Astrocytic clearance and fragmentation of toxic proteins in Alzheimer’s disease on large-scale brain networks

Abstract

The human brain is the most complicated biological structure on the planet. A major challenge of brain network modelling lies in its multi-scale spatio-temporal nature, covering scales from synapses to the whole brain. The coupled multiphysics and biochemical activities which spread through such a complex system shape brain capacity inside a structure-function relationship that requires a particular mathematical framework. Next-generation coupled-based mathematical modelling approaches to brain networks and the analysis of data-driven dynamical systems are needed to advance state-of-the-art therapeutic strategies for treating neurodegenerative diseases (NDDs) that affect millions of people worldwide, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). Importantly, AD is marked by the presence of amyloid-beta (Aβ) plaques and tau (τ) proteins. Some disease-specific misfolded proteins can interact with healthy proteins to form long chains and aggregates of different sizes that have different transport properties and toxicity. An improved large-scale brain network model is proposed here to understand the pathogenesis of AD, especially the role of astrocytes in the presence of misfolded proteins (Aβ and τ). The idea involves astrocytic clearance, which assists in eliminating toxic Aβ via fragmentation. We use the general Smoluchowski theory of nucleation, aggregation, and fragmentation to predict the development and propagation of aggregates of misfolded proteins in the brain. It has been shown that the developed model leads to different size distributions and propagation along the network. We predicted that astrocytic clearance varies with the aggregate size, which is key to slowing down AD progression. The clearance and fragmentation of toxic proteins span several spatial and temporal scales, and this research will potentially yield new insight into the associated processes and brain networks in health and disease. Detailed multi-scale brain modelling provides a promising approach for consolidating, organizing, and bridging the data sets of data-driven brain network models.