Elucidating the Germination Transcriptional Program Using Small Molecules
Bibliographic record
Abstract
The transition from seed to seedling is mediated by germination, a complex process that starts with imbibition and completes with radicle emergence. To gain insight into the transcriptional program mediating germination, previous studies have compared the transcript profiles of dry, dormant, and germinating after-ripened Arabidopsis (Arabidopsis thaliana) seeds. While informative, these approaches did not distinguish the transcriptional responses due to imbibition, shifts in metabolism, or breaking of dormancy from those triggered by the initiation of germination. In this study, three mechanistically distinct small molecules that inhibit Arabidopsis seed germination (methotrexate, 2, 4-dinitrophenol, and cycloheximide) were identified using a small-molecule screen and used to probe the germination transcriptome. Germination-responsive transcripts were defined as those with significantly altered transcript abundance across all inhibitory treatments with respect to control germinating seeds, using data from ATH1 microarrays. This analysis identified numerous germination regulators as germination responsive, including the DELLA proteins GAI, RGA, and RGL3, the abscisic acid-insensitive proteins ABI4, ABI5, ABI8, and FRY1, and the gibberellin receptor GID1A. To help visualize these and other publicly available seed microarray data, we designed a seed mRNA expression browser using the electronic Fluorescent Pictograph platform. An overall decrease in gene expression and a 5-fold greater number of transcripts identified as statistically down-regulated in drug-inhibited seeds point to a role for mRNA degradation or turnover during seed germination. The genes identified in our study as responsive to germination define potential uncharacterized regulators of this process and provide a refined transcriptional signature for germinating Arabidopsis seeds.
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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 | 0.000 |
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".