Differential Response of Transcription Factors to Activated Kinases in Steroidogenic and Non-Steroidogenic Cells
Bibliographic record
Abstract
Hormone-induced Leydig cell steroidogenesis requires rapid changes in gene expression in response to various hormones, cytokines, and growth factors. These proteins act by binding to their receptors on the surface of Leydig cells leading to activation of multiple intracellular signaling cascades, downstream of which are several kinases, including protein kinase A (PKA), Ca2+/calmodulin-dependent protein kinase I (CAMKI), and extracellular signal-regulated protein kinase 1 and 2 (ERK1/2). These kinases participate in hormone-induced steroidogenesis by phosphorylating numerous proteins including transcription factors leading to increased steroidogenic gene expression. How these various kinases and transcription factors come together to appropriately induce steroidogenic gene expression in response to specific stimuli remains poorly understood. In the present work, we compared the effect of PKA, CAMKI and ERK1/2 on the transactivation potential of 15 transcription factors belonging to 5 distinct families on the activity of the Star gene promoter. We not only validated known cooperation between kinases and transcription factors, but we also identified novel cooperations that have not yet been before reported. Some transcription factors were found to respond to all three kinases, whereas others were only activated by one specific kinase. Differential responses were also observed within a family of transcription factors. The diverse response to kinases provides flexibility to ensure proper genomic response of steroidogenic cells to different stimuli.
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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 | 0.001 |
| 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".