Dissection of the <i>AtNRT2.1</i>:<i>AtNRT2.2</i> Inducible High-Affinity Nitrate Transporter Gene Cluster
Bibliographic record
Abstract
Using a new Arabidopsis (Arabidopsis thaliana) mutant (Atnrt2.1-nrt2.2) we confirm that concomitant disruption of NRT2.1 and NRT2.2 reduces inducible high-affinity transport system (IHATS) by up to 80%, whereas the constitutive high-affinity transport system (CHATS) was reduced by 30%. Nitrate influx via the low-affinity transport system (LATS) was unaffected. Shoot-to-root ratios were significantly reduced compared to wild-type plants, the major effect being upon shoot growth. In another mutant uniquely disrupted in NRT2.1 (Atnrt2.1), IHATS was reduced by up to 72%, whereas neither the CHATS nor the LATS fluxes were significantly reduced. Disruption of NRT2.1 in Atnrt2.1 caused a consistent and significant reduction of shoot-to-root ratios. IHATS influx and shoot-to-root ratios were restored to wild-type values when Atnrt2.1-nrt2.2 was transformed with a NRT2.1 cDNA isolated from Arabidopsis. Disruption of NRT2.2 in Atnrt2.2 reduced IHATS by 19% and this reduction was statistically significant only at 6 h after resupply of nitrate to nitrogen-deprived plants. Atnrt2.2 showed no significant reduction of CHATS, LATS, or shoot-to-root ratios. These results define NRT2.1 as the major contributor to IHATS. Nevertheless, when maintained on agar containing 0.25 mm KNO(3) as the sole nitrogen source, Atnrt2.1-nrt2.2 consistently exhibited greater stress and growth reduction than Atnrt2.1. Evidence from real-time PCR revealed that NRT2.2 transcript abundance was increased almost 3-fold in Atnrt2.1. These findings suggest that NRT2.2 normally makes only a small contribution to IHATS, but when NRT2.1 is lost, this contribution increases, resulting in a partial compensation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".