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P4‐048: IDENTIFYING PRESYMPTOMATIC GENE SIGNATURES PREDICTIVE OF RESILIENCE TO ALZHEIMER'S
Author(s) -
Neuner Sarah M.,
Philip Vivek,
Huentelman Matthew J.,
Kaczorowski Catherine C.
Publication year - 2018
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2018.06.2450
Subject(s) - biology , gene , disease , genetics , phenotype , alzheimer's disease , cognitive decline , epigenetics , genetic variation , neuroscience , dementia , medicine
Background: New genetic and genomic resources are identifying genetic risk factors for late-onset Alzheimer’s disease (LOAD) and characterizing this common dementia at the molecular level. Experimental studies in model organisms can validate these associations and elucidate the links between specific genetic factors and transcriptomic signatures. However, most transgenic animal models are based on rare, early-onset AD genes which may not reflect the full transcriptomic signatures and complete neuropathology of LOAD. Animal models based on LOAD-associated genes are necessary to connect common genetic variation with LOAD transcriptomes, thereby providing novel insights into basic biological mechanisms underlying the disease. Methods: We performed RNA-seq on whole brain samples from a panel of sixmonth-old female mice, each carrying one of the following mutations: homozygous deletions of APOE and CLU; hemizygous deletions of BIN1 and CD2AP; and a transgenic APOEe4. We also included a transgenic APP/PS1 model for comparison to early-onset variants. Weighted gene co-expression network analysis (WGCNA) was used to identify modules of correlated genes and each module was tested for differential expression by strain. We then compared mouse modules with human postmortem brain modules from the Accelerating Medicine’s Partnership for AD (AMP-AD) to determine the AD relevance of risk genes. Results: Mouse modules were significantly enriched in multiple AD-related pathways, including immune response, inflammation, lipid-processing, endocytosis and synaptic-cell-functioning. Various modules were significantly associated with APOE, APOEe4, CLU, andAPP/PS1mouse models.APOE, APOEe4, and APP/PS1 driven modules overlapped with AMP-AD inflammation and microglial modules; CLU driven modules overlapped with synaptic modules; and APP/PS1 modules separately overlapped with lipid-processing and metabolism modules. Furthermore, we found that immune/microglia related genes are up-regulated and synaptic genes are down-regulated in early-onset carriers. Conclusions: This study of LOAD mouse models provides a basis to dissect the role of AD risk genes in relevant AD pathologies. We determined that different genetic perturbations affect different molecular mechanism underlying AD, and mapped specific effects to each risk gene. Our approach provides a platform for further exploration into the causes and progression of AD by assessing animal models at different ages and/or with different combinations of LOAD risk variants.