发表机构
Ludwig Institute for Cancer Research, University of Oxford; Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford（剑桥路德维希癌症研究所，牛津大学; 沃尔森数学生物学中心，数学学院，牛津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过比较十五个数学模型，发现碎片化摄取机制结合特定死亡率函数能最好地重现巨噬细胞同类相食性胞葬作用中的脂质分布，揭示了碎片大小对摄取频率的影响及分布数据在模型区分中的关键作用。
AI 中文摘要
胞葬作用（efferocytosis），即通常由巨噬细胞对死亡细胞的清除，对于组织稳态和炎症消退至关重要。Ford等人（Proc. R. Soc. B, 2019）此前的实验表明，同类相食性胞葬作用将内源性脂质从垂死的巨噬细胞重新分布到存活群体中，但现有的数学模型无法重现所观察到的种群动态和脂质分布。在此，我们比较了十五个候选模型，这些模型结合了凋亡物质摄取的三种机制与巨噬细胞死亡率的五种形式。模型比较以赤池信息准则（Akaike Information Criterion）以及与观察到的脂质分布的定性一致性为指导。数值解表明，全细胞摄取模型预测了数据中不存在的内部最大值，而啃食（nibbling）摄取产生的分布过于集中在均值附近。相比之下，中间态的“碎片化”摄取模型在与线性脂质依赖性死亡率或指数时间依赖性死亡率结合时，提供了显著改善的一致性。拟合模型预测，来自死亡细胞的较小碎片比较大大碎片以更高的频率被摄取。这一分析为胞葬作用如何塑造巨噬细胞群体内脂质分布提供了新的见解，并强调了分布级数据对于区分重现相似群体平均动态的机制模型的重要性。
英文摘要
Efferocytosis, the clearance of dying cells typically by macrophages, is essential for tissue homeostasis and the resolution of inflammation. Previous experiments by Ford et al. (Proc. R. Soc. B, 2019) showed that cannibalistic efferocytosis redistributes endogenous lipid from dying macrophages into the surviving population, but existing mathematical models do not reproduce the observed population dynamics and lipid distributions. Here, fifteen candidate models are compared, combining three mechanisms of apoptotic material uptake with five forms of the macrophage death rate. Model comparison is guided by the Akaike Information Criterion and qualitative agreement with the observed lipid distributions. Numerical solutions show that whole-cell uptake models predict internal maxima that are absent from the data, whereas nibbling uptake produces distributions that are too concentrated about their means. By contrast, intermediate "fragmented" uptake models provide substantially improved agreement when combined with either linear lipid-dependent or exponential time-dependent death rates. The fitted models predict that smaller fragments from dying cells are ingested at higher frequency than larger ones. This analysis provides new insight into how efferocytosis shapes the distribution of lipid within macrophage populations and highlights the importance of distribution-level data for distinguishing between mechanistic models that reproduce similar population-average dynamics.
