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