Distinct Functional Signatures of Human Olfactory and Respiratory Mucus Revealed by Proteomics Combined With Machine Learning
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
Background The nasal cavity includestwo distinct epithelial regions: olfactory and respiratory which fulfilldifferent roles. Despite their differences, their mucus composition, however, is yet not well elucidated.Methods To analyze themucosal secretome, samples from the human olfactory mucus (OM) and respiratorymucus (RM) were collected in a volunteer study with 25 normosmic individualsand analyzed using label-free quantitative proteomics (LFQ), supervised machinelearning (Partial Least Squares Discriminant Analysis, PLS-DA), functionalenrichment via Gene Ontology (GO) and pathway analyses at Reactome database.Results A total of 1,780high-confidence proteins were quantified across 50 samples. The optimizedPLS-DA model achieved robust discrimination between OM and RM (AUC = 0.93 +/- 0.04), identifying distinct molecular signatures. Cross-validation across GO,Reactome, and PLS-DA macro-category analyses confirmed the robustness andbiological coherence of these findings.Conclusions Overall, this study defines twocomplementary mucosal ecosystems: a dynamic olfactory mucus optimized for highmitochondrial activity, (non-motile) ciliary renewal, and autophagy, and animmune-active respiratory mucus specialized in host defense, providing acomprehensive molecular framework of nasal regional specialization.
Details
| Original language | English |
|---|---|
| Article number | e70049 |
| Number of pages | 12 |
| Journal | Proteomics - Clinical Applications |
| Volume | 20 |
| Issue number | 4 |
| Publication status | Published - 17 Jul 2026 |
| Peer-reviewed | Yes |
External IDs
| PubMed | 42466543 |
|---|---|
| ORCID | /0000-0001-9713-0183/work/222089232 |
| ORCID | /0000-0001-6711-7359/work/222089743 |
| Scopus | 105044872786 |
Keywords
Keywords
- Human nasal olfactory and respiratory mucus, Pls-da, Proteomics, Reactome database, Secretome