Development of new mouse strains containing alleles of loci associated with higher risk of late‐onset Alzheimer's disease (LOAD). (1st February 2022) - Forner, Stefania; Kawauchi, Shimako; Neumann, Jonathan; Walker, Amber; MacGregor, Grant R; Green, Kim N; LaFerla, Frank; Tenner, Andrea J; ,
Abstract: Background: Efforts to develop therapy for Alzheimer's disease have consistently failed, despite success in preclinical trials in animal models. This suggests that current animal models do not recapitulate human AD in cellular mechanisms and/or physiological conditions. Development of new predictive animal models of LOAD are required to advance the field. The mission of MODEL‐AD is to develop, characterize, and distribute Next‐Gen preclinical models for LOAD using open science. Method: Our approach is to engineer mouse models to express combinations of genetic variants identified as risk factors in human LOAD populations in genome‐wide association studies (GWAS). To assess the role of LOAD risk alleles, homozygous mutations of GWAS‐risk factors are introduced on several AD platform models (such as a 5xFAD, and a humanized Aballele). We have developed 10 risk models so far (ABCA7, BIN1, PICALM, ABI3, TREM2, CLU, EPHA1, SPI1). Result: Prior to deep‐phenotyping, new models are analyzed by RNA‐seq from hippocampus and cortex. This approach enables us to capture early responses to the risk factors and correlate transcription profiles in the mouse models to key human disease processes and pathways. Analysis is being performed at 4 and 12 months of age and the candidate models are analyzed for pathological phenotypes to 24 months of age. Deep phenotyping is performed using clinically relevant measures including metabolomics, biomarkers, neuropathology, electrophysiology, imaging and cognitive assays. Conclusion: This comprehensive approach will determine key phenotypes and the therapeutic time window for each model. All models are made available without restriction from the Jackson Laboratory, and all data will be shared via the AD Knowledge Portal. For more information see www.model‐ad.org.