NMDA receptors mediate contractile responses in human airway smooth muscle cells
Bibliographic record
Abstract
Human airway smooth muscle (HASM) exhibits enhanced contractility in asthma. Inflammation is associated with airway hypercontractility, but factors that underpin these features are not fully elucidated. Glutamate toxicity associated with increased plasma glutamate concentrations was observed in airway inflammation, suggesting that multisubunit glutamate receptors, N-methyl-d-aspartate receptors (NMDA-R) contribute to airway hyperreactivity. We tested the hypothesis that HASM expresses NMDA-R subunits that can form functional receptors to mediate contractile responses to specific extracellular ligands. In cultured HASM cells, we measured NMDA-R subunit mRNA and protein abundance by quantitative PCR, immunoblotting, flow cytometry, and epifluorescence immunocytochemistry. We measured mRNA for a number of NMDA-R subunits, including the obligatory NR1 subunit, which we confirmed to be present as a protein. In vitro and ex vivo functional NMDA-R activation in HASM cells was measured using intracellular calcium flux (fura-2 AM), collagen gel contraction assays, and murine thin-cut lung slices (TCLS). NMDA, a pharmacological glutamate analog, induced cytosolic calcium mobilization in cultured HASM cells. We detected three different temporal patterns of calcium response, suggesting the presence of heterogeneous myocyte subpopulations. NMDA-R activation also induced airway contraction in murine TCLS and soft collagen gels seeded with HASM cells. Responses in cells, lung slices, and collagen gels were mediated by NMDA-R, as they could be blocked by (2R)-amino-5-phosphonopentanoate, a specific NMDA-R inhibitor. In summary, we reveal the presence of NMDA-R in HASM that mediate contractile responses via glutamatergic mechanisms. These findings suggest that accumulation of glutamate-like ligands for NMDA-R associated with airway inflammation contributes directly to airway hyperreactivity.
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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) | 0.004 | 0.002 |
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".