RGMa mediates reactive astrogliosis and glial scar formation through TGFβ1/Smad2/3 signaling after stroke
Bibliographic record
Abstract
In response to stroke, astrocytes become reactive astrogliosis and are a major component of a glial scar. This results in the formation of both a physical and chemical (production of chondroitin sulfate proteoglycans) barrier, which prevent neurite regeneration that, in turn, interferes with functional recovery. However, the mechanisms of reactive astrogliosis and glial scar formation are poorly understood. In this work, we hypothesized that repulsive guidance molecule a (RGMa) regulate reactive astrogliosis and glial scar formation. We first found that RGMa was strongly expressed by reactive astrocytes in the glial scar in a rat model of middle cerebral artery occlusion/reperfusion. Genetic or pharmacologic inhibition of RGMa in vivo resulted in a strong reduction of reactive astrogliosis and glial scarring as well as in a pronounced improvement in functional recovery. Furthermore, we showed that transforming growth factor β1 (TGFβ1) stimulated RGMa expression through TGFβ1 receptor activin-like kinase 5 (ALK5) in primary cultured astrocytes. Knockdown of RGMa abrogated key steps of reactive astrogliosis and glial scar formation induced by TGFβ1, including cellular hypertrophy, glial fibrillary acidic protein upregulation, cell migration, and CSPGs secretion. Finally, we demonstrated that RGMa co-immunoprecipitated with ALK5 and Smad2/3. TGFβ1-induced ALK5-Smad2/3 interaction and subsequent phosphorylation of Smad2/3 were impaired by RGMa knockdown. Taken together, we identified that after stroke, RGMa promotes reactive astrogliosis and glial scar formation by forming a complex with ALK5 and Smad2/3 to promote ALK5-Smad2/3 interaction to facilitate TGFβ1/Smad2/3 signaling, thereby inhibiting neurological functional recovery. RGMa may be a new therapeutic target for stroke.
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 | 0.001 |
| 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".