Exploitation of Astrocytes by Glioma Cells to Facilitate Invasiveness: A Mechanism Involving Matrix Metalloproteinase-2 and the Urokinase-Type Plasminogen Activator–Plasmin Cascade
Bibliographic record
Abstract
The presence of reactive astrocytes around glioma cells in the CNS suggests the possibility that these two cell types could be interacting. We addressed whether glioma cells use the astrocyte environment to modulate matrix metalloproteinase-2 (MMP-2), a proteolytic enzyme implicated in the invasiveness of glioma cells. We found that astrocytes in culture produce significant amounts of the pro-form of MMP-2 but undetectable levels of active MMP-2. However, after coculture with the U251N glioma line, astrocyte pro-MMP-2 was converted to the active form. The mechanism of pro-MMP-2 activation in glioma-astrocyte coculture was investigated and was found to involve the urokinase-type plasminogen activator (uPA)-plasmin cascade whereby uPA bound to uPA receptor (uPAR), leading to the conversion of plasminogen to plasmin. The latter cleaved pro-MMP-2 to generate its active form. Furthermore, key components (i.e., uPAR, uPA, and pro-MMP-2) were contributed principally by astrocytes, whereas the U251N glioma cells provided plasminogen. In correspondence with this biochemical cascade, the transmigration of U251N cells through Boyden invasion chambers coated with an extracellular matrix barrier was increased significantly in the presence of astrocytes, and this was inhibited by agents that disrupted the uPA-plasmin cascade. Finally, using resected human glioblastoma specimens, we found that tumor cells, but not astrocytes, expressed plasminogen in situ. We conclude that glioma cells exploit their astrocyte environment to activate MMP-2 and that this leads to the increased invasiveness of glioma cells.
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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) | 0.001 | 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 | 0.001 |
| 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".