Photoheterotroph improved the growth and nutrient levels of Chlorella vulgaris and the related molecular mechanism
Bibliographic record
Abstract
Microalgae are rich in fatty acids, proteins, and other nutrients, which have gained the general attention of researchers all over the world. For the development of Chlorella vulgaris in food and feed industry, this study was conducted to investigate the differences in C. vulgaris' growth and nutritional components under different culture conditions (autotrophic, heterotrophic, photoheterotrophic) and the internal factors through cell counting in combination with transcriptome and nutrient analyses. The results showed that, under the photoheterotrophic condition, Chlorella's growth and the contents of lipid and protein were significantly higher than that under the heterotrophic condition, and the moisture content was lower than that under the heterotrophic condition. The saturated fatty acid content under the photoheterotrophic condition was the lowest, while the polyunsaturated fatty acid content was significantly higher than those under the other two conditions. There were 46,583 differentially expressed genes (DEGs), including 33,039 up-regulated DEGs (70.93%) and 13,544 down-regulated DEGs (29.07%), under the photoheterotrophic condition in comparison with the autotrophic condition. The fold change between the two conditions of samples of up-regulated genes was higher than that of the down-regulated genes. The KEGG enrichment showed that the up-regulated DEGs in the photoheterotrophic condition were significantly enriched in 5 pathways, including protein processing in endoplasmic reticulum pathway, photosynthesis pathway, photosynthesis-antenna protein pathway, endocytosis pathway, and phosphonate and phosphinate metabolism pathway. DEGs related to fatty acid metabolic pathways were significantly enriched in the fatty acid biosynthesis pathway and the biosynthesis of unsaturated fatty acid pathway. The qPCR analysis showed that the expression pattern of the selected genes was consistent with that of transcriptome analysis. The results of this study lay a theoretical foundation for the large-scale production of Chlorella and its application in food, feed, and biodiesel. KEY POINTS: • Nutrient levels under photoheterotrophic condition were higher than other conditions. • Six important pathways were discovered that affect changes in nutritional composition. • Explored genes encode important enzymes in the differential metabolic pathways.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".