Tauopathy i n A D a n d F T D is associated with diverse disease syndromes, and e s p e c i a l l y f o r F T D , there is variability in phenotype even w i t h i n f a m i l i e s , a n d between patients with the same mutation. Currently, little is known about why abnormal tau can give rise to such varied phenotypes, but recent data have suggested that subtypes of tau (strains) might underlie this diversity. Our working hypothesis is that p h e n o t y p i c d i v e r s i t y i n t h e t a u o p a t h i e s r e f l e c t s t h e c o n t r i b u t i o n o f differentstrainsthatimpactdifferentbrainregions.Ourproposalaimstousebiochemicaland structural analysis techniques to define strains and sophisticated longitudinal imaging to study how and where in the cell strain variants of tau are propagated, and through the use of biosensors, study the effect of different tau strains on cellular processes. Strains will then be inoculated into mice to study pathology morphology and distribution, as well as functional impact. Together, the aims of this proposal will benefit patients w i t h A D a n d F T D by fostering a better understanding of tauopathy, and providing valid cell and animal models for the testing of therapeutics.
Alzheimer?s Disease (AD) and Frontotemporal Lobar Degeneration (FTD) are two neurodegenerative diseases that are characterized by accumulation of abnormal tau. Despite this commonality, the two diseases have significantly different forms and distribution of tau which gives rise to the different clinical manifestations. Using sophisticated cell biology, structural biology and mouse modeling techniques we will attempt to better understand the cause of the diversity in the tauopathies which could help with diagnostics and therapeutics.
|Myeku, Natura; Duff, Karen E (2018) Targeting the 26S Proteasome To Protect Against Proteotoxic Diseases. Trends Mol Med 24:18-29|
|Fu, Hongjun; Hardy, John; Duff, Karen E (2018) Selective vulnerability in neurodegenerative diseases. Nat Neurosci 21:1350-1358|
|Christiansen, Eric M; Yang, Samuel J; Ando, D Michael et al. (2018) In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images. Cell 173:792-803.e19|