Induction of neuronal phenotypes from NG2+ glial progenitors by inhibiting epidermal growth factor receptor in mouse spinal cord injury
Bibliographic record
Abstract
Besides neural stem cells, some glial cells, such as GFAP+ cells, radial glia, and oligodendrocyte progenitor cells can produce neuronal cells. Attractively, NG2+ glial progenitors exhibit lineage plasticity, and they rapidly proliferate and differentiate in response to central nervous system (CNS) injuries. These attributes of NG2+ glial progenitors make them a promising source of neurons. However, the potential of neuronal regeneration from NG2+ glial progenitors in CNS pathologies remains to be investigated. In this study, we showed that antagonizing epidermal growth factor receptor (EGFR) function with EGFR inhibitor caused a significant number of proliferative NG2+ glial progenitors to acquire neuronal phenotypes in contusive spinal cord injury (SCI), which presumably led to an accumulation of newly generated neurons and contributed to the improved neural behavioral performance of animals. In addition, the neuronal differentiation of glial progenitors induced by EGFR inhibitor was further confirmed with two different cell lines either in vitro or through ex vivo transplantation experiment. The inhibition of EGFR signaling pathway under the gliogenic conditions could induce these cells to acquire neuronal phenotypes. Furthermore, we find that the Ras-ERK axis played a key role in neuronal differentiation of NG2+ glial progenitors upon EGFR inhibition. Taken together, our studies suggest that the EGFR inhibitor could promote neurogenesis post SCI, mainly from the NG2+ glial progenitors. These findings support the possibility of evoking endogenous neuronal replacement from NG2+ glial progenitors and suggest that EGFR inhibition may be beneficial to CNS trauma.
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) | 0.001 | 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".