A common cofilin activity cycle in invasive tumor cells and inflammatory cells
Bibliographic record
Abstract
In many cell types, the formation of membrane protrusions and directional migration depend on the spatial and temporal regulation of the actin-binding protein cofilin. Cofilin, which is important for the regulation of actin-polymerization initiation, increases the number of actin free barbed ends through three mechanisms: its intrinsic actin-nucleation activity; binding and severing of existing actin filaments; and recycling actin monomers from old filaments to new ones through its actin-depolymerization activity. The increase in free barbed ends that is caused by cofilin initiates new actin polymerization, which can be amplified by the actin-nucleating ARP2/3 complex. Interestingly, different cell systems seem to have different mechanisms of activating cofilin. The initial activation of cofilin in mammary breast tumors is dependent on PLCgamma, whereas cofilin activation in neutrophils is additionally dependent on dephosphorylation, which is promoted through Rac2 signaling. Although the literature seems to be confusing and inconsistent, we propose that all of the data can be explained by a single activity-cycle model. In this Opinion, we give an overview of cofilin activation in both tumor cells and inflammatory cells, and demonstrate how the differences in cofilin activation that are observed in various cell types can be explained by different starting points in this single common activity cycle.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".