Comparing mRNA and sncRNA profiles during the maternal-to-embryonic transition in bovine IVF and scNT embryos
Bibliographic record
Abstract
Production of embryos with high developmental competence by somatic cell nuclear transfer (scNT) is far less efficient than for in vitro fertilized (IVF) embryos, likely due to an accumulation of errors in genome reprogramming that results in aberrant expression of RNA transcripts, including messenger RNAs (mRNA) and, possibly, microRNAs (miRNA). Thus, our objectives were to use RNAseq to determine the dynamics of mRNA expression in early developing scNT and IVF embryos in the context of the maternal-to-embryonic transition (MET) and to correlate apparent transcriptional dysregulation in cloned embryos with miRNA expression profiles. Comparisons between scNT and IVF embryos indicated large scale transcriptome differences, which were most evident at the 8-cell and morula stages for genes associated with biological functions critical for the MET. For two miRNAs previously identified as differentially expressed in scNT morulae, miR-34a and miR-345, negative correlations with some predicted mRNA targets were apparent, though not widespread among the majority of predicted targets. Moreover, although large-scale aberrations in expression of mRNAs were evident during the MET in cattle scNT embryos, these changes were not consistently correlated with aberrations in miRNA expression at the same developmental stage, suggesting that other mechanisms controlling gene expression may be involved.
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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 | 0.001 | 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".