The influence of<i>in vitro</i>fertilization and embryo culture on the embryo epigenetic constituents and the possible consequences in the bovine model
Bibliographic record
Abstract
Medically assisted reproductive technologies, such as in vitro embryo production, are increasingly being used to palliate infertility. Eggs are produced following a hormonal regimen that stimulates the ovaries to produce a large number of oocytes. Collected oocytes are then fertilized in vitro and allowed to develop in vitro until they are either frozen or transferred to mothers. There are controversial reports on the adverse impacts of these technologies on early embryos and their potential long-term effects. Using newly developed technological platforms that enable global gene expression and global DNA methylation profiling, we evaluated gene perturbations caused by such artificial procedures. We know that cells in the early embryo produce all cells in the body and are able to respond to their in vitro environment. However, it is not known whether gene perturbations are part of a normal response to the environment or are due to distress and will have long-term impacts. While the mouse is an established genetic model used for quality control of culture media in clinics, the bovine is a large mono-ovulating mammal with similar embryonic kinetics as humans during the studied developmental window. These model systems are critical to understand the effects of assisted reproduction without the confounding impact of infertility and without the limitations imposed by the scarcity of donated human samples and ethical issues. The data presented in this review come mostly from our own experimentation, publications, and collaborations. Together they demonstrate that the in vitro environment has a significant impact on embryos at the transcriptomic level and at the DNA methylation level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".