Phosphate or nitrate imbalance induces stronger molecular responses than combined nutrient deprivation in roots and leaves of chickpea plants
Bibliographic record
Abstract
Abstract The negative effects of phosphate (Pi) and/or nitrate (NO3−) fertilizers on the environment have raised an urgent need to develop crop varieties with higher Pi and/or nitrogen use efficiencies for cultivation in low‐fertility soils. Achieving this goal depends upon research that focuses on the identification of genes involved in plant responses to Pi and/or NO3− starvation. Although plant responses to individual deficiency in either Pi (–Pi/+NO3−) or NO3− (+Pi/–NO3−) have been separately studied, our understanding of plant responses to combined Pi and NO3− deficiency (–Pi/–NO3−) is still very limited. Using RNA‐sequencing approach, transcriptome changes in the roots and leaves of chickpea cultivated under –Pi/+NO3−, +Pi/–NO3− or –Pi/–NO3− conditions were investigated in a comparative manner. –Pi/–NO3− treatment displayed lesser effect on expression changes of genes related to Pi or NO3− transport, signalling networks, lipid remodelling, nitrogen and Pi scavenging/remobilization/recycling, carbon metabolism and hormone metabolism than –Pi/+NO3− or +Pi/–NO3− treatments. Therefore, the plant response to –Pi/–NO3− is not simply an additive result of plant responses to –Pi/+NO3− and +Pi/–NO3− treatments. Our results indicate that nutrient imbalance is a stronger stimulus for molecular reprogramming than an overall deficiency.
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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".