23. Constitutively Active Signal Transducer and Activator of Transcription-3; an Oncogene that Increases Gap Junctional, Intercellular Communication
Bibliographic record
Abstract
Gap junctions are specialized intercellular channels that connect the cytoplasm of adjacent cells and mediate the direct exchange of small ions and molecules between them. Gap junctional intercellular communication (GJIC) is usually blocked, or down regulated, in cells transformed by oncogenes, such as Src. One of the Src effector pathways leading to transformation and GJIC suppression is the Ras/Raf/MEK/ERK pathway that has a prominent etiological role in cell proliferation, differentiation and cell survival; inhibition of this pathway in vSrc transformed cells restores GJIC. In addition, the distinct downstream effector of Src that is obligatory for neoplasia is the signal transducer and activator of transcription-3 (Stat3). Stat3 is up regulated in a range of tumors, and a modified version, the constitutively active form of Stat3 (Stat3C), has been shown to function as an oncogene. To examine the role of Stat3 upon the Src-mediated, GJIC suppression, Stat3 was down regulated in rat liver epithelial T51B cells expressing activated Src. The extent of GJIC was determined by the migration of the fluorescent dye, Lucifer Yellow, through adherent cells subsequent to electroporation. The results demonstrate that, contrary to inhibition of the Ras pathway, Stat3 inhibition in cells expressing activated Src does not restore GJIC. On the contrary, Stat3 inhibition in normal cells with high GJIC levels eliminated junctional permeability. Interestingly, our results also demonstrate that expression of Stat3C T51B cells and human lung cancer SK-LuCi6 cells, which have extensive communication and low Src levels, increased GJIC. Therefore, Stat3 is actually required for and increases junctional permeability.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".