Cytokines reprogram airway sensory neurons in asthma
Bibliographic record
Abstract
ABSTRACT Nociceptor neurons play a crucial role in maintaining the body’s homeostasis by detecting and responding to potential dangers in the environment. However, this function can be detrimental during allergic reactions, since vagal nociceptors can contribute to immune cell infiltration, bronchial hypersensitivity, and mucus imbalance, in addition to causing pain and coughing. Despite this, the specific mechanisms by which nociceptors acquire pro-inflammatory characteristics during allergic reactions are not yet fully understood. In this study, we aimed to investigate the molecular profile of airway nociceptor neurons during allergic airway inflammation and identify the signals driving such reprogramming. Using retrograde tracing and lineage reporting, we identified a unique class of inflammatory vagal nociceptor neurons that exclusively innervate the airways. In the ovalbumin mouse model of airway inflammation, these neurons undergo significant reprogramming characterized by the upregulation of the NPY receptor Npy1r . A screening of cytokines and neurotrophins revealed that IL-1β, IL-13 and BDNF drive part of this reprogramming. IL-13 triggered Npy1r overexpression in nociceptors via the JAK/STAT6 pathway. In parallel, sympathetic neurons and macrophages release NPY in the bronchoalveolar fluid of asthmatic mice, which limits the excitability of nociceptor neurons. Single-cell RNA sequencing of lung immune cells has revealed that a cell-specific knockout of Npy1r in nociceptor neurons in asthmatic mice leads to an increase in airway inflammation mediated by T cells. Opposite findings were observed in asthmatic mice in which nociceptor neurons were chemically ablated. In summary, allergic airway inflammation reprograms airway nociceptor neurons to acquire a pro-inflammatory phenotype, while a compensatory mechanism involving NPY1R limits nociceptor neurons’ activity.
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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 | 0.001 |
| 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".