MT1-MMP Expression Levels and Catalytic Functions Dictate LDL Receptor-Related Protein-1 Ligand Internalization Capacity in U87 Glioblastoma Cells
Bibliographic record
Abstract
Modulations in cell surface receptor ectodomain proteolytic shedding impact on receptor function and cancer biomarker expression. As such, heavily pursued therapeutic avenues have exploited LDL receptor-related protein-1 (LRP-1)-mediated capacity in internalizing Angiopep-2 (An2), a brain-penetrating peptide that allows An2-drug conjugates to cross the blood-brain tumor barrier (BBTB). Given that LRP-1 is proteolytically shed from the cell surface through matrix metalloproteinase (MMP) activity, the balance between MMP expression/function and LRP-1-mediated An2 internalization is unknown. In this study, we found that membrane type-1 (MT1)-MMP expression increased from grade 1 to 4 brain tumors, while that of LRP-1 decreased inversely. MMP pharmacological inhibitors such as Ilomastat, Doxycycline and Actinonin increased in vitro An2 internalization by up to 2.5 fold within a human grade IV-derived U87 glioblastoma cell model. Transient siRNA-mediated MT1-MMP gene silencing resulted in increased basal An2 cell surface binding and intracellular uptake, while recombinant MT1-MMP overexpression reduced both cell surface LRP-1 expression as well as An2 internalization. The addition of Ilomastat to cells overexpressing recombinant MT1-MMP restored LRP-1 expression at the cell surface and An2 uptake to levels comparable to those observed in control cells. Collectively, our data suggest that MT1-MMP expression status dictates An2-mediated internalization processes in part by regulating cell surface LRP-1 functions. Such evidence prompts preclinical evaluations of combined MMP inhibitors/An2-drug conjugate administration to potentially increase the treatment of high-MT1-MMP-expressing brain tumors.
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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".