Shu Hui Cao1, Yan Qing Chen2, Yong Sun3, Yang Liu4, Su Hua Zheng4, Zhi Guo Zhang5, Chuan You Li2. 1. Department of Bacteriology and Immunology, Beijing Key Laboratory on Drug-Resistant Tuberculosis Research, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing 101149, China; Department of Laboratory Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China. 2. Department of Bacteriology and Immunology, Beijing Key Laboratory on Drug-Resistant Tuberculosis Research, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing 101149, China. 3. Department of Clinical Laboratory, Beijing Tuberculosis and Thoracic Tumour Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing 101149, China. 4. Department of Epidemiological Research, Beijing Tuberculosis and Thoracic Tumour Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing 101149, China. 5. Institute of Tuberculosis Prevention and Control of District Changping, Beijing 102200, China.
Abstract
OBJECTIVE: To identify potential serum biomarkers for distinguishing between latent tuberculosis infection (LTBI) and active tuberculosis (TB). METHODS: A proteome microarray containing 4,262 antigens was used for screening serum biomarkers of 40 serum samples from patients with LTBI and active TB at the systems level. The interaction network and functional classification of differentially expressed antigens were analyzed using STRING 10.0 and the TB database, respectively. Enzyme-linked immunosorbent assays (ELISA) were used to validate candidate antigens further using 279 samples. The diagnostic performances of candidate antigens were evaluated by receiver operating characteristic curve (ROC) analysis. Both antigen combination and logistic regression analysis were used to improve diagnostic ability. RESULTS: Microarray results showed that levels of 152 Mycobacterium tuberculosis (Mtb)-antigen- specific IgG were significantly higher in active TB patients than in LTBI patients (P < 0.05), and these differentially expressed antigens showed stronger associations with each other and were involved in various biological processes. Eleven candidate antigens were further validated using ELISA and showed consistent results in microarray analysis. ROC analysis showed that antigens Rv2031c, Rv1408, and Rv2421c had higher areas under the curve (AUCs) of 0.8520, 0.8152, and 0.7970, respectively. In addition, both antigen combination and logistic regression analysis improved the diagnostic ability. CONCLUSION: Several antigens have the potential to serve as serum biomarkers for discrimination between LTBI and active TB.
OBJECTIVE: To identify potential serum biomarkers for distinguishing between latent tuberculosis infection (LTBI) and active tuberculosis (TB). METHODS: A proteome microarray containing 4,262 antigens was used for screening serum biomarkers of 40 serum samples from patients with LTBI and active TB at the systems level. The interaction network and functional classification of differentially expressed antigens were analyzed using STRING 10.0 and the TB database, respectively. Enzyme-linked immunosorbent assays (ELISA) were used to validate candidate antigens further using 279 samples. The diagnostic performances of candidate antigens were evaluated by receiver operating characteristic curve (ROC) analysis. Both antigen combination and logistic regression analysis were used to improve diagnostic ability. RESULTS: Microarray results showed that levels of 152 Mycobacterium tuberculosis (Mtb)-antigen- specific IgG were significantly higher in active TB patients than in LTBI patients (P < 0.05), and these differentially expressed antigens showed stronger associations with each other and were involved in various biological processes. Eleven candidate antigens were further validated using ELISA and showed consistent results in microarray analysis. ROC analysis showed that antigens Rv2031c, Rv1408, and Rv2421c had higher areas under the curve (AUCs) of 0.8520, 0.8152, and 0.7970, respectively. In addition, both antigen combination and logistic regression analysis improved the diagnostic ability. CONCLUSION: Several antigens have the potential to serve as serum biomarkers for discrimination between LTBI and active TB.
Authors: Yean K Yong; Hong Y Tan; Alireza Saeidi; Won F Wong; Ramachandran Vignesh; Vijayakumar Velu; Rajaraman Eri; Marie Larsson; Esaki M Shankar Journal: Front Microbiol Date: 2019-12-18 Impact factor: 5.640