Bibliographic record
Abstract
Nodulation in legumes is a highly regulated symbiotic process crucial for biological nitrogen fixation, which is crucial for sustainable agriculture. This study investigates how epigenetic modifications-specifically DNA methylation, histone modifications, and small RNAs-coordinate gene expression programs during distinct stages of nodule development. We first investigated the epigenetic landscape of legume root cells, highlighting the dynamic changes in methylation and the histone code during nodule formation. We then delved into the role of epigenetic mechanisms in early symbiotic signaling events, such as nodulation factor recognition, root hair coiling, and infection filament formation, and analyzed chromatin remodeling during cortical cell reprogramming and nodule organogenesis. We further investigated how environmental conditions such as nutrient availability and abiotic stress influence these epigenetic responses and assessed the transgenerational inheritance of nodulation traits. A detailed case study in Medicago truncatula , utilizing mutants and whole-genome analyses, elucidated the functional importance of specific epigenetic marks during nodule formation. Finally, we explore the translational potential of manipulating epigenetic regulators through genome editing and breeding to enhance the legume-rhizobium symbiosis. This study highlights the importance of integrating epigenomics with functional and systems biology to explore new strategies to improve nitrogen fixation efficiency in legumes.
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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".