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Interaction between a MAPT variant causing frontotemporal dementia and mutant APP affects axonal transport.

Neurobiology of aging | 2018

In Alzheimer's disease, many indicators point to a central role for poor axonal transport, but the potential for stimulating axonal transport to alleviate the disease remains largely untested. Previously, we reported enhanced anterograde axonal transport of mitochondria in 8- to 11-month-old MAPTP301L knockin mice, a genetic model of frontotemporal dementia with parkinsonism-17T. In this study, we further characterized the axonal transport of mitochondria in younger MAPTP301L mice crossed with the familial Alzheimer's disease model, TgCRND8, aiming to test whether boosting axonal transport in young TgCRND8 mice can alleviate axonal swelling. We successfully replicated the enhancement of anterograde axonal transport in young MAPTP301L/P301L knockin animals. Surprisingly, we found that in the presence of the amyloid precursor protein mutations, MAPTP301L/P3101L impaired anterograde axonal transport. The numbers of plaque-associated axonal swellings or amyloid plaques in TgCRND8 brains were unaltered. These findings suggest that amyloid-β promotes an action of mutant tau that impairs axonal transport. As amyloid-β levels increase with age even without amyloid precursor protein mutation, we suggest that this rise could contribute to age-related decline in frontotemporal dementia.

Pubmed ID: 29729423 RIS Download

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Associated grants

  • Agency: Medical Research Council, United Kingdom
    Id: MR/L003813/1
  • Agency: Biotechnology and Biological Sciences Research Council, United Kingdom

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PRISM (tool)

RRID:SCR_005375

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.

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