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基于主体的模型展示了细胞因子之间非线性、复杂相互作用对肌肉再生的影响。

Agent-based model demonstrates the impact of nonlinear, complex interactions between cytokines on muscle regeneration.

作者信息

Haase Megan, Comlekoglu Tien, Petrucciani Alexa, Peirce Shayn M, Blemker Silvia S

出版信息

bioRxiv. 2024 Mar 7:2023.08.14.553247. doi: 10.1101/2023.08.14.553247.

Abstract

Muscle regeneration is a complex process due to dynamic and multiscale biochemical and cellular interactions, making it difficult to identify microenvironmental conditions that are beneficial to muscle recovery from injury using experimental approaches alone. To understand the degree to which individual cellular behaviors impact endogenous mechanisms of muscle recovery, we developed an agent-based model (ABM) using the Cellular Potts framework to simulate the dynamic microenvironment of a cross-section of murine skeletal muscle tissue. We referenced more than 100 published studies to define over 100 parameters and rules that dictate the behavior of muscle fibers, satellite stem cells (SSC), fibroblasts, neutrophils, macrophages, microvessels, and lymphatic vessels, as well as their interactions with each other and the microenvironment. We utilized parameter density estimation to calibrate the model to temporal biological datasets describing cross-sectional area (CSA) recovery, SSC, and fibroblast cell counts at multiple time points following injury. The calibrated model was validated by comparison of other model outputs (macrophage, neutrophil, and capillaries counts) to experimental observations. Predictions for eight model perturbations that varied cell or cytokine input conditions were compared to published experimental studies to validate model predictive capabilities. We used Latin hypercube sampling and partial rank correlation coefficient to identify perturbations of cytokine diffusion coefficients and decay rates to enhance CSA recovery. This analysis suggests that combined alterations of specific cytokine decay and diffusion parameters result in greater fibroblast and SSC proliferation compared to individual perturbations with a 13% increase in CSA recovery compared to unaltered regeneration at 28 days. These results enable guided development of therapeutic strategies that similarly alter muscle physiology (i.e. converting ECM-bound cytokines into freely diffusible forms as studied in cancer therapeutics or delivery of exogenous cytokines) during regeneration to enhance muscle recovery after injury.

摘要

肌肉再生是一个复杂的过程,因为存在动态且多尺度的生化和细胞相互作用,这使得仅使用实验方法来确定有利于肌肉从损伤中恢复的微环境条件变得困难。为了了解个体细胞行为对内源性肌肉恢复机制的影响程度,我们使用细胞Potts框架开发了一个基于代理的模型(ABM),以模拟小鼠骨骼肌组织横截面的动态微环境。我们参考了100多项已发表的研究,定义了100多个参数和规则,这些参数和规则决定了肌纤维、卫星干细胞(SSC)、成纤维细胞、中性粒细胞、巨噬细胞、微血管和淋巴管的行为,以及它们彼此之间和与微环境的相互作用。我们利用参数密度估计将模型校准到描述损伤后多个时间点的横截面积(CSA)恢复、SSC和成纤维细胞计数的时间生物学数据集。通过将其他模型输出(巨噬细胞、中性粒细胞和毛细血管计数)与实验观察结果进行比较,对校准后的模型进行了验证。将八个改变细胞或细胞因子输入条件的模型扰动的预测结果与已发表的实验研究进行比较,以验证模型的预测能力。我们使用拉丁超立方采样和偏秩相关系数来识别细胞因子扩散系数和衰减率的扰动,以增强CSA恢复。该分析表明,与单个扰动相比,特定细胞因子衰减和扩散参数的联合改变会导致成纤维细胞和SSC增殖增加,与28天未改变的再生相比,CSA恢复增加13%。这些结果有助于指导治疗策略的开发,在再生过程中类似地改变肌肉生理学(即如癌症治疗中所研究的将细胞外基质结合的细胞因子转化为可自由扩散的形式或递送外源性细胞因子),以增强损伤后肌肉的恢复。

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