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Robust Sampling of Defective Pathways in Alzheimer's Disease. Implications in Drug Repositioning.

ABSTRACT: We present the analysis of the defective genetic pathways of the Late-Onset Alzheimer's Disease (LOAD) compared to the Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using different sampling methodologies. These algorithms sample the uncertainty space that is intrinsic to any kind of highly underdetermined phenotype prediction problem, by looking for the minimum-scale signatures (header genes) corresponding to different random holdouts. The biological pathways can be identified performing posterior analysis of these signatures established via cross-validation holdouts and plugging the set of most frequently sampled genes into different ontological platforms. That way, the effect of helper genes, whose presence might be due to the high degree of under determinacy of these experiments and data noise, is reduced. Our results suggest that common pathways for Alzheimer's disease and MCI are mainly related to viral mRNA translation, influenza viral RNA transcription and replication, gene expression, mitochondrial translation, and metabolism, with these results being highly consistent regardless of the comparative methods. The cross-validated predictive accuracies achieved for the LOAD and MCI discriminations were 84% and 81.5%, respectively. The difference between LOAD and MCI could not be clearly established (74% accuracy). The most discriminatory genes of the LOAD-MCI discrimination are associated with proteasome mediated degradation and G-protein signaling. Based on these findings we have also performed drug repositioning using Dr. Insight package, proposing the following different typologies of drugs: isoquinoline alkaloids, antitumor antibiotics, phosphoinositide 3-kinase PI3K, autophagy inhibitors, antagonists of the muscarinic acetylcholine receptor and histone deacetylase inhibitors. We believe that the potential clinical relevance of these findings should be further investigated and confirmed with other independent studies.

SUBMITTER: Fernandez-Martinez JL

PROVIDER: S-EPMC7279419 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Robust Sampling of Defective Pathways in Alzheimer's Disease. Implications in Drug Repositioning.

Fernández-Martínez Juan Luis JL Álvarez-Machancoses Óscar Ó de Andrés-Galiana Enrique J EJ Bea Guillermina G Kloczkowski Andrzej A

International journal of molecular sciences 20200519 10

We present the analysis of the defective genetic pathways of the Late-Onset Alzheimer's Disease (LOAD) compared to the Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using different sampling methodologies. These algorithms sample the uncertainty space that is intrinsic to any kind of highly underdetermined phenotype prediction problem, by looking for the minimum-scale signatures (header genes) corresponding to different random holdouts. The biological pathways can be identified perfor ...[more]

PMID: 32438758

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Project description:Numerous clinical trials of drug candidates for Alzheimer's disease (AD) have failed, and computational drug repositioning approaches using omics data have been proposed as effective alternative approaches to the discovery of drug candidates. However, little multi-omics data is available for AD, due to limited availability of brain tissues. Even if omics data exist, systematic drug repurposing study for AD has suffered from lack of big data, insufficient clinical information, and difficulty in data integration on account of sample heterogeneity derived from poor diagnosis or shortage of qualified post-mortem tissue. In this study, we developed a proteotranscriptomic-based computational drug repositioning method named Drug Repositioning Perturbation Score/Class (DRPS/C) based on inverse associations between disease- and drug-induced gene and protein perturbation patterns, incorporating pharmacogenomic knowledge. We constructed a Drug-induced Gene Perturbation Signature Database (DGPSD) comprised of 61,019 gene signatures perturbed by 1,520 drugs from the Connectivity Map (CMap) and the L1000 CMap. Drugs were classified into three DRPCs (High, Intermediate, and Low) according to DRPSs that were calculated using drug- and disease-induced gene perturbation signatures from DGPSD and The Cancer Genome Atlas (TCGA), respectively. The DRPS/C method was evaluated using the area under the ROC curve, with a prescribed drug list from TCGA as the gold standard. Glioblastoma had the highest AUC. To predict anti-AD drugs, DRPS were calculated using DGPSD and AD-induced gene/protein perturbation signatures generated from RNA-seq, microarray and proteomic datasets in the Synapse database, and the drugs were classified into DRPCs. We predicted 31 potential anti-AD drug candidates commonly belonged to high DRPCs of transcriptomic and proteomic signatures. Of these, four drugs classified into the nervous system group of Anatomical Therapeutic Chemical (ATC) system are voltage-gated sodium channel blockers (bupivacaine, topiramate) and monamine oxidase inhibitors (selegiline, iproniazid), and their mechanism of action was inferred from a potential anti-AD drug perspective. Our approach suggests a shortcut to discover new efficacy of drugs for AD.

Project description:BackgroundDespite numerous clinical trials and decades of endeavour, there is still no effective cure for Alzheimer's disease. Computational drug repositioning approaches may be employed for the development of new treatment strategies for Alzheimer's patients since an extensive amount of omics data has been generated during pre-clinical and clinical studies. However, targeting the most critical pathophysiological mechanisms and determining drugs with proper pharmacodynamics and good efficacy are equally crucial in drug repurposing and often imbalanced in Alzheimer's studies.MethodsHere, we investigated central co-expressed genes upregulated in Alzheimer's disease to determine a proper therapeutic target. We backed our reasoning by checking the target gene's estimated non-essentiality for survival in multiple human tissues. We screened transcriptome profiles of various human cell lines perturbed by drug induction (for 6798 compounds) and gene knockout using data available in the Connectivity Map database. Then, we applied a profile-based drug repositioning approach to discover drugs targeting the target gene based on the correlations between these transcriptome profiles. We evaluated the bioavailability, functional enrichment profiles and drug-protein interactions of these repurposed agents and evidenced their cellular viability and efficacy in glial cell culture by experimental assays and Western blotting. Finally, we evaluated their pharmacokinetics to anticipate to which degree their efficacy can be improved.ResultsWe identified glutaminase as a promising drug target. Glutaminase overexpression may fuel the glutamate excitotoxicity in neurons, leading to mitochondrial dysfunction and other neurodegeneration hallmark processes. The computational drug repurposing revealed eight drugs: mitoxantrone, bortezomib, parbendazole, crizotinib, withaferin-a, SA-25547 and two unstudied compounds. We demonstrated that the proposed drugs could effectively suppress glutaminase and reduce glutamate production in the diseased brain through multiple neurodegeneration-associated mechanisms, including cytoskeleton and proteostasis. We also estimated the human blood-brain barrier permeability of parbendazole and SA-25547 using the SwissADME tool.ConclusionsThis study method effectively identified an Alzheimer's disease marker and compounds targeting the marker and interconnected biological processes by use of multiple computational approaches. Our results highlight the importance of synaptic glutamate signalling in Alzheimer's disease progression. We suggest repurposable drugs (like parbendazole) with well-evidenced activities that we linked to glutamate synthesis hereby and novel molecules (SA-25547) with estimated mechanisms for the treatment of Alzheimer's patients.

Project description:BACKGROUND:Alzheimer'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. METHODS:In 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. RESULTS:We 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). CONCLUSIONS:Using 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.

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Robust Sampling of Defective Pathways in Alzheimer's Disease. Implications in Drug Repositioning.

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Robust Sampling of Defective Pathways in Alzheimer's Disease. Implications in Drug Repositioning.

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