Identification of evolutionarily conserved gene networks mediating neurodegenerative dementia

Abstract

Identifying the mechanisms through which genetic risk causes dementia is an imperative for new therapeutic development. Here, we apply a multi-stage, systems biology approach to elucidate disease mechanisms in frontotemporal dementia (FTD). We identify two gene co-expression modules that are preserved in mice harboring mutations in MAPT, GRN, and other dementia mutations on diverse genetic backgrounds. We bridge the species divide via integration with proteomic and transcriptomic data from human brain to identify evolutionarily conserved, disease-relevant networks. We find that overexpression of miR-203, a hub of a putative regulatory miRNA module, re-capitulates mRNA co-expression patterns associated with disease state and induces neuronal cell death, establishing this miRNA as a regulator of neurodegeneration. Using a database of drug-mediated gene expression changes, we identify small molecules that can normalize the disease-associated modules and validate this experimentally. Our results highlight the utility of an integrative, cross-species, network approach to drug discovery.

Identification of conserved proteomic networks in neurodegenerative dementia

“…While previous AD proteomic studies incorporated one or two datasets (Johnson et al, 2018;McKenzie et al, 2017;Ping et al, 2018;Seyfried et al, 2017;Yu et al, 2018;Zhang et al, 2018), we integrated data representing five different cohorts and used a bioinformatics approach to define robust disease-associated proteomic networks. Our consensus WGCNA approach bypassed the need for batch correction, which can lead to the removal or compression of the disease signal (Gandal et al, 2018b;Swarup et al, 2019).…”

“…Like AD proteomic analyses, transcriptomic analyses also showed upregulation of inflammatory astrocytic and microglial signatures and downregulation of neuronal signatures (Mostafavi et al, 2018;Seyfried et al, 2017;Swarup et al, 2019;Wang et al, 2016). One transcriptomic study identified a mitochondrial module positively associated with AD (Mostafavi et al, 2018), which contrasted with our and other studies' (Johnson et al, 2018;Ping et al, 2018) proteomic mitochondrial modules that were negatively associated with AD.…”

Transcriptional signatures of progressive neuropathology in transgenic tau and amyloid mouse models

“…Read counts were analysed for differential expression using the R package DESeq2 64 (version 1. 16.1) downloaded from Bioconductor 14 .DESeq2 uses the raw read counts, applies an internal normalization method, and does estimation of library size, estimation of dispersion, and negative binomial generalized linear model fitting 64 . Data sets were filtered for non-expressed and lowly expressed genes (minimum of 6 counts across all samples), and similarity in the genome-wide expression profile between samples was visualized in a heatmap clustered by Euclidean distance ( Supplementary Figure 21 and Supplementary Figure 22) and a principal component analysis (PCA) plot of the first two principal components ( Supplementary Figure 23 and Supplementary Figure 24).…”

“…Mouse models of tau and amyloid have played a major role in defining critical pathology-related processes, including facilitating our understanding of the brain's transcriptional response to the production and gradual deposition of tau and amyloid into tangles and plaques 11 . Recent studies have identified widespread gene expression differences in transgenic mice harbouring a diverse range of AD-associated mutations [12][13][14][15][16][17] . However, most analyses to date have been undertaken on relatively small numbers of animals and have not attempted to directly relate transcriptional alterations to the progressive burden of pathology in the same mice.…”

Disruption of aRAC1-centred protein interaction network is associated with Alzheimer’s disease pathology and causes age-dependent neurodegeneration

“…Network analysis is effective not only for detecting differences in molecular networks between AD patients and healthy controls but also for identifying hub genes that interact with many genes as key drivers of pathologies (8)(9)(10)(11)(12)(13)(14). A protein interaction network (PIN) is a collection of physical protein-protein interactions validated by high-throughput techniques, such as yeast two-hybrid systems and mass spectrometry-based technologies.…”