The elucidation of stress memory inheritance in Brassica rapa plants
Bibliographic record
Abstract
Plants are able to maintain the memory of stress exposure throughout their ontogenesis and faithfully propagate it into the next generation. Recent evidence argues for the epigenetic nature of this phenomenon. Small RNAs (smRNAs) are one of the vital epigenetic factors because they can both affect gene expression at the place of their generation and maintain non-cell-autonomous gene regulation. Here, we have made an attempt to decipher the contribution of smRNAs to the heat-shock-induced transgenerational inheritance in Brassica rapa plants using sequencing technology. To do this, we have generated comprehensive profiles of a transcriptome and a small RNAome (smRNAome) from somatic and reproductive tissues of stressed plants and their untreated progeny. We have demonstrated that the highest tissue-specific alterations in the transcriptome and smRNAome profile are detected in tissues that were not directly exposed to stress, namely, in the endosperm and pollen. Importantly, we have revealed that the progeny of stressed plants exhibit the highest fluctuations at the smRNAome level but not at the transcriptome level. Additionally, we have uncovered the existence of heat-inducible and transgenerationally transmitted tRNA-derived small RNA fragments in plants. Finally, we suggest that miR168 and braAGO1 are involved in the stress-induced transgenerational inheritance 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) | 0.000 | 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".