Regulation of High-Affinity Nitrate Transporter Genes and High-Affinity Nitrate Influx by Nitrogen Pools in Roots of Barley
Bibliographic record
Abstract
To investigate the regulation of HvNRT2, genes that encode high-affinity NO(3)(-) transporters in barley (Hordeum vulgare) roots, seedlings were treated with 10 mM NO(3)(-) in the presence or absence of amino acids (aspartate, asparagine, glutamate [Glu], and glutamine [Gln]), NH(4)(+), and/or inhibitors of N assimilation. Although all amino acids decreased high-affinity (13)NO(3)(-) influx and HvNRT2 transcript abundance, there was substantial interconversion of administered amino acids, making it impossible to determine which amino acid(s) were responsible for the observed effects. To clarify the role of individual amino acids, plants were separately treated with tungstate, methionine sulfoximine, or azaserine (inhibitors of nitrate reductase, Gln synthetase, and Glu synthase, respectively). Tungstate increased the HvNRT2 transcript by 20% to 30% and decreased NO(3)(-) influx by 50%, indicating that NO(3)(-) itself does not regulate transcript abundance, but may exert post-transcriptional effects. Experiments with methionine sulfoximine suggested that NH(4)(+) may down-regulate HvNRT2 gene expression and high-affinity NO(3)(-) influx by effects operating at the transcriptional and post-transcriptional levels. Azaserine decreased HvNRT2 transcript levels and NO(3)(-) influx by 97% and 95%, respectively, while decreasing Glu and increasing Gln levels. This suggests that Gln (and not Glu) is responsible for down-regulating HvNRT2 expression, although it does not preclude a contributory effect of other amino acids.
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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) | 0.001 | 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".