Changes in the nitrate assimilation pathway serve as a component of maize stress response to competition cues
Bibliographic record
Abstract
The impact of plant competition cues on nitrate levels and their assimilation in major crop plants remains largely unknown. This study investigated how low red to far-red (R:FR) light, a signal of plant competition, affects nitrate levels and assimilation in maize and soybean seedlings. Maize and soybean seedlings were exposed to low R:FR light conditions (R:FR ≈ 0.5) that mimicked nearby weeds or artificial sources. Additional treatments included individual soybean seedlings subjected to a soybean canopy. Biochemical assays and RNA-sequencing were used to assess nitrate levels, assimilation-related enzymes, and gene expression. Low R:FR light led to increased leaf nitrate levels in maize by 95% and 52% compared to the weed-free control (R:FR ≈ 2.8), and in soybean by 50% and 63%, while ammonium levels remained unchanged. A 37% increase in leaf nitrate level was also observed in soybean exposed to canopy conditions. In maize, nitrate accumulation was linked to reduced activity of ferredoxin-dependent glutamine:2-oxoglutarate aminotransferase (fd-GOGAT), while activities of nitrate reductase, nitrite reductase, glutamine synthetase, and NADH-GOGAT were unaffected. RNA-sequencing of maize leaves did not show altered expression of tonoplast localized nitrate transporter genes. These findings suggest that low R:FR light, as a plant competition cue, promotes nitrate accumulation in maize and soybean, potentially by altering nitrate assimilation pathways rather than transport or storage. This response may influence crop growth and metabolism under competition stress.
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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 | 0.001 |
| 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".