Deciphering molecular bridges: Unveiling the interplay between metabolic syndrome and Alzheimer's disease through a systems biology approach and drug repurposing

Abstract

The association between Alzheimer's disease and metabolic disorders as significant risk factors is widely acknowledged. However, the intricate molecular mechanism intertwining these conditions remains elusive. To address this knowledge gap, we conducted a thorough investigation using a bioinformatics method to illuminate the molecular connections and pathways that provide novel perspectives on these disorders' pathological and clinical features. Microarray datasets (GSE5281, GSE122063) from the Gene Expression Omnibus (GEO) database facilitated the way to identify genes with differential expression in Alzheimer's disease (141 genes). Leveraging CoreMine, CTD, and Gene Card databases, we extracted genes associated with metabolic conditions, including hypertension, non-alcoholic fatty liver disease, and diabetes. Subsequent analysis uncovered overlapping genes implicated in metabolic conditions and Alzheimer's disease, revealing shared molecular links. We utilized String and HIPPIE databases to visualize these shared genes' protein-protein interactions (PPI) and constructed a PPI network using Cytoscape and MCODE plugin. SPP1, CD44, IGF1, and FLT1 were identified as crucial molecules in the main cluster of Alzheimer's disease and metabolic syndrome. Enrichment analysis by the DAVID dataset was employed and highlighted the SPP1 as a novel target, with its receptor CD44 playing a significant role in the inflammatory cascade and disruption of insulin signaling, contributing to the neurodegenerative aspects of Alzheimer's disease. ECM-receptor interactions, focal adhesion, and the PI3K/Akt pathways may all mediate these effects. Additionally, we investigated potential medications by repurposing the molecular links using the DGIdb database, revealing Tacrolimus and Calcitonin as promising candidates, particularly since they possess binding sites on the SPP1 molecule. In conclusion, our study unveils crucial molecular bridges between metabolic syndrome and AD, providing insights into their pathophysiology for therapeutic interventions.

Fig 1. Workflow algorithm.

Workflow for constructing a protein-protein interaction network. DM, diabetes mellitus; HTN, hypertension; NAFLD, non-alcoholic fatty liver disease; AD, Alzheimer’s disease; GEO, Gene Expression Omnibus; DE, differentially expressed; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein‑protein interaction network.

Fig 2. Identification of DEG.

The boxplots show the normalized data from GSE122063 (A) and GSE5281 (B). The volcano plots of GSE122063 (C) and GSE5281 (D) highlight the upregulated and downregulated genes in the Alzheimer’s group vs the healthy group, represented by red and blue colors, respectively. GSE122063 (E) and GSE5281 (F) heatmaps reveal gene expression patterns. Using the Venn diagram, 141 common differentially expressed genes (DEGs) were identified between the two datasets (G), indicative of Alzheimer’s disease.

Fig 3. Intersected genes.

Intersection of shared genes between DM(A), HTN(B), and NAFLD(C) from the text-mine datasets (Core Mine, CTD, Gene Cards) with Alzheimer’s disease-related DEGs using the Venn diagram tool. DM, diabetes mellitus; HTN, hypertension; NAFLD, non-alcoholic fatty liver disease; AD, Alzheimer’s disease.

Fig 4. Protein-protein interaction network of metabolic syndrome conditions and Alzheimer.

The protein-protein interaction networks of AD and MetS-related diseases include DM(A), HTN (B), and NAFLD (C). The size of the nodes represents their degree of connectivity within the network. Pink circles highlight clusters of interconnected nodes, while the green ones symbolize nodes outside the clusters.

Fig 5. PPI network of Alzheimer.

The Alzheimer’s PPI network comprises 89 nodes and 182 edges. In this network, upregulated genes are denoted by red circles, while blue circles represent downregulated genes. The yellow nodes indicate genes exhibiting distinctive regulation patterns between the GSE5281 and GSE122063 datasets. The network shows five distinct clusters constructed by merging the HIPPIE and String databases. The size of the nodes corresponds to their level of connectivity. A group of diamond shapes depicts nodes that are connected and form a cluster.

Fig 6. Functional enrichment analysis.

Functional enrichment analysis was conducted to analyze the shared genes between Alzheimer’s disease (AD) and metabolic diseases, namely diabetes (A), hypertension (B), and non-alcoholic fatty liver disease (NAFLD) (C). Additionally, the enrichment analysis of molecular links between AD and MetS (D) was demonstrated.

Fig 7. Drug repurposing of molecular links.

Repurposing Essential Molecular Linked Genes for Drug Discovery. Visualized by pink circles representing the genes and green ones symbolizing approved targeted drugs.