Sorghum root epigenetic landscape during limiting phosphorus conditions
Bibliographic record
Abstract
Abstract Efficient acquisition and use of available phosphorus from the soil is crucial for plant growth, development, and yield. With an ever‐increasing acreage of croplands with suboptimal available soil phosphorus, genetic improvement of sorghum germplasm for enhanced phosphorus acquisition from soil is crucial to increasing agricultural output and reducing inputs, while confronted with a growing world population and uncertain climate. Sorghum bicolor is a globally important commodity for food, fodder, and forage. Known for robust tolerance to heat, drought, and other abiotic stresses, its capacity for optimal phosphorus use efficiency (PUE) is still being investigated for optimized root system architectures (RSA). Whilst a few RSA‐influencing genes have been identified in sorghum and other grasses, the epigenetic impact on expression and tissue‐specific activation of candidate PUE genes remains elusive. Here, we present transcriptomic, epigenetic, and regulatory network profiling of RSA modulation in the BTx623 sorghum background in response to limiting phosphorus (LP) conditions. We show that during LP, sorghum RSA is remodeled to increase root length and surface area, likely enhancing its ability to acquire P. Global DNA 5‐methylcytosine and H3K4 and H3K27 trimethylation levels decrease in response to LP, while H3K4me3 peaks and DNA hypomethylated regions contain recognition motifs of numerous developmental and nutrient responsive transcription factors that display disparate expression patterns between different root tissues (primary root apex, elongation zone, and lateral root apex).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".