MicroRNA-574 Impacts Granulosa Cell Estradiol Production via Targeting TIMP3 and ERK1/2 Signaling Pathway
Bibliographic record
Abstract
Estradiol represents a key steroid ovarian hormone that not only plays a vital role in ovarian follicular development but also is associated with many other reproductive functions. Our primary study revealed that miR-574 expression decreased in porcine granulosa cells during development from small to large follicles, and the increase of ERK1/2 phosphorylation accompanies this change. Since it has been well established that the ERK1/2 activity is tightly associated with granulosa cell functions, including ovarian hormone production, we thus further investigate if the miRNA is involved in the regulation of estradiol production in granulosa cells. We found that overexpression of miR-574 decreased phosphorylated ERK1/2 without affecting the level of ERK1/2 protein, and on the other hand, the inhibition of miR-574 increased phosphorylated ERK1/2 level (P<0.05); meanwhile, overexpression of miR-574 increased estradiol production but knockdown of miR-574 decreased estradiol level in granulosa cells. To further identify the potential mechanism involved in the miR-574 regulatory effect, in silico screening was performed and revealed a potential binding site on the 3’UTR region of the tissue inhibitor of metalloproteinase 3 (TIMP3). Our gain-, loss- of function experiments, and luciferase reporter assay confirmed that TIMP3 is indeed the target of miR-574 in granulosa cell. Furthermore, the siRNA TIMP3 knockdown resulted in decreased phosphorylated ERK1/2, and an increase in estradiol production. In contrast, the addition of recombinant TIMP3 increased phosphorylated ERK1/2 level and decreased estradiol production. In summary, our results suggest that the miR-574-TIMP3-pERK1/2 cascade may be one of the pathways by which microRNAs regulate granulosa cell estradiol production.
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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) | 0.002 | 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".