Delta-6 desaturase (Fads2) deficiency alters triacylglycerol/fatty acid cycling in murine white adipose tissue
Bibliographic record
Abstract
The Δ-6 desaturase (D6D) enzyme is not only critical for the synthesis of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) from α-linolenic acid (ALA), but recent evidence suggests that it also plays a role in adipocyte lipid metabolism and body weight; however, the mechanisms remain largely unexplored. The goal of this study was to investigate if a D6D deficiency would inhibit triacylglycerol storage and alter lipolytic and lipogenic pathways in mouse white adipose tissue (WAT) depots due to a disruption in EPA and DHA production. Male C57BL/6J D6D knockout (KO) and wild-type (WT) mice were fed either a 7% w/w lard or flax (ALA rich) diet for 21 weeks. Energy expenditure, physical activity, and substrate utilization were measured with metabolic caging. Inguinal and epididymal WAT depots were analyzed for changes in tissue weight, fatty acid composition, adipocyte size, and markers of lipogenesis, lipolysis, and insulin signaling. KO mice had lower body weight, higher serum nonesterified fatty acids, smaller WAT depots, and reduced adipocyte size compared to WT mice without altered food intake, energy expenditure, or physical activity, regardless of the diet. Markers of lipogenesis and lipolysis were more highly expressed in KO mice compared to WT mice in both depots, regardless of the diet. These changes were concomitant with lower basal insulin signaling in WAT. Collectively, a D6D deficiency alters triacylglycerol/fatty acid cycling in WAT by promoting lipolysis and reducing fatty acid re-esterification, which may be partially attributed to a reduction in WAT insulin signaling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".