Evaluation of Mycobacterium tuberculosis enrichment in metagenomic samples using ONT adaptive sequencing and amplicon sequencing for identification and variant calling

Sensitive detection of Mycobacterium tuberculosis (TB) in small percentages in metagenomic samples is essential for microbial classification and drug resistance prediction. However, traditional methods, such as bacterial culture and microscopy, are time-consuming and sometimes have limited TB detect...

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Veröffentlicht in:Scientific reports 2023-03, Vol.13 (1), p.5237-10, Article 5237
Hauptverfasser: Su, Junhao, Lui, Wui Wang, Lee, YanLam, Zheng, Zhenxian, Siu, Gilman Kit-Hang, Ng, Timothy Ting-Leung, Zhang, Tong, Lam, Tommy Tsan-Yuk, Lao, Hiu-Yin, Yam, Wing-Cheong, Tam, Kingsley King-Gee, Leung, Kenneth Siu-Sing, Lam, Tak-Wah, Leung, Amy Wing-Sze, Luo, Ruibang
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Sprache:eng
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Zusammenfassung:Sensitive detection of Mycobacterium tuberculosis (TB) in small percentages in metagenomic samples is essential for microbial classification and drug resistance prediction. However, traditional methods, such as bacterial culture and microscopy, are time-consuming and sometimes have limited TB detection sensitivity. Oxford nanopore technologies (ONT) MinION sequencing allows rapid and simple sample preparation for sequencing. Its recently developed adaptive sequencing selects reads from targets while allowing real-time base-calling to achieve sequence enrichment or depletion during sequencing. Another common enrichment method is PCR amplification of the target TB genes. In this study, we compared both methods using ONT MinION sequencing for TB detection and variant calling in metagenomic samples using both simulation runs and those with synthetic and patient samples. We found that both methods effectively enrich TB reads from a high percentage of human (95%) and other microbial DNA. Adaptive sequencing with readfish and UNCALLDE achieved a 3.9-fold and 2.2-fold enrichment compared to the control run. We provide a simple automatic analysis framework to support the detection of TB for clinical use, openly available at https://github.com/HKU-BAL/ONT-TB-NF . Depending on the patient's medical condition and sample type, we recommend users evaluate and optimize their workflow for different clinical specimens to improve the detection limit.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-32378-x