Modeling the prion protein-mediated transport of extracellular vesicles on the neuron surface

TL;DR

This paper develops a mathematical model to understand how prion protein-bearing extracellular vesicles move along neuron surfaces, integrating experimental data to explore passive and active transport mechanisms relevant to neurodegenerative disease propagation.

Contribution

The study introduces a data-driven mathematical model that characterizes EV transport mechanisms on neurons, combining experimental data with theoretical modeling to distinguish passive and active movement processes.

Findings

Model effectively captures EV movement features

Transport mechanisms vary with treatment conditions

Sensitivity analysis supports model robustness

Abstract

Neurodegenerative diseases are among the leading causes of global mortality, characterized by the progressive deterioration of specific neuron populations, ultimately leading to cognitive decline and dementia. Extracellular vesicles (EVs) are believed to play a role in the early stages of these diseases, acting as carriers of pathogens and contributing to neuroinflammation and disease propagation. This study presents a mathematical model aimed at characterizing the movement of EVs bearing prion protein (PrP) on their surface along neuronal surfaces. The model, informed by experimental data, investigates the influence of PrP and actin polymerization on EV transport dynamics and explores the possible interplay between passive and active mechanisms. EVs isolated from non-human astrocytes were analyzed under three conditions: untreated control (Ctrl), neurons treated with Cytochalasin D (CytoD-HN), and EVs treated with Cytochalasin D (CytoD-EV). The mathematical model is data-driven, testing different hypotheses regarding the underlying transport mechanisms. In the CytoD-EV dataset, EV movement was modeled using a flashing Brownian ratchet to represent directed motion. For active transport in the CytoD-HN set, a symmetric periodic potential was used to describe EV rolling along the neuron surface. The Ctrl scenario incorporates both mechanisms, reflecting a more complex transport behavior. A sensitivity analysis and comparison between numerical predictions and experimental data suggest that the model effectively captures key features of EV motion, providing a quantitative framework to interpret different transport regimes. While some variability remains, the approach offers a promising basis for future investigations into the role of cytoskeletal dynamics in EV-mediated disease propagation.