Revealing the Molecular Mechanisms of Alzheimer's Disease Based on Network Analysis

Abstract

The complex pathology of Alzheimer's disease (AD) emphasises the need for comprehensive modelling of the disease, which may lead to the development of efficient treatment strategies. To address this challenge, we analysed transcriptome data of post-mortem human brain samples of healthy elders and individuals with late-onset AD from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) and Mayo Clinic (MayoRNAseq) studies in the AMP-AD consortium. In this context, we conducted several bioinformatics and systems medicine analyses including the construction of AD-specific co-expression networks and genome-scale metabolic modelling of the brain in AD patients to identify key genes, metabolites and pathways involved in the progression of AD. We identified AMIGO1 and GRPRASP2 as examples of commonly altered marker genes in AD patients. Moreover, we found alterations in energy metabolism, represented by reduced oxidative phosphorylation and ATPase activity, as well as the depletion of hexanoyl-CoA, pentanoyl-CoA, (2E)-hexenoyl-CoA and numerous other unsaturated fatty acids in the brain. We also observed that neuroprotective metabolites (e.g., vitamins, retinoids and unsaturated fatty acids) tend to be depleted in the AD brain, while neurotoxic metabolites (e.g., β-alanine, bilirubin) were more abundant. In summary, we systematically revealed the key genes and pathways related to the progression of AD, gained insight into the crucial mechanisms of AD and identified some possible targets that could be used in the treatment of AD.

Keywords: Alzheimer’s disease; energy metabolism; gene co-expression network; genome-scale metabolic model; reporter metabolite analysis.

Figure 1

Systematic representation of the pathophysiology of Alzheimer’s disease.

Figure 2

Workflow diagram.

Figure 3

Significantly enriched KEGG pathways for protein-coding genes in DLPFC, TCX and CBE.

Figure 3

Significantly enriched KEGG pathways for protein-coding genes in DLPFC, TCX and CBE.

Figure 3

Significantly enriched KEGG pathways for protein-coding genes in DLPFC, TCX and CBE.

Figure 4

Functional enrichment of significant modules determined from co-expression network by random walk algorithm. M8 (n = 2034), the single module from DLPFC, and M11 (n = 2422), the single module from TCX, were enriched for nearly all metabolic pathways partly due to their size. For instance, genes involved in vitamin, glycan and leukotriene metabolisms were abundant specifically for M8 and M11. Nevertheless, oxidative phosphorylation was the only significant enrichment for both modules (hypergeometric p-value 0.00034 and 0.00054). Aminoacyl t-RNA metabolism was enriched significantly (hypergeometric p-value < 0.0476) for M8 genes. Whereas some CBE modules (M57, M90, M195 and M244) were not enriched for any given annotation, others shared genes associated with synaptic activity and energy metabolism.

Figure 5

Group comparisons in terms of reaction content and gene expressions. (left) Comparison of GEMs based on reaction content showed in heatmap of Hamming distances and dendrogram. (right) Comparison of groups based on gene expressions showed in heatmap of Spearman correlations of mean TPMs and dendrogram.

Figure 6

Scatter plot of metabolic tasks succeeded or failed differently in at least one GEM.

Figure 7

Heatmap for metabolic pathways expressed at least 10% differently in one group of samples. Each colour tone closer to blood-red refers to 10% increase compared to other metabolic pathways, while each colour tone closer to deep blue 10% decrease compared to other metabolic pathways.

Figure 8

Reporter metabolites for: (a) down-regulated genes; and (b) up-regulated genes.

Figure 8

Reporter metabolites for: (a) down-regulated genes; and (b) up-regulated genes.

Figure 9

Sphingolipid biosynthesis and affected signalling pathways in DLPFC. There is an overall increase in sphingolipid synthesis. The increase in sphingosine-1-phosphate also induces MAPK and P13K/Akt signalling pathways, which are associated with cytoskeletal events, vasodilation, fibre formation and cell survival. Circles representing metabolites: red for up-regulated genes, red/blue is palmitoyl–CoA reported for both down-regulated and up-regulated genes. Boxes representing reaction catalysing genes. Arrows represent the direction of significant gene expression level change.