Determinants of nucleosome positioning and their influence on plant gene expression
Bibliographic record
Abstract
Nucleosome positioning influences the access of transcription factors (TFs) to their binding sites and gene expression. Studies in plant, animal, and fungal models demonstrate similar nucleosome positioning patterns along genes and correlations between occupancy and expression. However, the relationships among nucleosome positioning, cis-regulatory element accessibility, and gene expression in plants remain undefined. Here we showed that plant nucleosome depletion occurs on specific 6-mer motifs and this sequence-specific nucleosome depletion is predictive of expression levels. Nucleosome-depleted regions in Arabidopsis thaliana tend to have higher G/C content, unlike yeast, and are centered on specific G/C-rich 6-mers, suggesting that intrinsic sequence properties, such as G/C content, cannot fully explain plant nucleosome positioning. These 6-mer motif sites showed higher DNase I hypersensitivity and are flanked by strongly phased nucleosomes, consistent with known TF binding sites. Intriguingly, this 6-mer-specific nucleosome depletion pattern occurs not only in promoter but also in genic regions and is significantly correlated with higher gene expression level, a phenomenon also found in rice but not in yeast. Among the 6-mer motifs enriched in genes responsive to treatment with the defense hormone jasmonate, there are no significant changes in nucleosome occupancy, suggesting that these sites are potentially preconditioned to enable rapid response without changing chromatin state significantly. Our study provides a global assessment of the joint contribution of nucleosome occupancy and motif sequences that are likely cis-elements to the control of gene expression in plants. Our findings pave the way for further understanding the impact of chromatin state on plant transcriptional regulatory circuits.
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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".