Fyn Kinase regulates GluN2B subunit-dominant NMDA receptors in human induced pluripotent stem cell-derived neurons
Bibliographic record
Abstract
NMDA receptor (NMDAR)-mediated fast excitatory neurotransmission is implicated in a broad range of physiological and pathological processes in the mammalian central nervous system. The function and regulation of NMDARs have been extensively studied in neurons from rodents and other non-human species, and in recombinant expression systems. Here, we investigated human NMDARs in situ by using neurons produced by directed differentiation of human induced pluripotent stem cells (iPSCs). The resultant cells showed electrophysiological characteristics demonstrating that they are bona fide neurons. In particular, human iPSC-derived neurons expressed functional ligand-gated ion channels, including NMDARs, AMPA receptors, GABAA receptors, as well as glycine receptors. Pharmacological and electrophysiological properties of NMDAR-mediated currents indicated that these were dominated by receptors containing GluN2B subunits. The NMDAR currents were suppressed by genistein, a broad-spectrum tyrosine kinase inhibitor. The NMDAR currents were also inhibited by a Fyn-interfering peptide, Fyn(39-57), but not a Src-interfering peptide, Src(40-58). Together, these findings are the first evidence that tyrosine phosphorylation regulates the function of NMDARs in human iPSC-derived neurons. Our findings provide a basis for utilizing human iPSC-derived neurons in screening for drugs targeting NMDARs in neurological disorders.
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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".