Overview of Transgenic Mouse Models for Alzheimer's Disease. Issue 1 (19th August 2019)

• Record Type: Journal Article
• Authors: Myers, Ariana; McGonigle, Paul
• Editors: Crawley, Jacqueline N.; Gerfen, Charles R.; Rogawski, Michael A.; Sibley, David R.; Skolnick, Phil; Wray, Susan
• Is Part Of: Current protocols in neuroscience. Issue 89:Issue 1(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-08-19
• Subjects: Alzheimer's -- animal models -- mouse -- transgenic; Neurosciences -- Laboratory manuals; Neurosciences; Cytological Techniques; Histological Techniques; Nervous System -- anatomy & histology; Neurosciences; Laboratory Manual; Laboratory manuals; 612.8
• DOI: 10.1002/cpns.81
• Languages: English
• ISSNs: 1934-8576

Abstract

Abstract: This review describes several transgenic mouse models of Alzheimer's disease (AD), a devastating neurodegenerative disorder that causes progressive cognitive decline and is diagnosed postmortem by the presence of extracellular amyloid‐β (Aβ) plaques and intraneuronal tau neurofibrillary tangles in the cerebral cortex. Currently there is no intervention that cures, prevents, or even slows disease progression. Its complex etiology and pathology pose significant challenges for animal model development, and there is no single model that faithfully recapitulates both the pathological aspects and behavioral phenotypes of AD. Nearly 200 transgenic rodent models of AD have been generated primarily based on mutations linked to Aβ protein misprocessing in the familial form of the disease. More recent models incorporate mutations in tau protein, as well as mutations associated with the sporadic form of the disease. The salient features, strengths, limitations, and key differentiators for the most commonly used and best characterized of these models are considered here. While the translational utility of many of these models to assess the potential of novel therapeutics is in dispute, knowledge of the different models available and a detailed understanding of their features can aid in the selection of the optimal model to explore disease mechanisms or evaluate candidate medications. We comment on the predictive utility of these models considering recent clinical trial failures and discuss trends and future directions in the development of models for AD based on the plethora of clinical data that have been generated over the last decade. © 2019 by John Wiley & Sons, Inc.