Gene analysis of major signaling pathways regulated by gonadotropins in human ovarian granulosa tumor cells (KGN)†
Bibliographic record
Abstract
The female reproductive function largely depends on timing and coordination between follicle-stimulating hormone (FSH) and luteinizing hormone. Even though it was suggested that these hormones act on granulosa cells via shared signaling pathways, mainly protein kinases A, B, and C (PKA, PKB, and PKC), there is still very little information available on how these signaling pathways are regulated by each hormone to provide such differences in gene expression throughout folliculogenesis. To obtain a global picture of the principal upstream factors involved in PKA, PKB, and PKC signaling in granulosa cells, human granulosa-like tumor cells (KGN) were treated with FSH or specific activators (forskolin, SC79, and phorbol 12-myristate 13-acetate) for each pathway to analyze gene expression with RNA-seq technology. Normalization and cutoffs (FC 1.5, P ≤ 0.05) revealed 3864 differentially expressed genes between treatments. Analysis of major upstream regulators showed that PKA is a master kinase of early cell differentiation as its activation resulted in the gene expression profile that accompanies granulosa cell differentiation. Our data also revealed that the activation of PKC in granulosa cells is also a strong differentiation signal that could control "advanced" differentiation in granulosa cells and the inflammatory cascade that occurs in the dominant follicle. According to our results, PKB activation provides support for PKA-stimulated gene expression and is also involved in granulosa cell survival throughout follicular development. Taken together, our results provide new information on PKA, PKB, and PKC signaling pathways and their roles in stimulating a follicle at the crossroad between maturation/ovulation and atresia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".