The role of glutamine synthetase isozymes in enhancing nitrogen use efficiency of N-efficient winter wheat
Bibliographic record
Abstract
Glutamine synthetase (GS) isozymes play critical roles in nitrogen (N) metabolism. However, the exact relationship between GS and nitrogen use efficiency (NUE) remain unclear. We have selected and compared two wheat cultivars, YM49 and XN509, which were identified as the N-efficient and N-inefficient genotypes, respectively. In this study, agronomical, morphological, physiological and biochemical approaches were performed. The results showed that TaGS1 was high expressed post-anthesis, and TaGS2 was highly expressed pre-anthesis in N-efficient genotype compared to N-inefficient genotype. GS1 and GS2 isozymes were also separated by native-PAGE and found that the spatial and temporal distribution of GS isozymes, their expression of gene and protein subunits in source-sink-flow organs during development periods triggered the pool strength and influenced the N flow. According to the physiological role of GS isozymes, we illustrated four metabolic regulation points, by which acting collaboratively in different organs, accelerating the transport of nutrients to the grain. It suggested that the regulation of GS isozymes may promote flow strength and enhance NUE by a complex C-N metabolic mechanism. The relative activity or amount of GS1 and GS2 isozymes could be a potential marker to predict and select wheat genotypes with enhanced NUE.
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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) | 0.000 | 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 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".