Stimulation of nodulation in<i>Medicago truncatula</i>by low concentrations of ammonium: quantitative reverse transcription PCR analysis of selected genes
Bibliographic record
Abstract
Although mineral nitrogen generally has negative effects on nodulation in legume-rhizobia symbioses, low concentrations of ammonium stimulate nodulation in some legumes. In this study, the effects of ammonium and nitrate on growth, nodulation and expression of 2 nitrogen transport and 12 putative nodulation-related genes of the model symbiosis of Medicago truncatula - Sinorhizobium meliloti are investigated. After 3 weeks of hydroponic growth, whole-plant nodulation was enhanced in all the ammonium treatments and up to three-fold in the 0.5 mM treatment compared with the zero-nitrogen control. Specific nodulation (nodules g(-1) root dry weight) was greatly stimulated in the 0.1 and 0.5 mM NH4+ treatments, to a lower extent in the 0.1 mM NO3- treatment, and inhibited in all other treatments. Expression of the 14 selected genes was observed at 0, 6, 12 and 24 h after exposure to rhizobia and nitrogen. Expression of nitrogen transporter genes increased significantly, but responses of the three genes putatively associated with symbiosis signaling/nodule initiation were mixed. There were infrequent responses of genes coding for an ABA-activated protein kinase or a gibberellin-regulated protein, but an ethylene-responsive element-binding factor showed increased expression in various treatments and sampling times. Three auxin-responsive genes and three cytokinin-responsive genes showed varied responses to ammonium and nitrate. This study indicates that low concentrations of ammonium stimulate nodulation in M. truncatula, but the data were inconclusive in verifying the hypothesis that a relatively high ratio of cytokinin to auxin in roots may be an underlying mechanism in this stimulation of nodulation.
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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) | 0.001 | 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".