Lack of FSH support enhances LIF–STAT3 signaling in granulosa cells of atretic follicles in cattle
Bibliographic record
Abstract
Subordinate follicles (SFs) of bovine follicular waves undergo atresia due to declining FSH concentrations; however, the signalling mechanisms have not been fully deciphered. We used an FSH-induced co-dominance model to determine the effect of FSH on signalling pathways in granulosa cells of the second-largest follicles (SF in control cows and co-dominant follicle (co-DF2) in FSH-treated cows). The SF was smaller than DF in control cows while diameters of co-DF1 and co-DF2 in FSH-treated cows were similar. The presence of cleaved CASP3 protein confirmed that granulosa cells of SFs, but not of DFs and co-DFs, were apoptotic. To determine the effect of FSH on molecular characteristics of the second-largest follicles, we generated relative variables for the second largest follicle in each cow. For this, variables of SF or co-DF2 were divided by the variables of the largest follicle DF or co-DF1 in each cow. There was higher transcript abundance of MAPK1/3 and AKT1/2/3 but lower abundance of phosphorylated MAPK3/1 in SF than co-DF2 granulosa cells. Abundance of mRNA and phosphorylated protein of STAT3 was higher in granulosa cells of control SF than FSH-treated co-DF2. SF granulosa cells had higher levels of LIFR and IL6ST transcripts, the two receptors involved in STAT3 activation. Further, lower transcript abundance of interleukin 6 receptor (IL6R), another receptor involved in STAT3 activation, indicated that STAT3 activation in SF granulosa cells could be mainly due to leukemia inhibitory factor (LIF) signalling. These results indicate that atresia due to lack of FSH is associated with activated LIF-STAT3 signalling in SF granulosa cells, as FSH treatment reversed such activation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".