Calmodulin as a versatile calcium signal transducer in plants
Bibliographic record
Abstract
Summary The complexity of Ca2+ patterns observed in eukaryotic cells, including plants, has led to the hypothesis that specific patterns of Ca2+ propagation, termed Ca2+ signatures, encode information and relay it to downstream elements (effectors) for translation into appropriate cellular responses. Ca2+‐binding proteins (sensors) play a key role in decoding Ca2+ signatures and transducing signals by activating specific targets and pathways. Calmodulin is a Ca2+ sensor known to modulate the activity of many mammalian proteins, whose targets in plants are now being actively characterized. Plants possess an interesting and rapidly growing list of calmodulin targets with a variety of cellular roles. Nevertheless, many targets appear to be unique to plants and remain uncharacterized, calling for a concerted effort to elucidate their functions. Moreover, the extended family of calmodulin‐related proteins in plants consists of evolutionarily divergent members, mostly of unknown function, although some have recently been implicated in stress responses. It is hoped that advances in functional genomics, and the research tools it generates, will help to explain themultiplicity of calmodulin genes in plants, and to identify their downstream effectors. This review summarizes current knowledge of the Ca2+–calmodulin messenger system in plants and presents suggestions for future areas of research. Contents I. Introduction 36 II. CaM isoforms and CaM‐like proteins 37 III. CaM‐target proteins 42 IV. CaM and nuclear functions 46 V. Regulation of ion transport 49 VI. CaM and plant responses to environmental stimuli 52 VII. Conclusions and future studies 58 Acknowledgements 59 References 59
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".