Vimentin Regulates Collagen Remodeling Through Interaction with Myosin 10
Bibliographic record
Abstract
Vimentin (Vim) marks epithelial-mesenchymal transition and increased Vim expression is associated with tumor progression and poor prognosis in colorectal cancer (CRC). Unconventional myosin 10 (Myo10) is an actin-based motor protein whose expression correlates with cancer metastasis, but the interactions of Myo10 with Vim in migrating cells are not defined. Sections of human invasive CRC exhibited prominent colocalization of Myo10 with Vim in cell extensions localized to the subjacent matrix, suggesting an association with local invasion. In mouse fibroblasts, Myo10 co-precipitated with Vim. Surface plasmon resonance showed high affinity binding of Myo10 to Vim (Kd 57 nM). By STED microscopy and FRET analysis, we found that Myo10 interacted with Vim at the termini of cell extensions. Deletion of Vim promoted accumulation of Myo10 at the cell periphery and reduced Myo10 at the tips of extensions. Drug- mediated disassembly of Vim filaments promoted Myo10 retraction from cell extensions into the cell body. FRAP showed that the mobility of Myo10 along cell extensions was impacted by Vim. Myo10 and Vim were required for pericellular collagen degradation by MT1-MMP. Vim deletion reduced the transit of MT1-MMP to the tips of cell extensions and down-regulated collagenolytic activity, which mediated cell invasion of the matrix. Deletion of Vim and Myo10 was associated with reduced collagen fiber alignment and tractional remodeling of the collagen matrix. CRC SW480 cells exhibited similar changes in Myo10 mobility and collagen remodeling after Vim knockdown, similar to mEFs, suggesting that Vim-Myo10 interactions are an important feature of motile cells. Collectively these data indicate that Vim binding to Myo10 promotes collagen remodeling and the generation of cell extensions required for cell migration through soft connective tissues.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".