Datasets used for generating the classification models.

*<p>NDAD are non-demented individuals with intermediate AD neuropathology.</p

Mean accuracies of predictions of AD severity obtained from various classification models.

<p>Each bar represents the mean accuracy of 20 classification models built using cross-validation based on cortex (A) neuronal (control, NDAD and AD samples), (B) astrocytes (early and advanced AD samples) and (C) whole tissue (control and AD samples) and on hippocampus (D) neuronal (control, NDAD and AD samples) and (E) whole tissue (control and AD samples) gene expression data, using all available genes (leftmost columns) or genes from specific biological processes. Standard deviations (SD) are shown as error bars. Two classifiers results are presented for each case: one classifier using all genes annotated to that biological process, and another classifier that imposes an additional feature selection for only top selected genes (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045879#s4" target="_blank">Methods</a>).</p

Analyzing Gene Expression from Whole Tissue vs. Different Cell Types Reveals the Central Role of Neurons in Predicting Severity of Alzheimer’s Disease

<div><p>Alterations in gene expression resulting from Alzheimer’s disease have received considerable attention in recent years. Although expression has been investigated separately in whole brain tissue, in astrocytes and in neurons, a rigorous comparative study quantifying the relative utility of these sources in predicting the progression of Alzheimer’s disease has been lacking. Here we analyze gene expression from neurons, astrocytes and whole tissues across different brain regions, and compare their ability to predict Alzheimer’s disease progression by building pertaining classification models based on gene expression sets annotated to different biological processes. Remarkably, we find that predictions based on neuronal gene expression are significantly more accurate than those based on astrocyte or whole tissue expression. The findings explicate the central role of neurons, particularly as compared to glial cells, in the pathogenesis of Alzheimer’s disease, and emphasize the importance of measuring gene expression in the most relevant (pathogenically ‘proximal’) single cell types.</p> </div

The list of GO terms used in the current study and the number of genes annotated to each process.

<p>The list of GO terms used in the current study and the number of genes annotated to each process.</p

Integrating Transcriptomics with Metabolic Modeling Predicts Biomarkers and Drug Targets for Alzheimer's Disease

<div><p>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.</p></div