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

Shiri Stempler,Keren Yizhak,Eytan Ruppin,Stephen S Fong

doi:10.1371/journal.pone.0105383

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.

Highlights

Alzheimer’s disease (AD) is the most common form of dementia
As transcriptional regulation plays a major role in controlling metabolic functions [13], and there is a large body of transcriptome data available for study, we approached this problem using integrative Metabolic Analysis Tool (iMAT), a computational method to systematically predict metabolic behavior by incorporating gene expression data into a Genome-Scale Metabolic Modeling (GSMM) [18]
To account for metabolic flux activity that is not reflected in the mRNA expression data, iMAT considers the mRNA levels as cues for the likelihood that the enzyme in question carries a metabolic flux in its associated reaction(s), and leverages the GSMM to accumulate these cues into a global flux behavior that is stochiometrically consistent and maintains mass balance across the entire network [27]

Summary

Introduction

Alzheimer’s disease (AD) is the most common form of dementia. It is estimated that AD affects more than 35 million patients worldwide and its incidence is expected to increase with the aging of the population. Extensive investigations of AD have taken place over the past few decades, its pathogenesis has yet to be elucidated. No treatment is available to prevent or halt the progression of AD. The clinical diagnosis of AD is not possible until a patient reaches the dementia phase of the disease [1]. A more accurate and earlier diagnosis of AD could enable the use of potential disease-modifying drugs and there is a need for biological markers for the early stages of AD [2]

Methods

Results

Conclusion