Chondroitin Sulfate Proteoglycans Negatively Modulate Spinal Cord Neural Precursor Cells by Signaling Through LAR and RPTPσ and Modulation of the Rho/ROCK Pathway
Bibliographic record
Abstract
Multipotent adult neural precursor cells (NPCs) have tremendous intrinsic potential to repair the damaged spinal cord. However, evidence shows that the regenerative capabilities of endogenous and transplanted NPCs are limited in the microenvironment of spinal cord injury (SCI). We previously demonstrated that injury-induced upregulation of matrix chondroitin sulfate proteoglycans (CSPGs) restricts the survival, migration, integration, and differentiation of NPCs following SCI. CSPGs are long-lasting components of the astroglial scar that are formed around the lesion. Our recent in vivo studies demonstrated that removing CSPGs from the SCI environment enhances the potential of transplanted and endogenous adult NPCs for spinal cord repair; however, the mechanisms by which CSPGs regulate NPCs remain unclear. In this study, using in vitro models recapitulating the extracellular matrix of SCI, we investigated the direct role of CSPGs in modulating the properties of adult spinal cord NPCs. We show that CSPGs significantly decrease NPCs growth, attachment, survival, proliferation, and oligodendrocytes differentiation. Moreover, using genetic models, we show that CSPGs regulate NPCs by signaling on receptor protein tyrosine phosphate sigma (RPTPσ) and leukocyte common antigen-related phosphatase (LAR). Intracellularly, CSPGs inhibitory effects are mediated through Rho/ROCK pathway and inhibition of Akt and Erk1/2 phosphorylation. Downregulation of RPTPσ and LAR and blockade of ROCK in NPCs attenuates the inhibitory effects of CSPGS. Our work provide novel evidence uncovering how upregulation of CSPGs challenges the response of NPCs in their post-SCI niche and identifies new therapeutic targets for enhancing NPC-based therapies for SCI repair.
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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 | 0.000 | 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 | 0.000 |
| 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".