FGF-16 is a target for adrenergic stimulation through NF- B activation in postnatal cardiac cells and adult mouse heart
Bibliographic record
Abstract
AIMS: The fibroblast growth factor (FGF) family plays an important role in cardiac growth and development. However, only FGF-16 RNA levels are reported to increase during the perinatal period and to be expressed preferentially in the myocardium, suggesting control at the transcriptional level and a role for FGF-16 in the postnatal heart. Beyond the identification of two TATA-like elements (TATA1 and TATA2) in the mouse FGF-16 promoter region and the preferential cardiac activity of TATA2, there is no report of Fgf-16 gene regulation. Assessment of promoter sequences, however, reveals putative nuclear factor-kappaB (NF-kappaB) elements, suggesting that Fgf-16 is regulated via NF-kappaB activation and thereby implicated in a number of cardiac events. Thus, the Fgf-16 gene was investigated as a target for NF-kappaB activation in cardiac cells. METHODS AND RESULTS: Assessments of Fgf-16 promoter activity were made using truncated and transfected hybrid genes with NF-kappaB inhibitors and/or beta-adrenergic stimulation via isoproterenol (IsP) treatment (a known NF-kappaB activator) in culture, and on endogenous mouse and human Fgf-16 genes in situ. The mouse Fgf-16 promoter region was stimulated in response to IsP treatment, but this response was lost with NF-kappaB inhibitor pretreatment. Deletion analysis revealed IsP responsiveness linked to sequences between TATA2 and TATA1 and, more specifically, a NF-kappaB element upstream and adjacent to TATA1 that associates with NF-kappaB p50/p65 subunits in chromatin. Finally, TATA1 and the proximal NF-kappaB element are conserved in the human genome and responsive to IsP. CONCLUSION: The mouse and human Fgf-16 gene is a target for NF-kappaB activation in the postnatal heart.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".