<scp>SlERF</scp>.<scp>H6</scp> mediates the orchestration of ethylene and gibberellin signaling that suppresses <scp>bitter‐SGA</scp> biosynthesis in tomato
Bibliographic record
Abstract
Summary Steroidal glycoalkaloids (SGAs) constitute a characteristic class of antinutritional metabolites that are found in certain Solanum species. Despite the considerable studies on SGA biosynthesis, the mechanisms of crosstalk between hormone signaling pathways that regulate SGA content still remain to be elucidated. Here, we performed a metabolic genome‐wide association study (mGWAS) based on the levels of SGA metabolites and identified SlERF.H6 as a negative regulator of bitter‐SGA biosynthesis. SlERF.H6 repressed the expression of SGA biosynthetic glycoalkaloid metabolism ( GAME ) genes and caused a subsequent decrease in the abundance of bitter SGAs. Furthermore, SlERF.H6 were shown to act downstream of GAME9, a regulator of SGA biosynthesis in tomato. We also uncovered the interplay between ethylene and gibberellin (GA) signaling in regulating SGA biosynthesis. SlERF.H6, acting as a downstream component in ethylene signaling, modulated GA content by inhibiting SlGA2ox12 expression. Increasing levels of endogenous GA 12 and GA 53 in SlERF.H6‐OE could inhibit of GA on SGA biosynthesis. Additionally, 1‐aminocyclopropane‐1‐carboxylic acid (ACC) treatment decreased the stability of SlERF.H6, weakening its inhibition on GAME genes and SlGA2ox12 , and caused bitter‐SGA accumulation. Our findings reveal a key role of SlERF.H6 in the regulation of SGA biosynthesis through the coordinated ethylene‐gibberellin signaling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".