American journal of respiratory and critical care medicine · 2016
Genetic Determinants of Drug Resistance in Mycobacterium tuberculosis and Their Diagnostic Value.
СтатьяПоддержка исследования: вне правительства СШАПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Farhat Maha R, Sultana Razvan, Iartchouk Oleg, Bozeman Sam, Galagan James, Sisk Peter, Stolte Christian, Nebenzahl-Guimaraes Hanna, Jacobson Karen, Sloutsky Alexander, Kaur Devinder, Posey James, Kreiswirth Barry N, Kurepina Natalia, Rigouts Leen, Streicher Elizabeth M, Victor Tommie C, Warren Robin M, van Soolingen Dick, Murray Megan
Аннотация
RATIONALE: The development of molecular diagnostics that detect both the presence of Mycobacterium tuberculosis in clinical samples and drug resistance-conferring mutations promises to revolutionize patient care and interrupt transmission by ensuring early diagnosis. However, these tools require the identification of genetic determinants of resistance to the full range of antituberculosis drugs.
OBJECTIVES: To determine the optimal molecular approach needed, we sought to create a comprehensive catalog of resistance mutations and assess their sensitivity and specificity in diagnosing drug resistance.
METHODS: We developed and validated molecular inversion probes for DNA capture and deep sequencing of 28 drug-resistance loci in M. tuberculosis. We used the probes for targeted sequencing of a geographically diverse set of 1,397 clinical M. tuberculosis isolates with known drug resistance phenotypes. We identified a minimal set of mutations to predict resistance to first- and second-line antituberculosis drugs and validated our predictions in an independent dataset. We constructed and piloted a web-based database that provides public access to the sequence data and prediction tool.
MEASUREMENTS AND MAIN RESULTS: The predicted resistance to rifampicin and isoniazid exceeded 90% sensitivity and specificity but was lower for other drugs. The number of mutations needed to diagnose resistance is large, and for the 13 drugs studied it was 238 across 18 genetic loci.
CONCLUSIONS: These data suggest that a comprehensive M. tuberculosis drug resistance diagnostic will need to allow for a high dimension of mutation detection. They also support the hypothesis that currently unknown genetic determinants, potentially discoverable by whole-genome sequencing, encode resistance to second-line tuberculosis drugs.
OBJECTIVES: To determine the optimal molecular approach needed, we sought to create a comprehensive catalog of resistance mutations and assess their sensitivity and specificity in diagnosing drug resistance.
METHODS: We developed and validated molecular inversion probes for DNA capture and deep sequencing of 28 drug-resistance loci in M. tuberculosis. We used the probes for targeted sequencing of a geographically diverse set of 1,397 clinical M. tuberculosis isolates with known drug resistance phenotypes. We identified a minimal set of mutations to predict resistance to first- and second-line antituberculosis drugs and validated our predictions in an independent dataset. We constructed and piloted a web-based database that provides public access to the sequence data and prediction tool.
MEASUREMENTS AND MAIN RESULTS: The predicted resistance to rifampicin and isoniazid exceeded 90% sensitivity and specificity but was lower for other drugs. The number of mutations needed to diagnose resistance is large, and for the 13 drugs studied it was 238 across 18 genetic loci.
CONCLUSIONS: These data suggest that a comprehensive M. tuberculosis drug resistance diagnostic will need to allow for a high dimension of mutation detection. They also support the hypothesis that currently unknown genetic determinants, potentially discoverable by whole-genome sequencing, encode resistance to second-line tuberculosis drugs.