suffer because they exhibit artifacts arising from the over-expression APP (WT or mutant) and in some cases mutant presenilins.
“These studies all suffer because they exhibit artifacts arising from the over-expression APP (WT or mutant) and in some cases mutant presenilins (Balducci and Forloni, 2011; Saito et al., 2014; Sasaguri et al., 2017).”
From β-amyloid accumulation enhances microtubule associated protein tau pathology in an APPNL-G-F/MAPTP301S mouse model of Alzheimer’s disease (2024) · cites this paper as Sasaguri et al., 2017
The ‘Limitations of first-generation mouse models’ section says APP overexpression produces additional APP fragments and warns that some phenotypes might result from APP or APP/PS overexpression rather than normal disease pathology. This supports the concern about artifacts, including in models involving presenilin, but the paper describes potential effects in some models; it does not establish that all the studies exhibit such artifacts. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe patterns of labeling of Aβ and MAPT are consistent with prior studies of APPNL-G-F and P301S MAPT mice.
“These patterns of labeling or Aβ and MAPT are consistent with prior studies of APPNL-G-F and P301S MAPT mice (Yoshiyama et al., 2007; Saito et al., 2014; Sasaguri et al., 2017; Apicco et al., 2018).”
From β-amyloid accumulation enhances microtubule associated protein tau pathology in an APPNL-G-F/MAPTP301S mouse model of Alzheimer’s disease (2024) · cites this paper as Sasaguri et al., 2017
The Modeling preclinical AD section shows Aβ labeling in the cortex of an App NL-G-F mouse, and the Second-generation mouse models section describes amyloid pathology in that model. The paper does not describe labeling patterns in P301S MAPT mice; its discussion of tau models concerns other mutations. It therefore supports the Aβ-model portion, but not the attributed MAPT-model comparison. (LLM, conf=0.85)
Experimental — accuracy not yet measured
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 [20]
The Second-generation mouse models section (page unavailable) reports Aβ deposition with astrocyte and microglial infiltration in App knock-in mice, along with age- and model-dependent cognitive deficits. The First-generation mouse models section describes Aβ-associated neuroinflammation and behavioral phenotypes in APP transgenic mice. The review discusses epilepsy-related findings in a transgenic line but does not establish network hyperexcitability as a feature of both model types; it notes that a related channel change was not observed in the knock-in mice studied. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAnimal models cannot fully recapitulate AD symptoms.
“A large number of animal models have been developed to test these hypotheses, but cannot fully recapitulate AD symptoms (LaFerla and Green, 2012; Sasaguri et al., 2017).”
From Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression (2020) · cites this paper as Sasaguri et al., 2017
Transgenic animal models are invaluable tools for studying pathogenic mechanisms and testing therapeutics of AD.
“transgenic animal models are an invaluable tool for studying pathogenic mechanisms and testing therapeutics of AD (Kitazawa et al., 2012; Jankowsky and Zheng, 2017; Sasaguri et al., 2017).”
From Age-dependent microstructure alterations in 5xFAD mice by high-resolution diffusion tensor imaging (2022) · cites this paper as Sasaguri et al., 2017
Novel APP knock-in models such as APPNL, APPNL-F, or APPNL-G-F overproduce Aβ1-42 in the absence of potentially confounding APP overexpression, but still contain one or more independent FAD mutations, which might result in an abnormal Aβ conformation.
“While novel APP knock-in models, such as APPNL, APPNL-F, or APPNL-G-F mice overproduce Aβ1–42 in the absence of potentially confounding APP overexpression [139], these mice still contain one or more independent FAD mutations, which might result in an abnormal Aβ conformation [140].”
From Neuron Loss in Alzheimer’s Disease: Translation in Transgenic Mouse Models (2020) · cites this paper as [140]
None of these App-KI mice exhibit tau pathology or severe neuronal loss, suggesting that they are suitable models for preclinical AD.
“None of these App-KI mice exhibit tau pathology or severe neuronal loss, suggesting that they are suitable models for preclinical AD [9].”
From Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease (2019) · cites this paper as [9]
App KI mice may model preclinical stages of AD, characterized by progressive amyloidosis and gliosis without prominent cognitive deterioration, tau pathology, or neurodegeneration.
“As suggested,15,20 App KI mice may model pre-clinical stages of AD, characterized by progressive amyloidosis and gliosis without prominent cognitive deterioration, tau pathology, or neurodegeneration.”
From Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models (2024) · cites this paper as [20]
An additional copy of HSA21 genes can alter APP biology, including Aβ deposition, in other APP transgenic models.
“Previous studies have indicated that an additional copy of HSA21 genes can alter APP biology, including Aβ deposition in other APP transgenic models.”
From Interferon signaling modulates Down syndrome-associated Alzheimer’s disease pathology in a mouse model (2025) · cites this paper as [61]
The First-generation mouse models section describes APP transgenic mice with Aβ deposits, but does not address whether an additional copy of HSA21 genes alters APP biology or deposition. Searches for HSA21, trisomy 21, Down syndrome, and human chromosome 21 found no passage making that connection. (LLM, conf=0.74)
Experimental — accuracy not yet measured
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The Amyloid Precursor Protein—A Novel Player within the Molecular Array of Presynaptic Nanomachines
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Flagged first, then by citations
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Scheltens, P., De Strooper, B., Kivipelto, M., Holstege, H., Chételat, G., Teunissen, C. E., Cummings, J., & van der Flier, W. M. (2021) · The Lancet · doi:10.1016/s0140-6736(20)32205-4 · Cited by 5,465 · see citing works
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Self-citationThe Amyloid-β Oligomer Hypothesis: Beginning of the Third Decade
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The role of adult hippocampal neurogenesis in brain health and disease
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Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression
Bai, B., Wang, X., Li, Y., Chen, P.-C., Yu, K., Dey, K. K., Yarbro, J. M., Han, X., Lutz, B. M., Rao, S., et al. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.015 · Cited by 632 · see citing works
“A large number of animal models have been developed to test these hypotheses, but cannot fully recapitulate AD symptoms (LaFerla and Green, 2012; Sasaguri et al., 2017).”
BackgroundAmyloid-β-independent regulators of tau pathology in Alzheimer disease
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Animal models of neurodegenerative diseases
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Modeling Alzheimer’s disease with iPSC-derived brain cells
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White matter aging drives microglial diversity
Safaiyan, S., Besson-Girard, S., Kaya, T., Cantuti-Castelvetri, L., Liu, L., Ji, H., Schifferer, M., Gouna, G., Usifo, F., Kannaiyan, N., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.01.027 · Cited by 477 · see citing works
Dementia in Down syndrome: unique insights for Alzheimer disease research
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Ilievski, V., Zuchowska, P. K., Green, S. J., Toth, P. T., Ragozzino, M. E., Le, K., Aljewari, H. W., O’Brien-Simpson, N. M., Reynolds, E. C., & Watanabe, K. (2018) · PLOS ONE · doi:10.1371/journal.pone.0204941 · Cited by 372 · see citing works
The clinical promise of biomarkers of synapse damage or loss in Alzheimer’s disease
the Synaptic Health Endpoints Working Group, Colom-Cadena, M., Spires-Jones, T., Zetterberg, H., Blennow, K., Caggiano, A., DeKosky, S. T., Fillit, H., Harrison, J. E., Schneider, L. S., et al. (2020) · Alzheimer's Research & Therapy · doi:10.1186/s13195-020-00588-4 · Cited by 328 · see citing works
Immune Signaling in Neurodegeneration
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“, 2017); however, the utility of these mice as robust preclinical models for AD has been challenged (Drummond and Wisniewski, 2017; Onos et al., 2016; Sasaguri et al., 2017).”
BackgroundMolecular Pathogenesis of the Tauopathies
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Self-citationMicroglia in Alzheimer’s Disease: Activated, Dysfunctional or Degenerative
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Modeling Sporadic Alzheimer's Disease in Human Brain Organoids under Serum Exposure
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Recent advances in the modeling of Alzheimer’s disease
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Self-citationA unifying hypothesis for Alzheimer's disease: from plaques to neurodegeneration
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Critical review: involvement of endoplasmic reticulum stress in the aetiology of Alzheimer's disease
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Self-citationUltrastructural evidence of microglial heterogeneity in Alzheimer’s disease amyloid pathology
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Neuron Loss in Alzheimer’s Disease: Translation in Transgenic Mouse Models
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“While novel APP knock-in models, such as APPNL, APPNL-F, or APPNL-G-F mice overproduce Aβ1–42 in the absence of potentially confounding APP overexpression [139], these mice still contain one or more independent FAD mutations, which might result in an abnormal Aβ conformation [140].”
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Perspectives for New and More Efficient Multifunctional Ligands for Alzheimer′s Disease Therapy
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Age-dependent microstructure alterations in 5xFAD mice by high-resolution diffusion tensor imaging
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- Authors
- Sasaguri, H., Nilsson, P., Hashimoto, S., Nagata, K., Saito, T., De Strooper, B., Hardy, J., Vassar, R., Winblad, B., & Saido, T. C.
- Affiliations
- Tokyo Medical and Dental University (JP)
- RIKEN Center for Brain Science (JP)
- UCL Queen Square Institute of Neurology (GB)
- Northwestern University (US)
- Karolinska Institutet (SE)
- Corresponding author
- Sasaguri, Hiroki
- Saido, Takaomi C
- ORCID
- Venue
- The EMBO Journal
- Published
- 2017-08-02
- Volume
- 36
- Issue
- 17
- Pages
- 2473-2487
- ISSN
- 0261-4189 (ISSN-L)
- 1460-2075
- PMC
- PMC5579350
- OpenAlex
- W2740213823
- Cited by
- 793
- References
- 153
- Citations by year
- 2026: 80 · 2025: 82 · 2024: 104 · 2023: 74 · 2022: 97 · 2021: 104 · 2020: 91 · 2019: 81 · 2018: 72 · 2017: 7
- FWCI
- 8.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- The EMBO Journal (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Lirias (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Ministry of Education, Culture, Sports, Science, and Technology (26290019, 15K19036)
- RIKEN
- Japan Science and Technology Agency Precursory Research for Embryonic Science and Technology (JPMJPR1285)
- Strategic Research Program for Brain Sciences (17dm0107071h0002)
- Japan Agency for Medical Research and Development
- Alzheimerfonden
- Hållstens forskningsstiftelse
- Svenska Forskningsrådet Formas
- HHS | National Institutes of Health (AG022560, AG030142)
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- Alzheimer's disease, amyloid precursor protein, transgenic mice, familial Alzheimer's disease, amyloid-beta deposition, presenilin, preclinical drug testing, model limitations
- MeSH terms
- Alzheimer Disease, Animals, Disease Models, Animal, Humans, Amyloid beta-Protein Precursor
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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