The regulation of smooth muscle contractility by zipper-interacting protein kinaseThis paper is one of a selection of papers published in this Special Issue, entitled Young Investigators' Forum.
Bibliographic record
Abstract
Smooth muscle contractility is mainly regulated by phosphorylation of the 20 kDa myosin light chains (LC20), a process that is controlled by the opposing activities of myosin light chain kinase (MLCK) and myosin light chain phosphatase (MLCP). Recently, intensive research has revealed that various protein kinase networks including Rho-kinase, integrin-linked kinase, zipper-interacting protein kinase (ZIPK), and protein kinase C (PKC) are involved in the regulation of LC20 phosphorylation and have important roles in modulating smooth muscle contractile responses to Ca2+ (i.e., Ca2+ sensitization and Ca2+ desensitization). Here, we review the general background and structure of ZIPK and summarize our current understanding of its involvement in a number of cell processes including cell death (apoptosis), cell motility, and smooth muscle contraction. ZIPK has been found to induce the diphosphorylation of LC20 at Ser-19 and Thr-18 in a Ca2+-independent manner and to regulate MLCP activity directly through its phosphorylation of the myosin-targeting subunit of MLCP or indirectly through its phosphorylation of the PKC-potentiated inhibitory protein of MLCP. Future investigations of ZIPK function in smooth muscle will undoubtably focus on determining the mechanisms that regulate its cellular activity, including the identification of upstream signaling pathways, the characterization of autoinhibitory domains and regulatory phosphorylation sites, and the development of specific inhibitor compounds.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".