Insulin signalling promotes dendrite and synapse regeneration and restores circuit function after axonal injury
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dendrite pathology and synapse disassembly are critical features of chronic neurodegenerative diseases. In spite of this, the capacity of injured neurons to regenerate dendrites has been largely ignored. Here, we show that, upon axonal injury, retinal ganglion cells undergo rapid dendritic retraction and massive synapse loss that preceded neuronal death. Human recombinant insulin, administered as eye drops or systemically after dendritic arbour shrinkage and prior to cell loss, promoted robust regeneration of dendrites and successful reconnection with presynaptic targets. Insulin-mediated regeneration of excitatory postsynaptic sites on retinal ganglion cell dendritic processes increased neuronal survival and rescued light-triggered retinal responses. Further, we show that axotomy-induced dendrite retraction triggered substantial loss of the mammalian target of rapamycin (mTOR) activity exclusively in retinal ganglion cells, and that insulin fully reversed this response. Targeted loss-of-function experiments revealed that insulin-dependent activation of mTOR complex 1 (mTORC1) is required for new dendritic branching to restore arbour complexity, while complex 2 (mTORC2) drives dendritic process extension thus re-establishing field area. Our findings demonstrate that neurons in the mammalian central nervous system have the intrinsic capacity to regenerate dendrites and synapses after injury, and provide a strong rationale for the use of insulin and/or its analogues as pro-regenerative therapeutics for intractable neurodegenerative diseases including glaucoma.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it