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complex(p(HGNC:MAPT), p(HGNC:MAPT), p(HGNC:MAPT))

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Tau Trimers Are the Minimal Propagation Unit Spontaneously Internalized to Seed Intracellular Aggregation v1.0.0

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complex(p(HGNC:MAPT), p(HGNC:MAPT), p(HGNC:MAPT)) hasComponent p(HGNC:MAPT) View Subject | View Object

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complex(p(HGNC:MAPT), p(HGNC:MAPT), p(HGNC:MAPT)) increases tloc(a(HBP:"Tau fibrils"), fromLoc(MESH:"Extracellular Space"), toLoc(MESH:"Intracellular Space")) View Subject | View Object

Within the limits of our detection, neither monomer nor dimer was internalized (Fig. 5, A and B). By contrast, trimers and larger species were readily internalized, with trimer uptake being slightly less efficient (Fig. 5, A and B). We did not observe an upper limit of size in terms of uptake. PubMed:25887395

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complex(p(HGNC:MAPT), p(HGNC:MAPT), p(HGNC:MAPT)) increases a(HBP:"Tau aggregates") View Subject | View Object

Only Tau RD oligomers of n  3 increased luciferase signal, defining these assemblies as the minimal size sufficient for spontaneous cell uptake and seeding (Fig. 6C). PubMed:25887395

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complex(p(HGNC:MAPT), p(HGNC:MAPT), p(HGNC:MAPT)) increases a(HBP:"Tau aggregates") View Subject | View Object

Next we analyzed fractions for seeding activity in the P301S- Nluc/Cluc HEK293 cell lines by a split-luciferase complemen- tation assay, comparing AD and control brains. Trimers and larger BD Tau assemblies induced intracellular aggregation, but not monomer or dimer. PubMed:25887395

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MeSH
Brain
MeSH
Alzheimer Disease

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BEL Commons is developed and maintained in an academic capacity by Charles Tapley Hoyt and Daniel Domingo-Fernández at the Fraunhofer SCAI Department of Bioinformatics with support from the IMI project, AETIONOMY. It is built on top of PyBEL, an open source project. Please feel free to contact us here to give us feedback or report any issues. Also, see our Publishing Notes and Data Protection information.

If you find BEL Commons useful in your work, please consider citing: Hoyt, C. T., Domingo-Fernández, D., & Hofmann-Apitius, M. (2018). BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language. Database, 2018(3), 1–11.