Manipulation of the Myc Interactome to Enhance Nerve Regeneration in a Murine Model
Bibliographic record
Abstract
OBJECTIVE: This study was undertaken to explore manipulation of the Myc protein interactome, members of an oncogene group, in enhancing the intrinsic growth of injured peripheral adult postmitotic neurons and the nerves they supply. New approaches to enhance adult neuron growth properties are a key strategy in improving nerve regeneration. METHODS: Expression and impact of Myc interactome members c-Myc, N-Myc, Mad1, and Max were evaluated within naive and "preconditioned" adult sensory neurons and Schwann cells (SCs), using siRNA and transfection of CRISPR/Cas9 or luciferase reporter in vitro. Morphological, behavioral, and electrophysiological indices of nerve regeneration were analyzed in vivo. RESULTS: c-Myc, N-Myc, Max, and Mad were expressed in adult sensory neurons and in partnering SCs. In vitro knockdown (KD) of either Mad1 or Max, competitive inhibitors of Myc, unleashed heightened neurite outgrowth in both naive uninjured or preconditioned adult neurons. In contrast, KD or inhibition of both isoforms of Myc was required to suppress growth. In SCs, Mad1 KD not only enhanced migratory behavior but also conditioned increased outgrowth in separately cultured adult sensory neurons. In vivo, local Mad1 KD improved electrophysiological, behavioral, and structural indices of nerve regeneration out to 60 days of follow-up. INTERPRETATION: Members of the Myc interactome, specifically Mad1, are novel targets for improving nerve regeneration. Unleashing of Myc growth signaling through Mad1 KD enhances the regrowth of both peripheral neurons and SCs to facilitate better regrowth of nerves. ANN NEUROL 2024;96:216-230.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".