Modifying Lipid Rafts Promotes Regeneration and Functional Recovery
Bibliographic record
Abstract
Ideal strategies to ameliorate CNS damage should promote both neuronal survival and axon regeneration. The receptor Neogenin promotes neuronal apoptosis. Its ligand prevents death, but the resulting repulsive guidance molecule a (RGMa)-Neogenin interaction also inhibits axonal growth, countering any prosurvival benefits. Here, we explore strategies to inhibit Neogenin, thus simultaneously enhancing survival and regeneration. We show that bone morphogenetic protein (BMP) and RGMa-dependent recruitment of Neogenin into lipid rafts requires an interaction between RGMa and Neogenin subdomains. RGMa or Neogenin peptides that prevent this interaction, BMP inhibition by Noggin, or reduction of membrane cholesterol all block Neogenin raft localization, promote axon outgrowth, and prevent neuronal apoptosis. Blocking Neogenin raft association influences axonal pathfinding, enhances survival in the developing CNS, and promotes survival and regeneration in the injured adult optic nerve and spinal cord. Moreover, lowering cholesterol disrupts rafts and restores locomotor function after spinal cord injury. These data reveal a unified strategy to promote both survival and regeneration in the CNS.
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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".