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Discovery of Novel Drug Candidates for Alzheimer's Disease by Molecular Network Modeling.

ABSTRACT: To identify the molecular mechanisms and novel therapeutic agents of late-onset Alzheimer's disease (AD), we performed integrative network analysis using multiple transcriptomic profiles of human brains. With the hypothesis that AD pathology involves the whole cerebrum, we first identified co-expressed modules across multiple cerebral regions of the aging human brain. Among them, two modules (M3 and M8) consisting of 1,429 protein-coding genes were significantly enriched with AD-correlated genes. Differential expression analysis of microarray, bulk RNA-sequencing (RNA-seq) data revealed the dysregulation of M3 and M8 across different cerebral regions in both normal aging and AD. The cell-type enrichment analysis and differential expression analysis at the single-cell resolution indicated the extensive neuronal vulnerability in AD pathogenesis. Transcriptomic-based drug screening from Connectivity Map proposed Gly-His-Lys acetate salt (GHK) as a potential drug candidate that could probably restore the dysregulated genes of the M3 and M8 network. Pretreatment with GHK showed a neuroprotective effect against amyloid-beta-induced injury in differentiated human neuron-like SH-SY5Y cells. Taken together, our findings uncover a dysregulated network disrupted across multiple cerebral regions in AD and propose pretreatment with GHK as a novel neuroprotective strategy against AD.

SUBMITTER: Zhou J

PROVIDER: S-EPMC9051440 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Discovery of Novel Drug Candidates for Alzheimer's Disease by Molecular Network Modeling.

Zhou Jiaxin J Li Qingyong Q Wu Wensi W Zhang Xiaojun X Zuo Zhiyi Z Lu Yanan Y Zhao Huiying H Wang Zhi Z

Frontiers in aging neuroscience 20220415

To identify the molecular mechanisms and novel therapeutic agents of late-onset Alzheimer's disease (AD), we performed integrative network analysis using multiple transcriptomic profiles of human brains. With the hypothesis that AD pathology involves the whole cerebrum, we first identified co-expressed modules across multiple cerebral regions of the aging human brain. Among them, two modules (M3 and M8) consisting of 1,429 protein-coding genes were significantly enriched with AD-correlated genes ...[more]

PMID: 35493947

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Project description:BackgroundAlzheimer's disease (AD) is a multifactorial and complex neuropathology that involves impairment of many intricate molecular mechanisms. Despite recent advances, AD pathophysiological characterization remains incomplete, which hampers the development of effective treatments. In fact, currently, there are no effective pharmacological treatments for AD. Integrative strategies such as transcription regulatory network and master regulator analyses exemplify promising new approaches to study complex diseases and may help in the identification of potential pharmacological targets.MethodsIn this study, we used transcription regulatory network and master regulator analyses on transcriptomic data of human hippocampus to identify transcription factors (TFs) that can potentially act as master regulators in AD. All expression profiles were obtained from the Gene Expression Omnibus database using the GEOquery package. A normal hippocampus transcription factor-centered regulatory network was reconstructed using the ARACNe algorithm. Master regulator analysis and two-tail gene set enrichment analysis were employed to evaluate the inferred regulatory units in AD case-control studies. Finally, we used a connectivity map adaptation to prospect new potential therapeutic interventions by drug repurposing.ResultsWe identified TFs with already reported involvement in AD, such as ATF2 and PARK2, as well as possible new targets for future investigations, such as CNOT7, CSRNP2, SLC30A9, and TSC22D1. Furthermore, Connectivity Map Analysis adaptation suggested the repositioning of six FDA-approved drugs that can potentially modulate master regulator candidate regulatory units (Cefuroxime, Cyproterone, Dydrogesterone, Metrizamide, Trimethadione, and Vorinostat).ConclusionsUsing a transcription factor-centered regulatory network reconstruction we were able to identify several potential molecular targets and six drug candidates for repositioning in AD. Our study provides further support for the use of bioinformatics tools as exploratory strategies in neurodegenerative diseases research, and also provides new perspectives on molecular targets and drug therapies for future investigation and validation in AD.

Project description:Despite the massive investment in Alzheimer's disease (AD), there are still no disease-modifying treatments (DMTs) for AD. One major reason is attributed to the limitation of clinical "one-size-fits-all" approach, since the same AD treatment solely based on clinical diagnosis was unlikely to achieve good clinical efficacy. In recent years, computational approaches based on multiomics data have provided an unprecedented opportunity for drug discovery since they can substantially lower the costs and boost the efficiency. In this study, we intended to identify potential drug candidates for different pathological stages of AD by computationally repurposing Food and Drug Administration (FDA) approved drugs. First, we assembled gene expression data from three different AD pathological stages, which include mild cognitive impairment (MCI) and early and late stages of AD (EAD, LAD). We next quantified the network distances between drug target networks and AD modules by utilizing a network proximity approach, and identified 193 candidates that possessed significant associations with AD. After searching for previous literature evidence, 63 out of 193 (32.6%) predicted drugs were demonstrated to exert therapeutic effects on AD. We further explored the novel mechanism of action (MOA) for these drug candidates by determining the specific brain cells they might function on based on AD patient single cell transcriptomic data. Additionally, we selected several promising candidates that could cross the blood brain barrier together with confirmed neuroprotective effects, and subsequently determined the antioxidative activity of these compounds. Experimental results showed that azathioprine decreased the reactive oxygen species (ROS) and malondialdehyde (MDA) levels and improved the superoxide dismutase (SOD) activity in APP-SH-SY5Y cells. Finally, we deciphered the potential MOA of azathioprine against AD via network analysis and validated several apoptosis-related proteins (Caspase 3, Cleaved Caspase 3, Bax, Bcl2) through western blotting. In summary, this study presented an effective computational strategy utilizing omics data for AD drug repurposing, which provides a new perspective for drug discovery and development.

Dataset Information

Discovery of Novel Drug Candidates for Alzheimer's Disease by Molecular Network Modeling.

Publications

Discovery of Novel Drug Candidates for Alzheimer's Disease by Molecular Network Modeling.

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