FKHRL1 and its homologs are new targets of nerve growth factor Trk receptor signaling
Bibliographic record
Abstract
We report that the Forkhead family of transcription factors, FKHRL1, FKHR and AFX are novel components of neurotrophin receptor signaling. NGF rapidly induced the phosphorylation of FKHRL1 in PC12 cells. This effect is mediated by high-affinity TrkA receptor as nerve growth factor (NGF) induced the phosphorylation of FKHRL1 only in TrkA expressing cells and not p75-expressing cells. Additional experiments with various kinase inhibitors, the transient expression of constitutively active and dominant-negative Akt, and in vitro kinase assay revealed that phosphatidylinositol-3 (PtdIns3)/Akt kinase mediated the actions of NGF. Similar data were obtained for brain-derived neurotrophic factor (BDNF), neurotrophin-3 (NT-3) and neurotrophin-4 (NT-4) in primary cortical cultured neurons. These findings demonstrate for the first time that the phosphorylation of the Forkhead family of transcription factors can be modulated by neurotrophins via Trk receptors and PtdIns3K/Akt kinase (but not MAP or S6p70 kinases) in neuronal and non-neuronal cells. Moreover, survival assays with the PtdIns3 kinase inhibitor LY294002, active and dominant-negative forms of Akt indicate that the phosphorylation of FKHRL1 plays a role in neurotrophins-mediated cell survival.
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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".