Integrating transcriptomics with metabolic modeling predicts biomarkers and drug targets for Alzheimer's disease

53Citations
Citations of this article
149Readers
Mendeley users who have this article in their library.

Abstract

Accumulating evidence links numerous abnormalities in cerebral metabolism with the progression of Alzheimer's disease (AD), beginning in its early stages. Here, we integrate transcriptomic data from AD patients with a genome-scale computational human metabolic model to characterize the altered metabolism in AD, and employ state-of-the-art metabolic modelling methods to predict metabolic biomarkers and drug targets in AD. The metabolic descriptions derived are first tested and validated on a large scale versus existing AD proteomics and metabolomics data. Our analysis shows a significant decrease in the activity of several key metabolic pathways, including the carnitine shuttle, folate metabolism and mitochondrial transport. We predict several metabolic biomarkers of AD progression in the blood and the CSF, including succinate and prostaglandin D2. Vitamin D and steroid metabolism pathways are enriched with predicted drug targets that could mitigate the metabolic alterations observed. Taken together, this study provides the first network wide view of the metabolic alterations associated with AD progression. Most importantly, it offers a cohort of new metabolic leads for the diagnosis of AD and its treatment. © 2014 Stempler et al.

Cite

CITATION STYLE

APA

Stempler, S., Yizhak, K., & Ruppin, E. (2014). Integrating transcriptomics with metabolic modeling predicts biomarkers and drug targets for Alzheimer’s disease. PLoS ONE, 9(8). https://doi.org/10.1371/journal.pone.0105383

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free