Laboratory of Systems Medicine of Healthy Aging, Lobachevsky Univeristy, Nizhny Novgorod, Russia.
Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia.
Semin Immunopathol. 2020 Oct;42(5):647-665. doi: 10.1007/s00281-020-00816-x. Epub 2020 Oct 9.
Brain aging is a complex process involving many functions of our body and described by the interplay of a sleep pattern and changes in the metabolic waste concentration regulated by the microglial function and the glymphatic system. We review the existing modelling approaches to this topic and derive a novel mathematical model to describe the crosstalk between these components within the conceptual framework of inflammaging. Analysis of the model gives insight into the dynamics of garbage concentration and linked microglial senescence process resulting from a normal or disrupted sleep pattern, hence, explaining an underlying mechanism behind healthy or unhealthy brain aging. The model incorporates accumulation and elimination of garbage, induction of glial activation by garbage, and glial senescence by over-activation, as well as the production of pro-inflammatory molecules by their senescence-associated secretory phenotype (SASP). Assuming that insufficient sleep leads to the increase of garbage concentration and promotes senescence, the model predicts that if the accumulation of senescent glia overcomes an inflammaging threshold, further progression of senescence becomes unstoppable even if a normal sleep pattern is restored. Inverting this process by "rejuvenating the brain" is only possible via a reset of concentration of senescent glia below this threshold. Our model approach enables analysis of space-time dynamics of senescence, and in this way, we show that heterogeneous patterns of inflammation will accelerate the propagation of senescence profile through a network, confirming a negative effect of heterogeneity.
大脑衰老涉及我们身体的许多功能,是一个复杂的过程,由睡眠模式的相互作用以及由小胶质细胞功能和糖质系统调节的代谢废物浓度变化来描述。我们回顾了这个主题的现有建模方法,并推导出了一个新的数学模型,以描述这些组成部分在炎症衰老概念框架内的相互作用。该模型的分析深入了解了垃圾浓度的动态变化以及由正常或紊乱的睡眠模式引起的小胶质细胞衰老过程,从而解释了健康或不健康的大脑衰老背后的潜在机制。该模型包含了垃圾的积累和清除、垃圾诱导的神经胶质激活以及过度激活引起的神经胶质衰老,以及它们衰老相关分泌表型(SASP)产生的促炎分子。假设睡眠不足会导致垃圾浓度增加并促进衰老,那么该模型预测,如果衰老的神经胶质细胞的积累超过炎症衰老的阈值,即使恢复正常的睡眠模式,衰老的进一步进展也将无法阻止。通过“使大脑年轻化”来反转这个过程,只有将衰老的神经胶质细胞的浓度重置到这个阈值以下才有可能。我们的模型方法能够分析衰老的时空动态,通过这种方式,我们表明异质的炎症模式将通过网络加速衰老特征的传播,从而证实了异质性的负面影响。