VAMP3, syntaxin-13 and SNAP23 are involved in secretion of matrix metalloproteinases, degradation of the extracellular matrix and cell invasion
Bibliographic record
Abstract
Cellular remodeling of the extracellular matrix (ECM), an essential component of many physiological and pathological processes, is dependent on the trafficking and secretion of matrix metalloproteinases (MMPs). Soluble NSF attachment protein receptor (SNARE)-mediated membrane traffic has documented roles in cell-ECM interactions and the present study specifically examines SNARE function in the trafficking of MMPs during ECM degradation. Using the invasive human fibrosarcoma cell line HT-1080, we demonstrate that a plasma membrane SNARE, SNAP23, and an endosomal v-SNARE, VAMP3 (also known as cellubrevin), partly colocalize with MMP2 and MMP9, and that inhibition of these SNAREs using dominant-negative SNARE mutants impaired secretion of the MMPs. Inhibition of VAMP3, SNAP23 or syntaxin-13 using dominant-negative SNARES, RNA interference or tetanus toxin impaired trafficking of membrane type 1 MMP to the cell surface. Consistent with these observations, we found that blocking the function of these SNAREs reduced the ability of HT-1080 cells to degrade a gelatin substrate in situ and impaired invasion of HT-1080 cells in vitro. The results reveal the importance of VAMP3, syntaxin-13 and SNAP23 in the trafficking of MMP during degradation of ECM substrates and subsequent cellular invasion.
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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 | 0.000 | 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 | 0.000 |
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".