Temporal Control of Trichome Distribution by MicroRNA156-Targeted<i>SPL</i>Genes in<i>Arabidopsis thaliana</i>
Bibliographic record
Abstract
The production and distribution of plant trichomes is temporally and spatially regulated. After entering into the flowering stage, Arabidopsis thaliana plants have progressively reduced numbers of trichomes on the inflorescence stem, and the floral organs are nearly glabrous. We show here that SQUAMOSA PROMOTER BINDING PROTEIN LIKE (SPL) genes, which define an endogenous flowering pathway and are targeted by microRNA 156 (miR156), temporally control the trichome distribution during flowering. Plants overexpressing miR156 developed ectopic trichomes on the stem and floral organs. By contrast, plants with elevated levels of SPLs produced fewer trichomes. During plant development, the increase in SPL transcript levels is coordinated with the gradual loss of trichome cells on the stem. The MYB transcription factor genes TRICHOMELESS1 (TCL1) and TRIPTYCHON (TRY) are negative regulators of trichome development. We show that SPL9 directly activates TCL1 and TRY expression through binding to their promoters and that this activation is independent of GLABROUS1 (GL1). The phytohormones cytokinin and gibberellin were reported to induce trichome formation on the stem and inflorescence via the C2H2 transcription factors GIS, GIS2, and ZFP8, which promote GL1 expression. We show that the GIS-dependent pathway does not affect the regulation of TCL1 and TRY by miR156-targeted SPLs, represented by SPL9. These results demonstrate that the miR156-regulated SPLs establish a direct link between developmental programming and trichome distribution.
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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 | 0.001 |
| 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".