Predicting Reactive Astrogliosis Propagation by Bayesian Computational Modeling: the Repeater Stations Model.

Area of Science:

• Neuroscience
• Computational Biology
• Cell Biology

Background:

• Reactive astrogliosis is a common response to brain injury and neurodegenerative diseases.
• The mechanisms driving the rapid, widespread propagation of reactive astrogliosis are not fully understood.
• Damage-associated molecular patterns (DAMP) are implicated in initiating astrogliosis, but their role in propagation is unclear.

Purpose of the Study:

• To computationally model and identify the most probable mechanisms of reactive astrogliosis propagation.
• To differentiate between proposed models of astrogliosis spread, including DAMP diffusion and glial cell involvement.
• To elucidate the roles of DAMP, microglia, and soluble mediators in sustaining reactive astrogliosis.

Main Methods:

• Developed a Bayesian computational model incorporating experimental data of GFAP-immunostained reactive astrocytes.
• Defined five astrocyte types based on morphometrical cues and mapped their positions post-ischemia.
• Modeled DAMP concentration changes and glial secretion of soluble mediators to simulate astrogliosis evolution.

Main Results:

• DAMP diffusion alone could not explain the observed reactive astrogliosis propagation.
• Two computational models accurately reproduced experimental data, highlighting the critical roles of microglia.
• Glial secretion of soluble mediators was identified as essential for sustaining the reactive signal and activating adjacent astrocytes.

Conclusions:

• Reactive astrogliosis propagation is not solely driven by DAMP diffusion.
• Glial cells, particularly microglia, act as crucial 'repeater stations' for the injury signal.
• Soluble mediators secreted by glial cells are vital for propagating reactive astrogliosis throughout the brain.