Enhanced Neurite Outgrowth in PC12 Cells Mediated by Connexin Hemichannels and ATP
Bibliographic record
Abstract
Gap junctions have traditionally been described as transmembrane channels that facilitate intercellular communication via the passage of small molecules. Connexins, the basic building blocks of gap junctions, are expressed in most mammalian tissues including the developing and adult central nervous system. During brain development, connexins are temporally and spatially regulated suggesting they play an important role in the proper formation of the central nervous system. In the current study, connexins 32 and 43 were overexpressed in PC12 cells to determine whether connexins are involved in neuronal differentiation. Both connexin 32 and 43 were appropriately trafficked to the cell membrane following overexpression and resulted in the formation of functional gap junctions. Connexin overexpression was found to cause enhanced neurite outgrowth in PC12 cells treated with nerve growth factor to initiate neuritogenesis. Surprisingly, however, enhanced neurite outgrowth was found to be the consequence of functional hemichannel formation as opposed to traditional intercellular communication. Additional analysis revealed that ATP was released into the media likely through hemichannels and acted on purinergic receptors to cause enhanced neurite outgrowth. Collectively, the results of the current study suggest that connexins may play an important role in neuronal differentiation by non-traditional mechanisms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".