Focal adhesions and Ras are functionally and spatially integrated to mediate IL‐1 activation of ERK
Bibliographic record
Abstract
In connective tissue cells, IL-1-induced ERK activation leading to matrix metalloproteinase (MMP)-3 expression is dependent on cooperative interactions between focal adhesions and the endoplasmic reticulum (ER). As Ras can be activated on the ER, we investigated the role of Ras in IL-1 signaling and focal adhesion formation. We found that constitutively active H-Ras, K-Ras or N-Ras enhanced focal adhesion maturation and β1-integrin activation. IL-1 promoted the accumulation of Ras isoforms in ER and focal adhesion fractions, as shown in cells cotransfected with GFP-tagged Ras isoforms and YFP-ER protein and by analysis of subcellular fractions enriched for ER or focal adhesion proteins. Dominant-negative H-Ras or K-Ras reduced accumulation of H-Ras and K-Ras in focal adhesions induced by IL-1 and also blocked ERK activation and focal adhesion maturation. Ras-GRF was enriched constitutively in focal adhesion fractions and was required for Ras recruitment to focal adhesions. We conclude that Ras activation and IL-1 signaling are interactive processes that regulate the maturation of focal adhesions, which, in turn, is required for ERK activation.
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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 | 0.001 |
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".