Syntaxin 7 contributes to breast cancer cell invasion by promoting invadopodia formation
Bibliographic record
Abstract
Invasion in various cancer cells requires coordinated delivery of signaling proteins, adhesion proteins, actin-remodeling proteins and proteases to matrix-degrading structures called invadopodia. Vesicular trafficking involving SNAREs plays a crucial role in the delivery of cargo to the target membrane. Screening of 13 SNAREs from the endocytic and recycling route using a gene silencing approach coupled with functional assays identified syntaxin 7 (STX7) as an important player in MDA-MB-231 cell invasion. Total internal reflection fluorescence microscopy (TIRF-M) studies revealed that STX7 resides near invadopodia and co-traffics with MT1-MMP (also known as MMP14), indicating a possible role for this SNARE in protease trafficking. STX7 depletion reduced the number of invadopodia and their associated degradative activity. Immunoprecipitation studies revealed that STX7 forms distinct SNARE complexes with VAMP2, VAMP3, VAMP7, STX4 and SNAP23. Depletion of VAMP2, VAMP3 or STX4 abrogated invadopodia formation, phenocopying what was seen upon lack of STX7. Whereas depletion of STX4 reduced MT1-MMP level at the cell surfaces, STX7 silencing significantly reduced the invadopodia-associated MT1-MMP pool and increased the non-invadosomal pool. This study highlights STX7 as a major contributor towards the invadopodia formation during cancer cell invasion. This article has an associated First Person interview with the first author of the paper.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".