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

项目总结

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.