Aggregate Trends of Apolipoprotein E on Cognition in Transgenic Alzheimer's Disease Mice

• Type of Material: Article
• Authors: Yassin Watson, Georgia Institute of Technology; Brenae Nelson, Georgia Institute of Technology; Jamie H Kluesner, Georgia Institute of Technology; Caroline Tanzy, Georgia Institute of Technology; Shreya Ramesh, Georgia Institute of Technology; Zoey Patel, Georgia Institute of Technology; Kaci Hernandez Kluesner, Georgia Institute of Technology; Anita Singh, Georgia Institute of Technology; Vibha Murthy, Georgia Institute of Technology; Cassie Mitchell, Emory University
• Language: English
• Date: 2021-01-01
• Publisher: IOS PRESS
• Publication Version: Final Published Version
• Copyright Statement: © 2021 – The authors. Published by IOS Press
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3233/JAD-210492
• Title of Journal or Parent Work: JOURNAL OF ALZHEIMERS DISEASE
• Volume: 83
• Issue: 1
• Start Page: 435
• End Page: 450
• Keywords: E KNOCKOUT MICE; Life Sciences & Biomedicine; GENDER; BEHAVIORAL DEFICITS; Neurosciences; AGE; treatment; APOE GENOTYPE; MOUSE MODELS; MORRIS WATER MAZE; Age; Alzheimer's disease; SPATIAL MEMORY; APOE; cognitive function; gender; RISK-FACTORS; Morris water maze; ASSOCIATION; Science & Technology; Neurosciences & Neurology
• Research Categories: Health Sciences, Pathology

Abstract

Background: Apolipoprotein E (APOE) genotypes typically increase risk of amyloid-β deposition and onset of clinical Alzheimer's disease (AD). However, cognitive assessments in APOE transgenic AD mice have resulted in discord. Objective: Analysis of 31 peer-reviewed AD APOE mouse publications (n=3,045 mice) uncovered aggregate trends between age, APOE genotype, gender, modulatory treatments, and cognition. Methods: T-tests with Bonferroni correction (significance=p<0.002) compared age-normalized Morris water maze (MWM) escape latencies in wild type (WT), APOE2 knock-in (KI2), APOE3 knock-in (KI3), APOE4 knock-in (KI4), and APOE knock-out (KO) mice. Positive treatments (t+) to favorably modulate APOE to improve cognition, negative treatments (t-) to perturb etiology and diminish cognition, and untreated (t0) mice were compared. Machine learning with random forest modeling predicted MWM escape latency performance based on 12 features: mouse genotype (WT, KI2, KI3, KI4, KO), modulatory treatment (t+, t-, t0), mouse age, and mouse gender (male=g_m; female=g_f, mixed gender=g_mi). Results: KI3 mice performed significantly better in MWM, but KI4 and KO performed significantly worse than WT. KI2 performed similarly to WT. KI4 performed significantly worse compared to every other genotype. Positive treatments significantly improved cognition in WT, KI4, and KO compared to untreated. Interestingly, negative treatments in KI4 also significantly improved mean MWM escape latency. Random forest modeling resulted in the following feature importance for predicting superior MWM performance: [KI3, age, g_m, KI4, t0, t+, KO, WT, g_mi, t-, g_f, KI2]=[0.270, 0.094, 0.092, 0.088, 0.077, 0.074, 0.069, 0.061, 0.058, 0.054, 0.038, 0.023]. Conclusion: APOE3, age, and male gender was most important for predicting superior mouse cognitive performance.