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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".