Fads2 knockout mice reveal that ALA prevention of hepatic steatosis is dependent on delta-6 desaturase activity
Bibliographic record
Abstract
The production of the omega-3 long-chain polyunsaturated fatty acids (n-3 LCPUFA) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) from alpha-linolenic acid (ALA) relies on the delta-6 desaturase (D6D) enzyme encoded by the Fads2 gene. While EPA and DHA reduce hepatic triacylglycerol (TAG) storage and regulate lipogenesis, the independent impact of ALA is less understood. To address this gap in knowledge, hepatic fatty acid metabolism was investigated in male wild-type (WT) and Fads2 knockout (KO) mice fed diets (16% kcal from fat) containing either lard (no n-3 LCPUFA), flaxseed oil (ALA-rich), or menhaden oil (EPA/DHA rich) for 21 weeks. Fat content and composition, as well as markers of lipogenesis, glyceroneogenesis, and TAG synthesis, were analyzed using histology, gas chromatography, and reverse transcription quantitative PCR (RT-qPCR). Mice fed the menhaden diet had significantly lower hepatic TAG compared to both lard- and flax-fed mice, concomitant with changes in n-3 and n-6 LCPUFA in both TAG and phospholipid (PL) fractions (all P < 0.05). Flax-fed WT mice had lower liver TAG content compared to their KO counterparts. Menhaden-fed mice had significantly lower expression of key lipogenic (Scd1, Srebp-1c, Fasn, Fads1, and Fads2), glyceroneogenic (Pck1), and TAG synthesis (Agpat3) genes compared to lard, with flax-fed mice showing some intermediate effects. Gene expression effects were independent of D6D activity, since no differences were detected between WT and KO mice fed the same diet. This study demonstrates that EPA/DHA and not ALA itself is critical for the prevention of hepatic steatosis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".