Individual metabolic network for the accurate detection of Alzheimer's disease based on FDGPET imaging

AbstractMetabolic aberrations are a prominent feature of Alzheimer’s Disease (AD). Different neuronal subtypes have selective vulnerability in AD. Despite the recent advance of single cell and single nucleus RNA-seq of AD brains, genome-scale metabolic network changes in neuronal subtypes have not been systematically studied with detail. To bridge this knowledge gap, I developed a computational method called perturb-Met that can uncover transcriptional dysregulation centered at hundreds of metabolites in a metabolic network. perturb-Met successfully recapitulated known glycolysis, cholesterol, and other metabolic defects in APOE4-neurons and microglia, many of which are missed by current methods. Applying perturb-Met on AD snRNA-seq data, I revealed that the four neuronal subtypes in the entorhinal cortex shows subtype-specific metabolic changes, namely mitochondrial complex I metabolism, ganglioside metabolism, galactose and heparan sulfate metabolism, as well as glucose and lipid metabolism, respectively. perturb-Met also revealed significant changes in protein glycosylation in the neuron subtype specifically found in AD brains. These subtype-specific metabolic changes may potentially underlie their selective vulnerability in AD. perturb-Met is a valuable tool to discover potential metabolic network changes in many other single cell or bulk transcriptomic studies.

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Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI

Psychiatry Research Neuroimaging ◽

10.1016/j.pscychresns.2012.04.007 ◽

2013 ◽

Vol 212 (3) ◽

pp. 230-236 ◽

Cited By ~ 62

Author(s):

Juergen Dukart ◽

Karsten Mueller ◽

Henryk Barthel ◽

Arno Villringer ◽

Osama Sabri ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Fdg Pet ◽

Meta Analysis ◽

Svm Classification ◽

Accurate Detection

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EEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease

Scientific Reports ◽

10.1038/s41598-017-06165-4 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 28

Author(s):

Sonja Simpraga ◽

Ricardo Alvarez-Jimenez ◽

Huibert D. Mansvelder ◽

Joop M. A. van Gerven ◽

Geert Jan Groeneveld ◽

...

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Accurate Detection

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A Novel Method for Drug Screen to Regulate G Protein-Coupled Receptors in the Metabolic Network of Alzheimer’s Disease

BioMed Research International ◽

10.1155/2018/5486403 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Yang Li ◽

Wei Zheng ◽

Wuyun Qiqige ◽

Shujuan Cao ◽

Jishou Ruan ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Metabolic Network ◽

G Protein ◽

Neurodegenerative Disorder ◽

G Protein Coupled Receptors ◽

Drug Screen ◽

Autodock Vina ◽

Position Information ◽

G Protein Coupled

Alzheimer’s disease (AD) is a chronic and progressive neurodegenerative disorder and the pathogenesis of AD is poorly understood. G protein-coupled receptors (GPCRs) are involved in numerous key AD pathways and play a key role in the pathology of AD. To fully understand the pathogenesis of AD and design novel drug therapeutics, analyzing the connection between AD and GPCRs is of great importance. In this paper, we firstly build and analyze the AD-related pathway by consulting the KEGG pathway of AD and a mass of literature and collect 25 AD-related GPCRs for drug discovery. Then the ILbind and AutoDock Vina tools are integrated to find out potential drugs related to AD. According to the analysis of DUD-E dataset, we select five drugs, that is, Acarbose (ACR), Carvedilol (CVD), Digoxin (DGX), NADH (NAI), and Telmisartan (TLS), by sorting the ILbind scores (≥0.73). Then depending on their AutoDock Vina scores and pocket position information, the binding patterns of these five drugs are obtained. We analyze the regulation function of GPCRs in the metabolic network of AD based on the drug screen results, which may be helpful for the study of the off-target effect and the side effect of drugs.

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