Transcriptome and translatome profiling of Col-0 and grp7grp8 under ABA treatment in Arabidopsis
Bibliographic record
Abstract
Abscisic acid (ABA) is a crucial phytohormone that regulates plant growth and stress responses. While substantial knowledge exists about transcriptional regulation, the molecular mechanisms underlying ABA-triggered translational regulation remain unclear. Recent advances in deep sequencing of ribosome footprints (Ribo-seq) enable the mapping and quantification of mRNA translation efficiency. Additionally, RNA-binding proteins (RBPs) play essential roles in translational regulation by interacting with target RNA molecules, making the identification of binding sites via UV crosslinking and immunoprecipitation (CLIP) critical for understanding RBP function. Glycine-rich RNA-binding proteins (GRPs), a prominent class of RBPs in plants, are responsive to ABA. In this study, RNA-seq and Ribo-seq analyses were conducted on 3-day-old Col-0 and grp7grp8 seedlings of Arabidopsis thaliana, treated with either ABA or mock solutions. These analyses facilitated deep sequencing of total mRNA and mRNA fragments protected by translating ribosomes. Additionally, CLIP-seq analysis of pGRP7::GRP7-GFP grp7-1 identified RNA bound by GRP7. This multi-omics dataset allows for a comprehensive investigation of the plant's response to ABA from various perspectives, providing a significant resource for studying ABA-regulated mRNA translation efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".