Low sulfated heparins target multiple proteins for central nervous system repair
Bibliographic record
Abstract
The lack of endogenous repair following spinal cord injury (SCI) accounts for the frequent permanent deficits for which effective treatments are absent. Previously, we demonstrated that low sulfated modified heparin mimetics (LS-mHeps) attenuate astrocytosis, suggesting they may represent a novel therapeutic approach. mHeps are glycomolecules with structural similarities to resident heparan sulfates (HS), which modulate cell signaling by both sequestering ligands, and acting as cofactors in the formation of ligand-receptor complexes. To explore whether mHeps can affect the myelination and neurite outgrowth necessary for repair after SCI, we created lesioned or demyelinated neural cell co-cultures and exposed them with a panel of mHeps with varying degrees and positions of their sulfate moieties. LS-mHep7 enhanced neurite outgrowth and myelination, whereas highly sulfated mHeps (HS-mHeps) had attenuating effects. LS-mHeps had no effects on myelination or neurite extension in developing, uninjured myelinating cultures, suggesting they might exert their proregenerating effects by modulating or sequestering inhibitory factors secreted after injury. To investigate this, we examined conditioned media from cultures using chemokine arrays and conducted an unbiased proteomics approach by applying TMT-LC/MS to mHep7 affinity purified conditioned media from these cultures. Multiple protein factors reported to play a role in damage or repair mechanisms were identified, including amyloid betaA4. Amyloid beta peptide (1-42) was validated as an important candidate by treating myelination cultures and shown to inhibit myelination. Thus, we propose that LS-mHeps exert multiple beneficial effects on mechanisms supporting enhanced repair, and represent novel candidates as therapeutics for CNS damage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".