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

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.

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

“…Increased pathology in female mice is also seen in the 5xFAD mouse model, which uses the same Thy1 mini-gene regulating expression of the cDNA’s in the transgenes. The promoter in this Thy1 mini-gene was found to contain an estrogen response like element that can produce greater expression in females ( Sadleir et al, 2015 ), which we recently replicated with our phenotyping efforts ( Forner et al, 2021 ). Hence, the female sex-bias in pathology in both 3xTg-AD and 5xFAD AD models is likely due to increased expression from the Thy-1 mini-gene, rather than reflecting some inherent female-specific bias in AD susceptibility as found in the human population.…”

“…Every reported n represents the number of independent biological replicates. The sample sizes are similar with those found in prior studies conducted by MODEL-AD ( Forner et al, 2021 ) and were not predetermined using statistical methods. Electrophysiology, immunohistochemical, and biochemical data were analyzed using Student’s t -test, one-way ANOVA, or two-way ANOVA via Prism v.9 (GraphPad, La Jolla, CA, United States).…”

“…We explored Thy1 promotor expression, which drives the transgenes in both 3xTg-AD and 5xFAD mice [data derived from Balderrama-Gutierrez et al (2021) ] and ( Forner et al, 2021 , respectively. Thy1 expression was elevated in 3xTg-AD cortex relative to wild-type Thy1 (which does not drive a transgene but sets the background level) only at 18 months of age, consistent with the lack of pathology that we observe there ( Figure 9F ).…”

“…To navigate the innumerable different AD animal models available, a standardized deep-phenotyping characterizations of current and future animal models will be a useful tool to compare advantages and limitations unique to each model. Here, we demonstrate the utility of one such deep-phenotyping pipeline using the 3xTg-AD mouse model, in tandem with a prior characterization of the 5xFAD mouse model ( Forner et al, 2021 ). The fact that these pathologies in the brains of 3xTg-AD mice develop slowly, and in an age-related fashion may be advantageous to studies that focus on late-onset AD and on the interactions between amyloid and tau pathologies.…”

“…An early objective of MODEL-AD is to establish phenotyping pipelines for robust and reproducible analysis of the newly developed mouse models of LOAD at independent sites. For an initial test of one such pipeline, we are performing an in-depth phenotyping of two popular animal models widely used within the AD research community; 5xFAD mice ( Oakley et al, 2006 ; Forner et al, 2021 ) and 3xTg-AD mice ( Oddo et al, 2003 ). In addition to validation of the phenotyping pipeline, the results of these analyses will better inform the AD field about the various pathologies that arise, and at which ages and in which brain regions, such that investigators can select the most appropriate model for their hypothesis/experiment.…”