Transcriptional memories mediate the plasticity of cold stress responses to enable morphological acclimation in <i>Brachypodium distachyon</i>
Bibliographic record
Abstract
Plants that successfully acclimate to stress can resume growth under stressful conditions. The grass Brachypodium distachyon can grow a cold-adaptive morphology during cold acclimation. Studies on transcriptional memory (TM) have revealed that plants can be primed for stress by adjusting their transcriptional responses, but the function of TM in stress acclimation is not well understood. We investigated the function of TM during cold acclimation in B. distachyon. Quantitative polymerase chain reaction (qPCR), RNA-seq and chromatin immunoprecipitation qPCR analyses were performed on plants exposed to repeated episodes of cold to characterize the presence and stability of TM during the stress and growth responses of cold acclimation. Transcriptional memory mainly dampened stress responses as growth resumed and as B. distachyon became habituated to cold stress. Although permanent on vernalization gene VRN1, TMs were short-term and reversible on cold-stress genes. Growing under cold conditions also coincided with the acquisition of new and targeted cold-induced transcriptional responses. Overall, TM provided plasticity to cold stress responses during cold acclimation in B. distachyon, leading to stress habituation, acquired stress responses, and resumed growth. Our study shows that chromatin-associated TMs are involved in tuning plant responses to environmental change and, as such, regulate both stress and developmental components that characterize cold-climate adaptation in B. distachyon.
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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) | 0.002 | 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".