A cryptic START domain regulates deeply conserved transcription factors
Bibliographic record
Abstract
Transcription factors (TFs) integrate a diverse array of inputs to achieve the exquisite control of gene expression necessary for life. In plants, this is exemplified by the deeply conserved CLASS III HOMEODOMAIN LEUCINE ZIPPER (HD-ZIPIII) family of TFs. HD-ZIPIII activity is controlled by inputs at transcriptional, post-transcriptional, and post-translational levels. As part of their multidomain architecture, HD-ZIPIII TFs contain a StAR-related lipid transfer (START) domain, a ubiquitously distributed evolutionary module that binds various types of lipophilic ligands. Here, we show that HD-ZIPIII and HD-ZIPIV proteins contain a cryptic, deeply conserved START domain which we term the disorder-containing START domain (dSTART). The dSTART domain is required for HD-ZIPIII developmental function, controlling their subcellular localization and DNA-binding properties. The dSTART domain also helps discriminate responsive from non-responsive binding sites across the HD-ZIPIII shared genetic network. Finally, we identify candidate ligands of the dSTART domain including several species of phosphatidylglycerol and phosphatidic acid. The identification and functional characterization of a cryptic START domain provides new mechanistic insights into a deeply conserved family of TFs with roles in nearly all aspects of plant development.
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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) | 0.002 | 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".