MGIT-seq for the Identification of Nontuberculous Mycobacteria and Drug Resistance: a Prospective Study.

Clicks: 57
ID: 276655
2023
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
Abstract
Because nontuberculous mycobacterial pulmonary disease is a considerable health burden, a simple and clinically applicable analytical protocol enabling the identification of subspecies and drug-resistant disease is required to determine the treatment strategy. We aimed to develop a simplified workflow consisting only of direct sequencing of mycobacterial growth indicator tube cultures (MGIT-seq). In total, 138 patients were prospectively enrolled between April 2021 and May 2022, and culture-positive MGIT broths were subjected to sequencing using MinION, a portable next-generation sequencer. Sequence analysis was conducted to identify species using core genome multilocus sequence typing and to predict macrolide and amikacin (AMK) resistance based on previously reported mutations in , and (41). The results were compared to clinical tests for species identification and drug susceptibility. A total of 116 patients with positive MGIT cultures were included in the analysis. MGIT-seq yielded 99.1% accuracy in species-level identification and identified 98 isolates (84.5%) at the subspecies level. Macrolide and AMK resistance were detected in 19.4% and 1.9% of Mycobacterium avium complex (MAC) and Mycobacterium abscessus isolates. The predicted macrolide and AMK resistance was consistent with the results of conventional drug susceptibility tests, with specificities of 97.6% and 100.0%, respectively. Direct MGIT-seq has achieved comprehensive identification and drug resistance detection of nontuberculous mycobacteria, which could be applicable to determine the treatment strategy by a single test in clinical practice.
Reference Key
fukushima2023mgitseqjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Fukushima, Kiyoharu;Matsumoto, Yuki;Matsuki, Takanori;Saito, Haruko;Motooka, Daisuke;Komukai, Sho;Fukui, Eriko;Yamuchi, June;Nitta, Tadayoshi;Niitsu, Takayuki;Abe, Yuko;Nabeshima, Hiroshi;Nagahama, Yasuharu;Nii, Takuro;Tsujino, Kazuyuki;Miki, Keisuke;Kitada, Seigo;Kumanogoh, Atsushi;Akira, Shizuo;Nakamura, Shota;Kida, Hiroshi;
Journal Journal of clinical microbiology
Year 2023
DOI
e01626-22
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.