Aggrecan components differentially modulate nerve growth factor–responsive and neurotrophin‐3‐responsive dorsal root ganglion neurite growth
Bibliographic record
Abstract
Aggrecan is one of the major chondroitin sulfate proteoglycans (CSPGs) expressed in the central nervous system. The signaling pathways activated downstream of cell interaction with aggrecan and with CSPGs in general and the importance of chondroitin sulfate-glycosaminoglycan side chains in their inhibition are unclear. Therefore, to analyze the effect of different components of aggrecan in inhibiting neurite growth, neurite outgrowth was quantified in an in vitro model in which chick dorsal root ganglion (DRG) explants were grown on substrates containing aggrecan bound to hyaluronan and link protein as a macromolecular aggregate, aggrecan monomers, hyaluronan, or ChABC-treated aggrecan. Aggrecan aggregate, aggrecan monomer, and hyaluronan inhibited neurite outgrowth from nerve growth factor (NGF)- and neurotrophin-3 (NT3)-responsive DRG neurons. Aggrecan inhibition was dependent on its chondroitin sulfate-glycosaminoglycans, as ChABC digestion alleviated neurite inhibition because of aggrecan. Growth cones displayed full or partial collapse on aggrecan aggregate, hyaluronan, and ChABC-treated aggrecan. Inhibition of Rho kinase (ROCK) with Y27632 increased neurite growth on some but not all of the aggrecan components tested. With NGF in the culture medium, Y27632 increased neurite outgrowth on aggrecan aggregate, monomers, and ChABC-treated aggrecan, but not on hyaluronan. The ROCK inhibitor also increased NT3-responsive outgrowth on aggrecan aggregate and hyaluronan, but not on ChABC-treated aggrecan. This study showed that the matrix proteoglycan aggrecan and its components have multiple effects on neurite outgrowth and that some of these effects involve the Rho/ROCK pathway.
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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".