Estrous cycle impacts microRNA content in extracellular vesicles that modulate bovine cumulus cell transcripts during in vitro maturation
Bibliographic record
Abstract
Extracellular vesicles (EVs) are nanoparticles secreted by ovarian follicle cells. Extracellular vesicles are an important form of intercellular communication, since they carry bioactive contents, such as microRNAs (miRNAs), mRNAs, and proteins. MicroRNAs are small noncoding RNA capable of modulating mRNA translation. Thus, EVs can play a role in follicle and oocyte development. However, it is not clear if EV contents vary with the estrous cycle stage. The aim of this study was to investigate the bovine miRNA content in EVs obtained from follicles at different estrous cycle stages, which are associated with different progesterone (P4) levels in the follicular fluid (FF). We collected FF from 3 to 6 mm follicles and evaluated the miRNA profile of the EVs and their effects on cumulus-oocyte complexes during in vitro maturation. We observed that EVs from low P4 group have a higher abundance of miRNAs predicted to modulate pathways, such as MAPK, RNA transport, Hippo, Cell cycle, FoxO, oocyte meiosis, and TGF-beta. Additionally, EVs were taken up by cumulus cells and, thus, affected the RNA global profile 9 h after EV supplementation. Cumulus cells supplemented with EVs from low P4 presented upregulated genes that could modulate biological processes, such as oocyte development, immune responses, and Notch signaling compared with genes of cumulus cells in the EV free media or with EVs from high P4 follicles. In conclusion, our results demonstrate that EV miRNA contents are distinct in follicles exposed to different estrous cycle stage. Supplementation with EVs impacts gene expression and biological processes in cumulus cells.
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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".