The magnesium transporter NIPAL1 is a pancreatic islet–expressed protein that conditionally impacts insulin secretion
Bibliographic record
Abstract
Type 2 diabetes is a chronic metabolic disease characterized by pancreatic β-cell dysfunction and peripheral insulin resistance. Among individuals with type 2 diabetes, ∼30% exhibit hypomagnesemia. Hypomagnesemia has been linked to insulin resistance through reduced tyrosine kinase activity of the insulin receptor; however, its impact on pancreatic β-cell function is unknown. In this study, through analysis of several single-cell RNA-sequencing data sets in tandem with quantitative PCR validation in both murine and human islets, we identified NIPAL1 (NIPA-like domain containing 1), encoding a magnesium influx transporter, as an islet-enriched gene. A series of immunofluorescence experiments confirmed NIPAL1 's magnesium-dependent expression and that it specifically localizes to the Golgi in Min6-K8 cells, a pancreatic β-cell–like cell line (mouse insulinoma 6 clone K8). Under varying magnesium concentrations, NIPAL1 knockdown decreased both basal insulin secretion and total insulin content; in contrast, its overexpression increased total insulin content. Although the expression, distribution, and magnesium responsiveness of NIPAL1 in α-TC6 glucagonoma cells (a pancreatic α-cell line) were similar to the observations in Min6-K8 cells, no effect was observed on glucagon secretion in α-TC6 cells under the conditions studied. Overall, these results suggest that NIPAL1 expression is regulated by extracellular magnesium and that down-regulation of this transporter decreases glucose-stimulated insulin secretion and intracellular insulin content, particularly under conditions of hypomagnesemia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".