Identification of α-Tomatine 23-Hydroxylase Involved in the Detoxification of a Bitter Glycoalkaloid
Bibliographic record
Abstract
Tomato plants (Solanum lycopersicum) contain steroidal glycoalkaloid α-tomatine, which functions as a chemical barrier to pathogens and predators. α-Tomatine accumulates in all tissues and at particularly high levels in leaves and immature green fruits. The compound is toxic and causes a bitter taste, but its presence decreases through metabolic conversion to nontoxic esculeoside A during fruit ripening. This study identifies the gene encoding a 23-hydroxylase of α-tomatine, which is a key to this process. Some 2-oxoglutarate-dependent dioxygenases were selected as candidates for the metabolic enzyme, and Solyc02g062460, designated Sl23DOX, was found to encode α-tomatine 23-hydroxylase. Biochemical analysis of the recombinant Sl23DOX protein demonstrated that it catalyzes the 23-hydroxylation of α-tomatine and the product spontaneously isomerizes to neorickiioside B, which is an intermediate in α-tomatine metabolism that appears during ripening. Leaves of transgenic tomato plants overexpressing Sl23DOX accumulated not only neorickiioside B but also another intermediate, lycoperoside C (23-O-acetylated neorickiioside B). Furthermore, the ripe fruits of Sl23DOX-silenced transgenic tomato plants contained lower levels of esculeoside A but substantially accumulated α-tomatine. Thus, Sl23DOX functions as α-tomatine 23-hydroxylase during the metabolic processing of toxic α-tomatine in tomato fruit ripening and is a key enzyme in the domestication of cultivated tomatoes.
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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".