6. Effect of Erk vs. PI3 Kinase Activation by the Middle Tumour Antigen of Polyoma Virus Upon Gap Junctional, Intercellular Communication of Cultured Cells
Bibliographic record
Abstract
Gap junctions are protein channels that permit the passage of small molecules and ions between adjacent cells. Gap junctional permeability is thought to lead to decreased cellular proliferation. In fact, a number of oncogenes, such as the middle tumour antigen of polyoma virus (mT), are known to interrupt gap junctional, intercellular communication (GJIC). The Ras/Raf/Mek/Erk and phosphoinositide 3‐kinase (PI3 kinase)/Akt signalling pathways, two commonly studied pathways in malignancy, are both activated by mT, and are both required for complete transformation by mT. This study is the first to investigate the effects of Erk vs. PI3 kinase activation by mT on GJIC in cultured cells. To this effect, two mT mutants,impaired in their ability to activate either the Erk or PI3 kinase pathway, were expressed through retroviral infection in rat liver epithelial T51B cells which have extensive GJIC. A novel in situ electroporation technique was used to quantitate the degree of GJIC in T51B cells expressing each mT mutant. The results showed that the Erk‐activating mutant exhibited the interrupted GJIC that is characteristic of mT expression, while the PI3 kinase‐activating mutant displayed levels of GJIC comparable to those observed in wild‐type T51B cells. This suggests that Erk activation by mT is sufficient to suppress GJIC, while PI3 kinase activation, despite its contribution to neoplastic transformation, is unable to interrupt GJIC. Given the importance of GJIC in cellular proliferation, apoptosis and differentiation, identifying the pathways affecting gap junctional permeability may yield novel targets for cancer therapy.
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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) | 0.002 | 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".