A Multidisciplinary Approach Establishes a Link between Transglutaminase 2 and the Kv10.1 Voltage-Dependent K+ Channel in Breast Cancer
Bibliographic record
Abstract
Since the multifunctionality of transglutaminase 2 (TG2) includes extra- and intracellular functions, we investigated the effects of intracellular administration of TG2 inhibitors in three breast cancer cell lines, MDA-MB-231, MDA-MB-436 and MDA-MB-468, which are representative of different triple-negative phenotypes, using a patch-clamp technique. The first cell line has a highly voltage-dependent a membrane current, which is low in the second and almost absent in the third one. While applying a voltage protocol to responsive single cells, injection of TG2 inhibitors triggered a significant decrease of the current in MDA-MB-231 that we attributed to voltage-dependent K+ channels using the specific inhibitors 4-aminopyridine and astemizole. Since the Kv10.1 channel plays a dominant role as a marker of cell migration and survival in breast cancer, we investigated its relationship with TG2 by immunoprecipitation. Our data reveal their physical interaction affects membrane currents in MDA-MB-231 but not in the less sensitive MDA-MB-436 cells. We further correlated the efficacy of TG2 inhibition with metabolic changes in the supernatants of treated cells, resulting in increased concentration of methyl- and dimethylamines, representing possible response markers. In conclusion, our findings highlight the interference of TG2 inhibitors with the Kv10.1 channel as a potential therapeutic tool depending on the specific features of cancer cells.
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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 | 0.001 |
| 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".