Differential regulation of FGFR3 by PTPN1 and PTPN2
Bibliographic record
Abstract
Aberrant expression and activation of FGFR3 is associated with disease states including bone dysplasia and malignancies of bladder, cervix, and bone marrow. MS analysis of protein-phosphotyrosine in multiple myeloma cells revealed a prevalent phosphorylated motif, D/EYYR/K, derived from the kinase domain activation loops of tyrosine kinases including FGFR3 corresponding to a recognition sequence of protein-tyrosine phosphatase PTPN1. Knockdown of PTPN1 or the related enzyme PTPN2 by RNAi resulted in ligand-independent activation of FGFR3. Modulation of FGFR3 activation loop phosphorylation by both PTPN1 and PTPN2 was a function of receptor trafficking and phosphotyrosine phosphatase (PTP) compartmentalization. The FGFR3 activation loop motif DYYKK(650) is altered to DYYKE(650) in the oncogenic variant FGFR3(K650E) , and consequently it is constitutively fully activated and unaffected by activation loop phosphorylation. FGFR3(K650E) was nevertheless remarkably sensitive to negative regulation by PTPN1 and PTPN2. This suggests that in addition to modulating FGFR3 phosphorylation, PTPN1 and PTPN2 constrain the kinase domain by fostering an inactive-state. Loss of this constraint in response to ligand or impaired PTPN1/N2 may initiate FGFR3 activation. These results suggest a model wherein PTP expression levels may define conditions that select for ectopic FGFR3 expression and activation during tumorigenesis.
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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".