An ABA‐regulated and Golgi‐localized protein phosphatase controls water loss during leaf senescence in Arabidopsis
Bibliographic record
Abstract
It is known that a senescing leaf loses water faster than a non-senescing leaf and that ABA has an important role in promoting leaf senescence. However, questions such as why water loss is faster, how water loss is regulated, and how ABA functions in leaf senescence are not well understood. Here we report on the identification and functional analysis of a leaf senescence associated gene called SAG113. The RNA blot and GUS reporter analyses all show that SAG113 is expressed in senescing leaves and is induced by ABA in Arabidopsis. The SAG113 expression levels are significantly reduced in aba2 and abi4 mutants. A GFP fusion protein analysis revealed that SAG113 protein is localized in the Golgi apparatus. SAG113 encodes a protein phosphatase that belongs to the PP2C family and is able to functionally complement a yeast PP2C-deficient mutant TM126 (ptc1Δ). Leaf senescence is delayed in the SAG113 knockout mutant compared with that in the wild type, stomatal movement in the senescing leaves of SAG113 knockouts is more sensitive to ABA than that of the wild type, and the rate of water loss in senescing leaves of SAG113 knockouts is significantly reduced. In contrast, inducible over-expression of SAG113 results in a lower sensitivity of stomatal movement to ABA treatment, more rapid water loss, and precocious leaf senescence. No other aspects of growth and development, including seed germination, were observed. These findings suggest that SAG113, a negative regulator of ABA signal transduction, is specifically involved in the control of water loss during leaf senescence.
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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 | 0.000 |
| 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) | 0.000 | 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".