MAR 14, 2019 09:00 AM PDT

A Gene Expression Based Screening Platform for Mouse Models of Late-Onset Alzheimer's Disease

Presented At Neuroscience 2019
SPONSORED BY: Nanostring Technologies
C.E. CREDITS: P.A.C.E. CE | Florida CE
Speakers
  • Associate Professor, The Jackson Laboratory
    Biography
      Greg Carter's laboratory combines genetic, genomic, imaging, and other data resources to understand the causes and progression of Alzheimer's disease. Greg is also the director of the Bioinformatics and Data Management Core of the IU/JAX Model Organism Development and Evaluation for Late-onset Alzheimer's Disease (MODEL-AD) Center. As a lead investigator for MODEL-AD, he is primarily focused on creating new animal models that better replicate human Alzheimer's disease and related-dementias. These models are being used to dissect the genetics, genomics, and neuropathology of late-onset Alzheimer's, and provide vital models for preclinical testing of candidate therapeutics.

    Abstract:

    Late-onset Alzheimer’s disease (LOAD) is the most common form of dementia worldwide. To date, animal models of Alzheimer’s have focused on rare familial mutations, due to a lack of frank neuropathology from models based on common disease genes. Recent multi-cohort studies of postmortem human brain transcriptomes have identified a set of 30 gene co-expression modules associated with LOAD, providing a molecular catalog of relevant endophenotypes. This resource enables precise gene-based alignment between new animal models and human molecular signatures of disease. Here, we describe a new resource to efficiently screen mouse models for LOAD relevance. A new NanoString nCounter® Mouse AD Panel was designed to correlate key human disease processes and pathways with mRNA expression from mouse brains. Analysis of two mouse models based on LOAD genetics, carrying APOE4 and TREM2.R47H alleles, demonstrated overlaps with distinct human AD modules that, in turn, are functionally enriched in key disease-associated pathways. Comprehensive comparison with full transcriptome data from same-sample RNA-seq shows strong correlation between gene expression changes independent of experimental platform. Taken together, we show that the nCounter Mouse AD Panel offers a rapid, cost-effective and highly reproducible approach to assess disease relevance of potential LOAD mouse models.

    Learning Objectives: 

    1. Reproducibly screen potential LOAD mouse models to assess disease relevance
    2. Understand design of a new resource, the Mouse AD Panel, to translate human disease processes and pathways to mouse models


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