Auxin-responsive<i>SAUR39</i>gene modulates auxin level in rice
Bibliographic record
Abstract
The signaling molecule auxin plays a central role in several aspects of plant growth and developmental processes. Underlying optimization of these processes are complex mechanisms orchestrating the expression of genes involved in controlling auxin level, movement and signalling. The small auxin-up RNA (SAUR) family comprises a large set of genes whose expressions are early auxin-responsive. However, the function of these genes is largely unknown. Loss-of-function mutants in a number of Arabidopsis SAUR genes did not show any marked phenotypic differences to wild-type plants likely due to compensatory functions of conserved members of the SAUR gene family. We have recently shown that a rice SAUR39 gene negatively regulates auxin synthesis and transport in rice. Here we propose a model that constitutive induction of SAUR39 gene expression reduces growth and seed yield in rice plants due to the presence of a lower auxin level, reduced polar auxin transport, less chlorophyll and increased sugar and anthocyanin contents. In wild-type plants, the SAUR39 gene is expressed at a low level and is transiently induced by changes in external auxin or other environmental stimuli, but within hours of this change its expression is reduced to the low constitutive level. This homeostatic mechanism is essential for optimal plant growth and seed yield and its disruption due to the constitutive overexpression of SAUR39 leads to a set of negative pleiotropic phenotypes.
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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 | 0.000 |
| 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".