Substrate preferences of long-chain acyl-CoA synthetase and diacylglycerol acyltransferase contribute to enrichment of flax seed oil with α-linolenic acid
Bibliographic record
Abstract
Seed oil from flax (Linum usitatissimum) is enriched in α-linolenic acid (ALA; 18:3Δ9cis,12cis,15cis), but the biochemical processes underlying the enrichment of flax seed oil with this polyunsaturated fatty acid are not fully elucidated. Here, a potential process involving the catalytic actions of long-chain acyl-CoA synthetase (LACS) and diacylglycerol acyltransferase (DGAT) is proposed for ALA enrichment in triacylglycerol (TAG). LACS catalyzes the ATP-dependent activation of free fatty acid to form acyl-CoA, which in turn may serve as an acyl-donor in the DGAT-catalyzed reaction leading to TAG. To test this hypothesis, flax LACS and DGAT cDNAs were functionally expressed in Saccharomyces cerevisiae strains to probe their possible involvement in the enrichment of TAG with ALA. Among the identified flax LACSs, LuLACS8A exhibited significantly enhanced specificity for ALA over oleic acid (18:1Δ9cis) or linoleic acid (18:2Δ9cis,12cis). Enhanced α-linolenoyl-CoA specificity was also observed in the enzymatic assay of flax DGAT2 (LuDGAT2-3), which displayed ∼20 times increased preference toward α-linolenoyl-CoA over oleoyl-CoA. Moreover, when LuLACS8A and LuDGAT2-3 were co-expressed in yeast, both in vitro and in vivo experiments indicated that the ALA-containing TAG enrichment process was operative between LuLACS8A- and LuDGAT2-3-catalyzed reactions. Overall, the results support the hypothesis that the cooperation between the reactions catalyzed by LACS8 and DGAT2 may represent a route to enrich ALA production in the flax seed oil.
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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".