Isolation and characterization of a gene encoding a drought-induced cysteine protease in tomato (<i>Lycopersicon esculentum</i>)
Bibliographic record
Abstract
In a previous study, a 65 kDa protein, TDI-65, was found to be accumulated in the leaves of drought-stressed tomato (Lycopersicon esculentum cv. Starfire) plants. The protein level returns to control level when the drought-stressed plants are rewatered. Antibodies raised against the purified protein were used to elucidate the subcellular localization of the protein. The protein was found to be mainly localized in the nuclei and chloroplasts of drought-stressed leaf cells. To identify the nature of the protein, a cDNA library was constructed and screened by the purified anti-TDI-65 antibody. A cDNA clone designated tdi-65 was isolated and characterized. The deduced amino acid sequences of tdi-65 protein has extensive homology with known cysteine proteases such as actinidin and papain. Northern blot analysis revealed that tdi-65 mRNA is 10-fold higher in drought-stressed plants as compared to control and rewatered plants. Similar results were observed in the tomato cultivar Ailsa and its near isogenic abscisic acid (ABA)-deficient mutant line, flacca, suggesting that the gene does not require ABA for its expression under drought conditions. Based on the previous immunolocalization findings we suggest that tdi-65 encoded cysteine protease functions in relation to drought-induced senescence and programmed cell death.
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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) | 0.000 | 0.001 |
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".