Novel mouse models of late‐onset Alzheimer’s disease

Abstract

AbstractBackgroundThe Model Organism Development and Evaluation for Late‐onset AD (MODEL‐AD) Consortium was established to generate and characterize more translatable animal models for late‐onset Alzheimer’s disease (LOAD) based on human genetic risk variants. While numerous genetic risk loci for LOAD have been identified, few have been experimentally verified in vivo and in many cases the risk variants have not been validated. The development of new models incorporating LOAD genetic risk should improve our understanding of disease mechanisms and improve preclinical testing of potential therapeutics.MethodCoding and non‐coding LOAD risk variants were prioritized based on human data sets, with the goal of targeting diverse pathways (e.g., neuroinflammation, vascular risk, metabolic function, lipid homeostasis). Risk variants in Abca7, Adamts4, Bin1, Erc2, Mthfr, Mtmr4, Plcg2, Ptk2b, Slc6a17, Snx1, Sorl1 and other loci were engineered into mouse models expressing humanized APOE4 and the Trem2*R47H risk variant; in more recent models, a humanized Abeta allele was also included. A novel transcriptomic panel, based on clinical LOAD samples (Preuss et al, 2020) was used to evaluate how well each model replicated clinical transcriptomic changes with disease. Models were aligned to ROSMAP clinical subtypes.ResultWe have identified specific human AD‐related pathways disrupted in an age‐dependent manner in these novel mouse models. Specifically, mouse models carrying human AD risk variants Abca7*A1527G, Mthfr*C677T, and Plcg2*M28L exhibited transcriptomics changes similar to those seen in LOAD patients. Most models could be readily matched to either the inflammatory or non‐inflammatory subtype.ConclusionWe have prioritized mouse models expressing LOAD risk variants in Abca7, Mthfr, and Plcg2 for comprehensive phenotyping using clinically relevant measures including transcriptomics and proteomics, biomarkers, neuropathology and in vivo imaging at advanced ages (24 months). Ongoing projects will use human data to guide how to combine alleles to create models that match multi‐omic signatures of AD subtypes and test the effects of environmental risk factors such as high‐fat diet. Through this effort we aim to develop improved models for testing targeted therapeutics.