Time of day shapes Arabidopsis drought transcriptomes
Bibliographic record
Abstract
Under natural conditions, it is common for plants to experience water deprivation (drought) for periods of days or longer. Plants respond to drought stress by reconfiguring their transcriptome activity. Transcriptome changes in response to drought are dynamic, and are shaped by mitigating factors like time during the diurnal cycle. To date, analyses of drought-induced transcriptome remodelling have concentrated on dynamic changes induced by rapid desiccation, or changes at a single time point following gradual water stress. To gain insights into the dynamics of transcriptome reconfiguration in response to gradual drying of the soil, the drought-induced transcriptomes of Arabidopsis thaliana were examined at four time points over a single diel period - midday, late day, midnight, and pre-dawn. Transcriptome reconfigurations were induced by drought in advance of changes to relative water content, leaf water loss, and chlorophyll content. Comparative analyses support the hypothesis that the drought-responsive transcriptomes were shaped by invocation of distinct hormonal and stress response pathways at different times of the day. While a core set of genes were drought responsive at multiple time points throughout the day, the magnitude of the response varied in a manner dependent on the time of day. Moreover, analysis of a single time point would fail to identify suites of drought-responsive genes that can only be detected through assessment of the dynamics of diurnal changes, emphasising the value of characterising multiple time-of-day-specific drought transcriptomes.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".