Modulating auxin response stabilizes tomato fruit set
Bibliographic record
Abstract
Fruit formation depends on successful fertilization and is highly sensitive to weather fluctuations that affect pollination. Auxin promotes fruit initiation and growth following fertilization. Class A auxin response factors (Class A ARFs) repress transcription in the absence of auxin and activate transcription in its presence. Here, we explore how multiple members of the ARF family regulate fruit set and fruit growth in tomato (Solanum lycopersicum) and Arabidopsis thaliana, and test whether reduction of SlARF activity improves yield stability in fluctuating temperatures. We found that several tomato Slarf mutant combinations produced seedless parthenocarpic fruits, most notably mutants deficient in SlARF8A and SlARF8B genes. Arabidopsis Atarf8 mutants deficient in the orthologous gene had less complete parthenocarpy than did tomato Slarf8a Slarf8b mutants. Conversely, Atarf6 Atarf8 double mutants had reduced fruit growth after fertilization. AtARF6 and AtARF8 likely switch from repression to activation of fruit growth in response to a fertilization-induced auxin increase in gynoecia. Tomato plants with reduced SlARF8A and SlARF8B gene dosage had substantially higher yield than the wild type under controlled or ambient hot and cold growth conditions. In field trials, partial reduction in the SlARF8 dose increased yield under extreme temperature with minimal pleiotropic effects. The stable yield of the mutant plants resulted from a combination of early onset of fruit set, more fruit-bearing branches and more flowers setting fruits. Thus, ARF8 proteins mediate the control of fruit set, and relieving this control with Slarf8 mutations may be utilized in breeding to increase yield stability in tomato and other crops.
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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) | 0.003 | 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".