Manipulation of Environmental Stress Towards Lutein Production in Chlorella fusca Cell Culture
Bibliographic record
Abstract
All carotenoids originate from a single, common precursor, phytoene. The colour of carotenoids is determinedby desaturation, isomerization, cyclization, hydroxylation and epoxidation of the 40-carbon phytoene. The conjugated double-bond structure and nature of end ring groups confer on the carotenoids properties such as colour and antioxidant activity. Algae may become major sources of carotenoids but the extent of environmental stress and genetic influences on algae carotenoid biosynthesis are poorly understood. Carotenoid biosynthesis can be influenced by many aspects and is liable to geometric isomerization with the existence of oxygen, light and heat which affect the colour degradation and oxidation. Therefore, in this study carotenoid biogenesis is investigated in cell culture of Chlorella fusca as a potential model system for rapid initiation, and extraction of carotenoids by providing stringent control of genetic, developmental and environmental factors. The value of this experimental system for investigating key factors controlling the carotenoid accumulation is then tested by assessing the effects of environmental variables, such as drought stress, light intensity, nutrient strength and media formulation on carotenoid accumulation. Our findings revealed that the conversion of violaxanthin to lutein is due to irradiance stress condition, nutrient strength as well as drought stress. As a result, manipulation of environmental variables will up-regulate lutein concentration. This reaction will restrict the supply of precursors for ABA biosynthesis and the algae cell culture responds by increasing carotenogenic metabolic flux to compensate for this restriction. In conclusion, selecting the appropriate algae species for the appropriate environmental conditions is not only important for yield production, but also for nutritional value quality of carotenoid.
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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) | 0.001 | 0.000 |
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".