Effect of CLA and Other C18 Unsaturated Fatty Acids on DGAT in Bovine Milk Fat Biosynthetic Systems
Bibliographic record
Abstract
Production of dairy products with increased amounts of nutraceutic FA such as conjugated linoleic acid (CLA) represents a recent approach for dairy producers and processors to increase the value of their products. The effect of CLA and other FA on the expression of diacylglycerol acyltransferase-1 (DGAT-1) and DGAT-2, and DGAT activity were investigated in bovine mammary gland epithelial (MAC-T) cells. DGAT gene expression analyses were also conducted using bovine mammary gland tissue from dairy cows. In the studies with MAC-T cells, there were no significant effects of CLA isomers or other FA on DGAT1 expression, whereas all FA tested showed enhanced DGAT2 expression (P < 0.05 to P < 0.001), with alpha-linolenic acid (alpha-18:3) having the greatest effect. Additionally, DGAT2 expression was co-ordinated with expression of lysophosphatidic acid acyltransferase (LPAAT), an observation that was also apparent in mammary gland from lactating dairy cows. In contrast, treatment of MAC-T cells with trans-10, cis-12 18:2 or alpha-18:3 resulted in a significant (P < 0.05) decrease in overall DGAT enzyme activity, although the mechanisms resulting in these effects are unclear. Competition assays using microsomes from bovine mammary gland tissue and 1-[(14)C]oleoyl-CoA suggested that DGAT activity was more selective for oleoyl (cis-9 18:1)-CoA than cis-9, trans-11 18:2-, trans-10, cis-12 18:2- or cis-9, cis-12 18:2-CoA. Collectively, the results suggest the relationship between trans-10, cis-12 18:2 and reduced TAG production in bovine milk is not linked to the production of DGAT1 or DGAT2 transcripts, but probably involves effects of this CLA isomer at events beyond transcription, such as post-translational and/or enzyme activity effects.
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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 | 0.001 | 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".