Differential Proteomics Reveals Multiple Components in Retrogradely Transported Axoplasm After Nerve Injury
Bibliographic record
Abstract
Information on axonal damage is conveyed to neuronal cell bodies by a number of signaling modalities, including the post-translational modification of axoplasmic proteins. Retrograde transport of a subset of such proteins is thought to induce or enhance a regenerative response in the cell body. Here we report the use of a differential 2D-PAGE approach to identify injury-correlated retrogradely transported proteins in nerves of the mollusk Lymnaea. A comprehensive series of gels at different pI ranges allowed resolution of approximately 4000 spots by silver staining, and 172 of these were found to differ between lesioned versus control nerves. Mass spectrometric sequencing of 134 differential spots allowed their assignment to over 40 different proteins, some belonging to a vesicular ensemble blocked by the lesion and others comprising an up-regulated ensemble highly enriched in calpain cleavage products of an intermediate filament termed RGP51 (retrograde protein of 51 kDa). Inhibition of RGP51 expression by RNA interference inhibits regenerative outgrowth of adult Lymnaea neurons in culture. These results implicate regulated proteolysis in the formation of retrograde injury signaling complexes after nerve lesion and suggest that this signaling modality utilizes a wide range of protein components.
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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 | 0.001 | 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) | 0.001 | 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".