GAS2 encodes a 2-oxoglutarate dependent dioxygenase involved in ABA catabolism
Bibliographic record
Abstract
Liu et al. 1 recently reported the characterisation of Arabidopsis thaliana GAS2 (Gain of Function in ABA-modulated Seed Germination 2), which was described as an enzyme catalysing the stereospecific hydration of GA 12 to produce GA 12 16, 17-dihydro-16α-ol (DHGA 12 ). A second paper describes the conversion of GA 12 to an unidentified product by GAS2 and reports that this enzyme does not convert ABA 2 . As previously reported 3 , we did not find any conversion of [17- 14 C]-labelled or [1-,7-,12-,18- 14 C 4 ]-labelled GA 12 by GAS2. Instead, we present here data showing that the recombinant GAS2 enzyme catabolizes abscisic acid (ABA) to phaseic acid (PA) and further to a second product, putative 8’-carboxy-ABA (compound A; Fig. 1a ). Fig. 1: GAS2 is an ABA catabolising oxidase. a The proposed ABA catabolic pathway catalysed by GAS2. b Metabolism of 3’,5’,5’,7’,7’,7’- d 6 -ABA incubated with 70 µL cell lysate containing recombinant ATGA20ox1 and cofactors (negative control, top lane). Metabolism of d 6 -ABA incubated with indicated volumes of cell lysates containing recombinant GAS2 and cofactors as described in Methods. Chromatograms of characteristic single ions are shown in the first row for ABA (extracted ion m/z 194.2), in the middle row for PA ( m/z 125.2), and in the right row for compound A ( m/z 194.2). Compounds that were identified on the basis of their full scan mass spectra of the methyl ester derivatives are shown in red. Similar results were obtained with two recombinant GAS2 preparations, produced from independent isolated E.coli clones, each were incubated at seven different lysate concentrations. Full size image
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".