Adaptive epigenetic memory of ancestral temperature regime in <i>Arabidopsis thaliana</i> This paper is one of a selection of papers published in a Special Issue from the National Research Council of Canada – Plant Biotechnology Institute.
Bibliographic record
Abstract
Although certain acquired nongenetic (i.e., epigenetic) traits are known to be heritable in plants, little is known currently about whether environmental parameters can induce adaptive epigenetic responses in plants and whether such effects can persist through generations. We used an experimental design based on classical genetics principles to assess whether plants respond to the environmental conditions of their ancestors in an adaptive epigenetic manner. An extensive examination of genetically identical Arabidopsis thaliana (L.) Heynh lines exposed to mild heat (30 °C) or cold (16 °C) treatments in the parental and F 1 generations revealed that the prior elevated temperature regime lead to a greater than fivefold improvement in fitness (seed production per individual) for plants exposed to heat in a later generation (F 3 ). The heat-specific fitness improvements among F 3 plants were observed even though the heat-treated parental and F 1 generations were followed by a generation grown at a normal temperature (F 2 ) and point towards a temperature-induced adaptive epigenetic phenomenon. No such adaptive responses were detected for cold-treated plants, indicating that there are distinctive biological processes inherent to these two temperature regimes. Overall, the data are consistent with the existence of an environmentally induced epigenetic and heritable adaptive response in plants.
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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".