The transcription factors, STOP1 and TCP20, are required for root system architecture alterations in response to nitrate deficiency
Bibliographic record
Abstract
Nitrate distribution in soils is often heterogeneous. Plants have adapted to this by modifying their root system architecture (RSA). Previous studies showed that NITRATE-TRANSPORTER1.1 (NRT1.1), which also transports auxin, helps inhibit lateral root primordia (LRP) emergence in nitrate-poor patches, by preferentially transporting auxin away from the LRP. In this study, we identified the regulatory system for this response involving the transcription factor (TF), SENSITIVE-TO-PROTON-RHIZOTOXICITY1 (STOP1), which is accumulated in the nuclei of LRP cells under nitrate deficiency and directly regulates Arabidopsis NRT1.1 expression. Mutations in STOP1 mimic the root phenotype of the loss-of-function NRT1.1 mutant under nitrate deficiency, compared to wild-type plants, including increased LR growth and higher DR5promoter activity (i.e., higher LRP auxin signaling/activity). Nitrate deficiency–induced LR growth inhibition was almost completely reversed when STOP1 and the TF, TEOSINTE-BRANCHED1,-CYCLOIDEA,-PCF-DOMAIN-FAMILY-PROTEIN20 (TCP20), a known activator of NRT1.1 expression, were both mutated. Thus, the STOP1-TCP20 system is required for activation of NRT1.1 expression under nitrate deficiency, leading to reduced LR growth in nitrate-poor regions. We found this STOP1-mediated system is more active as growth media becomes more acidic, which correlates with reductions in soil nitrate as the soil pH becomes more acidic. STOP1 has been shown to be involved in RSA modifications in response to phosphate deficiency and increased potassium uptake, hence, our findings indicate that root growth regulation in response to low availability of the major fertilizer nutrients, nitrogen, phosphorus and potassium, all involve STOP1, which may allow plants to maintain appropriate root growth under the complex and varying soil distribution of nutrients.
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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) | 0.002 | 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".