Whole‐plant and organ‐level nitrogen isotope discrimination indicates modification of partitioning of assimilation, fluxes and allocation of nitrogen in knockout lines of<i>Arabidopsis thaliana</i>
Bibliographic record
Abstract
The nitrogen isotope composition (δ¹⁵N) of plants has potential to provide time-integrated information on nitrogen uptake, assimilation and allocation. Here, we take advantage of existing T-DNA and γ-ray mutant lines of Arabidopsis thaliana to modify whole-plant and organ-level nitrogen isotope composition. Nitrate reductase 2 (nia2), nitrate reductase 1 (nia1) and nitrate transporter (nrt2) mutant lines and the Col-0 wild type were grown hydroponically under steady-state NO₃⁻ conditions at either 100 or 1000 μM NO₃⁻ for 35 days. There were no significant effects on whole-plant discrimination and growth in the assimilatory mutants (nia2 and nia1). Pronounced root vs leaf differences in δ¹⁵N, however, indicated that nia2 had an increased proportion of nitrogen assimilation of NO₃⁻ in leaves while nia1 had an increased proportion of assimilation in roots. These observations are consistent with reported ratios of nia1 and nia2 gene expression levels in leaves and roots. Greater whole-plant discrimination in nrt2 indicated an increase in efflux of unassimilated NO₃⁻ back to the rooting medium. This phenotype was associated with an overall reduction in NO₃⁻ uptake, assimilation and decreased partitioning of NO₃⁻ assimilation to the leaves, presumably because of decreased symplastic intercellular movement of NO₃⁻ in the root. Although the results were more varied than expected, they are interpretable within the context of expected mechanisms of whole-plant and organ-level nitrogen isotope discrimination that indicate variation in nitrogen fluxes, assimilation and allocation between lines.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".