Comparative Proteomics Analysis of Growth-Primed Adult Dorsal Root Ganglia Reveals Key Molecular Mediators for Peripheral Nerve Regeneration
Bibliographic record
Abstract
Abstract Injuries to peripheral nerves are frequent, yet no drug therapies are available for effective nerve repair. The slow growth rate of axons and inadequate access to growth factors challenge natural repair of nerves. A better understanding of the molecules that can promote the rate of axon growth may reveal therapeutic opportunities. Molecular profiling of injured neurons at early intervals of injury, when regeneration is at the maximum, has been the gold standard for exploring growth promoters. A complementary in vitro regenerative priming model was recently shown to induce enhanced outgrowth in adult sensory neurons. In this work, we exploited the in vitro priming model to reveal novel candidates for adult nerve regeneration. We performed a whole-tissue proteomics analysis of the in vitro primed dorsal root ganglia (DRGs) from adult SD rats and compared their molecular profile with that of the in vivo primed, and control DRGs. The proteomics data generated are available via ProteomeXchange with identifier PXD031927. From the follow-up analysis, Bioinformatics interventions, and literature curation, we identified several molecules that were differentially expressed in the primed DRGs with a potential to modulate adult nerve regrowth. We then validated the growth promoting roles of mesencephalic astrocyte-derived neurotrophic factor (MANF), one of the hits we identified, in adult rat sensory neurons. Overall, in this study, we explored two growth priming paradigm and shortlisted several candidates, and validated MANF, as potential targets for adult nerve regeneration. We also demonstrate that the in vitro priming model is a valid tool for adult nerve regeneration studies.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".