Stimulation of synaptic activity promotes TFEB-mediated clearance of pathological MAPT/Tau in cellular and mouse models of tauopathies
Bibliographic record
Abstract
Synapses represent an important target of Alzheimer disease (AD), and alterations of their excitability are among the earliest changes associated with AD development. Synaptic activation has been shown to be protective in models of AD, and deep brain stimulation (DBS), a surgical strategy that modulates neuronal activity to treat neurological and psychiatric disorders, produced positive effects in AD patients. However, the molecular mechanisms underlying the protective role(s) of brain stimulation are still elusive. We have previously demonstrated that induction of synaptic activity exerts protection in mouse models of AD and frontotemporal dementia (FTD) by enhancing the macroautophagy/autophagy flux and lysosomal degradation of pathological MAPT/Tau. We now provide evidence that TFEB (transcription factor EB), a master regulator of lysosomal biogenesis and autophagy, is a key mediator of this cellular response. In cultured primary neurons from FTD-transgenic mice, synaptic stimulation inhibits MTORC1 signaling, thus promoting nuclear translocation of TFEB, which, in turn, induces clearance of MAPT/Tau oligomers. Conversely, synaptic activation fails to promote clearance of toxic MAPT/Tau in neurons expressing constitutively active RRAG GTPases, which sequester TFEB in the cytosol, or upon TFEB depletion. Activation of TFEB is also confirmed in vivo in DBS-stimulated AD mice. We also demonstrate that DBS reduces pathological MAPT/Tau and promotes neuroprotection in Parkinson disease patients with tauopathy. Altogether our findings indicate that stimulation of synaptic activity promotes TFEB-mediated clearance of pathological MAPT/Tau. This mechanism, underlying the protective effect of DBS, provides encouraging support for the use of synaptic stimulation as a therapeutic treatment against tauopathies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".