Functional live-cell imaging demonstrates that beta1-integrin promotes type IV collagen degradation by breast and prostate cancer cells.
Bibliographic record
Abstract
The ability of tumor cells to adhere to, migrate on, and remodel extracellular matrices is mediated by cell surface receptors such as beta1-integrins. Here we conducted functional live-cell imaging in real time to investigate the effects of modulating beta1-integrin expression and function on proteolytic remodeling of the extracellular matrix. Human breast and prostate cancer cells were grown on reconstituted basement membrane containing a quenched fluorescent form of collagen IV. Generation of cleavage products and the resulting increases in fluorescence were imaged and quantified. Decreases in the expression and activity of beta1-integrin reduced digestion of quenched fluorescent-collagen IV by the breast and prostate cancer cells and correspondingly their invasion through and migration on reconstituted basement membrane. Decreased extracellular matrix degradation also was associated with changes in the constituents of proteolytic pathways: decreases in secretion of the cysteine protease cathepsin B, the matrix metalloproteinase (MMP)-13, and tissue inhibitors of metalloproteinases (TIMP)-1 and 2; a decrease in expression of MMP-14 or membrane type 1 MMP; and an increase in secretion of TIMP-3. This is the first study to demonstrate through functional live-cell imaging that downregulation of beta1-integrin expression and function reduces proteolysis of collagen IV by breast and prostate cancer cells.
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) | 0.002 | 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".