Systematic Phenotyping and Characterization of the 3xTg-AD Mouse Model of Alzheimer’s Disease

Autor: Dominic I. Javonillo, Kristine M. Tran, Jimmy Phan, Edna Hingco, Enikö A. Kramár, Celia da Cunha, Stefania Forner, Shimako Kawauchi, Giedre Milinkeviciute, Angela Gomez-Arboledas, Jonathan Neumann, Crystal E. Banh, Michelle Huynh, Dina P. Matheos, Narges Rezaie, Joshua A. Alcantara, Ali Mortazavi, Marcelo A. Wood, Andrea J. Tenner, Grant R. MacGregor, Kim N. Green, Frank M. LaFerla
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Frontiers in Neuroscience, Vol 15 (2022)
Druh dokumentu: article
ISSN: 1662-453X
DOI: 10.3389/fnins.2021.785276
Popis: Animal models of disease are valuable resources for investigating pathogenic mechanisms and potential therapeutic interventions. However, for complex disorders such as Alzheimer’s disease (AD), the generation and availability of innumerous distinct animal models present unique challenges to AD researchers and hinder the success of useful therapies. Here, we conducted an in-depth analysis of the 3xTg-AD mouse model of AD across its lifespan to better inform the field of the various pathologies that appear at specific ages, and comment on drift that has occurred in the development of pathology in this line since its development 20 years ago. This modern characterization of the 3xTg-AD model includes an assessment of impairments in long-term potentiation followed by quantification of amyloid beta (Aβ) plaque burden and neurofibrillary tau tangles, biochemical levels of Aβ and tau protein, and neuropathological markers such as gliosis and accumulation of dystrophic neurites. We also present a novel comparison of the 3xTg-AD model with the 5xFAD model using the same deep-phenotyping characterization pipeline and show plasma NfL is strongly driven by plaque burden. The results from these analyses are freely available via the AD Knowledge Portal (https://modeladexplorer.org/). Our work demonstrates the utility of a characterization pipeline that generates robust and standardized information relevant to investigating and comparing disease etiologies of current and future models of AD.
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