Protein networks in induced sputum from smokers and COPD patients

Abstract

RationaleSubtypes of cigarette smoke-induced disease affect different lung structures and may have distinct pathophysiological mechanisms.ObjectiveTo determine if proteomic classification of the cellular and vascular origins of sputum proteins can characterize these mechanisms and phenotypes.Subjects and methodsIndividual sputum specimens from lifelong nonsmokers (n=7) and smokers with normal lung function (n=13), mucous hypersecretion with normal lung function (n=11), obstructed airflow without emphysema (n=15), and obstruction plus emphysema (n=10) were assessed with mass spectrometry. Data reduction, logarithmic transformation of spectral counts, and Cytoscape network-interaction analysis were performed. The original 203 proteins were reduced to the most informative 50. Sources were secretory dimeric IgA, submucosal gland serous and mucous cells, goblet and other epithelial cells, and vascular permeability.ResultsEpithelial proteins discriminated nonsmokers from smokers. Mucin 5AC was elevated in healthy smokers and chronic bronchitis, suggesting a continuum with the severity of hypersecretion determined by mechanisms of goblet-cell hyperplasia. Obstructed airflow was correlated with glandular proteins and lower levels of Ig joining chain compared to other groups. Emphysema subjects’ sputum was unique, with high plasma proteins and components of neutrophil extracellular traps, such as histones and defensins. In contrast, defensins were correlated with epithelial proteins in all other groups. Protein-network interactions were unique to each group.ConclusionThe proteomes were interpreted as complex “biosignatures” that suggest distinct pathophysiological mechanisms for mucin 5AC hypersecretion, airflow obstruction, and inflammatory emphysema phenotypes. Proteomic phenotyping may improve genotyping studies by selecting more homogeneous study groups. Each phenotype may require its own mechanistically based diagnostic, risk-assessment, drug- and other treatment algorithms.

Searching for biomarkers of chronic obstructive pulmonary disease using proteomics: The current state

“…The correlation of their levels with FEV1/FVC ratio was the proof of their relationship with disease severity. The fact that these results were in good agreement with previously published data confirmed that a few of proteins identified could actually be considered reliable biomarkers of the disease.…”

“…This study was a good model for studying the ability of sputum profiling to capture the complex and reversible physiological response to cigarette smoke exposure. It is worth pointing out that, also in this case, several of the differentially expressed proteins identified were the same previously indicated as potential signatures of COPD .…”

“…The bioinformatic analysis of these data, performed by Baraniuk et al , allowed to deepen the information they contained. It was observed that epithelial proteins were significantly up‐regulated in non‐smokers compared to the other groups and that, in agreement with goblet‐cell hyperplasia and mucus hypersecretion, MUC5AC was up‐regulated in healthy smokers and chronic bronchitis patients compared to nonsmokers and COPD patients.…”

Proteomics: Clinical and research applications in respiratory diseases

“…identified a higher abundance of mucin 5AC in sputum from COPD and healthy smokers. Patients with emphysema features had higher levels of defensins and protein components of neutrophil extracellular traps (NETS) . Lee et al .…”

“…Patients with emphysema features had higher levels of defensins and protein components of neutrophil extracellular traps (NETS). 79 Lee et al employed MALDI-TOF-MS in tissue samples from COPD patients and healthy smokers. 19 They reported significant upregulation of MMP13 mainly in alveolar macrophages and thioredoxin-like 2 (TXL2) in bronchial epithelium compared with healthy smokers.…”