The <scp>miR396b</scp>‐<scp>MsGRF1c</scp> module positively associates with alfalfa nodule senescence
Bibliographic record
Abstract
The miR396-GRF module plays a vital role in the growth and development of plant organs. However, its function in the development of leguminous nodules remains unclear. Here, we observed significant upregulation of miR396b in alfalfa (Medicago sativa L.) senescent nodules, especially in the upper part of the nitrogen fixation region. Transgenic lines overexpressing or sequestering miR396b were generated to examine its role in nodule senescence. Here, we report that miR396b acts as a positive regulator in nodule senescence by affecting the accumulation of reactive oxygen species (ROS) in different regions of a nodule. Among the 10 miR396b-targeted MsGRFs, MsGRF1c showed the most significant transcriptional suppression by miR396b in nodules. By overexpressing rMsGRF1c (a synonymous mutation of MsGRF1c resistant to miR396b cleavage) and suppression of MsGRF1c activity via MsGRF1c-SRDX overexpression in alfalfa, we confirmed that MsGRF1c delayed nodule senescence by regulating ROS distribution and promoted alfalfa above-ground biomass yield after sinorhizobium inoculation. A glutamine synthetase gene (MsGS2), which showed differentially expressed in the RNA sequencing data, was markedly upregulated in the nodule senescent region and repressed by MsGRF1c. Then, MsGS2 was experimentally validated as a direct transcriptional target of MsGRF1c. Transient interference of MsGS2 expression in nodules via antisense oligodeoxynucleotide treatment proved its essential role in regulating ROS distribution and nitrogen fixation efficiency in alfalfa nodules. Our results shed light on the miR396b-MsGRF1c-MsGS2 pathway that plays a vital role in regulating alfalfa nodule senescence by affecting ROS distribution, and we propose an alternative way to create new alfalfa germplasm with enhanced nitrogen fixation capacity and biomass yield.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".