DNAR-02. Nuclear potassium governs nuclear integrity and tumour growth in medulloblastoma
Bibliographic record
Abstract
Abstract Potassium is the most abundant intracellular cation. Potassium flux across cell membranes is controlled by potassium channels. While cytoplasmic potassium has been extensively studied, very little is known about the function of potassium in the cell nucleus. Here, we discover that a nuclear potassium channel complex composed of KCNG1 and KCNB1 subunits is a regulator of nuclear potassium in medulloblastoma, the most common malignant pediatric brain tumour. Using genetically encoded potassium sensors, we show that KCNG1 regulates nuclear potassium level independently of cytoplasmic potassium level. KCNG1 knockdown alters the genomic architecture of medulloblastoma cells by stabilizing G-quadruplex DNA and inducing DNA damage. Ultimately, KCNG1 deficiency results in nuclear swelling, thereby compromising nuclear envelope integrity, promoting the formation of micronuclei, and inducing DNA spillage into the cytoplasm to activate the immune-stimulatory cGAS/STING pathway. Kcng1 knockout in a genetically engineered mouse model of medulloblastoma remodels the immune microenvironment, reduces tumour growth, and prolongs mouse survival. Altogether, we identified a potassium channel complex that regulates nuclear potassium to govern nuclear integrity, tumour immune microenvironment, and medulloblastoma growth. Our results demonstrate that targeting nuclear potassium can be leveraged to treat medulloblastoma.
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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".