Dynamics of small non-coding RNAs in bovine scNT embryos through the maternal-to-embryonic transition
Bibliographic record
Abstract
The efficiency of somatic cell nuclear transfer (scNT) for production of viable offspring is relatively low as compared to in vitro fertilization (IVF), presumably due to deficiencies in epigenetic reprogramming of the donor cell genome. Such defects may also involve the population of small non-coding RNAs (sncRNAs), which are important during early embryonic development. The objective of this study was to examine dynamic changes in relative abundance of sncRNAs during the maternal-to-embryonic transition (MET) in bovine embryos produced by scNT as compared to IVF by using RNA sequencing. When comparing populations of miRNA in scNT versus IVF embryos, only miR-2340, miR-345, and miR34a were differentially expressed in morulae, though many more miRNAs were differentially expressed when comparing across developmental stages. Also of interest, distinct populations of piwi-interacting like RNAs (pilRNAs) were identified in bovine embryos prior to and during embryonic genome activation (EGA) as compared bovine embryos post-EGA and differentiated cells. Overall, sncRNA sequencing analysis of preimplantation embryos revealed largely similar profiles of sncRNAs for IVF and scNT embryos at the 2-cell, 8-cell, morula, and blastocyst stages of development. However, these sncRNA profiles, including miRNA, piRNA, and tRNA fragments, were notably distinct prior to and after completion of the MET.
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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".