Optogenetic dissection of cellular and circuit mechanisms of network dysfunction and amyloid deposition in mouse models of Alzheimer's disease in vivo

总结

SUMMARY A contributing factor to the failure of clinical trials has been the translational limitations of transgenic (TG) overexpression models in which physiological and endogenously-regulated pathogenic interactions are not achieved. In addition, standard behavioral tests may lack sensitivity to identify robust and reproducible behavioral deficits in newly develop knock-in (KI) models. To address or mitigate these limitations, we propose to study the next generation of newly developed KI mouse models of AD without transgene overexpression using the latest machine learning approaches for behavioral phenotyping and chronic wireless EEG/EMG recordings. Specifically, we propose to study knock-in (KI) mice that express humanized Ab under the control of the mouse App locus with or without FAD mutations, including AppAβ/Aβ, AppNL-F/NL-F, and AppNL-G-F/NL-G-F mice using state-of- the-art chronic in vivo electrophysiological recordings and machine learning approaches for behavioral phenotyping. The parent grant heavily focuses on the J20 TG model of AD, because App-KI mice show no prominent cognitive deficits in standard behavioral tests, including the Morris water maze test,10 and because it is unknown if App-KI have robust network abnormalities described in J20 and other TG-APP mouse models and AD patients, including altered gamma oscillations, theta-gamma coupling, epileptiform spikes and seizures.1,3,9,11- 15. The proposed experiments and genotypes expand the focus of our parent grant and will develop tools and procedures that will be directly used by the parent grant to improve behavioral and brain network characterization of TG and KI models of AD. We propose the following aims: Aim 1. Develop and apply machine learning approaches to identify behavioral alterations in late-onset AppAβ/Aβ and early-onset AppNL-F/NL-F and AppNL-G-F/NL- G-F AD mice. Aim 2. Determine electrophysiological phenotypes by chronic wireless EEG/EMG recordings during aging and disease progression in late-onset AppAβ/Aβ and early-onset AppNL-F/NL-F and AppNL-G-F/NL-G-F AD mice. Aim 3. Determine relationships between behavioral alterations and neuronal network dysfunction in late-onset AppAβ/Aβ and early-onset AppNL-F/NL-F and AppNL-G-F/NL-G-F AD mice. In addition, this diversity supplement grant will significantly enhance the research potential of the candidate and further her ability to pursue a research career. The candidate will gain valuable relevant experience with electrophysiological data analysis and mouse behavioral assays during the funding period. These novel in vivo approaches and technical skills will help the candidate to address unexplored questions of AD pathogenesis, thereby creating a technical and conceptual path towards independence. The proposed supplement will provide the conceptual and technological foundation needed to support the production of preliminary data for the candidate’s future F32 grant application.