14 Signatures of pre-natal nutrition in cattle: adaptations in DNA methylation, gene expression, and post-natal growth.
Bibliographic record
Abstract
Exploring pre-natal nutrition to modulate and improve post-natal growth and development in cattle continues to reveal new opportunities for researchers, and ultimately cattle producers. Effects of pre-natal dietary interventions have been shown to be markedly time- and treatment-specific, although a few particular patterns of changes in post-natal growth present themselves recurrently. Male calves, both bulls and steers, appear to be more susceptible to alterations in pre-natal maternal nutrition and, when the opportunity presents, turn it into increased growth performance at a later stage of life (Fitzsimmons unpublished, Añez-Osuna et al. 2018). Male calves may also develop differences in growth patterns in association with pre-natal nutrition even if no differences in weight are detected at birth. Elucidating the how and why behind these changes is more challenging, as well as any long-term effects upon animal health and longevity. Examination of gene expression patterns within tissues that are highly connected with growth and metabolism in cattle, for example skeletal muscles and liver, shows that expression of genes connected to overall growth such as the IGF family, myogenesis (MYOD1, MYOG, etc.), adipogenesis (PPARG), as well as microRNAs that modulate proliferation and differentiation in skeletal muscle, can be associated with pre-natal maternal diet (Paradis et al. 2017). Furthermore, DNA methylation patterns of differentially-methylated regions (DMRs) near the genes for IGF2 and IGF2R in the bovine have been found to be significantly associated with pre-natal maternal diet, and the degree of methylation of IGF2 DMR2 to be negatively correlated with IGF2 expression (Paradis et al. 2017, Fitzsimmons et al. 2017). Dissecting the causes and effects of these associations still presents a great challenge, but the possibility of explaining more of the ‘E’ in the classic equation of Phenotype = Genotype (G) + Environment (E) + G X E is a fascinating and exciting opportunity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".