Drug repositioning targeting glutaminase reveals drug candidates for the treatment of Alzheimer's disease patients

Abstract

Background: Despite 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.

Methods: Here, 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.

Results: We 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.

Conclusions: This 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.

Keywords: Alzheimer’s disease; Anti-carcinogenic drugs; Gene co-expression network analysis; Glutaminase; Parbendazole; Profile-based computational drug repositioning.

Fig. 1

A The flowchart illustrating the identification of candidate target genes. To summarise, transcriptome data from Religious Orders Study/Memory and Ageing Project (ROSMAP) and MayoClinic cohorts sampled from the dorsolateral prefrontal cortex (purple in the illustration), temporal cortex (light green in the illustration) and cerebellum (light red in the illustration) of the AD patients and non-AD elders have been analysed by systems biology methods, including GCN. 95 shared and central co-expressed genes were in our interest. Hub genes overexpressed at least in one brain region in AD were used for further analysis. B Heatmap showing the log-twofold changes of GLS, KLC1 and NDRG1, which have been overexpressed either on the disease dorsolateral prefrontal cortex, temporal cortex or cerebellum. The blue-to-red colour transition indicates overexpression in AD samples. Significant differential expressions (adjusted p-value < 0.05) labelled with “*”. C Boxplot showing the GLS essentiality scores in nervous system cell lineages. Boxes represent the interquartile ranges where a median is a middle vertical line in a box

Fig. 2

The workflow of drug repositioning methodology

Fig. 3

Bubble plots showing the enriched functional pathways regulated by GLS knockdown and candidate drugs, based on (A) MSigDB hallmark pathways (n = 50) and (B) KEGG pathways significantly changed by GLS knockdown (n = 61). The red colour shows a larger normalised enrichment score (NES)/enrichment, and the blue colour shows a larger negative NES/repression. The size of the “bubble” represents the reliability of enrichment scores, which is calculated as -log(FDR adjusted and weighted Fisher aggregated p-value). The circle represents significant enrichments/repressions, and the triangle represents non-significant enrichment/repressions

Fig. 4

A GLS-centred drug-protein interaction network for four candidate repurposable drugs and compounds with a shared mechanism of action. All these drugs indirectly target glutaminase (demonstrated as GLS) as displayed. All drug–target-associated protein connections were examined, and those proteins on the shortest paths (path length = 4) between drugs and target protein are shown. All proteins are represented by their respective protein symbols

Fig. 5

Bar plots showing the metabolic activity of drug-induced glial cells and cytotoxicity of memantine, parbendazole and bortezomib at different doses based on MTT assay and LDH release assay, after drug treatment on Day 2. A significant change in cell viability percentage representing metabolic activity compared to control or OD absorbance at 450 nm representing lysed cells compared to negative control is indicated by the * sign

Fig. 6

Bar plots showing the glutamate content in glial cells in comparison to cell viability for different doses of memantine, parbendazole and bortezomib, after drug treatment on Day 1 and Day 2. A significant change in the ratio compared to the control is indicated by the * sign

Fig. 7

Western blot images showing the protein levels of GLS, GLS2, and GAPDH (negative control) after drug treatment on Day 2. Relative band intensities were quantified by Image J software and calculated in reference to controls of each drug–protein group, where DMSO band intensities are 1.00

Fig. 8

Visual representation of drug-induced MOA and affected neurodegenerative hallmarks