Differential Sulfation Remodelling of Heparan Sulfate by Extracellular 6-<i>O</i>-Sulfatases Regulates Fibroblast Growth Factor-Induced Boundary Formation by Glial Cells: Implications for Glial Cell Transplantation
Bibliographic record
Abstract
Previously, it has been shown that rat Schwann cells (SCs), but not olfactory ensheathing cells (OECs), form a boundary with astrocytes, due to a SC-specific secreted factor. Here, we identify highly sulfated heparan sulfates (HSs) and fibroblast growth factors (FGFs) 1 and 9 as possible determinants of boundary formation induced by rat SCs. Disaccharide analysis of HS in SC-conditioned and rat OEC-conditioned media showed that SCs secrete more highly sulfated HS than OECs. The dependence of the boundary-forming activity on high levels of sulfation was confirmed using a panel of semisynthetic modified heparins with variable levels of sulfation. Furthermore, extracellular HS 6-O-endosulfatase enzymes, Sulf 1 and Sulf 2, were expressed at a significantly lower level by SCs compared with OECs, and siRNA reduction of Sulfs in OECs was, in itself, sufficient to induce boundary formation. This demonstrates a key role for remodelling (reduction) of HS 6-O-sulfation by OECs, compared with SCs, to suppress boundary formation. Furthermore, specific anti-FGF1 and anti-FGF9 antibodies disrupted SC-astrocyte boundary formation, supporting a role for an HS sulfation-dependent FGF signaling mechanism via FGF receptors on astrocytes. We propose a model in which FGF1 and FGF9 signaling is differentially modulated by patterns of glial cell HS sulfation, dependent on Sulf 1 and Sulf 2 expression, to control FGF receptor 3-IIIb-mediated astrocytic responses. Moreover, these data suggest manipulation of HS sulfation after CNS injury as a potential novel approach for therapeutic intervention in CNS 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".