Connexin Over-Expression Differentially Suppresses Glioma Growth and Contributes to the Bystander Effect Following HSV-Thymidine Kinase Gene Therapy
Bibliographic record
Abstract
Neoplastic transformation is frequently associated with a loss of gap junctional intercellular communication and reduced expression of connexins. The introduction of connexin genes into tumor cells reverses the proliferative characteristics of such cells. However, there is very little comparative information on the effects of different connexins on cancer cell growth. We hypothesized that Cx26, Cx32, or Cx43 would display differential growth suppression of C6 glioma cells and uniquely modulate the bystander effect following transduction of C6 cells with HSVtk followed by suicide gene therapy. The bystander phenomenon is the death of a greater number of tumor cells than are expressing the HSVtk gene, presumably due to the passage of toxic molecules through gap junction channels. To test this hypothesis, we used retroviral vectors to infect C6 glioma cells producing connexin-expressing and HSVtk-expressing cell lines. All three connexin-expressing cell lines grew significantly slower than GFP-infected or native C6 cells. Cx32 and Cx26 were significantly more effective at mediating the bystander effect in cocultures of C6-connexin cells with C6-HSVtk cells. These studies indicate that connexins have unique properties that contribute to their tumor suppressive function.
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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 | 0.001 |
| 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".