Abstract
Alzheimer’s disease is a complex disorder encompassing multiple pathological features with associated genetic and molecular culprits. However, target-based therapeutic strategies have so far proved ineffective. The aim of this study is to develop a methodology harnessing the transcriptional changes associated with Alzheimer’s disease to develop a high content quantitative disease phenotype that can be used to repurpose existing drugs. Firstly, the Alzheimer’s disease gene expression landscape covering severe disease stage, early pathology progression, cognitive decline and animal models of the disease has been defined and used to select a set of 153 drugs tending to oppose disease-associated changes in the context of immortalised human cancer cell lines. The selected compounds have then been assayed in the more biologically relevant setting of iPSC-derived cortical neuron cultures. It is shown that 51 of the drugs drive expression changes consistently opposite to those seen in Alzheimer’s disease. It is hoped that the iPSC profiles will serve as a useful resource for drug repositioning within the context of neurodegenerative disease and potentially aid in generating novel multi-targeted therapeutic strategies.
Highlights
Global gene expression profiling can be thought of as a high content quantitative phenotypic measure characterising tissue[1], cell type in, for example, the heterogeneous context of the brain[2,3,4] and revealing diversity within a previously thought homogeneous population[5]
The results indicate that at the global level there is a degree of correspondence between the connectivity map project (CMAP) and induced pluripotent stem cell (iPSC) profiles
The animal model data naturally separates into those based on the 5xFAD, which is consistent with Alzheimer’s disease (AD) as can be seen in Supplementary Table 3, and those based on 3xTG, showing little overlap with AD profiles or internal consistency
Summary
Global gene expression profiling can be thought of as a high content quantitative phenotypic measure characterising tissue[1], cell type in, for example, the heterogeneous context of the brain[2,3,4] and revealing diversity within a previously thought homogeneous population[5]. It has been established that disease-associated expression changes can distinguish between disease states and are consistent across independent data sets, facilitating the identification of robust biomarkers[7]. In a recent development, diseaseassociated gene expression changes have begun to be inferred from genomic risk variant data with the Genotype-Tissue Expression repository[20] and harnessed to predict repurposing candidates for major psychiatric conditions[21]. There is some intriguing psychotherapeutic association of the candidate drugs in this approach, the predicted transcriptional perturbation does
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