Page 62 - BH-2-4

Brain & Heart Alzheimer’s disease: Gene and protein network analysis

population ages, understanding and addressing this utilized a gene expression dataset from the entorhinal
condition has become increasingly urgent. The etiology cortex of 10 patients with mid-stage AD (GSE4757)
of AD is notoriously complex, ranging from molecular for a multifaceted investigation integrating advanced
dysfunction to cellular dysfunction. Central to AD bioinformatics techniques and computational tools. This
pathogenesis is the pathological accumulation of amyloid- investigation encompassed three crucial dimensions:
beta (Aβ) peptides and neurofibrillary tangles (NFTs) differential gene expression analysis, gene ontology (GO)
composed of hyperphosphorylated tau, which collectively enrichment analysis, and protein–protein interaction
contribute to synaptic degradation and neuronal death. (PPI) network analysis. Each dimension provided a unique
1-3
The trafficking of amyloid precursor proteins (APP) perspective on the molecular landscape of AD, thereby
and secretases within neurons, modulated by numerous increasing the knowledge of this disease.
proteins, further exacerbates Aβ production and In our study, numerous differentially expressed genes
accumulation, thereby influencing the disease process. (DEGs) were identified 12,13 by comparing AD samples
4
Although Aβ-targeted therapeutic strategies have been with control samples within GSE4757, illuminating the
extensively investigated, most have failed to demonstrate extensive genetic alterations underlying AD. These DEGs
14
significant clinical efficacy. Therefore, the search for new may serve as potential biomarkers for early diagnosis and
protein targets or biomarkers for AD is both important promising targets for treatment in AD. Accordingly, GO
and urgent, potentially offering new approaches for the enrichment analysis was performed on these DEGs to
5
15
prevention and treatment of AD. reveal the biological functions and pathways significantly
Recent studies have unraveled that dysregulated APP associated with AD. The GO results underscored the
processing, fueled by alterations in the Aβ40/42 ratio, multifaceted nature of AD pathology and offered a robust
is a pivotal contributor to AD’s molecular pathology. foundation for further exploration of specific molecular
In addition, APP processing dysregulation is often pathways contributing to AD pathogenesis. Finally, the PPI
propelled by mutations in genes, including PSEN1, which network was analyzed using the STRING database (https://
are common in familial AD. These genetic mutations string-db.org/) 16,17 and Cytoscape software to unveil densely
primarily affect the cleavage of APP by γ-secretase, connected regions and hub genes within the AD-related
skewing the production of Aβ peptides. Consequently, network. The results highlighted ribosomal proteins
6
a convoluted interplay among gene mutations, protein as key players in AD, hinting at potential connections
processing, and intracellular transport orchestrates AD’s between protein synthesis processes and AD pathogenesis.
molecular landscape. Understanding these multifaceted These results challenge traditional paradigms and open a
molecular interactions is crucial for the development of novel avenue for research on the molecular basis of AD.
targeted therapeutic strategies. In addition, the molecular Furthermore, several genes, including RPL15 and RPS19,
intricacies of AD can be further unraveled by analyzing were identified as hub genes by the CytoHubba plugin in
nuances in mechanisms, such as the trafficking of APP Cytoscape, which may serve as key factors in the intricate
7
and the activity of secretases. As research progresses, interaction network of AD. The identification of these
it is increasingly necessary to develop a multi-targeted hub genes sheds light on the critical nodes orchestrating
approach addressing various aspects of the molecular basis AD-related pathways.
for effectively combating AD. 8,9 This study probed the molecular mechanisms of
In several studies, AD animal models have been AD using bioinformatics and computational biology,
utilized to examine disease-related changes in the brain. providing critical insights into genetic alterations,
10
Nevertheless, these animal studies have limitations, one disrupted pathways, and central players in AD. Through
of which is the inability to fully replicate the pathology data and computational analyses, this multidimensional
of human AD. In addition, key metabolic pathways and exploration enriched the understanding of AD pathology
regulators in AD have been analyzed using epigenomic, and advanced the search for effective treatments and
transcriptomic, proteomic, metabolomic, and genomic diagnostic tools for AD.
profiles from human-derived samples, facilitating the
search for diverse targets for preventive or therapeutic 2. Materials and methods
interventions. 11 2.1. Data preprocessing
It is well-reported that the entorhinal cortex is the GSE4757 is a public gene expression dataset in the Gene
first cortical region influenced by the neurodegenerative Expression Omnibus (GEO) database (https://www.ncbi.
process of AD, followed by the hippocampus and limbic nlm.nih.gov/geo/) maintained by the National Center
system, and, ultimately, the neocortex. In our study, we for Biotechnology Information. This dataset pertains to a
9

Volume 2 Issue 4 (2024) 2 doi: 10.36922/bh.2906