Proteomic, metabolomic and lipidomic approaches to unravel the roles of polyunsaturated fatty acid nutrition in the developing liver
Bibliographic record
Abstract
Polyunsaturated fatty acids (PUFA) regulate metabolism in adult liver; PUFA accumulation in fetal and infant liver depends on diet. We used combined proteomics, metabolomics and lipidomics to address whether PUFA nutrition impacts metabolic development in neonatal rat liver. Rats were fed as % energy, 3.9% 18:2n‐6 and 1.5% 18:3n‐3 (high PUFA), or 1 % 18:2n‐6 and <0.1% 18:3n‐3 (low PUFA) in gestation and lactation. On day 3 postnatal, lipidomics using HPLC and GLC showed lower n‐3/n‐6 PUFA, but no difference in lipid classes in low compared to high PUFA offspring. Liver proteins were resolved on 2D gels, resolving over 800 proteins. PDQuest analysis showed 24 proteins up‐regulated and 1 down‐regulated over 3 fold in the high compared to low PUFA group, and these were identified by MALDI‐TOF MS. Up‐regulated proteins included F‐1,6‐biphosphatase 1, G‐3‐P dehydrogenase, galactokinase 1, catalase and 60 kDa heat shock protein, with argininosuccinate synthase down‐regulated. GC‐MS profiling of small molecules in liver extracts, with principal component analysis to address changes in flux through metabolic pathways showed higher gluconeogenic amino acids in the high PUFA group. Finally, integration of results from the proteomic and metabolomic analyses into metabolic pathways shows that maternal lipid nutrition impacts pathways of gluconeogenesis and oxidative stress in developing liver. Funded by CIHR Grant Funding Source Michael Smith Fdn Health Research Studentship
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".