Zinc supports transcription and improves meiotic competence of growing bovine oocytes
Bibliographic record
Abstract
In the last years, many studies focused on the understanding of the possible role of zinc in the control of mammalian oogenesis, mainly on oocyte maturation and fertilization. However, little is known about the role of zinc at earlier stages, when the growing oocyte is actively transcribing molecules that will regulate and sustain subsequent stages of oocyte and embryonic development. In this study, we used the bovine model to gain insights into the possible involvement of zinc in oocyte development. We first mined the EmbryoGENE transcriptomic dataset, which revealed that several zinc transporters and methallothionein are impacted by physiological conditions throughout the final phase of oocyte growth and differentiation. We then observed that zinc supplementation during in vitro culture of growing oocytes is beneficial to the acquisition of meiotic competence when subsequently subjected to standard in vitro maturation. Furthermore, we tested the hypothesis that zinc supplementation might support transcription in growing oocytes. This hypothesis was indirectly confirmed by the experimental evidence that the content of labile zinc in the oocyte decreases when a major drop in transcription occurs in vivo. Accordingly, we observed that zinc sequestration with a zinc chelator rapidly reduced global transcription in growing oocytes, which was reversed by zinc supplementation in the culture medium. Finally, zinc supplementation impacted the chromatin state by reducing the level of global DNA methylation, which is consistent with the increased transcription. In conclusion, our study suggests that altering zinc availability by culture-medium supplementation supports global transcription, ultimately enhancing meiotic competence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".