Phytochemicals from Bacopa monnieri as small molecule modulators of MARK4: A multi-modal strategy for preventing Alzheimer's disease-causing tau aggregation.

IF 3 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
S Rehan Ahmad, Md Zeyaullah, Yousef Zahrani, Mohammad Suhail Khan, Khursheed Muzammil, Adam Dawria
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Abstract

Neurodegenerative tauopathies, such as Alzheimer's disease, are closely associated with the dysregulation of tau phosphorylation, a process regulated in part by the serine/threonine kinase MARK4. In this study, we explored phytochemicals derived from Bacopa monnieri as potential natural inhibitors of MARK4. Using pressurized liquid extraction with an ethanol-water mixture, we efficiently extracted bioactive compounds from Bacopa leaves. LC-MS analysis identified 25 distinct phytoconstituents spanning flavonoids, triterpenoids, cucurbitacins, sterols, and alkaloids. In silico analysis revealed that several compounds, including oroxindin, cucurbitacin B, and bacosine, bind strongly to the catalytic pocket of MARK4. Molecular dynamics simulations confirmed their stability within the MARK4 active site, with oroxindin demonstrating the most favorable thermodynamic and conformational profile. Principal component and free energy landscape analyses further supported their capacity to stabilize MARK4 in low-energy conformations. Microscale thermophoresis further validated high-affinity binding of MARK4 with oroxindin, while other four compounds also show strong interaction with MARK4. MTT assays in SH-SY5Y cells confirmed the non-cytotoxic nature of all five lead compounds across a concentration range of 10 nM to 10 μM. Cellular assays revealed a significant reduction in Tau-GFP aggregates upon treatment with the compounds, particularly oroxindin. These results highlight oroxindin and other Bacopa monnieri phytochemicals as promising natural inhibitors of MARK4, with potential to attenuate tau pathology in neurodegenerative diseases.

假马尾草中的植物化学物质作为MARK4的小分子调节剂:预防阿尔茨海默病引起的tau聚集的多模式策略
神经退行性tau病,如阿尔茨海默病,与tau磷酸化的失调密切相关,这一过程部分由丝氨酸/苏氨酸激酶MARK4调节。在这项研究中,我们探索了从假马齿苋中提取的植物化学物质作为MARK4的潜在天然抑制剂。利用乙醇-水混合物的加压液体萃取,我们有效地提取了假马齿苋叶中的生物活性化合物。LC-MS分析鉴定出25种不同的植物成分,包括类黄酮、三萜、葫芦素、甾醇和生物碱。硅分析显示,几种化合物,包括oroxindin,葫芦素B和烟叶碱,与MARK4的催化袋结合强烈。分子动力学模拟证实了它们在MARK4活性位点内的稳定性,其中oroxintin表现出最有利的热力学和构象剖面。主成分分析和自由能景观分析进一步支持了它们在低能构象中稳定MARK4的能力。微尺度热泳进一步验证了MARK4与oroxindin的高亲和力结合,同时其他四种化合物也显示出与MARK4的强相互作用。SH-SY5Y细胞的MTT试验证实,在10 nM至10 μM的浓度范围内,所有五种先导化合物均无细胞毒性。细胞分析显示,在用化合物处理后,Tau-GFP聚集体显着减少,特别是氧化素。这些结果突出了oroxindin和其他假马齿苋植物化学物质作为有希望的天然MARK4抑制剂,具有减轻神经退行性疾病中tau病理的潜力。
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来源期刊
Journal of molecular graphics & modelling
Journal of molecular graphics & modelling 生物-计算机:跨学科应用
CiteScore
5.50
自引率
6.90%
发文量
216
审稿时长
35 days
期刊介绍: The Journal of Molecular Graphics and Modelling is devoted to the publication of papers on the uses of computers in theoretical investigations of molecular structure, function, interaction, and design. The scope of the journal includes all aspects of molecular modeling and computational chemistry, including, for instance, the study of molecular shape and properties, molecular simulations, protein and polymer engineering, drug design, materials design, structure-activity and structure-property relationships, database mining, and compound library design. As a primary research journal, JMGM seeks to bring new knowledge to the attention of our readers. As such, submissions to the journal need to not only report results, but must draw conclusions and explore implications of the work presented. Authors are strongly encouraged to bear this in mind when preparing manuscripts. Routine applications of standard modelling approaches, providing only very limited new scientific insight, will not meet our criteria for publication. Reproducibility of reported calculations is an important issue. Wherever possible, we urge authors to enhance their papers with Supplementary Data, for example, in QSAR studies machine-readable versions of molecular datasets or in the development of new force-field parameters versions of the topology and force field parameter files. Routine applications of existing methods that do not lead to genuinely new insight will not be considered.
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