MAP4K4 regulates biomechanical forces at adherens junctions and focal adhesions to promote collective cell migration
Bibliographic record
Abstract
ABSTRACT Collective cell migration is important for normal development and tissue homeostasis, but can also promote cancer metastasis. To migrate collectively, cells need to coordinate their protrusion formation, rear retraction, adhesion sites dynamics, as well as forces generation and transmission. Nevertheless, the regulatory mechanisms coordinating these processes remain elusive. Using the A431 carcinoma cell line, we identify the kinase MAP4K4 as a central regulator of collective migration. We show that MAP4K4 inactivation blocks the migration of clusters while its overexpression decreases cluster cohesion. MAP4K4 regulates protrusion and retraction dynamics, remodels the actomyosin cytoskeleton, and controls the stability of both cell-cell and cell substrate adhesion. MAP4K4 promotes focal adhesion disassembly through the phosphorylation of Moesin, an actin and plasma membrane cross-linker, but disassembles adherens junctions through a Moesin-independent mechanism. By analyzing traction and intercellular forces, we found that the stabilization of adhesion sites in MAP4K4 loss of function leads to a tensional disequilibrium throughout the cell cluster, increasing the traction forces exerted onto the substrate and the tension loading at the cell-cell adhesions. Together, our results indicates that MAP4K4 activity is a key regulator of biomechanical forces at adhesion sites, promoting collective migration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".