Pannexin 1 binds β-catenin to modulate melanoma cell growth and metabolism
Bibliographic record
Abstract
Melanoma is the most aggressive skin malignancy with increasing incidence worldwide. Pannexin1 (PANX1), a member of the pannexin family of channel-forming glycoproteins, regulates cellular processes in melanoma cells including proliferation, migration, and invasion/metastasis. However, the mechanisms responsible for coordinating and regulating PANX1 function remain unclear. Here, we demonstrated a direct interaction between the C-terminal region of PANX1 and the N-terminal portion of β-catenin, a key transcription factor in the Wnt pathway. At the protein level, β-catenin was significantly decreased when PANX1 was either knocked down or inhibited by two PANX1 blockers, Probenecid and Spironolactone. Immunofluorescence imaging showed a disrupted pattern of β-catenin localization at the cell membrane in PANX1-deficient cells, and transcription of several Wnt target genes, including MITF, was suppressed. In addition, a mitochondrial stress test revealed that the metabolism of PANX1-deficient cells was impaired, indicating a role for PANX1 in the regulation of the melanoma cell metabolic profile. Taken together, our data show that PANX1 directly interacts with β-catenin to modulate growth and metabolism in melanoma cells. These findings provide mechanistic insight into PANX1-mediated melanoma progression and may be applicable to other contexts where PANX1 and β-catenin interact as a potential new component of the Wnt signaling pathway.
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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".