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

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.

Stratification of the Gut Microbiota Composition Landscape across the Alzheimer's Disease Continuum in a Turkish Cohort.

Alzheimer's disease (AD) is a heterogeneous disorder that spans a continuum with multiple phases, including preclinical, mild cognitive impairment, and dementia. Unlike for most other chronic diseases, human studies reporting on AD gut microbiota in the literature are very limited. With the scarcity of approved drugs for AD therapies, the rational and precise modulation of gut microbiota composition using diet and other tools is a promising approach to the management of AD. Such an approach could be personalized if an AD continuum can first be deconstructed into multiple strata based on specific microbiota features by using single or multiomics techniques. However, stratification of AD gut microbiota has not been systematically investigated before, leaving an important research gap for gut microbiota-based therapeutic approaches. Here, we analyze 16S rRNA amplicon sequencing of stool samples from 27 patients with mild cognitive impairment, 47 patients with AD, and 51 nondemented control subjects by using tools compatible with the compositional nature of microbiota. To stratify the AD gut microbiota community, we applied four machine learning techniques, including partitioning around the medoid clustering and fitting a probabilistic Dirichlet mixture model, the latent Dirichlet allocation model, and we performed topological data analysis for population-scale microbiome stratification based on the Mapper algorithm. These four distinct techniques all converge on Prevotella and Bacteroides stratification of the gut microbiota across the AD continuum, while some methods provided fine-scale resolution in stratifying the community landscape. Finally, we demonstrate that the signature taxa and neuropsychometric parameters together robustly classify the groups. Our results provide a framework for precision nutrition approaches aiming to modulate the AD gut microbiota. IMPORTANCE The prevalence of AD worldwide is estimated to reach 131 million by 2050. Most disease-modifying treatments and drug trials have failed, due partly to the heterogeneous and complex nature of the disease. Recent studies demonstrated that gut dybiosis can influence normal brain function through the so-called "gut-brain axis." Modulation of the gut microbiota, therefore, has drawn strong interest in the clinic in the management of the disease. However, there is unmet need for microbiota-informed stratification of AD clinical cohorts for intervention studies aiming to modulate the gut microbiota. Our study fills in this gap and draws attention to the need for microbiota stratification as the first step for microbiota-based therapy. We demonstrate that while Prevotella and Bacteroides clusters are the consensus partitions, the newly developed probabilistic methods can provide fine-scale resolution in partitioning the AD gut microbiome landscape.

Revealing the Molecular Mechanisms of Alzheimer's Disease Based on Network Analysis.

The complex pathology of Alzheimer's disease (AD) emphasises the need for comprehensive modelling of the disease, which may lead to the development of efficient treatment strategies. To address this challenge, we analysed transcriptome data of post-mortem human brain samples of healthy elders and individuals with late-onset AD from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) and Mayo Clinic (MayoRNAseq) studies in the AMP-AD consortium. In this context, we conducted several bioinformatics and systems medicine analyses including the construction of AD-specific co-expression networks and genome-scale metabolic modelling of the brain in AD patients to identify key genes, metabolites and pathways involved in the progression of AD. We identified AMIGO1 and GRPRASP2 as examples of commonly altered marker genes in AD patients. Moreover, we found alterations in energy metabolism, represented by reduced oxidative phosphorylation and ATPase activity, as well as the depletion of hexanoyl-CoA, pentanoyl-CoA, (2E)-hexenoyl-CoA and numerous other unsaturated fatty acids in the brain. We also observed that neuroprotective metabolites (e.g., vitamins, retinoids and unsaturated fatty acids) tend to be depleted in the AD brain, while neurotoxic metabolites (e.g., ß-alanine, bilirubin) were more abundant. In summary, we systematically revealed the key genes and pathways related to the progression of AD, gained insight into the crucial mechanisms of AD and identified some possible targets that could be used in the treatment of AD.

Multi-omics approaches for revealing the complexity of cardiovascular disease.

The development and progression of cardiovascular disease (CVD) can mainly be attributed to the narrowing of blood vessels caused by atherosclerosis and thrombosis, which induces organ damage that will result in end-organ dysfunction characterized by events such as myocardial infarction or stroke. It is also essential to consider other contributory factors to CVD, including cardiac remodelling caused by cardiomyopathies and co-morbidities with other diseases such as chronic kidney disease. Besides, there is a growing amount of evidence linking the gut microbiota to CVD through several metabolic pathways. Hence, it is of utmost importance to decipher the underlying molecular mechanisms associated with these disease states to elucidate the development and progression of CVD. A wide array of systems biology approaches incorporating multi-omics data have emerged as an invaluable tool in establishing alterations in specific cell types and identifying modifications in signalling events that promote disease development. Here, we review recent studies that apply multi-omics approaches to further understand the underlying causes of CVD and provide possible treatment strategies by identifying novel drug targets and biomarkers. We also discuss very recent advances in gut microbiota research with an emphasis on how diet and microbial composition can impact the development of CVD. Finally, we present various biological network analyses and other independent studies that have been employed for providing mechanistic explanation and developing treatment strategies for end-stage CVD, namely myocardial infarction and stroke.

A systems biology approach for studying neurodegenerative diseases.

Neurodegenerative diseases (NDDs), such as Alzheimer's (AD) and Parkinson's (PD), are among the leading causes of lost years of healthy life and exert a great strain on public healthcare systems. Despite being first described more than a century ago, no effective cure exists for AD or PD. Although extensively characterised at the molecular level, traditional neurodegeneration research remains marred by narrow-sense approaches surrounding amyloid ß (Aß), tau, and α-synuclein (α-syn). A systems biology approach enables the integration of multi-omics data and informs discovery of biomarkers, drug targets, and treatment strategies. Here, we present a comprehensive timeline of high-throughput data collection, and associated biotechnological advancements and computational analysis related to AD and PD. We hereby propose that a philosophical change in the definitions of AD and PD is now needed.