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 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 | 0.000 | 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) | 0.000 | 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".