Sex Dimorphism of Allergen-Induced Secreted Proteins in Murine and Human Lungs
Bibliographic record
Abstract
Biological sex influences disease severity, prevalence and response to therapy in allergic asthma. However, allergen-mediated sex-specific changes in lung protein biomarkers remain undefined. Here, we report sex-related differences in specific proteins secreted in the lungs of both mice and humans, in response to inhaled allergens. Female and male BALB/c mice (7-8 weeks) were intranasally challenged with the allergen house dust mite (HDM) for 2 weeks. Bronchoalveolar lavage fluid (BALF) was collected 24 hour after the last HDM challenge from allergen-naïve and HDM-challenged mice (N=10 per group, each sex). In a human study, adult participants were exposed to nebulized (2 min) allergens (based on individual sensitivity), BALF was obtained after 24 hour (N=5 each female and male). The BALF samples were examined in immunoblots for the abundance of 10 proteins shown to increase in response to allergen in both murine and human BALF, selected from proteomics studies. We showed significant sex-bias in allergen-driven increase in five out of the 10 selected proteins. Of these, increase in eosinophil peroxidase (EPX) was significantly higher in females compared to males, in both mice and human BALF. We also showed specific sex-related differences between murine and human samples. For example, allergen-driven increase in S100A8 and S100A9 was significantly higher in BALF of females compared to males in mice, but significantly higher in males compared to females in humans. Overall, this study provides sex-specific protein biomarkers that are enhanced in response to allergen in murine and human lungs, informing and motivating translational research in allergic asthma.
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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) | 0.002 | 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".