Molecular insights into the pathogenesis of Alzheimer's disease and its relationship to normal aging

Abstract

Alzheimer's disease (AD) is a complex neurodegenerative disorder that diverges from the process of normal brain aging by unknown mechanisms. We analyzed the global structure of age- and disease-dependent gene expression patterns in three regions from more than 600 brains. Gene expression variation could be almost completely explained by four transcriptional biomarkers that we named BioAge (biological age), Alz (Alzheimer), Inflame (inflammation), and NdStress (neurodegenerative stress). BioAge captures the first principal component of variation and includes genes statistically associated with neuronal loss, glial activation, and lipid metabolism. Normally BioAge increases with chronological age, but in AD it is prematurely expressed as if some of the subjects were 140 years old. A component of BioAge, Lipa, contains the AD risk factor APOE and reflects an apparent early disturbance in lipid metabolism. The rate of biological aging in AD patients, which cannot be explained by BioAge, is associated instead with NdStress, which includes genes related to protein folding and metabolism. Inflame, comprised of inflammatory cytokines and microglial genes, is broadly activated and appears early in the disease process. In contrast, the disease-specific biomarker Alz was selectively present only in the affected areas of the AD brain, appears later in pathogenesis, and is enriched in genes associated with the signaling and cell adhesion changes during the epithelial to mesenchymal (EMT) transition. Together these biomarkers provide detailed description of the aging process and its contribution to Alzheimer's disease progression.

Figure 1. Gene expression in PFC1.

The heat map shows hierarchical clustering of the 4000 most variable genes. The samples (rows) are sorted according to the values of the first principal component of the complete dataset and labeled according to diagnosis (normal samples in black, AD samples in red on the right).

Figure 2. Aging score versus chronological age in PFC1.

The box plots (A) demonstrate the distribution of BioAge in different 5-year long age segments and list the ANOVA p-values for the BioAge separation between normal and AD subjects in each chronological age segment. (B) Prediction of chronological age in the independent normal cohort using BioAge. The postmortem prefrontal cortex samples from individual of different age were profiled in an earlier study (GSE1572) . BioAge was calculated based on average expression of several hundred genes from Table S2 (see Methods).

Figure 3. Disease-specific metagenes.

(A) Clustered gene-gene correlation matrix demonstrating strong mutual correlations between genes that were differentially expressed between AD and non-demented samples from PFC1. Three outlined clusters correspond to NdStress, Alz, and Inflame. The coregulation of these genes is also shown in the panel (B). Each colored line represents expression levels of individual genes in 55 PFC1 samples from non-demented and AD subjects sorted in the order of increasing BioAge. Only representative samples that scored in the top or bottom 3% for any of the biomarkers were selected for this figure to improve visualization.

Figure 4. Plot matrix of mutual relationships between key aging and disease-specific biomarkers as well as chronological age.

Each biomarker is represented by its score in each sample based on the average gene expression of contributing genes (see Methods). Non-demented PFC1 samples are shown by black dots. AD samples are shown by red dots. All pairwise relationships between the biomarkers and with chronological age are shown.

Figure 5. Correlation between biomarker scores in PFC1 and VC1 of the same individuals.

Each plot shows relationships between the biomarker values in PFC1 and VC1. Samples from non-demented and AD subjects are shown in black and red respectively.

Figure 6. Comparison of NdStress and Alz in AD and HD. AD samples of PFC2 are colored in red.

HD samples are colored in green. The reference biomarker scores corresponding to non demented individuals are represented by dashed lines.

Figure 7. Disease progression model.

The trajectories of BioAge changes as a function of time reflect the relatively constant rate of aging in non-demented subjects (black), and acceleration of the rate of aging in AD (red). The dots represent the postmortem state of the brain captured by gene expression profiling. The state transition model defines several broad categories for normal brains N0–N3 and for diseased states A1 and A2. The sequence of transitions and associated gene expression biomarkers are shown by arrows.