Epidermal growth factor receptor-extracellular-regulated kinase blockade upregulates TRIM32 signaling cascade and promotes neurogenesis after spinal cord injury
Bibliographic record
Abstract
Nerve regeneration is blocked after spinal cord injury (SCI) by a complex myelin-associated inhibitory (MAI) microenvironment in the lesion site; however, the underlying mechanisms are not fully understood. During the process of neural stem cell (NSC) differentiation, pathway inhibitors were added to quantitatively assess the effects on neuronal differentiation. Immunoprecipitation and lentivirus-induced overexpression were used to examine effects in vitro. In vivo, animal experiments and lineage tracing methods were used to identify nascent neurogenesis after SCI. In vitro results indicated that myelin inhibited neuronal differentiation by activating the epidermal growth factor receptor (EGFR)-extracellular-regulated kinase (ERK) signaling cascade. Subsequently, we found that tripartite motif (TRIM) 32, a neuronal fate-determining factor, was inhibited. Moreover, inhibition of EGFR-ERK promoted TRIM32 expression and enhanced neuronal differentiation in the presence of myelin. We further demonstrated that ERK interacts with TRIM32 to regulate neuronal differentiation. In vivo results indicated that EGFR-ERK blockade increased TRIM32 expression and promoted neurogenesis in the injured area, thus enhancing functional recovery after SCI. Our results showed that EGFR-ERK blockade antagonized MAI of neuronal differentiation of NSCs through regulation of TRIM32 by ERK. Collectively, these findings may provide potential new targets for SCI repair.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".