鉴定CXCR4抑制剂作为阿尔茨海默病和帕金森病潜在治疗剂的初步研究。

IF 1.5 4区 生物学 Q4 CELL BIOLOGY Integrative Biology Pub Date : 2023-04-11 DOI:10.1093/intbio/zyad012
Rahul Tripathi, Pravir Kumar
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引用次数: 0

摘要

已知神经退行性疾病(ndd)表现出遗传重叠和共同的病理生理。本研究旨在发现两种主要与年龄相关的进行性神经退行性疾病阿尔茨海默病(AD)和帕金森病(PD)的共同遗传结构。GSE67333(含AD患者样本)和GSE114517(含PD患者样本)的基因表达谱从国家生物技术信息中心管理的基因表达Omnibus (GEO)功能基因组数据库中检索。使用web应用程序GREIN (GEO RNA-seq Experiments Interactive Navigator)鉴定差异表达基因(DEGs)。从GSE67333数据集中共鉴定出617个deg(239个上调,379个下调)。同样,从GSE114517数据集中鉴定出723个deg(378个上调,344个下调)。构建了deg的蛋白-蛋白相互作用网络,并从各自数据集的网络中鉴定出前50个枢纽基因。在两个数据集之间的四个共同枢纽基因中,选择了C-X-C趋化因子受体4型(CXCR4),因为它的基因表达特征谱和两个数据集之间的差异表达方向相同。选择Mavorixafor作为参比药物是由于其已知的对CXCR4的抑制活性及其穿过血脑屏障的能力。对51个与Mavorixafor结构相似的分子进行分子对接和分子动力学模拟,发现ZINC49067615和ZINC103242147两个新分子。这项初步研究可能有助于预测治疗阿尔茨海默病和帕金森病的分子靶点和诊断标记。我们的研究证实了CXCR4抑制剂治疗阿尔茨海默病和帕金森病的治疗相关性。我们想透露以下关于这项研究的见解。通过分析mRNA测序数据,我们发现阿尔茨海默病和帕金森病在转录水平上的共同特征。这些特征被用来通过计算分析来确定这些疾病的假定治疗剂。因此,我们提出了两个稳定的新化合物ZINC49067615和ZINC103242147,它们与CXCR4具有较强的亲和力,并且具有良好的药动学性质。这些化合物与CXCR4主要残基的相互作用也被描述。
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Preliminary study to identify CXCR4 inhibitors as potential therapeutic agents for Alzheimer's and Parkinson's diseases.

Neurodegenerative disorders (NDDs) are known to exhibit genetic overlap and shared pathophysiology. This study aims to find the shared genetic architecture of Alzheimer's disease (AD) and Parkinson's disease (PD), two major age-related progressive neurodegenerative disorders. The gene expression profiles of GSE67333 (containing samples from AD patients) and GSE114517 (containing samples from PD patients) were retrieved from the Gene Expression Omnibus (GEO) functional genomics database managed by the National Center for Biotechnology Information. The web application GREIN (GEO RNA-seq Experiments Interactive Navigator) was used to identify differentially expressed genes (DEGs). A total of 617 DEGs (239 upregulated and 379 downregulated) were identified from the GSE67333 dataset. Likewise, 723 DEGs (378 upregulated and 344 downregulated) were identified from the GSE114517 dataset. The protein-protein interaction networks of the DEGs were constructed, and the top 50 hub genes were identified from the network of the respective dataset. Of the four common hub genes between two datasets, C-X-C chemokine receptor type 4 (CXCR4) was selected due to its gene expression signature profile and the same direction of differential expression between the two datasets. Mavorixafor was chosen as the reference drug due to its known inhibitory activity against CXCR4 and its ability to cross the blood-brain barrier. Molecular docking and molecular dynamics simulation of 51 molecules having structural similarity with Mavorixafor was performed to find two novel molecules, ZINC49067615 and ZINC103242147. This preliminary study might help predict molecular targets and diagnostic markers for treating Alzheimer's and Parkinson's diseases. Insight Box Our research substantiates the therapeutic relevance of CXCR4 inhibitors for the treatment of Alzheimer's and Parkinson's diseases. We would like to disclose the following insights about this study. We found common signatures between Alzheimer's and Parkinson's diseases at transcriptional levels by analyzing mRNA sequencing data. These signatures were used to identify putative therapeutic agents for these diseases through computational analysis. Thus, we proposed two novel compounds, ZINC49067615 and ZINC103242147, that were stable, showed a strong affinity with CXCR4, and exhibited good pharmacokinetic properties. The interaction of these compounds with major residues of CXCR4 has also been described.

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来源期刊
Integrative Biology
Integrative Biology 生物-细胞生物学
CiteScore
4.90
自引率
0.00%
发文量
15
审稿时长
1 months
期刊介绍: Integrative Biology publishes original biological research based on innovative experimental and theoretical methodologies that answer biological questions. The journal is multi- and inter-disciplinary, calling upon expertise and technologies from the physical sciences, engineering, computation, imaging, and mathematics to address critical questions in biological systems. Research using experimental or computational quantitative technologies to characterise biological systems at the molecular, cellular, tissue and population levels is welcomed. Of particular interest are submissions contributing to quantitative understanding of how component properties at one level in the dimensional scale (nano to micro) determine system behaviour at a higher level of complexity. Studies of synthetic systems, whether used to elucidate fundamental principles of biological function or as the basis for novel applications are also of interest.
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