Comparative proteomic analysis of serum diagnosis patterns of sputum smear-positive pulmonary tuberculosis based on magnetic bead separation and mass spectrometry analysis.

A major challenge in pulmonary tuberculosis (TB) control is early and accurate diagnosis of sputum smear negative pulmonary TB (SSN-PTB). The patients with SSN-PTB have to wait for a longer period of time before receiving proper treatment than sputum smear positive pulmonary TB (SSP-PTB) patients due to delay in diagnosis. The purpose of this study is to discover potential serum protein biomarkers for SSN-PTB. Surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF MS) combined with weak cation exchange (WCX) magnetic beads was used to screen serum samples from SSN-PTB patients (N = 66), SSP-PTB patients (N = 49), and healthy volunteers (N = 80). The serum protein profiles were analyzed with Biomarker Wizard system. A classification model was established using Biomarker Pattern Software (BPS). Fifty-eight protein peaks were identified to exhibit significant differences between SSN-PTB, SSP-PTB and healthy control groups (P < 0.05), among which 6 peaks were found to be down-regulated, while 10 peaks were up-regulated gradually in the healthy control, SSN-PTB, and SSP-PTB groups. Twenty-three discriminating m/z peaks were detected between SSN-PTB patients and healthy controls (P < 0.01, Fold ≥ 1.5). The classification tree combined with three protein peaks (2747.0, 4480.0, and 9410.1 Da) could distinguish SSN-PTB patients from healthy controls with a sensitivity of 83.33% and a specificity of 82.50%. Early diagnosis of SSN-PTB disease is critical in order to reduce morbidity and mortality associated with TB. The study will help to clarify the role of differential proteins in the pathogenesis of TB.