Maha R Farhat1,2, Razvan Sultana3, Oleg Iartchouk4, Sam Bozeman5, James Galagan6,7,8, Peter Sisk9, Christian Stolte10, Hanna Nebenzahl-Guimaraes11,12,13,14,15, Karen Jacobson16,17, Alexander Sloutsky2,18, Devinder Kaur18, James Posey19, Barry N Kreiswirth20, Natalia Kurepina20, Leen Rigouts21,22, Elizabeth M Streicher17, Tommie C Victor17, Robin M Warren17, Dick van Soolingen12,13,14, Megan Murray2,23. 1. 1 Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts. 2. 2 Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts. 3. 3 Genomics England, Queen Mary University, London, United Kingdom. 4. 4 Novartis Institutes for Biomedical Research, Cambridge, Massachusetts. 5. 5 Abt Associates, Boston, Massachusetts. 6. 6 Department of Biomedical Engineering. 7. 7 Department of Microbiology, and. 8. 8 Bioinformatics Program, Boston University, Boston, Massachusetts. 9. 9 Gen9, Inc., Cambridge, Massachusetts. 10. 10 CSIRO, North Ryde, New South Wales, Australia. 11. 11 National Institute for Public Health and the Environment, Bilthoven, the Netherlands. 12. 12 Department of Pulmonary Diseases and. 13. 13 Department of Medical Microbiology, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands. 14. 14 Life and Health Sciences Research Institute, School of Health Sciences, University of Minho, Braga, Portugal. 15. 15 Life and Health Sciences Research Institute/3Bs, PT Government Associate Laboratory, Braga/Guimaraes, Portugal. 16. 16 Section of Infectious Diseases, Boston University School of Medicine, Boston, Massachusetts. 17. 17 DST/NRF Center of Excellence for Biomedical TB Research/SAMRC Center for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa. 18. 18 University of Massachusetts Medical School, Worcester, Massachusetts. 19. 19 Division of Tuberculosis Elimination, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia. 20. 20 Public Health Research Institute Tuberculosis Center, Rutgers University, Newark, New Jersey. 21. 21 Mycobacteriology, Institute of Tropical Medicine, Antwerp, Belgium. 22. 22 Biomedical Sciences, Antwerp University, Antwerp, Belgium; and. 23. 23 Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts.
Abstract
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.
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 clinicalM. 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.
Entities:
Keywords:
molecular diagnostics; multidrug-resistant tuberculosis; sensitivity and specificity
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