Tyrosine phosphatase SHP‐2 regulates IL‐1 signaling in fibroblasts through focal adhesions
Bibliographic record
Abstract
Interleukin-1beta (IL-1beta) mediates destruction of matrix collagens in diverse inflammatory diseases including arthritis, periodontitis, and pulmonary fibrosis by activating fibroblasts, cells that interact with matrix proteins through integrin-based adhesions. In vitro, IL-1beta signaling is modulated by focal adhesions, supramolecular protein complexes that are enriched with tyrosine kinases and phosphatases. We assessed the importance of tyrosine phosphatases in regulating cell-matrix interactions and IL-1beta signaling. In human gingival fibroblasts plated on fibronectin, IL-1beta enhanced the maturation of focal adhesions as defined by morphology and enrichment with paxillin and alpha-actinin. IL-1beta also induced activation of ERK and recruitment of phospho-ERK to focal complexes/adhesions. Treatment with the potent tyrosine phosphatase inhibitor pervanadate, in the absence of IL-1beta, recapitulated many of these responses indicating the importance of tyrosine phosphatases. Immunoblotting of collagen bead-associated complexes revealed that the tyrosine phosphatase, SHP-2, was also enriched in focal complexes/adhesions. Depletion of SHP-2 by siRNA or by homologous recombination markedly altered IL-1beta-induced ERK activation and maturation of focal adhesions. IL-1beta-induced tyrosine phosphorylation of SHP-2 on residue Y542 promoted focal adhesion maturation. Association of Gab1 with SHP-2 in focal adhesions correlated temporally with activation of ERK and was abrogated in cells expressing mutant (Y542F) SHP-2. We conclude that IL-1beta mediated maturation of focal adhesions is dependent on tyrosine phosphorylation of SHP-2 at Y542, leading to recruitment of Gab1, a process that may influence the downstream activation of ERK.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".