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Project 2: Identify and enhance LOAD-related signatures in outbred and genetically-engineered marmosets

项目 2：识别并增强近交系和基因工程狨猴中与 LOAD 相关的特征

基本信息

批准号：
10494776
负责人：
Gregory W Carter
金额：
$ 39.66万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10494776
关键词：
Aging Alzheimer&apos s Disease Alzheimer&apos s disease model Alzheimer&apos s disease risk Animal Model Autopsy Behavioral Bioinformatics Biological Biological Markers Biological Models Callithrix Cell model Clinical Clinical Research Cognitive Cohort Studies Communities Complement Data Data Aggregation Dementia Dimensions Disease Disease Outcome Disease Progression Disease model Drug Targeting Engineering Evaluation Event Foundations Future Generations Genetic Genetic Engineering Genetic Variation Genome Genomics Genotype Goals Human Human Genetics Image Impaired cognition Knowledge Portal Laboratories Late Onset Alzheimer Disease Link Longevity Methods Modeling Molecular Outcome Outcome Study Pathology Pathway interactions Phenotype Population Primates Process Proteins Research Rodent Model Serum Statistical Models Structure Study models Testing Tissues Validation Variant Work analytical method clinically relevant data integration data modeling data-driven model efficacy testing functional genomics genetic analysis genetic association genetic variant genome-wide genomic data human data human study in vivo imaging molecular marker molecular scale mouse model multi-scale modeling multimodality multiple data types multiple omics neuropathology nonhuman primate phenotypic data pre-clinical research preclinical efficacy preclinical study presenilin-1 risk variant targeted treatment whole genome

项目摘要

PROJECT SUMMARY PROJECT 2 Determining the early molecular and cellular events in the origins and progression of late-onset Alzheimer’s disease (LOAD) will require an analytical approach that integrates genetic, molecular, in vivo imaging, and behavioral data. Many clinical studies with this goal are currently underway, which increasingly complement genetic data with genome-scale molecular data from biofluids and post-mortem tissues, in vivo imaging data of structure and neuropathology, and detailed cognitive data collected over disease progression. Transforming the outcomes of these studies into targeted therapeutic strategies requires translatable animal model systems, both for understanding the biological underpinnings of disease outcomes and preclinical efficacy testing of candidate treatments. The marmoset is potentially the most promising non-human primate model of LOAD, providing an analytical bridge between human studies and high-capacity cell and rodent model systems. Laboratory marmosets with outbred genetics can potentially provide a range of genotypic and phenotypic variation in relevant clinical outcomes. This standing variation can be augmented by genetically engineering variants at specific risk loci, as we have demonstrated with PSEN1. Phenotypic changes in multi-omic, imaging, cognitive, and cellular outcomes can be rigorously studied in an aging primate with an intermediate lifespan. However, to date there have not been systematic studies of aging marmosets at scale. In this project, we will initiate these systematic studies through integrated analyses of genetics and LOAD-related phenotypes in aging marmosets. We will then rigorously test correspondences between human and marmosets at all biological levels, from genetic to multi-scale models. Our goal is to develop the marmoset into a mature platform for preclinical research, which we will pursue with the following three aims: (1) assess natural genetic variation in outbred marmosets as a model Alzheimer’s disease risk in humans; (2) integrate genetic, genomic, and phenotype data to establish robust statistical models of disease in marmosets; and (3) evaluate disease relevance of models by aligning molecular markers of Alzheimer’s disease in marmosets with human study cohorts. Through this work, we expect to lay the foundations for LOAD-related functional genomics in marmosets, provide an expanded view of the impact of natural genetic variation in laboratory marmosets, prioritize genetic variants to engineer in marmosets, and create the first models of LOAD-related marmoset pathology at multiple scales.

项目总结