The modulation of TRPM7 currents by nafamostat mesilate depends directly upon extracellular concentrations of divalent cations
Bibliographic record
Abstract
Concentrations of extracellular divalent cations (Ca2+ and Mg2+) fall substantially during intensive synaptic transmission as well as during some pathophysiological conditions such as epilepsy and brain ischemia. Here we report that a synthetic serine protease inhibitor, nafamostat mesylate (NM), and several of its analogues, block recombinant TRPM7 currents expressed in HEK293T cells in inverse relationship to the concentration of extracellular divalent cations. Lowering extracellular Ca2+ and Mg2+ also evokes a divalent-sensitive non-selective cation current that is mediated by TRPM7 expression in hippocampal neurons. In cultured hippocampal neurons, NM blocked these TRPM7-mediated currents with an apparent affinity of 27 μM, as well as the paradoxical Ca2+ influx associated with lowering extracellular Ca2+. Unexpectedly, pre-exposure to NM strongly potentiated TRPM7 currents. In the presence of physiological concentrations of extracellular divalent cations, NM activates TRPM7. The stimulating effects of NM on TRPM7 currents are also inversely related to extracellular Ca2+ and Mg2+. DAPI and HSB but not netropsin, blocked and stimulated TRPM7. In contrast, mono-cationic, the metabolites of NM, p-GBA and AN, as well as protease inhibitor leupeptin and gabexate failed to substantially modulate TRPM7. NM thus provides a molecular template for the design of putative modulators of TRPM7.
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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".