DNA methylation dynamics of sperm cell lineage development in tomato
Bibliographic record
Abstract
During the sexual reproduction of higher plants, DNA methylation and transcription are broadly changed to reshape a microspore into two sperm cells (SCs) and a vegetative cell (VC). However, when and how the DNA methylation of SCs is established remains not fully understood. Here we investigate the DNA methylation (5 mC) dynamics of SC lineage and the VC in tomato using whole-genome bisulfite sequencing. We find the asymmetric division of the microspore gives its two daughter cells differential methylome. Compared with the generative cell (GC), the VC is hypomethylated at CG sites while hypermethylated at CHG and CHH sites, with the majority of differentially methylation regions targeted to transposable elements (TEs). SCs have a nearly identical DNA methylome to the GC, suggesting that the methylation landscape in SCs may be pre-established following the asymmetric division or inherited from the GC. The random forest classifier for predicting gene and TE expression shows that methylation within the gene body is a more powerful predictor for gene expression. Among all tested samples, gene and TE expression in the microspore may be more predictable by DNA methylation. Our results depict an intact DNA methylome landscape of SC lineage in higher plants, and reveal that the impact of DNA methylation on transcription is variant in different cell types.
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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".