<i>Arabidopsis</i> Calmodulin-like Proteins CML13 and CML14 Interact with Calmodulin-Binding Transcriptional Activators and Function in Salinity Stress Response
Bibliographic record
Abstract
Eukaryotic cells use calcium ions (Ca2+) as second messengers, particularly in response to abiotic and biotic stresses. These signals are detected by Ca2+ sensor proteins, such as calmodulin (CaM), which regulate the downstream target proteins. Plants also possess many CaM-like proteins (CMLs), most of which remain unstudied. We recently demonstrated that Arabidopsis CML13 and CML14 interact with proteins containing isoleucine/glutamine (IQ) domains, including CaM-binding transcriptional activators (CAMTAs). Here, we show that CaM, CML13 and CML14 bind all six members of the Arabidopsis CAMTA family. Using a combination of in planta and in vitro protein-interaction assays, we tested 11 members of the CaM/CML family and demonstrated that only CaM, CML13 and CML14 bind to CAMTA IQ domains. CaM, CML13 and CML14 showed Ca2+-independent binding to the IQ region of CAMTA6 and CAMTA3, and CAMTA6 in vitro exhibited some specificity toward individual IQ domains within CAMTA6 in split-luciferase in planta assays. We show that cml13 mutants exhibited enhanced salinity tolerance during germination compared to wild-type plants, a phenotype similar to camta6 mutants. In contrast, plants overexpressing CML13-GFP or CML14-GFP in the wild-type background showed increased NaCl sensitivity. Under mannitol stress, cml13 mutants were more susceptible than camta6 mutants or wild-type plants. The phenotype of cml13 mutants could be rescued with the wild-type CML13 gene. Several salinity-marker genes under CAMTA6 control were similarly misregulated in both camta6 and cml13 mutants, further supporting a role for CML13 in CAMTA6 function. Collectively, our data suggest that CML13 and CML14 participate in abiotic stress signaling as CAMTA effectors.
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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) | 0.001 | 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".