<scp>B4</scp> Raf‐like <scp>MAPKKK RAF24</scp> regulates <i>Arabidopsis thaliana</i> flowering time through <scp>HISTONE MONO</scp> ‐ <scp>UBIQUITINATION</scp> 2
Bibliographic record
Abstract
The timing of flowering is a critical agronomic trait governed by an extensive and sophisticated regulatory network. To date, limited understanding of how posttranslational modifications regulate flowering time exists. Here, using Arabidopsis, we resolve a role for the B4 Raf-like MAPKKK protein kinase RAF24 in regulating flowering time. Loss of RAPIDLY ACCELERATED FIBROSARCOMA24 (RAF24) led to premature flowering time through altered expression of FLC and FT. Comparative phosphoproteomic analysis of raf24 and wild-type plants revealed a list of known flowering-related phosphoproteins from distinct flowering pathways displaying downregulated phosphorylation. Of these, the RING-type ubiquitin ligase HISTONE MONO-UBIQUITINATION 2 (HUB2) lacked phosphorylation in the absence of RAF24. Absence of RAF24 induced H2Bub1 overaccumulation, with protein-protein interactome analysis of HUB2 in the presence and absence of RAF24 influencing HUB2 protein interaction partners, such as H2B. HUB2 was also found to physically interact with SUCROSE NONFERMENTING KINASE 2.4 (SnRK2.4) and SnRK2.6, known substrates of RAF24. Using phospho-mimetic and phospho-ablative plant lines, we then validated the importance of HUB2 phosphorylation at serine 314 (S314) in maintaining appropriate flowering time. Our findings uncovered a novel biological role of RAF24, as a higher-order flowering regulator, while further implicating HUB2 as a centerpiece of flowering time regulation.
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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) | 0.002 | 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".