Comparative transcriptome analysis reveals significant differences in gene expression and signalling pathways between developmental and dark/starvation‐induced senescence in Arabidopsis
Bibliographic record
Abstract
An analysis of changes in global gene expression patterns during developmental leaf senescence in Arabidopsis has identified more than 800 genes that show a reproducible increase in transcript abundance. This extensive change illustrates the dramatic alterations in cell metabolism that underpin the developmental transition from a photosynthetically active leaf to a senescing organ which functions as a source of mobilizable nutrients. Comparison of changes in gene expression patterns during natural leaf senescence with those identified, when senescence is artificially induced in leaves induced to senesce by darkness or during sucrose starvation-induced senescence in cell suspension cultures, has shown not only similarities but also considerable differences. The data suggest that alternative pathways for essential metabolic processes such as nitrogen mobilization are used in different senescent systems. Gene expression patterns in the senescent cell suspension cultures are more similar to those for dark-induced senescence and this may be a consequence of sugar starvation in both tissues. Gene expression analysis in senescing leaves of plant lines defective in signalling pathways involving salicylic acid (SA), jasmonic acid (JA) and ethylene has shown that these three pathways are all required for expression of many genes during developmental senescence. The JA/ethylene pathways also appear to operate in regulating gene expression in dark-induced and cell suspension senescence whereas the SA pathway is not involved. The importance of the SA pathway in the senescence process is illustrated by the discovery that developmental leaf senescence, but not dark-induced senescence, is delayed in plants defective in the SA 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".