Genetic variation in transcriptional regulation of wheat seed starch content and its conversion to bioethanol
Bibliographic record
Abstract
Abstract Starch deposited in the endosperm of cereal seeds serves as a source of food and animal feed, and as a substrate for bioethanol production. To gain insights into the molecular mechanisms underlying genetic variation in seed size and seed starch content, this study investigated transcriptional regulation of starch biosynthesis genes during seed filling in two wheat genotypes that exhibit contrasting phenotype in seed size. Our data showed that variation in starch accumulation during seed filling is closely associated with modulations in the expression patterns of specific starch biosynthesis genes including TaAGPL1, TaAGPS2, TaGBSSI, TaSSI, TaSSIV, TaSBEIIa, TaISA1, and TaISA3, as well as alterations in the activity of AGPase, GBSS, and SS enzymes. Consistently, the genotype that produces larger seeds that are characterized by a higher seed starch content generated higher amounts of fermentable sugars and bioethanol before and after fermentation, respectively. Since the amount of phenol per seed dry weight is higher in the genotype that produces smaller seeds, the prevalence of lower starch to bioethanol conversion efficiency in this genotype, despite the higher amount of glucose generated per bioavailable starch, suggests that the phenolic compounds interfere with the fermentation process and thereby affecting the bioethanol yield.
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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 | 0.000 |
| 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".