Sputum Proteomics in Nontuberculous Mycobacterial Lung Disease

Abstract

Background: Nontuberculous mycobacterial (NTM) infections are difficult to diagnose and treat. Biomarkers to identify patients with active infection or at risk of disease progression would have clinical utility. Sputum is the most frequently used matrix for the diagnosis of NTM lung disease.

Research question: Can sputum proteomics be used to identify NTM-associated inflammatory profiles in sputum?

Study design and methods: Patients with NTM lung disease and a matched cohort of patients with COPD, bronchiectasis (BE), and cystic fibrosis (CF) without NTM lung disease were enrolled from two hospitals in the United Kingdom. Liquid chromatography-tandem mass spectrometry was used to identify proteomic biomarkers associated with underlying diagnosis (COPD, BE, and CF), the presence of NTM lung disease defined according to American Thoracic Society/Infectious Diseases Society of America criteria, and severity of NTM. A subset of patients receiving guideline-concordant NTM treatment were studied to identify protein changes associated with treatment response.

Results: This study analyzed 95 sputum samples from 55 subjects (BE, n = 21; COPD, n = 19; CF, n = 15). Underlying disease and infection with Pseudomonas aeruginosa were the strongest drivers of sputum protein profiles. Comparing protein abundance in COPD, BE, and CF found that 12 proteins were upregulated in CF compared with COPD, including MPO, AZU1, CTSG, CAT, and RNASE3, with 21 proteins downregulated, including SCGB1A1, IGFBP2, SFTPB, GC, and CFD. Across CF, BE, and COPD, NTM infection (n = 15) was not associated with statistically significant differences in sputum protein profiles compared with those without NTM. Two proteins associated with iron chelation were significantly downregulated in severe NTM disease. NTM treatment was associated with heterogeneous changes in the sputum protein profile. Patients with NTM and a decrease in immune response proteins had a subjective symptomatic improvement.

Interpretation: Sputum proteomics identified candidate biomarkers of NTM severity and treatment response. However, underlying lung disease and typical bacterial pathogens such as P aeruginosa are also key determinants of the sputum proteomic profile.

Keywords: COPD; NTM; biomarkers; bronchiectasis; cystic fibrosis.