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Substantial Improvement in Nontuberculous Mycobacterial Identification Using ASTA MicroIDSys Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry with an Upgraded Database

Authors
 Junhyup Song  ;  Shinyoung Yoon  ;  Yongha In  ;  Daewon Kim  ;  Hyukmin Lee  ;  Dongeun Yong  ;  Kyoungwon Lee 
Citation
 ANNALS OF LABORATORY MEDICINE, Vol.42(3) : 358-362, 2022-05 
Journal Title
ANNALS OF LABORATORY MEDICINE
ISSN
 2234-3806 
Issue Date
2022-05
MeSH
Culture Media ; Humans ; Lasers ; Mycobacterium* ; Nontuberculous Mycobacteria* / genetics ; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
Keywords
Database upgrade ; Identification ; Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry ; Nontuberculous mycobacteria ; Performance evaluation
Abstract
Identifying Mycobacterium using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is challenging. We evaluated the performance of MALDI-TOF MS in identifying nontuberculous mycobacteria (NTM) using the ASTA MicroIDSys system (ASTA Inc., Suwon, Korea) with the MycoDB v1.95s and upgraded MycoDB v2.0-beta databases. We tested 124 NTM isolates collected from Ogawa medium at Severance Hospital, Seoul, Korea, between January and April 2019. MicroIDSys scores were categorized into three groups: ≥140, reliable identification; 130-139, ambiguous identification; and <130, invalid identification. To validate the results, we used the reverse blot hybridization assay (Molecutech REBA MycoID, YD Diagnostics Corp., Korea). Initial analysis using MycoDB v1.95s resulted in 26.6% (33/124) reliable, 43.5% (54/124) ambiguous, and 29.8% (37/124) invalid identifications. Re-analysis using the upgraded MycoDB v2.0-beta database resulted in 94.4% (117/124) reliable, 4.0% (5/124) ambiguous, and 1.6% invalid (2/124) identifications. The percentage of reliable identifications that matched with the reference increased from 26.6% (33/124) with MycoDB v1.95s to 93.5% (116/124) with MycoDB v2.0-beta. The upgraded databases enable substantially improved NTM identification through deep learning in the inference algorithm and by considering more axes in the correlation analysis. MALDI-TOF MS using the upgraded database unambiguously identified most NTM species. Our study lays a foundation for applying MALDI-TOF MS for the simple and rapid identification of NTM isolated from solid media.
Files in This Item:
T202205020.pdf Download
DOI
10.3343/alm.2022.42.3.358
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Laboratory Medicine (진단검사의학교실) > 1. Journal Papers
Yonsei Authors
Song, Junhyup(송준협)
Yong, Dong Eun(용동은) ORCID logo https://orcid.org/0000-0002-1225-8477
Lee, Kyungwon(이경원) ORCID logo https://orcid.org/0000-0003-3788-2134
Lee, Hyuk Min(이혁민) ORCID logo https://orcid.org/0000-0002-8523-4126
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/191406
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