Genetic Regulation by NLA and MicroRNA827 for Maintaining Nitrate-Dependent Phosphate Homeostasis in Arabidopsis
Bibliographic record
Abstract
Plants need abundant nitrogen and phosphorus for higher yield. Improving plant genetics for higher nitrogen and phosphorus use efficiency would save potentially billions of dollars annually on fertilizers and reduce global environmental pollution. This will require knowledge of molecular regulators for maintaining homeostasis of these nutrients in plants. Previously, we reported that the NITROGEN LIMITATION ADAPTATION (NLA) gene is involved in adaptive responses to low-nitrogen conditions in Arabidopsis, where nla mutant plants display abrupt early senescence. To understand the molecular mechanisms underlying NLA function, two suppressors of the nla mutation were isolated that recover the nla mutant phenotype to wild type. Map-based cloning identified these suppressors as the phosphate (Pi) transport-related genes PHF1 and PHT1.1. In addition, NLA expression is shown to be regulated by the low-Pi induced microRNA miR827. Pi analysis revealed that the early senescence in nla mutant plants was due to Pi toxicity. These plants accumulated over five times the normal Pi content in shoots specifically under low nitrate and high Pi but not under high nitrate conditions. Also the Pi overaccumulator pho2 mutant shows Pi toxicity in a nitrate-dependent manner similar to the nla mutant. Further, the nitrate and Pi levels are shown to have an antagonistic crosstalk as displayed by their differential effects on flowering time. The results demonstrate that NLA and miR827 have pivotal roles in regulating Pi homeostasis in plants in a nitrate-dependent fashion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".