Regulation and action of early growth response 1 in bovine granulosa cells
Bibliographic record
Abstract
Abstract Fibroblast growth factors (FGF) modify cell proliferation and differentiation through receptor tyrosine kinases, which stimulate the expression of transcription factors including members of the early growth response (EGR) family. In ovarian granulosa cells, most FGFs activate typical response genes, although the role of EGR proteins has not been described. In the present study, we determined the regulation of EGR mRNA by FGFs and explored the role of EGR1 in the regulation of FGF-response genes. Addition of FGF1, FGF2, FGF4 or FGF8b increased EGR1 and EGR3 mRNA levels, whereas FGF18 increased only EGR1 mRNA abundance. No mRNA encoding EGR2 or EGR4 was detected. Overexpression of EGR1 increased EGR3 mRNA levels as well as the FGF-response genes SPRY2, NR4A1 and FOSL1 and also increased the phosphorylation of MAPK3/1. Knockdown of EGR3 did not alter the ability of FGF8b to stimulate SPRY2 mRNA levels. These data demonstrate the regulation of EGR1 and EGR3 mRNA abundance by FGFs in granulosa cells and suggest that EGR1 is likely an upstream component of FGF signaling in granulosa cells.
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 | 0.000 |
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".