Vacuolar cysteine proteases of wheat (Triticum aestivum L.) are common to leaf senescence induced by different factors
Bibliographic record
Abstract
Cellular proteins are extensively degraded during leaf senescence, and this correlates with an up-regulation of protease gene expression, particularly cysteine proteases. The objectives of this work were (i) to detect cysteine proteases associated with senescence of wheat leaves under different conditions and (ii) to find out their subcellular location. Activity labelling of cysteine proteases with the biotinylated inhibitor DCG-04 detected five bands at 27, 36, 39, 42, and 46 kDa in leaves of wheat senescing under continuous darkness. In-gel activity assays showed that these proteases are only active in an acid milieu (pH 4), and their activity increased several-fold in senescing leaves. Fractionation experiments showed that the senescence-associated cysteine proteases of 36, 39, 42, and 46 kDa localize to a vacuolar-enriched fraction. The vacuolar cysteine proteases of 36, 39, and 42 kDa increased in activity in attached flag leaves senescing naturally during post-anthesis, and in attached leaves of plants subjected to a period of water deficit. Thus, the activity of these vacuolar cysteine proteases is associated with developmental (post-anthesis) senescence and with senescence induced by stress factors (i.e. protracted darkness or drought). This suggests that vacuoles are involved in senescence-associated cellular degradation, and that different senescence-inducing factors may converge on a single degradation pathway.
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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".