In vitroculture and somatic cell nuclear transfer affect imprinting of SNRPN gene in pre- and post-implantation stages of development in cattle
Bibliographic record
Abstract
BACKGROUND: Embryo in vitro manipulations during early development are thought to increase mortality by altering the epigenetic regulation of some imprinted genes. Using a bovine interspecies model with a single nucleotide polymorphism, we assessed the imprinting status of the small nuclear ribonucleoprotein polypeptide N (SNRPN) gene in bovine embryos produced by artificial insemination (AI), in vitro culture (IVF) and somatic cell nuclear transfer (SCNT) and correlated allelic expression with the DNA methylation patterns of a differentially methylated region (DMR) located on the SNRPN promoter. RESULTS: In the AI group, SNRPN maternal expression is silenced at day 17 and 40 of development and a third of the alleles analyzed are methylated in the DMR. In the IVF group, maternal transcripts were identified at day 17 but methylation levels were similar to the AI group. However, day-40 fetuses in the IVF group showed significantly less methylation when compared to the AI group and SNRPN expression was mostly paternal in all fetal tissues studied, except in placenta. Finally, the SCNT group presented severe loss of DMR methylation in both day-17 embryos and 40 fetuses and biallelic expression was observed in all stages and tissues analyzed. CONCLUSION: Together these results suggest that artificial reproductive techniques, such as prolonged in vitro culture and SCNT, lead to abnormal reprogramming of imprinting of SNRPN gene by altering methylation levels at this locus.
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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".