发表机构
University of Manitoba（曼尼托巴大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究构建突变感知的淀粉样-β聚集与脑连接组耦合模型，发现突变主要影响聚集时序与累积负担，连接组决定空间传播，为家族性阿尔茨海默病机制提供概念验证。
AI 中文摘要
家族性淀粉样-$\beta$（A$\beta$）变体改变聚集动力学，但其与结构性脑连接组的相互作用仍未被完全理解。我们开发了一个突变感知的机制模型，将粗粒度的单体-寡聚体-原纤维聚集-断裂系统与540节点布达佩斯参考连接组组件上的图扩散耦合。实验性A$\beta_{42}$成核评分将七种变体相对于野生型的初级成核速率进行了缩放。通过突变评分不确定性、替代动力学映射、全局敏感性分析、种子和边权重扰动、保持度数的随机连接组、空间传播分析、合成ABC-SMC参数恢复、后验预测和化学朗之万模拟来检验鲁棒性。E22G显示出最早的阈值交叉和最大的累积寡聚体负担，而A2V在所选映射下延迟。突变排名在测试的网络扰动中持续存在，尽管区域负担模式依赖于拓扑结构。连接组中与种子区域的距离与较晚的寡聚体到达相关（Spearman $\rho\approx0.92$）。在推断的参数不确定性下，平均时序排名相关性为0.990，并且在每次后验抽取中累积负担排序均被保留，而精确峰值幅度排序则较不稳定。随机集成中位数保留了确定性排序，尽管轨迹之间存在重叠。在此概念验证框架内，突变依赖性动力学主要影响聚集时间和累积负担，而连接性则塑造空间传播。标称背景速率、模型时间单位和合成参数恢复将解释限制为机制比较而非临床校准预测。
英文摘要
Familial amyloid-$β$ (A$β$) variants alter aggregation kinetics, but their interaction with structural brain connectivity remains incompletely understood. We developed a mutation-aware mechanistic model coupling a coarse-grained monomer--oligomer--fibril aggregation--fragmentation system to graph diffusion on the 540-node Budapest Reference Connectome component. Experimental A$β_{42}$ nucleation scores scaled primary nucleation rates for seven variants relative to wild type. Robustness was examined using mutation-score uncertainty, alternative kinetic mappings, global sensitivity analysis, seed and edge-weight perturbations, degree-preserving randomized connectomes, spatial propagation analysis, synthetic ABC-SMC parameter recovery, posterior prediction, and Chemical Langevin simulations. E22G showed the earliest threshold crossing and greatest cumulative oligomer burden, whereas A2V was delayed under the selected mapping. Mutation rankings persisted across tested network perturbations, although regional burden patterns depended on topology. Connectome distance from seed regions was associated with later oligomer arrival (Spearman $ρ\approx0.92$). Under inferred parameter uncertainty, the mean timing-rank correlation was 0.990 and cumulative-burden ordering was preserved in every posterior draw, while exact peak-amplitude ordering was less stable. Stochastic ensemble medians retained the deterministic ordering despite overlap among trajectories. Within this proof-of-concept framework, mutation-dependent kinetics primarily influence aggregation timing and cumulative burden, while connectivity shapes spatial propagation. Nominal background rates, model-time units, and synthetic parameter recovery limit interpretation to mechanistic comparisons rather than clinically calibrated prediction.
