Early Gestational Hepatic Lipidomic Profiles Are Modulated by One-Carbon Metabolite Supplementation and Nutrient Restriction in Beef Heifers and Fetuses
Bibliographic record
Abstract
Background: Maternal nutrition during early gestation induces metabolic adaptations that support maternal health and fetal development. This study evaluated the effects of maternal one-carbon metabolite (OCM: methionine, choline, folate, and vitamin B12) supplementation and restricted rates of maternal gain on the hepatic lipid profiles of dams and fetuses at day 63 of gestation. Methods: Thirty-one crossbred Angus heifers were inseminated and assigned to a 2 × 2 factorial design with two factors: maternal dietary intake (control [CON]; 0.60 kg/day average daily gain [ADG] vs. restricted [RES]; −0.23 kg/day ADG) and OCM supplementation (supplemented [+OCM] vs. not supplemented [−OCM]). The four resulting groups (CON − OCM, CON + OCM, RES − OCM, RES + OCM) were maintained for 63 days post-breeding. Maternal and fetal liver samples were collected, and lipidomic profiling was performed using ultra-performance liquid chromatography–tandem mass-spectrometry. Results: In maternal liver, 485 lipid metabolites were detected, with 243 differing significantly in maternal gain. RES heifers showed increased levels (p ≤ 0.05) of acylcarnitines, plasmalogens, lysoplasmalogens, glycosphingolipids, and sphingomyelins. Additionally, RES combined with OCM supplementation led to the accumulation of secondary bile acids and a depletion of monoacylglycerols (p ≤ 0.05) in maternal liver. In fetal liver, 487 lipid metabolites were detected, but treatment effects were minimal. Conclusions: Maternal rate of gain significantly influenced hepatic lipid metabolism in the maternal liver, while fetal liver lipid profiles remained relatively unaffected. These findings underscore the significant role of dietary intake/rate of gain compared with OCM supplementation in modulating hepatic lipid metabolism and highlight the maternal liver’s metabolic adaptations during early pregnancy.
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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".