Deep functional redundancy between <scp>FAMA</scp> and <scp>FOUR LIPS</scp> in stomatal development
Bibliographic record
Abstract
Functional redundancy arises between gene paralogs as well as non-homologous genes that play a common role at a shared node. The bHLH transcription factor FAMA, along with the paralogous MYB genes, FOUR LIPS (FLP) and MYB88 all ensure that Arabidopsis stomata contain just two guard cells (GCs) by enforcing a single symmetric precursor cell division before stomatal maturity. Consistent with this function, FLP and FAMA exhibit the same expression pattern in which both translational GFP fusions emit fluorescence just before and after symmetric division; however, FAMA but not FLP is required to confer GC fate. Strikingly, swapping the genes and promoters of the FLP and FAMA genes results in the reciprocal complementation of respective loss-of-function mutants. Thus, an FLP transgene can restore GC fate to a fama mutant background. FAMA, FLP and the FLP paralog MYB88 were previously shown to influence higher order functions in stomatal development, including maintaining and stabilizing stomatal fate. Here we show that these overlapping functions are likely to also involve interactions between FLP and FAMA with the RETINOBLASTOMA-RELATED (RBR) protein.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".