Characterization of nitrite uptake in <i>Arabidopsis thaliana</i>: evidence for a nitrite‐specific transporter
Bibliographic record
Abstract
Nitrite-specific plasma membrane transporters have been described in bacteria, algae and fungi, but there is no evidence of a nitrite-specific plasma membrane transporter in higher plants. We have used 13NO2(-) to characterize nitrite influx into roots of Arabidopsis thaliana. Hydroponically grown Arabidopsis mutants, defective in high-affinity nitrate transport, were used to distinguish between nitrate and nitrite uptake by means of the short-lived tracers 13NO2(-) and 13NO3(-). This approach allowed us to characterize a nitrite-specific transporter. The Atnar2.1-2 mutant, lacking a functional high-affinity nitrate transport system, is capable of nitrite influx that is constitutive and thermodynamically active. The corresponding fluxes conform to a rectangular hyperbola, exhibiting saturation at concentrations above 200 μM (Km = 185 μM and Vmax = 1.89 μmol g(-1) FW h(-1)). Nitrite influx via the putative nitrite transporter is not subject to competitive inhibition by nitrate but is downregulated after 6 h exposure to ammonium. These results signify the existence of a nitrite-specific transporter in Arabidopsis. This transporter enables Atnar2.1-2 mutants, which are incapable of sustained growth on low nitrate, to maintain significant growth on low nitrite. In wild-type plants, this nitrite flux may increase nitrogen acquisition and also participate in the induction of genes specifically induced by nitrite.
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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) | 0.001 | 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".