School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
PLoS One. 2024 May 29;19(5):e0304410. doi: 10.1371/journal.pone.0304410. eCollection 2024.
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.
阿尔茨海默病和代谢紊乱作为重要的风险因素之间的关联已被广泛认可。然而,将这些情况联系起来的复杂分子机制仍难以捉摸。为了解决这一知识空白,我们使用生物信息学方法进行了全面调查,以阐明分子联系和途径,为这些疾病的病理和临床特征提供新的视角。我们从基因表达综合数据库(GEO)中使用微阵列数据集(GSE5281、GSE122063),确定了阿尔茨海默病中具有差异表达的基因(141 个基因)。利用 CoreMine、CTD 和 Gene Card 数据库,我们提取了与代谢疾病相关的基因,包括高血压、非酒精性脂肪肝和糖尿病。进一步的分析揭示了与代谢疾病和阿尔茨海默病相关的重叠基因,揭示了共同的分子联系。我们利用 String 和 HIPPIE 数据库可视化这些共享基因的蛋白质-蛋白质相互作用(PPI),并使用 Cytoscape 和 MCODE 插件构建 PPI 网络。在阿尔茨海默病和代谢综合征的主要簇中,我们鉴定了 SPP1、CD44、IGF1 和 FLT1 等关键分子。通过 DAVID 数据集进行的富集分析突出了 SPP1 作为一个新的靶点,其受体 CD44 在炎症级联和胰岛素信号转导中断中发挥重要作用,导致阿尔茨海默病的神经退行性方面。ECM-受体相互作用、焦点粘连和 PI3K/Akt 途径可能都介导这些作用。此外,我们通过使用 DGIdb 数据库重新利用分子联系来研究潜在的药物,发现他克莫司和降钙素是有前途的候选药物,特别是因为它们在 SPP1 分子上具有结合位点。总之,我们的研究揭示了代谢综合征和 AD 之间的关键分子桥梁,为它们的病理生理学提供了治疗干预的见解。