Stat3 is a positive regulator of gap junctional intercellular communication in cultured, human lung carcinoma cells
Bibliographic record
Abstract
BACKGROUND: Neoplastic transformation of cultured cells by a number of oncogenes such as src suppresses gap junctional, intercellular communication (GJIC); however, the role of Src and its effector Signal transducer and activator of transcription-3 (Stat3) upon GJIC in non small cell lung cancer (NSCLC) has not been defined. Immunohistochemical analysis revealed high Src activity in NSCLC biopsy samples compared to normal tissues. Here we explored the potential effect of Src and Stat3 upon GJIC, by assessing the levels of tyr418-phosphorylated Src and tyr705-phosphorylated Stat3, respectively, in a panel of NSCLC cell lines. METHODS: Gap junctional communication was examined by electroporating the fluorescent dye Lucifer yellow into cells grown on a transparent electrode, followed by observation of the migration of the dye to the adjacent, non-electroporated cells under fluorescence illumination. RESULTS: An inverse relationship between Src activity levels and GJIC was noted; in five lines with high Src activity GJIC was absent, while two lines with extensive GJIC (QU-DB and SK-LuCi6) had low Src levels, similar to a non-transformed, immortalised lung epithelial cell line. Interestingly, examination of the mechanism indicated that Stat3 inhibition in any of the NSCLC lines expressing high endogenous Src activity levels, or in cells where Src was exogenously transduced, did not restore GJIC. On the contrary, Stat3 downregulation in immortalised lung epithelial cells or in the NSCLC lines displaying extensive GJIC actually suppressed junctional permeability. CONCLUSIONS: Our findings demonstrate that although Stat3 is generally growth promoting and in an activated form it can act as an oncogene, it is actually required for gap junctional communication both in nontransformed lung epithelial cells and in certain lung cancer lines that retain extensive GJIC.
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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) | 0.002 | 0.001 |
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".