Galectin-3 Mediates Cross-Talk between K-Ras and Let-7c Tumor Suppressor microRNA
Bibliographic record
Abstract
BACKGROUND: Galectin-3 (Gal-3) and active (GTP-bound) K-Ras contribute to the malignant phenotype of many human tumors by increasing the rate of cell proliferation, survival, and migration. These Gal-3-mediated effects result from a selective binding to K-Ras.GTP, causing increased nanoclustering in the cell membrane and leading to robust Ras signaling. Regulation of the interactions between Gal-3 and active K-Ras is not fully understood. METHODS AND FINDINGS: To gain a better understanding of what regulates the critical interactions between these two proteins, we examined the role of Gal-3 in the regulation of K-Ras by using Gal-3-knockout mouse embryonic-fibroblasts (Gal-3-/- MEFs) and/or Gal-3/Gal-1 double-knockout MEFs. We found that knockout of Gal-3 induced strong downregulation (∼60%) of K-Ras and K-Ras.GTP. The downregulation was somewhat more marked in the double-knockout MEFs, in which we also detected robust inhibition(∼50%) of ERK and Akt activation. These additional effects are probably attributable to inhibition of the weak interactions of K-Ras.GTP with Gal-1. Re-expression of Gal-3 reversed the phenotype of the Gal-3-/- MEFs and dramatically reduced the disappearance of K-Ras in the presence of cycloheximide to the levels seen in wild-type MEFs. Furthermore, phosphorylation of Gal-3 by casein kinase-1 (CK-1) induced translocation of Gal-3 from the nucleus to the cytoplasm and the plasma membrane, leading to K-Ras stabilization accompanied by downregulation of the tumor suppressor miRNA let-7c, known to negatively control K-Ras transcription. CONCLUSIONS: Our results suggest a novel cross-talk between Gal-3-mediated downregulation of let 7c microRNA (which in turn negatively regulates K-Ras transcription) and elucidates the association among Gal-3 let-7c and K-Ras transcription/translation, cellular compartmentalization and activity.
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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 | 0.000 | 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 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".