Genetic perturbations of disease risk genes in mice capture transcriptomic signatures of late-onset Alzheimer’s disease

Abstract

BackgroundNew genetic and genomic resources have identified multiple genetic risk factors for late-onset Alzheimer’s disease (LOAD) and characterized 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. Animal models based on LOAD-associated genes can potentially connect common genetic variation with LOAD transcriptomes, thereby providing novel insights into basic biological mechanisms underlying the disease.MethodsWe performed RNA-Seq on whole brain samples from a panel of six-month-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 APOEε4. Similar data from a transgenic APP/PS1 model was included for comparison to early-onset variant effects. 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 LOAD-related processes affected by each genetic risk factor.ResultsMouse modules were significantly enriched in multiple AD-related processes, including immune response, inflammation, lipid processing, endocytosis, and synaptic cell function. WGCNA modules were significantly associated with Apoe−/−, APOEε4, Clu−/−, and APP/PS1 mouse models. Apoe−/−, GFAP-driven APOEε4, 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.ConclusionsThis study of genetic 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 mechanisms comprising 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.

Model organism development and evaluation for late‐onset Alzheimer's disease: MODEL‐AD

“…This extensive phenotyping will occur at multiple ages, up to 24 months of age, in male and female mice and, in addition to more traditional phenotyping assays (eg, behavior, biochemistry, and neuropathology), will include relevant in vivo positron emission tomography (PET)/magnetic resonance (MR) imaging with autoradiography validation of tracer compounds, blood and cerebrospinal fluid (CSF) biomarkers, synaptic physiology analyses (eg, basal synaptic transmission, long‐term potentiation, axon excitability, and transmitter release kinetics), and molecular profiling by RNA sequencing. Genomic data will be systematically compared to analogous human data from the AMP‐AD Consortium to identify the specific disease‐related pathways and modules modified in each strain (Pandey et al., 2019; Johnson et al., 2018; Logsdon et al., 2019) 26‐28 . We have developed a new NanoString nCounter Mouse AD panel to specifically assess modifications of LOAD‐associated transcriptome modules that will be used in primary screening of all new mouse strains.…”

APOEe4.Trem2*R47H Mice Show Changes in Alzheimer’s Disease-Relevant Processes in the Absence of Amyloid Plaques

“…We employed a weighted gene co-expression network analysis (WGCNA) used to identify modules of correlated genes. Each module was tested for differential expression by strain, then compared with human postmortem brain modules from the Accelerating Medicine's Partnership for AD (AMP-AD) to determine the LOAD-related processes affected by each genetic risk factor (6,68,69). This will be a useful tool in identifying differentially expressed genes correlated with molecular pathways tied to inflammation and identifying a mouse strain that exhibits a similar transcriptional signature to human patients with true neuroinflammation.…”

Transcriptomically unique endolysosomal and homeostatic microglia populations in Alzheimer’s disease and aged human brain

“…Multiple studies have reproducibly identified differential expression of at least 20 AD risk genes in brain from both mouse and human single cell/nucleus or bulk RNAseq studies (Rangaraju et al, 2018; Grubman et al, 2019; Pandey et al, 2019; Sala Frigerio et al, 2019; van Rooij et al, 2019; Nguyen et al, 2020; Olah et al, 2020; Rexach et al, 2020; Sierksma et al, 2020; Srinivasan et al, 2020; Gerrits et al, 2021). Several of these studies have identified expression of AD risk genes as microglia-specific (Grubman et al, 2019; Sierksma et al, 2020; Srinivasan et al, 2020).…”

“…Using a pseudobulk approach, we tested transcriptomic differences between AD and control individuals. Previous bulk RNAseq studies have identified upregulation of genes associated with inflammation in AD brain (Mills et al, 2013; Humphries et al, 2015; Friedman et al, 2018; Pandey et al, 2019; van Rooij et al, 2019; Rexach et al, 2020). While studies in mouse models have begun to differentiate different forms of inflammatory phenotypes or the timing of inflammation in AD, these have yet to be defined in human (Friedman et al, 2018; Rangaraju et al, 2018; Rexach et al, 2020).…”