Cytokines reprogram airway sensory neurons in asthma
Bibliographic record
Abstract
Nociceptor neurons play a crucial role in maintaining the body's homeostasis by detecting and responding to potential environmental dangers. However, this function can be detrimental during allergic reactions, as vagal nociceptors 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 investigate the changes in 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 identify a specific class of inflammatory vagal nociceptor neurons that exclusively innervate the airways. In the ovalbumin mouse model of allergic airway inflammation, these neurons undergo significant reprogramming characterized by the upregulation of the neuropeptide Y (NPY) receptor Npy1r. A screening of cytokines and neurotrophins reveals that interleukin 1β (IL-1β), IL-13, and brain-derived neurotrophic factor (BDNF) drive part of this reprogramming. IL-13 triggers Npy1r overexpression in nociceptors via the JAK/STAT6 pathway. In parallel, NPY is released into the bronchoalveolar fluid of asthmatic mice, which limits the excitability of nociceptor neurons. Single-cell RNA sequencing of lung immune cells reveals that a cell-specific knockout of NPY1R in nociceptor neurons in asthmatic mice altered T cell infiltration. Opposite findings are observed in asthmatic mice in which nociceptor neurons are chemically ablated. In summary, allergic airway inflammation reprograms airway nociceptor neurons to acquire a pro-inflammatory phenotype, while a compensatory mechanism involving NPY1R limits the activity of nociceptor neurons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".