Shaheen Hina, Melnik Roderick, Singh Sundeep
Faculty of Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada.
MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada.
Stat Anal Data Min. 2024 Apr;17(2). doi: 10.1002/sam.11679. Epub 2024 Apr 9.
The abnormal aggregation of extracellular amyloid- in senile plaques resulting in calcium dyshomeostasis is one of the primary symptoms of Alzheimer's disease (AD). Significant research efforts have been devoted in the past to better understand the underlying molecular mechanisms driving deposition and dysregulation. Importantly, synaptic impairments, neuronal loss, and cognitive failure in AD patients are all related to the buildup of intraneuronal accumulation. Moreover, increasing evidence show a feed-forward loop between and levels, i.e. disrupts neuronal levels, which in turn affects the formation of . To better understand this interaction, we report a novel stochastic model where we analyze the positive feedback loop between and using ADNI data. A good therapeutic treatment plan for AD requires precise predictions. Stochastic models offer an appropriate framework for modelling AD since AD studies are observational in nature and involve regular patient visits. The etiology of AD may be described as a multi-state disease process using the approximate Bayesian computation method. So, utilizing ADNI data from 2-year visits for AD patients, we employ this method to investigate the interplay between and levels at various disease development phases. Incorporating the ADNI data in our physics-based Bayesian model, we discovered that a sufficiently large disruption in either metabolism or intracellular homeostasis causes the relative growth rate in both and , which corresponds to the development of AD. The imbalance of ions causes disorders by directly or indirectly affecting a variety of cellular and subcellular processes, and the altered homeostasis may worsen the abnormalities of ion transportation and deposition. This suggests that altering the balance or the balance between and by chelating them may be able to reduce disorders associated with AD and open up new research possibilities for AD therapy.
细胞外淀粉样蛋白在老年斑中的异常聚集导致钙稳态失调,是阿尔茨海默病(AD)的主要症状之一。过去人们投入了大量研究精力来更好地理解驱动沉积和失调的潜在分子机制。重要的是,AD患者的突触损伤、神经元丢失和认知功能障碍都与神经元内聚集物的积累有关。此外,越来越多的证据表明淀粉样蛋白和tau蛋白水平之间存在前馈回路,即淀粉样蛋白破坏神经元tau蛋白水平,进而影响淀粉样蛋白的形成。为了更好地理解这种相互作用,我们报告了一种新颖的随机模型,在该模型中我们使用ADNI数据来分析淀粉样蛋白和tau蛋白之间的正反馈回路。针对AD的良好治疗方案需要精确的预测。随机模型为AD建模提供了一个合适的框架,因为AD研究本质上是观察性的,且涉及定期的患者随访。AD的病因可以用近似贝叶斯计算方法描述为一个多状态疾病过程。因此,利用AD患者两年随访的ADNI数据,我们采用这种方法来研究在疾病发展的各个阶段淀粉样蛋白和tau蛋白水平之间的相互作用。将ADNI数据纳入我们基于物理学的贝叶斯模型中,我们发现淀粉样蛋白代谢或细胞内tau蛋白稳态中足够大的破坏会导致淀粉样蛋白和tau蛋白的相对生长速率增加,这与AD的发展相对应。离子失衡通过直接或间接影响各种细胞和亚细胞过程导致tau蛋白紊乱,而改变的稳态可能会使离子运输和沉积的异常恶化。这表明通过螯合来改变淀粉样蛋白平衡或淀粉样蛋白与tau蛋白之间的平衡可能能够减少与AD相关的紊乱,并为AD治疗开辟新的研究可能性。