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Computational modeling in Alzheimer's disease

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2010, Doctor of Philosophy, Ohio State University, Integrated Biomedical Sciences.

Intracellular neurofibrillary lesions composed of the microtubule-associated protein tau and extracellular amyloid plaques assembled from the Aβ peptide are the pathological hallmarks that characterize Alzeheimer’s disease. Unlike amyloid plaques, the spatial and temporal appearance of tau lesions correlates closely with the progression of disease. Therefore, identifying the underlying mechanisms of tau lesion formation is of critical importance for understanding the disease. Also, identifying radiotracers that are able to selectively detect neurofibrillary tangles is critical for accurate diagnosis and staging of AD.

In order to understand the mechanism of tau filament formation, the fibrillization of recombinant full-length four-repeat human tau was examined in vitro using a small molecule inducer of aggregation, Thiazine red, under near physiological conditions. Resulting data were then fit to a simple homogeneous nucleation model with constraints from filament dissociation rate, critical concentration, and mass-per-length measurements using computational methods. As a result, we propose a model of the tau aggregation pathway suggesting four key steps that must be overcome for filaments to form in disease. Mutations in the MAPT gene encoding tau protein lead to neurofibrillary lesions, neurodegeneration, and cognitive decline. Although it depends on the location within the coding sequence, most tau missense mutations have been shown to increase aggregation propensity. Kinetic constants for the nucleation and extension phases were estimated through direct measurements and mathematical simulation, and data show that different mutations act at different steps in the aggregation pathway by increasing the rate of filament nucleation and/or extension rates.

In Alzheimer’s disease, tau protein associated with neurofibrillary lesions is mostly found to be hyperphosphorylated. Hyperphosphorylation of tau acts to increase the free tau concentration available for aggregation. A pseudophosphorylation mutant T212E was used to investigate the effects of negative charge incorporated by phosphorylation. Results indicated that pseudophosphorylation increased tau aggregation rate by increasing the rate of filament formation and also by stabilizing mature filaments and decreasing the rate of filament dissociation.

Postmortem histopathological examination of amyloid plaques and neurofibrillary tangles in the brain is still the only method to definitively confirm the diagnosis of AD. Because tau can serve as a surrogate marker for neurodegeneration in AD and other tauopathies, detection of tau aggregates is of practical importance for disease diagnosis and staging. Here, we use mathematical and pharmacokinetic modeling methods to develop a simplified compartment model describing the binding of radiotracer in AD brain.

Jeff Kuret (Advisor)
William Hayton (Committee Member)
Kun Huang (Committee Member)
Andrej Rotter (Committee Member)
146 p.

Recommended Citations

Citations

  • Kim, S. (2010). Computational modeling in Alzheimer's disease [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1267541374

    APA Style (7th edition)

  • Kim, Sohee. Computational modeling in Alzheimer's disease. 2010. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1267541374.

    MLA Style (8th edition)

  • Kim, Sohee. "Computational modeling in Alzheimer's disease." Doctoral dissertation, Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1267541374

    Chicago Manual of Style (17th edition)