Variation of DNA methylation patterns associated with gene expression in rice (<i>Oryza sativa</i>) exposed to cadmium
Bibliographic record
Abstract
We report genome-wide single-base resolution maps of methylated cytosines and transcriptome change in Cd-exposed rice. Widespread differences were identified in CG and non-CG methylation marks between Cd-exposed and Cd-free rice genomes. There are 2320 non-redundant differentially methylated regions detected in the genome. RNA sequencing revealed 2092 DNA methylation-modified genes differentially expressed under Cd exposure. More genes were found hypermethylated than those hypomethylated in CG, CHH and CHG (where H is A, C or T) contexts in upstream, gene body and downstream regions. Many of the genes were involved in stress response, metal transport and transcription factors. Most of the DNA methylation-modified genes were transcriptionally altered under Cd stress. A subset of loss of function mutants defective in DNA methylation and histone modification activities was used to identify transcript abundance of selected genes. Compared with wide type, mutation of MET1 and DRM2 resulted in general lower transcript levels of the genes under Cd stress. Transcripts of OsIRO2, OsPR1b and Os09g02214 in drm2 were significantly reduced. A commonly used DNA methylation inhibitor 5-azacytidine was employed to investigate whether DNA demethylation affected physiological consequences. 5-azacytidine provision decreased general DNA methylation levels of selected genes, but promoted growth of rice seedlings and Cd accumulation in rice plant.
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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".