Serum proteomics analysis for differentiation among Mycobacterium tuberculosis infection categories

Inhalation of Mycobacterium tuberculosis (Mtb) bacilli can lead to a range of TB categories including early clearance (EC), latent TB infection (LTBI) and active TB (ATB). There are few biomarkers available to differentiate among these TB categories: effective new biomarkers are badly needed. Here, we analyzed the serum proteins from 26 ATB cases, 20 LTBI cases, 34 EC cases and 38 healthy controls (HC) using label-free LC-MS/MS. The results were analyzed using MaxQuant software and matched to three different bacterial proteomics databases, including Mtb, Mycobacterium spp. and normal lung flora. PCA of protein candidates using the three proteomics databases revealed 44.5% differentiation power to differentiate among four TB categories. There were 289 proteins that showed potential for distinguishing between each pair of groups among TB categories. There were 50 candidate protein markers specifically found in ATB and LTBI but not in HC and EC groups. Decision trees using the top five candidate biomarkers (A0A1A2RWZ9, A0A1A3FMY8, A0A1A3KIY2, A0A5C7MJH5 and A0A1X0XYR3) had 92.31% accuracy to differentiate among TB categories and the accuracy was increased to 100% when using 10 candidate biomarkers. Our study shows that proteins expressed from Mycobacterium spp. have the potential to be used to differentiate among TB categories.