Probenecid Disrupts a Novel Pannexin 1-Collapsin Response Mediator Protein 2 Interaction and Increases Microtubule Stability
Bibliographic record
Abstract
Neurite formation relies on finely-tuned control of the cytoskeleton. Here we identified a novel protein-protein interaction between the ion and metabolite channel protein Panx1 and collapsin response mediator protein 2 (Crmp2), a positive regulator of microtubule polymerization and stabilization. Panx1 and Crmp2 co-precipitated from both Neuro-2a (N2a) cells and mouse ventricular zone tissue. In vitro binding assays between purified proteins revealed the interaction occurs directly between the Panx1 C-terminus and Crmp2. Because Crmp2 is a well-established microtubule-stabilizing protein, and we previously observed a marked increase in neurite formation following treatment with the Panx1 blocker, probenecid, in N2a cells and ventricular zone neural precursor cells, we investigated the impact of probenecid on the Panx1-Crmp2 interaction. Probenecid treatment significantly disrupted the Panx1-Crmp2 interaction by both immunoprecipitation and proximity ligation analysis, without altering overall Crmp2 protein expression levels. In the presence of probenecid, Crmp2 was concentrated at the distal ends of growing neurites. Moreover, probenecid treatment increased tubulin polymerization and microtubule stability in N2a cells. These results reveal that probenecid disrupts a novel interaction between Panx1 and the microtubule stabilizer, Crmp2, and also increases microtubule stability.
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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".