The Na<sup>+</sup>/Ca<sup>2+</sup> antiporter slr0681 affects carotenoid production in <scp><i>Synechocystis</i> sp. PCC</scp> 6803 under high‐light stress
Bibliographic record
Abstract
BACKGROUND: Carotenoids play key roles in photosynthesis and are widely used in foods as natural pigments, antioxidants, and health-promoting compounds. Enhancing carotenoid production in microalgae via biotechnology has become an important area of research. RESULTS: )-free medium, the cell density of Δslr0681 decreased by 29% compared to the wild type (WT). After 8 days of HL treatment, the total carotenoid contents decreased by 35% in Δslr0681, and the contents of individual carotenoids were altered: myxoxanthophyll, echinenone, and β-carotene contents increased by 10%, 50%, and 40%, respectively, while zeaxanthin contents decreased by ~40% in Δslr0681 versus the WT. The expression patterns of carotenoid metabolic pathway genes also differed: ipi expression increased by 1.2- to 8.5-fold, whereas crtO and crtR expression decreased by ~90% and 60%, respectively, in ∆slr0681 versus the WT. In addition, in ∆slr0681, the expression level of psaB (encoding a photosystem I structural protein) doubled, whereas the expression levels of the photosystem II genes psbA2 and psbD decreased by ~53% and 84%, respectively, compared to the WT. CONCLUSION: These findings suggest that slr0681 plays important roles in regulating carotenoid biosynthesis and structuring of the photosystems in Synechocystis sp. This study provides a theoretical basis for the genetic engineering of microalgae photosystems to increase their economic benefits and lays the foundation for developing microalgae germplasm resources with high carotenoid contents. © 2023 Society of Chemical Industry.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".