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Wang HY, Kuo CH, Chung CR, Lin WY, Wang YC, Lin TW, Yu JR, Lu JJ, Wu TS. Rapid and Accurate Discrimination of Mycobacterium abscessus Subspecies Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Spectrum and Machine Learning Algorithms. Biomedicines 2022; 11:biomedicines11010045. [PMID: 36672552 PMCID: PMC9856018 DOI: 10.3390/biomedicines11010045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 12/18/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
Mycobacterium abscessus complex (MABC) has been reported to cause complicated infections. Subspecies identification of MABC is crucial for adequate treatment due to different antimicrobial resistance properties amid subspecies. However, long incubation days are needed for the traditional antibiotic susceptibility testing (AST). Delayed effective antibiotics administration often causes unfavorable outcomes. Thus, we proposed a novel approach to identify subspecies and potential antibiotic resistance, guiding early and accurate treatment. Subspecies of MABC isolates were determined by secA1, rpoB, and hsp65. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) spectra were analyzed, and informative peaks were detected by random forest (RF) importance. Machine learning (ML) algorithms were used to build models for classifying MABC subspecies based on spectrum. The models were validated by repeated five-fold cross-validation to avoid over-fitting. In total, 102 MABC isolates (52 subspecies abscessus and 50 subspecies massiliense) were analyzed. Top informative peaks including m/z 6715, 4739, etc. were identified. RF model attained AUROC of 0.9166 (95% CI: 0.9072-0.9196) and outperformed other algorithms in discriminating abscessus from massiliense. We developed a MALDI-TOF based ML model for rapid and accurate MABC subspecies identification. Due to the significant correlation between subspecies and corresponding antibiotics resistance, this diagnostic tool guides a more precise and timelier MABC subspecies-specific treatment.
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Affiliation(s)
- Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333423, Taiwan
| | - Chi-Heng Kuo
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333423, Taiwan
| | - Chia-Ru Chung
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
| | | | - Yu-Chiang Wang
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Ting-Wei Lin
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333423, Taiwan
| | - Jia-Ruei Yu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333423, Taiwan
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333423, Taiwan
- School of Medicine, Chang Gung University, Taoyuan City 333323, Taiwan
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan City 333323, Taiwan
| | - Ting-Shu Wu
- Division of Infectious Diseases, Departments of Internal Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan City 333423, Taiwan
- Correspondence: ; Tel.: +886-3-3281200-7955
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