A novel systems biology approach to evaluate mouse models of late-onset Alzheimer's disease.

Authors

Christoph Preuss, The Jackson LaboratoryFollow
Ravi S Pandey, The Jackson LaboratoryFollow
Erin Piazza
Alexander D Fine, The Jackson LaboratoryFollow
Asli Uyar, The Jackson LaboratoryFollow
Thanneer Perumal
Dylan Garceau, The Jackson LaboratoryFollow
Kevin P Kotredes, The Jackson LaboratoryFollow
Harriet M. Williams, The Jackson LaboratoryFollow
Lara M Mangravite
Bruce T Lamb
Adrian L Oblak
Gareth R Howell, The Jackson LaboratoryFollow
Michael Sasner, The Jackson LaboratoryFollow
Benjamin A Logsdon
MODEL-AD Consortium
Gregory W. Carter, The Jackson LaboratoryFollow

Document Type

Article

Publication Date

11-10-2020

Keywords

JMG

JAX Source

Mol Neurodegener 2020 Nov 10; 15(1):67

Volume

15

Issue

1

First Page

67

Last Page

67

ISSN

1750-1326

PMID

33172468

DOI

https://doi.org/10.1186/s13024-020-00412-5

Grant

AG054345

Abstract

BACKGROUND: 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.

RESULTS: 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 from mouse brains. Analysis of the 5xFAD mouse, a widely used amyloid pathology model, and three mouse models based on LOAD genetics carrying APOE4 and TREM2*R47H alleles demonstrated overlaps with distinct human AD modules that, in turn, were functionally enriched in key disease-associated pathways. Comprehensive comparison with full transcriptome data from same-sample RNA-Seq showed strong correlation between gene expression changes independent of experimental platform.

CONCLUSIONS: 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.

Comments

We thank the many institutions and their staff that provided support for this study and who were involved in this collaboration. We would like to acknowledge Jamie Kuhar for her critically reading of the manuscript.

This article is licensed under a Creative Commons Attribution 4.0 International License,

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