Maternal Mineral Nutrition Regulates Fetal Genomic Programming in Cattle: A Review
Bibliographic record
Abstract
Maternal mineral nutrition during the critical phases of fetal development may leave lifetime impacts on the productivity of an individual. Most research within the developmental origins of the health and disease (DOHaD) field is focused on the role of macronutrients in the genome function and programming of the developing fetus. On the other hand, there is a paucity of knowledge about the role of micronutrients and, specifically, minerals in regulating the epigenome of livestock species, especially cattle. Therefore, this review will address the effects of the maternal dietary mineral supply on the fetal developmental programming from the embryonic to the postnatal phases in cattle. To this end, we will draw a parallel between findings from our cattle model research with data from model animals, cell lines, and other livestock species. The coordinated role and function of different mineral elements in feto-maternal genomic regulation underlies the establishment of pregnancy and organogenesis and, ultimately, affects the development and functioning of metabolically important tissues, such as the fetal liver, skeletal muscle, and, importantly, the placenta. Through this review, we will delineate the key regulatory pathways involved in fetal programming based on the dietary maternal mineral supply and its crosstalk with epigenomic regulation in cattle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".