Generation of <i>Chlorella vulgaris</i> starch mutants and their biomass and lipid productivities under different culture media
Bibliographic record
Abstract
ABSTRACT Background Microalgae are an important feedstock for the production of a wide variety of products, including biodiesel. Biodiesel, composed of fatty acid alkyl esters, is produced through the transesterification reaction of triacylglycerol (TAG). Microalgae store their energy reserves primarily as starch and TAGs. Therefore, several studies have focused on understanding the partitioning of carbon precursors between starch and TAG biosynthetic pathways. In this study, 5 starch mutants of Chlorella vulgaris were developed and cultured on different culture media. Results Chlorella vulgaris starch mutants were generated through UV-random mutagenesis. Five starch mutants were selected for this study: four low-starch producing mutants (st27, st29, st43 and st54) and one high-starch producing mutant (st80). The starch mutants were cultured on media with different organic carbon sources, and lipid and biomass productivity were measured. Mixotrophic growth on glucose resulted in the highest lipid productivity in all the mutants, including st80, without compromising growth, whereas photoautotrophic growth generally did not result in changes in lipid productivity of the starch mutants. The highest increase in lipid productivity was observed for st27, with a 3.8-fold higher lipid productivity than wildtype. Conclusions All starch mutants increased their lipid productivities when grown mixotrophically on glucose, suggesting the overflow hypothesis could explain the partitioning of carbon between starch and TAGs. Out of the mutants generated in this work, st27 resulted in the highest increases in lipid productivities, reaching an increase of 380% when grown mixotrophically on glucose, without compromising growth. The high-starch producing mutant st80 provides insight into a possibility to develop starch- and TAG-rich microalgal biomass.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".