EPA and DHA in microalgae: Health benefits, biosynthesis, and metabolic engineering advances
Bibliographic record
Abstract
Abstract Eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are ω ‐3 very long‐chain polyunsaturated fatty acids (VLC‐PUFAs) that offer a wide range of human health benefits impacting cardiovascular, anti‐inflammatory, and neurological health. It is widely known that humans inefficiently synthesize these compounds and as such rely on exogenous dietary sources, such as marine fish oils. Unfortunately, the production of marine fish oils is an unsustainable process and has suffered a dramatic fall in recent years due to overfishing and climate change, as the demand for EPA and DHA continues to rise. Therefore, there is an urgent need to develop alternative, sustainable sources for consumable EPA and DHA. Metabolic engineering of marine microalgae to improve their EPA and DHA productivity is regarded as a promising option that has received increasing commercial attention in recent years. In this mini‐review, we describe several notable health benefits of EPA and DHA, summarize the natural sources and biosynthesis of VLC‐PUFAS, as well as the recent advances in metabolic engineering of EPA and DHA production in representative microalgal and protist species, including Schizochytrium sp., Phaeodactylum tricornutum , and Nannochloropsis oceanica .
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".