The Protein Tyrosine Phosphatase SHP-2 Regulates Interleukin-1-induced ERK Activation in Fibroblasts
Bibliographic record
Abstract
Focal adhesion complexes are actin-rich, cytoskeletal structures that mediate cell adhesion to the substratum and also selectively regulate signal transduction pathways required for interleukin (IL)-1beta signaling to the MAP kinase, ERK. IL-1-induced ERK activation is markedly diminished in fibroblasts deprived of focal adhesions whereas activation of p38 and JNK is unaffected. While IL-1 signaling is known to involve the activity of protein and lipid kinases including MAP kinases, FAK, and PI3K, little is known about the role of phosphatases in the regulation of IL-1 signal generation and attenuation. Here we demonstrate that SHP-2, a protein tyrosine phosphatase present in focal adhesions, modulates IL-1-induced ERK activation and the transient actin stress fiber disorganization that occurs following IL-1 treatment in human gingival fibroblasts. Using a combination of immunoblotting, immunoprecipitation, and immunostaining we show that SHP-2 is present in nascent focal adhesions and undergoes phosphorylation on tyrosine 542 in response to IL-1 stimulation. Blocking anti-SHP-2 antibodies, electoporated into the cytosol of fibroblasts, inhibited IL-1-induced ERK activation, actin filament assembly, and cell contraction, indicating a role for SHP-2 in these processes. In summary, our data indicate that SHP-2, a focal adhesion-associated protein, participates in IL-1-induced ERK activation likely via an adaptor function.
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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) | 0.001 | 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 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".