Collagen/β1 integrin signaling up-regulates the ABCC1/MRP-1 transporter in an ERK/MAPK-dependent manner
Bibliographic record
Abstract
The mechanisms by which β1 integrins regulate chemoresistance of cancer cells are still poorly understood. In this study, we report that collagen/β1 integrin signaling inhibits doxorubicin-induced apoptosis of Jurkat and HSB2 leukemic T-cells by up-regulating the expression and function of the ATP-binding cassette C 1 (ABCC1) transporter, also known as multidrug resistance-associated protein 1. We find that collagen but not fibronectin reduces intracellular doxorubicin content and up-regulates the expression levels of ABCC1. Inhibition and knockdown studies show that up-regulation of ABCC1 is necessary for collagen-mediated reduction of intracellular doxorubicin content and collagen-mediated inhibition of doxorubicin-induced apoptosis. We also demonstrate that activation of the extracellular signal-regulated kinase (ERK)/mitogen-activated protein kinase signaling pathway is involved in collagen-induced reduction of intracellular doxorubicin accumulation, collagen-induced up-regulation of ABCC1 expression levels, and collagen-mediated cell survival. Finally, collagen-mediated up-regulation of ABCC1 expression and function also requires actin polymerization. Taken together, our results indicate for the first time that collagen/β1 integrin/ERK signaling up-regulates the expression and function of ABCC1 and suggest that its activation could represent an important pathway in cancer chemoresistance. Thus simultaneous targeting of collagen/β1 integrin and ABCC1 may be more efficient in preventing drug resistance than targeting each pathway alone.
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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) | 0.002 | 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".