Fibroblast growth factor receptor 4 (FGFR4) mediates signaling to the prolactin but not the FGFR4 promoter
Bibliographic record
Abstract
Fibroblast growth factor receptors (FGFRs) have been implicated in a multitude of activities. Signaling of the 23 members of the FGF family is mediated through FGFR1-4. We show that FGF-19, which selectively binds FGFR4, can induce prolactin (PRL) but not growth hormone expression. FGF-19 also stimulated MAPK activation, an effect that was abrogated by a soluble dominant negative (dn) form of FGFR4. The response of the pituitary PRL promoter to FGF maps to an Ets-Pit1 binding site. We have previously shown that the hematopoietic zinc finger-containing transcription factor Ikaros (Ik) regulates FGFR4 as part of an overlapping site with that for an Ets-type factor in the FGFR4 promoter. Thus, we examined whether FGF-19 might regulate its own receptor through the Ets-Ik element in the FGFR4 promoter. Ets stimulated and dn-Ets inhibited basal FGFR4 and PRL promoter activity. In contrast, Ets enhanced FGF-19-induced PRL activation but failed to confer an effect for FGF-19 on the FGFR4 promoter. We conclude that FGFR4 mediates FGF-19 signaling to the PRL promoter. Our data also suggest a possible functional role for Ik in sorting Ets signals to the FGFR4 promoter, as distinct from the PRL promoter, where Ets partners with Pit1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".