<i>SAUR39</i>, a Small Auxin-Up RNA Gene, Acts as a Negative Regulator of Auxin Synthesis and Transport in Rice
Bibliographic record
Abstract
The phytohormone auxin plays a critical role for plant growth by regulating the expression of a set of genes. One large auxin-responsive gene family of this type is the small auxin-up RNA (SAUR) genes, although their function is largely unknown. The expression of the rice (Oryza sativa) SAUR39 gene showed rapid induction by transient change in different environmental factors, including auxin, nitrogen, salinity, cytokinin, and anoxia. Transgenic rice plants overexpressing the SAUR39 gene resulted in lower shoot and root growth, altered shoot morphology, smaller vascular tissue, and lower yield compared with wild-type plants. The SAUR39 gene was expressed at higher levels in older leaves, unlike auxin biosynthesis, which occurs largely in the meristematic region. The transgenic plants had a lower auxin level and a reduced polar auxin transport as well as the down-regulation of some putative auxin biosynthesis and transporter genes. Biochemical analysis also revealed that transgenic plants had lower chlorophyll content, higher levels of anthocyanin, abscisic acid, sugar, and starch, and faster leaf senescence compared with wild-type plants at the vegetative stage. Most of these phenomena have been shown to be negatively correlated with auxin level and transport. Transcript profiling revealed that metabolic perturbations in overexpresser plants were largely due to transcriptional changes of genes involved in photosynthesis, senescence, chlorophyll production, anthocyanin accumulation, sugar synthesis, and transport. The lower growth and yield of overexpresser plants was largely recovered by exogenous auxin application. Taken together, the results suggest that SAUR39 acts as a negative regulator for auxin synthesis and transport.
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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 | 0.000 |
| 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".