Ghosh Preetam, Rana Pratip, Rangachari Vijayaraghavan, Saha Jhinuk, Steen Edward, Vaidya Ashwin
Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23220, USA.
Department of Chemistry and Biochemistry, School of Mathematics and Natural Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA.
R Soc Open Sci. 2020 Apr 29;7(4):191814. doi: 10.1098/rsos.191814. eCollection 2020 Apr.
Aggregation of amyloid- (A) peptides is a significant event that underpins Alzheimer's disease (AD). A aggregates, especially the low-molecular weight oligomers, are the primary toxic agents in AD pathogenesis. Therefore, there is increasing interest in understanding their formation and behaviour. In this paper, we use our previously established results on heterotypic interactions between A and fatty acids (FAs) to investigate off-pathway aggregation under the control of FA concentrations to develop a mathematical framework that captures the mechanism. Our framework to define and simulate the competing on- and off-pathways of A aggregation is based on the principles of game theory. Together with detailed simulations and biophysical experiments, our models describe the dynamics involved in the mechanisms of A aggregation in the presence of FAs to adopt multiple pathways. Specifically, our reduced-order computations indicate that the emergence of off- or on-pathway aggregates are tightly controlled by a narrow set of rate constants, and one could alter such parameters to populate a particular oligomeric species. These models agree with the detailed simulations and experimental data on using FA as a heterotypic partner to modulate the temporal parameters. Predicting spatio-temporal landscape along competing pathways for a given heterotypic partner such as lipids is a first step towards simulating scenarios in which the generation of specific 'conformer strains' of A could be predicted. This approach could be significant in deciphering the mechanisms of amyloid aggregation and strain generation, which are ubiquitously observed in many neurodegenerative diseases.
淀粉样β（Aβ）肽的聚集是阿尔茨海默病（AD）的一个重要事件。Aβ聚集体，尤其是低分子量寡聚体，是AD发病机制中的主要毒性因子。因此，人们对了解它们的形成和行为越来越感兴趣。在本文中，我们利用我们之前关于Aβ与脂肪酸（FAs）之间异型相互作用的研究结果，在FA浓度控制下研究非经典聚集，以建立一个能够捕捉其机制的数学框架。我们定义和模拟Aβ聚集竞争的经典和非经典途径的框架基于博弈论原理。结合详细的模拟和生物物理实验，我们的模型描述了在存在FAs的情况下Aβ聚集机制中涉及的动力学，以采用多种途径。具体而言，我们的降阶计算表明，非经典或经典聚集体的出现受到一组狭窄的速率常数的严格控制，人们可以改变这些参数以形成特定的寡聚体物种。这些模型与关于使用FA作为异型伙伴来调节时间参数的详细模拟和实验数据一致。预测给定异型伙伴（如脂质）沿竞争途径的时空格局是模拟可预测Aβ特定“构象菌株”生成的场景的第一步。这种方法在破译淀粉样蛋白聚集和菌株生成机制方面可能具有重要意义，这些机制在许多神经退行性疾病中普遍存在。
