随着年龄的增长，衰老细胞的稳定积累创造了有助于癌症进化的组织环境。衰老细胞状态具有高度异质性。 “深度衰老”细胞依靠健康的线粒体来提供强大的促炎分泌组，包括细胞因子、生长和转化信号。然而，衰老的生理触发因素，如活性氧 (ROS)，也会引发线粒体功能障碍，以及足够的能量不足来改变其分泌组并引起慢性氧化应激，这种状态称为线粒体功能障碍相关衰老 (MiDAS)。在这里，我们提供了导致 MiDAS 的分子过程的机制假设以及可测试的预测。为此，我们建立了一个布尔调节网络模型，该模型定性地捕获细胞周期进程（G1/S 边界的过度融合、有丝分裂中的裂变）、细胞凋亡（裂变和功能障碍）和葡萄糖饥饿（可逆）过程中线粒体动力学的关键方面。超融合），以及响应 SIRT3 敲低或氧化应激的 MiDAS。我们的模型重申了 NAD 和外部丙酮酸的保护作用。我们提供了关于 MiDAS 的生长因子和葡萄糖依赖性及其在活性氧 (ROS) 诱导衰老的不同阶段的可逆性的可测试预测。我们的模型提供了对 DNA 损伤诱导衰老的不同阶段、癌症中衰老与上皮间质转化之间关系的机制见解，并为构建组织衰老的多尺度模型奠定了基础。版权所有 © 2024。由 Elsevier Inc. 出版。

The steady accumulation of senescent cells with aging creates tissue environments that aid cancer evolution. Aging cell states are highly heterogeneous. 'Deep senescent' cells rely on healthy mitochondria to fuel a strong proinflammatory secretome, including cytokines, growth and transforming signals. Yet, the physiological triggers of senescence such as reactive oxygen species (ROS) can also trigger mitochondrial dysfunction, and sufficient energy deficit to alter their secretome and cause chronic oxidative stress - a state termed Mitochondrial Dysfunction-Associated Senescence (MiDAS). Here, we offer a mechanistic hypothesis for the molecular processes leading to MiDAS, along with testable predictions. To do this we have built a Boolean regulatory network model that qualitatively captures key aspects of mitochondrial dynamics during cell cycle progression (hyper-fusion at the G1/S boundary, fission in mitosis), apoptosis (fission and dysfunction) and glucose starvation (reversible hyper-fusion), as well as MiDAS in response to SIRT3 knockdown or oxidative stress. Our model reaffirms the protective role of NAD+ and external pyruvate. We offer testable predictions about the growth factor- and glucose-dependence of MiDAS and its reversibility at different stages of reactive oxygen species (ROS)-induced senescence. Our model provides mechanistic insights into the distinct stages of DNA-damage induced senescence, the relationship between senescence and epithelial-to-mesenchymal transition in cancer and offers a foundation for building multiscale models of tissue aging.Copyright © 2024. Published by Elsevier Inc.