PTEN Inhibition to Facilitate Intrinsic Regenerative Outgrowth of Adult Peripheral Axons
Bibliographic record
Abstract
In vivo regeneration of peripheral neurons is constrained and rarely complete, and unfortunately patients with major nerve trunk transections experience only limited recovery. Intracellular inhibition of neuronal growth signals may be among these constraints. In this work, we investigated the role of PTEN (phosphatase and tensin homolog deleted on chromosome 10) during regeneration of peripheral neurons in adult Sprague Dawley rats. PTEN inhibits phosphoinositide 3-kinase (PI3-K)/Akt signaling, a common and central outgrowth and survival pathway downstream of neuronal growth factors. While PI3-K and Akt outgrowth signals were expressed and activated within adult peripheral neurons during regeneration, PTEN was similarly expressed and poised to inhibit their support. PTEN was expressed in neuron perikaryal cytoplasm, nuclei, regenerating axons, and Schwann cells. Adult sensory neurons in vitro responded to both graded pharmacological inhibition of PTEN and its mRNA knockdown using siRNA. Both approaches were associated with robust rises in the plasticity of neurite outgrowth that were independent of the mTOR (mammalian target of rapamycin) pathway. Importantly, this accelerated outgrowth was in addition to the increased outgrowth generated in neurons that had undergone a preconditioning lesion. Moreover, following severe nerve transection injuries, local pharmacological inhibition of PTEN or siRNA knockdown of PTEN at the injury site accelerated axon outgrowth in vivo. The findings indicated a remarkable impact on peripheral neuron plasticity through PTEN inhibition, even within a complex regenerative milieu. Overall, these findings identify a novel route to propagate intrinsic regeneration pathways within axons to benefit nerve repair.
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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 | 0.001 |
| 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".