Investigating the Role of Liver ELOVL2 on Sex Specific Differences in Docosahexaenoic Acid (DHA) Metabolism
Bibliographic record
Abstract
The omega-3 (n-3) polyunsaturated fatty acids (PUFA), eicosapentaenoic acid (EPA,20:5n-3) and docosahexaenoic acid (DHA, 22:6n-3), are involved in the regulation of numerous physiological functions and play important roles for brain and cardiovascular function and health. Sex differences in DHA status and metabolism are believed to be hormonally regulated, primarily via the action of estrogen. Elongation of very long-chain fatty acids-like 2 (ELOVL2), which is a DHA biosynthesis gene and a genomic target of estrogen, may play an important role in regulating sex differences in DHA status and metabolism. However, the role of ELOVL2 in the development of sex differences in DHA synthesis and metabolism remains to be fully elucidated. The research carried out in this thesis used a liver specific-Elovl2 knockout (KO) model to investigate how the in vivo hepatic activity of ELOVL2 influences sex differences in DHA status and synthesis rates in response to EPA supplementation. Using a dietary switch model, male and female liver specific-Elovl2 KO and control mice were equilibrated to low carbon-13 abundance EPA (δ13C-EPA) diet for 5 weeks followed by switching to high δ13C-EPA diet. Following diet switch, changes in tissue/serum levels and δ13C of DPAn-3 and DHA were measured over time. The δ13C of DPAn-3 and DHA were determined by compound specific isotope analysis (CSIA) and modeled by one phase exponential decay to calculate DPAn-3 and DHA synthesis/turnover rates and half lives in male and female KO and control mice. Liver specific Elovl2 KO led to a significant reduction in liver and serum DHA levels and liver DHA synthesis/turnover rates in a sex dependent manner, indicating that hepatic ELOVL2 activity is necessary for regulating sex differences in DHA status and metabolism.
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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 | 0.001 |
| 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".