Activation of telomerase via madecassic acid enhances the developmental competence of the SCNT-derived bovine embryos
Bibliographic record
Abstract
Somatic cell nuclear transfer (SCNT) is important in assisted reproductive technologies. However, its reprogramming efficiency remains low. A considerable drawback of SCNT-cloned embryos is the reduction in telomerase activity, which is crucial for DNA stability and genetic and epigenetic reprogramming. The present study aimed to examine the effects of madecassic acid (MA), a potent telomerase activator, on the developmental rate, embryonic genome activation, and implantation potential of SCNT-derived bovine embryos. The treatment of bovine signal cell-cloned zygotes with 3.0 μg/mL MA significantly increased embryo cleavage (71.5%) and blastocyst rate (28.1%) compared with that in non-treated (control) SCNT-cloned bovine embryos. In addition, MA treatment enhanced the bovine granulosa cells' telomerase activity and telomerase expression are assessed using qTRAP assay and ELISA. Of note, MA enhanced the expression of embryonic genome activation (EGA)-related genes including NFYA , SP1 , DPRX , GSC , CTNNB1 , DUX , and ARGFX in MA-treated cloned embryos compared to the control group. Moreover, MA-treatment of cloned embryos showed substantially less DNA damage than the control SCNT embryos. Mechanistically, MA activation of telomerase reverse transcriptase (TERT) significantly enhanced the nuclear localization of β-catenin and c-Myc and improved EGA. Reduction in the nuclear localization of this triose may be the leading cause of reduced EGA in cloned embryos. In conclusion, MA impacted the EGA reprogramming and development of cloned bovine embryos via probable activation of TERT. This telomerase activator may have the application of improving SCNT-cloned bovine embryos.
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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".