Turnover of brain DHA in mice is accurately determined by tracer-free natural abundance carbon isotope ratio analysis
Bibliographic record
Abstract
The brain is highly enriched in the long-chain omega-3 (n-3) PUFA DHA. Due to the limited capacity for local DHA synthesis in the brain, it relies on a continual supply from the circulation to replenish metabolized DHA. Previous studies investigating brain DHA turnover and metabolism have relied on isotope tracers to determine brain fatty acid kinetics; however, this approach is cumbersome and costly. We applied natural abundance carbon isotope ratio analysis via high-precision gas chromatography combustion isotope ratio mass spectrometry, without the use of labeled tracers, to determine the half-life of brain DHA in mice following a dietary switch experiment. Mice fed diets containing either α-linolenic acid (ALA) or DHA as the sole dietary n-3 PUFA were switched onto diets containing ALA, DHA, or ALA + DHA at 6 weeks of age, while control mice were maintained on their respective background diet. We measured brain DHA carbon isotope ratios (reported as δ 13 C DHA signatures) over a 168-day time course. Brain δ 13 C DHA signatures of control mice maintained on background diets over the time course were stable ( P > 0.05). Brain δ 13 C DHA signatures of mice switched to the DHA or ALA + DHA diet from the ALA diet changed over time, yielding brain incorporation half-lives of 40 and 34 days, respectively. These half-lives determined by natural abundance carbon isotope ratio analysis were consistent with estimates from kinetic isotope tracer studies. Our results demonstrate the feasibility of natural abundance carbon isotope ratio analysis in the study of fatty acid metabolism without the use of isotopically labeled fatty acid tracers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".