A proposed role for selective autophagy in regulating auxin-dependent lateral root development under phosphate starvation in Arabidopsis
Bibliographic record
Abstract
Plants respond to limited soil nutrient availability by inducing more lateral roots (LR) to increase the root surface area. At the cellular level, nutrient starvation triggers the process of autophagy through which bulk degradation of cellular materials is achieved to facilitate nutrient mobilization. Whether there is any link between the cellular autophagy and induction of LR had remained unknown. We recently showed that the S-Domain receptor Kinase (ARK2) and U Box/Armadillo Repeat-Containing E3 ligase (PUB9) module is required for lateral root formation under phosphate starvation in Arabidopsis thaliana.(1) We also showed that PUB9 localized to autophagic bodies following either activation by ARK2 or under phosphate starvation and ark2-1/pub9-1 plants displayed lateral root defects with inability to accumulate auxin in the root tips under phosphate starvation.(1) Supplementing exogenous auxin was sufficient to rescue the LR defects in ark2-1/pub9-1 mutant. Blocking of autophagic responses in wild-type Arabidopsis also resulted in inhibition of both lateral roots and auxin accumulation in the root tips indicating the importance of autophagy in mediating auxin accumulation under phosphate starved conditions.(1) Here, we propose a model for ARK2/AtPUB9 module in regulation of lateral root development via selective autophagy.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".