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.

Introduction

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder defined by β-amyloid plaques and neurofibrillary tangles accumulation. AD progresses from the hippocampus through the frontal and other brain regions, leading to gradual cognitive impairment and memory loss [1]. It is the most common cause of dementia, and its impact on society is significant, with an estimated 66 million affected in 2030 and double every 20 years [2]. Growing research has linked metabolic syndrome to the progression of AD, making it a significant risk factor to address[1].

Method

Study design

We used bioinformatics approaches to identify dysregulated genes in AD and MetS, including DM, hypertension, and NAFLD.Then, we intersected Alzheimer’s DEG and diabetes, hypertension, and NAFLD as metabolic conditions separately. Then, three protein-protein interaction (PPI) networks of shared genes between both AD and MetS conditions were constructed.Ultimately, we analyzed the data to identify the essential molecular links between AD and MetS by finding the shared genes through PPI networks. To validate the results, A PPI network of differentially expressed genes (DEGs) in AD was constructed. MCODE Plugin was used to identify highly connected regions, and the associations between AD clusters and MetS-related genes were evaluated using Fisher’s exact test. Finally, we enriched the shared genes between both conditions (AD and Mets) and the molecular links. Additionally, drug repurposing was applied for the molecular links.

Result

Data pre-processing and identification of AD-associated DEGs

The datasets from GSE 5281 and GSE122063 were subjected to normalization using Ge-workbench. Two boxplots of normalized data from both GSEs are shown in Fig 2A and 2B, respectively. These boxplots show that the expression data quality was reliable and the normalization was sound. Genes with a log fold change (Ⅰlog FCⅠ) greater than one and an adjusted p-value < 0.05 were selected as differentially expressed genes (DEGs). In GSE5281, 2424 DEGs were identified, comprising 1514 upregulated genes and 910 downregulated genes. In GSE122063, 904 genes were identified as DEGs, among which 311 were upregulated, and 593 were downregulated. The expression of all DEGs from both GSEs is shown in two volcano plots (Fig 2C and 2D). Using the Venn diagram tool, 141 common genes were identified as AD-associated DEGs, consisting of 43 upregulated and 98 downregulated genes (Fig 2G, S1 File). Two heatmaps visualize the cluster analysis results of the DEGs of both GSE (Fig 2E and 2F).

Discussion

Metabolic syndrome has emerged as a significant risk factor for Alzheimer’s disease, sharing common pathological features such as mitochondrial dysfunction, insulin resistance, and oxidative stress [4, 14]. Although previous studies have shed light on the relationship between Alzheimer’s and metabolic syndrome, our understanding of the molecular mechanisms that connect these diseases remains incomplete. In our study, we aimed to unravel the intricate molecular links between Alzheimer’s and metabolic syndrome to advance our knowledge of the pathogenesis of these conditions. Our analysis identified four essential genes—SPP1, IGF1, CD44, and FLT1—that serve as potential molecular links between Alzheimer’s and metabolic syndrome.

Conclusion

Finally, we concluded that SPP1, IGF1, CD44, and FLT1 may play a significant role in the pathogenesis of both conditions. Therefore, these genes can be considered molecular links or shared genes between Alzheimer’s disease and metabolic syndrome. Furthermore, we suggest that targeted drug therapies for these molecular links, mainly focusing on SPP1, may be a promising approach for treating both Alzheimer’s disease and metabolic syndrome. These findings open up opportunities for novel treatments to address the shared molecular mechanisms underlying these conditions and potentially help patients with comorbidities. Overall, the results provide new insights into the molecular mechanisms underlying the relationship between Alzheimer’s disease and metabolic syndrome and have the potential to guide the development of more effective therapies for patients with both conditions.

Acknowledgments

The graphical abstract was created with https://app.biorender.com.

Citation: Azizan Z, Zali H, Mirmotalebisohi SA, Bazrgar M, Ahmadiani A (2024) Deciphering molecular bridges: Unveiling the interplay between metabolic syndrome and Alzheimer’s disease through a systems biology approach and drug repurposing. PLoS ONE 19(5): e0304410. https://doi.org/10.1371/journal.pone.0304410

Editor: Gurudeeban Selvaraj, Concordia University, CANADA

Received: December 3, 2023; Accepted: May 10, 2024; Published: May 29, 2024

Copyright: © 2024 Azizan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This study was supported by a grant from Shahid Beheshti University of Medical Sciences (NO.43007175-1). The funders had no role in study design, data collection and analysis, publication decisions, or manuscript preparation.

Competing interests: The authors have declared that no competing interests exist.

Abbreviation: Osteopontin (OPN)/SPP1, secreted phosphoprotein 1; IGF-1, Insulin-like growth factor 1; FLT1, Fms Related Receptor Tyrosine Kinase 1; AD, Alzheimer’s Disease; MetS, metabolic Syndrome; DM, diabetes mellitus; HTN, hypertension; NAFLD, non-alcoholic fatty liver disease; PPI, protein-protein interaction; DEG, differentially expression gene; GEO, Gene Expression Omnibus; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0304410#abstract0