MsSPL9 Modulates Nodulation under Nitrate Sufficiency Condition in Medicago sativa
Bibliographic record
Abstract
Nodulation in Leguminous spp. is induced by common environmental cues, such as low nitrogen availability conditions, in the presence of the specific Rhizobium spp. in the rhizosphere. Medicago sativa (alfalfa) is an important nitrogen-fixing forage crop that is widely cultivated around the world and relied upon as a staple source of forage in livestock feed. Although alfalfa’s relationship with these bacteria is one of the most efficient between rhizobia and legume plants, breeding for nitrogen-related traits in this crop has received little attention. In this report, we investigate the role of Squamosa-Promoter Binding Protein-Like 9 (SPL9), a target of miR156, in nodulation in alfalfa. Transgenic alfalfa plants with SPL9-silenced (SPL9-RNAi) and overexpressed (35S::SPL9) were compared to wild-type (WT) alfalfa for phenotypic changes in nodulation in the presence and absence of nitrogen. Phenotypic analyses showed that silencing of MsSPL9 in alfalfa caused an increase in the number of nodules. Moreover, the characterization of phenotypic and molecular parameters revealed that MsSPL9 regulates nodulation under a high concentration of nitrate (10 mM KNO3) by regulating the transcription levels of the nitrate-responsive genes Nitrate Reductase1 (NR1), NR2, Nitrate transporter 2.5 (NRT2.5), and a shoot-controlled autoregulation of nodulation (AON) gene, Super numeric nodules (SUNN). While MsSPL9–overexpressing transgenic plants have dramatically increased transcript levels of SUNN, NR1, NR2, and NRT2.5, reducing MsSPL9 caused downregulation of these genes and displayed a nitrogen-starved phenotype, as downregulation of the MsSPL9 transcript levels caused a nitrate-tolerant nodulation phenotype. Taken together, our results suggest that MsSPL9 regulates nodulation in alfalfa in response to nitrate.
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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".