Calmodulin-Mediated Cell Cycle Regulation: New Mechanisms for Old Observations
Bibliographic record
Abstract
The significance of divalent calcium ions (Ca(2+)) to cell cycle progression has been a subject of study for several decades, with a regulatory role for Ca(2+) suggested in distinct cell types and multiple organisms. Our interest in proliferative vascular diseases led us to focus on mammalian vascular smooth muscle cells (VSMC) in particular, in which we and others had shown that a coordinate elevation in the intracellular free Ca(2+) concentration is required for G(1) to S phase cell cycle progression. However, the molecular basis for this Ca(2+)-sensitive cell cycle transition was not known. Our recent discovery of a functional protein-protein interaction between the late G1-active cyclin E1 and the major calcium signal-transducing factor calmodulin (CaM) sheds new light on the mechanism(s) through which Ca2+ concentrations regulate cell cycle. Having identified a CaM-binding site on cyclin E1, our studies support a direct role for CaM in mediating Ca2+-sensitive cyclin E/CDK2 activity and G1 to S phase transitions in VSMC. The CaM binding site identified on cyclin E1 has a Kd for CaM consistent with that of known CaM-binding proteins, and is composed of a 22 amino acids N-terminal sequence that is highly conserved across several mammalian species. Deletion of this binding site abolished CaM binding and Ca2+-sensitive cyclin E/Cdk2 activity. Here we provide our perspectives on the literature supporting a role for Ca2+ in cell cycle regulation, focusing on the evidence implicating CaM in this functionality, and discuss the potential for therapeutic modulation of CaM-dependent cell cycle machinery.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| 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".