Abstract

Prion disease is characterized by a chain reaction in which infectious misfolded proteins force native proteins into a similar pathogenic structure. Recent studies have reinforced the hypothesis that the prion paradigm–the templated growth and spreading of misfolded proteins–could help explain the progression of a variety of neurodegenerative disorders. However, our current understanding of prion-like growth and spreading is rather empirical. Here we show that a physics-based reaction-diffusion model can explain the growth and spreading of misfolded protein in Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. To characterize the progression of misfolded proteins across the brain, we combine the classical Fisher–Kolmogorov equation for population dynamics with an anisotropic diffusion model and simulate misfolding across a sagittal section and across the entire brain. In a systematic sensitivity analysis, we probe the role of the individual model parameters and show that the misfolded protein concentration is sensitive to the coefficients of growth, extracellular diffusion, and axonal transport, to the axonal fiber orientation, and to the initial seeding region. Our model correctly predicts amyloid-β deposits and tau inclusions in Alzheimer’s disease, α-synuclein inclusions in Parkinson’s disease, and TDP-43 inclusions in amyotrophic lateral sclerosis and displays excellent agreement with the histological patterns in diseased human brains. When integrated across the brain, our concentration profiles result in biomarker curves that display a striking similarity with the sigmoid shape and qualitative timeline of clinical biomarker models. Our results suggest that misfolded proteins in various neurodegenerative disorders grow and spread according to a universal law that follows the basic physical principles of nonlinear reaction and anisotropic diffusion. Our findings substantiate the notion of a common underlying principle for the pathogenesis of a wide variety of neurodegenerative disorders, the prion paradigm. A more quantitative understanding of the growth and spreading of misfolded amyloid-β, tau, α-synuclein, and TDP-43 would allow us to establish a prognostic timeframe of disease progression. This could have important clinical implications, ranging from more accurate estimates of the socioeconomic burden of neurodegeneration to a more informed design of clinical trials and pharmacological intervention.

Highlights

  • Our prion-like spreading model in Eqs. (8) and (9) has five parameters that control the pattern formation during neurodegeneration: The growth rate α characterizes the local increase of the misfolded protein concentration; the extracellular diffusion dext characterizes the isotropic spreading of misfolded protein through the extracellular space, the axonal transport daxn characterizes the anisotropic spreading of misfolded protein through intracellular transport, the axonal orientation n characterizes the anatomic pathway of fast spreading, and the seeding region B∗ characterizes the regional onset of neurodegeneration

  • When increasing spreading, either extracellular diffusion dext or axonal transport daxn, the initial seeding region no longer coincides with the region that first reaches the critical concentration of 95%

  • Our sensitivity analysis suggests that the simulated activation pattern is rather insensitive to the growth rate α, which mainly affects the timing of activation but not the overall pattern, and highly sensitive to both extracellular diffusion dext and axonal transport daxn, which affect both the timing of activation and the emerging concentration pattern

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Summary

Objectives

Rather than choosing between the monomeric and polymeric seeding models (Aguzzi et al, 2001) as illustrated in Fig. 2, the objective of this study is to establish the simplest possible model that can explain the common features of various types of neurodegenerative disorders: the growth and spreading of misfolded proteins. Rather than choosing between the monomeric and polymeric seeding models (Aguzzi et al, 2001) as illustrated, the objective of this study is to establish the simplest possible model that can explain the common features of various types of neurodegenerative disorders: the growth and spreading of misfolded proteins. The objective of this study was to identify common features of neurodegeneration using physics-based modeling and computational simulation. Rather than engaging in this biochemistry-based discussion, the objective of this study was to identify unified principles of a variety of neurodegenerative disorders via physics-based modeling

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