Association between epidermal growth factor receptor gene polymorphisms and susceptibility to Parkinson's disease

Aging-related biomarkers for the diagnosis of Parkinson's disease based on bioinformatics analysis and machine learning

Parkinson's disease (PD) is a multifactorial disease that lacks reliable biomarkers for its diagnosis. It is now clear that aging is the greatest risk factor for developing PD. Therefore, it is necessary to identify novel biomarkers associated with aging in PD. In this study, we downloaded aging-related genes from the Human Ageing Gene Database. To screen and verify biomarkers for PD, we used whole-blood RNA-Seq data from 11 PD patients and 13 healthy control (HC) subjects as a training dataset and three datasets retrieved from the Gene Expression Omnibus (GEO) database as validation datasets. Using the limma package in R, 1435 differentially expressed genes (DEGs) were found in the training dataset. Of these genes, 29 genes were found to occur in both DEGs and 307 aging-related genes. By using machine learning algorithms (LASSO, RF, SVM, and RR), Venn diagrams, and LASSO regression, four of these genes were determined to be potential PD biomarkers; these were further validated in external validation datasets and by qRT-PCR in the peripheral blood mononuclear cells (PBMCs) of 10 PD patients and 10 HC subjects. Based on the biomarkers, a diagnostic model was developed that had reliable predictive ability for PD. Two of the identified biomarkers demonstrated a meaningful correlation with immune cell infiltration status in the PD patients and HC subjects. In conclusion, four aging-related genes were identified as robust diagnostic biomarkers and may serve as potential targets for PD therapeutics.

Bioinformatics analysis of the potential receptor and therapeutic drugs for Alzheimer's disease with comorbid Parkinson's disease

Background

Now, there are no sensitive biomarkers for improving Alzheimer's disease (AD) and comorbid Parkinson's disease (PD). The aim of the present study was to analyze differentially expressed genes (DEGs) in brain tissue from AD and PD patients via bioinformatics analysis, as well as to explore precise diagnostic and therapeutic targets for AD and comorbid PD.

Methods

GFE122063 and GSE7621 data sets from GEO in NCBI, were used to screen differentially expressed genes (DEGs) for AD and PD, and identify the intersected genes, respectively. Intersected genes were analyzed by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Then, STRING site and Cytoscape were used to construct a protein-protein interaction (PPI) network, CytoNCA algorithm to analyze and evaluate centrality, Mcode plug-in to analyze module, and Cytohubba to screen key genes. Combined GO-KEGG enrichment analysis with Cytoscape algorithm to screen the key gene in AD complicated with PD. Then, the DEGs for AD and PD were imported into the Association Map (CMap) online platform to screen out the top 10 small molecule drugs, and using molecular docking techniques to evaluate the interactions between small molecule drugs and key genes receptors.

Results

In total, 231 upregulated genes and 300 downregulated genes were identified. GO analysis revealed that the DEGs were highly enriched in signal transduction, and KEGG analysis revealed that the DEGs were associated with the MAPK and PI3K-Akt signaling pathways. Epidermal growth factor receptor (EGFR) was identified as a potential receptor gene in AD and comorbid PD. EGFR was upregulated in both AD and PD, and the proteins that interact with EGFR were enriched in the Ras/Raf/MAPK and PI3K/Akt signaling pathways. Semagacestat was identified as a drug with therapeutic potential for treating AD complicated with PD. There was a high binding affinity between semagacestat and EGFRNTD, with seven hydrogen bonds and one hydrophobic bond.

Discussion

Semagacestat may improve the health of patients with AD complicated with PD through the regulation of the Ras/Raf/MAPK and PI3K/Akt signaling pathways by EGFR, providing evidence supporting the structural modification of semagacestat to develop a more effective drug for treating AD complicated with PD.

Identification of SV2C and DENR as Key Biomarkers for Parkinson's Disease Based on Bioinformatics, Machine Learning, and Experimental Verification

The objective of this study is to investigate the potential biomarkers and therapeutic target genes for Parkinson's disease (PD). We analyzed four datasets (GSE8397, GSE20292, GSE20163, GSE20164) from the Gene Expression Omnibus database. We employed weighted gene co-expression network analysis and differential expression analysis to select genes and perform functional analysis. We applied three algorithms, namely, random forest, support vector machine recursive feature elimination, and least absolute shrinkage and selection operator, to identify hub genes, perform functional analysis, and assess their clinical diagnostic potential using receiver operating characteristic (ROC) curve analysis. We employed the xCell website to evaluate differences in the composition patterns of immune cells in the GEO datasets. We also collected serum samples from PD patients and established PD cell model to validate the expression of hub genes using enzyme-linked immunosorbent assay and quantitative real-time polymerase chain reaction. Our findings identified SV2C and DENR as two hub genes for PD and decreased in PD brain tissue compared with controls. ROC analysis showed effectively value of SV2C and DENR to diagnose PD, and they were downregulated in the serum of PD patients and cell model. Functional analysis revealed that dopamine vesicle transport and synaptic vesicle recycling are crucial pathways in PD. Besides, the differences in the composition of immune cells, especially basophils and T cells, were discovered between PD and controls. In summary, our study identifies SV2C and DENR as potential biomarkers for diagnosing PD and provides a new perspective for exploring the molecular mechanisms of PD.

Genomic profile of Parkinson's disease in Asians

Parkinson's Disease (PD) has witnessed an alarming rise in prevalence, highlighting the suboptimal nature of early diagnostic and therapeutic strategies. To address this issue, genetic testing has emerged as a potential avenue. In this comprehensive review, we have meticulously summarized the variants associated with PD in Asian populations. Our review reveals that these variants exert their influence on diverse biological pathways, encompassing the autophagy-lysosome pathway, cholesterol metabolism, circadian rhythm regulation, immune system response, and synaptic function. Conventionally, PD has been linked to other diseases; however, our findings shed light on a shared genetic susceptibility among these conditions, implying an underlying pathophysiological mechanism that unifies them. Moreover, it is noteworthy that these PD-associated variants can significantly impact drug responses during therapeutic interventions. This review not only provides a consolidated overview of the genetic variants associated with PD in Asian populations but also contributes novel insights into the intricate relationships between PD and other diseases by elucidating shared genetic components. These findings underscore the importance of personalized approaches in diagnosing and treating PD based on individual genetic profiles to optimize patient outcomes.

UBL3 Interacts with Alpha-Synuclein in Cells and the Interaction Is Downregulated by the EGFR Pathway Inhibitor Osimertinib

Ubiquitin-like 3 (UBL3) acts as a post-translational modification (PTM) factor and regulates protein sorting into small extracellular vesicles (sEVs). sEVs have been reported as vectors for the pathology propagation of neurodegenerative diseases, such as α-synucleinopathies. Alpha-synuclein (α-syn) has been widely studied for its involvement in α-synucleinopathies. However, it is still unknown whether UBL3 interacts with α-syn, and is influenced by drugs or compounds. In this study, we investigated the interaction between UBL3 and α-syn, and any ensuing possible functional and pathological implications. We found that UBL3 can interact with α-syn by the Gaussia princeps based split luciferase complementation assay in cells and immunoprecipitation, while cysteine residues at its C-terminal, which are considered important as PTM factors for UBL3, were not essential for the interaction. The interaction was upregulated by 1-methyl-4-phenylpyridinium exposure. In drug screen results, the interaction was significantly downregulated by the treatment of osimertinib. These results suggest that UBL3 interacts with α-syn in cells and is significantly downregulated by epidermal growth factor receptor (EGFR) pathway inhibitor osimertinib. Therefore, the UBL3 pathway may be a new therapeutic target for α-synucleinopathies in the future.

Integrated network analysis identifying potential novel drug candidates and targets for Parkinson's disease

This study aimed to identify potential novel drug candidates and targets for Parkinson's disease. First, 970 genes that have been reported to be related to PD were collected from five databases, and functional enrichment analysis of these genes was conducted to investigate their potential mechanisms. Then, we collected drugs and related targets from DrugBank, narrowed the list by proximity scores and Inverted Gene Set Enrichment analysis of drug targets, and identified potential drug candidates for PD treatment. Finally, we compared the expression distribution of the candidate drug-target genes between the PD group and the control group in the public dataset with the largest sample size (GSE99039) in Gene Expression Omnibus. Ten drugs with an FDR < 0.1 and their corresponding targets were identified. Some target genes of the ten drugs significantly overlapped with PD-related genes or already known therapeutic targets for PD. Nine differentially expressed drug-target genes with p < 0.05 were screened. This work will facilitate further research into the possible efficacy of new drugs for PD and will provide valuable clues for drug design.

Pharmacological Inhibition of Brain EGFR Activation By a BBB-penetrating Inhibitor, AZD3759, Attenuates α-synuclein Pathology in a Mouse Model of α-Synuclein Propagation

Aggregation and deposition of α-synuclein (α-syn) in Lewy bodies within dopamine neurons of substantia nigra (SN) is the pathological hallmark of Parkinson's disease (PD). These toxic α-syn aggregates are believed to propagate from neuron-to-neuron and spread the α-syn pathology throughout the brain beyond dopamine neurons in a prion-like manner. Targeting propagation of such α-syn aggregates is of high interest but requires identifying pathways involving in this process. Evidence from previous Alzheimer's disease reports suggests that EGFR may be involved in the prion-like propagation and seeding of amyloid-β. We show here that EGFR regulates the uptake of exogenous α-syn-PFFs and the levels of endogenous α-syn in cell cultures and a mouse model of α-syn propagation, respectively. Thus, we tested the therapeutic potentials of AZD3759, a highly selective BBB-penetrating EGFR inhibitor, in a preclinical mouse model of α-syn propagation. AZD3759 decreases activated EGFR levels in the brain and reduces phosphorylated α-synuclein (pSyn) pathology in brain sections, including striatum and SN. As AZD3759 is already in the clinic, this paper's results suggest a possible repositioning of AZD3759 as a disease-modifying approach for PD.