Glucose enhances indolic glucosinolate biosynthesis without reducing primary sulfur assimilation
Bibliographic record
Abstract
The effect of glucose as a signaling molecule on induction of aliphatic glucosinolate biosynthesis was reported in our former study. Here, we further investigated the regulatory mechanism of indolic glucosinolate biosynthesis by glucose in Arabidopsis. Glucose exerted a positive influence on indolic glucosinolate biosynthesis, which was demonstrated by induced accumulation of indolic glucosinolates and enhanced expression of related genes upon glucose treatment. Genetic analysis revealed that MYB34 and MYB51 were crucial in maintaining the basal indolic glucosinolate accumulation, with MYB34 being pivotal in response to glucose signaling. The increased accumulation of indolic glucosinolates and mRNA levels of MYB34, MYB51, and MYB122 caused by glucose were inhibited in the gin2-1 mutant, suggesting an important role of HXK1 in glucose-mediated induction of indolic glucosinolate biosynthesis. In contrast to what was known on the function of ABI5 in glucose-mediated aliphatic glucosinolate biosynthesis, ABI5 was not required for glucose-induced indolic glucosinolate accumulation. In addition, our results also indicated that glucose-induced glucosinolate accumulation was due to enhanced sulfur assimilation instead of directed sulfur partitioning into glucosinolate biosynthesis. Thus, our data provide new insights into molecular mechanisms underlying glucose-regulated glucosinolate biosynthesis.
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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".