Airway Smooth Muscle Cells Express Functional Neurokinin-1 Receptors and the Nerve-Derived Preprotachykinin-A Gene
Bibliographic record
Abstract
Preprotachykinin-A (PPT-A) gene-derived neuropeptides, namely substance P (SP) and neurokinin (NK)A, and their receptors participate in allergen-induced airway responses. Whether airway smooth muscle cells (ASMC) may react directly to SP through expression of the NK-1 receptor or express the gene for the synthesis of SP, the PPT-A gene, is unknown. We demonstrated using reverse transcription-polymerase chain reaction that tracheal SMC (TSMC) from atopic Brown Norway rats contained mRNA transcripts for the full-length isoform of the NK-1 receptor. Flow cytometric analysis indicated that the NK-1 receptor was expressed on the surface of TSMC. This receptor was functional as demonstrated by calcium mobilization in response to SP stimulation. The expression of the NK-1 receptor was not altered in passively sensitized TSMC in response to antigenic stimulation, although this stimulation increased the expression of the chemokine RANTES (regulated on activation, normal T cells expressed and secreted). Using different sets of PCR primers, we showed that TSMC also express the beta, alpha, and its alternative splicing product delta, and possibly the gamma mRNA transcript isoforms of the PPT-A gene. Gene sequencing of the PCR-amplified beta isoform confirmed that it is a transcript product of the rat PPT-A gene, and the production of SP by TSMC was confirmed by enzyme immunoassay. We also showed the beta isoform increased after cell stimulation with rat sera, whether sensitized or not. In conclusion, both the PPT-A gene and NK-1 receptors are expressed by TSMC, which suggests the possibility of autocrine neuropeptidergic mechanisms in these cells. However, these mechanisms are not upregulated by passive sensitization.
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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".