<i>Arabidopsis</i> CML13 and CML14 Have Essential and Overlapping Roles in Plant Development
Bibliographic record
Abstract
Calmodulin (CaM)-like proteins (CMLs) are the largest family of calcium-binding proteins in plants, yet the functions of most CMLs are unknown. Arabidopsis CML13 and CML14 are closely related paralogs that interact with the isoleucine-glutamine (IQ) domains of myosins, IQ-domain proteins and CaM-binding transcription activators (CAMTAs). Here, we explored the physiological roles of CML13 and CML14 during development by using dexamethasone (Dex)-inducible RNA silencing to suppress either CML13 or CML14 transcript levels. In the absence of inducible suppression, CML13- and CML14-RNA-interference lines were indistinguishable from wild-type (WT) plants throughout development. In contrast, induction of silencing treatment led to rapid increases in RNA-hairpin production that correlated with a targeted reduction in CML13 or CML14 transcript levels and a range of developmental and morphological effects. RNA-suppression treatment did not impair the germination of CML13- or 14-RNA-interference lines, but these seedlings were chlorotic, displayed high mortality and failed to achieve seedling establishment. Under Dex treatment, seeds of CML13- and CML14-RNA-interference lines exhibited differential sensitivity to exogenous ABA compared to WT seeds. Induced RNA suppression of mature plants led to reduced silique length, shorter roots and rapid leaf senescence in CML13- and 14-RNA-interference plants, which correlated with increased gene expression of the senescence marker Senescence-Associated Gene13 (SAG13). Plants induced for RNA suppression at 2 weeks post-germination exhibited a much stronger phenotype than treatment of 3-, 4- or 5-week-old plants. Collectively, our data indicate that both CML13 and CML14 are essential for normal development and function across a broad range of tissues and developmental stages.
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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".