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.
Highlights
Alzheimer’s disease (AD) is the most frequently diagnosed neurodegenerative disorder worldwide and the sixth leading cause of death in the United States and is on the rise [1]
We investigated the functional profiles of differentially expressed genes (DEGs) from each brain region using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) [17] term enrichment analysis (Figure 3)
Based on the network analysis and differential expression analysis (DEA) results, we found a wide change in energy metabolism, chaperones and synaptic activity
Summary
Alzheimer’s disease (AD) is the most frequently diagnosed neurodegenerative disorder worldwide and the sixth leading cause of death in the United States and is on the rise [1]. Rational development of medical approaches (e.g., sophisticated brain imaging studies and discovery of novel candidate genes, proteins, and other substances in blood or cerebrospinal fluid) are fundamental for better understanding of the molecular factors that contribute to disease progression, and for improving the early diagnosis and treatment decisions. GEnome-scale metabolic Models (GEMs) provide quantitative information on how the different metabolites are linked to each gene and each reaction in the network [8,14,15]. We first conducted differential expression analysis (DEA) and gene set enrichment analyses (GSEA) to collect baseline biological information.
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