High-Affinity Nitrate Transport in Roots of Arabidopsis Depends on Expression of the <i>NAR2</i>-Like Gene <i>AtNRT3.1</i>
Bibliographic record
Abstract
The NAR2 protein of Chlamydomonas reinhardtii has no known transport activity yet it is required for high-affinity nitrate uptake. Arabidopsis (Arabidopsis thaliana) possesses two genes, AtNRT3.1 and AtNRT3.2, that are similar to the C. reinhardtii NAR2 gene. AtNRT3.1 accounts for greater than 99% of NRT3 mRNA and is induced 6-fold by nitrate. AtNRT3.2 was expressed constitutively at a very low level and did not compensate for the loss of AtNRT3.1 in two Atnrt3.1 mutants. Nitrate uptake by roots and nitrate induction of gene expression were analyzed in two T-DNA mutants, Atnrt3.1-1 and Atnrt3.1-2, disrupted in the AtNRT3.1 promoter and coding regions, respectively, in 5-week-old plants. Nitrate induction of the nitrate transporter genes AtNRT1.1 and AtNRT2.1 was reduced in Atnrt3.1 mutant plants, and this reduced expression was correlated with reduced nitrate concentrations in the tissues. Constitutive high-affinity influx was reduced by 34% and 89%, respectively, in Atnrt3.1-1 and Atnrt3.1-2 mutant plants, while high-affinity nitrate-inducible influx was reduced by 92% and 96%, respectively, following induction with 1 mm KNO(3) after 7 d of nitrogen deprivation. By contrast, low-affinity influx appeared to be unaffected. Thus, the constitutive high-affinity influx and nitrate-inducible high-affinity influx (but not the low-affinity influx) of higher plant roots require a functional AtNRT3 (NAR2) gene.
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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 | 0.001 |
| 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".