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Chen H, Li S, Zhao W, Deng J, Yan Z, Zhang T, Wen SA, Guo H, Li L, Yuan J, Zhang H, Ma L, Zheng J, Gao M, Pang Y. A Peptidomic Approach to Identify Novel Antigen Biomarkers for the Diagnosis of Tuberculosis. Infect Drug Resist 2022; 15:4617-4626. [PMID: 36003990 PMCID: PMC9394730 DOI: 10.2147/idr.s373652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 08/01/2022] [Indexed: 11/23/2022] Open
Abstract
Background Here, we conducted a peptidomic study in murine model to identify novel antigen biomarkers for the diagnosis of tuberculosis (TB) with improved performance. Methods Four recombinant proteins, including Mycobacterium tuberculosis protein 32 (MPT32), Mycobacterium tuberculosis protein 64 (MPT64), culture filtrate protein 10 (CFP10), and phosphate ABC transporter substrate-binding lipoprotein (PstS1) were expressed and intravenously injected into BALB/c mice. The serum were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The concentrations of candidate peptides in serum of suspected TB patients were determined using competitive enzyme-linked immunosorbent assay. Results A total of 65 peptides from 4 MTB precursor recombinant proteins were identified in mouse serum by LC-MS/MS, of which 5 peptides were selected as candidates for serological analysis. The concentrations of peptides MPT64-2, CFP10-2 and PstS1-2 in TB patients were significantly higher than those in non-TB patients. MPT64-2 exhibited the most promising sensitivity (81.4%), followed by PstS1-2 and CFP10-2. In addition, PstS1-2 had the highest specificity (93.3%), followed by CFP10-2 and MPT64-2. According to the area under the curve (AUC), MPT64-2 (AUC = 0.863), PstS1-2 (AUC = 0.812) and CFP10-2 (AUC = 0.809) exhibited better diagnostic validity. Conclusion We develop an effective approach to identify new antigen biomarkers via LC-MS/MS-based peptidomics. Multiple peptides exhibit promising efficacy in diagnosis of active TB patients.
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Affiliation(s)
- Hongmei Chen
- Department of Tuberculosis, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China
| | - Shanshan Li
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China
| | - Weijie Zhao
- Clinical Trial Agency Office, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China
| | - Jiaheng Deng
- Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Zhuohong Yan
- Department of Central Laboratory, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China
| | - Tingting Zhang
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China
| | - Shu' An Wen
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China
| | - Haiping Guo
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China
| | - Lei Li
- Electral Safety Research & Development Center, Beijing Normal University, Zhuhai, People's Republic of China
| | - Jianfeng Yuan
- Electral Safety Research & Development Center, Beijing Normal University, Zhuhai, People's Republic of China
| | - Hongtao Zhang
- Department of Central Laboratory, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China
| | - Liping Ma
- Department of Tuberculosis, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China
| | - Jianhua Zheng
- Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Mengqiu Gao
- Department of Tuberculosis, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China
| | - Yu Pang
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing, People's Republic of China
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