Abstract

Alzheimer’s disease (AD) is the most frequent cause of neurodegenerative dementia and affects nearly 50 million people worldwide. Early stage diagnosis of AD is challenging, and there is presently no effective treatment for AD. The specific genetic alterations and pathological mechanisms of the development and progression of dementia remain poorly understood. Therefore, identifying essential genes and molecular pathways that are associated with this disease’s pathogenesis will help uncover potential treatments. In an attempt to achieve a more comprehensive understanding of the molecular pathogenesis of AD, we integrated the differentially expressed genes (DEGs) from six microarray datasets of AD patients and controls. We identified ATPase H+ transporting V1 subunit A (ATP6V1A), BCL2 interacting protein 3 (BNIP3), calmodulin-dependent protein kinase IV (CAMK4), TOR signaling pathway regulator-like (TIPRL), and the translocase of outer mitochondrial membrane 70 (TOMM70) as upregulated DEGs common to the five datasets. Our analyses revealed that these genes exhibited brain-specific gene co-expression clustering with OPA1, ITFG1, OXCT1, ATP2A2, MAPK1, CDK14, MAP2K4, YWHAB, PARK2, CMAS, HSPA12A, and RGS17. Taking the mean relative expression levels of this geneset in different brain regions into account, we found that the frontal cortex (BA9) exhibited significantly (p < 0.05) higher expression levels of these DEGs, while the hippocampus exhibited the lowest levels. These DEGs are associated with mitochondrial dysfunction, inflammation processes, and various pathways involved in the pathogenesis of AD. Finally, our blood–brain barrier (BBB) predictions using the support vector machine (SVM) and LiCABEDS algorithm and molecular docking analysis suggested that antrocin is permeable to the BBB and exhibits robust ligand–receptor interactions with high binding affinities to CAMK4, TOMM70, and T1PRL. Our results also revealed good predictions for ADMET properties, drug-likeness, adherence to Lipinskís rules, and no alerts for pan-assay interference compounds (PAINS) Conclusions: These results suggest a new molecular signature for AD parthenogenesis and antrocin as a potential therapeutic agent. Further investigation is warranted.

Highlights

  • Alzheimer’s disease (AD), which is characterized by functional impairment, progressive cognitive dysfunction, and memory loss, is the most frequent cause of neurodegenerative dementia in aging populations, affecting nearly 50 million people worldwide [1].Worldwide prevalence rates range from 1.0% at 60 years of age to 30–50% by 85 years of age [2,3]

  • Deregulated Expressions of ATP6V1A, BCL2 interacting protein 3 (BNIP3), CAMK4, TOR signaling pathway regulator-like (TIPRL), and translocase of outer mitochondrial membrane 70 (TOMM70) Associated with the Pathology of Neurodegenerative Dementia

  • We found that that ATP6V1A, BNIP3, CAMK4, TIPRL, and TOMM70 were colocalized with the markers of different subcellular localizations: ATP6V1A, BNIP3, CAMK4, cytosol),and andTOMM70

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Summary

Introduction

Alzheimer’s disease (AD), which is characterized by functional impairment, progressive cognitive dysfunction, and memory loss, is the most frequent cause of neurodegenerative dementia in aging populations, affecting nearly 50 million people worldwide [1].Worldwide prevalence rates range from 1.0% at 60 years of age to 30–50% by 85 years of age [2,3]. Alzheimer’s disease (AD), which is characterized by functional impairment, progressive cognitive dysfunction, and memory loss, is the most frequent cause of neurodegenerative dementia in aging populations, affecting nearly 50 million people worldwide [1]. The pathology of AD is irreversible, and early stage diagnosis is paramount to halting the progression of the disease and avoiding deterioration [5,6]. Major clinical manifestations of AD and other dementias are mild cognitive impairment (MCI) and subjective cognitive decline (SCD). The early detection of AD, SCD, MCI, and other dementias is still challenging [7,8]. The current diagnostic tools have several limitations and are unable to detect the disease in its early stages [9]

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