Bibliographic record
Abstract
The engagement of cadherins, cell-to-cell adhesion proteins, triggers a dramatic increase in the levels and activity of the Rac/Cdc42 GTPases, through the inhibition of proteasomal degradation. This leads to an increase in transcription and secretion of IL6 family cytokines, activation of their common receptor, gp130, in an autocrine manner and phosphorylation of the signal transducer and activator of transcription-3 (Stat3) on tyrosine-705 by the Jak kinases. Stat3 subsequently dimerizes, migrates to the nucleus and activates the transcription of genes involved in cell division and survival. The Src oncogene also increases Rac levels, leading to secretion of IL6 family cytokines and gp130 activation, which triggers a Stat3-ptyr705 increase. Interestingly, at the same time, Src downregulates cadherins in a quantitative manner, while cadherins are required to preserve gp130 levels for IL6 family signalling. Therefore, a fine balance between Src527F/Rac/IL6 and Src527F/cadherin/gp130 levels is in existence, which is required for Stat3 activation. This further demonstrates the important role of cadherins in the activation of Stat3, through preservation of gp130 function. Conversely, the absence of cadherin engagement correlates with low Stat3 activity: In sparsely growing cells, both gp130 and Stat3-ptyr705 levels are very low, despite the fact that cSrc is active in the FAK (focal adhesion kinase)/cSrc complex, which further indicates that the engagement of cadherins is important for Stat3 activation, not just their presence. Furthermore, the caveolin-1 protein downregulates Stat3 through binding and sequestration of cadherins to the scaffolding domain of caveolin-1. We hypothesize that the cadherins/Rac/gp130 axis may be a conserved pathway to Stat3 activation in a number of systems. This fact could have significant implications in Stat3 biology, as well as in drug testing and development.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".