Tau antagonizes end-binding protein tracking at microtubule ends through a phosphorylation-dependent mechanism
Bibliographic record
Abstract
Proper regulation of microtubule dynamics is essential for cell functions and involves various microtubule-associated proteins (MAPs). Among them, end-binding proteins (EBs) accumulate at microtubule plus ends, whereas structural MAPs bind along the microtubule lattice. Recent data indicate that the structural MAP tau modulates EB subcellular localization in neurons. However, the molecular determinants of EB/tau interaction remain unknown, as is the effect of this interplay on microtubule dynamics. Here we investigate the mechanisms governing EB/tau interaction in cell-free systems and cellular models. We find that tau inhibits EB tracking at microtubule ends. Tau and EBs form a complex via the C-terminal region of EBs and the microtubule-binding sites of tau. These two domains are required for the inhibitory activity of tau on EB localization to microtubule ends. Moreover, the phosphomimetic mutation S262E within tau microtubule-binding sites impairs EB/tau interaction and prevents the inhibitory effect of tau on EB comets. We further show that microtubule dynamic parameters vary, depending on the combined activities of EBs and tau proteins. Overall our results demonstrate that tau directly antagonizes EB function through a phosphorylation-dependent mechanism. This study highlights a novel role for tau in EB regulation, which might be impaired in neurodegenerative disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".