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Scientific reports2026; 16(1); 5880; doi: 10.1038/s41598-026-38766-3

Proteomic profiling of equine airway mucus reveals compositional changes in asthmatic phenotypes.

Abstract: Mucus hypersecretion and accumulation are hallmark features of equine asthma (EA), a meaningful respiratory disorder in horses occurring in mild to moderate (MEA) and severe (SEA) forms. Changes of the proteomic composition of airway mucus in EA are poorly understood. Using label-free quantitative liquid chromatography-mass spectrometry, we analyzed airway mucus from SEA (n = 10), MEA (n = 6), and healthy (n = 8) horses. We identified and quantified 2,275 proteins including gel-forming mucins MUC5AC and MUC5B and membrane-bound mucins MUC1 and MUC4. Compared with healthy controls, 130 proteins (SEA) and 103 (MEA) were significantly increased. 38 were elevated in SEA relative to MEA, 10 were higher in MEA. MUC4 was markedly increased in both, correlated with bronchoalveolar lavage neutrophils (ρ = 0.790, p = 4.9E-06), and distinguished excellently between healthy and asthmatics (AUC = 1.0, 95% CI: 1-1), similar to 23 other proteins. MUC5AC was elevated in both, whereas MUC5B only in SEA. MUC1 did not differ between groups. Changes in mucus-modifying proteins, including glycosyltransferases and aquaporins, suggest altered mucus properties in EA. Functional enrichment analyses revealed inflammation-, tissue remodeling- and coagulation-linked GO terms and pathways in EA. The distinct proteomic profiles add to the understanding of EA and may offer novel targets for phenotype-specific biomarkers and therapy.
© 2026. The Author(s).
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Publication Date: 2026-02-10 PubMed ID: 41667845PubMed Central: PMC12894910DOI: 10.1038/s41598-026-38766-3Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This research investigates changes in the protein composition of airway mucus in horses suffering from equine asthma, identifying differences between mild to moderate and severe forms compared to healthy horses.
  • The study reveals specific proteins that increase in diseased states and highlights potential biomarkers and therapeutic targets based on these proteomic changes.

Background

  • Equine asthma (EA) is a common respiratory disease in horses characterized by mucus hypersecretion and mucus accumulation in the airways.
  • EA presents in two main forms: severe equine asthma (SEA) and mild to moderate equine asthma (MEA), both impacting horse respiratory health.
  • The proteomic composition—meaning the full set of proteins present—in airway mucus under these conditions was previously not well-understood.

Study Objective

  • To analyze and compare the protein profiles of airway mucus from horses with SEA, MEA, and healthy controls.
  • To identify protein changes associated with different asthma phenotypes and find markers that could differentiate disease severity.

Methods

  • Samples of airway mucus were collected from three groups of horses: SEA (10 horses), MEA (6 horses), and healthy controls (8 horses).
  • Protein analysis was conducted using label-free quantitative liquid chromatography-mass spectrometry (LC-MS), a technique that separates and quantifies proteins without labeling.
  • Identified proteins were quantified, and comparative analysis performed to determine which proteins were increased or decreased among groups.

Key Findings

  • A total of 2,275 proteins were identified and quantified in the mucus samples.
  • Among these were key mucin proteins: gel-forming mucins MUC5AC and MUC5B, and membrane-bound mucins MUC1 and MUC4.
  • Compared to healthy horses:
    • 130 proteins were significantly increased in SEA samples.
    • 103 proteins were significantly increased in MEA samples.
  • Comparing SEA to MEA:
    • 38 proteins were elevated specifically in SEA.
    • 10 proteins were higher in MEA.
  • MUC4 was notably increased in both MEA and SEA and showed a strong correlation with neutrophil counts in bronchoalveolar lavage fluid (rho = 0.790, p = 4.9 x 10^-6), indicating a relationship with airway inflammation.
  • MUC4 also distinguished asthmatic from healthy horses with perfect accuracy (AUC = 1.0), alongside 23 other proteins showing similar diagnostic value.
  • MUC5AC levels were elevated in both SEA and MEA, while MUC5B was increased only in SEA.
  • MUC1 levels did not differ significantly between groups.

Additional Observations

  • Proteins involved in mucus modification such as glycosyltransferases (which add sugar groups to proteins) and aquaporins (water channel proteins) showed altered levels, implying changes in mucus properties like viscosity and hydration in EA.
  • Functional enrichment analyses (bioinformatics methods that identify overrepresented biological functions and pathways) indicated involvement of:
    • Inflammation-related processes
    • Tissue remodeling pathways
    • Coagulation pathways

    These reflect the complex pathological environment in asthmatic airways.

Implications and Conclusions

  • The distinct proteomic profiles characterized in this study enhance understanding of the molecular differences between severe and mild to moderate equine asthma.
  • Identification of mucins, especially MUC4, as potential biomarkers opens avenues for improved diagnostic accuracy and disease monitoring in horses.
  • Alterations in mucus-related proteins suggest potential targets for therapies aimed at modifying mucus properties to alleviate disease symptoms.
  • Overall, the findings contribute valuable information toward phenotype-specific biomarker discovery and personalized treatment strategies in equine respiratory medicine.

Cite This Article

APA
Bartenschlager F, Kuropka B, Schmitz P, Dumke F, Landmann K, Gruber AD, Weise C, Schnabel CL, Gehlen H, Mundhenk L. (2026). Proteomic profiling of equine airway mucus reveals compositional changes in asthmatic phenotypes. Sci Rep, 16(1), 5880. https://doi.org/10.1038/s41598-026-38766-3

Publication

Scientific reports
ISSN: 2045-2322
NlmUniqueID: 101563288
Country: England
Language: English
Volume: 16
Issue: 1
Pages: 5880
PII: 5880

Researcher Affiliations

Bartenschlager, Florian
  • Institute of Veterinary Pathology, Faculty of Veterinary Medicine, Freie Universität Berlin, Berlin, Germany.
Kuropka, Benno
  • Institute of Chemistry and Biochemistry, Department of Biology, Chemistry, Pharmacy , Freie Universität Berlin, Berlin, Germany.
Schmitz, Philip
  • Equine Clinic, Faculty of Veterinary Medicine, Freie Universität Berlin, Berlin, Germany.
Dumke, Fiona
  • Institute of Veterinary Pathology, Faculty of Veterinary Medicine, Freie Universität Berlin, Berlin, Germany.
Landmann, Katharina
  • Institute of Veterinary Pathology, Faculty of Veterinary Medicine, Freie Universität Berlin, Berlin, Germany.
Gruber, Achim D
  • Institute of Veterinary Pathology, Faculty of Veterinary Medicine, Freie Universität Berlin, Berlin, Germany.
Weise, Christoph
  • Institute of Chemistry and Biochemistry, Department of Biology, Chemistry, Pharmacy , Freie Universität Berlin, Berlin, Germany.
Schnabel, Christiane L
  • Institute of Immunology, Faculty of Veterinary Medicine, Leipzig University, Leipzig, Germany.
Gehlen, Heidrun
  • Equine Clinic, Faculty of Veterinary Medicine, Freie Universität Berlin, Berlin, Germany.
Mundhenk, Lars
  • Institute of Veterinary Pathology, Faculty of Veterinary Medicine, Freie Universität Berlin, Berlin, Germany. Lars.mundhenk@fu-berlin.de.

MeSH Terms

  • Animals
  • Horses
  • Asthma / metabolism
  • Asthma / veterinary
  • Asthma / pathology
  • Mucus / metabolism
  • Proteomics / methods
  • Horse Diseases / metabolism
  • Horse Diseases / pathology
  • Phenotype
  • Proteome / metabolism
  • Mucins / metabolism
  • Mucin 5AC / metabolism
  • Female
  • Respiratory Mucosa / metabolism
  • Male
  • Mucin-5B / metabolism

Conflict of Interest Statement

Declarations. Competing interests: The authors FB, BK, CW, FD, ADG, HG, and LM are listed as inventors on a European patent application titled “Biomarkers for Diagnosing Equine Asthma” (applicant: Freie Universität Berlin, application number: EP 4 260 906 A1, published: 10/18/2023, aspect of manuscript covered in patent application: Identification of new protein biomarkers for equine asthma such as MUC4). PS, KL and CLS declare no conflict of interest.

References

This article includes 106 references
  1. Couetil LL. Inflammatory airway disease of horses–revised consensus statement.. 503–515 (2016).
    doi: 10.1111/jvim.13824pmc: PMC4913592pubmed: 26806374google scholar: lookup
  2. Wysocka B, Kluciński W. Usefulness of the assessment of discharge accumulation in the lower airways and tracheal septum thickening in the differential diagnosis of recurrent airway obstruction (RAO) and inflammatory airway disease (IAD) in the horse.. 247–253 (2014).
    doi: 10.2478/pjvs-2014-0035pubmed: 24988850google scholar: lookup
  3. Drespling J. Endoscopically assessed mucus parameters in equine asthma: relationship to clinical history and cytological findings data.. .
    pubmed: 40704584doi: 10.1111/evj.70002google scholar: lookup
  4. Maxie, G. . Vol. 2Elsevier health sciences, (2015).
  5. Larsen M. Associations between clinical signs, endoscopic and cytological findings in equine Bronchoalveolar lavage samples.. .
    doi: 10.1111/eve.14174google scholar: lookup
  6. Ferrari CR. Horses with pasture asthma have airway remodeling that is characteristic of human asthma.. 144–158 (2018).
    doi: 10.1177/0300985817741729pubmed: 29254472google scholar: lookup
  7. Williams K, Roman J. Studying human respiratory disease in animals–role of induced and naturally occurring models.. 220–232 (2016).
    doi: 10.1002/path.4658pubmed: 26467890google scholar: lookup
  8. Léguillette R. Recurrent airway obstruction—heaves.. 63–86 (2003).
    doi: 10.1016/S0749-0739(02)00067-6pubmed: 12747662google scholar: lookup
  9. Bullone M, Lavoie JP. The equine asthma model of airway remodeling: from a veterinary to a human perspective.. 223–236 (2020).
    doi: 10.1007/s00441-019-03117-4pubmed: 31713728google scholar: lookup
  10. Woodrow JS, Sheats MK, Cooper B, Bayless R, Asthma. The use of animal models and their translational utility.. 1091 (2023).
    doi: 10.3390/cells12071091pmc: PMC10093022pubmed: 37048164google scholar: lookup
  11. Bullone M, Lavoie JP. Asthma of horses and men--how can equine heaves help Us better understand human asthma immunopathology and its functional consequences?. 97–105 (2015).
    doi: 10.1016/j.molimm.2014.12.005pubmed: 25547716google scholar: lookup
  12. Leduc L, Leclère M, Lavoie JP. Towards personalized medicine for the treatment of equine asthma.. 106125 (2024).
    doi: 10.1016/j.tvjl.2024.106125pubmed: 38704018google scholar: lookup
  13. Fahy JV, Dickey BF. Airway mucus function and dysfunction.. 2233–2247 (2010).
    doi: 10.1056/NEJMra0910061pmc: PMC4048736pubmed: 21121836google scholar: lookup
  14. Hill DB, Button B, Rubinstein M, Boucher RC. Physiology and pathophysiology of human airway mucus.. 1757–1836 (2022).
    doi: 10.1152/physrev.00004.2021pmc: PMC9665957pubmed: 35001665google scholar: lookup
  15. Bansil R, Turner BS. The biology of mucus: Composition, synthesis and organization.. 3–15 (2018).
    doi: 10.1016/j.addr.2017.09.023pubmed: 28970050google scholar: lookup
  16. Jefcoat AM. Persistent mucin glycoprotein alterations in equine recurrent airway obstruction. L704–712 (2001).
    doi: 10.1152/ajplung.2001.281.3.L704pubmed: 11504699google scholar: lookup
  17. Gerber V, King M, Schneider DA, Robinson NE. Tracheobronchial mucus viscoelasticity during environmental challenge in horses with recurrent airway obstruction. 411–417 (2000).
    doi: 10.2746/042516400777591183pubmed: 11037263google scholar: lookup
  18. Rousseau K. Muc5b and Muc5ac are the major oligomeric mucins in equine airway mucus. L1396–1404 (2007).
    doi: 10.1152/ajplung.00444.2006pubmed: 17293373google scholar: lookup
  19. Rousseau K. Muc5b is the major polymeric mucin in mucus from thoroughbred horses with and without airway mucus accumulation. PLoS One e19678 (2011).
    doi: 10.1371/journal.pone.0019678pmc: PMC3094342pubmed: 21602926google scholar: lookup
  20. Gerber V. Mucin genes in horse airways: MUC5AC, but not MUC2, May play a role in recurrent airway obstruction. 252–257 (2003).
    doi: 10.2746/042516403776148291pubmed: 12755427google scholar: lookup
  21. Bright L. Functional modelling of an equine Bronchoalveolar lavage fluid proteome provides experimental confirmation and functional annotation of equine genome sequences. 395–405 (2011).
    doi: 10.1111/j.1365-2052.2010.02158.xpubmed: 21749422google scholar: lookup
  22. Bright LA. Modeling the pasture-associated severe equine asthma Bronchoalveolar lavage fluid proteome identifies molecular events mediating neutrophilic airway inflammation. 43–63 (2019).
    doi: 10.2147/VMRR.S194427pmc: PMC6504673pubmed: 31119093google scholar: lookup
  23. Feutz MM. Proteomic analysis of Bronchoalveolar lavage fluid in an equine model of asthma during a natural antigen exposure trial. 123–131 (2012).
    doi: 10.5584/jiomics.v2i2.112google scholar: lookup
  24. Racine J. Comparison of genomic and proteomic data in recurrent airway obstruction affected horses using ingenuity pathway analysis. 1–10 (2011).
    doi: 10.1186/1746-6148-7-48pmc: PMC3174119pubmed: 21843342google scholar: lookup
  25. Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. 1315–1316 (2010).
    doi: 10.1097/JTO.0b013e3181ec173dpubmed: 20736804google scholar: lookup
  26. Cohen, J. 2nd edn, Vol. 567 (Routledge, 2013).
  27. Couetil L. Equine asthma: current Understanding and future directions. 450 (2020).
    doi: 10.3389/fvets.2020.00450pmc: PMC7438831pubmed: 32903600google scholar: lookup
  28. Ward C. Airway inflammation, basement membrane thickening and bronchial hyperresponsiveness in asthma. 309–316 (2002).
    doi: 10.1136/thorax.57.4.309pmc: PMC1746305pubmed: 11923548google scholar: lookup
  29. Woodruff PG. T-helper type 2–driven inflammation defines major subphenotypes of asthma. 388–395 (2009).
    doi: 10.1164/rccm.200903-0392OCpmc: PMC2742757pubmed: 19483109google scholar: lookup
  30. Setlakwe, E. L., Lemos, K. R., Lavoie-Lamoureux, A., Duguay, J. D. & Lavoie, J. P. Airway collagen and elastic fiber content correlates with lung function in equine heaves. , L252–260. 10.1152/ajplung.00019.2014 (2014).n
    doi: 10.1152/ajplung.00019.2014pubmed: 24879055google scholar: lookup
  31. Bullone, M., Chevigny, M., Allano, M., Martin, J. G. & Lavoie, J. P. Technical and physiological determinants of airway smooth muscle mass in endobronchial biopsy samples of asthmatic horses. . , 806–815. 10.1152/japplphysiol.00468.2014 (2014).n
    doi: 10.1152/japplphysiol.00468.2014pubmed: 25103978google scholar: lookup
  32. Herszberg, B., Ramos-Barbón, D., Tamaoka, M., Martin, J. G. & Lavoie, J. P. Heaves, an asthma-like equine disease, involves airway smooth muscle remodeling. , 382–388. 10.1016/j.jaci.2006.03.044 (2006).n
    doi: 10.1016/j.jaci.2006.03.044pubmed: 16890762google scholar: lookup
  33. Leclere, M. et al. Effect of antigenic exposure on airway smooth muscle remodeling in an equine model of chronic asthma. , 181–187. 10.1165/rcmb.2010-0300OC (2011).n
    doi: 10.1165/rcmb.2010-0300OCpubmed: 20935189google scholar: lookup
  34. Range, F., Mundhenk, L. & Gruber, A. D. A soluble secreted glycoprotein (eCLCA1) is overexpressed due to goblet cell hyperplasia and metaplasia in horses with recurrent airway obstruction. , 901–911. 10.1354/vp.44-6-901 (2007).n
    doi: 10.1354/vp.44-6-901pubmed: 18039903google scholar: lookup
  35. Lugo, J. et al. Airway inflammation is associated with mucous cell metaplasia and increased intraepithelial stored mucosubstances in horses. , 293–301. 10.1016/j.tvjl.2005.04.018 (2006).n
    doi: 10.1016/j.tvjl.2005.04.018pubmed: 15925524google scholar: lookup
  36. Bessonnat, A., Hélie, P., Grimes, C. & Lavoie, J. P. Airway remodeling in horses with mild and moderate asthma. , 285–291. 10.1111/jvim.16333 (2022).n
    doi: 10.1111/jvim.16333pmc: PMC8783337pubmed: 34877706google scholar: lookup
  37. ORDOÑEZ, C. L. et al. Mild and moderate asthma is associated with airway goblet cell hyperplasia and abnormalities in mucin gene expression. , 517–523. 10.1164/ajrccm.163.2.2004039 (2001).n
    doi: 10.1164/ajrccm.163.2.2004039pubmed: 11179133google scholar: lookup
  38. Tan, H. T. T. et al. Tight junction, mucin, and inflammasome-related molecules are differentially expressed in eosinophilic, mixed, and neutrophilic experimental asthma in mice. , 294–307. 10.1111/all.13619 (2019).n
    doi: 10.1111/all.13619pubmed: 30267575google scholar: lookup
  39. Evans, C. M. et al. The polymeric mucin Muc5ac is required for allergic airway hyperreactivity. , 6281. 10.1038/ncomms7281 (2015).n
    doi: 10.1038/ncomms7281pmc: PMC4333679pubmed: 25687754google scholar: lookup
  40. Tajiri, T. et al. Pathophysiological relevance of sputum MUC5AC and MUC5B levels in patients with mild asthma. , 193–199. 10.1016/j.alit.2021.09.003 (2022).
    doi: 10.1016/j.alit.2021.09.003pubmed: 34656442google scholar: lookup
  41. Lachowicz-Scroggins, M. E. et al. Abnormalities in MUC5AC and MUC5B protein in airway mucus in asthma. , 1296–1299. 10.1164/rccm.201603-0526LE (2016).n
    doi: 10.1164/rccm.201603-0526LEpmc: PMC5114443pubmed: 27845589google scholar: lookup
  42. Nath, S. & Mukherjee, P. MUC1: a multifaceted oncoprotein with a key role in cancer progression. , 332–342. 10.1016/j.molmed.2014.02.007 (2014).n
    doi: 10.1016/j.molmed.2014.02.007pmc: PMC5500204pubmed: 24667139google scholar: lookup
  43. Chaturvedi, P., Singh, A. P. & Batra, S. K. Structure, evolution, and biology of the MUC4 mucin. , 966–981. 10.1096/fj.07-9673rev (2008).n
    doi: 10.1096/fj.07-9673revpmc: PMC2835492pubmed: 18024835google scholar: lookup
  44. Soto, P., Zhang, J. & Carraway, K. L. Enzymatic cleavage as a processing step in the maturation of Muc4/sialomucin complex. , 1267–1274. 10.1002/jcb.20718 (2006).n
    doi: 10.1002/jcb.20718pubmed: 16329125google scholar: lookup
  45. Kato, K., Lillehoj, E. P., Lu, W. & Kim, K. C. MUC1: the first respiratory mucin with an anti-inflammatory function. 10.3390/jcm6120110 (2017).
    pmc: PMC5742799pubmed: 29186029
  46. Blalock, T. D., Spurr-Michaud, S. J., Tisdale, A. S. & Gipson, I. K. Release of membrane-associated mucins from ocular surface epithelia. , 1864–1871. 10.1167/iovs.07-1081 (2008).n
    doi: 10.1167/iovs.07-1081pmc: PMC2622730pubmed: 18436821google scholar: lookup
  47. Joo, N. S. et al. Proteomic analysis of pure human airway gland mucus reveals a large component of protective proteins. . , e0116756. 10.1371/journal.pone.0116756 (2015).n
    doi: 10.1371/journal.pone.0116756pmc: PMC4338240pubmed: 25706550google scholar: lookup
  48. Damera, G., Xia, B. & Sachdev, G. P. IL-4 induced MUC4 enhancement in respiratory epithelial cells in vitro is mediated through JAK-3 selective signaling. , 1–12. 10.1186/1465-9921-7-39 (2006).n
    doi: 10.1186/1465-9921-7-39pmc: PMC1435893pubmed: 16551361google scholar: lookup
  49. Zhou, X. et al. Sialylation of MUC4β N-glycans by ST6GAL1 orchestrates human airway epithelial cell differentiation associated with type-2 inflammation. . 10.1172/jci.insight.122475 (2019).
    pmc: PMC6483602pubmed: 30730306
  50. Hattori, T., Zhou, X., Trudeau, J. B. & Wenzel, S. E. MUC4 protein is increased in severe asthmatic bronchial epithelial cells. , A2404
  51. Padoan, E. et al. Gene expression profiles of the Immuno-Transcriptome in equine asthma. , 4. 10.3390/ani13010004 (2023).
    doi: 10.3390/ani13010004pmc: PMC9817691pubmed: 36611613google scholar: lookup
  52. Fahy, J. V. Goblet cell and mucin gene abnormalities in Asthma*. , 320S–326S. 10.1378/chest.122.6_suppl.320S (2002).n
    doi: 10.1378/chest.122.6_suppl.320Spubmed: 12475809google scholar: lookup
  53. Gorman, H., Moreau, F., Dufour, A. & Chadee, K. IgGFc-binding protein and MUC2 mucin produced by colonic goblet-like cells spatially interact non-covalently and regulate wound healing. 14–2023. 10.3389/fimmu.2023.1211336 (2023).
    pmc: PMC10285406pubmed: 37359538
  54. Gorman, H., Moreau, F., Kim, A. & Chadee, K. FCGBP stabilizes colonic MUC2 mucin structural integrity in innate host defense against entamoeba histolytica. 10.1096/fasebj.2021.35.S1.00451 (2021).
  55. Ehrencrona, E. et al. The IgGFc-binding protein FCGBP is secreted with all GDPH sequences cleaved but maintained by interfragment disulfide bonds. 10.1016/j.jbc.2021.100871 (2021).
    pmc: PMC8267560pubmed: 34126068
  56. Park, S. W. et al. The protein disulfide isomerase AGR2 is essential for production of intestinal mucus. . , 6950–6955. 10.1073/pnas.0808722106 (2009).n
    doi: 10.1073/pnas.0808722106pmc: PMC2678445pubmed: 19359471google scholar: lookup
  57. Schroeder, B. W. et al. AGR2 is induced in asthma and promotes allergen-induced mucin overproduction. , 178–185. 10.1165/rcmb.2011-0421OC (2012).n
    doi: 10.1165/rcmb.2011-0421OCpmc: PMC3423459pubmed: 22403803google scholar: lookup
  58. Varki, A., Cummings, R. D., Esko, J. D. et al. Essentials of Glycobiology [Internet]. 3rd edn. Cold Spring Harbor Laboratory Press (2015–2017). https://www.ncbi.nlm.nih.gov/books/NBK310274/.
    pubmed: 27010055
  59. Bennett, E. P. et al. Control of mucin-type O-glycosylation: A classification of the polypeptide GalNAc-transferase gene family. , 736–756. 10.1093/glycob/cwr182 (2011).n
    doi: 10.1093/glycob/cwr182pmc: PMC3409716pubmed: 22183981google scholar: lookup
  60. Venkitachalam, S. et al. Biochemical and functional characterization of glycosylation-associated mutational landscapes in colon cancer. , 23642. 10.1038/srep23642 (2016).n
    doi: 10.1038/srep23642pmc: PMC4804330pubmed: 27004849google scholar: lookup
  61. Solatycka, A. et al. MUC1 in human and murine mammary carcinoma cells decreases the expression of core 2 β1,6-N-acetylglucosaminyltransferase and β-galactoside α2,3-sialyltransferase. , 1042–1054. 10.1093/glycob/cws075 (2012).n
    doi: 10.1093/glycob/cws075pubmed: 22534569google scholar: lookup
  62. Xia, B., Royall, J. A., Damera, G., Sachdev, G. P. & Cummings, R. D. Altered O-glycosylation and sulfation of airway mucins associated with cystic fibrosis. , 747–775. 10.1093/glycob/cwi061 (2005).n
    doi: 10.1093/glycob/cwi061pubmed: 15994837google scholar: lookup
  63. Harris, E. S. et al. Reduced sialylation of airway mucin impairs mucus transport by altering the biophysical properties of mucin. , 16568. 10.1038/s41598-024-66510-2 (2024).n
    doi: 10.1038/s41598-024-66510-2pmc: PMC11255327pubmed: 39019950google scholar: lookup
  64. Crouzier, T. et al. Modulating mucin hydration and lubrication by deglycosylation and polyethylene glycol binding. . , 1500308. 10.1002/admi.201500308 (2015).
    doi: 10.1002/admi.201500308google scholar: lookup
  65. Kameyama, A., Nishijima, R. & Yamakoshi, K. Bmi-1 regulates mucin levels and mucin O-glycosylation in the submandibular gland of mice. . , e0245607. 10.1371/journal.pone.0245607 (2021).n
    doi: 10.1371/journal.pone.0245607pmc: PMC7815129pubmed: 33465144google scholar: lookup
  66. Jenssen, A. O., Olav, S., Harbitz, O. & and The importance of lysozyme for the viscosity of sputum from patients with chronic obstructive lung disease. , 727–731. 10.3109/00365518009095588 (1980).n
    doi: 10.3109/00365518009095588pubmed: 7280551google scholar: lookup
  67. Ablimit, A. et al. Changes in water channel Aquaporin 1 and Aquaporin 5 in the small airways and the alveoli in a rat asthma model. , 68–73. 10.1016/j.micron.2012.10.016 (2013).n
    doi: 10.1016/j.micron.2012.10.016pubmed: 23199524google scholar: lookup
  68. Verkman, A. S. Role of Aquaporins in lung liquid physiology. , 324–330. 10.1016/j.resp.2007.02.012 (2007).n
    doi: 10.1016/j.resp.2007.02.012pmc: PMC3315286pubmed: 17369110google scholar: lookup
  69. Erickson, N. A., Gruber, A. D. & Mundhenk, L. The family of chloride channel Regulator, Calcium-activated proteins in the feline respiratory tract: A comparative perspective on airway diseases in man and animal models. , 39–53. 10.1016/j.jcpa.2019.10.193 (2020).n
    doi: 10.1016/j.jcpa.2019.10.193pubmed: 31955802google scholar: lookup
  70. Fernandez-Blanco, J. A. et al. Attached stratified mucus separates bacteria from the epithelial cells in COPD lungs. . 10.1172/jci.insight.120994 (2018).
    pmc: PMC6171804pubmed: 30185674
  71. Plog, S., Mundhenk, L., Klymiuk, N. & Gruber, A. D. Genomic, tissue expression, and protein characterization of pCLCA1, a putative modulator of cystic fibrosis in the pig. , 1169–1181. 10.1369/jhc.2009.954594 (2009).n
    doi: 10.1369/jhc.2009.954594pmc: PMC2778090pubmed: 19755716google scholar: lookup
  72. Leverkoehne, I. & Gruber, A. D. The murine mCLCA3 (Alias gob-5) protein is located in the mucin granule membranes of Intestinal, Respiratory, and uterine goblet cells. . , 829–838. 10.1177/002215540205000609 (2002).
    doi: 10.1177/002215540205000609pubmed: 12019299google scholar: lookup
  73. Anton, F., Leverkoehne, I., Mundhenk, L., Thoreson, W. B. & Gruber, A. D. Overexpression of eCLCA1 in small airways of horses with recurrent airway obstruction. , 1011–1021. 10.1369/jhc.4A6599.2005 (2005).n
    doi: 10.1369/jhc.4A6599.2005pmc: PMC1383431pubmed: 15879574google scholar: lookup
  74. Lycke, N., Erlandsson, L., Ekman, L., Schön, K. & Leanderson, T. Lack of J chain inhibits the transport of gut IgA and abrogates the development of intestinal antitoxic protection. , 913–919. 10.4049/jimmunol.163.2.913 (1999).n
    doi: 10.4049/jimmunol.163.2.913pubmed: 10395687google scholar: lookup
  75. Kawasaki, K., Ohta, Y., Castro, C. D. & Flajnik, M. F. The Immunoglobulin J chain is an evolutionarily co-opted chemokine. . , e2318995121. 10.1073/pnas.2318995121 (2024).n
    doi: 10.1073/pnas.2318995121pmc: PMC10801876pubmed: 38215184google scholar: lookup
  76. Mostov, K. E. Transepithelial transport of Immunoglobulins. , 63–84. 10.1146/annurev.iy.12.040194.000431 (1994).n
    doi: 10.1146/annurev.iy.12.040194.000431pubmed: 8011293google scholar: lookup
  77. Wei, H. & Wang, J. Y. Role of polymeric Immunoglobulin receptor in IgA and IgM transcytosis. 10.3390/ijms22052284 (2021).
    pmc: PMC7956327pubmed: 33668983
  78. Jentsch, M. C. et al. Aspergillus fumigatus binding IgA and IgG1 are increased in Bronchoalveolar lavage fluid of horses with neutrophilic asthma. 10.3389/fimmu.2024.1406794 (2024).
    pmc: PMC11215007pubmed: 38953030
  79. Robinson, N. E. et al. Coughing, mucus accumulation, airway obstruction, and airway inflammation in control horses and horses affected with recurrent airway obstruction. , 550–557. 10.2460/ajvr.2003.64.550 (2003).n
    doi: 10.2460/ajvr.2003.64.550pubmed: 12755293google scholar: lookup
  80. Richard, E. A., Fortier, G. D., Lekeux, P. M. & Van Erck, E. Laboratory findings in respiratory fluids of the poorly-performing horse. , 115–122. 10.1016/j.tvjl.2009.05.003 (2010).n
    doi: 10.1016/j.tvjl.2009.05.003pubmed: 19481964google scholar: lookup
  81. Gerber, V., Ii, S., Robinson, N. & H. & Owner assessment in judging the efficacy of airway disease treatment. , 153–158. 10.1111/j.2042-3306.2010.00156.x (2011).n
    doi: 10.1111/j.2042-3306.2010.00156.xpubmed: 21592208google scholar: lookup
  82. Fogarty, U. & Buckley, T. Bronchoalveolar lavage findings in horses with exercise intolerance. , 434–437. 10.1111/j.2042-3306.1991.tb03756.x (1991).n
    doi: 10.1111/j.2042-3306.1991.tb03756.xpubmed: 1778160google scholar: lookup
  83. Couetil, L. & Denicola, D. Blood gas, plasma lactate and Bronchoalveolar lavage cytology analyses in racehorses with respiratory disease. , 77–82. 10.1111/j.2042-3306.1999.tb05193.x (1999).
    doi: 10.1111/j.2042-3306.1999.tb05193.xpubmed: 10659227google scholar: lookup
  84. Wasko, A. et al. Evaluation of a risk-screening questionnaire to detect equine lung inflammation: results of a large field study. , 145–152. 10.1111/j.2042-3306.2010.00150.x (2011).n
    doi: 10.1111/j.2042-3306.2010.00150.xpubmed: 21592207google scholar: lookup
  85. Beekman, L., Tohver, T. & Léguillette, R. Comparison of cytokine mRNA expression in the Bronchoalveolar lavage fluid of horses with inflammatory airway disease and Bronchoalveolar lavage mastocytosis or neutrophilia using REST software analysis. , 153–161. 10.1111/j.1939-1676.2011.00847.x (2012).n
    doi: 10.1111/j.1939-1676.2011.00847.xpubmed: 22168153google scholar: lookup
  86. Hughes, K. J. et al. Evaluation of cytokine mRNA expression in Bronchoalveolar lavage cells from horses with inflammatory airway disease. , 82–89. 10.1016/j.vetimm.2010.11.018 (2011).n
    doi: 10.1016/j.vetimm.2010.11.018pubmed: 21194756google scholar: lookup
  87. Hoffman, A., Mazan, M. & Ellenberg, S. Association between Bronchoalveolar lavage cytologic features and airway reactivity in horses with a history of exercise intolerance. , 176–181. 10.2460/ajvr.1998.59.02.176 (1998).n
    doi: 10.2460/ajvr.1998.59.02.176pubmed: 9492932google scholar: lookup
  88. Couëtil, L. L., Rosenthal, F. S., DeNicola, D. B. & Chilcoat, C. D. Clinical signs, evaluation of Bronchoalveolar lavage fluid, and assessment of pulmonary function in horses with inflammatory respiratory disease. , 538–546. 10.2460/ajvr.2001.62.538 (2001).n
    doi: 10.2460/ajvr.2001.62.538pubmed: 11327461google scholar: lookup
  89. Derksen, F. J., Scott, J. S., Miller, D. C., Slocombe, R. F. & Robinson, N. E. Bronchoalveolar lavage in ponies with recurrent airway obstruction (heaves). , 1066–1070. 10.1164/arrd.1985.132.5.1066 (1985).n
    doi: 10.1164/arrd.1985.132.5.1066pubmed: 4062037google scholar: lookup
  90. Hare, J. E. & Viel, L. Pulmonary eosinophilia associated with increased airway responsiveness in young racing horses. , 163–170. 10.1111/j.1939-1676.1998.tb02112.x (1998).n
    doi: 10.1111/j.1939-1676.1998.tb02112.xpubmed: 9595377google scholar: lookup
  91. Mundhenk, L., Bartenschlager, F., Gruber, A., Gehlen, H., Weise, C., Kuropka, B., Dumke, F. L., Biomarkers for Diagnosing Equine Asthma. Patent: EP4260906 (2023).
  92. Rappsilber, J., Mann, M. & Ishihama, Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using stagetips. , 1896–1906. 10.1038/nprot.2007.261 (2007).n
    doi: 10.1038/nprot.2007.261pubmed: 17703201google scholar: lookup
  93. Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. , 2301–2319. 10.1038/nprot.2016.136 (2016).n
    doi: 10.1038/nprot.2016.136pubmed: 27809316google scholar: lookup
  94. Pajic, P. et al. A mechanism of gene evolution generating mucin function. , eabm8757. 10.1126/sciadv.abm8757 (2022).n
    doi: 10.1126/sciadv.abm8757pmc: PMC9417175pubmed: 36026444google scholar: lookup
  95. Tyanova, S. et al. The perseus computational platform for comprehensive analysis of (prote)omics data. . , 731–740. 10.1038/nmeth.3901 (2016).n
    doi: 10.1038/nmeth.3901pubmed: 27348712google scholar: lookup
  96. McKinney, W. Data structures for statistical computing in python. (51-56). 10.25080/Majora-92bf1922-00a (2010).
  97. Harris, C. R. et al. Array programming with numpy. , 357–362. 10.1038/s41586-020-2649-2 (2020).n
    doi: 10.1038/s41586-020-2649-2pmc: PMC7759461pubmed: 32939066google scholar: lookup
  98. Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in python. . , 261–272. 10.1038/s41592-019-0686-2 (2020).n
    doi: 10.1038/s41592-019-0686-2pmc: PMC7056644pubmed: 32015543google scholar: lookup
  99. Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with python. , 92–96. 10.25080/Majora-92bf1922-011 (2010).
    doi: 10.25080/Majora-92bf1922-011google scholar: lookup
  100. Pedregosa, F. et al. Scikit-learn: machine learning in python. , 2825–2830. 10.48550/arXiv.1201.0490 (2011).
    doi: 10.48550/arXiv.1201.0490google scholar: lookup
  101. Waskom, M. L. Seaborn: statistical data visualization. , 3021. 10.21105/joss.03021 (2021).
    doi: 10.21105/joss.03021google scholar: lookup
  102. Wickham, H. & Sievert, C. Vol. 10 (Springer, 2009).
  103. Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. , 764–766. 10.1016/j.jesp.2013.03.013 (2013).
    doi: 10.1016/j.jesp.2013.03.013google scholar: lookup
  104. Locard-Paulet, M., Doncheva, N. T., Morris, J. H. & Jensen, L. J. Functional analysis of MS-Based proteomics data: from protein groups to networks. 10.1016/j.mcpro.2024.100871 (2024).
    pmc: PMC11667155pubmed: 39486590
  105. McClain, S. Bioinformatic screening and detection of allergen cross-reactive IgE-binding epitopes. , 1600676. 10.1002/mnfr.201600676 (2017).n
    doi: 10.1002/mnfr.201600676pmc: PMC5573986pubmed: 28191711google scholar: lookup
  106. Perez-Riverol, Y. et al. The PRIDE database at 20 years: 2025 update. 10.1093/nar/gkae1011 (2025). D543-D553.
    pmc: PMC11701690pubmed: 39494541

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